modeling_llava.py

# coding=utf-8
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PreTrainedModel, SiglipVisionModel
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    ModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.utils import (
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
)

try:
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
except Exception as exp:
    print(exp)


from transformers.configuration_utils import PretrainedConfig
from transformers import SiglipVisionConfig


logger = logging.get_logger(__name__)


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,
        qk_layernorm=False,
        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.qk_layernorm = qk_layernorm
        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,
        )

    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 LlavaConfig(PretrainedConfig):
    model_type = "mc-llava"
    is_composition = False

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        ignore_index=-100,
        image_token_index=50297,
        projector_hidden_act="gelu",
        projector_tokens_num=1,
        vocab_size=51200,
        **kwargs,
    ):
        self.ignore_index = ignore_index
        self.image_token_index = image_token_index
        self.projector_hidden_act = projector_hidden_act
        self.projector_tokens_num = projector_tokens_num
        self.vocab_size = vocab_size

        self.text_config = text_config
        if isinstance(self.text_config, dict):
            text_config["model_type"] = (
                text_config["model_type"] if "model_type" in text_config else "phi"
            )
            self.text_config = PhiConfig(**text_config)
            self.vocab_size = self.text_config.vocab_size

        self.vision_config = vision_config
        if isinstance(self.vision_config, dict):
            self.vision_config = SiglipVisionConfig(**vision_config)
            self.vision_embed_dim = self.vision_config.hidden_size

        super().__init__(**kwargs)


# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(
        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
    )
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->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).float().to(device) / self.dim)
        )
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings,
            device=self.inv_freq.device,
            dtype=torch.get_default_dtype(),
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )

        freqs = torch.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.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->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=self.inv_freq.dtype
        )
        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.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->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).float().to(device) / self.dim)
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )

        freqs = torch.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.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
    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 `layer_idx` is not recommended and will "
                "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.q_proj = nn.Linear(
            self.hidden_size, self.num_heads * self.head_dim, bias=True
        )
        self.k_proj = nn.Linear(
            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
        )
        self.v_proj = nn.Linear(
            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
        )
        self.dense = nn.Linear(
            self.num_heads * self.head_dim, self.hidden_size, bias=True
        )

        self.qk_layernorm = config.qk_layernorm
        if self.qk_layernorm:
            self.q_layernorm = nn.LayerNorm(
                config.hidden_size // self.num_heads,
                eps=config.layer_norm_eps,
                elementwise_affine=True,
            )
            self.k_layernorm = nn.LayerNorm(
                config.hidden_size // self.num_heads,
                eps=config.layer_norm_eps,
                elementwise_affine=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}")

    # Phi-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
    @torch.autocast("cpu", enabled=False)
    @torch.autocast("cuda", enabled=False)
    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,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        if self.qk_layernorm:
            query_states = self.q_layernorm(query_states)
            key_states = self.k_layernorm(key_states)

        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,
            }
            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:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(
            attn_weights, dim=-1, dtype=torch.float32
        ).to(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.dense(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class PhiFlashAttention2(PhiAttention):
    # 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,
        **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 = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        if self.qk_layernorm:
            query_states = self.q_layernorm(query_states)
            key_states = self.k_layernorm(key_states)

        # 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,
            }
            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 = self._flash_attention_forward(
            query_states,
            key_states,
            value_states,
            attention_mask,
            q_len,
            dropout=attn_dropout,
            softmax_scale=None,
        )

        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
        attn_output = self.dense(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
    def _flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        query_length,
        dropout=0.0,
        softmax_scale=None,
    ):
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            (
                query_states,
                key_states,
                value_states,
                indices_q,
                cu_seq_lens,
                max_seq_lens,
            ) = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(
                attn_output_unpad, indices_q, batch_size, query_length
            )
        else:
            attn_output = flash_attn_func(
                query_states,
                key_states,
                value_states,
                dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

        return attn_output

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
    def _upad_input(
        self, query_layer, key_layer, value_layer, attention_mask, query_length
    ):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
            indices_k,
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
            indices_k,
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
                indices_k,
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
                query_layer, attention_mask
            )

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


PHI_ATTENTION_CLASSES = {
    "flash_attention_2": PhiFlashAttention2,
    "eager": PhiAttention,
}


class PhiDecoderLayer(nn.Module):
    def __init__(self, config: PhiConfig, layer_idx: int):
        super().__init__()
        if is_flash_attn_2_available():
            config._attn_implementation = "flash_attention_2"
        self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](
            config, layer_idx=layer_idx
        )
        self.mlp = PhiMLP(config)
        self.input_layernorm = 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,
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        attn_outputs, self_attn_weights, present_key_value = 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,
        )
        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


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_cache_class = True


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.embed_tokens = nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.embed_dropout = nn.Dropout(config.embd_pdrop)
        self.layers = nn.ModuleList(
            [
                PhiDecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self.final_layernorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps
        )
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    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,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        past_key_values_length = 0

        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:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length,
                seq_length + past_key_values_length,
                dtype=torch.long,
                device=device,
            )
            position_ids = position_ids.unsqueeze(0)

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        inputs_embeds = self.embed_dropout(inputs_embeds)

        # Attention mask.
        if self._use_flash_attention_2:
            # 2d mask is passed through the layers
            attention_mask = (
                attention_mask
                if (attention_mask is not None and 0 in attention_mask)
                else None
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )

        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.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,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.final_layernorm(hidden_states)

        # 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,
        )


class PhiForCausalLM(PhiPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
    def __init__(self, config):
        super().__init__(config)
        self.model = PhiModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)

        # 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.model.embed_tokens

    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
    def get_output_embeddings(self):
        return self.lm_head

    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
    def set_output_embeddings(self, new_embeddings):
        self.lm_head = 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

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    @staticmethod
    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(
                    past_state.index_select(0, beam_idx.to(past_state.device))
                    for past_state in layer_past
                ),
            )
        return reordered_past


class PhiForSequenceClassification(PhiPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = PhiModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        model_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = model_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError(
                "Cannot handle batch sizes > 1 if no padding token is defined."
            )
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = (
                    torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                )
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else:
                sequence_lengths = -1

        pooled_logits = logits[
            torch.arange(batch_size, device=logits.device), sequence_lengths
        ]

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (
                    labels.dtype == torch.long or labels.dtype == torch.int
                ):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(
                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
                )
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + model_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=model_outputs.past_key_values,
            hidden_states=model_outputs.hidden_states,
            attentions=model_outputs.attentions,
        )


class PhiForTokenClassification(PhiPreTrainedModel):
    def __init__(self, config: PhiConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.model = PhiModel(config)
        if (
            hasattr(config, "classifier_dropout")
            and config.classifier_dropout is not None
        ):
            classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        model_outputs = self.model(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = model_outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            batch_size, seq_length = labels.shape
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                logits.view(batch_size * seq_length, self.num_labels),
                labels.view(batch_size * seq_length),
            )

        if not return_dict:
            output = (logits,) + model_outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=model_outputs.hidden_states,
            attentions=model_outputs.attentions,
        )


@dataclass
class LlavaCausalLMOutputWithPast(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_features: Optional[torch.FloatTensor] = None


class SiglipVisionEncoder(nn.Module):
    def __init__(self, config: LlavaConfig):
        super().__init__()
        self.vision_tower = SiglipVisionModel(config.vision_config)

        self.coord_embed = nn.Sequential(
            nn.Linear(2, config.vision_embed_dim),
            nn.GELU(),
            nn.Linear(config.vision_embed_dim, config.vision_embed_dim),
        )

        self.num_tokens = 728

    def feature_select(self, image_forward_outs, coord_feature, num_tokens=None):
        image_features = image_forward_outs
        image_features = image_features[:, 1:]
        if num_tokens is None:
            num_tokens = self.num_tokens
        split_size = int(num_tokens / image_features.shape[0])
        sum = 0
        output_list = []
        for i in range(image_features.shape[0]):
            if i == image_features.shape[0] - 1:
                size = num_tokens - sum
            else:
                size = split_size
                sum += size
            chunk_output = image_features[i, -size:, :]
            chunk_output = chunk_output + coord_feature[i]
            output_list.append(chunk_output)
        image_features = torch.cat(output_list)
        return image_features

    def process_image_chunks(self, image_tensor, coord_tensor, num_tokens=None):
        if image_tensor.shape[0] > 50:
            image_forward_out = []
            for i in range(0, image_tensor.shape[0], 50):
                part_forward_out = self.vision_tower(
                    image_tensor[i : i + 50], output_hidden_states=True
                ).hidden_states[-1]
                image_forward_out.append(part_forward_out)
            image_forward_out = torch.cat(image_forward_out, dim=0)
        else:
            image_forward_out = self.vision_tower(
                image_tensor, output_hidden_states=True
            ).hidden_states[-1]
        coord_feature = self.coord_embed(coord_tensor)
        if len(coord_feature.shape) == 1:
            coord_feature = coord_feature.unsqueeze(0)
        image_feature = self.feature_select(
            image_forward_out, coord_feature, num_tokens
        ).to(image_tensor.dtype)
        return image_feature

    def forward(
        self, images: List[torch.Tensor], coords: List[torch.Tensor], num_tokens=None
    ):
        image_features = []
        for i, image in enumerate(images):
            image_feature = self.process_image_chunks(image, coords[i], num_tokens)
            image_features.append(image_feature)
        image_features = torch.stack(image_features)
        return image_features


class LlavaMultiModalProjector(nn.Module):
    def __init__(self, config: LlavaConfig):
        super().__init__()

        self.linear_1 = nn.Linear(
            config.vision_embed_dim,
            config.text_config.hidden_size,
            bias=True,
        )
        self.act = nn.GELU()
        self.linear_2 = nn.Linear(
            config.text_config.hidden_size,
            config.text_config.hidden_size,
            bias=True,
        )

    def forward(self, image_features):
        hidden_states = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


class LlavaPreTrainedModel(PreTrainedModel):
    config_class = LlavaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LlavaVisionAttention"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True

    def __init__(self, config):
        super().__init__(config)

    def _init_weights(self, module):
        return

    @property
    def _supports_sdpa(self):
        """
        Retrieve language_model's attribute to check whether the model supports
        SDPA or not.
        """
        return self.language_model._supports_sdpa


class LlavaForCausalLM(LlavaPreTrainedModel):
    def __init__(self, config: LlavaConfig):
        super().__init__(config)
        self.vision_model = SiglipVisionEncoder(config)

        self.multi_modal_projector = LlavaMultiModalProjector(config)
        self.vocab_size = config.vocab_size
        self.language_model = PhiForCausalLM(config.text_config)
        self.pad_token_id = (
            self.config.pad_token_id if self.config.pad_token_id is not None else -1
        )
        self.post_init()

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    def get_output_embeddings(self):
        return self.language_model.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.language_model.set_output_embeddings(new_embeddings)

    def set_decoder(self, decoder):
        self.language_model.transformer = decoder

    def get_decoder(self):
        return self.language_model.transformer

    def tie_weights(self):
        return self.language_model.tie_weights()

    def resize_token_embeddings(
        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None
    ) -> nn.Embedding:
        model_embeds = self.language_model.resize_token_embeddings(
            new_num_tokens, pad_to_multiple_of
        )
        # update vocab size
        self.config.text_config.vocab_size = model_embeds.num_embeddings
        self.config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds

    def _merge_input_ids_with_image_features(
        self, image_features, inputs_embeds, input_ids, attention_mask, position_ids
    ):
        num_images, num_image_patches, embed_dim = image_features.shape
        batch_size, sequence_length = input_ids.shape
        left_padding = not torch.sum(
            input_ids[:, -1] == torch.tensor(self.pad_token_id)
        )
        # 1. Create a mask to know where special image tokens are
        special_image_token_mask = input_ids == self.config.image_token_index
        num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
        # Compute the maximum embed dimension
        max_embed_dim = (
            num_special_image_tokens.max() * (num_image_patches - 1)
        ) + sequence_length
        batch_indices, non_image_indices = torch.where(
            input_ids != self.config.image_token_index
        )

        # 2. Compute the positions where text should be written
        # Calculate new positions for text tokens in merged image-text sequence.
        # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
        # `torch.cumsum` computes how each image token shifts subsequent text token positions.
        # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
        new_token_positions = (
            torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1)
            - 1
        )
        nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
        if left_padding:
            new_token_positions += nb_image_pad[:, None]  # offset for left padding
        text_to_overwrite = new_token_positions[batch_indices, non_image_indices]

        # 3. Create the full embedding, already padded to the maximum position
        final_embedding = torch.zeros(
            batch_size,
            max_embed_dim,
            embed_dim,
            dtype=inputs_embeds.dtype,
            device=inputs_embeds.device,
        )
        final_attention_mask = torch.zeros(
            batch_size,
            max_embed_dim,
            dtype=attention_mask.dtype,
            device=inputs_embeds.device,
        )
        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
        # set the corresponding tensors into their correct target device.
        target_device = inputs_embeds.device
        batch_indices, non_image_indices, text_to_overwrite = (
            batch_indices.to(target_device),
            non_image_indices.to(target_device),
            text_to_overwrite.to(target_device),
        )
        attention_mask = attention_mask.to(target_device)

        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[
            batch_indices, non_image_indices
        ]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[
            batch_indices, non_image_indices
        ]

        # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
        image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
        image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[
            :, None
        ].to(target_device)

        if image_to_overwrite.sum() != image_features.shape[:-1].numel():
            raise ValueError(
                f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
                f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
            )

        final_embedding[image_to_overwrite] = (
            image_features.contiguous().reshape(-1, embed_dim).to(target_device)
        )
        final_attention_mask |= image_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_(
            (final_attention_mask == 0), 1
        )
        return final_embedding, final_attention_mask, position_ids

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        image_features: torch.FloatTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, LlavaCausalLMOutputWithPast]:
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)
            if image_features is not None and input_ids.shape[1] != 1:
                (
                    inputs_embeds,
                    attention_mask,
                    position_ids,
                ) = self._merge_input_ids_with_image_features(
                    image_features,
                    inputs_embeds,
                    input_ids,
                    attention_mask,
                    position_ids,
                )
            else:
                # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
                # generation with cache
                if past_key_values is not None and image_features is not None and input_ids.shape[1] == 1:
                    # Retrieve the first layer to inspect the logits and mask out the hidden states
                    # that are set to 0
                    first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]

                    # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                    batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)

                    # Get the target length
                    target_seqlen = first_layer_past_key_value.shape[-1] + 1

                    extended_attention_mask = torch.ones(
                        (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
                        dtype=attention_mask.dtype,
                        device=attention_mask.device,
                    )

                    # Filter out only the tokens that can be un-attended, this can happen
                    # if one uses Llava + Fused modules where the cache on the
                    # first iteration is already big enough, or if one passes custom cache
                    valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                    new_batch_index = batch_index[valid_indices]
                    new_non_attended_tokens = non_attended_tokens[valid_indices]

                    # Zero-out the places where we don't need to attend
                    extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0

                    attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
                    position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1

        outputs = self.language_model(
            input_ids=None,
            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,
        )

        logits = outputs[0]

        if not return_dict:
            output = (logits,) + outputs[1:]
            return output

        return LlavaCausalLMOutputWithPast(
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_features=image_features,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        attention_mask=None,
        image_features=None,
        **kwargs,
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if (
                attention_mask is not None
                and attention_mask.shape[1] > input_ids.shape[1]
            ):
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]+image_features.shape[1]-1:
                past_length -= image_features.shape[1]-1
                input_ids = input_ids[:, past_length:]
                attention_mask = attention_mask[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "image_features": image_features,
            }
        )
        return model_inputs

    def _reorder_cache(self, *args, **kwargs):
        return self.language_model._reorder_cache(*args, **kwargs)