# adapted from https://huggingface.co/apple/aimv2-huge-patch14-448 (modification: add gradient checkpoint support) from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import functional as F from transformers.modeling_outputs import BaseModelOutputWithNoAttention from transformers.modeling_utils import PreTrainedModel from flash_attn.layers.rotary import apply_rotary_emb from flash_attn import flash_attn_varlen_func from .configuration_aimv2 import AIMv2Config __all__ = ["AIMv2Model"] class RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(dim)) self.eps = eps def forward(self, x: torch.Tensor) -> torch.Tensor: output = self._norm(x.float()).type_as(x) return output * self.weight def extra_repr(self) -> str: return f"{tuple(self.weight.shape)}, eps={self.eps}" def _norm(self, x: torch.Tensor) -> torch.Tensor: return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) class AIMv2SwiGLUFFN(nn.Module): def __init__(self, config: AIMv2Config): super().__init__() hidden_features = config.intermediate_size in_features = config.hidden_size bias = config.use_bias self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) self.fc2 = nn.Linear(hidden_features, in_features, bias=bias) self.fc3 = nn.Linear(in_features, hidden_features, bias=bias) def forward(self, x: torch.Tensor) -> torch.Tensor: x = F.silu(self.fc1(x)) * self.fc3(x) x = self.fc2(x) return x # copied from qwen2.5-vl 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 # Note: in qwen2-vl and qwen2.5-vl, 3d convolution is used. class AIMv2PatchEmbed(nn.Module): def __init__(self, config: AIMv2Config): super().__init__() self.config = config self.proj = nn.Conv2d( config.num_channels, config.hidden_size, kernel_size=(config.patch_size, config.patch_size), stride=(config.patch_size, config.patch_size), ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.view(-1, self.config.num_channels * self.config.temporal_patch_size, self.config.patch_size, self.config.patch_size) x = self.proj(x).view(-1, self.config.hidden_size) #.flatten(2).transpose(1, 2) # token_len x hidden_size x = self.norm(x) return x class AIMv2ViTPreprocessor(nn.Module): def __init__(self, config: AIMv2Config): super().__init__() num_patches = (config.image_size // config.patch_size) ** 2 self.patchifier = AIMv2PatchEmbed(config) self.preserve_original_pe = config.preserve_original_pe self.hidden_stride = config.hidden_stride if self.preserve_original_pe: self.interpolate_pe_method = config.interpolate_pe_method self.pos_embed = nn.Parameter(torch.zeros((1, num_patches, config.hidden_size))) def forward(self, x: torch.Tensor, grid_thws: Optional[torch.Tensor] = None) -> torch.Tensor: tokens = self.patchifier(x) if self.preserve_original_pe: assert grid_thws is not None pos_embed_new = torch.zeros_like(tokens) if self.interpolate_pe_method == 'one_dim': pos_embed = self.pos_embed.transpose(1,2).to(tokens.device) elif self.interpolate_pe_method == 'two_dim': ori_h = ori_w = int(self.pos_embed.shape[1] ** 0.5) pos_embed = self.pos_embed.reshape(1, ori_h, ori_w, -1).permute(0,3,1,2) else: raise TypeError("The interpolation method for pe should be one_dim, two_dim.") cnt = 0 for t, h, w in grid_thws: num_patches = h * w thw = t * h * w if self.interpolate_pe_method == 'one_dim': pe = F.interpolate(pos_embed, size=num_patches, mode='linear', align_corners=False).transpose(1,2) elif self.interpolate_pe_method == 'two_dim': # 1, 1024, 32, 32 pe = F.interpolate(pos_embed, size=(h,w), mode='bicubic', align_corners=False) # 1, 1024, 1024 pe = pe.permute(0,2,3,1).reshape(1, h*w, -1) # 1024, 1024 pe = pe[0].repeat(t,1) # 1, 16, 2, 16, 2, 1024 pe = pe.reshape(t, h//self.hidden_stride, self.hidden_stride, w//self.hidden_stride, self.hidden_stride, -1) # 1024, 1024 pe = pe.permute(0,1,3,2,4,5).reshape(thw,-1) pos_embed_new[cnt:cnt+thw] = pe cnt += thw tokens = tokens + pos_embed_new return tokens # copied from qwen2.5-vl def apply_rotary_pos_emb_flashatt( q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: cos = cos.chunk(2, dim=-1)[0].contiguous() sin = sin.chunk(2, dim=-1)[0].contiguous() q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q) k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k) return q_embed, k_embed class AIMv2FlashAttention2(nn.Module): def __init__(self, config: AIMv2Config) -> None: super().__init__() dim = config.hidden_size self.num_heads = config.num_attention_heads self.qkv = nn.Linear(dim, dim * 3, bias=config.qkv_bias) self.proj = nn.Linear(dim, dim, bias=config.use_bias) self.use_rope = not config.disable_rope def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, 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) if self.use_rope: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_flashatt(q.unsqueeze(0), k.unsqueeze(0), cos, sin) q = q.squeeze(0) k = k.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 AIMv2Block(nn.Module): def __init__(self, config: AIMv2Config): super().__init__() self.attn = AIMv2FlashAttention2(config) self.norm_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp = AIMv2SwiGLUFFN(config) self.norm_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, x: torch.Tensor, cu_seqlens: torch.Tensor, position_embeddings: torch.Tensor ) -> torch.Tensor: x = x + self.attn(self.norm_1(x), cu_seqlens=cu_seqlens, position_embeddings=position_embeddings) x = x + self.mlp(self.norm_2(x)) return x class AIMv2Transformer(nn.Module): def __init__(self, config: AIMv2Config): super().__init__() self.blocks = nn.ModuleList( [AIMv2Block(config) for _ in range(config.num_hidden_layers)] ) self.post_trunk_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False self.rotary_pos_emb = VisionRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2) self.hidden_stride = config.hidden_stride self.patch_size = config.patch_size self.window_size = config.window_size self.spatial_merge_unit = config.hidden_stride * config.hidden_stride self.fullatt_block_indexes = config.fullatt_block_indexes # copied from qwen2.5_vl 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.hidden_stride, self.hidden_stride, w // self.hidden_stride, self.hidden_stride, ) 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.hidden_stride, self.hidden_stride, w // self.hidden_stride, self.hidden_stride, ) 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 get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.hidden_stride // self.patch_size # patch (after merge) number in each window for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.hidden_stride, # number of patch after merge grid_w // self.hidden_stride, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward( self, tokens: torch.Tensor, grid_thws: torch.Tensor, output_hidden_states: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, ...]]]: # RoPE, modified from qwen2.5_vl rotary_pos_emb = self.rot_pos_emb(grid_thws) window_index, cu_window_seqlens = self.get_window_index(grid_thws) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=tokens.device, dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = tokens.size() tokens = tokens.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) tokens = tokens[window_index, :, :] tokens = tokens.reshape(seq_len, -1) rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) position_embeddings = (emb.cos(), emb.sin()) cu_seqlens = torch.repeat_interleave(grid_thws[:, 1] * grid_thws[:, 2], grid_thws[:, 0]).cumsum( dim=0, # Select dtype based on the following factors: # - FA2 requires that cu_seqlens_q must have dtype int32 # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw # See https://github.com/huggingface/transformers/pull/34852 for more information dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32, ) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) reverse_indices = torch.argsort(window_index) hidden_states = () if output_hidden_states else None for index, block in enumerate(self.blocks): if self.fullatt_block_indexes is None or index in self.fullatt_block_indexes: cu_seqlens_tmp = cu_seqlens else: cu_seqlens_tmp = cu_window_seqlens if self.gradient_checkpointing and self.training: tokens = self._gradient_checkpointing_func(block.__call__, tokens, cu_seqlens_tmp, position_embeddings) else: tokens = block(tokens, cu_seqlens_tmp, position_embeddings) if output_hidden_states: tokens_ = tokens.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) hidden_states += (tokens_[reverse_indices,:].reshape(seq_len, -1),) tokens = self.post_trunk_norm(tokens) tokens = tokens.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) tokens = tokens[reverse_indices,:].reshape(seq_len, -1) return tokens, hidden_states class AIMv2PretrainedModel(PreTrainedModel): config_class = AIMv2Config base_model_prefix = "aimv2" supports_gradient_checkpointing = True main_input_name = "pixel_values" _no_split_modules = ["AIMv2ViTPreprocessor", "AIMv2Block"] _supports_sdpa = True class AIMv2Model(AIMv2PretrainedModel): def __init__(self, config: AIMv2Config): super().__init__(config) self.preprocessor = AIMv2ViTPreprocessor(config) self.trunk = AIMv2Transformer(config) def forward( self, pixel_values: torch.Tensor, grid_thws: torch.Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[ Tuple[torch.Tensor], Tuple[torch.Tensor, Tuple[torch.Tensor, ...]], BaseModelOutputWithNoAttention, ]: if output_hidden_states is None: output_hidden_states = self.config.output_hidden_states if return_dict is None: return_dict = self.config.use_return_dict x = self.preprocessor(pixel_values, grid_thws=grid_thws) x, hidden_states = self.trunk( x, grid_thws=grid_thws, output_hidden_states=output_hidden_states ) if not return_dict: res = (x,) res += (hidden_states,) if output_hidden_states else () return res return BaseModelOutputWithNoAttention( last_hidden_state=x, hidden_states=hidden_states, )