""" CLIP Model Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ import functools import inspect from copy import deepcopy import os import random import copy from contextlib import nullcontext from argparse import Namespace from dataclasses import dataclass import functools import logging import math from typing import Tuple, Union, Callable, Optional from torchvision.ops import roi_align import numpy as np import torch import torch.nn.functional as F from torch import nn from torch.utils.checkpoint import checkpoint # apply the non-reentrant variant of checkpoint if 'use_reentrant' in inspect.signature(checkpoint).parameters: checkpoint = functools.partial(checkpoint, use_reentrant=False) from .timm_model import TimmModel from .utils import freeze_batch_norm_2d, to_2tuple from .resnet import ModifiedResNet from .l0module import L0Module def load_state_dict(model, state_dict): model.load_state_dict(state_dict, strict=True) class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor, hidden_z=None): ''' x: (N, L, C) hidden_z: (C,) ''' self.hidden_z = hidden_z orig_type = x.dtype if hidden_z is None: x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) else: assert len(self.normalized_shape) == 1 # [TODO] weighted layer norm remaining_index = torch.where(hidden_z != 0)[0] compressed_input = torch.index_select( x, dim=-1, index=remaining_index) compressed_weight = self.weight[remaining_index] compressed_bias = self.bias[remaining_index] normalized_shape = len(remaining_index) normed_input = F.layer_norm( compressed_input, [normalized_shape], compressed_weight, compressed_bias, self.eps) x = x.new_zeros(x.shape) x[..., remaining_index] = normed_input.to(orig_type) return x.to(orig_type) def prune(self): if self.hidden_z is None: return self hidden_z = self.hidden_z assert len(self.normalized_shape) == 1 remaining_index = torch.where(hidden_z != 0)[0] compressed_weight = self.weight[remaining_index] compressed_bias = self.bias[remaining_index] # m = self m = LayerNorm(remaining_index.shape[0]).to(self.weight.device) m.normalized_shape = (len(remaining_index),) m.weight.data = compressed_weight.contiguous() m.bias.data = compressed_bias.contiguous() return m def prune_mul_hidden(self): if self.hidden_z is None: return self hidden_z = self.hidden_z assert len(self.normalized_shape) == 1 remaining_index = torch.where(hidden_z != 0)[0] compressed_weight = self.weight[remaining_index] * \ hidden_z[remaining_index] compressed_bias = self.bias[remaining_index] * \ hidden_z[remaining_index] m = self m.normalized_shape = (len(remaining_index),) m.weight.data = compressed_weight.contiguous() m.bias.data = compressed_bias.contiguous() return m class QuickGELU(nn.Module): # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class Mlp(nn.Module): def __init__(self, d_model, mlp_width, act_layer=nn.GELU, scale_fc=False): super().__init__() self.d_model = d_model self.mlp_width = mlp_width self.c_fc = nn.Linear(d_model, mlp_width) assert not scale_fc # self.ln = LayerNorm(mlp_width) if scale_fc else nn.Identity() self.act_layer = act_layer self.scale_fc = scale_fc self.gelu = act_layer() self.c_proj = nn.Linear(mlp_width, d_model) def forward(self, x, hidden_z=None, intermediate_z=None): ''' x: (N, L, C) intermediate_z: (mlp_width,) or (1, 1, mlp_width) hidden_z: (embed_dim,) or (1, 1, embed_dim) ''' self.hidden_z = hidden_z self.intermediate_z = intermediate_z x = self.c_fc(x) x = self.gelu(x) if intermediate_z is not None: x = torch.mul(x, intermediate_z) x = self.c_proj(x) if hidden_z is not None: x = torch.mul(x, hidden_z) return x def prune(self): device = self.c_fc.weight.device if self.hidden_z is None: self.hidden_z = torch.ones( (self.d_model,), dtype=torch.bool, device=device) if self.intermediate_z is None: self.intermediate_z = torch.ones( (self.mlp_width,), dtype=torch.bool, device=device) hidden_r = torch.where(self.hidden_z != 0)[0] intermediate_r = torch.where(self.intermediate_z != 0)[0] d_model = len(hidden_r) mlp_width = len(intermediate_r) # m = self m = copy.deepcopy(self) m.c_fc = nn.Linear(hidden_r.shape[0], intermediate_r.shape[0]) m.c_proj = nn.Linear(intermediate_r.shape[0], hidden_r.shape[0]) m.d_model = d_model m.mlp_width = mlp_width m.c_fc.weight = nn.Parameter( (self.c_fc.weight[intermediate_r][:, hidden_r]).contiguous()) m.c_fc.bias = nn.Parameter( (self.c_fc.bias[intermediate_r]).contiguous()) m.c_proj.weight = nn.Parameter(((self.c_proj.weight * self.intermediate_z.view(1, -1) * self.hidden_z.view(-1, 1))[hidden_r][:, intermediate_r]).contiguous()) m.c_proj.bias = nn.Parameter( ((self.c_proj.bias * self.hidden_z)[hidden_r]).contiguous()) return m class MultiheadAttention(nn.MultiheadAttention): def prune(self): device = self.in_proj_weight.device if self.hidden_z is None: self.hidden_z = torch.ones( (self.embed_dim,), dtype=torch.bool, device=device) if self.head_z is None: self.head_z = torch.ones( (self.num_heads,), dtype=torch.bool, device=device) hidden_r = torch.where(self.hidden_z != 0)[0] head_r = torch.where(self.head_z != 0)[0] d_model = len(hidden_r) d_head = len(head_r) org_num_heads = self.num_heads org_head_dim = self.head_dim org_embed_dim = self.embed_dim mod = self mod.use_naive_compute = True mod.embed_dim = d_model mod.head_dim = self.head_dim mod.num_heads = d_head inter_dim = d_head * self.head_dim mod.in_proj_weight = nn.Parameter(self.in_proj_weight.view( 3, org_num_heads, org_head_dim, org_embed_dim)[:, head_r][..., hidden_r].reshape(-1, d_model)) if self.in_proj_bias is not None: mod.in_proj_bias = nn.Parameter(self.in_proj_bias.view( 3, org_num_heads, org_head_dim)[:, head_r].reshape(-1)) mod.out_proj.weight = nn.Parameter( ((self.out_proj.weight * self.hidden_z.view(-1, 1)). view(org_embed_dim, org_num_heads, org_head_dim) * self.head_z.view(1, org_num_heads, 1))[hidden_r][:, head_r].reshape(d_model, -1) ) if self.out_proj.bias is not None: mod.out_proj.bias = nn.Parameter( (self.out_proj.bias * self.hidden_z.view(-1,)). view(org_embed_dim)[hidden_r].reshape(-1) ) return mod class ResidualAttentionBlock(nn.Module): def __init__( self, d_model: int, n_head: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, scale_cosine_attn: bool = False, scale_heads: bool = False, scale_attn: bool = False, scale_fc: bool = False, ): super().__init__() self.ln_1 = LayerNorm(d_model) # FIXME torchscript issues need to be resolved for custom attention # if scale_cosine_attn or scale_heads: # self.attn = Attention( # d_model, n_head, # scaled_cosine=scale_cosine_attn, # scale_heads=scale_heads, # ) self.attn = MultiheadAttention(d_model, n_head) assert not scale_attn self.ln_attn = LayerNorm(d_model) if scale_attn else nn.Identity() self.ln_2 = LayerNorm(d_model) mlp_width = int(d_model * mlp_ratio) self.mlp = Mlp(d_model, mlp_width, act_layer, scale_fc) def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, *, head_z: Optional[torch.Tensor] = None, hidden_z: Optional[torch.Tensor] = None, ): self.attn.head_z = head_z self.attn.hidden_z = hidden_z if (head_z is None and hidden_z is None and not getattr(self.attn, 'use_naive_compute', False)): return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] else: # the following code does not support `attn_mask` # x: (length, batch_size, embed_dim) n_head = self.attn.num_heads length, batch_size, d_model = x.shape ws = self.attn.in_proj_weight.chunk(3) bs = self.attn.in_proj_bias.chunk(3) dim_per_head = len(ws[0]) // n_head # (length, batch_size, n_head * dim_per_head) q, k, v = [F.linear(x, w, b) for w, b in zip(ws, bs)] # (batch_size * n_head, length, d_head) q = q.reshape(length, batch_size * n_head, -1).transpose(0, 1) k = k.reshape(length, batch_size * n_head, -1).transpose(0, 1) v = v.reshape(length, batch_size * n_head, -1).transpose(0, 1) scale = dim_per_head ** -0.5 q *= scale # (batch_size * n_head, length, length) sim = q @ k.transpose(1, 2) if attn_mask is not None: sim += attn_mask sim = torch.softmax(sim, -1) # (batch_size * n_head, length, head_dim) out = sim @ v if head_z is not None: out = out.view(batch_size, n_head, length, dim_per_head) # head_z: (1, n_head, 1, 1) out *= head_z.view(1, -1, 1, 1) out = out.view(batch_size * n_head, length, dim_per_head) out = out.transpose(0, 1).reshape(length, batch_size, -1) out = F.linear(out, self.attn.out_proj.weight, self.attn.out_proj.bias) if hidden_z is not None: out = torch.mul(out, hidden_z) return out def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, hidden_z: Optional[torch.Tensor] = None, heads_z: Optional[torch.Tensor] = None, mha_z: Optional[torch.Tensor] = None, intermediate_z: Optional[torch.Tensor] = None, ffn_z: Optional[torch.Tensor] = None): self.hidden_z = hidden_z self.heads_z = heads_z self.mha_z = mha_z self.intermediate_z = intermediate_z self.ffn_z = ffn_z # x: (length, batch_size, embed_dim) e.g. 50, 128, 768 for vision if self.attention is not None: attn_out = self.attention(self.ln_1(x, hidden_z=hidden_z), attn_mask=attn_mask, head_z=heads_z, hidden_z=hidden_z) if mha_z is not None: # a number attn_out = attn_out.mul(mha_z) x = x + attn_out if self.mlp is not None: ln_2_out = self.ln_2(x, hidden_z=hidden_z) mlp_out = self.mlp(ln_2_out, intermediate_z=intermediate_z, hidden_z=hidden_z) if ffn_z is not None: # a number mlp_out = mlp_out.mul(ffn_z) x = x + mlp_out return x def prune(self): mod = self if (self.mha_z is not None and self.mha_z.item() == 0) or (self.heads_z).sum() == 0: mod.ln_1 = None mod.attn = None mod.attention = None else: mod.ln_1 = mod.ln_1.prune() mod.attn = mod.attn.prune() if self.mha_z is not None: mod.attn.out_proj.weight.data *= self.mha_z mod.attn.out_proj.bias.data *= self.mha_z if self.ffn_z is not None and self.ffn_z.item() == 0: mod.ln_2 = None mod.mlp = None else: mod.ln_2 = mod.ln_2.prune() mod.mlp = mod.mlp.prune() if self.ffn_z is not None: mod.mlp.c_proj.weight.data *= self.ffn_z mod.mlp.c_proj.bias.data *= self.ffn_z return mod def csa_attn(self, x, mode, hidden_z=None, heads_z=None, mha_z=None): """ Cross-Self Attention: uses both q@q and k@k attention. """ x = self.ln_1(x, hidden_z=hidden_z) attn_layer = self.attn # Set attention masks attn_layer.head_z = heads_z attn_layer.hidden_z = hidden_z num_heads = attn_layer.num_heads length, bsz, embed_dim = x.size() head_dim = embed_dim // num_heads scale = head_dim ** -0.5 # Get q, k, v ws = attn_layer.in_proj_weight.chunk(3) bs = attn_layer.in_proj_bias.chunk(3) if attn_layer.in_proj_bias is not None else (None, None, None) q, k, v = [F.linear(x, w, b) for w, b in zip(ws, bs)] # Reshape for multi-head attention q = q.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) # (bsz*num_heads, length, head_dim) k = k.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) v = v.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) # Compute q@q and k@k attention q_attn = torch.bmm(q, q.transpose(1, 2))# scale k_attn = torch.bmm(k, k.transpose(1, 2))# scale attn_weights = F.softmax(q_attn, dim=-1) + F.softmax(k_attn, dim=-1) # Apply attention to values attn_output = torch.bmm(attn_weights, v) # Apply head mask if needed if heads_z is not None: attn_output = attn_output.view(bsz, num_heads, length, head_dim) attn_output = attn_output * heads_z.view(1, -1, 1, 1) attn_output = attn_output.view(bsz * num_heads, length, head_dim) # Reshape back attn_output = attn_output.transpose(0, 1).reshape(length, bsz, embed_dim) # Apply output projection attn_output = F.linear(attn_output, attn_layer.out_proj.weight, attn_layer.out_proj.bias) # Apply hidden mask and mha_z if needed if hidden_z is not None: attn_output = torch.mul(attn_output, hidden_z) if mha_z is not None: attn_output = attn_output.mul(mha_z) if "distill" in mode: # Return attention output and extra features (q, k excluding class token) return attn_output, (q[:, 1:], k[:, 1:]) else: return attn_output def ss_attn(self, x, mode, hidden_z=None, heads_z=None, mha_z=None): """ Self-Self Attention: uses either q@q or k@k attention based on mode. """ x = self.ln_1(x, hidden_z=hidden_z) attn_layer = self.attn # Set attention masks attn_layer.head_z = heads_z attn_layer.hidden_z = hidden_z num_heads = attn_layer.num_heads length, bsz, embed_dim = x.size() head_dim = embed_dim // num_heads scale = head_dim ** -0.5 # Get q, k, v ws = attn_layer.in_proj_weight.chunk(3) bs = attn_layer.in_proj_bias.chunk(3) if attn_layer.in_proj_bias is not None else (None, None, None) q, k, v = [F.linear(x, w, b) for w, b in zip(ws, bs)] # Reshape for multi-head attention q = q.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) # (bsz*num_heads, length, head_dim) k = k.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) v = v.reshape(length, bsz * num_heads, head_dim).transpose(0, 1) # Compute attention based on mode if mode == "qq" or mode == "qq_vfm_distill": q_attn = torch.bmm(q, q.transpose(1, 2)) # scale attn_weights = F.softmax(q_attn, dim=-1) extra_feats = q[:, 1:] if "distill" in mode else None elif mode == "kk" or mode == "kk_vfm_distill": k_attn = torch.bmm(k, k.transpose(1, 2)) # scale attn_weights = F.softmax(k_attn, dim=-1) extra_feats = k[:, 1:] if "distill" in mode else None else: raise NotImplementedError(f"The mode '{mode}' is not implemented for ss_attn.") # Apply attention to values attn_output = torch.bmm(attn_weights, v) # Apply head mask if needed if heads_z is not None: attn_output = attn_output.view(bsz, num_heads, length, head_dim) attn_output = attn_output * heads_z.view(1, -1, 1, 1) attn_output = attn_output.view(bsz * num_heads, length, head_dim) # Reshape back attn_output = attn_output.transpose(0, 1).reshape(length, bsz, embed_dim) # Apply output projection attn_output = F.linear(attn_output, attn_layer.out_proj.weight, attn_layer.out_proj.bias) # Apply hidden mask and mha_z if needed if hidden_z is not None: attn_output = torch.mul(attn_output, hidden_z) if mha_z is not None: attn_output = attn_output.mul(mha_z) if "distill" in mode: return attn_output, extra_feats else: return attn_output class Transformer(nn.Module): def __init__(self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU): super().__init__() self.width = width self.layers = layers self.grad_checkpointing = False assert width % heads == 0 self.head_dim = width // heads self.num_heads = heads self.mlp_ratio = mlp_ratio self.resblocks = nn.ModuleList([ ResidualAttentionBlock( width, heads, mlp_ratio, act_layer=act_layer) for _ in range(layers) ]) def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, hidden_z: Optional[torch.Tensor] = None, heads_z: Optional[torch.Tensor] = None, mha_z: Optional[torch.Tensor] = None, intermediate_z: Optional[torch.Tensor] = None, ffn_z: Optional[torch.Tensor] = None): return self.infer_blocks(x, attn_mask, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z) def infer_blocks(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, block_idxs=None, hidden_z: Optional[torch.Tensor] = None, heads_z: Optional[torch.Tensor] = None, mha_z: Optional[torch.Tensor] = None, intermediate_z: Optional[torch.Tensor] = None, ffn_z: Optional[torch.Tensor] = None): num_layers = self.layers if hidden_z is not None: assert hidden_z.shape == (self.width,) if heads_z is not None: if heads_z.ndim == 5: heads_z = heads_z.view(num_layers, self.num_heads) assert heads_z.shape in [(num_layers, self.num_heads), (self.num_heads,)], ( heads_z.shape, (num_layers, self.num_heads)) if mha_z is not None: assert mha_z.shape == (num_layers,), mha_z.shape if intermediate_z is not None: if intermediate_z.ndim == 4: intermediate_z = intermediate_z.view(num_layers, -1) assert intermediate_z.shape in [ (num_layers, self.mlp_ratio * self.width), (self.mlp_ratio * self.width,)], intermediate_z.shape if ffn_z is not None: assert ffn_z.shape == (num_layers,), ffn_z.shape def _get_zi(z, i, ndim=2): if z is None: return None if z.ndim == ndim: return z[i] return z block_idxs = block_idxs or list(range(self.layers)) for i in block_idxs: r = self.resblocks[i] if self.grad_checkpointing and not torch.jit.is_scripting(): x = checkpoint(r, x, attn_mask, hidden_z, _get_zi(heads_z, i), _get_zi(mha_z, i, ndim=1), _get_zi(intermediate_z, i), _get_zi(ffn_z, i, ndim=1)) else: x = r(x, attn_mask=attn_mask, hidden_z=hidden_z, heads_z=_get_zi(heads_z, i), mha_z=_get_zi(mha_z, i, ndim=1), intermediate_z=_get_zi(intermediate_z, i), ffn_z=_get_zi(ffn_z, i, ndim=1)) return x @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable def extra_repr(self): return f'grad_checkpointing={self.grad_checkpointing}' def prune(self): mod = self for i in range(len(self.resblocks)): self.resblocks[i] = self.resblocks[i].prune() return mod def extract_feature_map(self, x, mode='vanilla', hidden_z=None, heads_z=None, mha_z=None, intermediate_z=None, ffn_z=None): """ Extract feature map from transformer, supporting different modes. Supports vanilla, qq, kk, csa, and their distill variants. """ def _get_zi(z, i, ndim=2): """Helper function to get z value for layer i""" if z is None: return None if z.ndim == ndim: return z[i] return z # Process all layers except the last one for i in range(self.layers - 1): r = self.resblocks[i] x = r(x, attn_mask=None, hidden_z=hidden_z, heads_z=_get_zi(heads_z, i) if heads_z is not None else None, mha_z=_get_zi(mha_z, i, ndim=1) if mha_z is not None else None, intermediate_z=_get_zi(intermediate_z, i) if intermediate_z is not None else None, ffn_z=_get_zi(ffn_z, i, ndim=1) if ffn_z is not None else None) # Process the last layer based on mode r = self.resblocks[-1] last_heads_z = _get_zi(heads_z, self.layers - 1) if heads_z is not None else None last_mha_z = _get_zi(mha_z, self.layers - 1, ndim=1) if mha_z is not None else None last_intermediate_z = _get_zi(intermediate_z, self.layers - 1) if intermediate_z is not None else None last_ffn_z = _get_zi(ffn_z, self.layers - 1, ndim=1) if ffn_z is not None else None if mode == 'vanilla': x = r(x, attn_mask=None, hidden_z=hidden_z, heads_z=last_heads_z, mha_z=last_mha_z, intermediate_z=last_intermediate_z, ffn_z=last_ffn_z) return x elif mode in ['csa', 'csa_vfm_distill']: # For csa mode, only return attention output without residual connection and MLP # This matches EVA CLIP's forward_without_rcffn behavior result = r.csa_attn(x, mode, hidden_z=hidden_z, heads_z=last_heads_z, mha_z=last_mha_z) if 'distill' in mode: return result[0], result[1] # attn_out, extra_feats else: return result # attn_out only elif mode in ['qq', 'kk', 'qq_vfm_distill', 'kk_vfm_distill']: # For qq/kk mode, only return attention output without residual connection and MLP # This matches EVA CLIP's forward_without_rcffn behavior result = r.ss_attn(x, mode, hidden_z=hidden_z, heads_z=last_heads_z, mha_z=last_mha_z) if 'distill' in mode: return result[0], result[1] # attn_out, extra_feats else: return result # attn_out only else: raise NotImplementedError(f"The mode '{mode}' is not implemented.") class VisualTransformer(nn.Module): def __init__( self, image_size: int, patch_size: int, width: int, layers: int, heads: int, mlp_ratio: float, output_dim: int, act_layer: Callable = nn.GELU, teacher_width: int = -1, ): super().__init__() self.image_size = to_2tuple(image_size) self.patch_size = to_2tuple(patch_size) self.grid_size = ( self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) self.output_dim = output_dim self.embed_dim = width self.layers = layers self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) scale = width ** -0.5 self.class_embedding = nn.Parameter(scale * torch.randn(width)) self.positional_embedding = nn.Parameter( scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) self.ln_pre = LayerNorm(width) self.transformer = Transformer( width, layers, heads, mlp_ratio, act_layer=act_layer) self.head_dim = width // heads self.ln_post = LayerNorm(width) # image proj if teacher_width > 0: self.proj = nn.Parameter(torch.empty( teacher_width, output_dim), requires_grad=False) else: self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) def lock(self, unlocked_groups=0, freeze_bn_stats=False): for param in self.parameters(): param.requires_grad = False def _unlock(x): if isinstance(x, list): for g in x: _unlock(g) else: if isinstance(x, torch.nn.Parameter): x.requires_grad = True else: for p in x.parameters(): p.requires_grad = True for blk in self.transformer.resblocks[-unlocked_groups:]: _unlock(blk) if freeze_bn_stats: freeze_batch_norm_2d(self) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.transformer.set_grad_checkpointing(enable) def get_proj_feature(self, x): if self.proj is not None: x = x @ self.proj return x def extra_repr(self): return 'image_size={}, output_dim={}'.format(self.image_size, self.output_dim) def prune(self): hidden_r = torch.where(self.hidden_z != 0)[0] self.conv1.weight = nn.Parameter( (self.conv1.weight.data * self.hidden_z.view(-1, 1, 1, 1))[hidden_r]) if self.conv1.bias is not None: self.conv1.bias = nn.Parameter( (self.conv1.bias * self.hidden_z.view(-1,))[hidden_r]) self.class_embedding = nn.Parameter( (self.class_embedding * self.hidden_z.view(-1,))[hidden_r]) self.positional_embedding = nn.Parameter( (self.positional_embedding * self.hidden_z.view(1, -1))[:, hidden_r]) self.ln_pre = self.ln_pre.prune() self.transformer = self.transformer.prune() self.ln_post = self.ln_post.prune() if self.embed_dim_z is not None: embed_dim_r = self.embed_dim_z > 0 self.proj = nn.Parameter((self.proj * self.hidden_z.view(-1, 1) * self.embed_dim_z.view(1, -1))[hidden_r][:, embed_dim_r]) else: self.proj = nn.Parameter( (self.proj * self.hidden_z.view(-1, 1))[hidden_r]) return self def forward(self, x: torch.Tensor, hidden_z: Optional[torch.Tensor] = None, heads_z: Optional[torch.Tensor] = None, mha_z: Optional[torch.Tensor] = None, intermediate_z: Optional[torch.Tensor] = None, ffn_z: Optional[torch.Tensor] = None, embed_dim_z: Optional[torch.Tensor] = None): self.hidden_z = hidden_z self.embed_dim_z = embed_dim_z x = x.to(self.conv1.weight.device) x = self.conv1(x) # shape = [*, width, grid, grid] # shape = [*, width, grid ** 2] x = x.reshape(x.shape[0], x.shape[1], -1) x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] # the first token is the class token. x = torch.cat( [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, 1 + grid ** 2, width] x = x + self.positional_embedding.to(x.dtype) # 128, 50, 768 if hidden_z is not None: x = torch.mul(x, hidden_z) x = self.ln_pre(x, hidden_z=hidden_z) x = x.permute(1, 0, 2) # NLD -> LND 50, 128, 768 x = self.transformer(x, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z) x = x.permute(1, 0, 2) # LND -> NLD # select class token x = self.ln_post(x[:, 0, :], hidden_z=hidden_z) if self.proj is not None: x = self.get_proj_feature(x) return x def _global_pool(self, x: torch.Tensor): """Separate class token and patch tokens.""" return x[:, 0], x[:, 1:] def encode_dense(self, x, keep_shape=False, mode='vanilla', hidden_z=None, heads_z=None, mha_z=None, intermediate_z=None, ffn_z=None, embed_dim_z=None): """ Encode dense feature map from images. Similar to OpenAI CLIP's encode_dense but adapted for TinyCLIP. """ self.hidden_z = hidden_z self.embed_dim_z = embed_dim_z x = x.to(self.conv1.weight.device) x = self.conv1(x) # shape = [*, width, grid, grid] bs, _, h, w = x.shape # shape = [*, width, grid ** 2] x = x.reshape(x.shape[0], x.shape[1], -1) x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] # the first token is the class token. x = torch.cat( [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, 1 + grid ** 2, width] # Handle positional embedding if (h, w) == self.grid_size: pe = self.positional_embedding.to(x.dtype) else: pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype) x = x + pe if hidden_z is not None: x = torch.mul(x, hidden_z) x = self.ln_pre(x, hidden_z=hidden_z) x = x.permute(1, 0, 2) # NLD -> LND # For TinyCLIP, we support vanilla mode and distill modes if 'distill' in mode: x, extra_feats = self.transformer.extract_feature_map( x, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z) else: x = self.transformer.extract_feature_map( x, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z) x = x.permute(1, 0, 2) # LND -> NLD # Use _global_pool to separate class token and patch tokens _, tokens = self._global_pool(x) tokens = self.ln_post(tokens, hidden_z=hidden_z) if self.proj is not None: tokens = tokens @ self.proj feature_map = tokens.view(bs, h * w, -1) feature_map = F.normalize(feature_map, dim=-1) if keep_shape: feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2) if 'distill' in mode: return feature_map, extra_feats else: return feature_map def extract_roi_features(self, x, normed_boxes, mode="vanilla", size=(1, 1), hidden_z=None, heads_z=None, mha_z=None, intermediate_z=None, ffn_z=None, embed_dim_z=None): """ Extract ROI features from images using normalized boxes. """ if mode in ["qq_vfm_distill", "kk_vfm_distill", "csa_vfm_distill"]: x, extra_feats = self.encode_dense( x, keep_shape=True, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) boxes = self._denormalize_boxes(normed_boxes, x) roi_feats = roi_align( x, boxes, output_size=size, spatial_scale=1.0, sampling_ratio=-1, aligned=True ) if size == (1, 1): roi_feats = roi_feats[..., 0, 0] else: roi_feats = roi_feats.flatten(start_dim=-2).transpose(-2, -1).contiguous() return roi_feats, extra_feats else: x = self.encode_dense( x, keep_shape=True, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) boxes = self._denormalize_boxes(normed_boxes, x) roi_feats = roi_align( x, boxes, output_size=size, spatial_scale=1.0, sampling_ratio=-1, aligned=True ) if size == (1, 1): roi_feats = roi_feats[..., 0, 0] else: roi_feats = roi_feats.flatten(start_dim=-2).transpose(-2, -1).contiguous() return roi_feats def mask_pool(self, x, masks, mode="vanilla", hidden_z=None, heads_z=None, mha_z=None, intermediate_z=None, ffn_z=None, embed_dim_z=None): """ Pool features using masks. """ feature_map = self.encode_dense( x, keep_shape=False, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) num_masks_per_image = [len(masks_per_image) for masks_per_image in masks] masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w feature_map = torch.repeat_interleave( feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0) features = (feature_map * masks.unsqueeze(-1)).sum(1) / (masks.sum(1, keepdim=True) + 1e-12) return features @staticmethod def _denormalize_boxes(normed_boxes, x): """ Denormalize boxes from [0, 1] to pixel coordinates. """ h, w = x.shape[-2:] denormed_boxes = [] for boxes in normed_boxes: new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes! new_boxes[:, [0, 2]] *= w new_boxes[:, [1, 3]] *= h denormed_boxes.append(new_boxes) return denormed_boxes def rescale_positional_embedding(self, out_size, dtype): """ Rescale positional embedding to match output size. """ h, w = out_size rescaled_positional_embedding = \ self.positional_embedding.new_zeros(1 + h*w, self.positional_embedding.shape[1]) rescaled_positional_embedding[0] = self.positional_embedding[0] pe_2d = self.positional_embedding[1:].T.contiguous().view( 1, -1, *self.grid_size) pe_2d = F.interpolate(pe_2d, out_size, mode='bicubic', align_corners=False).view(-1, h*w) rescaled_positional_embedding[1:] = pe_2d.T.contiguous() return rescaled_positional_embedding.to(dtype=dtype) @dataclass class CLIPVisionCfg: layers: Union[Tuple[int, int, int, int], int] = 12 width: int = 768 teacher_width: int = -1 head_width: int = 64 mlp_ratio: float = 4.0 patch_size: int = 16 image_size: Union[Tuple[int, int], int] = 224 timm_model_name: str = None # a valid model name overrides layers, width, patch_size # use (imagenet) pretrained weights for named model timm_model_pretrained: bool = False # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') timm_pool: str = 'avg' # linear projection for timm model output ('linear', 'mlp', '') timm_proj: str = 'linear' @dataclass class CLIPTextCfg: context_length: int = 77 vocab_size: int = 49408 width: int = 512 teacher_width: int = -1 heads: int = 8 layers: int = 12 class ImageEncoder(nn.Module): def __init__(self, embed_dim, vision_cfg, quick_gelu, l0_module_image=False, mask_cfg=None): super().__init__() act_layer = QuickGELU if quick_gelu else nn.GELU if vision_cfg.timm_model_name: self.visual = TimmModel( vision_cfg.timm_model_name, pretrained=vision_cfg.timm_model_pretrained, pool=vision_cfg.timm_pool, proj=vision_cfg.timm_proj, embed_dim=embed_dim, image_size=vision_cfg.image_size ) act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models elif isinstance(vision_cfg.layers, (tuple, list)): vision_heads = vision_cfg.width * 32 // vision_cfg.head_width self.visual = ModifiedResNet( layers=vision_cfg.layers, output_dim=embed_dim, heads=vision_heads, image_size=vision_cfg.image_size, width=vision_cfg.width ) else: vision_heads = vision_cfg.width // vision_cfg.head_width self.visual = VisualTransformer( image_size=vision_cfg.image_size, patch_size=vision_cfg.patch_size, width=vision_cfg.width, layers=vision_cfg.layers, heads=vision_heads, mlp_ratio=vision_cfg.mlp_ratio, output_dim=embed_dim, act_layer=act_layer, teacher_width=vision_cfg.teacher_width, ) self.init_parameters() if l0_module_image: logging.info('use l0_module_vision') config_mask = Namespace() config_mask.hidden_size = vision_cfg.width config_mask.intermediate_size = 4 * vision_cfg.width config_mask.num_attention_heads = vision_heads config_mask.num_hidden_layers = vision_cfg.layers config_mask.sparsity_warmup = mask_cfg.sparsity_warmup config_mask.sparsity = mask_cfg.sparsity config_mask.start_sparsity = mask_cfg.start_sparsity self.l0_module = L0Module(config_mask, lagrangian_warmup=config_mask.sparsity_warmup, start_sparsity=config_mask.start_sparsity, target_sparsity=config_mask.sparsity, pruning_type=["hidden", "heads", "intermediate"]) else: self.l0_module = None self.mask = None def init_parameters(self): if hasattr(self.visual, 'init_parameters'): self.visual.init_parameters() def forward(self, image, normalized=False, **mask): if self.l0_module is not None: mask = self.l0_module.forward() self.mask = mask image_features = self.visual(image, **mask) embed_dim_z = mask.get('embed_dim_z', None) if embed_dim_z is not None: image_features = image_features.mul(embed_dim_z) if normalized: image_features = F.normalize(image_features, dim=-1) return image_features def prune(self): self.visual = self.visual.prune() return self class TextEncoder(nn.Module): def __init__(self, embed_dim, text_cfg, quick_gelu, l0_module_text, mask_cfg=None): super().__init__() act_layer = QuickGELU if quick_gelu else nn.GELU self.context_length = text_cfg.context_length if text_cfg.layers > 0: self.transformer = Transformer( width=text_cfg.width, layers=text_cfg.layers, heads=text_cfg.heads, act_layer=act_layer, ) else: self.transformer = None self.text_projection = None if text_cfg.layers > 0: self.vocab_size = text_cfg.vocab_size self.token_embedding = nn.Embedding( text_cfg.vocab_size, text_cfg.width) self.positional_embedding = nn.Parameter( torch.empty(self.context_length, text_cfg.width)) self.ln_final = LayerNorm(text_cfg.width) if text_cfg.teacher_width > 0: self.text_projection = nn.Parameter(torch.empty( text_cfg.width, embed_dim), requires_grad=False) else: self.text_projection = nn.Parameter( torch.empty(text_cfg.width, embed_dim)) self.register_buffer( 'attn_mask', self.build_attention_mask(), persistent=False) else: self.token_embedding = None self.init_parameters() if l0_module_text: logging.info('use l0_module_text') config_mask = Namespace() config_mask.hidden_size = text_cfg.width config_mask.intermediate_size = 4 * text_cfg.width config_mask.num_attention_heads = text_cfg.heads config_mask.num_hidden_layers = text_cfg.layers config_mask.sparsity_warmup = mask_cfg.sparsity_warmup config_mask.sparsity = mask_cfg.sparsity config_mask.start_sparsity = mask_cfg.start_sparsity self.l0_module = L0Module(config_mask, lagrangian_warmup=config_mask.sparsity_warmup, start_sparsity=config_mask.start_sparsity, target_sparsity=config_mask.sparsity, pruning_type=["hidden", "heads", "intermediate"]) else: self.l0_module = None self.mask = None def init_parameters(self): if self.transformer is not None: nn.init.normal_(self.token_embedding.weight, std=0.02) nn.init.normal_(self.positional_embedding, std=0.01) proj_std = (self.transformer.width ** -0.5) * \ ((2 * self.transformer.layers) ** -0.5) attn_std = self.transformer.width ** -0.5 fc_std = (2 * self.transformer.width) ** -0.5 for block in self.transformer.resblocks: nn.init.normal_(block.attn.in_proj_weight, std=attn_std) nn.init.normal_(block.attn.out_proj.weight, std=proj_std) nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) if self.text_projection is not None: nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) def build_attention_mask(self): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(self.context_length, self.context_length) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return mask def encode_text(self, text, normalized=False, hidden_z: Optional[torch.Tensor] = None, heads_z: Optional[torch.Tensor] = None, mha_z: Optional[torch.Tensor] = None, intermediate_z: Optional[torch.Tensor] = None, ffn_z: Optional[torch.Tensor] = None, embed_dim_z: Optional[torch.Tensor] = None, ): self.hidden_z = hidden_z self.embed_dim_z = embed_dim_z text = text.to(self.token_embedding.weight.device) x = self.token_embedding(text) # [batch_size, n_ctx, d_model] x = x + self.positional_embedding if hidden_z is not None: x = torch.mul(x, hidden_z) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer(x, attn_mask=self.attn_mask, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_final(x, hidden_z) # if hidden_z is not None: # x = torch.mul(x, hidden_z) x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] # x.shape = [batch_size, n_ctx, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) x = self.get_proj_feature(x) if embed_dim_z is not None: x = x.mul(embed_dim_z) if normalized: x = F.normalize(x, dim=-1) return x def get_proj_feature(self, x): return x @ self.text_projection def forward(self, text, normalized=False): mask = dict() if self.l0_module is not None: mask = self.l0_module.forward() self.mask = mask return self.encode_text(text, normalized=normalized, **mask) def prune(self): device = self.token_embedding.weight.device if self.hidden_z is None: self.hidden_z = torch.ones( self.text_projection.size(0), device=device) if self.embed_dim_z is None: self.embed_dim_z = torch.ones( self.text_projection.size(1), device=device) mod = self self_copy = copy.deepcopy(self) hidden_r = self.hidden_z > 0 mod.token_embedding = nn.Embedding( self_copy.token_embedding.weight.shape[0], hidden_r.sum()) mod.positional_embedding = nn.Parameter( torch.empty(self_copy.context_length, hidden_r.sum())) mod.token_embedding.weight = nn.Parameter( (self_copy.token_embedding.weight * self_copy.hidden_z.view(1, -1))[:, hidden_r]) mod.positional_embedding = nn.Parameter( (self_copy.positional_embedding * self_copy.hidden_z.view(1, -1))[:, hidden_r]) mod.transformer = self.transformer.prune() mod.ln_final = self.ln_final.prune() embed_dim_r = self.embed_dim_z > 0 mod.text_projection = nn.Parameter( (self.text_projection * self.hidden_z.view(-1, 1) * self.embed_dim_z.view(1, -1))[hidden_r][:, embed_dim_r]) return mod class LogitScale(nn.Module): def __init__(self): super().__init__() self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def forward(self, dummy): return self.logit_scale class FNBlock(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, *args, **kwargs): return self.fn(*args, **kwargs) class FakeDDP(nn.Module): def __init__(self, module): super().__init__() self.module = module def forward(self, *args, **kwargs): return self.module(*args, **kwargs) class CLIPBase(nn.Module): def __init__(self, image_encoder, text_encoder): super().__init__() self._image_encoder = image_encoder self._text_encoder = text_encoder self._logit_scale = LogitScale() # autocast context self.image_autocast = nullcontext self.text_autocast = nullcontext self.logit_autocast = nullcontext # copy the module without ddp self._without_ddp = [self._image_encoder, self._text_encoder, self._logit_scale] self.used_ddp = False def set_autocast(self, image_autocast, text_autocast, logit_autocast): self.image_autocast = image_autocast self.text_autocast = text_autocast self.logit_autocast = logit_autocast @property def image_encoder_without_ddp(self): return self._without_ddp[0] @image_encoder_without_ddp.setter def image_encoder_without_ddp(self, encoder): assert self.used_ddp is False self._image_encoder = encoder self._without_ddp[0] = self._image_encoder @property def text_encoder_without_ddp(self): return self._without_ddp[1] @text_encoder_without_ddp.setter def text_encoder_without_ddp(self, encoder): assert self.used_ddp is False self._text_encoder = encoder self._without_ddp[1] = self._text_encoder @property def logit_scale_without_ddp(self): return self._without_ddp[2] @logit_scale_without_ddp.setter def logit_scale_without_ddp(self, logit_scale): assert self.used_ddp is False self._logit_scale = logit_scale self._without_ddp[2] = self._logit_scale @property def visual(self): return self.image_encoder_without_ddp.visual @property def transformer(self): return self.text_encoder_without_ddp.transformer @property def text_encoder_without_ddp(self): return self._without_ddp[1] @property def logit_scale_without_ddp(self): return self._without_ddp[2] def get_teacher(self): return self.teacher[0] def use_teacher_image(self): def teacher_image_encoder_fn(image, normalized=False): teacher = self.get_teacher() with torch.no_grad(): return teacher.encode_image(image, normalized=normalized) self._image_encoder = FNBlock(teacher_image_encoder_fn) class EmptyVisual(nn.Module): def __init__(self): super().__init__() self.layers = 0 self._image_encoder.visual = EmptyVisual() self._without_ddp[0] = self._image_encoder def use_teacher_text(self): def teacher_text_encoder_fn(text, normalized=False): teacher = self.get_teacher() with torch.no_grad(): return teacher.encode_text(text, normalized=normalized) self._text_encoder = FNBlock(teacher_text_encoder_fn) class EmptyTransformer(nn.Module): def __init__(self): super().__init__() self.layers = 0 self._text_encoder.transformer = EmptyTransformer() self._text_encoder.token_embedding = None self._without_ddp[1] = self._text_encoder def ddpify(self, ddp_fn): def _ddp_fn(module): cnt = sum([p.numel() for p in module.parameters() if p.requires_grad]) if cnt > 0: return ddp_fn(module) return FakeDDP(module) self._image_encoder = _ddp_fn(self.image_encoder_without_ddp) self._text_encoder = _ddp_fn(self.text_encoder_without_ddp) self._logit_scale = _ddp_fn(self.logit_scale_without_ddp) self.used_ddp = True def forward(self, image, text, normalized=True): image_features = text_features = None if image is not None: with self.image_autocast(): image_features = self._image_encoder( image, normalized=normalized) if text is not None: with self.text_autocast(): text_features = self._text_encoder(text, normalized=normalized) with self.logit_autocast(): logit_scale = self._logit_scale(torch.tensor(0)) return image_features, text_features, logit_scale.exp() def encode_image(self, image, normalize=False): """ Encode image to features. Compatible with OpenAI CLIP's encode_image interface. """ with self.image_autocast(): return self._image_encoder(image, normalized=normalize) def encode_text(self, text, normalized=False): with self.text_autocast(): return self._text_encoder(text, normalized=normalized) @property def logit_scale(self): return self.logit_scale_without_ddp.logit_scale def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) def lock_text_tower(self, unlocked_groups=0, freeze_bn_stats=False): assert unlocked_groups == 0, 'partial locking not currently supported for this model' tower = self.text_encoder_without_ddp for param in tower.parameters(): param.requires_grad = False if freeze_bn_stats: freeze_batch_norm_2d(tower) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): visual = self.image_encoder_without_ddp.visual transformer = self.text_encoder_without_ddp.transformer if hasattr(visual, 'set_grad_checkpointing'): visual.set_grad_checkpointing(enable) if transformer is not None and hasattr(transformer, 'set_grad_checkpointing'): transformer.set_grad_checkpointing(enable) def image_named_params(self): return self._image_encoder.named_parameters() def text_named_params(self): return self._text_encoder.named_parameters() def joint_named_params(self): return self._logit_scale.named_parameters() def load_state_dict(self, state_dict, strict=True): state_dict = convert_to_new_checkpoint(state_dict, self.used_ddp) if not any(k.startswith('_image_encoder') for k in state_dict.keys()): self.use_teacher_image() for m in ['module.', '']: flag = f'_image_encoder.{m}visual.model.head.0.weight' if flag in state_dict: # LN state_dict[f'_image_encoder.{m}visual.ln_post.weight'] = state_dict.pop( f'_image_encoder.{m}visual.model.head.0.weight') state_dict[f'_image_encoder.{m}visual.ln_post.bias'] = state_dict.pop( f'_image_encoder.{m}visual.model.head.0.bias') # FC state_dict[f'_image_encoder.{m}visual.proj'] = state_dict.pop( f'_image_encoder.{m}visual.model.head.1.weight').T new_state_dict = state_dict.copy() for k, v in new_state_dict.items(): if '.module' in k: state_dict[k.replace('.module', '')] = v state_dict.pop(k) return super().load_state_dict(state_dict, strict=strict) class CLIP(CLIPBase): def __init__( self, embed_dim: int, vision_cfg: CLIPVisionCfg, text_cfg: CLIPTextCfg, quick_gelu: bool = False, mask_image: bool = False, mask_text: bool = False, sparsity_warmup: int = 1000, sparsity: float = 0.25, start_sparsity: float = 0.0, freeze_text: bool = True, ): vision_ocfg = None text_ocfg = None if isinstance(vision_cfg, dict): vision_ocfg = vision_cfg.pop('configs', None) vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(text_cfg, dict): text_ocfg = text_cfg.pop('configs', None) text_cfg = CLIPTextCfg(**text_cfg) mask_cfg = Namespace() mask_cfg.sparsity_warmup = sparsity_warmup mask_cfg.sparsity = sparsity mask_cfg.start_sparsity = start_sparsity if vision_ocfg is None: image_encoder = ImageEncoder(embed_dim, vision_cfg, quick_gelu, l0_module_image=mask_image, mask_cfg=mask_cfg) if text_ocfg is None: text_encoder = TextEncoder(embed_dim, text_cfg, quick_gelu, l0_module_text=mask_text, mask_cfg=mask_cfg) super().__init__(image_encoder, text_encoder) # Freeze text encoder at initialization if freeze_text: print(f'Freeze text encoder parameters', flush=True) self.lock_text_tower() self.text_encoder_without_ddp.eval() def train(self, mode: bool = True): """Override train() to ensure text encoder stays frozen even in training mode.""" if not isinstance(mode, bool): raise ValueError("training mode is expected to be boolean") self.training = mode # Set image encoder to training/eval mode based on mode if mode: logging.info(f'========Set image encoder as train mode========') else: logging.info(f'========Set image encoder as eval mode========') self.image_encoder_without_ddp.train(mode) # Always keep text encoder in eval mode (frozen) logging.info(f'========Set text encoder as eval mode (frozen)========') self.text_encoder_without_ddp.train(False) # Ensure text encoder parameters remain frozen for param in self.text_encoder_without_ddp.parameters(): param.requires_grad = False return self def encode_dense(self, image, normalize=False, keep_shape=False, mode="vanilla"): """ Encode dense feature map from images. Compatible with OpenAI CLIP's encode_dense interface. """ visual = self.visual if not isinstance(visual, VisualTransformer): raise NotImplementedError("encode_dense is only supported for VisualTransformer") # Get mask parameters if available mask = getattr(self.image_encoder_without_ddp, 'mask', None) if mask is None or not isinstance(mask, dict): mask = {} hidden_z = mask.get('hidden_z', None) heads_z = mask.get('heads_z', None) mha_z = mask.get('mha_z', None) intermediate_z = mask.get('intermediate_z', None) ffn_z = mask.get('ffn_z', None) embed_dim_z = mask.get('embed_dim_z', None) with self.image_autocast(): if mode in ["qq_vfm_distill", "kk_vfm_distill", "csa_vfm_distill"]: features, extra_features = visual.encode_dense( image, keep_shape=keep_shape, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) if normalize: if keep_shape: features = F.normalize(features, dim=1) else: features = F.normalize(features, dim=-1) return features, extra_features else: features = visual.encode_dense( image, keep_shape=keep_shape, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) if normalize: if keep_shape: features = F.normalize(features, dim=1) else: features = F.normalize(features, dim=-1) return features def encode_pseudo_boxes(self, image, normed_boxes, normalize=False, mode="vanilla", size=(1, 1)): """ Encode ROI features from images using normalized boxes. Compatible with OpenAI CLIP's encode_pseudo_boxes interface. """ visual = self.visual if not isinstance(visual, VisualTransformer): raise NotImplementedError("encode_pseudo_boxes is only supported for VisualTransformer") # Get mask parameters if available mask = getattr(self.image_encoder_without_ddp, 'mask', None) if mask is None or not isinstance(mask, dict): mask = {} hidden_z = mask.get('hidden_z', None) heads_z = mask.get('heads_z', None) mha_z = mask.get('mha_z', None) intermediate_z = mask.get('intermediate_z', None) ffn_z = mask.get('ffn_z', None) embed_dim_z = mask.get('embed_dim_z', None) with self.image_autocast(): if mode in ["qq_vfm_distill", "kk_vfm_distill", "csa_vfm_distill"]: box_features, clip_dense_feats = visual.extract_roi_features( image, normed_boxes, mode=mode, size=size, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) if normalize: box_features = F.normalize(box_features, dim=-1) return box_features, clip_dense_feats else: box_features = visual.extract_roi_features( image, normed_boxes, mode=mode, size=size, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) if normalize: box_features = F.normalize(box_features, dim=-1) return box_features def encode_masks(self, image, masks, normalize=True, mask_attn=False, mode="vanilla"): """ Encode mask-pooled features from images. Compatible with OpenAI CLIP's encode_masks interface. """ visual = self.visual if not isinstance(visual, VisualTransformer): raise NotImplementedError("encode_masks is only supported for VisualTransformer") # Get mask parameters if available mask = getattr(self.image_encoder_without_ddp, 'mask', None) if mask is None or not isinstance(mask, dict): mask = {} hidden_z = mask.get('hidden_z', None) heads_z = mask.get('heads_z', None) mha_z = mask.get('mha_z', None) intermediate_z = mask.get('intermediate_z', None) ffn_z = mask.get('ffn_z', None) embed_dim_z = mask.get('embed_dim_z', None) with self.image_autocast(): mask_pooled = visual.mask_pool( image, masks, mode=mode, hidden_z=hidden_z, heads_z=heads_z, mha_z=mha_z, intermediate_z=intermediate_z, ffn_z=ffn_z, embed_dim_z=embed_dim_z) if normalize: mask_pooled = F.normalize(mask_pooled, dim=-1) return mask_pooled def convert_to_new_checkpoint(state_dict, used_ddp=False): if '_logit_scale.module.logit_scale' in state_dict: if not used_ddp: new_checkpoint = dict() for k, v in state_dict.items(): sp = k.split('.') assert sp[1] == 'module', (sp, state_dict.keys()) k = '.'.join(sp[:1] + sp[2:]) new_checkpoint[k] = v state_dict = new_checkpoint return state_dict if '_logit_scale.logit_scale' in state_dict: if used_ddp: new_checkpoint = dict() for k, v in state_dict.items(): sp = k.split('.') k = '.'.join(sp[:1] + ['module'] + sp[1:]) new_checkpoint[k] = v state_dict = new_checkpoint return state_dict image_prefix = '_image_encoder.' text_prefix = '_text_encoder.' logit_scale_prefix = '_logit_scale.' if used_ddp: image_prefix += 'module.' text_prefix += 'module.' logit_scale_prefix += 'module.' new_checkpoint = dict() if 'module.logit_scale' in state_dict: # remove the prefix module state_dict = {k[len('module.'):]: v for k, v in state_dict.items()} if 'logit_scale' in state_dict: # old CLIP checkpoint for k, v in state_dict.items(): if k.startswith('visual.'): new_checkpoint[image_prefix + k] = v elif k == 'logit_scale': new_checkpoint[logit_scale_prefix + 'logit_scale'] = v else: new_checkpoint[text_prefix + k] = v else: new_checkpoint = state_dict return new_checkpoint def convert_weights_to_fp16(model: nn.Module): """Convert applicable model parameters to fp16""" def _convert_weights_to_fp16(l): if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): l.weight.data = l.weight.data.half() if l.bias is not None: l.bias.data = l.bias.data.half() if isinstance(l, (nn.MultiheadAttention, )): for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: tensor = getattr(l, attr) if tensor is not None: tensor.data = tensor.data.half() for name in ["text_projection", "proj"]: if hasattr(l, name): attr = getattr(l, name) if attr is not None: attr.data = attr.data.half() model.apply(_convert_weights_to_fp16) def build_model_from_openai_state_dict(state_dict: dict): vit = "visual.proj" in state_dict if vit: vision_width = state_dict["visual.conv1.weight"].shape[0] vision_layers = len( [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] grid_size = round( (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) image_size = vision_patch_size * grid_size else: counts: list = [ len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] vision_layers = tuple(counts) vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] output_width = round( (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) vision_patch_size = None assert output_width ** 2 + \ 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] image_size = output_width * 32 embed_dim = state_dict["text_projection"].shape[1] context_length = state_dict["positional_embedding"].shape[0] vocab_size = state_dict["token_embedding.weight"].shape[0] transformer_width = state_dict["ln_final.weight"].shape[0] transformer_heads = transformer_width // 64 transformer_layers = len(set( k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) vision_cfg = CLIPVisionCfg( layers=vision_layers, width=vision_width, patch_size=vision_patch_size, image_size=image_size, ) text_cfg = CLIPTextCfg( context_length=context_length, vocab_size=vocab_size, width=transformer_width, heads=transformer_heads, layers=transformer_layers ) model = CLIP( embed_dim, vision_cfg=vision_cfg, text_cfg=text_cfg, quick_gelu=True, # OpenAI models were trained with QuickGELU ) for key in ["input_resolution", "context_length", "vocab_size"]: state_dict.pop(key, None) convert_weights_to_fp16(model) model.load_state_dict(state_dict) return model.eval() def trace_model(model, batch_size=256, device=torch.device('cpu')): model.eval() image_size = model.visual.image_size example_images = torch.ones( (batch_size, 3, image_size, image_size), device=device) example_text = torch.zeros( (batch_size, model.context_length), dtype=torch.int, device=device) model = torch.jit.trace_module( model, inputs=dict( forward=(example_images, example_text), encode_text=(example_text,), encode_image=(example_images,) )) model.visual.image_size = image_size return model def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): # Rescale the grid of position embeddings when loading from state_dict old_pos_embed = state_dict.get('visual.positional_embedding', None) if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): return grid_size = to_2tuple(model.visual.grid_size) # FIXME detect different token configs (ie no class token, or more) extra_tokens = 1 new_seq_len = grid_size[0] * grid_size[1] + extra_tokens if new_seq_len == old_pos_embed.shape[0]: return if extra_tokens: pos_emb_tok, pos_emb_img = old_pos_embed[: extra_tokens], old_pos_embed[extra_tokens:] else: pos_emb_tok, pos_emb_img = None, old_pos_embed old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) pos_emb_img = pos_emb_img.reshape( 1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) pos_emb_img = F.interpolate( pos_emb_img, size=grid_size, mode=interpolation, align_corners=True, ) pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape( 1, grid_size[0] * grid_size[1], -1)[0] if pos_emb_tok is not None: new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) else: new_pos_embed = pos_emb_img state_dict['visual.positional_embedding'] = new_pos_embed @torch.no_grad() def load_pruned_model(model, pruned_state_dict, strict=True): ''' A full model loads the pruned state dict. Inputs: model_state_dict: the full model weights pruned_state_dict: the pruned model weights ''' def _copy_to_full_weight(dst, src): assert dst.ndim == src.ndim, (dst.ndim, src.ndim) dst.zero_() dims = src.shape if len(dims) == 0: dst.copy_(src) else: slices = [slice(0, d) for d in dims] dst[slices].copy_(src) for _ in range(2): pruned_state_dict = { k.replace('module.', ''): v for k, v in pruned_state_dict.items()} lambda_init_value = 10.0 model_state_dict = model.state_dict() head_dim = model.transformer.head_dim pruned_state_dict = {k.replace('image_encoder_without_ddp', '_image_encoder'). replace('text_encoder_without_ddp', '_text_encoder'): v for k, v in pruned_state_dict.items()} for name, dst in model_state_dict.items(): # auto weight inheritance model weight prefix dst_shape = dst.shape # copy weights if name in pruned_state_dict: src = pruned_state_dict[name] if 'attn.in_proj_weight' in name: # reshape: (3 * num_heads * head_dim, embed_dim) -> (3, num_heads, head_dim, embed_dim) assert len(src.shape) == 2 _copy_to_full_weight(dst.view(3, -1, head_dim, dst_shape[-1]), src.view(3, -1, head_dim, src.shape[-1])) elif 'attn.in_proj_bias' in name: # reshape: (3 * num_heads * head_dim,) -> (3, num_heads, head_dim) assert len(src.shape) == 1 _copy_to_full_weight(dst.view(3, -1, head_dim), src.view(3, -1, head_dim)) else: _copy_to_full_weight(dst, src) else: if '.resblocks.' in name: # the layer has been pruned. dst.zero_() model_state_dict['_logit_scale.logit_scale'] = pruned_state_dict['_logit_scale.logit_scale'] # prune hidden dimensions encoder_names = ['_image_encoder', '_text_encoder'] hidden_size_img = pruned_state_dict['_image_encoder.visual.ln_pre.weight'].shape[0] hidden_size_txt = pruned_state_dict['_text_encoder.positional_embedding'].shape[1] hidden_sizes = [hidden_size_img, hidden_size_txt] for ename, hidden_size in zip(encoder_names, hidden_sizes): # reset lambda in l0 module model_state_dict[f'{ename}.l0_module.lambda_1'].fill_( lambda_init_value) model_state_dict[f'{ename}.l0_module.lambda_2'].fill_( lambda_init_value) # prune the last dimensions model_state_dict[f'{ename}.l0_module.hidden_loga'][hidden_size:].fill_( -lambda_init_value) def _get_layer_id(name): return int(name.split('resblocks.')[1].split('.')[0]) for ename in encoder_names: # get the depth of the encoder encoder_keys = list(k for k in model_state_dict.keys() if ename in k) encoder_depth = max(_get_layer_id(k) for k in encoder_keys if 'resblocks' in k) + 1 pruned_encoder_keys = list( k for k in pruned_state_dict.keys() if ename in k) in_proj_weight_shapes = [None for _ in range(encoder_depth)] mlp_c_fc_shapes = [None for _ in range(encoder_depth)] for k in pruned_encoder_keys: if 'in_proj_weight' in k: d = _get_layer_id(k) in_proj_weight_shapes[d] = pruned_state_dict[k].shape elif 'mlp.c_fc.weight' in k: d = _get_layer_id(k) mlp_c_fc_shapes[d] = pruned_state_dict[k].shape for d in range(encoder_depth): # set heads_loga if in_proj_weight_shapes[d] is not None: num_heads = in_proj_weight_shapes[d][0] // head_dim // 3 model_state_dict[f'{ename}.l0_module.heads_loga'][d, num_heads:].fill_(-lambda_init_value) else: # all heads have been pruned model_state_dict[f'{ename}.l0_module.heads_loga'][d, :].fill_(-lambda_init_value) # set intermediate_loga if mlp_c_fc_shapes[d] is not None: inter_size = mlp_c_fc_shapes[d][0] model_state_dict[f'{ename}.l0_module.intermediate_loga'][d, inter_size:].fill_(-lambda_init_value) else: # all intermediate dimensions have been pruned model_state_dict[f'{ename}.l0_module.intermediate_loga'][d, :].fill_(-lambda_init_value) return model.load_state_dict(model_state_dict, strict=strict) def prune_model(model): device = next(model.parameters()).device with torch.no_grad(): model.image_encoder_without_ddp.eval() image_size = (1, 3) + model.image_encoder_without_ddp.visual.image_size image = torch.randn(image_size, device=device) model.image_encoder_without_ddp(image) model.image_encoder_without_ddp = model.image_encoder_without_ddp.prune() assert hasattr(model.image_encoder_without_ddp, 'l0_module') model.image_encoder_without_ddp.l0_module = None with torch.no_grad(): model.text_encoder_without_ddp.eval() context_length = model.text_encoder_without_ddp.context_length text = torch.zeros((1, context_length), dtype=torch.long, device=device) model.text_encoder_without_ddp(text) model.text_encoder_without_ddp = model.text_encoder_without_ddp.prune() assert hasattr(model.text_encoder_without_ddp, 'l0_module') model.text_encoder_without_ddp.l0_module = None return model