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config.json

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+{
+  "_name_or_path": "vikhyatk/moondream2",
+  "architectures": [
+    "Moondream"
+  ],
+  "auto_map": {
+    "AutoConfig": "vikhyatk/moondream2--configuration_moondream.MoondreamConfig",
+    "AutoModelForCausalLM": "vikhyatk/moondream2--moondream.Moondream"
+  },
+  "model_type": "moondream1",
+  "text_config": {
+    "model_type": "phi"
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.2"
+}

configuration_moondream.py

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+from transformers import PretrainedConfig
+
+
+class PhiConfig(PretrainedConfig):
+    model_type = "phi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=51200,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="gelu_new",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        partial_rotary_factor=0.5,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
+            )
+
+
+class MoondreamConfig(PretrainedConfig):
+    model_type = "moondream1"
+
+    def __init__(self, **kwargs):
+        self.text_config = PhiConfig(**kwargs.pop("text_config", {}))
+        super().__init__(**kwargs)

fourier_features.py

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+# Adopted from https://github.com/crowsonkb/k-diffusion/blob/transformer-model-v2/k_diffusion/layers.py
+
+import torch
+import torch.nn as nn
+import math
+
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1.0):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.register_buffer(
+            "weight", torch.randn([out_features // 2, in_features]) * std
+        )
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.44.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9fe382ec0e9fdaeb26f3b498f01e40047443c9ec77a5373a363ecc41d2c6924
+size 3736040266

modeling_phi.py

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+# coding=utf-8
+# Copyright 2023 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.
+
+"""PyTorch Phi model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    get_torch_version,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    is_torchdynamo_compiling,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_moondream import PhiConfig
+
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "PhiConfig"
+
+
+# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+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,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+):
+    """
+    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 plcae the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+    """
+    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:
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
+            dtype=dtype,
+            device=device,
+        )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        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
+            mask_length = attention_mask.shape[-1]
+            padding_mask = (
+                causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            )
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[
+                :, :, :, :mask_length
+            ].masked_fill(padding_mask, min_dtype)
+
+    return causal_mask
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Phi
+class PhiRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                / self.dim
+            )
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings,
+            device=self.inv_freq.device,
+            dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi
+class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi
+class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base
+                ** (
+                    torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                    / self.dim
+                )
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+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)
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, 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`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        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[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
+class PhiMLP(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
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class PhiAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.partial_rotary_factor = config.partial_rotary_factor
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.Wqkv = nn.Linear(
+            self.hidden_size, 3 * self.num_heads * self.head_dim, bias=True
+        )
+        self.out_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=True
+        )
+
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = PhiRotaryEmbedding(
+                int(self.partial_rotary_factor * self.head_dim),
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = PhiLinearScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def 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: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
+        attn_weights = torch.matmul(
+            query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights += causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class PhiFlashAttention2(PhiAttention):
+    """
+    Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # PhiFlashAttention2 attention does not support output_attentions
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_dropout = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32.
+
+        if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=attn_dropout,
+            softmax_scale=None,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class PhiSdpaAttention(PhiAttention):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.require_contiguous_qkv = version.parse(
+            get_torch_version()
+        ) < version.parse("2.2.0")
+
+    """
+    SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from PhiAttention.forward
+    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: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not "
+                "support `output_attentions=True`. Falling back to the manual attention implementation, but specifying "
+                "the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can "
+                'be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                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,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
+        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
+        # Reference: https://github.com/pytorch/pytorch/issues/112577
+        if (
+            self.require_contiguous_qkv
+            and query_states.device.type == "cuda"
+            and attention_mask is not None
+        ):
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+PHI_ATTENTION_CLASSES = {
+    "eager": PhiAttention,
+    "flash_attention_2": PhiFlashAttention2,
+    "sdpa": PhiSdpaAttention,
+}
+
+
+class PhiDecoderLayer(nn.Module):
+    def __init__(self, config: PhiConfig, layer_idx: int):
+        super().__init__()
+        self.mixer = PHI_ATTENTION_CLASSES[config._attn_implementation](
+            config, layer_idx=layer_idx
+        )
+        self.mlp = PhiMLP(config)
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.resid_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,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 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)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            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.ln(hidden_states)
+
+        # Self Attention
+        attn_outputs, self_attn_weights, present_key_value = self.mixer(
+            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,
+        )
+        attn_outputs = self.resid_dropout(attn_outputs)
+
+        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+PHI_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 ([`PhiConfig`]):
+            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 Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class PhiPreTrainedModel(PreTrainedModel):
+    config_class = PhiConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PhiDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class Embedding(nn.Module):
+    def __init__(self, config: PhiConfig):
+        super().__init__()
+        self.wte = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+
+    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
+        return self.wte(input_ids)
+
+PHI_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *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`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        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 Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class PhiModel(PhiPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
+
+    Args:
+        config: PhiConfig
+    """
+
+    def __init__(self, config: PhiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embd = Embedding(config)
+        self.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [
+                PhiDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._use_sdpa = config._attn_implementation == "sdpa"
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embd.wte
+
+    def set_input_embeddings(self, value):
+        self.embd.wte = value
+
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        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
+
+        use_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
+            use_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embd(input_ids)
+
+        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
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.h:
+            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,
+                    output_attentions,
+                    use_cache,
+                    past_key_values,
+                    cache_position,
+                )
+            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,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache()
+                if use_legacy_cache
+                else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # 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)
+
+        # 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
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                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]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        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 = _prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            min_dtype=min_dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            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
+
+
+class CausalLMHead(nn.Module):
+    """Causal Language Modeling head. Simplified version."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.linear = nn.Linear(config.hidden_size, config.vocab_size)
+
+    def forward(self, hidden_states):
+        return self.linear(self.ln(hidden_states))
+
+
+class PhiForCausalLM(PhiPreTrainedModel):
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = PhiModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = CausalLMHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.transformer.embd.wte
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.transformer.embd.wte = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.lm_head.linear
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.linear = new_embeddings
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_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.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, PhiForCausalLM
+
+        >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
+
+        >>> 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\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        num_logits_to_keep=0,
+        **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif (
+                input_ids.shape[1] != cache_position.shape[0]
+            ):  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {
+                "input_ids": input_ids.clone(memory_format=torch.contiguous_format),
+                "inputs_embeds": None,
+            }
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "num_logits_to_keep": num_logits_to_keep,
+            }
+        )
+        return model_inputs

moondream.py

@@ -0,0 +1,230 @@
+import torch
+
+from typing import List, Union, Literal, Optional
+from transformers import PreTrainedModel
+from PIL import Image
+
+from .configuration_moondream import PhiConfig
+from .configuration_moondream import MoondreamConfig
+from .vision_encoder import VisionEncoder
+from .region_model import RegionModel
+from .modeling_phi import PhiForCausalLM
+
+class Moondream(PreTrainedModel):
+    config_class = MoondreamConfig
+    _supports_flash_attn_2 = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vision_encoder = VisionEncoder(
+            use_flash_attn=config._attn_implementation == "flash_attention_2"
+        )
+        self.region_model = RegionModel()
+
+        if type(config.text_config) == dict:
+            phi_config = PhiConfig(
+                **config.text_config, attn_implementation=config._attn_implementation
+            )
+        else:
+            phi_config = config.text_config
+        self.text_model = PhiForCausalLM(phi_config)
+
+    @property
+    def device(self):
+        return self.text_model.device
+
+    def encode_image(self, image):
+        with torch.no_grad():
+            return self.vision_encoder(image)
+
+    def input_embeds(self, prompt, image_embeds, tokenizer):
+        def _tokenize(txt):
+            return tokenizer(
+                txt, return_tensors="pt", add_special_tokens=False
+            ).input_ids.to(self.device)
+
+        text_emb = self.text_model.get_input_embeddings()
+
+        # Add BOS token
+        embeds = []
+        embeds.append(
+            text_emb((torch.tensor([[tokenizer.bos_token_id]], device=self.device)))
+        )
+
+        if "<image>" not in prompt:
+            embeds.append(text_emb(_tokenize(prompt)))
+        else:
+            assert prompt.count("<image>") == 1
+            before, after = prompt.split("<image>")
+            if len(before) > 0:
+                embeds.append(text_emb(_tokenize(before)))
+            embeds.append(image_embeds.to(self.device))
+            if len(after) > 0:
+                embeds.append(text_emb(_tokenize(after)))
+
+        return torch.cat(embeds, dim=1)
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def generate(
+        self,
+        image_embeds,
+        prompt,
+        tokenizer,
+        max_new_tokens=128,
+        **kwargs,
+    ):
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "max_new_tokens": max_new_tokens,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+            attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+    # Note: Not ready for use yet, intended for September release.
+    def caption(
+        self,
+        images: List[Image.Image],
+        tokenizer,
+        length: Optional[Literal["short"]] = None,
+        **kwargs,
+    ):
+        image_embeds = self.encode_image(images)
+
+        templated_prompts = [
+            f"<image>\n\n{'Short caption' if length == 'short' else 'Caption'}:" for _ in images
+        ]
+        inputs_embeds = torch.stack([
+            self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
+            for prompt, image_embed in zip(templated_prompts, image_embeds)
+        ])
+        attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
+
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "repetition_penalty": 1.2,
+            "max_new_tokens": 512,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return [
+            x.strip()
+            for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        ]
+
+    def answer_question(
+        self,
+        image_embeds,
+        question,
+        tokenizer,
+        chat_history="",
+        result_queue=None,
+        max_new_tokens=256,
+        **kwargs,
+    ):
+        prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
+        answer = self.generate(
+            image_embeds,
+            prompt,
+            tokenizer=tokenizer,
+            max_new_tokens=max_new_tokens,
+            **kwargs,
+        )[0]
+        cleaned_answer = answer.strip()
+
+        # Use the result_queue to pass the result if it is provided
+        if result_queue:
+            result_queue.put(cleaned_answer)
+        else:
+            return cleaned_answer
+
+    def batch_answer(
+        self,
+        images,
+        prompts,
+        tokenizer,
+        **kwargs,
+    ):
+        image_embeds = self.encode_image(images)
+
+        templated_prompts = [
+            f"<image>\n\nQuestion: {prompt}\n\nAnswer:" for prompt in prompts
+        ]
+        prompt_embs = [
+            self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
+            for prompt, image_embed in zip(templated_prompts, image_embeds)
+        ]
+
+        bos_emb = prompt_embs[0][0]
+        max_len = max([p.shape[0] for p in prompt_embs])
+
+        inputs_embeds = torch.cat(
+            [
+                torch.cat([bos_emb.repeat(max_len - p.shape[0], 1), p]).unsqueeze(0)
+                for p in prompt_embs
+            ],
+            dim=0,
+        )
+        attention_mask = torch.cat(
+            [
+                torch.cat(
+                    [
+                        torch.zeros(
+                            1,
+                            max_len - p.shape[0],
+                            device=self.device,
+                            dtype=torch.long,
+                        ),
+                        torch.ones(1, p.shape[0], device=self.device, dtype=torch.long),
+                    ],
+                    dim=1,
+                )
+                for p in prompt_embs
+            ],
+            dim=0,
+        )
+
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "max_new_tokens": 512,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return [
+            x.strip()
+            for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        ]
+
+    def detect(self, image: Image.Image, query: str, tokenizer):
+        pass

region_model.py

@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+from .fourier_features import FourierFeatures
+
+class RegionModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.position_features = FourierFeatures(2, 256)
+        self.position_encoder = nn.Linear(256, 2048)
+        self.size_features = FourierFeatures(2, 256)
+        self.size_encoder = nn.Linear(256, 2048)
+
+        self.position_decoder = nn.Linear(2048, 2)
+        self.size_decoder = nn.Linear(2048, 2)
+        self.confidence_decoder = nn.Linear(2048, 1)
+
+    def encode_position(self, position):
+        return self.position_encoder(self.position_features(position))
+
+    def encode_size(self, size):
+        return self.size_encoder(self.size_features(size))
+
+    def decode_position(self, x):
+        return self.position_decoder(x)
+
+    def decode_size(self, x):
+        return self.size_decoder(x)
+
+    def decode_confidence(self, x):
+        return self.confidence_decoder(x)
+
+    def encode(self, position, size):
+        return torch.stack(
+            [self.encode_position(position), self.encode_size(size)], dim=0
+        )
+
+    def decode(self, position_logits, size_logits):
+        return (
+            self.decode_position(position_logits),
+            self.decode_size(size_logits),
+            self.decode_confidence(size_logits),
+        )

vision_encoder.py

@@ -0,0 +1,325 @@
+from typing import Union
+
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from torch import nn
+from einops import rearrange
+import PIL
+from torchvision.transforms.v2 import (
+    Compose,
+    Resize,
+    InterpolationMode,
+    ToImage,
+    ToDtype,
+    Normalize,
+)
+from transformers.utils import is_flash_attn_2_available
+
+try:
+    if is_flash_attn_2_available():
+        from flash_attn.modules.mha import FlashSelfAttention
+    else:
+        FlashSelfAttention = None
+except ImportError:
+    FlashSelfAttention = None
+
+
+class Attention(nn.Module):
+
+    def __init__(self, dim, num_heads=16, use_flash_attn=False):
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.proj = nn.Linear(dim, dim)
+
+        if use_flash_attn and FlashSelfAttention is not None:
+            self.flash_attn = FlashSelfAttention()
+        else:
+            self.flash_attn = None
+
+        torch.nn.init.kaiming_normal_(
+            self.qkv.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.proj.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.flash_attn is not None:
+            qkv = self.qkv(x)
+            qkv = rearrange(
+                qkv, "... (three h d) -> ... three h d", three=3, h=self.num_heads
+            )
+            attn_output = self.flash_attn(qkv)
+            output = rearrange(attn_output, "... h d -> ... (h d)")
+            output = self.proj(output)
+            return output
+        else:
+            B, N, C = x.shape
+            qkv = (
+                self.qkv(x)
+                .reshape(B, N, 3, self.num_heads, self.head_dim)
+                .permute(2, 0, 3, 1, 4)
+            )
+            q, k, v = qkv.unbind(0)
+
+            x = F.scaled_dot_product_attention(q, k, v)
+
+            x = x.transpose(1, 2).reshape(B, N, C)
+            x = self.proj(x)
+            return x
+
+
+class VitBlock(nn.Module):
+
+    def __init__(self, embed_dim, use_flash_attn=False):
+        super().__init__()
+        self.attn = Attention(embed_dim, use_flash_attn=use_flash_attn)
+        self.mlp = MLP(embed_dim, 4304)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.norm2 = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class VisionTransformer(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+
+        embed_len = 729
+        embed_dim = 1152
+
+        self.patch_embed = LinearPatchEmbedding()
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.blocks = nn.Sequential(
+            *[VitBlock(embed_dim, use_flash_attn=use_flash_attn) for _ in range(27)]
+        )
+        self.norm = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+        x = x + self.pos_embed
+        for block in self.blocks:
+            x = block(x)
+        return self.norm(x)
+
+
+class EncoderWrapper(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+        self.model = nn.ModuleDict({"visual": VisionTransformer(use_flash_attn)})
+
+    def forward(self, x):
+        return self.model["visual"](x)
+
+
+class LinearPatchEmbedding(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(588, 1152)
+
+    def forward(self, x):
+        b, c, hp1, wp2 = x.shape
+        p1, p2 = 14, 14
+        h, w = hp1 // p1, wp2 // p2
+        x = x.reshape(b, c, h, p1, w, p2)
+        x = x.permute(0, 2, 4, 1, 3, 5)
+        x = x.reshape(b, h * w, c * p1 * p2)
+
+        return self.linear(x)
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        out_features: int = None,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = nn.GELU(approximate="tanh")
+        self.fc2 = nn.Linear(hidden_features, out_features)
+
+        torch.nn.init.kaiming_normal_(
+            self.fc1.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.fc2.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class VisionProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        image_embedding_dim = 1152
+        model_dim = 2048
+        hidden_dim = model_dim * 4
+
+        self.mlp = MLP(image_embedding_dim * 2, hidden_dim, model_dim)
+
+    @property
+    def device(self):
+        return self.mlp.fc1.weight.device
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+def create_patches(image, patch_size=(378, 378)):
+    assert image.dim() == 3, "Image must be in CHW format"
+
+    _, height, width = image.shape  # Channels, Height, Width
+    patch_height, patch_width = patch_size
+
+    if height == patch_height and width == patch_width:
+        return []
+
+    # Iterate over the image and create patches
+    patches = []
+    for i in range(0, height, patch_height):
+        row_patches = []
+        for j in range(0, width, patch_width):
+            patch = image[:, i : i + patch_height, j : j + patch_width]
+            row_patches.append(patch)
+        patches.append(torch.stack(row_patches))
+    return patches
+
+
+class VisionEncoder(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+
+        self.encoder = EncoderWrapper(use_flash_attn)
+        self.projection = VisionProjection()
+        self.supported_sizes = [(378, 378), (378, 756), (756, 378), (756, 756)]
+
+    @property
+    def device(self):
+        return self.projection.mlp.fc1.weight.device
+
+    @property
+    def dtype(self):
+        return self.projection.mlp.fc1.weight.dtype
+
+    def preprocess(self, image: PIL.Image.Image):
+        width, height = image.size
+        max_dim = max(width, height)
+        if max_dim < 512:
+            im_size = (378, 378)
+        else:
+            aspect_ratio = width / height
+            im_size = min(
+                self.supported_sizes,
+                key=lambda size: (
+                    abs((size[1] / size[0]) - aspect_ratio),
+                    abs(size[0] - width) + abs(size[1] - height),
+                ),
+            )
+
+        return Compose(
+            [
+                Resize(size=im_size, interpolation=InterpolationMode.BICUBIC),
+                ToImage(),
+                ToDtype(torch.float32, scale=True),
+                Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ]
+        )(image)
+
+    def forward(
+        self, images: Union[PIL.Image.Image, list[PIL.Image.Image], torch.Tensor]
+    ) -> torch.Tensor:
+        im_list = None
+        if isinstance(images, torch.Tensor):
+            # Input must have dimensions (B, C, H, W)
+            assert (
+                len(images.shape) == 4
+            ), "Tensor input must have dimensions (B, C, H, W)"
+            im_list = list(images)
+        elif isinstance(images, PIL.Image.Image):
+            im_list = [images]
+        elif isinstance(images, list):
+            im_list = images
+        else:
+            raise ValueError(
+                "Input must be a PIL image, list of PIL images, or a tensor"
+            )
+
+        # Preprocess unless the images are already tensors (indicating that
+        # they have already been preprocessed)
+        if not isinstance(im_list[0], torch.Tensor):
+            im_list = [self.preprocess(im.convert("RGB")) for im in im_list]
+
+        patches = [create_patches(im) for im in im_list]
+        flat_patches = [patch for image_patches in patches for patch in image_patches]
+
+        # Images may be variable size, and need to be resized to a common size after
+        # creating patches.
+        resized_images = [
+            F.interpolate(im.unsqueeze(0), size=(378, 378), mode="bilinear")
+            for im in im_list
+        ]
+
+        combined_images = torch.cat([*resized_images, *flat_patches], dim=0)
+        combined_images = combined_images.to(self.device, dtype=self.dtype)
+
+        combined_features = self.encoder(combined_images)
+
+        full_img_features = combined_features[: len(im_list)]
+        patch_features = (
+            combined_features[len(im_list) :].transpose(1, 2).view(-1, 1152, 27, 27)
+        )
+
+        # Reshape patch features back to their original structure
+        reshaped_patch_features = []
+        patch_idx = 0
+        for i, patch_set in enumerate(patches):
+            if len(patch_set) == 0:
+                reshaped_patch_features.append(
+                    full_img_features[i].transpose(0, 1).view(1152, 27, 27)
+                )
+            else:
+                sample_features = []
+                for row_patches in patch_set:
+                    row_len = len(row_patches)
+                    row_features = patch_features[
+                        patch_idx : patch_idx + row_len
+                    ]  # row_len, T, C
+                    row_features = torch.cat(
+                        list(row_features), dim=2
+                    )  # T, C * row_len
+                    patch_idx += row_len
+                    sample_features.append(row_features)
+                sample_features = torch.cat(sample_features, dim=1)
+                sample_features = F.interpolate(
+                    sample_features.unsqueeze(0), size=(27, 27), mode="bilinear"
+                ).squeeze(0)
+                reshaped_patch_features.append(sample_features)
+        reshaped_patch_features = (
+            torch.stack(reshaped_patch_features).view(-1, 1152, 729).transpose(1, 2)
+        )
+
+        final_features = torch.cat([full_img_features, reshaped_patch_features], dim=2)
+
+        return self.projection(final_features)