diff --git "a/modeling_phi4_multimodal.py" "b/modeling_phi4_multimodal.py"
new file mode 100644--- /dev/null
+++ "b/modeling_phi4_multimodal.py"
@@ -0,0 +1,2388 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/phi4_multimodal/modular_phi4_multimodal.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_phi4_multimodal.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+
+import math
+import warnings
+from functools import wraps
+from typing import Callable, List, Optional, Tuple, Union, Any
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPast,
+    BaseModelOutputWithPooling,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    torch_int,
+)
+from .configuration_phi4_multimodal import Phi4MultimodalAudioConfig, Phi4MultimodalConfig, Phi4MultimodalVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def set_attribute_for_modules(module: "torch.nn.Module", key: str, value: Any):
+    """
+    Set a value to a module and all submodules.
+    """
+    setattr(module, key, value)
+    for submodule in module.children():
+        set_attribute_for_modules(submodule, key, value)
+
+
+def del_attribute_from_modules(module: "torch.nn.Module", key: str):
+    """
+    Delete a value from a module and all submodules.
+    """
+    # because we might remove it previously in case it's a shared module, e.g. activation function
+    if hasattr(module, key):
+        delattr(module, key)
+
+    for submodule in module.children():
+        del_attribute_from_modules(submodule, key)
+
+
+def can_return_tuple(func):
+    """
+    Decorator to wrap model method, to call output.to_tuple() if return_dict=False passed as a kwarg or
+    use_return_dict=False is set in the config.
+
+    Note:
+        output.to_tuple() convert output to tuple skipping all `None` values.
+    """
+
+    @wraps(func)
+    def wrapper(self, *args, **kwargs):
+        is_requested_to_return_tuple = kwargs.pop("return_dict", True) is False
+        is_configured_to_return_tuple = self.config.use_return_dict is False if hasattr(self, "config") else False
+
+        # The following allows to convert output to tuple ONLY on top level forward call,
+        # while internal modules of the model will return Output objects
+        # to be able to use name-based attribute access in modeling code.
+
+        # We will check if we are on top level module, if so, turn off to tuple conversion for all
+        # underling calls.
+        is_top_level_module = getattr(self, "_is_top_level_module", True)
+        if is_configured_to_return_tuple and is_top_level_module:
+            set_attribute_for_modules(self, "_is_top_level_module", False)
+
+        try:
+            output = func(self, *args, **kwargs)
+            if is_requested_to_return_tuple or (is_configured_to_return_tuple and is_top_level_module):
+                output = output.to_tuple()
+        finally:
+            # Remove the flag after the model forward call is finished.
+            if is_configured_to_return_tuple and is_top_level_module:
+                del_attribute_from_modules(self, "_is_top_level_module")
+
+        return output
+
+    return wrapper
+
+
+def dynamic_rope_update(rope_forward):
+    """
+    Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE
+    (i.e. a RoPE implementation that may recompute its frequencies in the forward pass).
+
+    Args:
+        rope_forward (Callable):
+            The forward pass of the RoPE implementation.
+
+    Returns:
+        The decorated forward pass.
+    """
+
+    def longrope_frequency_update(self, position_ids, device):
+        """Longrope uses long factor if sequence is larger than original pretraining length, short otherwise."""
+        seq_len = torch.max(position_ids) + 1
+        if hasattr(self.config, "original_max_position_embeddings"):
+            original_max_position_embeddings = self.config.original_max_position_embeddings
+        else:
+            original_max_position_embeddings = self.config.max_position_embeddings
+        if seq_len > original_max_position_embeddings:
+            if not hasattr(self, "long_inv_freq"):
+                self.long_inv_freq, _ = self.rope_init_fn(
+                    self.config, device, seq_len=original_max_position_embeddings + 1
+                )
+            self.register_buffer("inv_freq", self.long_inv_freq, persistent=False)
+        else:
+            # This .to() is needed if the model has been moved to a device after being initialized (because
+            # the buffer is automatically moved, but not the original copy)
+            self.original_inv_freq = self.original_inv_freq.to(device)
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+
+    def dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            # This .to() is needed if the model has been moved to a device after being initialized (because
+            # the buffer is automatically moved, but not the original copy)
+            self.original_inv_freq = self.original_inv_freq.to(device)
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @wraps(rope_forward)
+    def wrapper(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            dynamic_frequency_update(self, position_ids, device=x.device)
+        elif self.rope_type == "longrope":
+            longrope_frequency_update(self, position_ids, device=x.device)
+        return rope_forward(self, x, position_ids)
+
+    return wrapper
+
+
+class Phi4MultimodalVisionMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+def simple_eager_attention_forward(
+    module: nn.Module,
+    query_states: torch.Tensor,
+    key_states: torch.Tensor,
+    value_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class Phi4MultimodalVisionAttention(nn.Module):
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        self.scaling = self.head_dim**-0.5
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """Input shape: Batch x Time x Channel"""
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        attention_interface: Callable = simple_eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1)
+        attn_output = self.out_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Phi4MultimodalVisionEncoderLayer(nn.Module):
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.self_attn = Phi4MultimodalVisionAttention(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = Phi4MultimodalVisionMLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`.
+            attention_mask (`torch.FloatTensor`):
+                Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+        """
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class Phi4MultimodalVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`Phi4MultimodalVisionEncoderLayer`].
+
+    Args:
+        config: Phi4MultimodalVisionConfig
+    """
+
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList(
+            [Phi4MultimodalVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.gradient_checkpointing = False
+
+    # Ignore copy
+    @can_return_tuple
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> BaseModelOutput:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                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.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=encoder_states,
+            attentions=all_attentions,
+        )
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.0))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    tensor.clamp_(min=a, max=b)
+
+
+def trunc_normal_tf_(
+    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
+) -> torch.Tensor:
+    """Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \\leq \text{mean} \\leq b`.
+
+    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
+    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
+    and the result is subsequently scaled and shifted by the mean and std args.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    """
+    with torch.no_grad():
+        _trunc_normal_(tensor, 0, 1.0, a, b)
+        tensor.mul_(std).add_(mean)
+
+
+def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == "fan_in":
+        denom = fan_in
+    elif mode == "fan_out":
+        denom = fan_out
+    elif mode == "fan_avg":
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
+    elif distribution == "normal":
+        with torch.no_grad():
+            tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        with torch.no_grad():
+            tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
+
+
+def default_flax_embed_init(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="normal")
+
+
+class Phi4MultimodalVisionPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = Phi4MultimodalVisionConfig
+    base_model_prefix = "phi4_vision"
+    supports_gradient_checkpointing = True
+
+    _no_split_modules = ["Phi4MultimodalVisionEncoderLayer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, Phi4MultimodalVisionEmbeddings):
+            width = (
+                self.config.hidden_size
+                if isinstance(self.config, Phi4MultimodalVisionConfig)
+                else self.config.hidden_size
+            )
+            nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
+        elif isinstance(module, nn.Embedding):
+            default_flax_embed_init(module.weight)
+        elif isinstance(module, Phi4MultimodalVisionAttention):
+            nn.init.normal_(module.q_proj.weight)
+            nn.init.normal_(module.k_proj.weight)
+            nn.init.normal_(module.v_proj.weight)
+            nn.init.normal_(module.out_proj.weight)
+            nn.init.zeros_(module.q_proj.bias)
+            nn.init.zeros_(module.k_proj.bias)
+            nn.init.zeros_(module.v_proj.bias)
+            nn.init.zeros_(module.out_proj.bias)
+        elif isinstance(module, Phi4MultimodalVisionMLP):
+            nn.init.normal_(module.fc1.weight)
+            nn.init.normal_(module.fc2.weight)
+            nn.init.normal_(module.fc1.bias, std=1e-6)
+            nn.init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, Phi4MultimodalVisionMultiheadAttentionPoolingHead):
+            nn.init.normal_(module.probe.data)
+            nn.init.normal_(module.attention.in_proj_weight.data)
+            nn.init.zeros_(module.attention.in_proj_bias.data)
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            lecun_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class Phi4MultimodalVisionEmbeddings(nn.Module):
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__()
+        self.config = config
+        self.patch_size = config.patch_size
+        self.num_patches_per_side = config.image_size // self.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=config.hidden_size,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+        self.position_embedding = nn.Embedding(self.num_patches_per_side**2, config.hidden_size)
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing and no class embeddings.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """
+
+        num_patches = embeddings.shape[1]
+        num_positions = self.position_embedding.weight.shape[0]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+            return self.position_embedding(self.position_ids)
+
+        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+        dim = embeddings.shape[-1]
+
+        new_height = height // self.patch_size
+        new_width = width // self.patch_size
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor:
+        batch_size = pixel_values.size(0)
+
+        patch_embeds = self.patch_embedding(pixel_values)
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+        max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
+        max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
+        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
+        position_ids = torch.full((batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
+
+        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
+            nb_patches_h = p_attn_mask[:, 0].sum()
+            nb_patches_w = p_attn_mask[0].sum()
+
+            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
+            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
+
+            bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
+            bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
+
+            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
+            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
+
+        position_ids = position_ids.to(self.position_embedding.weight.device)
+
+        embeddings = embeddings + self.position_embedding(position_ids)
+        return embeddings
+
+
+class Phi4MultimodalVisionMultiheadAttentionPoolingHead(nn.Module):
+    """Multihead Attention Pooling."""
+
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__()
+
+        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
+        self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = Phi4MultimodalVisionMLP(config)
+
+    def forward(self, hidden_state, attention_mask):
+        batch_size = hidden_state.shape[0]
+        probe = self.probe.repeat(batch_size, 1, 1)
+
+        hidden_state = self.attention(
+            query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask
+        )[0]
+
+        residual = hidden_state
+        hidden_state = self.layernorm(hidden_state)
+        hidden_state = residual + self.mlp(hidden_state)
+
+        return hidden_state[:, 0]
+
+
+class Phi4MultimodalVisionModel(Phi4MultimodalVisionPreTrainedModel):
+    config_class = Phi4MultimodalVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: Phi4MultimodalVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = Phi4MultimodalVisionEmbeddings(config)
+        self.encoder = Phi4MultimodalVisionEncoder(config)
+        self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.head = Phi4MultimodalVisionMultiheadAttentionPoolingHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.embeddings.patch_embedding
+
+    def forward(
+        self,
+        pixel_values,
+        patch_attention_mask: Optional[torch.BoolTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> BaseModelOutputWithPooling:
+        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
+        )
+
+        batch_size = pixel_values.size(0)
+        if patch_attention_mask is None:
+            patch_attention_mask = torch.ones(
+                size=(
+                    batch_size,
+                    pixel_values.size(2) // self.config.patch_size,
+                    pixel_values.size(3) // self.config.patch_size,
+                ),
+                dtype=torch.bool,
+                device=pixel_values.device,
+            )
+
+        hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)
+
+        patch_attention_mask = patch_attention_mask.view(batch_size, -1)
+        # The call to `_upad_input` in `_flash_attention_forward` is expensive
+        # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
+        # avoiding passing the attention_mask, which is equivalent to attending to the full sequence
+        if not torch.any(~patch_attention_mask):
+            attention_mask = None
+        else:
+            attention_mask = (
+                _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
+                if not self.config._attn_implementation == "flash_attention_2"
+                else patch_attention_mask
+            )
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        pooled_output = self.head(
+            hidden_state=last_hidden_state,
+            attention_mask=patch_attention_mask,
+        )
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class Phi4MultimodalImageEmbedding(nn.Module):
+    """Image embedding."""
+
+    def __init__(self, config: Phi4MultimodalConfig):
+        super().__init__()
+        self.config = config
+        self.layer_idx = config.vision_config.feature_layer
+        self.crop_size = config.vision_config.crop_size
+        self.image_dim_out = config.vision_config.hidden_size
+
+        n_patches = config.vision_config.image_size // config.vision_config.patch_size
+        if n_patches % 2 != 0:
+            self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1))
+            n_patches += 1
+        self.num_img_tokens = (n_patches // 2) ** 2
+
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.img_processor = Phi4MultimodalVisionModel._from_config(config.vision_config)
+        self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2)
+        self.img_projection_up = nn.Linear(self.image_dim_out, config.hidden_size)
+        self.img_projection_down = nn.Linear(config.hidden_size, config.hidden_size)
+        self.global_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, self.image_dim_out]))
+        self.sub_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out]))
+
+    def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor:
+        img_processor_output = self.img_processor(
+            img_embeds, patch_attention_mask=attention_mask, output_hidden_states=True
+        )
+        img_feature = img_processor_output.hidden_states[self.layer_idx]
+
+        patch_feature = img_feature
+        # reshape to 2D tensor
+        width = int(math.sqrt(patch_feature.size(1)))
+        patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1))
+        # convert to NCHW
+        patch_feature = patch_feature.permute(0, 3, 1, 2)
+        if getattr(self, "img_processor_padding", None) is not None:
+            patch_feature = self.img_processor_padding(patch_feature)
+        patch_feature = self.image_token_compression(patch_feature)
+        # convert to NHWC
+        patch_feature = patch_feature.permute(0, 2, 3, 1)
+        patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1))
+        return patch_feature
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.Tensor,
+        image_pixel_values: torch.FloatTensor,
+        image_sizes: Optional[torch.Tensor] = None,
+        image_attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        image_pixel_values = image_pixel_values.to(self.img_processor.embeddings.patch_embedding.weight.dtype)
+
+        target_device = self.img_projection_up.bias.device
+        target_dtype = self.img_projection_up.bias.dtype
+
+        batch_size = image_pixel_values.shape[0]
+
+        img_features = self.get_img_features(
+            image_pixel_values.flatten(0, 1),
+            attention_mask=image_attention_mask.flatten(0, 1).to(dtype=bool, device=target_device),
+        )
+        base_feat_size = int(np.sqrt(img_features.shape[1]))
+        img_features = img_features.view(batch_size, -1, base_feat_size**2, self.image_dim_out)
+        image_sizes = image_sizes.view(-1, 2)
+
+        output_imgs = []
+        for idx in range(batch_size):
+            height, width = image_sizes[idx]
+            height_ratio = height // self.crop_size
+            width_ratio = width // self.crop_size
+            area_ratio = height_ratio * width_ratio
+
+            global_img = img_features[idx, :1]
+            global_img = global_img.reshape(1, base_feat_size, base_feat_size, self.image_dim_out).contiguous()
+            temporary_extensor = self.sub_img_feature_extensor.repeat(1, base_feat_size, 1, 1)
+            global_img = torch.cat([global_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out)
+
+            sub_img = img_features[idx, 1:]
+            sub_img = sub_img[:area_ratio]
+            sub_img = (
+                sub_img.reshape(height_ratio, width_ratio, base_feat_size, base_feat_size, self.image_dim_out)
+                .transpose(1, 2)
+                .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size, self.image_dim_out)
+                .contiguous()
+            )
+
+            if image_attention_mask is not None:
+                reshaped_image_attention_mask = (
+                    image_attention_mask[idx, 1 : area_ratio + 1, 0::2, 0::2]
+                    .reshape(height_ratio, width_ratio, base_feat_size, base_feat_size)
+                    .transpose(1, 2)
+                    .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size)
+                )
+                useful_height = int(reshaped_image_attention_mask[0, :, 0].sum().item())
+                useful_width = int(reshaped_image_attention_mask[0, 0, :].sum().item())
+                sub_img = sub_img[:, :useful_height, :useful_width]
+                temporary_extensor = self.sub_img_feature_extensor.repeat(1, useful_height, 1, 1)
+            else:
+                temporary_extensor = self.sub_img_feature_extensor.repeat(1, height_ratio * base_feat_size, 1, 1)
+
+            sub_img = torch.cat([sub_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out)
+
+            # Merge global and sub
+            output_imgs.append(torch.cat([sub_img, self.global_img_feature_extensor, global_img], dim=1))
+
+        img_set_tensor = []
+        for output_img in output_imgs:
+            output_img = output_img.to(device=target_device, dtype=target_dtype)
+            img_feature_proj = self.img_projection_up(output_img)
+            img_feature_proj = nn.functional.gelu(img_feature_proj)
+            img_feature_proj = self.img_projection_down(img_feature_proj)
+            img_set_tensor.append(img_feature_proj)
+
+        merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0)
+        merged_img_set_tensor = merged_img_set_tensor.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+
+        with torch.no_grad():
+            positions_tuple = torch.nonzero(input_ids == self.config.vision_config.image_token_id, as_tuple=True)
+
+        # Temporarily disable autocast to avoid issue on bf16 tensors
+        # Ref: https://github.com/pytorch/pytorch/issues/132715
+        with torch.autocast(device_type=inputs_embeds.device.type, enabled=False):
+            image_embeds = inputs_embeds.index_put(
+                indices=positions_tuple, values=merged_img_set_tensor, accumulate=False
+            )
+
+        image_embeds = self.drop(image_embeds)
+
+        return image_embeds
+
+
+########################################################## AUDIO #############################################
+
+
+class Phi4MultimodalAudioMLP(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.layer_norm = nn.LayerNorm(config.hidden_size)
+        self.act_fn = ACT2FN[config.activation]
+        self.gate_up_proj = nn.Linear(config.hidden_size, config.intermediate_size * 2)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, hidden_states):
+        hidden_states = self.layer_norm(hidden_states)
+        up_states = self.gate_up_proj(hidden_states)
+        up_states, gate = up_states.chunk(2, dim=-1)
+        up_states = up_states * self.act_fn(gate)
+        up_states = self.dropout(up_states)
+        hidden_states = self.down_proj(up_states)
+        out = self.dropout(hidden_states)
+
+        return out
+
+
+class Phi4MultimodalAudioAttention(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.config = config
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.dropout_rate
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        **kwargs,
+    ):
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        attention_interface: Callable = simple_eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, _ = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output
+
+
+class Phi4MultimodalAudioDepthWiseSeperableConv1d(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig, padding: int = 0):
+        super().__init__()
+        self.dw_conv = nn.Conv1d(
+            config.hidden_size,
+            config.hidden_size * config.depthwise_multiplier,
+            config.kernel_size,
+            1,
+            padding=padding,
+            groups=config.hidden_size,
+        )
+        self.pw_conv = nn.Conv1d(
+            config.hidden_size * config.depthwise_multiplier, config.depthwise_seperable_out_channel, 1, 1, 0
+        )
+
+    def forward(self, hidden_states):
+        return self.pw_conv(self.dw_conv(hidden_states))
+
+
+class Phi4MultimodalAudioGluPointWiseConv(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.config = config
+        self.output_dim = config.ext_pw_out_channel
+
+        self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel * 2, kernel_size=1, stride=1)
+        self.glu_act = ACT2FN[config.conv_glu_type]
+        self.b1 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1))
+        self.b2 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1))
+
+    def forward(self, hidden_states):
+        # we assume the input always has the #channel (#dim) in the last dimension of the
+        # tensor, so need to switch the dimension first for 1D-Conv case
+        hidden_states = hidden_states.permute([0, 2, 1])
+        hidden_states = self.ext_pw_conv_1d(hidden_states)
+        out = hidden_states[:, 0 : self.output_dim, :] + self.b1
+        out = out * self.glu_act(hidden_states[:, self.output_dim : self.output_dim * 2, :] + self.b2)
+        return out.permute([0, 2, 1])
+
+
+class Phi4MultimodalAudioConvModule(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.config = config
+        self.kernel_size = config.kernel_size
+
+        self.layer_norm = nn.LayerNorm(config.hidden_size)
+        self.glu = Phi4MultimodalAudioGluPointWiseConv(config)
+        self.dw_sep_conv_1d = Phi4MultimodalAudioDepthWiseSeperableConv1d(config, padding=config.kernel_size - 1)
+        self.act = ACT2FN[config.conv_activation]
+        self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel, kernel_size=1, stride=1)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, hidden_states: torch.Tensor):
+        hidden_states = self.glu(self.layer_norm(hidden_states))
+        hidden_states = self.dw_sep_conv_1d(hidden_states.permute([0, 2, 1]))
+
+        if self.kernel_size > 1:
+            hidden_states = hidden_states[:, :, : -(self.kernel_size - 1)]
+
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.ext_pw_conv_1d(hidden_states)
+        out = self.dropout(hidden_states.permute([0, 2, 1]))
+        return out
+
+
+class Phi4MultimodalAudioConformerEncoderLayer(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+
+        self.feed_forward_in = Phi4MultimodalAudioMLP(config)
+        self.self_attn = Phi4MultimodalAudioAttention(config)
+        self.conv = Phi4MultimodalAudioConvModule(config)
+        self.feed_forward_out = Phi4MultimodalAudioMLP(config)
+        self.layer_norm_att = nn.LayerNorm(config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ):
+        residual = hidden_states + 0.5 * self.feed_forward_in(hidden_states)
+        hidden_states = self.layer_norm_att(residual)
+
+        hidden_states = residual + self.self_attn(hidden_states, attention_mask)
+        hidden_states = hidden_states + self.conv(hidden_states)
+        hidden_states = hidden_states + 0.5 * self.feed_forward_out(hidden_states)
+
+        out = self.layer_norm(hidden_states)
+
+        return out
+
+
+class Phi4MultimodalAudioNemoConvSubsampling(torch.nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.subsampling_factor = config.time_reduction
+        self.sampling_num = int(math.log(self.subsampling_factor, 2))
+        self.act_fn = ACT2FN[config.nemo_activation]
+        conv_channels = config.nemo_conv_channels
+
+        layers = [
+            nn.Conv2d(1, conv_channels, kernel_size=3, stride=2, padding=1),
+            self.act_fn,
+        ]
+        for _ in range(self.sampling_num - 1):
+            layers.extend(
+                [
+                    nn.Conv2d(conv_channels, conv_channels, kernel_size=3, stride=2, padding=1, groups=conv_channels),
+                    nn.Conv2d(conv_channels, conv_channels, kernel_size=1, stride=1, padding=0, groups=1),
+                    self.act_fn,
+                ]
+            )
+
+        # Aggregate the layers
+        self.conv = torch.nn.Sequential(*layers)
+        self.out = torch.nn.Linear(conv_channels * config.nemo_final_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor, mask: Optional[torch.Tensor]):
+        # Unsqueeze Channel Axis
+        hidden_states = hidden_states.unsqueeze(1)
+        hidden_states = self.conv(hidden_states)
+
+        # Flatten Channel and Frequency Axes
+        b, _, t, _ = hidden_states.size()
+        hidden_states = self.out(hidden_states.transpose(1, 2).reshape(b, t, -1))
+
+        if mask is None:
+            return hidden_states, None
+
+        max_audio_length = hidden_states.shape[1]
+        feature_lens = mask.sum(1)
+        padding_length = torch.ceil(feature_lens / self.subsampling_factor)
+        arange_ = torch.arange(0, max_audio_length, device=hidden_states.device)
+        pad_mask = arange_.expand(padding_length.size(0), -1) < padding_length.unsqueeze(1)
+        return hidden_states, pad_mask.unsqueeze(1)
+
+
+class Phi4MultimodalAudioRelativeAttentionBias(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+
+        self.max_distance = config.bias_max_distance
+        self.symmetric = config.bias_symmetric
+        self.num_buckets = self.max_distance
+        if not config.bias_symmetric:
+            self.num_buckets *= 2
+        self.bias_values = nn.Embedding(self.num_buckets, config.num_attention_heads)
+
+    def forward(self, x):
+        # instantiate bias compatible with shape of x
+        max_pos = x.size(1)
+        context_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[:, None]
+        memory_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position
+        # clipping to a maximum distance using ops that play well with ONNX export
+        relative_position = relative_position.masked_fill(relative_position < -self.max_distance, -self.max_distance)
+        relative_position = relative_position.masked_fill(
+            relative_position > self.max_distance - 1, self.max_distance - 1
+        )
+
+        # mapping from relative position to index in the bias parameter
+        bias_idx = relative_position
+        bias_idx = bias_idx.abs() if self.symmetric else bias_idx + self.num_buckets // 2
+
+        att_bias = self.bias_values(bias_idx)
+        att_bias = att_bias.permute(2, 0, 1).unsqueeze(0)
+
+        return att_bias
+
+
+class Phi4MultimodalAudioMeanVarianceNormLayer(nn.Module):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.register_buffer("global_mean", torch.zeros(config.input_size))
+        self.register_buffer("global_invstd", torch.ones(config.input_size))
+
+    def forward(self, x):
+        return (x - self.global_mean) * self.global_invstd
+
+
+class Phi4MultimodalAudioPreTrainedModel(PreTrainedModel):
+    config_class = Phi4MultimodalAudioConfig
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Phi4MultimodalAudioConformerEncoderLayer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)):
+            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_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, Phi4MultimodalAudioGluPointWiseConv):
+            module.b1.data.zero_()
+            module.b2.data.zero_()
+
+
+def unfold_tensor(tensor, max_seq_len):
+    """
+    For a given tensor with shape of (N, T, D), if sequence length T is longer than max_seq_len,
+    this function unfold it to a (NT', max_seq_len, D) where T' is T // max_seq_len.
+    Args:
+        tensor: N, T, D
+    """
+    _, _, D = tensor.shape
+    tensor = tensor.transpose(-1, -2)
+    # N x D x 1 x T => N x (D x max_seq_len) x T'
+    tensor = F.unfold(tensor[..., None, :], kernel_size=(1, max_seq_len), stride=(1, max_seq_len))
+
+    new_bsz, _, slen = tensor.shape
+    tensor = tensor.view(new_bsz, -1, max_seq_len, slen)
+    tensor = tensor.permute(0, 3, 2, 1)
+    tensor = tensor.view(-1, max_seq_len, D).contiguous()
+    return tensor
+
+
+def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0):
+    """
+    The function is very important for Transformer Transducer Streaming mode
+    Args:
+        xs_len (int): sequence length
+        chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. It also supports adaptive chunk size [0,10,15,45]
+        left_window (int): how many left chunks can be seen
+        right_window (int): how many right chunks can be seen. It is used for chunk overlap model.
+        Returns:
+            mask (torch.Tensor): a mask tensor for streaming model
+    """
+    chunk_start_idx = torch.Tensor(chunk_start_idx).long()
+    start_pad = torch.nn.functional.pad(
+        chunk_start_idx, (1, 0)
+    )  # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48]
+    end_pad = torch.nn.functional.pad(
+        chunk_start_idx, (0, 1), value=x_len
+    )  # append x_len to the end, so it becomes [0,18,36,48, x_len]
+    seq_range = torch.arange(0, x_len).unsqueeze(-1)
+    idx = ((seq_range < end_pad) & (seq_range >= start_pad)).nonzero()[:, 1]
+    seq_range_expand = torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1)
+    idx_left = idx - left_window
+    idx_left[idx_left < 0] = 0
+    boundary_left = start_pad[idx_left]
+    mask_left = seq_range_expand >= boundary_left.unsqueeze(-1)
+    idx_right = idx + right_window
+    idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx)
+    boundary_right = end_pad[idx_right]
+    mask_right = seq_range_expand < boundary_right.unsqueeze(-1)
+    return mask_left & mask_right
+
+
+class Phi4MultimodalAudioModel(Phi4MultimodalAudioPreTrainedModel):
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.encoder_embedding = Phi4MultimodalAudioMeanVarianceNormLayer(config)
+        self.embed = Phi4MultimodalAudioNemoConvSubsampling(config)
+        self.relative_attention_bias_layer = Phi4MultimodalAudioRelativeAttentionBias(config)
+        self.encoders = nn.ModuleList(
+            [Phi4MultimodalAudioConformerEncoderLayer(config) for _ in range(config.num_blocks)]
+        )
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk):
+        # Create mask matrix for streaming
+        # S stores start index. if chunksize is 18, s is [0,18,36,....]
+        chunk_start_idx = np.arange(0, seq_len, chunk_size)
+        # avoid randomness when run evaluation or decoding
+        if self.training and np.random.rand() > 0.5:
+            # Either first or last chunk is not complete.
+            # If only the last one is not complete, EOS is not effective
+            chunk_start_idx = seq_len - chunk_start_idx
+            chunk_start_idx = chunk_start_idx[::-1]
+            chunk_start_idx = chunk_start_idx[:-1]
+            chunk_start_idx = np.insert(chunk_start_idx, 0, 0)
+
+        enc_streaming_mask = (
+            adaptive_enc_mask(seq_len, chunk_start_idx, left_window=left_chunk)
+            .unsqueeze(0)
+            .expand([batch_size, -1, -1])
+        )
+        return enc_streaming_mask
+
+    def forward_embeddings(self, hidden_states, masks):
+        """Forwarding the inputs through the top embedding layers"""
+        seq_len = math.ceil(hidden_states.shape[1] / self.config.time_reduction)
+        if seq_len <= 0:
+            raise ValueError(
+                f"The squence length after time reduction is invalid: {seq_len}. Your input feature is too short."
+            )
+
+        batch_size = hidden_states.shape[0]
+
+        enc_streaming_mask = self._streaming_mask(seq_len, batch_size, self.config.chunk_size, self.config.left_chunk)
+        enc_streaming_mask = enc_streaming_mask.to(hidden_states.device)
+
+        hidden_states, masks = self.embed(hidden_states, masks)
+
+        streaming_mask = enc_streaming_mask
+        if streaming_mask is not None and masks is not None:
+            hs_mask = masks & streaming_mask
+        elif masks is not None:
+            hs_mask = masks
+        else:
+            hs_mask = streaming_mask
+
+        return hidden_states, hs_mask, masks
+
+    def calculate_hs_mask(self, hidden_states, device, mask):
+        max_audio_length = hidden_states.shape[1]
+        batch_size = hidden_states.shape[0]
+        enc_streaming_mask = self._streaming_mask(
+            max_audio_length, batch_size, self.config.chunk_size, self.config.left_chunk
+        )
+        enc_streaming_mask = enc_streaming_mask.to(device)
+        if mask is None:
+            return enc_streaming_mask
+
+        feature_lens = mask.sum(1)
+        padding_length = feature_lens
+        pad_mask = torch.arange(0, max_audio_length, device=device).expand(
+            padding_length.size(0), -1
+        ) < padding_length.unsqueeze(1)
+        pad_mask = pad_mask.unsqueeze(1)
+        pad_mask = pad_mask & enc_streaming_mask
+        return pad_mask
+
+    def forward(self, hidden_states: torch.Tensor, mask: Optional[torch.Tensor]):
+        hidden_states = self.encoder_embedding(hidden_states)
+        hidden_states, hs_mask, mask = self.forward_embeddings(hidden_states, mask)
+
+        unfolded = False
+        bs, seq_len, _ = hidden_states.shape
+        max_seq_len = 500  # maxium position for absolute positional encoding
+        if seq_len > max_seq_len:
+            # audio sequence is longer than max_seq_len, unfold it into chunks of max_seq_len
+            unfolded = True
+            # the unfold op will drop residual frames, pad it to the multiple of max_seq_len
+            if seq_len % max_seq_len > 0:
+                chunk_pad_size = max_seq_len - (seq_len % max_seq_len)
+            else:
+                chunk_pad_size = 0
+            if chunk_pad_size > 0:
+                hidden_states_pad = F.pad(hidden_states, (0, 0, 0, chunk_pad_size), "constant", 0)
+                hidden_states = hidden_states_pad.to(hidden_states.device)
+
+            hidden_states = unfold_tensor(hidden_states, max_seq_len)
+            masks_unfold = None
+            if mask is not None:
+                # revise hs_mask here because the previous calculated hs_mask did not consider extra pad
+                subsampled_pad_mask = mask.squeeze(1)  # [bz, subsampled_unmask_seq_len]
+                extra_padded_subsamlped_pad_mask = F.pad(
+                    subsampled_pad_mask, (0, chunk_pad_size), "constant", False
+                )  # extra padding to the pad mask
+                extra_padded_subsamlped_pad_mask = extra_padded_subsamlped_pad_mask.unsqueeze(-1).float()
+                masks_unfold = unfold_tensor(
+                    extra_padded_subsamlped_pad_mask, max_seq_len
+                )  # unfold the pad mask like we did to the input tensor
+                masks_unfold = masks_unfold.squeeze(-1).bool()  # unfold op does not support bool tensor
+            hs_mask = self.calculate_hs_mask(
+                hidden_states, hidden_states.device, masks_unfold
+            )  # calculate hs_mask based on the unfolded pad mask
+
+        relative_attention_bias = self.relative_attention_bias_layer(hidden_states)
+        attention_mask = hs_mask.unsqueeze(1) + relative_attention_bias
+
+        for layer in self.encoders:
+            if self.gradient_checkpointing and self.training:
+                hidden_states = self._gradient_checkpointing_func(
+                    layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                )
+            else:
+                hidden_states = layer(hidden_states, attention_mask)
+
+        if unfolded:
+            embed_dim = hidden_states.shape[-1]
+            hidden_states = hidden_states.reshape(bs, -1, embed_dim)
+            # if we ever padded before unfolding, we need to remove the padding
+            if chunk_pad_size > 0:
+                hidden_states = hidden_states[:, :-chunk_pad_size, :]
+
+        return hidden_states
+
+
+class Phi4MultimodalAudioEmbedding(nn.Module):
+    def __init__(self, config: Phi4MultimodalConfig):
+        super().__init__()
+        self.config = config
+        self.layer_idx = config.audio_config.feature_layer
+
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.encoder = Phi4MultimodalAudioModel._from_config(config.audio_config)
+        self.up_proj_for_speech = nn.Linear(
+            config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size
+        )
+        self.down_proj_for_speech = nn.Linear(config.hidden_size, config.hidden_size)
+        self.up_proj_for_vision_speech = nn.Linear(
+            config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size
+        )
+        self.down_proj_for_vision_speech = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.Tensor,
+        audio_input_features: torch.FloatTensor,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+        audio_projection_mode="speech",
+    ) -> torch.FloatTensor:
+        with torch.no_grad():
+            positions_tuple = torch.nonzero(input_ids == self.config.audio_config.audio_token_id, as_tuple=True)
+
+        up_proj = self.up_proj_for_speech if audio_projection_mode == "speech" else self.up_proj_for_vision_speech
+        down_proj = (
+            self.down_proj_for_speech if audio_projection_mode == "speech" else self.down_proj_for_vision_speech
+        )
+
+        target_device = up_proj.bias.device
+        target_dtype = up_proj.bias.dtype
+
+        audio_input_features = audio_input_features.to(device=target_device, dtype=target_dtype)
+
+        audio_encoder_hidden_states = self.encoder(audio_input_features, audio_attention_mask)
+        audio_encoder_hidden_states = up_proj(audio_encoder_hidden_states)
+        audio_encoder_hidden_states = nn.functional.gelu(audio_encoder_hidden_states)
+        audio_embeds = down_proj(audio_encoder_hidden_states)
+
+        merged_audio_embeds = torch.cat(
+            [audio_embeds[i, : audio_embed_sizes[i], :] for i in range(len(audio_embed_sizes))], dim=0
+        )
+        merged_audio_embeds = merged_audio_embeds.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+        # Temporarily disable autocast to avoid issue on bf16 tensors
+        # Ref: https://github.com/pytorch/pytorch/issues/132715
+        with torch.autocast(device_type=inputs_embeds.device.type, enabled=False):
+            audio_embeds = inputs_embeds.index_put(
+                indices=positions_tuple, values=merged_audio_embeds, accumulate=False
+            )
+
+        audio_embeds = self.drop(audio_embeds)
+
+        return audio_embeds
+
+
+class Phi4MultimodalRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Phi4MultimodalRMSNorm 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)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Phi4MultimodalMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.activation_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        up_states = self.gate_up_proj(hidden_states)
+
+        gate, up_states = up_states.chunk(2, dim=-1)
+        up_states = up_states * self.activation_fn(gate)
+
+        return self.down_proj(up_states)
+
+
+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 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)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+
+    rotary_dim = cos.shape[-1]
+    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+    q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1)
+    k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1)
+    return q_embed, k_embed
+
+
+class Phi4MultimodalAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Phi4MultimodalConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.config.num_attention_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(hidden_shape).transpose(1, 2)
+        key_states = key_states.view(hidden_shape).transpose(1, 2)
+        value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=getattr(self.config, "sliding_window", None),
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Phi4MultimodalDecoderLayer(nn.Module):
+    def __init__(self, config: Phi4MultimodalConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Phi4MultimodalAttention(config=config, layer_idx=layer_idx)
+        self.mlp = Phi4MultimodalMLP(config)
+        self.input_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.config = config
+        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
+        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *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_value (`Cache`, *optional*): cached past key and value projection states
+            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`).
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            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,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + self.resid_attn_dropout(hidden_states)  # main diff with Llama
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.resid_mlp_dropout(hidden_states)  # main diff with Llama
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class Phi4MultimodalFeatureEmbedding(nn.Module):
+    """Image-audio embedding."""
+
+    def __init__(self, config: Phi4MultimodalConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.image_token_id = config.vision_config.image_token_id
+        self.audio_token_id = config.audio_config.audio_token_id
+        self.image_embed = Phi4MultimodalImageEmbedding(config)
+        self.audio_embed = Phi4MultimodalAudioEmbedding(config)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.Tensor,
+        image_pixel_values: Optional[torch.FloatTensor] = None,
+        audio_input_features: Optional[torch.FloatTensor] = None,
+        image_sizes=None,
+        image_attention_mask=None,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+    ) -> torch.FloatTensor:
+        with torch.no_grad():
+            image_position_mask = (input_ids == self.config.vision_config.image_token_id).unsqueeze(-1)
+            non_image_position_mask = ~image_position_mask
+
+        image_embeds = None
+        audio_embeds = None
+        if image_pixel_values is not None and (input_ids == self.image_token_id).any():
+            image_embeds = self.image_embed(
+                input_ids,
+                inputs_embeds,
+                image_pixel_values=image_pixel_values,
+                image_sizes=image_sizes,
+                image_attention_mask=image_attention_mask,
+            )
+        if audio_input_features is not None and (input_ids == self.audio_token_id).any():
+            audio_projection_mode = "vision" if image_pixel_values is not None else "speech"
+            audio_embeds = self.audio_embed(
+                input_ids,
+                inputs_embeds,
+                audio_input_features=audio_input_features,
+                audio_embed_sizes=audio_embed_sizes,
+                audio_attention_mask=audio_attention_mask,
+                audio_projection_mode=audio_projection_mode,
+            )
+
+        # merge image and audio
+        if image_embeds is not None and audio_embeds is not None:
+            inputs_embeds = image_embeds * image_position_mask + audio_embeds * non_image_position_mask
+        elif image_embeds is not None:
+            inputs_embeds = image_embeds
+        elif audio_embeds is not None:
+            inputs_embeds = audio_embeds
+
+        return inputs_embeds
+
+
+class Phi4MultimodalRotaryEmbedding(nn.Module):
+    def __init__(self, config: Phi4MultimodalConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+PHI4_MULTIMODAL_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 ([`Phi4MultimodalConfig`]):
+            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 Phi4Multimodal Model outputting raw hidden-states without any specific head on top.",
+    PHI4_MULTIMODAL_START_DOCSTRING,
+)
+class Phi4MultimodalPreTrainedModel(PreTrainedModel):
+    config_class = Phi4MultimodalConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Phi4MultimodalDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+    _version = "0.0.5"
+
+    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_()
+        elif isinstance(module, Phi4MultimodalRMSNorm):
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, Phi4MultimodalImageEmbedding):
+            module.global_img_feature_extensor.data.zero_()
+            module.sub_img_feature_extensor.data.zero_()
+
+
+PHI4_MULTIMODAL_MODEL_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 indices in `input_values`. 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)
+        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 (`Cache`)`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+            See our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+
+            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.
+        image_pixel_values (`torch.FloatTensor`, *optional*):
+            If the input contains images, these correspond to the pixel values after transformations (as returned by
+            the Processor)
+        image_sizes (`torch.LongTensor`, *optional*):
+            If the input contains images, these correspond to size of each image.
+        image_attention_mask (`torch.LongTensor`, *optional*):
+            Attention mask for the images.
+        audio_input_features (`torch.FloatTensor`, *optional*):
+            If the input contains audio samples, these correspond to the values after transformation (as returned by
+            the Processor).
+        audio_embed_sizes (`torch.Tensor`, *optional*):
+            Size of the audio inputs.
+        audio_attention_mask (`torch.Tensor, *optional*):
+            Attention mask for the audio inputs.
+        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.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi4Multimodal Model outputting raw hidden-states without any specific head on top.",
+    PHI4_MULTIMODAL_START_DOCSTRING,
+)
+class Phi4MultimodalModel(Phi4MultimodalPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi4MultimodalMMDecoderLayer`]
+    Args:
+        config: Phi4MultimodalMMConfig
+    """
+
+    def __init__(self, config: Phi4MultimodalConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList(
+            [Phi4MultimodalDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Phi4MultimodalRotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        self.embed_dropout = nn.Dropout(config.embd_pdrop)
+
+        self.embed_tokens_extend = Phi4MultimodalFeatureEmbedding(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
+    @add_start_docstrings_to_model_forward(PHI4_MULTIMODAL_MODEL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[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,
+        image_pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.LongTensor] = None,
+        image_attention_mask=None,
+        audio_input_features: Optional[torch.FloatTensor] = None,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or 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
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            inputs_embeds = self.embed_tokens_extend(
+                input_ids,
+                inputs_embeds,
+                image_pixel_values=image_pixel_values,
+                audio_input_features=audio_input_features,
+                image_sizes=image_sizes,
+                image_attention_mask=image_attention_mask,
+                audio_embed_sizes=audio_embed_sizes,
+                audio_attention_mask=audio_attention_mask,
+            )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                    **kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            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,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: Union[torch.Tensor, "BlockMask"],
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool = False,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and past_key_values is not None:
+                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+                if is_padding_right:
+                    raise ValueError(
+                        "You are attempting to perform batched generation with padding_side='right'"
+                        " this may lead to unexpected behaviour for Flash Attention version of Phi4Multimodal. Make sure to "
+                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                    )
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+        if self.config._attn_implementation == "flex_attention":
+            if isinstance(attention_mask, torch.Tensor):
+                attention_mask = make_flex_block_causal_mask(attention_mask)
+            return attention_mask
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not (using_static_cache or using_sliding_window_cache)
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                sliding_window=self.config.sliding_window,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        # SlidingWindowCache or StaticCache
+        if using_sliding_window_cache or using_static_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        # DynamicCache or no cache
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+            config=self.config,
+            past_key_values=past_key_values,
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu", "npu"]
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        config: Phi4MultimodalConfig,
+        past_key_values: Cache,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            device (`torch.device`):
+                The device to place the 4D attention mask on.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+            config (`Phi4MultimodalConfig`):
+                The model's configuration class
+            past_key_values (`Cache`):
+                The cache class that is being used currently to generate
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            if config.get_text_config().sliding_window is not None:
+                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
+                # the check is needed to verify is current checkpoint was trained with sliding window or not
+                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
+                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
+                        cache_position.reshape(-1, 1) - config.get_text_config().sliding_window
+                    )
+                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
+            causal_mask *= diagonal_attend_mask
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                if attention_mask.shape[-1] > target_length:
+                    attention_mask = attention_mask[:, :target_length]
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        return causal_mask
+
+
+class Phi4MultimodalForCausalLM(Phi4MultimodalPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Phi4MultimodalModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @can_return_tuple
+    @add_start_docstrings_to_model_forward(PHI4_MULTIMODAL_MODEL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=Phi4MultimodalConfig)
+    def forward(
+        self,
+        input_ids: Optional[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,
+        image_pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.LongTensor] = None,
+        image_attention_mask=None,
+        audio_input_features: Optional[torch.FloatTensor] = None,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        r"""
+            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]`.
+
+            logits_to_keep (`int` or `torch.Tensor`, *optional*):
+                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+                This is useful when using packed tensor format (single dimension for batch and sequence length).
+        Returns:
+
+        Example:
+        ```python
+        >>> from transformers import AutoTokenizer, Phi4MultimodalForCausalLM
+        >>> model = Phi4MultimodalForCausalLM.from_pretrained("TBA")
+        >>> tokenizer = AutoTokenizer.from_pretrained("TBA")
+        >>> prompt = "This is an example script ."
+        >>> 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]
+        'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
+        ```"""
+
+        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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            image_pixel_values=image_pixel_values,
+            image_sizes=image_sizes,
+            image_attention_mask=image_attention_mask,
+            audio_input_features=audio_input_features,
+            audio_embed_sizes=audio_embed_sizes,
+            audio_attention_mask=audio_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size)
+
+        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,
+        image_pixel_values=None,
+        image_sizes=None,
+        image_attention_mask=None,
+        audio_input_features=None,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        logits_to_keep=0,
+        **kwargs,
+    ):
+        # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
+        # process
+
+        # When the first time input length reached long and short factor switching point, enforce re-compute cache
+        # It will cause downside of slower at this single token position, however, better than current failure.
+        if (
+            past_key_values
+            and self.config.rope_scaling
+            and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
+        ):
+            past_length = cache_position[0]
+            if past_length <= self.config.original_max_position_embeddings:
+                past_key_values = None
+
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            image_pixel_values=image_pixel_values,
+            image_sizes=image_sizes,
+            image_attention_mask=image_attention_mask,
+            audio_input_features=audio_input_features,
+            audio_embed_sizes=audio_embed_sizes,
+            audio_attention_mask=audio_attention_mask,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            use_cache=use_cache,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+        return model_inputs
+
+
+__all__ = [
+    "Phi4MultimodalAudioPreTrainedModel",
+    "Phi4MultimodalAudioModel",
+    "Phi4MultimodalVisionPreTrainedModel",
+    "Phi4MultimodalVisionModel",
+    "Phi4MultimodalPreTrainedModel",
+    "Phi4MultimodalModel",
+    "Phi4MultimodalForCausalLM",
+]
+
+
+Phi4MultimodalForCausalLM.register_for_auto_class("AutoModelForCausalLM")
\ No newline at end of file