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Fix failing GPU errors for influential examples #1081

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Fix failing GPU errors for influential examples #1081

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aab5714
Fix failing GPU errors
NarineK Dec 5, 2022
a8eca00
Fix dtype and formatting
NarineK Dec 5, 2022
de2d9bc
add dtype to jacobian_projection
NarineK Dec 5, 2022
7cc1066
Remove einsum and bring back previous changes
NarineK Dec 11, 2022
c538345
Remove unnecessary comment
NarineK Dec 11, 2022
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5 changes: 3 additions & 2 deletions captum/influence/_core/tracincp_fast_rand_proj.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1380,6 +1380,7 @@ def _set_projections_tracincp_fast_rand_proj(
1
] # this is the dimension of the input of the last fully-connected layer
device = batch_jacobians.device
dtype = batch_jacobians.dtype

# choose projection if needed
# without projection, the dimension of the intermediate quantities returned
Expand Down Expand Up @@ -1409,8 +1410,8 @@ def _set_projections_tracincp_fast_rand_proj(
)

projection_quantities = jacobian_projection.to(
device
), layer_input_projection.to(device)
device=device, dtype=dtype
), layer_input_projection.to(device=device, dtype=dtype)

return projection_quantities

Expand Down
19 changes: 13 additions & 6 deletions tests/influence/_utils/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,35 +190,42 @@ def get_random_model_and_data(
BasicLinearNet(in_features, hidden_nodes, out_features)
if not unpack_inputs
else MultLinearNet(in_features, hidden_nodes, out_features, num_inputs)
)
).double()

num_checkpoints = 5

for i in range(num_checkpoints):
net.linear1.weight.data = torch.normal(3, 4, (hidden_nodes, in_features))
net.linear2.weight.data = torch.normal(5, 6, (out_features, hidden_nodes))
net.linear1.weight.data = torch.normal(
3, 4, (hidden_nodes, in_features)
).double()
net.linear2.weight.data = torch.normal(
5, 6, (out_features, hidden_nodes)
).double()
if unpack_inputs:
net.pre.weight.data = torch.normal(
3, 4, (in_features, in_features * num_inputs)
)
if hasattr(net, "pre"):
net.pre.weight.data = net.pre.weight.data.double()
checkpoint_name = "-".join(["checkpoint-reg", str(i + 1) + ".pt"])
net_adjusted = _wrap_model_in_dataparallel(net) if use_gpu else net
torch.save(net_adjusted.state_dict(), os.path.join(tmpdir, checkpoint_name))

num_samples = 50
num_train = 32
all_labels = torch.normal(1, 2, (num_samples, out_features))
all_labels = torch.normal(1, 2, (num_samples, out_features)).double()
train_labels = all_labels[:num_train]
test_labels = all_labels[num_train:]

if unpack_inputs:
all_samples = [
torch.normal(0, 1, (num_samples, in_features)) for _ in range(num_inputs)
torch.normal(0, 1, (num_samples, in_features)).double()
for _ in range(num_inputs)
]
train_samples = [ts[:num_train] for ts in all_samples]
test_samples = [ts[num_train:] for ts in all_samples]
else:
all_samples = torch.normal(0, 1, (num_samples, in_features))
all_samples = torch.normal(0, 1, (num_samples, in_features)).double()
train_samples = all_samples[:num_train]
test_samples = all_samples[num_train:]

Expand Down