shuffle_train=False,
)
model = model_cfg.base(*model_cfg.args,
num_classes=num_classes,
**model_cfg.kwargs)
model.cuda()
print("Loading model %s" % args.file)
checkpoint = torch.load(args.file)
model.load_state_dict(checkpoint["state_dict"])
criterion = torch.nn.CrossEntropyLoss()
if args.use_test:
loader = loaders["test"]
else:
loader = loaders["train"]
max_eval, min_eval, hvps, pos_evals, neg_evals = min_max_hessian_eigs(
model, loader, criterion, use_cuda=True, verbose=True)
print("Maximum eigenvalue: ", max_eval)
print("Minimum eigenvalue: ", min_eval)
print("Number of Hessian vector products: ", hvps)
print("Saving all eigenvalues to ", args.save_path)
np.savez(args.save_path,
pos_evals=pos_evals.cpu().numpy(),
neg_evals=neg_evals.cpu().numpy())
def crunch_hessian_eigs(surf_file, net, w, s, d, dataloader, comm, rank, args):
"""
Calculate eigen values of the hessian matrix of a given model in parallel
using mpi reduce. This is the synchronized version.
"""
f = h5py.File(surf_file, 'r+' if rank == 0 else 'r')
min_eig, max_eig = [], []
xcoordinates = f['xcoordinates'][:]
ycoordinates = f['ycoordinates'][:] if 'ycoordinates' in f.keys() else None
if 'min_eig' not in f.keys():
shape = xcoordinates.shape if ycoordinates is None else (len(xcoordinates), len(ycoordinates))
max_eig = -np.ones(shape=shape)
min_eig = np.ones(shape=shape)
if rank == 0:
f['min_eig'] = min_eig
f['max_eig'] = max_eig
else:
min_eig = f['min_eig'][:]
max_eig = f['max_eig'][:]
# Generate a list of all indices that need to be filled in.
# The coordinates of each unfilled index are stored in 'coords'.
inds, coords, inds_nums = scheduler.get_job_indices(max_eig, xcoordinates, ycoordinates, comm)
print('Computing %d values for rank %d' % (len(inds), rank))
criterion = nn.CrossEntropyLoss() # set the loss function criteria
# Loop over all un-calculated coords
start_time = time.time()
total_sync = 0.0
for count, ind in enumerate(inds):
# Get the coordinates of the points being calculated
coord = coords[count]
# Load the weights corresponding to those coordinates into the net
if args.dir_type == 'weights':
net_plotter.set_weights(net.module if args.ngpu > 1 else net, w, d, coord)
elif args.dir_type == 'states':
net_plotter.set_states(net.module if args.ngpu > 1 else net, s, d, coord)
# Compute the eign values of the hessian matrix
compute_start = time.time()
maxeig, mineig, iter_count = hess_vec_prod.min_max_hessian_eigs(net, dataloader, \
criterion, rank=rank, use_cuda=args.cuda,
verbose=True)
compute_time = time.time() - compute_start
# Record the result in the local array
max_eig.ravel()[ind] = maxeig
min_eig.ravel()[ind] = mineig
# Send updated plot data to the master node
sync_start_time = time.time()
max_eig = mpi4pytorch.reduce_max(comm, max_eig)
min_eig = mpi4pytorch.reduce_min(comm, min_eig)
sync_time = time.time() - sync_start_time
total_sync += sync_time
# Only the master node writes to the file - this avoids write conflicts
if rank == 0:
f['max_eig'][:] = max_eig
f['min_eig'][:] = min_eig
print("rank: %d %d/%d (%0.2f%%) %d\t %s \tmaxeig:%8.5f \tmineig:%8.5f \titer: %d \ttime:%.2f \tsync:%.2f" % ( \
rank, count + 1, len(inds), 100.0 * (count + 1) / len(inds), ind, str(coord), \
maxeig, mineig, iter_count, compute_time, sync_time))
# This is only needed to make MPI run smoothly. If this process has less work
# than the rank0 process, then we need to keep calling allreduce so the rank0 process doesn't block
for i in range(max(inds_nums) - len(inds)):
max_eig = mpi4pytorch.reduce_max(comm, max_eig)
min_eig = mpi4pytorch.reduce_min(comm, min_eig)
total_time = time.time() - start_time
print('Rank %d done! Total time: %f Sync: %f ' % (rank, total_time, total_sync))
f.close()
def crunch_hessian_eigs(surf_file, net, w, s, d, dataloader, comm, rank, args):
"""
Calculate eigen values of the hessian matrix of a given model in parallel
using mpi reduce. This is the synchronized version.
"""
f = h5py.File(surf_file, 'r+' if rank == 0 else 'r')
min_eig, max_eig = [], []
xcoordinates = f['xcoordinates'][:]
ycoordinates = f['ycoordinates'][:] if 'ycoordinates' in f.keys() else None
if 'min_eig' not in f.keys():
shape = xcoordinates.shape if ycoordinates is None else (len(xcoordinates),len(ycoordinates))
max_eig = -np.ones(shape=shape)
min_eig = np.ones(shape=shape)
if rank == 0:
f['min_eig'] = min_eig
f['max_eig'] = max_eig
else:
min_eig = f['min_eig'][:]
max_eig = f['max_eig'][:]
# Generate a list of all indices that need to be filled in.
# The coordinates of each unfilled index are stored in 'coords'.
inds, coords, inds_nums = scheduler.get_job_indices(max_eig, xcoordinates, ycoordinates, comm)
print('Computing %d values for rank %d'% (len(inds), rank))
criterion = nn.CrossEntropyLoss() # set the loss function criteria
# Loop over all un-calculated coords
start_time = time.time()
total_sync = 0.0
for count, ind in enumerate(inds):
# Get the coordinates of the points being calculated
coord = coords[count]
# Load the weights corresponding to those coordinates into the net
if args.dir_type == 'weights':
net_plotter.set_weights(net.module if args.ngpu > 1 else net, w, d, coord)
elif args.dir_type == 'states':
net_plotter.set_states(net.module if args.ngpu > 1 else net, s, d, coord)
# Compute the eign values of the hessian matrix
compute_start = time.time()
maxeig, mineig, iter_count = hess_vec_prod.min_max_hessian_eigs(net, dataloader, \
criterion, rank=rank, use_cuda=args.cuda, verbose=True)
compute_time = time.time() - compute_start
# Record the result in the local array
max_eig.ravel()[ind] = maxeig
min_eig.ravel()[ind] = mineig
# Send updated plot data to the master node
sync_start_time = time.time()
max_eig = mpi4pytorch.reduce_max(comm, max_eig)
min_eig = mpi4pytorch.reduce_min(comm, min_eig)
sync_time = time.time() - sync_start_time
total_sync += sync_time
# Only the master node writes to the file - this avoids write conflicts
if rank == 0:
f['max_eig'][:] = max_eig
f['min_eig'][:] = min_eig
print("rank: %d %d/%d (%0.2f%%) %d\t %s \tmaxeig:%8.5f \tmineig:%8.5f \titer: %d \ttime:%.2f \tsync:%.2f" % ( \
rank, count + 1, len(inds), 100.0 * (count + 1)/len(inds), ind, str(coord), \
maxeig, mineig, iter_count, compute_time, sync_time))
# This is only needed to make MPI run smoothly. If this process has less work
# than the rank0 process, then we need to keep calling allreduce so the rank0 process doesn't block
for i in range(max(inds_nums) - len(inds)):
max_eig = mpi4pytorch.reduce_max(comm, max_eig)
min_eig = mpi4pytorch.reduce_min(comm, min_eig)
total_time = time.time() - start_time
print('Rank %d done! Total time: %f Sync: %f '%(rank, total_time, total_sync))
f.close()