def reduce_dict_hvd(input_dict, average=True):
"""
Args:
input_dict (dict): all the values will be reduced
average (bool): whether to do average or sum
Reduce the values in the dictionary from all processes so that all processes
have the averaged results. Returns a dict with the same fields as
input_dict, after reduction.
"""
global _USE_HVD
world_size = get_world_size()
if world_size < 2:
return input_dict
with torch.no_grad():
names = []
values = []
# sort the keys so that they are consistent across processes
for k in sorted(input_dict.keys()):
names.append(k)
values.append(input_dict[k])
values = torch.stack(values, dim=0)
if _USE_HVD: # TODO: check this in hvd
hvd.allreduce_(values,
op=hvd.Average if average else hvd.Adasum,
name="reduce_dict")
else:
dist.all_reduce(values)
if average:
values /= world_size
reduced_dict = {k: v for k, v in zip(names, values)}
return reduced_dict
def test_horovod_allreduce_inplace(self):
"""Test that the allreduce correctly sums 1D, 2D, 3D tensors."""
hvd.init()
size = hvd.size()
dtypes = [torch.IntTensor, torch.LongTensor,
torch.FloatTensor, torch.DoubleTensor]
if torch.cuda.is_available():
dtypes += [torch.cuda.IntTensor, torch.cuda.LongTensor,
torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
torch.manual_seed(1234)
tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
tensor = tensor.type(dtype)
multiplied = tensor * size
hvd.allreduce_(tensor, average=False)
max_difference = tensor.sub(multiplied).max()
# Threshold for floating point equality depends on number of
# ranks, since we're comparing against precise multiplication.
if size <= 3 or dtype in [torch.IntTensor, torch.LongTensor,
torch.cuda.IntTensor, torch.cuda.LongTensor]:
threshold = 0
elif size < 10:
threshold = 1e-4
elif size < 15:
threshold = 5e-4
else:
break
assert max_difference <= threshold, 'hvd.allreduce produces incorrect results'
def all_reduce_and_rescale_tensors(tensors, rescale_denom):
"""All-reduce and rescale tensors at once (as a flattened tensor)
Args:
tensors: list of Tensors to all-reduce
rescale_denom: denominator for rescaling summed Tensors
"""
# buffer size in bytes, determine equiv. # of elements based on data type
sz = sum(t.numel() for t in tensors)
buffer_t = tensors[0].new(sz).zero_()
# copy tensors into buffer_t
offset = 0
for t in tensors:
numel = t.numel()
buffer_t[offset:offset + numel].copy_(t.view(-1))
offset += numel
# all-reduce and rescale
hvd.allreduce_(buffer_t[:offset])
buffer_t.div_(rescale_denom)
# copy all-reduced buffer back into tensors
offset = 0
for t in tensors:
numel = t.numel()
t.view(-1).copy_(buffer_t[offset:offset + numel])
offset += numel
def test_horovod_allreduce_inplace(self):
"""Test that the allreduce correctly sums 1D, 2D, 3D tensors."""
hvd.init()
size = hvd.size()
dtypes = [torch.IntTensor, torch.LongTensor,
torch.FloatTensor, torch.DoubleTensor]
if torch.cuda.is_available():
dtypes += [torch.cuda.IntTensor, torch.cuda.LongTensor,
torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
torch.manual_seed(1234)
tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
tensor = tensor.type(dtype)
multiplied = tensor * size
hvd.allreduce_(tensor, average=False)
max_difference = tensor.sub(multiplied).max()
# Threshold for floating point equality depends on number of
# ranks, since we're comparing against precise multiplication.
if size <= 3 or dtype in [torch.IntTensor, torch.LongTensor,
torch.cuda.IntTensor, torch.cuda.LongTensor]:
threshold = 0
elif size < 10:
threshold = 1e-4
elif size < 15:
threshold = 5e-4
else:
break
assert max_difference <= threshold, 'hvd.allreduce produces incorrect results'
def test_stability(self):
hvd.init()
# TODO support non-MPI Adasum operation
if not hvd.mpi_enabled():
self.skipTest("MPI not enabled")
device = torch.device('cuda:{}'.format(hvd.local_rank())) if torch.cuda.is_available() else torch.device('cpu')
np.random.seed(2)
torch.manual_seed(2)
size = hvd.size()
local_size = hvd.local_size()
rank = hvd.rank()
for data_type in self.data_types:
N = 1024
a = np.random.normal(0, np.finfo(data_type).tiny, (N, 1)).astype(np.float64)
r = np.random.normal(0, 1, (size, 1)).astype(np.float64)
q = np.dot(a,r.T).astype(data_type).astype(np.float64)
tensor = np.zeros(N,dtype=data_type)
tensor[:] = q[:,hvd.rank()]
tensor = torch.from_numpy(tensor).to(device)
hvd.allreduce_(tensor, op=hvd.Adasum)
expected = np.sum(q,axis=1) / size
comp = self.are_close(data_type, expected, tensor.cpu().numpy())
if comp:
print('Stability test passed')
else:
print('computed: ', tensor)
print('expected: ', expected)
print('off by: ', self.diff_ratio(expected,tensor.cpu().numpy()))
assert comp
def all_reduce_and_rescale_tensors_chunked(tensors,
rescale_denom,
buffer_size=10485760):
"""All-reduce and rescale tensors in chunks of the specified size.
Args:
tensors: list of Tensors to all-reduce
rescale_denom: denominator for rescaling summed Tensors
buffer_size: all-reduce chunk size in bytes
"""
# buffer size in bytes, determine equiv. # of elements based on data type
buffer_t = tensors[0].new(
math.ceil(buffer_size / tensors[0].element_size())).zero_()
buffer = []
def all_reduce_buffer():
# copy tensors into buffer_t
offset = 0
for t in buffer:
numel = t.numel()
buffer_t[offset:offset + numel].copy_(t.view(-1))
offset += numel
# all-reduce and rescale
hvd.allreduce_(buffer_t[:offset])
buffer_t.div_(rescale_denom)
# copy all-reduced buffer back into tensors
offset = 0
for t in buffer:
numel = t.numel()
t.view(-1).copy_(buffer_t[offset:offset + numel])
offset += numel
filled = 0
for t in tensors:
sz = t.numel() * t.element_size()
if sz > buffer_size:
# tensor is bigger than buffer, all-reduce and rescale directly
hvd.allreduce_(t)
t.div_(rescale_denom)
elif filled + sz > buffer_size:
# buffer is full, all-reduce and replace buffer with grad
all_reduce_buffer()
buffer = [t]
filled = sz
else:
# add tensor to buffer
buffer.append(t)
filled += sz
if len(buffer) > 0:
all_reduce_buffer()
def clip_grad_norm_2_by_global_(grad, max_norm, name=None):
max_norm = float(max_norm)
grad_square_sum = torch.sum(grad.square())
total_norm = torch.sqrt(allreduce_(grad_square_sum, average=True, name=name))
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1:
grad.data.mul_(clip_coef)
return grad
def all_reduce_buffer():
# copy tensors into buffer_t
offset = 0
for t in buffer:
numel = t.numel()
buffer_t[offset:offset + numel].copy_(t.view(-1))
offset += numel
# all-reduce and rescale
hvd.allreduce_(buffer_t[:offset])
buffer_t.div_(rescale_denom)
# copy all-reduced buffer back into tensors
offset = 0
for t in buffer:
numel = t.numel()
t.view(-1).copy_(buffer_t[offset:offset + numel])
offset += numel
def test_stability_2(self):
hvd.init()
# TODO support non-MPI Adasum operation
if not hvd.mpi_enabled():
return
device = torch.device('cuda:{}'.format(hvd.local_rank(
))) if torch.cuda.is_available() else torch.device('cpu')
np.random.seed(2)
torch.manual_seed(2)
size = hvd.size()
local_size = hvd.local_size()
rank = hvd.rank()
for data_type in self.data_types:
N = 1024
dt_min = np.finfo(data_type).tiny.astype(np.float64)
dt_max = math.sqrt(np.finfo(data_type).max.astype(np.float64))
a = np.random.normal(0, 1, (N, 1)).astype(np.float64)
r = np.array([
dt_max**(float(i + 1) / float(size)) *
dt_min**(float(size - i - 1) / float(size))
for i in range(size)
]).reshape(size, 1).astype(np.float64)
np.random.shuffle(r)
q = np.dot(a, r.T).astype(data_type).astype(np.float64)
tensor = np.zeros(N, dtype=data_type)
tensor[:] = q[:, hvd.rank()]
tensor = torch.from_numpy(tensor).to(device)
hvd.allreduce_(tensor, op=hvd.Adasum)
expected = np.sum(q, axis=1) / size
comp = self.are_close(data_type, expected, tensor.cpu().numpy())
if comp:
print('Stability 2 test passed')
else:
print('computed: ', tensor)
print('expected: ', expected)
print('off by: ',
self.diff_ratio(expected,
tensor.cpu().numpy()))
assert comp
def compress(self, tensor, name=""):
if tensor.dim() == 1:
return [tensor], None
shape = tensor.size()
matrix = tensor.view([shape[0], -1])
q = self.q_memory[name]
# q, _ = torch.qr(q)
orthogonalize(q)
p = torch.mm(matrix, q)
p = allreduce_(p)
# p, _ = torch.qr(p)
orthogonalize(p)
q = torch.mm(matrix.t(), p)
q = allreduce_(q)
ctx = p, q, shape
self.q_memory[name] = q
return [], ctx
def test_horovod_allreduce_multi_gpu(self):
"""Test that the allreduce works on multiple GPUs."""
# Only do this test if there are GPUs available.
if not torch.cuda.is_available():
return
hvd.init()
local_rank = hvd.local_rank()
size = hvd.size()
iter = 0
dtypes = [
torch.cuda.IntTensor, torch.cuda.LongTensor,
torch.cuda.FloatTensor, torch.cuda.DoubleTensor
]
if _fp16_supported:
dtypes += [torch.cuda.HalfTensor]
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
iter += 1
torch.manual_seed(1234)
tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
device = local_rank * 2 + (iter + local_rank) % 2
tensor = tensor.cuda(device).type(dtype)
multiplied = tensor * size
hvd.allreduce_(tensor, average=False)
max_difference = tensor.sub(multiplied).max()
# Threshold for floating point equality depends on number of
# ranks, since we're comparing against precise multiplication.
if size <= 3 or dtype in [
torch.cuda.IntTensor, torch.cuda.LongTensor
]:
threshold = 0
elif size < 10:
threshold = 1e-4
elif size < 15:
threshold = 5e-4
else:
break
assert max_difference <= threshold, 'hvd.allreduce produces incorrect results'
def test_horovod_allreduce_multi_gpu(self):
"""Test that the allreduce works on multiple GPUs."""
# Only do this test if there are GPUs available.
if not torch.cuda.is_available():
return
hvd.init()
local_rank = hvd.local_rank()
size = hvd.size()
iter = 0
dtypes = [torch.cuda.IntTensor, torch.cuda.LongTensor,
torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
iter += 1
torch.manual_seed(1234)
tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
device = local_rank * 2 + (iter + local_rank) % 2
tensor = tensor.cuda(device).type(dtype)
multiplied = tensor * size
hvd.allreduce_(tensor, average=False)
max_difference = tensor.sub(multiplied).max()
# Threshold for floating point equality depends on number of
# ranks, since we're comparing against precise multiplication.
if size <= 3 or dtype in [torch.cuda.IntTensor, torch.cuda.LongTensor]:
threshold = 0
elif size < 10:
threshold = 1e-4
elif size < 15:
threshold = 5e-4
else:
break
assert max_difference <= threshold, 'hvd.allreduce produces incorrect results'
def compensate(self, tensor, name):
"""Update the tensor with the residuals."""
# https://github.com/synxlin/deep-gradient-compression/blob/master/dgc/memory.py
grad = self.get_grad(name)
if self.gradient_clipping:
tensor_squ_sum = torch.sum(grad * grad)
clipping_val = torch.sqrt(allreduce_(tensor_squ_sum, average=True, name=name))
grad = grad.clamp(-clipping_val, clipping_val)
mmt = self.get_momentum(name)
vec = self.get_velocity(name)
if self.momentum_masking:
mmt.mul_(self.momentum).add_(grad)
vec.add_(mmt)
else:
vec.mul_(self.momentum).add_(grad)
def compensate(self, tensor, name):
"""Update the tensor with the residuals."""
if self.gradient_clipping:
tensor_squ_sum = torch.sum(tensor * tensor)
clipping_val = torch.sqrt(
allreduce_(tensor_squ_sum, average=True, name=name))
tensor = tensor.clamp(-clipping_val, clipping_val)
if name in self.residuals:
self.residuals[
name] = self.momentum * self.residuals[name] + tensor
else:
self.residuals[name] = tensor
if name in self.gradients:
self.gradients[name] += self.residuals[name]
tensor = self.gradients[name]
else:
self.gradients[name] = tensor
return tensor
def array_reduce_(arr: Array, average: bool = True) -> None:
t = torch.from_numpy(arr)
hvd.allreduce_(t, average=average)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
num_parameters = sum([np.prod(p.size()) for p in model_parameters])
print(f'Number of trainable parameters in model: {num_parameters}')
model.logger.add_text(f'hyperparams', '{num_parameters}', 0)
if root_process:
print("Load data")
# get dataset for training and testing of the model
if root_process:
train_set = datasets.MNIST(root="data/mnist", train=True, transform=transform_ops, download=True)
test_set = datasets.MNIST(root="data/mnist", train=False, transform=transform_ops, download=True)
# if distributed over multiple GPU's, set-up barrier a barrier ensuring that all the processes have loaded the data
if distributed:
hvd.allreduce_(torch.Tensor(0), name='barrier')
# get dataset for training and testing of the model
if not root_process:
train_set = datasets.MNIST(root="data/mnist", train=True, transform=transform_ops, download=True)
test_set = datasets.MNIST(root="data/mnist", train=True, transform=transform_ops, download=True)
# setup data sampler
if distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, num_replicas=hvd.size(), rank=hvd.rank())
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_set, num_replicas=hvd.size(), rank=hvd.rank())
# setup mini-batch enumerator for both train-set and test-set
train_loader = torch.utils.data.DataLoader(
model.train()
output = model(data)
loss = criterion(output, target)
large_batch_loss += loss.item()
loss.backward()
if inner_loop % large_ratio == 0:
num_updates += 1
optimizer.step()
optimizer.zero_grad()
if num_updates % args.comm_interval == args.comm_interval - 1:
allreduce_parameters(model.state_dict())
if batch_idx * large_ratio % 25 == 0:
print('Train Epoch: {} [{}/{}]\tLoss: {}'.format(epoch, batch_idx * len(data), len(train_sampler), large_batch_loss))
cur_batch_loss = torch.FloatTensor([loss.item()])
hvd.allreduce_(cur_batch_loss)
cur_batch_loss = float(cur_batch_loss)
train_losses.append((time.clock() - start_time, epoch, batch_idx, cur_batch_loss))
large_batch_loss = 0
if batch_idx % 100 == 0:
model.eval()
try:
inputs, labels = next(testset_iterator)
except StopIteration:
testset_iterator = iter(test_loader)
inputs, labels = next(testset_iterator)
outputs = model(inputs)
loss = criterion(outputs, labels)
accuracy = outputs.data.max(1)[1].eq(labels).sum().item() / outputs.data.shape[0]
loss = metric_average(loss, 'avg_loss')
def forward(data_loader,
model,
criterion,
epoch=0,
training=True,
optimizer=None,
U=None,
V=None):
# hvd
# if args.gpus and len(args.gpus) > 1:
# model = torch.nn.DataParallel(model, args.gpus)
batch_time = AverageMeter()
pruning_time = AverageMeter()
select_time = AverageMeter()
comm_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
masks = [torch.zeros(w.size()).cuda() for w in list(model.parameters())]
for i, (inputs, target) in enumerate(data_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpus is not None:
target = target.cuda(async=True)
input_var = Variable(inputs.type(args.type), volatile=not training)
target_var = Variable(target)
# compute output
if not training:
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input_var.size(0))
top1.update(prec1[0], input_var.size(0))
top5.update(prec5[0], input_var.size(0))
else:
# mini_inputs = input_var.chunk(args.batch_size // args.mini_batch_size)
# mini_targets = target_var.chunk(args.batch_size // args.mini_batch_size)
#TODO for debug shoul be delete
optimizer.zero_grad()
# fjr simulate distributed senario
# acc_grad = []
# if torch.cuda.is_available():
# acc_grad = [torch.zeros(w.size()).cuda() for w in list(model.parameters())]
# else:
# print("gpu is not avaiable for acc_grad allocation")
# for k, mini_input_var in enumerate(mini_inputs):
output = model(input_var)
loss = criterion(output, target_var)
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input_var.size(0))
top1.update(prec1[0], input_var.size(0))
top5.update(prec5[0], input_var.size(0))
loss.backward()
if args.use_pruning:
clip_grad_norm(model.parameters(), 5. * (hvd.size()**-0.5))
idx = 0
for u, v, p in zip(U, V, model.parameters()):
prune_begin = time.time()
if args.use_pruning:
# TODO how to set rho (momentum)
g = p.grad.data / hvd.size()
g += p.data * args.weight_decay / hvd.size()
if args.use_nesterov:
u = args.momentum * (u + g)
v = v + u + g
else:
u = args.momentum * u + g
v = v + u
select_begin = time.time()
ratio = 1 - 0.999
if args.use_sync and i % args.sync_interval == 0:
masks[idx] = 1
else:
if args.use_warmup:
# print("iter", i, "node ", k, " pruning layer ", idx)
if (epoch == 0):
ratio = 1 - 0.75
elif (epoch == 1):
ratio = 1 - 0.9375
elif (epoch == 2):
ratio = 1 - 0.984375
elif (epoch == 3):
ratio = 1 - 0.996
else:
ratio = 1 - 0.999
else:
ratio = 1 - 0.999
#masks[idx], compressed_val, compressed_idx = select_top_k(v, ratio, masks[idx])
masks[
idx], compressed_val, compressed_idx = select_top_k_appr(
v, ratio, masks[idx])
select_time.update(time.time() - select_begin)
# TODO check compress
p_tmp = v * masks[idx]
g_ref = hvd.allreduce(p_tmp, average=False)
v = v * (1 - masks[idx])
u = u * (1 - masks[idx])
comm_begin = time.time()
g_size = p.grad.data.size()
msg_size = len(compressed_val)
# print("compressed_val size is, ", msg_size)
gathered_val = hvd.allgather(compressed_val)
gathered_idx = hvd.allgather(compressed_idx)
p.grad.data = p.grad.data.view(-1)
p.grad.data.zero_()
# print("gathered_val size is, ", len(gathered_val))
# print("val", gathered_val)
# print("idx", gathered_idx)
for node_idx in range(hvd.size()):
p.grad.data[gathered_idx[node_idx *
msg_size:(node_idx + 1) *
msg_size]] += gathered_val[
node_idx *
msg_size:(node_idx + 1) *
msg_size]
p.grad.data = p.grad.data.view(g_size)
comm_time.update(time.time() - comm_begin)
U[idx] = u #new_residue
V[idx] = v
else:
p.grad.data = p.grad.data / hvd.size()
hvd.allreduce_(p.grad.data, average=False)
idx += 1
pruning_time.update(time.time() - prune_begin)
# Master
idx = 0
if args.use_pruning:
pass
else:
for p in list(model.parameters()):
# print("accumulated sparsity is", check_sparsity(g))
# TODO 1. use pytorch sgd optimizer to calculate mom and weight_decay, set mom and wd
# used with pruning
# TODO 2. implement weight_decay and momentum by myself, set mom=0 and wd = 0
# used with baseline
g = p.grad.data
g += p.data * args.weight_decay
V[idx] = args.momentum * V[idx] + g
p.grad.data = V[k][idx]
clip_grad_norm(model.parameters(), 5.)
idx = idx + 1
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
if hvd.local_rank() == 0:
logging.info(
'{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Prune {pruning_time.val:.9f} ({pruning_time.avg:.3f})\t'
'Select {select_time.val:.9f} ({select_time.avg:.3f})\t'
'Communication {comm_time.val:.9f} ({comm_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(data_loader),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time,
pruning_time=pruning_time,
select_time=select_time,
comm_time=comm_time,
loss=losses,
top1=top1,
top5=top5))
return {
'loss': losses.avg,
'prec1': top1.avg,
'prec5': top5.avg,
'U': U,
'V': V
}
def clip_grad_value_by_global_norm_(grad, name=None):
grad_square_sum = torch.sum(grad.square())
clip_value = torch.sqrt(allreduce_(grad_square_sum, average=True, name=name))
grad.data.clamp_(min=-clip_value, max=clip_value)
def batch_translate(self, input_path, output_path, field=0, remove_subword_tokens=True, max_length=100, resume=False):
"""Translate a file."""
# Check whether using multiple GPUs
try:
import horovod.torch as hvd
except ImportError:
pass
# If using multigpu, then separate the input file
if self._is_multigpu:
sync_tensor = torch.tensor(0)
tmp_output_path = "/tmp/{}.{}".format(os.path.basename(output_path), hvd.local_rank())
else:
sync_tensor = None
tmp_output_path = output_path
result_map = {}
if self._is_multigpu and resume and os.path.exists(tmp_output_path):
for line in open(tmp_output_path):
pair = line.strip("\n").split("\t")
if len(pair) != 2:
print(line)
id, line = pair
result_map[int(id)] = line
print("loaded {} computed results".format(len(result_map)))
fout = open(tmp_output_path, "w")
test_lines = list(open(input_path))
err = 0
for i, line in enumerate(test_lines):
# Gather error counts in multigpu mode
if self._is_multigpu:
if i % (10 * hvd.size()) == 0:
sync_tensor.fill_(err)
hvd.allreduce_(sync_tensor, average=False)
if i % hvd.size() != hvd.local_rank():
continue
# Translate
pair = line.strip().split("\t")
src_sent = pair[field]
if len(src_sent.split()) > max_length:
result = "x"
else:
if i in result_map:
result = result_map[i]
else:
result, _ = self.translate("<s> {} </s>".format(src_sent))
if result is None:
result = ""
if remove_subword_tokens:
if "▁" in result:
result = "".join(result.split()).replace("▁", " ").strip()
else:
result = result.replace("@@ ", "")
if not result:
err += 1
# Write the results and print progress
if self._is_multigpu:
fout.write("{}\t{}\n".format(i, result))
else:
fout.write("{}\n".format(result))
fout.flush()
if self._is_multigpu and hvd.local_rank() == 0:
sys.stdout.write("translating: {:.0f}% err: {} \r".format(float(i + 1) * 100 / len(test_lines),
int(sync_tensor)))
elif not self._is_multigpu:
sys.stdout.write("translating: {:.0f}% err: {} \r".format(float(i + 1) * 100 / len(test_lines), err))
sys.stdout.flush()
if is_root_node():
sys.stdout.write("\n")
fout.close()
if self._is_multigpu:
# Wait for all process to end
hvd.allreduce_(sync_tensor, average=False)
# Concatenate all separated translation results
if hvd.local_rank() == 0:
results = []
for i in range(hvd.size()):
for line in open("/tmp/{}.{}".format(os.path.basename(output_path), i)):
id, result = line.strip("\n").split("\t")
results.append((int(id), result))
results.sort()
with open(output_path, "w") as fout:
for _, result in results:
fout.write(result + "\n")
def tensor_mean_and_var(t: Tensor) -> Tuple[Tensor, Tensor]:
mean = hvd.allreduce_(t.mean(dim=0))
var = hvd.allreduce_((t - mean).pow(2).mean(dim=0))
return mean, var
def get_eigen(model,
inputs,
targets,
criterion,
maxIter=50,
tol=1e-3,
comm=True):
"""
compute the top eigenvalues of model parameters and
the corresponding eigenvectors.
change the model to evaluation mode, otherwise the batch Normalization Layer will change.
If you call this functino during training, remember to change the mode back to training mode.
model.eval()
"""
model.eval()
# torch.no_grad()
#model_copy = squeezenet1_1(pretrained=False)
#model_copy.load_state_dict(model.state_dict())
#optimizer = optim.SGD(model_copy.parameters(), lr=0.001 * hvd.size(), momentum=0.9)
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward(create_graph=True)
params, gradsH = get_params_grad(model)
v = [torch.randn(p.size()) for p in params]
v = normalization(v)
if comm:
hvd.broadcast_parameters(v, root_rank=0)
eigenvalue = None
for i in range(maxIter):
print(i)
model.zero_grad()
Hv = hessian_vector_product(gradsH, params, v)
if comm:
handles = []
for i in range(len(Hv)):
handles.append(
hvd.allreduce_async_(
Hv[i],
name='reduce random vector update {}'.format(i)))
for handle in handles:
hvd.synchronize(handle)
eigenvalue_tmp = group_product(Hv, v).item()
v = normalization(Hv)
if eigenvalue == None:
eigenvalue = eigenvalue_tmp
else:
if abs(eigenvalue - eigenvalue_tmp) < tol:
if comm:
return eigenvalue_tmp, v
else:
eigenvalue = eigenvalue_tmp
if not comm:
print("{} is here".format(hvd.rank()))
eigenvalue = torch.FloatTensor([eigenvalue])
hvd.allreduce_(eigenvalue, name='eigenvalue')
print("allreduced eigs for rank {}".format(hvd.rank()))
eigenvalue = float(eigenvalue)
if hvd.rank() == 0:
print("No Communication eigenvalue approximated at {}".format(
eigenvalue))
return eigenvalue, v
