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hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/maskformer/__init__.py
|
# Copyright 2022 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"]
_import_structure["image_processing_maskformer"] = ["MaskFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_maskformer"] = [
"MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"MaskFormerForInstanceSegmentation",
"MaskFormerModel",
"MaskFormerPreTrainedModel",
]
_import_structure["modeling_maskformer_swin"] = [
"MaskFormerSwinBackbone",
"MaskFormerSwinModel",
"MaskFormerSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/maskformer/image_processing_maskformer.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""Image processor class for MaskFormer."""
import math
import warnings
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
PaddingMode,
get_resize_output_image_size,
pad,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
TensorType,
is_torch_available,
is_torch_tensor,
logging,
)
logger = logging.get_logger(__name__)
if TYPE_CHECKING:
from transformers import MaskFormerForInstanceSegmentationOutput
if is_torch_available():
import torch
from torch import nn
# Copied from transformers.models.detr.image_processing_detr.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
def get_max_height_width(
images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
"""
Get the maximum height and width across all images in a batch.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images[0])
if input_data_format == ChannelDimension.FIRST:
_, max_height, max_width = max_across_indices([img.shape for img in images])
elif input_data_format == ChannelDimension.LAST:
max_height, max_width, _ = max_across_indices([img.shape for img in images])
else:
raise ValueError(f"Invalid channel dimension format: {input_data_format}")
return (max_height, max_width)
# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
"""
Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
Args:
image (`np.ndarray`):
Image to make the pixel mask for.
output_size (`Tuple[int, int]`):
Output size of the mask.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
mask = np.zeros(output_size, dtype=np.int64)
mask[:input_height, :input_width] = 1
return mask
# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
def binary_mask_to_rle(mask):
"""
Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
format.
"""
if is_torch_tensor(mask):
mask = mask.numpy()
pixels = mask.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return list(runs)
# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
"""
segment_ids = torch.unique(segmentation)
run_length_encodings = []
for idx in segment_ids:
mask = torch.where(segmentation == idx, 1, 0)
rle = binary_mask_to_rle(mask)
run_length_encodings.append(rle)
return run_length_encodings
# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
labels (`torch.Tensor`):
A tensor of shape `(num_queries)`.
object_mask_threshold (`float`):
A number between 0 and 1 used to binarize the masks.
Raises:
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
Returns:
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
< `object_mask_threshold`.
"""
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
raise ValueError("mask, scores and labels must have the same shape!")
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
return masks[to_keep], scores[to_keep], labels[to_keep]
# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
# Get the mask associated with the k class
mask_k = mask_labels == k
mask_k_area = mask_k.sum()
# Compute the area of all the stuff in query k
original_area = (mask_probs[k] >= mask_threshold).sum()
mask_exists = mask_k_area > 0 and original_area > 0
# Eliminate disconnected tiny segments
if mask_exists:
area_ratio = mask_k_area / original_area
if not area_ratio.item() > overlap_mask_area_threshold:
mask_exists = False
return mask_exists, mask_k
# Copied from transformers.models.detr.image_processing_detr.compute_segments
def compute_segments(
mask_probs,
pred_scores,
pred_labels,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
segments: List[Dict] = []
if target_size is not None:
mask_probs = nn.functional.interpolate(
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
)[0]
current_segment_id = 0
# Weigh each mask by its prediction score
mask_probs *= pred_scores.view(-1, 1, 1)
mask_labels = mask_probs.argmax(0) # [height, width]
# Keep track of instances of each class
stuff_memory_list: Dict[str, int] = {}
for k in range(pred_labels.shape[0]):
pred_class = pred_labels[k].item()
should_fuse = pred_class in label_ids_to_fuse
# Check if mask exists and large enough to be a segment
mask_exists, mask_k = check_segment_validity(
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
)
if mask_exists:
if pred_class in stuff_memory_list:
current_segment_id = stuff_memory_list[pred_class]
else:
current_segment_id += 1
# Add current object segment to final segmentation map
segmentation[mask_k] = current_segment_id
segment_score = round(pred_scores[k].item(), 6)
segments.append(
{
"id": current_segment_id,
"label_id": pred_class,
"was_fused": should_fuse,
"score": segment_score,
}
)
if should_fuse:
stuff_memory_list[pred_class] = current_segment_id
return segmentation, segments
# TODO: (Amy) Move to image_transforms
def convert_segmentation_map_to_binary_masks(
segmentation_map: "np.ndarray",
instance_id_to_semantic_id: Optional[Dict[int, int]] = None,
ignore_index: Optional[int] = None,
reduce_labels: bool = False,
):
if reduce_labels and ignore_index is None:
raise ValueError("If `reduce_labels` is True, `ignore_index` must be provided.")
if reduce_labels:
segmentation_map = np.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
# Get unique ids (class or instance ids based on input)
all_labels = np.unique(segmentation_map)
# Drop background label if applicable
if ignore_index is not None:
all_labels = all_labels[all_labels != ignore_index]
# Generate a binary mask for each object instance
binary_masks = [(segmentation_map == i) for i in all_labels]
binary_masks = np.stack(binary_masks, axis=0) # (num_labels, height, width)
# Convert instance ids to class ids
if instance_id_to_semantic_id is not None:
labels = np.zeros(all_labels.shape[0])
for label in all_labels:
class_id = instance_id_to_semantic_id[label + 1 if reduce_labels else label]
labels[all_labels == label] = class_id - 1 if reduce_labels else class_id
else:
labels = all_labels
return binary_masks.astype(np.float32), labels.astype(np.int64)
def get_maskformer_resize_output_image_size(
image: np.ndarray,
size: Union[int, Tuple[int, int], List[int], Tuple[int]],
max_size: Optional[int] = None,
size_divisor: int = 0,
default_to_square: bool = True,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
"""
Computes the output size given the desired size.
Args:
image (`np.ndarray`):
The input image.
size (`int` or `Tuple[int, int]` or `List[int]` or `Tuple[int]`):
The size of the output image.
max_size (`int`, *optional*):
The maximum size of the output image.
size_divisor (`int`, *optional*, defaults to 0):
If `size_divisor` is given, the output image size will be divisible by the number.
default_to_square (`bool`, *optional*, defaults to `True`):
Whether to default to square if no size is provided.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If unset, will use the inferred format from the input.
Returns:
`Tuple[int, int]`: The output size.
"""
output_size = get_resize_output_image_size(
input_image=image,
size=size,
default_to_square=default_to_square,
max_size=max_size,
input_data_format=input_data_format,
)
if size_divisor > 0:
height, width = output_size
height = int(math.ceil(height / size_divisor) * size_divisor)
width = int(math.ceil(width / size_divisor) * size_divisor)
output_size = (height, width)
return output_size
class MaskFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a MaskFormer image processor. The image processor can be used to prepare image(s) and optional targets
for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int`, *optional*, defaults to 800):
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
height / width, size)`.
size_divisor (`int`, *optional*, defaults to 32):
Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
Swin Transformer.
resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
to `True`.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the input to a certain `scale`.
rescale_factor (`float`, *optional*, defaults to `1/ 255`):
Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input with mean and standard deviation.
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
ImageNet std.
ignore_index (`int`, *optional*):
Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
denoted with 0 (background) will be replaced with `ignore_index`.
do_reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
The background label will be replaced by `ignore_index`.
"""
model_input_names = ["pixel_values", "pixel_mask"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size_divisor: int = 32,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
ignore_index: Optional[int] = None,
do_reduce_labels: bool = False,
**kwargs,
):
if "size_divisibility" in kwargs:
warnings.warn(
"The `size_divisibility` argument is deprecated and will be removed in v4.27. Please use "
"`size_divisor` instead.",
FutureWarning,
)
size_divisor = kwargs.pop("size_divisibility")
if "max_size" in kwargs:
warnings.warn(
"The `max_size` argument is deprecated and will be removed in v4.27. Please use size['longest_edge']"
" instead.",
FutureWarning,
)
# We make max_size a private attribute so we can pass it as a default value in the preprocess method whilst
# `size` can still be pass in as an int
self._max_size = kwargs.pop("max_size")
else:
self._max_size = 1333
if "reduce_labels" in kwargs:
warnings.warn(
"The `reduce_labels` argument is deprecated and will be removed in v4.27. Please use "
"`do_reduce_labels` instead.",
FutureWarning,
)
do_reduce_labels = kwargs.pop("reduce_labels")
size = size if size is not None else {"shortest_edge": 800, "longest_edge": self._max_size}
size = get_size_dict(size, max_size=self._max_size, default_to_square=False)
super().__init__(**kwargs)
self.do_resize = do_resize
self.size = size
self.resample = resample
self.size_divisor = size_divisor
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 IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.ignore_index = ignore_index
self.do_reduce_labels = do_reduce_labels
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
created using from_dict and kwargs e.g. `MaskFormerImageProcessor.from_pretrained(checkpoint, max_size=800)`
"""
image_processor_dict = image_processor_dict.copy()
if "max_size" in kwargs:
image_processor_dict["max_size"] = kwargs.pop("max_size")
if "size_divisibility" in kwargs:
image_processor_dict["size_divisibility"] = kwargs.pop("size_divisibility")
return super().from_dict(image_processor_dict, **kwargs)
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
size_divisor: int = 0,
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format=None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize the image to the given size. Size can be min_size (scalar) or `(height, width)` tuple. If size is an
int, smaller edge of the image will be matched to this number.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
The size of the output image.
size_divisor (`int`, *optional*, defaults to 0):
If `size_divisor` is given, the output image size will be divisible by the number.
resample (`PILImageResampling` resampling filter, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use when resizing the image.
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.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
if "max_size" in kwargs:
warnings.warn(
"The `max_size` parameter is deprecated and will be removed in v4.27. "
"Please specify in `size['longest_edge'] instead`.",
FutureWarning,
)
max_size = kwargs.pop("max_size")
else:
max_size = None
size = get_size_dict(size, max_size=max_size, default_to_square=False)
if "shortest_edge" in size and "longest_edge" in size:
size, max_size = size["shortest_edge"], size["longest_edge"]
elif "height" in size and "width" in size:
size = (size["height"], size["width"])
max_size = None
else:
raise ValueError(
"Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
f" {size.keys()}."
)
size = get_maskformer_resize_output_image_size(
image=image,
size=size,
max_size=max_size,
size_divisor=size_divisor,
default_to_square=False,
input_data_format=input_data_format,
)
image = resize(
image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
)
return image
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
def rescale(
self,
image: np.ndarray,
rescale_factor: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Rescale the image by the given factor. image = image * rescale_factor.
Args:
image (`np.ndarray`):
Image to rescale.
rescale_factor (`float`):
The value to use for rescaling.
data_format (`str` or `ChannelDimension`, *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.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the input image. If unset, 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.
"""
return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
def convert_segmentation_map_to_binary_masks(
self,
segmentation_map: "np.ndarray",
instance_id_to_semantic_id: Optional[Dict[int, int]] = None,
ignore_index: Optional[int] = None,
reduce_labels: bool = False,
):
reduce_labels = reduce_labels if reduce_labels is not None else self.reduce_labels
ignore_index = ignore_index if ignore_index is not None else self.ignore_index
return convert_segmentation_map_to_binary_masks(
segmentation_map=segmentation_map,
instance_id_to_semantic_id=instance_id_to_semantic_id,
ignore_index=ignore_index,
reduce_labels=reduce_labels,
)
def __call__(self, images, segmentation_maps=None, **kwargs) -> BatchFeature:
return self.preprocess(images, segmentation_maps=segmentation_maps, **kwargs)
def _preprocess(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
size_divisor: int = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
if do_resize:
image = self.resize(
image, size=size, size_divisor=size_divisor, resample=resample, input_data_format=input_data_format
)
if do_rescale:
image = self.rescale(image, rescale_factor=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
return image
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
size_divisor: int = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""Preprocesses a single image."""
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if is_scaled_image(image) and do_rescale:
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:
input_data_format = infer_channel_dimension_format(image)
image = self._preprocess(
image=image,
do_resize=do_resize,
size=size,
size_divisor=size_divisor,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
input_data_format=input_data_format,
)
if data_format is not None:
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
return image
def _preprocess_mask(
self,
segmentation_map: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
size_divisor: int = 0,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""Preprocesses a single mask."""
segmentation_map = to_numpy_array(segmentation_map)
# Add channel dimension if missing - needed for certain transformations
if segmentation_map.ndim == 2:
added_channel_dim = True
segmentation_map = segmentation_map[None, ...]
input_data_format = ChannelDimension.FIRST
else:
added_channel_dim = False
if input_data_format is None:
input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
# TODO: (Amy)
# Remork segmentation map processing to include reducing labels and resizing which doesn't
# drop segment IDs > 255.
segmentation_map = self._preprocess(
image=segmentation_map,
do_resize=do_resize,
resample=PILImageResampling.NEAREST,
size=size,
size_divisor=size_divisor,
do_rescale=False,
do_normalize=False,
input_data_format=input_data_format,
)
# Remove extra channel dimension if added for processing
if added_channel_dim:
segmentation_map = segmentation_map.squeeze(0)
return segmentation_map
def preprocess(
self,
images: ImageInput,
segmentation_maps: Optional[ImageInput] = None,
instance_id_to_semantic_id: Optional[Dict[int, int]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
size_divisor: Optional[int] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
ignore_index: Optional[int] = None,
do_reduce_labels: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> BatchFeature:
if "pad_and_return_pixel_mask" in kwargs:
warnings.warn(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in v4.27",
FutureWarning,
)
if "reduce_labels" in kwargs:
warnings.warn(
"The `reduce_labels` argument is deprecated and will be removed in v4.27. Please use"
" `do_reduce_labels` instead.",
FutureWarning,
)
if do_reduce_labels is not None:
raise ValueError(
"Cannot use both `reduce_labels` and `do_reduce_labels`. Please use `do_reduce_labels` instead."
)
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False, max_size=self._max_size)
size_divisor = size_divisor if size_divisor is not None else self.size_divisor
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
ignore_index = ignore_index if ignore_index is not None else self.ignore_index
do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
if do_resize is not None and size is None or size_divisor is None:
raise ValueError("If `do_resize` is True, `size` and `size_divisor` must be provided.")
if do_rescale is not None and rescale_factor is None:
raise ValueError("If `do_rescale` is True, `rescale_factor` must be provided.")
if do_normalize is not None and (image_mean is None or image_std is None):
raise ValueError("If `do_normalize` is True, `image_mean` and `image_std` must be provided.")
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
if segmentation_maps is not None and not valid_images(segmentation_maps):
raise ValueError(
"Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
images = make_list_of_images(images)
if segmentation_maps is not None:
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
if segmentation_maps is not None and len(images) != len(segmentation_maps):
raise ValueError("Images and segmentation maps must have the same length.")
images = [
self._preprocess_image(
image,
do_resize=do_resize,
size=size,
size_divisor=size_divisor,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
data_format=data_format,
input_data_format=input_data_format,
)
for image in images
]
if segmentation_maps is not None:
segmentation_maps = [
self._preprocess_mask(
segmentation_map, do_resize, size, size_divisor, input_data_format=input_data_format
)
for segmentation_map in segmentation_maps
]
encoded_inputs = self.encode_inputs(
images,
segmentation_maps,
instance_id_to_semantic_id,
ignore_index,
do_reduce_labels,
return_tensors,
input_data_format=input_data_format,
)
return encoded_inputs
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Pad an image with zeros to the given size.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
output_height, output_width = output_size
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
def pad(
self,
images: List[np.ndarray],
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> BatchFeature:
"""
Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
in the batch and optionally returns their corresponding pixel mask.
Args:
image (`np.ndarray`):
Image to pad.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to return a pixel mask.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
pad_size = get_max_height_width(images, input_data_format=input_data_format)
padded_images = [
self._pad_image(
image,
pad_size,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
for image in images
]
data = {"pixel_values": padded_images}
if return_pixel_mask:
masks = [
make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
for image in images
]
data["pixel_mask"] = masks
return BatchFeature(data=data, tensor_type=return_tensors)
def encode_inputs(
self,
pixel_values_list: List[ImageInput],
segmentation_maps: ImageInput = None,
instance_id_to_semantic_id: Optional[Union[List[Dict[int, int]], Dict[int, int]]] = None,
ignore_index: Optional[int] = None,
reduce_labels: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
MaskFormer addresses semantic segmentation with a mask classification paradigm, thus input segmentation maps
will be converted to lists of binary masks and their respective labels. Let's see an example, assuming
`segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
each mask.
Args:
pixel_values_list (`List[ImageInput]`):
List of images (pixel values) to be padded. Each image should be a tensor of shape `(channels, height,
width)`.
segmentation_maps (`ImageInput`, *optional*):
The corresponding semantic segmentation maps with the pixel-wise annotations.
(`bool`, *optional*, defaults to `True`):
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
If left to the default, will return a pixel mask that is:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
instance_id_to_semantic_id (`List[Dict[int, int]]` or `Dict[int, int]`, *optional*):
A mapping between object instance ids and class ids. If passed, `segmentation_maps` is treated as an
instance segmentation map where each pixel represents an instance id. Can be provided as a single
dictionary with a global/dataset-level mapping or as a list of dictionaries (one per image), to map
instance ids in each image separately.
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model.
- **pixel_mask** -- Pixel mask to be fed to a model (when `=True` or if `pixel_mask` is in
`self.model_input_names`).
- **mask_labels** -- Optional list of mask labels of shape `(labels, height, width)` to be fed to a model
(when `annotations` are provided).
- **class_labels** -- Optional list of class labels of shape `(labels)` to be fed to a model (when
`annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
`mask_labels[i][j]` if `class_labels[i][j]`.
"""
ignore_index = self.ignore_index if ignore_index is None else ignore_index
reduce_labels = self.do_reduce_labels if reduce_labels is None else reduce_labels
pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]
if input_data_format is None:
input_data_format = infer_channel_dimension_format(pixel_values_list[0])
encoded_inputs = self.pad(
pixel_values_list, return_tensors=return_tensors, input_data_format=input_data_format
)
if segmentation_maps is not None:
mask_labels = []
class_labels = []
pad_size = get_max_height_width(pixel_values_list, input_data_format=input_data_format)
# Convert to list of binary masks and labels
for idx, segmentation_map in enumerate(segmentation_maps):
segmentation_map = to_numpy_array(segmentation_map)
if isinstance(instance_id_to_semantic_id, list):
instance_id = instance_id_to_semantic_id[idx]
else:
instance_id = instance_id_to_semantic_id
# Use instance2class_id mapping per image
masks, classes = self.convert_segmentation_map_to_binary_masks(
segmentation_map, instance_id, ignore_index=ignore_index, reduce_labels=reduce_labels
)
# We add an axis to make them compatible with the transformations library
# this will be removed in the future
masks = [mask[None, ...] for mask in masks]
masks = [
self._pad_image(
image=mask,
output_size=pad_size,
constant_values=ignore_index,
input_data_format=ChannelDimension.FIRST,
)
for mask in masks
]
masks = np.concatenate(masks, axis=0)
mask_labels.append(torch.from_numpy(masks))
class_labels.append(torch.from_numpy(classes))
# we cannot batch them since they don't share a common class size
encoded_inputs["mask_labels"] = mask_labels
encoded_inputs["class_labels"] = class_labels
return encoded_inputs
def post_process_segmentation(
self, outputs: "MaskFormerForInstanceSegmentationOutput", target_size: Tuple[int, int] = None
) -> "torch.Tensor":
"""
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
The outputs from [`MaskFormerForInstanceSegmentation`].
target_size (`Tuple[int, int]`, *optional*):
If set, the `masks_queries_logits` will be resized to `target_size`.
Returns:
`torch.Tensor`:
A tensor of shape (`batch_size, num_class_labels, height, width`).
"""
logger.warning(
"`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_instance_segmentation`",
FutureWarning,
)
# class_queries_logits has shape [BATCH, QUERIES, CLASSES + 1]
class_queries_logits = outputs.class_queries_logits
# masks_queries_logits has shape [BATCH, QUERIES, HEIGHT, WIDTH]
masks_queries_logits = outputs.masks_queries_logits
if target_size is not None:
masks_queries_logits = torch.nn.functional.interpolate(
masks_queries_logits,
size=target_size,
mode="bilinear",
align_corners=False,
)
# remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
# mask probs has shape [BATCH, QUERIES, HEIGHT, WIDTH]
masks_probs = masks_queries_logits.sigmoid()
# now we want to sum over the queries,
# $ out_{c,h,w} = \sum_q p_{q,c} * m_{q,h,w} $
# where $ softmax(p) \in R^{q, c} $ is the mask classes
# and $ sigmoid(m) \in R^{q, h, w}$ is the mask probabilities
# b(atch)q(uery)c(lasses), b(atch)q(uery)h(eight)w(idth)
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
return segmentation
def post_process_semantic_segmentation(
self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None
) -> "torch.Tensor":
"""
Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
Returns:
`List[torch.Tensor]`:
A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
`torch.Tensor` correspond to a semantic class id.
"""
class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
# Remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
batch_size = class_queries_logits.shape[0]
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if batch_size != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
semantic_segmentation = []
for idx in range(batch_size):
resized_logits = torch.nn.functional.interpolate(
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = segmentation.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
return_binary_maps: Optional[bool] = False,
) -> List[Dict]:
"""
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into instance segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
return_coco_annotation (`bool`, *optional*, defaults to `False`):
If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
return_binary_maps (`bool`, *optional*, defaults to `False`):
If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
(one per detected instance).
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
`True`. Set to `None` if no mask if found above `threshold`.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- An integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
if return_coco_annotation and return_binary_maps:
raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
# [batch_size, num_queries, num_classes+1]
class_queries_logits = outputs.class_queries_logits
# [batch_size, num_queries, height, width]
masks_queries_logits = outputs.masks_queries_logits
device = masks_queries_logits.device
num_classes = class_queries_logits.shape[-1] - 1
num_queries = class_queries_logits.shape[-2]
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(class_queries_logits.shape[0]):
mask_pred = masks_queries_logits[i]
mask_cls = class_queries_logits[i]
scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
labels_per_image = labels[topk_indices]
topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
mask_pred = mask_pred[topk_indices]
pred_masks = (mask_pred > 0).float()
# Calculate average mask prob
mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
pred_masks.flatten(1).sum(1) + 1e-6
)
pred_scores = scores_per_image * mask_scores_per_image
pred_classes = labels_per_image
segmentation = torch.zeros(masks_queries_logits.shape[2:]) - 1
if target_sizes is not None:
segmentation = torch.zeros(target_sizes[i]) - 1
pred_masks = torch.nn.functional.interpolate(
pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
)[0]
instance_maps, segments = [], []
current_segment_id = 0
for j in range(num_queries):
score = pred_scores[j].item()
if not torch.all(pred_masks[j] == 0) and score >= threshold:
segmentation[pred_masks[j] == 1] = current_segment_id
segments.append(
{
"id": current_segment_id,
"label_id": pred_classes[j].item(),
"was_fused": False,
"score": round(score, 6),
}
)
current_segment_id += 1
instance_maps.append(pred_masks[j])
# Return segmentation map in run-length encoding (RLE) format
if return_coco_annotation:
segmentation = convert_segmentation_to_rle(segmentation)
# Return a concatenated tensor of binary instance maps
if return_binary_maps and len(instance_maps) != 0:
segmentation = torch.stack(instance_maps, dim=0)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[Dict]:
"""
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
predictions. Only supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
The outputs from [`MaskFormerForInstanceSegmentation`].
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (`Set[int]`, *optional*):
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction in batch. If left to None, predictions will not be
resized.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
to the corresponding `target_sizes` entry.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- an integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
if label_ids_to_fuse is None:
logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=label_ids_to_fuse,
target_size=target_size,
)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/nezha/configuration_nezha.py
|
from ... import PretrainedConfig
NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class NezhaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Nezha
[sijunhe/nezha-cn-base](https://huggingface.co/sijunhe/nezha-cn-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, optional, defaults to 21128):
Vocabulary size of the NEZHA model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`NezhaModel`].
hidden_size (`int`, optional, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, optional, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, optional, defaults to 3072):
The dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler.
hidden_dropout_prob (`float`, optional, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, optional, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
(e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, optional, defaults to 2):
The vocabulary size of the *token_type_ids* passed into [`NezhaModel`].
initializer_range (`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
classifier_dropout (`float`, optional, defaults to 0.1):
The dropout ratio for attached classifiers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
Example:
```python
>>> from transformers import NezhaConfig, NezhaModel
>>> # Initializing an Nezha configuration
>>> configuration = NezhaConfig()
>>> # Initializing a model (with random weights) from the Nezha-base style configuration model
>>> model = NezhaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
pretrained_config_archive_map = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "nezha"
def __init__(
self,
vocab_size=21128,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
max_relative_position=64,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
classifier_dropout=0.1,
pad_token_id=0,
bos_token_id=2,
eos_token_id=3,
use_cache=True,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.max_relative_position = max_relative_position
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/nezha/modeling_nezha.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""PyTorch Nezha model."""
import math
import os
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
NextSentencePredictorOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_nezha import NezhaConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "sijunhe/nezha-cn-base"
_CONFIG_FOR_DOC = "NezhaConfig"
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"sijunhe/nezha-cn-base",
"sijunhe/nezha-cn-large",
"sijunhe/nezha-base-wwm",
"sijunhe/nezha-large-wwm",
# See all Nezha models at https://huggingface.co/models?filter=nezha
]
def load_tf_weights_in_nezha(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
class NezhaRelativePositionsEncoding(nn.Module):
"""Implement the Functional Relative Position Encoding"""
def __init__(self, length, depth, max_relative_position=127):
super().__init__()
vocab_size = max_relative_position * 2 + 1
range_vec = torch.arange(length)
range_mat = range_vec.repeat(length).view(length, length)
distance_mat = range_mat - torch.t(range_mat)
distance_mat_clipped = torch.clamp(distance_mat, -max_relative_position, max_relative_position)
final_mat = distance_mat_clipped + max_relative_position
embeddings_table = torch.zeros(vocab_size, depth)
position = torch.arange(0, vocab_size, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, depth, 2).float() * (-math.log(10000.0) / depth))
embeddings_table[:, 0::2] = torch.sin(position * div_term)
embeddings_table[:, 1::2] = torch.cos(position * div_term)
flat_relative_positions_matrix = final_mat.view(-1)
one_hot_relative_positions_matrix = torch.nn.functional.one_hot(
flat_relative_positions_matrix, num_classes=vocab_size
).float()
positions_encoding = torch.matmul(one_hot_relative_positions_matrix, embeddings_table)
my_shape = list(final_mat.size())
my_shape.append(depth)
positions_encoding = positions_encoding.view(my_shape)
self.register_buffer("positions_encoding", positions_encoding, persistent=False)
def forward(self, length):
return self.positions_encoding[:length, :length, :]
class NezhaEmbeddings(nn.Module):
"""Construct the embeddings from word and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"token_type_ids", torch.zeros((1, config.max_position_embeddings), dtype=torch.long), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class NezhaSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.relative_positions_encoding = NezhaRelativePositionsEncoding(
length=config.max_position_embeddings,
depth=self.attention_head_size,
max_relative_position=config.max_relative_position,
)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
batch_size, num_attention_heads, from_seq_length, to_seq_length = attention_scores.size()
relations_keys = self.relative_positions_encoding(to_seq_length)
query_layer_t = query_layer.permute(2, 0, 1, 3)
query_layer_r = query_layer_t.contiguous().view(
from_seq_length, batch_size * num_attention_heads, self.attention_head_size
)
key_position_scores = torch.matmul(query_layer_r, relations_keys.permute(0, 2, 1))
key_position_scores_r = key_position_scores.view(
from_seq_length, batch_size, num_attention_heads, from_seq_length
)
key_position_scores_r_t = key_position_scores_r.permute(1, 2, 0, 3)
attention_scores = attention_scores + key_position_scores_r_t
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in NezhaModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
relations_values = self.relative_positions_encoding(to_seq_length)
attention_probs_t = attention_probs.permute(2, 0, 1, 3)
attentions_probs_r = attention_probs_t.contiguous().view(
from_seq_length, batch_size * num_attention_heads, to_seq_length
)
value_position_scores = torch.matmul(attentions_probs_r, relations_values)
value_position_scores_r = value_position_scores.view(
from_seq_length, batch_size, num_attention_heads, self.attention_head_size
)
value_position_scores_r_t = value_position_scores_r.permute(1, 2, 0, 3)
context_layer = context_layer + value_position_scores_r_t
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Nezha
class NezhaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class NezhaAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = NezhaSelfAttention(config)
self.output = NezhaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nezha
class NezhaIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nezha
class NezhaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class NezhaLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = NezhaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = NezhaAttention(config)
self.intermediate = NezhaIntermediate(config)
self.output = NezhaOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Nezha
class NezhaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([NezhaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Nezha
class NezhaPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nezha
class NezhaPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nezha
class NezhaLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = NezhaPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nezha
class NezhaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->Nezha
class NezhaOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->Nezha
class NezhaPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class NezhaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NezhaConfig
load_tf_weights = load_tf_weights_in_nezha
base_model_prefix = "nezha"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@dataclass
class NezhaForPreTrainingOutput(ModelOutput):
"""
Output type of [`NezhaForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
NEZHA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`NezhaConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
NEZHA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Nezha Model transformer outputting raw hidden-states without any specific head on top.",
NEZHA_START_DOCSTRING,
)
class NezhaModel(NezhaPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = NezhaEmbeddings(config)
self.encoder = NezhaEncoder(config)
self.pooler = NezhaPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""
Nezha Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
sentence prediction (classification)` head.
""",
NEZHA_START_DOCSTRING,
)
class NezhaForPreTraining(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaPreTrainingHeads(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NezhaForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
next_sentence_label: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], NezhaForPreTrainingOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked),
the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, NezhaForPreTraining
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("sijunhe/nezha-cn-base")
>>> model = NezhaForPreTraining.from_pretrained("sijunhe/nezha-cn-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.prediction_logits
>>> seq_relationship_logits = outputs.seq_relationship_logits
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return NezhaForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings("""Nezha Model with a `language modeling` head on top.""", NEZHA_START_DOCSTRING)
class NezhaForMaskedLM(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `NezhaForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.nezha = NezhaModel(config, add_pooling_layer=False)
self.cls = NezhaOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
if self.config.pad_token_id is None:
raise ValueError("The PAD token should be defined for generation")
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings(
"""Nezha Model with a `next sentence prediction (classification)` head on top.""",
NEZHA_START_DOCSTRING,
)
class NezhaForNextSentencePrediction(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, NezhaForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("sijunhe/nezha-cn-base")
>>> model = NezhaForNextSentencePrediction.from_pretrained("sijunhe/nezha-cn-base")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
>>> logits = outputs.logits
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
```
"""
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Nezha Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
NEZHA_START_DOCSTRING,
)
class NezhaForSequenceClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Nezha Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
NEZHA_START_DOCSTRING,
)
class NezhaForMultipleChoice(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
print(pooled_output.shape)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
print(logits.shape)
print(num_choices)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Nezha Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
NEZHA_START_DOCSTRING,
)
class NezhaForTokenClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Nezha Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
NEZHA_START_DOCSTRING,
)
class NezhaForQuestionAnswering(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/nezha/__init__.py
|
# Copyright 2022 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_nezha"] = [
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Team and The HuggingFace Inc. team. 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.
""" Decision Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class DecisionTransformerConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to
instantiate a Decision Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the standard
DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as
part of the architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
state_dim (`int`, *optional*, defaults to 17):
The state size for the RL environment
act_dim (`int`, *optional*, defaults to 4):
The size of the output action space
hidden_size (`int`, *optional*, defaults to 128):
The size of the hidden layers
max_ep_len (`int`, *optional*, defaults to 4096):
The maximum length of an episode in the environment
action_tanh (`bool`, *optional*, defaults to True):
Whether to use a tanh activation on action prediction
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DecisionTransformerModel`].
n_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_layer (`int`, *optional*, defaults to 3):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 1):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*):
Dimensionality of the inner feed-forward layers. If unset, will default to 4 times `n_embd`.
activation_function (`str`, *optional*, defaults to `"gelu"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import DecisionTransformerConfig, DecisionTransformerModel
>>> # Initializing a DecisionTransformer configuration
>>> configuration = DecisionTransformerConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = DecisionTransformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "decision_transformer"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
state_dim=17,
act_dim=4,
hidden_size=128,
max_ep_len=4096,
action_tanh=True,
vocab_size=1,
n_positions=1024,
n_layer=3,
n_head=1,
n_inner=None,
activation_function="relu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.state_dim = state_dim
self.act_dim = act_dim
self.hidden_size = hidden_size
self.max_ep_len = max_ep_len
self.action_tanh = action_tanh
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Team The HuggingFace Inc. team. 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.
""" PyTorch DecisionTransformer model."""
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_decision_transformer import DecisionTransformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "edbeeching/decision-transformer-gym-hopper-medium"
_CONFIG_FOR_DOC = "DecisionTransformerConfig"
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"edbeeching/decision-transformer-gym-hopper-medium",
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
]
# Copied from transformers.models.gpt2.modeling_gpt2.load_tf_weights_in_gpt2
def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
"""Load tf checkpoints in a pytorch model"""
try:
import re
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(gpt2_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split("/")
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+\d+", m_name):
scope_names = re.split(r"(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except ValueError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Attention with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2Attention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.split_size = self.embed_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
if self.is_cross_attention:
self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
else:
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
self.num_heads = self.num_heads - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / torch.full(
[], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
attn_weights = attn_weights / float(self.layer_idx + 1)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
# Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
bsz, num_heads, q_seq_len, dk = query.size()
_, _, k_seq_len, _ = key.size()
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if self.scale_attn_by_inverse_layer_idx:
scale_factor /= float(self.layer_idx + 1)
# Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
with autocast(enabled=False):
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `DecisionTransformerGPT2Attention(..., is_cross_attention=True)`."
)
query = self.q_attn(hidden_states)
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
value = self._split_heads(value, self.num_heads, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
present = (key, value)
else:
present = None
if self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
else:
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2MLP with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2MLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Block with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2Block(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = DecisionTransformerGPT2Attention(config, layer_idx=layer_idx)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if config.add_cross_attention:
self.crossattention = DecisionTransformerGPT2Attention(
config, is_cross_attention=True, layer_idx=layer_idx
)
self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = DecisionTransformerGPT2MLP(inner_dim, config)
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions, cross_attentions)
class DecisionTransformerGPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DecisionTransformerConfig
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if "c_proj" in name and "weight" in name:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
class DecisionTransformerGPT2Model(DecisionTransformerGPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList(
[DecisionTransformerGPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)]
)
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Model.forward
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# GPT2Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.add_cross_attention and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
@dataclass
class DecisionTransformerOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
state_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, state_dim)`):
Environment state predictions
action_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, action_dim)`):
Model action predictions
return_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 1)`):
Predicted returns for each state
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
state_preds: torch.FloatTensor = None
action_preds: torch.FloatTensor = None
return_preds: torch.FloatTensor = None
hidden_states: torch.FloatTensor = None
attentions: torch.FloatTensor = None
last_hidden_state: torch.FloatTensor = None
class DecisionTransformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DecisionTransformerConfig
base_model_prefix = "decision_transformer"
main_input_name = "states"
supports_gradient_checkpointing = False
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
DECISION_TRANSFORMER_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`~DecisionTransformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
DECISION_TRANSFORMER_INPUTS_DOCSTRING = r"""
Args:
states (`torch.FloatTensor` of shape `(batch_size, episode_length, state_dim)`):
The states for each step in the trajectory
actions (`torch.FloatTensor` of shape `(batch_size, episode_length, act_dim)`):
The actions taken by the "expert" policy for the current state, these are masked for auto regressive
prediction
rewards (`torch.FloatTensor` of shape `(batch_size, episode_length, 1)`):
The rewards for each state, action
returns_to_go (`torch.FloatTensor` of shape `(batch_size, episode_length, 1)`):
The returns for each state in the trajectory
timesteps (`torch.LongTensor` of shape `(batch_size, episode_length)`):
The timestep for each step in the trajectory
attention_mask (`torch.FloatTensor` of shape `(batch_size, episode_length)`):
Masking, used to mask the actions when performing autoregressive prediction
"""
@add_start_docstrings("The Decision Transformer Model", DECISION_TRANSFORMER_START_DOCSTRING)
class DecisionTransformerModel(DecisionTransformerPreTrainedModel):
"""
The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL
setting. Refer to the paper for more details: https://arxiv.org/abs/2106.01345
"""
def __init__(self, config):
super().__init__(config)
self.config = config
self.hidden_size = config.hidden_size
# note: the only difference between this GPT2Model and the default Huggingface version
# is that the positional embeddings are removed (since we'll add those ourselves)
self.encoder = DecisionTransformerGPT2Model(config)
self.embed_timestep = nn.Embedding(config.max_ep_len, config.hidden_size)
self.embed_return = torch.nn.Linear(1, config.hidden_size)
self.embed_state = torch.nn.Linear(config.state_dim, config.hidden_size)
self.embed_action = torch.nn.Linear(config.act_dim, config.hidden_size)
self.embed_ln = nn.LayerNorm(config.hidden_size)
# note: we don't predict states or returns for the paper
self.predict_state = torch.nn.Linear(config.hidden_size, config.state_dim)
self.predict_action = nn.Sequential(
*([nn.Linear(config.hidden_size, config.act_dim)] + ([nn.Tanh()] if config.action_tanh else []))
)
self.predict_return = torch.nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(DECISION_TRANSFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=DecisionTransformerOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
states: Optional[torch.FloatTensor] = None,
actions: Optional[torch.FloatTensor] = None,
rewards: Optional[torch.FloatTensor] = None,
returns_to_go: Optional[torch.FloatTensor] = None,
timesteps: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], DecisionTransformerOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import DecisionTransformerModel
>>> import torch
>>> model = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
>>> # evaluation
>>> model = model.to(device)
>>> model.eval()
>>> env = gym.make("Hopper-v3")
>>> state_dim = env.observation_space.shape[0]
>>> act_dim = env.action_space.shape[0]
>>> state = env.reset()
>>> states = torch.from_numpy(state).reshape(1, 1, state_dim).to(device=device, dtype=torch.float32)
>>> actions = torch.zeros((1, 1, act_dim), device=device, dtype=torch.float32)
>>> rewards = torch.zeros(1, 1, device=device, dtype=torch.float32)
>>> target_return = torch.tensor(TARGET_RETURN, dtype=torch.float32).reshape(1, 1)
>>> timesteps = torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
>>> attention_mask = torch.zeros(1, 1, device=device, dtype=torch.float32)
>>> # forward pass
>>> with torch.no_grad():
... state_preds, action_preds, return_preds = model(
... states=states,
... actions=actions,
... rewards=rewards,
... returns_to_go=target_return,
... timesteps=timesteps,
... attention_mask=attention_mask,
... return_dict=False,
... )
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, seq_length = states.shape[0], states.shape[1]
if attention_mask is None:
# attention mask for GPT: 1 if can be attended to, 0 if not
attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
# embed each modality with a different head
state_embeddings = self.embed_state(states)
action_embeddings = self.embed_action(actions)
returns_embeddings = self.embed_return(returns_to_go)
time_embeddings = self.embed_timestep(timesteps)
# time embeddings are treated similar to positional embeddings
state_embeddings = state_embeddings + time_embeddings
action_embeddings = action_embeddings + time_embeddings
returns_embeddings = returns_embeddings + time_embeddings
# this makes the sequence look like (R_1, s_1, a_1, R_2, s_2, a_2, ...)
# which works nice in an autoregressive sense since states predict actions
stacked_inputs = (
torch.stack((returns_embeddings, state_embeddings, action_embeddings), dim=1)
.permute(0, 2, 1, 3)
.reshape(batch_size, 3 * seq_length, self.hidden_size)
)
stacked_inputs = self.embed_ln(stacked_inputs)
# to make the attention mask fit the stacked inputs, have to stack it as well
stacked_attention_mask = (
torch.stack((attention_mask, attention_mask, attention_mask), dim=1)
.permute(0, 2, 1)
.reshape(batch_size, 3 * seq_length)
)
device = stacked_inputs.device
# we feed in the input embeddings (not word indices as in NLP) to the model
encoder_outputs = self.encoder(
inputs_embeds=stacked_inputs,
attention_mask=stacked_attention_mask,
position_ids=torch.zeros(stacked_attention_mask.shape, device=device, dtype=torch.long),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
x = encoder_outputs[0]
# reshape x so that the second dimension corresponds to the original
# returns (0), states (1), or actions (2); i.e. x[:,1,t] is the token for s_t
x = x.reshape(batch_size, seq_length, 3, self.hidden_size).permute(0, 2, 1, 3)
# get predictions
return_preds = self.predict_return(x[:, 2]) # predict next return given state and action
state_preds = self.predict_state(x[:, 2]) # predict next state given state and action
action_preds = self.predict_action(x[:, 1]) # predict next action given state
if not return_dict:
return (state_preds, action_preds, return_preds)
return DecisionTransformerOutput(
last_hidden_state=encoder_outputs.last_hidden_state,
state_preds=state_preds,
action_preds=action_preds,
return_preds=return_preds,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/decision_transformer/__init__.py
|
# Copyright 2020 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_decision_transformer": [
"DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"DecisionTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_decision_transformer"] = [
"DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"DecisionTransformerGPT2Model",
"DecisionTransformerGPT2PreTrainedModel",
"DecisionTransformerModel",
"DecisionTransformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
DecisionTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
DecisionTransformerGPT2Model,
DecisionTransformerGPT2PreTrainedModel,
DecisionTransformerModel,
DecisionTransformerPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/configuration_detr.py
|
# coding=utf-8
# Copyright 2021 Facebook AI Research and The HuggingFace Inc. team. 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.
""" DETR model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class DetrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DETR
[facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
case it will default to `ResNetConfig()`.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_queries (`int`, *optional*, defaults to 100):
Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetrModel`] can
detect in a single image. For COCO, we recommend 100 queries.
d_model (`int`, *optional*, defaults to 256):
Dimension of the layers.
encoder_layers (`int`, *optional*, defaults to 6):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
backbone (`str`, *optional*, defaults to `"resnet50"`):
Name of convolutional backbone to use in case `use_timm_backbone` = `True`. Supports any convolutional
backbone from the timm package. For a list of all available models, see [this
page](https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model).
use_pretrained_backbone (`bool`, *optional*, defaults to `True`):
Whether to use pretrained weights for the backbone. Only supported when `use_timm_backbone` = `True`.
dilation (`bool`, *optional*, defaults to `False`):
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
`use_timm_backbone` = `True`.
class_cost (`float`, *optional*, defaults to 1):
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
mask_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
bbox_loss_coefficient (`float`, *optional*, defaults to 5):
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (`float`, *optional*, defaults to 0.1):
Relative classification weight of the 'no-object' class in the object detection loss.
Examples:
```python
>>> from transformers import DetrConfig, DetrModel
>>> # Initializing a DETR facebook/detr-resnet-50 style configuration
>>> configuration = DetrConfig()
>>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
>>> model = DetrModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "detr"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__(
self,
use_timm_backbone=True,
backbone_config=None,
num_channels=3,
num_queries=100,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=8,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
init_xavier_std=1.0,
auxiliary_loss=False,
position_embedding_type="sine",
backbone="resnet50",
use_pretrained_backbone=True,
dilation=False,
class_cost=1,
bbox_cost=5,
giou_cost=2,
mask_loss_coefficient=1,
dice_loss_coefficient=1,
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
eos_coefficient=0.1,
**kwargs,
):
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
# set timm attributes to None
dilation, backbone, use_pretrained_backbone = None, None, None
self.use_timm_backbone = use_timm_backbone
self.backbone_config = backbone_config
self.num_channels = num_channels
self.num_queries = num_queries
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.init_xavier_std = init_xavier_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.num_hidden_layers = encoder_layers
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.dilation = dilation
# Hungarian matcher
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
# Loss coefficients
self.mask_loss_coefficient = mask_loss_coefficient
self.dice_loss_coefficient = dice_loss_coefficient
self.bbox_loss_coefficient = bbox_loss_coefficient
self.giou_loss_coefficient = giou_loss_coefficient
self.eos_coefficient = eos_coefficient
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model
@classmethod
def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
"""Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration.
Args:
backbone_config ([`PretrainedConfig`]):
The backbone configuration.
Returns:
[`DetrConfig`]: An instance of a configuration object
"""
return cls(backbone_config=backbone_config, **kwargs)
class DetrOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-5
@property
def default_onnx_opset(self) -> int:
return 12
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/modeling_detr.py
|
# coding=utf-8
# Copyright 2021 Facebook AI Research The HuggingFace Inc. team. 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.
""" PyTorch DETR model."""
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_scipy_available,
is_timm_available,
is_vision_available,
logging,
replace_return_docstrings,
requires_backends,
)
from ..auto import AutoBackbone
from .configuration_detr import DetrConfig
if is_scipy_available():
from scipy.optimize import linear_sum_assignment
if is_timm_available():
from timm import create_model
if is_vision_available():
from transformers.image_transforms import center_to_corners_format
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "DetrConfig"
_CHECKPOINT_FOR_DOC = "facebook/detr-resnet-50"
DETR_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/detr-resnet-50",
# See all DETR models at https://huggingface.co/models?filter=detr
]
@dataclass
class DetrDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through a layernorm. This is useful when training the model with auxiliary decoding losses.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
"""
intermediate_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
class DetrModelOutput(Seq2SeqModelOutput):
"""
Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through a layernorm. This is useful when training the model with auxiliary decoding losses.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
"""
intermediate_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
class DetrObjectDetectionOutput(ModelOutput):
"""
Output type of [`DetrForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.
loss_dict (`Dict`, *optional*):
A dictionary containing the individual losses. Useful for logging.
logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
Classification logits (including no-object) for all queries.
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use [`~DetrImageProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (`list[Dict]`, *optional*):
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
`pred_boxes`) for each decoder layer.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
auxiliary_outputs: Optional[List[Dict]] = None
last_hidden_state: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class DetrSegmentationOutput(ModelOutput):
"""
Output type of [`DetrForSegmentation`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.
loss_dict (`Dict`, *optional*):
A dictionary containing the individual losses. Useful for logging.
logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
Classification logits (including no-object) for all queries.
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use [`~DetrImageProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`):
Segmentation masks logits for all queries. See also
[`~DetrImageProcessor.post_process_semantic_segmentation`] or
[`~DetrImageProcessor.post_process_instance_segmentation`]
[`~DetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic
segmentation masks respectively.
auxiliary_outputs (`list[Dict]`, *optional*):
Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
`pred_boxes`) for each decoder layer.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
pred_masks: torch.FloatTensor = None
auxiliary_outputs: Optional[List[Dict]] = None
last_hidden_state: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
# BELOW: utilities copied from
# https://github.com/facebookresearch/detr/blob/master/backbone.py
class DetrFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
def __init__(self, n):
super().__init__()
self.register_buffer("weight", torch.ones(n))
self.register_buffer("bias", torch.zeros(n))
self.register_buffer("running_mean", torch.zeros(n))
self.register_buffer("running_var", torch.ones(n))
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
num_batches_tracked_key = prefix + "num_batches_tracked"
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x):
# move reshapes to the beginning
# to make it user-friendly
weight = self.weight.reshape(1, -1, 1, 1)
bias = self.bias.reshape(1, -1, 1, 1)
running_var = self.running_var.reshape(1, -1, 1, 1)
running_mean = self.running_mean.reshape(1, -1, 1, 1)
epsilon = 1e-5
scale = weight * (running_var + epsilon).rsqrt()
bias = bias - running_mean * scale
return x * scale + bias
def replace_batch_norm(model):
r"""
Recursively replace all `torch.nn.BatchNorm2d` with `DetrFrozenBatchNorm2d`.
Args:
model (torch.nn.Module):
input model
"""
for name, module in model.named_children():
if isinstance(module, nn.BatchNorm2d):
new_module = DetrFrozenBatchNorm2d(module.num_features)
if not module.weight.device == torch.device("meta"):
new_module.weight.data.copy_(module.weight)
new_module.bias.data.copy_(module.bias)
new_module.running_mean.data.copy_(module.running_mean)
new_module.running_var.data.copy_(module.running_var)
model._modules[name] = new_module
if len(list(module.children())) > 0:
replace_batch_norm(module)
class DetrConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
self.config = config
if config.use_timm_backbone:
requires_backends(self, ["timm"])
kwargs = {}
if config.dilation:
kwargs["output_stride"] = 16
backbone = create_model(
config.backbone,
pretrained=config.use_pretrained_backbone,
features_only=True,
out_indices=(1, 2, 3, 4),
in_chans=config.num_channels,
**kwargs,
)
else:
backbone = AutoBackbone.from_config(config.backbone_config)
# replace batch norm by frozen batch norm
with torch.no_grad():
replace_batch_norm(backbone)
self.model = backbone
self.intermediate_channel_sizes = (
self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
)
backbone_model_type = config.backbone if config.use_timm_backbone else config.backbone_config.model_type
if "resnet" in backbone_model_type:
for name, parameter in self.model.named_parameters():
if config.use_timm_backbone:
if "layer2" not in name and "layer3" not in name and "layer4" not in name:
parameter.requires_grad_(False)
else:
if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
parameter.requires_grad_(False)
def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
# send pixel_values through the model to get list of feature maps
features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps
out = []
for feature_map in features:
# downsample pixel_mask to match shape of corresponding feature_map
mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
out.append((feature_map, mask))
return out
class DetrConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embedding = position_embedding
def forward(self, pixel_values, pixel_mask):
# send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
out = self.conv_encoder(pixel_values, pixel_mask)
pos = []
for feature_map, mask in out:
# position encoding
pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))
return out, pos
class DetrSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None):
super().__init__()
self.embedding_dim = embedding_dim
self.temperature = temperature
self.normalize = normalize
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
if scale is None:
scale = 2 * math.pi
self.scale = scale
def forward(self, pixel_values, pixel_mask):
if pixel_mask is None:
raise ValueError("No pixel mask provided")
y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale
dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
class DetrLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256):
super().__init__()
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Embedding(50, embedding_dim)
def forward(self, pixel_values, pixel_mask=None):
height, width = pixel_values.shape[-2:]
width_values = torch.arange(width, device=pixel_values.device)
height_values = torch.arange(height, device=pixel_values.device)
x_emb = self.column_embeddings(width_values)
y_emb = self.row_embeddings(height_values)
pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
pos = pos.permute(2, 0, 1)
pos = pos.unsqueeze(0)
pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
return pos
def build_position_encoding(config):
n_steps = config.d_model // 2
if config.position_embedding_type == "sine":
# TODO find a better way of exposing other arguments
position_embedding = DetrSinePositionEmbedding(n_steps, normalize=True)
elif config.position_embedding_type == "learned":
position_embedding = DetrLearnedPositionEmbedding(n_steps)
else:
raise ValueError(f"Not supported {config.position_embedding_type}")
return position_embedding
class DetrAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if self.head_dim * num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
return tensor if object_queries is None else tensor + object_queries
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
object_queries: Optional[torch.Tensor] = None,
key_value_states: Optional[torch.Tensor] = None,
spatial_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
position_embeddings = kwargs.pop("position_ebmeddings", None)
key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if key_value_position_embeddings is not None and spatial_position_embeddings is not None:
raise ValueError(
"Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
if key_value_position_embeddings is not None:
logger.warning_once(
"key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead"
)
spatial_position_embeddings = key_value_position_embeddings
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size, target_len, embed_dim = hidden_states.size()
# add position embeddings to the hidden states before projecting to queries and keys
if object_queries is not None:
hidden_states_original = hidden_states
hidden_states = self.with_pos_embed(hidden_states, object_queries)
# add key-value position embeddings to the key value states
if spatial_position_embeddings is not None:
key_value_states_original = key_value_states
key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
source_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
raise ValueError(
f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (batch_size, 1, target_len, source_len):
raise ValueError(
f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
f" {attention_mask.size()}"
)
attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
class DetrEncoderLayer(nn.Module):
def __init__(self, config: DetrConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetrAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
object_queries: torch.Tensor = None,
output_attentions: bool = False,
**kwargs,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
values.
object_queries (`torch.FloatTensor`, *optional*):
Object queries (also called content embeddings), to be added to the hidden states.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
object_queries=object_queries,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if self.training:
if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class DetrDecoderLayer(nn.Module):
def __init__(self, config: DetrConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetrAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = DetrAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
object_queries: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
**kwargs,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
values.
object_queries (`torch.FloatTensor`, *optional*):
object_queries that are added to the hidden states
in the cross-attention layer.
query_position_embeddings (`torch.FloatTensor`, *optional*):
position embeddings that are added to the queries and keys
in the self-attention layer.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
object_queries=query_position_embeddings,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
object_queries=query_position_embeddings,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
spatial_position_embeddings=object_queries,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
class DetrClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor):
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class DetrPreTrainedModel(PreTrainedModel):
config_class = DetrConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [r"DetrConvEncoder", r"DetrEncoderLayer", r"DetrDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
xavier_std = self.config.init_xavier_std
if isinstance(module, DetrMHAttentionMap):
nn.init.zeros_(module.k_linear.bias)
nn.init.zeros_(module.q_linear.bias)
nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std)
nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std)
elif isinstance(module, DetrLearnedPositionEmbedding):
nn.init.uniform_(module.row_embeddings.weight)
nn.init.uniform_(module.column_embeddings.weight)
if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
DETR_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`DetrConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
DETR_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using [`AutoImageProcessor`]. See [`DetrImageProcessor.__call__`] for details.
pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
Not used by default. Can be used to mask object queries.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class DetrEncoder(DetrPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`DetrEncoderLayer`].
The encoder updates the flattened feature map through multiple self-attention layers.
Small tweak for DETR:
- object_queries are added to the forward pass.
Args:
config: DetrConfig
"""
def __init__(self, config: DetrConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
self.layers = nn.ModuleList([DetrEncoderLayer(config) for _ in range(config.encoder_layers)])
# in the original DETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
inputs_embeds=None,
attention_mask=None,
object_queries=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
- 1 for pixel features that are real (i.e. **not masked**),
- 0 for pixel features that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Object queries that are added to the queries in each self-attention layer.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states = inputs_embeds
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
# we add object_queries as extra input to the encoder_layer
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
object_queries=object_queries,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class DetrDecoder(DetrPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some small tweaks for DETR:
- object_queries and query_position_embeddings are added to the forward pass.
- if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers.
Args:
config: DetrConfig
"""
def __init__(self, config: DetrConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)])
# in DETR, the decoder uses layernorm after the last decoder layer output
self.layernorm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
object_queries=None,
query_position_embeddings=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
The query embeddings that are passed into the decoder.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`:
- 1 for queries that are **not masked**,
- 0 for queries that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
in `[0, 1]`:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Object queries that are added to the queries and keys in each cross-attention layer.
query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
, *optional*): Position embeddings that are added to the values and keys in each self-attention layer.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if inputs_embeds is not None:
hidden_states = inputs_embeds
input_shape = inputs_embeds.size()[:-1]
combined_attention_mask = None
if attention_mask is not None and combined_attention_mask is not None:
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
combined_attention_mask = combined_attention_mask + _prepare_4d_attention_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# optional intermediate hidden states
intermediate = () if self.config.auxiliary_loss else None
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
combined_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
object_queries=object_queries,
query_position_embeddings=query_position_embeddings,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if self.config.auxiliary_loss:
hidden_states = self.layernorm(hidden_states)
intermediate += (hidden_states,)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# finally, apply layernorm
hidden_states = self.layernorm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
# stack intermediate decoder activations
if self.config.auxiliary_loss:
intermediate = torch.stack(intermediate)
if not return_dict:
return tuple(
v
for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate]
if v is not None
)
return DetrDecoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
intermediate_hidden_states=intermediate,
)
@add_start_docstrings(
"""
The bare DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without
any specific head on top.
""",
DETR_START_DOCSTRING,
)
class DetrModel(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone = DetrConvEncoder(config)
object_queries = build_position_encoding(config)
self.backbone = DetrConvModel(backbone, object_queries)
# Create projection layer
self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1)
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
self.encoder = DetrEncoder(config)
self.decoder = DetrDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def freeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(False)
def unfreeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(True)
@add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=DetrModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.FloatTensor] = None,
encoder_outputs: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], DetrModelOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, DetrModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
>>> model = DetrModel.from_pretrained("facebook/detr-resnet-50")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> # the last hidden states are the final query embeddings of the Transformer decoder
>>> # these are of shape (batch_size, num_queries, hidden_size)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 100, 256]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, num_channels, height, width = pixel_values.shape
device = pixel_values.device
if pixel_mask is None:
pixel_mask = torch.ones(((batch_size, height, width)), device=device)
# First, sent pixel_values + pixel_mask through Backbone to obtain the features
# pixel_values should be of shape (batch_size, num_channels, height, width)
# pixel_mask should be of shape (batch_size, height, width)
features, object_queries_list = self.backbone(pixel_values, pixel_mask)
# get final feature map and downsampled mask
feature_map, mask = features[-1]
if mask is None:
raise ValueError("Backbone does not return downsampled pixel mask")
# Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
projected_feature_map = self.input_projection(feature_map)
# Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
# In other words, turn their shape into (batch_size, sequence_length, hidden_size)
flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
flattened_mask = mask.flatten(1)
# Fourth, sent flattened_features + flattened_mask + position embeddings through encoder
# flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
# flattened_mask is a Tensor of shape (batch_size, heigth*width)
if encoder_outputs is None:
encoder_outputs = self.encoder(
inputs_embeds=flattened_features,
attention_mask=flattened_mask,
object_queries=object_queries,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1)
queries = torch.zeros_like(query_position_embeddings)
# decoder outputs consists of (dec_features, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
inputs_embeds=queries,
attention_mask=None,
object_queries=object_queries,
query_position_embeddings=query_position_embeddings,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=flattened_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return DetrModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
)
@add_start_docstrings(
"""
DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks
such as COCO detection.
""",
DETR_START_DOCSTRING,
)
class DetrForObjectDetection(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# DETR encoder-decoder model
self.model = DetrModel(config)
# Object detection heads
self.class_labels_classifier = nn.Linear(
config.d_model, config.num_labels + 1
) # We add one for the "no object" class
self.bbox_predictor = DetrMLPPredictionHead(
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
)
# Initialize weights and apply final processing
self.post_init()
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
@add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=DetrObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.FloatTensor] = None,
encoder_outputs: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[List[dict]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], DetrObjectDetectionOutput]:
r"""
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, DetrForObjectDetection
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
>>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # convert outputs (bounding boxes and class logits) to COCO API
>>> target_sizes = torch.tensor([image.size[::-1]])
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
... 0
... ]
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... print(
... f"Detected {model.config.id2label[label.item()]} with confidence "
... f"{round(score.item(), 3)} at location {box}"
... )
Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98]
Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66]
Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76]
Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93]
Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# First, sent images through DETR base model to obtain encoder + decoder outputs
outputs = self.model(
pixel_values,
pixel_mask=pixel_mask,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
# class logits + predicted bounding boxes
logits = self.class_labels_classifier(sequence_output)
pred_boxes = self.bbox_predictor(sequence_output).sigmoid()
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
# First: create the matcher
matcher = DetrHungarianMatcher(
class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
)
# Second: create the criterion
losses = ["labels", "boxes", "cardinality"]
criterion = DetrLoss(
matcher=matcher,
num_classes=self.config.num_labels,
eos_coef=self.config.eos_coefficient,
losses=losses,
)
criterion.to(self.device)
# Third: compute the losses, based on outputs and labels
outputs_loss = {}
outputs_loss["logits"] = logits
outputs_loss["pred_boxes"] = pred_boxes
if self.config.auxiliary_loss:
intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
outputs_class = self.class_labels_classifier(intermediate)
outputs_coord = self.bbox_predictor(intermediate).sigmoid()
auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
outputs_loss["auxiliary_outputs"] = auxiliary_outputs
loss_dict = criterion(outputs_loss, labels)
# Fourth: compute total loss, as a weighted sum of the various losses
weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
weight_dict["loss_giou"] = self.config.giou_loss_coefficient
if self.config.auxiliary_loss:
aux_weight_dict = {}
for i in range(self.config.decoder_layers - 1):
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
weight_dict.update(aux_weight_dict)
loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
if not return_dict:
if auxiliary_outputs is not None:
output = (logits, pred_boxes) + auxiliary_outputs + outputs
else:
output = (logits, pred_boxes) + outputs
return ((loss, loss_dict) + output) if loss is not None else output
return DetrObjectDetectionOutput(
loss=loss,
loss_dict=loss_dict,
logits=logits,
pred_boxes=pred_boxes,
auxiliary_outputs=auxiliary_outputs,
last_hidden_state=outputs.last_hidden_state,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, for tasks
such as COCO panoptic.
""",
DETR_START_DOCSTRING,
)
class DetrForSegmentation(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# object detection model
self.detr = DetrForObjectDetection(config)
# segmentation head
hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads
intermediate_channel_sizes = self.detr.model.backbone.conv_encoder.intermediate_channel_sizes
self.mask_head = DetrMaskHeadSmallConv(
hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size
)
self.bbox_attention = DetrMHAttentionMap(
hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(DETR_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=DetrSegmentationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.FloatTensor] = None,
encoder_outputs: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[List[dict]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], DetrSegmentationOutput]:
r"""
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each
dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels,
bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves
should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a
`torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a
`torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`.
Returns:
Examples:
```python
>>> import io
>>> import requests
>>> from PIL import Image
>>> import torch
>>> import numpy
>>> from transformers import AutoImageProcessor, DetrForSegmentation
>>> from transformers.image_transforms import rgb_to_id
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
>>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
>>> # Segmentation results are returned as a list of dictionaries
>>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)])
>>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
>>> panoptic_seg = result[0]["segmentation"]
>>> # Get prediction score and segment_id to class_id mapping of each segment
>>> panoptic_segments_info = result[0]["segments_info"]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, num_channels, height, width = pixel_values.shape
device = pixel_values.device
if pixel_mask is None:
pixel_mask = torch.ones((batch_size, height, width), device=device)
# First, get list of feature maps and position embeddings
features, object_queries_list = self.detr.model.backbone(pixel_values, pixel_mask=pixel_mask)
# Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
feature_map, mask = features[-1]
batch_size, num_channels, height, width = feature_map.shape
projected_feature_map = self.detr.model.input_projection(feature_map)
# Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
# In other words, turn their shape into (batch_size, sequence_length, hidden_size)
flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
flattened_mask = mask.flatten(1)
# Fourth, sent flattened_features + flattened_mask + position embeddings through encoder
# flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
# flattened_mask is a Tensor of shape (batch_size, heigth*width)
if encoder_outputs is None:
encoder_outputs = self.detr.model.encoder(
inputs_embeds=flattened_features,
attention_mask=flattened_mask,
object_queries=object_queries,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output)
query_position_embeddings = self.detr.model.query_position_embeddings.weight.unsqueeze(0).repeat(
batch_size, 1, 1
)
queries = torch.zeros_like(query_position_embeddings)
# decoder outputs consists of (dec_features, dec_hidden, dec_attn)
decoder_outputs = self.detr.model.decoder(
inputs_embeds=queries,
attention_mask=None,
object_queries=object_queries,
query_position_embeddings=query_position_embeddings,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=flattened_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = decoder_outputs[0]
# Sixth, compute logits, pred_boxes and pred_masks
logits = self.detr.class_labels_classifier(sequence_output)
pred_boxes = self.detr.bbox_predictor(sequence_output).sigmoid()
memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width)
mask = flattened_mask.view(batch_size, height, width)
# FIXME h_boxes takes the last one computed, keep this in mind
# important: we need to reverse the mask, since in the original implementation the mask works reversed
# bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32)
bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask)
seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]])
pred_masks = seg_masks.view(batch_size, self.detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1])
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
# First: create the matcher
matcher = DetrHungarianMatcher(
class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
)
# Second: create the criterion
losses = ["labels", "boxes", "cardinality", "masks"]
criterion = DetrLoss(
matcher=matcher,
num_classes=self.config.num_labels,
eos_coef=self.config.eos_coefficient,
losses=losses,
)
criterion.to(self.device)
# Third: compute the losses, based on outputs and labels
outputs_loss = {}
outputs_loss["logits"] = logits
outputs_loss["pred_boxes"] = pred_boxes
outputs_loss["pred_masks"] = pred_masks
if self.config.auxiliary_loss:
intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1]
outputs_class = self.class_labels_classifier(intermediate)
outputs_coord = self.bbox_predictor(intermediate).sigmoid()
auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
outputs_loss["auxiliary_outputs"] = auxiliary_outputs
loss_dict = criterion(outputs_loss, labels)
# Fourth: compute total loss, as a weighted sum of the various losses
weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
weight_dict["loss_giou"] = self.config.giou_loss_coefficient
weight_dict["loss_mask"] = self.config.mask_loss_coefficient
weight_dict["loss_dice"] = self.config.dice_loss_coefficient
if self.config.auxiliary_loss:
aux_weight_dict = {}
for i in range(self.config.decoder_layers - 1):
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
weight_dict.update(aux_weight_dict)
loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
if not return_dict:
if auxiliary_outputs is not None:
output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs
else:
output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs
return ((loss, loss_dict) + output) if loss is not None else output
return DetrSegmentationOutput(
loss=loss,
loss_dict=loss_dict,
logits=logits,
pred_boxes=pred_boxes,
pred_masks=pred_masks,
auxiliary_outputs=auxiliary_outputs,
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def _expand(tensor, length: int):
return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1)
# taken from https://github.com/facebookresearch/detr/blob/master/models/segmentation.py
class DetrMaskHeadSmallConv(nn.Module):
"""
Simple convolutional head, using group norm. Upsampling is done using a FPN approach
"""
def __init__(self, dim, fpn_dims, context_dim):
super().__init__()
if dim % 8 != 0:
raise ValueError(
"The hidden_size + number of attention heads must be divisible by 8 as the number of groups in"
" GroupNorm is set to 8"
)
inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64]
self.lay1 = nn.Conv2d(dim, dim, 3, padding=1)
self.gn1 = nn.GroupNorm(8, dim)
self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1)
self.gn2 = nn.GroupNorm(min(8, inter_dims[1]), inter_dims[1])
self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1)
self.gn3 = nn.GroupNorm(min(8, inter_dims[2]), inter_dims[2])
self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1)
self.gn4 = nn.GroupNorm(min(8, inter_dims[3]), inter_dims[3])
self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1)
self.gn5 = nn.GroupNorm(min(8, inter_dims[4]), inter_dims[4])
self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1)
self.dim = dim
self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1)
self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1)
self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=1)
nn.init.constant_(m.bias, 0)
def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]):
# here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with
# the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32).
# We expand the projected feature map to match the number of heads.
x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1)
x = self.lay1(x)
x = self.gn1(x)
x = nn.functional.relu(x)
x = self.lay2(x)
x = self.gn2(x)
x = nn.functional.relu(x)
cur_fpn = self.adapter1(fpns[0])
if cur_fpn.size(0) != x.size(0):
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
x = self.lay3(x)
x = self.gn3(x)
x = nn.functional.relu(x)
cur_fpn = self.adapter2(fpns[1])
if cur_fpn.size(0) != x.size(0):
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
x = self.lay4(x)
x = self.gn4(x)
x = nn.functional.relu(x)
cur_fpn = self.adapter3(fpns[2])
if cur_fpn.size(0) != x.size(0):
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
x = self.lay5(x)
x = self.gn5(x)
x = nn.functional.relu(x)
x = self.out_lay(x)
return x
class DetrMHAttentionMap(nn.Module):
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None):
super().__init__()
self.num_heads = num_heads
self.hidden_dim = hidden_dim
self.dropout = nn.Dropout(dropout)
self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5
def forward(self, q, k, mask: Optional[Tensor] = None):
q = self.q_linear(q)
k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias)
queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads)
keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1])
weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head)
if mask is not None:
weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min)
weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size())
weights = self.dropout(weights)
return weights
def dice_loss(inputs, targets, num_boxes):
"""
Compute the DICE loss, similar to generalized IOU for masks
Args:
inputs: A float tensor of arbitrary shape.
The predictions for each example.
targets: A float tensor with the same shape as inputs. Stores the binary
classification label for each element in inputs (0 for the negative class and 1 for the positive
class).
"""
inputs = inputs.sigmoid()
inputs = inputs.flatten(1)
numerator = 2 * (inputs * targets).sum(1)
denominator = inputs.sum(-1) + targets.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
return loss.sum() / num_boxes
def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
"""
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
Args:
inputs (`torch.FloatTensor` of arbitrary shape):
The predictions for each example.
targets (`torch.FloatTensor` with the same shape as `inputs`)
A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class
and 1 for the positive class).
alpha (`float`, *optional*, defaults to `0.25`):
Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
gamma (`int`, *optional*, defaults to `2`):
Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
Returns:
Loss tensor
"""
prob = inputs.sigmoid()
ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
# add modulating factor
p_t = prob * targets + (1 - prob) * (1 - targets)
loss = ce_loss * ((1 - p_t) ** gamma)
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
loss = alpha_t * loss
return loss.mean(1).sum() / num_boxes
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
class DetrLoss(nn.Module):
"""
This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1)
we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair
of matched ground-truth / prediction (supervise class and box).
A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes`
parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is
the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to
be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2
(`max_obj_id` + 1). For more details on this, check the following discussion
https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223"
Args:
matcher (`DetrHungarianMatcher`):
Module able to compute a matching between targets and proposals.
num_classes (`int`):
Number of object categories, omitting the special no-object category.
eos_coef (`float`):
Relative classification weight applied to the no-object category.
losses (`List[str]`):
List of all the losses to be applied. See `get_loss` for a list of all available losses.
"""
def __init__(self, matcher, num_classes, eos_coef, losses):
super().__init__()
self.matcher = matcher
self.num_classes = num_classes
self.eos_coef = eos_coef
self.losses = losses
empty_weight = torch.ones(self.num_classes + 1)
empty_weight[-1] = self.eos_coef
self.register_buffer("empty_weight", empty_weight)
# removed logging parameter, which was part of the original implementation
def loss_labels(self, outputs, targets, indices, num_boxes):
"""
Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim
[nb_target_boxes]
"""
if "logits" not in outputs:
raise KeyError("No logits were found in the outputs")
source_logits = outputs["logits"]
idx = self._get_source_permutation_idx(indices)
target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(
source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
)
target_classes[idx] = target_classes_o
loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight)
losses = {"loss_ce": loss_ce}
return losses
@torch.no_grad()
def loss_cardinality(self, outputs, targets, indices, num_boxes):
"""
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
"""
logits = outputs["logits"]
device = logits.device
target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
# Count the number of predictions that are NOT "no-object" (which is the last class)
card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float())
losses = {"cardinality_error": card_err}
return losses
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
if "pred_boxes" not in outputs:
raise KeyError("No predicted boxes found in outputs")
idx = self._get_source_permutation_idx(indices)
source_boxes = outputs["pred_boxes"][idx]
target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)
loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none")
losses = {}
losses["loss_bbox"] = loss_bbox.sum() / num_boxes
loss_giou = 1 - torch.diag(
generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes))
)
losses["loss_giou"] = loss_giou.sum() / num_boxes
return losses
def loss_masks(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the masks: the focal loss and the dice loss.
Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
"""
if "pred_masks" not in outputs:
raise KeyError("No predicted masks found in outputs")
source_idx = self._get_source_permutation_idx(indices)
target_idx = self._get_target_permutation_idx(indices)
source_masks = outputs["pred_masks"]
source_masks = source_masks[source_idx]
masks = [t["masks"] for t in targets]
# TODO use valid to mask invalid areas due to padding in loss
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
target_masks = target_masks.to(source_masks)
target_masks = target_masks[target_idx]
# upsample predictions to the target size
source_masks = nn.functional.interpolate(
source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
)
source_masks = source_masks[:, 0].flatten(1)
target_masks = target_masks.flatten(1)
target_masks = target_masks.view(source_masks.shape)
losses = {
"loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
"loss_dice": dice_loss(source_masks, target_masks, num_boxes),
}
return losses
def _get_source_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
source_idx = torch.cat([source for (source, _) in indices])
return batch_idx, source_idx
def _get_target_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
target_idx = torch.cat([target for (_, target) in indices])
return batch_idx, target_idx
def get_loss(self, loss, outputs, targets, indices, num_boxes):
loss_map = {
"labels": self.loss_labels,
"cardinality": self.loss_cardinality,
"boxes": self.loss_boxes,
"masks": self.loss_masks,
}
if loss not in loss_map:
raise ValueError(f"Loss {loss} not supported")
return loss_map[loss](outputs, targets, indices, num_boxes)
def forward(self, outputs, targets):
"""
This performs the loss computation.
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
losses applied, see each loss' doc.
"""
outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}
# Retrieve the matching between the outputs of the last layer and the targets
indices = self.matcher(outputs_without_aux, targets)
# Compute the average number of target boxes across all nodes, for normalization purposes
num_boxes = sum(len(t["class_labels"]) for t in targets)
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
# (Niels): comment out function below, distributed training to be added
# if is_dist_avail_and_initialized():
# torch.distributed.all_reduce(num_boxes)
# (Niels) in original implementation, num_boxes is divided by get_world_size()
num_boxes = torch.clamp(num_boxes, min=1).item()
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "auxiliary_outputs" in outputs:
for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
indices = self.matcher(auxiliary_outputs, targets)
for loss in self.losses:
if loss == "masks":
# Intermediate masks losses are too costly to compute, we ignore them.
continue
l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
class DetrMLPPredictionHead(nn.Module):
"""
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
height and width of a bounding box w.r.t. an image.
Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
def forward(self, x):
for i, layer in enumerate(self.layers):
x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
# taken from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
class DetrHungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are
un-matched (and thus treated as non-objects).
Args:
class_cost:
The relative weight of the classification error in the matching cost.
bbox_cost:
The relative weight of the L1 error of the bounding box coordinates in the matching cost.
giou_cost:
The relative weight of the giou loss of the bounding box in the matching cost.
"""
def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
super().__init__()
requires_backends(self, ["scipy"])
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
raise ValueError("All costs of the Matcher can't be 0")
@torch.no_grad()
def forward(self, outputs, targets):
"""
Args:
outputs (`dict`):
A dictionary that contains at least these entries:
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
* "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates.
targets (`List[dict]`):
A list of targets (len(targets) = batch_size), where each target is a dict containing:
* "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of
ground-truth
objects in the target) containing the class labels
* "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates.
Returns:
`List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
"""
batch_size, num_queries = outputs["logits"].shape[:2]
# We flatten to compute the cost matrices in a batch
out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]
out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4]
# Also concat the target labels and boxes
target_ids = torch.cat([v["class_labels"] for v in targets])
target_bbox = torch.cat([v["boxes"] for v in targets])
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
class_cost = -out_prob[:, target_ids]
# Compute the L1 cost between boxes
bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)
# Compute the giou cost between boxes
giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))
# Final cost matrix
cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()
sizes = [len(v["boxes"]) for v in targets]
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
# below: bounding box utilities taken from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
def _upcast(t: Tensor) -> Tensor:
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
if t.is_floating_point():
return t if t.dtype in (torch.float32, torch.float64) else t.float()
else:
return t if t.dtype in (torch.int32, torch.int64) else t.int()
def box_area(boxes: Tensor) -> Tensor:
"""
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
Args:
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
< x2` and `0 <= y1 < y2`.
Returns:
`torch.FloatTensor`: a tensor containing the area for each box.
"""
boxes = _upcast(boxes)
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# modified from torchvision to also return the union
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
iou = inter / union
return iou, union
def generalized_box_iou(boxes1, boxes2):
"""
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
Returns:
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
"""
# degenerate boxes gives inf / nan results
# so do an early check
if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
iou, union = box_iou(boxes1, boxes2)
top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2]
area = width_height[:, :, 0] * width_height[:, :, 1]
return iou - (area - union) / area
# below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306
def _max_by_axis(the_list):
# type: (List[List[int]]) -> List[int]
maxes = the_list[0]
for sublist in the_list[1:]:
for index, item in enumerate(sublist):
maxes[index] = max(maxes[index], item)
return maxes
class NestedTensor(object):
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
cast_mask = mask.to(device)
else:
cast_mask = None
return NestedTensor(cast_tensor, cast_mask)
def decompose(self):
return self.tensors, self.mask
def __repr__(self):
return str(self.tensors)
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
if tensor_list[0].ndim == 3:
max_size = _max_by_axis([list(img.shape) for img in tensor_list])
batch_shape = [len(tensor_list)] + max_size
batch_size, num_channels, height, width = batch_shape
dtype = tensor_list[0].dtype
device = tensor_list[0].device
tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
for img, pad_img, m in zip(tensor_list, tensor, mask):
pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
m[: img.shape[1], : img.shape[2]] = False
else:
raise ValueError("Only 3-dimensional tensors are supported")
return NestedTensor(tensor, mask)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/convert_detr_original_pytorch_checkpoint_to_pytorch.py
|
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
"""Convert DETR checkpoints with timm backbone."""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
rename_keys = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
)
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
)
)
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
]
)
def rename_key(state_dict, old, new):
val = state_dict.pop(old)
state_dict[new] = val
def rename_backbone_keys(state_dict):
new_state_dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model")
new_state_dict[new_key] = value
else:
new_state_dict[key] = value
return new_state_dict
def read_in_q_k_v(state_dict, is_panoptic=False):
prefix = ""
if is_panoptic:
prefix = "detr."
# first: transformer encoder
for i in range(6):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6):
# read in weights + bias of input projection layer of self-attention
in_proj_weight = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
in_proj_weight_cross_attn = state_dict.pop(
f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"
)
in_proj_bias_cross_attn = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias")
# next, add query, keys and values (in that order) of cross-attention to the state dict
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :]
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[256:512, :]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:]
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_detr_checkpoint(model_name, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our DETR structure.
"""
# load default config
config = DetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
config.backbone = "resnet101"
if "dc5" in model_name:
config.dilation = True
is_panoptic = "panoptic" in model_name
if is_panoptic:
config.num_labels = 250
else:
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# load image processor
format = "coco_panoptic" if is_panoptic else "coco_detection"
image_processor = DetrImageProcessor(format=format)
# prepare image
img = prepare_img()
encoding = image_processor(images=img, return_tensors="pt")
pixel_values = encoding["pixel_values"]
logger.info(f"Converting model {model_name}...")
# load original model from torch hub
detr = torch.hub.load("facebookresearch/detr", model_name, pretrained=True).eval()
state_dict = detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
src = "detr." + src
rename_key(state_dict, src, dest)
state_dict = rename_backbone_keys(state_dict)
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict, is_panoptic=is_panoptic)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr")
and not key.startswith("class_labels_classifier")
and not key.startswith("bbox_predictor")
):
val = state_dict.pop(key)
state_dict["detr.model" + key[4:]] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
val = state_dict.pop(key)
state_dict["detr." + key] = val
elif key.startswith("bbox_attention") or key.startswith("mask_head"):
continue
else:
val = state_dict.pop(key)
state_dict[prefix + key] = val
else:
if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# finally, create HuggingFace model and load state dict
model = DetrForSegmentation(config) if is_panoptic else DetrForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
# verify our conversion
original_outputs = detr(pixel_values)
outputs = model(pixel_values)
assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4)
assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4)
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4)
# Save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
args = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/convert_detr_to_pytorch.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
"""Convert DETR checkpoints with native (Transformers) backbone."""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_detr_config(model_name):
# initialize config
if "resnet-50" in model_name:
backbone_config = ResNetConfig.from_pretrained("microsoft/resnet-50")
elif "resnet-101" in model_name:
backbone_config = ResNetConfig.from_pretrained("microsoft/resnet-101")
else:
raise ValueError("Model name should include either resnet50 or resnet101")
config = DetrConfig(use_timm_backbone=False, backbone_config=backbone_config)
# set label attributes
is_panoptic = "panoptic" in model_name
if is_panoptic:
config.num_labels = 250
else:
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
return config, is_panoptic
def create_rename_keys(config):
# here we list all keys to be renamed (original name on the left, our name on the right)
rename_keys = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight"))
rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight"))
rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias"))
rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean"))
rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var"))
# stages
for stage_idx in range(len(config.backbone_config.depths)):
for layer_idx in range(config.backbone_config.depths[stage_idx]):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
)
)
# 3 convs
for i in range(3):
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
)
)
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
)
)
# fmt: on
for i in range(config.encoder_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
f"encoder.layers.{i}.self_attn.out_proj.weight",
)
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight")
)
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
f"decoder.layers.{i}.self_attn.out_proj.weight",
)
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
)
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
)
)
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
]
)
return rename_keys
def rename_key(state_dict, old, new):
val = state_dict.pop(old)
state_dict[new] = val
def read_in_q_k_v(state_dict, is_panoptic=False):
prefix = ""
if is_panoptic:
prefix = "detr."
# first: transformer encoder
for i in range(6):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6):
# read in weights + bias of input projection layer of self-attention
in_proj_weight = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
in_proj_weight_cross_attn = state_dict.pop(
f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"
)
in_proj_bias_cross_attn = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias")
# next, add query, keys and values (in that order) of cross-attention to the state dict
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :]
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[256:512, :]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:]
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_detr_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
"""
Copy/paste/tweak model's weights to our DETR structure.
"""
# load default config
config, is_panoptic = get_detr_config(model_name)
# load original model from torch hub
model_name_to_original_name = {
"detr-resnet-50": "detr_resnet50",
"detr-resnet-101": "detr_resnet101",
}
logger.info(f"Converting model {model_name}...")
detr = torch.hub.load("facebookresearch/detr", model_name_to_original_name[model_name], pretrained=True).eval()
state_dict = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(config):
if is_panoptic:
src = "detr." + src
rename_key(state_dict, src, dest)
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict, is_panoptic=is_panoptic)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr")
and not key.startswith("class_labels_classifier")
and not key.startswith("bbox_predictor")
):
val = state_dict.pop(key)
state_dict["detr.model" + key[4:]] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
val = state_dict.pop(key)
state_dict["detr." + key] = val
elif key.startswith("bbox_attention") or key.startswith("mask_head"):
continue
else:
val = state_dict.pop(key)
state_dict[prefix + key] = val
else:
if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# finally, create HuggingFace model and load state dict
model = DetrForSegmentation(config) if is_panoptic else DetrForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
# verify our conversion on an image
format = "coco_panoptic" if is_panoptic else "coco_detection"
processor = DetrImageProcessor(format=format)
encoding = processor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
original_outputs = detr(pixel_values)
outputs = model(pixel_values)
assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-3)
assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-3)
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
# Upload model and image processor to the hub
logger.info("Uploading PyTorch model and image processor to the hub...")
model.push_to_hub(f"nielsr/{model_name}")
processor.push_to_hub(f"nielsr/{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
args = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/feature_extraction_detr.py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. 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.
"""Feature extractor class for DETR."""
import warnings
from ...image_transforms import rgb_to_id as _rgb_to_id
from ...utils import logging
from .image_processing_detr import DetrImageProcessor
logger = logging.get_logger(__name__)
def rgb_to_id(x):
warnings.warn(
"rgb_to_id has moved and will not be importable from this module from v5. "
"Please import from transformers.image_transforms instead.",
FutureWarning,
)
return _rgb_to_id(x)
class DetrFeatureExtractor(DetrImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use DetrImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/image_processing_detr.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""Image processor class for DETR."""
import io
import pathlib
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
PaddingMode,
center_to_corners_format,
corners_to_center_format,
id_to_rgb,
pad,
rescale,
resize,
rgb_to_id,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_coco_detection_annotations,
valid_coco_panoptic_annotations,
valid_images,
)
from ...utils import (
ExplicitEnum,
TensorType,
is_flax_available,
is_jax_tensor,
is_scipy_available,
is_tf_available,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
logging,
)
if is_torch_available():
import torch
from torch import nn
if is_vision_available():
import PIL
if is_scipy_available():
import scipy.special
import scipy.stats
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
SUPPORTED_ANNOTATION_FORMATS = (AnnotionFormat.COCO_DETECTION, AnnotionFormat.COCO_PANOPTIC)
def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
"""
Computes the output image size given the input image size and the desired output size.
Args:
image_size (`Tuple[int, int]`):
The input image size.
size (`int`):
The desired output size.
max_size (`int`, *optional*):
The maximum allowed output size.
"""
height, width = image_size
if max_size is not None:
min_original_size = float(min((height, width)))
max_original_size = float(max((height, width)))
if max_original_size / min_original_size * size > max_size:
size = int(round(max_size * min_original_size / max_original_size))
if (height <= width and height == size) or (width <= height and width == size):
return height, width
if width < height:
ow = size
oh = int(size * height / width)
else:
oh = size
ow = int(size * width / height)
return (oh, ow)
def get_resize_output_image_size(
input_image: np.ndarray,
size: Union[int, Tuple[int, int], List[int]],
max_size: Optional[int] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
"""
Computes the output image size given the input image size and the desired output size. If the desired output size
is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output
image size is computed by keeping the aspect ratio of the input image size.
Args:
input_image (`np.ndarray`):
The image to resize.
size (`int` or `Tuple[int, int]` or `List[int]`):
The desired output size.
max_size (`int`, *optional*):
The maximum allowed output size.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred from the input image.
"""
image_size = get_image_size(input_image, input_data_format)
if isinstance(size, (list, tuple)):
return size
return get_size_with_aspect_ratio(image_size, size, max_size)
def get_numpy_to_framework_fn(arr) -> Callable:
"""
Returns a function that converts a numpy array to the framework of the input array.
Args:
arr (`np.ndarray`): The array to convert.
"""
if isinstance(arr, np.ndarray):
return np.array
if is_tf_available() and is_tf_tensor(arr):
import tensorflow as tf
return tf.convert_to_tensor
if is_torch_available() and is_torch_tensor(arr):
import torch
return torch.tensor
if is_flax_available() and is_jax_tensor(arr):
import jax.numpy as jnp
return jnp.array
raise ValueError(f"Cannot convert arrays of type {type(arr)}")
def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
"""
Squeezes an array, but only if the axis specified has dim 1.
"""
if axis is None:
return arr.squeeze()
try:
return arr.squeeze(axis=axis)
except ValueError:
return arr
def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
image_height, image_width = image_size
norm_annotation = {}
for key, value in annotation.items():
if key == "boxes":
boxes = value
boxes = corners_to_center_format(boxes)
boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32)
norm_annotation[key] = boxes
else:
norm_annotation[key] = value
return norm_annotation
# Copied from transformers.models.vilt.image_processing_vilt.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
# Copied from transformers.models.vilt.image_processing_vilt.get_max_height_width
def get_max_height_width(
images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
"""
Get the maximum height and width across all images in a batch.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images[0])
if input_data_format == ChannelDimension.FIRST:
_, max_height, max_width = max_across_indices([img.shape for img in images])
elif input_data_format == ChannelDimension.LAST:
max_height, max_width, _ = max_across_indices([img.shape for img in images])
else:
raise ValueError(f"Invalid channel dimension format: {input_data_format}")
return (max_height, max_width)
# Copied from transformers.models.vilt.image_processing_vilt.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
"""
Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
Args:
image (`np.ndarray`):
Image to make the pixel mask for.
output_size (`Tuple[int, int]`):
Output size of the mask.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
mask = np.zeros(output_size, dtype=np.int64)
mask[:input_height, :input_width] = 1
return mask
# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L33
def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
"""
Convert a COCO polygon annotation to a mask.
Args:
segmentations (`List[List[float]]`):
List of polygons, each polygon represented by a list of x-y coordinates.
height (`int`):
Height of the mask.
width (`int`):
Width of the mask.
"""
try:
from pycocotools import mask as coco_mask
except ImportError:
raise ImportError("Pycocotools is not installed in your environment.")
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = np.asarray(mask, dtype=np.uint8)
mask = np.any(mask, axis=2)
masks.append(mask)
if masks:
masks = np.stack(masks, axis=0)
else:
masks = np.zeros((0, height, width), dtype=np.uint8)
return masks
# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L50
def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by DETR.
"""
image_height, image_width = get_image_size(image, channel_dim=input_data_format)
image_id = target["image_id"]
image_id = np.asarray([image_id], dtype=np.int64)
# Get all COCO annotations for the given image.
annotations = target["annotations"]
annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0]
classes = [obj["category_id"] for obj in annotations]
classes = np.asarray(classes, dtype=np.int64)
# for conversion to coco api
area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32)
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64)
boxes = [obj["bbox"] for obj in annotations]
# guard against no boxes via resizing
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
new_target = {}
new_target["image_id"] = image_id
new_target["class_labels"] = classes[keep]
new_target["boxes"] = boxes[keep]
new_target["area"] = area[keep]
new_target["iscrowd"] = iscrowd[keep]
new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64)
if annotations and "keypoints" in annotations[0]:
keypoints = [obj["keypoints"] for obj in annotations]
keypoints = np.asarray(keypoints, dtype=np.float32)
num_keypoints = keypoints.shape[0]
keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
new_target["keypoints"] = keypoints[keep]
if return_segmentation_masks:
segmentation_masks = [obj["segmentation"] for obj in annotations]
masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width)
new_target["masks"] = masks[keep]
return new_target
def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
Args:
masks: masks in format `[number_masks, height, width]` where N is the number of masks
Returns:
boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
def prepare_coco_panoptic_annotation(
image: np.ndarray,
target: Dict,
masks_path: Union[str, pathlib.Path],
return_masks: bool = True,
input_data_format: Union[ChannelDimension, str] = None,
) -> Dict:
"""
Prepare a coco panoptic annotation for DETR.
"""
image_height, image_width = get_image_size(image, channel_dim=input_data_format)
annotation_path = pathlib.Path(masks_path) / target["file_name"]
new_target = {}
new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64)
new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64)
new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64)
if "segments_info" in target:
masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32)
masks = rgb_to_id(masks)
ids = np.array([segment_info["id"] for segment_info in target["segments_info"]])
masks = masks == ids[:, None, None]
masks = masks.astype(np.uint8)
if return_masks:
new_target["masks"] = masks
new_target["boxes"] = masks_to_boxes(masks)
new_target["class_labels"] = np.array(
[segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64
)
new_target["iscrowd"] = np.asarray(
[segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64
)
new_target["area"] = np.asarray(
[segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32
)
return new_target
def get_segmentation_image(
masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False
):
h, w = input_size
final_h, final_w = target_size
m_id = scipy.special.softmax(masks.transpose(0, 1), -1)
if m_id.shape[-1] == 0:
# We didn't detect any mask :(
m_id = np.zeros((h, w), dtype=np.int64)
else:
m_id = m_id.argmax(-1).reshape(h, w)
if deduplicate:
# Merge the masks corresponding to the same stuff class
for equiv in stuff_equiv_classes.values():
for eq_id in equiv:
m_id[m_id == eq_id] = equiv[0]
seg_img = id_to_rgb(m_id)
seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST)
return seg_img
def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray:
final_h, final_w = target_size
np_seg_img = seg_img.astype(np.uint8)
np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
m_id = rgb_to_id(np_seg_img)
area = [(m_id == i).sum() for i in range(n_classes)]
return area
def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
probs = scipy.special.softmax(logits, axis=-1)
labels = probs.argmax(-1, keepdims=True)
scores = np.take_along_axis(probs, labels, axis=-1)
scores, labels = scores.squeeze(-1), labels.squeeze(-1)
return scores, labels
def post_process_panoptic_sample(
out_logits: np.ndarray,
masks: np.ndarray,
boxes: np.ndarray,
processed_size: Tuple[int, int],
target_size: Tuple[int, int],
is_thing_map: Dict,
threshold=0.85,
) -> Dict:
"""
Converts the output of [`DetrForSegmentation`] into panoptic segmentation predictions for a single sample.
Args:
out_logits (`torch.Tensor`):
The logits for this sample.
masks (`torch.Tensor`):
The predicted segmentation masks for this sample.
boxes (`torch.Tensor`):
The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
processed_size (`Tuple[int, int]`):
The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
after data augmentation but before batching.
target_size (`Tuple[int, int]`):
The target size of the image, `(height, width)` corresponding to the requested final size of the
prediction.
is_thing_map (`Dict`):
A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
threshold (`float`, *optional*, defaults to 0.85):
The threshold used to binarize the segmentation masks.
"""
# we filter empty queries and detection below threshold
scores, labels = score_labels_from_class_probabilities(out_logits)
keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)
cur_scores = scores[keep]
cur_classes = labels[keep]
cur_boxes = center_to_corners_format(boxes[keep])
if len(cur_boxes) != len(cur_classes):
raise ValueError("Not as many boxes as there are classes")
cur_masks = masks[keep]
cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR)
cur_masks = safe_squeeze(cur_masks, 1)
b, h, w = cur_masks.shape
# It may be that we have several predicted masks for the same stuff class.
# In the following, we track the list of masks ids for each stuff class (they are merged later on)
cur_masks = cur_masks.reshape(b, -1)
stuff_equiv_classes = defaultdict(list)
for k, label in enumerate(cur_classes):
if not is_thing_map[label]:
stuff_equiv_classes[label].append(k)
seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True)
area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores))
# We filter out any mask that is too small
if cur_classes.size() > 0:
# We know filter empty masks as long as we find some
filtered_small = np.array([a <= 4 for a in area], dtype=bool)
while filtered_small.any():
cur_masks = cur_masks[~filtered_small]
cur_scores = cur_scores[~filtered_small]
cur_classes = cur_classes[~filtered_small]
seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True)
area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores))
filtered_small = np.array([a <= 4 for a in area], dtype=bool)
else:
cur_classes = np.ones((1, 1), dtype=np.int64)
segments_info = [
{"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a}
for i, (cat, a) in enumerate(zip(cur_classes, area))
]
del cur_classes
with io.BytesIO() as out:
PIL.Image.fromarray(seg_img).save(out, format="PNG")
predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
return predictions
def resize_annotation(
annotation: Dict[str, Any],
orig_size: Tuple[int, int],
target_size: Tuple[int, int],
threshold: float = 0.5,
resample: PILImageResampling = PILImageResampling.NEAREST,
):
"""
Resizes an annotation to a target size.
Args:
annotation (`Dict[str, Any]`):
The annotation dictionary.
orig_size (`Tuple[int, int]`):
The original size of the input image.
target_size (`Tuple[int, int]`):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
The resampling filter to use when resizing the masks.
"""
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
ratio_height, ratio_width = ratios
new_annotation = {}
new_annotation["size"] = target_size
for key, value in annotation.items():
if key == "boxes":
boxes = value
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
new_annotation["boxes"] = scaled_boxes
elif key == "area":
area = value
scaled_area = area * (ratio_width * ratio_height)
new_annotation["area"] = scaled_area
elif key == "masks":
masks = value[:, None]
masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
masks = masks.astype(np.float32)
masks = masks[:, 0] > threshold
new_annotation["masks"] = masks
elif key == "size":
new_annotation["size"] = target_size
else:
new_annotation[key] = value
return new_annotation
# TODO - (Amy) make compatible with other frameworks
def binary_mask_to_rle(mask):
"""
Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
format.
"""
if is_torch_tensor(mask):
mask = mask.numpy()
pixels = mask.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return list(runs)
# TODO - (Amy) make compatible with other frameworks
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
"""
segment_ids = torch.unique(segmentation)
run_length_encodings = []
for idx in segment_ids:
mask = torch.where(segmentation == idx, 1, 0)
rle = binary_mask_to_rle(mask)
run_length_encodings.append(rle)
return run_length_encodings
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
labels (`torch.Tensor`):
A tensor of shape `(num_queries)`.
object_mask_threshold (`float`):
A number between 0 and 1 used to binarize the masks.
Raises:
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
Returns:
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
< `object_mask_threshold`.
"""
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
raise ValueError("mask, scores and labels must have the same shape!")
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
return masks[to_keep], scores[to_keep], labels[to_keep]
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
# Get the mask associated with the k class
mask_k = mask_labels == k
mask_k_area = mask_k.sum()
# Compute the area of all the stuff in query k
original_area = (mask_probs[k] >= mask_threshold).sum()
mask_exists = mask_k_area > 0 and original_area > 0
# Eliminate disconnected tiny segments
if mask_exists:
area_ratio = mask_k_area / original_area
if not area_ratio.item() > overlap_mask_area_threshold:
mask_exists = False
return mask_exists, mask_k
def compute_segments(
mask_probs,
pred_scores,
pred_labels,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
segments: List[Dict] = []
if target_size is not None:
mask_probs = nn.functional.interpolate(
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
)[0]
current_segment_id = 0
# Weigh each mask by its prediction score
mask_probs *= pred_scores.view(-1, 1, 1)
mask_labels = mask_probs.argmax(0) # [height, width]
# Keep track of instances of each class
stuff_memory_list: Dict[str, int] = {}
for k in range(pred_labels.shape[0]):
pred_class = pred_labels[k].item()
should_fuse = pred_class in label_ids_to_fuse
# Check if mask exists and large enough to be a segment
mask_exists, mask_k = check_segment_validity(
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
)
if mask_exists:
if pred_class in stuff_memory_list:
current_segment_id = stuff_memory_list[pred_class]
else:
current_segment_id += 1
# Add current object segment to final segmentation map
segmentation[mask_k] = current_segment_id
segment_score = round(pred_scores[k].item(), 6)
segments.append(
{
"id": current_segment_id,
"label_id": pred_class,
"was_fused": should_fuse,
"score": segment_score,
}
)
if should_fuse:
stuff_memory_list[pred_class] = current_segment_id
return segmentation, segments
class DetrImageProcessor(BaseImageProcessor):
r"""
Constructs a Detr image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls 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[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Controls 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. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_normalize:
Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
`preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
overridden by the `do_pad` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values", "pixel_mask"]
def __init__(
self,
format: Union[str, AnnotionFormat] = AnnotionFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
do_pad: bool = True,
**kwargs,
) -> None:
if "pad_and_return_pixel_mask" in kwargs:
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
)
max_size = kwargs.pop("max_size")
else:
max_size = None if size is None else 1333
size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
size = get_size_dict(size, max_size=max_size, default_to_square=False)
super().__init__(**kwargs)
self.format = format
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 IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
created using from_dict and kwargs e.g. `DetrImageProcessor.from_pretrained(checkpoint, size=600,
max_size=800)`
"""
image_processor_dict = image_processor_dict.copy()
if "max_size" in kwargs:
image_processor_dict["max_size"] = kwargs.pop("max_size")
if "pad_and_return_pixel_mask" in kwargs:
image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
return super().from_dict(image_processor_dict, **kwargs)
def prepare_annotation(
self,
image: np.ndarray,
target: Dict,
format: Optional[AnnotionFormat] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Dict:
"""
Prepare an annotation for feeding into DETR model.
"""
format = format if format is not None else self.format
if format == AnnotionFormat.COCO_DETECTION:
return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
target = prepare_coco_detection_annotation(
image, target, return_segmentation_masks, input_data_format=input_data_format
)
elif format == AnnotionFormat.COCO_PANOPTIC:
return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
target = prepare_coco_panoptic_annotation(
image,
target,
masks_path=masks_path,
return_masks=return_segmentation_masks,
input_data_format=input_data_format,
)
else:
raise ValueError(f"Format {format} is not supported.")
return target
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
int, smaller edge of the image will be matched to this number.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary containing the size to resize to. Can contain the keys `shortest_edge` and `longest_edge` or
`height` and `width`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
)
max_size = kwargs.pop("max_size")
else:
max_size = None
size = get_size_dict(size, max_size=max_size, default_to_square=False)
if "shortest_edge" in size and "longest_edge" in size:
size = get_resize_output_image_size(
image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
)
elif "height" in size and "width" in size:
size = (size["height"], size["width"])
else:
raise ValueError(
"Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
f" {size.keys()}."
)
image = resize(
image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
)
return image
def resize_annotation(
self,
annotation,
orig_size,
size,
resample: PILImageResampling = PILImageResampling.NEAREST,
) -> Dict:
"""
Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched
to this number.
"""
return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)
# TODO (Amy) - update to use `rescale_factor` instead of `scale`
def rescale(
self,
image: np.ndarray,
rescale_factor: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Rescale the image by the given factor. image = image * rescale_factor.
Args:
image (`np.ndarray`):
Image to rescale.
rescale_factor (`float`):
The value to use for rescaling.
data_format (`str` or `ChannelDimension`, *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.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the input image. If unset, 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.
"""
return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
"""
Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
`[center_x, center_y, width, height]` format.
"""
return normalize_annotation(annotation, image_size=image_size)
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Pad an image with zeros to the given size.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
output_height, output_width = output_size
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def pad(
self,
images: List[np.ndarray],
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> BatchFeature:
"""
Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
in the batch and optionally returns their corresponding pixel mask.
Args:
image (`np.ndarray`):
Image to pad.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to return a pixel mask.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
pad_size = get_max_height_width(images, input_data_format=input_data_format)
padded_images = [
self._pad_image(
image,
pad_size,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
for image in images
]
data = {"pixel_values": padded_images}
if return_pixel_mask:
masks = [
make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
for image in images
]
data["pixel_mask"] = masks
return BatchFeature(data=data, tensor_type=return_tensors)
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample=None, # PILImageResampling
do_rescale: Optional[bool] = None,
rescale_factor: Optional[Union[int, float]] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
format: Optional[Union[str, AnnotionFormat]] = None,
return_tensors: Optional[Union[TensorType, str]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> BatchFeature:
"""
Preprocess an image or a batch of images so that it can be used by the model.
Args:
images (`ImageInput`):
Image or batch of images 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`.
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
- "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a
dictionary. An image can have no annotations, in which case the list should be empty.
If annotation is for segmentation, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
- "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.
- "file_name" (`str`): The file name of the image.
return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks):
Whether to return segmentation masks.
masks_path (`str` or `pathlib.Path`, *optional*):
Path to the directory containing the segmentation masks.
do_resize (`bool`, *optional*, defaults to self.do_resize):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to self.size):
Size of the image after resizing.
resample (`PILImageResampling`, *optional*, defaults to self.resample):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to self.do_rescale):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to self.rescale_factor):
Rescale factor to use when rescaling the image.
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):
Mean to use when normalizing the image.
image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
Standard deviation to use when normalizing the image.
do_pad (`bool`, *optional*, defaults to self.do_pad):
Whether to pad the image.
format (`str` or `AnnotionFormat`, *optional*, defaults to self.format):
Format of the annotations.
return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors):
Type of tensors to return. If `None`, will return the list of images.
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.
"""
if "pad_and_return_pixel_mask" in kwargs:
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead."
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
max_size = None
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size")
do_resize = self.do_resize if do_resize is None else do_resize
size = self.size if size is None else size
size = get_size_dict(size=size, max_size=max_size, default_to_square=False)
resample = self.resample if resample is None else resample
do_rescale = self.do_rescale if do_rescale is None else do_rescale
rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
do_normalize = self.do_normalize if do_normalize is None else do_normalize
image_mean = self.image_mean if image_mean is None else image_mean
image_std = self.image_std if image_std is None else image_std
do_pad = self.do_pad if do_pad is None else do_pad
format = self.format if format is None else format
if do_resize is not None and size is None:
raise ValueError("Size and max_size must be specified if do_resize is True.")
if do_rescale is not None and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_normalize is not None and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
images = make_list_of_images(images)
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):
raise ValueError(
f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
format = AnnotionFormat(format)
if annotations is not None:
if format == AnnotionFormat.COCO_DETECTION and not valid_coco_detection_annotations(annotations):
raise ValueError(
"Invalid COCO detection annotations. Annotations must a dict (single image) of list of dicts "
"(batch of images) with the following keys: `image_id` and `annotations`, with the latter "
"being a list of annotations in the COCO format."
)
elif format == AnnotionFormat.COCO_PANOPTIC and not valid_coco_panoptic_annotations(annotations):
raise ValueError(
"Invalid COCO panoptic annotations. Annotations must a dict (single image) of list of dicts "
"(batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with "
"the latter being a list of annotations in the COCO format."
)
elif format not in SUPPORTED_ANNOTATION_FORMATS:
raise ValueError(
f"Unsupported annotation format: {format} must be one of {SUPPORTED_ANNOTATION_FORMATS}"
)
if (
masks_path is not None
and format == AnnotionFormat.COCO_PANOPTIC
and not isinstance(masks_path, (pathlib.Path, str))
):
raise ValueError(
"The path to the directory containing the mask PNG files should be provided as a"
f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
)
# All transformations expect numpy arrays
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
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])
# prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
if annotations is not None:
prepared_images = []
prepared_annotations = []
for image, target in zip(images, annotations):
target = self.prepare_annotation(
image,
target,
format,
return_segmentation_masks=return_segmentation_masks,
masks_path=masks_path,
input_data_format=input_data_format,
)
prepared_images.append(image)
prepared_annotations.append(target)
images = prepared_images
annotations = prepared_annotations
del prepared_images, prepared_annotations
# transformations
if do_resize:
if annotations is not None:
resized_images, resized_annotations = [], []
for image, target in zip(images, annotations):
orig_size = get_image_size(image, input_data_format)
resized_image = self.resize(
image, size=size, max_size=max_size, resample=resample, input_data_format=input_data_format
)
resized_annotation = self.resize_annotation(
target, orig_size, get_image_size(resized_image, input_data_format)
)
resized_images.append(resized_image)
resized_annotations.append(resized_annotation)
images = resized_images
annotations = resized_annotations
del resized_images, resized_annotations
else:
images = [
self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_rescale:
images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
if do_normalize:
images = [
self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
]
if annotations is not None:
annotations = [
self.normalize_annotation(annotation, get_image_size(image, input_data_format))
for annotation, image in zip(annotations, images)
]
if do_pad:
# Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
data = self.pad(
images, return_pixel_mask=True, data_format=data_format, input_data_format=input_data_format
)
else:
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
data = {"pixel_values": images}
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
if annotations is not None:
encoded_inputs["labels"] = [
BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
]
return encoded_inputs
# POSTPROCESSING METHODS - TODO: add support for other frameworks
# inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
def post_process(self, outputs, target_sizes):
"""
Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
original image size (before any data augmentation). For visualization, this should be the image size
after data augment, but before padding.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
logger.warning_once(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if len(out_logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
prob = nn.functional.softmax(out_logits, -1)
scores, labels = prob[..., :-1].max(-1)
# convert to [x0, y0, x1, y1] format
boxes = center_to_corners_format(out_bbox)
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :]
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
return results
def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5):
"""
Converts the output of [`DetrForSegmentation`] into image segmentation predictions. Only supports PyTorch.
Args:
outputs ([`DetrSegmentationOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
threshold (`float`, *optional*, defaults to 0.9):
Threshold to use to filter out queries.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
in the batch as predicted by the model.
"""
logger.warning_once(
"`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_semantic_segmentation`.",
)
out_logits, raw_masks = outputs.logits, outputs.pred_masks
empty_label = out_logits.shape[-1] - 1
preds = []
def to_tuple(tup):
if isinstance(tup, tuple):
return tup
return tuple(tup.cpu().tolist())
for cur_logits, cur_masks, size in zip(out_logits, raw_masks, target_sizes):
# we filter empty queries and detection below threshold
cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
cur_scores = cur_scores[keep]
cur_labels = cur_labels[keep]
cur_masks = cur_masks[keep]
cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
cur_masks = (cur_masks.sigmoid() > mask_threshold) * 1
predictions = {"scores": cur_scores, "labels": cur_labels, "masks": cur_masks}
preds.append(predictions)
return preds
# inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
def post_process_instance(self, results, outputs, orig_target_sizes, max_target_sizes, threshold=0.5):
"""
Converts the output of [`DetrForSegmentation`] into actual instance segmentation predictions. Only supports
PyTorch.
Args:
results (`List[Dict]`):
Results list obtained by [`~DetrImageProcessor.post_process`], to which "masks" results will be added.
outputs ([`DetrSegmentationOutput`]):
Raw outputs of the model.
orig_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
image size (before any data augmentation).
max_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the maximum size (h, w) of each image of the batch. For evaluation, this must be the
original image size (before any data augmentation).
threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and masks for an
image in the batch as predicted by the model.
"""
logger.warning_once(
"`post_process_instance` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_instance_segmentation`.",
)
if len(orig_target_sizes) != len(max_target_sizes):
raise ValueError("Make sure to pass in as many orig_target_sizes as max_target_sizes")
max_h, max_w = max_target_sizes.max(0)[0].tolist()
outputs_masks = outputs.pred_masks.squeeze(2)
outputs_masks = nn.functional.interpolate(
outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False
)
outputs_masks = (outputs_masks.sigmoid() > threshold).cpu()
for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):
img_h, img_w = t[0], t[1]
results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1)
results[i]["masks"] = nn.functional.interpolate(
results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest"
).byte()
return results
# inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
def post_process_panoptic(self, outputs, processed_sizes, target_sizes=None, is_thing_map=None, threshold=0.85):
"""
Converts the output of [`DetrForSegmentation`] into actual panoptic predictions. Only supports PyTorch.
Args:
outputs ([`DetrSegmentationOutput`]):
Raw outputs of the model.
processed_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
Torch Tensor (or list) containing the size (h, w) of each image of the batch, i.e. the size after data
augmentation but before batching.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`, *optional*):
Torch Tensor (or list) corresponding to the requested final size `(height, width)` of each prediction.
If left to None, it will default to the `processed_sizes`.
is_thing_map (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
Dictionary mapping class indices to either True or False, depending on whether or not they are a thing.
If not set, defaults to the `is_thing_map` of COCO panoptic.
threshold (`float`, *optional*, defaults to 0.85):
Threshold to use to filter out queries.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing a PNG string and segments_info values for
an image in the batch as predicted by the model.
"""
logger.warning_once(
"`post_process_panoptic is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_panoptic_segmentation`.",
)
if target_sizes is None:
target_sizes = processed_sizes
if len(processed_sizes) != len(target_sizes):
raise ValueError("Make sure to pass in as many processed_sizes as target_sizes")
if is_thing_map is None:
# default to is_thing_map of COCO panoptic
is_thing_map = {i: i <= 90 for i in range(201)}
out_logits, raw_masks, raw_boxes = outputs.logits, outputs.pred_masks, outputs.pred_boxes
if not len(out_logits) == len(raw_masks) == len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits and masks"
)
empty_label = out_logits.shape[-1] - 1
preds = []
def to_tuple(tup):
if isinstance(tup, tuple):
return tup
return tuple(tup.cpu().tolist())
for cur_logits, cur_masks, cur_boxes, size, target_size in zip(
out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes
):
# we filter empty queries and detection below threshold
cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
cur_scores = cur_scores[keep]
cur_labels = cur_labels[keep]
cur_masks = cur_masks[keep]
cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
cur_boxes = center_to_corners_format(cur_boxes[keep])
h, w = cur_masks.shape[-2:]
if len(cur_boxes) != len(cur_labels):
raise ValueError("Not as many boxes as there are classes")
# It may be that we have several predicted masks for the same stuff class.
# In the following, we track the list of masks ids for each stuff class (they are merged later on)
cur_masks = cur_masks.flatten(1)
stuff_equiv_classes = defaultdict(lambda: [])
for k, label in enumerate(cur_labels):
if not is_thing_map[label.item()]:
stuff_equiv_classes[label.item()].append(k)
def get_ids_area(masks, scores, dedup=False):
# This helper function creates the final panoptic segmentation image
# It also returns the area of the masks that appears on the image
m_id = masks.transpose(0, 1).softmax(-1)
if m_id.shape[-1] == 0:
# We didn't detect any mask :(
m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)
else:
m_id = m_id.argmax(-1).view(h, w)
if dedup:
# Merge the masks corresponding to the same stuff class
for equiv in stuff_equiv_classes.values():
if len(equiv) > 1:
for eq_id in equiv:
m_id.masked_fill_(m_id.eq(eq_id), equiv[0])
final_h, final_w = to_tuple(target_size)
seg_img = PIL.Image.fromarray(id_to_rgb(m_id.view(h, w).cpu().numpy()))
seg_img = seg_img.resize(size=(final_w, final_h), resample=PILImageResampling.NEAREST)
np_seg_img = torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes()))
np_seg_img = np_seg_img.view(final_h, final_w, 3)
np_seg_img = np_seg_img.numpy()
m_id = torch.from_numpy(rgb_to_id(np_seg_img))
area = []
for i in range(len(scores)):
area.append(m_id.eq(i).sum().item())
return area, seg_img
area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)
if cur_labels.numel() > 0:
# We know filter empty masks as long as we find some
while True:
filtered_small = torch.as_tensor(
[area[i] <= 4 for i, c in enumerate(cur_labels)], dtype=torch.bool, device=keep.device
)
if filtered_small.any().item():
cur_scores = cur_scores[~filtered_small]
cur_labels = cur_labels[~filtered_small]
cur_masks = cur_masks[~filtered_small]
area, seg_img = get_ids_area(cur_masks, cur_scores)
else:
break
else:
cur_labels = torch.ones(1, dtype=torch.long, device=cur_labels.device)
segments_info = []
for i, a in enumerate(area):
cat = cur_labels[i].item()
segments_info.append({"id": i, "isthing": is_thing_map[cat], "category_id": cat, "area": a})
del cur_labels
with io.BytesIO() as out:
seg_img.save(out, format="PNG")
predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
preds.append(predictions)
return preds
# inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
):
"""
Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score threshold to keep object detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
`(height, width)` of each image in the batch. If unset, predictions will not be resized.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if target_sizes is not None:
if len(out_logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
prob = nn.functional.softmax(out_logits, -1)
scores, labels = prob[..., :-1].max(-1)
# Convert to [x0, y0, x1, y1] format
boxes = center_to_corners_format(out_bbox)
# Convert from relative [0, 1] to absolute [0, height] coordinates
if target_sizes is not None:
if isinstance(target_sizes, List):
img_h = torch.Tensor([i[0] for i in target_sizes])
img_w = torch.Tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :]
results = []
for s, l, b in zip(scores, labels, boxes):
score = s[s > threshold]
label = l[s > threshold]
box = b[s > threshold]
results.append({"scores": score, "labels": label, "boxes": box})
return results
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
"""
Converts the output of [`DetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`DetrForSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*):
A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
batch. If unset, predictions will not be resized.
Returns:
`List[torch.Tensor]`:
A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
`torch.Tensor` correspond to a semantic class id.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
# Remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
batch_size = class_queries_logits.shape[0]
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if batch_size != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
semantic_segmentation = []
for idx in range(batch_size):
resized_logits = nn.functional.interpolate(
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = segmentation.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
# inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
) -> List[Dict]:
"""
Converts the output of [`DetrForSegmentation`] into instance segmentation predictions. Only supports PyTorch.
Args:
outputs ([`DetrForSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction. If unset, predictions will not be resized.
return_coco_annotation (`bool`, *optional*):
Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
`True`. Set to `None` if no mask if found above `threshold`.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- An integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=[],
target_size=target_size,
)
# Return segmentation map in run-length encoding (RLE) format
if return_coco_annotation:
segmentation = convert_segmentation_to_rle(segmentation)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
# inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[Dict]:
"""
Converts the output of [`DetrForSegmentation`] into image panoptic segmentation predictions. Only supports
PyTorch.
Args:
outputs ([`DetrForSegmentation`]):
The outputs from [`DetrForSegmentation`].
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (`Set[int]`, *optional*):
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to
the corresponding `target_sizes` entry.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- an integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
if label_ids_to_fuse is None:
logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=label_ids_to_fuse,
target_size=target_size,
)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/detr/__init__.py
|
# Copyright 2020 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"]
_import_structure["image_processing_detr"] = ["DetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_detr"] = [
"DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"DetrForObjectDetection",
"DetrForSegmentation",
"DetrModel",
"DetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_detr import DetrFeatureExtractor
from .image_processing_detr import DetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_detr import (
DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
DetrForObjectDetection,
DetrForSegmentation,
DetrModel,
DetrPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/configuration_idefics.py
|
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" Idefics model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json",
"HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json",
}
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
intermediate_size (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
"""
model_type = "idefics"
attribute_map = {
"hidden_size": "embed_dim",
}
def __init__(
self,
embed_dim=768,
image_size=224,
intermediate_size=5120,
patch_size=14,
num_hidden_layers=32,
num_attention_heads=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
self.embed_dim = embed_dim
self.image_size = image_size
self.intermediate_size = intermediate_size
self.patch_size = patch_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.layer_norm_eps = layer_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
super().__init__(**kwargs)
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_resampler (`bool`, *optional*, defaults to `False`):
Whether or not to use the resampler
resampler_n_latents (`int`, *optional*, defaults to ):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 6):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
Whether or not to use qk layer norms in perceiver
"""
model_type = "idefics"
def __init__(
self,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
qk_layer_norms_perceiver=False,
**kwargs,
):
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_dim
self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
super().__init__(**kwargs)
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
additional_vocab_size (`int`, *optional`, defaults to 0):
Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~IdeficsModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
Initialization type for the alphas.
alphas_initializer_range (`float`, *optional*, defaults to 0.0):
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.
alpha_type (`str`, *optional*, defaults to `"float"`):
Whether the gating alphas should be vectors or single floats.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0)
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1)
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing text layers when `freeze_text_layers` is `True`
freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
Example:
```python
>>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics"
is_composition = False
def __init__(
self,
vocab_size=32000,
additional_vocab_size=0,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
dropout=0.0,
hidden_act="silu",
initializer_range=0.02,
alpha_initializer="zeros",
alphas_initializer_range=0.0,
alpha_type="float",
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
cross_layer_interval=1,
qk_layer_norms=False,
freeze_text_layers=True,
freeze_text_module_exceptions=[],
freeze_lm_head=False,
freeze_vision_layers=True,
freeze_vision_module_exceptions=[],
use_resampler=False,
vision_config=None,
perceiver_config=None,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.cross_layer_interval = cross_layer_interval
self.qk_layer_norms = qk_layer_norms
self.freeze_vision_layers = freeze_vision_layers
self.freeze_text_layers = freeze_text_layers
self.freeze_text_module_exceptions = freeze_text_module_exceptions
self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
self.freeze_lm_head = freeze_lm_head
self.use_resampler = use_resampler
if perceiver_config is None:
self.perceiver_config = IdeficsPerceiverConfig()
elif isinstance(perceiver_config, dict):
self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, IdeficsPerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = IdeficsVisionConfig()
elif isinstance(vision_config, dict):
self.vision_config = IdeficsVisionConfig(**vision_config)
elif isinstance(vision_config, IdeficsVisionConfig):
self.vision_config = vision_config
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
# updates the config object with `kwargs` from from_pretrained, so during the instantiation
# of this object many attributes have default values and haven't yet been overridden.
# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/modeling_idefics.py
|
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" PyTorch Idefics model."""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import PreTrainedModel
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from ...modeling_outputs import ModelOutput
from ...modeling_utils import PretrainedConfig
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_idefics import IdeficsConfig
from .perceiver import IdeficsPerceiverResampler
from .vision import IdeficsVisionTransformer
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "IdeficsConfig"
IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = [
"HuggingFaceM4/idefics-9b",
"HuggingFaceM4/idefics-80b",
# See all Idefics models at https://huggingface.co/models?filter=idefics
]
@dataclass
class IdeficsBaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class IdeficsCausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = (
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None)
model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None)
model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None)
model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
if model_kwargs["image_attention_mask"] is not None:
model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select(
0, expanded_return_idx
)
if model_kwargs["pixel_values"] is not None:
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx)
elif model_kwargs["image_encoder_embeddings"] is not None:
model_kwargs["image_encoder_embeddings"] = model_kwargs["image_encoder_embeddings"].index_select(
0, expanded_return_idx
)
elif model_kwargs["perceiver_embeddings"] is not None:
model_kwargs["perceiver_embeddings"] = model_kwargs["perceiver_embeddings"].index_select(
0, expanded_return_idx
)
return input_ids, model_kwargs
def update_model_kwargs_for_generation(outputs, model_kwargs):
# must have this key set to at least None
if "past_key_values" in outputs:
model_kwargs["past_key_values"] = outputs.past_key_values
else:
model_kwargs["past_key_values"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
# update attention masks
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
model_kwargs["image_attention_mask"] = last_mask
# Get the precomputed image_hidden_states
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
return model_kwargs
def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
pixel_values = kwargs.get("pixel_values", None)
image_encoder_embeddings = kwargs.get("image_encoder_embeddings", None)
perceiver_embeddings = kwargs.get("perceiver_embeddings", None)
image_attention_mask = kwargs.get("image_attention_mask", None)
interpolate_pos_encoding = kwargs.get("interpolate_pos_encoding", False)
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"pixel_values": pixel_values,
"image_encoder_embeddings": image_encoder_embeddings,
"perceiver_embeddings": perceiver_embeddings,
"image_attention_mask": image_attention_mask,
"interpolate_pos_encoding": interpolate_pos_encoding,
}
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": nn.LayerNorm,
"Linear": nn.Linear,
"Embedding": nn.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
for module in model.modules():
if module_exceptions and any(isinstance(module, t) for t in module_exceptions_mapped):
module.requires_grad_(True) # Explicitely setting it to true to avoid any mistakes
else:
module.requires_grad_(False)
return model
class IdeficsDecoupledEmbedding(nn.Embedding):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
then it will create `num_additional_embeddings` additional parameters that are always trained. If
`num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
"""
def __init__(
self,
num_embeddings,
num_additional_embeddings,
embedding_dim,
partially_freeze: Optional[bool] = False,
device=None,
dtype=None,
padding_idx=None,
**kwargs,
) -> None:
"""
Args:
num_embeddings (`int`):
Size of the dictionary of embeddings
num_additional_embeddings (`int`):
Number of additional embeddings. Only useful when you `partially_freeze=True`.
embedding_dim (`int`):
The size of each embedding vector
partially_freeze: (`bool`, *optional*, defaults to `False`):
If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
padding_idx (`int`, *optional*):
The padding index (needs to be less than num_embeddings)
Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
`max_norm` or `norm_type`. We are not supporting these.
"""
if padding_idx is not None and padding_idx > num_embeddings:
raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
super().__init__(
num_embeddings=num_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
padding_idx=padding_idx,
**kwargs,
)
self.num_embeddings = num_embeddings
self.padding_idx = padding_idx
self.num_additional_embeddings = num_additional_embeddings
self.partially_freeze = partially_freeze
if partially_freeze:
self.weight.requires_grad_(False)
if self.num_additional_embeddings > 0:
self.additional_embedding = nn.Embedding(
num_embeddings=self.num_additional_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
)
def forward(self, input_ids):
"""
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the 2nd embedding
2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd
embedding starts from 0 and not num_embeddings
3. perform the 2nd embedding lookup
4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
5. perform the 1st embedding lookup
6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but
then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices -
i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are
usually relatively short it's probably not faster or if faster not by much - but might be a good idea to
measure.
"""
if self.num_additional_embeddings == 0:
return F.embedding(input_ids, self.weight)
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = F.embedding(input_ids, self.weight)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
def extra_repr(self) -> str:
return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
self.num_embeddings,
self.num_additional_embeddings,
self.embedding_dim,
self.partially_freeze,
)
class IdeficsDecoupledLinear(nn.Linear):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
in_features: int,
out_features: int,
out_additional_features: int = 0,
bias: bool = True,
partially_freeze: bool = True,
device=None,
dtype=None,
) -> None:
"""
out_additional_features: int. Number of additional trainable dimensions. Only makes sense when
`partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra
parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear.
"""
super().__init__(in_features, out_features, bias, device, dtype)
self.out_additional_features = out_additional_features
self.partially_freeze = partially_freeze
self.in_features = in_features
self.out_features = out_features
if partially_freeze:
self.weight.requires_grad_(False)
if bias:
self.bias.requires_grad_(False)
if out_additional_features > 0:
self.additional_fc = nn.Linear(
in_features=in_features,
out_features=out_additional_features,
bias=bias,
device=device,
dtype=dtype,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = F.linear(input, self.weight, self.bias)
if self.out_additional_features > 0:
additional_features = self.additional_fc(input)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
# this was adapted from LlamaRMSNorm
class IdeficsRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
IdeficsRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
ALL_LAYERNORM_LAYERS.append(IdeficsRMSNorm)
# this was adapted from LlamaRotaryEmbedding
class IdeficsEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# this was adapted from LlamaMLP
class IdeficsMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
# this was adapted from LlamaAttention
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: PretrainedConfig = None,
qk_layer_norms: bool = False,
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.dropout = dropout
if (self.head_dim * num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {num_heads})."
)
self.is_cross_attention = is_cross_attention
if not hasattr(nn.functional, "scaled_dot_product_attention"):
raise ValueError("this model requires pytorch 2.0 or higher")
if self.is_cross_attention:
kv_input_dim = (
self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
)
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(
kv_input_dim,
num_heads * self.head_dim,
bias=False,
)
else:
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.v_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.o_proj = nn.Linear(
num_heads * self.head_dim,
hidden_size,
bias=False,
)
self.rotary_emb = IdeficsEmbedding(self.head_dim)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
if not is_cross_attention:
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
else:
_, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = (
self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if not is_cross_attention:
cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len))
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_output = nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
# this was adapted from LlamaDecoderLayer
class IdeficsDecoderLayer(nn.Module):
def __init__(self, config: IdeficsConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.dropout,
config=config,
)
self.mlp = IdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, config: IdeficsConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.cross_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
is_cross_attention=True,
dropout=config.dropout,
config=config,
qk_layer_norms=config.qk_layer_norms,
)
self.mlp = IdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
if config.alpha_initializer == "zeros":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1))
self.alpha_dense = nn.Parameter(torch.zeros(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer == "ones":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.ones(1))
self.alpha_dense = nn.Parameter(torch.ones(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer in {"normal", "gaussian", "random"}:
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
self.alpha_dense = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))
)
self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
else:
raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!")
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
cross_attention_gate: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
image_attention_mask (`torch.FloatTensor`, *optional*): image attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
cross_attention_gate (`torch.FloatTensor`, *optional*):
gate of size `(batch, seq_len)` used to zero-out cross-attention output for tokens attending no images.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if cross_attention_gate is None:
raise ValueError(
"`cross_attention_gate` is required for Idefics cross attention module to zero-out the cross-attention hidden_states attending to no images."
)
if past_key_value is not None:
raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
# Fill in zeros for cross_attention hidden_states of tokens attending to no images
hidden_states[cross_attention_gate == 0] = hidden_states[cross_attention_gate == 0].fill_(0)
hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
def _init_weights(self, module):
# important: this ported version of Idefics isn't meant for training from scratch - only
# inference and fine-tuning - so the proper init weights code has been removed - the m4 code
# base should be used for training from scratch and it contains the correct code.
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
LLAMA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class IdeficsModel(IdeficsPreTrainedModel):
"""
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
Args:
config: IdeficsConfig
"""
def __init__(self, config: IdeficsConfig):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = IdeficsDecoupledEmbedding(
num_embeddings=config.vocab_size,
num_additional_embeddings=config.additional_vocab_size,
embedding_dim=config.hidden_size,
partially_freeze=config.freeze_text_layers,
padding_idx=self.padding_idx,
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = IdeficsVisionTransformer(config.vision_config)
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = IdeficsPerceiverResampler(
config,
config.vision_config.embed_dim,
perceiver_config.resampler_depth,
perceiver_config.resampler_n_heads,
perceiver_config.resampler_head_dim,
perceiver_config.resampler_n_latents,
)
self.layers = nn.ModuleList([IdeficsDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[IdeficsGatedCrossAttentionLayer(config) for _ in range(num_cross_layers)]
)
self.gradient_checkpointing = False
self.norm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
# Initialize weights and apply final processing
self.post_init()
self.freeze_relevant_params(config)
def freeze_relevant_params(self, config=None):
if config is None:
config = self.config
if config.freeze_text_layers:
self.freeze_text_layers(config.freeze_text_module_exceptions)
if config.freeze_vision_layers:
freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
def freeze_text_layers(self, module_exceptions=[]):
for module in [self.layers, self.norm]:
freeze_model(module, module_exceptions=module_exceptions)
def freeze_vision_layers(self, module_exceptions=[]):
freeze_model(self.vision_model, module_exceptions=module_exceptions)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
perceiver_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, IdeficsBaseModelOutputWithPast]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
elif position_ids is None:
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0)
if (pixel_values, image_encoder_embeddings, perceiver_embeddings).count(None) != 2:
raise ValueError(
"Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None."
)
elif pixel_values is not None:
pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility
batch_size, num_images = pixel_values.shape[:2]
pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
).last_hidden_state
elif image_encoder_embeddings is not None:
batch_size, num_images, image_seq_len, image_hidden_size = image_encoder_embeddings.size()
image_hidden_states = image_encoder_embeddings.to(dtype=self.dtype, device=device)
image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
if self.config.use_resampler:
if perceiver_embeddings is None:
perceiver_embeddings = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = perceiver_embeddings.size(1), perceiver_embeddings.size(2)
else:
batch_size, num_images, image_seq_len, image_hidden_size = perceiver_embeddings.size()
image_hidden_states = perceiver_embeddings
elif perceiver_embeddings is None:
image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
else:
raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True")
image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
# # Hack to use the model in full language modeling mode
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
if image_hidden_states is not None:
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
else:
image_attention_mask = None
# cross_attention_gate:
# For any tokens attending to no images, the hidden_states comming out of the cross-attention should be zeroed-out.
# `image_attention_mask` has shape [bsz, 1, num_images, hidden_size] with elements equal to either 0.0 or a very negative number.
# If any of the elements are 0.0, then the token is attending to at least one image and the gate value is 1. Otherwise the gate value is 0.
# `cross_attention_gate` has shape [bsz, seq_len] with elements equal to either 0.0 or 1.0.
cross_attention_gate = ((((image_attention_mask == 0.0).any(dim=-1)).to(dtype=self.dtype)).squeeze(dim=1)).to(
device
)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
cross_attention_gate,
output_attentions,
use_cache,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
if self.gradient_checkpointing and self.training:
past_key_value = None
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = self._gradient_checkpointing_func(
vblock,
decoder_layer,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
cross_attention_gate,
output_attentions,
use_cache,
idx,
self.cross_layer_interval,
self.gated_cross_attn_layers,
)
else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size)
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states]
if v is not None
)
return IdeficsBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config, vision_model=None):
super().__init__(config)
self.model = IdeficsModel(config)
self.lm_head = IdeficsDecoupledLinear(
in_features=config.hidden_size,
out_features=config.vocab_size,
out_additional_features=config.additional_vocab_size,
bias=False,
partially_freeze=config.freeze_lm_head,
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of
IdeficsDecoupledLinear and IdeficsDecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(self.config, "tie_word_embeddings", True):
output_embeddings.weight = input_embeddings.weight
if input_embeddings.num_additional_embeddings > 0:
assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight
if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
output_embeddings.out_features = input_embeddings.num_embeddings
if hasattr(output_embeddings, "out_additional_features") and hasattr(
input_embeddings, "num_additional_embeddings"
):
output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=IdeficsCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
perceiver_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, IdeficsCausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, IdeficsForVisionText2Text
>>> model = IdeficsForVisionText2Text.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_encoder_embeddings=image_encoder_embeddings,
perceiver_embeddings=perceiver_embeddings,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return IdeficsCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
image_hidden_states = kwargs.pop("image_hidden_states", None)
if image_hidden_states is not None:
if self.config.use_resampler:
kwargs["perceiver_embeddings"] = image_hidden_states
else:
kwargs["image_encoder_embeddings"] = image_hidden_states
kwargs["pixel_values"] = None
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder):
return update_model_kwargs_for_generation(outputs, model_kwargs)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/vision.py
|
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. 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.
""" PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...utils import ModelOutput, logging
from .configuration_idefics import IdeficsVisionConfig
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
# Heavily inspired from https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/vit/modeling_vit.py#L82
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
pos_embed = self.position_embedding(self.position_ids)
num_positions = pos_embed.shape[1] - 1
if num_patches == num_positions and height == width:
return pos_embed
class_pos_embed = pos_embed[:, 0]
patch_pos_embed = pos_embed[:, 1:]
embed_dim = embeddings.shape[-1]
num_h_patches = height // self.config.patch_size
num_w_patches = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
sqrt_num_positions = math.sqrt(num_positions)
patch_pos_embed = patch_pos_embed.reshape(1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
fp32_upcasting = patch_pos_embed.dtype == torch.bfloat16
if fp32_upcasting:
logger.warning_once(
"Upcasting patch_pos_embed to fp32 for interpolation since `upsample_bicubic2d_out_frame` in nn.functional.interpolate "
"is not implemented for 'torch.bfloat16' dtype. This will result in a slight overhead."
)
patch_pos_embed = patch_pos_embed.to(torch.float)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(num_h_patches / sqrt_num_positions, num_w_patches / sqrt_num_positions),
mode="bicubic",
align_corners=False,
)
if fp32_upcasting:
patch_pos_embed = patch_pos_embed.to(torch.bfloat16)
if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]:
raise ValueError(
f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})"
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, embed_dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size or width != self.image_size:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`"
)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = IdeficsVisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([IdeficsVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = IdeficsVisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/perceiver.py
|
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from .configuration_idefics import IdeficsConfig
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
# Create Latents for Perceiver
self.latents = nn.Parameter(torch.randn(self.n_latents, self.embed_dim), requires_grad=True)
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = nn.ModuleList(
[
nn.ModuleList(
[
IdeficsPerceiverAttention(self.embed_dim, self.n_heads, self.head_dim, self.qk_layer_norms),
IdeficsMLP(self.intermediate_dim, config),
]
)
for _ in range(depth)
]
)
self.layer_norm = nn.LayerNorm(self.embed_dim)
def forward(self, context: torch.Tensor) -> torch.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = self.latents.repeat(context.shape[0], 1, 1)
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class IdeficsPerceiverAttention(nn.Module):
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = nn.LayerNorm(self.embed_dim)
self.latents_layer_norm = nn.LayerNorm(self.embed_dim)
if self.qk_layer_norms:
self.q_layer_norm = nn.LayerNorm(self.head_dim)
self.k_layer_norm = nn.LayerNorm(self.head_dim)
self.qk_scale = self.head_dim**-0.5
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.output_proj = nn.Linear(self.n_heads * self.head_dim, embed_dim, bias=False)
def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`torch.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`torch.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = context.shape[:3]
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(torch.cat([context, latents], dim=-2))
v = self.v_proj(torch.cat([context, latents], dim=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
# einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
q, k, v = [x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(1, 2) for x in (q, k, v)]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach())
attn = stabilized_scores.softmax(dim=-1)
# Attend & project back to output...
resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v)
# einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads)
return self.output_proj(resampled.transpose(1, 2).flatten(-2))
class IdeficsMLP(nn.Module):
def __init__(self, intermediate_size, config: IdeficsConfig):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm(self.embed_dim)
self.fc = nn.Linear(self.embed_dim, intermediate_size, bias=False)
self.act = nn.ReLU()
self.c_proj = nn.Linear(intermediate_size, self.embed_dim, bias=False)
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/processing_idefics.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""
Processor class for IDEFICS.
"""
from typing import Callable, List, Optional, Union
from urllib.parse import urlparse
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
from ...utils import TensorType, is_torch_available
if is_torch_available():
import torch
IMAGE_TOKEN = "<image>"
# copied from m4.training.packing
def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
# This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
# If any of images index are more than num_classes, set them to -1.
# Words after the max number of images allowed have been seen don't attend on anything
if num_classes != -1:
incremental_mask[incremental_mask >= num_classes] = -1
negatives = incremental_mask == -1
incremental_mask[negatives] = 0
attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
attn_mask[negatives, :] = 0
return attn_mask
# copied from m4.training.packing
def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
image_attention_mask = torch.full_like(input_ids, fill_value=-1)
next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx, token_id in enumerate(input_ids[batch_idx]):
if token_id == image_token_id:
count += 1
image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
image_attention_mask[batch_idx][idx] = count
if seen_eod:
image_attention_mask[batch_idx][idx] = -1
if token_id == eod_token_id:
seen_eod = True
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
token_id = input_ids[batch_idx][idx]
if token_id == image_token_id:
count += 1
next_image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
next_image_attention_mask[batch_idx][idx] = count
if token_id == eod_token_id:
seen_eod = True
if seen_eod:
next_image_attention_mask[batch_idx][idx] = -1
non_negative_indices = next_image_attention_mask[batch_idx] != -1
next_image_attention_mask[batch_idx][non_negative_indices] -= count
next_image_attention_mask[batch_idx][non_negative_indices] *= -1
return image_attention_mask, next_image_attention_mask
def is_url(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
if " " in string:
return False
result = urlparse(string)
return all([result.scheme, result.netloc])
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`IdeficsImageProcessor`):
An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
tokenizer (`LlamaTokenizerFast`):
An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "IdeficsImageProcessor"
tokenizer_class = "LlamaTokenizerFast"
def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
self.default_image_dims = (
self.image_processor.image_num_channels,
self.image_processor.image_size,
self.image_processor.image_size,
)
self.tokenizer_was_trained_with_end_of_utterance_token = (
True
if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
else False
)
def __call__(
self,
prompts: Union[List[TextInput], List[List[TextInput]]],
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
transform: Callable = None,
add_eos_token=False,
add_end_of_utterance_token=None,
debug=False,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchEncoding:
"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process.
Args:
prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
transform (`Callable`, *optional*):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
set of transforms will be applied to the images
add_eos_token (`bool`, *optional*, defaults to `False`):
Adds `eos_token` at the end of the final prompt if True`
add_end_of_utterance_token (`bool`, *optional*)
Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
image). If `None` the tokenizer will be checked instead and if this token is found in
`additional_special_tokens` then the value will be `True`.
debug (`bool`, *optional*, defaults to `False`):
`True` value will help debug prompt generation by dumping useful information
return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
The type of tensors to return. Can be one of:
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
Returns:
a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
directly passed to `model.generate`
Detailed explanation:
Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
entry into the prompt.
Example:
```python
checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
"User:",
img,
"Describe this image.\nAssistant: An image of two kittens in grass.\n",
"User:",
"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
"Describe this image.\nAssistant:",
]
inputs = processor(prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
In this example the `prompts` will be converted into:
```
<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:'
```
and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
`pixel_values` dict entry of the return value.
This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.
To do training do:
```python
image_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.image_mean, std=self.image_std),
]
)
inputs = processor(prompts, transform=image_transform, return_tensors="pt")
```
In order to help debug prompt generation enable `debug=True` which will show you what's happening.
"""
# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
if add_end_of_utterance_token is None:
add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
# turn non-batched prompts into batched
if not any(isinstance(i, list) for i in prompts):
prompts = [prompts]
fake_token = "<fake_token_around_image>"
image_token = "<image>"
end_of_utterance_token = "<end_of_utterance>"
def image_tokens(last_was_image):
if last_was_image:
return image_token + fake_token
else:
return fake_token + image_token + fake_token
all_prompts = []
all_images = []
for sample in prompts:
# the model was trained on samples starting with <s>
full_text = f"{self.tokenizer.bos_token}"
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
image_objects = []
last_was_image = False
last_was_text = False
for i, item in enumerate(sample):
if i > 0:
last_was_text = True if not last_was_image else False
if isinstance(item, str):
item = item.strip(" ")
if is_url(item):
image = self.image_processor.fetch_images(item)
full_text += image_tokens(last_was_image)
image_objects.append(image)
last_was_image = True
else:
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
if add_end_of_utterance_token and last_was_text:
full_text += end_of_utterance_token
full_text += item
last_was_image = False
else:
# must be an image obj
full_text += image_tokens(last_was_image)
image_objects.append(item)
last_was_image = True
if add_eos_token:
full_text += self.tokenizer.eos_token
if debug is True:
print(f"{full_text=}")
image_objects = self.image_processor(image_objects, transform=transform)
all_prompts.append(full_text)
all_images.append(image_objects)
text_encoding = self.tokenizer(
text=all_prompts,
add_special_tokens=False,
padding=padding,
truncation=truncation,
max_length=max_length,
)
all_texts = text_encoding["input_ids"]
max_seq_len = max(len(x) for x in all_texts)
# max_num_images has to be at least 1 even when there are no images
max_num_images = max(len(x) for x in all_images)
max_num_images = max(1, max_num_images)
at_least_one_image = sum(len(x) for x in all_images) > 0
output_input_ids = []
output_images = []
output_attention_masks = []
for text, images in zip(all_texts, all_images):
padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
unpadded_seq_len = len(text)
start = max_seq_len - unpadded_seq_len
padded_input_ids[start:] = text[:max_seq_len]
attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
attention_mask[start:] = 1
image_count = padded_input_ids.count(self.image_token_id)
local_max_num_images = min(image_count, max_num_images)
current_images = images[:local_max_num_images]
if len(current_images) > 0:
padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
padded_image_tensor[: current_images.size(0)] = current_images
else:
padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
output_images.append(padded_image_tensor)
output_input_ids.append(torch.tensor(padded_input_ids))
output_attention_masks.append(attention_mask)
output_input_ids = torch.stack(output_input_ids)
output_images = torch.stack(output_images)
output_attention_masks = torch.stack(output_attention_masks)
if at_least_one_image:
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
image_attention_mask = incremental_to_binary_attention_mask(
image_attention_mask, num_classes=max_num_images
)
else:
# in full language mode we set the image mask to all-0s
image_attention_mask = torch.zeros(
output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
)
return BatchFeature(
data={
"input_ids": output_input_ids,
"attention_mask": output_attention_masks,
"pixel_values": output_images,
"image_attention_mask": image_attention_mask,
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/image_processing_idefics.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""Image processor class for Idefics."""
from typing import Callable, Dict, List, Optional, Union
from PIL import Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available
IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to 224):
Resize to image size
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_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 `IDEFICS_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.
image_num_channels (`int`, *optional*, defaults to 3):
Number of image channels.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
image_size: int = 224,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_num_channels: Optional[int] = 3,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.image_size = image_size
self.image_num_channels = image_num_channels
self.image_mean = image_mean
self.image_std = image_std
def preprocess(
self,
images: ImageInput,
image_num_channels: Optional[int] = 3,
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
**kwargs,
) -> TensorType.PYTORCH:
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
image_size (`int`, *optional*, defaults to `self.image_size`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_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 `IDEFICS_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.
transform (`Callable`, *optional*, defaults to `None`):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images
Returns:
a PyTorch tensor of the processed images
"""
image_size = image_size if image_size is not None else self.image_size
image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
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
size = (image_size, image_size)
if isinstance(images, list) and len(images) == 0:
return []
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# For training a user needs to pass their own set of transforms as a Callable.
# For reference this is what was used in the original IDEFICS training:
# transform = transforms.Compose([
# convert_to_rgb,
# transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
# transforms.ToTensor(),
# transforms.Normalize(mean=image_mean, std=image_std),
# ])
if transform is not None:
if not is_torch_available():
raise ImportError("To pass in `transform` torch must be installed")
import torch
images = [transform(x) for x in images]
return torch.stack(images)
# for inference we do the exact transforms that were used to train IDEFICS
images = [convert_to_rgb(x) for x in images]
# further transforms expect numpy arrays
images = [to_numpy_array(x) for x in images]
images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
images = [self.rescale(image=image, scale=1 / 255) for image in images]
images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
# TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"]
return images
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/idefics/__init__.py
|
# Copyright 2022 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {"configuration_idefics": ["IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP", "IdeficsConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_idefics"] = ["IdeficsImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_idefics"] = [
"IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST",
"IdeficsForVisionText2Text",
"IdeficsModel",
"IdeficsPreTrainedModel",
]
_import_structure["processing_idefics"] = ["IdeficsProcessor"]
if TYPE_CHECKING:
from .configuration_idefics import IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP, IdeficsConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_idefics import IdeficsImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_idefics import (
IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST,
IdeficsForVisionText2Text,
IdeficsModel,
IdeficsPreTrainedModel,
)
from .processing_idefics import IdeficsProcessor
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/encodec/modeling_encodec.py
|
# coding=utf-8
# Copyright 2023 Meta Platforms, Inc. and affiliates, and the HuggingFace Inc. team. 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.
""" PyTorch EnCodec model."""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_encodec import EncodecConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "EncodecConfig"
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/encodec_24khz",
"facebook/encodec_48khz",
# See all EnCodec models at https://huggingface.co/models?filter=encodec
]
@dataclass
class EncodecOutput(ModelOutput):
"""
Args:
audio_codes (`torch.FloatTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_values (`torch.FlaotTensor` of shape `(batch_size, sequence_length)`, *optional*)
Decoded audio values, obtained using the decoder part of Encodec.
"""
audio_codes: torch.FloatTensor = None
audio_values: torch.FloatTensor = None
@dataclass
class EncodecEncoderOutput(ModelOutput):
"""
Args:
audio_codes (`torch.FloatTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_scales (`torch.Tensor` of shape `(batch_size, nb_chunks)`, *optional*):
Scaling factor for each `audio_codes` input. This is used to unscale each chunk of audio when decoding.
"""
audio_codes: torch.FloatTensor = None
audio_scales: torch.FloatTensor = None
@dataclass
class EncodecDecoderOutput(ModelOutput):
"""
Args:
audio_values (`torch.FloatTensor` of shape `(batch_size, segment_length)`, *optional*):
Decoded audio values, obtained using the decoder part of Encodec.
"""
audio_values: torch.FloatTensor = None
class EncodecConv1d(nn.Module):
"""Conv1d with asymmetric or causal padding and normalization."""
def __init__(
self, config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, dilation: int = 1
):
super().__init__()
self.causal = config.use_causal_conv
self.pad_mode = config.pad_mode
self.norm_type = config.norm_type
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'
)
# warn user on unusual setup between dilation and stride
if stride > 1 and dilation > 1:
logger.warning(
"EncodecConv1d has been initialized with stride > 1 and dilation > 1"
f" (kernel_size={kernel_size} stride={stride}, dilation={dilation})."
)
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, dilation=dilation)
if self.norm_type == "weight_norm":
self.conv = nn.utils.weight_norm(self.conv)
elif self.norm_type == "time_group_norm":
self.norm = nn.GroupNorm(1, out_channels)
@staticmethod
def _get_extra_padding_for_conv1d(
hidden_states: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0
) -> int:
"""See `pad_for_conv1d`."""
length = hidden_states.shape[-1]
n_frames = (length - kernel_size + padding_total) / stride + 1
ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
return ideal_length - length
@staticmethod
def _pad1d(hidden_states: torch.Tensor, paddings: Tuple[int, int], mode: str = "zero", value: float = 0.0):
"""Tiny wrapper around torch.nn.functional.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happens.
"""
length = hidden_states.shape[-1]
padding_left, padding_right = paddings
if not mode == "reflect":
return nn.functional.pad(hidden_states, paddings, mode, value)
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
hidden_states = nn.functional.pad(hidden_states, (0, extra_pad))
padded = nn.functional.pad(hidden_states, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]
def forward(self, hidden_states):
kernel_size = self.conv.kernel_size[0]
stride = self.conv.stride[0]
dilation = self.conv.dilation[0]
kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
padding_total = kernel_size - stride
extra_padding = self._get_extra_padding_for_conv1d(hidden_states, kernel_size, stride, padding_total)
if self.causal:
# Left padding for causal
hidden_states = self._pad1d(hidden_states, (padding_total, extra_padding), mode=self.pad_mode)
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
hidden_states = self._pad1d(
hidden_states, (padding_left, padding_right + extra_padding), mode=self.pad_mode
)
hidden_states = self.conv(hidden_states)
if self.norm_type == "time_group_norm":
hidden_states = self.norm(hidden_states)
return hidden_states
class EncodecConvTranspose1d(nn.Module):
"""ConvTranspose1d with asymmetric or causal padding and normalization."""
def __init__(self, config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1):
super().__init__()
self.causal = config.use_causal_conv
self.trim_right_ratio = config.trim_right_ratio
self.norm_type = config.norm_type
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'
)
self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride)
if config.norm_type == "weight_norm":
self.conv = nn.utils.weight_norm(self.conv)
elif config.norm_type == "time_group_norm":
self.norm = nn.GroupNorm(1, out_channels)
if not (self.causal or self.trim_right_ratio == 1.0):
raise ValueError("`trim_right_ratio` != 1.0 only makes sense for causal convolutions")
def forward(self, hidden_states):
kernel_size = self.conv.kernel_size[0]
stride = self.conv.stride[0]
padding_total = kernel_size - stride
hidden_states = self.conv(hidden_states)
if self.norm_type == "time_group_norm":
hidden_states = self.norm(hidden_states)
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
# removed at the very end, when keeping only the right length for the output,
# as removing it here would require also passing the length at the matching layer
# in the encoder.
if self.causal:
# Trim the padding on the right according to the specified ratio
# if trim_right_ratio = 1.0, trim everything from right
padding_right = math.ceil(padding_total * self.trim_right_ratio)
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
# unpad
end = hidden_states.shape[-1] - padding_right
hidden_states = hidden_states[..., padding_left:end]
return hidden_states
class EncodecLSTM(nn.Module):
"""
LSTM without worrying about the hidden state, nor the layout of the data. Expects input as convolutional layout.
"""
def __init__(self, config, dimension):
super().__init__()
self.lstm = nn.LSTM(dimension, dimension, config.num_lstm_layers)
def forward(self, hidden_states):
hidden_states = hidden_states.permute(2, 0, 1)
hidden_states = self.lstm(hidden_states)[0] + hidden_states
hidden_states = hidden_states.permute(1, 2, 0)
return hidden_states
class EncodecResnetBlock(nn.Module):
"""
Residual block from SEANet model as used by EnCodec.
"""
def __init__(self, config: EncodecConfig, dim: int, dilations: List[int]):
super().__init__()
kernel_sizes = (config.residual_kernel_size, 1)
if len(kernel_sizes) != len(dilations):
raise ValueError("Number of kernel sizes should match number of dilations")
hidden = dim // config.compress
block = []
for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
in_chs = dim if i == 0 else hidden
out_chs = dim if i == len(kernel_sizes) - 1 else hidden
block += [nn.ELU()]
block += [EncodecConv1d(config, in_chs, out_chs, kernel_size, dilation=dilation)]
self.block = nn.ModuleList(block)
if config.use_conv_shortcut:
self.shortcut = EncodecConv1d(config, dim, dim, kernel_size=1)
else:
self.shortcut = nn.Identity()
def forward(self, hidden_states):
residual = hidden_states
for layer in self.block:
hidden_states = layer(hidden_states)
return self.shortcut(residual) + hidden_states
class EncodecEncoder(nn.Module):
"""SEANet encoder as used by EnCodec."""
def __init__(self, config: EncodecConfig):
super().__init__()
model = [EncodecConv1d(config, config.audio_channels, config.num_filters, config.kernel_size)]
scaling = 1
# Downsample to raw audio scale
for ratio in reversed(config.upsampling_ratios):
current_scale = scaling * config.num_filters
# Add residual layers
for j in range(config.num_residual_layers):
model += [EncodecResnetBlock(config, current_scale, [config.dilation_growth_rate**j, 1])]
# Add downsampling layers
model += [nn.ELU()]
model += [EncodecConv1d(config, current_scale, current_scale * 2, kernel_size=ratio * 2, stride=ratio)]
scaling *= 2
model += [EncodecLSTM(config, scaling * config.num_filters)]
model += [nn.ELU()]
model += [EncodecConv1d(config, scaling * config.num_filters, config.hidden_size, config.last_kernel_size)]
self.layers = nn.ModuleList(model)
def forward(self, hidden_states):
for layer in self.layers:
hidden_states = layer(hidden_states)
return hidden_states
class EncodecDecoder(nn.Module):
"""SEANet decoder as used by EnCodec."""
def __init__(self, config: EncodecConfig):
super().__init__()
scaling = int(2 ** len(config.upsampling_ratios))
model = [EncodecConv1d(config, config.hidden_size, scaling * config.num_filters, config.kernel_size)]
model += [EncodecLSTM(config, scaling * config.num_filters)]
# Upsample to raw audio scale
for ratio in config.upsampling_ratios:
current_scale = scaling * config.num_filters
# Add upsampling layers
model += [nn.ELU()]
model += [
EncodecConvTranspose1d(config, current_scale, current_scale // 2, kernel_size=ratio * 2, stride=ratio)
]
# Add residual layers
for j in range(config.num_residual_layers):
model += [EncodecResnetBlock(config, current_scale // 2, (config.dilation_growth_rate**j, 1))]
scaling //= 2
# Add final layers
model += [nn.ELU()]
model += [EncodecConv1d(config, config.num_filters, config.audio_channels, config.last_kernel_size)]
self.layers = nn.ModuleList(model)
def forward(self, hidden_states):
for layer in self.layers:
hidden_states = layer(hidden_states)
return hidden_states
class EncodecEuclideanCodebook(nn.Module):
"""Codebook with Euclidean distance."""
def __init__(self, config: EncodecConfig):
super().__init__()
embed = torch.zeros(config.codebook_size, config.codebook_dim)
self.codebook_size = config.codebook_size
self.register_buffer("inited", torch.Tensor([True]))
self.register_buffer("cluster_size", torch.zeros(config.codebook_size))
self.register_buffer("embed", embed)
self.register_buffer("embed_avg", embed.clone())
def quantize(self, hidden_states):
embed = self.embed.t()
scaled_states = hidden_states.pow(2).sum(1, keepdim=True)
dist = -(scaled_states - 2 * hidden_states @ embed + embed.pow(2).sum(0, keepdim=True))
embed_ind = dist.max(dim=-1).indices
return embed_ind
def encode(self, hidden_states):
shape = hidden_states.shape
# pre-process
hidden_states = hidden_states.reshape((-1, shape[-1]))
# quantize
embed_ind = self.quantize(hidden_states)
# post-process
embed_ind = embed_ind.view(*shape[:-1])
return embed_ind
def decode(self, embed_ind):
quantize = nn.functional.embedding(embed_ind, self.embed)
return quantize
class EncodecVectorQuantization(nn.Module):
"""
Vector quantization implementation. Currently supports only euclidean distance.
"""
def __init__(self, config: EncodecConfig):
super().__init__()
self.codebook = EncodecEuclideanCodebook(config)
def encode(self, hidden_states):
hidden_states = hidden_states.permute(0, 2, 1)
embed_in = self.codebook.encode(hidden_states)
return embed_in
def decode(self, embed_ind):
quantize = self.codebook.decode(embed_ind)
quantize = quantize.permute(0, 2, 1)
return quantize
class EncodecResidualVectorQuantizer(nn.Module):
"""Residual Vector Quantizer."""
def __init__(self, config: EncodecConfig):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.num_quantizers = config.num_quantizers
self.layers = nn.ModuleList([EncodecVectorQuantization(config) for _ in range(config.num_quantizers)])
def get_num_quantizers_for_bandwidth(self, bandwidth: Optional[float] = None) -> int:
"""Return num_quantizers based on specified target bandwidth."""
bw_per_q = math.log2(self.codebook_size) * self.frame_rate
num_quantizers = self.num_quantizers
if bandwidth is not None and bandwidth > 0.0:
num_quantizers = int(max(1, math.floor(bandwidth * 1000 / bw_per_q)))
return num_quantizers
def encode(self, embeddings: torch.Tensor, bandwidth: Optional[float] = None) -> torch.Tensor:
"""
Encode a given input tensor with the specified frame rate at the given bandwidth. The RVQ encode method sets
the appropriate number of quantizers to use and returns indices for each quantizer.
"""
num_quantizers = self.get_num_quantizers_for_bandwidth(bandwidth)
residual = embeddings
all_indices = []
for layer in self.layers[:num_quantizers]:
indices = layer.encode(residual)
quantized = layer.decode(indices)
residual = residual - quantized
all_indices.append(indices)
out_indices = torch.stack(all_indices)
return out_indices
def decode(self, codes: torch.Tensor) -> torch.Tensor:
"""Decode the given codes to the quantized representation."""
quantized_out = torch.tensor(0.0, device=codes.device)
for i, indices in enumerate(codes):
layer = self.layers[i]
quantized = layer.decode(indices)
quantized_out = quantized_out + quantized
return quantized_out
class EncodecPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EncodecConfig
base_model_prefix = "encodec"
main_input_name = "input_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
nn.init.uniform_(module.bias, a=-k, b=k)
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LSTM):
for name, param in module.named_parameters():
if "weight" in name:
nn.init.xavier_uniform_(param)
elif "bias" in name:
nn.init.constant_(param, 0.0)
ENCODEC_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`EncodecConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
ENCODEC_INPUTS_DOCSTRING = r"""
Args:
input_values (`torch.FloatTensor` of shape `(batch_size, channels, sequence_length)`, *optional*):
Raw audio input converted to Float and padded to the approriate length in order to be encoded using chunks
of length self.chunk_length and a stride of `config.chunk_stride`.
padding_mask (`torch.BoolTensor` of shape `(batch_size, channels, sequence_length)`, *optional*):
Mask to avoid computing scaling factors on padding token indices (can we avoid computing conv on these+).
Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
<Tip warning={true}>
`padding_mask` should always be passed, unless the input was truncated or not padded. This is because in
order to process tensors effectively, the input audio should be padded so that `input_length % stride =
step` with `step = chunk_length-stride`. This ensures that all chunks are of the same shape
</Tip>
bandwidth (`float`, *optional*):
The target bandwidth. Must be one of `config.target_bandwidths`. If `None`, uses the smallest possible
bandwidth. bandwidth is represented as a thousandth of what it is, e.g. 6kbps bandwidth is represented as
`bandwidth == 6.0`
audio_codes (`torch.FloatTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_scales (`torch.Tensor` of shape `(batch_size, nb_chunks)`, *optional*):
Scaling factor for each `audio_codes` input.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The EnCodec neural audio codec model.",
ENCODEC_START_DOCSTRING,
)
class EncodecModel(EncodecPreTrainedModel):
def __init__(self, config: EncodecConfig):
super().__init__(config)
self.config = config
self.encoder = EncodecEncoder(config)
self.decoder = EncodecDecoder(config)
self.quantizer = EncodecResidualVectorQuantizer(config)
self.bits_per_codebook = int(math.log2(self.config.codebook_size))
if 2**self.bits_per_codebook != self.config.codebook_size:
raise ValueError("The codebook_size must be a power of 2.")
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def _encode_frame(
self, input_values: torch.Tensor, bandwidth: float, padding_mask: int
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
Encodes the given input using the underlying VQVAE. If `config.normalize` is set to `True` the input is first
normalized. The padding mask is required to compute the correct scale.
"""
length = input_values.shape[-1]
duration = length / self.config.sampling_rate
if self.config.chunk_length_s is not None and duration > 1e-5 + self.config.chunk_length_s:
raise RuntimeError(f"Duration of frame ({duration}) is longer than chunk {self.config.chunk_length_s}")
scale = None
if self.config.normalize:
# if the padding is non zero
input_values = input_values * padding_mask
mono = torch.sum(input_values, 1, keepdim=True) / input_values.shape[1]
scale = mono.pow(2).mean(dim=-1, keepdim=True).sqrt() + 1e-8
input_values = input_values / scale
embeddings = self.encoder(input_values)
codes = self.quantizer.encode(embeddings, bandwidth)
codes = codes.transpose(0, 1)
return codes, scale
def encode(
self,
input_values: torch.Tensor,
padding_mask: torch.Tensor = None,
bandwidth: Optional[float] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], EncodecEncoderOutput]:
"""
Encodes the input audio waveform into discrete codes.
Args:
input_values (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Float values of the input audio waveform.
padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Padding mask used to pad the `input_values`.
bandwidth (`float`, *optional*):
The target bandwidth. Must be one of `config.target_bandwidths`. If `None`, uses the smallest possible
bandwidth. bandwidth is represented as a thousandth of what it is, e.g. 6kbps bandwidth is represented
as bandwidth == 6.0
Returns:
A list of frames containing the discrete encoded codes for the input audio waveform, along with rescaling
factors for each chunk when `normalize` is True. Each frames is a tuple `(codebook, scale)`, with
`codebook` of shape `[batch_size, num_codebooks, frames]`.
"""
return_dict = return_dict if return_dict is not None else self.config.return_dict
if bandwidth is None:
bandwidth = self.config.target_bandwidths[0]
if bandwidth not in self.config.target_bandwidths:
raise ValueError(
f"This model doesn't support the bandwidth {bandwidth}. "
f"Select one of {self.config.target_bandwidths}."
)
_, channels, input_length = input_values.shape
if channels < 1 or channels > 2:
raise ValueError(f"Number of audio channels must be 1 or 2, but got {channels}")
chunk_length = self.config.chunk_length
if chunk_length is None:
chunk_length = input_length
stride = input_length
else:
stride = self.config.chunk_stride
if padding_mask is None:
padding_mask = torch.ones_like(input_values).bool()
encoded_frames = []
scales = []
step = chunk_length - stride
if (input_length % stride) - step != 0:
raise ValueError(
"The input length is not properly padded for batched chunked decoding. Make sure to pad the input correctly."
)
for offset in range(0, input_length - step, stride):
mask = padding_mask[..., offset : offset + chunk_length].bool()
frame = input_values[:, :, offset : offset + chunk_length]
encoded_frame, scale = self._encode_frame(frame, bandwidth, mask)
encoded_frames.append(encoded_frame)
scales.append(scale)
encoded_frames = torch.stack(encoded_frames)
if not return_dict:
return (encoded_frames, scales)
return EncodecEncoderOutput(encoded_frames, scales)
@staticmethod
def _linear_overlap_add(frames: List[torch.Tensor], stride: int):
# Generic overlap add, with linear fade-in/fade-out, supporting complex scenario
# e.g., more than 2 frames per position.
# The core idea is to use a weight function that is a triangle,
# with a maximum value at the middle of the chunk.
# We use this weighting when summing the frames, and divide by the sum of weights
# for each positions at the end. Thus:
# - if a frame is the only one to cover a position, the weighting is a no-op.
# - if 2 frames cover a position:
# ... ...
# / \/ \
# / /\ \
# S T , i.e. S offset of second frame starts, T end of first frame.
# Then the weight function for each one is: (t - S), (T - t), with `t` a given offset.
# After the final normalization, the weight of the second frame at position `t` is
# (t - S) / (t - S + (T - t)) = (t - S) / (T - S), which is exactly what we want.
#
# - if more than 2 frames overlap at a given point, we hope that by induction
# something sensible happens.
if len(frames) == 0:
raise ValueError("`frames` cannot be an empty list.")
device = frames[0].device
dtype = frames[0].dtype
shape = frames[0].shape[:-1]
total_size = stride * (len(frames) - 1) + frames[-1].shape[-1]
frame_length = frames[0].shape[-1]
time_vec = torch.linspace(0, 1, frame_length + 2, device=device, dtype=dtype)[1:-1]
weight = 0.5 - (time_vec - 0.5).abs()
sum_weight = torch.zeros(total_size, device=device, dtype=dtype)
out = torch.zeros(*shape, total_size, device=device, dtype=dtype)
offset: int = 0
for frame in frames:
frame_length = frame.shape[-1]
out[..., offset : offset + frame_length] += weight[:frame_length] * frame
sum_weight[offset : offset + frame_length] += weight[:frame_length]
offset += stride
if sum_weight.min() == 0:
raise ValueError(f"`sum_weight` minimum element must be bigger than zero: {sum_weight}`")
return out / sum_weight
def _decode_frame(self, codes: torch.Tensor, scale: Optional[torch.Tensor] = None) -> torch.Tensor:
codes = codes.transpose(0, 1)
embeddings = self.quantizer.decode(codes)
outputs = self.decoder(embeddings)
if scale is not None:
outputs = outputs * scale.view(-1, 1, 1)
return outputs
def decode(
self,
audio_codes: torch.Tensor,
audio_scales: torch.Tensor,
padding_mask: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor], EncodecDecoderOutput]:
"""
Decodes the given frames into an output audio waveform.
Note that the output might be a bit bigger than the input. In that case, any extra steps at the end can be
trimmed.
Args:
audio_codes (`torch.FloatTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_scales (`torch.Tensor` of shape `(batch_size, nb_chunks)`, *optional*):
Scaling factor for each `audio_codes` input.
padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Padding mask used to pad the `input_values`.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
return_dict = return_dict or self.config.return_dict
chunk_length = self.config.chunk_length
if chunk_length is None:
if len(audio_codes) != 1:
raise ValueError(f"Expected one frame, got {len(audio_codes)}")
audio_values = self._decode_frame(audio_codes[0], audio_scales[0])
else:
decoded_frames = []
for frame, scale in zip(audio_codes, audio_scales):
frames = self._decode_frame(frame, scale)
decoded_frames.append(frames)
audio_values = self._linear_overlap_add(decoded_frames, self.config.chunk_stride or 1)
# truncate based on padding mask
if padding_mask is not None and padding_mask.shape[-1] < audio_values.shape[-1]:
audio_values = audio_values[..., : padding_mask.shape[-1]]
if not return_dict:
return (audio_values,)
return EncodecDecoderOutput(audio_values)
@add_start_docstrings_to_model_forward(ENCODEC_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=EncodecOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values: torch.Tensor,
padding_mask: Optional[torch.Tensor] = None,
bandwidth: Optional[float] = None,
audio_codes: Optional[torch.Tensor] = None,
audio_scales: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor], EncodecOutput]:
r"""
Returns:
Examples:
```python
>>> from datasets import load_dataset
>>> from transformers import AutoProcessor, EncodecModel
>>> dataset = load_dataset("ashraq/esc50")
>>> audio_sample = dataset["train"]["audio"][0]["array"]
>>> model_id = "facebook/encodec_24khz"
>>> model = EncodecModel.from_pretrained(model_id)
>>> processor = AutoProcessor.from_pretrained(model_id)
>>> inputs = processor(raw_audio=audio_sample, return_tensors="pt")
>>> outputs = model(**inputs)
>>> audio_codes = outputs.audio_codes
>>> audio_values = outputs.audio_values
```"""
return_dict = return_dict or self.config.return_dict
if padding_mask is None:
padding_mask = torch.ones_like(input_values).bool()
if audio_codes is not None and audio_scales is None:
raise ValueError("You specified `audio_codes` but did not specify the `audio_scales`")
if audio_scales is not None and audio_codes is None:
raise ValueError("You specified `audio_scales` but did not specify the `audio_codes`")
if audio_scales is None and audio_codes is None:
audio_codes, audio_scales = self.encode(input_values, padding_mask, bandwidth, False)
audio_values = self.decode(audio_codes, audio_scales, padding_mask, return_dict=return_dict)[0]
if not return_dict:
return (audio_codes, audio_values)
return EncodecOutput(audio_codes=audio_codes, audio_values=audio_values)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/encodec/convert_encodec_checkpoint_to_pytorch.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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.
"""Convert EnCodec checkpoints."""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
logger = logging.get_logger("transformers.models.encodec")
MAPPING_QUANTIZER = {
"quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
"quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
"quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
"quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
MAPPING_ENCODER = {
"encoder.model.0.conv.conv": "encoder.layers.0.conv",
"encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
"encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
"encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
"encoder.model.3.conv.conv": "encoder.layers.3.conv",
"encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
"encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
"encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
"encoder.model.6.conv.conv": "encoder.layers.6.conv",
"encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
"encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
"encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
"encoder.model.9.conv.conv": "encoder.layers.9.conv",
"encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
"encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
"encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
"encoder.model.12.conv.conv": "encoder.layers.12.conv",
"encoder.model.13.lstm": "encoder.layers.13.lstm",
"encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
MAPPING_ENCODER_48K = {
"encoder.model.0.conv.norm": "encoder.layers.0.norm",
"encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
"encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
"encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
"encoder.model.3.conv.norm": "encoder.layers.3.norm",
"encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
"encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
"encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
"encoder.model.6.conv.norm": "encoder.layers.6.norm",
"encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
"encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
"encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
"encoder.model.9.conv.norm": "encoder.layers.9.norm",
"encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
"encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
"encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
"encoder.model.12.conv.norm": "encoder.layers.12.norm",
"encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
MAPPING_DECODER = {
"decoder.model.0.conv.conv": "decoder.layers.0.conv",
"decoder.model.1.lstm": "decoder.layers.1.lstm",
"decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
"decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
"decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
"decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
"decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
"decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
"decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
"decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
"decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
"decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
"decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
"decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
"decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
"decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
"decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
"decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
"decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
MAPPING_DECODER_48K = {
"decoder.model.0.conv.norm": "decoder.layers.0.norm",
"decoder.model.3.convtr.norm": "decoder.layers.3.norm",
"decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
"decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
"decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
"decoder.model.6.convtr.norm": "decoder.layers.6.norm",
"decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
"decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
"decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
"decoder.model.9.convtr.norm": "decoder.layers.9.norm",
"decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
"decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
"decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
"decoder.model.12.convtr.norm": "decoder.layers.12.norm",
"decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
"decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
"decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
"decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
MAPPING_24K = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
MAPPING_48K = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = []
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "running_mean":
hf_pointer.running_mean.data = value
elif weight_type == "running_var":
hf_pointer.running_var.data = value
elif weight_type == "num_batches_tracked":
hf_pointer.num_batches_tracked.data = value
elif weight_type == "weight_ih_l0":
hf_pointer.weight_ih_l0.data = value
elif weight_type == "weight_hh_l0":
hf_pointer.weight_hh_l0.data = value
elif weight_type == "bias_ih_l0":
hf_pointer.bias_ih_l0.data = value
elif weight_type == "bias_hh_l0":
hf_pointer.bias_hh_l0.data = value
elif weight_type == "weight_ih_l1":
hf_pointer.weight_ih_l1.data = value
elif weight_type == "weight_hh_l1":
hf_pointer.weight_hh_l1.data = value
elif weight_type == "bias_ih_l1":
hf_pointer.bias_ih_l1.data = value
elif weight_type == "bias_hh_l1":
hf_pointer.bias_hh_l1.data = value
else:
hf_pointer.data = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
for key in ignore_keys:
if key.endswith(".*"):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def recursively_load_weights(orig_dict, hf_model, model_name):
unused_weights = []
if model_name == "encodec_24khz" or "encodec_32khz":
MAPPING = MAPPING_24K
elif model_name == "encodec_48khz":
MAPPING = MAPPING_48K
else:
raise ValueError(f"Unsupported model: {model_name}")
for name, value in orig_dict.items():
if should_ignore(name, IGNORE_KEYS):
logger.info(f"{name} was ignored")
continue
is_used = False
for key, mapped_key in MAPPING.items():
if "*" in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
key = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("embed") and name.endswith("embed_avg"):
continue
is_used = True
if "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "weight_ih_l0" in name:
weight_type = "weight_ih_l0"
elif "weight_hh_l0" in name:
weight_type = "weight_hh_l0"
elif "bias_ih_l0" in name:
weight_type = "bias_ih_l0"
elif "bias_hh_l0" in name:
weight_type = "bias_hh_l0"
elif "weight_ih_l1" in name:
weight_type = "weight_ih_l1"
elif "weight_hh_l1" in name:
weight_type = "weight_hh_l1"
elif "bias_ih_l1" in name:
weight_type = "bias_ih_l1"
elif "bias_hh_l1" in name:
weight_type = "bias_hh_l1"
elif "bias" in name:
weight_type = "bias"
elif "weight" in name:
weight_type = "weight"
elif "running_mean" in name:
weight_type = "running_mean"
elif "running_var" in name:
weight_type = "running_var"
elif "num_batches_tracked" in name:
weight_type = "num_batches_tracked"
else:
weight_type = None
set_recursively(hf_model, mapped_key, value, name, weight_type)
continue
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
@torch.no_grad()
def convert_checkpoint(
model_name,
checkpoint_path,
pytorch_dump_folder_path,
config_path=None,
repo_id=None,
):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = EncodecConfig.from_pretrained(config_path)
else:
config = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
config.upsampling_ratios = [8, 5, 4, 4]
config.target_bandwidths = [2.2]
config.num_filters = 64
config.sampling_rate = 32_000
config.codebook_size = 2048
config.use_causal_conv = False
config.normalize = False
config.use_conv_shortcut = False
elif model_name == "encodec_48khz":
config.upsampling_ratios = [8, 5, 4, 2]
config.target_bandwidths = [3.0, 6.0, 12.0, 24.0]
config.sampling_rate = 48_000
config.audio_channels = 2
config.use_causal_conv = False
config.norm_type = "time_group_norm"
config.normalize = True
config.chunk_length_s = 1.0
config.overlap = 0.01
else:
raise ValueError(f"Unknown model name: {model_name}")
model = EncodecModel(config)
feature_extractor = EncodecFeatureExtractor(
feature_size=config.audio_channels,
sampling_rate=config.sampling_rate,
chunk_length_s=config.chunk_length_s,
overlap=config.overlap,
)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
original_checkpoint = torch.load(checkpoint_path)
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
original_checkpoint = original_checkpoint["best_state"]
recursively_load_weights(original_checkpoint, model, model_name)
model.save_pretrained(pytorch_dump_folder_path)
if repo_id:
print("Pushing to the hub...")
feature_extractor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="encodec_24khz",
type=str,
help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
args = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/encodec/configuration_encodec.py
|
# coding=utf-8
# Copyright 2023 Meta Platforms, Inc. and affiliates, and the HuggingFace Inc. team. 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.
""" EnCodec model configuration"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
"facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}
class EncodecConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`EncodecModel`]. It is used to instantiate a
Encodec model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the
[facebook/encodec_24khz](https://huggingface.co/facebook/encodec_24khz) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
target_bandwidths (`List[float]`, *optional*, defaults to `[1.5, 3.0, 6.0, 12.0, 24.0]`):
The range of diffent bandwiths the model can encode audio with.
sampling_rate (`int`, *optional*, defaults to 24000):
The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
audio_channels (`int`, *optional*, defaults to 1):
Number of channels in the audio data. Either 1 for mono or 2 for stereo.
normalize (`bool`, *optional*, defaults to `False`):
Whether the audio shall be normalized when passed.
chunk_length_s (`float`, *optional*):
If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded.
overlap (`float`, *optional*):
Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following
formulae : `int((1.0 - self.overlap) * self.chunk_length)`.
hidden_size (`int`, *optional*, defaults to 128):
Intermediate representation dimension.
num_filters (`int`, *optional*, defaults to 32):
Number of convolution kernels of first `EncodecConv1d` down sampling layer.
num_residual_layers (`int`, *optional*, defaults to 1):
Number of residual layers.
upsampling_ratios (`Sequence[int]` , *optional*, defaults to `[8, 5, 4, 2]`):
Kernel size and stride ratios. The encoder uses downsampling ratios instead of upsampling ratios, hence it
will use the ratios in the reverse order to the ones specified here that must match the decoder order.
norm_type (`str`, *optional*, defaults to `"weight_norm"`):
Normalization method. Should be in `["weight_norm", "time_group_norm"]`
kernel_size (`int`, *optional*, defaults to 7):
Kernel size for the initial convolution.
last_kernel_size (`int`, *optional*, defaults to 7):
Kernel size for the last convolution layer.
residual_kernel_size (`int`, *optional*, defaults to 3):
Kernel size for the residual layers.
dilation_growth_rate (`int`, *optional*, defaults to 2):
How much to increase the dilation with each layer.
use_causal_conv (`bool`, *optional*, defaults to `True`):
Whether to use fully causal convolution.
pad_mode (`str`, *optional*, defaults to `"reflect"`):
Padding mode for the convolutions.
compress (`int`, *optional*, defaults to 2):
Reduced dimensionality in residual branches (from Demucs v3).
num_lstm_layers (`int`, *optional*, defaults to 2):
Number of LSTM layers at the end of the encoder.
trim_right_ratio (`float`, *optional*, defaults to 1.0):
Ratio for trimming at the right of the transposed convolution under the `use_causal_conv = True` setup. If
equal to 1.0, it means that all the trimming is done at the right.
codebook_size (`int`, *optional*, defaults to 1024):
Number of discret codes that make up VQVAE.
codebook_dim (`int`, *optional*):
Dimension of the codebook vectors. If not defined, uses `hidden_size`.
use_conv_shortcut (`bool`, *optional*, defaults to `True`):
Whether to use a convolutional layer as the 'skip' connection in the `EncodecResnetBlock` block. If False,
an identity function will be used, giving a generic residual connection.
Example:
```python
>>> from transformers import EncodecModel, EncodecConfig
>>> # Initializing a "facebook/encodec_24khz" style configuration
>>> configuration = EncodecConfig()
>>> # Initializing a model (with random weights) from the "facebook/encodec_24khz" style configuration
>>> model = EncodecModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "encodec"
def __init__(
self,
target_bandwidths=[1.5, 3.0, 6.0, 12.0, 24.0],
sampling_rate=24_000,
audio_channels=1,
normalize=False,
chunk_length_s=None,
overlap=None,
hidden_size=128,
num_filters=32,
num_residual_layers=1,
upsampling_ratios=[8, 5, 4, 2],
norm_type="weight_norm",
kernel_size=7,
last_kernel_size=7,
residual_kernel_size=3,
dilation_growth_rate=2,
use_causal_conv=True,
pad_mode="reflect",
compress=2,
num_lstm_layers=2,
trim_right_ratio=1.0,
codebook_size=1024,
codebook_dim=None,
use_conv_shortcut=True,
**kwargs,
):
self.target_bandwidths = target_bandwidths
self.sampling_rate = sampling_rate
self.audio_channels = audio_channels
self.normalize = normalize
self.chunk_length_s = chunk_length_s
self.overlap = overlap
self.hidden_size = hidden_size
self.num_filters = num_filters
self.num_residual_layers = num_residual_layers
self.upsampling_ratios = upsampling_ratios
self.norm_type = norm_type
self.kernel_size = kernel_size
self.last_kernel_size = last_kernel_size
self.residual_kernel_size = residual_kernel_size
self.dilation_growth_rate = dilation_growth_rate
self.use_causal_conv = use_causal_conv
self.pad_mode = pad_mode
self.compress = compress
self.num_lstm_layers = num_lstm_layers
self.trim_right_ratio = trim_right_ratio
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim if codebook_dim is not None else hidden_size
self.use_conv_shortcut = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'
)
super().__init__(**kwargs)
# This is a property because you might want to change the chunk_length_s on the fly
@property
def chunk_length(self) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate)
# This is a property because you might want to change the chunk_length_s on the fly
@property
def chunk_stride(self) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length))
@property
def frame_rate(self) -> int:
hop_length = np.prod(self.upsampling_ratios)
return math.ceil(self.sampling_rate / hop_length)
@property
def num_quantizers(self) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/encodec/feature_extraction_encodec.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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.
"""Feature extractor class for EnCodec."""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
logger = logging.get_logger(__name__)
class EncodecFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs an EnCodec feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
Instantiating a feature extractor with the defaults will yield a similar configuration to that of the
[facebook/encodec_24khz](https://huggingface.co/facebook/encodec_24khz) architecture.
Args:
feature_size (`int`, *optional*, defaults to 1):
The feature dimension of the extracted features. Use 1 for mono, 2 for stereo.
sampling_rate (`int`, *optional*, defaults to 24000):
The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
padding_value (`float`, *optional*, defaults to 0.0):
The value that is used to fill the padding values.
chunk_length_s (`float`, *optional*):
If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded.
overlap (`float`, *optional*):
Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following
formulae : `int((1.0 - self.overlap) * self.chunk_length)`.
"""
model_input_names = ["input_values", "padding_mask"]
def __init__(
self,
feature_size: int = 1,
sampling_rate: int = 24000,
padding_value: float = 0.0,
chunk_length_s: float = None,
overlap: float = None,
**kwargs,
):
super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
self.chunk_length_s = chunk_length_s
self.overlap = overlap
# This is a property because you might want to change the chunk_length_s on the fly
@property
def chunk_length(self) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate)
# This is a property because you might want to change the chunk_length_s on the fly
@property
def chunk_stride(self) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length))
def __call__(
self,
raw_audio: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Optional[Union[bool, str, PaddingStrategy]] = None,
truncation: Optional[bool] = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
sampling_rate: Optional[int] = None,
) -> BatchFeature:
"""
Main method to featurize and prepare for the model one or several sequence(s).
Args:
raw_audio (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float
values, a list of numpy arrays or a list of list of float values. The numpy array must be of shape
`(num_samples,)` for mono audio (`feature_size = 1`), or `(2, num_samples)` for stereo audio
(`feature_size = 2`).
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, *optional*, defaults to `False`):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
sampling_rate (`int`, *optional*):
The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass
`sampling_rate` at the forward call to prevent silent errors.
"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
f" {self.sampling_rate} and not {sampling_rate}."
)
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug."
)
if padding and truncation:
raise ValueError("Both padding and truncation were set. Make sure you only set one.")
elif padding is None:
# by default let's pad the inputs
padding = True
is_batched = bool(
isinstance(raw_audio, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list)))
)
if is_batched:
raw_audio = [np.asarray(audio, dtype=np.float32).T for audio in raw_audio]
elif not is_batched and not isinstance(raw_audio, np.ndarray):
raw_audio = np.asarray(raw_audio, dtype=np.float32)
elif isinstance(raw_audio, np.ndarray) and raw_audio.dtype is np.dtype(np.float64):
raw_audio = raw_audio.astype(np.float32)
# always return batch
if not is_batched:
raw_audio = [np.asarray(raw_audio).T]
# verify inputs are valid
for idx, example in enumerate(raw_audio):
if example.ndim > 2:
raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}")
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels")
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels")
padded_inputs = None
input_values = BatchFeature({"input_values": raw_audio})
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
max_length = min(array.shape[0] for array in raw_audio)
nb_step = int(np.floor(max_length / self.chunk_stride))
max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
max_length = max(array.shape[0] for array in raw_audio)
nb_step = int(np.ceil(max_length / self.chunk_stride))
max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
padding = "max_length"
else:
padded_inputs = input_values
# normal padding on batch
if padded_inputs is None:
padded_inputs = self.pad(
input_values,
max_length=max_length,
truncation=truncation,
padding=padding,
return_attention_mask=padding,
)
if padding:
padded_inputs["padding_mask"] = padded_inputs.pop("attention_mask")
input_values = []
for example in padded_inputs.pop("input_values"):
if self.feature_size == 1:
example = example[..., None]
input_values.append(example.T)
padded_inputs["input_values"] = input_values
if return_tensors is not None:
padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
return padded_inputs
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/encodec/__init__.py
|
# Copyright 2023 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_import_structure = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_encodec"] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/configuration_blenderbot.py
|
# coding=utf-8
# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. 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.
""" Blenderbot model configuration"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
logger = logging.get_logger(__name__)
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
# See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class BlenderbotConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlenderbotModel`]. It is used to instantiate an
Blenderbot model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Blenderbot
[facebook/blenderbot-3B](https://huggingface.co/facebook/blenderbot-3B) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the Blenderbot model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`BlenderbotModel`] or [`TFBlenderbotModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, defaults to 128):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
Example:
```python
>>> from transformers import BlenderbotConfig, BlenderbotModel
>>> # Initializing a Blenderbot facebook/blenderbot-3B style configuration
>>> configuration = BlenderbotConfig()
>>> # Initializing a model (with random weights) from the facebook/blenderbot-3B style configuration
>>> model = BlenderbotModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "blenderbot"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=8008,
max_position_embeddings=128,
encoder_layers=2,
encoder_ffn_dim=10240,
encoder_attention_heads=32,
decoder_layers=24,
decoder_ffn_dim=10240,
decoder_attention_heads=32,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="gelu",
d_model=2560,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=1,
scale_embedding=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
encoder_no_repeat_ngram_size=3,
forced_eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
)
class BlenderbotOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
common_inputs["decoder_input_ids"] = {0: "batch"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
elif self.task == "causal-lm":
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
_, num_decoder_layers = self.num_layers
for i in range(num_decoder_layers):
common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
else:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
]
)
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_outputs = super().outputs
else:
common_outputs = super(OnnxConfigWithPast, self).outputs
if self.use_past:
num_encoder_layers, _ = self.num_layers
for i in range(num_encoder_layers):
common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, seq_length, is_pair, framework
)
# Generate decoder inputs
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, decoder_seq_length, is_pair, framework
)
decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
common_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
encoder_shape = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
decoder_past_length = decoder_seq_length
decoder_shape = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
_, num_decoder_layers = self.num_layers
for _ in range(num_decoder_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
)
)
return common_inputs
def _generate_dummy_inputs_for_causal_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, seq_length, is_pair, framework
)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
past_key_values_length = seqlen
_, num_decoder_layers = self.num_layers
num_encoder_attention_heads, _ = self.num_attention_heads
past_shape = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
mask_dtype = common_inputs["attention_mask"].dtype
common_inputs["attention_mask"] = torch.cat(
[common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
common_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_decoder_layers)
]
return common_inputs
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
batch_size = compute_effective_axis_dimension(
batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
)
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
)
# Generate dummy inputs according to compute batch and sequence
dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
return common_inputs
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.generate_dummy_inputs
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
elif self.task == "causal-lm":
common_inputs = self._generate_dummy_inputs_for_causal_lm(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
else:
common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
return common_inputs
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
if self.task in ["default", "seq2seq-lm"]:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
else:
flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
flattened_output, name, idx, t
)
def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str):
if direction not in ["inputs", "outputs"]:
raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')
name = "past_key_values" if direction == "inputs" else "present"
_, num_decoder_layers = self.num_layers
encoder_sequence = "past_encoder_sequence"
decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"
for i in range(num_decoder_layers):
inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence}
inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence}
inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence}
inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence}
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py
|
# coding=utf-8
# Copyright 2021 The Facebook, Inc and The HuggingFace Inc. team. 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.
""" TF 2.0 Blenderbot model."""
from __future__ import annotations
import os
import random
import warnings
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
# Public API
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ContextManagers,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_blenderbot import BlenderbotConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill"
_CONFIG_FOR_DOC = "BlenderbotConfig"
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
start_tokens = tf.fill(
(shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
)
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100,
tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
shifted_input_ids,
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz = input_ids_shape[0]
tgt_len = input_ids_shape[1]
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
mask_cond = tf.range(shape_list(mask)[-1])
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TFBlenderbotLearnedPositionalEmbedding(tf.keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(
self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: tf.Tensor | None = None
):
"""Input is expected to be of size [bsz x seqlen]."""
if position_ids is None:
seq_len = input_shape[1]
position_ids = tf.range(seq_len, delta=1, name="range")
position_ids += past_key_values_length
return super().call(tf.cast(position_ids, dtype=tf.int32))
# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Blenderbot
class TFBlenderbotAttention(tf.keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = tf.keras.layers.Dropout(dropout)
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor | None]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {shape_list(attn_weights)}"
),
)
if attention_mask is not None:
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {shape_list(attention_mask)}"
),
)
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weights, axis=-1)
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.num_heads],
message=(
f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
f" {shape_list(layer_head_mask)}"
),
)
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
attn_weights, (bsz, self.num_heads, tgt_len, src_len)
)
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {shape_list(attn_output)}"
),
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
# Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot
class TFBlenderbotEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
layer_head_mask: tf.Tensor,
training: Optional[bool] = False,
):
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, self_attn_weights, _ = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
)
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
return hidden_states, self_attn_weights
# Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot
class TFBlenderbotDecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
name="self_attn",
is_decoder=True,
)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFBlenderbotAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decoder=True,
)
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | None = None,
past_key_value: Tuple[tf.Tensor] | None = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`tf.Tensor`):
cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
*(decoder_attention_heads,)*
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
*(decoder_attention_heads,)*
past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
return (
hidden_states,
self_attn_weights,
cross_attn_weights,
present_key_value,
)
class TFBlenderbotPreTrainedModel(TFPreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix = "model"
BLENDERBOT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`BlenderbotConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
BLENDERBOT_GENERATION_EXAMPLE = r"""
Conversation example::
```py
>>> from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration
>>> mname = "facebook/blenderbot-400M-distill"
>>> model = TFBlenderbotForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
>>> inputs = tokenizer([UTTERANCE], return_tensors="tf")
>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
>>> NEXT_UTTERANCE = (
... "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. "
... "Are they trying to lose weight or are they just trying to be healthier?</s> "
... "<s> I'm not sure."
... )
>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="tf")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
```
"""
BLENDERBOT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tf.FloatTensor`, *optional*):
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). Set to `False` during training, `True` during generation
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@keras_serializable
class TFBlenderbotEncoder(tf.keras.layers.Layer):
config_class = BlenderbotConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBlenderbotEncoderLayer`].
Args:
config: BlenderbotConfig
"""
def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.layerdrop = config.encoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
# is used with a name ending in `/`, that name replaces the current name scope.
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.dropout(hidden_states, training=training)
# check attention mask and invert
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(head_mask)[0]}."
),
)
# encoder layers
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop): # skip the layer
continue
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
)
if output_attentions:
all_attentions += (attn,)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@keras_serializable
class TFBlenderbotDecoder(tf.keras.layers.Layer):
config_class = BlenderbotConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotDecoderLayer`]
Args:
config: BlenderbotConfig
embed_tokens: output embedding
"""
def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.layers = [TFBlenderbotDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
position_ids=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`tf.Tensor` of shape
`(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids`
you can choose to directly pass an embedded representation. This is useful if you want more control
over how to convert `input_ids` indices into associated vectors than the model's internal embedding
lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
# embed positions
if position_ids is None:
positions = self.embed_positions(input_shape, past_key_values_length)
else:
positions = self.embed_positions(input_shape, position_ids=position_ids)
if inputs_embeds is None:
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
hidden_states = inputs_embeds
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
)
if attention_mask is not None:
combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
hidden_states = hidden_states + positions
hidden_states = self.dropout(hidden_states, training=training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_values = () if use_cache else None
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
shape_list(attn_mask)[0],
len(self.layers),
message=(
f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(attn_mask)[0]}."
),
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=head_mask[idx] if head_mask is not None else None,
cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
past_key_value=past_key_value,
)
if use_cache:
present_key_values += (present_key_value,)
if output_attentions:
all_self_attns += (layer_self_attn,)
if encoder_hidden_states is not None:
all_cross_attns += (layer_cross_attn,)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attns,
)
@keras_serializable
class TFBlenderbotMainLayer(tf.keras.layers.Layer):
config_class = BlenderbotConfig
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = tf.keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std),
name="model.shared",
)
# Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
self.shared.load_weight_prefix = "model.shared"
self.encoder = TFBlenderbotEncoder(config, self.shared, name="encoder")
self.decoder = TFBlenderbotDecoder(config, self.shared, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_position_ids=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
encoder_outputs = TFBaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple()
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.",
BLENDERBOT_START_DOCSTRING,
)
class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
def __init__(self, config: BlenderbotConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBlenderbotMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
if pretrained_model_name_or_path == "facebook/blenderbot-90M":
from ..blenderbot_small import TFBlenderbotSmallModel
warnings.warn(
"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
" checkpoint `facebook/small_blenderbot-90M` with"
" `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`"
" instead.",
FutureWarning,
)
return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
@unpack_inputs
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
decoder_head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: List[tf.Tensor] | None = None,
inputs_embeds: tf.Tensor | None = None,
decoder_inputs_embeds: tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
class BiasLayer(tf.keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(name=name, **kwargs)
# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
def call(self, x):
return x + self.bias
@add_start_docstrings(
"The BLENDERBOT Model with a language modeling head. Can be used for summarization.",
BLENDERBOT_START_DOCSTRING,
)
class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBlenderbotMainLayer(config, name="model")
self.use_cache = config.use_cache
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
)
def get_decoder(self):
return self.model.decoder
def get_encoder(self):
return self.model.encoder
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
)
self.bias_layer.bias.assign(value["final_logits_bias"])
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
if pretrained_model_name_or_path == "facebook/blenderbot-90M":
from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration
warnings.warn(
"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
" checkpoint `facebook/small_blenderbot-90M` with"
" `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`"
" instead.",
FutureWarning,
)
return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path)
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
@unpack_inputs
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
decoder_head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: List[tf.Tensor] | None = None,
inputs_embeds: tf.Tensor | None = None,
decoder_inputs_embeds: tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
if labels is not None:
labels = tf.where(
labels == self.config.pad_token_id,
tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
labels,
)
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
lm_logits = self.bias_layer(lm_logits)
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 of d outputs
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
cross_attentions=outputs.cross_attentions, # index 4 of d outputs
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
encoder_attentions=outputs.encoder_attentions, # 2 of e out
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no past_key_values
decoder_position_ids = tf.range(decoder_input_ids.shape[1])
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_position_ids": decoder_position_ids,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py
|
# coding=utf-8
# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. 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.
""" PyTorch Blenderbot model."""
import copy
import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ..blenderbot_small import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel
from .configuration_blenderbot import BlenderbotConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "BlenderbotConfig"
_CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill"
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/blenderbot-3B",
# See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
]
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class BlenderbotLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Blenderbot
class BlenderbotAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[BlenderbotConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
BLENDERBOT_ATTENTION_CLASSES = {"default": BlenderbotAttention}
# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Blenderbot, MBART->BLENDERBOT
class BlenderbotEncoderLayer(nn.Module):
def __init__(self, config: BlenderbotConfig):
super().__init__()
self.embed_dim = config.d_model
attn_type = "flash_attention_2" if getattr(config, "_flash_attn_2_enabled", False) else "default"
self.self_attn = BLENDERBOT_ATTENTION_CLASSES[attn_type](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Blenderbot, MBART->BLENDERBOT
class BlenderbotDecoderLayer(nn.Module):
def __init__(self, config: BlenderbotConfig):
super().__init__()
self.embed_dim = config.d_model
attn_type = "flash_attention_2" if getattr(config, "_flash_attn_2_enabled", False) else "default"
self.self_attn = BLENDERBOT_ATTENTION_CLASSES[attn_type](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
is_causal=True,
config=config,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BLENDERBOT_ATTENTION_CLASSES[attn_type](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class BlenderbotPreTrainedModel(PreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
"decoder_input_ids": input_ids,
}
return dummy_inputs
BLENDERBOT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BlenderbotConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
BLENDERBOT_GENERATION_EXAMPLE = r"""
Conversation example:
```python
>>> from transformers import AutoTokenizer, BlenderbotForConditionalGeneration
>>> mname = "facebook/blenderbot-400M-distill"
>>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
Human: My friends are cool but they eat too many carbs.
>>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
Bot: That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?
>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
Human: I'm not sure
>>> NEXT_UTTERANCE = (
... "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. "
... "Are they trying to lose weight or are they just trying to be healthier?</s> "
... "<s> I'm not sure."
... )
>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
Bot: I see. Well, it's good that they're trying to change their eating habits.
```
"""
BLENDERBOT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
`(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class BlenderbotEncoder(BlenderbotPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BlenderbotEncoderLayer`].
Args:
config: BlenderbotConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
self.embed_positions = BlenderbotLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BlenderbotEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# add final layer norm
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class BlenderbotDecoder(BlenderbotPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotDecoderLayer`]
Args:
config: BlenderbotConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
self.embed_positions = BlenderbotLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BlenderbotDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
control over how to convert `input_ids` indices into associated vectors than the model's internal
embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add final layer norm
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare Blenderbot Model outputting raw hidden-states without any specific head on top.",
BLENDERBOT_START_DOCSTRING,
)
class BlenderbotModel(BlenderbotPreTrainedModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
def __init__(self, config: BlenderbotConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = BlenderbotEncoder(config, self.shared)
self.decoder = BlenderbotDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
if pretrained_model_name_or_path == "facebook/blenderbot-90M":
warnings.warn(
"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
" checkpoint `facebook/small_blenderbot-90M` with"
" `BlenderbotSmallModel.from_pretrained('facebook/small_blenderbot-90M')` instead.",
FutureWarning,
)
return BlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)
return super(BlenderbotModel, cls).from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BlenderbotModel
>>> model = BlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 6, 1280]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The Blenderbot Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING
)
class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: BlenderbotConfig):
super().__init__(config)
self.model = BlenderbotModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
if pretrained_model_name_or_path == "facebook/blenderbot-90M":
warnings.warn(
"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
" checkpoint `facebook/small_blenderbot-90M` with"
" `BlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.",
FutureWarning,
)
return BlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path)
return super(BlenderbotForConditionalGeneration, cls).from_pretrained(
pretrained_model_name_or_path, *model_args, **kwargs
)
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
self._resize_final_logits_bias(new_embeddings.weight.shape[0])
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if decoder_input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = decoder_input_ids.shape[1] - 1
decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
return reordered_past
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Blenderbot
class BlenderbotDecoderWrapper(BlenderbotPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = BlenderbotDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Blenderbot, facebook/bart-base->facebook/blenderbot-400M-distill
class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BlenderbotDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BlenderbotForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> model = BlenderbotForCausalLM.from_pretrained("facebook/blenderbot-400M-distill", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/tokenization_blenderbot.py
|
# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. 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.
"""Tokenization class for Blenderbot."""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
characters the bpe code barfs on.
The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
tables between utf-8 bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class BlenderbotTokenizer(PreTrainedTokenizer):
"""
Constructs a Blenderbot tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import BlenderbotTokenizer
>>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")
>>> tokenizer.add_prefix_space = False
>>> tokenizer("Hello world")["input_ids"]
[47, 921, 86, 1085, 2]
>>> tokenizer(" Hello world")["input_ids"]
[6950, 1085, 2]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Blenderbot tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
**kwargs,
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = (
AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False)
if isinstance(mask_token, str)
else mask_token
)
# these special tokens are not part of the vocab.json, let's add them in the correct order
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
bpe_merges = merges_handle.read().split("\n")[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.add_prefix_space = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
super().__init__(
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def vocab_size(self):
return len(self.encoder)
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Blenderbot, RoBERTa->Blenderbot
def get_vocab(self):
vocab = dict(self.encoder).copy()
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Blenderbot, RoBERTa->Blenderbot
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Blenderbot, RoBERTa->Blenderbot
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.encode("utf-8")
) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Blenderbot, RoBERTa->Blenderbot
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Blenderbot, RoBERTa->Blenderbot
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Blenderbot, RoBERTa->Blenderbot
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Blenderbot, RoBERTa->Blenderbot
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Blenderbot sequence has the following format:
- single sequence: ` X </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*):
Will be ignored
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
return token_ids_0 + [self.eos_token_id]
@property
def default_chat_template(self):
"""
A very simple chat template that just adds whitespace between messages.
"""
logger.warning_once(
"\nNo chat template is defined for this tokenizer - using the default template "
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
"your model, please set `tokenizer.chat_template` to an appropriate template. "
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
)
return (
"{% for message in messages %}"
"{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}"
"{{ message['content'] }}"
"{% if not loop.last %}{{ ' ' }}{% endif %}"
"{% endfor %}"
"{{ eos_token }}"
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
|
# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. 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.
"""Fast Tokenization class for Blenderbot."""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128}
class BlenderbotTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Blenderbot tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2
tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import BlenderbotTokenizerFast
>>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")
>>> tokenizer("Hello world")["input_ids"]
[6950, 1085, 2]
>>> tokenizer(" Hello world")["input_ids"]
[6950, 1085, 2]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Blenderbot tokenizer detect beginning of words by the preceding space).
trim_offsets (`bool`, *optional*, defaults to `True`):
Whether the post processing step should trim offsets to avoid including whitespaces.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = BlenderbotTokenizer
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
trim_offsets=True,
**kwargs,
):
mask_token = (
AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False)
if isinstance(mask_token, str)
else mask_token
)
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
**kwargs,
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance.__getstate__())
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
state["sep"] = tuple(state["sep"])
if "cls" in state:
state["cls"] = tuple(state["cls"])
changes_to_apply = False
if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
state["add_prefix_space"] = add_prefix_space
changes_to_apply = True
if state.get("trim_offsets", trim_offsets) != trim_offsets:
state["trim_offsets"] = trim_offsets
changes_to_apply = True
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def mask_token(self) -> str:
"""
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
Blenderbot tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the *<mask>*.
"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return None
return str(self._mask_token)
@mask_token.setter
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Roberta.
"""
# Mask token behave like a normal word, i.e. include the space before it
# So we set lstrip to True
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
self._mask_token = value
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._batch_encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kwargs)
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Blenderbot sequence has the following format:
- single sequence: ` X </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*):
Will be ignored
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
return token_ids_0 + [self.eos_token_id]
@property
# Copied from transformers.models.blenderbot.tokenization_blenderbot.BlenderbotTokenizer.default_chat_template
def default_chat_template(self):
"""
A very simple chat template that just adds whitespace between messages.
"""
logger.warning_once(
"\nNo chat template is defined for this tokenizer - using the default template "
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
"your model, please set `tokenizer.chat_template` to an appropriate template. "
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
)
return (
"{% for message in messages %}"
"{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}"
"{{ message['content'] }}"
"{% if not loop.last %}{{ ' ' }}{% endif %}"
"{% endfor %}"
"{{ eos_token }}"
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/__init__.py
|
# Copyright 2020 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_blenderbot_fast"] = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_blenderbot"] = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_blenderbot"] = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_blenderbot"] = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py
|
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. 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.
""" Flax Blenderbot model."""
import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_blenderbot import BlenderbotConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "BlenderbotConfig"
_CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill"
BLENDERBOT_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`BlenderbotConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
"""
BLENDERBOT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
for denoising pre-training following the paper.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
BLENDERBOT_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
BLENDERBOT_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
for denoising pre-training following the paper.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
"""
Shift input ids one token to the right.
"""
shifted_input_ids = jnp.zeros_like(input_ids)
shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
return shifted_input_ids
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->Blenderbot
class FlaxBlenderbotAttention(nn.Module):
config: BlenderbotConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
# self_attention
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartEncoderLayer with MBart->Blenderbot
class FlaxBlenderbotEncoderLayer(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.fc1 = nn.Dense(
self.config.encoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->Blenderbot
class FlaxBlenderbotEncoderLayerCollection(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
self.layerdrop = self.config.encoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartDecoderLayer with MBart->Blenderbot
class FlaxBlenderbotDecoderLayer(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxBlenderbotAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.fc1 = nn.Dense(
self.config.decoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->Blenderbot
class FlaxBlenderbotDecoderLayerCollection(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
self.layerdrop = self.config.decoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop):
layer_outputs = (None, None, None)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
deterministic=deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class FlaxBlenderbotEncoder(nn.Module):
config: BlenderbotConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.pad_token_id
self.max_source_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
self.embed_positions = nn.Embed(
self.config.max_position_embeddings,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.layers = FlaxBlenderbotEncoderLayerCollection(self.config, self.dtype)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(position_ids)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
)
class FlaxBlenderbotDecoder(nn.Module):
config: BlenderbotConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.pad_token_id
self.max_target_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
self.embed_positions = nn.Embed(
self.config.max_position_embeddings,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.layers = FlaxBlenderbotDecoderLayerCollection(self.config, self.dtype)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# embed positions
positions = self.embed_positions(position_ids)
hidden_states = inputs_embeds + positions
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->Blenderbot
class FlaxBlenderbotModule(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
dtype=self.dtype,
)
self.encoder = FlaxBlenderbotEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
self.decoder = FlaxBlenderbotDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BlenderbotConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
# make sure initialization pass will work for FlaxBlenderbotForSequenceClassificationModule
input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
attention_mask = jnp.ones_like(input_ids)
decoder_input_ids = input_ids
decoder_attention_mask = jnp.ones_like(input_ids)
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(BLENDERBOT_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=BlenderbotConfig)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_ids, attention_mask, position_ids, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings(BLENDERBOT_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(
output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=BlenderbotConfig
)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxBlenderbotAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# prepare decoder inputs
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings(
"The bare MBart Model transformer outputting raw hidden-states without any specific head on top.",
BLENDERBOT_START_DOCSTRING,
)
class FlaxBlenderbotModel(FlaxBlenderbotPreTrainedModel):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxBlenderbotModule
append_call_sample_docstring(FlaxBlenderbotModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->Blenderbot
class FlaxBlenderbotForConditionalGenerationModule(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxBlenderbotModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.model.shared.num_embeddings,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
position_ids=position_ids,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["shared"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return output
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"The Blenderbot Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING
)
class FlaxBlenderbotForConditionalGeneration(FlaxBlenderbotPreTrainedModel):
module_class = FlaxBlenderbotForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(BLENDERBOT_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=BlenderbotConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxBlenderbotAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = module.model.variables["params"]["shared"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states)
lm_logits += module.final_logits_bias
return lm_logits, outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
FLAX_BLENDERBOT_CONDITIONAL_GENERATION_DOCSTRING = r"""
Returns:
Conversation example::
```py
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([UTTERANCE], max_length=1024, return_tensors="np")
>>> # Generate Reply
>>> reply_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5, early_stopping=True).sequences
>>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])
```
"""
overwrite_call_docstring(
FlaxBlenderbotForConditionalGeneration,
BLENDERBOT_INPUTS_DOCSTRING + FLAX_BLENDERBOT_CONDITIONAL_GENERATION_DOCSTRING,
)
append_replace_return_docstrings(
FlaxBlenderbotForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/blenderbot/convert_blenderbot_original_pytorch_checkpoint_to_pytorch.py
|
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
"""Convert Blenderbot checkpoint."""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
PATTERNS = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin", "q_proj"],
["k_lin", "k_proj"],
["v_lin", "v_proj"],
["out_lin", "out_proj"],
["norm_embeddings", "layernorm_embedding"],
["position_embeddings", "embed_positions"],
["embeddings", "embed_tokens"],
["ffn.lin", "fc"],
]
def rename_state_dict_key(k):
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
k = k.replace(parlai_name, hf_name)
if k.startswith("encoder"):
k = k.replace(".attn", ".self_attn")
k = k.replace("norm1", "self_attn_layer_norm")
k = k.replace("norm2", "final_layer_norm")
elif k.startswith("decoder"):
k = k.replace("norm1", "self_attn_layer_norm")
k = k.replace("norm2", "encoder_attn_layer_norm")
k = k.replace("norm3", "final_layer_norm")
return k
def rename_layernorm_keys(sd):
keys = [
"model.encoder.layernorm_embedding.weight",
"model.encoder.layernorm_embedding.bias",
"model.decoder.layernorm_embedding.weight",
"model.decoder.layernorm_embedding.bias",
]
for k in keys:
v = sd.pop(k)
new_k = k.replace("layernorm_embedding", "layer_norm")
assert new_k not in sd
sd[new_k] = v
IGNORE_KEYS = ["START"]
@torch.no_grad()
def convert_parlai_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_json_path):
"""
Copy/paste/tweak model's weights to our BERT structure.
"""
model = torch.load(checkpoint_path, map_location="cpu")
sd = model["model"]
cfg = BlenderbotConfig.from_json_file(config_json_path)
m = BlenderbotForConditionalGeneration(cfg)
valid_keys = m.model.state_dict().keys()
failures = []
mapping = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
new_k = rename_state_dict_key(k)
if new_k not in valid_keys:
failures.append([k, new_k])
else:
mapping[new_k] = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(sd)
m.model.load_state_dict(mapping, strict=True)
m.half()
m.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
parser.add_argument(
"--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
)
args = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/swiftformer/configuration_swiftformer.py
|
# coding=utf-8
# Copyright 2023 MBZUAI and The HuggingFace Inc. team. 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.
""" SwiftFormer model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"MBZUAI/swiftformer-xs": "https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json",
}
class SwiftFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwiftFormerModel`]. It is used to instantiate an
SwiftFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SwiftFormer
[MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels
depths (`List[int]`, *optional*, defaults to `[3, 3, 6, 4]`):
Depth of each stage
embed_dims (`List[int]`, *optional*, defaults to `[48, 56, 112, 220]`):
The embedding dimension at each stage
mlp_ratio (`int`, *optional*, defaults to 4):
Ratio of size of the hidden dimensionality of an MLP to the dimensionality of its input.
downsamples (`List[bool]`, *optional*, defaults to `[True, True, True, True]`):
Whether or not to downsample inputs between two stages.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (string). `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
down_patch_size (`int`, *optional*, defaults to 3):
The size of patches in downsampling layers.
down_stride (`int`, *optional*, defaults to 2):
The stride of convolution kernels in downsampling layers.
down_pad (`int`, *optional*, defaults to 1):
Padding in downsampling layers.
drop_path_rate (`float`, *optional*, defaults to 0.0):
Rate at which to increase dropout probability in DropPath.
use_layer_scale (`bool`, *optional*, defaults to `True`):
Whether to scale outputs from token mixers.
layer_scale_init_value (`float`, *optional*, defaults to 1e-05):
Factor by which outputs from token mixers are scaled.
batch_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the batch normalization layers.
Example:
```python
>>> from transformers import SwiftFormerConfig, SwiftFormerModel
>>> # Initializing a SwiftFormer swiftformer-base-patch16-224 style configuration
>>> configuration = SwiftFormerConfig()
>>> # Initializing a model (with random weights) from the swiftformer-base-patch16-224 style configuration
>>> model = SwiftFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "swiftformer"
def __init__(
self,
num_channels=3,
depths=[3, 3, 6, 4],
embed_dims=[48, 56, 112, 220],
mlp_ratio=4,
downsamples=[True, True, True, True],
hidden_act="gelu",
down_patch_size=3,
down_stride=2,
down_pad=1,
drop_path_rate=0.0,
use_layer_scale=True,
layer_scale_init_value=1e-5,
batch_norm_eps=1e-5,
**kwargs,
):
super().__init__(**kwargs)
self.num_channels = num_channels
self.depths = depths
self.embed_dims = embed_dims
self.mlp_ratio = mlp_ratio
self.downsamples = downsamples
self.hidden_act = hidden_act
self.down_patch_size = down_patch_size
self.down_stride = down_stride
self.down_pad = down_pad
self.drop_path_rate = drop_path_rate
self.use_layer_scale = use_layer_scale
self.layer_scale_init_value = layer_scale_init_value
self.batch_norm_eps = batch_norm_eps
class SwiftFormerOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py
|
# coding=utf-8
# Copyright 2023 MBZUAI and The HuggingFace Inc. team. 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.
""" PyTorch SwiftFormer model."""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2CLS
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_swiftformer import SwiftFormerConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "SwiftFormerConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "MBZUAI/swiftformer-xs"
_EXPECTED_OUTPUT_SHAPE = [1, 220, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "MBZUAI/swiftformer-xs"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"MBZUAI/swiftformer-xs",
# See all SwiftFormer models at https://huggingface.co/models?filter=swiftformer
]
class SwiftFormerPatchEmbedding(nn.Module):
"""
Patch Embedding Layer constructed of two 2D convolutional layers.
Input: tensor of shape `[batch_size, in_channels, height, width]`
Output: tensor of shape `[batch_size, out_channels, height/4, width/4]`
"""
def __init__(self, config: SwiftFormerConfig):
super().__init__()
in_chs = config.num_channels
out_chs = config.embed_dims[0]
self.patch_embedding = nn.Sequential(
nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(out_chs // 2, eps=config.batch_norm_eps),
nn.ReLU(),
nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(out_chs, eps=config.batch_norm_eps),
nn.ReLU(),
)
def forward(self, x):
return self.patch_embedding(x)
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Swiftformer
class SwiftFormerDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class SwiftFormerEmbeddings(nn.Module):
"""
Embeddings layer consisting of a single 2D convolutional and batch normalization layer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height/stride, width/stride]`
"""
def __init__(self, config: SwiftFormerConfig, index: int):
super().__init__()
patch_size = config.down_patch_size
stride = config.down_stride
padding = config.down_pad
embed_dims = config.embed_dims
in_chans = embed_dims[index]
embed_dim = embed_dims[index + 1]
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding)
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
self.norm = nn.BatchNorm2d(embed_dim, eps=config.batch_norm_eps)
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class SwiftFormerConvEncoder(nn.Module):
"""
`SwiftFormerConvEncoder` with 3*3 and 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim: int):
super().__init__()
hidden_dim = int(config.mlp_ratio * dim)
self.depth_wise_conv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)
self.norm = nn.BatchNorm2d(dim, eps=config.batch_norm_eps)
self.point_wise_conv1 = nn.Conv2d(dim, hidden_dim, kernel_size=1)
self.act = nn.GELU()
self.point_wise_conv2 = nn.Conv2d(hidden_dim, dim, kernel_size=1)
self.drop_path = nn.Identity()
self.layer_scale = nn.Parameter(torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
def forward(self, x):
input = x
x = self.depth_wise_conv(x)
x = self.norm(x)
x = self.point_wise_conv1(x)
x = self.act(x)
x = self.point_wise_conv2(x)
x = input + self.drop_path(self.layer_scale * x)
return x
class SwiftFormerMlp(nn.Module):
"""
MLP layer with 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, in_features: int):
super().__init__()
hidden_features = int(in_features * config.mlp_ratio)
self.norm1 = nn.BatchNorm2d(in_features, eps=config.batch_norm_eps)
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
act_layer = ACT2CLS[config.hidden_act]
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, in_features, 1)
self.drop = nn.Dropout(p=0.0)
def forward(self, x):
x = self.norm1(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class SwiftFormerEfficientAdditiveAttention(nn.Module):
"""
Efficient Additive Attention module for SwiftFormer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim: int = 512):
super().__init__()
self.to_query = nn.Linear(dim, dim)
self.to_key = nn.Linear(dim, dim)
self.w_g = nn.Parameter(torch.randn(dim, 1))
self.scale_factor = dim**-0.5
self.proj = nn.Linear(dim, dim)
self.final = nn.Linear(dim, dim)
def forward(self, x):
query = self.to_query(x)
key = self.to_key(x)
query = torch.nn.functional.normalize(query, dim=-1)
key = torch.nn.functional.normalize(key, dim=-1)
query_weight = query @ self.w_g
scaled_query_weight = query_weight * self.scale_factor
scaled_query_weight = scaled_query_weight.softmax(dim=-1)
global_queries = torch.sum(scaled_query_weight * query, dim=1)
global_queries = global_queries.unsqueeze(1).repeat(1, key.shape[1], 1)
out = self.proj(global_queries * key) + query
out = self.final(out)
return out
class SwiftFormerLocalRepresentation(nn.Module):
"""
Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim: int):
super().__init__()
self.depth_wise_conv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)
self.norm = nn.BatchNorm2d(dim, eps=config.batch_norm_eps)
self.point_wise_conv1 = nn.Conv2d(dim, dim, kernel_size=1)
self.act = nn.GELU()
self.point_wise_conv2 = nn.Conv2d(dim, dim, kernel_size=1)
self.drop_path = nn.Identity()
self.layer_scale = nn.Parameter(torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
def forward(self, x):
input = x
x = self.depth_wise_conv(x)
x = self.norm(x)
x = self.point_wise_conv1(x)
x = self.act(x)
x = self.point_wise_conv2(x)
x = input + self.drop_path(self.layer_scale * x)
return x
class SwiftFormerEncoderBlock(nn.Module):
"""
SwiftFormer Encoder Block for SwiftFormer. It consists of (1) Local representation module, (2)
SwiftFormerEfficientAdditiveAttention, and (3) MLP block.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels,height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim: int, drop_path: float = 0.0) -> None:
super().__init__()
layer_scale_init_value = config.layer_scale_init_value
use_layer_scale = config.use_layer_scale
self.local_representation = SwiftFormerLocalRepresentation(config, dim=dim)
self.attn = SwiftFormerEfficientAdditiveAttention(config, dim=dim)
self.linear = SwiftFormerMlp(config, in_features=dim)
self.drop_path = SwiftFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True
)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True
)
def forward(self, x):
x = self.local_representation(x)
batch_size, channels, height, width = x.shape
if self.use_layer_scale:
x = x + self.drop_path(
self.layer_scale_1
* self.attn(x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels))
.reshape(batch_size, height, width, channels)
.permute(0, 3, 1, 2)
)
x = x + self.drop_path(self.layer_scale_2 * self.linear(x))
else:
x = x + self.drop_path(
self.attn(x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels))
.reshape(batch_size, height, width, channels)
.permute(0, 3, 1, 2)
)
x = x + self.drop_path(self.linear(x))
return x
class SwiftFormerStage(nn.Module):
"""
A Swiftformer stage consisting of a series of `SwiftFormerConvEncoder` blocks and a final
`SwiftFormerEncoderBlock`.
Input: tensor in shape `[batch_size, channels, height, width]`
Output: tensor in shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, index: int) -> None:
super().__init__()
layer_depths = config.depths
dim = config.embed_dims[index]
depth = layer_depths[index]
blocks = []
for block_idx in range(depth):
block_dpr = config.drop_path_rate * (block_idx + sum(layer_depths[:index])) / (sum(layer_depths) - 1)
if depth - block_idx <= 1:
blocks.append(SwiftFormerEncoderBlock(config, dim=dim, drop_path=block_dpr))
else:
blocks.append(SwiftFormerConvEncoder(config, dim=dim))
self.blocks = nn.ModuleList(blocks)
def forward(self, input):
for block in self.blocks:
input = block(input)
return input
class SwiftFormerEncoder(nn.Module):
def __init__(self, config: SwiftFormerConfig) -> None:
super().__init__()
self.config = config
embed_dims = config.embed_dims
downsamples = config.downsamples
layer_depths = config.depths
# Transformer model
network = []
for i in range(len(layer_depths)):
stage = SwiftFormerStage(config=config, index=i)
network.append(stage)
if i >= len(layer_depths) - 1:
break
if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
# downsampling between two stages
network.append(SwiftFormerEmbeddings(config, index=i))
self.network = nn.ModuleList(network)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
all_hidden_states = (hidden_states,) if output_hidden_states else None
for block in self.network:
hidden_states = block(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
)
class SwiftFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwiftFormerConfig
base_model_prefix = "swiftformer"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Conv2d, nn.Linear)):
nn.init.trunc_normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, (nn.LayerNorm)):
nn.init.constant_(module.bias, 0)
nn.init.constant_(module.weight, 1.0)
SWIFTFORMER_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`SwiftFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SWIFTFORMER_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare SwiftFormer Model transformer outputting raw hidden-states without any specific head on top.",
SWIFTFORMER_START_DOCSTRING,
)
class SwiftFormerModel(SwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig):
super().__init__(config)
self.config = config
self.patch_embed = SwiftFormerPatchEmbedding(config)
self.encoder = SwiftFormerEncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SWIFTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
r""" """
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.patch_embed(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return tuple(v for v in encoder_outputs if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=encoder_outputs.last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
SwiftFormer Model transformer with an image classification head on top (e.g. for ImageNet).
""",
SWIFTFORMER_START_DOCSTRING,
)
class SwiftFormerForImageClassification(SwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig) -> None:
super().__init__(config)
embed_dims = config.embed_dims
self.num_labels = config.num_labels
self.swiftformer = SwiftFormerModel(config)
# Classifier head
self.norm = nn.BatchNorm2d(embed_dims[-1], eps=config.batch_norm_eps)
self.head = nn.Linear(embed_dims[-1], self.num_labels) if self.num_labels > 0 else nn.Identity()
self.dist_head = nn.Linear(embed_dims[-1], self.num_labels) if self.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SWIFTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# run base model
outputs = self.swiftformer(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs.last_hidden_state if return_dict else outputs[0]
# run classification head
sequence_output = self.norm(sequence_output)
sequence_output = sequence_output.flatten(2).mean(-1)
cls_out = self.head(sequence_output)
distillation_out = self.dist_head(sequence_output)
logits = (cls_out + distillation_out) / 2
# calculate loss
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/swiftformer/__init__.py
|
# Copyright 2023 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_import_structure = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_swiftformer"] = [
"SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwiftFormerForImageClassification",
"SwiftFormerModel",
"SwiftFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/swiftformer/convert_swiftformer_original_to_hf.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
"""Convert SwiftFormer checkpoints from the original implementation."""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
device = torch.device("cpu")
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
def get_expected_output(swiftformer_name):
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01])
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01])
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02])
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02])
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
def create_rename_keys(state_dict):
rename_keys = []
for k in state_dict.keys():
k_new = k
if ".pwconv" in k:
k_new = k_new.replace(".pwconv", ".point_wise_conv")
if ".dwconv" in k:
k_new = k_new.replace(".dwconv", ".depth_wise_conv")
if ".Proj." in k:
k_new = k_new.replace(".Proj.", ".proj.")
if "patch_embed" in k_new:
k_new = k_new.replace("patch_embed", "swiftformer.patch_embed.patch_embedding")
if "network" in k_new:
ls = k_new.split(".")
if ls[2].isdigit():
k_new = "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:])
else:
k_new = k_new.replace("network", "swiftformer.encoder.network")
rename_keys.append((k, k_new))
return rename_keys
@torch.no_grad()
def convert_swiftformer_checkpoint(swiftformer_name, pytorch_dump_folder_path, original_ckpt):
"""
Copy/paste/tweak model's weights to our SwiftFormer structure.
"""
# define default SwiftFormer configuration
config = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
config.num_labels = 1000
repo_id = "huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
config.depths = [3, 3, 6, 4]
config.embed_dims = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
config.depths = [3, 3, 9, 6]
config.embed_dims = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
config.depths = [4, 3, 10, 5]
config.embed_dims = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
config.depths = [4, 4, 12, 6]
config.embed_dims = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("https"):
checkpoint = torch.hub.load_state_dict_from_url(original_ckpt, map_location="cpu", check_hash=True)
else:
checkpoint = torch.load(original_ckpt, map_location="cpu")
state_dict = checkpoint
rename_keys = create_rename_keys(state_dict)
for rename_key_src, rename_key_dest in rename_keys:
rename_key(state_dict, rename_key_src, rename_key_dest)
# load HuggingFace model
hf_model = SwiftFormerForImageClassification(config).eval()
hf_model.load_state_dict(state_dict)
# prepare test inputs
image = prepare_img()
processor = ViTImageProcessor.from_pretrained("preprocessor_config")
inputs = processor(images=image, return_tensors="pt")
# compare outputs from both models
timm_logits = get_expected_output(swiftformer_name)
hf_logits = hf_model(inputs["pixel_values"]).logits
assert hf_logits.shape == torch.Size([1, 1000])
assert torch.allclose(hf_logits[0, 0:5], timm_logits, atol=1e-3)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {swiftformer_name} to {pytorch_dump_folder_path}")
hf_model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
args = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/falcon/modeling_falcon.py
|
# coding=utf-8
# Copyright 2023 the Falcon authors and HuggingFace Inc. team. 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.
"""PyTorch Falcon model."""
import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
logging,
)
from .configuration_falcon import FalconConfig
if is_flash_attn_2_available():
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
logger = logging.get_logger(__name__)
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [
"tiiuae/falcon-40b",
"tiiuae/falcon-40b-instruct",
"tiiuae/falcon-7b",
"tiiuae/falcon-7b-instruct",
"tiiuae/falcon-rw-7b",
"tiiuae/falcon-rw-1b",
]
_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
_CONFIG_FOR_DOC = "FalconConfig"
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
class FalconLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
hidden_states = input @ self.weight.T
if self.bias is None:
return hidden_states
return hidden_states + self.bias
# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Falcon
class FalconRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon
class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
"""FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
t = t / self.scaling_factor
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon
class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding):
"""FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def _prepare_4d_attention_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor:
"""
Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`.
"""
batch_size, total_length = mask.shape
seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length
expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
return expanded_mask.expand(batch_size, 1, seq_length, total_length)
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
batch_size, seq_length = attention_mask.shape
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
base = torch.tensor(
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
)
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
slopes = torch.pow(base, powers)
if closest_power_of_2 != num_heads:
extra_base = torch.tensor(
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
)
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
# => the query_length dimension will then be broadcasted correctly
# This is more or less identical to T5's relative position bias:
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
alibi = slopes[..., None].bfloat16() * arange_tensor
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
# Copied from transformers.models.bloom.modeling_bloom.dropout_add
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
"""
Dropout add function
Args:
x (`torch.tensor`, *required*):
input tensor
residual (`torch.tensor`, *required*):
residual tensor
prob (`float`, *required*):
dropout probability
training (`bool`, *required*):
training mode
"""
out = F.dropout(x, p=prob, training=training)
out = residual + out
return out
class FalconAttention(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.split_size = self.hidden_size
self.hidden_dropout = config.hidden_dropout
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if self.head_dim * self.num_heads != self.hidden_size:
raise ValueError(
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
f" {self.num_heads})."
)
if config.rotary:
self._init_rope()
# Layer-wise attention scaling
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
self.beta = self.inv_norm_factor
if config.new_decoder_architecture:
qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
elif config.multi_query:
qkv_out_dim = self.hidden_size + 2 * self.head_dim
else:
qkv_out_dim = 3 * self.hidden_size
self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
self.new_decoder_architecture = config.new_decoder_architecture
self.multi_query = config.multi_query
self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
self.attention_dropout = nn.Dropout(config.attention_dropout)
self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
# Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Falcon
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = FalconRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = FalconLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = FalconDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`
Args:
fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
Returns:
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
value: [batch_size, seq_length, num_heads, head_dim]
"""
if self.new_decoder_architecture:
batch, seq_len, _ = fused_qkv.shape
qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
query = qkv[:, :, :, :-2]
key = qkv[:, :, :, [-2]]
value = qkv[:, :, :, [-1]]
key = torch.broadcast_to(key, query.shape)
value = torch.broadcast_to(value, query.shape)
query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
return query, key, value
elif not self.multi_query:
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
else:
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
# Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
"""
Merge heads together over the last dimension
Args:
x (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
Returns:
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
"""
# What we want to achieve is:
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
batch_size_and_num_heads, seq_length, _ = x.shape
batch_size = batch_size_and_num_heads // self.num_heads
# First view to decompose the batch size
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
x = x.permute(0, 2, 1, 3)
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, query_length, _, _ = query_layer.shape
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
kv_seq_len = key_layer.shape[-2]
if layer_past is not None:
kv_seq_len += layer_past[0].shape[-2]
if alibi is None:
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
if layer_past is not None:
past_key, past_value = layer_past
# concatenate along seq_length dimension:
# - key: [batch_size, self.num_heads, kv_length, head_dim]
# - value: [batch_size, self.num_heads, kv_length, head_dim]
key_layer = torch.cat((past_key, key_layer), dim=-2)
value_layer = torch.cat((past_value, value_layer), dim=-2)
kv_length = key_layer.shape[-2]
if use_cache:
present = (key_layer, value_layer)
else:
present = None
if alibi is None:
if hasattr(F, "scaled_dot_product_attention") and not output_attentions:
# TODO: deprecate this once we add FA2 support in Falcon
logger.warning_once(
"The current implementation of Falcon calls `torch.scaled_dot_product_attention` directly, this will be deprecated in the"
" future in favor of the `BetterTransformer` API. Please install the latest optimum library with `pip install -U optimum` and call "
"`model.to_bettertransformer()` to benefit from `torch.scaled_dot_product_attention` and future performance optimizations."
)
attn_output = F.scaled_dot_product_attention(
query_layer, key_layer, value_layer, attention_mask, 0.0, is_causal=False
)
attention_scores = None
else:
attention_scores = query_layer @ key_layer.transpose(-1, -2)
attention_scores /= math.sqrt(self.head_dim)
attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
attn_output = attention_scores @ value_layer
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
attn_output = attn_output.permute(0, 2, 1, 3)
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
output_tensor = self.dense(attn_output)
if output_attentions:
return output_tensor, present, attention_scores
else:
return output_tensor, present
else:
matmul_result = query_layer @ key_layer.transpose(-1, -2)
# change view to [batch_size, num_heads, q_length, kv_length]
attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
input_dtype = attention_scores.dtype
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
attention_scores = attention_scores.to(torch.float32)
# Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
# adding (alibi * self.inv_norm_factor) to attention_mask. I think this would be mathematically
# equivalent and more performant, but there might be a numerical difference. If you're reading this
# and you'd like to experiment and maybe file a PR, feel free!
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
attention_logits *= self.inv_norm_factor
attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
# [batch_size, num_heads, q_length, kv_length]
attention_probs = self.attention_dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
# change view [batch_size, num_heads, q_length, kv_length]
attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
# matmul: [batch_size * num_heads, q_length, head_dim]
context_layer = (attention_probs_reshaped @ value_layer).flatten(0, 1)
# change view [batch_size, q_length, num_heads * head_dim]
context_layer = self._merge_heads(context_layer)
output_tensor = self.dense(context_layer)
if output_attentions:
return output_tensor, present, attention_probs
else:
return output_tensor, present
class FalconFlashAttention2(FalconAttention):
"""
Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, query_length, _, _ = query_layer.shape
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
kv_seq_len = key_layer.shape[-2]
if layer_past is not None:
kv_seq_len += layer_past[0].shape[-2]
if alibi is None:
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
if layer_past is not None and use_cache:
past_key, past_value = layer_past
# concatenate along seq_length dimension:
# - key: [batch_size, self.num_heads, kv_length, head_dim]
# - value: [batch_size, self.num_heads, kv_length, head_dim]
key_layer = torch.cat((past_key, key_layer), dim=-2)
value_layer = torch.cat((past_value, value_layer), dim=-2)
past_key_value = (key_layer, value_layer) if use_cache else None
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_layer = query_layer.transpose(1, 2)
key_layer = key_layer.transpose(1, 2)
value_layer = value_layer.transpose(1, 2)
if alibi is not None:
raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
attn_dropout = self.config.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in float16 just to be sure everything works as expected.
input_dtype = query_layer.dtype
if input_dtype == torch.float32:
# Handle the case where the model is quantized
if hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.query_key_value.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_layer = query_layer.to(target_dtype)
key_layer = key_layer.to(target_dtype)
value_layer = value_layer.to(target_dtype)
attn_output = self._flash_attention_forward(
query_layer, key_layer, value_layer, attention_mask, query_length, dropout=attn_dropout
)
attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
attn_output = self.dense(attn_weights)
if not output_attentions:
attn_weights = None
return attn_output, past_key_value, attn_weights
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
def _flash_attention_forward(
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
# Contains at least one padding token in the sequence
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
query_states, key_states, value_states, attention_mask, query_length
)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=self.is_causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
attn_output = flash_attn_func(
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=self.is_causal
)
return attn_output
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
value_layer = index_first_axis(
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
if query_length == kv_seq_len:
query_layer = index_first_axis(
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(
batch_size + 1, dtype=torch.int32, device=query_layer.device
) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q,
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
class FalconMLP(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
hidden_size = config.hidden_size
self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
self.act = nn.GELU()
self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
self.hidden_dropout = config.hidden_dropout
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.act(self.dense_h_to_4h(x))
x = self.dense_4h_to_h(x)
return x
class FalconDecoderLayer(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.self_attention = (
FalconAttention(config)
if not getattr(config, "_flash_attn_2_enabled", False)
else FalconFlashAttention2(config)
)
self.mlp = FalconMLP(config)
self.hidden_dropout = config.hidden_dropout
self.config = config
if config.new_decoder_architecture:
# The layer norm before self-attention
self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
# The layer norm before the MLP
self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
else:
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if not config.parallel_attn:
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
if self.config.new_decoder_architecture:
attention_layernorm_out = self.ln_attn(hidden_states)
mlp_layernorm_out = self.ln_mlp(hidden_states)
else:
attention_layernorm_out = self.input_layernorm(hidden_states)
# Self attention.
attn_outputs = self.self_attention(
attention_layernorm_out,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
alibi=alibi,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
**kwargs,
)
attention_output = attn_outputs[0]
if not self.config.new_decoder_architecture:
if self.config.parallel_attn:
mlp_layernorm_out = attention_layernorm_out
else:
residual = dropout_add(
attention_output, residual, self.config.attention_dropout, training=self.training
)
mlp_layernorm_out = self.post_attention_layernorm(residual)
outputs = attn_outputs[1:]
# MLP.
mlp_output = self.mlp(mlp_layernorm_out)
if self.config.new_decoder_architecture or self.config.parallel_attn:
mlp_output += attention_output
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, present, attentions
FALCON_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`FalconConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
FALCON_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]`
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`):
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
their past given to this model should not be passed as `input_ids` as they have already been computed.
Each element of `past_key_values` is a tuple (past_key, past_value):
- past_key: [batch_size * num_heads, head_dim, kv_length]
- past_value: [batch_size * num_heads, kv_length, head_dim]
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
`past_key_values`).
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class FalconPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FalconConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["FalconDecoderLayer"]
_supports_flash_attn_2 = True
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module: nn.Module):
"""Initialize the weights."""
if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@add_start_docstrings(
"The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
FALCON_START_DOCSTRING,
)
class FalconModel(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_alibi = config.alibi
# Embedding + LN Embedding
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
# Transformer blocks
self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
# Final Layer Norm
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
# Compute alibi tensor: check build_alibi_tensor documentation
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[-2]
if self.use_alibi:
mask = (
torch.ones(
(batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
)
if attention_mask is None
else attention_mask
)
alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
else:
alibi = None
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0)
if getattr(self.config, "_flash_attn_2_enabled", False):
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
alibi,
attention_mask,
position_ids,
head_mask[i],
layer_past,
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
@add_start_docstrings(
"The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
FALCON_START_DOCSTRING,
)
class FalconForCausalLM(FalconPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FalconConfig):
super().__init__(config)
self.transformer = FalconModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings: torch.Tensor):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
**kwargs,
) -> dict:
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
# Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE.
if not self.transformer.use_alibi and attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
return {
"input_ids": input_ids,
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def _reorder_cache(
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
Output shares the same memory storage as `past`.
"""
# Get a copy of `beam_idx` on all the devices where we need those indices.
device_to_beam_idx = {
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
}
reordered_past = tuple(
(
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
)
for layer_past in past
)
return reordered_past
@add_start_docstrings(
"""
The Falcon Model transformer with a sequence classification head on top (linear layer).
[`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
FALCON_START_DOCSTRING,
)
class FalconForSequenceClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1).to(logits.device)
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
FALCON_START_DOCSTRING,
)
class FalconForTokenClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifier_dropout
elif getattr(config, "hidden_dropout", None) is not None:
classifier_dropout = config.hidden_dropout
else:
classifier_dropout = 0.1
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
batch_size, seq_length = labels.shape
loss_fct = CrossEntropyLoss()
loss = loss_fct(
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
)
if not return_dict:
output = (logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
FALCON_START_DOCSTRING,
)
class FalconForQuestionAnswering(FalconPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = FalconModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/falcon/configuration_falcon.py
|
# coding=utf-8
# Copyright 2023 the Falcon authors and HuggingFace Inc. team. 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.
""" Falcon configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class FalconConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the
[tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65024):
Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FalconModel`]
hidden_size (`int`, *optional*, defaults to 4544):
Dimension of the hidden representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 71):
Number of attention heads for each attention layer in the Transformer encoder.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for MLP layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for attention layers.
num_kv_heads (`int`, *optional*):
Number of key-value heads to use per attention layer. If unset, defaults to the same value as
`num_attention_heads`.
alibi (`bool`, *optional*, defaults to `False`):
Whether to use ALiBi positional biases during self-attention.
new_decoder_architecture (`bool`, *optional*, defaults to `False`):
Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
arguments are ignored, as the new decoder always uses parallel attention.
multi_query (`bool`, *optional*, defaults to `True`):
Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
parallel_attn (`bool`, *optional*, defaults to `True`):
Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
bias (`bool`, *optional*, defaults to `False`):
Whether to use bias on Linear layers.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
Falcon models with RoPE support up to 2048 tokens.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
bos_token_id (`int`, *optional*, defaults to 11):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 11):
The id of the "end-of-sequence" token.
Example:
```python
>>> from transformers import FalconModel, FalconConfig
>>> # Initializing a small (2-layer) Falcon configuration
>>> configuration = FalconConfig(num_hidden_layers=2)
>>> # Initializing a model from the small configuration
>>> model = FalconModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "falcon"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65024,
hidden_size=4544,
num_hidden_layers=32,
num_attention_heads=71,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
hidden_dropout=0.0,
attention_dropout=0.0,
num_kv_heads=None,
alibi=False,
new_decoder_architecture=False,
multi_query=True,
parallel_attn=True,
bias=False,
max_position_embeddings=2048,
rope_theta=10000.0,
rope_scaling=None,
bos_token_id=11,
eos_token_id=11,
**kwargs,
):
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
n_embed = kwargs.pop("n_embed", None)
self.hidden_size = hidden_size if n_embed is None else n_embed
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
self.alibi = alibi
self.new_decoder_architecture = new_decoder_architecture
self.multi_query = multi_query # Ignored when new_decoder_architecture is True
self.parallel_attn = parallel_attn
self.bias = bias
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):
return self.hidden_size // self.num_attention_heads
@property
def rotary(self):
return not self.alibi
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if self.alibi:
raise ValueError("`rope_scaling` is not supported when `alibi` is `True`.")
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/falcon/convert_custom_code_checkpoint.py
|
import json
from argparse import ArgumentParser
from pathlib import Path
"""
This script converts Falcon custom code checkpoints to modern Falcon checkpoints that use code in the Transformers
library. After conversion, performance (especially for generation) should improve and the checkpoint can be loaded
without needing trust_remote_code=True.
"""
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument(
"--checkpoint_dir",
type=Path,
required=True,
help="Directory containing a custom code checkpoint to convert to a modern Falcon checkpoint.",
)
args = parser.parse_args()
if not args.checkpoint_dir.is_dir():
raise ValueError("--checkpoint_dir argument should be a directory!")
if (
not (args.checkpoint_dir / "configuration_RW.py").is_file()
or not (args.checkpoint_dir / "modelling_RW.py").is_file()
):
raise ValueError(
"The model directory should contain configuration_RW.py and modelling_RW.py files! Are you sure this is a custom code checkpoint?"
)
(args.checkpoint_dir / "configuration_RW.py").unlink()
(args.checkpoint_dir / "modelling_RW.py").unlink()
config = args.checkpoint_dir / "config.json"
text = config.read_text()
text = text.replace("RWForCausalLM", "FalconForCausalLM")
text = text.replace("RefinedWebModel", "falcon")
text = text.replace("RefinedWeb", "falcon")
json_config = json.loads(text)
del json_config["auto_map"]
if "n_head" in json_config:
json_config["num_attention_heads"] = json_config.pop("n_head")
if "n_layer" in json_config:
json_config["num_hidden_layers"] = json_config.pop("n_layer")
if "n_head_kv" in json_config:
json_config["num_kv_heads"] = json_config.pop("n_head_kv")
json_config["new_decoder_architecture"] = True
else:
json_config["new_decoder_architecture"] = False
bos_token_id = json_config.get("bos_token_id", 1)
eos_token_id = json_config.get("eos_token_id", 2)
config.unlink()
config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
tokenizer_config = args.checkpoint_dir / "tokenizer_config.json"
if tokenizer_config.is_file():
text = tokenizer_config.read_text()
json_config = json.loads(text)
if json_config["tokenizer_class"] == "PreTrainedTokenizerFast":
json_config["model_input_names"] = ["input_ids", "attention_mask"]
tokenizer_config.unlink()
tokenizer_config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
generation_config_path = args.checkpoint_dir / "generation_config.json"
generation_dict = {
"_from_model_config": True,
"bos_token_id": bos_token_id,
"eos_token_id": eos_token_id,
"transformers_version": "4.33.0.dev0",
}
generation_config_path.write_text(json.dumps(generation_dict, indent=2, sort_keys=True))
print("Done! Please double-check that the new checkpoint works as expected.")
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/falcon/__init__.py
|
# coding=utf-8
# Copyright 2023 the Falcon authors and HuggingFace Inc. team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_import_structure = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_falcon"] = [
"FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
"FalconForCausalLM",
"FalconModel",
"FalconPreTrainedModel",
"FalconForSequenceClassification",
"FalconForTokenClassification",
"FalconForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/image_processing_fuyu.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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.
"""Image processor class for Fuyu."""
import math
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import (
pad,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
is_valid_image,
make_list_of_images,
to_numpy_array,
)
from ...utils import (
TensorType,
is_torch_available,
is_torch_device,
is_torch_dtype,
logging,
requires_backends,
)
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
def make_list_of_list_of_images(
images: Union[List[List[ImageInput]], List[ImageInput], ImageInput]
) -> List[List[ImageInput]]:
if is_valid_image(images):
return [[images]]
if isinstance(images, list) and all(isinstance(image, list) for image in images):
return images
if isinstance(images, list):
return [make_list_of_images(image) for image in images]
raise ValueError("images must be a list of list of images or a list of images or an image.")
class FuyuBatchFeature(BatchFeature):
"""
BatchFeature class for Fuyu image processor and processor.
The outputs dictionary from the processors contains a mix of tensors and lists of tensors.
"""
def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
"""
Convert the inner content to tensors.
Args:
tensor_type (`str` or [`~utils.TensorType`], *optional*):
The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
`None`, no modification is done.
"""
if tensor_type is None:
return self
is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type=tensor_type)
def _convert_tensor(elem):
if is_tensor(elem):
return elem
return as_tensor(elem)
def _safe_convert_tensor(elem):
try:
return _convert_tensor(elem)
except: # noqa E722
if key == "overflowing_values":
raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
raise ValueError(
"Unable to create tensor, you should probably activate padding "
"with 'padding=True' to have batched tensors with the same length."
)
# Do the tensor conversion in batch
for key, value in self.items():
if isinstance(value, list) and isinstance(value[0], list):
# List[List[Any]] -> List[List[Tensor]]
self[key] = [[_safe_convert_tensor(elem) for elem in elems] for elems in value]
elif isinstance(value, list):
# List[Any] -> List[Tensor]
self[key] = [_safe_convert_tensor(elem) for elem in value]
else:
# Any -> Tensor
self[key] = _safe_convert_tensor(value)
return self
def to(self, *args, **kwargs) -> "BatchFeature":
"""
Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
different `dtypes` and sending the `BatchFeature` to a different `device`.
Args:
args (`Tuple`):
Will be passed to the `to(...)` function of the tensors.
kwargs (`Dict`, *optional*):
Will be passed to the `to(...)` function of the tensors.
Returns:
[`BatchFeature`]: The same instance after modification.
"""
requires_backends(self, ["torch"])
import torch # noqa
new_data = {}
device = kwargs.get("device")
# Check if the args are a device or a dtype
if device is None and len(args) > 0:
# device should be always the first argument
arg = args[0]
if is_torch_dtype(arg):
# The first argument is a dtype
pass
elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
device = arg
else:
# it's something else
raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
def _to(elem):
# check if v is a floating point
if torch.is_floating_point(elem):
# cast and send to device
return elem.to(*args, **kwargs)
if device is not None:
return elem.to(device=device)
return elem
# We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
for k, v in self.items():
if isinstance(v, list) and isinstance(v[0], list):
# Data structure is a list of lists
new_v = []
for elems in v:
new_v.append([_to(elem) for elem in elems])
new_data[k] = new_v
elif isinstance(v, list):
# Data structure is a list
new_data[k] = [_to(elem) for elem in v]
else:
new_data[k] = _to(v)
self.data = new_data
return self
class FuyuImageProcessor(BaseImageProcessor):
"""
This class should handle the image processing part before the main FuyuForCausalLM. In particular, it should
handle:
- Processing Images:
Taking a batch of images as input. If the images are variable-sized, it resizes them based on the desired patch
dimensions. The image output is always img_h, img_w of (1080, 1920)
Then, it patches up these images using the patchify_image function.
- Creating Image Input IDs:
For each patch, a placeholder ID is given to identify where these patches belong in a token sequence. For
variable-sized images, each line of patches is terminated with a newline ID.
- Image Patch Indices:
For each image patch, the code maintains an index where these patches should be inserted in a token stream.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image to `size`.
size (`Dict[str, int]`, *optional*, defaults to `{"height": 1080, "width": 1920}`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image to `size`.
padding_value (`float`, *optional*, defaults to 1.0):
The value to pad the image with.
padding_mode (`str`, *optional*, defaults to `"constant"`):
The padding mode to use when padding the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
image_mean (`float`, *optional*, defaults to 0.5):
The mean to use when normalizing the image.
image_std (`float`, *optional*, defaults to 0.5):
The standard deviation to use when normalizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `1 / 255`):
The factor to use when rescaling the image.
patch_size (`Dict[str, int]`, *optional*, defaults to `{"height": 30, "width": 30}`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
"""
model_input_names = [
"images",
"image_input_ids",
"image_patches",
"image_patch_indices_per_batch",
"image_patch_indices_per_subsequence",
]
def __init__(
self,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_pad: bool = True,
padding_value: float = 1.0,
padding_mode: str = "constant",
do_normalize: bool = True,
image_mean: Union[float, List[float]] = 0.5,
image_std: Union[float, List[float]] = 0.5,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
patch_size: Optional[Dict[str, int]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.do_resize = do_resize
self.size = size if size is not None else {"height": 1080, "width": 1920}
self.resample = resample
self.do_pad = do_pad
self.padding_value = padding_value
self.padding_mode = padding_mode
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.patch_size = patch_size if patch_size is not None else {"height": 30, "width": 30}
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = 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.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
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.
"""
image_height, image_width = get_image_size(image, input_data_format)
target_height, target_width = size["height"], size["width"]
if image_width <= target_width and image_height <= target_height:
return image
height_scale_factor = target_height / image_height
width_scale_factor = target_width / image_width
optimal_scale_factor = min(height_scale_factor, width_scale_factor)
new_height = int(image_height * optimal_scale_factor)
new_width = int(image_width * optimal_scale_factor)
scaled_image = resize(
image=image,
size=(new_height, new_width),
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
return scaled_image
def pad_image(
self,
image: np.ndarray,
size: Dict[str, int],
mode: str = "constant",
constant_values: float = 1.0,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Pad an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to pad.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
data_format (`ChannelDimension` or `str`, *optional*):
The data format of the output image. If unset, the same format as the input image is used.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
image_height, image_width = get_image_size(image, input_data_format)
target_height, target_width = size["height"], size["width"]
padding_top = 0
padding_left = 0
padding_bottom = target_height - image_height
padding_right = target_width - image_width
padded_image = pad(
image,
padding=((padding_top, padding_bottom), (padding_left, padding_right)),
mode=mode,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def preprocess(
self,
images,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Optional[PILImageResampling] = None,
do_pad: Optional[bool] = None,
padding_value: Optional[float] = None,
padding_mode: Optional[str] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[float] = None,
image_std: Optional[float] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
patch_size: Optional[Dict[str, int]] = None,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
return_tensors: Optional[TensorType] = None,
):
"""
Utility function to preprocess the images and extract necessary information about original formats.
Args:
images (`ImageInput`):
Images to preprocess. Expects a single image, a list or images or a list of lists of images. Pixel
values range from 0 to 255, or between 0 and 1 if `do_rescale` is `False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image to `size`.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether to pad the image to `size`.
padding_value (`float`, *optional*, defaults to `self.padding_value`):
The value to pad the image with.
padding_mode (`str`, *optional*, defaults to `self.padding_mode`):
The padding mode to use when padding the image.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float`, *optional*, defaults to `self.image_mean`):
The mean to use when normalizing the image.
image_std (`float`, *optional*, defaults to `self.image_std`):
The standard deviation to use when normalizing the image.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
The factor to use when rescaling the image.
patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format of 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.
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.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_pad = do_pad if do_pad is not None else self.do_pad
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
padding_value = padding_value if padding_value is not None else self.padding_value
padding_mode = padding_mode if padding_mode is not None else self.padding_mode
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
patch_size = patch_size if patch_size is not None else self.patch_size
if isinstance(images, list) and any(isinstance(elem, list) and len(elem) >= 2 for elem in images):
raise ValueError("Multiple images for a single sample are not yet supported.")
batch_images = make_list_of_list_of_images(images)
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_normalize and image_mean is None or image_std is None:
raise ValueError("image_mean and image_std must be specified if do_normalize is True.")
# All transformations expect numpy arrays.
batch_images = [[to_numpy_array(image) for image in images] for images in batch_images]
if is_scaled_image(batch_images[0][0]) and do_rescale:
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(batch_images[0][0])
original_image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]
if do_resize:
batch_images = [
[self.resize(image, size=size, input_data_format=input_data_format) for image in images]
for images in batch_images
]
image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]
image_unpadded_heights = [[image_size[0]] for image_size in image_sizes]
image_unpadded_widths = [[image_size[1]] for image_size in image_sizes]
# scale_h is the same as scale_w
image_scale_factors = [
[resized_size[0] / original_size[0]]
for original_size, resized_size in zip(original_image_sizes, image_sizes)
]
if do_pad:
batch_images = [
[
self.pad_image(
image,
size=size,
mode=padding_mode,
constant_values=padding_value,
input_data_format=input_data_format,
)
for image in images
]
for images in batch_images
]
if do_rescale:
batch_images = [
[self.rescale(image, scale=rescale_factor, input_data_format=input_data_format) for image in images]
for images in batch_images
]
if do_normalize:
batch_images = [
[
self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
for images in batch_images
]
if data_format is not None:
batch_images = [
[to_channel_dimension_format(image, data_format, input_data_format) for image in images]
for images in batch_images
]
data = {
"images": batch_images,
"image_unpadded_heights": image_unpadded_heights,
"image_unpadded_widths": image_unpadded_widths,
"image_scale_factors": image_scale_factors,
}
return FuyuBatchFeature(data=data, tensor_type=return_tensors)
def get_num_patches(self, image_height: int, image_width: int, patch_size: Dict[str, int] = None) -> int:
"""
Calculate number of patches required to encode an image.
Args:
image_height (`int`):
Height of the image.
image_width (`int`):
Width of the image.
patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
"""
patch_size = patch_size if patch_size is not None else self.patch_size
patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
if image_height % patch_height != 0:
raise ValueError(f"{image_height=} must be divisible by {patch_height}")
if image_width % patch_width != 0:
raise ValueError(f"{image_width=} must be divisible by {patch_width}")
num_patches_per_dim_h = image_height // patch_height
num_patches_per_dim_w = image_width // patch_width
num_patches = num_patches_per_dim_h * num_patches_per_dim_w
return num_patches
def patchify_image(self, image: "torch.Tensor", patch_size: Optional[Dict[str, int]] = None) -> "torch.Tensor":
"""
Convert an image into a tensor of patches.
Args:
image (`torch.Tensor`):
Image to convert. Shape: [batch, channels, height, width]
patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
"""
requires_backends(self, ["torch"])
patch_size = patch_size if patch_size is not None else self.patch_size
patch_height, patch_width = patch_size["height"], patch_size["width"]
# TODO refer to https://github.com/ArthurZucker/transformers/blob/0f0a3fe5ca5697ee58faeb5b53f049af720b5e98/src/transformers/models/vit_mae/modeling_vit_mae.py#L871
# torch implementation is faster but does not handle non-squares
batch_size, channels, _, _ = image.shape
unfolded_along_height = image.unfold(2, patch_height, patch_height)
patches = unfolded_along_height.unfold(3, patch_width, patch_width)
patches = patches.contiguous()
patches = patches.view(batch_size, channels, -1, patch_height, patch_width)
patches = patches.permute(0, 2, 3, 4, 1)
patches = patches.reshape(batch_size, -1, channels * patch_height * patch_width)
return patches
def preprocess_with_tokenizer_info(
self,
image_input: "torch.Tensor",
image_present: "torch.Tensor",
image_unpadded_h: "torch.Tensor",
image_unpadded_w: "torch.Tensor",
image_placeholder_id: int,
image_newline_id: int,
variable_sized: bool,
patch_size: Optional[Dict[str, int]] = None,
) -> FuyuBatchFeature:
"""Process images for model input. In particular, variable-sized images are handled here.
Args:
image_input (`torch.Tensor` of shape [batch_size, subsequence_size, num_channels, height, width]):
Tensor of images padded to model input size.
image_present (`torch.Tensor` of shape [batch_size, subsequence_size, num_images]):
Tensor of 1s and 0s indicating whether an image is present.
image_unpadded_h (`torch.Tensor` of shape [batch_size, subsequence_size]):
Tensor of unpadded image heights.
image_unpadded_w (`torch.Tensor` of shape [batch_size, subsequence_size]):
Tensor of unpadded image widths.
image_placeholder_id (int):
The id of the image placeholder token. Comes from an associated tokenizer.
image_newline_id (int):
The id of the image newline token. Comes from an associated tokenizer.
variable_sized (bool):
Whether to process images as variable-sized.
patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
Size of the patches.
"""
requires_backends(self, ["torch"])
patch_size = patch_size if patch_size is not None else self.patch_size
patch_height, patch_width = patch_size["height"], patch_size["width"]
# Only images that are present.
images: List[List[torch.Tensor]] = []
batch_image_patches: List[List[torch.Tensor]] = []
# Image input ids for every subsequence, including ones with no image present.
batch_image_input_ids: List[List[torch.Tensor]] = []
for batch_index in range(image_input.shape[0]):
image_input_ids = []
image_patches = []
for subseq_index in range(image_input.shape[1]):
if image_present[batch_index, subseq_index]:
image = image_input[batch_index, subseq_index]
image_height, image_width = image.shape[1], image.shape[2]
if variable_sized:
# The min() is required here due to floating point issues:
# math.ceil(torch.tensor(300).cuda() / 30) == 11
new_h = min(
image_height,
math.ceil(image_unpadded_h[batch_index, subseq_index] / patch_height) * patch_height,
)
new_w = min(
image_width,
math.ceil(image_unpadded_w[batch_index, subseq_index] / patch_width) * patch_width,
)
image = image[:, :new_h, :new_w]
image_height, image_width = new_h, new_w
num_patches = self.get_num_patches(image_height=image_height, image_width=image_width)
tensor_of_image_ids = torch.full(
[num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
)
patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
assert num_patches == patches.shape[0]
if variable_sized:
# Now terminate each line with |NEWLINE|.
tensor_of_image_ids = tensor_of_image_ids.reshape(-1, image_width // patch_width)
newline_ids = torch.full(
[tensor_of_image_ids.shape[0], 1],
image_newline_id,
dtype=torch.int32,
device=image_input.device,
)
tensor_of_image_ids = torch.cat([tensor_of_image_ids, newline_ids], dim=1)
tensor_of_image_ids = tensor_of_image_ids.reshape(-1)
images.append([image])
image_input_ids.append(tensor_of_image_ids)
image_patches.append(patches)
else:
image_input_ids.append(torch.tensor([], dtype=torch.int32, device=image_input.device))
batch_image_input_ids.append(image_input_ids)
batch_image_patches.append(image_patches)
# Create image_patch_input_indices, where non-negative values correspond to image patches to be inserted in
# the stream.
image_patch_indices_per_batch: List[List[torch.Tensor]] = []
image_patch_indices_per_subsequence: List[List[torch.Tensor]] = []
for sample_image_input_ids in batch_image_input_ids:
index_offset = 0
per_batch_indices = []
per_subsequence_indices = []
for subseq_image_input_ids in sample_image_input_ids:
# Indices of image patches.
patches_mask = subseq_image_input_ids == image_placeholder_id
num_patches = torch.count_nonzero(patches_mask)
indices = torch.arange(
num_patches, dtype=subseq_image_input_ids.dtype, device=subseq_image_input_ids.device
)
# Place those indices in the image input ids token stream, with -1 representing non-index tokens.
indices_in_stream_per_batch = torch.full_like(subseq_image_input_ids, -1)
indices_in_stream_per_subsequence = torch.full_like(subseq_image_input_ids, -1)
patches_inds = torch.nonzero(patches_mask, as_tuple=True)[0]
indices_in_stream_per_batch[patches_inds] = indices + index_offset
indices_in_stream_per_subsequence[patches_inds] = indices
per_batch_indices.append(indices_in_stream_per_batch)
per_subsequence_indices.append(indices_in_stream_per_subsequence)
index_offset += num_patches
image_patch_indices_per_batch.append(per_batch_indices)
image_patch_indices_per_subsequence.append(per_subsequence_indices)
return FuyuBatchFeature(
data={
"images": images,
"image_input_ids": batch_image_input_ids,
"image_patches": batch_image_patches,
"image_patch_indices_per_batch": image_patch_indices_per_batch,
"image_patch_indices_per_subsequence": image_patch_indices_per_subsequence,
}
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/configuration_fuyu.py
|
# coding=utf-8
# Copyright 2023 Adept AI and the HuggingFace Inc. team. 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.
""" Fuyu model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
FUYU_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"adept/fuyu-8b": "https://huggingface.co/adept/fuyu-8b/resolve/main/config.json",
}
class FuyuConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FuyuForCausalLM`]. It is used to instantiate an
Fuyu model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the
[adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 262144):
Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FuyuForCausalLM`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 16384):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 36):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 16384):
The maximum sequence length that this model might ever be used with.
image_size (`int`, *optional*, defaults to 300):
The input image size.
patch_size (`int`, *optional*, defaults to 30):
The input vision transformer encoding patch size.
num_channels (`int`, *optional*, defaults to 3):
The input image number of channels.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. Whether to tie weight embeddings
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie input and output embeddings.
rope_theta (`float`, *optional*, defaults to 25000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
qk_layernorm (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the Queries and Keys after projecting the hidden states
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio after applying the MLP to the hidden states.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio after computing the attention scores.
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
Percentage of the query and keys which will have rotary embedding.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the *beginning-of-sequence* token.
eos_token_id (`Union[int, List[int]]`, *optional*, defaults to 2):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize the `language``[`Aut`].
```python
>>> from transformers import FuyuConfig
>>> # Initializing a Fuyu fuyu-7b style configuration
>>> configuration = FuyuConfig()
```"""
model_type = "fuyu"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=262144,
hidden_size=4096,
intermediate_size=16384,
num_hidden_layers=36,
num_attention_heads=64,
hidden_act="relu2",
max_position_embeddings=16384,
image_size=300,
patch_size=30,
num_channels=3,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=25000.0,
rope_scaling=None,
qk_layernorm=True,
hidden_dropout=0.0,
attention_dropout=0.0,
partial_rotary_factor=0.5,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
text_config=None,
**kwargs,
):
if text_config is None:
text_config = {
"vocab_size": vocab_size,
"max_position_embeddings": max_position_embeddings,
"hidden_size": hidden_size,
"intermediate_size": intermediate_size,
"num_hidden_layers": num_hidden_layers,
"num_attention_heads": num_attention_heads,
"hidden_act": hidden_act,
"initializer_range": initializer_range,
"layer_norm_eps": layer_norm_eps,
"use_cache": use_cache,
"rope_theta": rope_theta,
"rope_scaling": rope_scaling,
"qk_layernorm": qk_layernorm,
"hidden_dropout": hidden_dropout,
"attention_dropout": attention_dropout,
"partial_rotary_factor": partial_rotary_factor,
"pad_token_id": pad_token_id,
"bos_token_id": bos_token_id,
"eos_token_id": eos_token_id,
"tie_word_embeddings": tie_word_embeddings,
}
logger.info("text_config is None. initializing the text model with default values.")
text_model_type = text_config["model_type"] if "model_type" in text_config else "persimmon"
self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.qk_layernorm = qk_layernorm
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.partial_rotary_factor = partial_rotary_factor
self._rope_scaling_validation()
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/processing_fuyu.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
"""
Image/Text processor class for GIT
"""
import re
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, TruncationStrategy
from ...utils import TensorType, is_torch_available, logging, requires_backends
if is_torch_available():
from .image_processing_fuyu import FuyuBatchFeature
logger = logging.get_logger(__name__)
if is_torch_available():
import torch
TEXT_REPR_BBOX_OPEN = "<box>"
TEXT_REPR_BBOX_CLOSE = "</box>"
TEXT_REPR_POINT_OPEN = "<point>"
TEXT_REPR_POINT_CLOSE = "</point>"
TOKEN_BBOX_OPEN_STRING = "<0x00>" # <bbox>
TOKEN_BBOX_CLOSE_STRING = "<0x01>" # </bbox>
TOKEN_POINT_OPEN_STRING = "<0x02>" # <point>
TOKEN_POINT_CLOSE_STRING = "<0x03>" # </point>
BEGINNING_OF_ANSWER_STRING = "<0x04>" # <boa>
def full_unpacked_stream_to_tensor(
all_bi_tokens_to_place: List[int],
full_unpacked_stream: List["torch.Tensor"],
fill_value: int,
batch_size: int,
new_seq_len: int,
offset: int,
) -> "torch.Tensor":
"""Takes an unpacked stream of tokens (i.e. a list of tensors, one for each item in the batch) and does
the required padding to create a single tensor for the batch of shape batch_size x new_seq_len.
"""
assert len(all_bi_tokens_to_place) == batch_size
assert len(full_unpacked_stream) == batch_size
# Create padded tensors for the full batch.
new_padded_tensor = torch.full(
[batch_size, new_seq_len],
fill_value=fill_value,
dtype=full_unpacked_stream[0].dtype,
device=full_unpacked_stream[0].device,
)
# Place each batch entry into the batch tensor.
for bi in range(batch_size):
tokens_to_place = all_bi_tokens_to_place[bi]
new_padded_tensor[bi, :tokens_to_place] = full_unpacked_stream[bi][offset : tokens_to_place + offset]
return new_padded_tensor
def construct_full_unpacked_stream(
num_real_text_tokens: Union[List[List[int]], "torch.Tensor"],
input_stream: "torch.Tensor",
image_tokens: List[List["torch.Tensor"]],
batch_size: int,
num_sub_sequences: int,
) -> List["torch.Tensor"]:
"""Takes an input_stream tensor of shape B x S x ?. For each subsequence, adds any required
padding to account for images and then unpacks the subsequences to create a single sequence per item in the batch.
Returns a list of tensors, one for each item in the batch."""
all_bi_stream = []
for batch_index in range(batch_size):
all_si_stream = []
# First, construct full token stream (including image placeholder tokens) and loss mask for each subsequence
# and append to lists. We use lists rather than tensors because each subsequence is variable-sized.
# TODO Remove this logic in a subsequent release since subsequences are not supported.
image_adjustment = image_tokens[batch_index][0]
subsequence_stream = torch.cat([image_adjustment, input_stream[batch_index, 0]], dim=0)
num_real_tokens = image_adjustment.shape[0] + num_real_text_tokens[batch_index][0]
all_si_stream.append(subsequence_stream[:num_real_tokens])
all_bi_stream.append(torch.cat(all_si_stream, dim=0))
return all_bi_stream
def _replace_string_repr_with_token_tags(prompt: str) -> str:
prompt = prompt.replace(TEXT_REPR_POINT_OPEN, TOKEN_POINT_OPEN_STRING)
prompt = prompt.replace(TEXT_REPR_POINT_CLOSE, TOKEN_POINT_CLOSE_STRING)
prompt = prompt.replace(TEXT_REPR_BBOX_OPEN, TOKEN_BBOX_OPEN_STRING)
prompt = prompt.replace(TEXT_REPR_BBOX_CLOSE, TOKEN_BBOX_CLOSE_STRING)
return prompt
def _segment_prompt_into_text_token_conversions(prompt: str) -> List:
"""
Given a string prompt, converts the prompt into a list of TextTokenConversions.
"""
# Wherever, we notice the [TOKEN_OPEN_STRING, TOKEN_CLOSE_STRING], we split the prompt
prompt_text_list: List = []
regex_pattern = re.compile(
f"({TOKEN_BBOX_OPEN_STRING}|{TOKEN_BBOX_CLOSE_STRING}|{TOKEN_POINT_OPEN_STRING}|{TOKEN_POINT_CLOSE_STRING})"
)
# Split by the regex pattern
prompt_split = regex_pattern.split(prompt)
for i, elem in enumerate(prompt_split):
if len(elem) == 0 or elem in [
TOKEN_BBOX_OPEN_STRING,
TOKEN_BBOX_CLOSE_STRING,
TOKEN_POINT_OPEN_STRING,
TOKEN_POINT_CLOSE_STRING,
]:
continue
prompt_text_list.append(
(elem, i > 1 and prompt_split[i - 1] in [TOKEN_BBOX_OPEN_STRING, TOKEN_POINT_OPEN_STRING])
)
return prompt_text_list
def _transform_coordinates_and_tokenize(prompt: str, scale_factor: float, tokenizer) -> List[int]:
"""
This function transforms the prompt in the following fashion:
- <box> <point> and </box> </point> to their respective token mappings
- extract the coordinates from the tag
- transform the coordinates into the transformed image space
- return the prompt tokens with the transformed coordinates and new tags
Bounding boxes and points MUST be in the following format: <box>y1, x1, y2, x2</box> <point>x, y</point> The spaces
and punctuation added above are NOT optional.
"""
# Make a namedtuple that stores "text" and "is_bbox"
# We want to do the following: Tokenize the code normally -> when we see a point or box, tokenize using the tokenize_within_tag function
# When point or box close tag, continue tokenizing normally
# First, we replace the point and box tags with their respective tokens
prompt = _replace_string_repr_with_token_tags(prompt)
# Tokenize the prompt
# Convert prompt into a list split
prompt_text_list = _segment_prompt_into_text_token_conversions(prompt)
transformed_prompt_tokens: List[int] = []
for elem in prompt_text_list:
if elem[1]:
# This is a location, we need to tokenize it
within_tag_tokenized = _transform_within_tags(elem[0], scale_factor, tokenizer)
# Surround the text with the open and close tags
transformed_prompt_tokens.extend(within_tag_tokenized)
else:
transformed_prompt_tokens.extend(tokenizer(elem[0], add_special_tokens=False).input_ids)
return transformed_prompt_tokens
def _transform_within_tags(text: str, scale_factor: float, tokenizer) -> List[int]:
"""
Given a bounding box of the fashion <box>1, 2, 3, 4</box> | <point>1, 2</point> This function is responsible for
converting 1, 2, 3, 4 into tokens of 1 2 3 4 without any commas.
"""
# Convert the text into a list of strings.
num_int_strs = text.split(",")
if len(num_int_strs) == 2:
# If there are any open or close tags, remove them.
token_space_open_string = tokenizer.vocab[TOKEN_POINT_OPEN_STRING]
token_space_close_string = tokenizer.vocab[TOKEN_POINT_CLOSE_STRING]
else:
token_space_open_string = tokenizer.vocab[TOKEN_BBOX_OPEN_STRING]
token_space_close_string = tokenizer.vocab[TOKEN_BBOX_CLOSE_STRING]
# Remove all spaces from num_ints
num_ints = [float(num.strip()) for num in num_int_strs]
# scale to transformed image siz
if len(num_ints) == 2:
num_ints_translated = scale_point_to_transformed_image(x=num_ints[0], y=num_ints[1], scale_factor=scale_factor)
elif len(num_ints) == 4:
num_ints_translated = scale_bbox_to_transformed_image(
top=num_ints[0],
left=num_ints[1],
bottom=num_ints[2],
right=num_ints[3],
scale_factor=scale_factor,
)
else:
raise ValueError(f"Invalid number of ints: {len(num_ints)}")
# Tokenize the text, skipping the
tokens = [tokenizer.vocab[str(num)] for num in num_ints_translated]
return [token_space_open_string] + tokens + [token_space_close_string]
def _tokenize_prompts_with_image_and_batch(
tokenizer,
prompts: List[List[str]],
scale_factors: Optional[List[List["torch.Tensor"]]],
max_tokens_to_generate: int,
max_position_embeddings: int,
add_BOS: bool, # Same issue with types as above
add_beginning_of_answer_token: bool,
) -> Tuple["torch.Tensor", "torch.Tensor"]:
"""
Given a set of prompts and number of tokens to generate:
- tokenize prompts
- set the sequence length to be the max of length of prompts plus the number of tokens we would like to generate
- pad all the sequences to this length so we can convert them into a 3D tensor.
"""
# If not tool use, tranform the coordinates while tokenizing
if scale_factors is not None:
transformed_prompt_tokens = []
for prompt_seq, scale_factor_seq in zip(prompts, scale_factors):
transformed_prompt_tokens.append(
[
_transform_coordinates_and_tokenize(prompt, scale_factor.item(), tokenizer)
for prompt, scale_factor in zip(prompt_seq, scale_factor_seq)
]
)
else:
transformed_prompt_tokens = [[tokenizer.tokenize(prompt) for prompt in prompt_seq] for prompt_seq in prompts]
prompts_tokens = transformed_prompt_tokens
if add_BOS:
bos_token = tokenizer.vocab["<s>"]
else:
bos_token = tokenizer.vocab["|ENDOFTEXT|"]
prompts_tokens = [[[bos_token] + x for x in prompt_seq] for prompt_seq in prompts_tokens]
if add_beginning_of_answer_token:
boa = tokenizer.vocab[BEGINNING_OF_ANSWER_STRING]
# Only add bbox open token to the last subsequence since that is what will be completed
for token_seq in prompts_tokens:
token_seq[-1].append(boa)
# Now we have a list of list of tokens which each list has a different
# size. We want to extend this list to:
# - incorporate the tokens that need to be generated
# - make all the sequences equal length.
# Get the prompts length.
prompts_length = [[len(x) for x in prompts_tokens_seq] for prompts_tokens_seq in prompts_tokens]
# Get the max prompts length.
max_prompt_len: int = np.max(prompts_length)
# Number of tokens in the each sample of the batch.
samples_length = min(max_prompt_len + max_tokens_to_generate, max_position_embeddings)
if max_prompt_len + max_tokens_to_generate > max_position_embeddings:
logger.warning(
f"Max subsequence prompt length of {max_prompt_len} + max tokens to generate {max_tokens_to_generate}",
f"exceeds context length of {max_position_embeddings}. Will generate as many tokens as possible.",
)
# Now update the list of list to be of the same size: samples_length.
for prompt_tokens_seq, prompts_length_seq in zip(prompts_tokens, prompts_length):
for prompt_tokens, prompt_length in zip(prompt_tokens_seq, prompts_length_seq):
if len(prompt_tokens) > samples_length:
raise ValueError("Length of subsequence prompt exceeds sequence length.")
padding_size = samples_length - prompt_length
prompt_tokens.extend([tokenizer.vocab["|ENDOFTEXT|"]] * padding_size)
# Now we are in a structured format, we can convert to tensors.
prompts_tokens_tensor = torch.tensor(prompts_tokens, dtype=torch.int64)
prompts_length_tensor = torch.tensor(prompts_length, dtype=torch.int64)
return prompts_tokens_tensor, prompts_length_tensor
# Simplified assuming self.crop_top = self.padding_top = 0
def original_to_transformed_h_coords(original_coords, scale_h):
return np.round(original_coords * scale_h).astype(np.int32)
# Simplified assuming self.crop_left = self.padding_left = 0
def original_to_transformed_w_coords(original_coords, scale_w):
return np.round(original_coords * scale_w).astype(np.int32)
def scale_point_to_transformed_image(x: float, y: float, scale_factor: float) -> List[int]:
x_scaled = original_to_transformed_w_coords(np.array([x / 2]), scale_factor)[0]
y_scaled = original_to_transformed_h_coords(np.array([y / 2]), scale_factor)[0]
return [x_scaled, y_scaled]
def scale_bbox_to_transformed_image(
top: float, left: float, bottom: float, right: float, scale_factor: float
) -> List[int]:
top_scaled = original_to_transformed_w_coords(np.array([top / 2]), scale_factor)[0]
left_scaled = original_to_transformed_h_coords(np.array([left / 2]), scale_factor)[0]
bottom_scaled = original_to_transformed_w_coords(np.array([bottom / 2]), scale_factor)[0]
right_scaled = original_to_transformed_h_coords(np.array([right / 2]), scale_factor)[0]
return [top_scaled, left_scaled, bottom_scaled, right_scaled]
class FuyuProcessor(ProcessorMixin):
r"""
Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor.
[`FuyuProcessor`] offers all the functionalities of [`FuyuImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~FuyuProcessor.__call__`] and [`~FuyuProcessor.decode`] for more information.
Args:
image_processor ([`FuyuImageProcessor`]):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`]):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "FuyuImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer):
super().__init__(image_processor=image_processor, tokenizer=tokenizer)
self.image_processor = image_processor
self.tokenizer = tokenizer
self.max_tokens_to_generate = 10
self.max_position_embeddings = 16384 # TODO Can't derive this from model files: where to set it?
self.pad_token_id = 0
self.dummy_image_index = -1
def _left_pad_inputs_with_attention_mask(self, model_inputs: List[Dict], return_attention_mask: bool):
max_length_input_ids = max(entry["input_ids"].shape[1] for entry in model_inputs)
max_length_image_patch_indices = max(entry["image_patches_indices"].shape[1] for entry in model_inputs)
batched_inputs = {"input_ids": [], "image_patches": [], "image_patches_indices": [], "attention_mask": []}
for entry in model_inputs:
for key, tensor in entry.items():
if key == "input_ids":
num_padding_tokens = max_length_input_ids - tensor.shape[1]
padded_input_ids = torch.cat(
[
torch.full((tensor.shape[0], num_padding_tokens), self.pad_token_id, dtype=torch.long),
tensor,
],
dim=1,
)
batched_inputs[key].append(padded_input_ids)
attention_mask = torch.cat(
[torch.zeros(tensor.shape[0], num_padding_tokens, dtype=torch.long), torch.ones_like(tensor)],
dim=1,
)
batched_inputs["attention_mask"].append(attention_mask)
elif key == "image_patches":
# For image_patches, we don't pad but just append them to the list.
batched_inputs[key].append(tensor)
else: # for image_patches_indices
num_padding_indices = max_length_image_patch_indices - tensor.shape[1]
padded_indices = torch.cat(
[
torch.full(
(tensor.shape[0], num_padding_indices), self.dummy_image_index, dtype=torch.long
),
tensor,
],
dim=1,
)
batched_inputs[key].append(padded_indices)
batched_keys = ["input_ids", "image_patches_indices"]
if return_attention_mask:
batched_keys.append("attention_mask")
for key in batched_keys:
batched_inputs[key] = torch.cat(batched_inputs[key], dim=0)
return batched_inputs
def get_sample_encoding(
self,
prompts,
scale_factors,
image_unpadded_heights,
image_unpadded_widths,
image_placeholder_id,
image_newline_id,
tensor_batch_images,
):
image_present = torch.ones(1, 1, 1)
model_image_input = self.image_processor.preprocess_with_tokenizer_info(
image_input=tensor_batch_images,
image_present=image_present,
image_unpadded_h=image_unpadded_heights,
image_unpadded_w=image_unpadded_widths,
image_placeholder_id=image_placeholder_id,
image_newline_id=image_newline_id,
variable_sized=True,
)
# FIXME max_tokens_to_generate is embedded into this processor's call.
prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
tokenizer=self.tokenizer,
prompts=prompts,
scale_factors=scale_factors,
max_tokens_to_generate=self.max_tokens_to_generate,
max_position_embeddings=self.max_position_embeddings,
add_BOS=True,
add_beginning_of_answer_token=True,
)
image_padded_unpacked_tokens = construct_full_unpacked_stream(
num_real_text_tokens=prompts_length,
input_stream=prompt_tokens,
image_tokens=model_image_input["image_input_ids"],
batch_size=1,
num_sub_sequences=self.subsequence_length,
)
# Construct inputs for image patch indices.
unpacked_image_patch_indices_per_batch = construct_full_unpacked_stream(
num_real_text_tokens=prompts_length,
input_stream=torch.full_like(prompt_tokens, -1),
image_tokens=model_image_input["image_patch_indices_per_batch"],
batch_size=1,
num_sub_sequences=self.subsequence_length,
)
max_prompt_length = max(x.shape[-1] for x in image_padded_unpacked_tokens)
max_seq_len_batch = min(max_prompt_length + self.max_tokens_to_generate, self.max_position_embeddings)
tokens_to_place = min(max_seq_len_batch, max(0, image_padded_unpacked_tokens[0].shape[0]))
# Use same packing logic for the image patch indices.
image_patch_input_indices = full_unpacked_stream_to_tensor(
all_bi_tokens_to_place=[tokens_to_place],
full_unpacked_stream=unpacked_image_patch_indices_per_batch,
fill_value=-1,
batch_size=1,
new_seq_len=max_seq_len_batch,
offset=0,
)
image_patches_tensor = torch.stack([img[0] for img in model_image_input["image_patches"]])
batch_encoding = {
"input_ids": image_padded_unpacked_tokens[0].unsqueeze(0),
"image_patches": image_patches_tensor,
"image_patches_indices": image_patch_input_indices,
}
return batch_encoding
def __call__(
self,
text=None,
images=None,
add_special_tokens: bool = True,
return_attention_mask: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_token_type_ids: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> "FuyuBatchFeature":
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to
encode the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
FuyuImageProcessor's [`~FuyuImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
text (`str`, `List[str]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `List[PIL.Image.Image]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
Returns:
[`FuyuBatchEncoding`]: A [`FuyuBatchEncoding`] with the following fields:
- **input_ids** -- Tensor of token ids to be fed to a model. Returned when `text` is not `None`.
- **image_patches** -- List of Tensor of image patches. Returned when `images` is not `None`.
- **image_patches_indices** -- Tensor of indices where patch embeddings have to be inserted by the model.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model when
`return_attention_mask=True`.
"""
requires_backends(self, ["torch"])
# --- Check input validity ---
if not return_attention_mask:
raise ValueError("`return_attention_mask=False` is not supported for this model.")
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be None.")
if text is not None and images is None:
logger.warning("You are processing a text with no associated image. Make sure it is intended.")
self.current_processor = self.tokenizer
text_encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
return text_encoding
if text is None and images is not None:
logger.warning("You are processing an image with no associated text. Make sure it is intended.")
prompts = [[""]]
if text is not None and images is not None:
if isinstance(text, str):
prompts = [[text]]
elif isinstance(text, list):
prompts = [[text_seq] for text_seq in text]
# --- Preprocess images using self.image_processor ---
# FIXME - We hard code "pt" here because the rest of the processing assumes torch tensors
image_encoding = self.image_processor.preprocess(images, return_tensors="pt")
batch_images = image_encoding["images"]
image_unpadded_heights = image_encoding["image_unpadded_heights"]
image_unpadded_widths = image_encoding["image_unpadded_widths"]
scale_factors = image_encoding["image_scale_factors"]
self.subsequence_length = 1 # Each batch contains only one sequence.
self.batch_size = len(batch_images)
# --- Use self.tokenizer to get the ids of special tokens to insert into image ids ---
image_placeholder_id = self.tokenizer("|SPEAKER|", add_special_tokens=False)["input_ids"][1]
image_newline_id = self.tokenizer("|NEWLINE|", add_special_tokens=False)["input_ids"][1]
tensor_batch_images = torch.stack([img[0] for img in batch_images]).unsqueeze(1)
# --- Use self.image_processor again to obtain the full token ids and batch inputs ---
all_encodings = []
for prompt, scale_factor, image_unpadded_height, image_unpadded_width, tensor_batch_image in zip(
prompts, scale_factors, image_unpadded_heights, image_unpadded_widths, tensor_batch_images
):
sample_encoding = self.get_sample_encoding(
prompts=[prompt],
scale_factors=[scale_factor],
image_unpadded_heights=torch.tensor([image_unpadded_height]),
image_unpadded_widths=torch.tensor([image_unpadded_width]),
image_placeholder_id=image_placeholder_id,
image_newline_id=image_newline_id,
tensor_batch_images=tensor_batch_image.unsqueeze(0),
)
all_encodings.append(sample_encoding)
batch_encoding = self._left_pad_inputs_with_attention_mask(
model_inputs=all_encodings, return_attention_mask=return_attention_mask
)
return FuyuBatchFeature(data=batch_encoding)
def post_process_box_coordinates(self, outputs, target_sizes=None):
"""
Transforms raw coordinates detected by [`FuyuForCausalLM`] to the original images' coordinate space.
Coordinates will be returned in "box" format, with the following pattern:
`<box>top, left, bottom, right</box>`
Point coordinates are not supported yet.
Args:
outputs ([`GenerateOutput`]):
Raw outputs from `generate`.
target_sizes (`torch.Tensor`, *optional*):
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
the batch. If set, found coordinates in the output sequence are rescaled to the target sizes. If left
to None, coordinates will not be rescaled.
Returns:
`GenerateOutput`: Same output type returned by `generate`, with output token ids replaced with
boxed and possible rescaled coordinates.
"""
def scale_factor_to_fit(original_size, target_size=None):
height, width = original_size
if target_size is None:
max_height = self.image_processor.size["height"]
max_width = self.image_processor.size["width"]
else:
max_height, max_width = target_size
if width <= max_width and height <= max_height:
return 1.0
return min(max_height / height, max_width / width)
def find_delimiters_pair(tokens, start_token, end_token):
start_id = self.tokenizer.convert_tokens_to_ids(start_token)
end_id = self.tokenizer.convert_tokens_to_ids(end_token)
starting_positions = (tokens == start_id).nonzero(as_tuple=True)[0]
ending_positions = (tokens == end_id).nonzero(as_tuple=True)[0]
if torch.any(starting_positions) and torch.any(ending_positions):
return (starting_positions[0], ending_positions[0])
return (None, None)
def tokens_to_boxes(tokens, original_size):
while (pair := find_delimiters_pair(tokens, TOKEN_BBOX_OPEN_STRING, TOKEN_BBOX_CLOSE_STRING)) != (
None,
None,
):
start, end = pair
if end != start + 5:
continue
# Retrieve transformed coordinates from tokens
coords = self.tokenizer.convert_ids_to_tokens(tokens[start + 1 : end])
# Scale back to original image size and multiply by 2
scale = scale_factor_to_fit(original_size)
top, left, bottom, right = [2 * int(float(c) / scale) for c in coords]
# Replace the IDs so they get detokenized right
replacement = f" {TEXT_REPR_BBOX_OPEN}{top}, {left}, {bottom}, {right}{TEXT_REPR_BBOX_CLOSE}"
replacement = self.tokenizer.tokenize(replacement)[1:]
replacement = self.tokenizer.convert_tokens_to_ids(replacement)
replacement = torch.tensor(replacement).to(tokens)
tokens = torch.cat([tokens[:start], replacement, tokens[end + 1 :]], 0)
return tokens
def tokens_to_points(tokens, original_size):
while (pair := find_delimiters_pair(tokens, TOKEN_POINT_OPEN_STRING, TOKEN_POINT_CLOSE_STRING)) != (
None,
None,
):
start, end = pair
if end != start + 3:
continue
# Retrieve transformed coordinates from tokens
coords = self.tokenizer.convert_ids_to_tokens(tokens[start + 1 : end])
# Scale back to original image size and multiply by 2
scale = scale_factor_to_fit(original_size)
x, y = [2 * int(float(c) / scale) for c in coords]
# Replace the IDs so they get detokenized right
replacement = f" {TEXT_REPR_POINT_OPEN}{x}, {y}{TEXT_REPR_POINT_CLOSE}"
replacement = self.tokenizer.tokenize(replacement)[1:]
replacement = self.tokenizer.convert_tokens_to_ids(replacement)
replacement = torch.tensor(replacement).to(tokens)
tokens = torch.cat([tokens[:start], replacement, tokens[end + 1 :]], 0)
return tokens
if target_sizes is None:
target_sizes = ((self.image_processor.size["height"], self.image_processor.size["width"]),) * len(outputs)
elif target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
if len(outputs) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as output sequences")
results = []
for seq, size in zip(outputs, target_sizes):
seq = tokens_to_boxes(seq, size)
seq = tokens_to_points(seq, size)
results.append(seq)
return results
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/modeling_fuyu.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc. team. 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.
""" PyTorch Fuyu model."""
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...modeling_outputs import CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...models.auto.modeling_auto import AutoModelForCausalLM
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_fuyu import FuyuConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "FuyuConfig"
FUYU_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`FuyuConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Fuyu Model outputting raw hidden-states without any specific head on top.",
FUYU_START_DOCSTRING,
)
class FuyuPreTrainedModel(PreTrainedModel):
config_class = FuyuConfig
base_model_prefix = "fuyu"
supports_gradient_checkpointing = True
_no_split_modules = []
_skip_keys_device_placement = "past_key_values"
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
FUYU_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
image_patches (`torch.FloatTensor` of shape `(batch_size, num_total_patches, patch_size_ x patch_size x num_channels)`, *optional*):
Image patches to be used as continuous embeddings. The patches are flattened and then projected to the
hidden size of the model.
image_patches_indices (`torch.LongTensor` of shape `(batch_size, num_total_patches + number_of_newline_tokens + number_of_text_tokens, patch_size_ x patch_size x num_channels )`, *optional*):
Indices indicating at which position the image_patches have to be inserted in input_embeds.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"Fuyu Model with a language modeling head on top for causal language model conditioned on image patches and text.",
FUYU_START_DOCSTRING,
)
class FuyuForCausalLM(FuyuPreTrainedModel):
def __init__(self, config: FuyuConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.language_model = AutoModelForCausalLM.from_config(config.text_config)
self.vision_embed_tokens = nn.Linear(
config.patch_size * config.patch_size * config.num_channels, config.hidden_size
)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def gather_continuous_embeddings(
self,
word_embeddings: torch.Tensor,
continuous_embeddings: List[torch.Tensor],
image_patch_input_indices: torch.Tensor,
) -> torch.Tensor:
"""This function places the continuous_embeddings into the word_embeddings at the locations
indicated by image_patch_input_indices. Different batch elements can have different numbers of continuous
embeddings.
Args:
word_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Tensor of word embeddings.
continuous_embeddings (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`):
Tensor of continuous embeddings. The length of the list is the batch size. Each entry is shape
[num_image_embeddings, hidden], and num_image_embeddings needs to match the number of non-negative
indices in image_patch_input_indices for that batch element.
image_patch_input_indices (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Tensor of indices of the image patches in the input_ids tensor.
"""
if not (word_embeddings.shape[0] == len(continuous_embeddings)):
raise ValueError(
f"Batch sizes must match! Got {len(continuous_embeddings)=} and {word_embeddings.shape[0]=}"
)
output_embeddings = word_embeddings.clone()
for batch_idx in range(word_embeddings.shape[0]):
# First, find the positions of all the non-negative values in image_patch_input_indices, those are the
# positions in word_embeddings that we want to replace with content from continuous_embeddings.
dst_indices = torch.nonzero(image_patch_input_indices[batch_idx] >= 0, as_tuple=True)[0]
# Next look up those indices in image_patch_input_indices to find the indices in continuous_embeddings that we
# want to use to replace the values in word_embeddings.
src_indices = image_patch_input_indices[batch_idx][dst_indices]
# Check if we have more indices than embeddings. Note that we could have fewer indices if images got truncated.
if src_indices.shape[0] > continuous_embeddings[batch_idx].shape[0]:
raise ValueError(
f"Number of continuous embeddings {continuous_embeddings[batch_idx].shape=} does not match "
f"number of continuous token ids {src_indices.shape=} in batch element {batch_idx}."
)
output_embeddings[batch_idx, dst_indices] = continuous_embeddings[batch_idx][src_indices]
return output_embeddings
@add_start_docstrings_to_model_forward(FUYU_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
image_patches: torch.Tensor = None, # [batch_size, num_total_patches, patch_size_ x patch_size x num_channels ]
image_patches_indices: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Examples:
```python
>>> from transformers import FuyuProcessor, FuyuForCausalLM
>>> from PIL import Image
>>> import requests
>>> processor = FuyuProcessor.from_pretrained("adept/fuyu-8b")
>>> model = FuyuForCausalLM.from_pretrained("adept/fuyu-8b")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "Generate a coco-style caption.\n"
>>> inputs = processor(text=text_prompt, images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=7)
>>> generation_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> print(generation_text)
'A bus parked on the side of a road.'
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_is or inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0)
if inputs_embeds is None:
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
if image_patches is not None and past_key_values is None:
patch_embeddings = [
self.vision_embed_tokens(patch.to(self.vision_embed_tokens.weight.dtype)).squeeze(0)
for patch in image_patches
]
inputs_embeds = self.gather_continuous_embeddings(
word_embeddings=inputs_embeds,
continuous_embeddings=patch_embeddings,
image_patch_input_indices=image_patches_indices,
)
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
labels=labels,
use_cache=use_cache,
return_dict=return_dict,
)
return outputs
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
image_patches=None,
image_patches_indices=None,
**kwargs,
):
if past_key_values:
input_ids = input_ids[:, -1:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
if image_patches_indices is not None:
model_inputs["image_patches_indices"] = image_patches_indices
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"image_patches_indices": image_patches_indices if past_key_values is None else None,
"image_patches": image_patches if past_key_values is None else None,
}
)
return model_inputs
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/convert_fuyu_model_weights_to_hf.py
|
# Copyright 2023 The HuggingFace Inc. team. 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.
import argparse
import os
import sys
import warnings
import flatdict
import torch
from transformers import FuyuConfig, FuyuForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
tokenizer_class = LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
)
tokenizer_class = LlamaTokenizer
"""
Sample usage: # TODO fix clone links from persimmon to fuyu
```
git clone https://github.com/adept-ai-labs/adept-inference
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
python src/transformers/models/fuyu/convert_fuyu_weights_to_hf.py --input_dir /path/to/downloaded/fuyu/weights/ --output_dir /output/path
```
Thereafter, models can be loaded via:
```py
from transformers import FuyuForCausalLM, FuyuTokenizer
model = FuyuForCausalLM.from_pretrained("/output/path")
tokenizer = FuyuTokenizer.from_pretrained("/output/path")
```
Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
"""
KEYS_TO_MODIFY_MAPPING = {
"self_attention": "self_attn",
"language_model.encoder": "language_model.model",
"word_embeddings_for_head": "language_model.lm_head",
"language_model.embedding.word_embeddings": "language_model.model.embed_tokens",
"vit_encoder.linear_encoder": "vision_embed_tokens",
}
KEYS_TO_REMOVE = {
"rotary_emb.inv_freq",
"image_patch_projection",
"image_patch_projection.weight",
"image_patch_projection.bias",
}
def rename_state_dict(state_dict):
model_state_dict = {}
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
# if KEYS_TO_REMOVE in key:
if key in KEYS_TO_REMOVE:
continue
model_state_dict[key] = value
return model_state_dict
def convert_fuyu_checkpoint(pytorch_dump_folder_path, ada_lib_path, pt_model_path, safe_serialization=False):
sys.path.insert(0, ada_lib_path)
model_state_dict_base = torch.load(pt_model_path, map_location="cpu")
state_dict = flatdict.FlatDict(model_state_dict_base["model"], ".")
state_dict = rename_state_dict(state_dict)
transformers_config = FuyuConfig()
model = FuyuForCausalLM(transformers_config).to(torch.bfloat16)
model.load_state_dict(state_dict)
model.save_pretrained(pytorch_dump_folder_path, safe_serialization=safe_serialization)
transformers_config.save_pretrained(pytorch_dump_folder_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_dir",
help="Location of Fuyu weights, which contains tokenizer.model and model folders",
)
parser.add_argument(
"--pt_model_path",
help="Location of Fuyu `model_optim_rng.pt`",
)
parser.add_argument(
"--output_dir",
help="Location to write HF model and tokenizer",
)
parser.add_argument(
"--ada_lib_path",
help="Location of original source code from adept to deserialize .pt checkpoint",
)
parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
args = parser.parse_args()
spm_path = os.path.join(args.input_dir, "adept_vocab.model")
convert_fuyu_checkpoint(
pytorch_dump_folder_path=args.output_dir,
pt_model_path=args.pt_model_path,
safe_serialization=args.safe_serialization,
ada_lib_path=args.ada_lib_path,
)
tokenizer = tokenizer_class(spm_path, bos_token="|ENDOFTEXT|", eos_token="|ENDOFTEXT|")
tokenizer.save_pretrained(args.output_dir)
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/fuyu/__init__.py
|
# Copyright 2023 AdeptAI and The HuggingFace Inc. team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_fuyu": ["FUYU_PRETRAINED_CONFIG_ARCHIVE_MAP", "FuyuConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_fuyu"] = ["FuyuImageProcessor"]
_import_structure["processing_fuyu"] = ["FuyuProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_fuyu"] = [
"FuyuForCausalLM",
"FuyuPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fuyu import FUYU_PRETRAINED_CONFIG_ARCHIVE_MAP, FuyuConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_fuyu import FuyuImageProcessor
from .processing_fuyu import FuyuProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fuyu import (
FuyuForCausalLM,
FuyuPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""
Speech processor class for Wav2Vec2
"""
import os
import warnings
from contextlib import contextmanager, nullcontext
from dataclasses import dataclass
from multiprocessing import Pool, get_context, get_start_method
from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import ModelOutput, logging, requires_backends
logger = logging.get_logger(__name__)
if TYPE_CHECKING:
from pyctcdecode import BeamSearchDecoderCTC
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils import PreTrainedTokenizerBase
ListOfDict = List[Dict[str, Union[int, str]]]
@dataclass
class Wav2Vec2DecoderWithLMOutput(ModelOutput):
"""
Output type of [`Wav2Vec2DecoderWithLM`], with transcription.
Args:
text (list of `str` or `str`):
Decoded logits in text from. Usually the speech transcription.
logit_score (list of `float` or `float`):
Total logit score of the beams associated with produced text.
lm_score (list of `float`):
Fused lm_score of the beams associated with produced text.
word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`):
Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets
can be used to compute time stamps for each word.
"""
text: Union[List[List[str]], List[str], str]
logit_score: Union[List[List[float]], List[float], float] = None
lm_score: Union[List[List[float]], List[float], float] = None
word_offsets: Union[List[List[ListOfDict]], List[ListOfDict], ListOfDict] = None
class Wav2Vec2ProcessorWithLM(ProcessorMixin):
r"""
Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor, a Wav2Vec2 CTC tokenizer and a decoder
with language model support into a single processor for language model boosted speech recognition decoding.
Args:
feature_extractor ([`Wav2Vec2FeatureExtractor`]):
An instance of [`Wav2Vec2FeatureExtractor`]. The feature extractor is a required input.
tokenizer ([`Wav2Vec2CTCTokenizer`]):
An instance of [`Wav2Vec2CTCTokenizer`]. The tokenizer is a required input.
decoder (`pyctcdecode.BeamSearchDecoderCTC`):
An instance of [`pyctcdecode.BeamSearchDecoderCTC`]. The decoder is a required input.
"""
feature_extractor_class = "Wav2Vec2FeatureExtractor"
tokenizer_class = "Wav2Vec2CTCTokenizer"
def __init__(
self,
feature_extractor: "FeatureExtractionMixin",
tokenizer: "PreTrainedTokenizerBase",
decoder: "BeamSearchDecoderCTC",
):
from pyctcdecode import BeamSearchDecoderCTC
super().__init__(feature_extractor, tokenizer)
if not isinstance(decoder, BeamSearchDecoderCTC):
raise ValueError(f"`decoder` has to be of type {BeamSearchDecoderCTC.__class__}, but is {type(decoder)}")
# make sure that decoder's alphabet and tokenizer's vocab match in content
missing_decoder_tokens = self.get_missing_alphabet_tokens(decoder, tokenizer)
if len(missing_decoder_tokens) > 0:
raise ValueError(
f"The tokens {missing_decoder_tokens} are defined in the tokenizer's "
"vocabulary, but not in the decoder's alphabet. "
f"Make sure to include {missing_decoder_tokens} in the decoder's alphabet."
)
self.decoder = decoder
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def save_pretrained(self, save_directory):
super().save_pretrained(save_directory)
self.decoder.save_to_dir(save_directory)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a [`Wav2Vec2ProcessorWithLM`] from a pretrained Wav2Vec2 processor.
<Tip>
This class method is simply calling Wav2Vec2FeatureExtractor's
[`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`], Wav2Vec2CTCTokenizer's
[`~tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`], and
[`pyctcdecode.BeamSearchDecoderCTC.load_from_hf_hub`].
Please refer to the docstrings of the methods above for more information.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a feature extractor file saved using the
[`~SequenceFeatureExtractor.save_pretrained`] method, e.g., `./my_model_directory/`.
- a path or url to a saved feature extractor JSON *file*, e.g.,
`./my_model_directory/preprocessor_config.json`.
**kwargs
Additional keyword arguments passed along to both [`SequenceFeatureExtractor`] and
[`PreTrainedTokenizer`]
"""
requires_backends(cls, "pyctcdecode")
from pyctcdecode import BeamSearchDecoderCTC
feature_extractor, tokenizer = super()._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
if os.path.isdir(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path):
decoder = BeamSearchDecoderCTC.load_from_dir(pretrained_model_name_or_path)
else:
# BeamSearchDecoderCTC has no auto class
kwargs.pop("_from_auto", None)
# snapshot_download has no `trust_remote_code` flag
kwargs.pop("trust_remote_code", None)
# make sure that only relevant filenames are downloaded
language_model_filenames = os.path.join(BeamSearchDecoderCTC._LANGUAGE_MODEL_SERIALIZED_DIRECTORY, "*")
alphabet_filename = BeamSearchDecoderCTC._ALPHABET_SERIALIZED_FILENAME
allow_patterns = [language_model_filenames, alphabet_filename]
decoder = BeamSearchDecoderCTC.load_from_hf_hub(
pretrained_model_name_or_path, allow_patterns=allow_patterns, **kwargs
)
# set language model attributes
for attribute in ["alpha", "beta", "unk_score_offset", "score_boundary"]:
value = kwargs.pop(attribute, None)
if value is not None:
cls._set_language_model_attribute(decoder, attribute, value)
# make sure that decoder's alphabet and tokenizer's vocab match in content
missing_decoder_tokens = cls.get_missing_alphabet_tokens(decoder, tokenizer)
if len(missing_decoder_tokens) > 0:
raise ValueError(
f"The tokens {missing_decoder_tokens} are defined in the tokenizer's "
"vocabulary, but not in the decoder's alphabet. "
f"Make sure to include {missing_decoder_tokens} in the decoder's alphabet."
)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=decoder)
@staticmethod
def _set_language_model_attribute(decoder: "BeamSearchDecoderCTC", attribute: str, value: float):
setattr(decoder.model_container[decoder._model_key], attribute, value)
@property
def language_model(self):
return self.decoder.model_container[self.decoder._model_key]
@staticmethod
def get_missing_alphabet_tokens(decoder, tokenizer):
from pyctcdecode.alphabet import BLANK_TOKEN_PTN, UNK_TOKEN, UNK_TOKEN_PTN
# we need to make sure that all of the tokenizer's except the special tokens
# are present in the decoder's alphabet. Retrieve missing alphabet token
# from decoder
tokenizer_vocab_list = list(tokenizer.get_vocab().keys())
# replace special tokens
for i, token in enumerate(tokenizer_vocab_list):
if BLANK_TOKEN_PTN.match(token):
tokenizer_vocab_list[i] = ""
if token == tokenizer.word_delimiter_token:
tokenizer_vocab_list[i] = " "
if UNK_TOKEN_PTN.match(token):
tokenizer_vocab_list[i] = UNK_TOKEN
# are any of the extra tokens no special tokenizer tokens?
missing_tokens = set(tokenizer_vocab_list) - set(decoder._alphabet.labels)
return missing_tokens
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context
[`~Wav2Vec2ProcessorWithLM.as_target_processor`] this method forwards all its arguments to
Wav2Vec2CTCTokenizer's [`~Wav2Vec2CTCTokenizer.__call__`]. Please refer to the docstring of the above two
methods for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.pop("text", None)
if len(args) > 0:
audio = args[0]
args = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process.")
if audio is not None:
inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif audio is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
def pad(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.pad`] and returns its output. If used in the context
[`~Wav2Vec2ProcessorWithLM.as_target_processor`] this method forwards all its arguments to
Wav2Vec2CTCTokenizer's [`~Wav2Vec2CTCTokenizer.pad`]. Please refer to the docstring of the above two methods
for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*args, **kwargs)
input_features = kwargs.pop("input_features", None)
labels = kwargs.pop("labels", None)
if len(args) > 0:
input_features = args[0]
args = args[1:]
if input_features is not None:
input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
if labels is not None:
labels = self.tokenizer.pad(labels, **kwargs)
if labels is None:
return input_features
elif input_features is None:
return labels
else:
input_features["labels"] = labels["input_ids"]
return input_features
def batch_decode(
self,
logits: np.ndarray,
pool: Optional[Pool] = None,
num_processes: Optional[int] = None,
beam_width: Optional[int] = None,
beam_prune_logp: Optional[float] = None,
token_min_logp: Optional[float] = None,
hotwords: Optional[Iterable[str]] = None,
hotword_weight: Optional[float] = None,
alpha: Optional[float] = None,
beta: Optional[float] = None,
unk_score_offset: Optional[float] = None,
lm_score_boundary: Optional[bool] = None,
output_word_offsets: bool = False,
n_best: int = 1,
):
"""
Batch decode output logits to audio transcription with language model support.
<Tip>
This function makes use of Python's multiprocessing. Currently, multiprocessing is available only on Unix
systems (see this [issue](https://github.com/kensho-technologies/pyctcdecode/issues/65)).
If you are decoding multiple batches, consider creating a `Pool` and passing it to `batch_decode`. Otherwise,
`batch_decode` will be very slow since it will create a fresh `Pool` for each call. See usage example below.
</Tip>
Args:
logits (`np.ndarray`):
The logits output vector of the model representing the log probabilities for each token.
pool (`multiprocessing.Pool`, *optional*):
An optional user-managed pool. If not set, one will be automatically created and closed. The pool
should be instantiated *after* `Wav2Vec2ProcessorWithLM`. Otherwise, the LM won't be available to the
pool's sub-processes.
<Tip>
Currently, only pools created with a 'fork' context can be used. If a 'spawn' pool is passed, it will
be ignored and sequential decoding will be used instead.
</Tip>
num_processes (`int`, *optional*):
If `pool` is not set, number of processes on which the function should be parallelized over. Defaults
to the number of available CPUs.
beam_width (`int`, *optional*):
Maximum number of beams at each step in decoding. Defaults to pyctcdecode's DEFAULT_BEAM_WIDTH.
beam_prune_logp (`int`, *optional*):
Beams that are much worse than best beam will be pruned Defaults to pyctcdecode's DEFAULT_PRUNE_LOGP.
token_min_logp (`int`, *optional*):
Tokens below this logp are skipped unless they are argmax of frame Defaults to pyctcdecode's
DEFAULT_MIN_TOKEN_LOGP.
hotwords (`List[str]`, *optional*):
List of words with extra importance, can be OOV for LM
hotword_weight (`int`, *optional*):
Weight factor for hotword importance Defaults to pyctcdecode's DEFAULT_HOTWORD_WEIGHT.
alpha (`float`, *optional*):
Weight for language model during shallow fusion
beta (`float`, *optional*):
Weight for length score adjustment of during scoring
unk_score_offset (`float`, *optional*):
Amount of log score offset for unknown tokens
lm_score_boundary (`bool`, *optional*):
Whether to have kenlm respect boundaries when scoring
output_word_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
n_best (`int`, *optional*, defaults to `1`):
Number of best hypotheses to return. If `n_best` is greater than 1, the returned `text` will be a list
of lists of strings, `logit_score` will be a list of lists of floats, and `lm_score` will be a list of
lists of floats, where the length of the outer list will correspond to the batch size and the length of
the inner list will correspond to the number of returned hypotheses . The value should be >= 1.
<Tip>
Please take a look at the Example of [`~Wav2Vec2ProcessorWithLM.decode`] to better understand how to
make use of `output_word_offsets`. [`~Wav2Vec2ProcessorWithLM.batch_decode`] works the same way with
batched output.
</Tip>
Returns:
[`~models.wav2vec2.Wav2Vec2DecoderWithLMOutput`].
Example:
See [Decoding multiple audios](#decoding-multiple-audios).
"""
from pyctcdecode.constants import (
DEFAULT_BEAM_WIDTH,
DEFAULT_HOTWORD_WEIGHT,
DEFAULT_MIN_TOKEN_LOGP,
DEFAULT_PRUNE_LOGP,
)
# set defaults
beam_width = beam_width if beam_width is not None else DEFAULT_BEAM_WIDTH
beam_prune_logp = beam_prune_logp if beam_prune_logp is not None else DEFAULT_PRUNE_LOGP
token_min_logp = token_min_logp if token_min_logp is not None else DEFAULT_MIN_TOKEN_LOGP
hotword_weight = hotword_weight if hotword_weight is not None else DEFAULT_HOTWORD_WEIGHT
# reset params at every forward call. It's just a `set` method in pyctcdecode
self.decoder.reset_params(
alpha=alpha, beta=beta, unk_score_offset=unk_score_offset, lm_score_boundary=lm_score_boundary
)
# create multiprocessing pool and list numpy arrays
# filter out logits padding
logits_list = [array[(array != -100.0).all(axis=-1)] for array in logits]
# create a pool if necessary while also using it as a context manager to close itself
if pool is None:
# fork is safe to use only on Unix, see "Contexts and start methods" section on
# multiprocessing's docs (https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods)
default_context = get_start_method()
if default_context == "fork":
cm = pool = get_context().Pool(num_processes)
else:
logger.warning(
"Parallel batch decoding is not currently supported in this platform. "
"Falling back to sequential decoding."
)
cm = nullcontext()
else:
# pool is managed by the user, so we don't need to close it
cm = nullcontext()
if num_processes is not None:
logger.warning(
"Parameter `num_process` was passed, but it will be ignored since `pool` was also specified."
)
# pyctcdecode
with cm:
decoded_beams = self.decoder.decode_beams_batch(
pool=pool,
logits_list=logits_list,
beam_width=beam_width,
beam_prune_logp=beam_prune_logp,
token_min_logp=token_min_logp,
hotwords=hotwords,
hotword_weight=hotword_weight,
)
# extract text and scores
batch_texts, logit_scores, lm_scores, word_offsets = [], [], [], []
for d in decoded_beams:
batch_texts.append([beam[0] for beam in d])
logit_scores.append([beam[-2] for beam in d])
lm_scores.append([beam[-1] for beam in d])
# word_offsets.append([{"word": t[0], "start_offset": t[1][0], "end_offset": t[1][1]} for t in d[0][1]])
word_offsets.append(
[
[
{"word": word, "start_offset": start_offset, "end_offset": end_offset}
for word, (start_offset, end_offset) in beam[1]
]
for beam in d
]
)
word_offsets = word_offsets if output_word_offsets else None
if n_best == 1:
return Wav2Vec2DecoderWithLMOutput(
text=[hyps[0] for hyps in batch_texts],
logit_score=[hyps[0] for hyps in logit_scores],
lm_score=[hyps[0] for hyps in lm_scores],
word_offsets=[hyps[0] for hyps in word_offsets] if word_offsets is not None else None,
)
else:
return Wav2Vec2DecoderWithLMOutput(
text=[hyps[:n_best] for hyps in batch_texts],
logit_score=[hyps[:n_best] for hyps in logit_scores],
lm_score=[hyps[:n_best] for hyps in lm_scores],
word_offsets=[hyps[:n_best] for hyps in word_offsets] if word_offsets is not None else None,
)
def decode(
self,
logits: np.ndarray,
beam_width: Optional[int] = None,
beam_prune_logp: Optional[float] = None,
token_min_logp: Optional[float] = None,
hotwords: Optional[Iterable[str]] = None,
hotword_weight: Optional[float] = None,
alpha: Optional[float] = None,
beta: Optional[float] = None,
unk_score_offset: Optional[float] = None,
lm_score_boundary: Optional[bool] = None,
output_word_offsets: bool = False,
n_best: int = 1,
):
"""
Decode output logits to audio transcription with language model support.
Args:
logits (`np.ndarray`):
The logits output vector of the model representing the log probabilities for each token.
beam_width (`int`, *optional*):
Maximum number of beams at each step in decoding. Defaults to pyctcdecode's DEFAULT_BEAM_WIDTH.
beam_prune_logp (`int`, *optional*):
A threshold to prune beams with log-probs less than best_beam_logp + beam_prune_logp. The value should
be <= 0. Defaults to pyctcdecode's DEFAULT_PRUNE_LOGP.
token_min_logp (`int`, *optional*):
Tokens with log-probs below token_min_logp are skipped unless they are have the maximum log-prob for an
utterance. Defaults to pyctcdecode's DEFAULT_MIN_TOKEN_LOGP.
hotwords (`List[str]`, *optional*):
List of words with extra importance which can be missing from the LM's vocabulary, e.g. ["huggingface"]
hotword_weight (`int`, *optional*):
Weight multiplier that boosts hotword scores. Defaults to pyctcdecode's DEFAULT_HOTWORD_WEIGHT.
alpha (`float`, *optional*):
Weight for language model during shallow fusion
beta (`float`, *optional*):
Weight for length score adjustment of during scoring
unk_score_offset (`float`, *optional*):
Amount of log score offset for unknown tokens
lm_score_boundary (`bool`, *optional*):
Whether to have kenlm respect boundaries when scoring
output_word_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
n_best (`int`, *optional*, defaults to `1`):
Number of best hypotheses to return. If `n_best` is greater than 1, the returned `text` will be a list
of strings, `logit_score` will be a list of floats, and `lm_score` will be a list of floats, where the
length of these lists will correspond to the number of returned hypotheses. The value should be >= 1.
<Tip>
Please take a look at the example below to better understand how to make use of `output_word_offsets`.
</Tip>
Returns:
[`~models.wav2vec2.Wav2Vec2DecoderWithLMOutput`].
Example:
```python
>>> # Let's see how to retrieve time steps for a model
>>> from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
>>> from datasets import load_dataset
>>> import datasets
>>> import torch
>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
>>> processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
>>> # load first sample of English common_voice
>>> dataset = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True)
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> dataset_iter = iter(dataset)
>>> sample = next(dataset_iter)
>>> # forward sample through model to get greedily predicted transcription ids
>>> input_values = processor(sample["audio"]["array"], return_tensors="pt").input_values
>>> with torch.no_grad():
... logits = model(input_values).logits[0].cpu().numpy()
>>> # retrieve word stamps (analogous commands for `output_char_offsets`)
>>> outputs = processor.decode(logits, output_word_offsets=True)
>>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate
>>> time_offset = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
>>> word_offsets = [
... {
... "word": d["word"],
... "start_time": round(d["start_offset"] * time_offset, 2),
... "end_time": round(d["end_offset"] * time_offset, 2),
... }
... for d in outputs.word_offsets
... ]
>>> # compare word offsets with audio `en_train_0/common_voice_en_19121553.mp3` online on the dataset viewer:
>>> # https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/en
>>> word_offsets[:4]
[{'word': 'THE', 'start_time': 0.68, 'end_time': 0.78}, {'word': 'TRACK', 'start_time': 0.88, 'end_time': 1.1}, {'word': 'APPEARS', 'start_time': 1.18, 'end_time': 1.66}, {'word': 'ON', 'start_time': 1.86, 'end_time': 1.92}]
```"""
from pyctcdecode.constants import (
DEFAULT_BEAM_WIDTH,
DEFAULT_HOTWORD_WEIGHT,
DEFAULT_MIN_TOKEN_LOGP,
DEFAULT_PRUNE_LOGP,
)
# set defaults
beam_width = beam_width if beam_width is not None else DEFAULT_BEAM_WIDTH
beam_prune_logp = beam_prune_logp if beam_prune_logp is not None else DEFAULT_PRUNE_LOGP
token_min_logp = token_min_logp if token_min_logp is not None else DEFAULT_MIN_TOKEN_LOGP
hotword_weight = hotword_weight if hotword_weight is not None else DEFAULT_HOTWORD_WEIGHT
# reset params at every forward call. It's just a `set` method in pyctcdecode
self.decoder.reset_params(
alpha=alpha, beta=beta, unk_score_offset=unk_score_offset, lm_score_boundary=lm_score_boundary
)
# pyctcdecode
decoded_beams = self.decoder.decode_beams(
logits,
beam_width=beam_width,
beam_prune_logp=beam_prune_logp,
token_min_logp=token_min_logp,
hotwords=hotwords,
hotword_weight=hotword_weight,
)
word_offsets = None
if output_word_offsets:
word_offsets = [
[
{"word": word, "start_offset": start_offset, "end_offset": end_offset}
for word, (start_offset, end_offset) in beam[2]
]
for beam in decoded_beams
]
logit_scores = [beam[-2] for beam in decoded_beams]
lm_scores = [beam[-1] for beam in decoded_beams]
hypotheses = [beam[0] for beam in decoded_beams]
if n_best > len(decoded_beams):
logger.info(
"N-best size is larger than the number of generated hypotheses, all hypotheses will be returned."
)
if n_best == 1:
return Wav2Vec2DecoderWithLMOutput(
text=hypotheses[0],
logit_score=logit_scores[0],
lm_score=lm_scores[0],
word_offsets=word_offsets[0] if word_offsets is not None else None,
)
else:
return Wav2Vec2DecoderWithLMOutput(
text=hypotheses[:n_best],
logit_score=logit_scores[:n_best],
lm_score=lm_scores[:n_best],
word_offsets=word_offsets[:n_best] if word_offsets is not None else None,
)
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the processor for processing the target. Useful for encoding the labels when fine-tuning
Wav2Vec2.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call."
)
self._in_target_context_manager = True
self.current_processor = self.tokenizer
yield
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/wav2vec2_with_lm/__init__.py
|
# Copyright 2021 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_import_structure = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/vit_msn/configuration_vit_msn.py
|
# coding=utf-8
# Copyright 2022 Facebook AI and The HuggingFace Inc. team. 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.
""" ViT MSN model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class ViTMSNConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViTMSNModel`]. It is used to instantiate an ViT
MSN model according to the specified arguments, defining the model architecture. Instantiating a configuration with
the defaults will yield a similar configuration to that of the ViT
[facebook/vit_msn_base](https://huggingface.co/facebook/vit_msn_base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
Example:
```python
>>> from transformers import ViTMSNModel, ViTMSNConfig
>>> # Initializing a ViT MSN vit-msn-base style configuration
>>> configuration = ViTConfig()
>>> # Initializing a model from the vit-msn-base style configuration
>>> model = ViTMSNModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vit_msn"
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-06,
image_size=224,
patch_size=16,
num_channels=3,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.qkv_bias = qkv_bias
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/vit_msn/modeling_vit_msn.py
|
# coding=utf-8
# Copyright 2022 Facebook AI and The HuggingFace Inc. team. 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.
""" PyTorch ViT MSN (masked siamese network) model."""
import collections.abc
import math
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_vit_msn import ViTMSNConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTMSNConfig"
_CHECKPOINT_FOR_DOC = "facebook/vit-msn-small"
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/vit-msn-small",
# See all ViTMSN models at https://huggingface.co/models?filter=vit_msn
]
class ViTMSNEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
self.patch_embeddings = ViTMSNPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.config = config
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
patch_window_height = height // self.config.patch_size
patch_window_width = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
patch_window_height, patch_window_width = patch_window_height + 0.1, patch_window_width + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(
patch_window_height / math.sqrt(num_positions),
patch_window_width / math.sqrt(num_positions),
),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if bool_masked_pos is not None:
seq_length = embeddings.shape[1]
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
# replace the masked visual tokens by mask_tokens
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
# add the [CLS] token to the embedded patch tokens
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.vit.modeling_vit.ViTPatchEmbeddings with ViT->ViTMSN
class ViTMSNPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
f" Expected {self.num_channels} but got {num_channels}."
)
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
return embeddings
# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTMSN
class ViTMSNSelfAttention(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTMSN
class ViTMSNSelfOutput(nn.Module):
"""
The residual connection is defined in ViTMSNLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMSN
class ViTMSNAttention(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.attention = ViTMSNSelfAttention(config)
self.output = ViTMSNSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_outputs = self.attention(hidden_states, head_mask, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTMSN
class ViTMSNIntermediate(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTMSN
class ViTMSNOutput(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMSN
class ViTMSNLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ViTMSNAttention(config)
self.intermediate = ViTMSNIntermediate(config)
self.output = ViTMSNOutput(config)
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in ViTMSN, layernorm is applied before self-attention
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# first residual connection
hidden_states = attention_output + hidden_states
# in ViTMSN, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_output,) + outputs
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMSN
class ViTMSNEncoder(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViTMSNLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class ViTMSNPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTMSNConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
# todo: Resort to https://github.com/facebookresearch/msn/blob/main/src/deit.py#L200-#L211
# when creating pre-training scripts.
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
VIT_MSN_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ViTMSNConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
VIT_MSN_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
for details.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ViTMSN Model outputting raw hidden-states without any specific head on top.",
VIT_MSN_START_DOCSTRING,
)
class ViTMSNModel(ViTMSNPreTrainedModel):
def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = ViTMSNEmbeddings(config, use_mask_token=use_mask_token)
self.encoder = ViTMSNEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> ViTMSNPatchEmbeddings:
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ViTMSNModel
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small")
>>> model = ViTMSNModel.from_pretrained("facebook/vit-msn-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
if not return_dict:
head_outputs = (sequence_output,)
return head_outputs + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Caution: We don't have the weights for the classification head yet. This class
# is here for the users that are interested to fine-tune the base model (ViTMSNModel).
@add_start_docstrings(
"""
ViTMSN Model with an image classification head on top e.g. for ImageNet.
""",
VIT_MSN_START_DOCSTRING,
)
class ViTMSNForImageClassification(ViTMSNPreTrainedModel):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.vit = ViTMSNModel(config)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ViTMSNForImageClassification
>>> import torch
>>> from PIL import Image
>>> import requests
>>> torch.manual_seed(2) # doctest: +IGNORE_RESULT
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small")
>>> model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
Kerry blue terrier
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.vit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output[:, 0, :])
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/vit_msn/convert_msn_to_pytorch.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert ViT MSN checkpoints from the original repository: https://github.com/facebookresearch/msn"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config, base_model=False):
rename_keys = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
rename_keys.append(
(f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")
)
rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))
# projection layer + position embeddings
rename_keys.extend(
[
("module.cls_token", "vit.embeddings.cls_token"),
("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("module.pos_embed", "vit.embeddings.position_embeddings"),
]
)
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("module.norm.weight", "layernorm.weight"),
("module.norm.bias", "layernorm.bias"),
]
)
# if just the base model, we should remove "vit" from all keys that start with "vit"
rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
]
)
return rename_keys
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, config, base_model=False):
for i in range(config.num_hidden_layers):
if base_model:
prefix = ""
else:
prefix = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight")
in_proj_bias = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
: config.hidden_size, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-config.hidden_size :, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def remove_classification_head_(state_dict):
ignore_keys = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(k, None)
def remove_projection_head(state_dict):
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
ignore_keys = [
"module.fc.fc1.weight",
"module.fc.fc1.bias",
"module.fc.bn1.weight",
"module.fc.bn1.bias",
"module.fc.bn1.running_mean",
"module.fc.bn1.running_var",
"module.fc.bn1.num_batches_tracked",
"module.fc.fc2.weight",
"module.fc.fc2.bias",
"module.fc.bn2.weight",
"module.fc.bn2.bias",
"module.fc.bn2.running_mean",
"module.fc.bn2.running_var",
"module.fc.bn2.num_batches_tracked",
"module.fc.fc3.weight",
"module.fc.fc3.bias",
]
for k in ignore_keys:
state_dict.pop(k, None)
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
def convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path):
config = ViTMSNConfig()
config.num_labels = 1000
repo_id = "datasets/huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
if "s16" in checkpoint_url:
config.hidden_size = 384
config.intermediate_size = 1536
config.num_attention_heads = 6
elif "l16" in checkpoint_url:
config.hidden_size = 1024
config.intermediate_size = 4096
config.num_hidden_layers = 24
config.num_attention_heads = 16
config.hidden_dropout_prob = 0.1
elif "b4" in checkpoint_url:
config.patch_size = 4
elif "l7" in checkpoint_url:
config.patch_size = 7
config.hidden_size = 1024
config.intermediate_size = 4096
config.num_hidden_layers = 24
config.num_attention_heads = 16
config.hidden_dropout_prob = 0.1
model = ViTMSNModel(config)
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["target_encoder"]
image_processor = ViTImageProcessor(size=config.image_size)
remove_projection_head(state_dict)
rename_keys = create_rename_keys(config, base_model=True)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, base_model=True)
model.load_state_dict(state_dict)
model.eval()
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = ViTImageProcessor(
size=config.image_size, image_mean=IMAGENET_DEFAULT_MEAN, image_std=IMAGENET_DEFAULT_STD
)
inputs = image_processor(images=image, return_tensors="pt")
# forward pass
torch.manual_seed(2)
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
expected_slice = torch.tensor([[-1.0915, -1.4876, -1.1809]])
elif "b16" in checkpoint_url:
expected_slice = torch.tensor([[14.2889, -18.9045, 11.7281]])
elif "l16" in checkpoint_url:
expected_slice = torch.tensor([[41.5028, -22.8681, 45.6475]])
elif "b4" in checkpoint_url:
expected_slice = torch.tensor([[-4.3868, 5.2932, -0.4137]])
else:
expected_slice = torch.tensor([[-0.1792, -0.6465, 2.4263]])
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3], expected_slice, atol=1e-4)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
args = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/vit_msn/__init__.py
|
# Copyright 2020 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vit_msn"] = [
"VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMSNModel",
"ViTMSNForImageClassification",
"ViTMSNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py
|
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. 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.
""" PyTorch KOSMOS-2 model."""
import math
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPooling,
CausalLMOutputWithCrossAttentions,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_kosmos2 import Kosmos2Config, Kosmos2TextConfig, Kosmos2VisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = Kosmos2Config
KOSMOS2_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/kosmos-2-patch14-224",
# See all KOSMOS-2 models at https://huggingface.co/models?filter=kosmos-2
]
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
KOSMOS2_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Kosmos2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
KOSMOS2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
KOSMOS2_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
1]`:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
KOSMOS2_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
1]`:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@dataclass
class Kosmos2ModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
the weighted average in the self-attention heads.
vision_model_output(`BaseModelOutputWithPooling`, *optional*):
The output of the [`Kosmos2VisionModel`].
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_embeds: Optional[torch.FloatTensor] = None
projection_attentions: Optional[Tuple[torch.FloatTensor]] = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
@dataclass
class Kosmos2ForConditionalGenerationModelOutput(ModelOutput):
"""
Model output class for `Kosmos2ForConditionalGeneration`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
the weighted average in the self-attention heads.
vision_model_output(`BaseModelOutputWithPooling`, *optional*):
The output of the [`Kosmos2VisionModel`].
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_embeds: Optional[torch.FloatTensor] = None
projection_attentions: Optional[Tuple[torch.FloatTensor]] = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Kosmos2
class Kosmos2VisionEmbeddings(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->Kosmos2Vision
class Kosmos2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Kosmos2Vision
class Kosmos2VisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Kosmos2Vision
class Kosmos2VisionEncoderLayer(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Kosmos2VisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Kosmos2VisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Kosmos2Vision
class Kosmos2VisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Kosmos2VisionEncoderLayer`].
Args:
config: Kosmos2VisionConfig
"""
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Kosmos2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Similar to `transformers.models.clip.modeling_clip.CLIPVisionTransformer` but without docstring for `forward`
class Kosmos2VisionTransformer(nn.Module):
# Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.__init__ with CLIPVision->Kosmos2Vision,CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2Vision
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = Kosmos2VisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = Kosmos2VisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Similar to `transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding` but allowing to pass `position_ids`
class Kosmos2TextSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.__init__
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.make_weights
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
if hasattr(self, "weights"):
# in forward put the weights on the correct dtype and device of the param
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
self.register_buffer("weights", emb_weights, persistent=False)
@staticmethod
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.get_embedding
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb.to(torch.get_default_dtype())
@torch.no_grad()
def forward(
self,
input_ids: torch.Tensor = None,
inputs_embeds: torch.Tensor = None,
past_key_values_length: int = 0,
position_ids: torch.Tensor = None,
):
if input_ids is not None:
bsz, seq_len = input_ids.size()
if position_ids is None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(
input_ids, self.padding_idx, past_key_values_length
).to(input_ids.device)
else:
bsz, seq_len = inputs_embeds.size()[:-1]
if position_ids is None:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
# expand embeddings if needed
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.create_position_ids_from_inputs_embeds
def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
class KosmosTextAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Similar to transformers.models.bart.modeling_bart.BartAttention.__init__ except an additional `inner_attn_ln`.
def __init__(
self,
config,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
add_inner_attn_layernorm: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
# End opy
self.inner_attn_ln = None
if add_inner_attn_layernorm:
self.inner_attn_ln = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def _shape(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, self.head_dim)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = encoder_hidden_states is not None
batch_size, seq_length = hidden_states.shape[:2]
# use encoder_hidden_states if cross attention
current_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
# checking that the `sequence_length` of the `past_key_value` is the same as the he provided
# `encoder_hidden_states` to support prefix tuning
if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
else:
key_states = self._shape(self.k_proj(current_states))
value_states = self._shape(self.v_proj(current_states))
if past_key_value is not None and not is_cross_attention:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
query_states = self._shape(self.q_proj(hidden_states) * self.scaling)
attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2))
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
src_len = key_states.size(2)
if attention_mask is not None:
if attention_mask.size() != (batch_size, 1, seq_length, src_len):
raise ValueError(
f"Attention mask should be of size {(batch_size, 1, seq_length, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
# attn_output = torch.bmm(attn_probs, value_states) ?
context_states = torch.matmul(attn_weights, value_states)
# attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) ?
context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
if self.inner_attn_ln is not None:
context_states = self.inner_attn_ln(context_states)
attn_output = self.out_proj(context_states)
return attn_output, attn_weights, past_key_value
class Kosmos2TextFFN(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(config.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, config.embed_dim)
self.ffn_layernorm = nn.LayerNorm(config.ffn_dim, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.ffn_layernorm(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
return hidden_states
class Kosmos2TextBlock(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.embed_dim = config.embed_dim
self.self_attn = KosmosTextAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
add_inner_attn_layernorm=True,
)
self.dropout = config.dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
if config.add_cross_attention:
self.encoder_attn = KosmosTextAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
add_inner_attn_layernorm=False,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.ffn = Kosmos2TextFFN(config)
self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
hidden_states = self.self_attn_layer_norm(hidden_states)
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
if not hasattr(self, "encoder_attn"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
# FFN
hidden_states = self.ffn(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class Kosmos2TextTransformer(nn.Module):
"""
Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2TextBlock`].
Args:
config: Kosmos2TextConfig
"""
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.config = config
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.embed_scale = math.sqrt(config.embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.embed_dim, padding_idx=config.pad_token_id)
self.embed_positions = Kosmos2TextSinusoidalPositionalEmbedding(
num_positions=config.max_position_embeddings,
embedding_dim=config.embed_dim,
padding_idx=config.pad_token_id,
)
self.layers = nn.ModuleList([Kosmos2TextBlock(config) for _ in range(config.layers)])
self.layer_norm = nn.LayerNorm(config.embed_dim, config.layer_norm_eps)
self.gradient_checkpointing = False
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward_embedding(
self,
input_ids,
inputs_embeds: torch.Tensor = None,
image_embeds: torch.Tensor = None,
img_input_mask: torch.Tensor = None,
past_key_values_length: int = 0,
position_ids: torch.Tensor = None,
):
# The argument `inputs_embeds` should be the one without being multiplied by `self.embed_scale`.
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if image_embeds is not None:
inputs_embeds[img_input_mask.to(dtype=torch.bool)] = image_embeds.to(inputs_embeds.device).view(
-1, image_embeds.size(-1)
)
inputs_embeds = inputs_embeds * self.embed_scale
# embed positions
positions = self.embed_positions(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
position_ids=position_ids,
)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
return hidden_states
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.shape
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
# We don't need img info. when `past_key_values_length` > 0
if past_key_values_length > 0:
image_embeds = None
image_embeds_position_mask = None
hidden_states = self.forward_embedding(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
image_embeds=image_embeds,
img_input_mask=image_embeds_position_mask,
past_key_values_length=past_key_values_length,
position_ids=position_ids,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, hidden_states, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_value_states = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
present_key_value_states += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add final layer norm
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_self_attns,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class Kosmos2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Kosmos2Config
supports_gradient_checkpointing = True
_no_split_modules = ["Kosmos2VisionEncoderLayer", "Kosmos2TextBlock"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(self, Kosmos2VisionModel):
factor = self.config.initializer_factor
elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
factor = self.config.vision_config.initializer_factor
if isinstance(self, (Kosmos2TextModel, Kosmos2TextForCausalLM)):
std = self.config.init_std
elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
std = self.config.text_config.init_std
if isinstance(module, Kosmos2VisionEmbeddings):
nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
elif isinstance(module, Kosmos2VisionAttention):
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (module.embed_dim**-0.5) * factor
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
if module.q_proj.bias is not None:
module.q_proj.bias.data.zero_()
if module.k_proj.bias is not None:
module.k_proj.bias.data.zero_()
if module.v_proj.bias is not None:
module.v_proj.bias.data.zero_()
if module.out_proj.bias is not None:
module.out_proj.bias.data.zero_()
elif isinstance(module, Kosmos2VisionMLP):
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
nn.init.normal_(module.fc1.weight, std=fc_std)
nn.init.normal_(module.fc2.weight, std=in_proj_std)
if module.fc1.bias is not None:
module.fc1.bias.data.zero_()
if module.fc2.bias is not None:
module.fc2.bias.data.zero_()
elif isinstance(module, Kosmos2VisionEncoderLayer):
module.layer_norm1.bias.data.zero_()
module.layer_norm1.weight.data.fill_(1.0)
module.layer_norm2.bias.data.zero_()
module.layer_norm2.weight.data.fill_(1.0)
elif isinstance(module, Kosmos2VisionTransformer):
module.pre_layrnorm.bias.data.zero_()
module.pre_layrnorm.weight.data.fill_(1.0)
module.post_layernorm.bias.data.zero_()
module.post_layernorm.weight.data.fill_(1.0)
elif isinstance(module, KosmosTextAttention):
nn.init.normal_(module.q_proj.weight, std=std)
nn.init.normal_(module.k_proj.weight, std=std)
nn.init.normal_(module.v_proj.weight, std=std)
nn.init.normal_(module.out_proj.weight, std=std)
if module.q_proj.bias is not None:
module.q_proj.bias.data.zero_()
if module.k_proj.bias is not None:
module.k_proj.bias.data.zero_()
if module.v_proj.bias is not None:
module.v_proj.bias.data.zero_()
if module.out_proj.bias is not None:
module.out_proj.bias.data.zero_()
elif isinstance(module, Kosmos2TextFFN):
nn.init.normal_(module.fc1.weight, std=std)
nn.init.normal_(module.fc2.weight, std=std)
if module.fc1.bias is not None:
module.fc1.bias.data.zero_()
if module.fc2.bias is not None:
module.fc2.bias.data.zero_()
elif isinstance(module, Kosmos2TextForCausalLM):
nn.init.normal_(module.lm_head.weight, std=std)
if module.lm_head.bias is not None:
module.lm_head.bias.data.zero_()
elif isinstance(module, Kosmos2ImageToTextProjection):
nn.init.normal_(module.dense.weight, std=std)
if module.dense.bias is not None:
module.dense.bias.data.zero_()
elif isinstance(module, Kosmos2TextTransformer):
module.embed_tokens.weight.data.normal_(mean=0.0, std=std)
if module.embed_tokens.padding_idx is not None:
module.embed_tokens.weight.data[module.embed_tokens.padding_idx].zero_()
class Kosmos2VisionModel(Kosmos2PreTrainedModel):
config_class = Kosmos2VisionConfig
main_input_name = "pixel_values"
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
def __init__(self, config: Kosmos2VisionConfig):
super().__init__(config)
self.model = Kosmos2VisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.get_input_embeddings with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
def get_input_embeddings(self) -> nn.Module:
return self.model.embeddings.patch_embedding
@add_start_docstrings_to_model_forward(KOSMOS2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Kosmos2VisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
return self.model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class Kosmos2TextModel(Kosmos2PreTrainedModel):
config_class = Kosmos2TextConfig
def __init__(self, config: Kosmos2TextConfig):
super().__init__(config)
self.model = Kosmos2TextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=Kosmos2TextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
Returns:
"""
return self.model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@add_start_docstrings(
"""
The text model from KOSMOS-2 with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel):
config_class = Kosmos2TextConfig
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: Kosmos2TextConfig):
super().__init__(config)
self.model = Kosmos2TextTransformer(config)
self.lm_head = nn.Linear(in_features=config.embed_dim, out_features=config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self) -> nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=Kosmos2TextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
image_embeds=None,
image_embeds_position_mask=None,
past_key_values=None,
attention_mask=None,
use_cache=None,
**model_kwargs,
):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
position_ids = None
# cut input_ids if past_key_values is used
if past_key_values is not None:
position_ids = create_position_ids_from_input_ids(
input_ids,
padding_idx=self.config.pad_token_id,
past_key_values_length=0,
)[:, -1:]
input_ids = input_ids[:, -1:]
# the image info. is already encoded into the past keys/values
image_embeds = None
image_embeds_position_mask = None
elif image_embeds_position_mask is not None:
# appending `False` to `image_embeds_position_mask` (because `input_ids` grows during generation)
batch_size, seq_len = input_ids.size()
mask_len = image_embeds_position_mask.size()[-1]
image_embeds_position_mask = torch.cat(
(
image_embeds_position_mask,
torch.zeros(size=(batch_size, seq_len - mask_len), dtype=torch.bool, device=input_ids.device),
),
dim=1,
)
return {
"input_ids": input_ids,
"image_embeds": image_embeds,
"image_embeds_position_mask": image_embeds_position_mask,
"past_key_values": past_key_values,
"attention_mask": attention_mask,
"position_ids": position_ids,
"use_cache": use_cache,
}
@staticmethod
# Copied from transformers.models.umt5.modeling_umt5.UMT5ForConditionalGeneration._reorder_cache
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
class Kosmos2ImageToTextProjection(nn.Module):
"""The layer that transforms the image model's output to part of the text model's input (namely, image features)"""
def __init__(self, config: Kosmos2Config):
super().__init__()
self.dense = nn.Linear(config.vision_config.hidden_size, config.text_config.embed_dim)
self.latent_query = nn.Parameter(torch.randn(config.latent_query_num, config.text_config.embed_dim))
self.x_attn = KosmosTextAttention(
config.text_config,
config.text_config.embed_dim,
config.text_config.attention_heads,
dropout=config.text_config.attention_dropout,
is_decoder=False,
add_inner_attn_layernorm=False,
)
def forward(self, features):
hidden_states = self.dense(features)
# shape = [batch, latent_query_num, h_dim]
latent_query = self.latent_query.unsqueeze(0).expand(hidden_states.size(0), -1, -1)
key_value_states = torch.cat([hidden_states, latent_query], dim=1)
hidden_states, attn_weights, _ = self.x_attn(
hidden_states=latent_query,
encoder_hidden_states=key_value_states,
past_key_value=None,
attention_mask=None,
output_attentions=None,
)
return hidden_states, attn_weights
@add_start_docstrings(
"""
KOSMOS-2 Model for generating text and image features. The model consists of a vision encoder and a language model.
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2Model(Kosmos2PreTrainedModel):
config_class = Kosmos2Config
main_input_name = "pixel_values"
def __init__(self, config: Kosmos2Config):
super().__init__(config)
self.text_model = Kosmos2TextModel(config.text_config)
self.vision_model = Kosmos2VisionModel(config.vision_config)
self.image_to_text_projection = Kosmos2ImageToTextProjection(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.model.embed_tokens
def set_input_embeddings(self, value):
self.text_model.model.embed_tokens = value
@add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Kosmos2ModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
image_embeds: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Kosmos2ModelOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Kosmos2Model
>>> model = Kosmos2Model.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = (
... "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863>"
... "</object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911>"
... "</object>"
... )
>>> inputs = processor(text=text, images=image, return_tensors="pt", add_eos_token=True)
>>> last_hidden_state = model(
... pixel_values=inputs["pixel_values"],
... input_ids=inputs["input_ids"],
... attention_mask=inputs["attention_mask"],
... image_embeds_position_mask=inputs["image_embeds_position_mask"],
... ).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 91, 2048]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_model_output = None
projection_attentions = None
if image_embeds is None:
if pixel_values is None:
raise ValueError("You have to specify either `pixel_values` or `image_embeds`.")
vision_model_output = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
outputs = outputs + (image_embeds, projection_attentions, vision_model_output)
return tuple(output for output in outputs if output is not None)
return Kosmos2ModelOutput(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_embeds=image_embeds,
projection_attentions=projection_attentions,
vision_model_output=vision_model_output,
)
@add_start_docstrings(
"""
KOSMOS-2 Model for generating text and bounding boxes given an image. The model consists of a vision encoder and a
language model.
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel):
config_class = Kosmos2Config
main_input_name = "pixel_values"
_tied_weights_keys = ["text_model.lm_head.weight"]
def __init__(self, config: Kosmos2Config):
super().__init__(config)
self.text_model = Kosmos2TextForCausalLM(config.text_config)
self.vision_model = Kosmos2VisionModel(config.vision_config)
self.image_to_text_projection = Kosmos2ImageToTextProjection(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.model.embed_tokens
def set_input_embeddings(self, value):
self.text_model.model.embed_tokens = value
def get_output_embeddings(self) -> nn.Module:
return self.text_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.text_model.set_output_embeddings(new_embeddings)
@add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Kosmos2ForConditionalGenerationModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
image_embeds: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Kosmos2ForConditionalGenerationModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Kosmos2ForConditionalGeneration
>>> model = Kosmos2ForConditionalGeneration.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "<grounding> An image of"
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
>>> generated_ids = model.generate(
... pixel_values=inputs["pixel_values"],
... input_ids=inputs["input_ids"],
... attention_mask=inputs["attention_mask"],
... image_embeds=None,
... image_embeds_position_mask=inputs["image_embeds_position_mask"],
... use_cache=True,
... max_new_tokens=64,
... )
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
>>> processed_text
'<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.'
>>> caption, entities = processor.post_process_generation(generated_text)
>>> caption
'An image of a snowman warming himself by a fire.'
>>> entities
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_model_output = None
projection_attentions = None
if image_embeds is None:
if pixel_values is None:
raise ValueError("You have to specify either `pixel_values` or `image_embeds`.")
vision_model_output = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
lm_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
outputs = lm_outputs + (image_embeds, projection_attentions, vision_model_output)
return tuple(output for output in outputs if output is not None)
return Kosmos2ForConditionalGenerationModelOutput(
loss=lm_outputs.loss,
logits=lm_outputs.logits,
past_key_values=lm_outputs.past_key_values,
hidden_states=lm_outputs.hidden_states,
attentions=lm_outputs.attentions,
image_embeds=image_embeds,
projection_attentions=projection_attentions,
vision_model_output=vision_model_output,
)
def generate(
self,
pixel_values: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
**kwargs,
):
# in order to allow `inputs` argument (as in `GenerationMixin`)
inputs = kwargs.pop("inputs", None)
if pixel_values is not None and inputs is not None:
raise ValueError(
f"`inputs`: {inputs} were passed alongside `pixel_values` which is not allowed."
f"Make sure to either pass `inputs` or pixel_values=..."
)
if pixel_values is None and inputs is not None:
pixel_values = inputs
if image_embeds is None:
vision_model_output = self.vision_model(pixel_values)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
output = self.text_model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
**kwargs,
)
return output
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/kosmos2/processing_kosmos2.py
|
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. 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.
"""Processor class for KOSMOS-2."""
import copy
import math
import re
from typing import List, Optional, Tuple, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput, is_batched
from ...processing_utils import ProcessorMixin
from ...tokenization_utils import AddedToken
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
from ...utils import TensorType
BboxInput = Union[
List[Tuple[int, int]],
List[Tuple[float, float, float, float]],
List[List[Tuple[int, int]]],
List[List[Tuple[float, float, float]]],
]
class Kosmos2Processor(ProcessorMixin):
r"""
Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single
processor.
[`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of
[`XLMRobertaTokenizerFast`]. See the docstring of [`~Kosmos2Processor.__call__`] and [`~Kosmos2Processor.decode`]
for more information.
Args:
image_processor (`CLIPImageProcessor`):
An instance of [`CLIPImageProcessor`]. The image processor is a required input.
tokenizer (`XLMRobertaTokenizerFast`):
An instance of ['XLMRobertaTokenizerFast`]. The tokenizer is a required input.
num_patch_index_tokens (`int`, *optional*, defaults to 1024):
The number of tokens that represent patch indices.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "CLIPImageProcessor"
tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024):
tokenizer.return_token_type_ids = False
self.eod_token = "</doc>"
self.boi_token = "<image>"
self.eoi_token = "</image>"
self.eoc_token = "</chunk>"
self.eol_token = "</line>"
self.bop_token = "<phrase>"
self.eop_token = "</phrase>"
self.boo_token = "<object>"
self.eoo_token = "</object>"
self.dom_token = "</delimiter_of_multi_objects/>"
self.grd_token = "<grounding>"
self.tag_tokens = [
self.eod_token,
self.boi_token,
self.eoi_token,
self.eoc_token,
self.eol_token,
self.bop_token,
self.eop_token,
self.boo_token,
self.eoo_token,
self.dom_token,
self.grd_token,
]
self.num_patch_index_tokens = num_patch_index_tokens
patch_index_tokens = [f"<patch_index_{str(x).zfill(4)}>" for x in range(self.num_patch_index_tokens)]
tokens_to_add = []
for token in self.tag_tokens + patch_index_tokens:
tokens_to_add.append(AddedToken(token, lstrip=True, rstrip=False, normalized=False))
tokenizer.add_tokens(tokens_to_add)
super().__init__(image_processor, tokenizer)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, List[TextInput]] = None,
bboxes: BboxInput = None,
num_image_tokens: Optional[int] = 64,
first_image_token_id: Optional[int] = None,
add_special_tokens: bool = True,
add_eos_token: bool = False,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and
[`XLMRobertaTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
The rest of this documentation shows the arguments specific to `Kosmos2Processor`.
Args:
bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, defaults to 64):
The number of (consecutive) places that are used to mark the placeholders to store image information.
This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using.
first_image_token_id (`int`, *optional*):
The token id that will be used for the first place of the subsequence that is reserved to store image
information. If unset, will default to `self.tokenizer.unk_token_id + 1`.
add_eos_token (`bool`, defaults to `False`):
Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`.
"""
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
encoding = BatchFeature()
if images is not None:
image_encoding = self.image_processor(images, return_tensors=return_tensors)
encoding.update(image_encoding)
if text is not None:
text = self.preprocess_examples(text, images, bboxes, num_image_tokens=num_image_tokens)
if add_special_tokens and not add_eos_token:
if isinstance(text, str):
text = f"{self.tokenizer.bos_token}{text}"
elif isinstance(text, list):
text = [f"{self.tokenizer.bos_token}{s}" for s in text]
text_encoding = self.tokenizer(
text=text,
add_special_tokens=(add_special_tokens and add_eos_token),
padding=padding and images is None,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of if images is None else pad_to_multiple_of,
return_attention_mask=return_attention_mask,
verbose=verbose,
return_tensors=return_tensors if images is None else None,
**kwargs,
)
encoding.update(text_encoding)
if text is not None and images is not None:
# Use the id of the first token after <unk>
if first_image_token_id is None:
first_image_token_id = self.tokenizer.unk_token_id + 1
# To see if we need one more `0` (for `<s>`) at the beginning of `image_embeds_position_mask`.
with_bos = add_special_tokens
# The first (actual) `<image>` token is always at the 1st or 2nd place (after `<s>` if any). Here we look
# for the second `<image>` token (which indicate the first image token).
start_index = int(with_bos) + 1
# Add `image_embeds_position_mask`: the leading and trailing `0` are for `boi` and `eoi` tokens. The `1` indicates
# the places of image tokens.
image_token_ids = list(range(first_image_token_id, first_image_token_id + num_image_tokens))
base_image_embeds_position_mask = [0] + [1] * num_image_tokens + [0]
# loop over `encoding["input_ids"]`
input_ids = []
image_embeds_position_mask = []
all_input_ids = encoding["input_ids"]
# not batched -> (changed to) batch of size 1
if isinstance(text, str):
all_input_ids = [all_input_ids]
encoding["attention_mask"] = [encoding["attention_mask"]]
for text_ids in all_input_ids:
# change the ids for the fake `<image>` tokens in `input_ids`
text_ids = text_ids[:start_index] + image_token_ids + text_ids[start_index + num_image_tokens :]
input_ids.append(text_ids)
mask = copy.copy(base_image_embeds_position_mask)
if with_bos:
# for `<s>`
mask = [0] + mask
# trailing part (which are not related to the image)
mask += [0] * (len(text_ids) - len(mask))
image_embeds_position_mask.append(mask)
if isinstance(text, list):
sorted_length = sorted(
[(idx, len(x)) for idx, x in enumerate(text_encoding.input_ids)], key=lambda x: x[-1]
)
_, min_len_not_padded = sorted_length[0]
idx, _ = sorted_length[-1]
text_encoding = self.tokenizer(
text=[text[idx]],
add_special_tokens=(add_special_tokens and add_eos_token),
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
return_tensors=None,
**kwargs,
)
max_len_padded = len(text_encoding.input_ids[0])
if min_len_not_padded != max_len_padded:
if self.tokenizer.padding_side == "right":
input_ids = [x + [self.tokenizer.pad_token_id] * (max_len_padded - len(x)) for x in input_ids]
image_embeds_position_mask = [
x + [0] * (max_len_padded - len(x)) for x in image_embeds_position_mask
]
encoding["attention_mask"] = [
x + [0] * (max_len_padded - len(x)) for x in encoding["attention_mask"]
]
elif self.tokenizer.padding_side == "left":
input_ids = [[self.tokenizer.pad_token_id] * (max_len_padded - len(x)) + x for x in input_ids]
image_embeds_position_mask = [
[0] * (max_len_padded - len(x)) + x for x in image_embeds_position_mask
]
encoding["attention_mask"] = [
[0] * (max_len_padded - len(x)) + x for x in encoding["attention_mask"]
]
# un-batch if necessary
if isinstance(text, str) and return_tensors is None:
input_ids = input_ids[0]
encoding["attention_mask"] = encoding["attention_mask"][0]
image_embeds_position_mask = image_embeds_position_mask[0]
# update (with the target tensor type if specified)
encoding.update(
BatchEncoding(
data={
"input_ids": input_ids,
"attention_mask": encoding["attention_mask"],
"image_embeds_position_mask": image_embeds_position_mask,
},
tensor_type=return_tensors,
)
)
return encoding
def _check_bboxes_for_single_text(self, bboxes):
"""
Check `bboxes` for a single text example. It could be
- `None`: no bounding box associated to a text.
- A list with each element being the bounding boxes associated to one `<phrase> ... </phrase>` pair found
in a text. This could be:
- `None`: no bounding box associated to a `<phrase> ... </phrase>` pair.
- A tuple of 2 integers: A single bounding box specified by patch indices.
- A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates.
- A list containing the above 2 tuple types: Multiple bounding boxes for a
`<phrase> ... </phrase>` pair.
"""
if bboxes is None:
return
elif not isinstance(bboxes, list):
raise ValueError("`bboxes` (for a single text example) should be `None` or a list.")
# `bbox` is the bounding boxes for a single <phrase> </phrase> pair
for bbox in bboxes:
if bbox is None:
continue
elif not isinstance(bbox, list):
bbox = [bbox]
for element in bbox:
if not isinstance(element, tuple) or not (
(len(element) == 2 and all(isinstance(x, int) for x in element))
or (len(element) == 4 and all(isinstance(x, float) for x in element))
):
raise ValueError(
"Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing "
"2 integers or 4 float point numbers, or a list containing such tuples. Also "
"make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in "
"batches or both for a single example."
)
def _preprocess_single_example(self, text, image, bboxes, img_info_tokens):
text = text.strip()
if image is not None:
# Add `<image> ... (fake) image tokens ... </image>`
text = f"{img_info_tokens} {text}"
# Add `<object> <patch_idx_xxxx> <patch_idx_yyy> </object>` after `<phrase> phrase text </phrase>`
text = self._insert_patch_index_tokens(text, bboxes)
return text
def preprocess_examples(
self,
texts: Union[TextInput, List[TextInput]],
images: ImageInput = None,
bboxes: BboxInput = None,
num_image_tokens: Optional[int] = 64,
) -> Union[str, List[str]]:
"""Add image and bounding box information to `texts` as image and patch index tokens.
Args:
texts (`Union[TextInput, List[TextInput]]`): The texts to be processed.
images (`ImageInput`, *optional*): The images associated to `texts`.
bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, *optional*, defaults to 64):
The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num`
attribute in `Kosmos2Config`.
Returns:
`Union[TextInput, List[TextInput]]`: The processed texts with image and patch index tokens.
"""
# These are fake `<image>` tokens enclosed between (the actual) `<image>` token and `</image>`.
img_tokens = [self.boi_token] * num_image_tokens
img_info_tokens = " ".join([self.boi_token] + img_tokens + [self.eoi_token])
# make batch to simplify processing logic
batched = True
if isinstance(texts, str):
batched = False
texts = [texts]
if images is None:
images = [None] * len(texts)
elif not is_batched(images):
images = [images]
if len(texts) != len(images):
raise ValueError(
f"The number of examples in `texts` and `images` should be the same. Got {len(texts)} v.s. {len(images)} instead."
)
if not batched:
self._check_bboxes_for_single_text(bboxes)
bboxes = [bboxes]
elif bboxes is not None:
if not isinstance(bboxes, list):
raise ValueError("`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.")
for x in bboxes:
self._check_bboxes_for_single_text(x)
else:
bboxes = [None] * len(texts)
if len(bboxes) != len(texts):
raise ValueError(
f"The number of examples in `texts` and `bboxes` should be the same. Got {len(texts)} v.s. {len(bboxes)} instead."
)
result = [
self._preprocess_single_example(text, image, bbox, img_info_tokens)
for text, image, bbox in zip(texts, images, bboxes)
]
# un-batch if necessary
if not batched:
result = result[0]
return result
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_generation(self, text, cleanup_and_extract=True):
caption = text.split(self.eoi_token)[-1]
if cleanup_and_extract:
return clean_text_and_extract_entities_with_bboxes(caption)
return caption
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def _insert_patch_index_tokens(self, text: str, bboxes: Union[List[Tuple[int]], List[Tuple[float]]]) -> str:
if bboxes is None or len(bboxes) == 0:
return text
matched_phrases = list(re.finditer(r"<phrase>.+?</phrase>", string=text))
if len(matched_phrases) != len(bboxes):
raise ValueError(
f"The number of elements in `bboxes` should be the same as the number of `<phrase> ... </phrase>` pairs in `text`. Got {len(matched_phrases)} v.s. {len(bboxes)} instead."
)
# insert object's patch index tokens
# the found `<phrase> ... </phrase>` pairs.
curr_pos = 0
buffer = []
for matched, bbox in zip(matched_phrases, bboxes):
_, end = matched.span()
buffer.append(text[curr_pos:end])
curr_pos = end
# A phrase without bbox
if bbox is None:
continue
# A phrase with a single bbox
if isinstance(bbox, tuple):
bbox = [bbox]
patch_index_strings = []
# A phrase could have multiple bboxes
if not all(box is not None for box in bbox):
raise ValueError(
"The multiple bounding boxes for a single phrase should not contain any `None` value."
)
for box in bbox:
patch_index_1, patch_index_2 = self._convert_bbox_to_patch_index_tokens(box)
patch_index_strings.append(f"{patch_index_1} {patch_index_2}")
# `bbox` being an empty list
if len(patch_index_strings) == 0:
continue
position_str = " </delimiter_of_multi_objects/> ".join(patch_index_strings)
buffer.append(f"<object> {position_str} </object>")
# remaining
if curr_pos < len(text):
buffer.append(text[curr_pos:])
text = "".join(buffer)
return text
def _convert_bbox_to_patch_index_tokens(
self, bbox: Union[Tuple[int, int], Tuple[float, float, float, float]]
) -> Tuple[str, str]:
# already computed patch indices
if len(bbox) == 2:
idx_1, idx_2 = bbox
# bbox specified with (normalized) coordinates
else:
# use `self.tokenizer` to get `num_patches_per_side`
num_patches_per_side = int(math.sqrt(self.num_patch_index_tokens))
idx_1, idx_2 = coordinate_to_patch_index(bbox, num_patches_per_side)
token_1 = f"<patch_index_{str(idx_1).zfill(4)}>"
token_2 = f"<patch_index_{str(idx_2).zfill(4)}>"
return token_1, token_2
def coordinate_to_patch_index(bbox: Tuple[float, float, float, float], num_patches_per_side: int) -> Tuple[int, int]:
"""Convert a bounding box to a pair of patch indices.
Args:
bbox (`Tuple[float, float, float, float]`):
The 4 coordinates of the bounding box, with the format being (x1, y1, x2, y2) specifying the upper-left and
lower-right corners of the box. It should have x2 > x1 and y2 > y1.
num_patches_per_side (`int`): the number of patches along each side.
Returns:
`Tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch.
"""
(x1, y1, x2, y2) = bbox
if not (x2 > x1 and y2 > y1):
raise ValueError("The coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.")
ul_x = math.floor(x1 * num_patches_per_side)
ul_y = math.floor(y1 * num_patches_per_side)
lr_x = math.ceil(x2 * num_patches_per_side - 1)
lr_y = math.ceil(y2 * num_patches_per_side - 1)
ul_idx = ul_y * num_patches_per_side + ul_x
lr_idx = lr_y * num_patches_per_side + lr_x
return ul_idx, lr_idx
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L35C1-L75C38
# (with format modifications)
def patch_index_to_coordinate(ul_idx: int, lr_idx: int, num_patches_per_side: int):
"""
Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a
bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
Args:
ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box.
lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box.
num_patches_per_side (`int`): the number of patches along each side.
Returns:
`Tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
"""
# Compute the size of each cell in the grid
cell_size = 1.0 / num_patches_per_side
# Compute the x and y indices of the upper-left and lower-right corners of the bounding box
ul_x = ul_idx % num_patches_per_side
ul_y = ul_idx // num_patches_per_side
lr_x = lr_idx % num_patches_per_side
lr_y = lr_idx // num_patches_per_side
# Compute the normalized coordinates of the bounding box
if ul_idx == lr_idx:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
elif ul_x == lr_x or ul_y == lr_y:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
else:
x1 = ul_x * cell_size + cell_size / 2
y1 = ul_y * cell_size + cell_size / 2
x2 = lr_x * cell_size + cell_size / 2
y2 = lr_y * cell_size + cell_size / 2
return x1, y1, x2, y2
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L4-L33
# (with format modifications)
def extract_entities_with_patch_indices(text):
"""Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices.
This functioin is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further
processing happens, including converting to normalized coordinates and whitespace character cleaning up.
Examples:
```python
>>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
>>> entities = extract_entities_with_patch_indices(text)
>>> entities
[(' a snowman', (31, 41), [(44, 863)]), (' a fire', (130, 137), [(5, 911)])]
```"""
# The regular expression pattern for matching the required formats
pattern = r"(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>"
# Find all matches in the given string
matches = re.finditer(pattern, text)
# Initialize an empty list to store the valid patch_index combinations
entities_with_patch_indices = []
for match in matches:
# span of a `phrase` that is between <phrase> and </phrase>
span = match.span(2)
phrase_tag, phrase, match_content = match.groups()
if not phrase_tag:
phrase = None
# We take the starting position of `<object>`
span = (match.span(0)[0], match.span(0)[0])
# Split the match_content by the delimiter to get individual patch_index pairs
patch_index_pairs = match_content.split("</delimiter_of_multi_objects/>")
entity_bboxes = []
for pair in patch_index_pairs:
# Extract the xxxx and yyyy values from the patch_index pair
x = re.search(r"<patch_index_(\d+)>", pair)
y = re.search(r"<patch_index_(\d+)>", pair[1:])
if x and y:
if phrase:
entity_bboxes.append((int(x.group(1)), int(y.group(1))))
else:
entity_bboxes.append((int(x.group(1)), int(y.group(1))))
if phrase:
entities_with_patch_indices.append((phrase, span, entity_bboxes))
else:
for bbox in entity_bboxes:
# fake entity name
entity = f"<patch_index_{bbox[0]}><patch_index_{bbox[1]}>"
entities_with_patch_indices.append((entity, span, [bbox]))
return entities_with_patch_indices
def adjust_entity_positions(entity, text):
"""Adjust the positions of the entities in `text` to be relative to the text with special fields removed."""
entity_name, (start, end) = entity
# computed the length of strings with special fields (tag tokens, patch index tokens, etc.) removed
adjusted_start = len(re.sub("<.*?>", "", text[:start]))
adjusted_end = len(re.sub("<.*?>", "", text[:end]))
adjusted_entity = (entity_name, (adjusted_start, adjusted_end))
return adjusted_entity
def _cleanup_spaces(text, entities):
"""Remove the spaces around the text and the entities in it."""
new_text = text.strip()
leading_spaces = len(text) - len(text.lstrip())
new_entities = []
for entity_name, (start, end), bboxes in entities:
entity_name_leading_spaces = len(entity_name) - len(entity_name.lstrip())
entity_name_trailing_spaces = len(entity_name) - len(entity_name.rstrip())
start = start - leading_spaces + entity_name_leading_spaces
end = end - leading_spaces - entity_name_trailing_spaces
entity_name = entity_name.strip()
new_entities.append((entity_name, (start, end), bboxes))
return new_text, new_entities
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L77-L87
# (with format modifications)
def clean_text_and_extract_entities_with_bboxes(text, num_patches_per_side=32):
"""Remove the tag tokens from `text`, extract entities in it with some cleaning up of white characters.
Examples:
```python
>>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
>>> clean_text, entities = clean_text_and_extract_entities_with_bboxes(text)
>>> clean_text
'An image of a snowman warming himself by a fire.'
>>> entities
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
```"""
# remove special fields (tag tokens, patch index tokens, etc.)
processed_text = re.sub("<.*?>", "", text)
entities_with_patch_indices = extract_entities_with_patch_indices(text)
entities = []
for item in entities_with_patch_indices:
entity, bboxes = item[0:2], item[2]
adjusted_entity = adjust_entity_positions(entity, text)
bboxes_in_coords = [patch_index_to_coordinate(bbox[0], bbox[1], num_patches_per_side) for bbox in bboxes]
entities.append(adjusted_entity + (bboxes_in_coords,))
return _cleanup_spaces(processed_text, entities)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/kosmos2/convert_kosmos2_original_pytorch_checkpoint_to_pytorch.py
|
import argparse
from fairseq.checkpoint_utils import load_checkpoint_to_cpu
from transformers import Kosmos2Config, Kosmos2ForConditionalGeneration
KEYS_TO_MODIFY_MAPPING = {
"gpt_model.decoder.output_projection": "text_model.lm_head",
"gpt_model.decoder": "text_model.model",
"img_connector": "image_to_text_projection",
"img_model.visual.class_embedding": "vision_model.model.embeddings.class_embedding",
"img_model.visual.positional_embedding": "vision_model.model.embeddings.position_embedding.weight",
"img_model.visual.conv1": "vision_model.model.embeddings.patch_embedding",
"img_model.visual": "vision_model.model",
"ln_pre": "pre_layrnorm",
"ln_post": "post_layernorm",
"transformer.resblocks": "encoder.layers",
"ts_attn": "self_attn",
"ln_1": "layer_norm1",
"ln_2": "layer_norm2",
"c_fc": "fc1",
"c_proj": "fc2",
}
KEYS_TO_IGNORE = [
# this buffer in the original code is only used to send weights to the desired device
"gpt_model.decoder.embed_positions._float_tensor",
# this weight is never used in the forward in the original KOSMOS-2)
"gpt_model.decoder.self_attn_sope.scale",
]
def rename_key(key):
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
return key
def convert_kosmos2_checkpoint_to_pytorch(checkpoint_path, pytorch_dump_folder_path):
state = load_checkpoint_to_cpu(checkpoint_path)
state_dict = state["model"]
state_dict_keys = list(state_dict.keys())
config = Kosmos2Config()
# This is necessary to match the results given by the original demo
config.text_config.no_repeat_ngram_size = 3
model = Kosmos2ForConditionalGeneration(config)
# convert (by renaming keys)
converted_state_dict = {}
for key in state_dict_keys:
if key in KEYS_TO_IGNORE:
continue
renamed_key = rename_key(key)
converted_state_dict[renamed_key] = state_dict[key]
# check weight loading
model.load_state_dict(converted_state_dict, strict=True)
# save the result
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--kosmos2_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_kosmos2_checkpoint_to_pytorch(args.kosmos2_checkpoint_path, args.pytorch_dump_folder_path)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/kosmos2/__init__.py
|
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_kosmos2": ["KOSMOS2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Kosmos2Config"],
"processing_kosmos2": ["Kosmos2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_kosmos2"] = [
"KOSMOS2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Kosmos2ForConditionalGeneration",
"Kosmos2Model",
"Kosmos2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_kosmos2 import KOSMOS2_PRETRAINED_CONFIG_ARCHIVE_MAP, Kosmos2Config
from .processing_kosmos2 import Kosmos2Processor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_kosmos2 import (
KOSMOS2_PRETRAINED_MODEL_ARCHIVE_LIST,
Kosmos2ForConditionalGeneration,
Kosmos2Model,
Kosmos2PreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/kosmos2/configuration_kosmos2.py
|
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. 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.
""" KOSMOS-2 model configuration"""
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
KOSMOS2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/kosmos-2-patch14-224": (
"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/config.json"
),
# See all KOSMOS-2 models at https://huggingface.co/models?filter=kosmos-2
}
class Kosmos2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a
KOSMOS-2 text decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the text decoder of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65037):
Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Kosmos2Model`].
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
embed_dim (`int`, *optional*, defaults to 2048):
Dimensionality of the layers and the pooler layer.
layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
ffn_dim (`int`, *optional*, defaults to 8192):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_embedding (`bool`, *optional*, defaults to `True`):
Scale embeddings by diving by sqrt(embed_dim).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
```"""
model_type = "kosmos_2_text_model"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "attention_heads",
"hidden_size": "embed_dim",
"num_hidden_layers": "layers",
}
def __init__(
self,
vocab_size=65037,
max_position_embeddings=2048,
embed_dim=2048,
layers=24,
ffn_dim=8192,
attention_heads=32,
activation_function="gelu",
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
layerdrop=0.0,
layer_norm_eps=1e-5,
init_std=0.02,
scale_embedding=True,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.embed_dim = embed_dim
self.layers = layers
self.ffn_dim = ffn_dim
self.attention_heads = attention_heads
self.activation_function = activation_function
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.init_std = init_std
self.scale_embedding = scale_embedding
self.use_cache = use_cache
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the text config dict if we are loading from Kosmos2Config
if config_dict.get("model_type") == "kosmos-2":
config_dict = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class Kosmos2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
KOSMOS-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
```"""
model_type = "kosmos_2_vision_model"
def __init__(
self,
hidden_size=1024,
intermediate_size=4096,
num_hidden_layers=24,
num_attention_heads=16,
num_channels=3,
image_size=224,
patch_size=14,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the vision config dict if we are loading from Kosmos2Config
if config_dict.get("model_type") == "kosmos-2":
config_dict = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class Kosmos2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a
KOSMOS-2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
latent_query_num (`int`, *optional*, defaults to 64):
The number of latent query tokens that represent the image features used in the text decoder component.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import Kosmos2Config, Kosmos2Model
>>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
>>> configuration = Kosmos2Config()
>>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
>>> model = Kosmos2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "kosmos-2"
is_composition = True
def __init__(
self,
text_config=None,
vision_config=None,
latent_query_num=64,
**kwargs,
):
super().__init__(**kwargs)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.")
self.text_config = Kosmos2TextConfig(**text_config)
self.vision_config = Kosmos2VisionConfig(**vision_config)
self.latent_query_num = latent_query_num
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/mra/convert_mra_pytorch_to_pytorch.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
"""Convert MRA checkpoints from the original repository. URL: https://github.com/mlpen/mra-attention"""
import argparse
import torch
from transformers import MraConfig, MraForMaskedLM
def rename_key(orig_key):
if "model" in orig_key:
orig_key = orig_key.replace("model.", "")
if "norm1" in orig_key:
orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
if "norm2" in orig_key:
orig_key = orig_key.replace("norm2", "output.LayerNorm")
if "norm" in orig_key:
orig_key = orig_key.replace("norm", "LayerNorm")
if "transformer" in orig_key:
layer_num = orig_key.split(".")[0].split("_")[-1]
orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
if "mha.attn" in orig_key:
orig_key = orig_key.replace("mha.attn", "attention.self")
if "mha" in orig_key:
orig_key = orig_key.replace("mha", "attention")
if "W_q" in orig_key:
orig_key = orig_key.replace("W_q", "self.query")
if "W_k" in orig_key:
orig_key = orig_key.replace("W_k", "self.key")
if "W_v" in orig_key:
orig_key = orig_key.replace("W_v", "self.value")
if "ff.0" in orig_key:
orig_key = orig_key.replace("ff.0", "intermediate.dense")
if "ff.2" in orig_key:
orig_key = orig_key.replace("ff.2", "output.dense")
if "ff" in orig_key:
orig_key = orig_key.replace("ff", "output.dense")
if "mlm_class" in orig_key:
orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
if "mlm" in orig_key:
orig_key = orig_key.replace("mlm", "cls.predictions.transform")
if "backbone.backbone.encoders" in orig_key:
orig_key = orig_key.replace("backbone.backbone.encoders", "encoder.layer")
if "cls" not in orig_key:
orig_key = "mra." + orig_key
return orig_key
def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if ("pooler" in key) or ("sen_class" in key):
continue
else:
orig_state_dict[rename_key(key)] = val
orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
orig_state_dict["mra.embeddings.position_ids"] = torch.arange(max_position_embeddings).expand((1, -1)) + 2
return orig_state_dict
def convert_mra_checkpoint(checkpoint_path, mra_config_file, pytorch_dump_path):
orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
config = MraConfig.from_json_file(mra_config_file)
model = MraForMaskedLM(config)
new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict)
print(model.load_state_dict(new_state_dict))
model.eval()
model.save_pretrained(pytorch_dump_path)
print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for Mra model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/mra/configuration_mra.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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.
""" MRA model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class MraConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MraModel`]. It is used to instantiate an MRA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Mra
[uw-madison/mra-base-512-4](https://huggingface.co/uw-madison/mra-base-512-4) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the Mra model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MraModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 1):
The vocabulary size of the `token_type_ids` passed when calling [`MraModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`.
block_per_row (`int`, *optional*, defaults to 4):
Used to set the budget for the high resolution scale.
approx_mode (`str`, *optional*, defaults to `"full"`):
Controls whether both low and high resolution approximations are used. Set to `"full"` for both low and
high resolution and `"sparse"` for only low resolution.
initial_prior_first_n_blocks (`int`, *optional*, defaults to 0):
The initial number of blocks for which high resolution is used.
initial_prior_diagonal_n_blocks (`int`, *optional*, defaults to 0):
The number of diagonal blocks for which high resolution is used.
Example:
```python
>>> from transformers import MraConfig, MraModel
>>> # Initializing a Mra uw-madison/mra-base-512-4 style configuration
>>> configuration = MraConfig()
>>> # Initializing a model (with random weights) from the uw-madison/mra-base-512-4 style configuration
>>> model = MraModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mra"
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=1,
initializer_range=0.02,
layer_norm_eps=1e-5,
position_embedding_type="absolute",
block_per_row=4,
approx_mode="full",
initial_prior_first_n_blocks=0,
initial_prior_diagonal_n_blocks=0,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.block_per_row = block_per_row
self.approx_mode = approx_mode
self.initial_prior_first_n_blocks = initial_prior_first_n_blocks
self.initial_prior_diagonal_n_blocks = initial_prior_diagonal_n_blocks
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/mra/__init__.py
|
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2023 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mra"] = [
"MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MraForMaskedLM",
"MraForMultipleChoice",
"MraForQuestionAnswering",
"MraForSequenceClassification",
"MraForTokenClassification",
"MraLayer",
"MraModel",
"MraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/mra/modeling_mra.py
|
# coding=utf-8
# Copyright 2023 University of Wisconsin-Madison and The HuggingFace Inc. team. 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.
""" PyTorch MRA model."""
import math
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.utils.cpp_extension import load
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_ninja_available,
is_torch_cuda_available,
logging,
)
from .configuration_mra import MraConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "uw-madison/mra-base-512-4"
_CONFIG_FOR_DOC = "MraConfig"
_TOKENIZER_FOR_DOC = "AutoTokenizer"
MRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"uw-madison/mra-base-512-4",
# See all Mra models at https://huggingface.co/models?filter=mra
]
def load_cuda_kernels():
global cuda_kernel
src_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "mra"
def append_root(files):
return [src_folder / file for file in files]
src_files = append_root(["cuda_kernel.cu", "cuda_launch.cu", "torch_extension.cpp"])
cuda_kernel = load("cuda_kernel", src_files, verbose=True)
import cuda_kernel
cuda_kernel = None
if is_torch_cuda_available() and is_ninja_available():
logger.info("Loading custom CUDA kernels...")
try:
load_cuda_kernels()
except Exception as e:
logger.warning(
"Failed to load CUDA kernels. Mra requires custom CUDA kernels. Please verify that compatible versions of"
f" PyTorch and CUDA Toolkit are installed: {e}"
)
else:
pass
def sparse_max(sparse_qk_prod, indices, query_num_block, key_num_block):
"""
Computes maximum values for softmax stability.
"""
if len(sparse_qk_prod.size()) != 4:
raise ValueError("sparse_qk_prod must be a 4-dimensional tensor.")
if len(indices.size()) != 2:
raise ValueError("indices must be a 2-dimensional tensor.")
if sparse_qk_prod.size(2) != 32:
raise ValueError("The size of the second dimension of sparse_qk_prod must be 32.")
if sparse_qk_prod.size(3) != 32:
raise ValueError("The size of the third dimension of sparse_qk_prod must be 32.")
index_vals = sparse_qk_prod.max(dim=-2).values.transpose(-1, -2)
index_vals = index_vals.contiguous()
indices = indices.int()
indices = indices.contiguous()
max_vals, max_vals_scatter = cuda_kernel.index_max(index_vals, indices, query_num_block, key_num_block)
max_vals_scatter = max_vals_scatter.transpose(-1, -2)[:, :, None, :]
return max_vals, max_vals_scatter
def sparse_mask(mask, indices, block_size=32):
"""
Converts attention mask to a sparse mask for high resolution logits.
"""
if len(mask.size()) != 2:
raise ValueError("mask must be a 2-dimensional tensor.")
if len(indices.size()) != 2:
raise ValueError("indices must be a 2-dimensional tensor.")
if mask.shape[0] != indices.shape[0]:
raise ValueError("mask and indices must have the same size in the zero-th dimension.")
batch_size, seq_len = mask.shape
num_block = seq_len // block_size
batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
mask = mask.reshape(batch_size, num_block, block_size)
mask = mask[batch_idx[:, None], (indices % num_block).long(), :]
return mask
def mm_to_sparse(dense_query, dense_key, indices, block_size=32):
"""
Performs Sampled Dense Matrix Multiplication.
"""
batch_size, query_size, dim = dense_query.size()
_, key_size, dim = dense_key.size()
if query_size % block_size != 0:
raise ValueError("query_size (size of first dimension of dense_query) must be divisible by block_size.")
if key_size % block_size != 0:
raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
dense_query = dense_query.reshape(batch_size, query_size // block_size, block_size, dim).transpose(-1, -2)
dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
if len(dense_query.size()) != 4:
raise ValueError("dense_query must be a 4-dimensional tensor.")
if len(dense_key.size()) != 4:
raise ValueError("dense_key must be a 4-dimensional tensor.")
if len(indices.size()) != 2:
raise ValueError("indices must be a 2-dimensional tensor.")
if dense_query.size(3) != 32:
raise ValueError("The third dimension of dense_query must be 32.")
if dense_key.size(3) != 32:
raise ValueError("The third dimension of dense_key must be 32.")
dense_query = dense_query.contiguous()
dense_key = dense_key.contiguous()
indices = indices.int()
indices = indices.contiguous()
return cuda_kernel.mm_to_sparse(dense_query, dense_key, indices.int())
def sparse_dense_mm(sparse_query, indices, dense_key, query_num_block, block_size=32):
"""
Performs matrix multiplication of a sparse matrix with a dense matrix.
"""
batch_size, key_size, dim = dense_key.size()
if key_size % block_size != 0:
raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
if sparse_query.size(2) != block_size:
raise ValueError("The size of the second dimension of sparse_query must be equal to the block_size.")
if sparse_query.size(3) != block_size:
raise ValueError("The size of the third dimension of sparse_query must be equal to the block_size.")
dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
if len(sparse_query.size()) != 4:
raise ValueError("sparse_query must be a 4-dimensional tensor.")
if len(dense_key.size()) != 4:
raise ValueError("dense_key must be a 4-dimensional tensor.")
if len(indices.size()) != 2:
raise ValueError("indices must be a 2-dimensional tensor.")
if dense_key.size(3) != 32:
raise ValueError("The size of the third dimension of dense_key must be 32.")
sparse_query = sparse_query.contiguous()
indices = indices.int()
indices = indices.contiguous()
dense_key = dense_key.contiguous()
dense_qk_prod = cuda_kernel.sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
dense_qk_prod = dense_qk_prod.transpose(-1, -2).reshape(batch_size, query_num_block * block_size, dim)
return dense_qk_prod
def transpose_indices(indices, dim_1_block, dim_2_block):
return ((indices % dim_2_block) * dim_1_block + torch.div(indices, dim_2_block, rounding_mode="floor")).long()
class MraSampledDenseMatMul(torch.autograd.Function):
@staticmethod
def forward(ctx, dense_query, dense_key, indices, block_size):
sparse_qk_prod = mm_to_sparse(dense_query, dense_key, indices, block_size)
ctx.save_for_backward(dense_query, dense_key, indices)
ctx.block_size = block_size
return sparse_qk_prod
@staticmethod
def backward(ctx, grad):
dense_query, dense_key, indices = ctx.saved_tensors
block_size = ctx.block_size
query_num_block = dense_query.size(1) // block_size
key_num_block = dense_key.size(1) // block_size
indices_T = transpose_indices(indices, query_num_block, key_num_block)
grad_key = sparse_dense_mm(grad.transpose(-1, -2), indices_T, dense_query, key_num_block)
grad_query = sparse_dense_mm(grad, indices, dense_key, query_num_block)
return grad_query, grad_key, None, None
@staticmethod
def operator_call(dense_query, dense_key, indices, block_size=32):
return MraSampledDenseMatMul.apply(dense_query, dense_key, indices, block_size)
class MraSparseDenseMatMul(torch.autograd.Function):
@staticmethod
def forward(ctx, sparse_query, indices, dense_key, query_num_block):
sparse_qk_prod = sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
ctx.save_for_backward(sparse_query, indices, dense_key)
ctx.query_num_block = query_num_block
return sparse_qk_prod
@staticmethod
def backward(ctx, grad):
sparse_query, indices, dense_key = ctx.saved_tensors
query_num_block = ctx.query_num_block
key_num_block = dense_key.size(1) // sparse_query.size(-1)
indices_T = transpose_indices(indices, query_num_block, key_num_block)
grad_key = sparse_dense_mm(sparse_query.transpose(-1, -2), indices_T, grad, key_num_block)
grad_query = mm_to_sparse(grad, dense_key, indices)
return grad_query, None, grad_key, None
@staticmethod
def operator_call(sparse_query, indices, dense_key, query_num_block):
return MraSparseDenseMatMul.apply(sparse_query, indices, dense_key, query_num_block)
class MraReduceSum:
@staticmethod
def operator_call(sparse_query, indices, query_num_block, key_num_block):
batch_size, num_block, block_size, _ = sparse_query.size()
if len(sparse_query.size()) != 4:
raise ValueError("sparse_query must be a 4-dimensional tensor.")
if len(indices.size()) != 2:
raise ValueError("indices must be a 2-dimensional tensor.")
_, _, block_size, _ = sparse_query.size()
batch_size, num_block = indices.size()
sparse_query = sparse_query.sum(dim=2).reshape(batch_size * num_block, block_size)
batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
global_idxes = (
torch.div(indices, key_num_block, rounding_mode="floor").long() + batch_idx[:, None] * query_num_block
).reshape(batch_size * num_block)
temp = torch.zeros(
(batch_size * query_num_block, block_size), dtype=sparse_query.dtype, device=sparse_query.device
)
output = temp.index_add(0, global_idxes, sparse_query).reshape(batch_size, query_num_block, block_size)
output = output.reshape(batch_size, query_num_block * block_size)
return output
def get_low_resolution_logit(query, key, block_size, mask=None, value=None):
"""
Compute low resolution approximation.
"""
batch_size, seq_len, head_dim = query.size()
num_block_per_row = seq_len // block_size
value_hat = None
if mask is not None:
token_count = mask.reshape(batch_size, num_block_per_row, block_size).sum(dim=-1)
query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
token_count[:, :, None] + 1e-6
)
key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
token_count[:, :, None] + 1e-6
)
if value is not None:
value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
token_count[:, :, None] + 1e-6
)
else:
token_count = block_size * torch.ones(batch_size, num_block_per_row, dtype=torch.float, device=query.device)
query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
if value is not None:
value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
low_resolution_logit = torch.matmul(query_hat, key_hat.transpose(-1, -2)) / math.sqrt(head_dim)
low_resolution_logit_row_max = low_resolution_logit.max(dim=-1, keepdims=True).values
if mask is not None:
low_resolution_logit = (
low_resolution_logit - 1e4 * ((token_count[:, None, :] * token_count[:, :, None]) < 0.5).float()
)
return low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat
def get_block_idxes(
low_resolution_logit, num_blocks, approx_mode, initial_prior_first_n_blocks, initial_prior_diagonal_n_blocks
):
"""
Compute the indices of the subset of components to be used in the approximation.
"""
batch_size, total_blocks_per_row, _ = low_resolution_logit.shape
if initial_prior_diagonal_n_blocks > 0:
offset = initial_prior_diagonal_n_blocks // 2
temp_mask = torch.ones(total_blocks_per_row, total_blocks_per_row, device=low_resolution_logit.device)
diagonal_mask = torch.tril(torch.triu(temp_mask, diagonal=-offset), diagonal=offset)
low_resolution_logit = low_resolution_logit + diagonal_mask[None, :, :] * 5e3
if initial_prior_first_n_blocks > 0:
low_resolution_logit[:, :initial_prior_first_n_blocks, :] = (
low_resolution_logit[:, :initial_prior_first_n_blocks, :] + 5e3
)
low_resolution_logit[:, :, :initial_prior_first_n_blocks] = (
low_resolution_logit[:, :, :initial_prior_first_n_blocks] + 5e3
)
top_k_vals = torch.topk(
low_resolution_logit.reshape(batch_size, -1), num_blocks, dim=-1, largest=True, sorted=False
)
indices = top_k_vals.indices
if approx_mode == "full":
threshold = top_k_vals.values.min(dim=-1).values
high_resolution_mask = (low_resolution_logit >= threshold[:, None, None]).float()
elif approx_mode == "sparse":
high_resolution_mask = None
else:
raise ValueError(f"{approx_mode} is not a valid approx_model value.")
return indices, high_resolution_mask
def mra2_attention(
query,
key,
value,
mask,
num_blocks,
approx_mode,
block_size=32,
initial_prior_first_n_blocks=0,
initial_prior_diagonal_n_blocks=0,
):
"""
Use Mra to approximate self-attention.
"""
if cuda_kernel is None:
return torch.zeros_like(query).requires_grad_()
batch_size, num_head, seq_len, head_dim = query.size()
meta_batch = batch_size * num_head
if seq_len % block_size != 0:
raise ValueError("sequence length must be divisible by the block_size.")
num_block_per_row = seq_len // block_size
query = query.reshape(meta_batch, seq_len, head_dim)
key = key.reshape(meta_batch, seq_len, head_dim)
value = value.reshape(meta_batch, seq_len, head_dim)
if mask is not None:
query = query * mask[:, :, None]
key = key * mask[:, :, None]
value = value * mask[:, :, None]
if approx_mode == "full":
low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat = get_low_resolution_logit(
query, key, block_size, mask, value
)
elif approx_mode == "sparse":
with torch.no_grad():
low_resolution_logit, token_count, low_resolution_logit_row_max, _ = get_low_resolution_logit(
query, key, block_size, mask
)
else:
raise Exception('approx_mode must be "full" or "sparse"')
with torch.no_grad():
low_resolution_logit_normalized = low_resolution_logit - low_resolution_logit_row_max
indices, high_resolution_mask = get_block_idxes(
low_resolution_logit_normalized,
num_blocks,
approx_mode,
initial_prior_first_n_blocks,
initial_prior_diagonal_n_blocks,
)
high_resolution_logit = MraSampledDenseMatMul.operator_call(
query, key, indices, block_size=block_size
) / math.sqrt(head_dim)
max_vals, max_vals_scatter = sparse_max(high_resolution_logit, indices, num_block_per_row, num_block_per_row)
high_resolution_logit = high_resolution_logit - max_vals_scatter
if mask is not None:
high_resolution_logit = high_resolution_logit - 1e4 * (1 - sparse_mask(mask, indices)[:, :, :, None])
high_resolution_attn = torch.exp(high_resolution_logit)
high_resolution_attn_out = MraSparseDenseMatMul.operator_call(
high_resolution_attn, indices, value, num_block_per_row
)
high_resolution_normalizer = MraReduceSum.operator_call(
high_resolution_attn, indices, num_block_per_row, num_block_per_row
)
if approx_mode == "full":
low_resolution_attn = (
torch.exp(low_resolution_logit - low_resolution_logit_row_max - 1e4 * high_resolution_mask)
* token_count[:, None, :]
)
low_resolution_attn_out = (
torch.matmul(low_resolution_attn, value_hat)[:, :, None, :]
.repeat(1, 1, block_size, 1)
.reshape(meta_batch, seq_len, head_dim)
)
low_resolution_normalizer = (
low_resolution_attn.sum(dim=-1)[:, :, None].repeat(1, 1, block_size).reshape(meta_batch, seq_len)
)
log_correction = low_resolution_logit_row_max.repeat(1, 1, block_size).reshape(meta_batch, seq_len) - max_vals
if mask is not None:
log_correction = log_correction * mask
low_resolution_corr = torch.exp(log_correction * (log_correction <= 0).float())
low_resolution_attn_out = low_resolution_attn_out * low_resolution_corr[:, :, None]
low_resolution_normalizer = low_resolution_normalizer * low_resolution_corr
high_resolution_corr = torch.exp(-log_correction * (log_correction > 0).float())
high_resolution_attn_out = high_resolution_attn_out * high_resolution_corr[:, :, None]
high_resolution_normalizer = high_resolution_normalizer * high_resolution_corr
context_layer = (high_resolution_attn_out + low_resolution_attn_out) / (
high_resolution_normalizer[:, :, None] + low_resolution_normalizer[:, :, None] + 1e-6
)
elif approx_mode == "sparse":
context_layer = high_resolution_attn_out / (high_resolution_normalizer[:, :, None] + 1e-6)
else:
raise Exception('config.approx_mode must be "full" or "sparse"')
if mask is not None:
context_layer = context_layer * mask[:, :, None]
context_layer = context_layer.reshape(batch_size, num_head, seq_len, head_dim)
return context_layer
class MraEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class MraSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = (
position_embedding_type if position_embedding_type is not None else config.position_embedding_type
)
self.num_block = (config.max_position_embeddings // 32) * config.block_per_row
self.num_block = min(self.num_block, int((config.max_position_embeddings // 32) ** 2))
self.approx_mode = config.approx_mode
self.initial_prior_first_n_blocks = config.initial_prior_first_n_blocks
self.initial_prior_diagonal_n_blocks = config.initial_prior_diagonal_n_blocks
def transpose_for_scores(self, layer):
new_layer_shape = layer.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
layer = layer.view(*new_layer_shape)
return layer.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask=None):
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
batch_size, num_heads, seq_len, head_dim = query_layer.size()
# revert changes made by get_extended_attention_mask
attention_mask = 1.0 + attention_mask / 10000.0
attention_mask = (
attention_mask.squeeze().repeat(1, num_heads, 1).reshape(batch_size * num_heads, seq_len).int()
)
# The CUDA kernels are most efficient with inputs whose size is a multiple of a GPU's warp size (32). Inputs
# smaller than this are padded with zeros.
gpu_warp_size = 32
if head_dim < gpu_warp_size:
pad_size = batch_size, num_heads, seq_len, gpu_warp_size - head_dim
query_layer = torch.cat([query_layer, torch.zeros(pad_size, device=query_layer.device)], dim=-1)
key_layer = torch.cat([key_layer, torch.zeros(pad_size, device=key_layer.device)], dim=-1)
value_layer = torch.cat([value_layer, torch.zeros(pad_size, device=value_layer.device)], dim=-1)
context_layer = mra2_attention(
query_layer.float(),
key_layer.float(),
value_layer.float(),
attention_mask.float(),
self.num_block,
approx_mode=self.approx_mode,
initial_prior_first_n_blocks=self.initial_prior_first_n_blocks,
initial_prior_diagonal_n_blocks=self.initial_prior_diagonal_n_blocks,
)
if head_dim < gpu_warp_size:
context_layer = context_layer[:, :, :, :head_dim]
context_layer = context_layer.reshape(batch_size, num_heads, seq_len, head_dim)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class MraSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MraAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = MraSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = MraSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, hidden_states, attention_mask=None):
self_outputs = self.self(hidden_states, attention_mask)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class MraIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class MraOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MraLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = MraAttention(config)
self.add_cross_attention = config.add_cross_attention
self.intermediate = MraIntermediate(config)
self.output = MraOutput(config)
def forward(self, hidden_states, attention_mask=None):
self_attention_outputs = self.attention(hidden_states, attention_mask)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class MraEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MraLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
)
else:
layer_outputs = layer_module(hidden_states, attention_mask)
hidden_states = layer_outputs[0]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutputWithCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
)
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform
class MraPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Mra
class MraLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MraPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Mra
class MraOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MraLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.yoso.modeling_yoso.YosoPreTrainedModel with Yoso->Mra,yoso->mra
class MraPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MraConfig
base_model_prefix = "mra"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
MRA_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MraConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MRA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MRA Model transformer outputting raw hidden-states without any specific head on top.",
MRA_START_DOCSTRING,
)
class MraModel(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = MraEmbeddings(config)
self.encoder = MraEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithCrossAttentions]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutputWithCrossAttentions(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""MRA Model with a `language modeling` head on top.""", MRA_START_DOCSTRING)
class MraForMaskedLM(MraPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.mra = MraModel(config)
self.cls = MraOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.yoso.modeling_yoso.YosoClassificationHead with Yoso->Mra
class MraClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.config = config
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = ACT2FN[self.config.hidden_act](x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""MRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks.""",
MRA_START_DOCSTRING,
)
class MraForSequenceClassification(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.classifier = MraClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""MRA Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks.""",
MRA_START_DOCSTRING,
)
class MraForMultipleChoice(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mra = MraModel(config)
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.mra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""MRA Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.""",
MRA_START_DOCSTRING,
)
class MraForTokenClassification(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""MRA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
MRA_START_DOCSTRING,
)
class MraForQuestionAnswering(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/modeling_regnet.py
|
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. 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.
""" PyTorch RegNet model."""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "RegNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/regnet-y-040"
_EXPECTED_OUTPUT_SHAPE = [1, 1088, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/regnet-y-040"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
REGNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class RegNetConvLayer(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
activation: Optional[str] = "relu",
):
super().__init__()
self.convolution = nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=kernel_size // 2,
groups=groups,
bias=False,
)
self.normalization = nn.BatchNorm2d(out_channels)
self.activation = ACT2FN[activation] if activation is not None else nn.Identity()
def forward(self, hidden_state):
hidden_state = self.convolution(hidden_state)
hidden_state = self.normalization(hidden_state)
hidden_state = self.activation(hidden_state)
return hidden_state
class RegNetEmbeddings(nn.Module):
"""
RegNet Embedddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: RegNetConfig):
super().__init__()
self.embedder = RegNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=3, stride=2, activation=config.hidden_act
)
self.num_channels = config.num_channels
def forward(self, pixel_values):
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
hidden_state = self.embedder(pixel_values)
return hidden_state
# Copied from transformers.models.resnet.modeling_resnet.ResNetShortCut with ResNet->RegNet
class RegNetShortCut(nn.Module):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2):
super().__init__()
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
self.normalization = nn.BatchNorm2d(out_channels)
def forward(self, input: Tensor) -> Tensor:
hidden_state = self.convolution(input)
hidden_state = self.normalization(hidden_state)
return hidden_state
class RegNetSELayer(nn.Module):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
def __init__(self, in_channels: int, reduced_channels: int):
super().__init__()
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
self.attention = nn.Sequential(
nn.Conv2d(in_channels, reduced_channels, kernel_size=1),
nn.ReLU(),
nn.Conv2d(reduced_channels, in_channels, kernel_size=1),
nn.Sigmoid(),
)
def forward(self, hidden_state):
# b c h w -> b c 1 1
pooled = self.pooler(hidden_state)
attention = self.attention(pooled)
hidden_state = hidden_state * attention
return hidden_state
class RegNetXLayer(nn.Module):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
groups = max(1, out_channels // config.groups_width)
self.shortcut = (
RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act),
RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act),
RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None),
)
self.activation = ACT2FN[config.hidden_act]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class RegNetYLayer(nn.Module):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
groups = max(1, out_channels // config.groups_width)
self.shortcut = (
RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act),
RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act),
RegNetSELayer(out_channels, reduced_channels=int(round(in_channels / 4))),
RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None),
)
self.activation = ACT2FN[config.hidden_act]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class RegNetStage(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
def __init__(
self,
config: RegNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
layer = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
self.layers = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
config,
in_channels,
out_channels,
stride=stride,
),
*[layer(config, out_channels, out_channels) for _ in range(depth - 1)],
)
def forward(self, hidden_state):
hidden_state = self.layers(hidden_state)
return hidden_state
class RegNetEncoder(nn.Module):
def __init__(self, config: RegNetConfig):
super().__init__()
self.stages = nn.ModuleList([])
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
config,
config.embedding_size,
config.hidden_sizes[0],
stride=2 if config.downsample_in_first_stage else 1,
depth=config.depths[0],
)
)
in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
self.stages.append(RegNetStage(config, in_channels, out_channels, depth=depth))
def forward(
self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
) -> BaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
class RegNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
# Copied from transformers.models.resnet.modeling_resnet.ResNetPreTrainedModel._init_weights
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
REGNET_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
REGNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top.",
REGNET_START_DOCSTRING,
)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class RegNetModel(RegNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = RegNetEmbeddings(config)
self.encoder = RegNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
) -> BaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values)
encoder_outputs = self.encoder(
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
REGNET_START_DOCSTRING,
)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class RegNetForImageClassification(RegNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.regnet = RegNetModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> ImageClassifierOutputWithNoAttention:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.regnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert RegNet checkpoints from timm and vissl."""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetY32gf, RegNetY64gf, RegNetY128gf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger()
@dataclass
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d)
if has_not_submodules:
self.traced.append(m)
def __call__(self, x: Tensor):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook))
self.module(x)
[x.remove() for x in self.handles]
return self
@property
def parametrized(self):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda x: len(list(x.state_dict().keys())) > 0, self.traced))
@dataclass
class ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 1
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
raise_if_mismatch: bool = True
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
hood we tracked all the operations in both modules.
"""
dest_traced = Tracker(self.dest)(x).parametrized
src_traced = Tracker(self.src)(x).parametrized
src_traced = list(filter(lambda x: type(x) not in self.src_skip, src_traced))
dest_traced = list(filter(lambda x: type(x) not in self.dest_skip, dest_traced))
if len(dest_traced) != len(src_traced) and self.raise_if_mismatch:
raise Exception(
f"Numbers of operations are different. Source module has {len(src_traced)} operations while"
f" destination module has {len(dest_traced)}."
)
for dest_m, src_m in zip(dest_traced, src_traced):
dest_m.load_state_dict(src_m.state_dict())
if self.verbose == 1:
print(f"Transfered from={src_m} to={dest_m}")
class FakeRegNetVisslWrapper(nn.Module):
"""
Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
"""
def __init__(self, model: nn.Module):
super().__init__()
feature_blocks: List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(("conv1", model.stem))
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("block"), f"Unexpected layer name {k}"
block_index = len(feature_blocks) + 1
feature_blocks.append((f"res{block_index}", v))
self._feature_blocks = nn.ModuleDict(feature_blocks)
def forward(self, x: Tensor):
return get_trunk_forward_outputs(
x,
out_feat_keys=None,
feature_blocks=self._feature_blocks,
)
class NameToFromModelFuncMap(dict):
"""
A Dictionary with some additional logic to return a function that creates the correct original model.
"""
def convert_name_to_timm(self, x: str) -> str:
x_split = x.split("-")
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])
def __getitem__(self, x: str) -> Callable[[], Tuple[nn.Module, Dict]]:
# default to timm!
if x not in self:
x = self.convert_name_to_timm(x)
val = partial(lambda: (timm.create_model(x, pretrained=True).eval(), None))
else:
val = super().__getitem__(x)
return val
class NameToOurModelFuncMap(dict):
"""
A Dictionary with some additional logic to return the correct hugging face RegNet class reference.
"""
def __getitem__(self, x: str) -> Callable[[], nn.Module]:
if "seer" in x and "in1k" not in x:
val = RegNetModel
else:
val = RegNetForImageClassification
return val
def manually_copy_vissl_head(from_state_dict, to_state_dict, keys: List[Tuple[str, str]]):
for from_key, to_key in keys:
to_state_dict[to_key] = from_state_dict[from_key].clone()
print(f"Copied key={from_key} to={to_key}")
return to_state_dict
def convert_weight_and_push(
name: str,
from_model_func: Callable[[], nn.Module],
our_model_func: Callable[[], nn.Module],
config: RegNetConfig,
save_directory: Path,
push_to_hub: bool = True,
):
print(f"Converting {name}...")
with torch.no_grad():
from_model, from_state_dict = from_model_func()
our_model = our_model_func(config).eval()
module_transfer = ModuleTransfer(src=from_model, dest=our_model, raise_if_mismatch=False)
x = torch.randn((1, 3, 224, 224))
module_transfer(x)
if from_state_dict is not None:
keys = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
keys = [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
to_state_dict = manually_copy_vissl_head(from_state_dict, our_model.state_dict(), keys)
our_model.load_state_dict(to_state_dict)
our_outputs = our_model(x, output_hidden_states=True)
our_output = (
our_outputs.logits if isinstance(our_model, RegNetForImageClassification) else our_outputs.last_hidden_state
)
from_output = from_model(x)
from_output = from_output[-1] if isinstance(from_output, list) else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
our_output = our_outputs.hidden_states[-1]
assert torch.allclose(from_output, our_output), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name,
commit_message="Add model",
use_temp_dir=True,
)
size = 224 if "seer" not in name else 384
# we can use the convnext one
image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
image_processor.push_to_hub(
repo_path_or_name=save_directory / name,
commit_message="Add image processor",
use_temp_dir=True,
)
print(f"Pushed {name}")
def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True):
filename = "imagenet-1k-id2label.json"
num_labels = 1000
expected_shape = (1, num_labels)
repo_id = "huggingface/label-files"
num_labels = num_labels
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
id2label = {int(k): v for k, v in id2label.items()}
id2label = id2label
label2id = {v: k for k, v in id2label.items()}
ImageNetPreTrainedConfig = partial(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
names_to_config = {
"regnet-x-002": ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type="x"
),
"regnet-x-004": ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type="x"
),
"regnet-x-006": ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type="x"
),
"regnet-x-008": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type="x"
),
"regnet-x-016": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type="x"
),
"regnet-x-032": ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type="x"
),
"regnet-x-040": ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type="x"
),
"regnet-x-064": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type="x"
),
"regnet-x-080": ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type="x"
),
"regnet-x-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type="x"
),
"regnet-x-160": ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type="x"
),
"regnet-x-320": ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type="x"
),
# y variant
"regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8),
"regnet-y-004": ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8
),
"regnet-y-006": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16
),
"regnet-y-008": ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16
),
"regnet-y-016": ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24
),
"regnet-y-032": ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24
),
"regnet-y-040": ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64
),
"regnet-y-064": ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72
),
"regnet-y-080": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56
),
"regnet-y-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112
),
"regnet-y-160": ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112
),
"regnet-y-320": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232
),
# models created by SEER -> https://arxiv.org/abs/2202.08360
"regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232),
"regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328),
"regnet-y-1280-seer": RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264
),
"regnet-y-2560-seer": RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640
),
"regnet-y-10b-seer": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
# finetuned on imagenet
"regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232
),
"regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328
),
"regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264
),
"regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640
),
"regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
}
names_to_ours_model_map = NameToOurModelFuncMap()
names_to_from_model_map = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(checkpoint_url: str, model_func: Callable[[], nn.Module]) -> Tuple[nn.Module, Dict]:
files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
model = model_func()
# check if we have a head, if yes add it
model_state_dict = files["classy_state_dict"]["base_model"]["model"]
state_dict = model_state_dict["trunk"]
model.load_state_dict(state_dict)
return model.eval(), model_state_dict["heads"]
# pretrained
names_to_from_model_map["regnet-y-320-seer"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch",
lambda: FakeRegNetVisslWrapper(RegNetY32gf()),
)
names_to_from_model_map["regnet-y-640-seer"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch",
lambda: FakeRegNetVisslWrapper(RegNetY64gf()),
)
names_to_from_model_map["regnet-y-1280-seer"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch",
lambda: FakeRegNetVisslWrapper(RegNetY128gf()),
)
names_to_from_model_map["regnet-y-10b-seer"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",
lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
),
)
# IN1K finetuned
names_to_from_model_map["regnet-y-320-seer-in1k"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch",
lambda: FakeRegNetVisslWrapper(RegNetY32gf()),
)
names_to_from_model_map["regnet-y-640-seer-in1k"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch",
lambda: FakeRegNetVisslWrapper(RegNetY64gf()),
)
names_to_from_model_map["regnet-y-1280-seer-in1k"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch",
lambda: FakeRegNetVisslWrapper(RegNetY128gf()),
)
names_to_from_model_map["regnet-y-10b-seer-in1k"] = partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",
lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
),
)
if model_name:
convert_weight_and_push(
model_name,
names_to_from_model_map[model_name],
names_to_ours_model_map[model_name],
names_to_config[model_name],
save_directory,
push_to_hub,
)
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
model_name,
names_to_from_model_map[model_name],
names_to_ours_model_map[model_name],
config,
save_directory,
push_to_hub,
)
return config, expected_shape
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
args = parser.parse_args()
pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/configuration_regnet.py
|
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. 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.
""" RegNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
}
class RegNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the RegNet
[facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"y"`):
The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with
`reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the
paper for a detailed explanation of how these layers were constructed.
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
If `True`, the first stage will downsample the inputs using a `stride` of 2.
Example:
```python
>>> from transformers import RegNetConfig, RegNetModel
>>> # Initializing a RegNet regnet-y-40 style configuration
>>> configuration = RegNetConfig()
>>> # Initializing a model from the regnet-y-40 style configuration
>>> model = RegNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "regnet"
layer_types = ["x", "y"]
def __init__(
self,
num_channels=3,
embedding_size=32,
hidden_sizes=[128, 192, 512, 1088],
depths=[2, 6, 12, 2],
groups_width=64,
layer_type="y",
hidden_act="relu",
**kwargs,
):
super().__init__(**kwargs)
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.groups_width = groups_width
self.layer_type = layer_type
self.hidden_act = hidden_act
# always downsample in the first stage
self.downsample_in_first_stage = True
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert RegNet 10B checkpoints vissl."""
# You need to install a specific version of classy vision
# pip install git+https://github.com/FrancescoSaverioZuppichini/ClassyVision.git@convert_weights
import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger()
@dataclass
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
name2module: Dict[str, nn.Module] = field(default_factory=OrderedDict)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d)
if has_not_submodules:
self.traced.append(m)
self.name2module[name] = m
def __call__(self, x: Tensor):
for name, m in self.module.named_modules():
self.handles.append(m.register_forward_hook(partial(self._forward_hook, name=name)))
self.module(x)
[x.remove() for x in self.handles]
return self
@property
def parametrized(self):
# check the len of the state_dict keys to see if we have learnable params
return {k: v for k, v in self.name2module.items() if len(list(v.state_dict().keys())) > 0}
class FakeRegNetVisslWrapper(nn.Module):
"""
Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
"""
def __init__(self, model: nn.Module):
super().__init__()
feature_blocks: List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(("conv1", model.stem))
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("block"), f"Unexpected layer name {k}"
block_index = len(feature_blocks) + 1
feature_blocks.append((f"res{block_index}", v))
self._feature_blocks = nn.ModuleDict(feature_blocks)
def forward(self, x: Tensor):
return get_trunk_forward_outputs(
x,
out_feat_keys=None,
feature_blocks=self._feature_blocks,
)
class FakeRegNetParams(RegNetParams):
"""
Used to instantiace a RegNet model from classy vision with the same depth as the 10B one but with super small
parameters, so we can trace it in memory.
"""
def get_expanded_params(self):
return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
"""
Returns a dictionary that maps from original model's key -> our implementation's keys
"""
# create our model (with small weights)
our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
if "in1k" in model_name:
our_model = RegNetForImageClassification(our_config)
else:
our_model = RegNetModel(our_config)
# create from model (with small weights)
from_model = FakeRegNetVisslWrapper(
RegNet(FakeRegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
)
with torch.no_grad():
from_model = from_model.eval()
our_model = our_model.eval()
x = torch.randn((1, 3, 32, 32))
# trace both
dest_tracker = Tracker(our_model)
dest_traced = dest_tracker(x).parametrized
pprint(dest_tracker.name2module)
src_tracker = Tracker(from_model)
src_traced = src_tracker(x).parametrized
# convert the keys -> module dict to keys -> params
def to_params_dict(dict_with_modules):
params_dict = OrderedDict()
for name, module in dict_with_modules.items():
for param_name, param in module.state_dict().items():
params_dict[f"{name}.{param_name}"] = param
return params_dict
from_to_ours_keys = {}
src_state_dict = to_params_dict(src_traced)
dst_state_dict = to_params_dict(dest_traced)
for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(), dst_state_dict.items()):
from_to_ours_keys[src_key] = dest_key
logger.info(f"{src_key} -> {dest_key}")
# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
if "in1k" in model_name:
from_to_ours_keys["0.clf.0.weight"] = "classifier.1.weight"
from_to_ours_keys["0.clf.0.bias"] = "classifier.1.bias"
return from_to_ours_keys
def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True):
filename = "imagenet-1k-id2label.json"
num_labels = 1000
repo_id = "huggingface/label-files"
num_labels = num_labels
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
id2label = {int(k): v for k, v in id2label.items()}
id2label = id2label
label2id = {v: k for k, v in id2label.items()}
ImageNetPreTrainedConfig = partial(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
names_to_config = {
"regnet-y-10b-seer": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
# finetuned on imagenet
"regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
}
# add seer weights logic
def load_using_classy_vision(checkpoint_url: str) -> Tuple[Dict, Dict]:
files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
# check if we have a head, if yes add it
model_state_dict = files["classy_state_dict"]["base_model"]["model"]
return model_state_dict["trunk"], model_state_dict["heads"]
names_to_from_model = {
"regnet-y-10b-seer": partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",
),
"regnet-y-10b-seer-in1k": partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",
),
}
from_to_ours_keys = get_from_to_our_keys(model_name)
if not (save_directory / f"{model_name}.pth").exists():
logger.info("Loading original state_dict.")
from_state_dict_trunk, from_state_dict_head = names_to_from_model[model_name]()
from_state_dict = from_state_dict_trunk
if "in1k" in model_name:
# add the head
from_state_dict = {**from_state_dict_trunk, **from_state_dict_head}
logger.info("Done!")
converted_state_dict = {}
not_used_keys = list(from_state_dict.keys())
regex = r"\.block.-part."
# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
for key in from_state_dict.keys():
# remove the weird "block[0,1]-part" from the key
src_key = re.sub(regex, "", key)
# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
dest_key = from_to_ours_keys[src_key]
# store the parameter with our key
converted_state_dict[dest_key] = from_state_dict[key]
not_used_keys.remove(key)
# check that all keys have been updated
assert len(not_used_keys) == 0, f"Some keys where not used {','.join(not_used_keys)}"
logger.info(f"The following keys were not used: {','.join(not_used_keys)}")
# save our state dict to disk
torch.save(converted_state_dict, save_directory / f"{model_name}.pth")
del converted_state_dict
else:
logger.info("The state_dict was already stored on disk.")
if push_to_hub:
logger.info(f"Token is {os.environ['HF_TOKEN']}")
logger.info("Loading our model.")
# create our model
our_config = names_to_config[model_name]
our_model_func = RegNetModel
if "in1k" in model_name:
our_model_func = RegNetForImageClassification
our_model = our_model_func(our_config)
# place our model to the meta device (so remove all the weights)
our_model.to(torch.device("meta"))
logger.info("Loading state_dict in our model.")
# load state dict
state_dict_keys = our_model.state_dict().keys()
PreTrainedModel._load_pretrained_model_low_mem(
our_model, state_dict_keys, [save_directory / f"{model_name}.pth"]
)
logger.info("Finally, pushing!")
# push it to hub
our_model.push_to_hub(
repo_path_or_name=save_directory / model_name,
commit_message="Add model",
output_dir=save_directory / model_name,
)
size = 384
# we can use the convnext one
image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
image_processor.push_to_hub(
repo_path_or_name=save_directory / model_name,
commit_message="Add image processor",
output_dir=save_directory / model_name,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
args = parser.parse_args()
pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/modeling_flax_regnet.py
|
# coding=utf-8
# Copyright 2023 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# 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.
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from transformers import RegNetConfig
from transformers.modeling_flax_outputs import (
FlaxBaseModelOutputWithNoAttention,
FlaxBaseModelOutputWithPooling,
FlaxBaseModelOutputWithPoolingAndNoAttention,
FlaxImageClassifierOutputWithNoAttention,
)
from transformers.modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
)
REGNET_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
REGNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`RegNetImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Copied from transformers.models.resnet.modeling_flax_resnet.Identity
class Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x
class FlaxRegNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
groups: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.kernel_size, self.kernel_size),
strides=self.stride,
padding=self.kernel_size // 2,
feature_group_count=self.groups,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity()
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(hidden_state)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxRegNetEmbeddings(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxRegNetConvLayer(
self.config.embedding_size,
kernel_size=3,
stride=2,
activation=self.config.hidden_act,
dtype=self.dtype,
)
def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
num_channels = pixel_values.shape[-1]
if num_channels != self.config.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
hidden_state = self.embedder(pixel_values, deterministic=deterministic)
return hidden_state
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetShortCut with ResNet->RegNet
class FlaxRegNetShortCut(nn.Module):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(1, 1),
strides=self.stride,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
return hidden_state
class FlaxRegNetSELayerCollection(nn.Module):
in_channels: int
reduced_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv_1 = nn.Conv(
self.reduced_channels,
kernel_size=(1, 1),
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
name="0",
) # 0 is the name used in corresponding pytorch implementation
self.conv_2 = nn.Conv(
self.in_channels,
kernel_size=(1, 1),
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
name="2",
) # 2 is the name used in corresponding pytorch implementation
def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
hidden_state = self.conv_1(hidden_state)
hidden_state = nn.relu(hidden_state)
hidden_state = self.conv_2(hidden_state)
attention = nn.sigmoid(hidden_state)
return attention
class FlaxRegNetSELayer(nn.Module):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
in_channels: int
reduced_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.pooler = partial(nn.avg_pool, padding=((0, 0), (0, 0)))
self.attention = FlaxRegNetSELayerCollection(self.in_channels, self.reduced_channels, dtype=self.dtype)
def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
pooled = self.pooler(
hidden_state,
window_shape=(hidden_state.shape[1], hidden_state.shape[2]),
strides=(hidden_state.shape[1], hidden_state.shape[2]),
)
attention = self.attention(pooled)
hidden_state = hidden_state * attention
return hidden_state
class FlaxRegNetXLayerCollection(nn.Module):
config: RegNetConfig
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
groups = max(1, self.out_channels // self.config.groups_width)
self.layer = [
FlaxRegNetConvLayer(
self.out_channels,
kernel_size=1,
activation=self.config.hidden_act,
dtype=self.dtype,
name="0",
),
FlaxRegNetConvLayer(
self.out_channels,
stride=self.stride,
groups=groups,
activation=self.config.hidden_act,
dtype=self.dtype,
name="1",
),
FlaxRegNetConvLayer(
self.out_channels,
kernel_size=1,
activation=None,
dtype=self.dtype,
name="2",
),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxRegNetXLayer(nn.Module):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxRegNetShortCut(
self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
if should_apply_shortcut
else Identity()
)
self.layer = FlaxRegNetXLayerCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxRegNetYLayerCollection(nn.Module):
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
groups = max(1, self.out_channels // self.config.groups_width)
self.layer = [
FlaxRegNetConvLayer(
self.out_channels,
kernel_size=1,
activation=self.config.hidden_act,
dtype=self.dtype,
name="0",
),
FlaxRegNetConvLayer(
self.out_channels,
stride=self.stride,
groups=groups,
activation=self.config.hidden_act,
dtype=self.dtype,
name="1",
),
FlaxRegNetSELayer(
self.out_channels,
reduced_channels=int(round(self.in_channels / 4)),
dtype=self.dtype,
name="2",
),
FlaxRegNetConvLayer(
self.out_channels,
kernel_size=1,
activation=None,
dtype=self.dtype,
name="3",
),
]
def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state)
return hidden_state
class FlaxRegNetYLayer(nn.Module):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxRegNetShortCut(
self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
if should_apply_shortcut
else Identity()
)
self.layer = FlaxRegNetYLayerCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxRegNetStageLayersCollection(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxRegNetXLayer if self.config.layer_type == "x" else FlaxRegNetYLayer
layers = [
# downsampling is done in the first layer with stride of 2
layer(
self.config,
self.in_channels,
self.out_channels,
stride=self.stride,
dtype=self.dtype,
name="0",
)
]
for i in range(self.depth - 1):
layers.append(
layer(
self.config,
self.out_channels,
self.out_channels,
dtype=self.dtype,
name=str(i + 1),
)
)
self.layers = layers
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = x
for layer in self.layers:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetStage with ResNet->RegNet
class FlaxRegNetStage(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxRegNetStageLayersCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
depth=self.depth,
dtype=self.dtype,
)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
return self.layers(x, deterministic=deterministic)
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetStageCollection with ResNet->RegNet
class FlaxRegNetStageCollection(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxRegNetStage(
self.config,
self.config.embedding_size,
self.config.hidden_sizes[0],
stride=2 if self.config.downsample_in_first_stage else 1,
depth=self.config.depths[0],
dtype=self.dtype,
name="0",
)
]
for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])):
stages.append(
FlaxRegNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1))
)
self.stages = stages
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
hidden_state = stage_module(hidden_state, deterministic=deterministic)
return hidden_state, hidden_states
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetEncoder with ResNet->RegNet
class FlaxRegNetEncoder(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxRegNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_state, hidden_states = self.stages(
hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic
)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return FlaxBaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetPreTrainedModel with ResNet->RegNet,resnet->regnet,RESNET->REGNET
class FlaxRegNetPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
module_class: nn.Module = None
def __init__(
self,
config: RegNetConfig,
input_shape=(1, 224, 224, 3),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
input_shape = (1, config.image_size, config.image_size, config.num_channels)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
rngs = {"params": rng}
random_params = self.module.init(rngs, pixel_values, return_dict=False)
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
def __call__(
self,
pixel_values,
params: dict = None,
train: bool = False,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
return self.module.apply(
{
"params": params["params"] if params is not None else self.params["params"],
"batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"],
},
jnp.array(pixel_values, dtype=jnp.float32),
not train,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=["batch_stats"] if train else False, # Returing tuple with batch_stats only when train is True
)
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetModule with ResNet->RegNet
class FlaxRegNetModule(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxRegNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxRegNetEncoder(self.config, dtype=self.dtype)
# Adaptive average pooling used in resnet
self.pooler = partial(
nn.avg_pool,
padding=((0, 0), (0, 0)),
)
def __call__(
self,
pixel_values,
deterministic: bool = True,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> FlaxBaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values, deterministic=deterministic)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(
last_hidden_state,
window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
).transpose(0, 3, 1, 2)
last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return FlaxBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top.",
REGNET_START_DOCSTRING,
)
class FlaxRegNetModel(FlaxRegNetPreTrainedModel):
module_class = FlaxRegNetModule
FLAX_VISION_MODEL_DOCSTRING = """
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, FlaxRegNetModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
>>> model = FlaxRegNetModel.from_pretrained("facebook/regnet-y-040")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxRegNetModel, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(
FlaxRegNetModel,
output_type=FlaxBaseModelOutputWithPooling,
config_class=RegNetConfig,
)
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetClassifierCollection with ResNet->RegNet
class FlaxRegNetClassifierCollection(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x)
# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetForImageClassificationModule with ResNet->RegNet,resnet->regnet,RESNET->REGNET
class FlaxRegNetForImageClassificationModule(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.regnet = FlaxRegNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxRegNetClassifierCollection(self.config, dtype=self.dtype)
else:
self.classifier = Identity()
def __call__(
self,
pixel_values=None,
deterministic: bool = True,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.regnet(
pixel_values,
deterministic=deterministic,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output[:, :, 0, 0])
if not return_dict:
output = (logits,) + outputs[2:]
return output
return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
REGNET_START_DOCSTRING,
)
class FlaxRegNetForImageClassification(FlaxRegNetPreTrainedModel):
module_class = FlaxRegNetForImageClassificationModule
FLAX_VISION_CLASSIF_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxRegNetForImageClassification
>>> from PIL import Image
>>> import jax
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
>>> model = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
>>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
```
"""
overwrite_call_docstring(FlaxRegNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
append_replace_return_docstrings(
FlaxRegNetForImageClassification,
output_type=FlaxImageClassifierOutputWithNoAttention,
config_class=RegNetConfig,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/modeling_tf_regnet.py
|
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. 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.
""" TensorFlow RegNet model."""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "RegNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/regnet-y-040"
_EXPECTED_OUTPUT_SHAPE = [1, 1088, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/regnet-y-040"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class TFRegNetConvLayer(tf.keras.layers.Layer):
def __init__(
self,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
activation: Optional[str] = "relu",
**kwargs,
):
super().__init__(**kwargs)
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
self.padding = tf.keras.layers.ZeroPadding2D(padding=kernel_size // 2)
self.convolution = tf.keras.layers.Conv2D(
filters=out_channels,
kernel_size=kernel_size,
strides=stride,
padding="VALID",
groups=groups,
use_bias=False,
name="convolution",
)
self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
self.activation = ACT2FN[activation] if activation is not None else tf.identity
def call(self, hidden_state):
hidden_state = self.convolution(self.padding(hidden_state))
hidden_state = self.normalization(hidden_state)
hidden_state = self.activation(hidden_state)
return hidden_state
class TFRegNetEmbeddings(tf.keras.layers.Layer):
"""
RegNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: RegNetConfig, **kwargs):
super().__init__(**kwargs)
self.num_channels = config.num_channels
self.embedder = TFRegNetConvLayer(
out_channels=config.embedding_size,
kernel_size=3,
stride=2,
activation=config.hidden_act,
name="embedder",
)
def call(self, pixel_values):
num_channels = shape_list(pixel_values)[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
hidden_state = self.embedder(pixel_values)
return hidden_state
class TFRegNetShortCut(tf.keras.layers.Layer):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, out_channels: int, stride: int = 2, **kwargs):
super().__init__(**kwargs)
self.convolution = tf.keras.layers.Conv2D(
filters=out_channels, kernel_size=1, strides=stride, use_bias=False, name="convolution"
)
self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
return self.normalization(self.convolution(inputs), training=training)
class TFRegNetSELayer(tf.keras.layers.Layer):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
def __init__(self, in_channels: int, reduced_channels: int, **kwargs):
super().__init__(**kwargs)
self.pooler = tf.keras.layers.GlobalAveragePooling2D(keepdims=True, name="pooler")
self.attention = [
tf.keras.layers.Conv2D(filters=reduced_channels, kernel_size=1, activation="relu", name="attention.0"),
tf.keras.layers.Conv2D(filters=in_channels, kernel_size=1, activation="sigmoid", name="attention.2"),
]
def call(self, hidden_state):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
pooled = self.pooler(hidden_state)
for layer_module in self.attention:
pooled = layer_module(pooled)
hidden_state = hidden_state * pooled
return hidden_state
class TFRegNetXLayer(tf.keras.layers.Layer):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1, **kwargs):
super().__init__(**kwargs)
should_apply_shortcut = in_channels != out_channels or stride != 1
groups = max(1, out_channels // config.groups_width)
self.shortcut = (
TFRegNetShortCut(out_channels, stride=stride, name="shortcut")
if should_apply_shortcut
else tf.keras.layers.Activation("linear", name="shortcut")
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
self.layers = [
TFRegNetConvLayer(out_channels, kernel_size=1, activation=config.hidden_act, name="layer.0"),
TFRegNetConvLayer(
out_channels, stride=stride, groups=groups, activation=config.hidden_act, name="layer.1"
),
TFRegNetConvLayer(out_channels, kernel_size=1, activation=None, name="layer.2"),
]
self.activation = ACT2FN[config.hidden_act]
def call(self, hidden_state):
residual = hidden_state
for layer_module in self.layers:
hidden_state = layer_module(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class TFRegNetYLayer(tf.keras.layers.Layer):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1, **kwargs):
super().__init__(**kwargs)
should_apply_shortcut = in_channels != out_channels or stride != 1
groups = max(1, out_channels // config.groups_width)
self.shortcut = (
TFRegNetShortCut(out_channels, stride=stride, name="shortcut")
if should_apply_shortcut
else tf.keras.layers.Activation("linear", name="shortcut")
)
self.layers = [
TFRegNetConvLayer(out_channels, kernel_size=1, activation=config.hidden_act, name="layer.0"),
TFRegNetConvLayer(
out_channels, stride=stride, groups=groups, activation=config.hidden_act, name="layer.1"
),
TFRegNetSELayer(out_channels, reduced_channels=int(round(in_channels / 4)), name="layer.2"),
TFRegNetConvLayer(out_channels, kernel_size=1, activation=None, name="layer.3"),
]
self.activation = ACT2FN[config.hidden_act]
def call(self, hidden_state):
residual = hidden_state
for layer_module in self.layers:
hidden_state = layer_module(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class TFRegNetStage(tf.keras.layers.Layer):
"""
A RegNet stage composed by stacked layers.
"""
def __init__(
self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, **kwargs
):
super().__init__(**kwargs)
layer = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
self.layers = [
# downsampling is done in the first layer with stride of 2
layer(config, in_channels, out_channels, stride=stride, name="layers.0"),
*[layer(config, out_channels, out_channels, name=f"layers.{i+1}") for i in range(depth - 1)],
]
def call(self, hidden_state):
for layer_module in self.layers:
hidden_state = layer_module(hidden_state)
return hidden_state
class TFRegNetEncoder(tf.keras.layers.Layer):
def __init__(self, config: RegNetConfig, **kwargs):
super().__init__(**kwargs)
self.stages = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
config,
config.embedding_size,
config.hidden_sizes[0],
stride=2 if config.downsample_in_first_stage else 1,
depth=config.depths[0],
name="stages.0",
)
)
in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:])
for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, config.depths[1:])):
self.stages.append(TFRegNetStage(config, in_channels, out_channels, depth=depth, name=f"stages.{i+1}"))
def call(
self, hidden_state: tf.Tensor, output_hidden_states: bool = False, return_dict: bool = True
) -> TFBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return TFBaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
@keras_serializable
class TFRegNetMainLayer(tf.keras.layers.Layer):
config_class = RegNetConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedder = TFRegNetEmbeddings(config, name="embedder")
self.encoder = TFRegNetEncoder(config, name="encoder")
self.pooler = tf.keras.layers.GlobalAveragePooling2D(keepdims=True, name="pooler")
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> TFBaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values, training=training)
encoder_outputs = self.encoder(
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
# Change to NCHW output format have uniformity in the modules
pooled_output = tf.transpose(pooled_output, perm=(0, 3, 1, 2))
last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2))
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
)
class TFRegNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
@property
def input_signature(self):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224), dtype=tf.float32)}
REGNET_START_DOCSTRING = r"""
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
REGNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top.",
REGNET_START_DOCSTRING,
)
class TFRegNetModel(TFRegNetPreTrainedModel):
def __init__(self, config: RegNetConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.regnet = TFRegNetMainLayer(config, name="regnet")
@unpack_inputs
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def call(
self,
pixel_values: tf.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.regnet(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state,
pooler_output=outputs.pooler_output,
hidden_states=outputs.hidden_states,
)
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
REGNET_START_DOCSTRING,
)
class TFRegNetForImageClassification(TFRegNetPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: RegNetConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.regnet = TFRegNetMainLayer(config, name="regnet")
# classification head
self.classifier = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels, name="classifier.1") if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
labels: Optional[tf.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.regnet(
pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
flattened_output = self.classifier[0](pooled_output)
logits = self.classifier[1](flattened_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/regnet/__init__.py
|
# Copyright 2022 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_regnet"] = [
"REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"RegNetForImageClassification",
"RegNetModel",
"RegNetPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_regnet"] = [
"TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRegNetForImageClassification",
"TFRegNetModel",
"TFRegNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_regnet"] = [
"FlaxRegNetForImageClassification",
"FlaxRegNetModel",
"FlaxRegNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_regnet import (
REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
RegNetForImageClassification,
RegNetModel,
RegNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_regnet import (
TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRegNetForImageClassification,
TFRegNetModel,
TFRegNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_regnet import (
FlaxRegNetForImageClassification,
FlaxRegNetModel,
FlaxRegNetPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/convert_esm.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert ESM checkpoint."""
import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory
import esm as esm_module
import torch
from esm.esmfold.v1.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.v1.pretrained import esmfold_v1
from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
EsmForMaskedLM,
EsmForSequenceClassification,
EsmIntermediate,
EsmLayer,
EsmOutput,
EsmSelfAttention,
EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_DATA = [
(
"protein1",
"MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
),
("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]
MODEL_MAPPING = {
"esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
"esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
"esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
"esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
"esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
"esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
"esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
"esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
"esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
"esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
"esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
"esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
"esmfold_v1": esmfold_v1,
}
restypes = list("ARNDCQEGHILKMFPSTWYV")
restypes_with_x = restypes + ["X"]
restypes_with_extras = restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]
def get_esmfold_tokenizer():
with TemporaryDirectory() as tempdir:
vocab = "\n".join(restypes_with_extras)
vocab_file = Path(tempdir) / "vocab.txt"
vocab_file.write_text(vocab)
hf_tokenizer = EsmTokenizer(vocab_file=str(vocab_file))
hf_tokenizer.pad_token_id = 0 # Overlaps with 'A' but that seems to be what they want
return hf_tokenizer
def transfer_and_check_weights(original_module, our_module):
status = our_module.load_state_dict(original_module.state_dict())
if status.missing_keys:
raise ValueError(f"Missing keys: {status.missing_keys}")
if status.unexpected_keys:
raise ValueError(f"Unexpected keys: {status.unexpected_keys}")
def convert_esm_checkpoint_to_pytorch(
model: str, pytorch_dump_folder_path: str, classification_head: bool, push_to_repo: str, auth_token: str
):
"""
Copy/paste/tweak esm's weights to our BERT structure.
"""
if model.startswith("esmfold"):
esm = MODEL_MAPPING[model]()
else:
esm, alphabet = MODEL_MAPPING[model]()
esm.eval() # disable dropout
if model.startswith("esmfold"):
embed_dim = esm.esm.embed_dim
num_layers = esm.esm.num_layers
num_attention_heads = esm.esm.attention_heads
intermediate_size = 4 * embed_dim
token_dropout = esm.esm.token_dropout
emb_layer_norm_before = False # This code path does not exist in ESM-2
position_embedding_type = "rotary"
is_folding_model = True
esmfold_config = EsmFoldConfig()
for key, val in esm.cfg.items():
if hasattr(esmfold_config, key) and key != "trunk":
setattr(esmfold_config, key, val)
for key, val in esm.cfg.trunk.items():
if hasattr(esmfold_config.trunk, key) and key != "structure_module":
setattr(esmfold_config.trunk, key, val)
for key, val in esm.cfg.trunk.structure_module.items():
if hasattr(esmfold_config.trunk.structure_module, key):
setattr(esmfold_config.trunk.structure_module, key, val)
elif hasattr(esm, "args"):
# Indicates an ESM-1b or ESM-1v model
embed_dim = esm.args.embed_dim
num_layers = esm.args.layers
num_attention_heads = esm.args.attention_heads
intermediate_size = esm.args.ffn_embed_dim
token_dropout = esm.args.token_dropout
emb_layer_norm_before = True if esm.emb_layer_norm_before else False
position_embedding_type = "absolute"
is_folding_model = False
esmfold_config = None
else:
# Indicates an ESM-2 model
embed_dim = esm.embed_dim
num_layers = esm.num_layers
num_attention_heads = esm.attention_heads
intermediate_size = 4 * embed_dim # This is hardcoded in ESM-2
token_dropout = esm.token_dropout
emb_layer_norm_before = False # This code path does not exist in ESM-2
position_embedding_type = "rotary"
is_folding_model = False
esmfold_config = None
if is_folding_model:
alphabet = esm.esm.alphabet
vocab_list = tuple(alphabet.all_toks)
mask_token_id = alphabet.mask_idx
pad_token_id = alphabet.padding_idx
if is_folding_model:
original_esm_model = esm.esm
else:
original_esm_model = esm
config = EsmConfig(
vocab_size=original_esm_model.embed_tokens.num_embeddings,
mask_token_id=mask_token_id,
hidden_size=embed_dim,
num_hidden_layers=num_layers,
num_attention_heads=num_attention_heads,
intermediate_size=intermediate_size,
max_position_embeddings=1026,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0,
pad_token_id=pad_token_id,
emb_layer_norm_before=emb_layer_norm_before,
token_dropout=token_dropout,
position_embedding_type=position_embedding_type,
is_folding_model=is_folding_model,
esmfold_config=esmfold_config,
vocab_list=vocab_list,
)
if classification_head:
config.num_labels = esm.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our ESM config:", config)
if model.startswith("esmfold"):
model_class = EsmForProteinFolding
elif classification_head:
model_class = EsmForSequenceClassification
else:
model_class = EsmForMaskedLM
model = model_class(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
model.esm.embeddings.word_embeddings.weight = original_esm_model.embed_tokens.weight
if position_embedding_type == "absolute":
model.esm.embeddings.position_embeddings.weight = original_esm_model.embed_positions.weight
if config.emb_layer_norm_before:
model.esm.embeddings.layer_norm.weight = original_esm_model.emb_layer_norm_before.weight
model.esm.embeddings.layer_norm.bias = original_esm_model.emb_layer_norm_before.bias
model.esm.encoder.emb_layer_norm_after.weight = original_esm_model.emb_layer_norm_after.weight
model.esm.encoder.emb_layer_norm_after.bias = original_esm_model.emb_layer_norm_after.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: EsmLayer = model.esm.encoder.layer[i]
# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
esm_layer = original_esm_model.layers[i]
# self attention
self_attn: EsmSelfAttention = layer.attention.self
assert (
esm_layer.self_attn.k_proj.weight.data.shape
== esm_layer.self_attn.q_proj.weight.data.shape
== esm_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
self_attn.query.weight.data = esm_layer.self_attn.q_proj.weight
self_attn.query.bias.data = esm_layer.self_attn.q_proj.bias
self_attn.key.weight.data = esm_layer.self_attn.k_proj.weight
self_attn.key.bias.data = esm_layer.self_attn.k_proj.bias
self_attn.value.weight.data = esm_layer.self_attn.v_proj.weight
self_attn.value.bias.data = esm_layer.self_attn.v_proj.bias
if getattr(esm_layer.self_attn, "rot_emb", None) is not None:
# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
# During the training of ESM-2 the model was converted to float16 precision, which also converts
# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
# we make sure the new model copies the data from the old inv_freq.
self_attn.rotary_embeddings.inv_freq.data = esm_layer.self_attn.rot_emb.inv_freq
# LayerNorm changes for pre-activation
layer.attention.LayerNorm.weight = esm_layer.self_attn_layer_norm.weight
layer.attention.LayerNorm.bias = esm_layer.self_attn_layer_norm.bias
layer.LayerNorm.weight = esm_layer.final_layer_norm.weight
layer.LayerNorm.bias = esm_layer.final_layer_norm.bias
# self-attention output
self_output: EsmSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = esm_layer.self_attn.out_proj.weight
self_output.dense.bias = esm_layer.self_attn.out_proj.bias
# intermediate
intermediate: EsmIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == esm_layer.fc1.weight.shape
intermediate.dense.weight = esm_layer.fc1.weight
intermediate.dense.bias = esm_layer.fc1.bias
# output
bert_output: EsmOutput = layer.output
assert bert_output.dense.weight.shape == esm_layer.fc2.weight.shape
bert_output.dense.weight = esm_layer.fc2.weight
bert_output.dense.bias = esm_layer.fc2.bias
# end of layer
if is_folding_model:
model.esm_s_combine.data = esm.esm_s_combine.data
model.af2_to_esm.data = esm.af2_to_esm.data
transfer_and_check_weights(esm.embedding, model.embedding)
transfer_and_check_weights(esm.esm_s_mlp, model.esm_s_mlp)
transfer_and_check_weights(esm.trunk, model.trunk)
transfer_and_check_weights(esm.distogram_head, model.distogram_head)
transfer_and_check_weights(esm.ptm_head, model.ptm_head)
transfer_and_check_weights(esm.lm_head, model.lm_head)
transfer_and_check_weights(esm.lddt_head, model.lddt_head)
elif classification_head:
model.classifier.dense.weight = esm.esm.classification_heads["mnli"].dense.weight
model.classifier.dense.bias = esm.classification_heads["mnli"].dense.bias
model.classifier.out_proj.weight = esm.classification_heads["mnli"].out_proj.weight
model.classifier.out_proj.bias = esm.classification_heads["mnli"].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = esm.lm_head.dense.weight
model.lm_head.dense.bias = esm.lm_head.dense.bias
model.lm_head.layer_norm.weight = esm.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = esm.lm_head.layer_norm.bias
model.lm_head.decoder.weight = esm.lm_head.weight
model.lm_head.bias = esm.lm_head.bias
# Contact prediction head
transfer_and_check_weights(esm.contact_head, model.esm.contact_head)
# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
if is_folding_model:
# Folding models aren't trained on masked inputs and don't like mask tokens.
sample_data = SAMPLE_DATA[:2]
else:
sample_data = SAMPLE_DATA
if is_folding_model:
hf_tokenizer = get_esmfold_tokenizer()
hf_tokens = hf_tokenizer(
[row[1] for row in sample_data], return_tensors="pt", padding=True, add_special_tokens=False
)
esmfold_aas, esmfold_mask, _, _, _ = esmfold_encode_sequences([row[1] for row in sample_data])
success = torch.all(hf_tokens["input_ids"] == esmfold_aas) and torch.all(
hf_tokens["attention_mask"] == esmfold_mask
)
else:
# Let's check that we get the same results.
batch_converter = alphabet.get_batch_converter()
batch_labels, batch_strs, batch_tokens = batch_converter(sample_data)
# Prepare tokenizer and make sure it matches
with TemporaryDirectory() as tempdir:
vocab = "\n".join(alphabet.all_toks)
vocab_file = Path(tempdir) / "vocab.txt"
vocab_file.write_text(vocab)
hf_tokenizer = EsmTokenizer(vocab_file=str(vocab_file))
hf_tokens = hf_tokenizer([row[1] for row in sample_data], return_tensors="pt", padding=True)
success = torch.all(hf_tokens["input_ids"] == batch_tokens)
print("Do both models tokenizers output the same tokens?", "🔥" if success else "💩")
if not success:
raise Exception("Tokenization does not match!")
with torch.no_grad():
if is_folding_model:
# Let's test the model in parts
# ESMFold always converts the ESM stem to float16, which requires float16 ops
# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
# original and the converted model on the GPU at the same time.
their_output = esm.cuda().infer([row[1] for row in sample_data])
our_output = model.cuda()(
input_ids=hf_tokens["input_ids"].cuda(), attention_mask=hf_tokens["attention_mask"].cuda()
)
else:
our_output = model(**hf_tokens, output_hidden_states=True)
our_output = our_output["logits"]
if classification_head:
their_output = esm.model.classification_heads["mnli"](esm.extract_features(batch_tokens))
else:
their_output = esm(hf_tokens["input_ids"], repr_layers=list(range(999)))
their_output = their_output["logits"]
if is_folding_model:
max_absolute_diff = torch.max(torch.abs(our_output["positions"] - their_output["positions"])).item()
success = torch.allclose(our_output["positions"], their_output["positions"], atol=1e-5)
else:
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
success = torch.allclose(our_output, their_output, atol=1e-5)
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-5
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
if not is_folding_model:
# Let's check contact prediction too
our_output = model.predict_contacts(hf_tokens["input_ids"], hf_tokens["attention_mask"])
their_output = esm.predict_contacts(hf_tokens["input_ids"])
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
success = torch.allclose(our_output, their_output, atol=1e-5)
print("Contact prediction testing:")
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-5
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
del esm # Free up some memory before continuing
print(f"Saving tokenizer to {pytorch_dump_folder_path}")
hf_tokenizer.save_pretrained(pytorch_dump_folder_path)
if push_to_repo:
model.push_to_hub(repo_id=push_to_repo, token_token=auth_token)
hf_tokenizer.push_to_hub(repo_id=push_to_repo, token_token=auth_token)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
args = parser.parse_args()
convert_esm_checkpoint_to_pytorch(
args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/configuration_esm.py
|
# coding=utf-8
# Copyright 2022 Meta and The HuggingFace Inc. team. 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.
""" ESM model configuration"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
# TODO Update this
ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class EsmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the ESM
[facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*):
Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ESMModel`].
mask_token_id (`int`, *optional*):
The index of the mask token in the vocabulary. This must be included in the config because of the
"mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
pad_token_id (`int`, *optional*):
The index of the padding token in the vocabulary. This must be included in the config because certain parts
of the ESM code use this instead of the attention mask.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 1026):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
emb_layer_norm_before (`bool`, *optional*):
Whether to apply layer normalization after embeddings but before the main stem of the network.
token_dropout (`bool`, defaults to `False`):
When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
Examples:
```python
>>> from transformers import EsmModel, EsmConfig
>>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
>>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
>>> # Accessing the model configuration >>> configuration = model.config
```"""
model_type = "esm"
def __init__(
self,
vocab_size=None,
mask_token_id=None,
pad_token_id=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=1026,
initializer_range=0.02,
layer_norm_eps=1e-12,
position_embedding_type="absolute",
use_cache=True,
emb_layer_norm_before=None,
token_dropout=False,
is_folding_model=False,
esmfold_config=None,
vocab_list=None,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.emb_layer_norm_before = emb_layer_norm_before
self.token_dropout = token_dropout
self.is_folding_model = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values.")
esmfold_config = EsmFoldConfig()
elif isinstance(esmfold_config, dict):
esmfold_config = EsmFoldConfig(**esmfold_config)
self.esmfold_config = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!")
self.vocab_list = get_default_vocab_list()
else:
self.vocab_list = vocab_list
else:
self.esmfold_config = None
self.vocab_list = None
if self.esmfold_config is not None and getattr(self.esmfold_config, "use_esm_attn_map", False):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!")
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = super().to_dict()
if isinstance(self.esmfold_config, EsmFoldConfig):
output["esmfold_config"] = self.esmfold_config.to_dict()
return output
@dataclass
class EsmFoldConfig:
esm_type: str = None
fp16_esm: bool = True
use_esm_attn_map: bool = False
esm_ablate_pairwise: bool = False
esm_ablate_sequence: bool = False
esm_input_dropout: float = 0
embed_aa: bool = True
bypass_lm: bool = False
lddt_head_hid_dim: int = 128
trunk: "TrunkConfig" = None
def __post_init__(self):
if self.trunk is None:
self.trunk = TrunkConfig()
elif isinstance(self.trunk, dict):
self.trunk = TrunkConfig(**self.trunk)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = asdict(self)
output["trunk"] = self.trunk.to_dict()
return output
@dataclass
class TrunkConfig:
num_blocks: int = 48
sequence_state_dim: int = 1024
pairwise_state_dim: int = 128
sequence_head_width: int = 32
pairwise_head_width: int = 32
position_bins: int = 32
dropout: float = 0
layer_drop: float = 0
cpu_grad_checkpoint: bool = False
max_recycles: int = 4
chunk_size: Optional[int] = 128
structure_module: "StructureModuleConfig" = None
def __post_init__(self):
if self.structure_module is None:
self.structure_module = StructureModuleConfig()
elif isinstance(self.structure_module, dict):
self.structure_module = StructureModuleConfig(**self.structure_module)
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}.")
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
f" {self.sequence_state_dim} and {self.sequence_state_dim}."
)
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
)
sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
)
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
)
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.")
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}.")
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = asdict(self)
output["structure_module"] = self.structure_module.to_dict()
return output
@dataclass
class StructureModuleConfig:
"""
Args:
sequence_dim:
Single representation channel dimension
pairwise_dim:
Pair representation channel dimension
ipa_dim:
IPA hidden channel dimension
resnet_dim:
Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
num_heads_ipa:
Number of IPA heads
num_qk_points:
Number of query/key points to generate during IPA
num_v_points:
Number of value points to generate during IPA
dropout_rate:
Dropout rate used throughout the layer
num_blocks:
Number of structure module blocks
num_transition_layers:
Number of layers in the single representation transition (Alg. 23 lines 8-9)
num_resnet_blocks:
Number of blocks in the angle resnet
num_angles:
Number of angles to generate in the angle resnet
trans_scale_factor:
Scale of single representation transition hidden dimension
epsilon:
Small number used in angle resnet normalization
inf:
Large number used for attention masking
"""
sequence_dim: int = 384
pairwise_dim: int = 128
ipa_dim: int = 16
resnet_dim: int = 128
num_heads_ipa: int = 12
num_qk_points: int = 4
num_v_points: int = 8
dropout_rate: float = 0.1
num_blocks: int = 8
num_transition_layers: int = 1
num_resnet_blocks: int = 2
num_angles: int = 7
trans_scale_factor: int = 10
epsilon: float = 1e-8
inf: float = 1e5
def to_dict(self):
return asdict(self)
def get_default_vocab_list():
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/modeling_esmfold.py
|
# coding=utf-8
# Copyright 2022 Meta and The HuggingFace Inc. team. 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.
import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...integrations.deepspeed import is_deepspeed_available
from ...modeling_outputs import ModelOutput
from ...utils import (
ContextManagers,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_scipy_available,
logging,
replace_return_docstrings,
)
from .configuration_esm import EsmConfig
from .modeling_esm import ESM_START_DOCSTRING, EsmModel, EsmPreTrainedModel
from .openfold_utils import (
OFProtein,
Rigid,
Rotation,
atom14_to_atom37,
chunk_layer,
compute_predicted_aligned_error,
compute_tm,
frames_and_literature_positions_to_atom14_pos,
make_atom14_masks,
residue_constants,
to_pdb,
torsion_angles_to_frames,
)
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/esmfold_v1"
_CONFIG_FOR_DOC = "EsmConfig"
@dataclass
class EsmForProteinFoldingOutput(ModelOutput):
"""
Output type of [`EsmForProteinFoldingOutput`].
Args:
frames (`torch.FloatTensor`):
Output frames.
sidechain_frames (`torch.FloatTensor`):
Output sidechain frames.
unnormalized_angles (`torch.FloatTensor`):
Predicted unnormalized backbone and side chain torsion angles.
angles (`torch.FloatTensor`):
Predicted backbone and side chain torsion angles.
positions (`torch.FloatTensor`):
Predicted positions of the backbone and side chain atoms.
states (`torch.FloatTensor`):
Hidden states from the protein folding trunk.
s_s (`torch.FloatTensor`):
Per-residue embeddings derived by concatenating the hidden states of each layer of the ESM-2 LM stem.
s_z (`torch.FloatTensor`):
Pairwise residue embeddings.
distogram_logits (`torch.FloatTensor`):
Input logits to the distogram used to compute residue distances.
lm_logits (`torch.FloatTensor`):
Logits output by the ESM-2 protein language model stem.
aatype (`torch.FloatTensor`):
Input amino acids (AlphaFold2 indices).
atom14_atom_exists (`torch.FloatTensor`):
Whether each atom exists in the atom14 representation.
residx_atom14_to_atom37 (`torch.FloatTensor`):
Mapping between atoms in the atom14 and atom37 representations.
residx_atom37_to_atom14 (`torch.FloatTensor`):
Mapping between atoms in the atom37 and atom14 representations.
atom37_atom_exists (`torch.FloatTensor`):
Whether each atom exists in the atom37 representation.
residue_index (`torch.FloatTensor`):
The index of each residue in the protein chain. Unless internal padding tokens are used, this will just be
a sequence of integers from 0 to `sequence_length`.
lddt_head (`torch.FloatTensor`):
Raw outputs from the lddt head used to compute plddt.
plddt (`torch.FloatTensor`):
Per-residue confidence scores. Regions of low confidence may indicate areas where the model's prediction is
uncertain, or where the protein structure is disordered.
ptm_logits (`torch.FloatTensor`):
Raw logits used for computing ptm.
ptm (`torch.FloatTensor`):
TM-score output representing the model's high-level confidence in the overall structure.
aligned_confidence_probs (`torch.FloatTensor`):
Per-residue confidence scores for the aligned structure.
predicted_aligned_error (`torch.FloatTensor`):
Predicted error between the model's prediction and the ground truth.
max_predicted_aligned_error (`torch.FloatTensor`):
Per-sample maximum predicted error.
"""
frames: torch.FloatTensor = None
sidechain_frames: torch.FloatTensor = None
unnormalized_angles: torch.FloatTensor = None
angles: torch.FloatTensor = None
positions: torch.FloatTensor = None
states: torch.FloatTensor = None
s_s: torch.FloatTensor = None
s_z: torch.FloatTensor = None
distogram_logits: torch.FloatTensor = None
lm_logits: torch.FloatTensor = None
aatype: torch.FloatTensor = None
atom14_atom_exists: torch.FloatTensor = None
residx_atom14_to_atom37: torch.FloatTensor = None
residx_atom37_to_atom14: torch.FloatTensor = None
atom37_atom_exists: torch.FloatTensor = None
residue_index: torch.FloatTensor = None
lddt_head: torch.FloatTensor = None
plddt: torch.FloatTensor = None
ptm_logits: torch.FloatTensor = None
ptm: torch.FloatTensor = None
aligned_confidence_probs: torch.FloatTensor = None
predicted_aligned_error: torch.FloatTensor = None
max_predicted_aligned_error: torch.FloatTensor = None
ESMFOLD_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
masking_pattern (`torch.LongTensor` of shape `({0})`, *optional*):
Locations of tokens to mask during training as a form of regularization. Mask values selected in `[0, 1]`.
num_recycles (`int`, *optional*, defaults to `None`):
Number of times to recycle the input sequence. If `None`, defaults to `config.num_recycles`. "Recycling"
consists of passing the output of the folding trunk back in as input to the trunk. During training, the
number of recycles should vary with each batch, to ensure that the model learns to output valid predictions
after each recycle. During inference, num_recycles should be set to the highest value that the model was
trained with for maximum accuracy. Accordingly, when this value is set to `None`, config.max_recycles is
used.
"""
def is_fp16_enabled():
# Autocast world
fp16_enabled = torch.get_autocast_gpu_dtype() == torch.float16
fp16_enabled = fp16_enabled and torch.is_autocast_enabled()
return fp16_enabled
def is_deepspeed_initialized():
if is_deepspeed_available():
return False
else:
try:
import deepspeed
# This is not available in all DeepSpeed versions.
return deepspeed.utils.is_initialized()
except Exception:
return False
def collate_dense_tensors(samples: List[torch.Tensor], pad_v: float = 0) -> torch.Tensor:
"""
Takes a list of tensors with the following dimensions:
[(d_11, ..., d_1K),
(d_21, ..., d_2K), ..., (d_N1, ..., d_NK)]
and stack + pads them into a single tensor of:
(N, max_i=1,N { d_i1 }, ..., max_i=1,N {diK})
"""
if len(samples) == 0:
return torch.Tensor()
if len({x.dim() for x in samples}) != 1:
raise RuntimeError(f"Samples has varying dimensions: {[x.dim() for x in samples]}")
(device,) = tuple({x.device for x in samples}) # assumes all on same device
max_shape = [max(lst) for lst in zip(*[x.shape for x in samples])]
result = torch.empty(len(samples), *max_shape, dtype=samples[0].dtype, device=device)
result.fill_(pad_v)
for i in range(len(samples)):
result_i = result[i]
t = samples[i]
result_i[tuple(slice(0, k) for k in t.shape)] = t
return result
def flatten_final_dims(t: torch.Tensor, no_dims: int):
return t.reshape(t.shape[:-no_dims] + (-1,))
def permute_final_dims(tensor: torch.Tensor, inds: List[int]):
zero_index = -1 * len(inds)
first_inds = list(range(len(tensor.shape[:zero_index])))
return tensor.permute(first_inds + [zero_index + i for i in inds])
def dict_multimap(fn, dicts):
first = dicts[0]
new_dict = {}
for k, v in first.items():
all_v = [d[k] for d in dicts]
if isinstance(v, dict):
new_dict[k] = dict_multimap(fn, all_v)
else:
new_dict[k] = fn(all_v)
return new_dict
def trunc_normal_init_(weights, scale=1.0, fan="fan_in"):
shape = weights.shape
scale = scale / max(1, shape[1])
if not is_scipy_available():
logger.warning(
"This init requires scipy, but scipy was not found, default to an approximation that might not be"
" equivalent."
)
std = math.sqrt(scale)
torch.nn.init.normal_(weights, std=std).clamp(min=0.0, max=2.0 * std)
else:
from scipy.stats import truncnorm
std = math.sqrt(scale) / truncnorm.std(a=-2, b=2, loc=0, scale=1)
samples = truncnorm.rvs(a=-2, b=2, loc=0, scale=std, size=weights.numel())
samples = np.reshape(samples, shape)
weights.copy_(torch.tensor(samples, device=weights.device))
def ipa_point_weights_init_(weights):
with torch.no_grad():
softplus_inverse_1 = 0.541324854612918
weights.fill_(softplus_inverse_1)
class EsmFoldLinear(nn.Linear):
"""
A Linear layer with built-in nonstandard initializations. Called just like torch.nn.Linear.
Implements the initializers in 1.11.4, plus some additional ones found in the code.
"""
def __init__(
self,
in_dim: int,
out_dim: int,
bias: bool = True,
init: str = "default",
init_fn: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None,
):
"""
Args:
in_dim:
The final dimension of inputs to the layer
out_dim:
The final dimension of layer outputs
bias:
Whether to learn an additive bias. True by default
init:
The initializer to use. Choose from:
"default": LeCun fan-in truncated normal initialization "relu": He initialization w/ truncated normal
distribution "glorot": Fan-average Glorot uniform initialization "gating": Weights=0, Bias=1 "normal":
Normal initialization with std=1/sqrt(fan_in) "final": Weights=0, Bias=0
Overridden by init_fn if the latter is not None.
init_fn:
A custom initializer taking weight and bias as inputs. Overrides init if not None.
"""
super().__init__(in_dim, out_dim, bias=bias)
if bias:
with torch.no_grad():
self.bias.fill_(0)
self.init = init
self.init_fn = init_fn
if init not in ["default", "relu", "glorot", "gating", "normal", "final"]:
raise ValueError("Invalid init string.")
class EsmFoldLayerNorm(nn.Module):
def __init__(self, c_in, eps=1e-5):
super().__init__()
self.c_in = (c_in,)
self.eps = eps
self.weight = nn.Parameter(torch.ones(c_in))
self.bias = nn.Parameter(torch.zeros(c_in))
def forward(self, x):
d = x.dtype
if d is torch.bfloat16 and not is_deepspeed_initialized():
with torch.cuda.amp.autocast(enabled=False):
out = nn.functional.layer_norm(x, self.c_in, self.weight.to(dtype=d), self.bias.to(dtype=d), self.eps)
else:
out = nn.functional.layer_norm(x, self.c_in, self.weight, self.bias, self.eps)
return out
@torch.jit.ignore
def softmax_no_cast(t: torch.Tensor, dim: int = -1) -> torch.Tensor:
"""
Softmax, but without automatic casting to fp32 when the input is of type bfloat16
"""
d = t.dtype
if d is torch.bfloat16 and not is_deepspeed_initialized():
with torch.cuda.amp.autocast(enabled=False):
s = torch.nn.functional.softmax(t, dim=dim)
else:
s = torch.nn.functional.softmax(t, dim=dim)
return s
class EsmFoldAttention(nn.Module):
"""
Standard multi-head attention using AlphaFold's default layer initialization. Allows multiple bias vectors.
"""
def __init__(
self,
c_q: int,
c_k: int,
c_v: int,
c_hidden: int,
no_heads: int,
gating: bool = True,
):
"""
Args:
c_q:
Input dimension of query data
c_k:
Input dimension of key data
c_v:
Input dimension of value data
c_hidden:
Per-head hidden dimension
no_heads:
Number of attention heads
gating:
Whether the output should be gated using query data
"""
super().__init__()
self.c_q = c_q
self.c_k = c_k
self.c_v = c_v
self.c_hidden = c_hidden
self.no_heads = no_heads
self.gating = gating
# DISCREPANCY: c_hidden is not the per-head channel dimension, as
# stated in the supplement, but the overall channel dimension.
self.linear_q = EsmFoldLinear(self.c_q, self.c_hidden * self.no_heads, bias=False, init="glorot")
self.linear_k = EsmFoldLinear(self.c_k, self.c_hidden * self.no_heads, bias=False, init="glorot")
self.linear_v = EsmFoldLinear(self.c_v, self.c_hidden * self.no_heads, bias=False, init="glorot")
self.linear_o = EsmFoldLinear(self.c_hidden * self.no_heads, self.c_q, init="final")
self.linear_g = None
if self.gating:
self.linear_g = EsmFoldLinear(self.c_q, self.c_hidden * self.no_heads, init="gating")
self.sigmoid = nn.Sigmoid()
def _prep_qkv(self, q_x: torch.Tensor, kv_x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
# [*, Q/K/V, H * C_hidden]
q = self.linear_q(q_x)
k = self.linear_k(kv_x)
v = self.linear_v(kv_x)
# [*, Q/K, H, C_hidden]
q = q.view(q.shape[:-1] + (self.no_heads, -1))
k = k.view(k.shape[:-1] + (self.no_heads, -1))
v = v.view(v.shape[:-1] + (self.no_heads, -1))
# [*, H, Q/K, C_hidden]
q = q.transpose(-2, -3)
k = k.transpose(-2, -3)
v = v.transpose(-2, -3)
q /= math.sqrt(self.c_hidden)
return q, k, v
def _wrap_up(self, o: torch.Tensor, q_x: torch.Tensor) -> torch.Tensor:
if self.linear_g is not None:
g = self.sigmoid(self.linear_g(q_x))
# [*, Q, H, C_hidden]
g = g.view(g.shape[:-1] + (self.no_heads, -1))
o = o * g
# [*, Q, H * C_hidden]
o = flatten_final_dims(o, 2)
# [*, Q, C_q]
o = self.linear_o(o)
return o
def forward(
self,
q_x: torch.Tensor,
kv_x: torch.Tensor,
biases: Optional[List[torch.Tensor]] = None,
use_memory_efficient_kernel: bool = False,
use_lma: bool = False,
lma_q_chunk_size: int = 1024,
lma_kv_chunk_size: int = 4096,
use_flash: bool = False,
flash_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
q_x:
[*, Q, C_q] query data
kv_x:
[*, K, C_k] key data
biases:
List of biases that broadcast to [*, H, Q, K]
use_memory_efficient_kernel:
Whether to use a custom memory-efficient attention kernel. This should be the default choice for most.
If none of the "use_<...>" flags are True, a stock PyTorch implementation is used instead
use_lma:
Whether to use low-memory attention (Staats & Rabe 2021). If none of the "use_<...>" flags are True, a
stock PyTorch implementation is used instead
lma_q_chunk_size:
Query chunk size (for LMA)
lma_kv_chunk_size:
Key/Value chunk size (for LMA)
Returns
[*, Q, C_q] attention update
"""
if use_lma and (lma_q_chunk_size is None or lma_kv_chunk_size is None):
raise ValueError("If use_lma is specified, lma_q_chunk_size and lma_kv_chunk_size must be provided")
if use_flash and biases is not None:
raise ValueError("use_flash is incompatible with the bias option. For masking, use flash_mask instead")
attn_options = [use_memory_efficient_kernel, use_lma, use_flash]
if sum(attn_options) > 1:
raise ValueError("Choose at most one alternative attention algorithm")
if biases is None:
biases = []
# [*, H, Q/K, C_hidden]
query, key, value = self._prep_qkv(q_x, kv_x)
key = permute_final_dims(key, (1, 0))
# [*, H, Q, K]
output = torch.matmul(query, key)
for b in biases:
output += b
output = softmax_no_cast(output, -1)
# [*, H, Q, C_hidden]
output = torch.matmul(output, value)
output = output.transpose(-2, -3)
output = self._wrap_up(output, q_x)
return output
class EsmFoldTriangleAttention(nn.Module):
def __init__(self, c_in, c_hidden, no_heads, starting=True, inf=1e9):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Overall hidden channel dimension (not per-head)
no_heads:
Number of attention heads
"""
super().__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_heads = no_heads
self.starting = starting
self.inf = inf
self.layer_norm = LayerNorm(self.c_in)
self.linear = EsmFoldLinear(c_in, self.no_heads, bias=False, init="normal")
self.mha = EsmFoldAttention(self.c_in, self.c_in, self.c_in, self.c_hidden, self.no_heads)
@torch.jit.ignore
def _chunk(
self,
x: torch.Tensor,
biases: List[torch.Tensor],
chunk_size: int,
use_memory_efficient_kernel: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"triangle! triangle!"
mha_inputs = {
"q_x": x,
"kv_x": x,
"biases": biases,
}
return chunk_layer(
partial(self.mha, use_memory_efficient_kernel=use_memory_efficient_kernel, use_lma=use_lma),
mha_inputs,
chunk_size=chunk_size,
no_batch_dims=len(x.shape[:-2]),
_out=x if inplace_safe else None,
)
def forward(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
use_memory_efficient_kernel: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"""
Args:
x:
[*, I, J, C_in] input tensor (e.g. the pair representation)
Returns:
[*, I, J, C_in] output tensor
"""
if mask is None:
# [*, I, J]
mask = x.new_ones(
x.shape[:-1],
)
if not self.starting:
x = x.transpose(-2, -3)
mask = mask.transpose(-1, -2)
# [*, I, J, C_in]
x = self.layer_norm(x)
# [*, I, 1, 1, J]
mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
# [*, H, I, J]
triangle_bias = permute_final_dims(self.linear(x), (2, 0, 1))
# [*, 1, H, I, J]
triangle_bias = triangle_bias.unsqueeze(-4)
biases = [mask_bias, triangle_bias]
if chunk_size is not None:
x = self._chunk(
x,
biases,
chunk_size,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_lma=use_lma,
inplace_safe=inplace_safe,
)
else:
x = self.mha(
q_x=x, kv_x=x, biases=biases, use_memory_efficient_kernel=use_memory_efficient_kernel, use_lma=use_lma
)
if not self.starting:
x = x.transpose(-2, -3)
return x
class EsmFoldTriangleMultiplicativeUpdate(nn.Module):
"""
Implements Algorithms 11 and 12.
"""
def __init__(self, config, _outgoing=True):
super().__init__()
c_hidden = config.pairwise_state_dim
self._outgoing = _outgoing
self.linear_a_p = EsmFoldLinear(c_hidden, c_hidden)
self.linear_a_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
self.linear_b_p = EsmFoldLinear(c_hidden, c_hidden)
self.linear_b_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
self.linear_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
self.linear_z = EsmFoldLinear(c_hidden, c_hidden, init="final")
self.layer_norm_in = LayerNorm(c_hidden)
self.layer_norm_out = LayerNorm(c_hidden)
self.sigmoid = nn.Sigmoid()
def _combine_projections(
self, a: torch.Tensor, b: torch.Tensor, _inplace_chunk_size: Optional[int] = None
) -> torch.Tensor:
if self._outgoing:
a = permute_final_dims(a, (2, 0, 1))
b = permute_final_dims(b, (2, 1, 0))
else:
a = permute_final_dims(a, (2, 1, 0))
b = permute_final_dims(b, (2, 0, 1))
if _inplace_chunk_size is not None:
# To be replaced by torch vmap
for i in range(0, a.shape[-3], _inplace_chunk_size):
a_chunk = a[..., i : i + _inplace_chunk_size, :, :]
b_chunk = b[..., i : i + _inplace_chunk_size, :, :]
a[..., i : i + _inplace_chunk_size, :, :] = torch.matmul(
a_chunk,
b_chunk,
)
p = a
else:
p = torch.matmul(a, b)
return permute_final_dims(p, (1, 2, 0))
def _inference_forward(
self,
z: torch.Tensor,
mask: Optional[torch.Tensor] = None,
inplace_chunk_size: Optional[int] = None,
with_add: bool = True,
):
"""
Args:
z:
A [*, N, N, C_z] pair representation
mask:
A [*, N, N] pair mask
inplace_chunk_size:
Size of chunks used in the main computation. Increase to trade memory for speed.
with_add:
If True, z is overwritten with (z + update). Otherwise, it is overwritten with (update).
Returns:
A reference to the overwritten z
More memory-efficient, inference-only version of the forward function. Uses in-place operations, fusion of the
addition that happens after this module in the Evoformer, a smidge of recomputation, and a cache of overwritten
values to lower peak memory consumption of this module from 5x the size of the input tensor z to 2.5x its size.
Useful for inference on extremely long sequences.
It works as follows. We will make reference to variables used in the default forward implementation below.
Naively, triangle multiplication attention requires the manifestation of 5 tensors the size of z: 1) z, the
"square" input tensor, 2) a, the first projection of z, 3) b, the second projection of b, 4) g, a z-sized mask,
and 5) a z-sized tensor for intermediate computations. For large N, this is prohibitively expensive; for
N=4000, for example, z is more than 8GB alone. To avoid this problem, we compute b, g, and all intermediate
tensors in small chunks, noting that the chunks required to compute a chunk of the output depend only on the
tensor a and corresponding vertical and horizontal chunks of z. This suggests an algorithm that loops over
pairs of chunks of z: hereafter "columns" and "rows" of z, even though each "column" and "row" in fact contains
inplace_chunk_size contiguous true columns and rows of z. Writing output chunks to a new tensor would bring
total memory consumption down to 3x the size of z. However, more memory can be saved by writing output chunks
directly to z in-place. WLOG, we choose to write output chunks vertically, overwriting the ith "column" of z at
the end of the ith iteration of the main loop. Despite this overwriting, the ith column is always one column
ahead of previously overwritten columns and can be recovered directly from z. After the first iteration,
however, the ith row of z is always at least partially overwritten. For this reason, we introduce the z-cache,
a tensor one-half the size of z. The z-cache initially contains the left half (2nd and 3rd quadrants) of z. For
0 < i < N/2, the missing left part of the ith row of z is recovered from this cache at the beginning of the ith
iteration. Once i exceeds n/2, the cache is "reoriented" to encompass the 3rd and 4th quadrants of z instead.
Though the 3rd quadrant of the original z is entirely overwritten at this point, it can be recovered from the
z-cache itself. Thereafter, the ith row of z can be recovered in its entirety from the reoriented z-cache.
After the final iteration, z has been completely overwritten and contains the triangular multiplicative update.
If with_add is True, it instead contains the sum of z and the triangular multiplicative update. In either case,
peak memory consumption is just 2.5x the size of z, disregarding memory used for chunks and other small
variables.
"""
if mask is None:
mask = z.new_ones(z.shape[:-1])
mask = mask.unsqueeze(-1)
def compute_projection_helper(pair, mask, a=True):
if a:
linear_g = self.linear_a_g
linear_p = self.linear_a_p
else:
linear_g = self.linear_b_g
linear_p = self.linear_b_p
pair = self.layer_norm_in(pair)
p = linear_g(pair)
p.sigmoid_()
p *= linear_p(pair)
p *= mask
p = permute_final_dims(p, (2, 0, 1))
return p
def compute_projection(pair, mask, a=True, chunked=True):
need_transpose = self._outgoing ^ a
if not chunked:
p = compute_projection_helper(pair, mask, a)
if need_transpose:
p = p.transpose(-1, -2)
else:
# This computation is chunked so as not to exceed our 2.5x
# budget with a large intermediate tensor
linear_g = self.linear_a_g if a else self.linear_b_g
c = linear_g.bias.shape[-1]
out_shape = pair.shape[:-3] + (c,) + pair.shape[-3:-1]
p = pair.new_zeros(out_shape)
for i in range(0, pair.shape[-3], inplace_chunk_size):
pair_chunk = pair[..., i : i + inplace_chunk_size, :, :]
pair_chunk = compute_projection_helper(
pair[..., i : i + inplace_chunk_size, :, :],
mask[..., i : i + inplace_chunk_size, :, :],
a,
)
if need_transpose:
pair_chunk = pair_chunk.transpose(-1, -2)
p[..., i : i + inplace_chunk_size] = pair_chunk
else:
p[..., i : i + inplace_chunk_size, :] = pair_chunk
del pair_chunk
return p
# We start by fully manifesting a. In addition to the input, this
# brings total memory consumption to 2x z (disregarding size of chunks)
# [*, N, N, c]
a = compute_projection(z, mask, True, chunked=True)
if inplace_chunk_size is not None:
n = a.shape[-1]
half_n = n // 2 + n % 2
row_dim = -3
col_dim = -2
b_chunk_dim = row_dim if self._outgoing else col_dim
def empty_slicer(t):
return [slice(None) for _ in t.shape]
def slice_tensor(t, start, end, dim):
# Slices start:end from the dim dimension of t
s = empty_slicer(t)
s[dim] = slice(start, end)
return t[s]
def flip_z_cache_(z_cache, z):
# "Reorient" the z_cache (see below), filling it with quadrants
# 3---recovered from the z_cache---and 4---recovered from z---
# of the input tensor z.
quadrant_3 = slice_tensor(z_cache, half_n, None, row_dim)
z_cache = z_cache.transpose(row_dim, col_dim)
# If n is odd, we need to shrink the z_cache by one row
z_cache = z_cache[..., : (n // 2), :, :]
# Move the 3rd quadrant of z into the
first_half_slicer = empty_slicer(z_cache)
first_half_slicer[col_dim] = slice(0, half_n)
z_cache[first_half_slicer] = quadrant_3
# Get the fourth quadrant of z
quadrant_4 = slice_tensor(z, half_n, None, row_dim)
quadrant_4 = slice_tensor(quadrant_4, half_n, None, col_dim)
# Insert said quadrant into the rotated z-cache
quadrant_3_slicer = empty_slicer(z_cache)
quadrant_3_slicer[col_dim] = slice(half_n, None)
z_cache[quadrant_3_slicer] = quadrant_4
return z_cache
# Initialize the z cache to the left half of z.
z_cache_shape = list(z.shape)
z_cache_shape[col_dim] = half_n
z_cache = z.new_zeros(z_cache_shape)
z_cache_slicer = empty_slicer(z_cache)
z_cache_slicer[col_dim] = slice(0, half_n)
z_cache.copy_(z[z_cache_slicer])
z_cache_rotated = False
# We need to reorient the z-cache at the halfway point, and we
# don't want a single chunk to straddle that point. We contract one
# of the chunks in the middle to address that problem.
i_range = list(range(0, half_n, inplace_chunk_size))
initial_offsets = [i_2 - i_1 for i_1, i_2 in zip(i_range, i_range[1:] + [half_n])]
after_half = list(range(half_n, n, inplace_chunk_size))
after_half_offsets = [inplace_chunk_size for _ in after_half]
combined_range_with_offsets = zip(i_range + after_half, initial_offsets + after_half_offsets)
for i, offset in combined_range_with_offsets:
if not z_cache_rotated and i >= half_n:
z_cache = flip_z_cache_(z_cache, z)
z_cache_rotated = True
z_chunk_b = slice_tensor(z, i, i + offset, b_chunk_dim)
mask_chunk = slice_tensor(mask, i, i + offset, b_chunk_dim)
z_chunk_b = z_chunk_b.clone()
if b_chunk_dim == col_dim:
z_chunk_b = slice_tensor(z, i, i + offset, col_dim)
else: # b_chunk_dim == row_dim
# In this case, the b-dimension (b_chunk_dim) is partially
# overwritten at the end of each iteration. We need to
# restore the missing component from the z-cache.
if not z_cache_rotated:
z_chunk_slicer = empty_slicer(z_chunk_b)
z_chunk_slicer[col_dim] = slice(0, half_n)
z_chunk_b[z_chunk_slicer] = slice_tensor(z_cache, i, i + offset, row_dim)
else:
z_cache_offset = i - half_n
z_chunk_b = slice_tensor(z_cache, z_cache_offset, z_cache_offset + offset, row_dim)
b_chunk = compute_projection(z_chunk_b, mask_chunk, a=False, chunked=False)
del z_chunk_b
x_chunk = torch.matmul(a, b_chunk)
x_chunk = permute_final_dims(x_chunk, (1, 2, 0))
x_chunk = self.layer_norm_out(x_chunk)
x_chunk = self.linear_z(x_chunk)
# The g dimension (col_dim) is parallel to and ahead of the
# overwrites in z. We can extract the g chunk normally.
z_chunk_g = slice_tensor(z, i, i + offset, col_dim)
g_chunk = self.linear_g(self.layer_norm_in(z_chunk_g))
g_chunk.sigmoid_()
del z_chunk_g
x_chunk *= g_chunk
# Write the columns into z in-place
z_slicer = empty_slicer(z)
z_slicer[col_dim] = slice(i, i + offset)
if with_add:
z[z_slicer] += x_chunk
else:
z[z_slicer] = x_chunk
else:
b = compute_projection(z, mask, False, False)
x = torch.matmul(a, b)
x = self.layer_norm_out(x)
x = self.linear_z(x)
g = self.linear_g(z)
g.sigmoid_()
x *= g
if with_add:
z += x
else:
z = x
return z
def forward(
self,
z: torch.Tensor,
mask: Optional[torch.Tensor] = None,
inplace_safe: bool = False,
_add_with_inplace: bool = False,
_inplace_chunk_size: Optional[int] = 256,
) -> torch.Tensor:
"""
Args:
x:
[*, N_res, N_res, C_z] input tensor
mask:
[*, N_res, N_res] input mask
Returns:
[*, N_res, N_res, C_z] output tensor
"""
if inplace_safe:
x = self._inference_forward(
z,
mask,
inplace_chunk_size=_inplace_chunk_size,
with_add=_add_with_inplace,
)
return x
if mask is None:
mask = z.new_ones(z.shape[:-1])
mask = mask.unsqueeze(-1)
z = self.layer_norm_in(z)
a = mask
a = a * self.sigmoid(self.linear_a_g(z))
a = a * self.linear_a_p(z)
b = mask
b = b * self.sigmoid(self.linear_b_g(z))
b = b * self.linear_b_p(z)
if is_fp16_enabled():
with torch.cuda.amp.autocast(enabled=False):
x = self._combine_projections(a.float(), b.float())
else:
x = self._combine_projections(a, b)
del a, b
x = self.layer_norm_out(x)
x = self.linear_z(x)
g = self.sigmoid(self.linear_g(z))
x = x * g
return x
class EsmFoldPreTrainedModel(EsmPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
# Subclass `EsMPreTrainedModel` to deal with special init
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, EsmFoldLinear):
with torch.no_grad():
if module.init_fn is not None:
module.init_fn(module.weight, module.bias)
elif module.init == "default":
trunc_normal_init_(module.weight, scale=1.0)
elif module.init == "relu":
trunc_normal_init_(module.weight, scale=2.0)
elif module.init == "glorot":
nn.init.xavier_uniform_(module.weight, gain=1)
elif module.init == "gating":
module.weight.fill_(0.0)
if module.bias:
module.bias.fill_(1.0)
elif module.init == "normal":
torch.nn.init.kaiming_normal_(module.weight, nonlinearity="linear")
elif module.init == "final":
module.weight.fill_(0.0)
elif isinstance(module, EsmFoldInvariantPointAttention):
ipa_point_weights_init_(module.head_weights)
elif isinstance(module, EsmFoldTriangularSelfAttentionBlock):
torch.nn.init.zeros_(module.tri_mul_in.linear_z.weight)
torch.nn.init.zeros_(module.tri_mul_in.linear_z.bias)
torch.nn.init.zeros_(module.tri_mul_out.linear_z.weight)
torch.nn.init.zeros_(module.tri_mul_out.linear_z.bias)
torch.nn.init.zeros_(module.tri_att_start.mha.linear_o.weight)
torch.nn.init.zeros_(module.tri_att_start.mha.linear_o.bias)
torch.nn.init.zeros_(module.tri_att_end.mha.linear_o.weight)
torch.nn.init.zeros_(module.tri_att_end.mha.linear_o.bias)
torch.nn.init.zeros_(module.sequence_to_pair.o_proj.weight)
torch.nn.init.zeros_(module.sequence_to_pair.o_proj.bias)
torch.nn.init.zeros_(module.pair_to_sequence.linear.weight)
torch.nn.init.zeros_(module.seq_attention.o_proj.weight)
torch.nn.init.zeros_(module.seq_attention.o_proj.bias)
torch.nn.init.zeros_(module.mlp_seq.mlp[-2].weight)
torch.nn.init.zeros_(module.mlp_seq.mlp[-2].bias)
torch.nn.init.zeros_(module.mlp_pair.mlp[-2].weight)
torch.nn.init.zeros_(module.mlp_pair.mlp[-2].bias)
else:
super()._init_weights(module)
class EsmFoldSelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads, head_width, gated=False):
super().__init__()
assert embed_dim == num_heads * head_width
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_width = head_width
self.proj = nn.Linear(embed_dim, embed_dim * 3, bias=False)
self.o_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.gated = gated
if gated:
self.g_proj = nn.Linear(embed_dim, embed_dim)
torch.nn.init.zeros_(self.g_proj.weight)
torch.nn.init.ones_(self.g_proj.bias)
self.rescale_factor = self.head_width**-0.5
torch.nn.init.zeros_(self.o_proj.bias)
def forward(self, x, mask=None, bias=None, indices=None):
"""
Basic self attention with optional mask and external pairwise bias. To handle sequences of different lengths,
use mask.
Inputs:
x: batch of input sequneces (.. x L x C) mask: batch of boolean masks where 1=valid, 0=padding position (..
x L_k) bias: batch of scalar pairwise attention biases (.. x Lq x Lk x num_heads)
Outputs:
sequence projection (B x L x embed_dim), attention maps (B x L x L x num_heads)
"""
t = self.proj(x).view(*x.shape[:2], self.num_heads, -1)
t = t.permute(0, 2, 1, 3)
q, k, v = t.chunk(3, dim=-1)
q = self.rescale_factor * q
a = torch.einsum("...qc,...kc->...qk", q, k)
# Add external attention bias.
if bias is not None:
a = a + bias.permute(0, 3, 1, 2)
# Do not attend to padding tokens.
if mask is not None:
mask = mask[:, None, None]
a = a.masked_fill(mask == False, -np.inf) # noqa: E712
a = nn.functional.softmax(a, dim=-1)
y = torch.einsum("...hqk,...hkc->...qhc", a, v)
y = y.reshape(*y.shape[:2], -1)
if self.gated:
y = self.g_proj(x).sigmoid() * y
y = self.o_proj(y)
return y, a.permute(0, 3, 1, 2)
class EsmFoldDropout(nn.Module):
"""
Implementation of dropout with the ability to share the dropout mask along a particular dimension.
"""
def __init__(self, r: float, batch_dim: Union[int, List[int]]):
super().__init__()
self.r = r
if isinstance(batch_dim, int):
batch_dim = [batch_dim]
self.batch_dim = batch_dim
self.dropout = nn.Dropout(self.r)
def forward(self, x: torch.Tensor) -> torch.Tensor:
shape = list(x.shape)
if self.batch_dim is not None:
for bd in self.batch_dim:
shape[bd] = 1
return x * self.dropout(x.new_ones(shape))
class EsmFoldSequenceToPair(nn.Module):
def __init__(self, sequence_state_dim, inner_dim, pairwise_state_dim):
super().__init__()
self.layernorm = nn.LayerNorm(sequence_state_dim)
self.proj = nn.Linear(sequence_state_dim, inner_dim * 2, bias=True)
self.o_proj = nn.Linear(2 * inner_dim, pairwise_state_dim, bias=True)
torch.nn.init.zeros_(self.proj.bias)
torch.nn.init.zeros_(self.o_proj.bias)
def forward(self, sequence_state):
"""
Inputs:
sequence_state: B x L x sequence_state_dim
Output:
pairwise_state: B x L x L x pairwise_state_dim
Intermediate state:
B x L x L x 2*inner_dim
"""
assert len(sequence_state.shape) == 3
s = self.layernorm(sequence_state)
s = self.proj(s)
q, k = s.chunk(2, dim=-1)
prod = q[:, None, :, :] * k[:, :, None, :]
diff = q[:, None, :, :] - k[:, :, None, :]
x = torch.cat([prod, diff], dim=-1)
x = self.o_proj(x)
return x
class EsmFoldPairToSequence(nn.Module):
def __init__(self, pairwise_state_dim, num_heads):
super().__init__()
self.layernorm = nn.LayerNorm(pairwise_state_dim)
self.linear = nn.Linear(pairwise_state_dim, num_heads, bias=False)
def forward(self, pairwise_state):
"""
Inputs:
pairwise_state: B x L x L x pairwise_state_dim
Output:
pairwise_bias: B x L x L x num_heads
"""
assert len(pairwise_state.shape) == 4
z = self.layernorm(pairwise_state)
pairwise_bias = self.linear(z)
return pairwise_bias
class EsmFoldResidueMLP(nn.Module):
def __init__(self, embed_dim, inner_dim, dropout=0):
super().__init__()
self.mlp = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, inner_dim),
nn.ReLU(),
nn.Linear(inner_dim, embed_dim),
nn.Dropout(dropout),
)
def forward(self, x):
return x + self.mlp(x)
class EsmFoldTriangularSelfAttentionBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
sequence_state_dim = config.sequence_state_dim
pairwise_state_dim = config.pairwise_state_dim
sequence_num_heads = sequence_state_dim // config.sequence_head_width
pairwise_num_heads = pairwise_state_dim // config.pairwise_head_width
self.layernorm_1 = nn.LayerNorm(sequence_state_dim)
self.sequence_to_pair = EsmFoldSequenceToPair(sequence_state_dim, pairwise_state_dim // 2, pairwise_state_dim)
self.pair_to_sequence = EsmFoldPairToSequence(pairwise_state_dim, sequence_num_heads)
self.seq_attention = EsmFoldSelfAttention(
sequence_state_dim, sequence_num_heads, config.sequence_head_width, gated=True
)
self.tri_mul_out = EsmFoldTriangleMultiplicativeUpdate(config, _outgoing=True)
self.tri_mul_in = EsmFoldTriangleMultiplicativeUpdate(config, _outgoing=False)
self.tri_att_start = EsmFoldTriangleAttention(
pairwise_state_dim, config.pairwise_head_width, pairwise_num_heads, inf=1e9, starting=True
)
self.tri_att_end = EsmFoldTriangleAttention(
pairwise_state_dim, config.pairwise_head_width, pairwise_num_heads, inf=1e9, starting=False
)
self.mlp_seq = EsmFoldResidueMLP(sequence_state_dim, 4 * sequence_state_dim, dropout=config.dropout)
self.mlp_pair = EsmFoldResidueMLP(pairwise_state_dim, 4 * pairwise_state_dim, dropout=config.dropout)
self.drop = nn.Dropout(config.dropout)
self.row_drop = EsmFoldDropout(config.dropout * 2, 2)
self.col_drop = EsmFoldDropout(config.dropout * 2, 1)
def forward(self, sequence_state, pairwise_state, mask=None, chunk_size=None, **__kwargs):
"""
Inputs:
sequence_state: B x L x sequence_state_dim pairwise_state: B x L x L x pairwise_state_dim mask: B x L boolean
tensor of valid positions
Output:
sequence_state: B x L x sequence_state_dim pairwise_state: B x L x L x pairwise_state_dim
"""
if len(sequence_state.shape) != 3:
raise ValueError(f"`sequence_state` should be a 3d-tensor, got {len(sequence_state.shape)} dims.")
if len(pairwise_state.shape) != 4:
raise ValueError(f"`pairwise_state` should be a 4d-tensor, got {len(pairwise_state.shape)} dims.")
if mask is not None and len(mask.shape) != 2:
raise ValueError(f"`mask` should be a 2d-tensor, got {len(mask.shape)} dims.")
batch_dim, seq_dim, sequence_state_dim = sequence_state.shape
pairwise_state_dim = pairwise_state.shape[3]
if sequence_state_dim != self.config.sequence_state_dim:
raise ValueError(
"`sequence_state` last dimension should be equal to `self.sequence_state_dim`. Got "
f"{sequence_state_dim} != {self.config.sequence_state_dim}."
)
if pairwise_state_dim != self.config.pairwise_state_dim:
raise ValueError(
"`pairwise_state` last dimension should be equal to `self.pairwise_state_dim`. Got "
f"{pairwise_state_dim} != {self.config.pairwise_state_dim}."
)
if batch_dim != pairwise_state.shape[0]:
raise ValueError(
f"`sequence_state` and `pairwise_state` have inconsistent batch size: {batch_dim} != "
f"{pairwise_state.shape[0]}."
)
if seq_dim != pairwise_state.shape[1] or seq_dim != pairwise_state.shape[2]:
raise ValueError(
f"`sequence_state` and `pairwise_state` have inconsistent sequence length: {seq_dim} != "
f"{pairwise_state.shape[1]} or {pairwise_state.shape[2]}."
)
# Update sequence state
bias = self.pair_to_sequence(pairwise_state)
# Self attention with bias + mlp.
y = self.layernorm_1(sequence_state)
y, _ = self.seq_attention(y, mask=mask, bias=bias)
sequence_state = sequence_state + self.drop(y)
sequence_state = self.mlp_seq(sequence_state)
# Update pairwise state
pairwise_state = pairwise_state + self.sequence_to_pair(sequence_state)
# Axial attention with triangular bias.
tri_mask = mask.unsqueeze(2) * mask.unsqueeze(1) if mask is not None else None
pairwise_state = pairwise_state + self.row_drop(self.tri_mul_out(pairwise_state, mask=tri_mask))
pairwise_state = pairwise_state + self.col_drop(self.tri_mul_in(pairwise_state, mask=tri_mask))
pairwise_state = pairwise_state + self.row_drop(
self.tri_att_start(pairwise_state, mask=tri_mask, chunk_size=chunk_size)
)
pairwise_state = pairwise_state + self.col_drop(
self.tri_att_end(pairwise_state, mask=tri_mask, chunk_size=chunk_size)
)
# MLP over pairs.
pairwise_state = self.mlp_pair(pairwise_state)
return sequence_state, pairwise_state
class EsmCategoricalMixture:
def __init__(self, param, bins=50, start=0, end=1):
# All tensors are of shape ..., bins.
self.logits = param
bins = torch.linspace(start, end, bins + 1, device=self.logits.device, dtype=self.logits.dtype)
self.v_bins = (bins[:-1] + bins[1:]) / 2
def log_prob(self, true):
# Shapes are:
# self.probs: ... x bins
# true : ...
true_index = (true.unsqueeze(-1) - self.v_bins[[None] * true.ndim]).abs().argmin(-1)
nll = self.logits.log_softmax(-1)
return torch.take_along_dim(nll, true_index.unsqueeze(-1), dim=-1).squeeze(-1)
def mean(self):
return (self.logits.softmax(-1) @ self.v_bins.unsqueeze(1)).squeeze(-1)
def categorical_lddt(logits, bins=50):
# Logits are ..., 37, bins.
return EsmCategoricalMixture(logits, bins=bins).mean()
def get_axial_mask(mask):
"""
Helper to convert B x L mask of valid positions to axial mask used in row column attentions.
Input:
mask: B x L tensor of booleans
Output:
mask: B x L x L tensor of booleans
"""
if mask is None:
return None
if len(mask.shape) != 2:
raise ValueError(f"`mask` should be a 2d-tensor, got {len(mask.shape)} dims.")
batch_dim, seq_dim = mask.shape
m = mask.unsqueeze(1).expand(batch_dim, seq_dim, seq_dim)
m = m.reshape(batch_dim * seq_dim, seq_dim)
return m
class EsmFoldRelativePosition(nn.Module):
def __init__(self, config):
super().__init__()
self.bins = config.position_bins
# Note an additional offset is used so that the 0th position
# is reserved for masked pairs.
self.embedding = torch.nn.Embedding(2 * self.bins + 2, config.pairwise_state_dim)
def forward(self, residue_index, mask=None):
"""
Input:
residue_index: B x L tensor of indices (dytpe=torch.long) mask: B x L tensor of booleans
Output:
pairwise_state: B x L x L x pairwise_state_dim tensor of embeddings
"""
if residue_index.dtype != torch.long:
raise ValueError(f"`residue_index` has dtype {residue_index.dtype}, it should be `torch.long`.")
if mask is not None and residue_index.shape != mask.shape:
raise ValueError(
f"`residue_index` and `mask` have inconsistent shapes: {residue_index.shape} != {mask.shape}."
)
diff = residue_index[:, None, :] - residue_index[:, :, None]
diff = diff.clamp(-self.bins, self.bins)
diff = diff + self.bins + 1 # Add 1 to adjust for padding index.
if mask is not None:
mask = mask[:, None, :] * mask[:, :, None]
diff[mask == False] = 0 # noqa: E712
output = self.embedding(diff)
return output
class EsmFoldAngleResnetBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.linear_1 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="relu")
self.linear_2 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="final")
self.relu = nn.ReLU()
def forward(self, a: torch.Tensor) -> torch.Tensor:
s_initial = a
a = self.relu(a)
a = self.linear_1(a)
a = self.relu(a)
a = self.linear_2(a)
return a + s_initial
class EsmFoldAngleResnet(nn.Module):
"""
Implements Algorithm 20, lines 11-14
"""
def __init__(self, config):
super().__init__()
self.config = config
self.linear_in = EsmFoldLinear(config.sequence_dim, config.resnet_dim)
self.linear_initial = EsmFoldLinear(config.sequence_dim, config.resnet_dim)
self.layers = nn.ModuleList()
for _ in range(config.num_resnet_blocks):
layer = EsmFoldAngleResnetBlock(config)
self.layers.append(layer)
self.linear_out = EsmFoldLinear(config.resnet_dim, config.num_angles * 2)
self.relu = nn.ReLU()
def forward(self, s: torch.Tensor, s_initial: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
s:
[*, C_hidden] single embedding
s_initial:
[*, C_hidden] single embedding as of the start of the StructureModule
Returns:
[*, no_angles, 2] predicted angles
"""
# NOTE: The ReLU's applied to the inputs are absent from the supplement
# pseudocode but present in the source. For maximal compatibility with
# the pretrained weights, I'm going with the source.
# [*, C_hidden]
s_initial = self.relu(s_initial)
s_initial = self.linear_initial(s_initial)
s = self.relu(s)
s = self.linear_in(s)
s = s + s_initial
for l in self.layers:
s = l(s)
s = self.relu(s)
# [*, no_angles * 2]
s = self.linear_out(s)
# [*, no_angles, 2]
s = s.view(s.shape[:-1] + (-1, 2))
unnormalized_s = s
norm_denom = torch.sqrt(
torch.clamp(
torch.sum(s**2, dim=-1, keepdim=True),
min=self.config.epsilon,
)
)
s = s / norm_denom
return unnormalized_s, s
class EsmFoldInvariantPointAttention(nn.Module):
"""
Implements Algorithm 22.
"""
def __init__(self, config):
super().__init__()
self.config = config
c_s = config.sequence_dim
c_z = config.pairwise_dim
self.hidden_dim = config.ipa_dim
self.num_heads = config.num_heads_ipa
self.num_qk_points = config.num_qk_points
self.num_v_points = config.num_v_points
# These linear layers differ from their specifications in the
# supplement. There, they lack bias and use Glorot initialization.
# Here as in the official source, they have bias and use the default
# Lecun initialization.
hc = config.ipa_dim * config.num_heads_ipa
self.linear_q = EsmFoldLinear(c_s, hc)
self.linear_kv = EsmFoldLinear(c_s, 2 * hc)
hpq = config.num_heads_ipa * config.num_qk_points * 3
self.linear_q_points = EsmFoldLinear(c_s, hpq)
hpkv = config.num_heads_ipa * (config.num_qk_points + config.num_v_points) * 3
self.linear_kv_points = EsmFoldLinear(c_s, hpkv)
self.linear_b = EsmFoldLinear(c_z, config.num_heads_ipa)
self.head_weights = nn.Parameter(torch.zeros((config.num_heads_ipa)))
concat_out_dim = config.num_heads_ipa * (c_z + config.ipa_dim + config.num_v_points * 4)
self.linear_out = EsmFoldLinear(concat_out_dim, c_s, init="final")
self.softmax = nn.Softmax(dim=-1)
self.softplus = nn.Softplus()
def forward(
self,
s: torch.Tensor,
z: Optional[torch.Tensor],
r: Rigid,
mask: torch.Tensor,
_offload_inference: bool = False,
_z_reference_list: Optional[Sequence[torch.Tensor]] = None,
) -> torch.Tensor:
"""
Args:
s:
[*, N_res, C_s] single representation
z:
[*, N_res, N_res, C_z] pair representation
r:
[*, N_res] transformation object
mask:
[*, N_res] mask
Returns:
[*, N_res, C_s] single representation update
"""
z = [z]
#######################################
# Generate scalar and point activations
#######################################
# [*, N_res, H * C_hidden]
q = self.linear_q(s)
kv = self.linear_kv(s)
# [*, N_res, H, C_hidden]
q = q.view(q.shape[:-1] + (self.num_heads, -1))
# [*, N_res, H, 2 * C_hidden]
kv = kv.view(kv.shape[:-1] + (self.num_heads, -1))
# [*, N_res, H, C_hidden]
k, v = torch.split(kv, self.hidden_dim, dim=-1)
# [*, N_res, H * P_q * 3]
q_pts = self.linear_q_points(s)
# This is kind of clunky, but it's how the original does it
# [*, N_res, H * P_q, 3]
q_pts = torch.split(q_pts, q_pts.shape[-1] // 3, dim=-1)
q_pts = torch.stack(q_pts, dim=-1)
q_pts = r[..., None].apply(q_pts)
# [*, N_res, H, P_q, 3]
q_pts = q_pts.view(q_pts.shape[:-2] + (self.num_heads, self.num_qk_points, 3))
# [*, N_res, H * (P_q + P_v) * 3]
kv_pts = self.linear_kv_points(s)
# [*, N_res, H * (P_q + P_v), 3]
kv_pts = torch.split(kv_pts, kv_pts.shape[-1] // 3, dim=-1)
kv_pts = torch.stack(kv_pts, dim=-1)
kv_pts = r[..., None].apply(kv_pts)
# [*, N_res, H, (P_q + P_v), 3]
kv_pts = kv_pts.view(kv_pts.shape[:-2] + (self.num_heads, -1, 3))
# [*, N_res, H, P_q/P_v, 3]
k_pts, v_pts = torch.split(kv_pts, [self.num_qk_points, self.num_v_points], dim=-2)
##########################
# Compute attention scores
##########################
# [*, N_res, N_res, H]
b = self.linear_b(z[0])
if _offload_inference:
assert sys.getrefcount(z[0]) == 2
z[0] = z[0].cpu()
# [*, H, N_res, N_res]
if is_fp16_enabled():
with torch.cuda.amp.autocast(enabled=False):
a = torch.matmul(
permute_final_dims(q.float(), (1, 0, 2)), # [*, H, N_res, C_hidden]
permute_final_dims(k.float(), (1, 2, 0)), # [*, H, C_hidden, N_res]
)
else:
a = torch.matmul(
permute_final_dims(q, (1, 0, 2)), # [*, H, N_res, C_hidden]
permute_final_dims(k, (1, 2, 0)), # [*, H, C_hidden, N_res]
)
a *= math.sqrt(1.0 / (3 * self.hidden_dim))
a += math.sqrt(1.0 / 3) * permute_final_dims(b, (2, 0, 1))
# [*, N_res, N_res, H, P_q, 3]
pt_att = q_pts.unsqueeze(-4) - k_pts.unsqueeze(-5)
pt_att = pt_att**2
# [*, N_res, N_res, H, P_q]
pt_att = sum(torch.unbind(pt_att, dim=-1))
head_weights = self.softplus(self.head_weights).view(*((1,) * len(pt_att.shape[:-2]) + (-1, 1)))
head_weights = head_weights * math.sqrt(1.0 / (3 * (self.num_qk_points * 9.0 / 2)))
pt_att = pt_att * head_weights
# [*, N_res, N_res, H]
pt_att = torch.sum(pt_att, dim=-1) * (-0.5)
# [*, N_res, N_res]
square_mask = mask.unsqueeze(-1) * mask.unsqueeze(-2)
square_mask = self.config.inf * (square_mask - 1)
# [*, H, N_res, N_res]
pt_att = permute_final_dims(pt_att, (2, 0, 1))
a = a + pt_att
a = a + square_mask.unsqueeze(-3)
a = self.softmax(a)
################
# Compute output
################
# [*, N_res, H, C_hidden]
o = torch.matmul(a, v.transpose(-2, -3).to(dtype=a.dtype)).transpose(-2, -3)
# [*, N_res, H * C_hidden]
o = flatten_final_dims(o, 2)
# [*, H, 3, N_res, P_v]
o_pt = torch.sum(
(a[..., None, :, :, None] * permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]),
dim=-2,
)
# [*, N_res, H, P_v, 3]
o_pt = permute_final_dims(o_pt, (2, 0, 3, 1))
o_pt = r[..., None, None].invert_apply(o_pt)
# [*, N_res, H * P_v]
o_pt_norm = flatten_final_dims(torch.sqrt(torch.sum(o_pt**2, dim=-1) + self.config.epsilon), 2)
# [*, N_res, H * P_v, 3]
o_pt = o_pt.reshape(*o_pt.shape[:-3], -1, 3)
if _offload_inference:
z[0] = z[0].to(o_pt.device)
# [*, N_res, H, C_z]
o_pair = torch.matmul(a.transpose(-2, -3), z[0].to(dtype=a.dtype))
# [*, N_res, H * C_z]
o_pair = flatten_final_dims(o_pair, 2)
# [*, N_res, C_s]
s = self.linear_out(
torch.cat((o, *torch.unbind(o_pt, dim=-1), o_pt_norm, o_pair), dim=-1).to(dtype=z[0].dtype)
)
return s
class EsmFoldBackboneUpdate(nn.Module):
"""
Implements part of Algorithm 23.
"""
def __init__(self, config):
super().__init__()
self.linear = EsmFoldLinear(config.sequence_dim, 6, init="final")
def forward(self, s: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
[*, N_res, C_s] single representation
Returns:
[*, N_res, 6] update vector
"""
# [*, 6]
update = self.linear(s)
return update
class EsmFoldStructureModuleTransitionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.linear_1 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
self.linear_2 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
self.linear_3 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="final")
self.relu = nn.ReLU()
def forward(self, s):
s_initial = s
s = self.linear_1(s)
s = self.relu(s)
s = self.linear_2(s)
s = self.relu(s)
s = self.linear_3(s)
s = s + s_initial
return s
class EsmFoldStructureModuleTransition(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList()
for _ in range(config.num_transition_layers):
l = EsmFoldStructureModuleTransitionLayer(config)
self.layers.append(l)
self.dropout = nn.Dropout(config.dropout_rate)
self.layer_norm = LayerNorm(config.sequence_dim)
def forward(self, s):
for l in self.layers:
s = l(s)
s = self.dropout(s)
s = self.layer_norm(s)
return s
class EsmFoldStructureModule(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# Buffers to be lazily initialized later
# self.default_frames
# self.group_idx
# self.atom_mask
# self.lit_positions
self.layer_norm_s = LayerNorm(config.sequence_dim)
self.layer_norm_z = LayerNorm(config.pairwise_dim)
self.linear_in = EsmFoldLinear(config.sequence_dim, config.sequence_dim)
self.ipa = EsmFoldInvariantPointAttention(config)
self.ipa_dropout = nn.Dropout(config.dropout_rate)
self.layer_norm_ipa = LayerNorm(config.sequence_dim)
self.transition = EsmFoldStructureModuleTransition(config)
self.bb_update = EsmFoldBackboneUpdate(config)
self.angle_resnet = EsmFoldAngleResnet(config)
def forward(
self,
evoformer_output_dict,
aatype,
mask=None,
_offload_inference=False,
):
"""
Args:
evoformer_output_dict:
Dictionary containing:
"single":
[*, N_res, C_s] single representation
"pair":
[*, N_res, N_res, C_z] pair representation
aatype:
[*, N_res] amino acid indices
mask:
Optional [*, N_res] sequence mask
Returns:
A dictionary of outputs
"""
s = evoformer_output_dict["single"]
if mask is None:
# [*, N]
mask = s.new_ones(s.shape[:-1])
# [*, N, C_s]
s = self.layer_norm_s(s)
# [*, N, N, C_z]
z = self.layer_norm_z(evoformer_output_dict["pair"])
z_reference_list = None
if _offload_inference:
assert sys.getrefcount(evoformer_output_dict["pair"]) == 2
evoformer_output_dict["pair"] = evoformer_output_dict["pair"].cpu()
z_reference_list = [z]
z = None
# [*, N, C_s]
s_initial = s
s = self.linear_in(s)
# [*, N]
rigids = Rigid.identity(
s.shape[:-1],
s.dtype,
s.device,
self.training,
fmt="quat",
)
outputs = []
for i in range(self.config.num_blocks):
# [*, N, C_s]
s = s + self.ipa(
s,
z,
rigids,
mask,
_offload_inference=_offload_inference,
_z_reference_list=z_reference_list,
)
s = self.ipa_dropout(s)
s = self.layer_norm_ipa(s)
s = self.transition(s)
# [*, N]
rigids = rigids.compose_q_update_vec(self.bb_update(s))
# To hew as closely as possible to AlphaFold, we convert our
# quaternion-based transformations to rotation-matrix ones
# here
backb_to_global = Rigid(
Rotation(rot_mats=rigids.get_rots().get_rot_mats(), quats=None),
rigids.get_trans(),
)
backb_to_global = backb_to_global.scale_translation(self.config.trans_scale_factor)
# [*, N, 7, 2]
unnormalized_angles, angles = self.angle_resnet(s, s_initial)
all_frames_to_global = self.torsion_angles_to_frames(backb_to_global, angles, aatype)
pred_xyz = self.frames_and_literature_positions_to_atom14_pos(all_frames_to_global, aatype)
scaled_rigids = rigids.scale_translation(self.config.trans_scale_factor)
preds = {
"frames": scaled_rigids.to_tensor_7(),
"sidechain_frames": all_frames_to_global.to_tensor_4x4(),
"unnormalized_angles": unnormalized_angles,
"angles": angles,
"positions": pred_xyz,
"states": s,
}
outputs.append(preds)
rigids = rigids.stop_rot_gradient()
del z, z_reference_list
if _offload_inference:
evoformer_output_dict["pair"] = evoformer_output_dict["pair"].to(s.device)
outputs = dict_multimap(torch.stack, outputs)
outputs["single"] = s
return outputs
def _init_residue_constants(self, float_dtype, device):
if not hasattr(self, "default_frames"):
self.register_buffer(
"default_frames",
torch.tensor(
residue_constants.restype_rigid_group_default_frame,
dtype=float_dtype,
device=device,
requires_grad=False,
),
persistent=False,
)
if not hasattr(self, "group_idx"):
self.register_buffer(
"group_idx",
torch.tensor(
residue_constants.restype_atom14_to_rigid_group,
device=device,
requires_grad=False,
),
persistent=False,
)
if not hasattr(self, "atom_mask"):
self.register_buffer(
"atom_mask",
torch.tensor(
residue_constants.restype_atom14_mask,
dtype=float_dtype,
device=device,
requires_grad=False,
),
persistent=False,
)
if not hasattr(self, "lit_positions"):
self.register_buffer(
"lit_positions",
torch.tensor(
residue_constants.restype_atom14_rigid_group_positions,
dtype=float_dtype,
device=device,
requires_grad=False,
),
persistent=False,
)
def torsion_angles_to_frames(self, r, alpha, f):
# Lazily initialize the residue constants on the correct device
self._init_residue_constants(alpha.dtype, alpha.device)
# Separated purely to make testing less annoying
return torsion_angles_to_frames(r, alpha, f, self.default_frames)
def frames_and_literature_positions_to_atom14_pos(self, r, f): # [*, N, 8] # [*, N]
# Lazily initialize the residue constants on the correct device
self._init_residue_constants(r.get_rots().dtype, r.get_rots().device)
return frames_and_literature_positions_to_atom14_pos(
r,
f,
self.default_frames,
self.group_idx,
self.atom_mask,
self.lit_positions,
)
class EsmFoldingTrunk(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
c_s = config.sequence_state_dim
c_z = config.pairwise_state_dim
self.pairwise_positional_embedding = EsmFoldRelativePosition(config)
self.blocks = nn.ModuleList([EsmFoldTriangularSelfAttentionBlock(config) for _ in range(config.num_blocks)])
self.recycle_bins = 15
self.recycle_s_norm = nn.LayerNorm(c_s)
self.recycle_z_norm = nn.LayerNorm(c_z)
self.recycle_disto = nn.Embedding(self.recycle_bins, c_z)
self.recycle_disto.weight[0].detach().zero_()
self.structure_module = EsmFoldStructureModule(config.structure_module)
self.trunk2sm_s = nn.Linear(c_s, config.structure_module.sequence_dim)
self.trunk2sm_z = nn.Linear(c_z, config.structure_module.pairwise_dim)
self.chunk_size = config.chunk_size
def set_chunk_size(self, chunk_size):
# This parameter means the axial attention will be computed
# in a chunked manner. This should make the memory used more or less O(L) instead of O(L^2).
# It's equivalent to running a for loop over chunks of the dimension we're iterative over,
# where the chunk_size is the size of the chunks, so 128 would mean to parse 128-lengthed chunks.
self.chunk_size = chunk_size
def forward(self, seq_feats, pair_feats, true_aa, residx, mask, no_recycles):
"""
Inputs:
seq_feats: B x L x C tensor of sequence features pair_feats: B x L x L x C tensor of pair features residx: B
x L long tensor giving the position in the sequence mask: B x L boolean tensor indicating valid residues
Output:
predicted_structure: B x L x (num_atoms_per_residue * 3) tensor wrapped in a Coordinates object
"""
device = seq_feats.device
s_s_0 = seq_feats
s_z_0 = pair_feats
if no_recycles is None:
no_recycles = self.config.max_recycles
else:
if no_recycles < 0:
raise ValueError("Number of recycles must not be negative.")
no_recycles += 1 # First 'recycle' is just the standard forward pass through the model.
def trunk_iter(s, z, residx, mask):
z = z + self.pairwise_positional_embedding(residx, mask=mask)
for block in self.blocks:
s, z = block(s, z, mask=mask, residue_index=residx, chunk_size=self.chunk_size)
return s, z
s_s = s_s_0
s_z = s_z_0
recycle_s = torch.zeros_like(s_s)
recycle_z = torch.zeros_like(s_z)
recycle_bins = torch.zeros(*s_z.shape[:-1], device=device, dtype=torch.int64)
for recycle_idx in range(no_recycles):
with ContextManagers([] if recycle_idx == no_recycles - 1 else [torch.no_grad()]):
# === Recycling ===
recycle_s = self.recycle_s_norm(recycle_s.detach()).to(device)
recycle_z = self.recycle_z_norm(recycle_z.detach()).to(device)
recycle_z += self.recycle_disto(recycle_bins.detach()).to(device)
s_s, s_z = trunk_iter(s_s_0 + recycle_s, s_z_0 + recycle_z, residx, mask)
# === Structure module ===
structure = self.structure_module(
{"single": self.trunk2sm_s(s_s), "pair": self.trunk2sm_z(s_z)},
true_aa,
mask.float(),
)
recycle_s = s_s
recycle_z = s_z
# Distogram needs the N, CA, C coordinates, and bin constants same as alphafold.
recycle_bins = EsmFoldingTrunk.distogram(
structure["positions"][-1][:, :, :3],
3.375,
21.375,
self.recycle_bins,
)
structure["s_s"] = s_s
structure["s_z"] = s_z
return structure
@staticmethod
def distogram(coords, min_bin, max_bin, num_bins):
# Coords are [... L x 3 x 3], where it's [N, CA, C] x 3 coordinates.
boundaries = torch.linspace(
min_bin,
max_bin,
num_bins - 1,
device=coords.device,
)
boundaries = boundaries**2
N, CA, C = [x.squeeze(-2) for x in coords.chunk(3, dim=-2)]
# Infer CB coordinates.
b = CA - N
c = C - CA
a = b.cross(c, dim=-1)
CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA
dists = (CB[..., None, :, :] - CB[..., :, None, :]).pow(2).sum(dim=-1, keepdims=True)
bins = torch.sum(dists > boundaries, dim=-1) # [..., L, L]
return bins
# TODO Add information to the docstring about any methods that convert to PDB format, or otherwise prepare
# the outputs for downstream use.
@add_start_docstrings(
"""
ESMForProteinFolding is the HuggingFace port of the original ESMFold model. It consists of an ESM-2 "stem" followed
by a protein folding "head", although unlike most other output heads, this "head" is similar in size and runtime to
the rest of the model combined! It outputs a dictionary containing predicted structural information about the input
protein(s).
""",
ESM_START_DOCSTRING,
)
class EsmForProteinFolding(EsmPreTrainedModel):
_no_split_modules = ["EsmFoldStructureModule", "EsmFoldTriangularSelfAttentionBlock"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.distogram_bins = 64
self.esm = EsmModel(config, add_pooling_layer=False)
self.esm.requires_grad_(False)
if self.config.esmfold_config.fp16_esm:
self.esm.half()
self.esm_feats = self.config.hidden_size
self.esm_attns = self.config.num_hidden_layers * self.config.num_attention_heads
self.esm_layers = self.config.num_hidden_layers
self.register_buffer("af2_to_esm", self._af2_to_esm_from_vocab_list(config.vocab_list))
self.esm_s_combine = nn.Parameter(torch.zeros(self.esm_layers + 1))
trunk_config = self.config.esmfold_config.trunk
c_s = trunk_config.sequence_state_dim
c_z = trunk_config.pairwise_state_dim
self.esm_s_mlp = nn.Sequential(
LayerNorm(self.esm_feats),
nn.Linear(self.esm_feats, c_s),
nn.ReLU(),
nn.Linear(c_s, c_s),
)
# 0 is padding, N is unknown residues, N + 1 is mask.
self.n_tokens_embed = residue_constants.restype_num + 3
self.pad_idx = 0
self.unk_idx = self.n_tokens_embed - 2
self.mask_idx = self.n_tokens_embed - 1
self.esm_dict_cls_idx = self.config.vocab_list.index("<cls>")
self.esm_dict_mask_idx = self.config.vocab_list.index("<mask>")
self.esm_dict_eos_idx = self.config.vocab_list.index("<eos>")
self.esm_dict_padding_idx = self.config.vocab_list.index("<pad>")
if self.config.esmfold_config.embed_aa:
self.embedding = nn.Embedding(self.n_tokens_embed, c_s, padding_idx=0)
self.trunk = EsmFoldingTrunk(trunk_config)
self.distogram_head = nn.Linear(c_z, self.distogram_bins)
self.ptm_head = nn.Linear(c_z, self.distogram_bins)
self.lm_head = nn.Linear(c_s, self.n_tokens_embed)
self.lddt_bins = 50
structure_module_config = trunk_config.structure_module
self.lddt_head = nn.Sequential(
nn.LayerNorm(structure_module_config.sequence_dim),
nn.Linear(structure_module_config.sequence_dim, self.config.esmfold_config.lddt_head_hid_dim),
nn.Linear(self.config.esmfold_config.lddt_head_hid_dim, self.config.esmfold_config.lddt_head_hid_dim),
nn.Linear(self.config.esmfold_config.lddt_head_hid_dim, 37 * self.lddt_bins),
)
@staticmethod
def _af2_to_esm_from_vocab_list(vocab_list: List[str]) -> torch.Tensor:
# Remember that t is shifted from residue_constants by 1 (0 is padding).
esm_reorder = [vocab_list.index("<pad>")] + [vocab_list.index(v) for v in residue_constants.restypes_with_x]
return torch.tensor(esm_reorder)
@add_start_docstrings_to_model_forward(ESMFOLD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=EsmForProteinFoldingOutput, config_class=EsmConfig)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
masking_pattern: Optional[torch.Tensor] = None,
num_recycles: Optional[int] = None,
) -> EsmForProteinFoldingOutput:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, EsmForProteinFolding
>>> model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
>>> inputs = tokenizer(["MLKNVQVQLV"], return_tensors="pt", add_special_tokens=False) # A tiny random peptide
>>> outputs = model(**inputs)
>>> folded_positions = outputs.positions
```
"""
cfg = self.config.esmfold_config
aa = input_ids # B x L
B = aa.shape[0]
L = aa.shape[1]
device = input_ids.device
if attention_mask is None:
attention_mask = torch.ones_like(aa, device=device)
if position_ids is None:
position_ids = torch.arange(L, device=device).expand_as(input_ids)
# === ESM ===
esmaa = self.af2_idx_to_esm_idx(aa, attention_mask)
if masking_pattern is not None:
masked_aa, esmaa, mlm_targets = self.bert_mask(aa, esmaa, attention_mask, masking_pattern)
else:
masked_aa = aa
mlm_targets = None
# We get sequence and pair representations from whatever version of ESM /
# configuration we are using. The sequence representation esm_s is always
# present. The pair embedding esm_z may be present depending on the
# configuration of the model. If esm_z is not used by the model then it
# is returned as None here.
esm_s = self.compute_language_model_representations(esmaa)
# Convert esm_s and esm_z, if present, to the precision used by the trunk and
# the structure module. These tensors may be a lower precision if, for example,
# we're running the language model in fp16 precision.
esm_s = esm_s.to(self.esm_s_combine.dtype)
if cfg.esm_ablate_sequence:
esm_s = esm_s * 0
esm_s = esm_s.detach()
# === preprocessing ===
esm_s = (self.esm_s_combine.softmax(0).unsqueeze(0) @ esm_s).squeeze(2)
s_s_0 = self.esm_s_mlp(esm_s)
s_z_0 = s_s_0.new_zeros(B, L, L, cfg.trunk.pairwise_state_dim)
if self.config.esmfold_config.embed_aa:
s_s_0 += self.embedding(masked_aa)
structure: dict = self.trunk(s_s_0, s_z_0, aa, position_ids, attention_mask, no_recycles=num_recycles)
# Documenting what we expect:
structure = {
k: v
for k, v in structure.items()
if k
in [
"s_z",
"s_s",
"frames",
"sidechain_frames",
"unnormalized_angles",
"angles",
"positions",
"states",
]
}
# Add BERT mask for the loss to use, if available.
if mlm_targets:
structure["mlm_targets"] = mlm_targets
disto_logits = self.distogram_head(structure["s_z"])
disto_logits = (disto_logits + disto_logits.transpose(1, 2)) / 2
structure["distogram_logits"] = disto_logits
lm_logits = self.lm_head(structure["s_s"])
structure["lm_logits"] = lm_logits
structure["aatype"] = aa
make_atom14_masks(structure)
# Of course, this doesn't respect the true mask because it doesn't know about it...
# We're not going to properly mask change of index tensors:
# "residx_atom14_to_atom37",
# "residx_atom37_to_atom14",
for k in [
"atom14_atom_exists",
"atom37_atom_exists",
]:
structure[k] *= attention_mask.unsqueeze(-1)
structure["residue_index"] = position_ids
lddt_head = self.lddt_head(structure["states"]).reshape(structure["states"].shape[0], B, L, -1, self.lddt_bins)
structure["lddt_head"] = lddt_head
plddt = categorical_lddt(lddt_head[-1], bins=self.lddt_bins)
structure["plddt"] = plddt
ptm_logits = self.ptm_head(structure["s_z"])
structure["ptm_logits"] = ptm_logits
structure["ptm"] = compute_tm(ptm_logits, max_bin=31, no_bins=self.distogram_bins)
structure.update(compute_predicted_aligned_error(ptm_logits, max_bin=31, no_bins=self.distogram_bins))
return EsmForProteinFoldingOutput(**structure)
def af2_idx_to_esm_idx(self, aa, mask):
# avoid indexing on different devices
if self.af2_to_esm.device != aa.device:
self.af2_to_esm = self.af2_to_esm.to(aa.device)
aa = (aa + 1).masked_fill(mask != 1, 0)
return self.af2_to_esm[aa]
def compute_language_model_representations(self, esmaa: torch.Tensor) -> torch.Tensor:
device = next(self.parameters()).device
B, L = esmaa.shape # B = batch size, L = sequence length.
if self.config.esmfold_config.bypass_lm:
esm_s = torch.zeros(B, L, self.esm_s_combine.size[0], -1, self.esm_feats, device=device)
return esm_s
bosi, eosi = self.esm_dict_cls_idx, self.esm_dict_eos_idx
bos = esmaa.new_full((B, 1), bosi)
eos = esmaa.new_full((B, 1), self.esm_dict_padding_idx)
esmaa = torch.cat([bos, esmaa, eos], dim=1)
# Use the first padding index as eos during inference.
esmaa[range(B), (esmaa != 1).sum(1)] = eosi
# _, esm_z, esm_s = self.esm(esmaa, return_pairs=self.config.esmfold_config.use_esm_attn_map)
# Because we do not support use_esm_attn_map in the HF port as it is not used in any public models,
# esm_z is always None
esm_hidden_states = self.esm(esmaa, attention_mask=esmaa != 1, output_hidden_states=True)["hidden_states"]
esm_s = torch.stack(esm_hidden_states, dim=2)
esm_s = esm_s[:, 1:-1] # B, L, nLayers, C
return esm_s
def bert_mask(self, aa, esmaa, mask, pattern):
new_aa = aa.clone()
target = aa.clone()
new_esmaa = esmaa.clone()
new_aa[pattern == 1] = self.mask_idx
target[pattern != 1] = 0
new_esmaa[pattern == 1] = self.esm_dict_mask_idx
return new_aa, new_esmaa, target
@torch.no_grad()
def infer(
self,
seqs: Union[str, List[str]],
position_ids=None,
):
if isinstance(seqs, str):
lst = [seqs]
else:
lst = seqs
# Returns the raw outputs of the model given an input sequence.
device = next(self.parameters()).device
aatype = collate_dense_tensors(
[
torch.from_numpy(
residue_constants.sequence_to_onehot(
sequence=seq,
mapping=residue_constants.restype_order_with_x,
map_unknown_to_x=True,
)
)
.to(device)
.argmax(dim=1)
for seq in lst
]
) # B=1 x L
mask = collate_dense_tensors([aatype.new_ones(len(seq)) for seq in lst])
position_ids = (
torch.arange(aatype.shape[1], device=device).expand(len(lst), -1)
if position_ids is None
else position_ids.to(device)
)
if position_ids.ndim == 1:
position_ids = position_ids.unsqueeze(0)
return self.forward(
aatype,
mask,
position_ids=position_ids,
)
@staticmethod
def output_to_pdb(output: Dict) -> List[str]:
"""Returns the pbd (file) string from the model given the model output."""
output = {k: v.to("cpu").numpy() for k, v in output.items()}
pdbs = []
final_atom_positions = atom14_to_atom37(output["positions"][-1], output)
final_atom_mask = output["atom37_atom_exists"]
for i in range(output["aatype"].shape[0]):
aa = output["aatype"][i]
pred_pos = final_atom_positions[i]
mask = final_atom_mask[i]
resid = output["residue_index"][i] + 1
pred = OFProtein(
aatype=aa,
atom_positions=pred_pos,
atom_mask=mask,
residue_index=resid,
b_factors=output["plddt"][i],
)
pdbs.append(to_pdb(pred))
return pdbs
def infer_pdb(self, seqs, *args, **kwargs) -> str:
"""Returns the pdb (file) string from the model given an input sequence."""
assert isinstance(seqs, str)
output = self.infer(seqs, *args, **kwargs)
return self.output_to_pdb(output)[0]
def infer_pdbs(self, seqs: List[str], *args, **kwargs) -> List[str]:
"""Returns the pdb (file) string from the model given an input sequence."""
output = self.infer(seqs, *args, **kwargs)
return self.output_to_pdb(output)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/tokenization_esm.py
|
# coding=utf-8
# Copyright 2022 Meta and The HuggingFace Inc. team. 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.
"""Tokenization classes for ESM."""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/esm2_t6_8M_UR50D": 1024,
"facebook/esm2_t12_35M_UR50D": 1024,
}
def load_vocab_file(vocab_file):
with open(vocab_file, "r") as f:
lines = f.read().splitlines()
return [l.strip() for l in lines]
class EsmTokenizer(PreTrainedTokenizer):
"""
Constructs an ESM tokenizer.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<unk>",
cls_token="<cls>",
pad_token="<pad>",
mask_token="<mask>",
eos_token="<eos>",
**kwargs,
):
self.all_tokens = load_vocab_file(vocab_file)
self._id_to_token = dict(enumerate(self.all_tokens))
self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
super().__init__(
unk_token=unk_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
eos_token=eos_token,
**kwargs,
)
# TODO, all the tokens are added? But they are also part of the vocab... bit strange.
# none of them are special, but they all need special splitting.
self.unique_no_split_tokens = self.all_tokens
self._update_trie(self.unique_no_split_tokens)
def _convert_id_to_token(self, index: int) -> str:
return self._id_to_token.get(index, self.unk_token)
def _convert_token_to_id(self, token: str) -> int:
return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
def _tokenize(self, text, **kwargs):
return text.split()
def get_vocab_size(self, with_added_tokens=False):
return len(self._id_to_token)
def get_vocab(self):
return {token: i for i, token in enumerate(self.all_tokens)}
def token_to_id(self, token: str) -> int:
return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
def id_to_token(self, index: int) -> str:
return self._id_to_token.get(index, self.unk_token)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
cls = [self.cls_token_id]
sep = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_1 is None:
if self.eos_token_id is None:
return cls + token_ids_0
else:
return cls + token_ids_0 + sep
elif self.eos_token_id is None:
raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
return cls + token_ids_0 + sep + token_ids_1 + sep # Multiple inputs always have an EOS token
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of ids of the first sequence.
token_ids_1 (`List[int]`, *optional*):
List of ids of the second sequence.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return [1 if token in self.all_special_ids else 0 for token in token_ids_0]
mask = [1] + ([0] * len(token_ids_0)) + [1]
if token_ids_1 is not None:
mask += [0] * len(token_ids_1) + [1]
return mask
def save_vocabulary(self, save_directory, filename_prefix):
vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt")
with open(vocab_file, "w") as f:
f.write("\n".join(self.all_tokens))
return (vocab_file,)
@property
def vocab_size(self) -> int:
return self.get_vocab_size(with_added_tokens=False)
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
return super()._add_tokens(new_tokens, special_tokens=True)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/modeling_esm.py
|
# coding=utf-8
# Copyright 2022 Meta and The HuggingFace Inc. team. 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.
""" PyTorch ESM model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_esm import EsmConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
_CONFIG_FOR_DOC = "EsmConfig"
ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/esm2_t6_8M_UR50D",
"facebook/esm2_t12_35M_UR50D",
# This is not a complete list of all ESM models!
# See all ESM models at https://huggingface.co/models?filter=esm
]
def rotate_half(x):
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(x, cos, sin):
cos = cos[:, :, : x.shape[-2], :]
sin = sin[:, :, : x.shape[-2], :]
return (x * cos) + (rotate_half(x) * sin)
def gelu(x):
"""
This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def symmetrize(x):
"Make layer symmetric in final two dimensions, used for contact prediction."
return x + x.transpose(-1, -2)
def average_product_correct(x):
"Perform average product correct, used for contact prediction."
a1 = x.sum(-1, keepdims=True)
a2 = x.sum(-2, keepdims=True)
a12 = x.sum((-1, -2), keepdims=True)
avg = a1 * a2
avg.div_(a12) # in-place to reduce memory
normalized = x - avg
return normalized
class RotaryEmbedding(torch.nn.Module):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int):
super().__init__()
# Generate and save the inverse frequency buffer (non trainable)
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
inv_freq = inv_freq
self.register_buffer("inv_freq", inv_freq)
self._seq_len_cached = None
self._cos_cached = None
self._sin_cached = None
def _update_cos_sin_tables(self, x, seq_dimension=2):
seq_len = x.shape[seq_dimension]
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
self._seq_len_cached = seq_len
t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self._cos_cached = emb.cos()[None, None, :, :]
self._sin_cached = emb.sin()[None, None, :, :]
return self._cos_cached, self._sin_cached
def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
return (
apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
)
class EsmContactPredictionHead(nn.Module):
"""Performs symmetrization, apc, and computes a logistic regression on the output features"""
def __init__(
self,
in_features: int,
bias=True,
eos_idx: int = 2,
):
super().__init__()
self.in_features = in_features
self.eos_idx = eos_idx
self.regression = nn.Linear(in_features, 1, bias)
self.activation = nn.Sigmoid()
def forward(self, tokens, attentions):
# remove eos token attentions
eos_mask = tokens.ne(self.eos_idx).to(attentions)
eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
attentions = attentions * eos_mask[:, None, None, :, :]
attentions = attentions[..., :-1, :-1]
# remove cls token attentions
attentions = attentions[..., 1:, 1:]
batch_size, layers, heads, seqlen, _ = attentions.size()
attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
# features: batch x channels x tokens x tokens (symmetric)
attentions = attentions.to(
self.regression.weight.device
) # attentions always float32, may need to convert to float16
attentions = average_product_correct(symmetrize(attentions))
attentions = attentions.permute(0, 2, 3, 1)
return self.activation(self.regression(attentions).squeeze(3))
class EsmEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
if config.emb_layer_norm_before:
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
else:
self.layer_norm = None
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
self.token_dropout = config.token_dropout
self.mask_token_id = config.mask_token_id
def forward(
self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
# embedding_scale factor here.
embeddings = inputs_embeds
# Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
# flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
# masked tokens are treated as if they were selected for input dropout and zeroed out.
# This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
# a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
# This is analogous to the way that dropout layers scale down outputs during evaluation when not
# actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
if self.token_dropout:
embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all ESM model training runs
src_lengths = attention_mask.sum(-1)
mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
embeddings.dtype
)
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings = embeddings + position_embeddings
if self.layer_norm is not None:
embeddings = self.layer_norm(embeddings)
if attention_mask is not None:
embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
# Matt: I think this line was copied incorrectly from BERT, disabling it for now.
# embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class EsmSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
self.rotary_embeddings = None
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
elif self.position_embedding_type == "rotary":
self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
# ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
# but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
# ESM code and fix rotary embeddings.
query_layer = query_layer * self.attention_head_size**-0.5
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
if self.position_embedding_type == "rotary":
query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
class EsmSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class EsmAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = EsmSelfAttention(config)
self.output = EsmSelfOutput(config)
self.pruned_heads = set()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
hidden_states_ln = self.LayerNorm(hidden_states)
self_outputs = self.self(
hidden_states_ln,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class EsmIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = gelu(hidden_states)
return hidden_states
class EsmOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class EsmLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = EsmAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = EsmAttention(config)
self.intermediate = EsmIntermediate(config)
self.output = EsmOutput(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise AttributeError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
" with cross-attention layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = self.feed_forward_chunk(attention_output)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
attention_output_ln = self.LayerNorm(attention_output)
intermediate_output = self.intermediate(attention_output_ln)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class EsmEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([EsmLayer(config) for _ in range(config.num_hidden_layers)])
self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
"`use_cache=False`..."
)
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = next_decoder_cache + (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if self.emb_layer_norm_after:
hidden_states = self.emb_layer_norm_after(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class EsmPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class EsmPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EsmConfig
base_model_prefix = "esm"
supports_gradient_checkpointing = True
_no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock", "EsmEmbeddings"]
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
ESM_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`EsmConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
ESM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
ESM_START_DOCSTRING,
)
class EsmModel(EsmPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = EsmEmbeddings(config)
self.encoder = EsmEncoder(config)
self.pooler = EsmPooler(config) if add_pooling_layer else None
self.contact_head = EsmContactPredictionHead(
in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
def predict_contacts(self, tokens, attention_mask):
attns = self(tokens, attention_mask=attention_mask, return_dict=True, output_attentions=True).attentions
attns = torch.stack(attns, dim=1) # Matches the original model layout
# In the original model, attentions for padding tokens are completely zeroed out.
# This makes no difference most of the time because the other tokens won't attend to them,
# but it does for the contact prediction task, which takes attentions as input,
# so we have to mimic that here.
attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
return self.contact_head(tokens, attns)
@add_start_docstrings("""ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING)
class EsmForMaskedLM(EsmPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.esm = EsmModel(config, add_pooling_layer=False)
self.lm_head = EsmLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
labels = labels.to(prediction_scores.device)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
class EsmLMHead(nn.Module):
"""ESM Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x) + self.bias
return x
@add_start_docstrings(
"""
ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ESM_START_DOCSTRING,
)
class EsmForSequenceClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.esm = EsmModel(config, add_pooling_layer=False)
self.classifier = EsmClassificationHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
ESM_START_DOCSTRING,
)
class EsmForTokenClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.esm = EsmModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
labels = labels.to(logits.device)
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class EsmClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/__init__.py
|
# Copyright 2022 Facebook and The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
"configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
"tokenization_esm": ["EsmTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_esm"] = [
"ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
"EsmForMaskedLM",
"EsmForSequenceClassification",
"EsmForTokenClassification",
"EsmModel",
"EsmPreTrainedModel",
]
_import_structure["modeling_esmfold"] = ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_esm"] = [
"TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFEsmForMaskedLM",
"TFEsmForSequenceClassification",
"TFEsmForTokenClassification",
"TFEsmModel",
"TFEsmPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
from .tokenization_esm import EsmTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmForMaskedLM,
EsmForSequenceClassification,
EsmForTokenClassification,
EsmModel,
EsmPreTrainedModel,
)
from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
TFEsmPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/esm/modeling_tf_esm.py
|
# coding=utf-8
# Copyright 2022 Meta and The HuggingFace Inc. team. 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.
""" PyTorch ESM model."""
from __future__ import annotations
import os
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.keras.activations import gelu
from tensorflow.keras.layers import Dense, Dropout, Embedding, Layer, LayerNormalization
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPoolingAndCrossAttentions,
TFMaskedLMOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
shape_list,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, stable_softmax
from ...utils import logging
from .configuration_esm import EsmConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
_CONFIG_FOR_DOC = "EsmConfig"
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/esm2_t6_8M_UR50D",
"facebook/esm2_t12_35M_UR50D",
# This is not a complete list of all ESM models!
# See all ESM models at https://huggingface.co/models?filter=esm
]
def rotate_half(x):
x1, x2 = tf.split(x, 2, axis=-1)
return tf.concat((-x2, x1), axis=-1)
def apply_rotary_pos_emb(x, cos, sin):
cos = cos[:, :, : tf.shape(x)[-2], :]
sin = sin[:, :, : tf.shape(x)[-2], :]
return (x * cos) + (rotate_half(x) * sin)
def symmetrize(x):
"Make layer symmetric in final two dimensions, used for contact prediction."
return x + tf.linalg.matrix_transpose(x) # Transposes last two dimensions only
def average_product_correct(x):
"Perform average product correct, used for contact prediction."
a1 = tf.reduce_sum(x, -1, keepdims=True)
a2 = tf.reduce_sum(x, -2, keepdims=True)
a12 = tf.reduce_sum(x, (-1, -2), keepdims=True)
avg = a1 * a2
avg = avg / a12
normalized = x - avg
return normalized
class TFRotaryEmbedding(Layer):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int, name=None):
super().__init__(name=name)
# Matt: The PyTorch version of this layer does a lot of work to cache values, but we just rely on TF compilation
# and/or XLA to sort out constants like that. It actually may not seem like this layer needs to be stateful at
# all when we benefit from TF compilation, but it does. The reason is that self.inv_freq is a buffer in the
# original implementation, but all the shared ESM checkpoints were trained with fp16 params. This means that
# the inv_freq tensor was stored as a float16, and we need to replicate those lower-precision values or our
# models give different outputs from the original.
self.dim = dim
def build(self, input_shape):
super().build(input_shape)
self.inv_freq = self.add_weight(
"inv_freq", shape=(self.dim // 2,), dtype=tf.float32, initializer=get_initializer(1.0), trainable=False
)
self.inv_freq.assign(
1.0 / (10000 ** (tf.range(start=0, limit=self.dim, delta=2, dtype=tf.float32) / self.dim))
)
def _compute_cos_sin(self, x, seq_dimension=2):
seq_len = tf.shape(x)[seq_dimension]
t = tf.range(seq_len, dtype=self.inv_freq.dtype)
freqs = tf.einsum("i, j -> ij", t, self.inv_freq) # Outer multiplication
emb = tf.concat((freqs, freqs), axis=-1)[None, None, :, :]
return tf.cos(emb), tf.sin(emb)
def call(self, q: tf.Tensor, k: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
cos_emb, sin_emb = self._compute_cos_sin(k, seq_dimension=-2)
return (
apply_rotary_pos_emb(q, cos_emb, sin_emb),
apply_rotary_pos_emb(k, cos_emb, sin_emb),
)
class TFEsmContactPredictionHead(Layer):
"""Performs symmetrization, apc, and computes a logistic regression on the output features"""
def __init__(
self,
in_features: int,
bias=True,
eos_idx: int = 2,
name=None,
):
super().__init__(name=name)
self.eos_idx = eos_idx
self.in_features = in_features
self.regression = Dense(1, use_bias=bias, activation="sigmoid", name="regression")
def build(self, input_shape):
super().build(input_shape)
with tf.name_scope("regression"):
self.regression.build((None, self.in_features))
def call(self, tokens, attentions):
# remove eos token attentions
eos_mask = tf.cast(tokens != self.eos_idx, attentions.dtype)
eos_mask = tf.expand_dims(eos_mask, 1) * tf.expand_dims(eos_mask, 2)
attentions = attentions * eos_mask[:, None, None, :, :]
attentions = attentions[..., :-1, :-1]
# remove cls token attentions
attentions = attentions[..., 1:, 1:]
batch_size, layers, heads, seqlen, _ = shape_list(attentions)
attentions = tf.reshape(attentions, (batch_size, layers * heads, seqlen, seqlen))
# features: batch x channels x tokens x tokens (symmetric)
attentions = average_product_correct(symmetrize(attentions))
attentions = tf.transpose(attentions, perm=(0, 2, 3, 1))
return tf.squeeze(self.regression(attentions), 3)
class TFEsmEmbeddings(Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, name=None):
super().__init__(name=name)
self.word_embeddings = Embedding(
config.vocab_size,
config.hidden_size,
embeddings_initializer=get_initializer(config.initializer_range),
name="word_embeddings",
)
self.position_embeddings = Embedding(
config.max_position_embeddings,
config.hidden_size,
embeddings_initializer=get_initializer(config.initializer_range),
name="position_embeddings",
)
if config.emb_layer_norm_before:
self.layer_norm = LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
else:
self.layer_norm = None
# Matt: I think this line was copied incorrectly from BERT, disabling for now
# self.dropout = Dropout(config.hidden_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.position_ids = tf.range(config.max_position_embeddings)[None, :]
self.padding_idx = config.pad_token_id
self.token_dropout = config.token_dropout
self.mask_token_id = config.mask_token_id
self.config = config
def call(
self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = self.word_embeddings(input_ids)
# Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
# embedding_scale factor here.
embeddings = inputs_embeds
# Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
# flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
# masked tokens are treated as if they were selected for input dropout and zeroed out.
# This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
# a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
# This is analogous to the way that dropout layers scale down outputs during evaluation when not
# actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
if self.token_dropout:
embeddings = tf.where((input_ids == self.mask_token_id)[:, :, None], 0.0, embeddings)
mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all ESM model training runs
src_lengths = tf.cast(tf.reduce_sum(attention_mask, axis=-1), tf.float32)
masked_tokens = input_ids == self.mask_token_id
mask_ratio_observed = tf.math.count_nonzero(masked_tokens, dtype=tf.float32, axis=-1) / src_lengths
embeddings = embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
if self.layer_norm is not None:
embeddings = self.layer_norm(embeddings)
if attention_mask is not None:
embeddings = embeddings * tf.cast(tf.expand_dims(attention_mask, -1), embeddings.dtype)
# Matt: I think this line was copied incorrectly from BERT, disabling it for now.
# embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: tf.Tensor
Returns: tf.Tensor
"""
input_shape = shape_list(inputs_embeds)[:-1]
sequence_length = input_shape[1]
position_ids = tf.range(
start=self.padding_idx + 1, limit=sequence_length + self.padding_idx + 1, dtype=tf.int64
)
return tf.broadcast_to(tf.expand_dims(position_ids, 0), input_shape)
class TFEsmSelfAttention(Layer):
def __init__(self, config, position_embedding_type=None, name=None):
super().__init__(name=name)
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = Dense(self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key")
self.value = Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
self.rotary_embeddings = None
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = Embedding(
2 * config.max_position_embeddings - 1,
self.attention_head_size,
embeddings_initializer=get_initializer(config.initializer_range),
)
elif self.position_embedding_type == "rotary":
self.rotary_embeddings = TFRotaryEmbedding(dim=self.attention_head_size, name="rotary_embeddings")
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
new_x_shape = shape_list(x)[:-1] + [self.num_attention_heads, self.attention_head_size]
x = tf.reshape(x, new_x_shape)
return tf.transpose(x, perm=(0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
output_attentions: Optional[bool] = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
# ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
# but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
# ESM code and fix rotary embeddings.
query_layer = query_layer * self.attention_head_size**-0.5
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
if self.position_embedding_type == "rotary":
query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = shape_list(hidden_states)[1]
position_ids_l = tf.expand_dims(tf.range(seq_length, dtype=tf.int64), -1)
position_ids_r = tf.expand_dims(tf.range(seq_length, dtype=tf.int64), 0)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = tf.cast(positional_embedding, query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = tf.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = tf.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = tf.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = attention_probs @ value_layer
context_layer = tf.transpose(context_layer, perm=(0, 2, 1, 3))
new_context_layer_shape = shape_list(context_layer)[:-2] + [self.all_head_size]
context_layer = tf.reshape(context_layer, new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
class TFEsmSelfOutput(Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, input_tensor, training=False):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states += input_tensor
return hidden_states
class TFEsmAttention(Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.self = TFEsmSelfAttention(config, name="self")
self.output_layer = TFEsmSelfOutput(config, name="output")
self.pruned_heads = set()
self.LayerNorm = LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
training=False,
):
hidden_states_ln = self.LayerNorm(hidden_states)
self_outputs = self.self(
hidden_states_ln,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
training,
)
attention_output = self.output_layer(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class TFEsmIntermediate(tf.keras.layers.Layer):
def __init__(self, config: EsmConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = tf.nn.gelu(hidden_states)
return hidden_states
class TFEsmOutput(Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, input_tensor, training=False):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states += input_tensor
return hidden_states
class TFEsmLayer(Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = TFEsmAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TFEsmAttention(config)
self.intermediate = TFEsmIntermediate(config, name="intermediate")
self.output_layer = TFEsmOutput(config, name="output")
self.LayerNorm = LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
training=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
training=training,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise AttributeError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
" with cross-attention layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
training=training,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layernorm_output = self.LayerNorm(attention_output)
intermediate_output = self.intermediate(hidden_states=layernorm_output)
layer_output = self.output_layer(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + outputs # add attentions if we output them
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
class TFEsmEncoder(Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.config = config
self.layer = [TFEsmLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
self.emb_layer_norm_after = LayerNormalization(epsilon=config.layer_norm_eps, name="emb_layer_norm_after")
def call(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
training=False,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
training,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if self.emb_layer_norm_after:
hidden_states = self.emb_layer_norm_after(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Esm
class TFEsmPooler(tf.keras.layers.Layer):
def __init__(self, config: EsmConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(inputs=first_token_tensor)
return pooled_output
class TFEsmPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EsmConfig
base_model_prefix = "esm"
ESM_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Keras [Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a
regular Keras model and refer to the TF/Keras documentation for all matters related to general usage and behavior.
Parameters:
config ([`EsmConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
ESM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
ESM_START_DOCSTRING,
)
class TFEsmMainLayer(Layer):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config, add_pooling_layer=True, name=None, **kwargs):
super().__init__(name=name, **kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TFEsmEmbeddings(config, name="embeddings")
self.encoder = TFEsmEncoder(config, name="encoder")
self.pooler = TFEsmPooler(config, name="pooler") if add_pooling_layer else None
self.contact_head = TFEsmContactPredictionHead(
in_features=self.config.num_hidden_layers * self.config.num_attention_heads, bias=True, name="contact_head"
)
def build(self, input_shape):
super().build(input_shape)
with tf.name_scope("contact_head"):
self.contact_head.build(input_shape)
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.word_embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
if not self.config.is_decoder:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
if past_key_values is None:
past_key_values_length = 0
past_key_values = [None] * len(self.encoder.layer)
else:
past_key_values_length = shape_list(past_key_values[0][0])[-2]
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
embedding_output = self.embeddings(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask_shape = shape_list(attention_mask)
mask_seq_length = seq_length + past_key_values_length
# Copied from `modeling_tf_t5.py`
# Provided a padding mask of dimensions [batch_size, mask_seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
if self.is_decoder:
seq_ids = tf.range(mask_seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
seq_ids[None, :, None],
)
causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype)
extended_attention_mask = causal_mask * attention_mask[:, None, :]
attention_mask_shape = shape_list(extended_attention_mask)
extended_attention_mask = tf.reshape(
extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
)
if past_key_values[0] is not None:
# attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length]
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = tf.reshape(
attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.is_decoder and encoder_attention_mask is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype)
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
if not return_dict:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
def predict_contacts(self, tokens, attention_mask):
attns = self(tokens, attention_mask=attention_mask, return_dict=True, output_attentions=True).attentions
attns = tf.stack(attns, axis=1) # Matches the original model layout
# In the original model, attentions for padding tokens are completely zeroed out.
# This makes no difference most of the time because the other tokens won't attend to them,
# but it does for the contact prediction task, which takes attentions as input,
# so we have to mimic that here.
attention_mask = tf.cast(attention_mask, attns.dtype)
attns *= attention_mask[:, None, None, None]
attns *= attention_mask[:, None, None, :, None]
return self.contact_head(tokens, attns)
@add_start_docstrings(
"The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
ESM_START_DOCSTRING,
)
class TFEsmModel(TFEsmPreTrainedModel):
def __init__(self, config: EsmConfig, add_pooling_layer=True, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.esm = TFEsmMainLayer(config, add_pooling_layer=add_pooling_layer, name="esm")
@unpack_inputs
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). Set to `False` during training, `True` during generation
"""
outputs = self.esm(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask)
@add_start_docstrings("""ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING)
class TFEsmForMaskedLM(TFEsmPreTrainedModel, TFMaskedLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"position_ids"]
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.esm = TFEsmMainLayer(config, add_pooling_layer=False, name="esm")
self.lm_head = TFEsmLMHead(config, name="lm_head")
if config.tie_word_embeddings:
# Ensure word embeddings are built so that we actually have something to tie
with tf.name_scope(os.path.join(self._name_scope(), "esm", "embeddings", "word_embeddings")):
self.esm.embeddings.word_embeddings.build((None, None))
self.lm_head.decoder = self.esm.embeddings.word_embeddings.weights[0]
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
def get_lm_head(self):
return self.lm_head
@unpack_inputs
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
masked_lm_loss = self.hf_compute_loss(labels=labels, logits=prediction_scores)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFMaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask)
class TFEsmLMHead(Layer):
"""ESM Head for masked language modeling."""
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.layer_norm = LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
if config.tie_word_embeddings:
self.decoder = None
else:
self.decoder = Dense(
config.vocab_size,
kernel_initializer=get_initializer(config.initializer_range),
name="decoder",
use_bias=False,
)
self.config = config
def build(self, input_shape):
super().build(input_shape)
# Separate bias to match the PT model and allow weight cross-loading to work
# Put it in the build so it gets the right name when adding it as a weight
self.bias = self.add_weight("bias", shape=(self.config.vocab_size,), initializer="zeros", trainable=True)
def get_bias(self):
return {"bias": self.bias}
def call(self, features):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
if self.config.tie_word_embeddings:
x = tf.matmul(x, self.decoder, transpose_b=True) + self.bias
else:
x = self.decoder(x) + self.bias
return x
@add_start_docstrings(
"""
ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ESM_START_DOCSTRING,
)
class TFEsmForSequenceClassification(TFEsmPreTrainedModel, TFSequenceClassificationLoss):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.esm = TFEsmMainLayer(config, add_pooling_layer=False, name="esm")
self.classifier = TFEsmClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
ESM_START_DOCSTRING,
)
class TFEsmForTokenClassification(TFEsmPreTrainedModel, TFTokenClassificationLoss):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.esm = TFEsmMainLayer(config, add_pooling_layer=False, name="esm")
self.dropout = Dropout(config.hidden_dropout_prob)
self.classifier = Dense(config.num_labels, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=training)
logits = self.classifier(sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class TFEsmClassificationHead(Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
self.dropout = Dropout(config.hidden_dropout_prob)
self.out_proj = Dense(
config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
activation="linear",
name="out_proj",
)
def call(self, features, training=False):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x, training=training)
x = self.dense(x)
x = self.dropout(x, training=training)
x = self.out_proj(x)
return x
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: tf.Tensor x:
Returns: tf.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = tf.cast(input_ids != padding_idx, tf.int64)
incremental_indices = (tf.cumsum(mask, axis=1) + past_key_values_length) * mask
return incremental_indices + padding_idx
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/tensor_utils.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
import torch
import torch.nn as nn
import torch.types
def add(m1: torch.Tensor, m2: torch.Tensor, inplace: bool) -> torch.Tensor:
# The first operation in a checkpoint can't be in-place, but it's
# nice to have in-place addition during inference. Thus...
if not inplace:
m1 = m1 + m2
else:
m1 += m2
return m1
def permute_final_dims(tensor: torch.Tensor, inds: List[int]) -> torch.Tensor:
zero_index = -1 * len(inds)
first_inds = list(range(len(tensor.shape[:zero_index])))
return tensor.permute(first_inds + [zero_index + i for i in inds])
def flatten_final_dims(t: torch.Tensor, no_dims: int) -> torch.Tensor:
return t.reshape(t.shape[:-no_dims] + (-1,))
def masked_mean(mask: torch.Tensor, value: torch.Tensor, dim: int, eps: float = 1e-4) -> torch.Tensor:
mask = mask.expand(*value.shape)
return torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim))
def pts_to_distogram(
pts: torch.Tensor, min_bin: torch.types.Number = 2.3125, max_bin: torch.types.Number = 21.6875, no_bins: int = 64
) -> torch.Tensor:
boundaries = torch.linspace(min_bin, max_bin, no_bins - 1, device=pts.device)
dists = torch.sqrt(torch.sum((pts.unsqueeze(-2) - pts.unsqueeze(-3)) ** 2, dim=-1))
return torch.bucketize(dists, boundaries)
def dict_multimap(fn: Callable[[list], Any], dicts: List[dict]) -> dict:
first = dicts[0]
new_dict = {}
for k, v in first.items():
all_v = [d[k] for d in dicts]
if isinstance(v, dict):
new_dict[k] = dict_multimap(fn, all_v)
else:
new_dict[k] = fn(all_v)
return new_dict
def one_hot(x: torch.Tensor, v_bins: torch.Tensor) -> torch.Tensor:
reshaped_bins = v_bins.view(((1,) * len(x.shape)) + (len(v_bins),))
diffs = x[..., None] - reshaped_bins
am = torch.argmin(torch.abs(diffs), dim=-1)
return nn.functional.one_hot(am, num_classes=len(v_bins)).float()
def batched_gather(data: torch.Tensor, inds: torch.Tensor, dim: int = 0, no_batch_dims: int = 0) -> torch.Tensor:
ranges: List[Union[slice, torch.Tensor]] = []
for i, s in enumerate(data.shape[:no_batch_dims]):
r = torch.arange(s)
r = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1))))
ranges.append(r)
remaining_dims: List[Union[slice, torch.Tensor]] = [slice(None) for _ in range(len(data.shape) - no_batch_dims)]
remaining_dims[dim - no_batch_dims if dim >= 0 else dim] = inds
ranges.extend(remaining_dims)
# Matt note: Editing this to get around the behaviour of using a list as an array index changing
# in recent Numpy versions
return data[tuple(ranges)]
T = TypeVar("T")
# With tree_map, a poor man's JAX tree_map
def dict_map(
fn: Callable[[T], Any], dic: Dict[Any, Union[dict, list, tuple, T]], leaf_type: Type[T]
) -> Dict[Any, Union[dict, list, tuple, Any]]:
new_dict: Dict[Any, Union[dict, list, tuple, Any]] = {}
for k, v in dic.items():
if isinstance(v, dict):
new_dict[k] = dict_map(fn, v, leaf_type)
else:
new_dict[k] = tree_map(fn, v, leaf_type)
return new_dict
@overload
def tree_map(fn: Callable[[T], Any], tree: T, leaf_type: Type[T]) -> Any:
...
@overload
def tree_map(fn: Callable[[T], Any], tree: dict, leaf_type: Type[T]) -> dict:
...
@overload
def tree_map(fn: Callable[[T], Any], tree: list, leaf_type: Type[T]) -> list:
...
@overload
def tree_map(fn: Callable[[T], Any], tree: tuple, leaf_type: Type[T]) -> tuple:
...
def tree_map(fn, tree, leaf_type):
if isinstance(tree, dict):
return dict_map(fn, tree, leaf_type)
elif isinstance(tree, list):
return [tree_map(fn, x, leaf_type) for x in tree]
elif isinstance(tree, tuple):
return tuple(tree_map(fn, x, leaf_type) for x in tree)
elif isinstance(tree, leaf_type):
return fn(tree)
else:
print(type(tree))
raise ValueError("Not supported")
tensor_tree_map = partial(tree_map, leaf_type=torch.Tensor)
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/rigid_utils.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from __future__ import annotations
from functools import lru_cache
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
import numpy as np
import torch
def rot_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication of two rotation matrix tensors. Written out by hand to avoid AMP downcasting.
Args:
a: [*, 3, 3] left multiplicand
b: [*, 3, 3] right multiplicand
Returns:
The product ab
"""
def row_mul(i: int) -> torch.Tensor:
return torch.stack(
[
a[..., i, 0] * b[..., 0, 0] + a[..., i, 1] * b[..., 1, 0] + a[..., i, 2] * b[..., 2, 0],
a[..., i, 0] * b[..., 0, 1] + a[..., i, 1] * b[..., 1, 1] + a[..., i, 2] * b[..., 2, 1],
a[..., i, 0] * b[..., 0, 2] + a[..., i, 1] * b[..., 1, 2] + a[..., i, 2] * b[..., 2, 2],
],
dim=-1,
)
return torch.stack(
[
row_mul(0),
row_mul(1),
row_mul(2),
],
dim=-2,
)
def rot_vec_mul(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
"""
Applies a rotation to a vector. Written out by hand to avoid transfer to avoid AMP downcasting.
Args:
r: [*, 3, 3] rotation matrices
t: [*, 3] coordinate tensors
Returns:
[*, 3] rotated coordinates
"""
x, y, z = torch.unbind(t, dim=-1)
return torch.stack(
[
r[..., 0, 0] * x + r[..., 0, 1] * y + r[..., 0, 2] * z,
r[..., 1, 0] * x + r[..., 1, 1] * y + r[..., 1, 2] * z,
r[..., 2, 0] * x + r[..., 2, 1] * y + r[..., 2, 2] * z,
],
dim=-1,
)
@lru_cache(maxsize=None)
def identity_rot_mats(
batch_dims: Tuple[int, ...],
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
requires_grad: bool = True,
) -> torch.Tensor:
rots = torch.eye(3, dtype=dtype, device=device, requires_grad=requires_grad)
rots = rots.view(*((1,) * len(batch_dims)), 3, 3)
rots = rots.expand(*batch_dims, -1, -1)
rots = rots.contiguous()
return rots
@lru_cache(maxsize=None)
def identity_trans(
batch_dims: Tuple[int, ...],
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
requires_grad: bool = True,
) -> torch.Tensor:
trans = torch.zeros((*batch_dims, 3), dtype=dtype, device=device, requires_grad=requires_grad)
return trans
@lru_cache(maxsize=None)
def identity_quats(
batch_dims: Tuple[int, ...],
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
requires_grad: bool = True,
) -> torch.Tensor:
quat = torch.zeros((*batch_dims, 4), dtype=dtype, device=device, requires_grad=requires_grad)
with torch.no_grad():
quat[..., 0] = 1
return quat
_quat_elements: List[str] = ["a", "b", "c", "d"]
_qtr_keys: List[str] = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements]
_qtr_ind_dict: Dict[str, int] = {key: ind for ind, key in enumerate(_qtr_keys)}
def _to_mat(pairs: List[Tuple[str, int]]) -> np.ndarray:
mat = np.zeros((4, 4))
for key, value in pairs:
ind = _qtr_ind_dict[key]
mat[ind // 4][ind % 4] = value
return mat
_QTR_MAT = np.zeros((4, 4, 3, 3))
_QTR_MAT[..., 0, 0] = _to_mat([("aa", 1), ("bb", 1), ("cc", -1), ("dd", -1)])
_QTR_MAT[..., 0, 1] = _to_mat([("bc", 2), ("ad", -2)])
_QTR_MAT[..., 0, 2] = _to_mat([("bd", 2), ("ac", 2)])
_QTR_MAT[..., 1, 0] = _to_mat([("bc", 2), ("ad", 2)])
_QTR_MAT[..., 1, 1] = _to_mat([("aa", 1), ("bb", -1), ("cc", 1), ("dd", -1)])
_QTR_MAT[..., 1, 2] = _to_mat([("cd", 2), ("ab", -2)])
_QTR_MAT[..., 2, 0] = _to_mat([("bd", 2), ("ac", -2)])
_QTR_MAT[..., 2, 1] = _to_mat([("cd", 2), ("ab", 2)])
_QTR_MAT[..., 2, 2] = _to_mat([("aa", 1), ("bb", -1), ("cc", -1), ("dd", 1)])
def quat_to_rot(quat: torch.Tensor) -> torch.Tensor:
"""
Converts a quaternion to a rotation matrix.
Args:
quat: [*, 4] quaternions
Returns:
[*, 3, 3] rotation matrices
"""
# [*, 4, 4]
quat = quat[..., None] * quat[..., None, :]
# [4, 4, 3, 3]
mat = _get_quat("_QTR_MAT", dtype=quat.dtype, device=quat.device)
# [*, 4, 4, 3, 3]
shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape)
quat = quat[..., None, None] * shaped_qtr_mat
# [*, 3, 3]
return torch.sum(quat, dim=(-3, -4))
def rot_to_quat(rot: torch.Tensor) -> torch.Tensor:
if rot.shape[-2:] != (3, 3):
raise ValueError("Input rotation is incorrectly shaped")
[[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = [[rot[..., i, j] for j in range(3)] for i in range(3)]
k = [
[
xx + yy + zz,
zy - yz,
xz - zx,
yx - xy,
],
[
zy - yz,
xx - yy - zz,
xy + yx,
xz + zx,
],
[
xz - zx,
xy + yx,
yy - xx - zz,
yz + zy,
],
[
yx - xy,
xz + zx,
yz + zy,
zz - xx - yy,
],
]
_, vectors = torch.linalg.eigh((1.0 / 3.0) * torch.stack([torch.stack(t, dim=-1) for t in k], dim=-2))
return vectors[..., -1]
_QUAT_MULTIPLY = np.zeros((4, 4, 4))
_QUAT_MULTIPLY[:, :, 0] = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]]
_QUAT_MULTIPLY[:, :, 1] = [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, -1, 0]]
_QUAT_MULTIPLY[:, :, 2] = [[0, 0, 1, 0], [0, 0, 0, -1], [1, 0, 0, 0], [0, 1, 0, 0]]
_QUAT_MULTIPLY[:, :, 3] = [[0, 0, 0, 1], [0, 0, 1, 0], [0, -1, 0, 0], [1, 0, 0, 0]]
_QUAT_MULTIPLY_BY_VEC = _QUAT_MULTIPLY[:, 1:, :]
_CACHED_QUATS: Dict[str, np.ndarray] = {
"_QTR_MAT": _QTR_MAT,
"_QUAT_MULTIPLY": _QUAT_MULTIPLY,
"_QUAT_MULTIPLY_BY_VEC": _QUAT_MULTIPLY_BY_VEC,
}
@lru_cache(maxsize=None)
def _get_quat(quat_key: str, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
return torch.tensor(_CACHED_QUATS[quat_key], dtype=dtype, device=device)
def quat_multiply(quat1: torch.Tensor, quat2: torch.Tensor) -> torch.Tensor:
"""Multiply a quaternion by another quaternion."""
mat = _get_quat("_QUAT_MULTIPLY", dtype=quat1.dtype, device=quat1.device)
reshaped_mat = mat.view((1,) * len(quat1.shape[:-1]) + mat.shape)
return torch.sum(reshaped_mat * quat1[..., :, None, None] * quat2[..., None, :, None], dim=(-3, -2))
def quat_multiply_by_vec(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
"""Multiply a quaternion by a pure-vector quaternion."""
mat = _get_quat("_QUAT_MULTIPLY_BY_VEC", dtype=quat.dtype, device=quat.device)
reshaped_mat = mat.view((1,) * len(quat.shape[:-1]) + mat.shape)
return torch.sum(reshaped_mat * quat[..., :, None, None] * vec[..., None, :, None], dim=(-3, -2))
def invert_rot_mat(rot_mat: torch.Tensor) -> torch.Tensor:
return rot_mat.transpose(-1, -2)
def invert_quat(quat: torch.Tensor) -> torch.Tensor:
quat_prime = quat.clone()
quat_prime[..., 1:] *= -1
inv = quat_prime / torch.sum(quat**2, dim=-1, keepdim=True)
return inv
class Rotation:
"""
A 3D rotation. Depending on how the object is initialized, the rotation is represented by either a rotation matrix
or a quaternion, though both formats are made available by helper functions. To simplify gradient computation, the
underlying format of the rotation cannot be changed in-place. Like Rigid, the class is designed to mimic the
behavior of a torch Tensor, almost as if each Rotation object were a tensor of rotations, in one format or another.
"""
def __init__(
self,
rot_mats: Optional[torch.Tensor] = None,
quats: Optional[torch.Tensor] = None,
normalize_quats: bool = True,
):
"""
Args:
rot_mats:
A [*, 3, 3] rotation matrix tensor. Mutually exclusive with quats
quats:
A [*, 4] quaternion. Mutually exclusive with rot_mats. If normalize_quats is not True, must be a unit
quaternion
normalize_quats:
If quats is specified, whether to normalize quats
"""
if (rot_mats is None and quats is None) or (rot_mats is not None and quats is not None):
raise ValueError("Exactly one input argument must be specified")
if (rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or (quats is not None and quats.shape[-1] != 4):
raise ValueError("Incorrectly shaped rotation matrix or quaternion")
# Force full-precision
if quats is not None:
quats = quats.to(dtype=torch.float32)
if rot_mats is not None:
rot_mats = rot_mats.to(dtype=torch.float32)
if quats is not None and normalize_quats:
quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True)
self._rot_mats = rot_mats
self._quats = quats
@staticmethod
def identity(
shape,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
requires_grad: bool = True,
fmt: str = "quat",
) -> Rotation:
"""
Returns an identity Rotation.
Args:
shape:
The "shape" of the resulting Rotation object. See documentation for the shape property
dtype:
The torch dtype for the rotation
device:
The torch device for the new rotation
requires_grad:
Whether the underlying tensors in the new rotation object should require gradient computation
fmt:
One of "quat" or "rot_mat". Determines the underlying format of the new object's rotation
Returns:
A new identity rotation
"""
if fmt == "rot_mat":
rot_mats = identity_rot_mats(
shape,
dtype,
device,
requires_grad,
)
return Rotation(rot_mats=rot_mats, quats=None)
elif fmt == "quat":
quats = identity_quats(shape, dtype, device, requires_grad)
return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
else:
raise ValueError(f"Invalid format: f{fmt}")
# Magic methods
def __getitem__(self, index: Any) -> Rotation:
"""
Allows torch-style indexing over the virtual shape of the rotation object. See documentation for the shape
property.
Args:
index:
A torch index. E.g. (1, 3, 2), or (slice(None,))
Returns:
The indexed rotation
"""
if type(index) != tuple:
index = (index,)
if self._rot_mats is not None:
rot_mats = self._rot_mats[index + (slice(None), slice(None))]
return Rotation(rot_mats=rot_mats)
elif self._quats is not None:
quats = self._quats[index + (slice(None),)]
return Rotation(quats=quats, normalize_quats=False)
else:
raise ValueError("Both rotations are None")
def __mul__(self, right: torch.Tensor) -> Rotation:
"""
Pointwise left multiplication of the rotation with a tensor. Can be used to e.g. mask the Rotation.
Args:
right:
The tensor multiplicand
Returns:
The product
"""
if not (isinstance(right, torch.Tensor)):
raise TypeError("The other multiplicand must be a Tensor")
if self._rot_mats is not None:
rot_mats = self._rot_mats * right[..., None, None]
return Rotation(rot_mats=rot_mats, quats=None)
elif self._quats is not None:
quats = self._quats * right[..., None]
return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
else:
raise ValueError("Both rotations are None")
def __rmul__(self, left: torch.Tensor) -> Rotation:
"""
Reverse pointwise multiplication of the rotation with a tensor.
Args:
left:
The left multiplicand
Returns:
The product
"""
return self.__mul__(left)
# Properties
@property
def shape(self) -> torch.Size:
"""
Returns the virtual shape of the rotation object. This shape is defined as the batch dimensions of the
underlying rotation matrix or quaternion. If the Rotation was initialized with a [10, 3, 3] rotation matrix
tensor, for example, the resulting shape would be [10].
Returns:
The virtual shape of the rotation object
"""
if self._rot_mats is not None:
return self._rot_mats.shape[:-2]
elif self._quats is not None:
return self._quats.shape[:-1]
else:
raise ValueError("Both rotations are None")
@property
def dtype(self) -> torch.dtype:
"""
Returns the dtype of the underlying rotation.
Returns:
The dtype of the underlying rotation
"""
if self._rot_mats is not None:
return self._rot_mats.dtype
elif self._quats is not None:
return self._quats.dtype
else:
raise ValueError("Both rotations are None")
@property
def device(self) -> torch.device:
"""
The device of the underlying rotation
Returns:
The device of the underlying rotation
"""
if self._rot_mats is not None:
return self._rot_mats.device
elif self._quats is not None:
return self._quats.device
else:
raise ValueError("Both rotations are None")
@property
def requires_grad(self) -> bool:
"""
Returns the requires_grad property of the underlying rotation
Returns:
The requires_grad property of the underlying tensor
"""
if self._rot_mats is not None:
return self._rot_mats.requires_grad
elif self._quats is not None:
return self._quats.requires_grad
else:
raise ValueError("Both rotations are None")
def get_rot_mats(self) -> torch.Tensor:
"""
Returns the underlying rotation as a rotation matrix tensor.
Returns:
The rotation as a rotation matrix tensor
"""
if self._rot_mats is not None:
return self._rot_mats
elif self._quats is not None:
return quat_to_rot(self._quats)
else:
raise ValueError("Both rotations are None")
def get_quats(self) -> torch.Tensor:
"""
Returns the underlying rotation as a quaternion tensor.
Depending on whether the Rotation was initialized with a quaternion, this function may call torch.linalg.eigh.
Returns:
The rotation as a quaternion tensor.
"""
if self._rot_mats is not None:
return rot_to_quat(self._rot_mats)
elif self._quats is not None:
return self._quats
else:
raise ValueError("Both rotations are None")
def get_cur_rot(self) -> torch.Tensor:
"""
Return the underlying rotation in its current form
Returns:
The stored rotation
"""
if self._rot_mats is not None:
return self._rot_mats
elif self._quats is not None:
return self._quats
else:
raise ValueError("Both rotations are None")
# Rotation functions
def compose_q_update_vec(self, q_update_vec: torch.Tensor, normalize_quats: bool = True) -> Rotation:
"""
Returns a new quaternion Rotation after updating the current object's underlying rotation with a quaternion
update, formatted as a [*, 3] tensor whose final three columns represent x, y, z such that (1, x, y, z) is the
desired (not necessarily unit) quaternion update.
Args:
q_update_vec:
A [*, 3] quaternion update tensor
normalize_quats:
Whether to normalize the output quaternion
Returns:
An updated Rotation
"""
quats = self.get_quats()
new_quats = quats + quat_multiply_by_vec(quats, q_update_vec)
return Rotation(
rot_mats=None,
quats=new_quats,
normalize_quats=normalize_quats,
)
def compose_r(self, r: Rotation) -> Rotation:
"""
Compose the rotation matrices of the current Rotation object with those of another.
Args:
r:
An update rotation object
Returns:
An updated rotation object
"""
r1 = self.get_rot_mats()
r2 = r.get_rot_mats()
new_rot_mats = rot_matmul(r1, r2)
return Rotation(rot_mats=new_rot_mats, quats=None)
def compose_q(self, r: Rotation, normalize_quats: bool = True) -> Rotation:
"""
Compose the quaternions of the current Rotation object with those of another.
Depending on whether either Rotation was initialized with quaternions, this function may call
torch.linalg.eigh.
Args:
r:
An update rotation object
Returns:
An updated rotation object
"""
q1 = self.get_quats()
q2 = r.get_quats()
new_quats = quat_multiply(q1, q2)
return Rotation(rot_mats=None, quats=new_quats, normalize_quats=normalize_quats)
def apply(self, pts: torch.Tensor) -> torch.Tensor:
"""
Apply the current Rotation as a rotation matrix to a set of 3D coordinates.
Args:
pts:
A [*, 3] set of points
Returns:
[*, 3] rotated points
"""
rot_mats = self.get_rot_mats()
return rot_vec_mul(rot_mats, pts)
def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:
"""
The inverse of the apply() method.
Args:
pts:
A [*, 3] set of points
Returns:
[*, 3] inverse-rotated points
"""
rot_mats = self.get_rot_mats()
inv_rot_mats = invert_rot_mat(rot_mats)
return rot_vec_mul(inv_rot_mats, pts)
def invert(self) -> Rotation:
"""
Returns the inverse of the current Rotation.
Returns:
The inverse of the current Rotation
"""
if self._rot_mats is not None:
return Rotation(rot_mats=invert_rot_mat(self._rot_mats), quats=None)
elif self._quats is not None:
return Rotation(
rot_mats=None,
quats=invert_quat(self._quats),
normalize_quats=False,
)
else:
raise ValueError("Both rotations are None")
# "Tensor" stuff
def unsqueeze(self, dim: int) -> Rotation:
"""
Analogous to torch.unsqueeze. The dimension is relative to the shape of the Rotation object.
Args:
dim: A positive or negative dimension index.
Returns:
The unsqueezed Rotation.
"""
if dim >= len(self.shape):
raise ValueError("Invalid dimension")
if self._rot_mats is not None:
rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2)
return Rotation(rot_mats=rot_mats, quats=None)
elif self._quats is not None:
quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1)
return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
else:
raise ValueError("Both rotations are None")
@staticmethod
def cat(rs: Sequence[Rotation], dim: int) -> Rotation:
"""
Concatenates rotations along one of the batch dimensions. Analogous to torch.cat().
Note that the output of this operation is always a rotation matrix, regardless of the format of input
rotations.
Args:
rs:
A list of rotation objects
dim:
The dimension along which the rotations should be concatenated
Returns:
A concatenated Rotation object in rotation matrix format
"""
rot_mats = torch.cat(
[r.get_rot_mats() for r in rs],
dim=dim if dim >= 0 else dim - 2,
)
return Rotation(rot_mats=rot_mats, quats=None)
def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rotation:
"""
Apply a Tensor -> Tensor function to underlying rotation tensors, mapping over the rotation dimension(s). Can
be used e.g. to sum out a one-hot batch dimension.
Args:
fn:
A Tensor -> Tensor function to be mapped over the Rotation
Returns:
The transformed Rotation object
"""
if self._rot_mats is not None:
rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,))
rot_mats = torch.stack(list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1)
rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3))
return Rotation(rot_mats=rot_mats, quats=None)
elif self._quats is not None:
quats = torch.stack(list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1)
return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
else:
raise ValueError("Both rotations are None")
def cuda(self) -> Rotation:
"""
Analogous to the cuda() method of torch Tensors
Returns:
A copy of the Rotation in CUDA memory
"""
if self._rot_mats is not None:
return Rotation(rot_mats=self._rot_mats.cuda(), quats=None)
elif self._quats is not None:
return Rotation(rot_mats=None, quats=self._quats.cuda(), normalize_quats=False)
else:
raise ValueError("Both rotations are None")
def to(self, device: Optional[torch.device], dtype: Optional[torch.dtype]) -> Rotation:
"""
Analogous to the to() method of torch Tensors
Args:
device:
A torch device
dtype:
A torch dtype
Returns:
A copy of the Rotation using the new device and dtype
"""
if self._rot_mats is not None:
return Rotation(
rot_mats=self._rot_mats.to(device=device, dtype=dtype),
quats=None,
)
elif self._quats is not None:
return Rotation(
rot_mats=None,
quats=self._quats.to(device=device, dtype=dtype),
normalize_quats=False,
)
else:
raise ValueError("Both rotations are None")
def detach(self) -> Rotation:
"""
Returns a copy of the Rotation whose underlying Tensor has been detached from its torch graph.
Returns:
A copy of the Rotation whose underlying Tensor has been detached from its torch graph
"""
if self._rot_mats is not None:
return Rotation(rot_mats=self._rot_mats.detach(), quats=None)
elif self._quats is not None:
return Rotation(
rot_mats=None,
quats=self._quats.detach(),
normalize_quats=False,
)
else:
raise ValueError("Both rotations are None")
class Rigid:
"""
A class representing a rigid transformation. Little more than a wrapper around two objects: a Rotation object and a
[*, 3] translation Designed to behave approximately like a single torch tensor with the shape of the shared batch
dimensions of its component parts.
"""
def __init__(self, rots: Optional[Rotation], trans: Optional[torch.Tensor]):
"""
Args:
rots: A [*, 3, 3] rotation tensor
trans: A corresponding [*, 3] translation tensor
"""
# (we need device, dtype, etc. from at least one input)
batch_dims, dtype, device, requires_grad = None, None, None, None
if trans is not None:
batch_dims = trans.shape[:-1]
dtype = trans.dtype
device = trans.device
requires_grad = trans.requires_grad
elif rots is not None:
batch_dims = rots.shape
dtype = rots.dtype
device = rots.device
requires_grad = rots.requires_grad
else:
raise ValueError("At least one input argument must be specified")
if rots is None:
rots = Rotation.identity(
batch_dims,
dtype,
device,
requires_grad,
)
elif trans is None:
trans = identity_trans(
batch_dims,
dtype,
device,
requires_grad,
)
assert rots is not None
assert trans is not None
if (rots.shape != trans.shape[:-1]) or (rots.device != trans.device):
raise ValueError("Rots and trans incompatible")
# Force full precision. Happens to the rotations automatically.
trans = trans.to(dtype=torch.float32)
self._rots = rots
self._trans = trans
@staticmethod
def identity(
shape: Tuple[int, ...],
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
requires_grad: bool = True,
fmt: str = "quat",
) -> Rigid:
"""
Constructs an identity transformation.
Args:
shape:
The desired shape
dtype:
The dtype of both internal tensors
device:
The device of both internal tensors
requires_grad:
Whether grad should be enabled for the internal tensors
Returns:
The identity transformation
"""
return Rigid(
Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt),
identity_trans(shape, dtype, device, requires_grad),
)
def __getitem__(self, index: Any) -> Rigid:
"""
Indexes the affine transformation with PyTorch-style indices. The index is applied to the shared dimensions of
both the rotation and the translation.
E.g.::
r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None) t = Rigid(r, torch.rand(10, 10, 3)) indexed =
t[3, 4:6] assert(indexed.shape == (2,)) assert(indexed.get_rots().shape == (2,))
assert(indexed.get_trans().shape == (2, 3))
Args:
index: A standard torch tensor index. E.g. 8, (10, None, 3),
or (3, slice(0, 1, None))
Returns:
The indexed tensor
"""
if type(index) != tuple:
index = (index,)
return Rigid(
self._rots[index],
self._trans[index + (slice(None),)],
)
def __mul__(self, right: torch.Tensor) -> Rigid:
"""
Pointwise left multiplication of the transformation with a tensor. Can be used to e.g. mask the Rigid.
Args:
right:
The tensor multiplicand
Returns:
The product
"""
if not (isinstance(right, torch.Tensor)):
raise TypeError("The other multiplicand must be a Tensor")
new_rots = self._rots * right
new_trans = self._trans * right[..., None]
return Rigid(new_rots, new_trans)
def __rmul__(self, left: torch.Tensor) -> Rigid:
"""
Reverse pointwise multiplication of the transformation with a tensor.
Args:
left:
The left multiplicand
Returns:
The product
"""
return self.__mul__(left)
@property
def shape(self) -> torch.Size:
"""
Returns the shape of the shared dimensions of the rotation and the translation.
Returns:
The shape of the transformation
"""
return self._trans.shape[:-1]
@property
def device(self) -> torch.device:
"""
Returns the device on which the Rigid's tensors are located.
Returns:
The device on which the Rigid's tensors are located
"""
return self._trans.device
def get_rots(self) -> Rotation:
"""
Getter for the rotation.
Returns:
The rotation object
"""
return self._rots
def get_trans(self) -> torch.Tensor:
"""
Getter for the translation.
Returns:
The stored translation
"""
return self._trans
def compose_q_update_vec(self, q_update_vec: torch.Tensor) -> Rigid:
"""
Composes the transformation with a quaternion update vector of shape [*, 6], where the final 6 columns
represent the x, y, and z values of a quaternion of form (1, x, y, z) followed by a 3D translation.
Args:
q_vec: The quaternion update vector.
Returns:
The composed transformation.
"""
q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:]
new_rots = self._rots.compose_q_update_vec(q_vec)
trans_update = self._rots.apply(t_vec)
new_translation = self._trans + trans_update
return Rigid(new_rots, new_translation)
def compose(self, r: Rigid) -> Rigid:
"""
Composes the current rigid object with another.
Args:
r:
Another Rigid object
Returns:
The composition of the two transformations
"""
new_rot = self._rots.compose_r(r._rots)
new_trans = self._rots.apply(r._trans) + self._trans
return Rigid(new_rot, new_trans)
def apply(self, pts: torch.Tensor) -> torch.Tensor:
"""
Applies the transformation to a coordinate tensor.
Args:
pts: A [*, 3] coordinate tensor.
Returns:
The transformed points.
"""
rotated = self._rots.apply(pts)
return rotated + self._trans
def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:
"""
Applies the inverse of the transformation to a coordinate tensor.
Args:
pts: A [*, 3] coordinate tensor
Returns:
The transformed points.
"""
pts = pts - self._trans
return self._rots.invert_apply(pts)
def invert(self) -> Rigid:
"""
Inverts the transformation.
Returns:
The inverse transformation.
"""
rot_inv = self._rots.invert()
trn_inv = rot_inv.apply(self._trans)
return Rigid(rot_inv, -1 * trn_inv)
def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rigid:
"""
Apply a Tensor -> Tensor function to underlying translation and rotation tensors, mapping over the
translation/rotation dimensions respectively.
Args:
fn:
A Tensor -> Tensor function to be mapped over the Rigid
Returns:
The transformed Rigid object
"""
new_rots = self._rots.map_tensor_fn(fn)
new_trans = torch.stack(list(map(fn, torch.unbind(self._trans, dim=-1))), dim=-1)
return Rigid(new_rots, new_trans)
def to_tensor_4x4(self) -> torch.Tensor:
"""
Converts a transformation to a homogenous transformation tensor.
Returns:
A [*, 4, 4] homogenous transformation tensor
"""
tensor = self._trans.new_zeros((*self.shape, 4, 4))
tensor[..., :3, :3] = self._rots.get_rot_mats()
tensor[..., :3, 3] = self._trans
tensor[..., 3, 3] = 1
return tensor
@staticmethod
def from_tensor_4x4(t: torch.Tensor) -> Rigid:
"""
Constructs a transformation from a homogenous transformation tensor.
Args:
t: [*, 4, 4] homogenous transformation tensor
Returns:
T object with shape [*]
"""
if t.shape[-2:] != (4, 4):
raise ValueError("Incorrectly shaped input tensor")
rots = Rotation(rot_mats=t[..., :3, :3], quats=None)
trans = t[..., :3, 3]
return Rigid(rots, trans)
def to_tensor_7(self) -> torch.Tensor:
"""
Converts a transformation to a tensor with 7 final columns, four for the quaternion followed by three for the
translation.
Returns:
A [*, 7] tensor representation of the transformation
"""
tensor = self._trans.new_zeros((*self.shape, 7))
tensor[..., :4] = self._rots.get_quats()
tensor[..., 4:] = self._trans
return tensor
@staticmethod
def from_tensor_7(t: torch.Tensor, normalize_quats: bool = False) -> Rigid:
if t.shape[-1] != 7:
raise ValueError("Incorrectly shaped input tensor")
quats, trans = t[..., :4], t[..., 4:]
rots = Rotation(rot_mats=None, quats=quats, normalize_quats=normalize_quats)
return Rigid(rots, trans)
@staticmethod
def from_3_points(
p_neg_x_axis: torch.Tensor, origin: torch.Tensor, p_xy_plane: torch.Tensor, eps: float = 1e-8
) -> Rigid:
"""
Implements algorithm 21. Constructs transformations from sets of 3 points using the Gram-Schmidt algorithm.
Args:
p_neg_x_axis: [*, 3] coordinates
origin: [*, 3] coordinates used as frame origins
p_xy_plane: [*, 3] coordinates
eps: Small epsilon value
Returns:
A transformation object of shape [*]
"""
p_neg_x_axis_unbound = torch.unbind(p_neg_x_axis, dim=-1)
origin_unbound = torch.unbind(origin, dim=-1)
p_xy_plane_unbound = torch.unbind(p_xy_plane, dim=-1)
e0 = [c1 - c2 for c1, c2 in zip(origin_unbound, p_neg_x_axis_unbound)]
e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane_unbound, origin_unbound)]
denom = torch.sqrt(sum(c * c for c in e0) + eps * torch.ones_like(e0[0]))
e0 = [c / denom for c in e0]
dot = sum((c1 * c2 for c1, c2 in zip(e0, e1)))
e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)]
denom = torch.sqrt(sum((c * c for c in e1)) + eps * torch.ones_like(e1[0]))
e1 = [c / denom for c in e1]
e2 = [
e0[1] * e1[2] - e0[2] * e1[1],
e0[2] * e1[0] - e0[0] * e1[2],
e0[0] * e1[1] - e0[1] * e1[0],
]
rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1)
rots = rots.reshape(rots.shape[:-1] + (3, 3))
rot_obj = Rotation(rot_mats=rots, quats=None)
return Rigid(rot_obj, torch.stack(origin_unbound, dim=-1))
def unsqueeze(self, dim: int) -> Rigid:
"""
Analogous to torch.unsqueeze. The dimension is relative to the shared dimensions of the rotation/translation.
Args:
dim: A positive or negative dimension index.
Returns:
The unsqueezed transformation.
"""
if dim >= len(self.shape):
raise ValueError("Invalid dimension")
rots = self._rots.unsqueeze(dim)
trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1)
return Rigid(rots, trans)
@staticmethod
def cat(ts: Sequence[Rigid], dim: int) -> Rigid:
"""
Concatenates transformations along a new dimension.
Args:
ts:
A list of T objects
dim:
The dimension along which the transformations should be concatenated
Returns:
A concatenated transformation object
"""
rots = Rotation.cat([t._rots for t in ts], dim)
trans = torch.cat([t._trans for t in ts], dim=dim if dim >= 0 else dim - 1)
return Rigid(rots, trans)
def apply_rot_fn(self, fn: Callable[[Rotation], Rotation]) -> Rigid:
"""
Applies a Rotation -> Rotation function to the stored rotation object.
Args:
fn: A function of type Rotation -> Rotation
Returns:
A transformation object with a transformed rotation.
"""
return Rigid(fn(self._rots), self._trans)
def apply_trans_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rigid:
"""
Applies a Tensor -> Tensor function to the stored translation.
Args:
fn:
A function of type Tensor -> Tensor to be applied to the translation
Returns:
A transformation object with a transformed translation.
"""
return Rigid(self._rots, fn(self._trans))
def scale_translation(self, trans_scale_factor: float) -> Rigid:
"""
Scales the translation by a constant factor.
Args:
trans_scale_factor:
The constant factor
Returns:
A transformation object with a scaled translation.
"""
return self.apply_trans_fn(lambda t: t * trans_scale_factor)
def stop_rot_gradient(self) -> Rigid:
"""
Detaches the underlying rotation object
Returns:
A transformation object with detached rotations
"""
return self.apply_rot_fn(lambda r: r.detach())
@staticmethod
def make_transform_from_reference(
n_xyz: torch.Tensor, ca_xyz: torch.Tensor, c_xyz: torch.Tensor, eps: float = 1e-20
) -> Rigid:
"""
Returns a transformation object from reference coordinates.
Note that this method does not take care of symmetries. If you provide the atom positions in the non-standard
way, the N atom will end up not at [-0.527250, 1.359329, 0.0] but instead at [-0.527250, -1.359329, 0.0]. You
need to take care of such cases in your code.
Args:
n_xyz: A [*, 3] tensor of nitrogen xyz coordinates.
ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates.
c_xyz: A [*, 3] tensor of carbon xyz coordinates.
Returns:
A transformation object. After applying the translation and rotation to the reference backbone, the
coordinates will approximately equal to the input coordinates.
"""
translation = -1 * ca_xyz
n_xyz = n_xyz + translation
c_xyz = c_xyz + translation
c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)]
norm = torch.sqrt(eps + c_x**2 + c_y**2)
sin_c1 = -c_y / norm
cos_c1 = c_x / norm
c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3))
c1_rots[..., 0, 0] = cos_c1
c1_rots[..., 0, 1] = -1 * sin_c1
c1_rots[..., 1, 0] = sin_c1
c1_rots[..., 1, 1] = cos_c1
c1_rots[..., 2, 2] = 1
norm = torch.sqrt(eps + c_x**2 + c_y**2 + c_z**2)
sin_c2 = c_z / norm
cos_c2 = torch.sqrt(c_x**2 + c_y**2) / norm
c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))
c2_rots[..., 0, 0] = cos_c2
c2_rots[..., 0, 2] = sin_c2
c2_rots[..., 1, 1] = 1
c2_rots[..., 2, 0] = -1 * sin_c2
c2_rots[..., 2, 2] = cos_c2
c_rots = rot_matmul(c2_rots, c1_rots)
n_xyz = rot_vec_mul(c_rots, n_xyz)
_, n_y, n_z = [n_xyz[..., i] for i in range(3)]
norm = torch.sqrt(eps + n_y**2 + n_z**2)
sin_n = -n_z / norm
cos_n = n_y / norm
n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))
n_rots[..., 0, 0] = 1
n_rots[..., 1, 1] = cos_n
n_rots[..., 1, 2] = -1 * sin_n
n_rots[..., 2, 1] = sin_n
n_rots[..., 2, 2] = cos_n
rots = rot_matmul(n_rots, c_rots)
rots = rots.transpose(-1, -2)
translation = -1 * translation
rot_obj = Rotation(rot_mats=rots, quats=None)
return Rigid(rot_obj, translation)
def cuda(self) -> Rigid:
"""
Moves the transformation object to GPU memory
Returns:
A version of the transformation on GPU
"""
return Rigid(self._rots.cuda(), self._trans.cuda())
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hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/residue_constants.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
"""Constants used in AlphaFold."""
import collections
import copy
import functools
from importlib import resources
from typing import Dict, List, Mapping, Sequence, Tuple
import numpy as np
# Internal import (35fd).
# Distance from one CA to next CA [trans configuration: omega = 180].
ca_ca = 3.80209737096
# Format: The list for each AA type contains chi1, chi2, chi3, chi4 in
# this order (or a relevant subset from chi1 onwards). ALA and GLY don't have
# chi angles so their chi angle lists are empty.
chi_angles_atoms: Dict[str, List[List[str]]] = {
"ALA": [],
# Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
"ARG": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "NE"], ["CG", "CD", "NE", "CZ"]],
"ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]],
"ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]],
"CYS": [["N", "CA", "CB", "SG"]],
"GLN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]],
"GLU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]],
"GLY": [],
"HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]],
"ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]],
"LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"LYS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "CE"], ["CG", "CD", "CE", "NZ"]],
"MET": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "SD"], ["CB", "CG", "SD", "CE"]],
"PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]],
"SER": [["N", "CA", "CB", "OG"]],
"THR": [["N", "CA", "CB", "OG1"]],
"TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"VAL": [["N", "CA", "CB", "CG1"]],
}
# If chi angles given in fixed-length array, this matrix determines how to mask
# them for each AA type. The order is as per restype_order (see below).
chi_angles_mask: List[List[float]] = [
[0.0, 0.0, 0.0, 0.0], # ALA
[1.0, 1.0, 1.0, 1.0], # ARG
[1.0, 1.0, 0.0, 0.0], # ASN
[1.0, 1.0, 0.0, 0.0], # ASP
[1.0, 0.0, 0.0, 0.0], # CYS
[1.0, 1.0, 1.0, 0.0], # GLN
[1.0, 1.0, 1.0, 0.0], # GLU
[0.0, 0.0, 0.0, 0.0], # GLY
[1.0, 1.0, 0.0, 0.0], # HIS
[1.0, 1.0, 0.0, 0.0], # ILE
[1.0, 1.0, 0.0, 0.0], # LEU
[1.0, 1.0, 1.0, 1.0], # LYS
[1.0, 1.0, 1.0, 0.0], # MET
[1.0, 1.0, 0.0, 0.0], # PHE
[1.0, 1.0, 0.0, 0.0], # PRO
[1.0, 0.0, 0.0, 0.0], # SER
[1.0, 0.0, 0.0, 0.0], # THR
[1.0, 1.0, 0.0, 0.0], # TRP
[1.0, 1.0, 0.0, 0.0], # TYR
[1.0, 0.0, 0.0, 0.0], # VAL
]
# The following chi angles are pi periodic: they can be rotated by a multiple
# of pi without affecting the structure.
chi_pi_periodic: List[List[float]] = [
[0.0, 0.0, 0.0, 0.0], # ALA
[0.0, 0.0, 0.0, 0.0], # ARG
[0.0, 0.0, 0.0, 0.0], # ASN
[0.0, 1.0, 0.0, 0.0], # ASP
[0.0, 0.0, 0.0, 0.0], # CYS
[0.0, 0.0, 0.0, 0.0], # GLN
[0.0, 0.0, 1.0, 0.0], # GLU
[0.0, 0.0, 0.0, 0.0], # GLY
[0.0, 0.0, 0.0, 0.0], # HIS
[0.0, 0.0, 0.0, 0.0], # ILE
[0.0, 0.0, 0.0, 0.0], # LEU
[0.0, 0.0, 0.0, 0.0], # LYS
[0.0, 0.0, 0.0, 0.0], # MET
[0.0, 1.0, 0.0, 0.0], # PHE
[0.0, 0.0, 0.0, 0.0], # PRO
[0.0, 0.0, 0.0, 0.0], # SER
[0.0, 0.0, 0.0, 0.0], # THR
[0.0, 0.0, 0.0, 0.0], # TRP
[0.0, 1.0, 0.0, 0.0], # TYR
[0.0, 0.0, 0.0, 0.0], # VAL
[0.0, 0.0, 0.0, 0.0], # UNK
]
# Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi,
# psi and chi angles:
# 0: 'backbone group',
# 1: 'pre-omega-group', (empty)
# 2: 'phi-group', (currently empty, because it defines only hydrogens)
# 3: 'psi-group',
# 4,5,6,7: 'chi1,2,3,4-group'
# The atom positions are relative to the axis-end-atom of the corresponding
# rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
# is defined such that the dihedral-angle-definiting atom (the last entry in
# chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
# format: [atomname, group_idx, rel_position]
rigid_group_atom_positions: Dict[str, List[Tuple[str, int, Tuple[float, float, float]]]] = {
"ALA": [
("N", 0, (-0.525, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.529, -0.774, -1.205)),
("O", 3, (0.627, 1.062, 0.000)),
],
"ARG": [
("N", 0, (-0.524, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.524, -0.778, -1.209)),
("O", 3, (0.626, 1.062, 0.000)),
("CG", 4, (0.616, 1.390, -0.000)),
("CD", 5, (0.564, 1.414, 0.000)),
("NE", 6, (0.539, 1.357, -0.000)),
("NH1", 7, (0.206, 2.301, 0.000)),
("NH2", 7, (2.078, 0.978, -0.000)),
("CZ", 7, (0.758, 1.093, -0.000)),
],
"ASN": [
("N", 0, (-0.536, 1.357, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.531, -0.787, -1.200)),
("O", 3, (0.625, 1.062, 0.000)),
("CG", 4, (0.584, 1.399, 0.000)),
("ND2", 5, (0.593, -1.188, 0.001)),
("OD1", 5, (0.633, 1.059, 0.000)),
],
"ASP": [
("N", 0, (-0.525, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, 0.000, -0.000)),
("CB", 0, (-0.526, -0.778, -1.208)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.593, 1.398, -0.000)),
("OD1", 5, (0.610, 1.091, 0.000)),
("OD2", 5, (0.592, -1.101, -0.003)),
],
"CYS": [
("N", 0, (-0.522, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, 0.000, 0.000)),
("CB", 0, (-0.519, -0.773, -1.212)),
("O", 3, (0.625, 1.062, -0.000)),
("SG", 4, (0.728, 1.653, 0.000)),
],
"GLN": [
("N", 0, (-0.526, 1.361, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, 0.000)),
("CB", 0, (-0.525, -0.779, -1.207)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.615, 1.393, 0.000)),
("CD", 5, (0.587, 1.399, -0.000)),
("NE2", 6, (0.593, -1.189, -0.001)),
("OE1", 6, (0.634, 1.060, 0.000)),
],
"GLU": [
("N", 0, (-0.528, 1.361, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.526, -0.781, -1.207)),
("O", 3, (0.626, 1.062, 0.000)),
("CG", 4, (0.615, 1.392, 0.000)),
("CD", 5, (0.600, 1.397, 0.000)),
("OE1", 6, (0.607, 1.095, -0.000)),
("OE2", 6, (0.589, -1.104, -0.001)),
],
"GLY": [
("N", 0, (-0.572, 1.337, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.517, -0.000, -0.000)),
("O", 3, (0.626, 1.062, -0.000)),
],
"HIS": [
("N", 0, (-0.527, 1.360, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, 0.000, 0.000)),
("CB", 0, (-0.525, -0.778, -1.208)),
("O", 3, (0.625, 1.063, 0.000)),
("CG", 4, (0.600, 1.370, -0.000)),
("CD2", 5, (0.889, -1.021, 0.003)),
("ND1", 5, (0.744, 1.160, -0.000)),
("CE1", 5, (2.030, 0.851, 0.002)),
("NE2", 5, (2.145, -0.466, 0.004)),
],
"ILE": [
("N", 0, (-0.493, 1.373, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, -0.000)),
("CB", 0, (-0.536, -0.793, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG1", 4, (0.534, 1.437, -0.000)),
("CG2", 4, (0.540, -0.785, -1.199)),
("CD1", 5, (0.619, 1.391, 0.000)),
],
"LEU": [
("N", 0, (-0.520, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.522, -0.773, -1.214)),
("O", 3, (0.625, 1.063, -0.000)),
("CG", 4, (0.678, 1.371, 0.000)),
("CD1", 5, (0.530, 1.430, -0.000)),
("CD2", 5, (0.535, -0.774, 1.200)),
],
"LYS": [
("N", 0, (-0.526, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, 0.000)),
("CB", 0, (-0.524, -0.778, -1.208)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.619, 1.390, 0.000)),
("CD", 5, (0.559, 1.417, 0.000)),
("CE", 6, (0.560, 1.416, 0.000)),
("NZ", 7, (0.554, 1.387, 0.000)),
],
"MET": [
("N", 0, (-0.521, 1.364, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, 0.000, 0.000)),
("CB", 0, (-0.523, -0.776, -1.210)),
("O", 3, (0.625, 1.062, -0.000)),
("CG", 4, (0.613, 1.391, -0.000)),
("SD", 5, (0.703, 1.695, 0.000)),
("CE", 6, (0.320, 1.786, -0.000)),
],
"PHE": [
("N", 0, (-0.518, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, 0.000, -0.000)),
("CB", 0, (-0.525, -0.776, -1.212)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.607, 1.377, 0.000)),
("CD1", 5, (0.709, 1.195, -0.000)),
("CD2", 5, (0.706, -1.196, 0.000)),
("CE1", 5, (2.102, 1.198, -0.000)),
("CE2", 5, (2.098, -1.201, -0.000)),
("CZ", 5, (2.794, -0.003, -0.001)),
],
"PRO": [
("N", 0, (-0.566, 1.351, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, 0.000)),
("CB", 0, (-0.546, -0.611, -1.293)),
("O", 3, (0.621, 1.066, 0.000)),
("CG", 4, (0.382, 1.445, 0.0)),
# ('CD', 5, (0.427, 1.440, 0.0)),
("CD", 5, (0.477, 1.424, 0.0)), # manually made angle 2 degrees larger
],
"SER": [
("N", 0, (-0.529, 1.360, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.518, -0.777, -1.211)),
("O", 3, (0.626, 1.062, -0.000)),
("OG", 4, (0.503, 1.325, 0.000)),
],
"THR": [
("N", 0, (-0.517, 1.364, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, -0.000)),
("CB", 0, (-0.516, -0.793, -1.215)),
("O", 3, (0.626, 1.062, 0.000)),
("CG2", 4, (0.550, -0.718, -1.228)),
("OG1", 4, (0.472, 1.353, 0.000)),
],
"TRP": [
("N", 0, (-0.521, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, 0.000)),
("CB", 0, (-0.523, -0.776, -1.212)),
("O", 3, (0.627, 1.062, 0.000)),
("CG", 4, (0.609, 1.370, -0.000)),
("CD1", 5, (0.824, 1.091, 0.000)),
("CD2", 5, (0.854, -1.148, -0.005)),
("CE2", 5, (2.186, -0.678, -0.007)),
("CE3", 5, (0.622, -2.530, -0.007)),
("NE1", 5, (2.140, 0.690, -0.004)),
("CH2", 5, (3.028, -2.890, -0.013)),
("CZ2", 5, (3.283, -1.543, -0.011)),
("CZ3", 5, (1.715, -3.389, -0.011)),
],
"TYR": [
("N", 0, (-0.522, 1.362, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, -0.000, -0.000)),
("CB", 0, (-0.522, -0.776, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG", 4, (0.607, 1.382, -0.000)),
("CD1", 5, (0.716, 1.195, -0.000)),
("CD2", 5, (0.713, -1.194, -0.001)),
("CE1", 5, (2.107, 1.200, -0.002)),
("CE2", 5, (2.104, -1.201, -0.003)),
("OH", 5, (4.168, -0.002, -0.005)),
("CZ", 5, (2.791, -0.001, -0.003)),
],
"VAL": [
("N", 0, (-0.494, 1.373, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, -0.000)),
("CB", 0, (-0.533, -0.795, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG1", 4, (0.540, 1.429, -0.000)),
("CG2", 4, (0.533, -0.776, 1.203)),
],
}
# A list of atoms (excluding hydrogen) for each AA type. PDB naming convention.
residue_atoms: Dict[str, List[str]] = {
"ALA": ["C", "CA", "CB", "N", "O"],
"ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"],
"ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"],
"ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"],
"CYS": ["C", "CA", "CB", "N", "O", "SG"],
"GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"],
"GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"],
"GLY": ["C", "CA", "N", "O"],
"HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"],
"ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"],
"LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"],
"LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"],
"MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"],
"PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"],
"PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"],
"SER": ["C", "CA", "CB", "N", "O", "OG"],
"THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"],
"TRP": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE2", "CE3", "CZ2", "CZ3", "CH2", "N", "NE1", "O"],
"TYR": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH"],
"VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"],
}
# Naming swaps for ambiguous atom names.
# Due to symmetries in the amino acids the naming of atoms is ambiguous in
# 4 of the 20 amino acids.
# (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities
# in LEU, VAL and ARG can be resolved by using the 3d constellations of
# the 'ambiguous' atoms and their neighbours)
# TODO: ^ interpret this
residue_atom_renaming_swaps: Dict[str, Dict[str, str]] = {
"ASP": {"OD1": "OD2"},
"GLU": {"OE1": "OE2"},
"PHE": {"CD1": "CD2", "CE1": "CE2"},
"TYR": {"CD1": "CD2", "CE1": "CE2"},
}
# Van der Waals radii [Angstroem] of the atoms (from Wikipedia)
van_der_waals_radius: Dict[str, float] = {
"C": 1.7,
"N": 1.55,
"O": 1.52,
"S": 1.8,
}
Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"])
BondAngle = collections.namedtuple(
"BondAngle",
["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"],
)
def map_structure_with_atom_order(in_list: list, first_call: bool = True) -> list:
# Maps strings in a nested list structure to their corresponding index in atom_order
if first_call:
in_list = copy.deepcopy(in_list)
for i in range(len(in_list)):
if isinstance(in_list[i], list):
in_list[i] = map_structure_with_atom_order(in_list[i], first_call=False)
elif isinstance(in_list[i], str):
in_list[i] = atom_order[in_list[i]]
else:
raise ValueError("Unexpected type when mapping nested lists!")
return in_list
@functools.lru_cache(maxsize=None)
def load_stereo_chemical_props() -> (
Tuple[
Mapping[str, List[Bond]],
Mapping[str, List[Bond]],
Mapping[str, List[BondAngle]],
]
):
"""Load stereo_chemical_props.txt into a nice structure.
Load literature values for bond lengths and bond angles and translate bond angles into the length of the opposite
edge of the triangle ("residue_virtual_bonds").
Returns:
residue_bonds: dict that maps resname --> list of Bond tuples residue_virtual_bonds: dict that maps resname -->
list of Bond tuples residue_bond_angles: dict that maps resname --> list of BondAngle tuples
"""
# TODO: this file should be downloaded in a setup script
stereo_chemical_props = resources.read_text("openfold.resources", "stereo_chemical_props.txt")
lines_iter = iter(stereo_chemical_props.splitlines())
# Load bond lengths.
residue_bonds: Dict[str, List[Bond]] = {}
next(lines_iter) # Skip header line.
for line in lines_iter:
if line.strip() == "-":
break
bond, resname, bond_length, stddev = line.split()
atom1, atom2 = bond.split("-")
if resname not in residue_bonds:
residue_bonds[resname] = []
residue_bonds[resname].append(Bond(atom1, atom2, float(bond_length), float(stddev)))
residue_bonds["UNK"] = []
# Load bond angles.
residue_bond_angles: Dict[str, List[BondAngle]] = {}
next(lines_iter) # Skip empty line.
next(lines_iter) # Skip header line.
for line in lines_iter:
if line.strip() == "-":
break
bond, resname, angle_degree, stddev_degree = line.split()
atom1, atom2, atom3 = bond.split("-")
if resname not in residue_bond_angles:
residue_bond_angles[resname] = []
residue_bond_angles[resname].append(
BondAngle(
atom1,
atom2,
atom3,
float(angle_degree) / 180.0 * np.pi,
float(stddev_degree) / 180.0 * np.pi,
)
)
residue_bond_angles["UNK"] = []
def make_bond_key(atom1_name: str, atom2_name: str) -> str:
"""Unique key to lookup bonds."""
return "-".join(sorted([atom1_name, atom2_name]))
# Translate bond angles into distances ("virtual bonds").
residue_virtual_bonds: Dict[str, List[Bond]] = {}
for resname, bond_angles in residue_bond_angles.items():
# Create a fast lookup dict for bond lengths.
bond_cache: Dict[str, Bond] = {}
for b in residue_bonds[resname]:
bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b
residue_virtual_bonds[resname] = []
for ba in bond_angles:
bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)]
bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)]
# Compute distance between atom1 and atom3 using the law of cosines
# c^2 = a^2 + b^2 - 2ab*cos(gamma).
gamma = ba.angle_rad
length = np.sqrt(bond1.length**2 + bond2.length**2 - 2 * bond1.length * bond2.length * np.cos(gamma))
# Propagation of uncertainty assuming uncorrelated errors.
dl_outer = 0.5 / length
dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer
dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer
dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer
stddev = np.sqrt(
(dl_dgamma * ba.stddev) ** 2 + (dl_db1 * bond1.stddev) ** 2 + (dl_db2 * bond2.stddev) ** 2
)
residue_virtual_bonds[resname].append(Bond(ba.atom1_name, ba.atom3name, length, stddev))
return (residue_bonds, residue_virtual_bonds, residue_bond_angles)
# Between-residue bond lengths for general bonds (first element) and for Proline
# (second element).
between_res_bond_length_c_n: Tuple[float, float] = (1.329, 1.341)
between_res_bond_length_stddev_c_n: Tuple[float, float] = (0.014, 0.016)
# Between-residue cos_angles.
between_res_cos_angles_c_n_ca: Tuple[float, float] = (-0.5203, 0.0353) # degrees: 121.352 +- 2.315
between_res_cos_angles_ca_c_n: Tuple[float, float] = (-0.4473, 0.0311) # degrees: 116.568 +- 1.995
# This mapping is used when we need to store atom data in a format that requires
# fixed atom data size for every residue (e.g. a numpy array).
atom_types: List[str] = [
"N",
"CA",
"C",
"CB",
"O",
"CG",
"CG1",
"CG2",
"OG",
"OG1",
"SG",
"CD",
"CD1",
"CD2",
"ND1",
"ND2",
"OD1",
"OD2",
"SD",
"CE",
"CE1",
"CE2",
"CE3",
"NE",
"NE1",
"NE2",
"OE1",
"OE2",
"CH2",
"NH1",
"NH2",
"OH",
"CZ",
"CZ2",
"CZ3",
"NZ",
"OXT",
]
atom_order: Dict[str, int] = {atom_type: i for i, atom_type in enumerate(atom_types)}
atom_type_num = len(atom_types) # := 37.
# A compact atom encoding with 14 columns
# pylint: disable=line-too-long
# pylint: disable=bad-whitespace
restype_name_to_atom14_names: Dict[str, List[str]] = {
"ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""],
"ARG": ["N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2", "", "", ""],
"ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""],
"ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""],
"CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""],
"GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", ""],
"GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", ""],
"GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""],
"HIS": ["N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2", "", "", "", ""],
"ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""],
"LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""],
"LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""],
"MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""],
"PHE": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "", "", ""],
"PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""],
"SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""],
"THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""],
"TRP": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", "CZ2", "CZ3", "CH2"],
"TYR": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH", "", ""],
"VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""],
"UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""],
}
# pylint: enable=line-too-long
# pylint: enable=bad-whitespace
# This is the standard residue order when coding AA type as a number.
# Reproduce it by taking 3-letter AA codes and sorting them alphabetically.
restypes: List[str] = [
"A",
"R",
"N",
"D",
"C",
"Q",
"E",
"G",
"H",
"I",
"L",
"K",
"M",
"F",
"P",
"S",
"T",
"W",
"Y",
"V",
]
restype_order: Dict[str, int] = {restype: i for i, restype in enumerate(restypes)}
restype_num = len(restypes) # := 20.
unk_restype_index = restype_num # Catch-all index for unknown restypes.
restypes_with_x: List[str] = restypes + ["X"]
restype_order_with_x: Dict[str, int] = {restype: i for i, restype in enumerate(restypes_with_x)}
def sequence_to_onehot(sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False) -> np.ndarray:
"""Maps the given sequence into a one-hot encoded matrix.
Args:
sequence: An amino acid sequence.
mapping: A dictionary mapping amino acids to integers.
map_unknown_to_x: If True, any amino acid that is not in the mapping will be
mapped to the unknown amino acid 'X'. If the mapping doesn't contain amino acid 'X', an error will be thrown.
If False, any amino acid not in the mapping will throw an error.
Returns:
A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of the sequence.
Raises:
ValueError: If the mapping doesn't contain values from 0 to
num_unique_aas - 1 without any gaps.
"""
num_entries = max(mapping.values()) + 1
if sorted(set(mapping.values())) != list(range(num_entries)):
raise ValueError(
"The mapping must have values from 0 to num_unique_aas-1 without any gaps. Got: %s"
% sorted(mapping.values())
)
one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32)
for aa_index, aa_type in enumerate(sequence):
if map_unknown_to_x:
if aa_type.isalpha() and aa_type.isupper():
aa_id = mapping.get(aa_type, mapping["X"])
else:
raise ValueError(f"Invalid character in the sequence: {aa_type}")
else:
aa_id = mapping[aa_type]
one_hot_arr[aa_index, aa_id] = 1
return one_hot_arr
restype_1to3: Dict[str, str] = {
"A": "ALA",
"R": "ARG",
"N": "ASN",
"D": "ASP",
"C": "CYS",
"Q": "GLN",
"E": "GLU",
"G": "GLY",
"H": "HIS",
"I": "ILE",
"L": "LEU",
"K": "LYS",
"M": "MET",
"F": "PHE",
"P": "PRO",
"S": "SER",
"T": "THR",
"W": "TRP",
"Y": "TYR",
"V": "VAL",
}
# NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple
# 1-to-1 mapping of 3 letter names to one letter names. The latter contains
# many more, and less common, three letter names as keys and maps many of these
# to the same one letter name (including 'X' and 'U' which we don't use here).
restype_3to1: Dict[str, str] = {v: k for k, v in restype_1to3.items()}
# Define a restype name for all unknown residues.
unk_restype = "UNK"
resnames: List[str] = [restype_1to3[r] for r in restypes] + [unk_restype]
resname_to_idx: Dict[str, int] = {resname: i for i, resname in enumerate(resnames)}
# The mapping here uses hhblits convention, so that B is mapped to D, J and O
# are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the
# remaining 20 amino acids are kept in alphabetical order.
# There are 2 non-amino acid codes, X (representing any amino acid) and
# "-" representing a missing amino acid in an alignment. The id for these
# codes is put at the end (20 and 21) so that they can easily be ignored if
# desired.
HHBLITS_AA_TO_ID: Dict[str, int] = {
"A": 0,
"B": 2,
"C": 1,
"D": 2,
"E": 3,
"F": 4,
"G": 5,
"H": 6,
"I": 7,
"J": 20,
"K": 8,
"L": 9,
"M": 10,
"N": 11,
"O": 20,
"P": 12,
"Q": 13,
"R": 14,
"S": 15,
"T": 16,
"U": 1,
"V": 17,
"W": 18,
"X": 20,
"Y": 19,
"Z": 3,
"-": 21,
}
# Partial inversion of HHBLITS_AA_TO_ID.
ID_TO_HHBLITS_AA: Dict[int, str] = {
0: "A",
1: "C", # Also U.
2: "D", # Also B.
3: "E", # Also Z.
4: "F",
5: "G",
6: "H",
7: "I",
8: "K",
9: "L",
10: "M",
11: "N",
12: "P",
13: "Q",
14: "R",
15: "S",
16: "T",
17: "V",
18: "W",
19: "Y",
20: "X", # Includes J and O.
21: "-",
}
restypes_with_x_and_gap: List[str] = restypes + ["X", "-"]
MAP_HHBLITS_AATYPE_TO_OUR_AATYPE: Tuple[int, ...] = tuple(
restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) for i in range(len(restypes_with_x_and_gap))
)
def _make_standard_atom_mask() -> np.ndarray:
"""Returns [num_res_types, num_atom_types] mask array."""
# +1 to account for unknown (all 0s).
mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32)
for restype, restype_letter in enumerate(restypes):
restype_name = restype_1to3[restype_letter]
atom_names = residue_atoms[restype_name]
for atom_name in atom_names:
atom_type = atom_order[atom_name]
mask[restype, atom_type] = 1
return mask
STANDARD_ATOM_MASK = _make_standard_atom_mask()
# A one hot representation for the first and second atoms defining the axis
# of rotation for each chi-angle in each residue.
def chi_angle_atom(atom_index: int) -> np.ndarray:
"""Define chi-angle rigid groups via one-hot representations."""
chi_angles_index = {}
one_hots = []
for k, v in chi_angles_atoms.items():
indices = [atom_types.index(s[atom_index]) for s in v]
indices.extend([-1] * (4 - len(indices)))
chi_angles_index[k] = indices
for r in restypes:
res3 = restype_1to3[r]
one_hot = np.eye(atom_type_num)[chi_angles_index[res3]]
one_hots.append(one_hot)
one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`.
one_hot = np.stack(one_hots, axis=0)
one_hot = np.transpose(one_hot, [0, 2, 1])
return one_hot
chi_atom_1_one_hot = chi_angle_atom(1)
chi_atom_2_one_hot = chi_angle_atom(2)
# An array like chi_angles_atoms but using indices rather than names.
chi_angles_atom_indices_list: List[List[List[str]]] = [chi_angles_atoms[restype_1to3[r]] for r in restypes]
chi_angles_atom_indices_ours: list = map_structure_with_atom_order(chi_angles_atom_indices_list)
chi_angles_atom_indices = np.array(
[chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) for chi_atoms in chi_angles_atom_indices_list]
)
# Mapping from (res_name, atom_name) pairs to the atom's chi group index
# and atom index within that group.
chi_groups_for_atom: Dict[Tuple[str, str], List[Tuple[int, int]]] = collections.defaultdict(list)
for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items():
for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res):
for atom_i, atom in enumerate(chi_group):
chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i))
chi_groups_for_atom = dict(chi_groups_for_atom)
def _make_rigid_transformation_4x4(ex: np.ndarray, ey: np.ndarray, translation: np.ndarray) -> np.ndarray:
"""Create a rigid 4x4 transformation matrix from two axes and transl."""
# Normalize ex.
ex_normalized = ex / np.linalg.norm(ex)
# make ey perpendicular to ex
ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized
ey_normalized /= np.linalg.norm(ey_normalized)
# compute ez as cross product
eznorm = np.cross(ex_normalized, ey_normalized)
m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose()
m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0)
return m
# create an array with (restype, atomtype) --> rigid_group_idx
# and an array with (restype, atomtype, coord) for the atom positions
# and compute affine transformation matrices (4,4) from one rigid group to the
# previous group
restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int)
restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int)
restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)
def _make_rigid_group_constants() -> None:
"""Fill the arrays above."""
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]:
atomtype = atom_order[atomname]
restype_atom37_to_rigid_group[restype, atomtype] = group_idx
restype_atom37_mask[restype, atomtype] = 1
restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position
atom14idx = restype_name_to_atom14_names[resname].index(atomname)
restype_atom14_to_rigid_group[restype, atom14idx] = group_idx
restype_atom14_mask[restype, atom14idx] = 1
restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
atom_positions: Dict[str, np.ndarray] = {
name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname]
}
# backbone to backbone is the identity transform
restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4)
# pre-omega-frame to backbone (currently dummy identity matrix)
restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4)
# phi-frame to backbone
mat = _make_rigid_transformation_4x4(
ex=atom_positions["N"] - atom_positions["CA"],
ey=np.array([1.0, 0.0, 0.0]),
translation=atom_positions["N"],
)
restype_rigid_group_default_frame[restype, 2, :, :] = mat
# psi-frame to backbone
mat = _make_rigid_transformation_4x4(
ex=atom_positions["C"] - atom_positions["CA"],
ey=atom_positions["CA"] - atom_positions["N"],
translation=atom_positions["C"],
)
restype_rigid_group_default_frame[restype, 3, :, :] = mat
# chi1-frame to backbone
if chi_angles_mask[restype][0]:
base_atom_names = chi_angles_atoms[resname][0]
base_atom_positions = [atom_positions[name] for name in base_atom_names]
mat = _make_rigid_transformation_4x4(
ex=base_atom_positions[2] - base_atom_positions[1],
ey=base_atom_positions[0] - base_atom_positions[1],
translation=base_atom_positions[2],
)
restype_rigid_group_default_frame[restype, 4, :, :] = mat
# chi2-frame to chi1-frame
# chi3-frame to chi2-frame
# chi4-frame to chi3-frame
# luckily all rotation axes for the next frame start at (0,0,0) of the
# previous frame
for chi_idx in range(1, 4):
if chi_angles_mask[restype][chi_idx]:
axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2]
axis_end_atom_position = atom_positions[axis_end_atom_name]
mat = _make_rigid_transformation_4x4(
ex=axis_end_atom_position,
ey=np.array([-1.0, 0.0, 0.0]),
translation=axis_end_atom_position,
)
restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat
_make_rigid_group_constants()
def make_atom14_dists_bounds(
overlap_tolerance: float = 1.5,
bond_length_tolerance_factor: int = 15,
) -> Dict[str, np.ndarray]:
"""compute upper and lower bounds for bonds to assess violations."""
restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32)
restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32)
restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32)
residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props()
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
atom_list = restype_name_to_atom14_names[resname]
# create lower and upper bounds for clashes
for atom1_idx, atom1_name in enumerate(atom_list):
if not atom1_name:
continue
atom1_radius = van_der_waals_radius[atom1_name[0]]
for atom2_idx, atom2_name in enumerate(atom_list):
if (not atom2_name) or atom1_idx == atom2_idx:
continue
atom2_radius = van_der_waals_radius[atom2_name[0]]
lower = atom1_radius + atom2_radius - overlap_tolerance
upper = 1e10
restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
# overwrite lower and upper bounds for bonds and angles
for b in residue_bonds[resname] + residue_virtual_bonds[resname]:
atom1_idx = atom_list.index(b.atom1_name)
atom2_idx = atom_list.index(b.atom2_name)
lower = b.length - bond_length_tolerance_factor * b.stddev
upper = b.length + bond_length_tolerance_factor * b.stddev
restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev
restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev
return {
"lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14)
"upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14)
"stddev": restype_atom14_bond_stddev, # shape (21,14,14)
}
restype_atom14_ambiguous_atoms = np.zeros((21, 14), dtype=np.float32)
restype_atom14_ambiguous_atoms_swap_idx: np.ndarray = np.tile(np.arange(14, dtype=int), (21, 1))
def _make_atom14_ambiguity_feats() -> None:
for res, pairs in residue_atom_renaming_swaps.items():
res_idx = restype_order[restype_3to1[res]]
for atom1, atom2 in pairs.items():
atom1_idx = restype_name_to_atom14_names[res].index(atom1)
atom2_idx = restype_name_to_atom14_names[res].index(atom2)
restype_atom14_ambiguous_atoms[res_idx, atom1_idx] = 1
restype_atom14_ambiguous_atoms[res_idx, atom2_idx] = 1
restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom1_idx] = atom2_idx
restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom2_idx] = atom1_idx
_make_atom14_ambiguity_feats()
def aatype_to_str_sequence(aatype: Sequence[int]) -> str:
return "".join([restypes_with_x[aatype[i]] for i in range(len(aatype))])
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/data_transforms.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def make_atom14_masks(protein: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Construct denser atom positions (14 dimensions instead of 37)."""
restype_atom14_to_atom37_list = []
restype_atom37_to_atom14_list = []
restype_atom14_mask_list = []
for rt in rc.restypes:
atom_names = rc.restype_name_to_atom14_names[rc.restype_1to3[rt]]
restype_atom14_to_atom37_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
restype_atom37_to_atom14_list.append(
[(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0) for name in rc.atom_types]
)
restype_atom14_mask_list.append([(1.0 if name else 0.0) for name in atom_names])
# Add dummy mapping for restype 'UNK'
restype_atom14_to_atom37_list.append([0] * 14)
restype_atom37_to_atom14_list.append([0] * 37)
restype_atom14_mask_list.append([0.0] * 14)
restype_atom14_to_atom37 = torch.tensor(
restype_atom14_to_atom37_list,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom37_to_atom14 = torch.tensor(
restype_atom37_to_atom14_list,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom14_mask = torch.tensor(
restype_atom14_mask_list,
dtype=torch.float32,
device=protein["aatype"].device,
)
protein_aatype = protein["aatype"].to(torch.long)
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
residx_atom14_to_atom37 = restype_atom14_to_atom37[protein_aatype]
residx_atom14_mask = restype_atom14_mask[protein_aatype]
protein["atom14_atom_exists"] = residx_atom14_mask
protein["residx_atom14_to_atom37"] = residx_atom14_to_atom37.long()
# create the gather indices for mapping back
residx_atom37_to_atom14 = restype_atom37_to_atom14[protein_aatype]
protein["residx_atom37_to_atom14"] = residx_atom37_to_atom14.long()
# create the corresponding mask
restype_atom37_mask = torch.zeros([21, 37], dtype=torch.float32, device=protein["aatype"].device)
for restype, restype_letter in enumerate(rc.restypes):
restype_name = rc.restype_1to3[restype_letter]
atom_names = rc.residue_atoms[restype_name]
for atom_name in atom_names:
atom_type = rc.atom_order[atom_name]
restype_atom37_mask[restype, atom_type] = 1
residx_atom37_mask = restype_atom37_mask[protein_aatype]
protein["atom37_atom_exists"] = residx_atom37_mask
return protein
def make_atom14_masks_np(batch: Dict[str, torch.Tensor]) -> Dict[str, np.ndarray]:
batch = tree_map(lambda n: torch.tensor(n, device=batch["aatype"].device), batch, np.ndarray)
out = tensor_tree_map(lambda t: np.array(t), make_atom14_masks(batch))
return out
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/chunk_utils.py
|
# Copyright 2021 AlQuraishi Laboratory
#
# 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.
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _fetch_dims(tree: Union[dict, list, tuple, torch.Tensor]) -> List[Tuple[int, ...]]:
shapes = []
if isinstance(tree, dict):
for v in tree.values():
shapes.extend(_fetch_dims(v))
elif isinstance(tree, (list, tuple)):
for t in tree:
shapes.extend(_fetch_dims(t))
elif isinstance(tree, torch.Tensor):
shapes.append(tree.shape)
else:
raise ValueError("Not supported")
return shapes
@torch.jit.ignore
def _flat_idx_to_idx(flat_idx: int, dims: Tuple[int, ...]) -> Tuple[int, ...]:
idx = []
for d in reversed(dims):
idx.append(flat_idx % d)
flat_idx = flat_idx // d
return tuple(reversed(idx))
@torch.jit.ignore
def _get_minimal_slice_set(
start: Sequence[int],
end: Sequence[int],
dims: Sequence[int],
start_edges: Optional[Sequence[bool]] = None,
end_edges: Optional[Sequence[bool]] = None,
) -> List[Tuple[slice, ...]]:
"""
Produces an ordered sequence of tensor slices that, when used in sequence on a tensor with shape dims, yields
tensors that contain every leaf in the contiguous range [start, end]. Care is taken to yield a short sequence of
slices, and perhaps even the shortest possible (I'm pretty sure it's the latter).
end is INCLUSIVE.
"""
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(l: List[bool]) -> None:
tally = True
for i in range(len(l)):
reversed_idx = -1 * (i + 1)
l[reversed_idx] &= tally
tally = l[reversed_idx]
if start_edges is None:
start_edges = [s == 0 for s in start]
reduce_edge_list(start_edges)
if end_edges is None:
end_edges = [e == (d - 1) for e, d in zip(end, dims)]
reduce_edge_list(end_edges)
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(start) == 0:
return [()]
elif len(start) == 1:
return [(slice(start[0], end[0] + 1),)]
slices: List[Tuple[slice, ...]] = []
path_list: List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(start, end):
if s == e:
path_list.append(slice(s, s + 1))
else:
break
path: Tuple[slice, ...] = tuple(path_list)
divergence_idx = len(path)
# start == end, and we're done
if divergence_idx == len(dims):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
sdi = start[divergence_idx]
return tuple(
path + (slice(sdi, sdi + 1),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :],
[d - 1 for d in dims[divergence_idx + 1 :]],
dims[divergence_idx + 1 :],
start_edges=start_edges[divergence_idx + 1 :],
end_edges=[True for _ in end_edges[divergence_idx + 1 :]],
)
)
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
edi = end[divergence_idx]
return tuple(
path + (slice(edi, edi + 1),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]],
end[divergence_idx + 1 :],
dims[divergence_idx + 1 :],
start_edges=[True for _ in start_edges[divergence_idx + 1 :]],
end_edges=end_edges[divergence_idx + 1 :],
)
)
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx], end[divergence_idx] + 1),))
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx], end[divergence_idx]),))
slices.extend(lower())
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper())
slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx] + 1),))
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper())
middle_ground = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx]),))
slices.extend(lower())
return slices
@torch.jit.ignore
def _chunk_slice(t: torch.Tensor, flat_start: int, flat_end: int, no_batch_dims: int) -> torch.Tensor:
"""
Equivalent to
t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end]
but without the need for the initial reshape call, which can be memory-intensive in certain situations. The only
reshape operations in this function are performed on sub-tensors that scale with (flat_end - flat_start), the chunk
size.
"""
batch_dims = t.shape[:no_batch_dims]
start_idx = list(_flat_idx_to_idx(flat_start, batch_dims))
# _get_minimal_slice_set is inclusive
end_idx = list(_flat_idx_to_idx(flat_end - 1, batch_dims))
# Get an ordered list of slices to perform
slices = _get_minimal_slice_set(
start_idx,
end_idx,
batch_dims,
)
sliced_tensors = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]) for s in sliced_tensors])
def chunk_layer(
layer: Callable,
inputs: Dict[str, Any],
chunk_size: int,
no_batch_dims: int,
low_mem: bool = False,
_out: Any = None,
_add_into_out: bool = False,
) -> Any:
"""
Implements the "chunking" procedure described in section 1.11.8.
Layer outputs and inputs are assumed to be simple "pytrees," consisting only of (arbitrarily nested) lists, tuples,
and dicts with torch.Tensor leaves.
Args:
layer:
The layer to be applied chunk-wise
inputs:
A (non-nested) dictionary of keyworded inputs. All leaves must be tensors and must share the same batch
dimensions.
chunk_size:
The number of sub-batches per chunk. If multiple batch dimensions are specified, a "sub-batch" is defined
as a single indexing of all batch dimensions simultaneously (s.t. the number of sub-batches is the product
of the batch dimensions).
no_batch_dims:
How many of the initial dimensions of each input tensor can be considered batch dimensions.
low_mem:
Avoids flattening potentially large input tensors. Unnecessary in most cases, and is ever so slightly
slower than the default setting.
Returns:
The reassembled output of the layer on the inputs.
"""
if not (len(inputs) > 0):
raise ValueError("Must provide at least one input")
initial_dims = [shape[:no_batch_dims] for shape in _fetch_dims(inputs)]
orig_batch_dims = tuple([max(s) for s in zip(*initial_dims)])
def _prep_inputs(t: torch.Tensor) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims]) == no_batch_dims:
t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
t = t.reshape(-1, *t.shape[no_batch_dims:])
else:
t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
return t
prepped_inputs: Dict[str, Any] = tensor_tree_map(_prep_inputs, inputs)
prepped_outputs = None
if _out is not None:
prepped_outputs = tensor_tree_map(lambda t: t.view([-1] + list(t.shape[no_batch_dims:])), _out)
flat_batch_dim = 1
for d in orig_batch_dims:
flat_batch_dim *= d
no_chunks = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(t: torch.Tensor) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
i = 0
out = prepped_outputs
for _ in range(no_chunks):
# Chunk the input
if not low_mem:
select_chunk = _select_chunk
else:
select_chunk = partial(
_chunk_slice,
flat_start=i,
flat_end=min(flat_batch_dim, i + chunk_size),
no_batch_dims=len(orig_batch_dims),
)
chunks: Dict[str, Any] = tensor_tree_map(select_chunk, prepped_inputs)
# Run the layer on the chunk
output_chunk = layer(**chunks)
# Allocate space for the output
if out is None:
out = tensor_tree_map(lambda t: t.new_zeros((flat_batch_dim,) + t.shape[1:]), output_chunk)
# Put the chunk in its pre-allocated space
if isinstance(output_chunk, dict):
def assign(d1: dict, d2: dict) -> None:
for k, v in d1.items():
if isinstance(v, dict):
assign(v, d2[k])
else:
if _add_into_out:
v[i : i + chunk_size] += d2[k]
else:
v[i : i + chunk_size] = d2[k]
assign(out, output_chunk)
elif isinstance(output_chunk, tuple):
for x1, x2 in zip(out, output_chunk):
if _add_into_out:
x1[i : i + chunk_size] += x2
else:
x1[i : i + chunk_size] = x2
elif isinstance(output_chunk, torch.Tensor):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
out[i : i + chunk_size] = output_chunk
else:
raise ValueError("Not supported")
i += chunk_size
out = tensor_tree_map(lambda t: t.view(orig_batch_dims + t.shape[1:]), out)
return out
class ChunkSizeTuner:
def __init__(
self,
# Heuristically, runtimes for most of the modules in the network
# plateau earlier than this on all GPUs I've run the model on.
max_chunk_size: int = 512,
):
self.max_chunk_size = max_chunk_size
self.cached_chunk_size: Optional[int] = None
self.cached_arg_data: Optional[tuple] = None
def _determine_favorable_chunk_size(self, fn: Callable, args: tuple, min_chunk_size: int) -> int:
logging.info("Tuning chunk size...")
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
candidates: List[int] = [2**l for l in range(int(math.log(self.max_chunk_size, 2)) + 1)]
candidates = [c for c in candidates if c > min_chunk_size]
candidates = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(chunk_size: int) -> bool:
try:
with torch.no_grad():
fn(*args, chunk_size=chunk_size)
return True
except RuntimeError:
return False
min_viable_chunk_size_index = 0
i = len(candidates) - 1
while i > min_viable_chunk_size_index:
viable = test_chunk_size(candidates[i])
if not viable:
i = (min_viable_chunk_size_index + i) // 2
else:
min_viable_chunk_size_index = i
i = (i + len(candidates) - 1) // 2
return candidates[min_viable_chunk_size_index]
def _compare_arg_caches(self, ac1: Iterable, ac2: Iterable) -> bool:
consistent = True
for a1, a2 in zip(ac1, ac2):
assert type(ac1) == type(ac2)
if isinstance(ac1, (list, tuple)):
consistent &= self._compare_arg_caches(a1, a2)
elif isinstance(ac1, dict):
a1_items = [v for _, v in sorted(a1.items(), key=lambda x: x[0])]
a2_items = [v for _, v in sorted(a2.items(), key=lambda x: x[0])]
consistent &= self._compare_arg_caches(a1_items, a2_items)
else:
consistent &= a1 == a2
return consistent
def tune_chunk_size(
self,
representative_fn: Callable,
args: tuple,
min_chunk_size: int,
) -> int:
consistent = True
arg_data: tuple = tree_map(lambda a: a.shape if isinstance(a, torch.Tensor) else a, args, object)
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data) == len(arg_data)
consistent = self._compare_arg_caches(self.cached_arg_data, arg_data)
else:
# Otherwise, we can reuse the precomputed value
consistent = False
if not consistent:
self.cached_chunk_size = self._determine_favorable_chunk_size(
representative_fn,
args,
min_chunk_size,
)
self.cached_arg_data = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/loss.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from typing import Dict, Optional, Tuple
import torch
def _calculate_bin_centers(boundaries: torch.Tensor) -> torch.Tensor:
step = boundaries[1] - boundaries[0]
bin_centers = boundaries + step / 2
bin_centers = torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1)], dim=0)
return bin_centers
def _calculate_expected_aligned_error(
alignment_confidence_breaks: torch.Tensor,
aligned_distance_error_probs: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
bin_centers = _calculate_bin_centers(alignment_confidence_breaks)
return (
torch.sum(aligned_distance_error_probs * bin_centers, dim=-1),
bin_centers[-1],
)
def compute_predicted_aligned_error(
logits: torch.Tensor,
max_bin: int = 31,
no_bins: int = 64,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""Computes aligned confidence metrics from logits.
Args:
logits: [*, num_res, num_res, num_bins] the logits output from
PredictedAlignedErrorHead.
max_bin: Maximum bin value
no_bins: Number of bins
Returns:
aligned_confidence_probs: [*, num_res, num_res, num_bins] the predicted
aligned error probabilities over bins for each residue pair.
predicted_aligned_error: [*, num_res, num_res] the expected aligned distance
error for each pair of residues.
max_predicted_aligned_error: [*] the maximum predicted error possible.
"""
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
aligned_confidence_probs = torch.nn.functional.softmax(logits, dim=-1)
predicted_aligned_error, max_predicted_aligned_error = _calculate_expected_aligned_error(
alignment_confidence_breaks=boundaries,
aligned_distance_error_probs=aligned_confidence_probs,
)
return {
"aligned_confidence_probs": aligned_confidence_probs,
"predicted_aligned_error": predicted_aligned_error,
"max_predicted_aligned_error": max_predicted_aligned_error,
}
def compute_tm(
logits: torch.Tensor,
residue_weights: Optional[torch.Tensor] = None,
max_bin: int = 31,
no_bins: int = 64,
eps: float = 1e-8,
**kwargs,
) -> torch.Tensor:
if residue_weights is None:
residue_weights = logits.new_ones(logits.shape[-2])
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
bin_centers = _calculate_bin_centers(boundaries)
torch.sum(residue_weights)
n = logits.shape[-2]
clipped_n = max(n, 19)
d0 = 1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8
probs = torch.nn.functional.softmax(logits, dim=-1)
tm_per_bin = 1.0 / (1 + (bin_centers**2) / (d0**2))
predicted_tm_term = torch.sum(probs * tm_per_bin, dim=-1)
normed_residue_mask = residue_weights / (eps + residue_weights.sum())
per_alignment = torch.sum(predicted_tm_term * normed_residue_mask, dim=-1)
weighted = per_alignment * residue_weights
argmax = (weighted == torch.max(weighted)).nonzero()[0]
return per_alignment[tuple(argmax)]
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/protein.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
"""Protein data type."""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
FeatureDict = Mapping[str, np.ndarray]
ModelOutput = Mapping[str, Any] # Is a nested dict.
PICO_TO_ANGSTROM = 0.01
@dataclasses.dataclass(frozen=True)
class Protein:
"""Protein structure representation."""
# Cartesian coordinates of atoms in angstroms. The atom types correspond to
# residue_constants.atom_types, i.e. the first three are N, CA, CB.
atom_positions: np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
aatype: np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
atom_mask: np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
residue_index: np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
b_factors: np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
chain_index: Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
remark: Optional[str] = None
# Templates used to generate this protein (prediction-only)
parents: Optional[Sequence[str]] = None
# Chain corresponding to each parent
parents_chain_index: Optional[Sequence[int]] = None
def from_proteinnet_string(proteinnet_str: str) -> Protein:
tag_re = r"(\[[A-Z]+\]\n)"
tags: List[str] = [tag.strip() for tag in re.split(tag_re, proteinnet_str) if len(tag) > 0]
groups: Iterator[Tuple[str, List[str]]] = zip(tags[0::2], [l.split("\n") for l in tags[1::2]])
atoms: List[str] = ["N", "CA", "C"]
aatype = None
atom_positions = None
atom_mask = None
for g in groups:
if "[PRIMARY]" == g[0]:
seq = g[1][0].strip()
for i in range(len(seq)):
if seq[i] not in residue_constants.restypes:
seq[i] = "X" # FIXME: strings are immutable
aatype = np.array(
[residue_constants.restype_order.get(res_symbol, residue_constants.restype_num) for res_symbol in seq]
)
elif "[TERTIARY]" == g[0]:
tertiary: List[List[float]] = []
for axis in range(3):
tertiary.append(list(map(float, g[1][axis].split())))
tertiary_np = np.array(tertiary)
atom_positions = np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.float32)
for i, atom in enumerate(atoms):
atom_positions[:, residue_constants.atom_order[atom], :] = np.transpose(tertiary_np[:, i::3])
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
mask = np.array(list(map({"-": 0, "+": 1}.get, g[1][0].strip())))
atom_mask = np.zeros(
(
len(mask),
residue_constants.atom_type_num,
)
).astype(np.float32)
for i, atom in enumerate(atoms):
atom_mask[:, residue_constants.atom_order[atom]] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=atom_positions,
atom_mask=atom_mask,
aatype=aatype,
residue_index=np.arange(len(aatype)),
b_factors=None,
)
def get_pdb_headers(prot: Protein, chain_id: int = 0) -> List[str]:
pdb_headers: List[str] = []
remark = prot.remark
if remark is not None:
pdb_headers.append(f"REMARK {remark}")
parents = prot.parents
parents_chain_index = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
parents = [p for i, p in zip(parents_chain_index, parents) if i == chain_id]
if parents is None or len(parents) == 0:
parents = ["N/A"]
pdb_headers.append(f"PARENT {' '.join(parents)}")
return pdb_headers
def add_pdb_headers(prot: Protein, pdb_str: str) -> str:
"""Add pdb headers to an existing PDB string. Useful during multi-chain
recycling
"""
out_pdb_lines: List[str] = []
lines = pdb_str.split("\n")
remark = prot.remark
if remark is not None:
out_pdb_lines.append(f"REMARK {remark}")
parents_per_chain: List[List[str]]
if prot.parents is not None and len(prot.parents) > 0:
parents_per_chain = []
if prot.parents_chain_index is not None:
parent_dict: Dict[str, List[str]] = {}
for p, i in zip(prot.parents, prot.parents_chain_index):
parent_dict.setdefault(str(i), [])
parent_dict[str(i)].append(p)
max_idx = max([int(chain_idx) for chain_idx in parent_dict])
for i in range(max_idx + 1):
chain_parents = parent_dict.get(str(i), ["N/A"])
parents_per_chain.append(chain_parents)
else:
parents_per_chain.append(list(prot.parents))
else:
parents_per_chain = [["N/A"]]
def make_parent_line(p: Sequence[str]) -> str:
return f"PARENT {' '.join(p)}"
out_pdb_lines.append(make_parent_line(parents_per_chain[0]))
chain_counter = 0
for i, l in enumerate(lines):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(l)
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(parents_per_chain):
chain_parents = parents_per_chain[chain_counter]
else:
chain_parents = ["N/A"]
out_pdb_lines.append(make_parent_line(chain_parents))
return "\n".join(out_pdb_lines)
def to_pdb(prot: Protein) -> str:
"""Converts a `Protein` instance to a PDB string.
Args:
prot: The protein to convert to PDB.
Returns:
PDB string.
"""
restypes = residue_constants.restypes + ["X"]
def res_1to3(r: int) -> str:
return residue_constants.restype_1to3.get(restypes[r], "UNK")
atom_types = residue_constants.atom_types
pdb_lines: List[str] = []
atom_mask = prot.atom_mask
aatype = prot.aatype
atom_positions = prot.atom_positions
residue_index = prot.residue_index.astype(np.int32)
b_factors = prot.b_factors
chain_index = prot.chain_index
if np.any(aatype > residue_constants.restype_num):
raise ValueError("Invalid aatypes.")
headers = get_pdb_headers(prot)
if len(headers) > 0:
pdb_lines.extend(headers)
n = aatype.shape[0]
atom_index = 1
prev_chain_index = 0
chain_tags = string.ascii_uppercase
chain_tag = None
# Add all atom sites.
for i in range(n):
res_name_3 = res_1to3(aatype[i])
for atom_name, pos, mask, b_factor in zip(atom_types, atom_positions[i], atom_mask[i], b_factors[i]):
if mask < 0.5:
continue
record_type = "ATOM"
name = atom_name if len(atom_name) == 4 else f" {atom_name}"
alt_loc = ""
insertion_code = ""
occupancy = 1.00
element = atom_name[0] # Protein supports only C, N, O, S, this works.
charge = ""
chain_tag = "A"
if chain_index is not None:
chain_tag = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
atom_line = (
f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
f"{res_name_3:>3} {chain_tag:>1}"
f"{residue_index[i]:>4}{insertion_code:>1} "
f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
f"{occupancy:>6.2f}{b_factor:>6.2f} "
f"{element:>2}{charge:>2}"
)
pdb_lines.append(atom_line)
atom_index += 1
should_terminate = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
should_terminate = True
prev_chain_index = chain_index[i + 1]
if should_terminate:
# Close the chain.
chain_end = "TER"
chain_termination_line = (
f"{chain_end:<6}{atom_index:>5} {res_1to3(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}"
)
pdb_lines.append(chain_termination_line)
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(prot, prev_chain_index))
pdb_lines.append("END")
pdb_lines.append("")
return "\n".join(pdb_lines)
def ideal_atom_mask(prot: Protein) -> np.ndarray:
"""Computes an ideal atom mask.
`Protein.atom_mask` typically is defined according to the atoms that are reported in the PDB. This function
computes a mask according to heavy atoms that should be present in the given sequence of amino acids.
Args:
prot: `Protein` whose fields are `numpy.ndarray` objects.
Returns:
An ideal atom mask.
"""
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def from_prediction(
features: FeatureDict,
result: ModelOutput,
b_factors: Optional[np.ndarray] = None,
chain_index: Optional[np.ndarray] = None,
remark: Optional[str] = None,
parents: Optional[Sequence[str]] = None,
parents_chain_index: Optional[Sequence[int]] = None,
) -> Protein:
"""Assembles a protein from a prediction.
Args:
features: Dictionary holding model inputs.
result: Dictionary holding model outputs.
b_factors: (Optional) B-factors to use for the protein.
chain_index: (Optional) Chain indices for multi-chain predictions
remark: (Optional) Remark about the prediction
parents: (Optional) List of template names
Returns:
A protein instance.
"""
return Protein(
aatype=features["aatype"],
atom_positions=result["final_atom_positions"],
atom_mask=result["final_atom_mask"],
residue_index=features["residue_index"] + 1,
b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]),
chain_index=chain_index,
remark=remark,
parents=parents,
parents_chain_index=parents_chain_index,
)
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/__init__.py
|
from .chunk_utils import chunk_layer
from .data_transforms import make_atom14_masks
from .feats import atom14_to_atom37, frames_and_literature_positions_to_atom14_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
| 0
|
hf_public_repos/transformers/src/transformers/models/esm
|
hf_public_repos/transformers/src/transformers/models/esm/openfold_utils/feats.py
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from typing import Dict, Tuple, overload
import torch
import torch.types
from torch import nn
from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather
@overload
def pseudo_beta_fn(aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: None) -> torch.Tensor:
...
@overload
def pseudo_beta_fn(
aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
...
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
is_gly = aatype == rc.restype_order["G"]
ca_idx = rc.atom_order["CA"]
cb_idx = rc.atom_order["CB"]
pseudo_beta = torch.where(
is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3),
all_atom_positions[..., ca_idx, :],
all_atom_positions[..., cb_idx, :],
)
if all_atom_masks is not None:
pseudo_beta_mask = torch.where(
is_gly,
all_atom_masks[..., ca_idx],
all_atom_masks[..., cb_idx],
)
return pseudo_beta, pseudo_beta_mask
else:
return pseudo_beta
def atom14_to_atom37(atom14: torch.Tensor, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
atom37_data = batched_gather(
atom14,
batch["residx_atom37_to_atom14"],
dim=-2,
no_batch_dims=len(atom14.shape[:-2]),
)
atom37_data = atom37_data * batch["atom37_atom_exists"][..., None]
return atom37_data
def build_template_angle_feat(template_feats: Dict[str, torch.Tensor]) -> torch.Tensor:
template_aatype = template_feats["template_aatype"]
torsion_angles_sin_cos = template_feats["template_torsion_angles_sin_cos"]
alt_torsion_angles_sin_cos = template_feats["template_alt_torsion_angles_sin_cos"]
torsion_angles_mask = template_feats["template_torsion_angles_mask"]
template_angle_feat = torch.cat(
[
nn.functional.one_hot(template_aatype, 22),
torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2], 14),
alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2], 14),
torsion_angles_mask,
],
dim=-1,
)
return template_angle_feat
def build_template_pair_feat(
batch: Dict[str, torch.Tensor],
min_bin: torch.types.Number,
max_bin: torch.types.Number,
no_bins: int,
use_unit_vector: bool = False,
eps: float = 1e-20,
inf: float = 1e8,
) -> torch.Tensor:
template_mask = batch["template_pseudo_beta_mask"]
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
# Compute distogram (this seems to differ slightly from Alg. 5)
tpb = batch["template_pseudo_beta"]
dgram = torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True)
lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2
upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)
to_concat = [dgram, template_mask_2d[..., None]]
aatype_one_hot: torch.LongTensor = nn.functional.one_hot(
batch["template_aatype"],
rc.restype_num + 2,
)
n_res = batch["template_aatype"].shape[-1]
to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2], n_res, -1, -1))
to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1))
n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
rigids = Rigid.make_transform_from_reference(
n_xyz=batch["template_all_atom_positions"][..., n, :],
ca_xyz=batch["template_all_atom_positions"][..., ca, :],
c_xyz=batch["template_all_atom_positions"][..., c, :],
eps=eps,
)
points = rigids.get_trans()[..., None, :, :]
rigid_vec = rigids[..., None].invert_apply(points)
inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec**2, dim=-1))
t_aa_masks = batch["template_all_atom_mask"]
template_mask = t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
inv_distance_scalar = inv_distance_scalar * template_mask_2d
unit_vector = rigid_vec * inv_distance_scalar[..., None]
if not use_unit_vector:
unit_vector = unit_vector * 0.0
to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))
to_concat.append(template_mask_2d[..., None])
act = torch.cat(to_concat, dim=-1)
act = act * template_mask_2d[..., None]
return act
def build_extra_msa_feat(batch: Dict[str, torch.Tensor]) -> torch.Tensor:
msa_1hot: torch.LongTensor = nn.functional.one_hot(batch["extra_msa"], 23)
msa_feat = [
msa_1hot,
batch["extra_has_deletion"].unsqueeze(-1),
batch["extra_deletion_value"].unsqueeze(-1),
]
return torch.cat(msa_feat, dim=-1)
def torsion_angles_to_frames(
r: Rigid,
alpha: torch.Tensor,
aatype: torch.Tensor,
rrgdf: torch.Tensor,
) -> Rigid:
# [*, N, 8, 4, 4]
default_4x4 = rrgdf[aatype, ...]
# [*, N, 8] transformations, i.e.
# One [*, N, 8, 3, 3] rotation matrix and
# One [*, N, 8, 3] translation matrix
default_r = r.from_tensor_4x4(default_4x4)
bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
bb_rot[..., 1] = 1
# [*, N, 8, 2]
alpha = torch.cat([bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2)
# [*, N, 8, 3, 3]
# Produces rotation matrices of the form:
# [
# [1, 0 , 0 ],
# [0, a_2,-a_1],
# [0, a_1, a_2]
# ]
# This follows the original code rather than the supplement, which uses
# different indices.
all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
all_rots[..., 0, 0] = 1
all_rots[..., 1, 1] = alpha[..., 1]
all_rots[..., 1, 2] = -alpha[..., 0]
all_rots[..., 2, 1:] = alpha
all_frames = default_r.compose(Rigid(Rotation(rot_mats=all_rots), None))
chi2_frame_to_frame = all_frames[..., 5]
chi3_frame_to_frame = all_frames[..., 6]
chi4_frame_to_frame = all_frames[..., 7]
chi1_frame_to_bb = all_frames[..., 4]
chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame)
chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)
all_frames_to_bb = Rigid.cat(
[
all_frames[..., :5],
chi2_frame_to_bb.unsqueeze(-1),
chi3_frame_to_bb.unsqueeze(-1),
chi4_frame_to_bb.unsqueeze(-1),
],
dim=-1,
)
all_frames_to_global = r[..., None].compose(all_frames_to_bb)
return all_frames_to_global
def frames_and_literature_positions_to_atom14_pos(
r: Rigid,
aatype: torch.Tensor,
default_frames: torch.Tensor,
group_idx: torch.Tensor,
atom_mask: torch.Tensor,
lit_positions: torch.Tensor,
) -> torch.Tensor:
# [*, N, 14]
group_mask = group_idx[aatype, ...]
# [*, N, 14, 8]
group_mask_one_hot: torch.LongTensor = nn.functional.one_hot(
group_mask,
num_classes=default_frames.shape[-3],
)
# [*, N, 14, 8]
t_atoms_to_global = r[..., None, :] * group_mask_one_hot
# [*, N, 14]
t_atoms_to_global = t_atoms_to_global.map_tensor_fn(lambda x: torch.sum(x, dim=-1))
# [*, N, 14, 1]
atom_mask = atom_mask[aatype, ...].unsqueeze(-1)
# [*, N, 14, 3]
lit_positions = lit_positions[aatype, ...]
pred_positions = t_atoms_to_global.apply(lit_positions)
pred_positions = pred_positions * atom_mask
return pred_positions
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py
|
# coding=utf-8
# Copyright 2022 NVIDIA and The HuggingFace Team. 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.
""" TF 2.0 GroupViT model."""
from __future__ import annotations
import collections.abc
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
TFModelInputType,
TFPreTrainedModel,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_tensorflow_probability_available,
logging,
replace_return_docstrings,
)
from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
logger = logging.get_logger(__name__)
# soft dependency
if is_tensorflow_probability_available():
try:
import tensorflow_probability as tfp
# On the first call, check whether a compatible version of TensorFlow is installed
# TensorFlow Probability depends on a recent stable release of TensorFlow
_ = tfp.distributions.Normal(loc=0.0, scale=1.0)
except ImportError:
logger.error(
"GroupViT models are not usable since `tensorflow_probability` can't be loaded. "
"It seems you have `tensorflow_probability` installed with the wrong tensorflow version."
"Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability."
)
_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"nvidia/groupvit-gcc-yfcc",
# See all GroupViT models at https://huggingface.co/models?filter=groupvit
]
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
return tf.math.reduce_mean(
tf.keras.metrics.sparse_categorical_crossentropy(
y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
)
)
# Copied from transformers.models.clip.modeling_tf_clip.clip_loss with clip->groupvit
def groupvit_loss(similarity: tf.Tensor) -> tf.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(tf.transpose(similarity))
return (caption_loss + image_loss) / 2.0
def hard_softmax(logits: tf.Tensor, dim: int) -> tf.Tensor:
y_soft = stable_softmax(logits, dim)
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
return ret
def gumbel_softmax(logits: tf.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> tf.Tensor:
gumbel_dist = tfp.distributions.Gumbel(0.0, 1.0)
gumbels = gumbel_dist.sample(tf.shape(logits), dtype=logits.dtype)
gumbels = (logits + gumbels) / tau # ~Gumbel(logits,tau)
y_soft = stable_softmax(gumbels, dim)
if hard:
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
else:
# Reparametrization trick.
ret = y_soft
return ret
def resize_attention_map(attentions: tf.Tensor, height: int, width: int, align_corners: bool = False) -> tf.Tensor:
"""
Args:
attentions (`tf.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
height (`int`): height of the output attention map
width (`int`): width of the output attention map
align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
Returns:
`tf.Tensor`: resized attention map of shape [batch_size, groups, height, width]
"""
scale = (height * width // attentions.shape[2]) ** 0.5
if height > width:
feat_width = int(np.round(width / scale))
feat_height = shape_list(attentions)[2] // feat_width
else:
feat_height = int(np.round(height / scale))
feat_width = shape_list(attentions)[2] // feat_height
batch_size = shape_list(attentions)[0]
groups = shape_list(attentions)[1] # number of group token
# [batch_size, groups, height x width, groups] -> [batch_size, groups, height, width]
attentions = tf.reshape(attentions, (batch_size, groups, feat_height, feat_width))
attentions = tf.transpose(attentions, perm=(0, 2, 3, 1))
if align_corners:
attentions = tf.compat.v1.image.resize(
attentions,
size=(height, width),
method="bilinear",
align_corners=align_corners,
)
else:
attentions = tf.image.resize(attentions, size=(height, width), method="bilinear")
attentions = tf.transpose(attentions, perm=(0, 3, 1, 2))
return attentions
def get_grouping_from_attentions(attentions: Tuple[tf.Tensor], hw_shape: Tuple[int]) -> tf.Tensor:
"""
Args:
attentions (`tuple(tf.Tensor)`: tuple of attention maps returned by `TFGroupViTVisionTransformer`
hw_shape (`tuple(int)`): height and width of the output attention map
Returns:
`tf.Tensor`: the attention map of shape [batch_size, groups, height, width]
"""
attn_maps = []
prev_attn_masks = None
for attn_masks in attentions:
# [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
attn_masks = tf.transpose(attn_masks, perm=(0, 2, 1))
if prev_attn_masks is None:
prev_attn_masks = attn_masks
else:
prev_attn_masks = tf.matmul(prev_attn_masks, attn_masks)
# [batch_size, height x width, num_groups] -> [batch_size, num_groups, height x width] -> [batch_size, num_groups, height, width]
cur_attn_map = resize_attention_map(tf.transpose(prev_attn_masks, perm=(0, 2, 1)), *hw_shape)
attn_maps.append(cur_attn_map)
# [batch_size, num_groups, height, width]
final_grouping = attn_maps[-1]
return tf.stop_gradient(final_grouping)
@dataclass
class TFGroupViTModelOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text (`tf.Tensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
segmentation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
text_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTTextModel`].
image_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTVisionModel`].
text_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTTextModel`].
vision_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTVisionModel`].
"""
loss: tf.Tensor | None = None
logits_per_image: tf.Tensor = None
logits_per_text: tf.Tensor = None
segmentation_logits: tf.Tensor = None
text_embeds: tf.Tensor = None
image_embeds: tf.Tensor = None
text_model_output: TFBaseModelOutputWithPooling = None
vision_model_output: TFBaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class TFGroupViTCrossAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.attn = TFGroupViTAttention(config, name="attn")
self.norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.norm_post = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post")
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.Tensor:
x = query
x = x + self.attn(query, encoder_hidden_states=key)[0]
x = x + self.mlp(self.norm2(x))
x = self.norm_post(x)
return x
class TFGroupViTAssignAttention(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.scale = config.hidden_size**-0.5
self.q_proj = tf.keras.layers.Dense(config.hidden_size, name="q_proj")
self.k_proj = tf.keras.layers.Dense(config.hidden_size, name="k_proj")
self.v_proj = tf.keras.layers.Dense(config.hidden_size, name="v_proj")
self.proj = tf.keras.layers.Dense(config.hidden_size, name="proj")
self.assign_eps = config.assign_eps
def get_attn(self, attn: tf.Tensor, gumbel: bool = True, hard: bool = True, training: bool = False) -> tf.Tensor:
if gumbel and training:
attn = gumbel_softmax(attn, dim=-2, hard=hard)
else:
if hard:
attn = hard_softmax(attn, dim=-2)
else:
attn = stable_softmax(attn, axis=-2)
return attn
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False):
value = key
# [batch_size, query_length, channels]
query = self.q_proj(query)
# [batch_size, key_length, channels]
key = self.k_proj(key)
# [batch_size, key_length, channels]
value = self.v_proj(value)
# [batch_size, query_length, key_length]
raw_attn = tf.matmul(query, key, transpose_b=True) * self.scale
attn = self.get_attn(raw_attn, training=training)
soft_attn = self.get_attn(raw_attn, training=training, gumbel=False, hard=False)
attn = attn / (tf.math.reduce_sum(attn, axis=-1, keepdims=True) + self.assign_eps)
out = tf.matmul(attn, value)
out = self.proj(out)
return out, soft_attn
class TFGroupViTTokenAssign(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
super().__init__(**kwargs)
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_tokens")
assign_mlp_ratio = (
config.assign_mlp_ratio
if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
else (config.assign_mlp_ratio, config.assign_mlp_ratio)
)
tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
self.mlp_inter = TFGroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group, name="mlp_inter")
self.norm_post_tokens = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="norm_post_tokens"
)
# norm on x
self.norm_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_x")
self.pre_assign_attn = TFGroupViTCrossAttentionLayer(config, name="pre_assign_attn")
self.assign = TFGroupViTAssignAttention(config, name="assign")
self.norm_new_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_new_x")
self.mlp_channels = TFGroupViTMLP(
config, config.hidden_size, channels_dim, config.hidden_size, name="mlp_channels"
)
def project_group_token(self, group_tokens: tf.Tensor) -> tf.Tensor:
"""
Args:
group_tokens (tf.Tensor): group tokens, [batch_size, num_group_tokens, channels]
Returns:
projected_group_tokens (tf.Tensor): [batch_size, num_output_groups, channels]
"""
# [B, num_output_groups, C] <- [B, num_group_tokens, C]
projected_group_tokens = self.mlp_inter(group_tokens)
projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
return projected_group_tokens
def call(self, image_tokens: tf.Tensor, group_tokens: tf.Tensor, training: bool = False):
"""
Args:
image_tokens (`tf.Tensor`): image tokens, of shape [batch_size, input_length, channels]
group_tokens (`tf.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
"""
group_tokens = self.norm_tokens(group_tokens)
image_tokens = self.norm_x(image_tokens)
# [batch_size, num_output_groups, channels]
projected_group_tokens = self.project_group_token(group_tokens)
projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
new_image_tokens += projected_group_tokens
new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
return new_image_tokens, attention
# Adapted from transformers.models.vit.modeling_tf_vit.TFViTPatchEmbeddings with ViT->GroupViT
class TFGroupViTPatchEmbeddings(tf.keras.layers.Layer):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels = config.num_channels
# hidden_size is a member as it will be required in the call method
self.hidden_size = config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_channels = num_channels
self.config = config
self.projection = tf.keras.layers.Conv2D(
filters=self.hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding="valid",
data_format="channels_last",
use_bias=True,
kernel_initializer=get_initializer(self.config.initializer_range),
bias_initializer="zeros",
name="projection",
)
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
if (
not interpolate_pos_encoding
and tf.executing_eagerly()
and (height != self.image_size[0] or width != self.image_size[1])
):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
)
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
projection = self.projection(pixel_values)
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
# In the TFGroupViTVisionEmbeddings the embeddings from this layer will be layer normalized
# LayerNormalization layer needs to have static last dimension (otherwise the test_keras_save_load fails with symbolic tensors)
# This is why we have used the hidden_size in the reshape method
embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, self.hidden_size))
return embeddings
# Adapted from transformers.vit.modeling_tf_vit.TFViTEmbeddings
class TFGroupViTVisionEmbeddings(tf.keras.layers.Layer):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFGroupViTPatchEmbeddings(config, name="patch_embeddings")
self.dropout = tf.keras.layers.Dropout(rate=config.dropout, name="dropout")
self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
self.config = config
def build(self, input_shape: tf.TensorShape):
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = self.add_weight(
shape=(1, num_patches, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="position_embeddings",
)
super().build(input_shape)
def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
batch_size, num_patches, dim = shape_list(embeddings)
num_positions = shape_list(self.position_embeddings)[1]
if num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
patch_pos_embed = tf.image.resize(
images=tf.reshape(
patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
),
size=(h0, w0),
method="bicubic",
)
patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
return patch_pos_embed
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
_, _, height, width = shape_list(pixel_values)
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
embeddings = self.layernorm(embeddings)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextEmbeddings with CLIP->GroupViT
class TFGroupViTTextEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.config = config
def build(self, input_shape: tf.TensorShape = None):
with tf.name_scope("token_embedding"):
self.weight = self.add_weight(
shape=(self.config.vocab_size, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="weight",
)
with tf.name_scope("position_embedding"):
self.position_embedding = self.add_weight(
shape=(self.config.max_position_embeddings, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="embeddings",
)
super().build(input_shape)
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
if input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embedding, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
final_embeddings = inputs_embeds + position_embeds
return final_embeddings
class TFGroupViTStage(tf.keras.layers.Layer):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.depth = depth
self.num_group_token = num_group_token
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(depth)]
if num_group_token > 0:
self.downsample = TFGroupViTTokenAssign(
config=config,
num_group_token=num_group_token,
num_output_group=num_output_group,
name="downsample",
)
else:
self.downsample = None
if num_prev_group_token > 0 and num_group_token > 0:
self.group_projector = [
tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="group_projector.0"),
TFGroupViTMixerMLP(
config, num_prev_group_token, config.hidden_size // 2, num_group_token, name="group_projector.1"
),
]
else:
self.group_projector = None
def build(self, input_shape: tf.TensorShape):
if self.num_group_token > 0:
self.group_token = self.add_weight(
shape=(1, self.num_group_token, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="group_token",
)
else:
self.group_token = None
super().build(input_shape)
@property
def with_group_token(self):
return self.group_token is not None
def split_x(self, x: tf.Tensor) -> tf.Tensor:
if self.with_group_token:
return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
else:
return x, None
def concat_x(self, x: tf.Tensor, group_token: tf.Tensor | None = None) -> tf.Tensor:
if group_token is None:
return x
return tf.concat([x, group_token], axis=1)
def call(
self,
hidden_states: tf.Tensor,
prev_group_token: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the grouping tensors of Grouping block.
"""
if self.with_group_token:
group_token = tf.tile(self.group_token, multiples=(shape_list(hidden_states)[0], 1, 1))
if self.group_projector is not None:
for layer in self.group_projector:
prev_group_token = layer(prev_group_token)
group_token = group_token + prev_group_token
else:
group_token = None
x = hidden_states
cat_x = self.concat_x(x, group_token)
for layer in self.layers:
layer_out = layer(
cat_x,
attention_mask=None,
causal_attention_mask=None,
output_attentions=None,
)
cat_x = layer_out[0]
x, group_token = self.split_x(cat_x)
attention = None
if self.downsample is not None:
x, attention = self.downsample(x, group_token)
outputs = (x, group_token)
if output_attentions:
outputs = outputs + (attention,)
return outputs
class TFGroupViTMLP(tf.keras.layers.Layer):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.activation_fn = get_tf_activation(config.hidden_act)
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
output_size = output_size if output_size is not None else hidden_size
self.fc1 = tf.keras.layers.Dense(intermediate_size, name="fc1")
self.fc2 = tf.keras.layers.Dense(output_size, name="fc2")
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class TFGroupViTMixerMLP(TFGroupViTMLP):
def call(self, x, training: bool = False):
x = super().call(hidden_states=tf.transpose(x, perm=(0, 2, 1)))
return tf.transpose(x, perm=(0, 2, 1))
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPAttention
class TFGroupViTAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = self.embed_dim // self.num_attention_heads
if self.attention_head_size * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_attention_heads})."
)
factor = config.initializer_factor
in_proj_std = (self.embed_dim**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (self.embed_dim**-0.5) * factor
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.q_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="q_proj"
)
self.k_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="k_proj"
)
self.v_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="v_proj"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_dropout)
self.out_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(out_proj_std), name="out_proj"
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention.transpose_for_scores
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor = None,
causal_attention_mask: tf.Tensor = None,
output_attentions: bool = None,
encoder_hidden_states: tf.Tensor = None,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""Input shape: Batch x Time x Channel"""
batch_size = shape_list(hidden_states)[0]
is_cross_attention = encoder_hidden_states is not None
mixed_query_layer = self.q_proj(inputs=hidden_states)
if is_cross_attention:
mixed_key_layer = self.k_proj(inputs=encoder_hidden_states)
mixed_value_layer = self.v_proj(inputs=encoder_hidden_states)
else:
mixed_key_layer = self.k_proj(inputs=hidden_states)
mixed_value_layer = self.v_proj(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
# Apply the causal attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, causal_attention_mask)
if attention_mask is not None:
# Apply the attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
_attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=_attention_probs)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, embed_dim)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.embed_dim))
attention_output = self.out_proj(attention_output)
# In TFBert, attention weights are returned after dropout.
# However, in CLIP, they are returned before dropout.
outputs = (attention_output, _attention_probs) if output_attentions else (attention_output,)
return outputs
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPEncoderLayer with CLIP->GroupViT
class TFGroupViTEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFGroupViTAttention(config, name="self_attn")
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
causal_attention_mask (`tf.Tensor`): causal attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`):
Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(inputs=hidden_states)
attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = attention_outputs[0]
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(inputs=hidden_states)
hidden_states = self.mlp(hidden_states=hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,) + attention_outputs[1:] # add attentions if we output them
return outputs
# Adapted from transformers.models.clip.modeling_tf_clip.TFGroupViTTextEncoder
class TFGroupViTTextEncoder(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutput]:
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class TFGroupViTVisionEncoder(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.stages = [
TFGroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=config.num_group_tokens[i],
num_output_group=config.num_output_groups[i],
num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
name=f"stages_._{i}",
)
for i in range(len(config.depths))
]
def call(
self,
hidden_states: tf.Tensor,
output_hidden_states: bool,
output_attentions: bool,
return_dict: bool,
training: bool = False,
) -> Union[tuple, TFBaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_groupings = () if output_attentions else None
group_tokens = None
for stage in self.stages:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = stage(hidden_states, group_tokens, output_attentions)
hidden_states = layer_outputs[0]
group_tokens = layer_outputs[1]
if output_attentions and layer_outputs[2] is not None:
all_groupings = all_groupings + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
)
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder
class TFGroupViTTextTransformer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTTextEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTTextEncoder(config, name="encoder")
self.final_layer_norm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="final_layer_norm"
)
# For `pooled_output` computation
self.eos_token_id = config.eos_token_id
def call(
self,
input_ids: TFModelInputType,
attention_mask: tf.Tensor,
position_ids: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
input_shape = shape_list(input_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids)
batch_size, seq_length = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = self._build_causal_attention_mask(batch_size, seq_length, dtype=embedding_output.dtype)
# check attention mask and invert
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
sequence_output = self.final_layer_norm(inputs=sequence_output)
if self.eos_token_id == 2:
# The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
# A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added
# ------------------------------------------------------------
# text_embeds.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
pooled_output = tf.gather_nd(
params=sequence_output,
indices=tf.stack(
values=(tf.range(input_shape[0], dtype=tf.int64), tf.math.argmax(input_ids, axis=-1)), axis=1
),
)
else:
# The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
pooled_output = tf.gather_nd(
params=sequence_output,
indices=tf.stack(
values=(
tf.range(input_shape[0], dtype=tf.int64),
tf.math.argmax(tf.cast(input_ids == self.eos_token_id, dtype=tf.int8), axis=-1),
),
axis=1,
),
)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32):
# It is possible with an unspecified sequence length for seq_length to be
# a runtime value, which is unsupported by tf.constant. Per the TensorFlow
# docs, tf.fill can handle runtime dynamic shapes:
# https://www.tensorflow.org/api_docs/python/tf/fill
diag = tf.cast(tf.fill((seq_length,), 0.0), dtype)
# set an additive 2D attention mask with all places being masked
to_mask = tf.cast(tf.fill((seq_length, seq_length), -10000.0), dtype)
# set diagonal & lower triangular parts to 0 (i.e. the places not to be masked)
# TIP: think the 2D matrix as the space of (query_seq, key_seq)
to_mask = tf.linalg.band_part(to_mask, 0, -1)
# to_mask = tf.linalg.band_part(to_mask, -1, 0)
to_mask = tf.linalg.set_diag(to_mask, diagonal=diag)
return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPVisionTransformer
class TFGroupViTVisionTransformer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTVisionEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTVisionEncoder(config, name="encoder")
self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
def call(
self,
pixel_values: TFModelInputType,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutputWithPooling]:
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# normalize the last hidden state
last_hidden_state = self.layernorm(last_hidden_state)
pooled_output = tf.math.reduce_mean(last_hidden_state, axis=1)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@keras_serializable
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextMainLayer with CLIP->GroupViT
class TFGroupViTTextMainLayer(tf.keras.layers.Layer):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.text_model = TFGroupViTTextTransformer(config, name="text_model")
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.text_model.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.text_model.embeddings.weight = value
self.text_model.embeddings.vocab_size = shape_list(value)[0]
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_model_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return text_model_outputs
@keras_serializable
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPVisionMainLayer with CLIP->GroupViT
class TFGroupViTVisionMainLayer(tf.keras.layers.Layer):
config_class = GroupViTVisionConfig
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.vision_model = TFGroupViTVisionTransformer(config, name="vision_model")
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.vision_model.embeddings
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_model_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return vision_model_outputs
@keras_serializable
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPMainLayer
class TFGroupViTMainLayer(tf.keras.layers.Layer):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
if not isinstance(config.text_config, GroupViTTextConfig):
raise ValueError(
"config.text_config is expected to be of type GroupViTTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, GroupViTVisionConfig):
raise ValueError(
"config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
f" {type(config.vision_config)}."
)
self.config = config
text_config = config.text_config
vision_config = config.vision_config
self.projection_dim = config.projection_dim
self.projection_intermediate_dim = config.projection_intermediate_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = TFGroupViTTextTransformer(text_config, name="text_model")
self.vision_model = TFGroupViTVisionTransformer(vision_config, name="vision_model")
self.visual_projection = [
tf.keras.layers.Dense(self.projection_intermediate_dim, name="visual_projection.0"),
tf.keras.layers.BatchNormalization(name="visual_projection.1", momentum=0.9, epsilon=1e-5),
tf.keras.layers.ReLU(name="visual_projection.2"),
tf.keras.layers.Dense(self.projection_dim, name="visual_projection.3"),
]
self.text_projection = [
tf.keras.layers.Dense(self.projection_intermediate_dim, name="text_projection.0"),
tf.keras.layers.BatchNormalization(name="text_projection.1", momentum=0.9, epsilon=1e-5),
tf.keras.layers.ReLU(name="text_projection.2"),
tf.keras.layers.Dense(self.projection_dim, name="text_projection.3"),
]
def build(self, input_shape: tf.TensorShape):
self.logit_scale = self.add_weight(
shape=(1,),
initializer=tf.keras.initializers.Constant(self.config.logit_scale_init_value),
trainable=True,
name="logit_scale",
)
super().build(input_shape)
@unpack_inputs
def get_text_features(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = text_outputs[1]
for layer in self.text_projection:
pooled_output = layer(pooled_output)
text_features = pooled_output
return text_features
@unpack_inputs
def get_image_features(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = vision_outputs[1]
for layer in self.visual_projection:
pooled_output = layer(pooled_output)
image_features = pooled_output
return image_features
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
pixel_values: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if output_segmentation:
output_attentions = True
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
image_embeds = vision_outputs[1]
for layer in self.visual_projection:
image_embeds = layer(image_embeds)
text_embeds = text_outputs[1]
for layer in self.text_projection:
text_embeds = layer(text_embeds)
# normalized features
image_embeds = image_embeds / tf.norm(image_embeds, axis=-1, keepdims=True)
text_embeds = text_embeds / tf.norm(text_embeds, axis=-1, keepdims=True)
# cosine similarity as logits
logit_scale = tf.math.exp(self.logit_scale)
logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
logits_per_image = tf.transpose(logits_per_text)
seg_logits = None
if output_segmentation:
# grouped features
# [batch_size_image, num_group, hidden_size]
image_group_embeds = vision_outputs[0]
# [batch_size_image*num_group, hidden_size]
image_group_embeds = tf.reshape(image_group_embeds, shape=(-1, shape_list(image_group_embeds)[-1]))
for layer in self.visual_projection:
image_group_embeds = layer(image_group_embeds)
if output_hidden_states:
attentions = vision_outputs[3]
else:
attentions = vision_outputs[2]
# [batch_size_image, num_group, height, width]
grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
# normalized features
image_group_embeds = image_group_embeds / tf.norm(
tensor=image_group_embeds, ord="euclidean", axis=-1, keepdims=True
)
# [batch_size_image x num_group, batch_size_text]
logits_per_image_group = tf.matmul(image_group_embeds, text_embeds, transpose_b=True) * logit_scale
# [batch_size_image, batch_size_text, num_group]
logits_per_image_group = tf.reshape(
logits_per_image_group, shape=(image_embeds.shape[0], -1, text_embeds.shape[0])
)
logits_per_image_group = tf.transpose(logits_per_image_group, perm=(0, 2, 1))
# [batch_size_image, batch_size_text, height x width]
flatten_grouping = tf.reshape(grouping, shape=(shape_list(grouping)[0], shape_list(grouping)[1], -1))
# [batch_size_image, batch_size_text, height, width]
seg_logits = tf.matmul(logits_per_image_group, flatten_grouping) * logit_scale
seg_logits = tf.reshape(
seg_logits, shape=(seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3])
)
loss = None
if return_loss:
loss = groupvit_loss(logits_per_text)[None, ...]
if not return_dict:
if seg_logits is not None:
output = (
logits_per_image,
logits_per_text,
seg_logits,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
else:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output
return TFGroupViTModelOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
segmentation_logits=seg_logits,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
class TFGroupViTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit"
GROUPVIT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
</Tip>
Args:
config ([`GroupViTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
GROUPVIT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
config_class = GroupViTTextConfig
main_input_name = "input_ids"
def __init__(self, config: GroupViTTextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTTextMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, TFGroupViTTextModel
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = TFGroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
outputs = self.groupvit(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTVisionMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def call(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTVisionModel
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = TFGroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
outputs = self.groupvit(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
@add_start_docstrings(GROUPVIT_START_DOCSTRING)
class TFGroupViTModel(TFGroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def get_text_features(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
the projection layer to the pooled output of [`TFGroupViTTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, TFGroupViTModel
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> text_features = model.get_text_features(**inputs)
```"""
text_features = self.groupvit.get_text_features(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return text_features
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
the projection layer to the pooled output of [`TFGroupViTVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTModel
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> image_features = model.get_image_features(**inputs)
```"""
image_features = self.groupvit.get_image_features(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return image_features
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFGroupViTModelOutput, config_class=GroupViTConfig)
def call(
self,
input_ids: TFModelInputType | None = None,
pixel_values: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTModel
>>> import tensorflow as tf
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = tf.math.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```"""
outputs = self.groupvit(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
return_loss=return_loss,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_segmentation=output_segmentation,
return_dict=return_dict,
training=training,
)
return outputs
def serving_output(self, output: TFGroupViTModelOutput) -> TFGroupViTModelOutput:
# TODO: As is this currently fails with saved_model=True, because
# TensorFlow cannot trace through nested dataclasses. Reference:
# https://github.com/huggingface/transformers/pull/16886
return output
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/groupvit/configuration_groupvit.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
""" GroupViT model configuration"""
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
logger = logging.get_logger(__name__)
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"nvidia/groupvit-gcc-yfcc": "https://huggingface.co/nvidia/groupvit-gcc-yfcc/resolve/main/config.json",
}
class GroupViTTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an
GroupViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 49408):
Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`GroupViTModel`].
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 1024):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 4):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 77):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import GroupViTTextConfig, GroupViTTextModel
>>> # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration
>>> configuration = GroupViTTextConfig()
>>> model = GroupViTTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "groupvit_text_model"
def __init__(
self,
vocab_size=49408,
hidden_size=256,
intermediate_size=1024,
num_hidden_layers=12,
num_attention_heads=4,
max_position_embeddings=77,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
pad_token_id=1,
bos_token_id=49406,
eos_token_id=49407,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.dropout = dropout
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the text config dict if we are loading from GroupViTConfig
if config_dict.get("model_type") == "groupvit":
config_dict = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class GroupViTVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate
an GroupViT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 384):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 1536):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
depths (`List[int]`, *optional*, defaults to [6, 3, 3]):
The number of layers in each encoder block.
num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]):
The number of group tokens for each stage.
num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]):
The number of output groups for each stage, 0 means no group.
num_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import GroupViTVisionConfig, GroupViTVisionModel
>>> # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration
>>> configuration = GroupViTVisionConfig()
>>> model = GroupViTVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "groupvit_vision_model"
def __init__(
self,
hidden_size=384,
intermediate_size=1536,
depths=[6, 3, 3],
num_hidden_layers=12,
num_group_tokens=[64, 8, 0],
num_output_groups=[64, 8, 8],
num_attention_heads=6,
image_size=224,
patch_size=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
assign_eps=1.0,
assign_mlp_ratio=[0.5, 4],
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.depths = depths
if num_hidden_layers != sum(depths):
logger.warning(
f"Manually setting num_hidden_layers to {num_hidden_layers}, but we expect num_hidden_layers ="
f" sum(depth) = {sum(depths)}"
)
self.num_hidden_layers = num_hidden_layers
self.num_group_tokens = num_group_tokens
self.num_output_groups = num_output_groups
self.num_attention_heads = num_attention_heads
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.assign_eps = assign_eps
self.assign_mlp_ratio = assign_mlp_ratio
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the vision config dict if we are loading from GroupViTConfig
if config_dict.get("model_type") == "groupvit":
config_dict = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class GroupViTConfig(PretrainedConfig):
r"""
[`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to
instantiate a GroupViT model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GroupViTTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GroupViTVisionConfig`].
projection_dim (`int`, *optional*, defaults to 256):
Dimentionality of text and vision projection layers.
projection_intermediate_dim (`int`, *optional*, defaults to 4096):
Dimentionality of intermediate layer of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The inital value of the *logit_scale* parameter. Default is used as per the original GroupViT
implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
"""
model_type = "groupvit"
def __init__(
self,
text_config=None,
vision_config=None,
projection_dim=256,
projection_intermediate_dim=4096,
logit_scale_init_value=2.6592,
**kwargs,
):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
text_config_dict = kwargs.pop("text_config_dict", None)
vision_config_dict = kwargs.pop("vision_config_dict", None)
super().__init__(**kwargs)
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
text_config = {}
# This is the complete result when using `text_config_dict`.
_text_config_dict = GroupViTTextConfig(**text_config_dict).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
message = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f'The value `text_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`text_config_dict` is provided which will be used to initialize `GroupViTTextConfig`. "
f'The value `text_config["{key}"]` will be overriden.'
)
logger.warning(message)
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if vision_config_dict is not None:
if vision_config is None:
vision_config = {}
# This is the complete result when using `vision_config_dict`.
_vision_config_dict = GroupViTVisionConfig(**vision_config_dict).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
_vision_config_dict["id2label"] = {
str(key): value for key, value in _vision_config_dict["id2label"].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
message = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f'values. The value `vision_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`vision_config_dict` is provided which will be used to initialize `GroupViTVisionConfig`."
f' The value `vision_config["{key}"]` will be overriden.'
)
logger.warning(message)
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `GroupViTTextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. initializing the `GroupViTVisionConfig` with default values.")
self.text_config = GroupViTTextConfig(**text_config)
self.vision_config = GroupViTVisionConfig(**vision_config)
self.projection_dim = projection_dim
self.projection_intermediate_dim = projection_intermediate_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_range = 0.02
self.initializer_factor = 1.0
self.output_segmentation = False
@classmethod
def from_text_vision_configs(cls, text_config: GroupViTTextConfig, vision_config: GroupViTVisionConfig, **kwargs):
r"""
Instantiate a [`GroupViTConfig`] (or a derived class) from groupvit text model configuration and groupvit
vision model configuration.
Returns:
[`GroupViTConfig`]: An instance of a configuration object
"""
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
class GroupViTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
def generate_dummy_inputs(
self,
processor: "ProcessorMixin",
batch_size: int = -1,
seq_length: int = -1,
framework: Optional["TensorType"] = None,
) -> Mapping[str, Any]:
text_input_dict = super().generate_dummy_inputs(
processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
)
image_input_dict = super().generate_dummy_inputs(
processor.image_processor, batch_size=batch_size, framework=framework
)
return {**text_input_dict, **image_input_dict}
@property
def default_onnx_opset(self) -> int:
return 14
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/groupvit/modeling_groupvit.py
|
# coding=utf-8
# Copyright 2022 NVIDIA and The HuggingFace Team. 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.
""" PyTorch GroupViT model."""
import collections.abc
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"nvidia/groupvit-gcc-yfcc",
# See all GroupViT models at https://huggingface.co/models?filter=groupvit
]
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->groupvit
def groupvit_loss(similarity: torch.Tensor) -> torch.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(similarity.t())
return (caption_loss + image_loss) / 2.0
def hard_softmax(logits: torch.Tensor, dim: int):
y_soft = logits.softmax(dim)
# Straight through.
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
return ret
def gumbel_softmax(logits: torch.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> torch.Tensor:
# more stable https://github.com/pytorch/pytorch/issues/41663
gumbel_dist = torch.distributions.gumbel.Gumbel(
torch.tensor(0.0, device=logits.device, dtype=logits.dtype),
torch.tensor(1.0, device=logits.device, dtype=logits.dtype),
)
gumbels = gumbel_dist.sample(logits.shape)
gumbels = (logits + gumbels) / tau # ~Gumbel(logits,tau)
y_soft = gumbels.softmax(dim)
if hard:
# Straight through.
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
else:
# Reparametrization trick.
ret = y_soft
return ret
def resize_attention_map(attentions, height, width, align_corners=False):
"""
Args:
attentions (`torch.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
height (`int`): height of the output attention map
width (`int`): width of the output attention map
align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
Returns:
`torch.Tensor`: resized attention map of shape [batch_size, groups, height, width]
"""
scale = (height * width // attentions.shape[2]) ** 0.5
if height > width:
feat_width = int(np.round(width / scale))
feat_height = attentions.shape[2] // feat_width
else:
feat_height = int(np.round(height / scale))
feat_width = attentions.shape[2] // feat_height
batch_size = attentions.shape[0]
groups = attentions.shape[1] # number of group token
# [batch_size, groups, height*width, groups] -> [batch_size, groups, height, width]
attentions = attentions.reshape(batch_size, groups, feat_height, feat_width)
attentions = nn.functional.interpolate(
attentions, size=(height, width), mode="bilinear", align_corners=align_corners
)
return attentions
def get_grouping_from_attentions(attentions, hw_shape):
"""
Args:
attentions (`tuple(torch.FloatTensor)`: tuple of attention maps returned by `GroupViTVisionTransformer`
hw_shape (`tuple(int)`): height and width of the output attention map
Returns:
`torch.Tensor`: the attention map of shape [batch_size, groups, height, width]
"""
attn_maps = []
with torch.no_grad():
prev_attn_masks = None
for attn_masks in attentions:
# [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
attn_masks = attn_masks.permute(0, 2, 1).contiguous()
if prev_attn_masks is None:
prev_attn_masks = attn_masks
else:
prev_attn_masks = prev_attn_masks @ attn_masks
# [batch_size, heightxwidth, num_groups] -> [batch_size, num_groups, heightxwidth] -> [batch_size, num_groups, height, width]
cur_attn_map = resize_attention_map(prev_attn_masks.permute(0, 2, 1).contiguous(), *hw_shape)
attn_maps.append(cur_attn_map)
# [batch_size, num_groups, height, width]
final_grouping = attn_maps[-1]
return final_grouping
class GroupViTCrossAttentionLayer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.attn = GroupViTAttention(config)
self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp = GroupViTMLP(config)
self.norm_post = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, query, key):
x = query
x = x + self.attn(query, encoder_hidden_states=key)[0]
x = x + self.mlp(self.norm2(x))
x = self.norm_post(x)
return x
class GroupViTAssignAttention(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.scale = config.hidden_size**-0.5
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.proj = nn.Linear(config.hidden_size, config.hidden_size)
self.assign_eps = config.assign_eps
def get_attn(self, attn, gumbel=True, hard=True):
if gumbel and self.training:
attn = gumbel_softmax(attn, dim=-2, hard=hard)
else:
if hard:
attn = hard_softmax(attn, dim=-2)
else:
attn = nn.functional.softmax(attn, dim=-2)
return attn
def forward(self, query, key):
value = key
# [batch_size, query_length, channels]
query = self.q_proj(query)
# [batch_size, key_length, channels]
key = self.k_proj(key)
# [batch_size, key_length, channels]
value = self.v_proj(value)
# [batch_size, query_length, key_length]
raw_attn = (query @ key.transpose(-2, -1)) * self.scale
attn = self.get_attn(raw_attn)
soft_attn = self.get_attn(raw_attn, gumbel=False, hard=False)
attn = attn / (attn.sum(dim=-1, keepdim=True) + self.assign_eps)
out = attn @ value
out = self.proj(out)
return out, soft_attn
class GroupViTTokenAssign(nn.Module):
def __init__(self, config: GroupViTVisionConfig, num_group_token, num_output_group):
super().__init__()
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
assign_mlp_ratio = (
config.assign_mlp_ratio
if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
else (config.assign_mlp_ratio, config.assign_mlp_ratio)
)
tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
self.mlp_inter = GroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group)
self.norm_post_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# norm on x
self.norm_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pre_assign_attn = GroupViTCrossAttentionLayer(config)
self.assign = GroupViTAssignAttention(config)
self.norm_new_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp_channels = GroupViTMLP(config, config.hidden_size, channels_dim, config.hidden_size)
def project_group_token(self, group_tokens):
"""
Args:
group_tokens (torch.Tensor): group tokens, [batch_size, num_group_tokens, channels]
Returns:
projected_group_tokens (torch.Tensor): [batch_size, num_output_groups, channels]
"""
# [B, num_output_groups, C] <- [B, num_group_tokens, C]
projected_group_tokens = self.mlp_inter(group_tokens)
projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
return projected_group_tokens
def forward(self, image_tokens, group_tokens):
"""
Args:
image_tokens (`torch.Tensor`): image tokens, of shape [batch_size, input_length, channels]
group_tokens (`torch.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
"""
group_tokens = self.norm_tokens(group_tokens)
image_tokens = self.norm_x(image_tokens)
# [batch_size, num_output_groups, channels]
projected_group_tokens = self.project_group_token(group_tokens)
projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
new_image_tokens += projected_group_tokens
new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
return new_image_tokens, attention
@dataclass
class GroupViTModelOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
segmentation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of
[`GroupViTTextModel`].
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`GroupViTVisionModel`].
text_model_output (`BaseModelOutputWithPooling`):
The output of the [`GroupViTTextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`GroupViTVisionModel`].
"""
loss: Optional[torch.FloatTensor] = None
logits_per_image: torch.FloatTensor = None
logits_per_text: torch.FloatTensor = None
segmentation_logits: torch.FloatTensor = None
text_embeds: torch.FloatTensor = None
image_embeds: torch.FloatTensor = None
text_model_output: BaseModelOutputWithPooling = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class GroupViTPatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
x = self.projection(pixel_values).flatten(2).transpose(1, 2)
return x
class GroupViTVisionEmbeddings(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.patch_embeddings = GroupViTPatchEmbeddings(
image_size=config.image_size,
patch_size=config.patch_size,
num_channels=config.num_channels,
embed_dim=config.hidden_size,
)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches, config.hidden_size))
self.dropout = nn.Dropout(config.dropout)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.config = config
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
npatch = embeddings.shape[1]
if npatch == self.position_embeddings.shape[1] and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
num_original_pos_embed = patch_pos_embed.shape[1]
dim = embeddings.shape[-1]
feat_height = height // self.config.patch_size
feat_width = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
feat_height, feat_width = feat_height + 0.1, feat_width + 0.1
original_height = original_width = math.sqrt(num_original_pos_embed)
reshaped_patch_pos_embed = patch_pos_embed.reshape(1, int(original_height), int(original_width), dim).permute(
0, 3, 1, 2
)
scale_factor = (feat_height / original_height, feat_width / original_width)
patch_pos_embed = nn.functional.interpolate(
reshaped_patch_pos_embed,
scale_factor=scale_factor,
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
embeddings = self.layernorm(embeddings)
batch_size, seq_len, _ = embeddings.size()
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->GroupViT
class GroupViTTextEmbeddings(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
embed_dim = config.hidden_size
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.token_embedding(input_ids)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
class GroupViTStage(nn.Module):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
):
super().__init__()
self.depth = depth
self.num_group_token = num_group_token
if num_group_token > 0:
self.group_token = nn.Parameter(torch.zeros(1, num_group_token, config.hidden_size))
else:
self.group_token = None
self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(depth)])
if num_group_token > 0:
self.downsample = GroupViTTokenAssign(
config=config,
num_group_token=num_group_token,
num_output_group=num_output_group,
)
else:
self.downsample = None
if num_prev_group_token > 0 and num_group_token > 0:
self.group_projector = nn.Sequential(
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps),
GroupViTMixerMLP(config, num_prev_group_token, config.hidden_size // 2, num_group_token),
)
else:
self.group_projector = None
@property
def with_group_token(self):
return self.group_token is not None
def split_x(self, x):
if self.with_group_token:
return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
else:
return x, None
def concat_x(self, x: torch.Tensor, group_token: Optional[torch.Tensor] = None) -> torch.Tensor:
if group_token is None:
return x
return torch.cat([x, group_token], dim=1)
def forward(
self,
hidden_states: torch.Tensor,
prev_group_token: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the grouping tensors of Grouping block.
"""
if self.with_group_token:
group_token = self.group_token.expand(hidden_states.size(0), -1, -1)
if self.group_projector is not None:
group_token = group_token + self.group_projector(prev_group_token)
else:
group_token = None
x = hidden_states
cat_x = self.concat_x(x, group_token)
for layer in self.layers:
layer_out = layer(cat_x, attention_mask=None, causal_attention_mask=None)
cat_x = layer_out[0]
x, group_token = self.split_x(cat_x)
attention = None
if self.downsample is not None:
x, attention = self.downsample(x, group_token)
outputs = (x, group_token)
if output_attentions:
outputs = outputs + (attention,)
return outputs
class GroupViTMLP(nn.Module):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
output_size = output_size if output_size is not None else hidden_size
self.fc1 = nn.Linear(hidden_size, intermediate_size)
self.fc2 = nn.Linear(intermediate_size, output_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class GroupViTMixerMLP(GroupViTMLP):
def forward(self, x):
x = super().forward(x.transpose(1, 2))
return x.transpose(1, 2)
class GroupViTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
is_cross_attention = encoder_hidden_states is not None
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
if is_cross_attention:
key_states = self._shape(self.k_proj(encoder_hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(encoder_hidden_states), -1, bsz)
else:
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->GroupViT
class GroupViTEncoderLayer(nn.Module):
def __init__(self, config: GroupViTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = GroupViTAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = GroupViTMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class GroupViTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
init_range = self.config.initializer_range
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
factor = self.config.initializer_factor
if isinstance(module, GroupViTTextEmbeddings):
module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
elif isinstance(module, GroupViTAttention):
factor = self.config.initializer_factor
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (module.embed_dim**-0.5) * factor
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
elif isinstance(module, GroupViTMLP):
factor = self.config.initializer_factor
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
nn.init.normal_(module.fc1.weight, std=fc_std)
nn.init.normal_(module.fc2.weight, std=in_proj_std)
GROUPVIT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`GroupViTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
GROUPVIT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class GroupViTVisionEncoder(nn.Module):
def __init__(self, config: GroupViTVisionConfig) -> None:
super().__init__()
self.config = config
self.stages = nn.ModuleList(
[
GroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=config.num_group_tokens[i],
num_output_group=config.num_output_groups[i],
num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
)
for i in range(len(config.depths))
]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
all_hidden_states = () if output_hidden_states else None
all_groupings = () if output_attentions else None
group_tokens = None
for i, stage in enumerate(self.stages):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = stage(hidden_states, group_tokens, output_attentions)
hidden_states = layer_outputs[0]
group_tokens = layer_outputs[1]
if output_attentions and layer_outputs[2] is not None:
all_groupings = all_groupings + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
)
class GroupViTTextEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self-attention layers. Each layer is a
[`GroupViTEncoderLayer`].
Args:
config: GroupViTTextConfig
"""
def __init__(self, config: GroupViTTextConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder, CLIP_TEXT->GROUPVIT_TEXT
class GroupViTTextTransformer(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTTextEmbeddings(config)
self.encoder = GroupViTTextEncoder(config)
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
# For `pooled_output` computation
self.eos_token_id = config.eos_token_id
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = _create_4d_causal_attention_mask(
input_shape, hidden_states.dtype, device=hidden_states.device
)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
if self.eos_token_id == 2:
# The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
# A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added
# ------------------------------------------------------------
# text_embeds.shape = [batch_size, sequence_length, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
]
else:
# The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
# We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
(input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
.int()
.argmax(dim=-1),
]
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class GroupViTTextModel(GroupViTPreTrainedModel):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig):
super().__init__(config)
self.text_model = GroupViTTextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.embeddings.token_embedding
def set_input_embeddings(self, value):
self.text_model.embeddings.token_embedding = value
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, GroupViTTextModel
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = GroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class GroupViTVisionTransformer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTVisionEmbeddings(config)
self.encoder = GroupViTVisionEncoder(config)
self.layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
hidden_states=hidden_states,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# normalize the last hidden state
last_hidden_state = self.layernorm(last_hidden_state)
pooled_output = last_hidden_state.mean(dim=1)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class GroupViTVisionModel(GroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig):
super().__init__(config)
self.vision_model = GroupViTVisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> GroupViTPatchEmbeddings:
return self.vision_model.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTVisionModel
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
return self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@add_start_docstrings(GROUPVIT_START_DOCSTRING)
class GroupViTModel(GroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig):
super().__init__(config)
if not isinstance(config.text_config, GroupViTTextConfig):
raise ValueError(
"config.text_config is expected to be of type GroupViTTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, GroupViTVisionConfig):
raise ValueError(
"config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
f" {type(config.vision_config)}."
)
text_config = config.text_config
vision_config = config.vision_config
self.projection_dim = config.projection_dim
self.projection_intermediate_dim = config.projection_intermediate_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = GroupViTTextTransformer(text_config)
self.vision_model = GroupViTVisionTransformer(vision_config)
self.visual_projection = nn.Sequential(
nn.Linear(self.vision_embed_dim, self.projection_intermediate_dim, bias=True),
nn.BatchNorm1d(self.projection_intermediate_dim),
nn.ReLU(inplace=True),
nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
)
self.text_projection = nn.Sequential(
nn.Linear(self.text_embed_dim, self.projection_intermediate_dim, bias=True),
nn.BatchNorm1d(self.projection_intermediate_dim),
nn.ReLU(inplace=True),
nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
)
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
applying the projection layer to the pooled output of [`GroupViTTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = text_outputs[1]
text_features = self.text_projection(pooled_output)
return text_features
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
applying the projection layer to the pooled output of [`GroupViTVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(pooled_output)
return image_features
@add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=GroupViTModelOutput, config_class=GroupViTConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, GroupViTModelOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_segmentation = (
output_segmentation if output_segmentation is not None else self.config.output_segmentation
)
if output_segmentation:
output_attentions = True
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
logits_per_image = logits_per_text.t()
seg_logits = None
if output_segmentation:
# grouped features
# [batch_size_image, num_group, hidden_size]
image_group_embeds = vision_outputs[0]
# [batch_size_image*num_group, hidden_size]
image_group_embeds = self.visual_projection(image_group_embeds.reshape(-1, image_group_embeds.shape[-1]))
if output_hidden_states:
attentions = vision_outputs[3]
else:
attentions = vision_outputs[2]
# [batch_size_image, num_group, height, width]
grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
# normalized features
image_group_embeds = image_group_embeds / image_group_embeds.norm(dim=-1, keepdim=True)
# [batch_size_image x num_group, batch_size_text]
logits_per_image_group = torch.matmul(image_group_embeds, text_embeds.t()) * logit_scale
# [batch_size_image, batch_size_text, num_group]
logits_per_image_group = logits_per_image_group.reshape(
image_embeds.shape[0], -1, text_embeds.shape[0]
).permute(0, 2, 1)
# [batch_size_image, batch_size_text, height x width]
flatten_grouping = grouping.reshape(grouping.shape[0], grouping.shape[1], -1)
# [batch_size_image, batch_size_text, height, width]
seg_logits = torch.matmul(logits_per_image_group, flatten_grouping) * logit_scale
seg_logits = seg_logits.reshape(
seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3]
)
loss = None
if return_loss:
loss = groupvit_loss(logits_per_text)
if not return_dict:
if seg_logits is not None:
output = (
logits_per_image,
logits_per_text,
seg_logits,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
else:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output
return GroupViTModelOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
segmentation_logits=seg_logits,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/groupvit/__init__.py
|
# Copyright 2022 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_groupvit"] = [
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_groupvit"] = [
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/groupvit/convert_groupvit_nvlab_to_hf.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""
Convert GroupViT checkpoints from the original repository.
URL: https://github.com/NVlabs/GroupViT
"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def rename_key(name):
# vision encoder
if "img_encoder.pos_embed" in name:
name = name.replace("img_encoder.pos_embed", "vision_model.embeddings.position_embeddings")
if "img_encoder.patch_embed.proj" in name:
name = name.replace("img_encoder.patch_embed.proj", "vision_model.embeddings.patch_embeddings.projection")
if "img_encoder.patch_embed.norm" in name:
name = name.replace("img_encoder.patch_embed.norm", "vision_model.embeddings.layernorm")
if "img_encoder.layers" in name:
name = name.replace("img_encoder.layers", "vision_model.encoder.stages")
if "blocks" in name and "res" not in name:
name = name.replace("blocks", "layers")
if "attn" in name and "pre_assign" not in name:
name = name.replace("attn", "self_attn")
if "proj" in name and "self_attn" in name and "text" not in name:
name = name.replace("proj", "out_proj")
if "pre_assign_attn.attn.proj" in name:
name = name.replace("pre_assign_attn.attn.proj", "pre_assign_attn.attn.out_proj")
if "norm1" in name:
name = name.replace("norm1", "layer_norm1")
if "norm2" in name and "pre_assign" not in name:
name = name.replace("norm2", "layer_norm2")
if "img_encoder.norm" in name:
name = name.replace("img_encoder.norm", "vision_model.layernorm")
# text encoder
if "text_encoder.token_embedding" in name:
name = name.replace("text_encoder.token_embedding", "text_model.embeddings.token_embedding")
if "text_encoder.positional_embedding" in name:
name = name.replace("text_encoder.positional_embedding", "text_model.embeddings.position_embedding.weight")
if "text_encoder.transformer.resblocks." in name:
name = name.replace("text_encoder.transformer.resblocks.", "text_model.encoder.layers.")
if "ln_1" in name:
name = name.replace("ln_1", "layer_norm1")
if "ln_2" in name:
name = name.replace("ln_2", "layer_norm2")
if "c_fc" in name:
name = name.replace("c_fc", "fc1")
if "c_proj" in name:
name = name.replace("c_proj", "fc2")
if "text_encoder" in name:
name = name.replace("text_encoder", "text_model")
if "ln_final" in name:
name = name.replace("ln_final", "final_layer_norm")
# projection layers
if "img_projector.linear_hidden." in name:
name = name.replace("img_projector.linear_hidden.", "visual_projection.")
if "img_projector.linear_out." in name:
name = name.replace("img_projector.linear_out.", "visual_projection.3.")
if "text_projector.linear_hidden" in name:
name = name.replace("text_projector.linear_hidden", "text_projection")
if "text_projector.linear_out" in name:
name = name.replace("text_projector.linear_out", "text_projection.3")
return name
def convert_state_dict(orig_state_dict, config):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
key_split = key.split(".")
stage_num, layer_num = int(key_split[2]), int(key_split[4])
dim = config.vision_config.hidden_size
if "weight" in key:
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.q_proj.weight"
] = val[:dim, :]
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.k_proj.weight"
] = val[dim : dim * 2, :]
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.v_proj.weight"
] = val[-dim:, :]
else:
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.q_proj.bias"
] = val[:dim]
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.k_proj.bias"
] = val[dim : dim * 2]
orig_state_dict[
f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.v_proj.bias"
] = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
key_split = key.split(".")
layer_num = int(key_split[3])
dim = config.text_config.hidden_size
if "weight" in key:
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
else:
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
else:
new_name = rename_key(key)
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
orig_state_dict[new_name] = val.squeeze_()
else:
orig_state_dict[new_name] = val
return orig_state_dict
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_groupvit_checkpoint(
checkpoint_path, pytorch_dump_folder_path, model_name="groupvit-gcc-yfcc", push_to_hub=False
):
"""
Copy/paste/tweak model's weights to the Transformers design.
"""
config = GroupViTConfig()
model = GroupViTModel(config).eval()
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
new_state_dict = convert_state_dict(state_dict, config)
missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(unexpected_keys) == 0)
# verify result
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
image = prepare_img()
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
if model_name == "groupvit-gcc-yfcc":
expected_logits = torch.tensor([[13.3523, 6.3629]])
elif model_name == "groupvit-gcc-redcaps":
expected_logits = torch.tensor([[16.1873, 8.6230]])
else:
raise ValueError(f"Model name {model_name} not supported.")
assert torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)
processor.save_pretrained(pytorch_dump_folder_path)
model.save_pretrained(pytorch_dump_folder_path)
print("Successfully saved processor and model to", pytorch_dump_folder_path)
if push_to_hub:
print("Pushing to the hub...")
processor.push_to_hub(model_name, organization="nielsr")
model.push_to_hub(model_name, organization="nielsr")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
)
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
parser.add_argument(
"--model_name",
default="groupvit-gccy-fcc",
type=str,
help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
)
args = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
""" OpenAI ImageGPT configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
logger = logging.get_logger(__name__)
IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class ImageGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the ImageGPT
[openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 512):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`].
n_positions (`int`, *optional*, defaults to 32*32):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 512):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
Activation function (can be one of the activation functions defined in src/transformers/activations.py).
Defaults to "quick_gelu".
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import ImageGPTConfig, ImageGPTModel
>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = ImageGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "imagegpt"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=512 + 1, # add one for start of sentence (sos) token
n_positions=32 * 32,
n_embd=512,
n_layer=24,
n_head=8,
n_inner=None,
activation_function="quick_gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
tie_word_embeddings=False,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.tie_word_embeddings = tie_word_embeddings
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class ImageGPTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
preprocessor: "FeatureExtractionMixin",
batch_size: int = 1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int = 3,
image_width: int = 32,
image_height: int = 32,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
The preprocessor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the model for (-1 means dynamic axis).
num_choices (`int`, *optional*, defaults to -1):
The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
seq_length (`int`, *optional*, defaults to -1):
The sequence length to export the model for (-1 means dynamic axis).
is_pair (`bool`, *optional*, defaults to `False`):
Indicate if the input is a pair (sentence 1, sentence 2)
framework (`TensorType`, *optional*, defaults to `None`):
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
num_channels (`int`, *optional*, defaults to 3):
The number of channels of the generated images.
image_width (`int`, *optional*, defaults to 40):
The width of the generated images.
image_height (`int`, *optional*, defaults to 40):
The height of the generated images.
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
inputs = dict(preprocessor(images=input_image, return_tensors=framework))
return inputs
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""Convert OpenAI Image GPT checkpoints."""
import argparse
import torch
from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
# Construct configuration depending on size
MODELS = {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
n_embd, n_head, n_layer = MODELS[model_size] # set model hyperparameters
config = ImageGPTConfig(n_embd=n_embd, n_layer=n_layer, n_head=n_head)
model = ImageGPTForCausalLM(config)
# Load weights from numpy
load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path}")
torch.save(model.state_dict(), pytorch_weights_dump_path)
print(f"Save configuration file to {pytorch_config_dump_path}")
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--imagegpt_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--model_size",
default=None,
type=str,
required=True,
help="Size of the model (can be either 'small', 'medium' or 'large').",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_imagegpt_checkpoint_to_pytorch(
args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/image_processing_imagegpt.py
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
"""Image processor class for ImageGPT."""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
logger = logging.get_logger(__name__)
def squared_euclidean_distance(a, b):
b = b.T
a2 = np.sum(np.square(a), axis=1)
b2 = np.sum(np.square(b), axis=0)
ab = np.matmul(a, b)
d = a2[:, None] - 2 * ab + b2[None, :]
return d
def color_quantize(x, clusters):
x = x.reshape(-1, 3)
d = squared_euclidean_distance(x, clusters)
return np.argmin(d, axis=1)
class ImageGPTImageProcessor(BaseImageProcessor):
r"""
Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution
(such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values"
(color clusters).
Args:
clusters (`np.ndarray` or `List[List[int]]`, *optional*):
The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overriden by `clusters`
in `preprocess`.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by
`do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
Size of the image after resizing. Can be overridden by `size` in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in
`preprocess`.
do_color_quantize (`bool`, *optional*, defaults to `True`):
Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
# clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_normalize: bool = True,
do_color_quantize: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 256, "width": 256}
size = get_size_dict(size)
self.clusters = np.array(clusters) if clusters is not None else None
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_normalize = do_normalize
self.do_color_quantize = do_color_quantize
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = 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.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
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,
)
def normalize(
self,
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Normalizes an images' pixel values to between [-1, 1].
Args:
image (`np.ndarray`):
Image to normalize.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)
image = image - 1
return image
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_normalize: bool = None,
do_color_quantize: Optional[bool] = None,
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> 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_normalize=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
has an effect if `do_resize` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image
do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`):
Whether to color quantize the image.
clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`):
Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if
`do_color_quantize` is set to `True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Only has an effect if `do_color_quantize` is set to `False`.
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
size = size if size is not None else self.size
size = get_size_dict(size)
resample = resample if resample is not None else self.resample
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
clusters = clusters if clusters is not None else self.clusters
clusters = np.array(clusters)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_normalize:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If you wish to do this, "
"make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].",
)
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_normalize:
images = [self.normalize(image=image, input_data_format=input_data_format) for image in images]
if do_color_quantize:
images = [to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
images = np.array(images)
images = color_quantize(images, clusters).reshape(images.shape[:-1])
# flatten to (batch_size, height*width)
batch_size = images.shape[0]
images = images.reshape(batch_size, -1)
# We need to convert back to a list of images to keep consistent behaviour across processors.
images = list(images)
else:
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
data = {"input_ids": images}
return BatchFeature(data=data, tensor_type=return_tensors)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/__init__.py
|
# Copyright 2020 The HuggingFace Team. 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_imagegpt"] = ["ImageGPTFeatureExtractor"]
_import_structure["image_processing_imagegpt"] = ["ImageGPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_imagegpt"] = [
"IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ImageGPTForCausalImageModeling",
"ImageGPTForImageClassification",
"ImageGPTModel",
"ImageGPTPreTrainedModel",
"load_tf_weights_in_imagegpt",
]
if TYPE_CHECKING:
from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
from .image_processing_imagegpt import ImageGPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_imagegpt import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
ImageGPTPreTrainedModel,
load_tf_weights_in_imagegpt,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py
|
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and HuggingFace Inc. team.
#
# 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.
"""PyTorch OpenAI ImageGPT model."""
import math
import os
import warnings
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_imagegpt import ImageGPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/imagegpt-small"
_CONFIG_FOR_DOC = "ImageGPTConfig"
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openai/imagegpt-small",
"openai/imagegpt-medium",
"openai/imagegpt-large",
# See all Image GPT models at https://huggingface.co/models?filter=imagegpt
]
def load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path):
"""
Load tf checkpoints in a pytorch model
"""
try:
import re
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(imagegpt_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
) or name[-1] in ["_step"]:
logger.info("Skipping {}".format("/".join(name)))
continue
pointer = model
if name[-1] not in ["wtet"]:
pointer = getattr(pointer, "transformer")
for m_name in name:
if re.fullmatch(r"[A-Za-z]+\d+", m_name):
scope_names = re.split(r"(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] in ["q_proj", "k_proj", "v_proj"]:
pointer = getattr(pointer, "c_attn")
pointer = getattr(pointer, "weight")
elif len(name) == 3 and name[1] == "attn" and scope_names[0] == "c_proj":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] == "wtet":
pointer = getattr(pointer, "lm_head")
pointer = getattr(pointer, "weight")
elif scope_names[0] == "sos":
pointer = getattr(pointer, "wte")
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if len(name) > 1 and name[1] == "attn" or name[-1] == "wtet" or name[-1] == "sos" or name[-1] == "wte":
pass # array is used to initialize only part of the pointer so sizes won't match
else:
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
if name[-1] == "q_proj":
pointer.data[:, : config.n_embd] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif name[-1] == "k_proj":
pointer.data[:, config.n_embd : 2 * config.n_embd] = torch.from_numpy(
array.reshape(config.n_embd, config.n_embd)
).T
elif name[-1] == "v_proj":
pointer.data[:, 2 * config.n_embd :] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif len(name) == 3 and name[1] == "attn" and name[2] == "c_proj":
pointer.data = torch.from_numpy(array.reshape(config.n_embd, config.n_embd))
elif name[-1] == "wtet":
pointer.data = torch.from_numpy(array)
elif name[-1] == "wte":
pointer.data[: config.vocab_size - 1, :] = torch.from_numpy(array)
elif name[-1] == "sos":
pointer.data[-1] = torch.from_numpy(array)
else:
pointer.data = torch.from_numpy(array)
return model
class ImageGPTLayerNorm(nn.Module):
def __init__(self, hidden_size: Tuple[int], eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.Tensor(hidden_size))
def forward(self, tensor: torch.Tensor) -> tuple:
# input is not mean centered
return (
tensor
/ torch.sqrt(torch.mean(torch.square(tensor), axis=-1, keepdim=True) + self.eps)
* self.weight.data[..., :]
)
class ImageGPTAttention(nn.Module):
def __init__(self, config, is_cross_attention: Optional[bool] = False, layer_idx: Optional[int] = None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.split_size = self.embed_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
if self.is_cross_attention:
self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
else:
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
self.num_heads = self.num_heads - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
attn_weights = attn_weights / float(self.layer_idx + 1)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
# Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
bsz, num_heads, q_seq_len, dk = query.size()
_, _, k_seq_len, _ = key.size()
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if self.scale_attn_by_inverse_layer_idx:
scale_factor /= float(self.layer_idx + 1)
# Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
with autocast(enabled=False):
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(*new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `ImageGPTAttention(..., is_cross_attention=True)`."
)
query = self.q_attn(hidden_states)
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
value = self._split_heads(value, self.num_heads, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
present = (key, value)
else:
present = None
if self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
else:
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class ImageGPTMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class ImageGPTBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = ImageGPTAttention(config, layer_idx=layer_idx)
self.ln_2 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if config.add_cross_attention:
self.crossattention = ImageGPTAttention(config, is_cross_attention=True, layer_idx=layer_idx)
self.ln_cross_attn = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = ImageGPTMLP(inner_dim, config)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + (outputs if use_cache else outputs[1:])
return outputs # hidden_states, present, (attentions, cross_attentions)
class ImageGPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ImageGPTConfig
load_tf_weights = load_tf_weights_in_imagegpt
base_model_prefix = "transformer"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, ImageGPTLayerNorm):
module.weight.data.fill_(1.0)
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if "c_proj" in name and "weight" in name:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
IMAGEGPT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ImageGPTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
IMAGEGPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
sequence tokens in the vocabulary.
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoImageProcessor`]. See [`ImageGPTImageProcessor.__call__`] for details.
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
their past given to this model should not be passed as `input_ids` as they have already been computed.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
`past_key_values`).
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ImageGPT Model transformer outputting raw hidden-states without any specific head on top.",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTModel(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([ImageGPTBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self.ln_f = ImageGPTLayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTModel.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# ImageGPTAttention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.add_cross_attention and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.transformer = ImageGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size - 1, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[bool] = None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# Omit tokens covered by past_key_values
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForCausalImageModeling
>>> import torch
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small")
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # unconditional generation of 8 images
>>> batch_size = 4
>>> context = torch.full((batch_size, 1), model.config.vocab_size - 1) # initialize with SOS token
>>> context = context.to(device)
>>> output = model.generate(
... input_ids=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40
... )
>>> clusters = image_processor.clusters
>>> height = image_processor.size["height"]
>>> width = image_processor.size["width"]
>>> samples = output[:, 1:].cpu().detach().numpy()
>>> samples_img = [
... np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [height, width, 3]).astype(np.uint8) for s in samples
... ] # convert color cluster tokens back to pixels
>>> f, axes = plt.subplots(1, batch_size, dpi=300)
>>> for img, ax in zip(samples_img, axes): # doctest: +IGNORE_RESULT
... ax.axis("off")
... ax.imshow(img)
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with an image classification head on top (linear layer).
[`ImageGPTForImageClassification`] average-pools the hidden states in order to do the classification.
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForImageClassification(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = ImageGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForImageClassification
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForImageClassification.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# average-pool the hidden states along the sequence dimension
pooled_hidden_states = hidden_states.mean(dim=1)
# project from (batch_size, hidden_size) to (batch_size, num_labels)
logits = self.score(pooled_hidden_states)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/imagegpt/feature_extraction_imagegpt.py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. 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.
"""Feature extractor class for ImageGPT."""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
logger = logging.get_logger(__name__)
class ImageGPTFeatureExtractor(ImageGPTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ImageGPTImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs)
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/tvp/configuration_tvp.py
|
# coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. 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.
""" TVP model configuration"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
TVP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"Intel/tvp-base": "https://huggingface.co/Intel/tvp-base/resolve/main/config.json",
}
class TvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Tvp
[Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model.
distance_loss_weight (`float`, *optional*, defaults to 1.0):
The weight of distance loss.
duration_loss_weight (`float`, *optional*, defaults to 0.1):
The weight of duration loss.
visual_prompter_type (`str`, *optional*, defaults to `"framepad"`):
Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad"
or "framedownpad"
visual_prompter_apply (`str`, *optional*, defaults to `"replace"`):
The way of applying visual prompt. Replace means use the value of prompt to change the original value in
visual inputs. Should be one of "replace", or "add", or "remove".
visual_prompt_size (`int`, *optional*, defaults to 96):
The size of visual prompt.
max_img_size (`int`, *optional*, defaults to 448):
The maximum size of frame.
num_frames (`int`, *optional*, defaults to 48):
The number of frames extracted from a video.
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`TvpModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
max_grid_col_position_embeddings (`int`, *optional*, defaults to 100):
The largest number of horizontal patches from a video frame.
max_grid_row_position_embeddings (`int`, *optional*, defaults to 100):
The largest number of vertical patches from a video frame.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability of hidden layers.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability of attention layers.
"""
model_type = "tvp"
def __init__(
self,
backbone_config=None,
distance_loss_weight=1.0,
duration_loss_weight=0.1,
visual_prompter_type="framepad",
visual_prompter_apply="replace",
visual_prompt_size=96,
max_img_size=448,
num_frames=48,
vocab_size=30522,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
max_position_embeddings=512,
max_grid_col_position_embeddings=100,
max_grid_row_position_embeddings=100,
hidden_dropout_prob=0.1,
hidden_act="gelu",
layer_norm_eps=1e-12,
initializer_range=0.02,
attention_probs_dropout_prob=0.1,
**kwargs,
):
super().__init__(**kwargs)
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
self.backbone_config = backbone_config
self.distance_loss_weight = distance_loss_weight
self.duration_loss_weight = duration_loss_weight
self.visual_prompter_type = visual_prompter_type
self.visual_prompter_apply = visual_prompter_apply
self.visual_prompt_size = visual_prompt_size
self.max_img_size = max_img_size
self.num_frames = num_frames
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.max_grid_col_position_embeddings = max_grid_col_position_embeddings
self.max_grid_row_position_embeddings = max_grid_row_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_dropout_prob = hidden_dropout_prob
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.attention_probs_dropout_prob = attention_probs_dropout_prob
@classmethod
def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
"""Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration.
Args:
backbone_config ([`PretrainedConfig`]):
The backbone configuration.
Returns:
[`TvpConfig`]: An instance of a configuration object
"""
return cls(backbone_config=backbone_config, **kwargs)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
output["backbone_config"] = self.backbone_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
| 0
|
hf_public_repos/transformers/src/transformers/models
|
hf_public_repos/transformers/src/transformers/models/tvp/image_processing_tvp.py
|
# coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. 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.
"""Image processor class for TVP."""
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
PaddingMode,
flip_channel_order,
pad,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
logger = logging.get_logger(__name__)
# Copied from transformers.models.vivit.image_processing_vivit.make_batched
def make_batched(videos) -> List[List[ImageInput]]:
if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
return videos
elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
return [videos]
elif is_valid_image(videos):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}")
def get_resize_output_image_size(
input_image: np.ndarray,
max_size: int = 448,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
height, width = get_image_size(input_image, input_data_format)
if height >= width:
ratio = width * 1.0 / height
new_height = max_size
new_width = new_height * ratio
else:
ratio = height * 1.0 / width
new_width = max_size
new_height = new_width * ratio
size = (int(new_height), int(new_width))
return size
class TvpImageProcessor(BaseImageProcessor):
r"""
Constructs a Tvp 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[str, int]` *optional*, defaults to `{"longest_edge": 448}`):
Size of the output image after resizing. The longest edge of the image will be resized to
`size["longest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by
`size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop`
parameter in the `preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the center crop. Can be overridden by the `crop_size` 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`):
Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter
in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the
`preprocess` method.
constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0):
The fill value to use when padding the image.
pad_mode (`PaddingMode`, *optional*, defaults to `PaddingMode.CONSTANT`):
Use what kind of mode in padding.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
do_flip_channel_order (`bool`, *optional*, defaults to `True`):
Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order`
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.
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.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_pad: bool = True,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = 0,
pad_mode: PaddingMode = PaddingMode.CONSTANT,
do_normalize: bool = True,
do_flip_channel_order: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"longest_edge": 448}
crop_size = crop_size if crop_size is not None else {"height": 448, "width": 448}
pad_size = pad_size if pad_size is not None else {"height": 448, "width": 448}
self.do_resize = do_resize
self.size = size
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.pad_size = pad_size
self.constant_values = constant_values
self.pad_mode = pad_mode
self.do_normalize = do_normalize
self.do_flip_channel_order = do_flip_channel_order
self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will
have the size `(h, w)`. If `size` is of the form `{"longest_edge": s}`, the output image will have its
longest edge of length `s` while keeping the aspect ratio of the original image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use when resiizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
size = get_size_dict(size, default_to_square=False)
if "height" in size and "width" in size:
output_size = (size["height"], size["width"])
elif "longest_edge" in size:
output_size = get_resize_output_image_size(image, size["longest_edge"], input_data_format)
else:
raise ValueError(f"Size must have 'height' and 'width' or 'longest_edge' as keys. Got {size.keys()}")
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def pad_image(
self,
image: np.ndarray,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = 0,
pad_mode: PaddingMode = PaddingMode.CONSTANT,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Pad an image with zeros to the given size.
Args:
image (`np.ndarray`):
Image to pad.
pad_size (`Dict[str, int]`)
Size of the output image with pad.
constant_values (`Union[float, Iterable[float]]`)
The fill value to use when padding the image.
pad_mode (`PaddingMode`)
The pad mode, default to PaddingMode.CONSTANT
data_format (`ChannelDimension` or `str`, *optional*)
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
max_height = pad_size.get("height", height)
max_width = pad_size.get("width", width)
pad_right, pad_bottom = max_width - width, max_height - height
if pad_right < 0 or pad_bottom < 0:
raise ValueError("The padding size must be greater than image size")
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=pad_mode,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_pad: bool = True,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = None,
pad_mode: PaddingMode = None,
do_normalize: bool = None,
do_flip_channel_order: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""Preprocesses a single image."""
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_pad and pad_size is None:
raise ValueError("Padding size must be specified if do_pad is True.")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_center_crop:
image = self.center_crop(image, size=crop_size, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(
image=image.astype(np.float32), mean=image_mean, std=image_std, input_data_format=input_data_format
)
if do_pad:
image = self.pad_image(
image=image,
pad_size=pad_size,
constant_values=constant_values,
pad_mode=pad_mode,
input_data_format=input_data_format,
)
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
image = flip_channel_order(image=image, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
return image
def preprocess(
self,
videos: Union[ImageInput, List[ImageInput], List[List[ImageInput]]],
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_pad: bool = None,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = None,
pad_mode: PaddingMode = None,
do_normalize: bool = None,
do_flip_channel_order: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> PIL.Image.Image:
"""
Preprocess an image or batch of images.
Args:
videos (`ImageInput` or `List[ImageInput]` or `List[List[ImageInput]]`):
Frames to preprocess.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after applying resize.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
has an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`):
Whether to centre crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the image after applying the centre crop.
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_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the
`preprocess` method.
constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0):
The fill value to use when padding the image.
pad_mode (`PaddingMode`, *optional*, defaults to "PaddingMode.CONSTANT"):
Use what kind of mode in padding.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`):
Whether to flip the channel order of the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the inferred 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_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
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_pad = do_pad if do_pad is not None else self.do_pad
pad_size = pad_size if pad_size is not None else self.pad_size
constant_values = constant_values if constant_values is not None else self.constant_values
pad_mode = pad_mode if pad_mode else self.pad_mode
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_flip_channel_order = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
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
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size")
if not valid_images(videos):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
videos = make_batched(videos)
videos = [
np.array(
[
self._preprocess_image(
image=img,
do_resize=do_resize,
size=size,
resample=resample,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_pad=do_pad,
pad_size=pad_size,
constant_values=constant_values,
pad_mode=pad_mode,
do_normalize=do_normalize,
do_flip_channel_order=do_flip_channel_order,
image_mean=image_mean,
image_std=image_std,
data_format=data_format,
input_data_format=input_data_format,
)
for img in video
]
)
for video in videos
]
data = {"pixel_values": videos}
return BatchFeature(data=data, tensor_type=return_tensors)
| 0
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