import math import logging from functools import partial from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.models.helpers import load_pretrained from timm.models.layers import Mlp from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from timm.models.resnet import resnet26d, resnet50d from timm.models.registry import register_model import numpy as np from .Quant import * from ._quan_base import * _logger = logging.getLogger(__name__) def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'patch_embed.proj', 'classifier': 'head', **kwargs } default_cfgs = { # patch models (my experiments) 'vit_small_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth', ), # patch models (weights ported from official Google JAX impl) 'vit_base_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), ), 'vit_base_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_base_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), # patch models, imagenet21k (weights ported from official Google JAX impl) 'vit_base_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_huge_patch14_224_in21k': _cfg( url='', # FIXME I have weights for this but > 2GB limit for github release binaries num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), # hybrid models (weights ported from official Google JAX impl) 'vit_base_resnet50_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_resnet50_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'), # hybrid models (my experiments) 'vit_small_resnet26d_224': _cfg(), 'vit_small_resnet50d_s3_224': _cfg(), 'vit_base_resnet26d_224': _cfg(), 'vit_base_resnet50d_224': _cfg(), # deit models (FB weights) 'vit_deit_tiny_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'), 'vit_deit_small_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'), 'vit_deit_base_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',), 'vit_deit_base_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth', input_size=(3, 384, 384), crop_pct=1.0), 'vit_deit_tiny_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth'), 'vit_deit_small_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth'), 'vit_deit_base_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth', ), 'vit_deit_base_distilled_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth', input_size=(3, 384, 384), crop_pct=1.0), } class Q_Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, nbits, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features drop_probs = to_2tuple(drop) self.fc1 = LinearQ(in_features, hidden_features, nbits_w=nbits, mode=Qmodes.kernel_wise) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) self.fc2 = LinearQ(hidden_features, out_features, nbits_w=nbits, mode=Qmodes.kernel_wise) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): x = self.fc1(x) # print(torch.max(x), torch.min(x)) x = self.act(x) x = torch.clip(x, -10., 10.) # print(torch.clip(x, -10., 10.)) x = self.drop1(x) x = self.fc2(x) x = self.drop2(x) return x class Q_Attention(nn.Module): def __init__(self, nbits, dim, num_heads=8, quantize_attn=True, qkv_bias=False, attn_drop=0., proj_drop=0.): super().__init__() assert dim % num_heads == 0, 'dim should be divisible by num_heads' self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.quantize_attn = quantize_attn self.norm_q = nn.LayerNorm(head_dim) self.norm_k = nn.LayerNorm(head_dim) if self.quantize_attn: self.qkv = LinearQ(dim, dim * 3, bias=qkv_bias, nbits_w=nbits, mode=Qmodes.kernel_wise) self.attn_drop = nn.Dropout(attn_drop) self.proj = LinearQ(dim, dim, nbits_w=nbits, mode=Qmodes.kernel_wise) self.q_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.k_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.v_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.attn_act = ActQ(nbits_a=nbits, in_features=self.num_heads) else: self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.q_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.k_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.v_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.attn_act = ActQ(nbits_a=nbits, in_features=self.num_heads) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) q = self.norm_q(q) k = self.norm_k(k) q = self.q_act(q) k = self.k_act(k) v = self.v_act(v) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) attn = self.attn_act(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Q_Block(nn.Module): def __init__(self, nbits, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Q_Attention(nbits, dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # 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) self.mlp = Q_Mlp(nbits=nbits, in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class Q_PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, nbits=4, 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.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = Conv2dQ(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) # nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): 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 lowbit_VisionTransformer(nn.Module): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__(self, nbits, 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=True, representation_size=None, distilled=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=Q_PatchEmbed, norm_layer=None, act_layer=None, weight_init=''): """ Args: nbits: nbits img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set distilled (bool): model includes a distillation token and head as in DeiT models drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer weight_init: (str): weight init scheme """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 2 if distilled else 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.patch_embed = embed_layer( nbits=nbits, 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.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential(*[ Q_Block( nbits=nbits, dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.norm = norm_layer(embed_dim) # Representation layer if representation_size and not distilled: self.num_features = representation_size self.pre_logits = nn.Sequential(OrderedDict([ ('fc', nn.Linear(embed_dim, representation_size)), ('act', nn.Tanh()) ])) else: self.pre_logits = nn.Identity() # Classifier head(s) self.head = LinearQ(self.num_features, num_classes, nbits_w=8) if num_classes > 0 else nn.Identity() # nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() self.head_dist = None if distilled: self.head_dist = LinearQ(self.embed_dim, self.num_classes, nbits_w=8) if num_classes > 0 else nn.Identity() # self.head = LinearQ(self.embed_dim, self.num_classes, nbits_w=8) if num_classes > 0 else nn.Identity() # nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'jax_nlhb', 'nlhb', '') head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0. trunc_normal_(self.pos_embed, std=.02) if self.dist_token is not None: trunc_normal_(self.dist_token, std=.02) if mode.startswith('jax'): # leave cls token as zeros to match jax impl named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self) else: trunc_normal_(self.cls_token, std=.02) self.apply(_init_vit_weights) def _init_weights(self, m): # this fn left here for compat with downstream users _init_vit_weights(m) @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token', 'dist_token'} def get_classifier(self): if self.dist_token is None: return self.head else: return self.head, self.head_dist 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() if self.num_tokens == 2: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): x = self.patch_embed(x) cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks if self.dist_token is None: x = torch.cat((cls_token, x), dim=1) else: x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1) x = self.pos_drop(x + self.pos_embed) x = self.blocks(x) x = self.norm(x) if self.dist_token is None: return self.pre_logits(x[:, 0]) else: return x[:, 0], x[:, 1] def forward(self, x): x = self.forward_features(x) if self.head_dist is not None: x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple if self.training and not torch.jit.is_scripting(): # during inference, return the average of both classifier predictions return x, x_dist else: return (x + x_dist) / 2 else: x = self.head(x) return x def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False): """ ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compatibility with prev hparam / downstream use cases (ie DeiT). * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl """ if isinstance(module, nn.Linear): if name.startswith('head'): nn.init.zeros_(module.weight) nn.init.constant_(module.bias, head_bias) elif name.startswith('pre_logits'): lecun_normal_(module.weight) nn.init.zeros_(module.bias) else: if jax_impl: nn.init.xavier_uniform_(module.weight) if module.bias is not None: if 'mlp' in name: nn.init.normal_(module.bias, std=1e-6) else: nn.init.zeros_(module.bias) else: trunc_normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif jax_impl and isinstance(module, nn.Conv2d): # NOTE conv was left to pytorch default in my original init lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) def resize_pos_embed(posemb, posemb_new): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) ntok_new = posemb_new.shape[1] if True: posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:] ntok_new -= 1 else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) gs_new = int(math.sqrt(ntok_new)) _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear') posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed(v, model.pos_embed) out_dict[k] = v return out_dict def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs): default_cfg = default_cfgs[variant] default_num_classes = default_cfg['num_classes'] default_img_size = default_cfg['input_size'][-1] num_classes = kwargs.pop('num_classes', default_num_classes) img_size = kwargs.pop('img_size', default_img_size) repr_size = kwargs.pop('representation_size', None) if repr_size is not None and num_classes != default_num_classes: # Remove representation layer if fine-tuning. This may not always be the desired action, # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface? _logger.warning("Removing representation layer for fine-tuning.") repr_size = None model_cls = DistilledVisionTransformer if distilled else VisionTransformer model = model_cls(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained( model, num_classes=num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=partial(checkpoint_filter_fn, model=model)) return model @register_model def fourbits_deit_small_patch16_224(pretrained=False, **kwargs): model = lowbit_VisionTransformer( nbits=4, patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) model.default_cfg = _cfg() if pretrained: torch.hub.load_state_dict_from_url( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth', map_location="cpu", check_hash=True ) return model @register_model def threebits_deit_small_patch16_224(pretrained=False, **kwargs): model = lowbit_VisionTransformer( nbits=3, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) model.default_cfg = _cfg() if pretrained: torch.hub.load_state_dict_from_url( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth', map_location="cpu", check_hash=True ) return model @register_model def twobits_deit_small_patch16_224(pretrained=False, **kwargs): model = lowbit_VisionTransformer( nbits=2, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) model.default_cfg = _cfg() if pretrained: torch.hub.load_state_dict_from_url( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth', map_location="cpu", check_hash=True ) return model