update

build_mlp.py

@@ -52,8 +52,8 @@ class CLIPVisionTower(nn.Module):
         self.vision_tower_name = vision_tower
         self.select_layer = -1
         self.select_feature = 'patch'
-        self.load_model()
-        self.resize_pos()
+        #self.load_model()
+        #self.resize_pos()
 
     def load_model(self):
         self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)

build_mlp.py~

@@ -0,0 +1,206 @@
+import torch
+import torch.nn as nn
+import re
+import os
+import math
+from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
+
+
+def build_vision_tower(path):
+    vision_tower = os.path.join(path, 'clip_l_336')
+    return CLIPVisionTower(vision_tower)
+
+
+def build_vision_projector():
+    projector_type = 'mlp2x_gelu'
+    mm_hidden_size = 1024
+    hidden_size = 4096
+
+    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
+    if mlp_gelu_match:
+        mlp_depth = int(mlp_gelu_match.group(1))
+        modules = [nn.Linear(mm_hidden_size, hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(hidden_size, hidden_size))
+        return nn.Sequential(*modules)
+
+    if projector_type == 'identity':
+        return IdentityMap()
+
+    raise ValueError(f'Unknown projector type: {projector_type}')
+
+class IdentityMap(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+    @property
+    def config(self):
+        return {"mm_projector_type": 'identity'}
+
+
+class CLIPVisionTower(nn.Module):
+    def __init__(self, vision_tower):
+        super().__init__()
+
+        self.is_loaded = False
+        self.is_resize_pos = False
+
+        self.vision_tower_name = vision_tower
+        self.select_layer = -1
+        self.select_feature = 'patch'
+        #self.load_model()
+        #self.resize_pos()
+
+    def load_model(self):
+        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+    def resize_pos(self):
+        pos_embed_checkpoint = self.vision_tower.vision_model.embeddings.position_embedding.weight
+        pos_embed_checkpoint = pos_embed_checkpoint.unsqueeze(0)
+        orig_size = 24
+        new_size = 16
+
+        if pos_embed_checkpoint.shape[1] == new_size ** 2 + 1:
+            self.is_resize_pos = True
+        else:
+            embedding_size = pos_embed_checkpoint.shape[-1]
+            num_extra_tokens = 1
+            new_num = new_size ** 2 + num_extra_tokens
+            print("Position interpolate from %dx%d to %dx%d" %
+                  (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
+                                            embedding_size).permute(
+                                                0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(pos_tokens,
+                                                         size=(new_size,
+                                                               new_size),
+                                                         mode='bicubic',
+                                                         align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+
+            new_pos_embed = new_pos_embed.squeeze(0)
+
+            self.vision_tower.vision_model.embeddings.position_embedding = torch.nn.Embedding(new_num, 1024)
+            self.vision_tower.vision_model.embeddings.position_embedding.weight = torch.nn.Parameter(new_pos_embed.to(pos_embed_checkpoint.dtype))
+            self.vision_tower.vision_model.embeddings.position_ids = torch.arange(new_num).expand((1, -1))
+
+            #self.vision_tower.vision_model.embeddings.position_embedding.weight = torch.nn.Parameter(new_pos_embed.to(pos_embed_checkpoint.device).to(pos_embed_checkpoint.dtype))
+            #self.vision_tower.vision_model.embeddings.position_ids = torch.arange(new_num).expand((1, -1)).to(pos_embed_checkpoint.device)
+            self.is_resize_pos = True
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == 'patch':
+            image_features = image_features[:, 1:]
+        elif self.select_feature == 'cls_patch':
+            image_features = image_features
+        else:
+            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+        return image_features
+
+    def forward(self, images):
+        if not self.is_loaded:
+            self.load_model()
+        if type(images) is list:
+            image_features = []
+            for image in images:
+                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
+                image_feature = self.feature_select(image_forward_out).to(image.dtype)
+                image_features.append(image_feature)
+        else:
+            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
+            image_features = self.feature_select(image_forward_outs).to(images.dtype)
+
+        return image_features
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        if self.is_loaded:
+            return self.vision_tower.config
+        else:
+            return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2
+
+class PLoRA(nn.Linear):
+    def __init__(self,
+                 in_features: int,
+                 out_features: int,
+                 bias: bool = True,
+                 device=None,
+                 dtype=None,
+                 lora_r=8,
+                 lora_alpha=16,
+                 lora_dropout=0.05,
+                 lora_len=0,
+                 **kwargs) -> None:
+        super().__init__(in_features, out_features, bias, device, dtype)
+        self.lora_r = lora_r
+        self.lora_alpha = lora_alpha
+        self.lora_len = lora_len
+        if lora_dropout > 0.:
+            self.lora_dropout = nn.Dropout(p=lora_dropout)
+        else:
+            self.lora_dropout = lambda x: x
+        self.lora_scaling = self.lora_alpha / self.lora_r
+
+        self.Plora_A = nn.Linear(in_features,
+                                self.lora_r,
+                                bias=False,
+                                device=device,
+                                dtype=dtype)
+        self.Plora_B = nn.Linear(self.lora_r,
+                                out_features,
+                                bias=False,
+                                device=device,
+                                dtype=dtype)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if hasattr(self, 'lora_A'):
+            # initialize A the same way as the default for nn.Linear and B to zero
+            nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
+            nn.init.zeros_(self.lora_B.weight)
+            #print ("lora weight init {} {}".format(torch.mean(self.lora_A.weight), torch.mean(self.lora_B.weight)))
+
+    def forward(self, x, im_mask=None):
+        res = super().forward(x)
+        if im_mask is not None:
+            if torch.sum(im_mask) > 0:
+                part_x = x[im_mask]
+                res[im_mask] += self.Plora_B(self.Plora_A(
+                    self.lora_dropout(part_x))) * self.lora_scaling
+            else:
+                part_x = x[:, :1]
+                res[:, :1] += self.Plora_B(self.Plora_A(
+                    self.lora_dropout(part_x))) * 0
+        return res

modeling_internlm2.py

@@ -1015,8 +1015,8 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
     def _set_gradient_checkpointing(self, module, value=False):
         if isinstance(module, InternLM2Model):
             module.gradient_checkpointing = value
-        if value:
-            self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value
+        #if value:
+        #    self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value
 
     def get_input_embeddings(self):
         return self.model.tok_embeddings
@@ -1064,7 +1064,9 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         return img_embeds
 
     def img2emb(self, image):
-        img_embeds = self.vision_proj(self.vit(image.to(self.device)))
+        bs = image.shape[0]
+        #img_embeds = self.vision_proj(self.vit(image.to(self.device)))
+        img_embeds = torch.ones(bs,5,4096).to(torch.bfloat16).to(self.device)
         atts_img = torch.ones(
             img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)
 

modeling_internlm2.py~

@@ -0,0 +1,1548 @@
+# # Copyright (c) InternLM. 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 InternLM2 model."""
+import copy
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from einops import rearrange
+from PIL import Image
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+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)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .build_mlp import PLoRA, build_vision_projector, build_vision_tower
+from .configuration_internlm import InternLMConfig as InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = 'InternLM2Config'
+
+
+# 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.tensor(torch.finfo(dtype).min, device=device),
+                      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 InternLM2RMSNorm(nn.Module):
+
+    def __init__(self, hidden_size, eps=1e-6):
+        """InternLM2RMSNorm 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 InternLM2RotaryEmbedding(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, persistent=False)
+
+        # 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().to(dtype), persistent=False)
+        self.register_buffer(
+            'sin_cached', emb.sin().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 InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding 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().to(dtype), persistent=False)
+        self.register_buffer(
+            'sin_cached', emb.sin().to(dtype), persistent=False)
+
+
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding 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, persistent=False)
+
+        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().to(dtype), persistent=False)
+        self.register_buffer(
+            'sin_cached', emb.sin().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.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
+    sin = sin.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
+    if q.size(2) == 1:
+        q_embed = (q * cos[:, :, -1:, :]) + (
+            rotate_half(q) * sin[:, :, -1:, :])
+    else:
+        q_embed = (q * cos) + (rotate_half(q) * sin)
+
+    if k.size(2) == 1:
+        k_embed = (k * cos[:, :, -1:, :]) + (
+            rotate_half(k) * sin[:, :, -1:, :])
+    else:
+        k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        #self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        #self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        #self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+        self.w1 = PLoRA(
+            self.hidden_size,
+            self.intermediate_size,
+            bias=False,
+            lora_r=256,
+            lora_alpha=256,
+            lora_len=576)
+        self.w3 = PLoRA(
+            self.hidden_size,
+            self.intermediate_size,
+            bias=False,
+            lora_r=256,
+            lora_alpha=256,
+            lora_len=576)
+        self.w2 = PLoRA(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            lora_r=256,
+            lora_alpha=256,
+            lora_len=576)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x, im_mask):
+        down_proj = self.w2(
+            self.act_fn(self.w1(x, im_mask)) * self.w3(x, im_mask), im_mask)
+
+        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)
+
+
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper."""
+
+    def __init__(self, config: InternLM2Config):
+        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.is_causal = True
+
+        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.wqkv = nn.Linear(
+        self.wqkv = PLoRA(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+            lora_r=256,
+            lora_alpha=256,
+            lora_len=576)
+
+        #self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self.wo = PLoRA(
+            self.num_heads * self.head_dim,
+            self.hidden_size,
+            bias=config.bias,
+            lora_r=256,
+            lora_alpha=256,
+            lora_len=576)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            scaling_factor = self.config.rope_scaling['factor']
+            if scaling_type == 'dynamic':
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor)
+            else:
+                raise ValueError(
+                    "Currently we only support rotary embedding's type being 'dynamic'."
+                )
+        return self.rotary_emb
+
+    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,
+        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,
+        im_mask: Optional[Tuple[torch.Tensor]] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`')
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states, im_mask)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., :self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.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.wo(attn_output, im_mask)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class InternLM2FlashAttention2(InternLM2Attention):
+    """InternLM2 flash attention module.
+
+    This module inherits from `InternLM2Attention` as the weights of the module
+    stays untouched. The only required change would be on the forward pass
+    where it needs to correctly call the public API of flash attention and deal
+    with padding tokens in case the input contains any of them.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        im_mask: Optional[Tuple[torch.Tensor]] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`')
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop('padding_mask')
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states, im_mask)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=self.num_heads + 2 * self.num_key_value_heads,
+            d=self.head_dim,
+            q=q_len,
+        )
+
+        query_states = qkv_states[..., :self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        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
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (InternLM2RMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, '_pre_quantization_dtype'):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f'The input hidden states seems to be silently casted in float32, this might be related to'
+                f' the fact you have upcasted embedding or layer norm layers in float32. We will cast back '
+                f'the input in {target_dtype}.')
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate)
+
+        attn_output = attn_output.reshape(bsz, q_len,
+                                          self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output, im_mask)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class InternLM2DecoderLayer(nn.Module):
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.attention = (
+            InternLM2Attention(config=config)
+            if not getattr(config, '_flash_attn_2_enabled', False) else
+            InternLM2FlashAttention2(config=config))
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        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,
+        im_mask: Optional[Tuple[torch.Tensor]] = None,
+        **kwargs,
+    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            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
+        """
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`')
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            im_mask=im_mask,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states, im_mask)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states, )
+
+        if output_attentions:
+            outputs += (self_attn_weights, )
+
+        if use_cache:
+            outputs += (present_key_value, )
+
+        return outputs
+
+
+InternLM2_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 ([`InternLM2Config`]):
+            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 InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['InternLM2DecoderLayer']
+    _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
+
+    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_()
+
+
+InternLM2_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 `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, decoder_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 `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """Transformer decoder consisting of *config.num_hidden_layers* layers.
+    Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = 'AutoModel'
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size,
+                                           config.hidden_size,
+                                           self.padding_idx)
+        self.layers = nn.ModuleList([
+            InternLM2DecoderLayer(config)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = InternLM2RMSNorm(
+            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.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = 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(InternLM2_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,
+                use_cache: Optional[bool] = None,
+                output_attentions: Optional[bool] = None,
+                output_hidden_states: Optional[bool] = None,
+                return_dict: Optional[bool] = None,
+                **kwargs) -> Union[Tuple, BaseModelOutputWithPast]:
+
+        im_mask = kwargs.get('im_mask', None)
+
+        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 input_ids and inputs_embeds at the same time'
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError(
+                'You have to specify either input_ids or 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)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+            im_mask = torch.zeros(inputs_embeds.shape[:2]).to(
+                inputs_embeds.device).bool()
+        # 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)
+
+        # embed positions
+        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, output_attentions, None,
+                                      im_mask)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    im_mask=im_mask,
+                )
+
+            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 InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    _auto_class = 'AutoModelForCausalLM'
+
+    _tied_weights_keys = ['output.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+        self.debug_flag = 1
+        self.tokenizer = None
+
+        self.max_length = config.max_length
+        print(f'Set max length to {self.max_length}')
+        self.debug_flag = 1
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.vit = build_vision_tower(config._name_or_path)
+        self.vision_proj = build_vision_projector()
+
+        self.vis_processor = transforms.Compose([
+            transforms.Resize((336, 336),
+                              interpolation=InterpolationMode.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
+                                 (0.26862954, 0.26130258, 0.27577711)),
+        ])
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, InternLM2Model):
+            module.gradient_checkpointing = value
+        #if value:
+        #    self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def encode_text(self, t, add_special_tokens=False):
+        t = t.replace('<|User|>:', '[UNUSED_TOKEN_146]user\n')
+        t = t.replace('<|Bot|>:', '[UNUSED_TOKEN_146]assistant\n')
+        t = t.replace('<TOKENS_UNUSED_0>', '[UNUSED_TOKEN_145]')
+        t = t.replace('<TOKENS_UNUSED_1>', '[UNUSED_TOKEN_145]')
+        t = t.replace('[UNUSED_TOKEN_0]', '[UNUSED_TOKEN_145]')
+        t = t.replace('[UNUSED_TOKEN_1]', '[UNUSED_TOKEN_145]')
+
+        text = t
+        token = self.tokenizer(
+            text, return_tensors='pt',
+            add_special_tokens=add_special_tokens).input_ids.to(self.device)
+        embs = self.model.tok_embeddings(token)
+        return embs
+
+    def encode_img(self, image):
+        if image is None:
+            return None
+        if isinstance(image, str):
+            image = Image.open(image).convert('RGB')
+            image = self.vis_processor(image).unsqueeze(0).to(self.device)
+        else:
+            assert isinstance(image, torch.Tensor)
+
+        img_embeds, atts_img, img_target = self.img2emb(image)
+        return img_embeds
+
+    def img2emb(self, image):
+        bs = image.shape[0]
+        #img_embeds = self.vision_proj(self.vit(image.to(self.device)))
+        img_embeds = 
+        atts_img = torch.ones(
+            img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)
+
+        img_target = torch.ones(
+            img_embeds.size()[:2], dtype=torch.long).to(
+                img_embeds.device) * -100
+
+        return img_embeds, atts_img, img_target
+
+    def prompt_wrap(self, img_embeds, prompt):
+        batch_size = img_embeds.shape[0]
+        p_before, p_after = prompt.split('<ImageHere>')
+        p_before_tokens = self.tokenizer(
+            p_before, return_tensors='pt',
+            add_special_tokens=True).to(img_embeds.device)
+
+        p_before_embeds = self.model.tok_embeddings(
+            p_before_tokens.input_ids).expand(batch_size, -1, -1)
+        wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds], dim=1)
+
+        wrapped_atts_img = torch.ones(
+            wrapped_img_embeds.size()[:-1],
+            dtype=torch.long).to(img_embeds.device)
+
+        wrapped_target = torch.ones(
+            batch_size, wrapped_img_embeds.shape[1], dtype=torch.long).to(
+                img_embeds.device) * -100
+
+        return wrapped_img_embeds, wrapped_atts_img, wrapped_target
+
+    def text2emb(self, text, add_special=False):
+        if type(text) == str:
+            new_text = []
+            for t in text:
+                t = t.replace('<|User|>:', '[UNUSED_TOKEN_146]user\n')
+                t = t.replace('<|Bot|>:', '[UNUSED_TOKEN_146]assistant\n')
+                t = t.replace('<TOKENS_UNUSED_0>', '[UNUSED_TOKEN_145]')
+                t = t.replace('<TOKENS_UNUSED_1>', '[UNUSED_TOKEN_145]')
+                new_text.append(t)
+            text = new_text
+        elif type(text) == list:
+            new_text = []
+            text_list = text
+            for text in text_list:
+                for t in text:
+                    t = t.replace('<|User|>:', '[UNUSED_TOKEN_146]user\n')
+                    t = t.replace('<|Bot|>:', '[UNUSED_TOKEN_146]assistant\n')
+                    t = t.replace('<TOKENS_UNUSED_0>', '[UNUSED_TOKEN_145]')
+                    t = t.replace('<TOKENS_UNUSED_1>', '[UNUSED_TOKEN_145]')
+                    new_text.append(t)
+            text = new_text
+        to_regress_tokens = self.tokenizer(
+            text,
+            return_tensors='pt',
+            padding='longest',
+            truncation=True,
+            max_length=self.max_length,
+            add_special_tokens=add_special).to(self.device)
+
+        targets = self.mask_human_targets(to_regress_tokens.input_ids)
+        targets = targets.to(self.device)
+
+        return to_regress_tokens, targets
+
+    def interleav_wrap(self, img_list, text_list):
+        wrap_embeds_list, wrap_atts_list = [], []
+        wrap_target_list, wrap_im_mask_list = [], []
+
+        for image, text in zip(img_list, text_list):
+            img_embeds, atts_img, img_target = self.img2emb(image)
+            text = text[0]
+            parts = text.split('<ImageHere>')
+            wrap_tokens, wrap_embeds, wrap_atts, wrap_im_mask = [], [], [], []
+            temp_len = 0
+            image_nums, im_len = img_embeds.shape[:2]
+            need_bos = True
+            for idx, part in enumerate(parts):
+                if len(part) > 0:
+                    part_tokens = self.tokenizer(
+                        part,
+                        return_tensors='pt',
+                        padding='longest',
+                        add_special_tokens=need_bos).to(self.device)
+                    if need_bos:
+                        need_bos = False
+                    wrap_tokens.append(part_tokens.input_ids)
+                    part_embeds = self.model.tok_embeddings(
+                        part_tokens.input_ids)
+                    wrap_embeds.append(part_embeds)
+                    wrap_atts.append(part_tokens.attention_mask)
+                    wrap_im_mask.append(
+                        torch.zeros(part_embeds.shape[:2]).to(self.device))
+
+                    temp_len += part_embeds.shape[1]
+                if idx < image_nums:
+                    wrap_tokens.append(img_target[idx].unsqueeze(0))
+                    wrap_embeds.append(img_embeds[idx].unsqueeze(0))
+                    wrap_atts.append(atts_img[idx].unsqueeze(0))
+                    wrap_im_mask.append(
+                        torch.ones_like(atts_img[idx].unsqueeze(0)))
+
+                    temp_len += im_len
+                if temp_len > self.max_length:
+                    break
+
+            wrap_tokens = torch.cat(wrap_tokens, dim=1)
+            wrap_embeds = torch.cat(wrap_embeds, dim=1)
+            wrap_atts = torch.cat(wrap_atts, dim=1)
+            wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
+
+            wrap_target = self.mask_human_targets(wrap_tokens).to(self.device)
+
+            wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device)
+            wrap_atts = wrap_atts[:, :self.max_length].to(self.device)
+            wrap_target = wrap_target[:, :self.max_length].to(self.device)
+            wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device)
+
+            wrap_embeds_list.append(wrap_embeds)
+            wrap_atts_list.append(wrap_atts)
+            wrap_target_list.append(wrap_target)
+            wrap_im_mask_list.append(wrap_im_mask)
+
+        wrap_embeds = torch.cat(wrap_embeds_list)
+        wrap_atts = torch.cat(wrap_atts_list)
+        wrap_target = torch.cat(wrap_target_list)
+        wrap_im_mask = torch.cat(wrap_im_mask_list)
+        return wrap_embeds, wrap_atts, wrap_target, wrap_im_mask
+
+    def mask_human_targets(self, input_ids, pure=False):
+        target_batch = []
+        for bs in range(input_ids.shape[0]):
+            cur_idx = 0
+            ids = input_ids[bs]
+            targets = copy.deepcopy(ids)
+            end_count = 0
+            last_eoa = 0
+            for i, temp_id in enumerate(ids):
+                if temp_id == 92542:
+                    if end_count % 2 == 0:
+                        targets[last_eoa:i + 6] = -100
+                    else:
+                        last_eoa = i + 1
+                    end_count += 1
+                elif temp_id == 2:  ### eos and following pad
+                    targets[i + 1:] = -100  #### loss on eos, but not on pad
+                    break
+            if temp_id != 2 and end_count % 2 == 0:  ### trunction, end at last question
+                targets[last_eoa +
+                        1:] = -100  #### mask all after the last answer
+
+            target_batch.append(targets.unsqueeze(0))
+            if self.debug_flag:
+                print('#### Warning! System meta is not support now')
+                targets_vis = targets.clone()
+                targets_vis[targets_vis == -100] = 92399
+                targets_vis_tokens = ''.join(
+                    self.tokenizer.convert_ids_to_tokens(targets_vis)).replace(
+                        '[UNUSED_TOKEN_2]', '  ')
+                # print(''.join(self.tokenizer.convert_ids_to_tokens(ids)))
+                print('-----------')
+                print([targets_vis_tokens])
+                print('-----------------------------')
+
+        target_batch = torch.cat(target_batch, dim=0)
+        return target_batch
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    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,
+                **kwargs) -> 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, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.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."
+        ```"""
+        samples = kwargs.get('samples', None)
+        if samples:
+            if self.debug_flag:
+                self.debug_flag += 1
+            if self.debug_flag > 5:
+                self.debug_flag = 0
+
+            if samples['data_type'][0] == 'text':
+                has_img = False
+            elif samples['data_type'][0] == 'multi':
+                has_img = True
+            else:
+                raise NotImplementedError
+
+            ### encode text
+            text = samples['text_input']
+            if has_img:
+                image = samples['image']
+                to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap(
+                    image, text)
+            else:
+                to_regress_tokens, targets = self.text2emb(
+                    text, add_special=True)
+                to_regress_embeds = self.model.tok_embeddings(
+                    to_regress_tokens.input_ids)
+                attention_mask = to_regress_tokens.attention_mask
+                im_mask = torch.zeros(to_regress_embeds.shape[:2]).cuda()
+
+            inputs_embeds = to_regress_embeds[:, :self.max_length]
+            attention_mask = attention_mask[:, :self.max_length]
+            targets = targets[:, :self.max_length]
+            im_mask = im_mask[:, :self.max_length].bool()
+            labels = targets
+            if self.debug_flag:
+                print(targets.shape, inputs_embeds.shape, attention_mask.shape)
+                le = len(samples['text_input'])
+                data_type = samples['data_type'][0]
+                print(
+                    f'DataType: {data_type}. Has Image: {has_img}. Current max length: {self.max_length}, BatchSize is {le}'
+                )
+                # if has_img:
+                #     print(img_embeds.shape)
+
+        else:
+            self.debug_flag = 0
+            im_mask = kwargs.get('im_mask', None)
+
+        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,
+            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,
+            im_mask=im_mask,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(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,
+                                      im_mask=None,
+                                      **kwargs):
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        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[:, -input_ids.shape[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}
+
+        im_mask = im_mask
+
+        model_inputs.update({
+            'position_ids': position_ids,
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+            'im_mask': im_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
+
+    def build_inputs(self,
+                     tokenizer,
+                     query: str,
+                     history: List[Tuple[str, str]] = []):
+        prompt = ''
+        for record in history:
+            prompt += f"""<|User|>:{record[0]}\n<|Bot|>:{record[1]}[UNUSED_TOKEN_0]\n"""
+        prompt += f"""<|User|>:{query}\n<|Bot|>:"""
+        return tokenizer([prompt], return_tensors='pt')
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history)
+        inputs = {
+            k: v.to(self.device)
+            for k, v in inputs.items() if torch.is_tensor(v)
+        }
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split('[UNUSED_TOKEN_0]')[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """Return a generator in format: (response, history) Eg.
+
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')]) ('你好，有什么可以帮助您的吗？', [('你好',
+        '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                'The version of `transformers` is too low. Please make sure '
+                'that you have installed `transformers>=4.28.0`.')
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ''
+                self.received_inputs = False
+                self.queue.put(
+                    (self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError('ChatStreamer only supports batch size 1')
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                token = self.tokenizer.decode([value[-1]],
+                                              skip_special_tokens=True)
+                if token.strip() != '[UNUSED_TOKEN_0]':
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()