from math import pi import torch from torch import nn from einops import rearrange, repeat import logging def broadcat(tensors, dim = -1): num_tensors = len(tensors) shape_lens = set(list(map(lambda t: len(t.shape), tensors))) assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' shape_len = list(shape_lens)[0] dim = (dim + shape_len) if dim < 0 else dim dims = list(zip(*map(lambda t: list(t.shape), tensors))) expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) expanded_dims.insert(dim, (dim, dims[dim])) expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) return torch.cat(tensors, dim = dim) def rotate_half(x): x = rearrange(x, '... (d r) -> ... d r', r = 2) x1, x2 = x.unbind(dim = -1) x = torch.stack((-x2, x1), dim = -1) return rearrange(x, '... d r -> ... (d r)') class VisionRotaryEmbeddingFast(nn.Module): def __init__( self, dim, pt_seq_len, ft_seq_len=None, custom_freqs = None, freqs_for = 'lang', theta = 10000, max_freq = 10, num_freqs = 1, patch_dropout = 0. ): super().__init__() if custom_freqs: freqs = custom_freqs elif freqs_for == 'lang': freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) elif freqs_for == 'pixel': freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi elif freqs_for == 'constant': freqs = torch.ones(num_freqs).float() else: raise ValueError(f'unknown modality {freqs_for}') if ft_seq_len is None: ft_seq_len = pt_seq_len t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len freqs = torch.einsum('..., f -> ... f', t, freqs) freqs = repeat(freqs, '... n -> ... (n r)', r = 2) freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) self.patch_dropout = patch_dropout self.register_buffer("freqs_cos", freqs_cos) self.register_buffer("freqs_sin", freqs_sin) logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') def forward(self, t, patch_indices_keep=None): if patch_indices_keep is not None: batch = t.size()[0] batch_indices = torch.arange(batch) batch_indices = batch_indices[..., None] freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) freqs_cos = freqs_cos[batch_indices, patch_indices_keep] freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') freqs_sin = freqs_sin[batch_indices, patch_indices_keep] freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') return t * freqs_cos + rotate_half(t) * freqs_sin return t * self.freqs_cos + rotate_half(t) * self.freqs_sin import torch.nn as nn import os from dataclasses import dataclass from typing import Optional, Tuple, Union from functools import partial import numpy as np import torch import torch.nn.functional as F from torch import nn # -------------------------------------------------------- # Adapted from https://github.com/microsoft/unilm/tree/master/beit # -------------------------------------------------------- import math import os from functools import partial import torch import torch.nn as nn import torch.nn.functional as F import logging try: from timm.models.layers import drop_path, to_2tuple, trunc_normal_ except: from timm.layers import drop_path, to_2tuple, trunc_normal_ class PatchDropout(nn.Module): """ https://arxiv.org/abs/2212.00794 """ def __init__(self, prob, exclude_first_token=True): super().__init__() assert 0 <= prob < 1. self.prob = prob self.exclude_first_token = exclude_first_token # exclude CLS token logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") def forward(self, x): if not self.training or self.prob == 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) if self.training and os.getenv('RoPE') == '1': return x, patch_indices_keep return x if os.getenv('ENV_TYPE') == 'deepspeed': try: from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint except: from torch.utils.checkpoint import checkpoint else: from torch.utils.checkpoint import checkpoint import xformers.ops as xops 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 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., 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., 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., proj_drop=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, C = x.shape if self.subln: if self.q_proj.weight.dtype == torch.uint8: import bitsandbytes as bnb q = bnb.matmul_4bit(x, self.q_proj.weight.t(), bias=self.q_bias, quant_state=self.q_proj.weight.quant_state) k = bnb.matmul_4bit(x, self.k_proj.weight.t(), bias=None, quant_state=self.k_proj.weight.quant_state) v = bnb.matmul_4bit(x, self.v_proj.weight.t(), bias=self.v_bias, quant_state=self.v_proj.weight.quant_state) else: 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: 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., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=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. 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, **kwargs): B, C, 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 ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).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=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=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): super().__init__() self.image_size = img_size self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models 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 self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) # trunc_normal_(self.mask_token, std=.02) self.apply(self._init_weights) self.fix_init_weight() if isinstance(self.head, nn.Linear): trunc_normal_(self.head.weight, std=.02) self.head.weight.data.mul_(init_scale) 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. 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 def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.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, freeze_bn_stats=False): 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, global_pool=''): 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 os.getenv('RoPE') == '1': 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 i, blk in enumerate(self.blocks): if i == len(self.blocks)-1: continue 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 class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm (with cast back to input dtype).""" def forward(self, x: torch.Tensor): orig_type = x.dtype x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x.to(orig_type) try: from apex.normalization import FusedLayerNorm except: FusedLayerNorm = LayerNorm print("Please 'pip install apex'") @dataclass class CLIPVisionCfg: layers: Union[Tuple[int, int, int, int], int] = 12 width: int = 768 head_width: int = 64 mlp_ratio: float = 4.0 patch_size: int = 16 image_size: Union[Tuple[int, int], int] = 224 ls_init_value: Optional[float] = None # layer scale initial value patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) drop_path_rate: Optional[float] = None # drop path rate timm_model_name: str = None # a valid model name overrides layers, width, patch_size timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') timm_proj_bias: bool = False # enable bias final projection eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size qkv_bias: bool = True fusedLN: bool = False xattn: bool = False postnorm: bool = False rope: bool = False pt_hw_seq_len: int = 16 # 224/14 intp_freq: bool = False naiveswiglu: bool = False subln: bool = False def _build_vision_tower( embed_dim: int, vision_cfg: CLIPVisionCfg ): if isinstance(vision_cfg, dict): vision_cfg = CLIPVisionCfg(**vision_cfg) if vision_cfg.eva_model_name: vision_heads = vision_cfg.width // vision_cfg.head_width norm_layer = LayerNorm visual = EVAVisionTransformer( img_size=vision_cfg.image_size, patch_size=vision_cfg.patch_size, num_classes=embed_dim, use_mean_pooling=vision_cfg.global_average_pool, #False init_values=vision_cfg.ls_init_value, patch_dropout=vision_cfg.patch_dropout, embed_dim=vision_cfg.width, depth=vision_cfg.layers, num_heads=vision_heads, mlp_ratio=vision_cfg.mlp_ratio, qkv_bias=vision_cfg.qkv_bias, drop_path_rate=vision_cfg.drop_path_rate, norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), xattn=vision_cfg.xattn, rope=vision_cfg.rope, postnorm=vision_cfg.postnorm, pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 intp_freq= vision_cfg.intp_freq, naiveswiglu= vision_cfg.naiveswiglu, subln= vision_cfg.subln ) return visual class Eva2LargeEncoder(nn.Module): def __init__(self, image_size=224): super(Eva2LargeEncoder, self).__init__() self.config = { "embed_dim": 768, "vision_cfg": { "image_size": 336, "layers": 24, "width": 1024, "drop_path_rate": 0, "head_width": 64, "mlp_ratio": 2.6667, "patch_size": 14, "eva_model_name": "eva-clip-l-14-336", "xattn": True, "fusedLN": True, "rope": True, "pt_hw_seq_len": 16, "intp_freq": True, "naiveswiglu": True, "subln": True } } self.config['vision_cfg']['image_size'] = image_size import os os.environ['delRoPE'] = '1' # to avoid error in rope params when changing image size self.model = _build_vision_tower(**self.config) def forward(self, images): encode = self.model(images, return_all_features=True)[:, 1:, :] return encode class CrossVisionModel(nn.Module): def __init__(self, config): super().__init__() self.vit = Eva2LargeEncoder(image_size=config.cross_image_size) self.pos_embed = nn.Parameter(torch.zeros((self.vit.config['vision_cfg']['image_size'] // self.vit.config['vision_cfg']['patch_size']) ** 2, self.vit.config['vision_cfg']['width'])) def forward(self, images): enc = self.vit(images) return enc + self.pos_embed.unsqueeze(0)