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"""
Processor class for Molmo.
"""
from typing import Optional
import PIL
from PIL import ImageOps
from PIL.Image import Image
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.processing_utils import (
TextKwargs,
ProcessingKwargs,
ProcessorMixin,
)
from transformers.tokenization_utils_base import TextInput
from transformers.utils import logging
from transformers import AutoTokenizer
from .image_preprocessing_molmo import MolmoImagesKwargs, MolmoImageProcessor
logger = logging.get_logger(__name__)
DEFAULT_IMAGE_PATCH_TOKEN = f"<im_patch>"
DEFAULT_IM_START_TOKEN = f"<im_start>"
DEFAULT_IM_END_TOKEN = f"<im_end>"
DEFAULT_IM_COL_TOKEN = f"<im_col>"
IMAGE_PROMPT = "<|image|>"
EXTRA_TOKENS = (DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_COL_TOKEN, IMAGE_PROMPT)
def get_special_token_ids(tokenizer):
ids = tokenizer.encode("".join(EXTRA_TOKENS), add_special_tokens=False)
assert len(ids) == len(EXTRA_TOKENS)
return {k: i for k, i in zip(EXTRA_TOKENS, ids)}
class MolmoTextKwargs(TextKwargs, total=False):
style: Optional[str]
system_prompt: Optional[str]
message_format: Optional[str]
always_start_with_space: Optional[bool]
sequence_length: Optional[int]
class MolmoProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: MolmoTextKwargs
images_kwargs: MolmoImagesKwargs
_defaults = {
"images_kwargs": {
"max_crops": 12,
"overlap_margins": [4, 4],
"base_image_input_size": [336, 336],
"image_token_length_w": 12,
"image_token_length_h": 12,
"image_patch_size": 14,
"image_padding_mask": True,
},
"text_kwargs": {
"style": "long_caption",
"system_prompt": "none",
"message_format": "role",
"always_start_with_space": True,
"sequence_length": 1536,
"padding": False,
},
}
class MolmoProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(self, image_processor: MolmoImageProcessor = None, tokenizer : AutoTokenizer = None, **kwargs):
# self.image_processor = image_processor
# self.tokenizer = tokenizer
super().__init__(image_processor, tokenizer)
self._special_tokens = None
@property
def special_token_ids(self):
if self._special_tokens is None:
self._special_tokens = get_special_token_ids(self.tokenizer)
return self._special_tokens
def get_tokens_input(self, prompt, message_format, always_start_with_space):
if message_format == "none" or message_format is None:
pass
elif message_format == "role":
prompt = "User: " + prompt + " Assistant:"
else:
raise NotImplementedError(f"Message format {message_format} not implemented")
if always_start_with_space:
prompt = " " + prompt
tokens = self.tokenizer.encode(prompt, add_special_tokens=False)
return tokens
def process(
self,
text: TextInput = None,
images: ImageInput = None,
**kwargs: Unpack[MolmoProcessorKwargs],
):
output_kwargs = self._merge_kwargs(
MolmoProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
tokens = self.get_tokens_input(
text,
output_kwargs["text_kwargs"]["message_format"],
output_kwargs["text_kwargs"]["always_start_with_space"],
)
image_token_id = self.special_token_ids[IMAGE_PROMPT]
if images is not None:
if not isinstance(images, (list, tuple)):
images = [images]
image_arrays = []
for image in images:
if isinstance(image, Image):
image = image.convert("RGB")
# Handle images with EXIF orientation tags, which PIL will ignore by default
# https://github.com/python-pillow/Pillow/issues/4703
img = ImageOps.exif_transpose(image)
image_arrays.append(np.array(image))
else:
assert len(image.shape) == 3 and image.shape[-1] == 3
image_arrays.append(image.astype(np.uint8))
images = image_arrays
# For now only support inserting images at the start
image_idx = [-1]*len(images)
else:
image_idx = None
sequence_length = output_kwargs["text_kwargs"]["sequence_length"]
image_patch_token_id = self.special_token_ids[DEFAULT_IMAGE_PATCH_TOKEN]
image_col_token_id = self.special_token_ids[DEFAULT_IM_COL_TOKEN]
image_start_token_id = self.special_token_ids[DEFAULT_IM_START_TOKEN]
image_end_token_id = self.special_token_ids[DEFAULT_IM_END_TOKEN]
out = self.image_processor.multimodal_preprocess(
images=images,
image_idx=image_idx,
tokens=np.asarray(tokens).astype(np.int32),
sequence_length=sequence_length,
image_patch_token_id=image_patch_token_id,
image_col_token_id=image_col_token_id,
image_start_token_id=image_start_token_id,
image_end_token_id=image_end_token_id,
**output_kwargs["images_kwargs"]
)
# Prepend BOS
# qwen2 and olmo do not have a BOS, and instead use EOS as a generic seperator token.
bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
decoder_input_tokens = np.pad(out["input_ids"], [[1, 0]], constant_values=bos)
out["input_ids"] = decoder_input_tokens
if "image_input_idx" in out:
# Shift patch mapping up by one since we added BOS
image_input_idx = out["image_input_idx"]
out["image_input_idx"] = np.where(image_input_idx < 0, image_input_idx, image_input_idx + 1)
for k, v in out.items():
out[k] = torch.from_numpy(v)
return out
MolmoProcessor.register_for_auto_class()
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