def setUp(self):
super(TestPdbBreakpoint, self).setUp()
# Sample computation that involves tensors with different numbers
# of dimensions
self.input1 = T.fmatrix()
self.input2 = T.fscalar()
self.output = T.dot((self.input1 - self.input2),
(self.input1 - self.input2).transpose())
# Declare the conditional breakpoint
self.breakpointOp = PdbBreakpoint("Sum of output too high")
self.condition = T.gt(self.output.sum(), 1000)
(self.monitored_input1, self.monitored_input2,
self.monitored_output) = self.breakpointOp(self.condition,
self.input1, self.input2,
self.output)
def test_pdbbreakpoint_op():
""" Test that PdbBreakpoint ops don't block gpu optimization"""
b = tensor.fmatrix()
# Create a function composed of a breakpoint followed by
# some computation
condition = tensor.gt(b.sum(), 0)
b_monitored = PdbBreakpoint(name='TestBreakpoint')(condition, b)
output = b_monitored**2
f = theano.function([b], output, mode=mode_with_gpu)
# Ensure that, in the compiled function, the computation following the
# breakpoint has been moved to the gpu.
topo = f.maker.fgraph.toposort()
assert isinstance(topo[-2].op, cuda.GpuElemwise)
assert topo[-1].op == cuda.host_from_gpu
def setUp(self):
super(TestPdbBreakpoint, self).setUp()
# Sample computation that involves tensors with different numbers
# of dimensions
self.input1 = T.fmatrix()
self.input2 = T.fscalar()
self.output = T.dot((self.input1 - self.input2),
(self.input1 - self.input2).transpose())
# Declare the conditional breakpoint
self.breakpointOp = PdbBreakpoint("Sum of output too high")
self.condition = T.gt(self.output.sum(), 1000)
(self.monitored_input1,
self.monitored_input2,
self.monitored_output) = self.breakpointOp(self.condition,
self.input1,
self.input2, self.output)
class TestPdbBreakpoint(utt.InferShapeTester):
def setUp(self):
super(TestPdbBreakpoint, self).setUp()
# Sample computation that involves tensors with different numbers
# of dimensions
self.input1 = T.fmatrix()
self.input2 = T.fscalar()
self.output = T.dot((self.input1 - self.input2),
(self.input1 - self.input2).transpose())
# Declare the conditional breakpoint
self.breakpointOp = PdbBreakpoint("Sum of output too high")
self.condition = T.gt(self.output.sum(), 1000)
(self.monitored_input1,
self.monitored_input2,
self.monitored_output) = self.breakpointOp(self.condition,
self.input1,
self.input2, self.output)
def test_infer_shape(self):
input1_value = numpy.arange(6).reshape(2, 3).astype("float32")
input2_value = 10.0
self._compile_and_check([self.input1, self.input2],
[self.monitored_input1,
self.monitored_input2,
self.monitored_output],
[input1_value, input2_value],
PdbBreakpoint)
def test_grad(self):
input1_value = numpy.arange(9).reshape(3, 3).astype("float32")
input2_value = 10.0
grads = [T.grad(self.monitored_input1.sum(), self.input1),
T.grad(self.monitored_input2.sum(), self.input2)]
# Add self.monitored_input1 as an output to the Theano function to
# prevent Theano from optimizing the PdbBreakpoint op out of the
# function graph
fct = theano.function([self.input1, self.input2],
grads + [self.monitored_input1])
gradients = fct(input1_value, input2_value)[:-1]
expected_gradients = [numpy.ones((3, 3), dtype="float32"),
numpy.array(1., dtype="float32")]
for i in range(len(gradients)):
numpy.testing.assert_allclose(gradients[i], expected_gradients[i])
def test_fprop(self):
input1_value = numpy.arange(9).reshape(3, 3).astype("float32")
input2_value = 10.0
fct = theano.function([self.input1, self.input2],
[self.monitored_input1, self.monitored_input2])
output = fct(input1_value, input2_value)
numpy.testing.assert_allclose(output[0], input1_value)
numpy.testing.assert_allclose(output[1], input2_value)
def test_connection_pattern(self):
node = self.monitored_output.owner
connection_pattern = self.breakpointOp.connection_pattern(node)
expected_pattern = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
assert connection_pattern == expected_pattern
class TestPdbBreakpoint(utt.InferShapeTester):
def setUp(self):
super(TestPdbBreakpoint, self).setUp()
# Sample computation that involves tensors with different numbers
# of dimensions
self.input1 = T.fmatrix()
self.input2 = T.fscalar()
self.output = T.dot((self.input1 - self.input2),
(self.input1 - self.input2).transpose())
# Declare the conditional breakpoint
self.breakpointOp = PdbBreakpoint("Sum of output too high")
self.condition = T.gt(self.output.sum(), 1000)
(self.monitored_input1, self.monitored_input2,
self.monitored_output) = self.breakpointOp(self.condition,
self.input1, self.input2,
self.output)
def test_infer_shape(self):
input1_value = numpy.arange(6).reshape(2, 3).astype("float32")
input2_value = 10.0
self._compile_and_check([self.input1, self.input2], [
self.monitored_input1, self.monitored_input2, self.monitored_output
], [input1_value, input2_value], PdbBreakpoint)
def test_grad(self):
input1_value = numpy.arange(9).reshape(3, 3).astype("float32")
input2_value = 10.0
grads = [
T.grad(self.monitored_input1.sum(), self.input1),
T.grad(self.monitored_input2.sum(), self.input2)
]
# Add self.monitored_input1 as an output to the Theano function to
# prevent Theano from optimizing the PdbBreakpoint op out of the
# function graph
fct = theano.function([self.input1, self.input2],
grads + [self.monitored_input1])
gradients = fct(input1_value, input2_value)[:-1]
expected_gradients = [
numpy.ones((3, 3), dtype="float32"),
numpy.array(1., dtype="float32")
]
for i in range(len(gradients)):
numpy.testing.assert_allclose(gradients[i], expected_gradients[i])
def test_fprop(self):
input1_value = numpy.arange(9).reshape(3, 3).astype("float32")
input2_value = 10.0
fct = theano.function([self.input1, self.input2],
[self.monitored_input1, self.monitored_input2])
output = fct(input1_value, input2_value)
numpy.testing.assert_allclose(output[0], input1_value)
numpy.testing.assert_allclose(output[1], input2_value)
def test_connection_pattern(self):
node = self.monitored_output.owner
connection_pattern = self.breakpointOp.connection_pattern(node)
expected_pattern = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
assert connection_pattern == expected_pattern
