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becf13a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | """
Custom optimizer constructor for viewpoint-conditioned training.
Supports parameter-wise learning rates for different model components:
- viewpoint_mlp: Higher LR for new viewpoint token module
- viewpoint_head: Higher LR for new viewpoint prediction head
- llm: Lower LR for pretrained LLM
- mar: Lower LR for pretrained MAR
- proj_in/proj_out: Medium LR for projection layers
"""
import torch.nn as nn
from mmengine.optim import DefaultOptimWrapperConstructor, OptimWrapper
from mmengine.registry import OPTIM_WRAPPER_CONSTRUCTORS, OPTIM_WRAPPERS, OPTIMIZERS
import inspect
@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
class ViewpointOptimWrapperConstructor(DefaultOptimWrapperConstructor):
"""
Custom optimizer wrapper constructor with parameter-wise learning rates.
Expects the following parameters in optim_wrapper_cfg:
- lr_viewpoint: Learning rate for viewpoint modules (default: 1e-3)
- lr_llm: Learning rate for LLM (default: 1e-5)
- lr_mar: Learning rate for MAR (default: 1e-5)
- lr_proj: Learning rate for projection layers (default: 1e-4)
"""
def __call__(self, model: nn.Module) -> OptimWrapper:
if hasattr(model, 'module'):
model = model.module
optim_wrapper_cfg = self.optim_wrapper_cfg.copy()
optim_wrapper_cfg.setdefault('type', 'OptimWrapper')
optimizer_cfg = self.optimizer_cfg.copy()
# Get base learning rate and weight decay
base_lr = optimizer_cfg.get('lr', 1e-5)
weight_decay = optimizer_cfg.pop('weight_decay', 0.02)
# Get component-specific learning rates (with fallbacks)
lr_viewpoint = optim_wrapper_cfg.pop('lr_viewpoint', 1e-3)
lr_llm = optim_wrapper_cfg.pop('lr_llm', 1e-5)
lr_mar = optim_wrapper_cfg.pop('lr_mar', 1e-5)
lr_proj = optim_wrapper_cfg.pop('lr_proj', 1e-4)
# Freeze parameters for components with lr=0
# This saves memory and computation by not computing gradients
frozen_components = []
component_lr_map = {
'viewpoint': lr_viewpoint, # Covers both viewpoint_mlp and viewpoint_head
'llm': lr_llm,
'mar': lr_mar,
'proj': lr_proj,
}
print("\n" + "="*80)
print("Viewpoint Optimizer: Checking for frozen components (lr=0)")
print("="*80)
for component_name, component_lr in component_lr_map.items():
if component_lr == 0:
frozen_components.append(component_name)
# Freeze parameters for this component
num_frozen = 0
for name, param in model.named_parameters():
# Match component name patterns
should_freeze = False
if component_name == 'viewpoint' and ('viewpoint_mlp' in name or 'viewpoint_head' in name):
should_freeze = True
elif component_name == 'llm' and 'llm' in name:
should_freeze = True
elif component_name == 'mar' and 'mar' in name:
should_freeze = True
elif component_name == 'proj' and ('proj_in' in name or 'proj_out' in name):
should_freeze = True
if should_freeze:
param.requires_grad = False
num_frozen += param.numel()
print(f" ✓ Frozen {component_name}: {num_frozen:,} parameters (lr=0)")
if not frozen_components:
print(" No components frozen (all have lr > 0)")
print("="*80 + "\n")
# Categorize parameters by component
viewpoint_mlp_params = []
viewpoint_head_params = []
llm_params = []
mar_params = []
proj_params = []
other_params = []
# Track no-decay parameters
viewpoint_mlp_no_decay = []
viewpoint_head_no_decay = []
llm_no_decay = []
mar_no_decay = []
proj_no_decay = []
other_no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
# Determine if parameter should have weight decay
# Skip bias, norms, and diffloss
apply_decay = True
if len(param.shape) == 1 or name.endswith(".bias") or 'diffloss' in name:
apply_decay = False
# Categorize by component
if 'viewpoint_mlp' in name:
if apply_decay:
viewpoint_mlp_params.append(param)
else:
viewpoint_mlp_no_decay.append(param)
elif 'viewpoint_head' in name:
if apply_decay:
viewpoint_head_params.append(param)
else:
viewpoint_head_no_decay.append(param)
elif 'llm' in name:
if apply_decay:
llm_params.append(param)
else:
llm_no_decay.append(param)
elif 'mar' in name:
if apply_decay:
mar_params.append(param)
else:
mar_no_decay.append(param)
elif 'proj_in' in name or 'proj_out' in name:
if apply_decay:
proj_params.append(param)
else:
proj_no_decay.append(param)
else:
if apply_decay:
other_params.append(param)
else:
other_no_decay.append(param)
# Build parameter groups
param_groups = []
# Viewpoint MLP (with decay)
if viewpoint_mlp_params:
param_groups.append({
'params': viewpoint_mlp_params,
'lr': lr_viewpoint,
'weight_decay': weight_decay,
'name': 'viewpoint_mlp_decay'
})
if viewpoint_mlp_no_decay:
param_groups.append({
'params': viewpoint_mlp_no_decay,
'lr': lr_viewpoint,
'weight_decay': 0.0,
'name': 'viewpoint_mlp_no_decay'
})
# Viewpoint Head (with decay)
if viewpoint_head_params:
param_groups.append({
'params': viewpoint_head_params,
'lr': lr_viewpoint,
'weight_decay': weight_decay,
'name': 'viewpoint_head_decay'
})
if viewpoint_head_no_decay:
param_groups.append({
'params': viewpoint_head_no_decay,
'lr': lr_viewpoint,
'weight_decay': 0.0,
'name': 'viewpoint_head_no_decay'
})
# LLM
if llm_params:
param_groups.append({
'params': llm_params,
'lr': lr_llm,
'weight_decay': weight_decay,
'name': 'llm_decay'
})
if llm_no_decay:
param_groups.append({
'params': llm_no_decay,
'lr': lr_llm,
'weight_decay': 0.0,
'name': 'llm_no_decay'
})
# MAR
if mar_params:
param_groups.append({
'params': mar_params,
'lr': lr_mar,
'weight_decay': weight_decay,
'name': 'mar_decay'
})
if mar_no_decay:
param_groups.append({
'params': mar_no_decay,
'lr': lr_mar,
'weight_decay': 0.0,
'name': 'mar_no_decay'
})
# Projection layers
if proj_params:
param_groups.append({
'params': proj_params,
'lr': lr_proj,
'weight_decay': weight_decay,
'name': 'proj_decay'
})
if proj_no_decay:
param_groups.append({
'params': proj_no_decay,
'lr': lr_proj,
'weight_decay': 0.0,
'name': 'proj_no_decay'
})
# Other parameters
if other_params:
param_groups.append({
'params': other_params,
'lr': base_lr,
'weight_decay': weight_decay,
'name': 'other_decay'
})
if other_no_decay:
param_groups.append({
'params': other_no_decay,
'lr': base_lr,
'weight_decay': 0.0,
'name': 'other_no_decay'
})
# Print parameter group statistics
print("\n" + "="*80)
print("Viewpoint Optimizer Parameter Groups:")
print("="*80)
for group in param_groups:
num_params = sum(p.numel() for p in group['params'])
print(f" {group['name']:30s} | LR: {group['lr']:.2e} | "
f"Weight Decay: {group['weight_decay']:.2e} | "
f"Params: {num_params:,}")
print("="*80 + "\n")
# Build optimizer
optimizer_cls = self.optimizer_cfg['type']
if isinstance(optimizer_cls, str):
with OPTIMIZERS.switch_scope_and_registry(None) as registry:
optimizer_cls = registry.get(self.optimizer_cfg['type'])
first_arg_name = next(iter(inspect.signature(optimizer_cls).parameters))
optimizer_cfg[first_arg_name] = param_groups
optimizer = OPTIMIZERS.build(optimizer_cfg)
# Build optimizer wrapper
optim_wrapper = OPTIM_WRAPPERS.build(
optim_wrapper_cfg, default_args=dict(optimizer=optimizer))
return optim_wrapper
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