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from typing import Any, Dict
from schema import Schema, Or
import schema
from data import Scenario, MergedDataset
from methods.base.alg import BaseAlg
from data import build_dataloader
from ..model import ElasticDNN_OfflineFMModel, ElasticDNN_OfflineMDModel
from ...model.base import ElasticDNNUtil
import torch.optim
import tqdm
import torch.nn.functional as F
from torch import nn
from utils.dl.common.env import create_tbwriter
import os
import random
import numpy as np
from copy import deepcopy
from utils.dl.common.model import LayerActivation, get_module
from utils.common.log import logger
class ElasticDNN_MDPretrainingAlg(BaseAlg):
"""
TODO: fine-tuned FM -> init MD -> trained MD -> construct indexes (only between similar weights) and fine-tune
"""
def get_required_models_schema(self) -> Schema:
return Schema({
'fm': ElasticDNN_OfflineFMModel,
'md': ElasticDNN_OfflineMDModel
})
def get_required_hyp_schema(self) -> Schema:
return Schema({
'launch_tbboard': bool,
'samples_size': (int, int, int, int),
'generate_md_width_ratio': int,
'FBS_r': int,
'FBS_ignore_layers': [str],
'train_batch_size': int,
'val_batch_size': int,
'num_workers': int,
'optimizer': str,
'md_optimizer_args': dict,
'indexes_optimizer_args': dict,
'scheduler': str,
'scheduler_args': dict,
'num_iters': int,
'val_freq': int,
'max_sparsity': float,
'min_sparsity': float,
'distill_loss_weight': float,
'index_loss_weight': float,
'val_num_sparsities': int,
'bn_cal_num_iters': int,
'index_guided_linear_comb_split_size': Or(int, None)
})
def upsample_2d_tensor(self, 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)
def two_params_diff_fast(self, trained_p: torch.Tensor, ref_p: torch.Tensor,
index: torch.Tensor,
split_size: int):
assert trained_p.dim() == ref_p.dim()
assert index.size(0) == trained_p.size(0) and index.size(1) == ref_p.size(0)
# print(trained_p.size(), ref_p.size(), index.size())
ref_p = ref_p.detach()
if trained_p.dim() > 1:
trained_p = trained_p.flatten(1)
ref_p = ref_p.flatten(1)
# the weight size of master DNN and foundation model may be totally different
# MD -> FM: upsample first
# FM -> MD: downsample first
if trained_p.size(1) < ref_p.size(1):
trained_p = self.upsample_2d_tensor(trained_p, ref_p.size(1))
index = index.unsqueeze(-1)
# linear_combed_ref_p = (ref_p.unsqueeze(0) * index).sum(1)
# else:
# print(trained_p.size(), ref_p.size(), index.size())
if split_size is None:
# old version: huge memory consumption, not recommended (although this is fastest)
# print('old version')
linear_combed_ref_p = (ref_p.unsqueeze(0) * index).sum(1)
else:
# new version
linear_combed_ref_p = 0
cur_split_size = split_size
while index.size(1) % cur_split_size != 0:
cur_split_size -= 1
# print(cur_split_size)
for i in range(0, index.size(1), cur_split_size):
# if not isinstance(linear_combed_ref_p, int):
# print(linear_combed_ref_p.size(), ref_p.unsqueeze(0)[:, i: i + cur_split_size].size(), index[:, i: i + cur_split_size].size())
linear_combed_ref_p += ref_p.unsqueeze(0)[:, i: i + cur_split_size] * index[:, i: i + cur_split_size]
linear_combed_ref_p = linear_combed_ref_p.sum(1)
diff = (linear_combed_ref_p - trained_p).norm(2) ** 2
return diff
def get_index_loss(self, fm, md, indexes, match_fn, split_size):
res = 0.
for name, p in md.named_parameters():
if p.dim() == 0:
continue
raw_p = match_fn(name, fm)
if raw_p is None:
continue
index = indexes[name]
# print(name)
res += self.two_params_diff_fast(p, raw_p, index, split_size)
return res
def bn_cal(self, model: nn.Module, train_loader, num_iters, device):
has_bn = False
for n, m in model.named_modules():
if isinstance(m, nn.BatchNorm2d):
has_bn = True
break
if not has_bn:
return {}
def bn_calibration_init(m):
""" calculating post-statistics of batch normalization """
if getattr(m, 'track_running_stats', False):
# reset all values for post-statistics
m.reset_running_stats()
# set bn in training mode to update post-statistics
m.training = True
with torch.no_grad():
model.eval()
model.apply(bn_calibration_init)
for _ in range(num_iters):
x, _ = next(train_loader)
model(x.to(device))
model.eval()
bn_stats = {}
for n, m in model.named_modules():
if isinstance(m, nn.BatchNorm2d):
bn_stats[n] = m
return bn_stats
def run(self, scenario: Scenario, hyps: Dict) -> Dict[str, Any]:
super().run(scenario, hyps)
# sanity check
# a= torch.tensor([[1, 2, 3], [1, 2, 4]])
# index = torch.tensor([[1, 2, 3],
# [1, 2, 4]])
# b = torch.tensor([[1, 2, 3], [1, 2, 4], [2, 3, 4]])
# print(self.two_params_diff_fast(a, b, index, hyps['index_guided_linear_comb_split_size']))
assert isinstance(self.models['md'], ElasticDNN_OfflineMDModel) # for auto completion
assert isinstance(self.models['fm'], ElasticDNN_OfflineFMModel) # for auto completion
# 1. add FBS
device = self.models['md'].device
if self.models['md'].models_dict['main'] == -1:
logger.info(f'init master DNN by reducing width of an adapted foundation model (already tuned by LoRA)...')
before_fm_model = deepcopy(self.models['fm'].models_dict['main'])
lora_util = self.models['fm'].get_lora_util()
lora_absorbed_fm_model = lora_util.absorb_lora_and_recover_net_structure(self.models['fm'].models_dict['main'],
torch.rand(hyps['samples_size']).to(device))
self.models['fm'].models_dict['main'] = lora_absorbed_fm_model
master_dnn = self.models['fm'].generate_md_by_reducing_width(hyps['generate_md_width_ratio'],
torch.rand(hyps['samples_size']).to(device))
self.models['fm'].models_dict['main'] = before_fm_model
elastic_dnn_util = self.models['fm'].get_elastic_dnn_util()
master_dnn = elastic_dnn_util.convert_raw_dnn_to_master_dnn_with_perf_test(master_dnn,
hyps['FBS_r'], hyps['FBS_ignore_layers'])
self.models['md'].models_dict['main'] = master_dnn
self.models['md'].to(device)
# 2. train (knowledge distillation, index relationship)
offline_datasets = scenario.get_offline_datasets()
train_dataset = MergedDataset([d['train'] for d in offline_datasets.values()])
val_dataset = MergedDataset([d['val'] for d in offline_datasets.values()])
train_loader = iter(build_dataloader(train_dataset, hyps['train_batch_size'], hyps['num_workers'],
True, None))
val_loader = build_dataloader(val_dataset, hyps['val_batch_size'], hyps['num_workers'],
False, False)
# 2.1 train whole master DNN (knowledge distillation)
for p in master_dnn.parameters():
p.requires_grad = True
self.models['md'].to_train_mode()
optimizer = torch.optim.__dict__[hyps['optimizer']]([
{'params': self.models['md'].models_dict['main'].parameters(), **hyps['md_optimizer_args']}
])
scheduler = torch.optim.lr_scheduler.__dict__[hyps['scheduler']](optimizer, **hyps['scheduler_args'])
tb_writer = create_tbwriter(os.path.join(self.res_save_dir, 'tb_log'), launch_tbboard=hyps['launch_tbboard'])
pbar = tqdm.tqdm(range(hyps['num_iters']), dynamic_ncols=True)
best_avg_val_acc = 0.
md_output_hook = None
for iter_index in pbar:
self.models['md'].to_train_mode()
self.models['fm'].to_eval_mode()
rand_sparsity = random.random() * (hyps['max_sparsity'] - hyps['min_sparsity']) + hyps['min_sparsity']
elastic_dnn_util.set_master_dnn_sparsity(self.models['md'].models_dict['main'], rand_sparsity)
x, y = next(train_loader)
x, y = x.to(device), y.to(device)
with torch.no_grad():
fm_output = self.models['fm'].infer(x)
if md_output_hook is None:
md_output_hook = LayerActivation(self.models['md'].models_dict['main'], False, device)
task_loss = self.models['md'].forward_to_get_task_loss(x, y)
md_output = md_output_hook.output
distill_loss = hyps['distill_loss_weight'] * self.models['md'].get_distill_loss(md_output, fm_output)
total_loss = task_loss + distill_loss
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
scheduler.step()
if (iter_index + 1) % hyps['val_freq'] == 0:
elastic_dnn_util.clear_cached_channel_attention_in_master_dnn(self.models['md'].models_dict['main'])
md_output_hook.remove()
md_output_hook = None
cur_md = self.models['md'].models_dict['main']
md_for_test = deepcopy(self.models['md'].models_dict['main'])
val_accs = {}
avg_val_acc = 0.
bn_stats = {}
for val_sparsity in np.linspace(hyps['min_sparsity'], hyps['max_sparsity'], num=hyps['val_num_sparsities']):
elastic_dnn_util.set_master_dnn_sparsity(md_for_test, val_sparsity)
bn_stats[f'{val_sparsity:.4f}'] = self.bn_cal(md_for_test, train_loader, hyps['bn_cal_num_iters'], device)
self.models['md'].models_dict['main'] = md_for_test
self.models['md'].to_eval_mode()
val_acc = self.models['md'].get_accuracy(val_loader)
val_accs[f'{val_sparsity:.4f}'] = val_acc
avg_val_acc += val_acc
avg_val_acc /= hyps['val_num_sparsities']
self.models['md'].models_dict['main'] = cur_md
self.models['md'].models_dict['bn_stats'] = bn_stats
self.models['md'].save_model(os.path.join(self.res_save_dir, 'models/md_last.pt'))
self.models['fm'].save_model(os.path.join(self.res_save_dir, 'models/fm_last.pt'))
if avg_val_acc > best_avg_val_acc:
best_avg_val_acc = avg_val_acc
self.models['md'].save_model(os.path.join(self.res_save_dir, 'models/md_best.pt'))
self.models['fm'].save_model(os.path.join(self.res_save_dir, 'models/fm_best.pt'))
tb_writer.add_scalars(f'losses', dict(task=task_loss, distill=distill_loss, total=total_loss), iter_index)
pbar.set_description(f'loss: {total_loss:.6f}')
if (iter_index + 1) >= hyps['val_freq']:
tb_writer.add_scalars(f'accs/val_accs', val_accs, iter_index)
tb_writer.add_scalar(f'accs/avg_val_acc', avg_val_acc, iter_index)
pbar.set_description(f'loss: {total_loss:.6f}, avg_val_acc: {avg_val_acc:.4f}')
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