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# 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
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss, LayerNorm
from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
is_flash_attn_2_available,
logging,
)
from .configuration_qwen2vit import Qwen2VLVisionConfig
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
logger = logging.get_logger(__name__)
# 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
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) -> torch.Tensor:
seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
freqs = torch.outer(seq, self.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
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 = 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) -> 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 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 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
QWEN2_VL_VISION_ATTENTION_CLASSES = {
"eager": VisionAttention,
"flash_attention_2": VisionFlashAttention2,
"sdpa": VisionSdpaAttention,
}
class Qwen2VLVisionBlock(nn.Module):
def __init__(self, config, attn_implementation: str = "sdpa") -> None:
super().__init__()
self.norm1 = LayerNorm(config.embed_dim, eps=1e-6)
self.norm2 = 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) -> 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 Qwen2VisionTower(PreTrainedModel):
config_class = Qwen2VLVisionConfig
_no_split_modules = ["Qwen2VLVisionBlock"]
base_model_prefix = "model"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = 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_()
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, "eager") 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) -> 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)
return self.merger(hidden_states)
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