File size: 11,807 Bytes
b84549f |
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 |
from copy import deepcopy
from typing import List
import torch
from methods.base.model import BaseModel
import tqdm
from torch import nn
import torch.nn.functional as F
from abc import abstractmethod
from methods.elasticdnn.model.base import ElasticDNNUtil
from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util
from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from utils.common.log import logger
from utils.dl.common.model import LayerActivation, get_parameter
class ElasticDNN_OnlineModel(BaseModel):
def __init__(self, name: str, models_dict_path: str, device: str, ab_options: dict):
super().__init__(name, models_dict_path, device)
assert [k in ab_options.keys() for k in ['md_to_fm_alpha', 'fm_to_md_alpha']]
self.ab_options = ab_options
def get_required_model_components(self) -> List[str]:
return ['fm', 'md', 'sd', 'indexes', 'bn_stats']
@torch.no_grad()
def generate_sd_by_target_samples(self, target_samples: torch.Tensor):
elastic_dnn_util = self.get_elastic_dnn_util()
if isinstance(target_samples, dict):
for k, v in target_samples.items():
if isinstance(v, torch.Tensor):
target_samples[k] = v.to(self.device)
else:
target_samples = target_samples.to(self.device)
sd, unpruned_indexes_of_layers = elastic_dnn_util.extract_surrogate_dnn_via_samples_with_perf_test(self.models_dict['md'], target_samples, True)
logger.debug(f'generate sd: \n{sd}')
return sd, unpruned_indexes_of_layers
@torch.no_grad()
def _compute_diff(self, old, new):
return (new - old).norm(1) / old.norm(1)
@torch.no_grad()
def sd_feedback_to_md(self, after_da_sd, unpruned_indexes_of_layers):
self.models_dict['sd'] = after_da_sd
self.before_da_md = deepcopy(self.models_dict['md'])
logger.info('\n\nsurrogate DNN feedback to master DNN...\n\n')
# one-to-one
cur_unpruned_indexes = None
cur_unpruned_indexes_name = None
for p_name, p in self.models_dict['sd'].named_parameters():
matched_md_param = self.get_md_matched_param_of_sd_param(p_name)
logger.debug(f'if feedback: {p_name}')
if matched_md_param is None:
continue
logger.debug(f'start feedback: {p_name}, {p.size()} -> {matched_md_param.size()}')
# average
# setattr(matched_md_module, matched_md_param_name, (matched_md_param + p) / 2.)
if p_name in unpruned_indexes_of_layers.keys():
cur_unpruned_indexes = unpruned_indexes_of_layers[p_name]
cur_unpruned_indexes_name = p_name
if p.size() != matched_md_param.size():
logger.debug(f'cur unpruned indexes: {cur_unpruned_indexes_name}, {cur_unpruned_indexes.size()}')
if p.dim() == 1: # norm
new_p = deepcopy(matched_md_param)
new_p[cur_unpruned_indexes] = p
elif p.dim() == 2: # linear
if p.size(0) < matched_md_param.size(0): # output pruned
new_p = deepcopy(matched_md_param)
new_p[cur_unpruned_indexes] = p
else: # input pruned
new_p = deepcopy(matched_md_param)
new_p[:, cur_unpruned_indexes] = p
p = new_p
assert p.size() == matched_md_param.size(), f'{p.size()}, {matched_md_param.size()}'
diff = self._compute_diff(matched_md_param, (matched_md_param + p) / 2.)
matched_md_param.copy_((matched_md_param + p) / 2.)
logger.debug(f'end feedback: {p_name}, diff: {diff:.6f}')
def infer(self, x, *args, **kwargs):
return self.models_dict['sd'](x)
def set_sd_sparsity(self, sparsity: float):
elastic_dnn_util = self.get_elastic_dnn_util()
elastic_dnn_util.clear_cached_channel_attention_in_master_dnn(self.models_dict['md'])
elastic_dnn_util.set_master_dnn_sparsity(self.models_dict['md'], sparsity)
@torch.no_grad()
def md_feedback_to_self_fm(self):
logger.info('\n\nmaster DNN feedback to self foundation model...\n\n')
# one-to-many
# def upsample_2d_tensor(p: torch.Tensor, target_len: int):
# assert p.dim() == 2 # regard 2d weight as (batch_size, 1d_vector_dim)
# return F.upsample(p.unsqueeze(1).unsqueeze(3),
# size=(target_len, 1),
# mode='bilinear').squeeze(3).squeeze(1)
for (p_name, p), before_p in zip(self.models_dict['md'].named_parameters(), self.before_da_md.parameters()):
matched_fm_param = self.get_fm_matched_param_of_md_param(p_name)
logger.debug(f'if feedback: {p_name}')
if matched_fm_param is None:
continue
# print(self.models_dict['indexes'].keys())
index = self.models_dict['indexes'][p_name]
logger.debug(f'start feedback: {p_name}, {p.size()} -> {matched_fm_param.size()}, index: {index.size()}')
p_update = p - before_p
t = False
if p.dim() > 1 and index.size(0) == p.size(1) and index.size(1) == matched_fm_param.size(1):
assert p.dim() == 2
p_update = p_update.T
matched_fm_param = matched_fm_param.T
t = True
logger.debug(f'transpose paramters')
if p.dim() == 2:
# p_update = upsample_2d_tensor(p_update, matched_fm_param.size(1))
p_update = p_update.unsqueeze(1)
index = index.unsqueeze(-1)
# fast
# agg_p_update = (p_update * index).sum(0)
# balanced agg
agg_p_update = 0
cur_split_size = 64
while index.size(0) % cur_split_size != 0:
cur_split_size -= 1
for i in range(0, index.size(0), cur_split_size):
agg_p_update += p_update[i: i + cur_split_size] * index[i: i + cur_split_size]
agg_p_update = agg_p_update.sum(0)
else:
agg_p_update = (p_update.unsqueeze(1) * index).sum(0)
new_fm_param = matched_fm_param + agg_p_update * self.ab_options['md_to_fm_alpha']
diff = self._compute_diff(matched_fm_param, new_fm_param)
# NOTE: matched_fm_param may not be reference, may be a deepcopy!!
# and only here matched_fm_param needs to be updated, so another method dedicated for updating is necessary here
# matched_fm_param.copy_(new_fm_param)
self.update_fm_param(p_name, new_fm_param.T if t else new_fm_param)
logger.debug(f'end feedback: {p_name}, diff: {diff:.6f} (md_to_fm_alpha={self.ab_options["md_to_fm_alpha"]:.4f})')
@abstractmethod
@torch.no_grad()
def update_fm_param(self, md_param_name, cal_new_fm_param_by_md_param):
"""
you should get the reference of fm_param and update it
"""
raise NotImplementedError
@torch.no_grad()
def aggregate_fms_to_self_fm(self, fms: List[nn.Module]):
# average task-agnositc parameters
logger.info('\n\naggregate foundation models to self foundation model...\n\n')
for p_name, self_p in self.models_dict['fm'].named_parameters():
logger.debug(f'if aggregate {p_name}')
if 'abs' in p_name or p_name.startswith('norm') or p_name.startswith('head'):
logger.debug(f'{p_name} belongs to LoRA parameters/task-specific head, i.e. task-specific parameters, skip')
continue
all_p = [get_parameter(fm, p_name) for fm in fms]
if any([_p is None for _p in all_p]):
continue
avg_p = sum(all_p) / len(all_p)
# [_p.copy_(avg_p) for _p in all_p]
diff = self._compute_diff(self_p, avg_p)
logger.debug(f'aggregate {p_name}, diff {diff:.6f}')
self_p.copy_(avg_p)
@torch.no_grad()
def fm_feedback_to_md(self):
logger.info('\n\nself foundation model feedback to master DNN...\n\n')
# one-to-many
# def downsample_2d_tensor(p: torch.Tensor, target_len: int):
# assert p.dim() == 2 # regard 2d weight as (batch_size, 1d_vector_dim)
# # return F.upsample(p.unsqueeze(1).unsqueeze(3),
# # size=(target_len, 1),
# # mode='bilinear').squeeze(3).squeeze(1)
# return F.interpolate(p.unsqueeze(1).unsqueeze(3), size=(target_len, 1), mode='bilinear').squeeze(3).squeeze(1)
for p_name, p in self.models_dict['md'].named_parameters():
matched_fm_param = self.get_fm_matched_param_of_md_param(p_name)
logger.debug(f'if feedback: {p_name}')
if matched_fm_param is None:
continue
index = self.models_dict['indexes'][p_name]
logger.debug(f'start feedback: {p_name}, {p.size()} -> {matched_fm_param.size()}, index: {index.size()}')
if p.dim() > 1 and index.size(0) == p.size(1) and index.size(1) == matched_fm_param.size(1):
assert p.dim() == 2
p = p.T
matched_fm_param = matched_fm_param.T
if p.dim() == 2:
# matched_fm_param = downsample_2d_tensor(matched_fm_param, p.size(1))
matched_fm_param = matched_fm_param.unsqueeze(0)
index = index.unsqueeze(-1)
# fast
# agg_p_update = (p_update * index).sum(0)
# balanced agg
agg_fm_param = 0
cur_split_size = 64
while index.size(1) % cur_split_size != 0:
cur_split_size -= 1
for i in range(0, index.size(1), cur_split_size):
agg_fm_param += matched_fm_param[:, i: i + cur_split_size] * index[:, i: i + cur_split_size]
agg_fm_param = agg_fm_param.sum(1)
# agg_fm_param = downsample_2d_tensor(agg_fm_param, p.size(1))
else:
agg_fm_param = (matched_fm_param.unsqueeze(0) * index).sum(1)
diff = self._compute_diff(p, agg_fm_param)
p.copy_(agg_fm_param * self.ab_options['fm_to_md_alpha'] + (1. - self.ab_options['fm_to_md_alpha']) * p)
logger.debug(f'end feedback: {p_name}, diff: {diff:.6f} (fm_to_md_alpha: {self.ab_options["fm_to_md_alpha"]:.4f})')
@abstractmethod
def get_elastic_dnn_util(self) -> ElasticDNNUtil:
pass
@abstractmethod
def get_task_head_params(self):
pass
@abstractmethod
def get_md_matched_param_of_sd_param(self, sd_param_name):
pass
@abstractmethod
def get_fm_matched_param_of_md_param(self, md_param_name):
pass
@abstractmethod
def get_md_matched_param_of_fm_param(self, fm_param_name):
pass |