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from typing import List
from data.dataloader import build_dataloader
# from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel
from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel
import torch
import sys
from torch import nn
from methods.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel
from methods.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg
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_to_md.vit import FM_to_MD_ViT_Util
from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from methods.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util
from methods.elasticdnn.model.bert import ElasticBertUtil
from utils.common.file import ensure_dir
from utils.dl.common.model import LayerActivation, get_module, get_parameter, set_module
from utils.common.exp import save_models_dict_for_init, get_res_save_dir
from data import build_scenario
from utils.dl.common.loss import CrossEntropyLossSoft
import torch.nn.functional as F
from utils.dl.common.env import create_tbwriter
import os
from utils.common.log import logger
from utils.common.data_record import write_json
# from methods.shot.shot import OnlineShotModel
from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg
import tqdm
from methods.feat_align.mmd import mmd_rbf
class ElasticDNN_SeClsOnlineModel(ElasticDNN_OnlineModel):
@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()}'
if 'classifier' in p_name:
continue
# if False:
# self.last_trained_cls_indexes
assert hasattr(self, 'last_trained_cls_indexes')
print(self.last_trained_cls_indexes)
diff = self._compute_diff(matched_md_param, p)
# matched_md_param[self.last_trained_cls_indexes].copy_(p[self.last_trained_cls_indexes.to(self.device)])
matched_md_param.copy_(p)
logger.debug(f'SPECIFIC FOR CL HEAD | end feedback: {p_name}, diff: {diff:.6f}')
else:
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 add_cls_in_head(self, num_cls):
head: nn.Linear = get_module(self.models_dict['md'], 'classifier')
new_head = nn.Linear(head.in_features, head.out_features + num_cls, head.bias is not None, device=self.device)
# nn.init.zeros_(new_head.weight.data)
# nn.init.zeros_(new_head.bias.data)
new_head.weight.data[0: head.out_features] = deepcopy(head.weight.data)
new_head.bias.data[0: head.out_features] = deepcopy(head.bias.data)
set_module(self.models_dict['md'], 'classifier', new_head)
set_module(self.models_dict['fm'], 'classifier', new_head)
def get_accuracy(self, test_loader, *args, **kwargs):
acc = 0
sample_num = 0
self.to_eval_mode()
with torch.no_grad():
pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), dynamic_ncols=True, leave=False)
for batch_index, (x, y) in pbar:
for k, v in x.items():
if isinstance(v, torch.Tensor):
x[k] = v.to(self.device)
y = y.to(self.device)
output = self.infer(x)
pred = F.softmax(output, dim=1).argmax(dim=1)
correct = torch.eq(pred, y).sum().item()
acc += correct
sample_num += len(y)
pbar.set_description(f'cur_batch_total: {len(y)}, cur_batch_correct: {correct}, '
f'cur_batch_acc: {(correct / len(y)):.4f}')
acc /= sample_num
return acc
def get_elastic_dnn_util(self) -> ElasticDNNUtil:
return ElasticBertUtil()
def get_fm_matched_param_of_md_param(self, md_param_name):
# only between qkv.weight, norm.weight/bias
self_param_name = md_param_name
fm = self.models_dict['fm']
if any([k in self_param_name for k in ['fbs', 'ab', 'embeddings']]):
return None
p = get_parameter(self.models_dict['md'], self_param_name)
if p.dim() == 0:
return None
elif p.dim() == 1 and 'LayerNorm' in self_param_name and 'weight' in self_param_name:
return get_parameter(fm, self_param_name)
# 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz
if ('query' in self_param_name or 'key' in self_param_name or \
'value' in self_param_name) and ('weight' in self_param_name):
ss = self_param_name.split('.')
fm_qkv_name = '.'.join(ss[0: -1]) + '.fc'
fm_qkv = get_module(fm, fm_qkv_name)
fm_abs_name = '.'.join(ss[0: -1]) + '.ab'
fm_abs = get_module(fm, fm_abs_name)
# NOTE: unrecoverable operation! multiply LoRA parameters to allow it being updated in update_fm_param()
# TODO: if fm will be used for inference, _mul_lora_weight will not be applied!
if not hasattr(fm_abs, '_mul_lora_weight'):
logger.debug(f'set _mul_lora_weight in {fm_abs_name}')
setattr(fm_abs, '_mul_lora_weight',
nn.Parameter(fm_abs[1].weight @ fm_abs[0].weight))
return torch.cat([
fm_qkv.weight.data, # task-agnositc params
fm_abs._mul_lora_weight.data # task-specific params (LoRA)
], dim=0)
# elif 'to_qkv.bias' in self_param_name:
# ss = self_param_name.split('.')
# fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias'
# return get_parameter(fm, fm_qkv_name)
elif 'dense' in self_param_name and 'weight' in self_param_name:
fm_param_name = self_param_name.replace('.linear', '')
return get_parameter(fm, fm_param_name)
# elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name:
# fm_param_name = self_param_name
# return get_parameter(fm, fm_param_name)
else:
# return get_parameter(fm, self_param_name)
return None
def update_fm_param(self, md_param_name, cal_new_fm_param_by_md_param):
if not ('query' in md_param_name or 'key' in md_param_name or 'value' in md_param_name):
matched_fm_param_ref = self.get_fm_matched_param_of_md_param(md_param_name)
matched_fm_param_ref.copy_(cal_new_fm_param_by_md_param)
else:
new_fm_attn_weight, new_fm_lora_weight = torch.chunk(cal_new_fm_param_by_md_param, 2, 0)
ss = md_param_name.split('.')
fm = self.models_dict['fm']
# update task-agnostic parameters
fm_qkv_name = '.'.join(ss[0: -1]) + '.fc'
fm_qkv = get_module(fm, fm_qkv_name)
fm_qkv.weight.data.copy_(new_fm_attn_weight)
# update task-specific parameters
fm_abs_name = '.'.join(ss[0: -1]) + '.ab'
fm_abs = get_module(fm, fm_abs_name)
fm_abs._mul_lora_weight.data.copy_(new_fm_lora_weight) # TODO: this will not be applied in inference!
def get_md_matched_param_of_fm_param(self, fm_param_name):
return super().get_md_matched_param_of_fm_param(fm_param_name)
def get_md_matched_param_of_sd_param(self, sd_param_name):
# raise NotImplementedError
# only between qkv.weight, norm.weight/bias
self_param_name = sd_param_name
md = self.models_dict['md']
if any([k in self_param_name for k in ['fbs', 'ab', 'embeddings']]):
return None
p = get_parameter(self.models_dict['sd'], self_param_name)
if p.dim() == 0:
return None
elif p.dim() == 1 and 'LayerNorm' in self_param_name and 'weight' in self_param_name:
return get_parameter(md, self_param_name)
if 'classifier' in self_param_name:
return get_parameter(md, self_param_name)
# 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz
if ('query' in self_param_name or 'key' in self_param_name or \
'value' in self_param_name) and ('weight' in self_param_name):
return get_parameter(md, self_param_name) # NOTE: no fbs in qkv!
# elif 'to_qkv.bias' in self_param_name:
# ss = self_param_name.split('.')
# fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias'
# return get_parameter(fm, fm_qkv_name)
elif 'intermediate.dense.0.weight' in self_param_name:
fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.weight'
return get_parameter(md, fm_param_name)
elif 'output.dense' in self_param_name and 'weight' in self_param_name:
fm_param_name = self_param_name
return get_parameter(md, fm_param_name)
else:
# return get_parameter(fm, self_param_name)
return None
def get_task_head_params(self):
head = get_module(self.models_dict['sd'], 'classifier')
return list(head.parameters())
from methods.gem.gem_el_bert import OnlineGEMModel
import tqdm
from methods.feat_align.mmd import mmd_rbf
from copy import deepcopy
class SeClsOnlineGEMModel(OnlineGEMModel):
def get_trained_params(self):
qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'query' in n or 'key' in n or 'value' in n or 'dense' in n or 'LayerNorm' in n]
return qkv_and_norm_params
def forward_to_get_task_loss(self, x, y):
return F.cross_entropy(self.infer(x), y)
def add_cls_in_head(self, num_cls):
return
head: nn.Linear = get_module(self.models_dict['main'], 'head')
new_head = nn.Linear(head.in_features, head.out_features + num_cls, head.bias is not None, device=self.device)
new_head.weight.data[0: head.out_features] = deepcopy(head.weight.data)
new_head.bias.data[0: head.out_features] = deepcopy(head.bias.data)
set_module(self.models_dict['main'], 'head', new_head)
def infer(self, x, *args, **kwargs):
return self.models_dict['main'](**x)
def get_accuracy(self, test_loader, *args, **kwargs):
_d = test_loader.dataset
from data import build_dataloader, split_dataset
if _d.__class__.__name__ == '_SplitDataset' and _d.underlying_dataset.__class__.__name__ == 'MergedDataset': # necessary for CL
print('\neval on merged datasets')
merged_full_dataset = _d.underlying_dataset.datasets
ratio = len(_d.keys) / len(_d.underlying_dataset)
if int(len(_d) * ratio) == 0:
ratio = 1.
# print(ratio)
# bs =
# test_loaders = [build_dataloader(split_dataset(d, min(max(test_loader.batch_size, int(len(d) * ratio)), len(d)))[0], # TODO: this might be overlapped with train dataset
# min(test_loader.batch_size, int(len(d) * ratio)),
# test_loader.num_workers, False, None) for d in merged_full_dataset]
test_loaders = []
for d in merged_full_dataset:
n = int(len(d) * ratio)
if n == 0:
n = len(d)
sub_dataset = split_dataset(d, min(max(test_loader.batch_size, n), len(d)))[0]
loader = build_dataloader(sub_dataset, min(test_loader.batch_size, n), test_loader.num_workers, False, None)
test_loaders += [loader]
accs = [self.get_accuracy(loader) for loader in test_loaders]
print(accs)
return sum(accs) / len(accs)
acc = 0
sample_num = 0
self.to_eval_mode()
with torch.no_grad():
pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), dynamic_ncols=True, leave=False)
for batch_index, (x, y) in pbar:
for k, v in x.items():
if isinstance(v, torch.Tensor):
x[k] = v.to(self.device)
y = y.to(self.device)
output = self.infer(x)
pred = F.softmax(output, dim=1).argmax(dim=1)
correct = torch.eq(pred, y).sum().item()
acc += correct
sample_num += len(y)
# if batch_index == 0:
# print(pred, y)
pbar.set_description(f'cur_batch_total: {len(y)}, cur_batch_correct: {correct}, '
f'cur_batch_acc: {(correct / len(y)):.4f}')
acc /= sample_num
return acc |