def nmt_test(model_path_pruned, DATA_PATH, GPU_ID, translate_param1_path, translate_param2_path, group_dict):
cuda.set_device(GPU_ID)
valid_data = torch.load(DATA_PATH + 'len50_pywmt14.valid.pt')
fields = onmt.IO.load_fields(torch.load(DATA_PATH + 'len50_pywmt14.vocab.pt'))
valid_data.fields = fields
checkpoint = torch.load(model_path_pruned, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
with cuda.device(GPU_ID):
ref_model = onmt.ModelConstructor.make_base_model(model_opt, fields, True, checkpoint)
ref_model.eval()
ref_model.generator.eval()
masked_model = MaskedModel(ref_model, group_dict, cuda.current_device(), cuda.current_device()) # ref_model is at current_device, no copy will happen
translate_opt, translate_dummy_opt = translate_opt_initialize(translate_param1_path, translate_param2_path, DATA_PATH, model_path_pruned, GPU_ID)
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = ref_model
tt=open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
translate_data = onmt.IO.ONMTDataset(
translate_opt.src, translate_opt.tgt, fields,
use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(
dataset=translate_data, device=GPU_ID,
batch_size=1, train=False, sort=False,
shuffle=False)
sparsity = masked_model.get_sparsity()
total_param = masked_model.total_parameters_of_pretrain()
tmp_fit1 = evaluate(masked_model, valid_data, fields)
tmp_fit2 = evaluate_trans(translator, references, prune_data, translate_data)
return total_param, sparsity, tmp_fit1, tmp_fit2
def main():
valid_data = torch.load(TRAIN_DATA + '.valid.pt')
fields = onmt.IO.load_fields(torch.load(TRAIN_DATA + '.vocab.pt'))
valid_data.fields = fields # we need to clear this assignment relationg if we want to transfere valid among threads
if GPU_ID == 0 or GPU_ID == 1:
cuda.set_device(GPU_ID)
with cuda.device(GPU_ID):
# '/fl/deepModels/tmp/rnnsearch_tmp.pt','/fl/deepModels/tmp/loungnet_tmp.pt'
checkpoint_path = '/fl/deepModels/tmp/rnnsearch_tmp.pt'
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
ref_model = onmt.ModelConstructor.make_base_model(
model_opt, fields, True, checkpoint)
ref_model.eval()
ref_model.generator.eval()
masked_model = MaskedModel(
ref_model, group_dict, cuda.current_device(),
cuda.current_device(
)) # ref_model is at current_device, no copy will happen
# train data loading
translate_opt, translate_dummy_opt = translate_opt_initialize(
'/fl/NMTSWPO/workspace/opennmt_translate_opt.pt',
'/fl/NMTSWPO/workspace/opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = masked_model
tt = open(translate_opt.tgt, 'r', encoding='utf-8')
references = [[t] for t in tt]
p = 0.3
translate_data = onmt.IO.ONMTDataset(translate_opt.src,
translate_opt.tgt,
fields,
use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data,
device=GPU_ID,
batch_size=1,
train=False,
sort=False,
shuffle=False)
tmp_crate = len(masked_model.group_name_list) * [p]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate_trans(translator, references, prune_data,
translate_data)
logger.scalar_summary('test_bleu', tmp_fit[1] * 100, int(p * 100))
logger.scalar_summary('test_ppl', tmp_fit[0], int(p * 100))
print('percentage %s => bleu (%.4f), ppl (%.4f)' %
(p * 100, tmp_fit[1] * 100, tmp_fit[0]))
def train(net, loader, ep, scheduler=None, writer=None):
global n_iter
if scheduler:
scheduler.step()
net.train()
loss_all, norm_all = [], []
train_iter = tqdm(loader)
for images, labels in train_iter:
n_iter += 1
images, labels = images.cuda(), labels.cuda()
embedding = net(images)
loss = criterion(embedding, labels)
loss_all.append(loss.item())
if writer:
writer.add_scalar('loss/train', loss.item(), n_iter)
print(cuda.memory_allocated(cuda.current_device()))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_iter.set_description("[Train][Epoch %d] Loss: %.5f" % (ep, loss.item()))
print('[Epoch %d] Loss: %.5f\n' % (ep, torch.Tensor(loss_all).mean()))
def check_devices():
for i in range(device_count()):
print("Found device {}:".format(i), get_device_name(i))
if device_count() == 0:
print("No GPU device found")
else:
print("Current cuda device is", get_device_name(current_device()))
def eval(net, loader, ep):
K = [1, 10, 100, 1000]
net.eval()
test_iter = tqdm(loader)
embeddings_all, labels_all = [], []
test_iter.set_description("[Eval][Epoch %d]" % ep)
with torch.no_grad():
for images, labels in test_iter:
images, labels = images.cuda(), labels.cuda()
embedding = net(images)
embeddings_all.append(embedding.data)
labels_all.append(labels.data)
print(cuda.memory_allocated(cuda.current_device()))
embeddings_all = torch.cat(embeddings_all).cpu()
labels_all = torch.cat(labels_all).cpu()
rec = recall(embeddings_all, labels_all, K=K)
print("Embedding Size: %d" % len(embeddings_all))
print(labels_all.sum())
for k, r in zip(K, rec):
print('[Epoch %d] Recall@%d: [%.4f]\n' % (ep, k+1, 100 * r))
return rec[0]
def which_processor():
"""Check if fastai/torch is using GPU or CPU"""
if is_available():
device_nr = current_device()
print(f"Fast.ai (Torch) is using GPU: {get_device_name(device_nr)}")
else:
print("Cuda is not available. Fast.ai/Torch is using CPU")
def test_sequential_move_to_cuda_via_to(self):
"""Test moving AnalogSequential to cuda (from CPU), using ``.to()``."""
if not cuda.is_compiled():
raise SkipTest('not compiled with CUDA support')
# Map the original tile classes to the expected ones after `cuda()`.
tile_classes = {
tiles.AnalogTile: tiles.CudaAnalogTile,
tiles.CudaAnalogTile: tiles.CudaAnalogTile
}
layer = self.get_layer()
expected_class = tile_classes[layer.analog_tile.tile.__class__]
expected_device = device('cuda', current_device())
# Create a container and move to cuda.
model = AnalogSequential(layer)
model.to(device('cuda'))
analog_tile = layer.analog_tile
self.assertEqual(analog_tile.device, expected_device)
self.assertEqual(analog_tile.get_analog_ctx().data.device,
expected_device)
if analog_tile.shared_weights is not None:
self.assertEqual(analog_tile.shared_weights.data.device,
expected_device)
self.assertEqual(analog_tile.shared_weights.data.size()[0],
analog_tile.tile.get_x_size())
self.assertEqual(analog_tile.shared_weights.data.size()[1],
analog_tile.tile.get_d_size())
# Assert the tile has been moved to cuda.
self.assertIsInstance(layer.analog_tile.tile, expected_class)
def get_device_id(self) -> Union[str, torch.device]:
if cuda.is_available():
device_id = self.non_default_device_to_use or cuda.current_device()
return torch.device('cuda', device_id)
elif self.fallback_to_cpu:
return "cpu"
else:
raise CudaNotAvailable("Cuda not available")
def get_gpu_statistics(self):
id = cuda.current_device()
print("Max memory allocated on GPU %d: %d bytes" %
(id, cuda.max_memory_allocated(id)))
print("Max memory cached on GPU %d: %d bytes" %
(id, cuda.max_memory_cached(id)))
print("Current memory allocated on GPU %d: %d bytes" %
(id, cuda.memory_allocated(id)))
print("Current memory cached on GPU %d: %d bytes" %
(id, cuda.memory_cached(id)))
def get_device(force_cpu: bool) \
-> Tuple[str, str]:
"""Gets the available device.
:param force_cpu: Force CPU usage?
:type force_cpu: bool
:return: Device and device name.
:rtype: str, str
"""
return ('cuda', cuda.get_device_name(cuda.current_device())) \
if cuda.is_available() and not force_cpu else \
('cpu', processor())
def enable_GPU(self):
# Get the GPU device name.
device_name = tf.test.gpu_device_name()
# The device name should look like the following:
if device_name == '/device:GPU:0':
print('Found GPU at: {}'.format(device_name))
else:
raise SystemError('GPU device not found')
# From torch:
device = cuda.current_device()
return device
def device_info(the_device: str) \
-> None:
"""Prints an informative message about the device that we are using.
:param the_device: The device.
:type the_device: str
"""
from torch.cuda import get_device_name, current_device
from platform import processor
actual_device = get_device_name(current_device()) \
if the_device.startswith('cuda') else processor()
cmd_msg(f'Using device: `{actual_device}`.')
def __init__(self, source_tile: AnalogTile):
if not cuda.is_compiled():
raise CudaError('aihwkit has not been compiled with CUDA support')
# Create a new instance of the rpu config.
new_rpu_config = deepcopy(source_tile.rpu_config)
# Create the tile, replacing the simulator tile.
super().__init__(source_tile.out_size, source_tile.in_size,
new_rpu_config, source_tile.bias,
source_tile.in_trans, source_tile.out_trans)
self.cuda(current_device())
def __init__(self, source_tile: FloatingPointTile):
if not cuda.is_compiled():
raise CudaError('aihwkit has not been compiled with CUDA support')
# Create a new instance of the rpu config.
new_rpu_config = deepcopy(source_tile.rpu_config)
# Create the tile, replacing the simulator tile.
super().__init__(source_tile.out_size, source_tile.in_size, new_rpu_config,
source_tile.bias, source_tile.in_trans, source_tile.out_trans)
self.tile = tiles.CudaFloatingPointTile(source_tile.tile)
# Set the cuda properties
self.stream = current_stream()
self.device = torch_device(current_device())
def __init__(self,
out_size: int,
in_size: int,
resistive_device: Optional[BaseResistiveDevice] = None,
bias: bool = False,
in_trans: bool = False,
out_trans: bool = False):
if not cuda.is_compiled():
raise RuntimeError(
'aihwkit has not been compiled with CUDA support')
super().__init__(out_size, in_size, resistive_device, bias, in_trans,
out_trans)
self.tile = tiles.CudaAnalogTile(self.tile)
self.stream = current_stream()
self.device = torch_device(current_device())
def make_loss_compute(model, tgt_vocab, dataset, gpu_id=None, copy_attn=False, copy_attn_force=False):
"""
This returns user-defined LossCompute object, which is used to
compute loss in train/validate process. You can implement your
own *LossCompute class, by subclassing LossComputeBase.
"""
if copy_attn:
compute = onmt.modules.CopyGeneratorLossCompute(
model.generator, tgt_vocab, dataset, copy_attn_force)
else:
compute = onmt.Loss.NMTLossCompute(model.generator, tgt_vocab)
if gpu_id == None:
gpu_id = cuda.current_device()
compute.cuda(gpu_id)
return compute
def __init__(self, source_tile: AnalogTile):
if not cuda.is_compiled():
raise RuntimeError(
'aihwkit has not been compiled with CUDA support')
# Create a new instance of the resistive device.
new_resistive_device = deepcopy(source_tile.resistive_device)
# Create the tile, replacing the simulator tile.
super().__init__(source_tile.out_size, source_tile.in_size,
new_resistive_device, source_tile.bias,
source_tile.in_trans, source_tile.out_trans)
self.tile = tiles.CudaAnalogTile(source_tile.tile)
# Set the cuda properties
self.stream = current_stream()
self.device = torch_device(current_device())
def config_cuda(use_cuda):
if not use_cuda:
print('Using cpu')
torch.device('cpu')
return 'cpu'
elif not cuda.is_available():
print('Cuda not found, using cpu')
torch.device('cpu')
return 'cpu'
print('Configuring cuda...')
torch.device('cuda')
cuda.set_device(0)
current_dev = cuda.current_device()
current_dev_name = cuda.get_device_name(current_dev)
current_dev_specs = cuda.get_device_properties(current_dev)
print(f'Current Device: {current_dev}')
print(f'Current Device Name: {current_dev_name}')
print(f'Current Device Specs: {current_dev_specs}')
print()
return 'cuda'
def test_save_with_cuda(self):
"""Whether model is correctly reconstructed after saving"""
if not cuda.is_compiled():
raise SkipTest('not compiled with CUDA support')
# Map the original tile classes to the expected ones after `cuda()`.
tile_classes = {
tiles.AnalogTile: tiles.CudaAnalogTile,
tiles.CudaAnalogTile: tiles.CudaAnalogTile
}
layer = self.get_layer()
model = AnalogSequential(layer)
model.cuda()
with TemporaryFile() as file:
save(model.state_dict(), file)
# Create a new model and load its state dict.
file.seek(0)
checkpoint = load(file)
model.load_state_dict(checkpoint)
expected_device = device('cuda', current_device())
expected_class = tile_classes[layer.analog_tile.tile.__class__]
analog_tile = model[0].analog_tile
self.assertEqual(analog_tile.device, expected_device)
self.assertEqual(analog_tile.get_analog_ctx().data.device,
expected_device)
if analog_tile.shared_weights is not None:
self.assertEqual(analog_tile.shared_weights.data.device,
expected_device)
self.assertEqual(analog_tile.shared_weights.data.size()[0],
analog_tile.tile.get_x_size())
self.assertEqual(analog_tile.shared_weights.data.size()[1],
analog_tile.tile.get_d_size())
# Assert the tile has been moved to cuda.
self.assertIsInstance(layer.analog_tile.tile, expected_class)
def __init__(self, model, use_cuda=None, model_name='nnTrainer_model'):
# Basics
super(nnTrainer, self).__init__()
self.model = model
self.model_name = model_name.split('.')[0]
self.results_path = 'results'
if not os.path.exists(self.results_path):
os.makedirs(self.results_path)
# Use CUDA?
self.use_cuda = use_cuda if (
use_cuda != None and cuda.is_available()) else cuda.is_available()
self.device = 'cpu' if (not self.use_cuda) else (
'cuda:' + str(cuda.current_device()))
self.device = torch.device(self.device)
clog('Model CUDA:', self.use_cuda, '| Device:', self.device)
# Current loss and loss history
self.train_loss = 0
self.valid_loss = 0
self.train_loss_hist = []
self.valid_loss_hist = []
def make_sequence_video(cfg):
with open(cfg) as fd:
data_specs = json.load(fd)
temp_size = data_specs['temp_size']
num_of_temp_features = data_specs['temp_features']
m = load_model(data_specs['model_path'],
DOTNetCNN.name(),
# ToNameNet.name(),
"model_params.json")
use_gpu = torch_cuda.is_available()
device = torch_device(torch_cuda.current_device()) if use_gpu else torch_device("cpu")
m.to(device)
r = get_images_classes(
data_specs['images_path'],
data_specs['info_dict'],
data_specs['class_of_interest']
)
train_d, val_d, test_d = split_data(
data_specs['positive_ev_path'],
r,
window_size=temp_size,
future_size=0,
shuffle=False
)
full_df = pd.concat([train_d, val_d, test_d], ignore_index=True)
info_path = data_specs["info_path"]
ds = PixelLevelDs(
full_df,
info_path=info_path,
add_polarity="neg" if num_of_temp_features > 1 else "",
)
# make_video(m, ds)
make_non_repeating_video(m, ds)
def main():
total_times = 100
run_times = 0
init_threshold = ...
start_t = time.time()
valid_data = torch.load(TRAIN_DATA + '.valid.pt')
fields = onmt.IO.load_fields(torch.load(TRAIN_DATA + '.vocab.pt'))
# fields = onmt.IO.load_fields_from_vocab(torch.load(TRAIN_DATA + '.vocab.pt'))
valid_data.fields = fields # we need to clear this assignment relationg if we want to transfere valid among threads
checkpoint = torch.load(weights, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
# masked_models = []
with cuda.device(GPU_ID):
ref_model = onmt.ModelConstructor.make_base_model(
model_opt, fields, True, checkpoint)
ref_model.eval()
ref_model.generator.eval()
masked_model = MaskedModel(
ref_model, group_dict, cuda.current_device(), cuda.current_device(
)) # ref_model is at current_device, no copy will happen
# masked_models.append(masked_model)
train_opt, _, _ = opt_initialize(checkpoint, 'opennmt_translate_opt.pt',
'opennmt_translate_dummy_opt.pt')
if GPU_ID:
cuda.set_device(GPU_ID)
# 只需要原始的accuracy
acc_of_no_prune = 0
get_acc_of_no_prune = False
print(time_now(), "start while")
while run_times < total_times:
print("-----------------------------------------")
print("-----------------------------------------")
print("start Iteration ", run_times)
print("----------------test model for masked_model------")
masked_model.make_evaluable()
tmp_fit = evaluate(masked_model, valid_data, fields)
print('acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_model.get_sparsity()
print('Sparsity: {}'.format(model_sparsity))
# init threshold
best_threshold = 0
itr_time = time.time()
xxx = np.arange(0., 1, 0.01)
print(time_now(), "start testing pruning")
masked_model.make_evaluable()
for i in range(len(xxx)):
# best_threshold = 0.2
# break
tmp_crate = len(masked_model.group_name_list) * [xxx[i]]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate(masked_model, valid_data, fields)
print('percentage %s => acc (%.4f), ppl (%.4f)' %
(xxx[i] * 100, tmp_fit[1], tmp_fit[0]))
if i == 0 and not get_acc_of_no_prune:
acc_of_no_prune = tmp_fit[1]
acc_of_no_prune = int(acc_of_no_prune * 10) / 10
get_acc_of_no_prune = True
elif acc_of_no_prune - tmp_fit[1] > acc_percent_prune:
best_threshold = xxx[i] - 0.01
break
# -------------------------------------------------
# Start writing
# prune again
print(time_now(), " init accuracy of model:", acc_of_no_prune)
print("accuracy constraint:", acc_percent_prune)
print("-------test------------:", get_acc_of_no_prune)
print(time_now(), " apply pruning with threshold:", best_threshold)
tmp_crate = len(masked_model.group_name_list) * [best_threshold]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
# print information
tmp_fit = evaluate(masked_model, valid_data, fields)
print('percentage %s => acc (%.4f), ppl (%.4f)' %
(best_threshold * 100, tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_model.get_sparsity()
print('Sparsity: {}'.format(model_sparsity))
#--------------- start retraining --------------
# first store model
print(time_now(), "start saving model")
_, saved_model = update_checkpoint(checkpoint, masked_model, run_times,
acc_percent_prune)
print(time_now(), "finish saving model:", saved_model)
model_for_train = masked_model
pretrained_leaf_dict = model_for_train.make_trainable()
optim = build_optim(model_for_train.masked_model, checkpoint,
train_opt, pretrained_leaf_dict)
print("finish building optim")
print(time_now(), "start loading data for retraining")
train = torch.load(train_opt.data + '.train.pt')
valid = torch.load(train_opt.data + '.valid.pt')
train_fields = load_fields(train, valid, checkpoint, train_opt)
print(time_now(), "finish data loading")
recovered = train_model(model_for_train, train, valid, train_fields,
optim, train_opt, run_times, acc_of_no_prune)
print(time_now(), "finish retraining ")
if not recovered:
exit()
else:
print("------------Accuracy recorverd!--------------------")
print("recovered accuracy:", acc_of_no_prune)
masked_model = MaskedModel(model_for_train.masked_model, group_dict,
cuda.current_device(),
cuda.current_device())
#-------------------------------------------------
print('------------- save checkpoint ---------------')
_, saved_model = update_checkpoint(checkpoint,
model_for_train,
run_times,
acc_percent_prune,
t=True)
print(time_now(), ' saving model:', saved_model)
print("-------------print evaluation info ---------------")
model_for_train.make_evaluable()
tmp_fit = evaluate(model_for_train, valid_data, fields)
print('acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
model_sparsity = model_for_train.get_sparsity()
print('Sparsity: {}'.format(model_sparsity))
print("----------------test model for masked_model------")
masked_model.make_evaluable()
tmp_fit = evaluate(masked_model, valid_data, fields)
print('acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_model.get_sparsity()
print('Sparsity: {}'.format(model_sparsity))
#--------------------------------------------------
print("BLEU evaluation:")
translate_opt, translate_dummy_opt = translate_opt_initialize(
'opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = model_for_train.masked_model
tt = open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
translate_data = onmt.IO.ONMTDataset(translate_opt.src,
translate_opt.tgt,
fields,
use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data,
device=GPU_ID,
batch_size=1,
train=False,
sort=False,
shuffle=False)
tmp_fit2 = evaluate_trans(translator, references, prune_data,
translate_data)
print('Finsished => bleu (%.4f), ppl (%.4f)' %
(tmp_fit2[1] * 100, tmp_fit2[0]))
#--------------------------------------------------
run_times += 1
from torch import cuda
import onmt
import onmt.io
import onmt.Models
import onmt.ModelConstructor
import onmt.modules
from onmt.Utils import use_gpu
import opts
import argparse
import glob
print torch.cuda.is_available()
print cuda.device_count()
print cuda.current_device()
parser = argparse.ArgumentParser(
description='train.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# opts.py
opts.add_md_help_argument(parser)
opts.model_opts(parser)
opts.train_opts(parser)
opt = parser.parse_args()
if opt.word_vec_size != -1:
opt.src_word_vec_size = opt.word_vec_size
opt.tgt_word_vec_size = opt.word_vec_size
import tensorflow as tf
hello = tf.constant("hello TensorFlow!")
sess=tf.Session()
sess.run(hello)
# find PyTorch packages
import pkg_resources
l = [d for d in pkg_resources.working_set if 'pytorch' in str(d)]
print(l)
# confirm PyTorch sees the GPU
from torch import cuda
import torch
print('PyTorch version', torch.__version__)
print('PyTorch cuda available', cuda.is_available())
print('PyTorch device count', cuda.device_count())
print('PyTorch device', cuda.get_device_name(cuda.current_device()))
# confirm Keras sees the GPU
from keras import backend
print('keras GPUs:', backend.tensorflow_backend._get_available_gpus())
import os
os.system('nvidia-smi')
os.system('nvcc --version')
import ray
ray.init(num_gpus=1)
print('ray GPU IDs', ray.get_gpu_ids())
def main():
total_times = 100
run_times = 0
init_threshold = ...
start_t = time.time()
valid_data = torch.load(TRAIN_DATA + '.valid.pt')
fields = onmt.IO.load_fields(torch.load(TRAIN_DATA + '.vocab.pt'))
# fields = onmt.IO.load_fields_from_vocab(torch.load(TRAIN_DATA + '.vocab.pt'))
valid_data.fields = fields # we need to clear this assignment relationg if we want to transfere valid among threads
checkpoint = torch.load(weights, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
masked_models = []
with cuda.device(GPU_ID):
ref_model = onmt.ModelConstructor.make_base_model(
model_opt, fields, True, checkpoint)
ref_model.eval()
ref_model.generator.eval()
masked_model = MaskedModel(
ref_model, group_dict, cuda.current_device(), cuda.current_device(
)) # ref_model is at current_device, no copy will happen
masked_models.append(masked_model)
if GPU_ID:
cuda.set_device(GPU_ID)
# 1 means 1% acc
acc_percent_prune = 1
# 只需要原始的accuracy
acc_of_no_prune = 0
get_acc_of_no_prune = False
print(time_now(), "start while")
while run_times < total_times:
print("-----------------------------------------")
print("start Iteration ", run_times)
# init threshold
best_threshold = 0
itr_time = time.time()
'''
display all the names of parameters
'''
'''
aa=ref_model.named_parameters
aa_namelist = [ak[0] for ak in aa]
'''
'''
test MP
'''
translate_opt, translate_dummy_opt = translate_opt_initialize(
'opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = masked_model
tt = open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
xxx = np.arange(0., 1, 0.01)
#for i in range(len(masked_model.group_name_list)):
# tmp_crate = len(masked_model.group_name_list)*[0.]
print(time_now(), "start testing pruning")
masked_model.make_evaluable()
for i in range(len(xxx)):
# best_threshold = 0.55
# break
translate_data = onmt.IO.ONMTDataset(translate_opt.src,
translate_opt.tgt,
fields,
use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data,
device=GPU_ID,
batch_size=1,
train=False,
sort=False,
shuffle=False)
tmp_crate = len(masked_model.group_name_list) * [xxx[i]]
#tmp_crate[i] = 0.01
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate(masked_model, valid_data, fields)
#tmp_fit = evaluate_trans(translator, references, prune_data, translate_data)
#logger.scalar_summary('test_bleu', tmp_fit[1]*100, int(xxx[i]*100))
#logger.scalar_summary('acc', tmp_fit[1], int(xxx[i]*100))
#logger.scalar_summary('ppl', tmp_fit[0], int(xxx[i]*100))
#logger.scalar_summary('test_ppl', tmp_fit[0], int(xxx[i]*100))
#print('group %s => acc (%.4f), ppl (%.4f)' % (masked_model.group_name_list[i], tmp_fit[1], tmp_fit[0]))
#print('percentage %s => bleu (%.4f), ppl (%.4f)' % (xxx[i]*100, tmp_fit[1]*100, tmp_fit[0]))
# print('percentage %s => acc (%.4f), ppl (%.4f)' % (xxx[i]*100, tmp_fit[1], tmp_fit[0]))
if i == 0 and not get_acc_of_no_prune:
acc_of_no_prune = tmp_fit[1]
acc_of_no_prune = int(acc_of_no_prune * 100) / 100
get_acc_of_no_prune = True
elif acc_of_no_prune - tmp_fit[1] > acc_percent_prune:
best_threshold = xxx[i] - 0.01
break
# -------------------------------------------------
# Start writing
# prune again
print(time_now(), " start accuracy:", acc_of_no_prune)
print("-------test------------:", get_acc_of_no_prune)
print(time_now(), " apply pruning with threshold:", best_threshold)
tmp_crate = len(masked_model.group_name_list) * [best_threshold]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
# print information
tmp_fit = evaluate(masked_model, valid_data, fields)
print('percentage %s => acc (%.4f), ppl (%.4f)' %
(best_threshold * 100, tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_model.get_sparsity()
print('Sparsity: {}'.format(model_sparsity))
#--------------- start retraining --------------
# first store model
print(time_now(), "start saving model")
_, saved_model = update_checkpoint(checkpoint, masked_model, run_times)
print(time_now(), "finish saving model")
print(time_now(), "start loading model")
checkpoint = torch.load(SAVE_MODEL_TMP_FOLDER + saved_model,
map_location=lambda storage, loc: storage)
train_opt, _, _ = opt_initialize(checkpoint,
'opennmt_translate_opt.pt',
'opennmt_translate_dummy_opt.pt')
# train data loading
print(time_now(), "start loading data for retraining")
train = torch.load(train_opt.data + '.train.pt')
valid = torch.load(train_opt.data + '.valid.pt')
print(time_now(), "finish data loading")
train_fields = load_fields(train, valid, checkpoint, train_opt)
model_for_train = init_train_model(checkpoint, train_opt, train_fields)
masked_model = MaskedModel(model_for_train, group_dict,
cuda.current_device(),
cuda.current_device())
masked_model.make_trainable()
print(time_now(), "building optm")
optim = build_optim(model_for_train, checkpoint, train_opt)
print(time_now(), "start restraining")
recovered = train_model(model_for_train, train, valid, train_fields,
optim, train_opt, run_times, acc_of_no_prune)
print(time_now(), "finish retraining ")
if not recovered:
exit()
else:
print("------------Accuracy recorverd!--------------------")
print("recovered accuracy:", acc_of_no_prune)
run_times += 1
masked_model.make_evaluable()
tmp_fit = evaluate(masked_model, valid_data, fields)
print("------------------for test-------------------")
print('percentage %s => acc (%.4f), ppl (%.4f)' %
(best_threshold * 100, tmp_fit[1], tmp_fit[0]))
def main():
data_path = "{}/data/penn".format(cfg.PROJECT_ROOT)
model_path = "{}/model/original_model/language_model/{}".format(
cfg.PROJECT_ROOT, 'lm_model_orignal.pt')
total_times = 20
run_times = 0
orginal_acc = 0
init_threshold = ...
start_t = time.time()
# get data
corpus = data.Corpus(data_path)
ntokens = len(corpus.dictionary)
eval_batch_size = TEST_BATCH_SIZE
train_data = batchify(corpus.train, TRAIN_BATCH_SIZE)
val_data = batchify(corpus.valid, TEST_BATCH_SIZE)
valid_data = val_data
test_data = batchify(corpus.test, TEST_BATCH_SIZE)
ref_model = None
# Load the best saved model.
with cuda.device(GPU_ID):
ff = open(model_path, 'rb')
ref_model = torch.load(ff)
ref_model.eval()
masked_model = MaskedModel(
ref_model, group_dict, cuda.current_device(), cuda.current_device(
)) # ref_model is at current_device, no copy will happen
#pdb.set_trace()
ff.close()
if GPU_ID:
cuda.set_device(GPU_ID)
print(time_now(), "get accuray of no pruning model")
masked_model.make_evaluable()
tmp_crate = len(masked_model.group_name_list) * [0]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate_lm(masked_model.masked_model, valid_data, corpus,
TEST_BATCH_SIZE)
# 只需要原始的accuracy
acc_of_no_prune = tmp_fit[1]
fit_of_no_prune = tmp_fit
original_acc = acc_of_no_prune
pruning_arr = []
ppl_arr = []
#acc_of_no_prune = int(acc_of_no_prune*10)/10
print("init accuracy of model:", acc_of_no_prune)
print("accuracy constraint:", acc_percent_prune)
init_threshold = [0]
while run_times < total_times:
print("-----------------------------------------")
print("-----------------------------------------")
print("-----------------------------------------")
print("start Iteration ", run_times)
print("test model---------------")
LR = LR_INIT
previous_pr = None
previous_fit = None
best_pr = None
best_fit = None
for prune_rate in range(1, 100):
tmp_crate = len(masked_model.group_name_list) * [0.01 * prune_rate]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate_lm(masked_model.masked_model, valid_data,
corpus, TEST_BATCH_SIZE)
print(
"each layer {} \% | {} % in total => validation acc {}\%, validation ppl {}"
.format(prune_rate,
masked_model.get_sparsity() * 100, tmp_fit[1] * 100.,
tmp_fit[0]))
if (not best_pr) and (tmp_fit[1] +
acc_percent_prune) < original_acc:
best_pr = previous_pr
best_fit = previous_fit
previous_pr = tmp_crate
previous_fit = tmp_fit
print('==============================')
print("The best pruning rates are: {}".format(best_pr))
if (not best_pr) or (best_pr[0] == init_threshold[0]):
print(
"Not better than last iteration of pruning, stop the process.")
exit()
masked_model.change_mask(best_pr, apply_MP_on_mask)
masked_model.apply_mask()
test_fit = evaluate_lm(masked_model.masked_model, test_data, corpus,
TEST_BATCH_SIZE)
print("{} \% => validation acc {}\%, validation ppl {}".format(
best_pr[0], best_fit[1] * 100., best_fit[0]))
print("{} \% => test acc {}\%, test ppl {}".format(
best_pr[0], test_fit[1] * 100., test_fit[0]))
print('==============================')
init_threshold = best_pr
saved_model_name = 'ncs_pruned_model_%s_iteration%s_%s_%s_acc_cons_%s.pt' % (
name_mark, run_times, Model_type, layer_group_type,
str(acc_percent_prune))
torch.save(masked_model.masked_model,
cfg.LM_MODEL_TMP_FOLDER + saved_model_name)
#--------------- start retraining --------------
model_for_train = masked_model
with open(cfg.LM_MODEL_TMP_FOLDER + saved_model_name, 'rb') as f:
model_tmp_load = torch.load(f)
model_for_train.masked_model = model_tmp_load
model_for_train.change_mask(init_threshold, apply_MP_on_mask)
model_for_train.apply_mask()
model_for_train.make_trainable()
recovered = False
best_val_loss = None
try:
for epoch in range(1, RETRAIN_EPOCHS + 1):
epoch_start_time = time.time()
train(model_for_train, ntokens, train_data, TRAIN_BATCH_SIZE,
SEQ_LEN, corpus, GRAD_CLIP, TRAIN_LOG_INTERVAL, epoch)
val_eval = evaluate_lm(model_for_train.masked_model, val_data,
corpus, TEST_BATCH_SIZE)
print('-' * 89)
print(
'| end of epoch {:3d} | time: {:5.2f}s | valid acc {:5.2f} | '
'valid ppl {:8.2f}'.format(
epoch, (time.time() - epoch_start_time), val_eval[1],
val_eval[0]))
val_loss = val_eval[2]
print('-' * 89)
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
with open(
"{}/{}{}_iterative_retrain_model_runtime{}_epoch_{}.pt"
.format(cfg.LM_MODEL_PATH, name_mark,
acc_percent_prune, run_times, epoch),
'wb') as f:
torch.save(model_for_train, f)
best_val_loss = val_loss
else:
# Anneal the learning rate if no improvement has been seen in the validation dataset.
LR /= 4.0
if val_eval[1] >= original_acc:
recovered = True
break
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
print(time_now(), "finish retraining ")
if not recovered:
exit()
else:
print("------------Accuracy recorverd!--------------------")
print("recovered accuracy (>= {})".format(acc_of_no_prune))
model_for_train.make_evaluable()
model_for_train.apply_mask()
ref_model = model_for_train.masked_model
print("validate acc of the model---------------")
tmp_fit = evaluate_lm(ref_model, valid_data, corpus, TEST_BATCH_SIZE)
print('ref_model', 'acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
print("-------------print TEST evaluation info ---------------")
tmp_fit = evaluate_lm(ref_model, test_data, corpus, TEST_BATCH_SIZE)
print('percentage %s => acc (%.4f), ppl (%.4f)' %
(init_threshold[0] * 100, tmp_fit[1], tmp_fit[0]))
masked_model = model_for_train
run_times += 1
def main():
total_times = 100
run_times = 0
init_threshold = ...
start_t = time.time()
valid_data = torch.load(TRAIN_DATA + '.valid.pt')
fields = onmt.IO.load_fields(torch.load(TRAIN_DATA + '.vocab.pt'))
# fields = onmt.IO.load_fields_from_vocab(torch.load(TRAIN_DATA + '.vocab.pt'))
valid_data.fields = fields # we need to clear this assignment relationg if we want to transfere valid among threads
checkpoint = torch.load(weights, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
masked_models = []
with cuda.device(GPU_ID):
ref_model = onmt.ModelConstructor.make_base_model(model_opt, fields, True, checkpoint)
ref_model.eval()
ref_model.generator.eval()
masked_model = MaskedModel(ref_model, group_dict, cuda.current_device(), cuda.current_device()) # ref_model is at current_device, no copy will happen
masked_models.append(masked_model)
train_opt, _, _ = opt_initialize(checkpoint, 'opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
if GPU_ID:
cuda.set_device(GPU_ID)
print("BLEU evaluation:")
translate_opt, translate_dummy_opt = translate_opt_initialize('opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = masked_model.masked_model
tt=open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
translate_data = onmt.IO.ONMTDataset(translate_opt.src, translate_opt.tgt,fields,use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data, device=GPU_ID,batch_size=1, train=False, sort=False,shuffle=False)
tmp_fit2 = evaluate_trans(translator, references, prune_data, translate_data)
print('Finsished => bleu (%.4f), ppl (%.4f)' % (tmp_fit2[1]*100, tmp_fit2[0]))
exit()
# print(time_now(), "get accuray of no pruning model")
# masked_model.make_evaluable()
# tmp_crate = len(masked_model.group_name_list)*[0]
# masked_model.change_mask(tmp_crate, apply_MP_on_mask)
# masked_model.apply_mask()
# tmp_fit = evaluate(masked_model, valid_data, fields)
# # 只需要原始的accuracy
# acc_of_no_prune = tmp_fit[1]
# acc_of_no_prune = int(acc_of_no_prune*10)/10
print("init accuracy of model:", acc_of_no_prune)
print("accuracy constraint:", acc_percent_prune)
while run_times < total_times:
print("-----------------------------------------")
print("-----------------------------------------")
print("-----------------------------------------")
print(time_now(), "start Iteration ", run_times)
print("test model---------------")
ref_model.eval()
ref_model.generator.eval()
tmp_fit = evaluate(ref_model, valid_data, fields)
print('ref_model','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
print("test model---------------")
masked_models[0].make_evaluable()
tmp_fit = evaluate(masked_models[0], valid_data, fields)
print('masked_models[0]','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_models[0].get_sparsity()
print('masked_models[0] Sparsity: {}'.format(model_sparsity))
itr_time = time.time()
for gpu_candidate in other_GPU_IDs:
with cuda.device(gpu_candidate):
masked_models.append(MaskedModel(ref_model, group_dict, GPU_ID, gpu_candidate)) # if the gpu_candidate is the same as ref_model, it will return the ref_model
#------------- Here -------------------------
# del ref_model
# do pruning
ncs_start = time.time()
print('Itration %d, model loading: %d sec' % (run_times, ncs_start - itr_time))
if run_times == 0:
if START_THRESHOLD is not None:
init_threshold = START_THRESHOLD
else:
init_threshold = len(masked_models[0].group_name_list)*[0.25]
# if run_times == 0:
# init_threshold = len(masked_models[0].group_name_list)*[0.25]
print("init threshold:", init_threshold)
prune_acc_now = acc_percent_prune+tmp_fit[1]-acc_of_no_prune
print('pruning acc now:', prune_acc_now)
best_found, saved_model, best_masked_model = NCS_MP(init_threshold, 0.05, fields, masked_models, valid_data, prune_acc_now, run_times, checkpoint)
init_threshold = best_found
#best_found, saved_model, best_masked_model = NCS_MP(init_threshold, 0.05, fields, masked_models, valid_data, 0.01, run_times, checkpoint)
end_t = time.time()
print('NCS Time: {} min'.format((end_t - itr_time)/60.))
print('Best found thresholds:')
for i in range(len(masked_models[0].group_name_list)):
print("layer {}: {}%".format(masked_models[0].group_name_list[i], 100*best_found[i]))
print("BLEU evaluation:")
translate_opt, translate_dummy_opt = translate_opt_initialize('opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = best_masked_model
tt=open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
translate_data = onmt.IO.ONMTDataset(translate_opt.src, translate_opt.tgt,fields,use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data, device=GPU_ID,batch_size=1, train=False, sort=False,shuffle=False)
tmp_fit = evaluate_trans(translator, references, prune_data, translate_data)
print('Finsished => bleu (%.4f), ppl (%.4f)' % (tmp_fit[1]*100, tmp_fit[0]))
# clear no used models
for gpu_model in masked_models:
del gpu_model
#--------------- start retraining --------------
model_for_train = best_masked_model
pretrained_leaf_dict = model_for_train.make_trainable()
optim = build_optim(model_for_train.masked_model, checkpoint, train_opt, pretrained_leaf_dict)
print(time_now(), "start loading data for retraining")
train = torch.load(train_opt.data+ '.train.pt')
valid = torch.load(train_opt.data + '.valid.pt')
train_fields = load_fields(train, valid, checkpoint, train_opt)
print(time_now(), "finish data loading")
model_for_train.change_mask(init_threshold, apply_MP_on_mask)
model_for_train.apply_mask()
model_for_train.make_trainable()
recovered = train_model(model_for_train, train, valid, train_fields, optim, train_opt, run_times, acc_of_no_prune)
print(time_now(), "finish retraining ")
if not recovered:
exit()
else:
print("------------Accuracy recorverd!--------------------")
print("recovered accuracy:", acc_of_no_prune)
model_for_train.make_evaluable()
ref_model = model_for_train.masked_model
masked_models = [MaskedModel(ref_model, group_dict, cuda.current_device(), cuda.current_device())]
print("test model---------------")
tmp_fit = evaluate(ref_model, valid_data, fields)
print('ref_model','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
print("test model---------------")
tmp_fit = evaluate(masked_models[0], valid_data, fields)
print('masked_models[0]','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
model_sparsity = masked_models[0].get_sparsity()
print('masked_models[0] Sparsity: {}'.format(model_sparsity))
print('------------- save checkpoint ---------------')
_, saved_model = update_checkpoint(checkpoint, model_for_train, run_times, acc_percent_prune, t=True)
print(time_now(), ' saving model:', saved_model)
print("-------------print evaluation info ---------------")
tmp_fit = evaluate(model_for_train, valid_data, fields)
print('percentage %s => acc (%.4f), ppl (%.4f)' % (best_found*100, tmp_fit[1], tmp_fit[0]))
#--------------------------------------------------
print("BLEU evaluation:")
translate_opt, translate_dummy_opt = translate_opt_initialize('opennmt_translate_opt.pt', 'opennmt_translate_dummy_opt.pt')
translator = init_translate_model(translate_opt, translate_dummy_opt)
del translator.model
translator.model = model_for_train.masked_model
tt=open(translate_opt.tgt, 'r')
references = [[t] for t in tt]
translate_data = onmt.IO.ONMTDataset(translate_opt.src, translate_opt.tgt,fields,use_filter_pred=False)
prune_data = onmt.IO.OrderedIterator(dataset=translate_data, device=GPU_ID,batch_size=1, train=False, sort=False,shuffle=False)
tmp_fit2 = evaluate_trans(translator, references, prune_data, translate_data)
print('Finsished => bleu (%.4f), ppl (%.4f)' % (tmp_fit2[1]*100, tmp_fit2[0]))
#--------------------------------------------------
run_times += 1
def main():
data_path = "{}/data/penn".format(DATA_PATH)
model_path = "{}/deepModels/torch_models/language-model/{}".format(MODEL_PATH, 'model.pt')
#model_path = "{}/deepModels/torch_models/language-model/{}".format(MODEL_PATH, 'lstm_3layer.pt')
total_times = 1
run_times = 0
orginal_acc = 0
init_threshold = ...
start_t = time.time()
# get data
corpus = data.Corpus(data_path)
ntokens = len(corpus.dictionary)
eval_batch_size = TEST_BATCH_SIZE
train_data = batchify(corpus.train, TRAIN_BATCH_SIZE)
val_data = batchify(corpus.valid, TEST_BATCH_SIZE)
valid_data = val_data
test_data = batchify(corpus.test, TEST_BATCH_SIZE)
ref_model = None
# Load the best saved model.
masked_models = []
with cuda.device(GPU_ID):
ff = open(model_path, 'rb')
ref_model = torch.load(ff)
ref_model.eval()
masked_model = MaskedModel(ref_model, group_dict, cuda.current_device(), cuda.current_device()) # ref_model is at current_device, no copy will happen
#pdb.set_trace()
masked_models.append(masked_model)
ff.close()
if GPU_ID:
cuda.set_device(GPU_ID)
print(time_now(), "get accuray of no pruning model")
masked_model.make_evaluable()
tmp_crate = len(masked_model.group_name_list)*[0]
masked_model.change_mask(tmp_crate, apply_MP_on_mask)
masked_model.apply_mask()
tmp_fit = evaluate_lm(masked_model.masked_model, valid_data, corpus, TEST_BATCH_SIZE)
# 只需要原始的accuracy
acc_of_no_prune = tmp_fit[1]
fit_of_no_prune = tmp_fit
original_acc = acc_of_no_prune
#acc_of_no_prune = int(acc_of_no_prune*10)/10
print("=============TiPO start========================")
print("init accuracy of model:", acc_of_no_prune)
print("accuracy constraint:", acc_percent_prune)
previous_pr = None
best_pr = None
ncs_std = 0.05
while run_times < total_times:
print("-----------------------------------------")
print("-----------------------------------------")
print("-----------------------------------------")
print("start Iteration ", run_times)
print("test model---------------")
LR = LR_INIT
#ref_model.generator.eval()
print("test model---------------")
masked_models[0].make_evaluable()
tmp_fit = evaluate_lm(masked_models[0].masked_model, valid_data, corpus, TEST_BATCH_SIZE)
print('masked_models[0]','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
if run_times == 0:
init_threshold = len(masked_models[0].group_name_list) * [0.6]
itr_time = time.time()
for gpu_candidate in other_GPU_IDs:
with cuda.device(gpu_candidate):
masked_models.append(MaskedModel(ref_model, group_dict, GPU_ID, gpu_candidate)) # if the gpu_candidate is the same as ref_model, it will return the ref_model
#------------- Here -------------------------
# del ref_model
# do pruning
ncs_start = time.time()
print('Itration %d, model loading: %d sec' % (run_times, ncs_start - itr_time))
print("init threshold:", init_threshold)
best_found, saved_model, best_masked_model = NCS_MP(init_threshold, ncs_std, masked_models, valid_data, corpus, acc_percent_prune, fit_of_no_prune, run_times)
#best_found, saved_model, best_masked_model = init_threshold, '/raid/lab_tk/liguiying/deepModels/torch_models/language-model/prune_tmp/ncs_pruned_model_test_iteration0_LM_time_acc_cons_0.01.pt', masked_models[0]
init_threshold = best_found
#best_found, saved_model, best_masked_model = NCS_MP(init_threshold, 0.05, fields, masked_models, valid_data, 0.01, run_times, checkpoint)
end_t = time.time()
print('NCS Time: {} min'.format((end_t - itr_time)/60.))
print('Best found thresholds:')
for i in range(len(masked_models[0].group_name_list)):
print("layer {}: {}%".format(masked_models[0].group_name_list[i], 100*best_found[i]))
print("TEST PPL evaluation:")
tmp_fit = evaluate_lm(best_masked_model.masked_model, test_data, corpus, TEST_BATCH_SIZE)
print('Finsished => acc (%.4f percent), ppl (%.4f)' % (tmp_fit[1]*100, tmp_fit[0]))
# clear no used models
for gpu_model in masked_models:
del gpu_model
if not best_pr :
best_pr = best_masked_model.get_sparsity()
else:
tmp_pr = best_masked_model.get_sparsity()
if best_pr > tmp_pr:
print("No improvement! Stop the PROCESS.")
exit()
elif best_pr == tmp_pr:
if tmp_fit[1] <fit_of_no_prune[1]:
ncs_std /= 10
else:
ncs_std *= 10
else:
best_pr = tmp_pr
#if run_times % 5 == 0:
# ncs_std /= 10
#--------------- start retraining --------------
model_for_train = best_masked_model
#pretrained_leaf_dict = model_for_train.make_trainable()
#print(model_for_train.map_dict.keys())
#pdb.set_trace()
#fix_no_leaf(model_for_train, pretrained_leaf_dict)
#pdb.set_trace()
with open(SAVE_MODEL_TMP_FOLDER + saved_model, 'rb') as f:
model_tmp_load = torch.load(f)
model_for_train.masked_model = model_tmp_load.masked_model
model_for_train.change_mask(init_threshold, apply_MP_on_mask)
model_for_train.apply_mask()
model_for_train.make_trainable()
recovered = False
best_val_loss = None
try:
for epoch in range(1, RETRAIN_EPOCHS + 1):
epoch_start_time = time.time()
train(model_for_train, ntokens, train_data, TRAIN_BATCH_SIZE, SEQ_LEN, corpus, GRAD_CLIP, TRAIN_LOG_INTERVAL, epoch)
val_eval = evaluate_lm(model_for_train.masked_model, val_data, corpus, TEST_BATCH_SIZE)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid acc {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_eval[1], val_eval[0]))
val_loss = val_eval[2]
print('-' * 89)
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
with open("{}/{}{}_iterative_retrain_model_runtime{}_epoch_{}.pt".format(SAVE_MODEL_FOLDER, name_mark, acc_percent_prune, run_times, epoch), 'wb') as f:
torch.save(model_for_train, f)
best_val_loss = val_loss
else:
# Anneal the learning rate if no improvement has been seen in the validation dataset.
LR /= 4.0
if val_eval[1] >= original_acc:
recovered = True
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
print(time_now(), "finish retraining ")
if not recovered:
print("NOT RECORVER!")
exit()
else:
print("------------Accuracy recorverd!--------------------")
print("recovered accuracy (>= {})".format(acc_of_no_prune))
model_for_train.make_evaluable()
model_for_train.apply_mask()
ref_model = model_for_train.masked_model
masked_models = [MaskedModel(ref_model, group_dict, cuda.current_device(), cuda.current_device())]
print("validate acc of the model---------------")
tmp_fit = evaluate_lm(ref_model, valid_data, corpus, TEST_BATCH_SIZE)
print('ref_model','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
tmp_fit = evaluate_lm(masked_models[0].masked_model, valid_data, corpus, TEST_BATCH_SIZE)
print('masked_models[0]','acc (%.4f), ppl (%.4f)' % (tmp_fit[1], tmp_fit[0]))
print('------------- save checkpoint ---------------')
saved_model = update_checkpoint(model_for_train, run_times, acc_percent_prune, t=True)
print(time_now(), ' saving model:', saved_model)
print("-------------print TEST evaluation info ---------------")
tmp_fit = evaluate_lm(model_for_train.masked_model, test_data, corpus, TEST_BATCH_SIZE)
print('percentage %s => acc (%.4f), ppl (%.4f)' % (model_for_train.get_sparsity()*100, tmp_fit[1], tmp_fit[0]))
run_times += 1
def get_memory_use():
device = cuda.current_device()
message = cuda.get_device_name(device) + ':\n'
message += 'allocated:' + str(cuda.memory_allocated(device)) + '/' + str(cuda.max_memory_allocated()) + '\n'
message += 'cached:' + str(cuda.memory_cached(device)) + '/' + str(cuda.max_memory_cached()) + '\n'
return message
import tensorflow as tf
#%% Check that gpu is available
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())
# confirm Keras sees the GPU
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0
# confirm PyTorch sees the GPU
from torch import cuda
assert cuda.is_available()
assert cuda.device_count() > 0
print(cuda.get_device_name(cuda.current_device()))
#%%
cb = [ModelCheckpoint("model.hdf5", save_best_only=True, period=3)]
model = Sequential()
model.add(CuDNNGRU(48, input_shape=(None, n_features)))
model.add(Dense(10, activation='relu'))
model.add(Dense(1))
model.summary()
#%%
# Compile and fit model
