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import numpy as np
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, core, model_helper, brew, test_util
class CopyOpsTest(test_util.TestCase):
def tearDown(self):
# Reset workspace after each test
# Otherwise, the multi-GPU test will use previously created tensors,
# which may have been placed on the wrong device
workspace.ResetWorkspace()
def run_test_copy_gradient(self, device_opt):
model = model_helper.ModelHelper(name="copy_test")
with core.DeviceScope(device_opt):
x = model.net.AddExternalInputs("x")
y = model.Copy(x, "y")
loss = model.AveragedLoss(y, "loss")
gradient_map = model.AddGradientOperators([loss])
workspace.FeedBlob(x, np.random.rand(32).astype(np.float32))
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
self.assertTrue(np.array_equal(
workspace.FetchBlob(x),
workspace.FetchBlob(y),
))
self.assertTrue(np.array_equal(
workspace.FetchBlob(gradient_map[x]),
workspace.FetchBlob(gradient_map[y]),
))
def test_copy_gradient_cpu(self):
self.run_test_copy_gradient(core.DeviceOption(caffe2_pb2.CPU, 0))
@unittest.skipIf(workspace.NumGpuDevices() < 1, "Need at least 1 GPU.")
def test_copy_gradient_gpu(self):
self.run_test_copy_gradient(core.DeviceOption(workspace.GpuDeviceType, 0))
@unittest.skipIf(workspace.NumGpuDevices() < 2, "Need at least 2 GPU.")
def test_copy_gradient_multiple_gpus(self):
model = model_helper.ModelHelper(name="copy_test")
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
x_cpu = model.net.AddExternalInputs("x_cpu")
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)):
x_gpu_1 = model.CopyCPUToGPU(x_cpu, "x_gpu_1")
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 1)):
x_gpu_2 = model.Copy(x_gpu_1, "x_gpu_2")
loss = model.AveragedLoss(x_gpu_2, "loss")
gradient_map = model.AddGradientOperators([loss])
workspace.FeedBlob("x_cpu", np.random.rand(32).astype(np.float32))
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
self.assertTrue(np.array_equal(
workspace.FetchBlob("x_gpu_1"),
workspace.FetchBlob("x_gpu_2"),
))
self.assertTrue(np.array_equal(
workspace.FetchBlob(gradient_map["x_gpu_1"]),
workspace.FetchBlob(gradient_map["x_gpu_2"]),
))
def get_op_with_output(model, output_blob_name):
for op in model.net.Proto().op:
if len(op.output) == 1 and op.output[0] == output_blob_name:
return op
return None
self.assertEqual(
get_op_with_output(model, "x_gpu_2_grad").device_option,
core.DeviceOption(workspace.GpuDeviceType, 1),
)
self.assertEqual(
get_op_with_output(model, "x_cpu_grad").device_option,
core.DeviceOption(workspace.GpuDeviceType, 0),
)
@unittest.skipIf(workspace.NumGpuDevices() < 1, "Need at least 1 GPU.")
def test_cpu2gpu_gpu2cpu_sparse_gradients(self):
model = model_helper.ModelHelper(name="copy_test")
v = model.param_init_net.UniformFill([], ["v"], shape=[16, 4])
indices = model.param_init_net.UniformFill([], ["v"], shape=[16, 4])
cpu_opt = core.DeviceOption(caffe2_pb2.CPU, 0)
gpu_opt = core.DeviceOption(workspace.GpuDeviceType, 0)
with core.DeviceScope(gpu_opt):
vcpu = model.CopyGPUToCPU(v, "vcpu")
with core.DeviceScope(cpu_opt):
g = model.Gather([vcpu, indices], "g")
with core.DeviceScope(gpu_opt):
ggpu = model.CopyCPUToGPU(g, "ggpu")
f = brew.fc(model, ggpu, "out", dim_in=4, dim_out=6)
(softmax, loss) = model.SoftmaxWithLoss(
[f, "label"],
["softmax", "loss"],
)
gradient_map = model.AddGradientOperators([loss])
self.assertTrue("v" in gradient_map)
self.assertTrue(isinstance(gradient_map['v'], core.GradientSlice))
@unittest.skipIf(workspace.NumGpuDevices() < 1, "Need at least 1 GPU.")
def test_cpu2gpu_gpu2cpu_gradients(self):
model = model_helper.ModelHelper(name="copy_test")
batch = 32
cpu_opt = core.DeviceOption(caffe2_pb2.CPU, 0)
gpu_opt = core.DeviceOption(workspace.GpuDeviceType, 0)
with core.NameScope("cpu"):
with core.DeviceScope(cpu_opt):
x_cpu = brew.fc(model, 'data', 'x_cpu', 16, 8)
with core.NameScope("gpu_0"):
with core.DeviceScope(gpu_opt):
x_gpu = model.CopyCPUToGPU(x_cpu, "x_gpu")
pred_gpu = brew.fc(model, x_gpu, "pred_gpu", 8, 4)
pred_cpu = model.CopyGPUToCPU(pred_gpu, "pred_cpu")
with core.DeviceScope(cpu_opt):
with core.NameScope("cpu"):
(softmax, loss) = model.SoftmaxWithLoss(
[pred_cpu, "label"],
["softmax", "loss"],
)
gradient_map = model.AddGradientOperators([loss])
# Add param updates (for cpu and gpu)
init_net = model.param_init_net
with core.DeviceScope(cpu_opt):
with core.NameScope("cpu"):
ONE = init_net.ConstantFill([], "ONE", shape=[1], value=1.)
LR = init_net.ConstantFill([], "LR", shape=[1], value=-2.0)
for param in model.GetParams():
model.WeightedSum(
[param, ONE, gradient_map[param], LR],
param,
)
with core.NameScope("gpu_0"):
with core.DeviceScope(gpu_opt):
ONE = init_net.ConstantFill([], "ONE", shape=[1], value=1.)
LR = init_net.ConstantFill([], "LR", shape=[1], value=-2.0)
for param in model.GetParams():
model.WeightedSum(
[param, ONE, gradient_map[param], LR],
param,
)
with core.DeviceScope(cpu_opt):
workspace.FeedBlob(
'cpu/data',
np.random.rand(batch, 16).astype(np.float32),
)
workspace.FeedBlob(
'cpu/label',
np.random.randint(4, size=batch).astype(np.int32),
)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
initial_params = {p: workspace.FetchBlob(p) for p in model.GetParams()}
workspace.RunNet(model.net.Proto().name)
updated_params = {p: workspace.FetchBlob(p) for p in model.GetParams()}
for p in model.GetParams():
g = gradient_map[p]
expected = initial_params[p] - 2.0 * workspace.FetchBlob(g)
actual = updated_params[p]
self.assertTrue(
np.array_equal(expected, updated_params[p]),
"Mismatch: {}: {}, {}".format(p, expected, actual),
)
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