def Center_loss_surgery(model):
# broadcast parameters
blobs = [
'gpu_' + str(gpu_id) + '/center_feature'
for gpu_id in range(cfg.NUM_GPUS)
]
data = workspace.FetchBlob(blobs[0])
for i, p in enumerate(blobs[1:]):
with c2_utils.CudaScope(i + 1):
workspace.FeedBlob(p, data)
# sync parameters
with c2_utils.CudaScope(0):
gradients = [
'gpu_' + str(gpu_id) + '/center_feature_g'
for gpu_id in range(cfg.NUM_GPUS)
]
if cfg.USE_NCCL:
model.net.NCCLAllreduce(gradients, gradients)
else:
muji.Allreduce(model.net, gradients, reduced_affix='')
with c2_utils.CudaScope(0):
gradients = [
'gpu_' + str(gpu_id) + '/center_feature_n_u'
for gpu_id in range(cfg.NUM_GPUS)
]
if cfg.USE_NCCL:
model.net.NCCLAllreduce(gradients, gradients)
else:
muji.Allreduce(model.net, gradients, reduced_affix='')
def build_data_parallel_model(model, single_gpu_build_func):
if model.train:
all_loss_gradients = {} # Will include loss gradients from all GPUs
# Build the model on each GPU with correct name and device scoping
for gpu_id in range(cfg.NUM_GPUS):
with core.NameScope('gpu_{}'.format(gpu_id)):
with core.DeviceScope(muji.OnGPU(gpu_id)):
all_loss_gradients.update(
single_gpu_build_func(model))
# Add backward pass on all GPUs
model.AddGradientOperators(all_loss_gradients)
if cfg.NUM_GPUS > 1:
# Need to all-reduce the per-GPU gradients if training with more
# than 1 GPU
all_params = model.TrainableParams()
assert len(all_params) % cfg.NUM_GPUS == 0, \
'This should not happen.'
# The model parameters are replicated on each GPU, get the number
# distinct parameter blobs (i.e., the number of parameter blobs on
# each GPU)
params_per_gpu = int(len(all_params) / cfg.NUM_GPUS)
with core.DeviceScope(muji.OnGPU(cfg.ROOT_GPU_ID)):
# Iterate over distinct parameter blobs
for i in range(params_per_gpu):
# Gradients from all GPUs for this parameter blob
gradients = [
model.param_to_grad[p]
for p in all_params[i::params_per_gpu]
]
if len(gradients) > 0:
if cfg.USE_NCCL:
model.net.NCCLAllreduce(gradients, gradients)
else:
muji.Allreduce(
model.net, gradients, reduced_affix='')
for gpu_id in range(cfg.NUM_GPUS):
# After all-reduce, all GPUs perform SGD updates on their identical
# params and gradients in parallel
add_parameter_update_ops(model, gpu_id)
else:
# Testing only supports running on a single GPU
with core.NameScope('gpu_{}'.format(cfg.ROOT_GPU_ID)):
with core.DeviceScope(muji.OnGPU(cfg.ROOT_GPU_ID)):
single_gpu_build_func(model)
def _add_allreduce_graph(model):
"""Construct the graph that performs Allreduce on the gradients."""
# Need to all-reduce the per-GPU gradients if training with more than 1 GPU
all_params = model.TrainableParams()
assert len(all_params) % cfg.NUM_GPUS == 0
# The model parameters are replicated on each GPU, get the number
# distinct parameter blobs (i.e., the number of parameter blobs on
# each GPU)
params_per_gpu = int(len(all_params) / cfg.NUM_GPUS)
with c2_utils.CudaScope(0):
# Iterate over distinct parameter blobs
for i in range(params_per_gpu):
# Gradients from all GPUs for this parameter blob
gradients = [
model.param_to_grad[p] for p in all_params[i::params_per_gpu]
]
if len(gradients) > 0:
if cfg.USE_NCCL:
model.net.NCCLAllreduce(gradients, gradients)
else:
muji.Allreduce(model.net, gradients, reduced_affix='')
def test_timings(self):
for n in range(2, workspace.NumCudaDevices()):
for in_place in [False, True]:
xs = [np.random.randn(1e7).astype(np.float32)
for i in range(n)]
inputs = [str("x_{}".format(i)) for i in range(n)]
prefix = "" if in_place else "o"
outputs = [str("{}x_{}".format(prefix, i)) for i in range(n)]
net = core.Net("test")
net.NCCLAllreduce(inputs, outputs)
net.RunAllOnGPU()
for i in range(n):
self.ws.create_blob(inputs[i]).feed(xs[i], gpu_device(i))
self.ws.run(net)
net_time = benchmark(self.ws, net)
vanilla = core.Net("vanilla")
muji.Allreduce(vanilla, inputs)
vanilla_time = benchmark(self.ws, vanilla)
print("Speedup for NCCL: {:.2f}".format(
vanilla_time / net_time))
def test_timings(self):
for n in range(2, workspace.NumCudaDevices()):
for in_place in [False, True]:
xs = [
np.random.randn(1e7).astype(np.float32) for i in range(n)
]
inputs = [str("x_{}".format(i)) for i in range(n)]
prefix = "" if in_place else "o"
outputs = [str("{}x_{}".format(prefix, i)) for i in range(n)]
net = core.Net("test")
net.NCCLAllreduce(inputs, outputs)
net.RunAllOnGPU()
for i in range(n):
workspace.FeedBlob(inputs[i], xs[i],
gpu_device(i).SerializeToString())
workspace.RunNetOnce(net.Proto().SerializeToString())
net_time = benchmark(net)
vanilla = core.Net("vanilla")
muji.Allreduce(vanilla, inputs)
vanilla_time = benchmark(vanilla)
print("Speedup for NCCL: {:.2f}".format(vanilla_time /
net_time))
