diff --git a/README.md b/README.md
index e7e2389..91fac3f 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,67 @@
-# DocOwl2_a14065830742978560965625
+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- document-understanding
+---
-DocOwl2
\ No newline at end of file
+# mPLUG-DocOwl2
+
+## Introduction
+mPLUG-DocOwl2 is a state-of-the-art Multimodal LLM for OCR-free Multi-page Document Understanding.
+
+Through a compressing module named High-resolution DocCompressor, each page is encoded with just 324 tokens.
+
+
+Github: [mPLUG-DocOwl](https://github.com/X-PLUG/mPLUG-DocOwl)
+
+SDK下载
+```bash
+#安装ModelScope
+pip install modelscope
+```
+```python
+#SDK模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('iic/DocOwl2')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/iic/DocOwl2.git
+```
+
+
+
+## Quickstart
+
+
+```python
+import torch
+import os
+from modelscope import AutoTokenizer, AutoModel
+from icecream import ic
+import time
+class DocOwlInfer():
+ def __init__(self, ckpt_path):
+ self.tokenizer = AutoTokenizer.from_pretrained(ckpt_path, use_fast=False)
+ self.model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map='auto')
+ self.model.init_processor(tokenizer=self.tokenizer, basic_image_size=504, crop_anchors='grid_12')
+
+ def inference(self, images, query):
+ messages = [{'role': 'USER', 'content': '<|image|>'*len(images)+query}]
+ answer = self.model.chat(messages=messages, images=images, tokenizer=self.tokenizer)
+ return answer
+docowl = DocOwlInfer(ckpt_path='$your_model_local_dir')
+images = [
+ '$your_model_local_dir'+'/examples/docowl2_page0.png',
+ '$your_model_local_dir'+'/examples/docowl2_page1.png',
+ '$your_model_local_dir'+'/examples/docowl2_page2.png',
+ '$your_model_local_dir'+'/examples/docowl2_page3.png',
+ '$your_model_local_dir'+'/examples/docowl2_page4.png',
+ '$your_model_local_dir'+'/examples/docowl2_page5.png',
+ ]
+answer = docowl.inference(images, query='what is this paper about? provide detailed information.')
+answer = docowl.inference(images, query='what is the third page about? provide detailed information.')
+```
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..3a93b17
--- /dev/null
+++ b/config.json
@@ -0,0 +1,59 @@
+{
+ "architectures": [
+ "mPLUGDocOwl2"
+ ],
+ "auto_map": {
+ "AutoConfig": "configuration_mplug_docowl.MPLUGDocOwlConfig",
+ "AutoModel": "modeling_mplug_docowl.MPLUGDocOwl2",
+ "AutoModelForCausalLM": "modeling_mplug_docowl.MPLUGDocOwl2"
+ },
+ "attention_bias": false,
+ "bos_token_id": 1,
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 4096,
+ "initializer_range": 0.02,
+ "intermediate_size": 11008,
+ "max_position_embeddings": 2048,
+ "model_type": "mplug_docowl",
+ "num_attention_heads": 32,
+ "num_hidden_layers": 32,
+ "num_key_value_heads": 32,
+ "pretraining_tp": 1,
+ "rms_norm_eps": 1e-06,
+ "rope_scaling": null,
+ "rope_theta": 10000.0,
+ "tie_word_embeddings": false,
+ "transformers_version": "4.39.3",
+ "use_cache": true,
+ "visual_config": {
+ "visual_hrcompressor": {
+ "layer": 2,
+ "high_reso_cross_num_att_heads": 16,
+ "high_reso_cross_hid_size": 4096,
+ "high_reso_cross_dropout": 0.0
+ },
+ "visual_hreducer": {
+ "conv_shape": "1x4",
+ "hidden_size": 1024
+ },
+ "visual_model": {
+ "attention_dropout": 0.0,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 1024,
+ "image_size": 504,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-06,
+ "model_type": "mplug_owl_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 24,
+ "patch_size": 14,
+ "projection_dim": 768,
+ "use_flash_attn": false
+ }
+ },
+ "vocab_size": 32000
+}
diff --git a/configuration.json b/configuration.json
new file mode 100644
index 0000000..3ddf3e3
--- /dev/null
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework":"Pytorch","task":"document-understanding"}
\ No newline at end of file
diff --git a/configuration_mplug_docowl.py b/configuration_mplug_docowl.py
new file mode 100644
index 0000000..c6bd7b6
--- /dev/null
+++ b/configuration_mplug_docowl.py
@@ -0,0 +1,358 @@
+# Copyright (c) Alibaba.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import copy
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+from transformers.utils import logging
+from transformers.models.auto import CONFIG_MAPPING
+
+
+class LlamaConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LLaMA-7B.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LlamaModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+ Llama 2 up to 4096, CodeLlama up to 16384.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+
+
+ ```python
+ >>> from transformers import LlamaModel, LlamaConfig
+
+ >>> # Initializing a LLaMA llama-7b style configuration
+ >>> configuration = LlamaConfig()
+
+ >>> # Initializing a model from the llama-7b style configuration
+ >>> model = LlamaModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+ model_type = "llama"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self._rope_scaling_validation()
+ self.attention_bias = attention_bias
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+ f"got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+
+
+class MplugOwlVisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MplugOwlVisionModel`]. It is used to instantiate
+ a
+ mPLUG-Owl vision encoder according to the specified arguments, defining the model architecture. Instantiating a
+ configuration defaults will yield a similar configuration to that of the mPLUG-Owl
+ [x-plug/x_plug-llama-7b](https://huggingface.co/x-plug/x_plug-llama-7b) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 32):
+ The size (resolution) of each patch.
+ hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+ The epsilon used by the layer normalization layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_factor (`float`, *optional*, defaults to 1):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+
+
+ ```"""
+
+ model_type = "mplug_owl_vision_model"
+
+ def __init__(
+ self,
+ hidden_size=1024,
+ intermediate_size=4096,
+ projection_dim=768,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ num_channels=3,
+ image_size=448,
+ patch_size=14,
+ hidden_act="quick_gelu",
+ layer_norm_eps=1e-6,
+ attention_dropout=0.0,
+ initializer_range=0.02,
+ initializer_factor=1.0,
+ use_flash_attn=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.projection_dim = projection_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+ self.initializer_range = initializer_range
+ self.initializer_factor = initializer_factor
+ self.attention_dropout = attention_dropout
+ self.layer_norm_eps = layer_norm_eps
+ self.hidden_act = hidden_act
+ self.use_flash_attn = use_flash_attn
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+ # get the vision config dict if we are loading from MplugOwlConfig
+ if config_dict.get("model_type") == "mplug-owl":
+ config_dict = config_dict["vision_config"]
+
+ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+ logger.warning(
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+ )
+
+ return cls.from_dict(config_dict, **kwargs)
+
+
+class MplugDocOwlHReducerConfig(PretrainedConfig):
+ model_type = "mplug_docowl_hreducer"
+
+ def __init__(
+ self,
+ hidden_size=1024,
+ initializer_range=0.02,
+ layer_norm_eps=1e-6,
+ conv_shape='1x4',
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.hidden_size = hidden_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.conv_shape = conv_shape
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+ # get the visual_abstractor config dict if we are loading from MplugOwlConfig
+ if config_dict.get("model_type") == "mplug-docowl":
+ config_dict = config_dict["hreducer_config"]
+
+ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+ logger.warning(
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+ )
+
+ return cls.from_dict(config_dict, **kwargs)
+
+
+class MplugDocOwlHRDocCompressorConfig(PretrainedConfig):
+ model_type = "mplug_docowl_hrcompressor"
+
+ def __init__(
+ self,
+ initializer_range=0.02,
+ layer_norm_eps=1e-6,
+ layer=2,
+ high_reso_cross_num_att_heads=16,
+ high_reso_cross_hid_size=4096,
+ high_reso_cross_dropout=0.0,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.layer = layer
+ self.high_reso_cross_num_att_heads=high_reso_cross_num_att_heads
+ self.high_reso_cross_hid_size=high_reso_cross_hid_size
+ self.high_reso_cross_dropout=high_reso_cross_dropout
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+ # get the visual_abstractor config dict if we are loading from MplugOwlConfig
+ if config_dict.get("model_type") == "mplug-docowl":
+ config_dict = config_dict["hrcompressor_config"]
+
+ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+ logger.warning(
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+ )
+
+ return cls.from_dict(config_dict, **kwargs)
+
+
+DEFAULT_VISUAL_CONFIG = {
+ "visual_model": MplugOwlVisionConfig().to_dict(),
+ "visual_hreducer": MplugDocOwlHReducerConfig().to_dict(),
+ "visual_hrcompressor": MplugDocOwlHRDocCompressorConfig().to_dict()
+}
+
+class MPLUGDocOwlConfig(LlamaConfig):
+ model_type = "mplug_docowl"
+ def __init__(self, visual_config=None, **kwargs):
+ if visual_config is None:
+ self.visual_config = DEFAULT_VISUAL_CONFIG
+ else:
+ self.visual_config = visual_config
+
+ super().__init__(
+ **kwargs,
+ )
+
+if __name__ == "__main__":
+ print(MplugOwlVisionConfig().to_dict())
\ No newline at end of file
diff --git a/constants.py b/constants.py
new file mode 100644
index 0000000..b632a10
--- /dev/null
+++ b/constants.py
@@ -0,0 +1,9 @@
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "./demo_logs"
+
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<|image|>"
diff --git a/examples/docowl2_page0.png b/examples/docowl2_page0.png
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diff --git a/examples/docowl2_page2.png b/examples/docowl2_page2.png
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diff --git a/examples/docowl2_page3.png b/examples/docowl2_page3.png
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diff --git a/examples/docowl2_page4.png b/examples/docowl2_page4.png
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diff --git a/examples/docowl2_page5.png b/examples/docowl2_page5.png
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diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..57dbab3
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,9 @@
+{
+ "bos_token_id": 1,
+ "eos_token_id": 2,
+ "max_length": 4096,
+ "pad_token_id": 0,
+ "temperature": 0.9,
+ "top_p": 0.6,
+ "transformers_version": "4.31.0"
+}
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000..6aaa9c7
--- /dev/null
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91364ade22619ab440b5cffa1528dcc4afcecbe497f4443a00e33c4f723bb42c
+size 17127218408
diff --git a/modeling_llama2_mam.py b/modeling_llama2_mam.py
new file mode 100644
index 0000000..b4108ce
--- /dev/null
+++ b/modeling_llama2_mam.py
@@ -0,0 +1,1048 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+# from .configuration_llama import LlamaConfig
+from .configuration_mplug_docowl import LlamaConfig
+
+from functools import partial
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz, tgt_len = input_ids_shape
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+ mask_cond = torch.arange(mask.size(-1), device=device)
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+ mask = mask.to(dtype)
+
+ if past_key_values_length > 0:
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = mask.size()
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+ inverted_mask = 1.0 - expanded_mask
+
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ LlamaRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ )
+
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.pretraining_tp = config.pretraining_tp
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ if self.pretraining_tp > 1:
+ slice = self.intermediate_size // self.pretraining_tp
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+ gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
+
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+ down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)]
+ down_proj = sum(down_proj)
+ else:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+
+LLAMA_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`LlamaConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+ LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+ config_class = LlamaConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, LlamaModel):
+ module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+ LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+ Args:
+ config: LlamaConfig
+ """
+
+ def __init__(self, config: LlamaConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(
+ input_shape,
+ inputs_embeds.dtype,
+ device=inputs_embeds.device,
+ past_key_values_length=past_key_values_length,
+ )
+
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+ inputs_embeds.device
+ )
+ combined_attention_mask = (
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+ )
+
+ return combined_attention_mask
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ # copy from mplug-owl2 (https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ modality_indicators: torch.Tensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ seq_length_with_past = seq_length
+ past_key_values_length = 0
+
+ if past_key_values is not None:
+ past_key_values_length = past_key_values[0][0].shape[2]
+ seq_length_with_past = seq_length_with_past + past_key_values_length
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+ else:
+ position_ids = position_ids.view(-1, seq_length).long()
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+ # embed positions
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+ )
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ hidden_states = inputs_embeds
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = () if use_cache else None
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ if self.gradient_checkpointing and self.training:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ # None for past_key_value
+ return module(*inputs, past_key_value, output_attentions)
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(decoder_layer),
+ hidden_states,
+ modality_indicators,
+ attention_mask,
+ position_ids,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ modality_indicators=modality_indicators,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = next_decoder_cache if use_cache else None
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = LlamaModel(config)
+ self.pretraining_tp = config.pretraining_tp
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ # copy from mplug-owl2 (https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ modality_indicators: torch.Tensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ modality_indicators=modality_indicators,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values:
+ input_ids = input_ids[:, -1:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -1].unsqueeze(-1)
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = LlamaModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+# copy from mplug-owl2 (https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)
+class MultiwayNetwork(nn.Module):
+
+ def __init__(self, module_provider, num_multiway=2):
+ super(MultiwayNetwork, self).__init__()
+
+ self.multiway = torch.nn.ModuleList([module_provider() for _ in range(num_multiway)])
+
+ def forward(self, hidden_states, multiway_indices):
+
+ if len(self.multiway) == 1:
+ return self.multiway[0](hidden_states)
+
+ output_hidden_states = torch.empty_like(hidden_states)
+
+ for idx, subway in enumerate(self.multiway):
+ local_indices = multiway_indices.eq(idx).nonzero(as_tuple=True)
+ hidden = hidden_states[local_indices].unsqueeze(1).contiguous()
+ if hidden.numel():
+ output = subway(hidden)
+ if isinstance(output, tuple):
+ output = output[0]
+ output = output.squeeze(1)
+ output_hidden_states[local_indices] = output
+
+ return output_hidden_states.contiguous()
+
+# copy from mplug-owl2 (https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)
+class LlamaAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: LlamaConfig):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = MultiwayNetwork(module_provider=partial(
+ nn.Linear, in_features=self.hidden_size, out_features=self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ )
+ self.v_proj = MultiwayNetwork(module_provider=partial(
+ nn.Linear, in_features=self.hidden_size, out_features=self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ )
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = LlamaRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ if scaling_type == "linear":
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ modality_indicators: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ padding_mask: Optional[torch.LongTensor] = None,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, )
+ key_states = self.k_proj(hidden_states, modality_indicators)
+ value_states = self.v_proj(hidden_states, modality_indicators)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value[0].shape[-2]
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+ past_key_value = (key_states, value_states) if use_cache else None
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+# copy from mplug-owl2 (https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)
+class LlamaDecoderLayer(nn.Module):
+ def __init__(self, config: LlamaConfig):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = LlamaAttention(config=config)
+ self.mlp = LlamaMLP(config)
+ self.input_layernorm = MultiwayNetwork(module_provider=partial(
+ LlamaRMSNorm, hidden_size=config.hidden_size, eps=config.rms_norm_eps
+ ))
+ self.post_attention_layernorm = MultiwayNetwork(module_provider=partial(
+ LlamaRMSNorm, hidden_size=config.hidden_size, eps=config.rms_norm_eps
+ ))
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ modality_indicators: torch.Tensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states, modality_indicators)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ modality_indicators=modality_indicators,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states, modality_indicators)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
diff --git a/modeling_mplug_docowl.py b/modeling_mplug_docowl.py
new file mode 100644
index 0000000..0d44cd9
--- /dev/null
+++ b/modeling_mplug_docowl.py
@@ -0,0 +1,398 @@
+# Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA)
+#
+# 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.
+
+from abc import ABC, abstractmethod
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from transformers import AutoConfig, AutoModelForCausalLM
+from .modeling_llama2_mam import LlamaConfig, LlamaModel, LlamaForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .configuration_mplug_docowl import (MPLUGDocOwlConfig, MplugOwlVisionConfig, MplugDocOwlHReducerConfig, MplugDocOwlHRDocCompressorConfig)
+from .visual_encoder import MplugOwlVisionModel, MplugDocOwlHReducerModel
+from .visual_compressor import MplugDocOwlHRDocCompressor
+from .processor import DocProcessor
+
+from .constants import IMAGE_TOKEN_INDEX, IGNORE_INDEX
+from icecream import ic
+
+from transformers import StoppingCriteria, TextStreamer
+
+class KeywordsStoppingCriteria(StoppingCriteria):
+ def __init__(self, keywords, tokenizer, input_ids):
+ self.keywords = keywords
+ self.keyword_ids = []
+ self.max_keyword_len = 0
+ for keyword in keywords:
+ cur_keyword_ids = tokenizer(keyword).input_ids
+ if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
+ cur_keyword_ids = cur_keyword_ids[1:]
+ if len(cur_keyword_ids) > self.max_keyword_len:
+ self.max_keyword_len = len(cur_keyword_ids)
+ self.keyword_ids.append(torch.tensor(cur_keyword_ids))
+ self.tokenizer = tokenizer
+ self.start_len = input_ids.shape[1]
+
+ def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+ assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO
+ offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
+ self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
+ for keyword_id in self.keyword_ids:
+ if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
+ return True
+ outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
+ for keyword in self.keywords:
+ if keyword in outputs:
+ return True
+ return False
+
+class MPLUGDocOwlMetaModel:
+ _no_split_modules = ["MplugOwlVisionModel", "MplugDocOwlHReducerModel", "MplugDocOwlHRDocCompressor"]
+ def __init__(self, config):
+ super(MPLUGDocOwlMetaModel, self).__init__(config)
+ self.vision_model = MplugOwlVisionModel(
+ MplugOwlVisionConfig(**config.visual_config["visual_model"])
+ )
+ v_img_row_tokens = int((config.visual_config["visual_model"]['image_size']/config.visual_config["visual_model"]['patch_size']))
+ v_img_col_tokens = v_img_row_tokens
+
+ self.vision2text = MplugDocOwlHReducerModel(
+ MplugDocOwlHReducerConfig(**config.visual_config["visual_hreducer"]), config.hidden_size
+ )
+
+ horizontal_reduce = int(config.visual_config["visual_hreducer"]['conv_shape'].split('x')[1])
+ v2t_img_col_tokens = int(v_img_row_tokens / horizontal_reduce)
+
+ self.hr_compressor = MplugDocOwlHRDocCompressor(
+ MplugDocOwlHRDocCompressorConfig(**config.visual_config["visual_hrcompressor"]),
+ config.hidden_size,
+ v2t_img_col_tokens
+ )
+
+ def get_vision_tower(self):
+ vision_model = getattr(self, 'vision_model', None)
+ if type(vision_model) is list:
+ vision_model = vision_model[0]
+ return vision_model
+
+ def get_vision2text(self):
+ vision2text = getattr(self, 'vision2text', None)
+ if type(vision2text) is list:
+ vision2text = vision2text[0]
+ return vision2text
+
+ def get_hrcompressor(self):
+ hrcompressor = getattr(self, 'hr_compressor', None)
+ if type(hrcompressor) is list:
+ hrcompressor = hrcompressor[0]
+ return hrcompressor
+
+class MPLUGDocOwlMetaForCausalLM(ABC):
+ @abstractmethod
+ def get_model(self):
+ pass
+
+ def encode_images(self, images, patch_positions):
+ image_features = self.get_model().vision_model(images).last_hidden_state
+ image_features = self.get_model().vision2text(encoder_hidden_states=image_features)
+ image_features = self.get_model().hr_compressor(hidden_states=image_features, patch_positions=patch_positions)
+ return image_features
+
+ def prepare_inputs_labels_for_multimodal(
+ self, input_ids, attention_mask, past_key_values, labels, images, patch_positions
+ ):
+ # ic(images.shape, patch_positions.shape)
+ if images is None or input_ids.shape[1] == 1:
+ if past_key_values is not None and images is not None and input_ids.shape[1] == 1:
+ attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
+ multiway_indices = torch.zeros_like(input_ids).long().to(self.device)
+ return input_ids, multiway_indices, attention_mask, past_key_values, None, labels
+
+ if type(images) is list or images.ndim == 5:
+ concat_images = torch.cat([image for image in images], dim=0)
+ image_features = self.encode_images(concat_images, patch_positions)
+ split_sizes = [image.shape[0] for image in images]
+ image_features = torch.split(image_features, split_sizes, dim=0)
+ image_features = [x.flatten(0, 1) for x in image_features]
+ else:
+ image_features = self.encode_images(images, patch_positions) # Sum(Crop Image Number) x L x d
+
+ new_input_embeds = []
+ new_modality_indicators = []
+ new_labels = [] if labels is not None else None
+ cur_image_idx = 0
+ for batch_idx, cur_input_ids in enumerate(input_ids):
+ if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
+ # multimodal LLM, but the current sample is not multimodal
+ # FIXME: this is a hacky fix, for deepspeed zero3 to work
+ half_len = cur_input_ids.shape[0] // 2
+ cur_image_features = image_features[cur_image_idx]
+ cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
+ cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
+ cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
+ new_input_embeds.append(cur_input_embeds)
+
+ cur_modality_indicators = torch.zeros(len(cur_input_embeds)).long().to(self.device)
+ new_modality_indicators.append(cur_modality_indicators)
+ if labels is not None:
+ new_labels.append(labels[batch_idx])
+ cur_image_idx += 1
+ continue
+ image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
+ cur_new_input_embeds = []
+ cur_modality_indicators = []
+ if labels is not None:
+ cur_labels = labels[batch_idx]
+ cur_new_labels = []
+ assert cur_labels.shape == cur_input_ids.shape
+ while image_token_indices.numel() > 0:
+ cur_image_features = image_features[cur_image_idx]
+ image_token_start = image_token_indices[0]
+ cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
+ cur_new_input_embeds.append(cur_image_features)
+
+ # Add modality indicator
+ assert image_token_start == len(cur_input_ids[:image_token_start])
+ cur_modality_indicators.append(torch.zeros(len(cur_input_ids[:image_token_start])).long())
+ cur_modality_indicators.append(torch.ones(len(cur_image_features)).long())
+
+ if labels is not None:
+ cur_new_labels.append(cur_labels[:image_token_start])
+ cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
+ cur_labels = cur_labels[image_token_start+1:]
+ cur_image_idx += 1
+ cur_input_ids = cur_input_ids[image_token_start+1:]
+ image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
+ if cur_input_ids.numel() > 0:
+ cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
+ cur_modality_indicators.append(torch.zeros(len(cur_input_ids)).long())
+ if labels is not None:
+ cur_new_labels.append(cur_labels)
+ cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
+ cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
+ new_input_embeds.append(cur_new_input_embeds)
+
+ # Modality
+ cur_modality_indicators = [x.to(device=self.device) for x in cur_modality_indicators]
+ cur_modality_indicators = torch.cat(cur_modality_indicators, dim=0)
+ new_modality_indicators.append(cur_modality_indicators)
+
+
+ if labels is not None:
+ cur_new_labels = torch.cat(cur_new_labels, dim=0)
+ new_labels.append(cur_new_labels)
+
+ if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
+ max_len = max(x.shape[0] for x in new_input_embeds)
+
+ # Embedding
+ new_input_embeds_align = []
+ for cur_new_embed in new_input_embeds:
+ cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
+ new_input_embeds_align.append(cur_new_embed)
+ new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
+
+ # Modality
+ new_modality_indicators_align = []
+ for cur_modality_indicator in new_modality_indicators:
+ cur_new_embed = torch.cat((cur_modality_indicator, torch.zeros(max_len - cur_modality_indicator.shape[0], dtype=cur_modality_indicator.dtype, device=cur_modality_indicator.device)), dim=0)
+ new_modality_indicators_align.append(cur_new_embed)
+ new_modality_indicators = torch.stack(new_modality_indicators_align, dim=0)
+
+ # Label
+ if labels is not None:
+ new_labels_align = []
+ _new_labels = new_labels
+ for cur_new_label in new_labels:
+ cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
+ new_labels_align.append(cur_new_label)
+ new_labels = torch.stack(new_labels_align, dim=0)
+
+ # Attention Mask
+ if attention_mask is not None:
+ new_attention_mask = []
+ for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
+ new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
+ new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
+ cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
+ new_attention_mask.append(cur_new_attention_mask)
+ attention_mask = torch.stack(new_attention_mask, dim=0)
+ assert attention_mask.shape == new_labels.shape
+ else:
+ new_input_embeds = torch.stack(new_input_embeds, dim=0)
+ new_modality_indicators = torch.stack(new_modality_indicators, dim=0)
+ if labels is not None:
+ new_labels = torch.stack(new_labels, dim=0)
+
+ if attention_mask is not None:
+ new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
+ attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
+ assert attention_mask.shape == new_input_embeds.shape[:2]
+ return None, new_modality_indicators, attention_mask, past_key_values, new_input_embeds, new_labels
+
+
+
+class MPLUGDocOwlLlamaModel(MPLUGDocOwlMetaModel, LlamaModel):
+ config_class = MPLUGDocOwlConfig
+
+ def __init__(self, config: MPLUGDocOwlConfig):
+ super(MPLUGDocOwlLlamaModel, self).__init__(config)
+
+
+class MPLUGDocOwl2(LlamaForCausalLM, MPLUGDocOwlMetaForCausalLM):
+ config_class = MPLUGDocOwlConfig
+
+ def __init__(self, config):
+ super(LlamaForCausalLM, self).__init__(config)
+ self.model = MPLUGDocOwlLlamaModel(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def init_processor(self, tokenizer, basic_image_size, crop_anchors):
+ self.processor = DocProcessor(tokenizer=tokenizer, image_size=basic_image_size, anchors=crop_anchors)
+ return self.processor
+
+ def get_model(self):
+ return self.model
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ # modality_indicators: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ images: Optional[torch.FloatTensor] = None,
+ patch_positions: Optional[torch.LongTensor] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+ # print('modeling_mplug_docow2.py patch_positions:', patch_positions)
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ input_ids, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \
+ self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images, patch_positions)
+ # ic(inputs_embeds.shape, labels.shape)
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ modality_indicators=modality_indicators,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict
+ )
+ # ic(outputs[0].shape)
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model/pipeline parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ # ic(loss.shape)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values:
+ input_ids = input_ids[:, -1:]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ "images": kwargs.get("images", None),
+ "patch_positions": kwargs.get("patch_positions", None),
+ }
+ )
+ return model_inputs
+
+ def chat(self, messages, images, tokenizer):
+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+ image_tensor, patch_positions, input_ids = self.processor(images=images, messages=messages)
+
+ image_tensor = image_tensor.to(self.model.device, dtype=torch.float16)
+ patch_positions = patch_positions.to(self.model.device)
+ input_ids = input_ids.unsqueeze(0).to(self.model.device)
+
+ stopping_criteria = KeywordsStoppingCriteria(["</s>"], tokenizer, input_ids)
+
+ with torch.inference_mode():
+ output_ids = self.generate(
+ input_ids,
+ images=image_tensor,
+ patch_positions=patch_positions,
+ do_sample=False,
+ temperature=1.0,
+ max_new_tokens=512,
+ streamer=streamer,
+ use_cache=True,
+ stopping_criteria=[stopping_criteria])
+
+ outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+
+ return outputs.replace('</s>', '')
+
+AutoConfig.register("mplug_docowl", MPLUGDocOwlConfig)
+AutoModelForCausalLM.register(MPLUGDocOwlConfig, MPLUGDocOwl2)
+
\ No newline at end of file
diff --git a/preprocessor_config.json b/preprocessor_config.json
new file mode 100644
index 0000000..d546b79
--- /dev/null
+++ b/preprocessor_config.json
@@ -0,0 +1,20 @@
+{
+ "crop_size": 448,
+ "do_center_crop": true,
+ "do_normalize": true,
+ "do_resize": true,
+ "feature_extractor_type": "CLIPFeatureExtractor",
+ "image_mean": [
+ 0.48145466,
+ 0.4578275,
+ 0.40821073
+ ],
+ "image_std": [
+ 0.26862954,
+ 0.26130258,
+ 0.27577711
+ ],
+ "resample": 3,
+ "size": 448
+}
+
diff --git a/processor.py b/processor.py
new file mode 100644
index 0000000..4e30f36
--- /dev/null
+++ b/processor.py
@@ -0,0 +1,226 @@
+from einops import rearrange, repeat
+import torch
+from torchvision import transforms
+from PIL import Image, ImageFile
+import random
+from torchvision.ops.boxes import box_area
+
+from torchvision.transforms.transforms import InterpolationMode
+from torchvision.transforms import functional as F
+import numpy as np
+from icecream import ic
+import re
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+from .constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
+
+def box_iou(boxes1, area1, boxes2, eps=1e-5):
+ area2 = box_area(boxes2)
+
+ lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
+ rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
+
+ wh = (rb - lt).clamp(min=0) # [N,M,2]
+ inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
+
+ union = area1[:, None] + area2 - inter
+
+ iou = inter / (union+eps)
+ return iou, union
+
+def anchor_rank(anchors, anchors_areas, input_image_size, eps=1e-5):
+ # anchors x1 y1 x2 y2
+
+ # image_size: (h, w)
+ # xyxy
+ input_image_bbox = torch.tensor([0, 0, input_image_size[1], input_image_size[0]]).unsqueeze(0)
+
+ boxes1 = anchors
+ boxes2 = input_image_bbox
+ boxes3 = anchors.clone()
+ # y2
+ boxes3[:,3] = input_image_size[0]/input_image_size[1]*anchors[:,2] # 用于算分辨率无关的iou
+
+ area1 = anchors_areas
+
+ iou, _ = box_iou(boxes1, area1, boxes2)
+ iou = iou.squeeze(1)
+ shape_iou, _ = box_iou(boxes1, area1, boxes3)
+ shape_iou = shape_iou.diag()
+ # 优先匹配形状接近 再匹配分辨率接近
+ index = torch.argmax(shape_iou*100+iou,dim=0)
+ return index
+
+class AnchorResize(torch.nn.Module):
+
+ def __init__(self, image_size, anchors, interpolation=InterpolationMode.BILINEAR, antialias=None):
+ super().__init__()
+ # xyxy
+ self.anchors = torch.tensor(
+ [[0, 0, _[1]*image_size[1], _[0]*image_size[0]]
+ for _ in anchors], requires_grad=False
+ )
+
+ self.anchor_areas = box_area(self.anchors)
+
+ self.interpolation = interpolation
+ self.antialias = antialias
+
+ def forward(self, img, skip_resize=False):
+ """
+ Args:
+ img (PIL Image or Tensor): Image to be scaled.
+
+ Returns:
+ PIL Image or Tensor: Rescaled image.
+ """
+ selected_anchor = anchor_rank(self.anchors, self.anchor_areas, (img.size[1], img.size[0]))
+ target_size = self.anchors[selected_anchor][2:].tolist() # w,h
+ if skip_resize:
+ # for debug
+ return selected_anchor
+ return F.resize(img, [target_size[1],target_size[0]], self.interpolation, max_size=None, antialias=self.antialias), selected_anchor
+
+ def __repr__(self) -> str:
+ detail = f"(size={self.image_size}, anchor={self.anchors}, interpolation={self.interpolation.value}, antialias={self.antialias})"
+ return f"{self.__class__.__name__}{detail}"
+
+
+class DocProcessor():
+ def __init__(self, tokenizer=None, image_size=504, anchors='grid_12'):
+ self.media_token= "<|image|>"
+ # h,w
+ if isinstance(image_size, int):
+ image_size = (image_size, image_size)
+ self.image_size = image_size
+ # h,w
+ # anchors = grid_dict[anchors]
+ max_crop = int(anchors.split('_')[1])
+ anchors = [(j, int(i/j)) for i in range(1,max_crop+1) for j in range(1, i+1) if i%j==0]
+ self.anchors = [tuple(_) for _ in anchors]
+ self.anchor_max = max([max(_) for _ in self.anchors])
+ # xywh -> xyxy
+ self.resizer = AnchorResize(image_size=image_size, anchors=anchors, interpolation=InterpolationMode.BICUBIC)
+ self.old_resizer = transforms.Resize(image_size,interpolation=InterpolationMode.BICUBIC)
+ self.image_transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+ ])
+ self.tokenizer = tokenizer
+
+ def _process_image(self, images):
+ new_images = []
+ new_patch_position = []
+ num_image_mult = []
+ for image in images:
+ nocut_image = self.image_transform(self.old_resizer(image)).unsqueeze(0)
+
+ image, selected_anchor = self.resizer(image)
+ image_input = self.image_transform(image) # h,w,3 -> 3,h,w
+ # rearrange(x,'B C (n1 h) (n2 w) -> (B n1 n2) C h w', n1=self.down_sample[0], n2=self.down_sample[1])
+ image_input = rearrange(image_input, 'C (num_h h) (num_w w) -> (num_h num_w) C h w', h=self.image_size[0], w=self.image_size[1])
+
+ image_input = torch.cat([nocut_image, image_input], dim=0)
+
+ anchor = self.anchors[selected_anchor] # w,h
+ patch_position = torch.cat([
+ repeat(torch.arange(anchor[0]), 'num_h -> num_h num_w 1', num_w=anchor[1]),
+ repeat(torch.arange(anchor[1]), 'num_w -> num_h num_w 1', num_h=anchor[0])],dim=2)
+ patch_position = rearrange(patch_position, 'num_h num_w p-> (num_h num_w) p', p=2) # num_patch, (ph,pw)
+
+ patch_position = torch.cat([torch.ones(1,2).long()*self.anchor_max, patch_position], dim=0)
+
+ new_images.append(image_input)
+ new_patch_position.append(patch_position)
+ num_image_mult.append(patch_position.shape[0])
+
+ new_images = torch.cat(new_images,dim=0)
+ new_patch_position = torch.cat(new_patch_position, dim=0)
+ return new_images, new_patch_position, num_image_mult
+
+ def __call__(self, images=None, messages=None):
+ assert images is not None
+ # print(images)
+
+ ## 1. process images
+ if not isinstance(images, list):
+ images = [images]
+ image_pils = []
+ for image in images:
+ if isinstance(image, str):
+ image = Image.open(image).convert('RGB')
+ else:
+
+ image = image.convert('RGB')
+ # ic(image.size)
+ image_pils.append(image)
+
+ image_data, patch_position, num_image_mult = self._process_image(image_pils)
+
+ ## 2. process text
+ # 2.1 add image ordinal token (e.g. <img 1>) before image placeholder <|image|>
+ image_index = 1 # start from 1
+ for m in messages:
+ try:
+ assert m['role'] in ['USER', 'ASSISTANT']
+ except Exception as e:
+ print("Unexpected role: "+m['role']+", only support 'USER' or 'ASSISTANT'")
+ exit(0)
+
+ if m['role'] == 'USER' and self.media_token in m.get('content', ''):
+ pattern = '|'.join(map(re.escape, [self.media_token]))
+ text_list = re.split(f'({pattern})', m['content'])
+ text = ''
+ for x in text_list:
+ if x == '<|image|>':
+ text += '<img '+str(image_index)+'><|image|>'
+ image_index += 1
+ else:
+ text += x
+ m['content'] = text
+
+ if messages[-1]['role'] == 'USER':
+ messages.append({'role':'ASSISTANT'})
+ else:
+ try:
+ assert messages[-1].get('content', '') == ''
+ except Exception as e:
+ print("Unexpected end message: "+str(messages[-1]), "only (role=='USER') or (role=='ASSISTANT' and content=='') are expected.")
+ exit(0)
+
+ # print('after adding img ordinal token: ', messages)
+ # 2.2 text tokenize
+ seps = [' ', '</s>']
+ prompt = ""
+ for i, m in enumerate(messages):
+ if 'content' in m:
+ prompt += m['role'] + ": " + m['content'] + seps[i % 2]
+ else:
+ prompt += m['role'] + ":"
+ ic(prompt)
+ assert self.media_token in prompt
+ input_ids = self.tokenizer_token(prompt)
+
+ return image_data, patch_position, input_ids
+
+
+ def tokenizer_token(self, prompt):
+ prompt_chunks = [self.tokenizer(chunk).input_ids if len(chunk) > 0 else [] for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]
+
+ def insert_separator(X, sep):
+ return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+
+ input_ids = []
+ offset = 0
+ if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == self.tokenizer.bos_token_id:
+ offset = 1
+ input_ids.append(prompt_chunks[0][0])
+
+ for x in insert_separator(prompt_chunks, [IMAGE_TOKEN_INDEX] * (offset + 1)):
+ input_ids.extend(x[offset:])
+
+ return torch.tensor(input_ids, dtype=torch.long)
+
\ No newline at end of file
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..14761dc
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+ "bos_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": "<unk>",
+ "unk_token": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tokenizer.model b/tokenizer.model
new file mode 100644
index 0000000..6c00c74
--- /dev/null
+++ b/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..508754b
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,35 @@
+{
+ "add_bos_token": true,
+ "add_eos_token": false,
+ "bos_token": {
+ "__type": "AddedToken",
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "clean_up_tokenization_spaces": false,
+ "eos_token": {
+ "__type": "AddedToken",
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "legacy": false,
+ "model_max_length": 4096,
+ "pad_token": null,
+ "padding_side": "right",
+ "sp_model_kwargs": {},
+ "tokenizer_class": "LlamaTokenizer",
+ "unk_token": {
+ "__type": "AddedToken",
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/visual_compressor.py b/visual_compressor.py
new file mode 100644
index 0000000..5196809
--- /dev/null
+++ b/visual_compressor.py
@@ -0,0 +1,426 @@
+import math
+from typing import Any, Optional, Tuple, Union
+
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, BaseModelOutputWithPastAndCrossAttentions
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from icecream import ic
+
+from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
+from einops import rearrange
+
+
+class MplugDocOwlVisualMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ in_features = config.high_reso_cross_hid_size
+ self.act = nn.SiLU()
+
+ ffn_hidden_size = int(2 * 4 * in_features / 3)
+ multiple_of = 256
+ ffn_hidden_size = multiple_of * ((ffn_hidden_size + multiple_of - 1) // multiple_of)
+
+ self.w1 = nn.Linear(in_features, ffn_hidden_size)
+ self.w2 = nn.Linear(ffn_hidden_size, in_features)
+ self.w3 = nn.Linear(in_features, ffn_hidden_size)
+ self.ffn_ln = nn.LayerNorm(ffn_hidden_size, eps=config.layer_norm_eps)
+
+ torch.nn.init.zeros_(self.w1.bias.data)
+ torch.nn.init.zeros_(self.w2.bias.data)
+ torch.nn.init.zeros_(self.w3.bias.data)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.act(self.w1(hidden_states)) * self.w3(hidden_states)
+ hidden_states = self.ffn_ln(hidden_states)
+ hidden_states = self.w2(hidden_states)
+ return hidden_states
+
+
+class FlashCrossAttention(torch.nn.Module):
+ """Implement the scaled dot product attention with softmax.
+ Arguments
+ ---------
+ softmax_scale: The temperature to use for the softmax attention.
+ (default: 1/sqrt(d_keys) where d_keys is computed at
+ runtime)
+ attention_dropout: The dropout rate to apply to the attention
+ (default: 0.0)
+ """
+ def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
+ device=None, dtype=None):
+ super().__init__()
+
+ self.softmax_scale = softmax_scale
+ self.dropout_p = attention_dropout
+
+ def forward(self, q, k, v, **kwargs):
+ """Implements the multihead softmax attention.
+ Arguments
+ ---------
+ q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
+
+ or
+
+ q: (Sum_q, H, D), k,v : (Sum_k, H, D),
+ must with batch_size, max_seqlen_q, max_seqlen_k, cu_seqlens_q, cu_seqlens_k in kwargs
+ """
+
+ assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
+ assert all((i.is_cuda for i in (q,k,v)))
+
+
+ if q.dim() == 4:
+ batch_size, seqlen_q = q.shape[0], q.shape[1]
+ q = rearrange(q, 'b s ... -> (b s) ...')
+ cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
+ device=q.device)
+ else:
+ batch_size, seqlen_q = kwargs['batch_size'], kwargs['max_seqlen_q']
+ cu_seqlens_q = kwargs['cu_seqlens_q']
+
+ if k.dim() == 4:
+ seqlen_k = k.shape[1]
+ k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [k, v]]
+ cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
+ device=q.device)
+ else:
+ seqlen_k = kwargs['max_seqlen_k']
+ cu_seqlens_k = kwargs['cu_seqlens_k']
+
+ # q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
+ # self.dropout_p = 0
+
+ """print('FlashCrossAttention: q.shape:', q.shape)
+ print('FlashCrossAttention: k.shape:', k.shape)
+ print('FlashCrossAttention: v.shape:', v.shape)
+ print('FlashCrossAttention: cu_seqlens_q:', cu_seqlens_q)
+ print('FlashCrossAttention: cu_seqlens_k:', cu_seqlens_k)"""
+
+ # print('visual_compressor.py q.shape:', q.shape, ' k.shape:', k.shape, ' v.shape:', v.shape)
+ output = flash_attn_unpadded_func(
+ q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
+ self.dropout_p if self.training else 0.0,
+ softmax_scale=self.softmax_scale, causal=False
+ )
+
+ if q.dim() == 4: # keep the shape of output shape same as the input query
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+ return output
+
+
+class MplugDocOwlVisualMultiHeadAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ if config.high_reso_cross_hid_size % config.high_reso_cross_num_att_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention heads (%d)"
+ % (config.high_reso_cross_hid_size, config.high_reso_cross_num_att_heads)
+ )
+ if config.high_reso_cross_hid_size // config.high_reso_cross_num_att_heads > 256:
+ raise ValueError(
+ "The hidden size of each head (%d) > 256 and is illegal for flash attention"
+ % (config.high_reso_cross_hid_size // config.high_reso_cross_num_att_heads)
+ )
+
+
+ self.num_attention_heads = config.high_reso_cross_num_att_heads
+ self.attention_head_size = int(config.high_reso_cross_hid_size / config.high_reso_cross_num_att_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.high_reso_cross_hid_size, self.all_head_size)
+ self.key = nn.Linear(config.high_reso_cross_hid_size, self.all_head_size)
+ self.value = nn.Linear(config.high_reso_cross_hid_size, self.all_head_size)
+ self.core_attention_flash = FlashCrossAttention(attention_dropout=config.high_reso_cross_dropout)
+
+ # bias init
+ torch.nn.init.zeros_(self.query.bias.data)
+ torch.nn.init.zeros_(self.key.bias.data)
+ torch.nn.init.zeros_(self.value.bias.data)
+
+ def transpose_for_scores(self, x):
+ # [B, S, D] -> [B, S, H, D] or [Sum_S, D] -> [Sum_S, H, D]
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(*new_x_shape)
+ return x
+
+ def forward(
+ self,
+ hidden_states,
+ encoder_hidden_states=None,
+ **kwargs
+ ):
+ # assert not torch.isnan(hidden_states).any()
+ # assert not torch.isnan(encoder_hidden_states).any()
+
+ key = self.transpose_for_scores(self.key(encoder_hidden_states))
+ value = self.transpose_for_scores(self.value(encoder_hidden_states))
+ query = self.transpose_for_scores(self.query(hidden_states))
+ # print('visual_compressor.py key(after projection): ', key.shape, key)
+ # print('visual_compressor.py value(after projection): ', value.shape, value)
+ # print('visual_compressor.py query(after projection): ', query.shape, query)
+ # assert not torch.isnan(key).any()
+ # assert not torch.isnan(value).any()
+ # assert not torch.isnan(query).any()
+ outputs = self.core_attention_flash(q=query, k=key, v=value, **kwargs)
+ outputs = rearrange(outputs, 's h d -> s (h d)').contiguous()
+ # print('visual_compressor.py outputs(after cross_att): ', outputs.shape, outputs)
+ return outputs
+
+
+class MplugDocOwlVisualCrossOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ dim = config.high_reso_cross_hid_size
+ self.out_proj = nn.Linear(dim, dim, bias=True)
+ self.norm2 = nn.LayerNorm(dim)
+ self.mlp = MplugDocOwlVisualMLP(config)
+
+ # bias init
+ torch.nn.init.zeros_(self.out_proj.bias.data)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ input_tensor = input_tensor + self.out_proj(hidden_states)
+ input_tensor = input_tensor + self.mlp(self.norm2(input_tensor))
+ return input_tensor
+
+
+class MplugDocOwlVisualCrossAttentionLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.attention = MplugDocOwlVisualMultiHeadAttention(config)
+ self.output = MplugDocOwlVisualCrossOutput(config)
+ self.norm1 = nn.LayerNorm(config.high_reso_cross_hid_size)
+ self.normk = nn.LayerNorm(config.high_reso_cross_hid_size)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ **kwargs
+ ) -> Tuple[torch.Tensor]:
+ # print('visual_compressor.py hidden_states: ', hidden_states.shape, hidden_states)
+ # print('visual_compressor.py encoder_hidden_states: ', encoder_hidden_states.shape, encoder_hidden_states)
+ # assert not torch.isnan(hidden_states).any()
+ # assert not torch.isnan(encoder_hidden_states).any()
+ hidden_states = self.norm1(hidden_states)
+ encoder_hidden_states = self.normk(encoder_hidden_states)
+ # print('visual_compressor.py hidden_states(after norm): ', hidden_states.shape, hidden_states)
+ # print('visual_compressor.py encoder_hidden_states(after norm): ', encoder_hidden_states.shape, encoder_hidden_states)
+ attention_output = self.attention(
+ hidden_states,
+ encoder_hidden_states,
+ **kwargs
+ )
+
+ outputs = self.output(attention_output, hidden_states)
+
+ return outputs
+
+
+class MplugDocOwlVisualCrossAttentionEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer_num = config.layer
+ self.layers = nn.ModuleList(
+ [MplugDocOwlVisualCrossAttentionLayer(config) for layer_idx in range(self.layer_num)]
+ )
+ self.gradient_checkpointing = True
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ **kwargs
+ ):
+ for i in range(self.layer_num):
+ layer_module = self.layers[i]
+ layer_outputs = layer_module(
+ hidden_states,
+ encoder_hidden_states,
+ **kwargs
+ )
+ hidden_states = layer_outputs
+
+ return hidden_states
+
+
+def ensemble_crop_feats(crop_feats, patch_positions, col_feat_num):
+ """
+ ensemble vision feats from different crops to a feature map according the position of the raw image
+ crop_feats: [N_crop, Len_feat, D]
+ patch_positions: [N_crop, 2], 2 == (rowl_index, col_index)
+ col_feat_num: the feature num of a row in a crop image
+ """
+ assert crop_feats.size(0) == patch_positions.size(0)
+ row_feats = []
+ crop_row = torch.max(patch_positions[:,0])+1 #
+ crop_feats = rearrange(crop_feats, '(R C) L D -> R C L D', R=crop_row) # [N_crop_row, N_crop_col, Len_feat, D]
+ crop_feats = rearrange(crop_feats, 'R C (X Y) D-> R C X Y D', Y=col_feat_num) # [N_crop_row, N_crop_col, Len_row_feat, Len_col_feat, D]
+ # 1. concatenate same row feats across crops; 2. ensemble row feats to get 1 feature map
+ hw_feats = rearrange(crop_feats, 'R C X Y D-> (R X) (C Y) D') # [N_crop_row x Len_row_feat, N_crop_col x Len_col_feat, D]
+
+ return hw_feats
+
+def group_window_feats(feats, window):
+ """
+ collect vision feats from a window (win_row, win_col) to 1 group
+ feats: [H, W, D]
+ window: (win_row, win_col)
+
+ return: [H/win_row, H/win_col, win_row x win_col, D]
+ """
+
+ group_feats = rearrange(feats, '(X R) (Y C) D -> (X Y) (R C) D', R=window[0], C=window[1]) # [H/win_row x H/win_col, win_row x win_col, D]
+ return group_feats
+
+
+def distinguish_global_crop_features(hidden_states, patch_positions, reorganize_crop_feats=True, col_feat_num=None, group_feats_by_crop_shape=False, keep_row_col=False):
+ """
+ distinguish global and crop features with the help of patcg_positions
+ # hidden_states: [B, s+1, h]
+ # (B is the sum of cropped num across samples in a micro_batch, s is the visual tokens, +1 means the vit end token)
+ # patch_positions: [B, 2],
+ # 2 == (rowl_index, col_index), the first crop is (0,0), global img is (anchor_max, anchor_max)
+
+ col_feat_num is used when reorganize_crop_feats == True
+
+ outputs:
+ img_global_features: list of [Len_global_feat, D]
+ img_crop_features: list of [Len_global_feat, D]
+ """
+ hidden_states = hidden_states[:, :-1, :] # remove the last vit end token emb
+ # the first crop is (0,0)
+ first_crop_indices = (patch_positions.sum(dim=-1) == 0).nonzero().squeeze(1) # Num_img
+ # the global image is before the first crop
+ global_indices = first_crop_indices - 1 # Num_img
+ # print('vision2text_model.py patch_positions:', patch_positions)
+ # print('vision2text_model.py global_indices:', global_indices)
+ # collect cropped vision features of an identical image
+ batch_size = hidden_states.size(0)
+ img_global_features = []
+ img_crop_features = [] # store list of Num_crop (variable) x Len_feat (fixed)
+ img_crop_positions = [] # store list of Num_crop (variable) x 2
+ for i in range(len(global_indices)):
+ index = global_indices[i]
+ img_global_features.append(hidden_states[index])
+ if i == (len(global_indices)-1):
+ img_crop_features.append(hidden_states[index+1:])
+ img_crop_positions.append(patch_positions[index+1:])
+ else:
+ next_index = global_indices[i+1]
+ img_crop_features.append(hidden_states[index+1:next_index])
+ img_crop_positions.append(patch_positions[index+1:next_index])
+
+ if reorganize_crop_feats:
+ for i in range(len(img_crop_features)):
+ img_crop_features[i] = ensemble_crop_feats(img_crop_features[i], img_crop_positions[i], col_feat_num) # [H W D]
+ if group_feats_by_crop_shape: # collect vision feats from a window (crop_row, crop_col) to 1 group
+ crop_row = torch.max(img_crop_positions[i][:,0])+1 #
+ crop_col = torch.max(img_crop_positions[i][:,1])+1 #
+ img_crop_features[i] = group_window_feats(img_crop_features[i], window=(crop_row, crop_col)) # [H/crop_row x W/crop_col, crop_row x crop_row, D]
+ else:
+ # img_crop_features = [rearrange(x, 'H W D -> (H W) D') for x in img_crop_features]
+ if not keep_row_col:
+ img_crop_featuress[i] = rearrange(img_crop_featuress[i], 'H W D -> (H W) D')
+ else:
+ img_crop_features = [rearrange(x, 'N L D -> (N L) D') for x in img_crop_features]
+
+ return img_global_features, img_crop_features
+
+
+class MplugDocOwlHRDocCompressor(PreTrainedModel):
+ """
+ After vision-to-text module, use low-resolution global features to select high-resolution crop features with cross-attention
+ the key/value from high-resolution crop features are contrained in a window size
+ positions of the features within the window in raw images are the same as the global query features
+ """
+ def __init__(self, config, output_hidden_size, v2t_img_col_tokens):
+ super().__init__(config)
+ self.use_flash_attn = True
+ assert self.use_flash_attn
+
+ self.v2t_img_col_tokens = v2t_img_col_tokens
+
+ self.compressor_crossatt = MplugDocOwlVisualCrossAttentionEncoder(config)
+
+ self.compressor_fc = torch.nn.Linear(output_hidden_size, output_hidden_size)
+
+ self.compressor_eos = torch.nn.Parameter(torch.randn(1, 1, output_hidden_size))
+
+
+ def forward(self, hidden_states, patch_positions=None):
+ # hidden_states: outputs of vision2textmodel: [Sum(crop), s+1, h]
+ # (Sum(crop) is the sum of cropped num across samples in a micro_batch, s is the visual tokens, +1 is the special vit_eos token added in H-Reducer)
+ # patch_positions: [Sum(crop), 2]
+
+ # print('visual_compressor.py HRDocCompressor hidden_states.shape:', hidden_states.shape)
+ # print('visual_compressor.py HRDocCompressor patch_positions.shape:', patch_positions.shape)
+
+ # N_img x [L_global (fixed), D], N_img x [L_global (fixed), Crop_row x Crop_Col (Variable), D]
+ img_global_features, img_crop_features = distinguish_global_crop_features(hidden_states,
+ patch_positions,
+ reorganize_crop_feats=True,
+ col_feat_num=self.v2t_img_col_tokens,
+ group_feats_by_crop_shape=True)
+
+ # cross-attention to accumulate high-resolution features
+ # if self.use_flash_attn: # flash_attn_varlen_func don't need to pad crop_features
+ img_global_features = torch.stack(img_global_features, dim=0).to(hidden_states.device) # Num_img x Len_global_feat x D
+ batch_size, global_feat_num, seqlen_q = img_global_features.shape[0], img_global_features.shape[1], 1
+ img_global_features = rearrange(img_global_features, 'b s ... -> (b s) ...')
+ cu_seqlens_q = torch.arange(0, batch_size*global_feat_num+1, step=1, dtype=torch.int32, device=img_global_features.device) # # (Num_img x Len_global_feat +1, )
+ cu_seqlens_k = [0]
+ max_seqlens_k = 0
+ for crop_feat in img_crop_features:
+ for i in range(crop_feat.shape[0]):
+ cu_seqlens_k.append(cu_seqlens_k[-1]+crop_feat.shape[1]) # same k within a image shares the seq len
+ max_seqlens_k = max(max_seqlens_k, crop_feat.size(1))
+
+ cu_seqlens_k = torch.tensor(cu_seqlens_k, dtype=torch.int32).to(hidden_states.device) # (Num_img x Len_global_feat+1, )
+ # cu_seqlens_k = torch.arange(0, (batch_size + 1) * max_seqlens_k, step=max_seqlens_k, dtype=torch.int32, device=img_global_features.device) # # (Num_img+1, )
+
+ img_crop_features = torch.cat([rearrange(x, 'N L D -> (N L) D') for x in img_crop_features], dim=0).to(hidden_states.device) # Sum(L_hr) x D
+ flash_kwargs = {
+ 'batch_size': batch_size*global_feat_num, # each feat in global feats use different keys
+ 'max_seqlen_q': seqlen_q, # key are unique for each query
+ 'max_seqlen_k': max_seqlens_k,
+ 'cu_seqlens_q': cu_seqlens_q, # the seq len of each q
+ 'cu_seqlens_k': cu_seqlens_k # the seq len of each k
+ }
+ # print('visual_compressor.py HRDocCompressor img_global_features.shape:', img_global_features.shape, img_global_features)
+ # print('visual_compressor.py HRDocCompressor img_crop_features.shape:', img_crop_features.shape, img_crop_features)
+ """print('visual_compressor.py HRDocCompressor cu_seqlens_q, cu_seqlens_q.shape:', cu_seqlens_q, cu_seqlens_q.shape)
+ print('visual_compressor.py HRDocCompressor cu_seqlens_k, cu_seqlens_k.shape:', cu_seqlens_k, cu_seqlens_k.shape)"""
+ # assert not torch.isnan(img_global_features).any()
+ # assert not torch.isnan(img_crop_features).any()
+ for x_name, x in self.compressor_crossatt.named_parameters():
+ try:
+ assert not torch.isnan(x).any()
+ # print('visual_compressor.py ', x_name, x.shape, x)
+ except Exception as e:
+ print(e)
+ print('visual_compressor.py nan', x_name, x.shape, x)
+ hidden_states = self.compressor_crossatt(
+ img_global_features.contiguous(), # Sum(L_global) x D
+ img_crop_features.contiguous(), # Sum(L_hr) x D
+ **flash_kwargs
+ ) # Sum(L_global) x D
+ hidden_states = rearrange(hidden_states, '(B S) D -> S B D', B=batch_size) # L_global x N_img x D
+
+ hidden_states = self.compressor_fc(hidden_states) # L_global x N_img x D
+
+ hidden_states = hidden_states.transpose(0, 1).contiguous() # N_img x L_global x D
+ # print('visual_compressor.py hidden_states:', hidden_states.shape)
+
+ hidden_states = torch.cat([hidden_states, self.compressor_eos.repeat(hidden_states.shape[0], 1, 1)], dim=1) # N_img x (L_global+1) x D
+ # print('visual_compressor.py HRDocCompressor hidden_states.shape:', hidden_states.shape)
+
+ return hidden_states
\ No newline at end of file
diff --git a/visual_encoder.py b/visual_encoder.py
new file mode 100644
index 0000000..a3b73bd
--- /dev/null
+++ b/visual_encoder.py
@@ -0,0 +1,501 @@
+import math
+from typing import Any, Optional, Tuple, Union
+
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, BaseModelOutputWithPastAndCrossAttentions
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from icecream import ic
+import einops
+from einops import rearrange
+
+def get_abs_pos(abs_pos, tgt_size):
+ # abs_pos: L, C
+ # tgt_size: M
+ # return: M, C
+ src_size = int(math.sqrt(abs_pos.size(0)))
+ tgt_size = int(math.sqrt(tgt_size))
+ dtype = abs_pos.dtype
+
+ if src_size != tgt_size:
+ return F.interpolate(
+ abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
+ size=(tgt_size, tgt_size),
+ mode="bicubic",
+ align_corners=False,
+ ).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
+ else:
+ return abs_pos
+
+# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+ """
+ grid_size: int of the grid height and width
+ return:
+ pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+ """
+ grid_h = np.arange(grid_size, dtype=np.float32)
+ grid_w = np.arange(grid_size, dtype=np.float32)
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ grid = np.stack(grid, axis=0)
+
+ grid = grid.reshape([2, 1, grid_size, grid_size])
+ pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+ if cls_token:
+ pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+ return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+ assert embed_dim % 2 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
+ emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
+
+ emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+ return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+ """
+ embed_dim: output dimension for each position
+ pos: a list of positions to be encoded: size (M,)
+ out: (M, D)
+ """
+ assert embed_dim % 2 == 0
+ omega = np.arange(embed_dim // 2, dtype=np.float32)
+ omega /= embed_dim / 2.
+ omega = 1. / 10000**omega # (D/2,)
+
+ pos = pos.reshape(-1) # (M,)
+ out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
+
+ emb_sin = np.sin(out) # (M, D/2)
+ emb_cos = np.cos(out) # (M, D/2)
+
+ emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
+ return emb
+
+
+
+class MplugOwlVisionEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))
+
+ self.patch_embed = nn.Conv2d(
+ in_channels=3,
+ out_channels=self.hidden_size,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ bias=False,
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, self.hidden_size))
+
+ self.pre_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ batch_size = pixel_values.size(0)
+ image_embeds = self.patch_embed(pixel_values)
+ image_embeds = image_embeds.flatten(2).transpose(1, 2)
+
+ class_embeds = self.cls_token.expand(batch_size, 1, -1).to(image_embeds.dtype)
+ embeddings = torch.cat([class_embeds, image_embeds], dim=1)
+ embeddings = embeddings + self.position_embedding[:, : embeddings.size(1)].to(image_embeds.dtype)
+ embeddings = self.pre_layernorm(embeddings)
+ return embeddings
+
+
+
+class MplugOwlVisionAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ if self.head_dim * self.num_heads != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.dropout = nn.Dropout(config.attention_dropout)
+
+ self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size)
+ self.dense = nn.Linear(self.hidden_size, self.hidden_size)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ head_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ bsz, seq_len, embed_dim = hidden_states.size()
+
+ mixed_qkv = self.query_key_value(hidden_states)
+
+ mixed_qkv = mixed_qkv.reshape(bsz, seq_len, self.num_heads, 3, embed_dim // self.num_heads).permute(
+ 3, 0, 2, 1, 4
+ ) # [3, b, np, sq, hn]
+ query_states, key_states, value_states = (
+ mixed_qkv[0],
+ mixed_qkv[1],
+ mixed_qkv[2],
+ )
+ # if self.config.use_flash_attn and flash_attn_func is not None:
+ if False:
+ # [b*sq, np, hn]
+ query_states = query_states.permute(0, 2, 1, 3).contiguous()
+ query_states = query_states.view(query_states.size(0) * query_states.size(1), query_states.size(2), -1)
+
+ key_states = key_states.permute(0, 2, 1, 3).contiguous()
+ key_states = key_states.view(key_states.size(0) * key_states.size(1), key_states.size(2), -1)
+
+ value_states = value_states.permute(0, 2, 1, 3).contiguous()
+ value_states = value_states.view(value_states.size(0) * value_states.size(1), value_states.size(2), -1)
+
+ cu_seqlens = torch.arange(
+ 0, (bsz + 1) * seq_len, step=seq_len, dtype=torch.int32, device=query_states.device
+ )
+
+ context_layer = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens,
+ cu_seqlens,
+ seq_len,
+ seq_len,
+ self.dropout if self.training else 0.0,
+ softmax_scale=self.scale,
+ causal=False,
+ return_attn_probs=False,
+ )
+ # [b*sq, np, hn] => [b, sq, np, hn]
+ context_layer = context_layer.view(bsz, seq_len, context_layer.size(1), context_layer.size(2))
+ else:
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
+
+ attention_scores = attention_scores * self.scale
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = torch.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)
+
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)
+ context_layer = context_layer.reshape(new_context_layer_shape)
+
+ output = self.dense(context_layer)
+
+ outputs = (output, attention_probs) if output_attentions else (output, None)
+
+ return outputs
+
+
+class QuickGELU(nn.Module):
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class MplugOwlMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = QuickGELU()
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class MplugOwlVisionEncoderLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = MplugOwlVisionAttention(config)
+ self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
+ self.mlp = MplugOwlMLP(config)
+ self.post_attention_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ output_attentions: Optional[bool] = False,
+ ) -> Tuple[torch.FloatTensor]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ `(config.encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ head_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + residual
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+
+ hidden_states = hidden_states + residual
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+class MplugOwlVisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`MplugOwlVisionEncoderLayer`].
+
+ Args:
+ config (`MplugOwlVisionConfig`):
+ The corresponding vision configuration for the `MplugOwlEncoder`.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([MplugOwlVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = True
+
+ def forward(
+ self,
+ inputs_embeds,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Embedded representation of the inputs. Should be float, not int tokens.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ if self.gradient_checkpointing and self.training:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs, output_attentions)
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(encoder_layer),
+ hidden_states,
+ attention_mask,
+ )
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class MplugOwlVisionModel(PreTrainedModel):
+ main_input_name = "pixel_values"
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+ self.hidden_size = config.hidden_size
+
+ self.embeddings = MplugOwlVisionEmbeddings(config)
+ self.encoder = MplugOwlVisionEncoder(config)
+ self.post_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
+
+ self.post_init()
+
+
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
+ r"""
+ Returns:
+
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ hidden_states = self.embeddings(pixel_values)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ last_hidden_state = self.post_layernorm(last_hidden_state)
+
+ pooled_output = last_hidden_state[:, 0, :]
+ pooled_output = self.post_layernorm(pooled_output)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+
+class MplugDocOwlHReducerModel(PreTrainedModel):
+ def __init__(self, config, language_hidden_size):
+ super().__init__(config)
+ self.config = config
+ self.ln_q = torch.nn.LayerNorm(self.config.hidden_size, eps=1e-6)
+ self.conv_shape = (int(self.config.conv_shape.split('x')[0]), int(self.config.conv_shape.split('x')[1])) #
+ self.conv_patch=self.conv_shape[0]*self.conv_shape[1]
+ ## feature interaction with a conv layer
+ self.reducer_before = torch.nn.Sequential(
+ nn.Conv2d(self.config.hidden_size, self.conv_patch*self.config.hidden_size, kernel_size=self.conv_shape, stride=self.conv_shape, bias=True),
+ nn.GELU()
+ )
+ ## reduce visual feature length with a conv layer
+ self.reducer = nn.Conv2d(self.config.hidden_size, self.config.hidden_size, kernel_size=self.conv_shape, stride=self.conv_shape, bias=True)
+ ## align visual features with language embedding with fc
+ self.visual_fc = torch.nn.Linear(self.config.hidden_size, language_hidden_size)
+ self.vit_eos = torch.nn.Parameter(torch.randn(1, 1, language_hidden_size))
+
+ self.post_init()
+
+ def forward(
+ self,
+ encoder_hidden_states=None
+ ):
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):
+ batch_size is the number of all images (global+crop) in a batch
+ Sequence of hidden-states at the output of the last layer of the encoder.
+ """
+ encoder_hidden_states = encoder_hidden_states[:,1:,:] # remove the first cls token
+ B, L, C = encoder_hidden_states.shape # B, 1024=(448/14)^2, 1024
+
+ ## feature interaction with a conv layer
+ encoder_hidden_states = rearrange(encoder_hidden_states, 'B (H W) D -> B D H W', H=int(math.sqrt(L)))
+ hidden_states = self.reducer_before(encoder_hidden_states) # B 4D H W/4
+ ## reduce seq length with a conv layer
+ """hidden_states = hidden_states.flatten(2).transpose(1, 2) # B 4D H W/4 -> B 4D H*W/4 -> B H*W/4 4D
+ hidden_states = rearrange(hidden_states, 'B L (X D) -> B (L X) D', X=self.conv_patch) # B (H W) D
+ hidden_states = rearrange(hidden_states, 'B (H W) D -> B D H W', H=int(math.sqrt(L))) # B D H W """
+ hidden_states = rearrange(hidden_states, 'B (X D) H W -> B D H (W X)', X=self.conv_patch) # B 4D H W/4 -> B D H W
+ sequence_output = self.reducer(hidden_states) # B,C,H,W -> B,C,H/conv_shape[1],W/(conv_shape[1])
+ sequence_output = sequence_output.flatten(2).transpose(1, 2) # B,C,H/conv_shape[1],W/(conv_shape[1]) -> B,C,L/conv_patch -> B,L/conv_patch,C
+ sequence_output = sequence_output.transpose(0, 1).contiguous() # L/conv_patch, B, C
+ ## align visual features with language embedding with fc
+ sequence_output = self.visual_fc(sequence_output) # L/conv_patch, B, h
+ sequence_output = sequence_output.transpose(0, 1).contiguous() # B, s/4, h
+ sequence_output = torch.cat([sequence_output, self.vit_eos.repeat(B, 1, 1)], dim=1)
+
+ return sequence_output
+
+
+