# 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 Donut checkpoints using the original `donut-python` library. URL: https://github.com/clovaai/donut""" import argparse import torch from datasets import load_dataset from donut import DonutModel from transformers import ( DonutImageProcessor, DonutProcessor, DonutSwinConfig, DonutSwinModel, MBartConfig, MBartForCausalLM, VisionEncoderDecoderModel, XLMRobertaTokenizerFast, ) def get_configs(model): original_config = model.config encoder_config = DonutSwinConfig( image_size=original_config.input_size, patch_size=4, depths=original_config.encoder_layer, num_heads=[4, 8, 16, 32], window_size=original_config.window_size, embed_dim=128, ) decoder_config = MBartConfig( is_decoder=True, is_encoder_decoder=False, add_cross_attention=True, decoder_layers=original_config.decoder_layer, max_position_embeddings=original_config.max_position_embeddings, vocab_size=len( model.decoder.tokenizer ), # several special tokens are added to the vocab of XLMRobertaTokenizer, see repo on the hub (added_tokens.json) scale_embedding=True, add_final_layer_norm=True, ) return encoder_config, decoder_config def rename_key(name): if "encoder.model" in name: name = name.replace("encoder.model", "encoder") if "decoder.model" in name: name = name.replace("decoder.model", "decoder") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "embeddings.norm") if name.startswith("encoder"): if "layers" in name: name = "encoder." + name if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name and "mask" not in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "encoder.layernorm.weight" if name == "encoder.norm.bias": name = "encoder.layernorm.bias" return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "qkv" in key: key_split = key.split(".") layer_num = int(key_split[3]) block_num = int(key_split[5]) dim = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = ( val[dim : dim * 2, :] ) orig_state_dict[ f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = ( val[:dim] ) orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = ( val[dim : dim * 2] ) orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = ( val[-dim:] ) elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]: # HuggingFace implementation doesn't use attn_mask buffer # and model doesn't use final LayerNorms for the encoder pass else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_donut_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False): # load original model original_model = DonutModel.from_pretrained(model_name).eval() # load HuggingFace model encoder_config, decoder_config = get_configs(original_model) encoder = DonutSwinModel(encoder_config) decoder = MBartForCausalLM(decoder_config) model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder) model.eval() state_dict = original_model.state_dict() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) # verify results on scanned document dataset = load_dataset("hf-internal-testing/example-documents") # no-script image = dataset["test"][0]["image"].convert("RGB") tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name, from_slow=True) image_processor = DonutImageProcessor( do_align_long_axis=original_model.config.align_long_axis, size=original_model.config.input_size[::-1] ) processor = DonutProcessor(image_processor, tokenizer) pixel_values = processor(image, return_tensors="pt").pixel_values if model_name == "naver-clova-ix/donut-base-finetuned-docvqa": task_prompt = "{user_input}" question = "When is the coffee break?" task_prompt = task_prompt.replace("{user_input}", question) elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip": task_prompt = "" elif model_name in [ "naver-clova-ix/donut-base-finetuned-cord-v1", "naver-clova-ix/donut-base-finetuned-cord-v1-2560", ]: task_prompt = "" elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2": task_prompt = "s_cord-v2>" elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket": task_prompt = "" elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]: # use a random prompt task_prompt = "hello world" else: raise ValueError("Model name not supported") prompt_tensors = original_model.decoder.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")[ "input_ids" ] original_patch_embed = original_model.encoder.model.patch_embed(pixel_values) patch_embeddings, _ = model.encoder.embeddings(pixel_values) assert torch.allclose(original_patch_embed, patch_embeddings, atol=1e-3) # verify encoder hidden states original_last_hidden_state = original_model.encoder(pixel_values) last_hidden_state = model.encoder(pixel_values).last_hidden_state assert torch.allclose(original_last_hidden_state, last_hidden_state, atol=1e-2) # verify decoder hidden states original_logits = original_model(pixel_values, prompt_tensors, None).logits logits = model(pixel_values, decoder_input_ids=prompt_tensors).logits assert torch.allclose(original_logits, logits, atol=1e-3) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model and processor to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model.push_to_hub("nielsr/" + model_name.split("/")[-1], commit_message="Update model") processor.push_to_hub("nielsr/" + model_name.split("/")[-1], commit_message="Update model") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="naver-clova-ix/donut-base-finetuned-docvqa", required=False, type=str, help="Name of the original model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, required=False, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model and processor to the 🤗 hub.", ) args = parser.parse_args() convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)