# -------------------------------------------------------- # Adapted from EVA CLIP # https://github.com/baaivision/EVA/tree/master/EVA-CLIP/rei/eva_clip # -------------------------------------------------------- import math import os from functools import partial import torch import torch.nn as nn import torch.nn.functional as f try: from timm.models.layers import drop_path as timm_drop_path, to_2tuple, trunc_normal_ except ImportError or ModuleNotFoundError: from timm.layers import drop_path as timm_drop_path, to_2tuple, trunc_normal_ from .rope_embeddings import VisionRotaryEmbeddingFast if os.getenv('ENV_TYPE') == 'deepspeed': try: from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint except ImportError or ModuleNotFoundError: from torch.utils.checkpoint import checkpoint else: from torch.utils.checkpoint import checkpoint try: import xformers.ops as xops except ImportError: xops = None class PatchDropout(nn.Module): """ https://arxiv.org/abs/2212.00794 """ def __init__(self, prob, exclude_first_token=True): super().__init__() assert 0 <= prob < 1.0 self.prob = prob self.exclude_first_token = exclude_first_token # exclude CLS token def forward(self, x): if not self.training or self.prob == 0.0: return x if self.exclude_first_token: cls_tokens, x = x[:, :1], x[:, 1:] else: cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) batch = x.size()[0] num_tokens = x.size()[1] batch_indices = torch.arange(batch) batch_indices = batch_indices[..., None] keep_prob = 1 - self.prob num_patches_keep = max(1, int(num_tokens * keep_prob)) rand = torch.randn(batch, num_tokens) patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices x = x[batch_indices, patch_indices_keep] if self.exclude_first_token: x = torch.cat((cls_tokens, x), dim=1) return x, patch_indices_keep class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return timm_drop_path(x, self.drop_prob, self.training) def extra_repr(self) -> str: return 'p={}'.format(self.drop_prob) class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, drop=0.0, subln=False, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) # x = self.drop(x) # commit this for the orignal BERT implement x = self.ffn_ln(x) x = self.fc2(x) x = self.drop(x) return x class SwiGLU(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.0, norm_layer=nn.LayerNorm, subln=False, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.w1 = nn.Linear(in_features, hidden_features) self.w2 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() self.w3 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x1 = self.w1(x) x2 = self.w2(x) hidden = self.act(x1) * x2 x = self.ffn_ln(hidden) x = self.w3(x) x = self.drop(x) return x class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm, ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads if attn_head_dim is not None: head_dim = attn_head_dim all_head_dim = head_dim * self.num_heads self.scale = qk_scale or head_dim**-0.5 self.subln = subln if self.subln: self.q_proj = nn.Linear(dim, all_head_dim, bias=False) self.k_proj = nn.Linear(dim, all_head_dim, bias=False) self.v_proj = nn.Linear(dim, all_head_dim, bias=False) else: self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) else: self.q_bias = None self.v_bias = None if window_size: self.window_size = window_size self.num_relative_distance = (2 * window_size[0] - 1) * ( 2 * window_size[1] - 1 ) + 3 self.relative_position_bias_table = nn.Parameter( torch.zeros(self.num_relative_distance, num_heads) ) # 2*Wh-1 * 2*Ww-1, nH # cls to token & token 2 cls & cls to cls # get pair-wise relative position index for each token inside the window coords_h = torch.arange(window_size[0]) coords_w = torch.arange(window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += window_size[1] - 1 relative_coords[:, :, 0] *= 2 * window_size[1] - 1 relative_position_index = torch.zeros( size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype, ) relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww relative_position_index[0, 0:] = self.num_relative_distance - 3 relative_position_index[0:, 0] = self.num_relative_distance - 2 relative_position_index[0, 0] = self.num_relative_distance - 1 self.register_buffer('relative_position_index', relative_position_index) else: self.window_size = None self.relative_position_bias_table = None self.relative_position_index = None self.attn_drop = nn.Dropout(attn_drop) self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() # self.proj = nn.Linear(all_head_dim, all_head_dim) self.proj = nn.Linear(all_head_dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.xattn = xattn self.xattn_drop = attn_drop self.rope = rope def forward(self, x, rel_pos_bias=None, attn_mask=None): b, n, _ = x.shape if self.subln: q = f.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) k = f.linear(input=x, weight=self.k_proj.weight, bias=None) v = f.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) q = q.reshape(b, n, self.num_heads, -1).permute( 0, 2, 1, 3 ) # B, num_heads, N, C k = k.reshape(b, n, self.num_heads, -1).permute(0, 2, 1, 3) v = v.reshape(b, n, self.num_heads, -1).permute(0, 2, 1, 3) else: qkv_bias = None if self.q_bias is not None: qkv_bias = torch.cat( ( self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias, ) ) qkv = f.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(b, n, 3, self.num_heads, -1).permute( 2, 0, 3, 1, 4 ) # 3, B, num_heads, N, C q, k, v = qkv[0], qkv[1], qkv[2] if self.rope: # slightly fast impl q_t = q[:, :, 1:, :] ro_q_t = self.rope(q_t) q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) k_t = k[:, :, 1:, :] ro_k_t = self.rope(k_t) k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) if self.xattn: if xops is None: raise ValueError( "Can't use xattn without xformers. Please 'pip install xformers'" ) q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C k = k.permute(0, 2, 1, 3) v = v.permute(0, 2, 1, 3) x = xops.memory_efficient_attention( q, k, v, p=self.xattn_drop, scale=self.scale, ) x = x.reshape(b, n, -1) x = self.inner_attn_ln(x) x = self.proj(x) x = self.proj_drop(x) else: q = q * self.scale attn = q @ k.transpose(-2, -1) if self.relative_position_bias_table is not None: relative_position_bias = self.relative_position_bias_table[ self.relative_position_index.view(-1) ].view( self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1, ) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) if rel_pos_bias is not None: attn = attn + rel_pos_bias.type_as(attn) if attn_mask is not None: attn_mask = attn_mask.bool() attn = attn.masked_fill(~attn_mask[:, None, None, :], float('-inf')) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(b, n, -1) x = self.inner_attn_ln(x) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, subln=False, naiveswiglu=False, ): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer, ) # NOTE: drop path for stochastic depth, we shall see if this is better # than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) if naiveswiglu: self.mlp = SwiGLU( in_features=dim, hidden_features=mlp_hidden_dim, subln=subln, norm_layer=norm_layer, ) else: self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, subln=subln, drop=drop, ) if init_values is not None and init_values > 0: self.gamma_1 = nn.Parameter( init_values * torch.ones((dim,)), requires_grad=True ) self.gamma_2 = nn.Parameter( init_values * torch.ones((dim,)), requires_grad=True ) else: self.gamma_1, self.gamma_2 = None, None self.postnorm = postnorm def forward(self, x, rel_pos_bias=None, attn_mask=None): if self.gamma_1 is None: if self.postnorm: x = x + self.drop_path( self.norm1( self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) ) ) x = x + self.drop_path(self.norm2(self.mlp(x))) else: x = x + self.drop_path( self.attn( self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask ) ) x = x + self.drop_path(self.mlp(self.norm2(x))) else: if self.postnorm: x = x + self.drop_path( self.gamma_1 * self.norm1( self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) ) ) x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) else: x = x + self.drop_path( self.gamma_1 * self.attn( self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask ) ) x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) return x class PatchEmbed(nn.Module): """Image to Patch Embedding""" def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size ) def forward(self, x, **_): target_dtype = self.proj.weight.dtype _, __, h, w = x.shape # FIXME look at relaxing size constraints assert h == self.img_size[0] and w == self.img_size[1], ( f"Input image size ({h}*{w}) doesn't match model " f'({self.img_size[0]}*{self.img_size[1]}).' ) x = self.proj(x.to(dtype=target_dtype)).flatten(2).transpose(1, 2) return x class RelativePositionBias(nn.Module): def __init__(self, window_size, num_heads): super().__init__() self.window_size = window_size self.num_relative_distance = (2 * window_size[0] - 1) * ( 2 * window_size[1] - 1 ) + 3 self.relative_position_bias_table = nn.Parameter( torch.zeros(self.num_relative_distance, num_heads) ) # 2*Wh-1 * 2*Ww-1, nH # cls to token & token 2 cls & cls to cls # get pair-wise relative position index for each token inside the window coords_h = torch.arange(window_size[0]) coords_w = torch.arange(window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += window_size[1] - 1 relative_coords[:, :, 0] *= 2 * window_size[1] - 1 relative_position_index = torch.zeros( size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype ) relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww relative_position_index[0, 0:] = self.num_relative_distance - 3 relative_position_index[0:, 0] = self.num_relative_distance - 2 relative_position_index[0, 0] = self.num_relative_distance - 1 self.register_buffer('relative_position_index', relative_position_index) def forward(self): relative_position_bias = self.relative_position_bias_table[ self.relative_position_index.view(-1) ].view( self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1, ) # Wh*Ww,Wh*Ww,nH return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww class EVAVisionTransformer(nn.Module): """Vision Transformer with support for patch or hybrid CNN input stage""" def __init__( self, img_size=224, patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0.0, use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False, proj_type=None, ): super().__init__() self.image_size = img_size self.num_classes = num_classes # num_features for consistency with other models self.num_features = self.embed_dim = embed_dim self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if use_abs_pos_emb: self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) else: self.pos_embed = None self.pos_drop = nn.Dropout(p=drop_rate) if use_shared_rel_pos_bias: self.rel_pos_bias = RelativePositionBias( window_size=self.patch_embed.patch_shape, num_heads=num_heads ) else: self.rel_pos_bias = None if rope: half_head_dim = embed_dim // num_heads // 2 hw_seq_len = img_size // patch_size self.rope = VisionRotaryEmbeddingFast( dim=half_head_dim, pt_seq_len=pt_hw_seq_len, ft_seq_len=hw_seq_len if intp_freq else None, patch_dropout=patch_dropout, ) else: self.rope = None self.naiveswiglu = naiveswiglu dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, depth) ] # stochastic depth decay rule self.use_rel_pos_bias = use_rel_pos_bias self.blocks = nn.ModuleList( [ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu, ) for i in range(depth) ] ) self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None if (num_classes == embed_dim) and (proj_type is None): self.head = nn.Identity() elif proj_type == 'linear': self.head = nn.Linear(embed_dim, num_classes, bias=qkv_bias) elif proj_type == 'mlp': hidden_size = (embed_dim + num_classes) // 2 self.proj = nn.Sequential( nn.Linear(embed_dim, hidden_size, bias=qkv_bias), nn.GELU(), nn.Linear(hidden_size, num_classes, bias=qkv_bias), ) if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=0.02) trunc_normal_(self.cls_token, std=0.02) self.apply(self._init_weights) self.fix_init_weight() if isinstance(self.head, nn.Linear): trunc_normal_(self.head.weight, std=0.02) self.head.weight.data.mul_(init_scale) if qkv_bias: self.head.bias.data.mul_(init_scale) # setting a patch_dropout of 0. would mean it is disabled and this function # would be the identity fn self.patch_dropout = ( PatchDropout(patch_dropout) if patch_dropout > 0.0 else nn.Identity() ) self.grad_checkpointing = grad_checkpointing def fix_init_weight(self): def rescale(param, _layer_id): param.div_(math.sqrt(2.0 * _layer_id)) for layer_id, layer in enumerate(self.blocks): rescale(layer.attn.proj.weight.data, layer_id + 1) if self.naiveswiglu: rescale(layer.mlp.w3.weight.data, layer_id + 1) else: rescale(layer.mlp.fc2.weight.data, layer_id + 1) def get_cast_dtype(self) -> torch.dtype: return self.blocks[0].mlp.fc2.weight.dtype @staticmethod def _init_weights(m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def get_num_layers(self): return len(self.blocks) def lock(self, unlocked_groups=0, *_, **__): assert ( unlocked_groups == 0 ), 'partial locking not currently supported for this model' for param in self.parameters(): param.requires_grad = False @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): return self.head def reset_classifier(self, num_classes, *_, **__): self.num_classes = num_classes self.head = ( nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() ) def forward_features(self, x, return_all_features=False): x = self.patch_embed(x) batch_size, seq_len, _ = x.size() cls_tokens = self.cls_token.expand( batch_size, -1, -1 ) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) if self.pos_embed is not None: x = x + self.pos_embed x = self.pos_drop(x) # a patch_dropout of 0. would mean it is disabled and this function would do # nothing but return what was passed in if self.rope is not None: if self.training and not isinstance(self.patch_dropout, nn.Identity): x, patch_indices_keep = self.patch_dropout(x) self.rope.forward = partial( self.rope.forward, patch_indices_keep=patch_indices_keep ) else: self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) x = self.patch_dropout(x) else: x = self.patch_dropout(x) rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None for blk in self.blocks: if self.grad_checkpointing: x = checkpoint(blk, x, (rel_pos_bias,)) else: x = blk(x, rel_pos_bias=rel_pos_bias) if not return_all_features: x = self.norm(x) if self.fc_norm is not None: return self.fc_norm(x.mean(1)) else: return x[:, 0] return x def forward(self, x, return_all_features=False): if return_all_features: return self.forward_features(x, return_all_features) x = self.forward_features(x) x = self.head(x) return x