def testTorchInterop(self):
workspace.RunOperatorOnce(core.CreateOperator(
"ConstantFill", [], "foo", shape=(4,), value=2, dtype=10))
t = workspace.FetchTorch("foo")
t.resize_(5)
t[4] = t[2] = 777
np.testing.assert_array_equal(t.numpy(), np.array([2,2,777,2,777]))
# this doesn't work because of variable / tensor confusion
# the underlying data tensor is not properly reshaped :(
np.testing.assert_array_equal(
workspace.FetchBlob("foo"), np.array([2,2,777,2]))
z = torch.ones((4,), dtype=torch.int64)
workspace.FeedBlob('bar', z)
workspace.RunOperatorOnce(
core.CreateOperator("Reshape", ['bar'], ['bar', '_'], shape=(2,2)))
# NOTE: `workspace.FeedBlob('bar', z)` above creates a shallow-copy of `z`
# and assign it to `bar` in the Caffe2 workspace. Since it's a shallow-copy,
# any sizes or strides change to `bar` will not be propagated back to `z`,
# and we need to call `z = workspace.FetchTorch("bar")` to manually put
# the value of `bar` back into `z`.
#
# In the near future, we won't need to perform the shallow-copying of `z` and
# can directly pass it into the Caffe2 workspace, as long as `z` doesn't require
# grad. At that point we won't need to use `z = workspace.FetchTorch("bar")`
# to fetch `z` from the Caffe2 workspace, since it will exactly be the same as
# the original tensor `z`.
z = workspace.FetchTorch("bar")
z[0,1] = 123
np.testing.assert_array_equal(
workspace.FetchBlob("bar"), np.array([[1,123],[1,1]]))
np.testing.assert_array_equal(z, np.array([[1,123],[1,1]]))
def testTorchInterop(self):
# CUDA has convenient mem stats, let's use them to make sure we didn't
# leak memory
initial_mem = torch.cuda.memory_allocated()
workspace.RunOperatorOnce(
core.CreateOperator("ConstantFill", [],
"foo",
shape=(4, ),
value=2,
dtype=10,
device_option=core.DeviceOption(
workspace.GpuDeviceType)))
t = workspace.FetchTorch("foo")
t.resize_(5)
self.assertTrue(t.is_cuda)
t[4] = t[2] = 777
np.testing.assert_array_equal(t.cpu().numpy(),
np.array([2, 2, 777, 2, 777]))
# this doesn't work because of variable / tensor confusion
# the underlying data tensor is not properly reshaped :(
np.testing.assert_array_equal(workspace.FetchBlob("foo"),
np.array([2, 2, 777, 2]))
z = torch.ones((4, ), dtype=torch.int64, device="cuda")
workspace.FeedBlob('bar', z)
workspace.RunOperatorOnce(
core.CreateOperator("Reshape", ['bar'], ['bar', '_'],
shape=(2, 2),
device_option=core.DeviceOption(
workspace.GpuDeviceType)))
# NOTE: `workspace.FeedBlob('bar', z)` above creates a shallow-copy of `z`
# and assign it to `bar` in the Caffe2 workspace. Since it's a shallow-copy,
# any sizes or strides change to `bar` will not be propagated back to `z`,
# and we need to call `z = workspace.FetchTorch("bar")` to manually put
# the value of `bar` back into `z`.
#
# In the near future, we won't need to perform the shallow-copying of `z` and
# can directly pass it into the Caffe2 workspace, as long as `z` doesn't require
# grad. At that point we won't need to use `z = workspace.FetchTorch("bar")`
# to fetch `z` from the Caffe2 workspace, since it will exactly be the same as
# the original tensor `z`.
z = workspace.FetchTorch("bar")
z[0, 1] = 123
np.testing.assert_array_equal(workspace.FetchBlob("bar"),
np.array([[1, 123], [1, 1]]))
np.testing.assert_array_equal(z.cpu(), np.array([[1, 123], [1, 1]]))
self.assertGreater(torch.cuda.memory_allocated(), initial_mem)
# clean up everything
del t
del z
workspace.ResetWorkspace()
self.assertEqual(torch.cuda.memory_allocated(), initial_mem)
def testTorchInterop(self):
workspace.RunOperatorOnce(
core.CreateOperator("ConstantFill", [],
"foo",
shape=(4, ),
value=2,
dtype=10))
t = workspace.FetchTorch("foo")
t.resize_(5)
t[4] = t[2] = 777
np.testing.assert_array_equal(t.numpy(), np.array([2, 2, 777, 2, 777]))
# this doesn't work because of variable / tensor confusion
# the underlying data tensor is not properly reshaped :(
np.testing.assert_array_equal(workspace.FetchBlob("foo"),
np.array([2, 2, 777, 2]))
z = torch.ones((4, ), dtype=torch.int64)
workspace.FeedBlob('bar', z)
workspace.RunOperatorOnce(
core.CreateOperator("Reshape", ['bar'], ['bar', '_'],
shape=(2, 2)))
z[0, 1] = 123
np.testing.assert_array_equal(workspace.FetchBlob("bar"),
np.array([[1, 123], [1, 1]]))
np.testing.assert_array_equal(z, np.array([[1, 123], [1, 1]]))
def testTorchInterop(self):
# CUDA has convenient mem stats, let's use them to make sure we didn't
# leak memory
initial_mem = torch.cuda.memory_allocated()
workspace.RunOperatorOnce(core.CreateOperator(
"ConstantFill", [], "foo", shape=(4,), value=2, dtype=10,
device_option=core.DeviceOption(workspace.GpuDeviceType)))
t = workspace.FetchTorch("foo")
t.resize_(5)
self.assertTrue(t.is_cuda)
t[4] = t[2] = 777
np.testing.assert_array_equal(
t.cpu().numpy(), np.array([2,2,777,2,777]))
# this doesn't work because of variable / tensor confusion
# the underlying data tensor is not properly reshaped :(
np.testing.assert_array_equal(
workspace.FetchBlob("foo"), np.array([2,2,777,2]))
z = torch.ones((4,), dtype=torch.int64, device="cuda")
workspace.FeedBlob('bar', z)
workspace.RunOperatorOnce(
core.CreateOperator("Reshape", ['bar'], ['bar', '_'], shape=(2,2),
device_option=core.DeviceOption(workspace.GpuDeviceType)))
z[0,1] = 123
np.testing.assert_array_equal(
workspace.FetchBlob("bar"), np.array([[1,123],[1,1]]))
np.testing.assert_array_equal(z.cpu(), np.array([[1,123],[1,1]]))
self.assertGreater(torch.cuda.memory_allocated(), initial_mem)
# clean up everything
del t
del z
workspace.ResetWorkspace()
self.assertEqual(torch.cuda.memory_allocated(), initial_mem)
def testTorchInterop(self):
workspace.RunOperatorOnce(core.CreateOperator(
"ConstantFill", [], "foo", shape=(4,), value=2, dtype=10))
t = workspace.FetchTorch("foo")
t.resize_(5)
t[4] = t[2] = 777
np.testing.assert_array_equal(t.numpy(), np.array([2,2,777,2,777]))
np.testing.assert_array_equal(
workspace.FetchBlob("foo"), np.array([2,2,777,2,777]))
z = torch.ones((4,), dtype=torch.int64)
workspace.FeedBlob('bar', z)
workspace.RunOperatorOnce(
core.CreateOperator("Reshape", ['bar'], ['bar', '_'], shape=(2,2)))
z[0,1] = 123
np.testing.assert_array_equal(
workspace.FetchBlob("bar"), np.array([[1,123],[1,1]]))
np.testing.assert_array_equal(z, np.array([[1,123],[1,1]]))
