KeyError: 'florence2_language'

[conceptofmind]

System Info

Ubuntu

Who can help?

No response

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

# coding=utf-8
# Copyright 2025 Microsoft 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.
import argparse
from collections import OrderedDict

import torch

from transformers import (
    AddedToken,
    AutoConfig,
    AutoModelForCausalLM,
    AutoProcessor,
    Florence2Config,
    Florence2ForConditionalGeneration,
    Florence2Processor,
    Florence2VisionConfig,
)


def convert_config(original_config: dict):
    new_config = Florence2VisionConfig(
        embed_dim=original_config["dim_embed"],
        max_temporal_embeddings=original_config["visual_temporal_embedding"]["max_temporal_embeddings"],
        max_pos_embeddings=original_config["image_pos_embed"]["max_pos_embeddings"],
        **original_config,
    )

    return new_config


def vision_conv_embeddings(idx):
    """
    The function helps in renaming vision convolution embedding layer weights.

    Args:
        idx: stage number in original model
    """
    convs = []
    convs.append(
        (
            f"vision_tower.convs.{idx}.proj.weight",
            f"model.vision_tower.convs.{idx}.conv.weight",
        )
    )
    convs.append(
        (
            f"vision_tower.convs.{idx}.proj.bias",
            f"model.vision_tower.convs.{idx}.conv.bias",
        )
    )
    convs.append(
        (
            f"vision_tower.convs.{idx}.norm.weight",
            f"model.vision_tower.convs.{idx}.norm.weight",
        )
    )
    convs.append(
        (
            f"vision_tower.convs.{idx}.norm.bias",
            f"model.vision_tower.convs.{idx}.norm.bias",
        )
    )
    return convs


def vision_spatial_block(stage_idx, block_idx):
    """
    The function helps in renaming vision spatial block layers weights.

    Args:
        idx: stage number in original model
        cnt: count of blocks in each stage
    """
    spatial_block = []
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.fn.dw.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.fn.dw.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv1.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.norm.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm1.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.norm.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm1.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.qkv.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.qkv.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.qkv.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.qkv.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.proj.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.proj.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.fn.proj.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.window_attn.proj.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.fn.dw.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.fn.dw.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.conv2.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.norm.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm2.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.norm.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.norm2.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc1.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc1.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc1.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc1.bias",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc2.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc2.weight",
        )
    )
    spatial_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fn.net.fc2.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.spatial_block.ffn.fc2.bias",
        )
    )
    return spatial_block


def vision_channel_block(stage_idx, block_idx):
    """
    The function helps in renaming vision channel block layers weights.

    Args:
        idx: stage number in original model
        cnt: count of blocks in each stage
    """
    channel_block = []
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.fn.dw.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.fn.dw.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv1.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.norm.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm1.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.norm.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm1.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.qkv.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.qkv.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.qkv.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.qkv.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.proj.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.proj.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.fn.proj.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.channel_attn.proj.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.fn.dw.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.fn.dw.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.conv2.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.norm.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm2.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.norm.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.norm2.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc1.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc1.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc1.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc1.bias",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc2.weight",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc2.weight",
        )
    )
    channel_block.append(
        (
            f"vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fn.net.fc2.bias",
            f"model.vision_tower.blocks.{stage_idx}.{block_idx}.channel_block.ffn.fc2.bias",
        )
    )
    return channel_block


def multi_modal_projector():
    """
    Function helps in renaming final classification layer
    """
    projector = []
    projector.append(("image_projection", "model.multi_modal_projector.image_projection.weight"))
    projector.append(("image_proj_norm.weight", "model.multi_modal_projector.image_proj_norm.weight"))
    projector.append(("image_proj_norm.bias", "model.multi_modal_projector.image_proj_norm.bias"))
    projector.append(
        (
            "image_pos_embed.row_embeddings.weight",
            "model.multi_modal_projector.image_position_embed.row_embeddings.weight",
        )
    )
    projector.append(
        (
            "image_pos_embed.column_embeddings.weight",
            "model.multi_modal_projector.image_position_embed.column_embeddings.weight",
        )
    )
    projector.append(
        (
            "visual_temporal_embed.pos_idx_to_embed",
            "model.multi_modal_projector.visual_temporal_embed.pos_idx_to_embed",
        )
    )
    return projector


def language_model(state_dict):
    language_state_dict_keys = []
    for key in state_dict.keys():
        if key.startswith("language_model.model") and "lm_head" not in key:
            new_key = key.replace("language_model.model.", "model.language_model.")
            language_state_dict_keys.append((key, new_key))
    language_state_dict_keys.append(("language_model.lm_head.weight", "lm_head.weight"))
    return language_state_dict_keys


def convert_florence2_checkpoint(hf_model_id, pytorch_dump_folder, output_hub_path):
    """
    Function to convert the microsoft florence2 checkpoint to huggingface checkpoint
    """

    hf_config = AutoConfig.from_pretrained(hf_model_id, trust_remote_code=True)
    hf_model = AutoModelForCausalLM.from_pretrained(
        hf_model_id, trust_remote_code=True, dtype=torch.float16, attn_implementation="eager"
    )
    hf_processor = AutoProcessor.from_pretrained(hf_model_id, trust_remote_code=True)
    huggingface_weights = OrderedDict()
    list_of_state_dict = []

    image_processor = hf_processor.image_processor

    tokenizer = hf_processor.tokenizer
    tokenizer.image_token = "<image>"
    tokenizer.add_tokens(AddedToken(tokenizer.image_token, special=True, normalized=False), special_tokens=True)
    tokenizer.image_token_id = tokenizer.encode(tokenizer.image_token, add_special_tokens=False)[0]
    tokenizer.extra_special_tokens = {"image_token": "<image>"}

    post_processor_config = {
        "ocr": {
            "pattern": r"(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>",
            "area_threshold": 0.0,
        },
        "phrase_grounding": {
            "banned_grounding_tokens": [
                "it",
                "I",
                "me",
                "mine",
                "you",
                "your",
                "yours",
                "he",
                "him",
                "his",
                "she",
                "her",
                "hers",
                "they",
                "them",
                "their",
                "theirs",
                "one",
                "oneself",
                "we",
                "us",
                "our",
                "ours",
                "you",
                "your",
                "yours",
                "they",
                "them",
                "their",
                "theirs",
                "mine",
                "yours",
                "his",
                "hers",
                "its",
                "ours",
                "yours",
                "theirs",
                "myself",
                "yourself",
                "himself",
                "herself",
                "itself",
                "ourselves",
                "yourselves",
                "themselves",
                "this",
                "that",
                "these",
                "those",
                "who",
                "whom",
                "whose",
                "which",
                "what",
                "who",
                "whom",
                "whose",
                "which",
                "that",
                "all",
                "another",
                "any",
                "anybody",
                "anyone",
                "anything",
                "each",
                "everybody",
                "everyone",
                "everything",
                "few",
                "many",
                "nobody",
                "none",
                "one",
                "several",
                "some",
                "somebody",
                "someone",
                "something",
                "each other",
                "one another",
                "myself",
                "yourself",
                "himself",
                "herself",
                "itself",
                "ourselves",
                "yourselves",
                "themselves",
                "the image",
                "image",
                "images",
                "the",
                "a",
                "an",
                "a group",
                "other objects",
                "lots",
                "a set",
            ],
        },
        "pure_text": {},
        "description_with_bboxes": {},
        "description_with_polygons": {},
        "polygons": {},
        "bboxes": {},
        "description_with_bboxes_or_polygons": {},
    }
    processor = Florence2Processor(
        image_processor=image_processor, tokenizer=tokenizer, post_processor_config=post_processor_config
    )

    vision_config = convert_config(hf_config.vision_config.__dict__)
    text_config = hf_config.text_config.__dict__
    config = Florence2Config(
        text_config=text_config,
        vision_config=vision_config,
        image_token_id=tokenizer.image_token_id,
        dtype=torch.float16,
    )

    for stage_idx in range(len(config.vision_config.embed_dim)):
        list_of_state_dict = list_of_state_dict + vision_conv_embeddings(stage_idx)
        for block_idx in range(config.vision_config.depths[stage_idx]):
            list_of_state_dict = list_of_state_dict + vision_spatial_block(stage_idx, block_idx)
            list_of_state_dict = list_of_state_dict + vision_channel_block(stage_idx, block_idx)

    original_weights = hf_model.state_dict()
    list_of_state_dict = list_of_state_dict + multi_modal_projector()
    list_of_state_dict = list_of_state_dict + language_model(original_weights)
    for i in range(len(list_of_state_dict)):
        if list_of_state_dict[i][0] == "image_projection":
            original_weights[list_of_state_dict[i][0]].transpose_(1, 0)
        huggingface_weights[list_of_state_dict[i][1]] = original_weights[list_of_state_dict[i][0]]

    model = Florence2ForConditionalGeneration(config)
    model.load_state_dict(huggingface_weights, strict=True, assign=True)
    model.tie_weights()
    # We add an image token so we resize the model and pad to 64 for performance reasons
    pad_shape = 64
    model.resize_token_embeddings(len(tokenizer), pad_shape)

    if pytorch_dump_folder:
        model.save_pretrained(pytorch_dump_folder)
        processor.save_pretrained(pytorch_dump_folder)

    if output_hub_path:
        model.push_to_hub(output_hub_path)
        processor.push_to_hub(output_hub_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--hf_model_id",
        default="why-tho/test",
        type=str,
        help="Name of the florence2 model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--output_hub_path",
        default="why-tho/test2",
        help="Location on the hub of the converted model",
    )

    args = parser.parse_args()
    convert_florence2_checkpoint(args.hf_model_id, args.pytorch_dump_folder_path, args.output_hub_path)

Expected behavior

Convert Florence 2 checkpoint from: https://huggingface.co/MiaoshouAI/Florence-2-large-PromptGen-v2.0

Created a new set of modeling code with the mixin: https://huggingface.co/why-tho/test/blob/main/modeling_florence2.py

Error:

generation_config.json: 100%|████████████████████████████████████████████████████████████████████████| 57.0/57.0 [00:00<00:00, 644kB/s]
Traceback (most recent call last):
  File "/home/henry/seidr/tests/test.py", line 531, in <module>
    convert_florence2_checkpoint(args.hf_model_id, args.pytorch_dump_folder_path, args.output_hub_path)
  File "/home/henry/seidr/tests/test.py", line 476, in convert_florence2_checkpoint
    config = Florence2Config(
  File "/home/henry/seidr/.venv/lib/python3.10/site-packages/transformers/models/florence2/configuration_florence2.py", line 195, in __init__
    text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
  File "/home/henry/seidr/.venv/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py", line 1004, in __getitem__
    raise KeyError(key)
KeyError: 'florence2_language'

[kowshik-thatinati]

Hi, I encountered this KeyError as well.

Possible fix:

1. Update transformers library: pip install --upgrade transformers
2. Or manually add the missing key in the model config:

from transformers import AutoConfig, AutoModel

config = AutoConfig.from_pretrained("model_name_or_path")
config.florence2_language = "desired_value"
model = AutoModel.from_pretrained("model_name_or_path", config=config)

Hope this helps!

[zucchini-nlp]

Yeah, we need to change the model type after loading the remote-code config as pointed in above comment. Prob same is true to the vision block

However instead of adding the key, we need to adjust existing one to correct value as text_config['model_type'] = "bart". Feel free to test it and submit a PR

[conceptofmind]

I am going to leave this open since I will not be able to contribute a PR for quite awhile. And I imagine someone else will run into the same issue.

[i3hz]

#41866
I've submitted a PR for this issue . Could please look into it and let me know if that is the expected solution?Thanks :)
@zucchini-nlp @conceptofmind