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"""
Processor class for Molmo2.
"""
from typing import List, Optional, Union, Dict, Tuple, Any
import dataclasses
import PIL
from PIL import ImageFile, ImageOps
try:
from typing import Unpack
except ImportError:
from typing_extensions import Unpack
import numpy as np
import torch
from transformers.image_utils import ImageInput
from transformers.video_utils import VideoInput
from transformers.processing_utils import (
ProcessingKwargs,
ProcessorMixin,
)
from transformers.feature_extraction_utils import BatchFeature
from transformers.tokenization_utils_base import TextInput, PreTokenizedInput
from transformers.utils import logging
from transformers import AutoTokenizer
from .image_processing import Molmo2ImagesKwargs, Molmo2ImageProcessor
from .video_processing import Molmo2VideoProcessorKwargs, Molmo2VideoProcessor
logger = logging.get_logger(__name__)
# Special tokens, these should be present in any tokenizer we use since the preprocessor uses them
IMAGE_PATCH_TOKEN = f"<im_patch>" # Where to insert high-res tokens
IMAGE_LOW_RES_TOKEN = f"<im_low>" # Where to insert low-res tokens
IM_START_TOKEN = f"<im_start>"
LOW_RES_IMAGE_START_TOKEN = f"<low_res_im_start>"
FRAME_START_TOKEN = f"<frame_start>"
IM_END_TOKEN = f"<im_end>"
FRAME_END_TOKEN= f"<frame_end>"
IM_COL_TOKEN = f"<im_col>"
IMAGE_PROMPT = "<|image|>"
VIDEO_PROMPT = "<|video|>"
@dataclasses.dataclass
class VideoFrames:
"Frames from a video and frame metadata"
frames: np.ndarray
"""Frames as RGB images"""
timestamps: np.ndarray
"""Timestamps for each frame"""
target_fps: Optional[float] = None
"""Target FPS used to sample the frames, if there was one
Negative values are also used to indicate that the target FPS is not known
"""
sampling_augmentation: Optional[str] = None
"""Augmentation used"""
def __post_init__(self):
assert len(self.timestamps) == len(self.frames)
assert len(self.frames.shape) == 4
assert self.frames.shape[-1] == 3
if self.target_fps is not None and self.target_fps >= 0:
self.target_fps = float(self.target_fps)
else:
self.target_fps = None
def __len__(self):
return len(self.frames)
@property
def sampled_fps(self) -> float:
if self.target_fps is None:
return 1 / (self.timestamps[1:] - self.timestamps[:-1]).mean()
else:
return self.target_fps
class Molmo2ProcessorKwargs(ProcessingKwargs, total=False):
"""Molmo2 processor kwargs"""
images_kwargs: Molmo2ImagesKwargs
videos_kwargs: Molmo2VideoProcessorKwargs
_defaults = {
"text_kwargs": {
"padding": False,
},
}
class Molmo2Processor(ProcessorMixin):
attributes = ["image_processor", "video_processor", "tokenizer"]
optional_attributes = [
"chat_template",
"time_mode",
"image_use_col_tokens",
"use_single_crop_col_tokens",
"use_single_crop_start_token",
"video_use_col_tokens",
"use_frame_special_tokens",
]
image_processor_class = "AutoImageProcessor"
video_processor_class = "AutoVideoProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor: Molmo2ImageProcessor = None,
video_processor: Molmo2VideoProcessor = None,
tokenizer: AutoTokenizer = None,
chat_template: Optional[str] = None,
time_mode: Optional[str] = "per-frame-compact",
image_use_col_tokens: Optional[bool] = True,
use_single_crop_col_tokens: Optional[bool] = None,
use_single_crop_start_token: Optional[bool] = True,
video_use_col_tokens: Optional[bool] = False,
use_frame_special_tokens: Optional[bool] = True,
**kwargs
) -> None:
super().__init__(
image_processor,
video_processor,
tokenizer,
chat_template=chat_template,
time_mode=time_mode,
image_use_col_tokens=image_use_col_tokens,
use_single_crop_col_tokens=use_single_crop_col_tokens,
use_single_crop_start_token=use_single_crop_start_token,
video_use_col_tokens=video_use_col_tokens,
use_frame_special_tokens=use_frame_special_tokens,
)
self.image_placeholder_token = IMAGE_PROMPT
self.video_placeholder_token = VIDEO_PROMPT
def get_image_tokens(self, image_grid: np.ndarray):
resized_h, resized_w, height, width = image_grid
per_row = np.full(width, IMAGE_PATCH_TOKEN)
if self.image_use_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
joint = [
[IM_START_TOKEN],
np.tile(per_row, [height]),
[IM_END_TOKEN],
]
per_row = np.full(resized_w, IMAGE_PATCH_TOKEN)
use_single_crop_col_tokens = (
self.image_use_col_tokens
if self.use_single_crop_col_tokens is None
else self.use_single_crop_col_tokens
)
image_start_token = (
LOW_RES_IMAGE_START_TOKEN
if self.use_single_crop_start_token
else IM_START_TOKEN
)
if use_single_crop_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
joint = [
[image_start_token],
np.tile(per_row, [resized_h]),
[IM_END_TOKEN],
] + joint
return np.concatenate(joint)
def get_video_string(
self,
video_grid: np.ndarray,
timestamps: np.ndarray,
sampled_fps: float,
sampling_augmentation: Optional[str] = None
):
average_time_delta = 1 / sampled_fps
prefix: str = None
if self.time_mode in ["sampled-fps-prefix", "numbered-frames"]:
if sampling_augmentation:
prefix = f"Aug={sampling_augmentation} FPS={sampled_fps:0.2f}"
else:
prefix = f"FPS={sampled_fps:0.2f}"
elif self.time_mode == "time-delta-prefix":
assert not sampling_augmentation
prefix = f"Sampling Delta {average_time_delta:0.2f}"
elif self.time_mode not in ["per-frame", "per-frame-compact"]:
not NotImplementedError(self.time_mode)
if self.use_frame_special_tokens:
start_token_id = FRAME_START_TOKEN
end_token_id = FRAME_END_TOKEN
else:
start_token_id = IM_START_TOKEN
end_token_id = IM_END_TOKEN
num_frames, h, w = video_grid
video_string: str = ""
for frame_idx, frame_time in enumerate(timestamps):
if self.time_mode == "numbered-frames":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"{frame_idx+1}: " # explicit whitespace before/after image tokens
elif self.time_mode == "per-frame":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"time {frame_time:.2f} " # explicit whitespace before/after image tokens
elif self.time_mode == "per-frame-compact":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"{frame_time:.1f} " # explicit whitespace before/after image tokens
else:
frame_prefix = None
if frame_prefix is not None:
video_string += frame_prefix
per_row = np.full(w, IMAGE_PATCH_TOKEN)
if self.video_use_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
extra_tokens = np.tile(per_row, [h])
video_tokens = [
[start_token_id],
extra_tokens,
[end_token_id],
]
video_string += "".join(np.concatenate(video_tokens, 0))
if prefix is not None:
video_string = prefix + " " + video_string
return video_string
def insert_bos_numpy(
self,
input_ids: np.ndarray,
attention_mask: np.ndarray,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] array with left padding
attention_mask: [B, S] array (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] array with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
need_to_expand = len(input_ids.shape) == 1
if need_to_expand:
input_ids = input_ids[None, :]
attention_mask = attention_mask[None, :]
B, S = input_ids.shape
# Handle zero-length sequence
if S == 0:
new_input_ids = np.full((B, 1), bos_token_id, dtype=input_ids.dtype)
new_attention_mask = np.ones((B, 1), dtype=attention_mask.dtype)
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).argmax(axis=-1) # [B]
bos_already_present = np.all(input_ids[np.arange(B), first_valid_index] == bos_token_id)
if bos_already_present:
if need_to_expand:
input_ids = input_ids[0]
attention_mask = attention_mask[0]
return input_ids, attention_mask
else:
new_input_ids = np.full((B, S+1), pad_token_id, dtype=input_ids.dtype)
new_attention_mask = np.zeros((B, S+1), dtype=attention_mask.dtype)
src_idx = np.tile(np.arange(S), (B, 1)) # [B, S]
valid_mask = src_idx >= first_valid_index[:, None] # [B, S]
tgt_idx = src_idx + 1 # shit right
batch_idx = np.tile(np.arange(B)[:, None], (1, S)) # [B, S]
# flatten valid_positions
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
new_input_ids[np.arange(B), insert_pos] = bos_token_id
new_attention_mask[np.arange(B), insert_pos] = 1
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
def insert_bos_torch(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] tensor with left padding
attention_mask: [B, S] tensor (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] tensor with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
B, S = input_ids.shape
device = input_ids.device
# Handle zero-length sequence
if S == 0:
new_input_ids = torch.full((B, 1), bos_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.ones((B, 1), dtype=attention_mask.dtype, device=device)
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).long().argmax(dim=-1) # [B]
bos_already_present = (input_ids[torch.arange(B), first_valid_index] == bos_token_id).all()
if bos_already_present:
return input_ids, attention_mask
else:
new_input_ids = torch.full((B, S+1), pad_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.zeros((B, S+1), dtype=attention_mask.dtype, device=device)
src_idx = torch.arange(S, device=device).expand(B, S) # [B, S]
valid_mask = src_idx >= first_valid_index.unsqueeze(1) # [B, S]
tgt_idx = src_idx + 1 # shift right
batch_idx = torch.arange(B, device=device).unsqueeze(1).expand_as(src_idx)
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
batch_indices = torch.arange(B, device=device)
new_input_ids[batch_indices, insert_pos] = bos_token_id
new_attention_mask[batch_indices, insert_pos] = 1
return new_input_ids, new_attention_mask
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
images: ImageInput = None,
videos: Union[Dict[str, Any], list[Dict[str, Any]]] = None,
**kwargs: Unpack[Molmo2ProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwargs` arguments to
Molmo2ImageProcessor's [`~Molmo2ImageProcessor.__call__`] if `vision_infos` is not `None`.
Args:
text (`str`, `list[str]`, `list[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`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
videos (`dict[str, Any]` or `list[dict[str, Any]]`):
The video or batch of videos to be prepared. Each video can be a dictionary with the following keys:
- `"frames"`: `np.ndarray` of shape (T, H, W, 3)
- `"timestamps"`: `np.ndarray` of shape (T,)
- `"sampled_fps"`: `float` (optional)
- `"sampling_augmentation"`: `str` (optional)
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
`BatchFeature`: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **image_token_pooling** -- Indices of the patches in `image_grids` to pool for each token in `image_tokens`.
Returned when `images` is not `None`.
- **image_grids** -- Grids of images. Returned when `images` is not `None`.
- **image_num_crops** -- Number of crops for each image. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **video_token_pooling** -- Indices of the patches in `video_grids` to pool for each token in `video_tokens`.
Returned when `videos` is not `None`.
- **video_grids** -- Grids of videos. Returned when `videos` is not `None`.
"""
output_kwargs = self._merge_kwargs(
Molmo2ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
image_grids = image_inputs["image_grids"]
else:
image_inputs = {}
image_grids = None
if videos is not None:
if not isinstance(videos, (list, tuple)):
videos = [videos]
videos = [VideoFrames(**video) for video in videos]
video_inputs = self.video_processor([video.frames for video in videos], **output_kwargs["videos_kwargs"])
video_grids = video_inputs["video_grids"]
else:
video_inputs = {}
video_grids = None
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
if image_grids is not None:
index = 0
for i in range(len(text)):
num_images = text[i].count(self.image_placeholder_token)
image_grids_i = image_grids[index:index+num_images]
for image_grid in image_grids_i:
image_tokens = self.get_image_tokens(image_grid)
image_string = "".join(image_tokens)
text[i] = text[i].replace(self.image_placeholder_token, image_string, 1)
index += num_images
if video_grids is not None:
index = 0
for i in range(len(text)):
num_videos = text[i].count(self.video_placeholder_token)
assert num_videos in {0, 1}, "At most one video is supported for now"
video_grids_i = video_grids[index:index+num_videos]
for video_grid in video_grids_i:
video_string = self.get_video_string(
video_grid,
videos[index].timestamps,
videos[index].sampled_fps,
videos[index].sampling_augmentation,
)
text[i] = text[i].replace(self.video_placeholder_token, video_string, 1)
index += num_videos
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
input_ids = text_inputs["input_ids"]
attention_mask = text_inputs["attention_mask"]
is_list = isinstance(input_ids, (list, tuple))
if is_list:
input_ids = np.array(input_ids)
attention_mask = np.array(attention_mask)
use_numpy = isinstance(attention_mask, np.ndarray)
if use_numpy and np.issubdtype(input_ids.dtype, np.floating):
input_ids = input_ids.astype(np.int64)
attention_mask = attention_mask.astype(np.int64)
elif not use_numpy and torch.is_floating_point(input_ids):
input_ids = input_ids.to(torch.int64)
attention_mask = attention_mask.to(torch.int64)
bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
if use_numpy:
input_ids, attention_mask = self.insert_bos_numpy(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
else:
input_ids, attention_mask = self.insert_bos_torch(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
if is_list:
input_ids = input_ids.tolist() # type: ignore
attention_mask = attention_mask.tolist() # type: ignore
text_inputs["input_ids"] = input_ids
text_inputs["attention_mask"] = attention_mask
return BatchFeature(
data={**text_inputs, **image_inputs, **video_inputs},
tensor_type=return_tensors,
)
def post_process_image_text_to_text(
self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
skip_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
**kwargs:
Additional arguments to be passed to the tokenizer's `batch_decode method`.
Returns:
`list[str]`: The decoded text.
"""
return self.tokenizer.batch_decode(
generated_outputs,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
class ImageMolmo2Processor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
optional_attributes = [
"chat_template",
"image_use_col_tokens",
"use_single_crop_col_tokens",
"use_single_crop_start_token",
]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor: Molmo2ImageProcessor = None,
tokenizer: AutoTokenizer = None,
chat_template: Optional[str] = None,
image_use_col_tokens: Optional[bool] = True,
use_single_crop_col_tokens: Optional[bool] = None,
use_single_crop_start_token: Optional[bool] = True,
**kwargs
) -> None:
super().__init__(
image_processor,
tokenizer,
chat_template=chat_template,
image_use_col_tokens=image_use_col_tokens,
use_single_crop_col_tokens=use_single_crop_col_tokens,
use_single_crop_start_token=use_single_crop_start_token,
)
self.image_placeholder_token = IMAGE_PROMPT
def get_image_tokens(self, image_grid: np.ndarray):
resized_h, resized_w, height, width = image_grid
per_row = np.full(width, IMAGE_PATCH_TOKEN)
if self.image_use_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
joint = [
[IM_START_TOKEN],
np.tile(per_row, [height]),
[IM_END_TOKEN],
]
per_row = np.full(resized_w, IMAGE_PATCH_TOKEN)
use_single_crop_col_tokens = (
self.image_use_col_tokens
if self.use_single_crop_col_tokens is None
else self.use_single_crop_col_tokens
)
image_start_token = (
LOW_RES_IMAGE_START_TOKEN
if self.use_single_crop_start_token
else IM_START_TOKEN
)
if use_single_crop_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
joint = [
[image_start_token],
np.tile(per_row, [resized_h]),
[IM_END_TOKEN],
] + joint
return np.concatenate(joint)
def insert_bos_numpy(
self,
input_ids: np.ndarray,
attention_mask: np.ndarray,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] array with left padding
attention_mask: [B, S] array (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] array with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
need_to_expand = len(input_ids.shape) == 1
if need_to_expand:
input_ids = input_ids[None, :]
attention_mask = attention_mask[None, :]
B, S = input_ids.shape
# Handle zero-length sequence
if S == 0:
new_input_ids = np.full((B, 1), bos_token_id, dtype=input_ids.dtype)
new_attention_mask = np.ones((B, 1), dtype=attention_mask.dtype)
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).argmax(axis=-1) # [B]
bos_already_present = np.all(input_ids[np.arange(B), first_valid_index] == bos_token_id)
if bos_already_present:
if need_to_expand:
input_ids = input_ids[0]
attention_mask = attention_mask[0]
return input_ids, attention_mask
else:
new_input_ids = np.full((B, S+1), pad_token_id, dtype=input_ids.dtype)
new_attention_mask = np.zeros((B, S+1), dtype=attention_mask.dtype)
src_idx = np.tile(np.arange(S), (B, 1)) # [B, S]
valid_mask = src_idx >= first_valid_index[:, None] # [B, S]
tgt_idx = src_idx + 1 # shit right
batch_idx = np.tile(np.arange(B)[:, None], (1, S)) # [B, S]
# flatten valid_positions
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
new_input_ids[np.arange(B), insert_pos] = bos_token_id
new_attention_mask[np.arange(B), insert_pos] = 1
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
def insert_bos_torch(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] tensor with left padding
attention_mask: [B, S] tensor (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] tensor with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
B, S = input_ids.shape
device = input_ids.device
# Handle zero-length sequence
if S == 0:
new_input_ids = torch.full((B, 1), bos_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.ones((B, 1), dtype=attention_mask.dtype, device=device)
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).long().argmax(dim=-1) # [B]
bos_already_present = (input_ids[torch.arange(B), first_valid_index] == bos_token_id).all()
if bos_already_present:
return input_ids, attention_mask
else:
new_input_ids = torch.full((B, S+1), pad_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.zeros((B, S+1), dtype=attention_mask.dtype, device=device)
src_idx = torch.arange(S, device=device).expand(B, S) # [B, S]
valid_mask = src_idx >= first_valid_index.unsqueeze(1) # [B, S]
tgt_idx = src_idx + 1 # shift right
batch_idx = torch.arange(B, device=device).unsqueeze(1).expand_as(src_idx)
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
batch_indices = torch.arange(B, device=device)
new_input_ids[batch_indices, insert_pos] = bos_token_id
new_attention_mask[batch_indices, insert_pos] = 1
return new_input_ids, new_attention_mask
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
images: ImageInput = None,
**kwargs: Unpack[Molmo2ProcessorKwargs],
) -> BatchFeature:
output_kwargs = self._merge_kwargs(
Molmo2ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
image_grids = image_inputs["image_grids"]
else:
image_inputs = {}
image_grids = None
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
if image_grids is not None:
index = 0
for i in range(len(text)):
num_images = text[i].count(self.image_placeholder_token)
image_grids_i = image_grids[index:index+num_images]
for image_grid in image_grids_i:
image_tokens = self.get_image_tokens(image_grid)
image_string = "".join(image_tokens)
text[i] = text[i].replace(self.image_placeholder_token, image_string, 1)
index += num_images
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
input_ids = text_inputs["input_ids"]
attention_mask = text_inputs["attention_mask"]
is_list = isinstance(input_ids, (list, tuple))
if is_list:
input_ids = np.array(input_ids)
attention_mask = np.array(attention_mask)
use_numpy = isinstance(attention_mask, np.ndarray)
if use_numpy and np.issubdtype(input_ids.dtype, np.floating):
input_ids = input_ids.astype(np.int64)
attention_mask = attention_mask.astype(np.int64)
elif not use_numpy and torch.is_floating_point(input_ids):
input_ids = input_ids.to(torch.int64)
attention_mask = attention_mask.to(torch.int64)
bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
if use_numpy:
input_ids, attention_mask = self.insert_bos_numpy(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
else:
input_ids, attention_mask = self.insert_bos_torch(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
if is_list:
input_ids = input_ids.tolist() # type: ignore
attention_mask = attention_mask.tolist() # type: ignore
text_inputs["input_ids"] = input_ids
text_inputs["attention_mask"] = attention_mask
return BatchFeature(
data={**text_inputs, **image_inputs},
tensor_type=return_tensors,
)
def post_process_image_text_to_text(
self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
skip_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
**kwargs:
Additional arguments to be passed to the tokenizer's `batch_decode method`.
Returns:
`list[str]`: The decoded text.
"""
return self.tokenizer.batch_decode(
generated_outputs,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
class VideoMolmo2Processor(ProcessorMixin):
attributes = ["video_processor", "tokenizer"]
optional_attributes = [
"chat_template",
"time_mode",
"video_use_col_tokens",
"use_frame_special_tokens",
]
video_processor_class = "AutoVideoProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
video_processor: Molmo2VideoProcessor = None,
tokenizer: AutoTokenizer = None,
chat_template: Optional[str] = None,
time_mode: Optional[str] = "per-frame-compact",
video_use_col_tokens: Optional[bool] = False,
use_frame_special_tokens: Optional[bool] = True,
**kwargs
) -> None:
super().__init__(
video_processor,
tokenizer,
chat_template=chat_template,
time_mode=time_mode,
video_use_col_tokens=video_use_col_tokens,
use_frame_special_tokens=use_frame_special_tokens,
)
self.video_placeholder_token = VIDEO_PROMPT
self.audio_tokenizer = None
def get_video_string(
self,
video_grid: np.ndarray,
timestamps: np.ndarray,
sampled_fps: float,
sampling_augmentation: Optional[str] = None
):
average_time_delta = 1 / sampled_fps
prefix: str = None
if self.time_mode in ["sampled-fps-prefix", "numbered-frames"]:
if sampling_augmentation:
prefix = f"Aug={sampling_augmentation} FPS={sampled_fps:0.2f}"
else:
prefix = f"FPS={sampled_fps:0.2f}"
elif self.time_mode == "time-delta-prefix":
assert not sampling_augmentation
prefix = f"Sampling Delta {average_time_delta:0.2f}"
elif self.time_mode not in ["per-frame", "per-frame-compact"]:
not NotImplementedError(self.time_mode)
if self.use_frame_special_tokens:
start_token_id = FRAME_START_TOKEN
end_token_id = FRAME_END_TOKEN
else:
start_token_id = IM_START_TOKEN
end_token_id = IM_END_TOKEN
num_frames, h, w = video_grid
video_string: str = ""
for frame_idx, frame_time in enumerate(timestamps):
if self.time_mode == "numbered-frames":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"{frame_idx+1}: " # explicit whitespace before/after image tokens
elif self.time_mode == "per-frame":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"time {frame_time:.2f} " # explicit whitespace before/after image tokens
elif self.time_mode == "per-frame-compact":
prev_space = " " if frame_idx > 0 else ""
frame_prefix = prev_space + f"{frame_time:.1f} " # explicit whitespace before/after image tokens
else:
frame_prefix = None
if frame_prefix is not None:
video_string += frame_prefix
per_row = np.full(w, IMAGE_PATCH_TOKEN)
if self.video_use_col_tokens:
per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
extra_tokens = np.tile(per_row, [h])
video_tokens = [
[start_token_id],
extra_tokens,
[end_token_id],
]
video_string += "".join(np.concatenate(video_tokens, 0))
if prefix is not None:
video_string = prefix + " " + video_string
return video_string
def insert_bos_numpy(
self,
input_ids: np.ndarray,
attention_mask: np.ndarray,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] array with left padding
attention_mask: [B, S] array (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] array with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
need_to_expand = len(input_ids.shape) == 1
if need_to_expand:
input_ids = input_ids[None, :]
attention_mask = attention_mask[None, :]
B, S = input_ids.shape
# Handle zero-length sequence
if S == 0:
new_input_ids = np.full((B, 1), bos_token_id, dtype=input_ids.dtype)
new_attention_mask = np.ones((B, 1), dtype=attention_mask.dtype)
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).argmax(axis=-1) # [B]
bos_already_present = np.all(input_ids[np.arange(B), first_valid_index] == bos_token_id)
if bos_already_present:
if need_to_expand:
input_ids = input_ids[0]
attention_mask = attention_mask[0]
return input_ids, attention_mask
else:
new_input_ids = np.full((B, S+1), pad_token_id, dtype=input_ids.dtype)
new_attention_mask = np.zeros((B, S+1), dtype=attention_mask.dtype)
src_idx = np.tile(np.arange(S), (B, 1)) # [B, S]
valid_mask = src_idx >= first_valid_index[:, None] # [B, S]
tgt_idx = src_idx + 1 # shit right
batch_idx = np.tile(np.arange(B)[:, None], (1, S)) # [B, S]
# flatten valid_positions
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
new_input_ids[np.arange(B), insert_pos] = bos_token_id
new_attention_mask[np.arange(B), insert_pos] = 1
if need_to_expand:
new_input_ids = new_input_ids[0]
new_attention_mask = new_attention_mask[0]
return new_input_ids, new_attention_mask
def insert_bos_torch(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
bos_token_id: int,
pad_token_id: int,
):
"""
Args:
input_ids: [B, S] tensor with left padding
attention_mask: [B, S] tensor (0 for pad, 1 for valid)
bos_token_id: int
pad_token_id: int
Returns:
input_ids_out: [B, S] or [B, S+1] tensor with bos inserted if needed
attention_mask_out: same shape as input_ids_out
"""
B, S = input_ids.shape
device = input_ids.device
# Handle zero-length sequence
if S == 0:
new_input_ids = torch.full((B, 1), bos_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.ones((B, 1), dtype=attention_mask.dtype, device=device)
return new_input_ids, new_attention_mask
first_valid_index = (attention_mask == 1).long().argmax(dim=-1) # [B]
bos_already_present = (input_ids[torch.arange(B), first_valid_index] == bos_token_id).all()
if bos_already_present:
return input_ids, attention_mask
else:
new_input_ids = torch.full((B, S+1), pad_token_id, dtype=input_ids.dtype, device=device)
new_attention_mask = torch.zeros((B, S+1), dtype=attention_mask.dtype, device=device)
src_idx = torch.arange(S, device=device).expand(B, S) # [B, S]
valid_mask = src_idx >= first_valid_index.unsqueeze(1) # [B, S]
tgt_idx = src_idx + 1 # shift right
batch_idx = torch.arange(B, device=device).unsqueeze(1).expand_as(src_idx)
flat_vals = input_ids[valid_mask]
flat_batch = batch_idx[valid_mask]
flat_tgt = tgt_idx[valid_mask]
new_input_ids[flat_batch, flat_tgt] = flat_vals
new_attention_mask[flat_batch, flat_tgt] = 1
insert_pos = first_valid_index
batch_indices = torch.arange(B, device=device)
new_input_ids[batch_indices, insert_pos] = bos_token_id
new_attention_mask[batch_indices, insert_pos] = 1
return new_input_ids, new_attention_mask
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
videos: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
**kwargs: Unpack[Molmo2ProcessorKwargs],
) -> BatchFeature:
output_kwargs = self._merge_kwargs(
Molmo2ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if videos is not None:
if not isinstance(videos, (list, tuple)):
videos = [videos]
videos = [VideoFrames(**video) for video in videos]
video_inputs = self.video_processor([video.frames for video in videos], **output_kwargs["videos_kwargs"])
video_grids = video_inputs["video_grids"]
else:
video_inputs = {}
video_grids = None
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
if video_grids is not None:
index = 0
for i in range(len(text)):
num_videos = text[i].count(self.video_placeholder_token)
assert num_videos in {0, 1}, "At most one video is supported for now"
video_grids_i = video_grids[index:index+num_videos]
for video_grid in video_grids_i:
video_string = self.get_video_string(
video_grid,
videos[index].timestamps,
videos[index].sampled_fps,
videos[index].sampling_augmentation,
)
text[i] = text[i].replace(self.video_placeholder_token, video_string, 1)
index += num_videos
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
input_ids = text_inputs["input_ids"]
attention_mask = text_inputs["attention_mask"]
is_list = isinstance(input_ids, (list, tuple))
if is_list:
input_ids = np.array(input_ids)
attention_mask = np.array(attention_mask)
use_numpy = isinstance(attention_mask, np.ndarray)
if use_numpy and np.issubdtype(input_ids.dtype, np.floating):
input_ids = input_ids.astype(np.int64)
attention_mask = attention_mask.astype(np.int64)
elif not use_numpy and torch.is_floating_point(input_ids):
input_ids = input_ids.to(torch.int64)
attention_mask = attention_mask.to(torch.int64)
bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
if use_numpy:
input_ids, attention_mask = self.insert_bos_numpy(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
else:
input_ids, attention_mask = self.insert_bos_torch(
input_ids, attention_mask, bos, self.tokenizer.pad_token_id
)
if is_list:
input_ids = input_ids.tolist() # type: ignore
attention_mask = attention_mask.tolist() # type: ignore
text_inputs["input_ids"] = input_ids
text_inputs["attention_mask"] = attention_mask
return BatchFeature(
data={**text_inputs, **video_inputs},
tensor_type=return_tensors,
)
def post_process_image_text_to_text(
self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
skip_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
**kwargs:
Additional arguments to be passed to the tokenizer's `batch_decode method`.
Returns:
`list[str]`: The decoded text.
"""
return self.tokenizer.batch_decode(
generated_outputs,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
def apply_chat_template(self, text: str, **kwargs):
return self.tokenizer.apply_chat_template(text, **kwargs)
Molmo2Processor.register_for_auto_class()
ImageMolmo2Processor.register_for_auto_class()
VideoMolmo2Processor.register_for_auto_class()
from transformers import AutoProcessor, AutoConfig
from molmo_r1.src.models.molmo2.config import Molmo2Config
AutoConfig.register("molmo2", Molmo2Config)
AutoProcessor.register(Molmo2Config, Molmo2Processor)
AutoProcessor.register(Molmo2Config, ImageMolmo2Processor)
AutoProcessor.register(Molmo2Config, VideoMolmo2Processor) |