Add a remote code file for transformers integration 🤗 (#2)

- Add a remote code file for transformers integration 🤗 (e5c6dc1a07b977d538d27829470a7a62e98f1e39)
- Update config.json (fbe8feb9086c566d4d867103a646a966f8922d22)
- Update README.md (8e093cf658178a113a2dc8396a5213a552d59475)
- Update README.md (69d7e3fdd67b10df146686be17bcf661bbc500e8)


Co-authored-by: Vaibhav Srivastav <[email protected]>

README.md

@@ -3,6 +3,8 @@ license: apple-amlr
 license_name: apple-ascl
 license_link: https://github.com/apple/ml-fastvlm/blob/main/LICENSE_MODEL
 library_name: ml-fastvlm
+tags:
+- transformers
 ---
 # FastVLM: Efficient Vision Encoding for Vision Language Models
 
@@ -51,6 +53,61 @@ python predict.py --model-path /path/to/checkpoint-dir \
                   --prompt "Describe the image."
 ```
 
+### Run inference with Transformers (Remote Code)
+To run inference with transformers we can leverage `trust_remote_code` along with the following snippet:
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MID = "apple/FastVLM-0.5B"
+IMAGE_TOKEN_INDEX = -200  # what the model code looks for
+
+# Load
+tok = AutoTokenizer.from_pretrained(MID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+# Build chat -> render to string (not tokens) so we can place <image> exactly
+messages = [
+    {"role": "user", "content": "<image>\nDescribe this image in detail."}
+]
+rendered = tok.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+
+pre, post = rendered.split("<image>", 1)
+
+# Tokenize the text *around* the image token (no extra specials!)
+pre_ids  = tok(pre,  return_tensors="pt", add_special_tokens=False).input_ids
+post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids
+
+# Splice in the IMAGE token id (-200) at the placeholder position
+img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype)
+input_ids = torch.cat([pre_ids, img_tok, post_ids], dim=1).to(model.device)
+attention_mask = torch.ones_like(input_ids, device=model.device)
+
+# Preprocess image via the model's own processor
+img = Image.open("test-2.jpg").convert("RGB")
+px = model.get_vision_tower().image_processor(images=img, return_tensors="pt")["pixel_values"]
+px = px.to(model.device, dtype=model.dtype)
+
+# Generate
+with torch.no_grad():
+    out = model.generate(
+        inputs=input_ids,
+        attention_mask=attention_mask,
+        images=px,
+        max_new_tokens=128,
+    )
+
+print(tok.decode(out[0], skip_special_tokens=True))
+```
 
 ## Citation
 If you found this model useful, please cite the following paper:
@@ -62,4 +119,4 @@ If you found this model useful, please cite the following paper:
   month = {June},
   year = {2025},
 }
-```
+```

config.json

@@ -3,6 +3,10 @@
   "architectures": [
     "LlavaQwen2ForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "llava_qwen.LlavaConfig",
+    "AutoModelForCausalLM": "llava_qwen.LlavaQwen2ForCausalLM"
+  },  
   "attention_dropout": 0.0,
   "bos_token_id": 151643,
   "eos_token_id": 151645,

llava_qwen.py

@@ -0,0 +1,2195 @@
+#    Copyright 2023 Haotian Liu
+#
+#    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 typing import List, Optional, Tuple, Union
+
+import re
+import copy
+from timm.models import create_model
+from abc import ABC, abstractmethod
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+import torch.nn.functional as F
+from torch.nn.init import normal_
+
+from transformers import CLIPImageProcessor
+from transformers import AutoConfig, AutoModelForCausalLM, Qwen2Config, Qwen2Model, Qwen2ForCausalLM
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.generation.utils import GenerateOutput
+
+from functools import partial
+from typing import List, Tuple, Optional, Union, Dict, Any
+
+from timm.models import register_model
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import DropPath, SqueezeExcite
+
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+LOGDIR = "."
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+IMAGE_PLACEHOLDER = "<image-placeholder>"
+
+class LlavaConfig(Qwen2Config):
+    model_type = "llava_qwen2"
+
+def _cfg(url="", **kwargs):
+    return {
+        "url": url,
+        "num_classes": 1000,
+        "input_size": (3, 256, 256),
+        "pool_size": None,
+        "crop_pct": 0.95,
+        "interpolation": "bicubic",
+        "mean": IMAGENET_DEFAULT_MEAN,
+        "std": IMAGENET_DEFAULT_STD,
+        "classifier": "head",
+        **kwargs,
+    }
+
+
+default_cfgs = {
+    "fastvit_t": _cfg(crop_pct=0.9),
+    "fastvit_s": _cfg(crop_pct=0.9),
+    "fastvit_m": _cfg(crop_pct=0.95),
+}
+
+
+class SEBlock(nn.Module):
+    """Squeeze and Excite module.
+    Pytorch implementation of `Squeeze-and-Excitation Networks` -
+    https://arxiv.org/pdf/1709.01507.pdf
+    """
+
+    def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
+        """Construct a Squeeze and Excite Module.
+        Args:
+            in_channels: Number of input channels.
+            rd_ratio: Input channel reduction ratio.
+        """
+        super(SEBlock, self).__init__()
+        self.reduce = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=int(in_channels * rd_ratio),
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+        self.expand = nn.Conv2d(
+            in_channels=int(in_channels * rd_ratio),
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        b, c, h, w = inputs.size()
+        # x = F.avg_pool2d(inputs, kernel_size=[h, w])
+        x = F.avg_pool2d(inputs, kernel_size=[16, 16])
+        x = self.reduce(x)
+        x = F.relu(x)
+        x = self.expand(x)
+        x = torch.sigmoid(x)
+        x = x.view(-1, c, 1, 1)
+        return inputs * x
+
+
+class MobileOneBlock(nn.Module):
+    """MobileOne building block.
+    This block has a multi-branched architecture at train-time
+    and plain-CNN style architecture at inference time
+    For more details, please refer to our paper:
+    `An Improved One millisecond Mobile Backbone` -
+    https://arxiv.org/pdf/2206.04040.pdf
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        use_act: bool = True,
+        use_scale_branch: bool = True,
+        num_conv_branches: int = 1,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a MobileOneBlock module.
+        Args:
+            in_channels: Number of channels in the input.
+            out_channels: Number of channels produced by the block.
+            kernel_size: Size of the convolution kernel.
+            stride: Stride size.
+            padding: Zero-padding size.
+            dilation: Kernel dilation factor.
+            groups: Group number.
+            inference_mode: If True, instantiates model in inference mode.
+            use_se: Whether to use SE-ReLU activations.
+            use_act: Whether to use activation. Default: ``True``
+            use_scale_branch: Whether to use scale branch. Default: ``True``
+            num_conv_branches: Number of linear conv branches.
+        """
+        super(MobileOneBlock, self).__init__()
+        self.inference_mode = inference_mode
+        self.groups = groups
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_conv_branches = num_conv_branches
+
+        # Check if SE-ReLU is requested
+        if use_se:
+            self.se = SEBlock(out_channels)
+        else:
+            self.se = nn.Identity()
+
+        if use_act:
+            self.activation = activation
+        else:
+            self.activation = nn.Identity()
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            # Re-parameterizable skip connection
+            # Fallback, sometimes batchnorm tensors
+            # do not get instantiated correctly on some processes
+            # when using deepspeed + accelerate
+            norm_layer = nn.BatchNorm2d(num_features=in_channels)
+            if norm_layer.weight.shape[0] == 0:
+                norm_layer.weight = nn.Parameter(torch.zeros(in_channels))
+            if norm_layer.bias.shape[0] == 0:
+                norm_layer.bias = nn.Parameter(torch.zeros(in_channels))
+
+            self.rbr_skip = (
+                norm_layer
+                if out_channels == in_channels and stride == 1
+                else None
+            )
+
+            # Re-parameterizable conv branches
+            if num_conv_branches > 0:
+                rbr_conv = list()
+                for _ in range(self.num_conv_branches):
+                    rbr_conv.append(
+                        self._conv_bn(kernel_size=kernel_size, padding=padding)
+                    )
+                self.rbr_conv = nn.ModuleList(rbr_conv)
+            else:
+                self.rbr_conv = None
+
+            # Re-parameterizable scale branch
+            self.rbr_scale = None
+            if not isinstance(kernel_size, int):
+                kernel_size = kernel_size[0]
+            if (kernel_size > 1) and use_scale_branch:
+                self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        # Inference mode forward pass.
+        if self.inference_mode:
+            return self.activation(self.se(self.reparam_conv(x)))
+
+        # Multi-branched train-time forward pass.
+        # Skip branch output
+        identity_out = 0
+        if self.rbr_skip is not None:
+            identity_out = self.rbr_skip(x)
+
+        # Scale branch output
+        scale_out = 0
+        if self.rbr_scale is not None:
+            scale_out = self.rbr_scale(x)
+
+        # Other branches
+        out = scale_out + identity_out
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                out += self.rbr_conv[ix](x)
+
+        return self.activation(self.se(out))
+
+    def reparameterize(self):
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
+        https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
+        architecture used at training time to obtain a plain CNN-like structure
+        for inference.
+        """
+        if self.inference_mode:
+            return
+        kernel, bias = self._get_kernel_bias()
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = kernel
+        self.reparam_conv.bias.data = bias
+
+        # Delete un-used branches
+        self.__delattr__("rbr_conv")
+        self.__delattr__("rbr_scale")
+        if hasattr(self, "rbr_skip"):
+            self.__delattr__("rbr_skip")
+
+        self.inference_mode = True
+
+    def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        # get weights and bias of scale branch
+        kernel_scale = 0
+        bias_scale = 0
+        if self.rbr_scale is not None:
+            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
+            # Pad scale branch kernel to match conv branch kernel size.
+            pad = self.kernel_size // 2
+            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
+
+        # get weights and bias of skip branch
+        kernel_identity = 0
+        bias_identity = 0
+        if self.rbr_skip is not None:
+            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
+
+        # get weights and bias of conv branches
+        kernel_conv = 0
+        bias_conv = 0
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
+                kernel_conv += _kernel
+                bias_conv += _bias
+
+        kernel_final = kernel_conv + kernel_scale + kernel_identity
+        bias_final = bias_conv + bias_scale + bias_identity
+        return kernel_final, bias_final
+
+    def _fuse_bn_tensor(
+        self, branch: Union[nn.Sequential, nn.BatchNorm2d]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with preceeding conv layer.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
+        Args:
+            branch: Sequence of ops to be fused.
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        if isinstance(branch, nn.Sequential):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        else:
+            assert isinstance(branch, nn.BatchNorm2d)
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.in_channels // self.groups
+
+                kernel_size = self.kernel_size
+                if isinstance(self.kernel_size, int):
+                    kernel_size = (self.kernel_size, self.kernel_size)
+
+                kernel_value = torch.zeros(
+                    (self.in_channels, input_dim, kernel_size[0], kernel_size[1]),
+                    dtype=branch.weight.dtype,
+                    device=branch.weight.device,
+                )
+                for i in range(self.in_channels):
+                    kernel_value[
+                        i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2
+                    ] = 1
+                self.id_tensor = kernel_value
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+        Returns:
+            Conv-BN module.
+        """
+        # Fallback, sometimes batchnorm tensors
+        # do not get instantiated correctly on some processes
+        # when using deepspeed + accelerate
+        norm_layer = nn.BatchNorm2d(num_features=self.out_channels)
+        if norm_layer.weight.shape[0] == 0:
+            norm_layer.weight = nn.Parameter(torch.zeros(self.out_channels))
+        if norm_layer.bias.shape[0] == 0:
+            norm_layer.bias = nn.Parameter(torch.zeros(self.out_channels))
+
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", norm_layer)
+        return mod_list
+
+
+class ReparamLargeKernelConv(nn.Module):
+    """Building Block of RepLKNet
+    This class defines overparameterized large kernel conv block
+    introduced in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
+    Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int,
+        groups: int,
+        small_kernel: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a ReparamLargeKernelConv module.
+        Args:
+            in_channels: Number of input channels.
+            out_channels: Number of output channels.
+            kernel_size: Kernel size of the large kernel conv branch.
+            stride: Stride size. Default: 1
+            groups: Group number. Default: 1
+            small_kernel: Kernel size of small kernel conv branch.
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+            activation: Activation module. Default: ``nn.GELU``
+        """
+        super(ReparamLargeKernelConv, self).__init__()
+
+        self.stride = stride
+        self.groups = groups
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.activation = activation
+
+        self.kernel_size = kernel_size
+        self.small_kernel = small_kernel
+        self.padding = kernel_size // 2
+
+        # Check if SE is requested
+        if use_se:
+            self.se = SqueezeExcite(out_channels, rd_ratio=0.25)
+        else:
+            self.se = nn.Identity()
+
+        if inference_mode:
+            self.lkb_reparam = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=self.padding,
+                dilation=1,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            self.lkb_origin = self._conv_bn(
+                kernel_size=kernel_size, padding=self.padding
+            )
+            if small_kernel is not None:
+                assert (
+                    small_kernel <= kernel_size
+                ), "The kernel size for re-param cannot be larger than the large kernel!"
+                self.small_conv = self._conv_bn(
+                    kernel_size=small_kernel, padding=small_kernel // 2
+                )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        if hasattr(self, "lkb_reparam"):
+            out = self.lkb_reparam(x)
+        else:
+            out = self.lkb_origin(x)
+            if hasattr(self, "small_conv"):
+                out += self.small_conv(x)
+
+        return self.activation(self.se(out))
+
+    def get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias.
+        Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        eq_k, eq_b = self._fuse_bn(self.lkb_origin.conv, self.lkb_origin.bn)
+        if hasattr(self, "small_conv"):
+            small_k, small_b = self._fuse_bn(self.small_conv.conv, self.small_conv.bn)
+            eq_b += small_b
+            eq_k += nn.functional.pad(
+                small_k, [(self.kernel_size - self.small_kernel) // 2] * 4
+            )
+        return eq_k, eq_b
+
+    def reparameterize(self) -> None:
+        """
+        Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
+        https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
+        architecture used at training time to obtain a plain CNN-like structure
+        for inference.
+        """
+        eq_k, eq_b = self.get_kernel_bias()
+        self.lkb_reparam = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.lkb_origin.conv.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+
+        self.lkb_reparam.weight.data = eq_k
+        self.lkb_reparam.bias.data = eq_b
+        self.__delattr__("lkb_origin")
+        if hasattr(self, "small_conv"):
+            self.__delattr__("small_conv")
+
+    @staticmethod
+    def _fuse_bn(
+        conv: torch.Tensor, bn: nn.BatchNorm2d
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with conv layer.
+        Args:
+            conv: Convolutional kernel weights.
+            bn: Batchnorm 2d layer.
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        kernel = conv.weight
+        running_mean = bn.running_mean
+        running_var = bn.running_var
+        gamma = bn.weight
+        beta = bn.bias
+        eps = bn.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int = 0) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+        Returns:
+            A nn.Sequential Conv-BN module.
+        """
+        # Fallback, sometimes batchnorm tensors
+        # do not get instantiated correctly on some processes
+        # when using deepspeed + accelerate
+        norm_layer = nn.BatchNorm2d(num_features=self.out_channels)
+        if norm_layer.weight.shape[0] == 0:
+            norm_layer.weight = nn.Parameter(torch.zeros(self.out_channels))
+        if norm_layer.bias.shape[0] == 0:
+            norm_layer.bias = nn.Parameter(torch.zeros(self.out_channels))
+
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", norm_layer)
+        return mod_list
+
+
+def convolutional_stem(
+    in_channels: int, out_channels: int, inference_mode: bool = False, use_scale_branch: bool = True,
+) -> nn.Sequential:
+    """Build convolutional stem with MobileOne blocks.
+    Args:
+        in_channels: Number of input channels.
+        out_channels: Number of output channels.
+        inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+    Returns:
+        nn.Sequential object with stem elements.
+    """
+    return nn.Sequential(
+        MobileOneBlock(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+            use_scale_branch=use_scale_branch
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=out_channels,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+            use_scale_branch=use_scale_branch
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+            use_scale_branch=use_scale_branch
+        ),
+    )
+
+
+class LayerNormChannel(nn.Module):
+    """
+    LayerNorm only for Channel Dimension.
+    Input: tensor in shape [B, C, H, W]
+    """
+    def __init__(self, num_features, eps=1e-05) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_features))
+        self.bias = nn.Parameter(torch.zeros(num_features))
+        self.eps = eps
+
+    def forward(self, x) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight.unsqueeze(-1).unsqueeze(-1) * x \
+            + self.bias.unsqueeze(-1).unsqueeze(-1)
+        return x
+
+
+class MHSA(nn.Module):
+    """Multi-headed Self Attention module.
+    Source modified from:
+    https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        """Build MHSA module that can handle 3D or 4D input tensors.
+        Args:
+            dim: Number of embedding dimensions.
+            head_dim: Number of hidden dimensions per head. Default: ``32``
+            qkv_bias: Use bias or not. Default: ``False``
+            attn_drop: Dropout rate for attention tensor.
+            proj_drop: Dropout rate for projection tensor.
+        """
+        super().__init__()
+        assert dim % head_dim == 0, "dim should be divisible by head_dim"
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shape = x.shape
+        B, C, H, W = shape
+        N = H * W
+        if len(shape) == 4:
+            x = torch.flatten(x, start_dim=2).transpose(-2, -1)  # (B, N, C)
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, self.head_dim)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        # trick here to make [email protected] more stable
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        if len(shape) == 4:
+            x = x.transpose(-2, -1).reshape(B, C, H, W)
+
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """Convolutional patch embedding layer."""
+
+    def __init__(
+        self,
+        patch_size: int,
+        stride: int,
+        in_channels: int,
+        embed_dim: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+    ) -> None:
+        """Build patch embedding layer.
+        Args:
+            patch_size: Patch size for embedding computation.
+            stride: Stride for convolutional embedding layer.
+            in_channels: Number of channels of input tensor.
+            embed_dim: Number of embedding dimensions.
+            inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+            use_se: If ``True`` SE block will be used.
+        """
+        super().__init__()
+        block = list()
+        block.append(
+            ReparamLargeKernelConv(
+                in_channels=in_channels,
+                out_channels=embed_dim,
+                kernel_size=patch_size,
+                stride=stride,
+                groups=in_channels,
+                small_kernel=3,
+                inference_mode=inference_mode,
+                use_se=use_se,
+            )
+        )
+        block.append(
+            MobileOneBlock(
+                in_channels=embed_dim,
+                out_channels=embed_dim,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                groups=1,
+                inference_mode=inference_mode,
+                use_se=False,
+                num_conv_branches=1,
+            )
+        )
+        self.proj = nn.Sequential(*block)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x
+
+
+class RepMixer(nn.Module):
+    """Reparameterizable token mixer.
+    For more details, please refer to our paper:
+    `FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=3,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Module.
+        Args:
+            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
+            kernel_size: Kernel size for spatial mixing. Default: 3
+            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
+            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+        """
+        super().__init__()
+        self.dim = dim
+        self.kernel_size = kernel_size
+        self.inference_mode = inference_mode
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.dim,
+                out_channels=self.dim,
+                kernel_size=self.kernel_size,
+                stride=1,
+                padding=self.kernel_size // 2,
+                groups=self.dim,
+                bias=True,
+            )
+        else:
+            self.norm = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+                use_scale_branch=False,
+                num_conv_branches=0,
+            )
+            self.mixer = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+            )
+            self.use_layer_scale = use_layer_scale
+            if use_layer_scale:
+                self.layer_scale = nn.Parameter(
+                    layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
+                )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            if self.use_layer_scale:
+                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
+            else:
+                x = x + self.mixer(x) - self.norm(x)
+            return x
+
+    def reparameterize(self) -> None:
+        """Reparameterize mixer and norm into a single
+        convolutional layer for efficient inference.
+        """
+        if self.inference_mode:
+            return
+
+        self.mixer.reparameterize()
+        self.norm.reparameterize()
+
+        if self.use_layer_scale:
+            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
+                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            )
+            b = torch.squeeze(self.layer_scale) * (
+                self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+            )
+        else:
+            w = (
+                self.mixer.id_tensor
+                + self.mixer.reparam_conv.weight
+                - self.norm.reparam_conv.weight
+            )
+            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.dim,
+            out_channels=self.dim,
+            kernel_size=self.kernel_size,
+            stride=1,
+            padding=self.kernel_size // 2,
+            groups=self.dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w
+        self.reparam_conv.bias.data = b
+
+        self.__delattr__("mixer")
+        self.__delattr__("norm")
+        if self.use_layer_scale:
+            self.__delattr__("layer_scale")
+
+
+class ConvFFN(nn.Module):
+    """Convolutional FFN Module."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        hidden_channels: Optional[int] = None,
+        out_channels: Optional[int] = None,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        """Build convolutional FFN module.
+        Args:
+            in_channels: Number of input channels.
+            hidden_channels: Number of channels after expansion. Default: None
+            out_channels: Number of output channels. Default: None
+            act_layer: Activation layer. Default: ``GELU``
+            drop: Dropout rate. Default: ``0.0``.
+        """
+        super().__init__()
+        out_channels = out_channels or in_channels
+        hidden_channels = hidden_channels or in_channels
+        self.conv = nn.Sequential()
+        self.conv.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=7,
+                padding=3,
+                groups=in_channels,
+                bias=False,
+            ),
+        )
+
+        # Fallback, sometimes batchnorm tensors
+        # do not get instantiated correctly on some processes
+        # when using deepspeed + accelerate
+        norm_layer = nn.BatchNorm2d(num_features=out_channels)
+        if norm_layer.weight.shape[0] == 0:
+            norm_layer.weight = nn.Parameter(torch.zeros(out_channels))
+        if norm_layer.bias.shape[0] == 0:
+            norm_layer.bias = nn.Parameter(torch.zeros(out_channels))
+
+        self.conv.add_module("bn", norm_layer)
+        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
+        self.drop = nn.Dropout(drop)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Conv2d):
+            normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class RepCPE(nn.Module):
+    """Implementation of conditional positional encoding.
+    For more details refer to paper:
+    `Conditional Positional Encodings for Vision Transformers <https://arxiv.org/pdf/2102.10882.pdf>`_
+    In our implementation, we can reparameterize this module to eliminate a skip connection.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        embed_dim: int = 768,
+        spatial_shape: Union[int, Tuple[int, int]] = (7, 7),
+        inference_mode=False,
+    ) -> None:
+        """Build reparameterizable conditional positional encoding
+        Args:
+            in_channels: Number of input channels.
+            embed_dim: Number of embedding dimensions. Default: 768
+            spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super(RepCPE, self).__init__()
+        if isinstance(spatial_shape, int):
+            spatial_shape = tuple([spatial_shape] * 2)
+        assert isinstance(spatial_shape, Tuple), (
+            f'"spatial_shape" must by a sequence or int, '
+            f"get {type(spatial_shape)} instead."
+        )
+        assert len(spatial_shape) == 2, (
+            f'Length of "spatial_shape" should be 2, '
+            f"got {len(spatial_shape)} instead."
+        )
+
+        self.spatial_shape = spatial_shape
+        self.embed_dim = embed_dim
+        self.in_channels = in_channels
+        self.groups = embed_dim
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.embed_dim,
+                kernel_size=self.spatial_shape,
+                stride=1,
+                padding=int(self.spatial_shape[0] // 2),
+                groups=self.embed_dim,
+                bias=True,
+            )
+        else:
+            self.pe = nn.Conv2d(
+                in_channels,
+                embed_dim,
+                spatial_shape,
+                1,
+                int(spatial_shape[0] // 2),
+                bias=True,
+                groups=embed_dim,
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            x = self.pe(x) + x
+            return x
+
+    def reparameterize(self) -> None:
+        # Build equivalent Id tensor
+        input_dim = self.in_channels // self.groups
+        kernel_value = torch.zeros(
+            (
+                self.in_channels,
+                input_dim,
+                self.spatial_shape[0],
+                self.spatial_shape[1],
+            ),
+            dtype=self.pe.weight.dtype,
+            device=self.pe.weight.device,
+        )
+        for i in range(self.in_channels):
+            kernel_value[
+                i,
+                i % input_dim,
+                self.spatial_shape[0] // 2,
+                self.spatial_shape[1] // 2,
+            ] = 1
+        id_tensor = kernel_value
+
+        # Reparameterize Id tensor and conv
+        w_final = id_tensor + self.pe.weight
+        b_final = self.pe.bias
+
+        # Introduce reparam conv
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.spatial_shape,
+            stride=1,
+            padding=int(self.spatial_shape[0] // 2),
+            groups=self.embed_dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w_final
+        self.reparam_conv.bias.data = b_final
+
+        self.__delattr__("pe")
+
+
+class RepMixerBlock(nn.Module):
+    """Implementation of Metaformer block with RepMixer as token mixer.
+    For more details on Metaformer structure, please refer to:
+    `MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 3,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Block.
+        Args:
+            dim: Number of embedding dimensions.
+            kernel_size: Kernel size for repmixer. Default: 3
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+
+        super().__init__()
+
+        self.token_mixer = RepMixer(
+            dim,
+            kernel_size=kernel_size,
+            use_layer_scale=use_layer_scale,
+            layer_scale_init_value=layer_scale_init_value,
+            inference_mode=inference_mode,
+        )
+
+        assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
+            mlp_ratio
+        )
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop Path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale = nn.Parameter(
+                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
+            )
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.layer_scale * self.convffn(x))
+        else:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+class AttentionBlock(nn.Module):
+    """Implementation of metaformer block with MHSA as token mixer.
+    For more details on Metaformer structure, please refer to:
+    `MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+    ):
+        """Build Attention Block.
+        Args:
+            dim: Number of embedding dimensions.
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            norm_layer: Normalization layer. Default: ``nn.BatchNorm2d``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+        """
+
+        super().__init__()
+
+        # Fallback, sometimes batchnorm tensors
+        # do not get instantiated correctly on some processes
+        # when using deepspeed + accelerate
+        norm_layer_ = norm_layer(num_features=dim)
+        if norm_layer_.weight.shape[0] == 0:
+            norm_layer_.weight = nn.Parameter(torch.zeros(dim))
+        if norm_layer_.bias.shape[0] == 0:
+            norm_layer_.bias = nn.Parameter(torch.zeros(dim))
+
+        self.norm = norm_layer_
+        self.token_mixer = MHSA(dim=dim)
+
+        assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
+            mlp_ratio
+        )
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale_1 = nn.Parameter(
+                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
+            )
+            self.layer_scale_2 = nn.Parameter(
+                layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True
+            )
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.layer_scale_2 * self.convffn(x))
+        else:
+            x = x + self.drop_path(self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+def basic_blocks(
+    dim: int,
+    block_index: int,
+    num_blocks: List[int],
+    token_mixer_type: str,
+    kernel_size: int = 3,
+    mlp_ratio: float = 4.0,
+    act_layer: nn.Module = nn.GELU,
+    norm_layer: nn.Module = nn.BatchNorm2d,
+    drop_rate: float = 0.0,
+    drop_path_rate: float = 0.0,
+    use_layer_scale: bool = True,
+    layer_scale_init_value: float = 1e-5,
+    inference_mode=False,
+) -> nn.Sequential:
+    """Build FastViT blocks within a stage.
+    Args:
+        dim: Number of embedding dimensions.
+        block_index: block index.
+        num_blocks: List containing number of blocks per stage.
+        token_mixer_type: Token mixer type.
+        kernel_size: Kernel size for repmixer.
+        mlp_ratio: MLP expansion ratio.
+        act_layer: Activation layer.
+        norm_layer: Normalization layer.
+        drop_rate: Dropout rate.
+        drop_path_rate: Drop path rate.
+        use_layer_scale: Flag to turn on layer scale regularization.
+        layer_scale_init_value: Layer scale value at initialization.
+        inference_mode: Flag to instantiate block in inference mode.
+    Returns:
+        nn.Sequential object of all the blocks within the stage.
+    """
+    blocks = []
+    for block_idx in range(num_blocks[block_index]):
+        block_dpr = (
+            drop_path_rate
+            * (block_idx + sum(num_blocks[:block_index]))
+            / (sum(num_blocks) - 1)
+        )
+        if token_mixer_type == "repmixer":
+            blocks.append(
+                RepMixerBlock(
+                    dim,
+                    kernel_size=kernel_size,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                    inference_mode=inference_mode,
+                )
+            )
+        elif token_mixer_type == "attention":
+            blocks.append(
+                AttentionBlock(
+                    dim,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                )
+            )
+        else:
+            raise ValueError(
+                "Token mixer type: {} not supported".format(token_mixer_type)
+            )
+    blocks = nn.Sequential(*blocks)
+    return blocks
+
+
+class GlobalPool2D(nn.Module):
+    """This class implements global pooling with linear projection."""
+
+    def __init__(self, in_dim: int, out_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+        scale = in_dim**-0.5
+        self.proj = nn.Parameter(scale * torch.randn(size=(in_dim, out_dim)))
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+    def pool(self, x) -> Tensor:
+        if x.dim() == 4:
+            dims = [-2, -1]
+        elif x.dim() == 5:
+            dims = [-3, -2, -1]
+        x = torch.mean(x, dim=dims, keepdim=False)
+        return x
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
+        # x is of shape [batch, in_dim]
+        assert (
+            x.dim() == 4
+        ), "Input should be 4-dimensional (Batch x in_dim x in_height x in_width). Got: {}".format(
+            x.shape
+        )
+
+        # [batch, in_dim, in_height, in_width] --> [batch, in_dim]
+        x = self.pool(x)
+        # [batch, in_dim]  x [in_dim, out_dim] --> [batch, out_dim]
+        x = x @ self.proj
+        return x
+
+
+class FastViT(nn.Module):
+    """
+    This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        layers,
+        token_mixers: Tuple[str, ...],
+        embed_dims=None,
+        mlp_ratios=None,
+        downsamples=None,
+        se_downsamples=None,
+        repmixer_kernel_size=3,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        act_layer: nn.Module = nn.GELU,
+        num_classes=1000,
+        pos_embs=None,
+        down_patch_size=7,
+        down_stride=2,
+        drop_rate=0.0,
+        drop_path_rate=0.0,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        init_cfg=None,
+        pretrained=None,
+        cls_ratio=2.0,
+        inference_mode=False,
+        stem_scale_branch=True,
+        **kwargs,
+    ) -> None:
+
+        super().__init__()
+
+        self.num_classes = num_classes
+        if len(layers) == 4:
+            self.out_indices = [0, 2, 4, 7]
+        elif len(layers) == 5:
+            self.out_indices = [0, 2, 4, 7, 10]
+        else:
+            raise NotImplementedError("FPN is not implemented for more than 5 stages.")
+
+        if pos_embs is None:
+            pos_embs = [None] * len(layers)
+
+        if se_downsamples is None:
+            se_downsamples = [False] * len(layers)
+
+        # Convolutional stem
+        self.patch_embed = convolutional_stem(3, embed_dims[0], inference_mode,
+                                              use_scale_branch=stem_scale_branch)
+
+        # Build the main stages of the network architecture
+        network = []
+        for i in range(len(layers)):
+            # Add position embeddings if requested
+            if pos_embs[i] is not None:
+                network.append(
+                    pos_embs[i](
+                        embed_dims[i], embed_dims[i], inference_mode=inference_mode
+                    )
+                )
+            stage = basic_blocks(
+                embed_dims[i],
+                i,
+                layers,
+                token_mixer_type=token_mixers[i],
+                kernel_size=repmixer_kernel_size,
+                mlp_ratio=mlp_ratios[i],
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                drop_rate=drop_rate,
+                drop_path_rate=drop_path_rate,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+                inference_mode=inference_mode,
+            )
+            network.append(stage)
+            if i >= len(layers) - 1:
+                break
+
+            # Patch merging/downsampling between stages.
+            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
+                network.append(
+                    PatchEmbed(
+                        patch_size=down_patch_size,
+                        stride=down_stride,
+                        in_channels=embed_dims[i],
+                        embed_dim=embed_dims[i + 1],
+                        inference_mode=inference_mode,
+                        use_se=se_downsamples[i + 1],
+                    )
+                )
+        self.network = nn.ModuleList(network)
+
+        # Classifier head
+        self.conv_exp = MobileOneBlock(
+            in_channels=embed_dims[-1],
+            out_channels=int(embed_dims[-1] * cls_ratio),
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=embed_dims[-1],
+            inference_mode=inference_mode,
+            use_se=True,
+            num_conv_branches=1,
+        )
+        self.head = (
+            nn.Linear(int(embed_dims[-1] * cls_ratio), num_classes)
+            if num_classes > 0
+            else nn.Identity()
+        )
+        self.apply(self.cls_init_weights)
+        self.init_cfg = copy.deepcopy(init_cfg)
+
+    def cls_init_weights(self, m: nn.Module) -> None:
+        """Init. for classification"""
+        if isinstance(m, nn.Linear):
+            normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward_embeddings(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        return x
+
+    def forward_tokens(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        for idx, block in enumerate(self.network):
+            x = block(x)
+        return x
+
+    def forward(self, x: torch.Tensor, *args, **kwargs) -> Union[Tensor, Dict[str, Tensor]]:
+        # input embedding
+        x = self.forward_embeddings(x)
+        # through backbone
+        x = self.forward_tokens(x)
+        # for image classification/embedding
+        x = self.conv_exp(x)
+        cls_out = self.head(x)
+
+        out_dict = dict()
+        if kwargs.get("return_image_embeddings", False):
+            out_dict.update({"logits": cls_out})
+            out_dict.update({"image_embeddings": x})
+            return out_dict
+        else:
+            return cls_out
+
+
+@register_model
+def fastvithd(pretrained=False, **kwargs):
+    """Instantiate FastViTHD model variant."""
+    layers = [2, 12, 24, 4, 2]
+    embed_dims = [96, 192, 384, 768, 1536]
+    mlp_ratios = [4, 4, 4, 4, 4]
+    downsamples = [True, True, True, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7)), partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        norm_layer=LayerNormChannel,
+        stem_scale_branch=False,
+        inference_mode=True,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_m"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model
+
+def load_model_config(
+        model_name: str,
+) -> Any:
+    model_cfg = {
+        "embed_dim": 768,
+        "image_cfg": {
+            "image_size": 1024,
+            "model_name": "fastvithd",
+            "embed_dim": 3072,
+            "patch_size": 64
+        },
+        "text_cfg": {
+            "context_length": 77,
+            "vocab_size": 49408,
+            "dim": 768,
+            "ffn_multiplier_per_layer": 4.0,
+            "n_heads_per_layer": 12,
+            "n_transformer_layers": 12,
+            "norm_layer": "layer_norm_fp32",
+            "causal_masking": False,
+            "model_name": "base"
+        }
+    }
+    return model_cfg
+
+
+class MCi(nn.Module):
+    """
+    This class implements `MCi Models <https://arxiv.org/pdf/2311.17049.pdf>`_
+    """
+
+    def __init__(self, model_name: str, *args, **kwargs) -> None:
+        super().__init__()
+        self.projection_dim = None
+        if "projection_dim" in kwargs:
+            self.projection_dim = kwargs.get("projection_dim")
+
+        # Create model
+        self.model = create_model(model_name, projection_dim=self.projection_dim)
+
+        # Build out projection head.
+        if self.projection_dim is not None:
+            if hasattr(self.model, "head"):
+                self.model.head = MCi._update_image_classifier(
+                    image_classifier=self.model.head, projection_dim=self.projection_dim
+                )
+
+    def forward(self, x: Any, *args, **kwargs) -> Any:
+        """A forward function of the model."""
+        x = self.model(x, *args, **kwargs)
+        return x
+
+    @staticmethod
+    def _get_in_feature_dimension(image_classifier: nn.Module) -> int:
+        """Return the input feature dimension to the image classification head."""
+        in_features = None
+        if isinstance(image_classifier, nn.Sequential):
+            # Classifier that uses nn.Sequential usually has global pooling and
+            # multiple linear layers. Find the first linear layer and get its
+            # in_features
+            for layer in image_classifier:
+                if isinstance(layer, nn.Linear):
+                    in_features = layer.in_features
+                    break
+        elif isinstance(image_classifier, nn.Linear):
+            in_features = image_classifier.in_features
+
+        if in_features is None:
+            raise NotImplementedError(
+                f"Cannot get input feature dimension of {image_classifier}."
+            )
+        return in_features
+
+    @staticmethod
+    def _update_image_classifier(
+        image_classifier: nn.Module, projection_dim: int, *args, **kwargs
+    ) -> nn.Module:
+        in_features = MCi._get_in_feature_dimension(image_classifier)
+        new_img_classifier = GlobalPool2D(in_dim=in_features, out_dim=projection_dim)
+        return new_img_classifier
+
+
+class MobileCLIPVisionTower(nn.Module):
+    def __init__(self, vision_tower, args, delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+        self.vision_tower_name = vision_tower
+        self.tune_vision_tower = getattr(args, 'unfreeze_mm_vision_tower', False)
+        self.input_image_size = int(vision_tower.split("_")[-1])
+
+        # Delay load is disabled for now
+        if not delay_load:
+            self.load_model()
+        elif getattr(args, 'unfreeze_mm_vision_tower', False):
+            self.load_model()
+        else:
+            model_cfg = load_model_config(self.vision_tower_name)
+            self.cfg_only = model_cfg
+
+    def load_model(self, device_map=None):
+        if self.is_loaded:
+            print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
+            return
+
+        # Load model config
+        model_cfg = load_model_config(self.vision_tower_name)
+
+        # Override default image resolution
+        model_cfg["image_cfg"]["image_size"] = self.input_image_size
+
+        self.cfg_only = model_cfg
+
+        # Build HF CLIPImageProcessor with MobileCLIP parameters
+        self.image_processor = CLIPImageProcessor(crop_size={"height": model_cfg["image_cfg"]["image_size"],
+                                                             "width": model_cfg["image_cfg"]["image_size"]},
+                                                  image_mean=[0.0, 0.0, 0.0],
+                                                  image_std=[1.0, 1.0, 1.0],
+                                                  size={"shortest_edge": model_cfg["image_cfg"]["image_size"]})
+
+        # Instantiate the image encoder
+        self.vision_tower = MCi(model_name=model_cfg["image_cfg"]["model_name"],
+                                           projection_dim=model_cfg["embed_dim"])
+
+        if not self.tune_vision_tower:
+            self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        # Features from penultimate layer
+        image_features = image_forward_outs["image_embeddings"]
+
+        # Reshape 4D tensor to 3D
+        B, C, H, W = image_features.shape
+        image_features = image_features.reshape(B, C, H*W)
+        image_features = image_features.transpose(1, 2)
+        return image_features
+
+    def forward(self, images):
+        if self.tune_vision_tower:
+            return self.forward_images(images)
+        else:
+            with torch.no_grad():
+                return self.forward_images(images)
+
+    def forward_images(self, images):
+        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), return_image_embeddings=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), return_image_embeddings=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 next(self.vision_tower.parameters()).dtype
+
+    @property
+    def device(self):
+        return next(self.vision_tower.parameters()).device
+
+    @property
+    def config(self):
+        return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        return self.config["image_cfg"]["embed_dim"]
+
+    @property
+    def num_patches_per_side(self):
+        return self.config["image_cfg"]["image_size"] // self.config["image_cfg"]["patch_size"]
+
+    @property
+    def num_patches(self):
+        return (self.config["image_cfg"]["image_size"] // self.config["image_cfg"]["patch_size"]) ** 2
+
+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'}
+
+def build_vision_projector(config, delay_load=False, **kwargs):
+    projector_type = getattr(config, 'mm_projector_type', 'linear')
+
+    if projector_type == 'linear':
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+
+    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(config.mm_hidden_size, config.hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+        return nn.Sequential(*modules)
+
+    if projector_type == 'identity':
+        return IdentityMap()
+
+    raise ValueError(f'Unknown projector type: {projector_type}')
+
+def build_vision_tower(vision_tower_cfg, **kwargs):
+    vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
+    return MobileCLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
+
+class LlavaMetaModel:
+
+    def __init__(self, config):
+        super(LlavaMetaModel, self).__init__(config)
+
+        if hasattr(config, "mm_vision_tower"):
+            self.vision_tower = build_vision_tower(config, delay_load=True)
+            self.mm_projector = build_vision_projector(config)
+
+            if 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
+                self.image_newline = nn.Parameter(
+                    torch.empty(config.hidden_size, dtype=self.dtype)
+                )
+
+    def get_vision_tower(self):
+        vision_tower = getattr(self, 'vision_tower', None)
+        if type(vision_tower) is list:
+            vision_tower = vision_tower[0]
+        return vision_tower
+
+    def initialize_vision_modules(self, model_args, fsdp=None):
+        vision_tower = model_args.vision_tower
+        mm_vision_select_layer = model_args.mm_vision_select_layer
+        mm_vision_select_feature = model_args.mm_vision_select_feature
+        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
+        mm_patch_merge_type = model_args.mm_patch_merge_type
+
+        self.config.mm_vision_tower = vision_tower
+
+        if self.get_vision_tower() is None:
+            vision_tower = build_vision_tower(model_args)
+
+            if fsdp is not None and len(fsdp) > 0:
+                self.vision_tower = [vision_tower]
+            else:
+                self.vision_tower = vision_tower
+        else:
+            if fsdp is not None and len(fsdp) > 0:
+                vision_tower = self.vision_tower[0]
+            else:
+                vision_tower = self.vision_tower
+            vision_tower.load_model()
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
+        self.config.mm_hidden_size = vision_tower.hidden_size
+        self.config.mm_vision_select_layer = mm_vision_select_layer
+        self.config.mm_vision_select_feature = mm_vision_select_feature
+        self.config.mm_patch_merge_type = mm_patch_merge_type
+
+        if getattr(self, 'mm_projector', None) is None:
+            self.mm_projector = build_vision_projector(self.config)
+
+            if 'unpad' in mm_patch_merge_type:
+                embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
+                self.image_newline = nn.Parameter(
+                    torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
+                )
+        else:
+            # In case it is frozen by LoRA
+            for p in self.mm_projector.parameters():
+                p.requires_grad = True
+
+        if pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
+
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+
+            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
+
+def select_best_resolution(original_size, possible_resolutions):
+    """
+    Selects the best resolution from a list of possible resolutions based on the original size.
+    Args:
+        original_size (tuple): The original size of the image in the format (width, height).
+        possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
+    Returns:
+        tuple: The best fit resolution in the format (width, height).
+    """
+    original_width, original_height = original_size
+    best_fit = None
+    max_effective_resolution = 0
+    min_wasted_resolution = float('inf')
+
+    for width, height in possible_resolutions:
+        scale = min(width / original_width, height / original_height)
+        downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)
+        effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
+        wasted_resolution = (width * height) - effective_resolution
+
+        if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
+            max_effective_resolution = effective_resolution
+            min_wasted_resolution = wasted_resolution
+            best_fit = (width, height)
+
+    return best_fit
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+    """
+    Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+    Args:
+        image_size (tuple): The size of the input image in the format (width, height).
+        grid_pinpoints (str): A string representation of a list of possible resolutions.
+        patch_size (int): The size of each image patch.
+    Returns:
+        tuple: The shape of the image patch grid in the format (width, height).
+    """
+    import ast
+    if type(grid_pinpoints) is list:
+        possible_resolutions = grid_pinpoints
+    else:
+        possible_resolutions = ast.literal_eval(grid_pinpoints)
+    width, height = select_best_resolution(image_size, possible_resolutions)
+    return width // patch_size, height // patch_size
+
+class LlavaMetaForCausalLM(ABC):
+
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+
+    def encode_images(self, images):
+        image_features = self.get_model().get_vision_tower()(images)
+        image_features = self.get_model().mm_projector(image_features)
+        return image_features
+
+    def prepare_inputs_labels_for_multimodal(
+        self, input_ids, position_ids, attention_mask, past_key_values, labels,
+        images, image_sizes=None
+    ):
+        vision_tower = self.get_vision_tower()
+        if vision_tower is None or images is None or input_ids.shape[1] == 1:
+            return input_ids, position_ids, attention_mask, past_key_values, None, labels
+
+        if type(images) is list or images.ndim == 5:
+            if type(images) is list:
+                images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
+            concat_images = torch.cat([image for image in images], dim=0)
+            image_features = self.encode_images(concat_images)
+            split_sizes = [image.shape[0] for image in images]
+            image_features = torch.split(image_features, split_sizes, dim=0)
+            mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
+            image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
+            if mm_patch_merge_type == 'flat':
+                image_features = [x.flatten(0, 1) for x in image_features]
+            elif mm_patch_merge_type.startswith('spatial'):
+                new_image_features = []
+                for image_idx, image_feature in enumerate(image_features):
+                    if image_feature.shape[0] > 1:
+                        base_image_feature = image_feature[0]
+                        image_feature = image_feature[1:]
+                        height = width = self.get_vision_tower().num_patches_per_side
+                        assert height * width == base_image_feature.shape[0]
+                        if image_aspect_ratio == 'anyres':
+                            if hasattr(self.get_vision_tower(), 's2_image_size'):
+                                img_size = self.get_vision_tower().s2_image_size
+                            elif isinstance(self.get_vision_tower().config, dict):
+                                img_size = self.get_vision_tower().config["image_cfg"]["image_size"]
+                            else:
+                                img_size = self.get_vision_tower().config.image_size
+
+                            num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, img_size)
+                            image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+                        else:
+                            raise NotImplementedError
+                        if 'unpad' in mm_patch_merge_type:
+                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                            image_feature = unpad_image(image_feature, image_sizes[image_idx])
+                            image_feature = torch.cat((
+                                image_feature,
+                                self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
+                            ), dim=-1)
+                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                        else:
+                            image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
+                            image_feature = image_feature.flatten(0, 3)
+                        image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+                    else:
+                        image_feature = image_feature[0]
+                        if 'unpad' in mm_patch_merge_type:
+                            image_feature = torch.cat((
+                                image_feature,
+                                self.model.image_newline[None].to(image_feature.device)
+                            ), dim=0)
+                    new_image_features.append(image_feature)
+                image_features = new_image_features
+            else:
+                raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
+        else:
+            image_features = self.encode_images(images)
+
+        # TODO: image start / end is not implemented here to support pretraining.
+        if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
+            raise NotImplementedError
+
+        # Let's just add dummy tensors if they do not exist,
+        # it is a headache to deal with None all the time.
+        # But it is not ideal, and if you have a better idea,
+        # please open an issue / submit a PR, thanks.
+        _labels = labels
+        _position_ids = position_ids
+        _attention_mask = attention_mask
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+        else:
+            attention_mask = attention_mask.bool()
+        if position_ids is None:
+            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
+        if labels is None:
+            labels = torch.full_like(input_ids, IGNORE_INDEX)
+
+        # remove the padding using attention_mask -- FIXME
+        _input_ids = input_ids
+        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
+        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
+
+        new_input_embeds = []
+        new_labels = []
+        cur_image_idx = 0
+        for batch_idx, cur_input_ids in enumerate(input_ids):
+            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
+            if num_images == 0:
+                cur_image_features = image_features[cur_image_idx]
+                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
+                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
+                new_input_embeds.append(cur_input_embeds)
+                new_labels.append(labels[batch_idx])
+                cur_image_idx += 1
+                continue
+
+            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
+            cur_input_ids_noim = []
+            cur_labels = labels[batch_idx]
+            cur_labels_noim = []
+            for i in range(len(image_token_indices) - 1):
+                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
+                cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
+            split_sizes = [x.shape[0] for x in cur_labels_noim]
+            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
+            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
+            cur_new_input_embeds = []
+            cur_new_labels = []
+
+            for i in range(num_images + 1):
+                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
+                cur_new_labels.append(cur_labels_noim[i])
+                if i < num_images:
+                    cur_image_features = image_features[cur_image_idx]
+                    cur_image_idx += 1
+                    cur_new_input_embeds.append(cur_image_features)
+                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
+
+            cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
+
+            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
+            cur_new_labels = torch.cat(cur_new_labels)
+
+            new_input_embeds.append(cur_new_input_embeds)
+            new_labels.append(cur_new_labels)
+
+        # Truncate sequences to max length as image embeddings can make the sequence longer
+        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
+        if tokenizer_model_max_length is not None:
+            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
+            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
+
+        # Combine them
+        max_len = max(x.shape[0] for x in new_input_embeds)
+        batch_size = len(new_input_embeds)
+
+        new_input_embeds_padded = []
+        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
+        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
+        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
+
+        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
+            cur_len = cur_new_embed.shape[0]
+            if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
+                new_input_embeds_padded.append(torch.cat((
+                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
+                    cur_new_embed
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, -cur_len:] = cur_new_labels
+                    attention_mask[i, -cur_len:] = True
+                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+            else:
+                new_input_embeds_padded.append(torch.cat((
+                    cur_new_embed,
+                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, :cur_len] = cur_new_labels
+                    attention_mask[i, :cur_len] = True
+                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+
+        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
+
+        if _labels is None:
+            new_labels = None
+        else:
+            new_labels = new_labels_padded
+
+        if _attention_mask is None:
+            attention_mask = None
+        else:
+            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
+
+        if _position_ids is None:
+            position_ids = None
+
+        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
+
+    def initialize_vision_tokenizer(self, model_args, tokenizer):
+        if model_args.mm_use_im_patch_token:
+            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+        if model_args.mm_use_im_start_end:
+            num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+            if num_new_tokens > 0:
+                input_embeddings = self.get_input_embeddings().weight.data
+                output_embeddings = self.get_output_embeddings().weight.data
+
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+
+                input_embeddings[-num_new_tokens:] = input_embeddings_avg
+                output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+            if model_args.pretrain_mm_mlp_adapter:
+                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+                assert num_new_tokens == 2
+                if input_embeddings.shape == embed_tokens_weight.shape:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
+                elif embed_tokens_weight.shape[0] == num_new_tokens:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
+                else:
+                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+        elif model_args.mm_use_im_patch_token:
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = False
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+
+class LlavaQwen2Model(LlavaMetaModel, Qwen2Model):
+    config_class = LlavaConfig
+
+    def __init__(self, config: Qwen2Config):
+        super(LlavaQwen2Model, self).__init__(config)
+
+
+class LlavaQwen2ForCausalLM(Qwen2ForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaConfig
+
+    def __init__(self, config):
+        super(Qwen2ForCausalLM, self).__init__(config)
+        self.model = LlavaQwen2Model(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_model(self):
+        return self.model
+
+    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,
+        images: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[List[List[int]]] = None,
+        return_dict: Optional[bool] = None,
+        cache_position=None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        if inputs_embeds is None:
+            (
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                inputs_embeds,
+                labels
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                labels,
+                images,
+                image_sizes
+            )
+
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+
+    @torch.no_grad()
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        images: Optional[torch.Tensor] = None,
+        image_sizes: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        position_ids = kwargs.pop("position_ids", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+        if "inputs_embeds" in kwargs:
+            raise NotImplementedError("`inputs_embeds` is not supported")
+
+        if images is not None:
+            (
+                inputs,
+                position_ids,
+                attention_mask,
+                _,
+                inputs_embeds,
+                _
+            ) = self.prepare_inputs_labels_for_multimodal(
+                inputs,
+                position_ids,
+                attention_mask,
+                None,
+                None,
+                images,
+                image_sizes=image_sizes
+            )
+        else:
+            inputs_embeds = self.get_model().embed_tokens(inputs)
+
+        return super().generate(
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            **kwargs
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
+                                      inputs_embeds=None, **kwargs):
+        images = kwargs.pop("images", None)
+        image_sizes = kwargs.pop("image_sizes", None)
+        inputs = super().prepare_inputs_for_generation(
+            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
+        )
+        if images is not None:
+            inputs['images'] = images
+        if image_sizes is not None:
+            inputs['image_sizes'] = image_sizes
+        return inputs
+
+
+AutoConfig.register("llava_qwen2", LlavaConfig)
+AutoModelForCausalLM.register(LlavaConfig, LlavaQwen2ForCausalLM)