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import numpy as np
import time
from caffe2.python import workspace, cnn, memonger, core
def has_blob(proto, needle):
for op in proto.op:
for inp in op.input:
if inp == needle:
return True
for outp in op.output:
if outp == needle:
return True
return False
def count_blobs(proto):
blobs = set()
for op in proto.op:
blobs = blobs.union(set(op.input)).union(set(op.output))
return len(blobs)
def count_shared_blobs(proto):
blobs = set()
for op in proto.op:
blobs = blobs.union(set(op.input)).union(set(op.output))
return len([b for b in blobs if "_shared" in b])
def test_shared_grads(
with_shapes,
create_model,
conv_blob,
last_out_blob,
data_blob='gpu_0/data',
label_blob='gpu_0/label',
num_labels=1000,
):
model = cnn.CNNModelHelper(
order="NCHW",
name="test",
cudnn_exhaustive_search=True,
)
with core.NameScope("gpu_0"):
data = model.net.AddExternalInput(data_blob)
label = model.net.AddExternalInput(label_blob)
(_softmax, loss) = create_model(
model,
data,
num_input_channels=3,
num_labels=num_labels,
label=label,
is_test=False,
)
param_to_grad = model.AddGradientOperators([loss])
(shapes, types) = workspace.InferShapesAndTypes(
[model.param_init_net, model.net],
{data_blob: [4, 3, 227, 227],
label_blob: [4]},
)
count_before = count_blobs(model.net.Proto())
optim_proto = memonger.share_grad_blobs(
model.net,
["gpu_0/loss"],
set(model.param_to_grad.values()),
"gpu_0/",
share_activations=True,
dont_share_blobs=set([str(param_to_grad[conv_blob])]),
blob_shapes=shapes if with_shapes else None,
)
count_after = count_blobs(optim_proto)
# Run model and compare results. We check that the loss is same
# and also that the final gradient (conv1_w_grad is same)
workspace.RunNetOnce(model.param_init_net)
data = np.random.rand(4, 3, 227, 227).astype(np.float32)
label = (np.random.rand(4) * num_labels).astype(np.int32)
workspace.FeedBlob(data_blob, data)
workspace.FeedBlob(label_blob, label)
workspace.RunNetOnce(model.net)
model.net.Proto().type = 'dag'
model.net.Proto().num_workers = 4
loss1 = workspace.FetchBlob(last_out_blob)
conv1_w_grad = workspace.FetchBlob(param_to_grad[conv_blob])
workspace.FeedBlob(param_to_grad[conv_blob], np.array([0.0]))
workspace.RunNetOnce(optim_proto)
optimized_loss1 = workspace.FetchBlob(last_out_blob)
optim_conv1_w_grad = workspace.FetchBlob(param_to_grad[conv_blob])
return [(count_after, count_before),
(loss1, optimized_loss1),
(conv1_w_grad, optim_conv1_w_grad)]
def test_forward_only(
create_model,
last_out_blob,
data_blob='gpu_0/data',
num_labels=1000,
):
model = cnn.CNNModelHelper(
order="NCHW",
name="test",
cudnn_exhaustive_search=True,
)
with core.NameScope("gpu_0"):
data = model.net.AddExternalInput(data_blob)
create_model(
model,
data,
num_input_channels=3,
num_labels=num_labels,
is_test=True
)
count_before = count_blobs(model.net.Proto())
optim_proto = memonger.optimize_inference_for_dag(
model.net, [data_blob], "gpu_0/"
)
count_after = count_blobs(optim_proto)
num_shared_blobs = count_shared_blobs(optim_proto)
# Run model and compare results
workspace.RunNetOnce(model.param_init_net)
data = np.random.rand(4, 3, 227, 227).astype(np.float32)
workspace.FeedBlob(data_blob, data)
workspace.RunNetOnce(model.net)
model.net.Proto().type = 'dag'
model.net.Proto().num_workers = 4
loss1 = workspace.FetchBlob(last_out_blob)
workspace.RunNetOnce(optim_proto)
optimized_loss1 = workspace.FetchBlob(last_out_blob)
return [(count_after, count_before),
(num_shared_blobs),
(loss1, optimized_loss1)]
def test_forward_only_fast_simplenet(
create_model,
last_out_blob,
data_blob="gpu_0/data",
num_labels=1000,
):
model = cnn.CNNModelHelper(
order="NCHW",
name="test",
cudnn_exhaustive_search=True,
)
with core.NameScope("gpu_0"):
data = model.net.AddExternalInput(data_blob)
create_model(
model,
data,
num_input_channels=3,
num_labels=num_labels,
is_test=True
)
count_before = count_blobs(model.net.Proto())
t = time.time()
optim_proto = memonger.optimize_inference_fast(
model.net.Proto(),
set([data_blob, last_out_blob]).union(
set(model.net.Proto().external_input))
)
print("Optimization took {} secs".format(time.time() - t))
count_after = count_blobs(optim_proto)
num_shared_blobs = count_shared_blobs(optim_proto)
print(count_after, count_before, num_shared_blobs)
# Run model and compare results
workspace.RunNetOnce(model.param_init_net)
data = np.random.rand(4, 3, 227, 227).astype(np.float32)
workspace.FeedBlob(data_blob, data)
model.net.Proto().type = 'simple'
workspace.RunNetOnce(model.net)
loss1 = workspace.FetchBlob(last_out_blob)
workspace.RunNetOnce(optim_proto)
optimized_loss1 = workspace.FetchBlob(last_out_blob)
return [(count_after, count_before),
(num_shared_blobs),
(loss1, optimized_loss1)]
|
