Upload 13 files

config.json

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+{
+  "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
+}

configuration_mplug_docowl.py

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+# 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())

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|>"

generation_config.json

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+{
+  "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"
+}

modeling_llama2_mam.py

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+# 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
+

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)
+    

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
+}
+

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)
+            

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
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

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
+  }
+}

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

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
+
+
+