def test_full_hessian_xent_mnist_multilayer():
"""Test regular and diagonal hessian computation."""
u.seed_random(1)
data_width = 3
batch_size = 2
d = [data_width**2, 6, 10]
o = d[-1]
n = batch_size
train_steps = 1
model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)
autograd_lib.register(model)
dataset = u.TinyMNIST(dataset_size=batch_size,
data_width=data_width,
original_targets=True)
trainloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False)
train_iter = iter(trainloader)
loss_fn = torch.nn.CrossEntropyLoss()
hess = defaultdict(float)
hess_diag = defaultdict(float)
for train_step in range(train_steps):
data, targets = next(train_iter)
activations = {}
def save_activations(layer, a, _):
activations[layer] = a
with autograd_lib.module_hook(save_activations):
output = model(data)
loss = loss_fn(output, targets)
def compute_hess(layer, _, B):
A = activations[layer]
BA = torch.einsum("nl,ni->nli", B, A)
hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)
hess_diag[layer] += torch.einsum("ni,nj->ij", B * B, A * A)
with autograd_lib.module_hook(compute_hess):
autograd_lib.backward_hessian(output,
loss='CrossEntropy',
retain_graph=True)
# compute Hessian through autograd
H_autograd = u.hessian(loss, model.layers[0].weight)
u.check_close(hess[model.layers[0]] / batch_size, H_autograd)
diag_autograd = torch.einsum('lili->li', H_autograd)
u.check_close(diag_autograd, hess_diag[model.layers[0]] / batch_size)
H_autograd = u.hessian(loss, model.layers[1].weight)
u.check_close(hess[model.layers[1]] / batch_size, H_autograd)
diag_autograd = torch.einsum('lili->li', H_autograd)
u.check_close(diag_autograd, hess_diag[model.layers[1]] / batch_size)
def _test_kfac_hessian_xent_mnist():
u.seed_random(1)
data_width = 3
batch_size = 2
d = [data_width**2, 10]
o = d[-1]
n = batch_size
train_steps = 1
model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)
autograd_lib.register(model)
dataset = u.TinyMNIST(dataset_size=batch_size,
data_width=data_width,
original_targets=True)
trainloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False)
train_iter = iter(trainloader)
loss_fn = torch.nn.CrossEntropyLoss()
activations = {}
hess = defaultdict(lambda: AttrDefault(float))
for train_step in range(train_steps):
data, targets = next(train_iter)
activations = {}
def save_activations(layer, a, _):
activations[layer] = a
with autograd_lib.module_hook(save_activations):
output = model(data)
loss = loss_fn(output, targets)
def compute_hess(layer, _, B):
A = activations[layer]
hess[layer].AA += torch.einsum("ni,nj->ij", A, A)
hess[layer].BB += torch.einsum("ni,nj->ij", B, B)
with autograd_lib.module_hook(compute_hess):
autograd_lib.backward_hessian(output,
loss='CrossEntropy',
retain_graph=True)
hess_factored = hess[model.layers[0]]
hess0 = torch.einsum('kl,ij->kilj', hess_factored.BB / n,
hess_factored.AA / o) # hess for sum loss
hess0 /= n # hess for mean loss
# compute Hessian through autograd
H_autograd = u.hessian(loss, model.layers[0].weight)
rel_error = torch.norm(
(hess0 - H_autograd).flatten()) / torch.norm(H_autograd.flatten())
assert rel_error < 0.01 # 0.0057
def test_full_hessian_xent_kfac2():
"""Test with uneven layers."""
u.seed_random(1)
torch.set_default_dtype(torch.float64)
batch_size = 1
d = [3, 2]
o = d[-1]
n = batch_size
train_steps = 1
model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=True, bias=False)
autograd_lib.register(model)
loss_fn = torch.nn.CrossEntropyLoss()
data = u.to_logits(torch.tensor([[0.7, 0.2, 0.1]]))
targets = torch.tensor([0])
data = data.repeat([3, 1])
targets = targets.repeat([3])
n = len(data)
activations = {}
hess = defaultdict(lambda: AttrDefault(float))
for i in range(n):
def save_activations(layer, A, _):
activations[layer] = A
hess[layer].AA += torch.einsum("ni,nj->ij", A, A)
with autograd_lib.module_hook(save_activations):
data_batch = data[i:i + 1]
targets_batch = targets[i:i + 1]
Y = model(data_batch)
o = Y.shape[1]
loss = loss_fn(Y, targets_batch)
def compute_hess(layer, _, B):
hess[layer].BB += torch.einsum("ni,nj->ij", B, B)
with autograd_lib.module_hook(compute_hess):
autograd_lib.backward_hessian(Y, loss='CrossEntropy')
# expand
hess_factored = hess[model.layers[0]]
hess0 = torch.einsum('kl,ij->kilj', hess_factored.BB / n,
hess_factored.AA / o) # hess for sum loss
hess0 /= n # hess for mean loss
# check against autograd
# 0.1459
Y = model(data)
loss = loss_fn(Y, targets)
hess_autograd = u.hessian(loss, model.layers[0].weight)
u.check_equal(hess_autograd, hess0)
def test_full_hessian_xent_multibatch():
u.seed_random(1)
torch.set_default_dtype(torch.float64)
batch_size = 1
d = [2, 2]
o = d[-1]
n = batch_size
train_steps = 1
model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=True, bias=True)
model.layers[0].weight.data.copy_(torch.eye(2))
autograd_lib.register(model)
loss_fn = torch.nn.CrossEntropyLoss()
data = u.to_logits(torch.tensor([[0.7, 0.3]]))
targets = torch.tensor([0])
data = data.repeat([3, 1])
targets = targets.repeat([3])
n = len(data)
activations = {}
hess = defaultdict(float)
def save_activations(layer, a, _):
activations[layer] = a
for i in range(n):
with autograd_lib.module_hook(save_activations):
data_batch = data[i:i + 1]
targets_batch = targets[i:i + 1]
Y = model(data_batch)
loss = loss_fn(Y, targets_batch)
def compute_hess(layer, _, B):
A = activations[layer]
BA = torch.einsum("nl,ni->nli", B, A)
hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)
with autograd_lib.module_hook(compute_hess):
autograd_lib.backward_hessian(Y, loss='CrossEntropy')
# check against autograd
# 0.1459
Y = model(data)
loss = loss_fn(Y, targets)
hess_autograd = u.hessian(loss, model.layers[0].weight)
hess0 = hess[model.layers[0]] / n
u.check_equal(hess_autograd, hess0)
def main():
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')
parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')
parser.add_argument('--logdir', type=str, default='/tmp/runs/curv_train_tiny/run')
parser.add_argument('--nonlin', type=int, default=1, help="whether to add ReLU nonlinearity between layers")
parser.add_argument('--bias', type=int, default=1, help="whether to add bias between layers")
parser.add_argument('--layer', type=int, default=-1, help="restrict updates to this layer")
parser.add_argument('--data_width', type=int, default=28)
parser.add_argument('--targets_width', type=int, default=28)
parser.add_argument('--hess_samples', type=int, default=1,
help='number of samples when sub-sampling outputs, 0 for exact hessian')
parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')
parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')
parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')
parser.add_argument('--dataset_size', type=int, default=60000)
parser.add_argument('--train_steps', type=int, default=100, help="this many train steps between stat collection")
parser.add_argument('--stats_steps', type=int, default=1000000, help="total number of curvature stats collections")
parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--weight_decay', type=float, default=0)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--dropout', type=int, default=0)
parser.add_argument('--swa', type=int, default=0)
parser.add_argument('--lmb', type=float, default=1e-3)
parser.add_argument('--train_batch_size', type=int, default=64)
parser.add_argument('--stats_batch_size', type=int, default=10000)
parser.add_argument('--stats_num_batches', type=int, default=1)
parser.add_argument('--run_name', type=str, default='noname')
parser.add_argument('--launch_blocking', type=int, default=0)
parser.add_argument('--sampled', type=int, default=0)
parser.add_argument('--curv', type=str, default='kfac',
help='decomposition to use for curvature estimates: zero_order, kfac, isserlis or full')
parser.add_argument('--log_spectra', type=int, default=0)
u.seed_random(1)
gl.args = parser.parse_args()
args = gl.args
u.seed_random(1)
gl.project_name = 'train_ciresan'
u.setup_logdir_and_event_writer(args.run_name)
print(f"Logging to {gl.logdir}")
d1 = 28 * 28
d = [784, 2500, 2000, 1500, 1000, 500, 10]
# number of samples per datapoint. Used to normalize kfac
model = u.SimpleFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout)
model = model.to(gl.device)
autograd_lib.register(model)
assert args.dataset_size >= args.stats_batch_size
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, original_targets=True,
dataset_size=args.dataset_size)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True, drop_last=True)
train_iter = u.infinite_iter(train_loader)
assert not args.full_batch, "fixme: validation still uses stats_iter"
if not args.full_batch:
stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=True,
drop_last=True)
stats_iter = u.infinite_iter(stats_loader)
else:
stats_iter = None
test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False,
original_targets=True,
dataset_size=args.dataset_size)
test_eval_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False,
drop_last=False)
train_eval_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False,
drop_last=False)
loss_fn = torch.nn.CrossEntropyLoss()
autograd_lib.add_hooks(model)
autograd_lib.disable_hooks()
gl.token_count = 0
last_outer = 0
for step in range(args.stats_steps):
epoch = gl.token_count // 60000
lr = optimizer.param_groups[0]['lr']
print('token_count', gl.token_count)
if last_outer:
u.log_scalars({"time/outer": 1000 * (time.perf_counter() - last_outer)})
print(f'time: {time.perf_counter() - last_outer:.2f}')
last_outer = time.perf_counter()
with u.timeit("validate"):
val_accuracy, val_loss = validate(model, test_eval_loader, f'test (epoch {epoch})')
train_accuracy, train_loss = validate(model, train_eval_loader, f'train (epoch {epoch})')
# save log
metrics = {'epoch': epoch, 'val_accuracy': val_accuracy, 'val_loss': val_loss,
'train_loss': train_loss, 'train_accuracy': train_accuracy,
'lr': optimizer.param_groups[0]['lr'],
'momentum': optimizer.param_groups[0].get('momentum', 0)}
u.log_scalars(metrics)
def mom_update(buffer, val):
buffer *= 0.9
buffer += val * 0.1
if not args.skip_stats:
# number of samples passed through
n = args.stats_batch_size * args.stats_num_batches
# quanti
forward_stats = defaultdict(lambda: AttrDefault(float))
hessians = defaultdict(lambda: AttrDefault(float))
jacobians = defaultdict(lambda: AttrDefault(float))
fishers = defaultdict(lambda: AttrDefault(float)) # empirical fisher/gradient
quad_fishers = defaultdict(lambda: AttrDefault(float)) # gradient statistics that depend on fisher (4th order moments)
train_regrets = defaultdict(list)
test_regrets1 = defaultdict(list)
test_regrets2 = defaultdict(list)
train_regrets_opt = defaultdict(list)
test_regrets_opt = defaultdict(list)
cosines = defaultdict(list)
dot_products = defaultdict(list)
hessians_histograms = defaultdict(lambda: AttrDefault(u.MyList))
jacobians_histograms = defaultdict(lambda: AttrDefault(u.MyList))
fishers_histograms = defaultdict(lambda: AttrDefault(u.MyList))
quad_fishers_histograms = defaultdict(lambda: AttrDefault(u.MyList))
current = None
current_histograms = None
for i in range(args.stats_num_batches):
activations = {}
backprops = {}
def save_activations(layer, A, _):
activations[layer] = A
forward_stats[layer].AA += torch.einsum("ni,nj->ij", A, A)
print('forward')
with u.timeit("stats_forward"):
with autograd_lib.module_hook(save_activations):
data, targets = next(stats_iter)
output = model(data)
loss = loss_fn(output, targets) * len(output)
def compute_stats(layer, _, B):
A = activations[layer]
if current == fishers:
backprops[layer] = B
# about 27ms per layer
with u.timeit('compute_stats'):
current[layer].BB += torch.einsum("ni,nj->ij", B, B) # TODO(y): index consistency
current[layer].diag += torch.einsum("ni,nj->ij", B * B, A * A)
current[layer].BA += torch.einsum("ni,nj->ij", B, A)
current[layer].a += torch.einsum("ni->i", A)
current[layer].b += torch.einsum("nk->k", B)
current[layer].norm2 += ((A * A).sum(dim=1) * (B * B).sum(dim=1)).sum()
# compute curvatures in direction of all gradiennts
if current is fishers:
assert args.stats_num_batches == 1, "not tested on more than one stats step, currently reusing aggregated moments"
hess = hessians[layer]
jac = jacobians[layer]
Bh, Ah = B @ hess.BB / n, A @ forward_stats[layer].AA / n
Bj, Aj = B @ jac.BB / n, A @ forward_stats[layer].AA / n
norms = ((A * A).sum(dim=1) * (B * B).sum(dim=1))
current[layer].min_norm2 = min(norms)
current[layer].median_norm2 = torch.median(norms)
current[layer].max_norm2 = max(norms)
norms2_hess = ((Ah * A).sum(dim=1) * (Bh * B).sum(dim=1))
norms2_jac = ((Aj * A).sum(dim=1) * (Bj * B).sum(dim=1))
current[layer].norm += norms.sum()
current_histograms[layer].norms.extend(torch.sqrt(norms))
current[layer].curv_hess += (skip_nans(norms2_hess / norms)).sum()
current_histograms[layer].curv_hess.extend(skip_nans(norms2_hess / norms))
current[layer].curv_hess_max += (skip_nans(norms2_hess / norms)).max()
current[layer].curv_hess_median += (skip_nans(norms2_hess / norms)).median()
current_histograms[layer].curv_jac.extend(skip_nans(norms2_jac / norms))
current[layer].curv_jac += (skip_nans(norms2_jac / norms)).sum()
current[layer].curv_jac_max += (skip_nans(norms2_jac / norms)).max()
current[layer].curv_jac_median += (skip_nans(norms2_jac / norms)).median()
current[layer].a_sparsity += torch.sum(A <= 0).float() / A.numel()
current[layer].b_sparsity += torch.sum(B <= 0).float() / B.numel()
current[layer].mean_activation += torch.mean(A)
current[layer].mean_activation2 += torch.mean(A*A)
current[layer].mean_backprop = torch.mean(B)
current[layer].mean_backprop2 = torch.mean(B*B)
current[layer].norms_hess += torch.sqrt(norms2_hess).sum()
current_histograms[layer].norms_hess.extend(torch.sqrt(norms2_hess))
current[layer].norms_jac += norms2_jac.sum()
current_histograms[layer].norms_jac.extend(torch.sqrt(norms2_jac))
normalized_moments = copy.copy(hessians[layer])
normalized_moments.AA = forward_stats[layer].AA
normalized_moments = u.divide_attributes(normalized_moments, n)
train_regrets_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=0, m=normalized_moments,
approx=args.curv)
test_regrets1_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=1, m=normalized_moments,
approx=args.curv)
test_regrets2_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=2, m=normalized_moments,
approx=args.curv)
test_regrets_opt_ = autograd_lib.offset_losses(A, B, alpha=None, offset=2,
m=normalized_moments, approx=args.curv)
train_regrets_opt_ = autograd_lib.offset_losses(A, B, alpha=None, offset=0,
m=normalized_moments, approx=args.curv)
cosines_ = autograd_lib.offset_cosines(A, B)
train_regrets[layer].extend(train_regrets_)
test_regrets1[layer].extend(test_regrets1_)
test_regrets2[layer].extend(test_regrets2_)
train_regrets_opt[layer].extend(train_regrets_opt_)
test_regrets_opt[layer].extend(test_regrets_opt_)
cosines[layer].extend(cosines_)
dot_products[layer].extend(autograd_lib.offset_dotprod(A, B))
# statistics of the form g.Sigma.g
elif current == quad_fishers:
hess = hessians[layer]
sigma = fishers[layer]
jac = jacobians[layer]
Bs, As = B @ sigma.BB / n, A @ forward_stats[layer].AA / n
Bh, Ah = B @ hess.BB / n, A @ forward_stats[layer].AA / n
Bj, Aj = B @ jac.BB / n, A @ forward_stats[layer].AA / n
norms = ((A * A).sum(dim=1) * (B * B).sum(dim=1))
norms2_hess = ((Ah * A).sum(dim=1) * (Bh * B).sum(dim=1))
norms2_jac = ((Aj * A).sum(dim=1) * (Bj * B).sum(dim=1))
norms_sigma = ((As * A).sum(dim=1) * (Bs * B).sum(dim=1))
current[layer].norm += norms.sum() # TODO(y) remove, redundant with norm2 above
current[layer].curv_sigma += (skip_nans(norms_sigma / norms)).sum()
current[layer].curv_sigma_max = skip_nans(norms_sigma / norms).max()
current[layer].curv_sigma_median = skip_nans(norms_sigma / norms).median()
current[layer].curv_hess += skip_nans(norms2_hess / norms).sum()
current[layer].curv_hess_max += skip_nans(norms2_hess / norms).max()
current[layer].lyap_hess_mean += skip_nans(norms_sigma / norms2_hess).mean()
current[layer].lyap_hess_max = max(skip_nans(norms_sigma/norms2_hess))
current[layer].lyap_jac_mean += skip_nans(norms_sigma / norms2_jac).mean()
current[layer].lyap_jac_max = max(skip_nans(norms_sigma/norms2_jac))
print('backward')
with u.timeit("backprop_H"):
with autograd_lib.module_hook(compute_stats):
current = hessians
current_histograms = hessians_histograms
autograd_lib.backward_hessian(output, loss='CrossEntropy', sampled=args.sampled,
retain_graph=True) # 600 ms
current = jacobians
current_histograms = jacobians_histograms
autograd_lib.backward_jacobian(output, sampled=args.sampled, retain_graph=True) # 600 ms
current = fishers
current_histograms = fishers_histograms
model.zero_grad()
loss.backward(retain_graph=True) # 60 ms
current = quad_fishers
current_histograms = quad_fishers_histograms
model.zero_grad()
loss.backward() # 60 ms
print('summarize')
for (i, layer) in enumerate(model.layers):
stats_dict = {'hessian': hessians, 'jacobian': jacobians, 'fisher': fishers}
# evaluate stats from
# https://app.wandb.ai/yaroslavvb/train_ciresan/runs/425pu650?workspace=user-yaroslavvb
for stats_name in stats_dict:
s = AttrDict()
stats = stats_dict[stats_name][layer]
for key in forward_stats[layer]:
# print(f'copying {key} in {stats_name}, {layer}')
try:
assert stats[key] == float()
except:
f"Trying to overwrite {key} in {stats_name}, {layer}"
stats[key] = forward_stats[layer][key]
diag: torch.Tensor = stats.diag / n
# jacobian:
# curv in direction of gradient goes down to roughly 0.3-1
# maximum curvature goes up to 1000-2000
#
# Hessian:
# max curv goes down to 1, in direction of gradient 0.0001
s.diag_l2 = torch.max(diag) # 40 - 3000 smaller than kfac l2 for jac
s.diag_fro = torch.norm(
diag) # jacobian grows to 0.5-1.5, rest falls, layer-5 has phase transition, layer-4 also
s.diag_trace = diag.sum() # jacobian grows 0-1000 (first), 0-150 (last). Almost same as kfac_trace (771 vs 810 kfac). Jacobian has up/down phase transition
s.diag_average = diag.mean()
# normalize for mean loss
BB = stats.BB / n
AA = stats.AA / n
# A_evals, _ = torch.symeig(AA) # averaging 120ms per hit, 90 hits
# B_evals, _ = torch.symeig(BB)
# s.kfac_l2 = torch.max(A_evals) * torch.max(B_evals) # 60x larger than diag_l2. layer0/hess has down/up phase transition. layer5/jacobian has up/down phase transition
s.kfac_trace = torch.trace(AA) * torch.trace(BB) # 0/hess down/up tr, 5/jac sharp phase transition
s.kfac_fro = torch.norm(stats.AA) * torch.norm(
stats.BB) # 0/hess has down/up tr, 5/jac up/down transition
# s.kfac_erank = s.kfac_trace / s.kfac_l2 # first layer has 25, rest 15, all layers go down except last, last noisy
# s.kfac_erank_fro = s.kfac_trace / s.kfac_fro / max(stats.BA.shape)
s.diversity = (stats.norm2 / n) / u.norm_squared(
stats.BA / n) # gradient diversity. Goes up 3x. Bottom layer has most diversity. Jacobian diversity much less noisy than everythingelse
# discrepancy of KFAC based on exact values of diagonal approximation
# average difference normalized by average diagonal magnitude
diag_kfac = torch.einsum('ll,ii->li', BB, AA)
s.kfac_error = (torch.abs(diag_kfac - diag)).mean() / torch.mean(diag.abs())
u.log_scalars(u.nest_stats(f'layer-{i}/{stats_name}', s))
# openai batch size stat
s = AttrDict()
hess = hessians[layer]
jac = jacobians[layer]
fish = fishers[layer]
quad_fish = quad_fishers[layer]
# the following check passes, but is expensive
# if args.stats_num_batches == 1:
# u.check_close(fisher[layer].BA, layer.weight.grad)
def trsum(A, B):
return (A * B).sum() # computes tr(AB')
grad = fishers[layer].BA / n
s.grad_fro = torch.norm(grad)
# get norms
s.lyap_hess_max = quad_fish.lyap_hess_max
s.lyap_hess_ave = quad_fish.lyap_hess_sum / n
s.lyap_jac_max = quad_fish.lyap_jac_max
s.lyap_jac_ave = quad_fish.lyap_jac_sum / n
s.hess_trace = hess.diag.sum() / n
s.jac_trace = jac.diag.sum() / n
# Version 1 of Jain stochastic rates, use Hessian for curvature
b = args.train_batch_size
s.hess_curv = trsum((hess.BB / n) @ grad @ (hess.AA / n), grad) / trsum(grad, grad)
s.jac_curv = trsum((jac.BB / n) @ grad @ (jac.AA / n), grad) / trsum(grad, grad)
# compute gradient noise statistics
# fish.BB has /n factor twice, hence don't need extra /n on fish.AA
# after sampling, hess_noise,jac_noise became 100x smaller, but normalized is unaffected
s.hess_noise = (trsum(hess.AA / n, fish.AA / n) * trsum(hess.BB / n, fish.BB / n))
s.jac_noise = (trsum(jac.AA / n, fish.AA / n) * trsum(jac.BB / n, fish.BB / n))
s.hess_noise_centered = s.hess_noise - trsum(hess.BB / n @ grad, grad @ hess.AA / n)
s.jac_noise_centered = s.jac_noise - trsum(jac.BB / n @ grad, grad @ jac.AA / n)
s.openai_gradient_noise = (fish.norms_hess / n) / trsum(hess.BB / n @ grad, grad @ hess.AA / n)
s.mean_norm = torch.sqrt(fish.norm2) / n
s.min_norm = torch.sqrt(fish.min_norm2)
s.median_norm = torch.sqrt(fish.median_norm2)
s.max_norm = torch.sqrt(fish.max_norm2)
s.enorms = u.norm_squared(grad)
s.a_sparsity = fish.a_sparsity
s.b_sparsity = fish.b_sparsity
s.mean_activation = fish.mean_activation
s.msr_activation = torch.sqrt(fish.mean_activation2)
s.mean_backprop = fish.mean_backprop
s.msr_backprop = torch.sqrt(fish.mean_backprop2)
s.norms_centered = fish.norm2 / n - u.norm_squared(grad)
s.norms_hess = fish.norms_hess / n
s.norms_jac = fish.norms_jac / n
s.hess_curv_grad = fish.curv_hess / n # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.
s.hess_curv_grad_max = fish.curv_hess_max # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.
s.hess_curv_grad_median = fish.curv_hess_median # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.
s.sigma_curv_grad = quad_fish.curv_sigma / n
s.sigma_curv_grad_max = quad_fish.curv_sigma_max
s.sigma_curv_grad_median = quad_fish.curv_sigma_median
s.band_bottou = 0.5 * lr * s.sigma_curv_grad / s.hess_curv_grad
s.band_bottou_stoch = 0.5 * lr * quad_fish.curv_ratio / n
s.band_yaida = 0.25 * lr * s.mean_norm**2
s.band_yaida_centered = 0.25 * lr * s.norms_centered
s.jac_curv_grad = fish.curv_jac / n # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.
s.jac_curv_grad_max = fish.curv_jac_max # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.
s.jac_curv_grad_median = fish.curv_jac_median # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.
# OpenAI gradient noise statistics
s.hess_noise_normalized = s.hess_noise_centered / (fish.norms_hess / n)
s.jac_noise_normalized = s.jac_noise / (fish.norms_jac / n)
train_regrets_, test_regrets1_, test_regrets2_, train_regrets_opt_, test_regrets_opt_, cosines_, dot_products_ = (torch.stack(r[layer]) for r in (train_regrets, test_regrets1, test_regrets2, train_regrets_opt, test_regrets_opt, cosines, dot_products))
s.train_regret = train_regrets_.median() # use median because outliers make it hard to see the trend
s.test_regret1 = test_regrets1_.median()
s.test_regret2 = test_regrets2_.median()
s.test_regret_opt = test_regrets_opt_.median()
s.train_regret_opt = train_regrets_opt_.median()
s.mean_dot_product = torch.mean(dot_products_)
s.median_dot_product = torch.median(dot_products_)
a = [1, 2, 3]
s.median_cosine = cosines_.median()
s.mean_cosine = cosines_.mean()
# get learning rates
L1 = s.hess_curv_grad / n
L2 = s.jac_curv_grad / n
diversity = (fish.norm2 / n) / u.norm_squared(grad)
robust_diversity = (fish.norm2 / n) / fish.median_norm2
dotprod_diversity = fish.median_norm2 / s.median_dot_product
s.lr1 = 2 / (L1 * diversity)
s.lr2 = 2 / (L2 * diversity)
s.lr3 = 2 / (L2 * robust_diversity)
s.lr4 = 2 / (L2 * dotprod_diversity)
hess_A = u.symeig_pos_evals(hess.AA / n)
hess_B = u.symeig_pos_evals(hess.BB / n)
fish_A = u.symeig_pos_evals(fish.AA / n)
fish_B = u.symeig_pos_evals(fish.BB / n)
jac_A = u.symeig_pos_evals(jac.AA / n)
jac_B = u.symeig_pos_evals(jac.BB / n)
u.log_scalars({f'layer-{i}/hessA_erank': erank(hess_A)})
u.log_scalars({f'layer-{i}/hessB_erank': erank(hess_B)})
u.log_scalars({f'layer-{i}/fishA_erank': erank(fish_A)})
u.log_scalars({f'layer-{i}/fishB_erank': erank(fish_B)})
u.log_scalars({f'layer-{i}/jacA_erank': erank(jac_A)})
u.log_scalars({f'layer-{i}/jacB_erank': erank(jac_B)})
gl.event_writer.add_histogram(f'layer-{i}/hist_hess_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_fish_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_jac_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())
s.hess_l2 = max(hess_A) * max(hess_B)
s.jac_l2 = max(jac_A) * max(jac_B)
s.fish_l2 = max(fish_A) * max(fish_B)
s.hess_trace = hess.diag.sum() / n
s.jain1_sto = 1/(s.hess_trace + 2 * s.hess_l2)
s.jain1_det = 1/s.hess_l2
s.jain1_lr = (1 / b) * (1/s.jain1_sto) + (b - 1) / b * (1/s.jain1_det)
s.jain1_lr = 2 / s.jain1_lr
s.regret_ratio = (
train_regrets_opt_ / test_regrets_opt_).median() # ratio between train and test regret, large means overfitting
u.log_scalars(u.nest_stats(f'layer-{i}', s))
# compute stats that would let you bound rho
if i == 0: # only compute this once, for output layer
hhh = hessians[model.layers[-1]].BB / n
fff = fishers[model.layers[-1]].BB / n
d = fff.shape[0]
L = u.lyapunov_spectral(hhh, 2 * fff, cond=1e-8)
L_evals = u.symeig_pos_evals(L)
Lcheap = fff @ u.pinv(hhh, cond=1e-8)
Lcheap_evals = u.eig_real(Lcheap)
u.log_scalars({f'mismatch/rho': d/erank(L_evals)})
u.log_scalars({f'mismatch/rho_cheap': d/erank(Lcheap_evals)})
u.log_scalars({f'mismatch/diagonalizability': erank(L_evals)/erank(Lcheap_evals)}) # 1 means diagonalizable
u.log_spectrum(f'mismatch/sigma', u.symeig_pos_evals(fff), loglog=False)
u.log_spectrum(f'mismatch/hess', u.symeig_pos_evals(hhh), loglog=False)
u.log_spectrum(f'mismatch/lyapunov', L_evals, loglog=True)
u.log_spectrum(f'mismatch/lyapunov_cheap', Lcheap_evals, loglog=True)
gl.event_writer.add_histogram(f'layer-{i}/hist_grad_norms', u.to_numpy(fishers_histograms[layer].norms.value()), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_grad_norms_hess', u.to_numpy(fishers_histograms[layer].norms_hess.value()), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_curv_jac', u.to_numpy(fishers_histograms[layer].curv_jac.value()), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_curv_hess', u.to_numpy(fishers_histograms[layer].curv_hess.value()), gl.get_global_step())
gl.event_writer.add_histogram(f'layer-{i}/hist_cosines', u.to_numpy(cosines[layer]), gl.get_global_step())
if args.log_spectra:
with u.timeit('spectrum'):
# 2/alpha
# s.jain1_lr = (1 / b) * s.jain1_sto + (b - 1) / b * s.jain1_det
# s.jain1_lr = 1 / s.jain1_lr
# hess.diag_trace, jac.diag_trace
# Version 2 of Jain stochastic rates, use Jacobian squared for curvature
s.jain2_sto = s.lyap_jac_max * s.jac_trace / s.lyap_jac_ave
s.jain2_det = s.jac_l2
s.jain2_lr = (1 / b) * s.jain2_sto + (b - 1) / b * s.jain2_det
s.jain2_lr = 1 / s.jain2_lr
u.log_spectrum(f'layer-{i}/hess_A', hess_A)
u.log_spectrum(f'layer-{i}/hess_B', hess_B)
u.log_spectrum(f'layer-{i}/hess_AB', u.outer(hess_A, hess_B).flatten())
u.log_spectrum(f'layer-{i}/jac_A', jac_A)
u.log_spectrum(f'layer-{i}/jac_B', jac_B)
u.log_spectrum(f'layer-{i}/fish_A', fish_A)
u.log_spectrum(f'layer-{i}/fish_B', fish_B)
u.log_scalars({f'layer-{i}/trace_ratio': fish_B.sum()/hess_B.sum()})
L = torch.eig(u.lyapunov_spectral(hess.BB, 2*fish.BB, cond=1e-8))[0]
L = L[:, 0] # extract real part
L = L.sort()[0]
L = torch.flip(L, [0])
L_cheap = torch.eig(fish.BB @ u.pinv(hess.BB, cond=1e-8))[0]
L_cheap = L_cheap[:, 0] # extract real part
L_cheap = L_cheap.sort()[0]
L_cheap = torch.flip(L_cheap, [0])
d = len(hess_B)
u.log_spectrum(f'layer-{i}/Lyap', L)
u.log_spectrum(f'layer-{i}/Lyap_cheap', L_cheap)
u.log_scalars({f'layer-{i}/dims': d})
u.log_scalars({f'layer-{i}/L_erank': erank(L)})
u.log_scalars({f'layer-{i}/L_cheap_erank': erank(L_cheap)})
u.log_scalars({f'layer-{i}/rho': d/erank(L)})
u.log_scalars({f'layer-{i}/rho_cheap': d/erank(L_cheap)})
model.train()
with u.timeit('train'):
for i in range(args.train_steps):
optimizer.zero_grad()
data, targets = next(train_iter)
model.zero_grad()
output = model(data)
loss = loss_fn(output, targets)
loss.backward()
optimizer.step()
if args.weight_decay:
for group in optimizer.param_groups:
for param in group['params']:
param.data.mul_(1 - args.weight_decay)
gl.token_count += data.shape[0]
gl.event_writer.close()
def test_grad_norms():
"""Test computing gradient norms using various methods."""
u.seed_random(1)
# torch.set_default_dtype(torch.float64)
data_width = 3
batch_size = 2
d = [data_width**2, 6, 10]
o = d[-1]
stats_steps = 2
num_samples = batch_size * stats_steps # number of samples used in computation of curvature stats
model: u.SimpleModel = u.SimpleMLP(d, nonlin=True, bias=True)
loss_fn = torch.nn.CrossEntropyLoss()
autograd_lib.register(model)
dataset = u.TinyMNIST(dataset_size=num_samples,
data_width=data_width,
original_targets=True)
stats_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False)
stats_iter = iter(stats_loader)
moments = defaultdict(lambda: AttrDefault(float))
norms = defaultdict(lambda: AttrDefault(MyList))
data_batches = []
targets_batches = []
for stats_step in range(stats_steps):
data, targets = next(stats_iter)
data_batches.append(data)
targets_batches.append(targets)
activations = {}
def forward_aggregate(layer, A, _):
activations[layer] = A
moments[layer].AA += torch.einsum('ni,nj->ij', A, A)
moments[layer].a += torch.einsum("ni->i", A)
with autograd_lib.module_hook(forward_aggregate):
output = model(data)
loss_fn(output, targets)
def backward_aggregate(layer, _, B):
A = activations[layer]
moments[layer].b += torch.einsum("nk->k", B)
moments[layer].BA += torch.einsum("nl,ni->li", B, A)
moments[layer].BB += torch.einsum("nk,nl->kl", B, B)
moments[layer].BABA += torch.einsum('nl,ni,nk,nj->likj', B, A, B,
A)
with autograd_lib.module_hook(backward_aggregate):
autograd_lib.backward_hessian(output,
loss='CrossEntropy',
retain_graph=True)
# compare against results using autograd
data = torch.cat(data_batches)
targets = torch.cat(targets_batches)
with autograd_lib.save_activations2() as activations:
loss = loss_fn(model(data), targets)
def normalize_moments(d, n):
result = AttrDict()
for val in d:
if type(d[val]) == torch.Tensor:
result[val] = d[val] / n
return result
def compute_norms(layer, _, B):
A = activations[layer]
for kind in ('zero_order', 'kfac', 'isserlis', 'full'):
normalized_moments = normalize_moments(moments[layer], num_samples)
norms_list = getattr(norms[layer], kind)
norms_list.extend(
autograd_lib.grad_norms(A, B, normalized_moments, approx=kind))
with autograd_lib.module_hook(compute_norms):
model.zero_grad()
(len(data) * loss).backward(retain_graph=True)
print(norms[model.layers[0]].zero_order.value())
for layer in model.layers:
output = model(data)
losses = torch.stack([
loss_fn(output[i:i + 1], targets[i:i + 1])
for i in range(len(data))
])
grads = u.jacobian(losses, layer.weight)
grad_norms = torch.einsum('nij,nij->n', grads, grads)
u.check_close(grad_norms, norms[layer].zero_order)
# test gradient norms with custom metric
kfac_norms, isserlis_norms, full_norms = [
u.to_pytorch(getattr(norms[layer], k))
for k in ('kfac', 'isserlis', 'full')
]
error_kfac = max(abs(kfac_norms - full_norms))
error_isserlis = max(abs(isserlis_norms - full_norms))
assert error_isserlis < 1e-4
assert error_kfac < 1e-4