def split_paras(paras):
num_layers = len(paras)
arch_paras = []
quan_paras = []
for i in range(num_layers):
para = paras[i]
arch_para = {}
quan_para = {}
for name, _ in ARCH_SPACE.items():
if name in para:
arch_para[name] = para[name]
if 'anchor_point' in para:
arch_para['anchor_point'] = para['anchor_point']
for name, _ in QUAN_SPACE.items():
if name in para:
quan_para[name] = para[name]
if arch_para != {}:
arch_paras.append(arch_para)
if quan_para != {}:
quan_paras.append(quan_para)
if arch_paras == []:
arch_paras = None
if quan_paras == []:
quan_paras = None
return arch_paras, quan_paras
def quantization_search(device, dir='experiment'):
dir = os.path.join(dir,
f"rLut={args.rLUT}, rThroughput={args.rThroughput}")
if os.path.exists(dir) is False:
os.makedirs(dir)
filepath = os.path.join(dir, f"quantization ({args.episodes} episodes)")
logger = get_logger(filepath)
csvfile = open(filepath + '.csv', mode='w+', newline='')
writer = csv.writer(csvfile)
logger.info(f"INFORMATION")
logger.info(f"mode: \t\t\t\t\t {'quantization'}")
logger.info(f"dataset: \t\t\t\t {args.dataset}")
logger.info(f"number of child network layers: \t {args.layers}")
logger.info(f"include stride: \t\t\t {not args.no_stride}")
logger.info(f"include pooling: \t\t\t {not args.no_pooling}")
logger.info(f"skip connection: \t\t\t {args.skip}")
logger.info(f"required # LUTs: \t\t\t {args.rLUT}")
logger.info(f"required throughput: \t\t\t {args.rThroughput}")
logger.info(f"Assumed frequency: \t\t\t {CLOCK_FREQUENCY}")
logger.info(f"training epochs: \t\t\t {args.epochs}")
logger.info(f"data augmentation: \t\t\t {args.augment}")
logger.info(f"batch size: \t\t\t\t {args.batch_size}")
logger.info(f"controller learning rate: \t\t {args.learning_rate}")
logger.info(f"architecture episodes: \t\t\t {args.episodes}")
logger.info(f"using multi gpus: \t\t\t {args.multi_gpu}")
logger.info(f"architecture space: ")
# for name, value in ARCH_SPACE.items():
# logger.info(name + f": \t\t\t\t {value}")
logger.info(f"quantization space: ")
for name, value in QUAN_SPACE.items():
logger.info(name + f": \t\t\t {value}")
agent = Agent(QUAN_SPACE,
args.layers,
lr=args.learning_rate,
device=torch.device('cpu'),
skip=False)
train_data, val_data = data.get_data(args.dataset,
device,
shuffle=True,
batch_size=args.batch_size,
augment=args.augment)
input_shape, num_classes = data.get_info(args.dataset)
writer.writerow(["ID"] +
["Layer {}".format(i)
for i in range(args.layers)] + ["Accuracy"] + [
"Partition (Tn, Tm)", "Partition (#LUTs)",
"Partition (#cycles)", "Total LUT", "Total Throughput"
] + ["Time"])
child_id, total_time = 0, 0
logger.info('=' * 50 + "Start exploring quantization space" + '=' * 50)
best_samples = BestSamples(5)
arch_paras = [{
'filter_height': 3,
'filter_width': 3,
'stride_height': 1,
'stride_width': 1,
'num_filters': 64,
'pool_size': 1
}, {
'filter_height': 7,
'filter_width': 5,
'stride_height': 1,
'stride_width': 1,
'num_filters': 48,
'pool_size': 1
}, {
'filter_height': 5,
'filter_width': 5,
'stride_height': 2,
'stride_width': 1,
'num_filters': 48,
'pool_size': 1
}, {
'filter_height': 3,
'filter_width': 5,
'stride_height': 1,
'stride_width': 1,
'num_filters': 64,
'pool_size': 1
}, {
'filter_height': 5,
'filter_width': 7,
'stride_height': 1,
'stride_width': 1,
'num_filters': 36,
'pool_size': 1
}, {
'filter_height': 3,
'filter_width': 1,
'stride_height': 1,
'stride_width': 2,
'num_filters': 64,
'pool_size': 2
}]
model, optimizer = child.get_model(input_shape,
arch_paras,
num_classes,
device,
multi_gpu=args.multi_gpu,
do_bn=False)
_, val_acc = backend.fit(model,
optimizer,
train_data=train_data,
val_data=val_data,
epochs=args.epochs,
verbosity=args.verbosity)
print(val_acc)
for e in range(args.episodes):
logger.info('-' * 130)
child_id += 1
start = time.time()
quan_rollout, quan_paras = agent.rollout()
logger.info("Sample Quantization ID: {}, Sampled actions: {}".format(
child_id, quan_rollout))
fpga_model = FPGAModel(rLUT=args.rLUT,
rThroughput=args.rThroughput,
arch_paras=arch_paras,
quan_paras=quan_paras)
if fpga_model.validate():
_, reward = backend.fit(model,
optimizer,
val_data=val_data,
quan_paras=quan_paras,
epochs=1,
verbosity=args.verbosity)
else:
reward = 0
agent.store_rollout(quan_rollout, reward)
end = time.time()
ep_time = end - start
total_time += ep_time
best_samples.register(child_id, quan_rollout, reward)
writer.writerow([child_id] +
[str(quan_paras[i]) for i in range(args.layers)] +
[reward] + list(fpga_model.get_info()) + [ep_time])
logger.info(f"Reward: {reward}, " + f"Elasped time: {ep_time}, " +
f"Average time: {total_time/(e+1)}")
logger.info(f"Best Reward: {best_samples.reward_list[0]}, " +
f"ID: {best_samples.id_list[0]}, " +
f"Rollout: {best_samples.rollout_list[0]}")
logger.info('=' * 50 + "Quantization sapce exploration finished" +
'=' * 50)
logger.info(f"Total elasped time: {total_time}")
logger.info(f"Best samples: {best_samples}")
csvfile.close()
def nested_search(device, dir='experiment'):
dir = os.path.join(dir,
f"rLut={args.rLUT}, rThroughput={args.rThroughput}")
if os.path.exists(dir) is False:
os.makedirs(dir)
filepath = os.path.join(dir, f"nested ({args.episodes} episodes)")
logger = get_logger(filepath)
csvfile = open(filepath + '.csv', mode='w+', newline='')
writer = csv.writer(csvfile)
logger.info(f"INFORMATION")
logger.info(f"mode: \t\t\t\t\t {'nested'}")
logger.info(f"dataset: \t\t\t\t {args.dataset}")
logger.info(f"number of child network layers: \t {args.layers}")
logger.info(f"include stride: \t\t\t {not args.no_stride}")
logger.info(f"include pooling: \t\t\t {not args.no_pooling}")
logger.info(f"skip connection: \t\t\t {args.skip}")
logger.info(f"required # LUTs: \t\t\t {args.rLUT}")
logger.info(f"required throughput: \t\t\t {args.rThroughput}")
logger.info(f"Assumed frequency: \t\t\t {CLOCK_FREQUENCY}")
logger.info(f"training epochs: \t\t\t {args.epochs}")
logger.info(f"data augmentation: \t\t\t {args.augment}")
logger.info(f"batch size: \t\t\t\t {args.batch_size}")
logger.info(f"controller learning rate: \t\t {args.learning_rate}")
logger.info(f"architecture episodes: \t\t\t {args.episodes1}")
logger.info(f"quantization episodes: \t\t\t {args.episodes2}")
logger.info(f"using multi gpus: \t\t\t {args.multi_gpu}")
logger.info(f"architecture space: ")
for name, value in ARCH_SPACE.items():
logger.info(name + f": \t\t\t\t {value}")
logger.info(f"quantization space: ")
for name, value in QUAN_SPACE.items():
logger.info(name + f": \t\t\t {value}")
train_data, val_data = data.get_data(args.dataset,
device,
shuffle=True,
batch_size=args.batch_size,
augment=args.augment)
input_shape, num_classes = data.get_info(args.dataset)
writer.writerow(["ID"] +
["Layer {}".format(i)
for i in range(args.layers)] + ["Accuracy"] + [
"Partition (Tn, Tm)", "Partition (#LUTs)",
"Partition (#cycles)", "Total LUT", "Total Throughput"
] + ["Time"])
arch_agent = Agent(ARCH_SPACE,
args.layers,
lr=args.learning_rate,
device=torch.device('cpu'),
skip=args.skip)
arch_id, total_time = 0, 0
logger.info('=' * 50 + "Start exploring architecture space" + '=' * 50)
best_arch = BestSamples(5)
for e1 in range(args.episodes1):
logger.info('-' * 130)
arch_id += 1
start = time.time()
arch_rollout, arch_paras = arch_agent.rollout()
logger.info("Sample Architecture ID: {}, Sampled arch: {}".format(
arch_id, arch_rollout))
model, optimizer = child.get_model(input_shape,
arch_paras,
num_classes,
device,
multi_gpu=args.multi_gpu,
do_bn=False)
backend.fit(model,
optimizer,
train_data,
val_data,
epochs=args.epochs,
verbosity=args.verbosity)
quan_id = 0
best_quan_reward = -1
logger.info('=' * 50 + "Start exploring quantization space" + '=' * 50)
quan_agent = Agent(QUAN_SPACE,
args.layers,
lr=args.learning_rate,
device=torch.device('cpu'),
skip=False)
for e2 in range(args.episodes2):
quan_id += 1
quan_rollout, quan_paras = quan_agent.rollout()
fpga_model = FPGAModel(rLUT=args.rLUT,
rThroughput=args.rThroughput,
arch_paras=arch_paras,
quan_paras=quan_paras)
if fpga_model.validate():
_, quan_reward = backend.fit(model,
optimizer,
val_data=val_data,
quan_paras=quan_paras,
epochs=1,
verbosity=args.verbosity)
else:
quan_reward = 0
logger.info(
"Sample Quantization ID: {}, Sampled Quantization: {}, reward: {}"
.format(quan_id, quan_rollout, quan_reward))
quan_agent.store_rollout(quan_rollout, quan_reward)
if quan_reward > best_quan_reward:
best_quan_reward = quan_reward
best_quan_rollout, best_quan_paras = quan_rollout, quan_paras
logger.info('=' * 50 + "Quantization space exploration finished" +
'=' * 50)
arch_reward = best_quan_reward
arch_agent.store_rollout(arch_rollout, arch_reward)
end = time.time()
ep_time = end - start
total_time += ep_time
best_arch.register(
arch_id,
utility.combine_rollout(arch_rollout, best_quan_rollout,
args.layers), arch_reward)
writer.writerow([arch_id] + [
str(arch_paras[i]) + '\n' + str(best_quan_paras[i])
for i in range(args.layers)
] + [arch_reward] + list(fpga_model.get_info()) + [ep_time])
logger.info(f"Reward: {arch_reward}, " + f"Elasped time: {ep_time}, " +
f"Average time: {total_time/(e1+1)}")
logger.info(f"Best Reward: {best_arch.reward_list[0]}, " +
f"ID: {best_arch.id_list[0]}, " +
f"Rollout: {best_arch.rollout_list[0]}")
logger.info('=' * 50 +
"Architecture & quantization sapce exploration finished" +
'=' * 50)
logger.info(f"Total elasped time: {total_time}")
logger.info(f"Best samples: {best_arch}")
csvfile.close()
def sync_search(device, dir='experiment'):
dir = os.path.join(
dir,
utility.cleanText(f"rLut-{args.rLUT}_rThroughput-{args.rThroughput}"))
if os.path.exists(dir) is False:
os.makedirs(dir)
filepath = os.path.join(
dir, utility.cleanText(f"joint_{args.episodes}-episodes"))
logger = utility.get_logger(filepath)
csvfile = open(filepath + '.csv', mode='w+', newline='')
writer = csv.writer(csvfile)
tb_writer = SummaryWriter(filepath)
logger.info(f"INFORMATION")
logger.info(f"mode: \t\t\t\t\t {'joint'}")
logger.info(f"dataset: \t\t\t\t {args.dataset}")
logger.info(f"number of child network layers: \t {args.layers}")
logger.info(f"seed: \t\t\t\t {args.seed}")
logger.info(f"gpu: \t\t\t\t {args.gpu}")
logger.info(f"include batchnorm: \t\t\t {args.batchnorm}")
logger.info(f"include stride: \t\t\t {not args.no_stride}")
logger.info(f"include pooling: \t\t\t {not args.no_pooling}")
logger.info(f"skip connection: \t\t\t {args.skip}")
logger.info(f"required # LUTs: \t\t\t {args.rLUT}")
logger.info(f"required throughput: \t\t\t {args.rThroughput}")
logger.info(f"Assumed frequency: \t\t\t {CLOCK_FREQUENCY}")
logger.info(f"training epochs: \t\t\t {args.epochs}")
logger.info(f"data augmentation: \t\t\t {args.augment}")
logger.info(f"batch size: \t\t\t\t {args.batch_size}")
logger.info(f"controller learning rate: \t\t {args.learning_rate}")
logger.info(f"controller learning rate: \t\t {args.learning_rate}")
logger.info(f"architecture episodes: \t\t\t {args.episodes}")
logger.info(f"using multi gpus: \t\t\t {args.multi_gpu}")
logger.info(f"architecture space: ")
for name, value in ARCH_SPACE.items():
logger.info(name + f": \t\t\t\t {value}")
logger.info(f"quantization space: ")
for name, value in QUAN_SPACE.items():
logger.info(name + f": \t\t\t {value}")
agent = Agent({
**ARCH_SPACE,
**QUAN_SPACE
},
args.layers,
lr=args.learning_rate,
device=torch.device('cpu'),
skip=args.skip)
train_data, val_data = data.get_data(args.dataset,
device,
shuffle=True,
batch_size=args.batch_size,
augment=args.augment)
input_shape, num_classes = data.get_info(args.dataset)
## (3,32,32) -> (1,3,32,32) add batch dimension
sample_input = utility.get_sample_input(device, input_shape)
writer.writerow(["ID"] +
["Layer {}".format(i)
for i in range(args.layers)] + ["Accuracy"] + [
"Partition (Tn, Tm)", "Partition (#LUTs)",
"Partition (#cycles)", "Total LUT", "Total Throughput"
] + ["Time"])
arch_id, total_time = 0, 0
best_reward = float('-inf')
logger.info('=' * 50 +
"Start exploring architecture & quantization space" + '=' * 50)
best_samples = BestSamples(5)
for e in range(args.episodes):
logger.info('-' * 130)
arch_id += 1
start = time.time()
rollout, paras = agent.rollout()
logger.info("Sample Architecture ID: {}, Sampled actions: {}".format(
arch_id, rollout))
arch_paras, quan_paras = utility.split_paras(paras)
fpga_model = FPGAModel(rLUT=args.rLUT,
rThroughput=args.rThroughput,
arch_paras=arch_paras,
quan_paras=quan_paras)
if fpga_model.validate():
model, optimizer = child.get_model(input_shape,
arch_paras,
num_classes,
device,
multi_gpu=args.multi_gpu,
do_bn=args.batchnorm)
if args.verbosity > 1:
print(model)
torchsummary.summary(model, input_shape)
if args.adapt:
num_w = utility.get_net_param(model)
macs = utility.get_net_macs(model, sample_input)
tb_writer.add_scalar('num_param', num_w, arch_id)
tb_writer.add_scalar('macs', macs, arch_id)
if args.verbosity > 1:
print(f"# of param: {num_w}, macs: {macs}")
_, val_acc = backend.fit(model,
optimizer,
train_data,
val_data,
quan_paras=quan_paras,
epochs=args.epochs,
verbosity=args.verbosity)
else:
val_acc = 0
if args.adapt:
## TODO: how to make arch_reward function with macs and latency?
arch_reward = val_acc
else:
arch_reward = val_acc
agent.store_rollout(rollout, arch_reward)
end = time.time()
ep_time = end - start
total_time += ep_time
best_samples.register(arch_id, rollout, arch_reward)
tb_writer.add_scalar('val_acc', val_acc, arch_id)
tb_writer.add_scalar('arch_reward', arch_reward, arch_id)
if arch_reward > best_reward:
best_reward = arch_reward
tb_writer.add_scalar('best_reward', best_reward, arch_id)
tb_writer.add_graph(model.eval(), (sample_input, ))
writer.writerow([arch_id] +
[str(paras[i])
for i in range(args.layers)] + [arch_reward] +
list(fpga_model.get_info()) + [ep_time])
logger.info(f"Reward: {arch_reward}, " + f"Elasped time: {ep_time}, " +
f"Average time: {total_time/(e+1)}")
logger.info(f"Best Reward: {best_samples.reward_list[0]}, " +
f"ID: {best_samples.id_list[0]}, " +
f"Rollout: {best_samples.rollout_list[0]}")
logger.info('=' * 50 +
"Architecture & quantization sapce exploration finished" +
'=' * 50)
logger.info(f"Total elasped time: {total_time}")
logger.info(f"Best samples: {best_samples}")
tb_writer.close()
csvfile.close()