from typing import List
from data.dataloader import build_dataloader
# from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel
from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel
import torch
import sys
from torch import nn
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel
from new_impl.cv.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg
from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util
from new_impl.cv.elasticdnn.model.vit import ElasticViTUtil
from utils.common.file import ensure_dir
from utils.dl.common.model import LayerActivation, get_module, get_parameter
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 new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg
import tqdm
from new_impl.cv.feat_align.mmd import mmd_rbf
class ElasticDNN_ClsOnlineModel(ElasticDNN_OnlineModel):
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:
x, y = x.to(self.device), 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 ElasticViTUtil()
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', 'cls_token', 'pos_embed']]):
# return None
# p = get_parameter(self.models_dict['md'], self_param_name)
# if p.dim() == 0:
# return None
# elif p.dim() == 1 and 'norm' in self_param_name and 'weight' in self_param_name:
# return get_parameter(fm, self_param_name)
if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]):
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 'qkv.weight' in self_param_name:
# ss = self_param_name.split('.')
# fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv'
# fm_qkv = get_module(fm, fm_qkv_name)
# fm_abs_name = '.'.join(ss[0: -1]) + '.abs'
# 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(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0)))
# 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 'mlp.fc1' 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
if ('attention.attention.projection_query' in self_param_name or 'attention.attention.projection_key' in self_param_name or \
'attention.attention.projection_value' in self_param_name) and ('weight' in self_param_name):
ss = self_param_name.split('.')
fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv'
fm_qkv = get_module(fm, fm_qkv_name)
fm_abs_name = '.'.join(ss[0: -1]) + '.abs'
fm_abs = get_module(fm, fm_abs_name)
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(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0)))
return torch.cat([
fm_qkv.weight.data, # task-agnositc params
fm_abs._mul_lora_weight.data # task-specific params (LoRA)
], dim=0)
elif ('attention.attention.projection_query' in self_param_name or 'attention.attention.projection_key' in self_param_name or \
'attention.attention.projection_value' in self_param_name) and ('bias' in self_param_name):
ss = self_param_name.split('.')
fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv.bias'
return get_parameter(fm, fm_qkv_name)
elif 'intermediate.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 'output.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)
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 ('attention.attention.projection_query.weight' in md_param_name or 'attention.attention.projection_key.weight' in md_param_name or 'attention.attention.projection_value.weight' 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]) + '.qkv'
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]) + '.abs'
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):
# 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', 'cls_token', 'pos_embed']]):
return None
p = get_parameter(self.models_dict['sd'], self_param_name)
if p.dim() == 0:
return None
elif p.dim() == 1 and 'norm' in self_param_name and 'weight' 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 'qkv.weight' in self_param_name:
return get_parameter(md, self_param_name)
# 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 'mlp.fc1.0.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 'mlp.fc2' 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'], 'head')
return list(head.parameters())
class ClsOnlineFeatAlignModel(OnlineFeatAlignModel):
def get_trained_params(self): # TODO: elastic fm only train a part of params
#qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'attention.attention.projection_query' in n or 'attention.attention.projection_key' in n or 'attention.attention.projection_value' in n or 'intermediate.dense' in n or 'output.dense' in n]
qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters()]
return qkv_and_norm_params
def get_feature_hook(self):
return LayerActivation(get_module(self.models_dict['main'], 'classifier'), False, self.device)
def forward_to_get_task_loss(self, x, y):
return F.cross_entropy(self.infer(x), y)
def get_mmd_loss(self, f1, f2):
return mmd_rbf(f1, f2)
def infer(self, x, *args, **kwargs):
return self.models_dict['main'](x).logits
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:
x, y = x.to(self.device), 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