finalf0 commited on
Commit
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1 Parent(s): ebe6d48
config.json ADDED
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+ {
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+ "_name_or_path": "openbmb/MiniCPM-Llama3-V-2_5",
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+ "architectures": [
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+ "MiniCPMV"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "configuration_minicpm.MiniCPMVConfig",
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+ "AutoModel": "modeling_minicpmv.MiniCPMV",
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+ "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
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+ },
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+ "batch_vision_input": true,
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+ "bos_token_id": 128000,
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+ "drop_vision_last_layer": false,
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+ "eos_token_id": 128001,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "image_size": 448,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 8192,
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+ "mm_use_im_start_end": true,
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+ "model_type": "minicpmv",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "patch_size": 14,
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+ "pretraining_tp": 1,
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+ "query_num": 96,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 500000.0,
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+ "slice_config": {
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+ "max_slice_nums": 9,
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+ "patch_size": 14,
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+ "model_type": "minicpmv"
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+ },
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+ "slice_mode": true,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.40.0",
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+ "use_cache": false,
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+ "vision_config": {
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+ "hidden_size": 1152,
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+ "image_size": 980,
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+ "intermediate_size": 4304,
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+ "model_type": "idefics2",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 27,
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+ "patch_size": 14
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+ },
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+ "vocab_size": 128256
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+ }
configuration_minicpm.py ADDED
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+ # coding=utf-8
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+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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+ #
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+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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+ # and OPT implementations in this library. It has been modified from its
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+ # original forms to accommodate minor architectural differences compared
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+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ MiniCPM model configuration"""
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+ import os
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+ from typing import Union
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+
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+ from transformers.utils import logging
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+ from transformers import LlamaConfig, PretrainedConfig
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+ from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionConfig
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class MiniCPMVSliceConfig(PretrainedConfig):
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+ model_type = "minicpmv"
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+
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+ def __init__(
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+ self,
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+ patch_size=14,
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+ max_slice_nums=9,
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+ scale_resolution=448,
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+ **kwargs,
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+ ):
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+ super().__init__(**kwargs)
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+ self.patch_size = patch_size
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+ self.max_slice_nums = max_slice_nums
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+ self.scale_resolution = scale_resolution
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+
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+ @classmethod
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+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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+ cls._set_token_in_kwargs(kwargs)
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+
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+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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+
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+ if config_dict.get("model_type") == "minicpmv":
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+ config_dict = config_dict["slice_config"]
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+
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+ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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+ logger.warning(
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+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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+ )
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+
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+ return cls.from_dict(config_dict, **kwargs)
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+
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+
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+
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+ class MiniCPMVConfig(LlamaConfig):
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+ model_type = "minicpmv"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+
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+ default_vision_config = {
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+ "hidden_size": 1152,
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+ "image_size": 980,
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+ "intermediate_size": 4304,
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+ "model_type": "idefics2",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 27,
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+ "patch_size": 14,
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+ }
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+
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+ def __init__(
80
+ self,
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+ use_cache=True,
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+ query_num=64,
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+ image_size=448,
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+ drop_vision_last_layer=True,
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+ batch_vision_input=True,
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+ slice_config=None,
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+ vision_config=None,
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+ **kwargs,
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+ ):
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+ self.use_cache = use_cache
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+ self.query_num = query_num
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+ self.image_size = image_size
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+ self.drop_vision_last_layer = drop_vision_last_layer
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+ self.batch_vision_input = batch_vision_input
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+
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+ if slice_config is None:
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+ self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
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+ else:
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+ self.slice_config = MiniCPMVSliceConfig(**slice_config)
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+ self.slice_mode = True
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+
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+ # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
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+ if vision_config is None:
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+ self.vision_config = Idefics2VisionConfig(**self.default_vision_config)
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+ logger.info("vision_config is None, using default vision config")
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+ elif isinstance(vision_config, dict):
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+ self.vision_config = Idefics2VisionConfig(**vision_config)
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+ elif isinstance(vision_config, Idefics2VisionConfig):
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+ self.vision_config = vision_config
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+
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+ self.patch_size = self.vision_config.patch_size
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+
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+ super().__init__(**kwargs)
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 128000,
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+ "eos_token_id": 128001,
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+ "transformers_version": "4.40.0"
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+ }
model.safetensors.index.json ADDED
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746
+ "vpm.post_layernorm.weight": "model-00007-of-00007.safetensors"
747
+ }
748
+ }
modeling_minicpmv.py ADDED
@@ -0,0 +1,655 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ from typing import List, Optional
3
+ import json
4
+ import torch
5
+ import torchvision
6
+ from copy import deepcopy
7
+ from PIL import Image
8
+ from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
9
+ from torchvision import transforms
10
+ from transformers import LlamaTokenizer, LlamaPreTrainedModel, LlamaForCausalLM, AutoModel, PreTrainedTokenizerFast
11
+ from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer
12
+
13
+ from .configuration_minicpm import MiniCPMVConfig
14
+ from .resampler import Resampler
15
+
16
+
17
+ class MiniCPMVPreTrainedModel(LlamaPreTrainedModel):
18
+ config_class = MiniCPMVConfig
19
+
20
+
21
+ class MiniCPMV(MiniCPMVPreTrainedModel):
22
+ def __init__(self, config):
23
+ super().__init__(config)
24
+
25
+ self.llm = LlamaForCausalLM(config)
26
+ self.vpm = self.init_vision_module()
27
+ self.vision_dim = self.vpm.embed_dim
28
+ self.embed_dim = self.llm.config.hidden_size
29
+ self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
30
+ self.transform = self.init_transform()
31
+
32
+ def init_vision_module(self):
33
+ # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
34
+ model = Idefics2VisionTransformer(self.config.vision_config)
35
+ if self.config.drop_vision_last_layer:
36
+ model.encoder.layers = model.encoder.layers[:-1]
37
+
38
+ setattr(model, 'embed_dim', model.embeddings.embed_dim)
39
+ setattr(model, 'patch_size', model.embeddings.patch_size)
40
+
41
+ return model
42
+
43
+ def init_resampler(self, embed_dim, vision_dim):
44
+ return Resampler(
45
+ num_queries=self.config.query_num,
46
+ embed_dim=embed_dim,
47
+ num_heads=embed_dim // 128,
48
+ kv_dim=vision_dim,
49
+ adaptive=True
50
+ )
51
+
52
+ def init_transform(self):
53
+ return transforms.Compose(
54
+ [
55
+ transforms.ToTensor(),
56
+ transforms.Normalize(
57
+ mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD
58
+ ),
59
+ ]
60
+ )
61
+
62
+ def get_vllm_embedding(self, data):
63
+ if 'vision_hidden_states' not in data:
64
+ dtype = self.vpm.embeddings.position_embedding.weight.dtype
65
+ device = self.vpm.embeddings.position_embedding.weight.device
66
+ tgt_sizes = data['tgt_sizes']
67
+ pixel_values_list = data['pixel_values']
68
+ vision_hidden_states = []
69
+ all_pixel_values = []
70
+ img_cnt = []
71
+ for pixel_values in pixel_values_list:
72
+ img_cnt.append(len(pixel_values))
73
+ all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values])
74
+
75
+ # exist image
76
+ if all_pixel_values:
77
+ tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
78
+
79
+ if self.config.batch_vision_input:
80
+ max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
81
+
82
+ all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True,
83
+ padding_value=0.0)
84
+ B, L, _ = all_pixel_values.shape
85
+ all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
86
+
87
+ patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
88
+ for i in range(B):
89
+ patch_attn_mask[i, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
90
+
91
+ vision_embedding = self.vpm(all_pixel_values.type(dtype), patch_attention_mask=patch_attn_mask).last_hidden_state
92
+ vision_embedding = self.resampler(vision_embedding, tgt_sizes)
93
+ else:
94
+ # get vision_embedding foreach
95
+ vision_embedding = []
96
+ for single_tgt_size, single_pixel_values in zip(tgt_sizes, all_pixel_values):
97
+ single_pixel_values = single_pixel_values.unsqueeze(0)
98
+ B, L, _ = single_pixel_values.shape
99
+ single_pixel_values = single_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
100
+ single_vision_embedding = self.vpm(single_pixel_values.type(dtype)).last_hidden_state
101
+ single_vision_embedding = self.resampler(single_vision_embedding, single_tgt_size.unsqueeze(0))
102
+ vision_embedding.append(single_vision_embedding)
103
+ vision_embedding = torch.vstack(vision_embedding)
104
+
105
+ start = 0
106
+ for pixel_values in pixel_values_list:
107
+ img_cnt = len(pixel_values)
108
+ if img_cnt > 0:
109
+ vision_hidden_states.append(vision_embedding[start: start + img_cnt])
110
+ start += img_cnt
111
+ else:
112
+ vision_hidden_states.append([])
113
+ else: # no image
114
+ if self.training:
115
+ dummy_image = torch.zeros(
116
+ (1, 3, 224, 224),
117
+ device=device, dtype=dtype
118
+ )
119
+ tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.patch_size)]]).type(torch.int32)
120
+ dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes)
121
+ else:
122
+ dummy_feature = []
123
+ for _ in range(len(pixel_values_list)):
124
+ vision_hidden_states.append(dummy_feature)
125
+
126
+ else:
127
+ vision_hidden_states = data['vision_hidden_states']
128
+
129
+ if hasattr(self.llm.config, 'scale_emb'):
130
+ vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb
131
+ else:
132
+ vllm_embedding = self.llm.model.embed_tokens(data['input_ids'])
133
+
134
+ vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance(
135
+ i, torch.Tensor) else i for i in vision_hidden_states]
136
+
137
+ bs = len(data['input_ids'])
138
+ for i in range(bs):
139
+ cur_vs_hs = vision_hidden_states[i]
140
+ if len(cur_vs_hs) > 0:
141
+ cur_vllm_emb = vllm_embedding[i]
142
+ cur_image_bound = data['image_bound'][i]
143
+ if len(cur_image_bound) > 0:
144
+ image_indices = torch.stack(
145
+ [torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
146
+ ).to(vllm_embedding.device)
147
+
148
+ cur_vllm_emb.scatter_(0, image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
149
+ cur_vs_hs.view(-1, cur_vs_hs.shape[-1]))
150
+ elif self.training:
151
+ cur_vllm_emb += cur_vs_hs[0].mean() * 0
152
+
153
+ return vllm_embedding, vision_hidden_states
154
+
155
+ def forward(self, data, **kwargs):
156
+ vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data)
157
+ position_ids = data["position_ids"]
158
+ if position_ids.dtype != torch.int64:
159
+ position_ids = position_ids.long()
160
+
161
+ return self.llm(
162
+ input_ids=None,
163
+ position_ids=position_ids,
164
+ inputs_embeds=vllm_embedding,
165
+ **kwargs
166
+ )
167
+
168
+ def _convert_to_tensors(
169
+ self, tokenizer, input_ids, max_inp_length: Optional[int] = None
170
+ ):
171
+ if max_inp_length is not None:
172
+ input_ids = input_ids[:max_inp_length]
173
+ input_ids = torch.tensor(input_ids, dtype=torch.int32)
174
+
175
+ image_start_tokens = torch.where(input_ids == tokenizer.im_start_id)[0]
176
+ # 跳过 im_start
177
+ image_start_tokens += 1
178
+ image_end_tokens = torch.where(input_ids == tokenizer.im_end_id)[0]
179
+ valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
180
+ image_bound = torch.hstack(
181
+ [
182
+ image_start_tokens[:valid_image_nums].unsqueeze(-1),
183
+ image_end_tokens[:valid_image_nums].unsqueeze(-1),
184
+ ]
185
+ )
186
+
187
+ model_input = {}
188
+ model_input["input_ids"] = input_ids.unsqueeze(0).to(self.device)
189
+ model_input["image_bound"] = image_bound
190
+
191
+ return model_input
192
+
193
+ def _process_list(
194
+ self, tokenizer, input_id_list, max_inp_length: Optional[int] = None
195
+ ):
196
+ pad_keys = ["input_ids"]
197
+ input_tensors = []
198
+ for input_ids in input_id_list:
199
+ input_tensors.append(
200
+ self._convert_to_tensors(tokenizer, input_ids, max_inp_length)
201
+ )
202
+ padded = {}
203
+ for key in pad_keys:
204
+ padded[key] = pad(input_tensors, key, padding_side="left").to(self.device)
205
+ padded["image_bound"] = [i["image_bound"] for i in input_tensors]
206
+ return padded
207
+
208
+ def _decode(self, inputs_embeds, tokenizer, **kwargs):
209
+ terminators = [
210
+ tokenizer.eos_token_id,
211
+ tokenizer.convert_tokens_to_ids("<|eot_id|>")
212
+ ]
213
+ output = self.llm.generate(
214
+ inputs_embeds=inputs_embeds,
215
+ pad_token_id=0,
216
+ eos_token_id=terminators,
217
+ **kwargs
218
+ )
219
+ return self._decode_text(output, tokenizer)
220
+
221
+ def _decode_text(self, result_ids, tokenizer):
222
+ result_text = []
223
+ for result in result_ids:
224
+ result = result[result != 0]
225
+ if result[0] == tokenizer.bos_id:
226
+ result = result[1:]
227
+ if result[-1] == tokenizer.eos_id or result[-1] == tokenizer.eot_id:
228
+ result = result[:-1]
229
+ result_text.append(tokenizer.decode(result).strip())
230
+ return result_text
231
+
232
+ def slice_image(self, image):
233
+ return slice_image(
234
+ image,
235
+ self.config.slice_config.max_slice_nums,
236
+ self.config.slice_config.scale_resolution,
237
+ self.config.slice_config.patch_size,
238
+ )
239
+
240
+ def get_slice_image_placeholder(self, image, tokenizer):
241
+ image_placeholder = (
242
+ tokenizer.im_start
243
+ + tokenizer.unk_token * self.config.query_num
244
+ + tokenizer.im_end
245
+ )
246
+
247
+ slice_images = []
248
+
249
+ source_image, patches, best_grid = slice_image(
250
+ image,
251
+ self.config.slice_config.max_slice_nums,
252
+ self.config.slice_config.scale_resolution,
253
+ self.config.slice_config.patch_size,
254
+ )
255
+
256
+ slice_images.append(source_image)
257
+ final_placeholder = image_placeholder
258
+
259
+ if len(patches) > 0:
260
+ for i in range(len(patches)):
261
+ for j in range(len(patches[0])):
262
+ slice_images.append(patches[i][j])
263
+
264
+ final_placeholder += get_grid_placeholder(
265
+ tokenizer, best_grid, self.config.query_num
266
+ )
267
+
268
+ return slice_images, final_placeholder
269
+
270
+ def reshape_by_patch(self, image_tensor):
271
+ """
272
+ :param image_tensor: shape [3, H, W]
273
+ :param patch_size:
274
+ :return: [3, patch_size, HW/patch_size]
275
+ """
276
+ patch_size = self.config.patch_size
277
+ patches = torch.nn.functional.unfold(
278
+ image_tensor,
279
+ (patch_size, patch_size),
280
+ stride=(patch_size, patch_size)
281
+ )
282
+
283
+ patches = patches.reshape(image_tensor.size(0), patch_size, patch_size, -1)
284
+ patches = patches.permute(0, 1, 3, 2).reshape(image_tensor.size(0), patch_size, -1)
285
+ return patches
286
+
287
+ def generate(
288
+ self,
289
+ input_id_list=None,
290
+ img_list=None,
291
+ tgt_sizes=None,
292
+ tokenizer=None,
293
+ max_inp_length: Optional[int] = None,
294
+ vision_hidden_states=None,
295
+ return_vision_hidden_states=False,
296
+ **kwargs
297
+ ):
298
+
299
+ assert input_id_list is not None
300
+ bs = len(input_id_list)
301
+ if img_list == None:
302
+ img_list = [[] for i in range(bs)]
303
+ assert bs == len(img_list)
304
+
305
+ model_inputs = self._process_list(tokenizer, input_id_list, max_inp_length)
306
+
307
+ if vision_hidden_states is None:
308
+ pixel_values = []
309
+ for i in range(bs):
310
+ img_inps = []
311
+ for img in img_list[i]:
312
+ img_inps.append(img.to(self.device))
313
+ if img_inps:
314
+ pixel_values.append(img_inps)
315
+ else:
316
+ pixel_values.append([])
317
+ model_inputs["pixel_values"] = pixel_values
318
+ model_inputs['tgt_sizes'] = tgt_sizes
319
+ else:
320
+ model_inputs["vision_hidden_states"] = vision_hidden_states
321
+
322
+ with torch.inference_mode():
323
+ (
324
+ model_inputs["inputs_embeds"],
325
+ vision_hidden_states,
326
+ ) = self.get_vllm_embedding(model_inputs)
327
+
328
+ result = self._decode(model_inputs["inputs_embeds"], tokenizer, **kwargs)
329
+
330
+ if return_vision_hidden_states:
331
+ return result, vision_hidden_states
332
+
333
+ return result
334
+
335
+ def chat(
336
+ self,
337
+ image,
338
+ msgs,
339
+ tokenizer,
340
+ vision_hidden_states=None,
341
+ max_new_tokens=1024,
342
+ sampling=True,
343
+ max_inp_length=2048,
344
+ **kwargs
345
+ ):
346
+ if isinstance(msgs, str):
347
+ msgs = json.loads(msgs)
348
+
349
+ copy_msgs = deepcopy(msgs)
350
+ assert len(copy_msgs) > 0, 'msgs is empty'
351
+
352
+ if image is not None and isinstance(copy_msgs[0]['content'], str):
353
+ copy_msgs[0]['content'] = [image, copy_msgs[0]['content']]
354
+
355
+ for i, msg in enumerate(copy_msgs):
356
+ role = msg["role"]
357
+ content = msg["content"]
358
+ assert role in ["user", "assistant"]
359
+ if i == 0:
360
+ assert role == "user", "The role of first msg should be user"
361
+ if isinstance(content, str):
362
+ content = [content]
363
+
364
+ images = []
365
+ tgt_sizes = []
366
+ cur_msgs = []
367
+ for c in content:
368
+ if isinstance(c, Image.Image):
369
+ image = c
370
+ if self.config.slice_mode:
371
+ slice_images, image_placeholder = self.get_slice_image_placeholder(
372
+ image, tokenizer
373
+ )
374
+ cur_msgs.append(image_placeholder)
375
+ for slice_image in slice_images:
376
+ slice_image = self.transform(slice_image)
377
+ H, W = slice_image.shape[1:]
378
+ images.append(self.reshape_by_patch(slice_image))
379
+ tgt_sizes.append(torch.Tensor([H // self.config.patch_size, W // self.config.patch_size]).type(torch.int32))
380
+ else:
381
+ images.append(self.transform(image))
382
+ cur_msgs.append(
383
+ tokenizer.im_start
384
+ + tokenizer.unk_token * self.config.query_num
385
+ + tokenizer.im_end
386
+ )
387
+ elif isinstance(c, str):
388
+ cur_msgs.append(c)
389
+
390
+ if tgt_sizes:
391
+ tgt_sizes = torch.vstack(tgt_sizes)
392
+
393
+ msg['content'] = '\n'.join(cur_msgs)
394
+
395
+ input_ids = tokenizer.apply_chat_template(copy_msgs, tokenize=True, add_generation_prompt=False)
396
+
397
+ if sampling:
398
+ generation_config = {
399
+ "top_p": 0.8,
400
+ "top_k": 100,
401
+ "temperature": 0.7,
402
+ "do_sample": True,
403
+ "repetition_penalty": 1.05
404
+ }
405
+ else:
406
+ generation_config = {
407
+ "num_beams": 3,
408
+ "repetition_penalty": 1.2,
409
+ }
410
+
411
+ generation_config.update(
412
+ (k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
413
+ )
414
+
415
+ with torch.inference_mode():
416
+ res, vision_hidden_states = self.generate(
417
+ input_id_list=[input_ids],
418
+ max_inp_length=max_inp_length,
419
+ img_list=[images],
420
+ tgt_sizes=[tgt_sizes],
421
+ tokenizer=tokenizer,
422
+ max_new_tokens=max_new_tokens,
423
+ vision_hidden_states=vision_hidden_states,
424
+ return_vision_hidden_states=True,
425
+ **generation_config
426
+ )
427
+ answer = res[0]
428
+
429
+ return answer
430
+
431
+
432
+ class PreTrainedTokenizerFastWrapper(PreTrainedTokenizerFast):
433
+ def __init__(self, **kwargs):
434
+ super().__init__(**kwargs)
435
+ self.eot_token = "<|eot_id|>"
436
+ self.im_start = "<image>"
437
+ self.im_end = "</image>"
438
+ self.ref_start = "<ref>"
439
+ self.ref_end = "</ref>"
440
+ self.box_start = "<box>"
441
+ self.box_end = "</box>"
442
+ self.quad_start = "<quad>"
443
+ self.quad_end = "</quad>"
444
+ self.slice_start = "<slice>"
445
+ self.slice_end = "</slice>"
446
+
447
+ @property
448
+ def eos_id(self):
449
+ return self.eos_token_id
450
+
451
+ @property
452
+ def bos_id(self):
453
+ return self.bos_token_id
454
+
455
+ @property
456
+ def unk_id(self):
457
+ return self.unk_token_id
458
+
459
+ @property
460
+ def eot_id(self):
461
+ return self.convert_tokens_to_ids(self.eot_token)
462
+
463
+ @property
464
+ def im_start_id(self):
465
+ return self.convert_tokens_to_ids(self.im_start)
466
+
467
+ @property
468
+ def im_end_id(self):
469
+ return self.convert_tokens_to_ids(self.im_end)
470
+
471
+ @staticmethod
472
+ def escape(text: str) -> str:
473
+ return text
474
+
475
+ @staticmethod
476
+ def unescape(text: str) -> str:
477
+ return text
478
+
479
+
480
+ def pad(orig_items, key, max_length=None, padding_value=0, padding_side="left"):
481
+ items = []
482
+ if isinstance(orig_items[0][key], list):
483
+ assert isinstance(orig_items[0][key][0], torch.Tensor)
484
+ for it in orig_items:
485
+ for tr in it[key]:
486
+ items.append({key: tr})
487
+ else:
488
+ assert isinstance(orig_items[0][key], torch.Tensor)
489
+ items = orig_items
490
+
491
+ batch_size = len(items)
492
+ shape = items[0][key].shape
493
+ dim = len(shape)
494
+ assert dim <= 3
495
+ if max_length is None:
496
+ max_length = 0
497
+ max_length = max(max_length, max(item[key].shape[-1] for item in items))
498
+ min_length = min(item[key].shape[-1] for item in items)
499
+ dtype = items[0][key].dtype
500
+
501
+ if dim == 1:
502
+ return torch.cat([item[key] for item in items], dim=0)
503
+ elif dim == 2:
504
+ if max_length == min_length:
505
+ return torch.cat([item[key] for item in items], dim=0)
506
+ tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
507
+ else:
508
+ tensor = (
509
+ torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
510
+ + padding_value
511
+ )
512
+
513
+ for i, item in enumerate(items):
514
+ if dim == 2:
515
+ if padding_side == "left":
516
+ tensor[i, -len(item[key][0]) :] = item[key][0].clone()
517
+ else:
518
+ tensor[i, : len(item[key][0])] = item[key][0].clone()
519
+ elif dim == 3:
520
+ if padding_side == "left":
521
+ tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
522
+ else:
523
+ tensor[i, : len(item[key][0]), :] = item[key][0].clone()
524
+
525
+ return tensor
526
+
527
+
528
+ def slice_image(
529
+ image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
530
+ ):
531
+ original_size = image.size
532
+ original_width, original_height = original_size
533
+ log_ratio = math.log(original_width / original_height)
534
+ ratio = original_width * original_height / (scale_resolution * scale_resolution)
535
+ multiple = min(math.ceil(ratio), max_slice_nums)
536
+
537
+ source_image = None
538
+ best_grid = None
539
+ patches = []
540
+
541
+ if multiple <= 1 or never_split:
542
+ # dont need to slice, upsample
543
+ best_size = find_best_resize(
544
+ original_size, scale_resolution, patch_size, allow_upscale=True
545
+ )
546
+ source_image = image.resize(best_size, Image.Resampling.BICUBIC)
547
+ else:
548
+ candidate_split_grids_nums = []
549
+ for i in [multiple - 1, multiple, multiple + 1]:
550
+ if i == 1 or i > max_slice_nums:
551
+ continue
552
+ candidate_split_grids_nums.append(i)
553
+
554
+ # source image, down-sampling and ensure divided by patch_size
555
+ best_resize = find_best_resize(original_size, scale_resolution, patch_size)
556
+ source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
557
+ candidate_grids = []
558
+
559
+ # find best grid
560
+ for split_grids_nums in candidate_split_grids_nums:
561
+ m = 1
562
+ while m <= split_grids_nums:
563
+ if split_grids_nums % m == 0:
564
+ candidate_grids.append([m, split_grids_nums // m])
565
+ m += 1
566
+
567
+ best_grid = [1, 1]
568
+ min_error = float("inf")
569
+ for grid in candidate_grids:
570
+ error = abs(log_ratio - math.log(grid[0] / grid[1]))
571
+ if error < min_error:
572
+ best_grid = grid
573
+ min_error = error
574
+
575
+ refine_size = get_refine_size(
576
+ original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
577
+ )
578
+
579
+ refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
580
+ patches = split_to_patches(refine_image, best_grid)
581
+
582
+ return source_image, patches, best_grid
583
+
584
+
585
+ def ensure_divide(length, patch_size):
586
+ return max(round(length / patch_size) * patch_size, patch_size)
587
+
588
+
589
+ def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
590
+ width, height = original_size
591
+ if (width * height > scale_resolution * scale_resolution) or allow_upscale:
592
+ r = width / height
593
+ height = int(scale_resolution / math.sqrt(r))
594
+ width = int(height * r)
595
+ best_width = ensure_divide(width, patch_size)
596
+ best_height = ensure_divide(height, patch_size)
597
+ return (best_width, best_height)
598
+
599
+
600
+ def get_refine_size(
601
+ original_size, grid, scale_resolution, patch_size, allow_upscale=False
602
+ ):
603
+ width, height = original_size
604
+ grid_x, grid_y = grid
605
+
606
+ refine_width = ensure_divide(width, grid_x)
607
+ refine_height = ensure_divide(height, grid_y)
608
+
609
+ grid_width = refine_width / grid_x
610
+ grid_height = refine_height / grid_y
611
+
612
+ best_grid_size = find_best_resize(
613
+ (grid_width, grid_height),
614
+ scale_resolution,
615
+ patch_size,
616
+ allow_upscale=allow_upscale,
617
+ )
618
+
619
+ refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
620
+
621
+ return refine_size
622
+
623
+
624
+ def split_to_patches(image, grid):
625
+ patches = []
626
+ width, height = image.size
627
+ grid_x = int(width / grid[0])
628
+ grid_y = int(height / grid[1])
629
+
630
+ for i in range(0, height, grid_y):
631
+ images = []
632
+ for j in range(0, width, grid_x):
633
+ box = (j, i, j + grid_x, i + grid_y)
634
+ patch = image.crop(box)
635
+ images.append(patch)
636
+ patches.append(images)
637
+
638
+ return patches
639
+
640
+
641
+ def get_grid_placeholder(tokenizer, grid, query_num):
642
+ image_placeholder = (
643
+ tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end
644
+ )
645
+
646
+ cols = grid[0]
647
+ rows = grid[1]
648
+ slices = []
649
+ for i in range(rows):
650
+ lines = []
651
+ for j in range(cols):
652
+ lines.append(image_placeholder)
653
+ slices.append("".join(lines))
654
+ slice_placeholder = tokenizer.slice_start + "\n".join(slices) + tokenizer.slice_end
655
+ return slice_placeholder
resampler.py ADDED
@@ -0,0 +1,163 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from functools import partial
2
+ import numpy as np
3
+
4
+ import torch
5
+ from torch import nn
6
+ from torch.nn.init import trunc_normal_
7
+
8
+ def get_2d_sincos_pos_embed(embed_dim, image_size):
9
+ """
10
+ image_size: image_size or (image_height, image_width)
11
+ return:
12
+ pos_embed: [image_height, image_width, embed_dim]
13
+ """
14
+ if isinstance(image_size, int):
15
+ grid_h_size, grid_w_size = image_size, image_size
16
+ else:
17
+ grid_h_size, grid_w_size = image_size[0], image_size[1]
18
+
19
+ grid_h = np.arange(grid_h_size, dtype=np.float32)
20
+ grid_w = np.arange(grid_w_size, dtype=np.float32)
21
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
22
+ grid = np.stack(grid, axis=0)
23
+
24
+ pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
25
+ return pos_embed
26
+
27
+
28
+ def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
29
+ assert embed_dim % 2 == 0
30
+
31
+ # use half of dimensions to encode grid_h
32
+ emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2)
33
+ emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2)
34
+
35
+ emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
36
+ return emb
37
+
38
+
39
+ def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
40
+ """
41
+ embed_dim: output dimension for each position
42
+ pos: a list of positions to be encoded: size (H, W)
43
+ out: (H, W, D)
44
+ """
45
+ assert embed_dim % 2 == 0
46
+ omega = np.arange(embed_dim // 2, dtype=np.float32)
47
+ omega /= embed_dim / 2.
48
+ omega = 1. / 10000 ** omega # (D/2,)
49
+
50
+ out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
51
+
52
+ emb_sin = np.sin(out) # (H, W, D/2)
53
+ emb_cos = np.cos(out) # (H, W, D/2)
54
+
55
+ emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
56
+ return emb
57
+
58
+
59
+ class Resampler(nn.Module):
60
+ """
61
+ A 2D perceiver-resampler network with one cross attention layers by
62
+ given learnable queries and 2d sincos pos_emb
63
+ Outputs:
64
+ A tensor with the shape of (batch_size, num_queries, embed_dim)
65
+ """
66
+
67
+ def __init__(
68
+ self,
69
+ num_queries,
70
+ embed_dim,
71
+ num_heads,
72
+ kv_dim=None,
73
+ norm_layer=partial(nn.LayerNorm, eps=1e-6),
74
+ adaptive=False,
75
+ max_size=(70, 70),
76
+ ):
77
+ super().__init__()
78
+ self.num_queries = num_queries
79
+ self.embed_dim = embed_dim
80
+ self.num_heads = num_heads
81
+ self.adaptive = adaptive
82
+ self.max_size = max_size
83
+
84
+ self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
85
+ trunc_normal_(self.query, std=.02)
86
+
87
+ if kv_dim is not None and kv_dim != embed_dim:
88
+ self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
89
+ else:
90
+ self.kv_proj = nn.Identity()
91
+
92
+ self.attn = nn.MultiheadAttention(embed_dim, num_heads)
93
+ self.ln_q = norm_layer(embed_dim)
94
+ self.ln_kv = norm_layer(embed_dim)
95
+
96
+ self.ln_post = norm_layer(embed_dim)
97
+ self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
98
+
99
+ self._set_2d_pos_cache(self.max_size)
100
+ self.apply(self._init_weights)
101
+
102
+ def _set_2d_pos_cache(self, max_size, device='cpu'):
103
+ pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
104
+ self.register_buffer("pos_embed", pos_embed, persistent=False)
105
+
106
+ def _adjust_pos_cache(self, tgt_sizes, device):
107
+ max_h = torch.max(tgt_sizes[:, 0])
108
+ max_w = torch.max(tgt_sizes[:, 1])
109
+ if max_h > self.max_size[0] or max_w > self.max_size[1]:
110
+ self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
111
+ self._set_2d_pos_cache(self.max_size, device)
112
+
113
+ def _init_weights(self, m):
114
+ if isinstance(m, nn.Linear):
115
+ trunc_normal_(m.weight, std=.02)
116
+ if isinstance(m, nn.Linear) and m.bias is not None:
117
+ nn.init.constant_(m.bias, 0)
118
+ elif isinstance(m, nn.LayerNorm):
119
+ nn.init.constant_(m.bias, 0)
120
+ nn.init.constant_(m.weight, 1.0)
121
+
122
+ def forward(self, x, tgt_sizes=None):
123
+ assert x.shape[0] == tgt_sizes.shape[0]
124
+ bs = x.shape[0]
125
+
126
+ device = x.device
127
+ dtype = x.dtype
128
+
129
+ patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
130
+
131
+ self._adjust_pos_cache(tgt_sizes, device=device)
132
+
133
+ max_patch_len = torch.max(patch_len)
134
+ key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
135
+
136
+ pos_embed = []
137
+ for i in range(bs):
138
+ tgt_h, tgt_w = tgt_sizes[i]
139
+ pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D
140
+ key_padding_mask[i, patch_len[i]:] = True
141
+
142
+ pos_embed = torch.nn.utils.rnn.pad_sequence(
143
+ pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D
144
+
145
+ x = self.kv_proj(x) # B * L * D
146
+ x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
147
+
148
+ q = self.ln_q(self.query) # Q * D
149
+
150
+ out = self.attn(
151
+ self._repeat(q, bs), # Q * B * D
152
+ x + pos_embed, # L * B * D + L * B * D
153
+ x,
154
+ key_padding_mask=key_padding_mask)[0]
155
+ # out: Q * B * D
156
+ x = out.permute(1, 0, 2) # B * Q * D
157
+
158
+ x = self.ln_post(x)
159
+ x = x @ self.proj
160
+ return x
161
+
162
+ def _repeat(self, query, N: int):
163
+ return query.unsqueeze(1).repeat(1, N, 1)
special_tokens_map.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|begin_of_text|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|end_of_text|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": "!",
17
+ "unk_token": {
18
+ "content": "<unk>",
19
+ "lstrip": false,
20
+ "normalized": false,
21
+ "rstrip": false,
22
+ "single_word": false
23
+ }
24
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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+ null
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+ ]
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+ },
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+ "bos_token": "<|begin_of_text|>",
2059
+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
2060
+ "clean_up_tokenization_spaces": true,
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+ ],
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+ "unk_token": "<unk>"
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+ }