modeling_qwen2vit.py

# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Qwen2-VL model."""

import math
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

from torch import Tensor

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, StaticCache
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    ModelOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_torch_npu_available,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from .configuration_qwen2vit import Qwen2VLConfig, Qwen2VLVisionConfig
from .modeling_rope_utils import ROPE_INIT_FUNCTIONS

from einops import rearrange

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "Qwen2VLConfig"

try:
    import xformers.ops as xops

    is_xformers_available = True
except Exception as e:
    is_xformers_available = False

if is_flash_attn_2_available():
    from flash_attn import flash_attn_varlen_func

    from transformers.modeling_flash_attention_utils import _flash_attention_forward
else:
    flash_attn_varlen_func = None


def init_weights(m):
    if isinstance(m, nn.Linear):
        # we use xavier_uniform following official JAX ViT:
        torch.nn.init.xavier_uniform_(m.weight)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.nn.LayerNorm):
        nn.init.constant_(m.bias, 0)
        nn.init.constant_(m.weight, 1.0)
    elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        w = m.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))


@dataclass
class Qwen2VLCausalLMOutputWithPast(ModelOutput):
    """
    Base class for Qwen2VL causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
            The rope index difference between sequence length and multimodal rope.
    """

    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
    rope_deltas: Optional[torch.LongTensor] = None


class Qwen2VLRotaryEmbedding(nn.Module):
    def __init__(
            self,
            dim=None,
            max_position_embeddings=2048,
            base=10000,
            device=None,
            scaling_factor=1.0,
            rope_type="default",
            config: Optional[Qwen2VLConfig] = None,
    ):
        super().__init__()
        # TODO (joao): remove the `if` below, only used for BC
        self.rope_kwargs = {}
        if config is None:
            logger.warning_once(
                "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the "
                "`config` argument. All other arguments will be removed in v4.46"
            )
            self.rope_kwargs = {
                "rope_type": rope_type,
                "factor": scaling_factor,
                "dim": dim,
                "base": base,
                "max_position_embeddings": max_position_embeddings,
            }
            self.rope_type = rope_type
            self.max_seq_len_cached = max_position_embeddings
            self.original_max_seq_len = max_position_embeddings
        else:
            # BC: "rope_type" was originally "type"
            if config.rope_scaling is not None:
                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
            else:
                self.rope_type = "default"
            self.max_seq_len_cached = config.max_position_embeddings
            self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(
                self.config, device, seq_len=seq_len, **self.rope_kwargs
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block. In contrast to other models, Qwen2_VL has different position ids for thw grids
        # So we expand the inv_freq to shape (3, ...)
        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (3, bs, 1, positions)
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


# 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)


def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
    orig_dtype = tensor.dtype
    tensor = tensor.float()
    cos = freqs.cos()
    sin = freqs.sin()
    cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
    sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
    output = (tensor * cos) + (rotate_half(tensor) * sin)
    output = output.to(orig_dtype)
    return output


def apply_rotary_pos_emb_vision_batch(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
    orig_dtype = tensor.dtype
    tensor = tensor.float()
    cos = freqs.cos()
    sin = freqs.sin()
    cos = cos.repeat(1, 1, 1, 2).float()
    sin = sin.repeat(1, 1, 1, 2).float()
    output = (tensor * cos) + (rotate_half(tensor) * sin)
    output = output.to(orig_dtype)
    return output


class VisionRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, seqlen: int, scale_factor: float = 1.0) -> torch.Tensor:
        # 使用 scale_factor 动态调整 inv_freq
        scaled_inv_freq = self.inv_freq * scale_factor
        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(seq, scaled_inv_freq)
        return freqs


class PatchEmbed(nn.Module):
    def __init__(
            self,
            patch_size: int = 14,
            temporal_patch_size: int = 2,
            in_channels: int = 3,
            embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.in_channels = in_channels
        self.embed_dim = embed_dim

        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        target_dtype = self.proj.weight.dtype
        if is_torch_npu_available():
            # if True:
            hidden_states = F.linear(hidden_states, self.proj.weight.view(self.embed_dim, -1))
        else:
            hidden_states = hidden_states.view(
                -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
            )
            hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
        return hidden_states


class PatchMerger(nn.Module):
    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size ** 2)
        self.ln_q = nn.LayerNorm(context_dim, eps=1e-6)
        self.mlp = nn.Sequential(
            nn.Linear(self.hidden_size, self.hidden_size),
            nn.GELU(),
            nn.Linear(self.hidden_size, dim),
        )

    def forward(self, x: torch.Tensor, grid_thw) -> torch.Tensor:
        x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
        return x


class VisionMlp(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, hidden_act: str) -> None:
        super().__init__()
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.act = ACT2FN[hidden_act]
        self.fc2 = nn.Linear(hidden_dim, dim)

    def forward(self, x) -> torch.Tensor:
        return self.fc2(self.act(self.fc1(x)))


class VisionAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16, ) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self,
            hidden_states: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)

        attention_mask = torch.full(
            [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype
        )
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1]: cu_seqlens[i], cu_seqlens[i - 1]: cu_seqlens[i]] = 0

        q = q.transpose(0, 1)
        k = k.transpose(0, 1)
        v = v.transpose(0, 1)
        attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim)
        attn_weights = attn_weights + attention_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.transpose(0, 1)
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


class BatchVisionAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self,
            hidden_states: torch.Tensor,  # [batch_size, seq_len, dim]
            attention_mask: torch.Tensor,  # [batch_size, 1, 1, seq_len]
            rotary_pos_emb: torch.Tensor = None  # [batch_size, seq_len, head_dim//2]
    ) -> torch.Tensor:
        batch_size, seq_len, _ = hidden_states.shape

        q, k, v = self.qkv(hidden_states).reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim).permute(2, 0,
                                                                                                                 3, 1,
                                                                                                                 4).unbind(
            0)
        # [batch_size, num_heads, seq_len, head_dim]

        if rotary_pos_emb is not None:
            rotary_pos_emb = rotary_pos_emb.unsqueeze(1)  # [batch_size, 1, seq_len, head_dim//2]
            q = apply_rotary_pos_emb_vision_batch(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision_batch(k, rotary_pos_emb)

        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        # Softmax
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)

        attn_output = torch.matmul(attn_weights, v)  # [batch_size, num_heads, seq_len, head_dim]
        attn_output = attn_output.transpose(1, 2).reshape(batch_size, seq_len, -1)
        return self.proj(attn_output)


class VisionXformerAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self,
            hidden_states: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]

        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)

        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb)
        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb)

        seqlens = [cu_seqlens[0]] + [cu_seqlens[i] - cu_seqlens[i - 1] for i in range(1, len(cu_seqlens))]
        attn_bias = xops.fmha.BlockDiagonalMask.from_seqlens(seqlens)

        attn_output = xops.memory_efficient_attention(
            q, k, v.unsqueeze(0),
            attn_bias=attn_bias,
            scale=1.0 / math.sqrt(self.head_dim)
        )
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


class BatchVisionXformerAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: torch.Tensor,  # [batch_size, 1, 1, seq_len]
            rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        batch_size, seq_len = hidden_states.shape

        q, k, v = self.qkv(hidden_states).reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim).permute(2, 0,
                                                                                                                 3, 1,
                                                                                                                 4).unbind(
            0)
        # [batch_size, num_heads, seq_len, head_dim]

        if rotary_pos_emb is not None:
            rotary_pos_emb = rotary_pos_emb.unsqueeze(1)  # [batch_size, 1, seq_len, head_dim//2]
            q = apply_rotary_pos_emb_vision_batch(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision_batch(k, rotary_pos_emb)

        attn_output = xops.memory_efficient_attention(
            q, k, v,
            attn_bias=attention_mask,
            scale=1.0 / math.sqrt(self.head_dim)
        )
        attn_output = attn_output.reshape(batch_size, seq_len, -1)
        return self.proj(attn_output)


class VisionFlashAttention2(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)

        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
            seq_length, -1
        )
        attn_output = self.proj(attn_output)
        return attn_output


class BatchVisionFlashAttention2(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        batch_size, seq_len, _ = hidden_states.shape

        q, k, v = self.qkv(hidden_states).reshape(batch_size, seq_len, 3, self.num_heads, -1).permute(2, 0, 3, 1,
                                                                                                      4).unbind(0)

        if rotary_pos_emb is not None:
            rotary_pos_emb = rotary_pos_emb.unsqueeze(1)  # [batch_size, 1, seq_len, head_dim//2]
            q = apply_rotary_pos_emb_vision_batch(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision_batch(k, rotary_pos_emb)

        q = rearrange(q, 'b h l d -> b l h d')
        k = rearrange(k, 'b h l d -> b l h d')
        v = rearrange(v, 'b h l d -> b l h d')

        attn_output = _flash_attention_forward(q, k, v).reshape(batch_size, seq_len, -1)
        attn_output = self.proj(attn_output)
        return attn_output


class VisionSdpaAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)

        attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool)
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1]: cu_seqlens[i], cu_seqlens[i - 1]: cu_seqlens[i]] = True
        q = q.transpose(0, 1)
        k = k.transpose(0, 1)
        v = v.transpose(0, 1)
        attn_output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0)
        attn_output = attn_output.transpose(0, 1)
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


class BatchVisionSdpaAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
            self,
            hidden_states: torch.Tensor,  # [batch_size, seq_len, dim]
            attention_mask: torch.Tensor = None,  # [batch_size, 1, 1, seq_len]
            rotary_pos_emb: torch.Tensor = None  # [batch_size, seq_len, head_dim//2]
    ) -> torch.Tensor:
        batch_size, seq_len, _ = hidden_states.shape
        q, k, v = self.qkv(hidden_states).reshape(batch_size, seq_len, 3, self.num_heads, -1).permute(2, 0, 3, 1,
                                                                                                      4).unbind(0)
        # [batch_size, num_heads, seq_len, head_dim]

        if rotary_pos_emb is not None:
            rotary_pos_emb = rotary_pos_emb.unsqueeze(1)  # [batch_size, 1, seq_len, head_dim//2]
            q = apply_rotary_pos_emb_vision_batch(q, rotary_pos_emb)
            k = apply_rotary_pos_emb_vision_batch(k, rotary_pos_emb)

        attn_output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(batch_size, seq_len, -1)
        attn_output = self.proj(attn_output)
        return attn_output


QWEN2_VL_VISION_ATTENTION_CLASSES = {
    "eager": VisionAttention,
    "flash_attention_2": VisionFlashAttention2,
    "sdpa": VisionSdpaAttention,
    "xformers": VisionXformerAttention,
}

QWEN2_VL_VISION_BATCH_ATTENTION_CLASSES = {
    "eager": BatchVisionAttention,
    "flash_attention_2": VisionFlashAttention2,
    "sdpa": BatchVisionSdpaAttention,
}


class Qwen2VLVisionBlock(nn.Module):
    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
        super().__init__()

        self.norm1 = nn.LayerNorm(config.embed_dim, eps=1e-6)
        self.norm2 = nn.LayerNorm(config.embed_dim, eps=1e-6)
        mlp_hidden_dim = int(config.embed_dim * config.mlp_ratio)

        self.attn = QWEN2_VL_VISION_ATTENTION_CLASSES[attn_implementation](
            config.embed_dim, num_heads=config.num_heads,
        )
        self.mlp = VisionMlp(dim=config.embed_dim, hidden_dim=mlp_hidden_dim, hidden_act=config.hidden_act)

    def forward(self, hidden_states, cu_seqlens, rotary_pos_emb, grid_thw) -> torch.Tensor:
        hidden_states = hidden_states + self.attn(
            self.norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
        )
        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
        return hidden_states


class Qwen2VLBatchVisionBlock(nn.Module):
    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
        super().__init__()
        self.norm1 = nn.LayerNorm(config.embed_dim, eps=1e-6)
        self.norm2 = nn.LayerNorm(config.embed_dim, eps=1e-6)
        mlp_hidden_dim = int(config.embed_dim * config.mlp_ratio)

        self.attn = QWEN2_VL_VISION_BATCH_ATTENTION_CLASSES[attn_implementation](
            config.embed_dim, num_heads=config.num_heads,
        )
        self.mlp = VisionMlp(config.embed_dim, hidden_dim=mlp_hidden_dim, hidden_act=config.hidden_act)

    def forward(
            self,
            hidden_states: torch.Tensor,  # [batch_size, seq_len, dim]
            attention_mask: torch.Tensor = None,  # [batch_size, 1, 1, seq_len]
            rotary_pos_emb: torch.Tensor = None  # [batch_size, seq_len, head_dim//2]
    ) -> torch.Tensor:
        # Attention
        hidden_states = hidden_states + self.attn(
            self.norm1(hidden_states), attention_mask=attention_mask, rotary_pos_emb=rotary_pos_emb
        )
        # MLP
        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
        return hidden_states


# 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.qwen2.modeling_qwen2.Qwen2RMSNorm
class Qwen2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Qwen2RMSNorm is equivalent to T5nn.LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2MLP
class Qwen2MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, hidden_state):
        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))


# Copied from transformers.models.llama.modeling_llama.repeat_kv
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 Qwen2VLPreTrainedModel(PreTrainedModel):
    config_class = Qwen2VLConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Qwen2VLDecoderLayer", "Qwen2VLVisionBlock"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_static_cache = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, (nn.Linear, nn.Conv3d)):
            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 Qwen2VisionTransformerPretrainedModel(Qwen2VLPreTrainedModel):
    config_class = Qwen2VLVisionConfig
    _no_split_modules = ["Qwen2VLVisionBlock"]

    def __init__(self, config) -> None:
        super().__init__(config)
        self.spatial_merge_size = config.spatial_merge_size

        self.patch_embed = PatchEmbed(
            patch_size=config.patch_size,
            temporal_patch_size=config.temporal_patch_size,
            in_channels=config.in_channels,
            embed_dim=config.embed_dim,
        )

        head_dim = config.embed_dim // config.num_heads
        self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)

        self.blocks = nn.ModuleList(
            [Qwen2VLVisionBlock(config, config.attn_implementation) for _ in range(config.depth)]
        )
        self.merger = PatchMerger(
            dim=config.hidden_size, context_dim=config.embed_dim, spatial_merge_size=config.spatial_merge_size
        )

    def get_dtype(self) -> torch.dtype:
        return self.blocks[0].mlp.fc2.weight.dtype

    def get_device(self) -> torch.device:
        return self.blocks[0].mlp.fc2.weight.device

    def rot_pos_emb(self, grid_thw):
        pos_ids = []
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            hpos_ids = hpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            )
            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
            hpos_ids = hpos_ids.flatten()

            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            wpos_ids = wpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            )
            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
            wpos_ids = wpos_ids.flatten()
            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor,
                output_hidden_states=False, org_forward=False, ) -> torch.Tensor:
        hidden_states = self.patch_embed(hidden_states)
        rotary_pos_emb = self.rot_pos_emb(grid_thw)

        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
            dim=0, dtype=torch.int32
        )
        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)

        for blk in self.blocks:
            hidden_states = blk(hidden_states,
                                cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb,
                                grid_thw=grid_thw)

        hidden_states = self.merger(hidden_states, grid_thw)
        return hidden_states