def step(self, action: float, model: Module):
_, _, current_statistics = count_flops_params(model,
self.dummy_input,
verbose=False)
current_statistics = {
result['name']: result
for result in current_statistics
}
index = self.pruning_op_names.index(self.current_op_name)
action = 1 - current_statistics[self.current_op_name][
self.target] / self.current_op_target
total_current_target = sum([
current_statistics[name][self.target]
for name in self.pruning_op_names
])
previous_pruning_target = self.under_pruning_target - total_current_target
rest_target = sum([
current_statistics[name][self.target]
for name in self.pruning_op_names[index + 1:]
])
self.layer_embedding[index][
-3] = previous_pruning_target / self.under_pruning_target # reduced
self.layer_embedding[index][
-2] = rest_target / self.under_pruning_target # rest
self.layer_embedding[index][-1] = action # last action
observation = self.layer_embedding[index, :].copy()
return action, 0, observation, self.is_final_layer()
def count_flops(model, log=None, device=None):
dummy_input = torch.rand([1, 3, 256, 256])
if device is not None:
dummy_input = dummy_input.to(device)
flops, params, results = count_flops_params(model, dummy_input)
print(f"FLOPs: {flops}, params: {params}")
if log is not None:
log.write(f"FLOPs: {flops}, params: {params}\n")
return flops, params
def __init__(self,
model: Module,
config_list: List[Dict],
dummy_input: Tensor,
total_sparsity: float,
max_sparsity_per_layer: Dict[str, float],
target: str = 'flops'):
pruning_op_names = []
[
pruning_op_names.extend(config['op_names'])
for config in config_list_canonical(model, config_list)
]
self.pruning_ops = OrderedDict()
self.pruning_types = []
for i, (name, layer) in enumerate(model.named_modules()):
if name in pruning_op_names:
op_type = type(layer).__name__
stride = np.power(np.prod(layer.stride), 1 /
len(layer.stride)) if hasattr(
layer, 'stride') else 0
kernel_size = np.power(np.prod(layer.kernel_size), 1 /
len(layer.kernel_size)) if hasattr(
layer, 'kernel_size') else 1
self.pruning_ops[name] = (i, op_type, stride, kernel_size)
self.pruning_types.append(op_type)
self.pruning_types = list(set(self.pruning_types))
self.pruning_op_names = list(self.pruning_ops.keys())
self.dummy_input = dummy_input
self.total_sparsity = total_sparsity
self.max_sparsity_per_layer = max_sparsity_per_layer
assert target in ['flops', 'params']
self.target = target
self.origin_target, self.origin_params_num, self.origin_statistics = count_flops_params(
model, dummy_input, verbose=False)
self.origin_statistics = {
result['name']: result
for result in self.origin_statistics
}
self.under_pruning_target = sum([
self.origin_statistics[name][self.target]
for name in self.pruning_op_names
])
self.excepted_pruning_target = self.total_sparsity * self.under_pruning_target
def correct_action(self, action: float, model: Module):
try:
op_name = next(self.ops_iter)
index = self.pruning_op_names.index(op_name)
_, _, current_statistics = count_flops_params(model,
self.dummy_input,
verbose=False)
current_statistics = {
result['name']: result
for result in current_statistics
}
total_current_target = sum([
current_statistics[name][self.target]
for name in self.pruning_op_names
])
previous_pruning_target = self.under_pruning_target - total_current_target
max_rest_pruning_target = sum([
current_statistics[name][self.target] *
self.max_sparsity_per_layer[name]
for name in self.pruning_op_names[index + 1:]
])
min_current_pruning_target = self.excepted_pruning_target - previous_pruning_target - max_rest_pruning_target
max_current_pruning_target_1 = self.origin_statistics[op_name][
self.target] * self.max_sparsity_per_layer[op_name] - (
self.origin_statistics[op_name][self.target] -
current_statistics[op_name][self.target])
max_current_pruning_target_2 = self.excepted_pruning_target - previous_pruning_target
max_current_pruning_target = min(max_current_pruning_target_1,
max_current_pruning_target_2)
min_action = min_current_pruning_target / current_statistics[
op_name][self.target]
max_action = max_current_pruning_target / current_statistics[
op_name][self.target]
if min_action > self.max_sparsity_per_layer[op_name]:
_logger.warning(
'[%s] min action > max sparsity per layer: %f > %f',
op_name, min_action, self.max_sparsity_per_layer[op_name])
action = max(0., min(max_action, max(min_action, action)))
self.current_op_name = op_name
self.current_op_target = current_statistics[op_name][self.target]
except StopIteration:
raise Error('Something goes wrong, this should not happen.')
return action
def _calculate_flops(self, eps=0.001):
"""FLOPs cost."""
flops_lut = [{} for i in range(self.cnt_layers)]
layer_id = 0
for stage_name in self.lut_ops:
stage_ops = self.lut_ops[stage_name]
ops_num = self.layer_num[stage_name]
for _ in range(ops_num):
for op_name in stage_ops:
layer_config = self.layer_configs[layer_id]
key_params = {"fm_size": layer_config[3]}
op = stage_ops[op_name](*layer_config[0:3], **key_params)
# measured in Flops
in_shape = self.layer_in_shapes[layer_id]
x = (1, in_shape[0], in_shape[1], in_shape[2])
flops, _, _ = count_flops_params(op, x, verbose=False)
flops = eps if flops == 0.0 else flops
flops_lut[layer_id][op_name] = float(flops)
layer_id += 1
return flops_lut
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.experiment_data_dir, exist_ok=True)
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=64,
)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'data', train=False, transform=transform),
batch_size=1000)
# Step1. Model Pretraining
model = NaiveModel().to(device)
criterion = torch.nn.NLLLoss()
optimizer = optim.Adadelta(model.parameters(), lr=args.pretrain_lr)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
flops, params, _ = count_flops_params(model, (1, 1, 28, 28), verbose=False)
if args.pretrained_model_dir is None:
args.pretrained_model_dir = os.path.join(args.experiment_data_dir,
f'pretrained.pth')
best_acc = 0
for epoch in range(args.pretrain_epochs):
train(args, model, device, train_loader, criterion, optimizer,
epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
model.load_state_dict(state_dict)
torch.save(state_dict, args.pretrained_model_dir)
print(f'Model saved to {args.pretrained_model_dir}')
else:
state_dict = torch.load(args.pretrained_model_dir)
model.load_state_dict(state_dict)
best_acc = test(args, model, device, criterion, test_loader)
dummy_input = torch.randn([1000, 1, 28, 28]).to(device)
time_cost = get_model_time_cost(model, dummy_input)
# 125.49 M, 0.85M, 93.29, 1.1012
print(
f'Pretrained model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_acc: .2f}, Time Cost: {time_cost}'
)
# Step2. Model Pruning
config_list = [{'sparsity': args.sparsity, 'op_types': ['Conv2d']}]
kw_args = {}
if args.dependency_aware:
dummy_input = torch.randn([1000, 1, 28, 28]).to(device)
print('Enable the dependency_aware mode')
# note that, not all pruners support the dependency_aware mode
kw_args['dependency_aware'] = True
kw_args['dummy_input'] = dummy_input
pruner = L1FilterPruner(model, config_list, **kw_args)
model = pruner.compress()
pruner.get_pruned_weights()
mask_path = os.path.join(args.experiment_data_dir, 'mask.pth')
model_path = os.path.join(args.experiment_data_dir, 'pruned.pth')
pruner.export_model(model_path=model_path, mask_path=mask_path)
pruner._unwrap_model() # unwrap all modules to normal state
# Step3. Model Speedup
m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
m_speedup.speedup_model()
print('model after speedup', model)
flops, params, _ = count_flops_params(model, dummy_input, verbose=False)
acc = test(args, model, device, criterion, test_loader)
time_cost = get_model_time_cost(model, dummy_input)
print(
f'Pruned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {acc: .2f}, Time Cost: {time_cost}'
)
# Step4. Model Finetuning
optimizer = optim.Adadelta(model.parameters(), lr=args.pretrain_lr)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
best_acc = 0
for epoch in range(args.finetune_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
model.load_state_dict(state_dict)
save_path = os.path.join(args.experiment_data_dir, f'finetuned.pth')
torch.save(state_dict, save_path)
flops, params, _ = count_flops_params(model, dummy_input, verbose=True)
time_cost = get_model_time_cost(model, dummy_input)
# FLOPs 28.48 M, #Params: 0.18M, Accuracy: 89.03, Time Cost: 1.03
print(
f'Finetuned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_acc: .2f}, Time Cost: {time_cost}'
)
print(f'Model saved to {save_path}')
# Step5. Model Quantization via QAT
config_list = [{
'quant_types': ['weight', 'output'],
'quant_bits': {
'weight': 8,
'output': 8
},
'op_names': ['conv1']
}, {
'quant_types': ['output'],
'quant_bits': {
'output': 8
},
'op_names': ['relu1']
}, {
'quant_types': ['weight', 'output'],
'quant_bits': {
'weight': 8,
'output': 8
},
'op_names': ['conv2']
}, {
'quant_types': ['output'],
'quant_bits': {
'output': 8
},
'op_names': ['relu2']
}]
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
quantizer = QAT_Quantizer(model, config_list, optimizer)
quantizer.compress()
# Step6. Quantization Aware Training
best_acc = 0
for epoch in range(1):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
calibration_path = os.path.join(args.experiment_data_dir,
'calibration.pth')
calibration_config = quantizer.export_model(model_path, calibration_path)
print("calibration_config: ", calibration_config)
# Step7. Model Speedup
batch_size = 32
input_shape = (batch_size, 1, 28, 28)
engine = ModelSpeedupTensorRT(model,
input_shape,
config=calibration_config,
batchsize=32)
engine.compress()
test_trt(engine, test_loader)
model, total_epoch=args.pretrain_epochs)
criterion = torch.nn.CrossEntropyLoss()
pre_best_acc = 0.0
best_state_dict = None
for i in range(args.pretrain_epochs):
trainer(model, optimizer, criterion)
scheduler.step()
acc = evaluator(model)
if acc > pre_best_acc:
pre_best_acc = acc
best_state_dict = model.state_dict()
print("Best accuracy: {}".format(pre_best_acc))
model.load_state_dict(best_state_dict)
pre_flops, pre_params, _ = count_flops_params(
model,
torch.randn([128, 3, 32, 32]).to(device))
g_epoch = 0
# Start to prune and speedup
print('\n' + '=' * 50 +
' START TO PRUNE THE BEST ACCURACY PRETRAINED MODEL ' + '=' * 50)
config_list = [{
'total_sparsity': 0.5,
'op_types': ['Conv2d'],
}]
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.SGD)(model.parameters(),
lr=0.01,
momentum=0.9,
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.experiment_data_dir, exist_ok=True)
# prepare model and data
train_loader, test_loader, criterion = get_data(args.dataset,
args.data_dir,
args.batch_size,
args.test_batch_size)
model, optimizer, scheduler = get_model_optimizer_scheduler(
args, device, train_loader, test_loader, criterion)
dummy_input = get_dummy_input(args, device)
flops, params, results = count_flops_params(model, dummy_input)
print(f"FLOPs: {flops}, params: {params}")
print('start pruning...')
model_path = os.path.join(
args.experiment_data_dir,
'pruned_{}_{}_{}.pth'.format(args.model, args.dataset, args.pruner))
mask_path = os.path.join(
args.experiment_data_dir,
'mask_{}_{}_{}.pth'.format(args.model, args.dataset, args.pruner))
pruner = get_pruner(model, args.pruner, device, optimizer,
args.dependency_aware)
model = pruner.compress()
if args.multi_gpu and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
if args.test_only:
test(args, model, device, criterion, test_loader)
best_top1 = 0
for epoch in range(args.fine_tune_epochs):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
top1 = test(args, model, device, criterion, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
# mask_path stores mask_dict of the pruned model
pruner.export_model(model_path=model_path, mask_path=mask_path)
if args.nni:
nni.report_final_result(best_top1)
if args.speed_up:
# reload the best checkpoint for speed-up
args.pretrained_model_dir = model_path
model, _, _ = get_model_optimizer_scheduler(args, device, train_loader,
test_loader, criterion)
model.eval()
apply_compression_results(model, mask_path, device)
# test model speed
start = time.time()
for _ in range(32):
use_mask_out = model(dummy_input)
print('elapsed time when use mask: ', time.time() - start)
m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
m_speedup.speedup_model()
flops, params, results = count_flops_params(model, dummy_input)
print(f"FLOPs: {flops}, params: {params}")
start = time.time()
for _ in range(32):
use_speedup_out = model(dummy_input)
print('elapsed time when use speedup: ', time.time() - start)
top1 = test(args, model, device, criterion, test_loader)
def main():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args = parse_args()
#########################################################################
# Prepare model, tokenizer, dataset, optimizer, and the scheduler
logger.setLevel(logging.INFO)
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
# Load dataset and tokenizer, and then preprocess the dataset
raw_dataset, is_regression, num_labels = get_raw_dataset(args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_fast=True)
processed_datasets = preprocess(args, tokenizer, raw_dataset)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["validation_matched" if args.task_name ==
"mnli" else "validation"]
# Load pretrained model
config = AutoConfig.from_pretrained(args.model_name,
num_labels=num_labels,
finetuning_task=args.task_name)
model = AutoModelForSequenceClassification.from_pretrained(args.model_name,
config=config)
model.to(device)
#########################################################################
# Finetune on the target GLUE task before pruning
optimizer, train_dataloader, eval_dataloader, data_collator = get_dataloader_and_optimizer(
args, tokenizer, model, train_dataset, eval_dataset)
train_steps = args.num_train_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=train_steps)
metric = load_metric("glue", args.task_name)
logger.info(
"================= Finetuning before pruning =================")
train_model(args, model, is_regression, train_dataloader, eval_dataloader,
optimizer, lr_scheduler, metric, device)
if args.output_dir is not None:
torch.save(model.state_dict(),
args.output_dir + "/model_before_pruning.pt")
if args.task_name == "mnli":
final_eval_for_mnli(args, model, processed_datasets, metric,
data_collator)
#########################################################################
# Pruning
optimizer, train_dataloader, eval_dataloader, data_collator = get_dataloader_and_optimizer(
args, tokenizer, model, train_dataset, eval_dataset)
dummy_input = next(iter(train_dataloader))["input_ids"].to(device)
flops, params, results = count_flops_params(model, dummy_input)
print(
f"Initial model FLOPs {flops / 1e6:.2f} M, #Params: {params / 1e6:.2f}M"
)
# Here criterion is embedded in the model. Upper levels can just pass None to trainer.
def trainer(model, optimizer, criterion, epoch):
return trainer_helper(model, train_dataloader, optimizer, device)
def forward_runner(model):
return forward_runner_helper(model, train_dataloader, device)
# example: prune different layers with different sparsity
attention_name_groups = list(
zip([
"bert.encoder.layer.{}.attention.self.query".format(i)
for i in range(12)
], [
"bert.encoder.layer.{}.attention.self.key".format(i)
for i in range(12)
], [
"bert.encoder.layer.{}.attention.self.value".format(i)
for i in range(12)
], [
"bert.encoder.layer.{}.attention.output.dense".format(i)
for i in range(12)
]))
kwargs = {
"ranking_criterion": args.ranking_criterion,
"global_sort": args.global_sort,
"num_iterations": args.num_iterations,
"epochs_per_iteration": args.epochs_per_iteration,
"attention_name_groups": attention_name_groups,
"head_hidden_dim": 64,
"trainer": trainer,
"optimizer": optimizer,
"forward_runner": forward_runner
}
config_list = [{
"sparsity":
args.sparsity,
"op_types": ["Linear"],
"op_names": [x for layer in attention_name_groups[:6] for x in layer]
}, {
"sparsity":
args.sparsity / 2,
"op_types": ["Linear"],
"op_names": [x for layer in attention_name_groups[6:] for x in layer]
}]
pruner = TransformerHeadPruner(model, config_list, **kwargs)
pruner.compress()
#########################################################################
# uncomment the following part to export the pruned model masks
# model_path = os.path.join(args.output_dir, "pruned_{}_{}.pth".format(args.model_name, args.task_name))
# mask_path = os.path.join(args.output_dir, "mask_{}_{}.pth".format(args.model_name, args.task_name))
# pruner.export_model(model_path=model_path, mask_path=mask_path)
#########################################################################
# Speedup
# Currently, speeding up Transformers through NNI ModelSpeedup is not supported because of shape inference issues.
# However, if you are using the transformers library, you can use the following workaround:
# The following code gets the head pruning decisions from the pruner and calls the _prune_heads() function
# implemented in models from the transformers library to speed up the model.
if args.speed_up:
speedup_rules = {}
for group_idx, group in enumerate(pruner.attention_name_groups):
# get the layer index
layer_idx = None
for part in group[0].split("."):
try:
layer_idx = int(part)
break
except:
continue
if layer_idx is not None:
speedup_rules[layer_idx] = pruner.pruned_heads[group_idx]
pruner._unwrap_model()
model.bert._prune_heads(speedup_rules)
print(model)
#########################################################################
# After pruning, finetune again on the target task
# Get the metric function
metric = load_metric("glue", args.task_name)
# re-initialize the optimizer and the scheduler
optimizer, _, _, data_collator = get_dataloader_and_optimizer(
args, tokenizer, model, train_dataset, eval_dataset)
lr_scheduler = get_scheduler(name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=train_steps)
logger.info("================= Finetuning after Pruning =================")
train_model(args, model, is_regression, train_dataloader, eval_dataloader,
optimizer, lr_scheduler, metric, device)
if args.output_dir is not None:
torch.save(model.state_dict(),
args.output_dir + "/model_after_pruning.pt")
if args.task_name == "mnli":
final_eval_for_mnli(args, model, processed_datasets, metric,
data_collator)
flops, params, results = count_flops_params(model, dummy_input)
print(
f"Final model FLOPs {flops / 1e6:.2f} M, #Params: {params / 1e6:.2f}M")
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.experiment_data_dir, exist_ok=True)
# prepare model and data
train_loader, test_loader, criterion = get_data(args.dataset,
args.data_dir,
args.batch_size,
args.test_batch_size)
model, optimizer, scheduler = get_model_optimizer_scheduler(
args, device, train_loader, test_loader, criterion)
dummy_input = get_dummy_input(args, device)
flops, params, results = count_flops_params(model, dummy_input)
print(f"FLOPs: {flops}, params: {params}")
print(f'start {args.pruner} pruning...')
def trainer(model, optimizer, criterion, epoch):
return train(args,
model,
device,
train_loader,
criterion,
optimizer,
epoch=epoch)
pruner_cls = str2pruner[args.pruner]
kw_args = {}
config_list = [{'sparsity': args.sparsity, 'op_types': ['Conv2d']}]
if args.pruner == 'level':
config_list = [{'sparsity': args.sparsity, 'op_types': ['default']}]
else:
if args.dependency_aware:
dummy_input = get_dummy_input(args, device)
print('Enable the dependency_aware mode')
# note that, not all pruners support the dependency_aware mode
kw_args['dependency_aware'] = True
kw_args['dummy_input'] = dummy_input
if args.pruner not in ('l1filter', 'l2filter', 'fpgm'):
# set only work for training aware pruners
kw_args['trainer'] = trainer
kw_args['optimizer'] = optimizer
kw_args['criterion'] = criterion
if args.pruner in ('mean_activation', 'apoz', 'taylorfo'):
kw_args['sparsifying_training_batches'] = 1
if args.pruner == 'slim':
kw_args['sparsifying_training_epochs'] = 1
if args.pruner == 'agp':
kw_args['pruning_algorithm'] = 'l1'
kw_args['num_iterations'] = 2
kw_args['epochs_per_iteration'] = 1
# Reproduced result in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
# Conv_1, Conv_8, Conv_9, Conv_10, Conv_11, Conv_12 are pruned with 50% sparsity, as 'VGG-16-pruned-A'
if args.pruner == 'slim':
config_list = [{
'sparsity': args.sparsity,
'op_types': ['BatchNorm2d'],
}]
else:
config_list = [{
'sparsity':
args.sparsity,
'op_types': ['Conv2d'],
'op_names': [
'feature.0', 'feature.24', 'feature.27', 'feature.30',
'feature.34', 'feature.37'
]
}]
pruner = pruner_cls(model, config_list, **kw_args)
# Pruner.compress() returns the masked model
model = pruner.compress()
pruner.get_pruned_weights()
# export the pruned model masks for model speedup
model_path = os.path.join(
args.experiment_data_dir,
'pruned_{}_{}_{}.pth'.format(args.model, args.dataset, args.pruner))
mask_path = os.path.join(
args.experiment_data_dir,
'mask_{}_{}_{}.pth'.format(args.model, args.dataset, args.pruner))
pruner.export_model(model_path=model_path, mask_path=mask_path)
if args.test_only:
test(args, model, device, criterion, test_loader)
if args.speed_up:
# Unwrap all modules to normal state
pruner._unwrap_model()
m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
m_speedup.speedup_model()
print('start finetuning...')
best_top1 = 0
save_path = os.path.join(args.experiment_data_dir, f'finetuned.pth')
for epoch in range(args.fine_tune_epochs):
print('# Epoch {} #'.format(epoch))
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
top1 = test(args, model, device, criterion, test_loader)
if top1 > best_top1:
best_top1 = top1
torch.save(model.state_dict(), save_path)
flops, params, results = count_flops_params(model, dummy_input)
print(
f'Finetuned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_top1: .2f}'
)
if args.nni:
nni.report_final_result(best_top1)
def main(args):
# prepare dataset
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader, val_loader, criterion = get_data(args.dataset, args.data_dir, args.batch_size, args.test_batch_size)
model, optimizer = get_trained_model_optimizer(args, device, train_loader, val_loader, criterion)
def short_term_fine_tuner(model, epochs=1):
for epoch in range(epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
def trainer(model, optimizer, criterion, epoch, callback):
return train(args, model, device, train_loader, criterion, optimizer, epoch=epoch, callback=callback)
def evaluator(model):
return test(model, device, criterion, val_loader)
# used to save the performance of the original & pruned & finetuned models
result = {'flops': {}, 'params': {}, 'performance':{}}
flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
result['flops']['original'] = flops
result['params']['original'] = params
evaluation_result = evaluator(model)
print('Evaluation result (original model): %s' % evaluation_result)
result['performance']['original'] = evaluation_result
# module types to prune, only "Conv2d" supported for channel pruning
if args.base_algo in ['l1', 'l2', 'fpgm']:
op_types = ['Conv2d']
elif args.base_algo == 'level':
op_types = ['default']
config_list = [{
'sparsity': args.sparsity,
'op_types': op_types
}]
dummy_input = get_dummy_input(args, device)
if args.pruner == 'L1FilterPruner':
pruner = L1FilterPruner(model, config_list)
elif args.pruner == 'L2FilterPruner':
pruner = L2FilterPruner(model, config_list)
elif args.pruner == 'FPGMPruner':
pruner = FPGMPruner(model, config_list)
elif args.pruner == 'NetAdaptPruner':
pruner = NetAdaptPruner(model, config_list, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator,
base_algo=args.base_algo, experiment_data_dir=args.experiment_data_dir)
elif args.pruner == 'ADMMPruner':
# users are free to change the config here
if args.model == 'LeNet':
if args.base_algo in ['l1', 'l2', 'fpgm']:
config_list = [{
'sparsity': 0.8,
'op_types': ['Conv2d'],
'op_names': ['conv1']
}, {
'sparsity': 0.92,
'op_types': ['Conv2d'],
'op_names': ['conv2']
}]
elif args.base_algo == 'level':
config_list = [{
'sparsity': 0.8,
'op_names': ['conv1']
}, {
'sparsity': 0.92,
'op_names': ['conv2']
}, {
'sparsity': 0.991,
'op_names': ['fc1']
}, {
'sparsity': 0.93,
'op_names': ['fc2']
}]
else:
raise ValueError('Example only implemented for LeNet.')
pruner = ADMMPruner(model, config_list, trainer=trainer, num_iterations=2, training_epochs=2)
elif args.pruner == 'SimulatedAnnealingPruner':
pruner = SimulatedAnnealingPruner(
model, config_list, evaluator=evaluator, base_algo=args.base_algo,
cool_down_rate=args.cool_down_rate, experiment_data_dir=args.experiment_data_dir)
elif args.pruner == 'AutoCompressPruner':
pruner = AutoCompressPruner(
model, config_list, trainer=trainer, evaluator=evaluator, dummy_input=dummy_input,
num_iterations=3, optimize_mode='maximize', base_algo=args.base_algo,
cool_down_rate=args.cool_down_rate, admm_num_iterations=30, admm_training_epochs=5,
experiment_data_dir=args.experiment_data_dir)
else:
raise ValueError(
"Pruner not supported.")
# Pruner.compress() returns the masked model
# but for AutoCompressPruner, Pruner.compress() returns directly the pruned model
model = pruner.compress()
evaluation_result = evaluator(model)
print('Evaluation result (masked model): %s' % evaluation_result)
result['performance']['pruned'] = evaluation_result
if args.save_model:
pruner.export_model(
os.path.join(args.experiment_data_dir, 'model_masked.pth'), os.path.join(args.experiment_data_dir, 'mask.pth'))
print('Masked model saved to %s' % args.experiment_data_dir)
# model speed up
if args.speed_up:
if args.pruner != 'AutoCompressPruner':
if args.model == 'LeNet':
model = LeNet().to(device)
elif args.model == 'vgg16':
model = VGG(depth=16).to(device)
elif args.model == 'resnet18':
model = ResNet18().to(device)
elif args.model == 'resnet50':
model = ResNet50().to(device)
model.load_state_dict(torch.load(os.path.join(args.experiment_data_dir, 'model_masked.pth')))
masks_file = os.path.join(args.experiment_data_dir, 'mask.pth')
m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
m_speedup.speedup_model()
evaluation_result = evaluator(model)
print('Evaluation result (speed up model): %s' % evaluation_result)
result['performance']['speedup'] = evaluation_result
torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_speed_up.pth'))
print('Speed up model saved to %s' % args.experiment_data_dir)
flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
result['flops']['speedup'] = flops
result['params']['speedup'] = params
if args.fine_tune:
if args.dataset == 'mnist':
optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
elif args.dataset == 'cifar10' and args.model == 'vgg16':
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
elif args.dataset == 'cifar10' and args.model == 'resnet18':
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
elif args.dataset == 'cifar10' and args.model == 'resnet50':
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
best_acc = 0
for epoch in range(args.fine_tune_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = evaluator(model)
if acc > best_acc:
best_acc = acc
torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_fine_tuned.pth'))
print('Evaluation result (fine tuned): %s' % best_acc)
print('Fined tuned model saved to %s' % args.experiment_data_dir)
result['performance']['finetuned'] = best_acc
with open(os.path.join(args.experiment_data_dir, 'result.json'), 'w+') as f:
json.dump(result, f)
def generate_tasks(self, task_result: TaskResult) -> List[Task]:
# append experience & update agent policy
if task_result.task_id != 'origin':
action, reward, observation, done = self.env.step(
self.action, task_result.compact_model)
self.T.append(
[reward, self.observation, observation, self.action, done])
self.observation = observation.copy()
if done:
final_reward = task_result.score - 1
# agent observe and update policy
for _, s_t, s_t1, a_t, d_t in self.T:
self.agent.observe(final_reward, s_t, s_t1, a_t, d_t)
if self.current_episode > self.warmup_episode:
self.agent.update_policy()
self.current_episode += 1
self.T = []
self.action = None
self.observation = None
# update current2origin_sparsity in log file
origin_model = torch.load(self._origin_model_path)
compact_model = task_result.compact_model
compact_model_masks = task_result.compact_model_masks
current2origin_sparsity, _, _ = compute_sparsity(
origin_model, compact_model, compact_model_masks,
self.temp_config_list)
self._tasks[task_result.task_id].state[
'current2origin_sparsity'] = current2origin_sparsity
current2origin_sparsity, _, _ = compute_sparsity(
origin_model, compact_model, compact_model_masks,
self.config_list_copy)
self._tasks[task_result.task_id].state[
'current_total_sparsity'] = current2origin_sparsity
flops, params, _ = count_flops_params(compact_model,
self.dummy_input,
verbose=False)
self._tasks[task_result.
task_id].state['current_flops'] = '{:.2f} M'.format(
flops / 1e6)
self._tasks[task_result.
task_id].state['current_params'] = '{:.2f} M'.format(
params / 1e6)
# generate new action
if self.current_episode < self.total_episode:
if self.observation is None:
self.observation = self.env.reset().copy()
self.temp_config_list = []
compact_model = torch.load(self._origin_model_path)
compact_model_masks = torch.load(self._origin_masks_path)
else:
compact_model = task_result.compact_model
compact_model_masks = task_result.compact_model_masks
if self.current_episode <= self.warmup_episode:
action = self.agent.random_action()
else:
action = self.agent.select_action(self.observation,
episode=self.current_episode)
action = action.tolist()[0]
self.action = self.env.correct_action(action, compact_model)
sub_config_list = [{
'op_names': [self.env.current_op_name],
'total_sparsity': self.action
}]
self.temp_config_list.extend(sub_config_list)
task_id = self._task_id_candidate
if self.env.is_first_layer() or self.env.is_final_layer():
task_config_list = self.temp_config_list
else:
task_config_list = sub_config_list
config_list_path = Path(self._intermediate_result_dir,
'{}_config_list.json'.format(task_id))
with Path(config_list_path).open('w') as f:
json_tricks.dump(task_config_list, f, indent=4)
model_path = Path(
self._intermediate_result_dir,
'{}_compact_model.pth'.format(task_result.task_id))
masks_path = Path(
self._intermediate_result_dir,
'{}_compact_model_masks.pth'.format(task_result.task_id))
torch.save(compact_model, model_path)
torch.save(compact_model_masks, masks_path)
task = Task(task_id, model_path, masks_path, config_list_path)
if not self.env.is_final_layer():
task.finetune = False
task.evaluate = False
self._tasks[task_id] = task
self._task_id_candidate += 1
return [task]
else:
return []
if __name__ == '__main__':
# model = MobileNetV2(n_class=10).to(device)
model = VGG().to(device)
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=5e-4)
scheduler = MultiStepLR(optimizer, milestones=[50, 75], gamma=0.1)
criterion = torch.nn.CrossEntropyLoss()
for i in range(100):
trainer(model, optimizer, criterion, i)
pre_best_acc = evaluator(model)
dummy_input = torch.rand(10, 3, 32, 32).to(device)
pre_flops, pre_params, _ = count_flops_params(model, dummy_input)
config_list = [{
'op_types': ['Conv2d'],
'total_sparsity': 0.5,
'max_sparsity_per_layer': 0.8
}]
# if you just want to keep the final result as the best result, you can pass evaluator as None.
# or the result with the highest score (given by evaluator) will be the best result.
ddpg_params = {
'hidden1': 300,
'hidden2': 300,
'lr_c': 1e-3,
'lr_a': 1e-4,
'warmup': 100,
