def testIdentifyGradientWorksOnMultipleLosses(self):
grad_debugger_1 = debug_gradients.GradientsDebugger()
grad_debugger_2 = debug_gradients.GradientsDebugger()
y = math_ops.add(self.w, -1.0, name="y")
debug_y = grad_debugger_1.identify_gradient(y)
z1 = math_ops.square(debug_y, name="z1")
debug_y = grad_debugger_2.identify_gradient(y)
z2 = math_ops.sqrt(debug_y, name="z2")
with grad_debugger_1:
gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
with grad_debugger_2:
gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
dz1_dy = grad_debugger_1.gradient_tensor(y)
dz2_dy = grad_debugger_2.gradient_tensor(y)
self.assertIsInstance(dz1_dy, ops.Tensor)
self.assertIsInstance(dz2_dy, ops.Tensor)
self.assertIsNot(dz1_dy, dz2_dy)
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(5.0**2, self.sess.run(z1))
self.assertAllClose(5.0**0.5, self.sess.run(z2))
self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy))
def testIdentifyGradientGivesCorrectTensorObjectWithTfGradients(self):
grad_debugger = debug_gradients.GradientsDebugger()
id_grad_w = grad_debugger.identify_gradient(self.w)
y = math_ops.add(id_grad_w, -1.0, name="y")
with grad_debugger:
grads = gradients_impl.gradients(y, [self.u, self.v])
self.assertEqual(2, len(grads))
u_grad = grads[0]
v_grad = grads[1]
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(5.0, self.sess.run(y))
self.assertAllClose(3.0, self.sess.run(u_grad))
self.assertAllClose(2.0, self.sess.run(v_grad))
# Fetch the gradient tensor with the x-tensor object.
w_grad = grad_debugger.gradient_tensor(self.w)
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
# Fetch the gradient tensor with the x-tensor's name.
w_grad = grad_debugger.gradient_tensor(self.w.name)
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
# Fetch the gradient tensor with the x-tensor name.
w_grad = grad_debugger.gradient_tensor(self.w.name)
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
def testWatchGradientsByXTensorNamesWorksWithoutContextManager(self):
y = math_ops.add(self.w, -1.0, name="y")
# The constructrion of the forward graph has completed.
# But we can still get the gradient tensors by using
# watch_gradients_by_tensor_names().
grad_debugger = debug_gradients.GradientsDebugger()
grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "w:0$")
grads = gradients_impl.gradients(y, [self.u, self.v])
self.assertEqual(2, len(grads))
u_grad = grads[0]
v_grad = grads[1]
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(5.0, self.sess.run(y))
self.assertAllClose(3.0, self.sess.run(u_grad))
self.assertAllClose(2.0, self.sess.run(v_grad))
w_grad = grad_debugger.gradient_tensor(self.w)
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
w_grad = grad_debugger.gradient_tensor("w:0")
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(
self):
grad_debugger = debug_gradients.GradientsDebugger()
grad_debugger.identify_gradient(self.w)
with self.assertRaisesRegexp(
ValueError, "The graph already contains an op named .*"):
grad_debugger.identify_gradient(self.w)
def testIdentifyGradientRaisesTypeErrorForNonTensorOrTensorNameInput(self):
grad_debugger = debug_gradients.GradientsDebugger()
with self.assertRaisesRegexp(
TypeError,
r"x_tensor must be a str or tf\.Tensor or tf\.Variable, but instead "
r"has type .*Operation.*"):
grad_debugger.gradient_tensor(variables.global_variables_initializer())
def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self):
grad_debugger_1 = debug_gradients.GradientsDebugger()
grad_debugger_2 = debug_gradients.GradientsDebugger()
id_grad_w = grad_debugger_1.identify_gradient(self.w)
y = math_ops.add(id_grad_w, -1.0, name="y")
# There are >1 gradient debuggers registered, and grad_debugger is not used
# as a context manager here, so the gradient w.r.t. self.w will not be
# registered.
gradients_impl.gradients(y, [self.u, self.v])
with self.assertRaisesRegexp(
LookupError,
r"This GradientsDebugger has not received any gradient tensor for "):
grad_debugger_1.gradient_tensor(self.w)
with self.assertRaisesRegexp(
LookupError,
r"This GradientsDebugger has not received any gradient tensor for "):
grad_debugger_2.gradient_tensor(self.w)
def testGradientsValuesFromDumpWorks(self):
y = math_ops.add(self.w, -1.0, name="y")
z = math_ops.square(y, name="z")
grad_debugger = debug_gradients.GradientsDebugger()
with grad_debugger.watch_gradients_by_tensors(self.sess.graph,
[self.w, self.u, y]):
train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(
z)
self.sess.run(variables.global_variables_initializer())
run_options = config_pb2.RunOptions(output_partition_graphs=True)
dump_dir = tempfile.mkdtemp()
debug_url = "file://" + dump_dir
debug_utils.watch_graph(run_options,
self.sess.graph,
debug_urls=debug_url)
run_metadata = config_pb2.RunMetadata()
self.assertAllClose(2.0, self.sess.run(self.u))
self.sess.run(train_op, options=run_options, run_metadata=run_metadata)
self.assertAllClose(-1.0, self.sess.run(self.u))
dump = debug_data.DebugDumpDir(
dump_dir, partition_graphs=run_metadata.partition_graphs)
dump.set_python_graph(self.sess.graph)
y_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, y, dump)
self.assertEqual(1, len(y_grad_values))
self.assertAllClose(10.0, y_grad_values[0])
w_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, self.w, dump)
self.assertEqual(1, len(w_grad_values))
self.assertAllClose(10.0, w_grad_values[0])
u_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, self.u, dump)
self.assertEqual(1, len(u_grad_values))
self.assertAllClose(30.0, u_grad_values[0])
with self.assertRaisesRegexp(
LookupError,
r"This GradientsDebugger has not received any gradient tensor for "
r"x-tensor v:0"):
debug_gradients.gradient_values_from_dump(grad_debugger, self.v,
dump)
# Cleanup.
shutil.rmtree(dump_dir)
def testIdentifyGradientTensorWorksWithGradientDescentOptimizer(self):
grad_debugger = debug_gradients.GradientsDebugger()
id_grad_w = grad_debugger.identify_gradient(self.w)
y = math_ops.add(id_grad_w, -1.0, name="y")
with grad_debugger:
gradient_descent.GradientDescentOptimizer(0.1).minimize(y)
self.sess.run(variables.global_variables_initializer())
# Fetch the gradient tensor with the x-tensor object.
w_grad = grad_debugger.gradient_tensor(self.w)
self.assertIsInstance(w_grad, ops.Tensor)
self.assertAllClose(1.0, self.sess.run(w_grad))
def testWatchGradientsByTensorCanWorkOnMultipleLosses(self):
y = math_ops.add(self.w, -1.0, name="y")
z1 = math_ops.square(y, name="z1")
z2 = math_ops.sqrt(y, name="z2")
grad_debugger_1 = debug_gradients.GradientsDebugger()
with grad_debugger_1.watch_gradients_by_tensors(self.sess.graph, y):
gradient_descent.GradientDescentOptimizer(0.1).minimize(z1)
grad_debugger_2 = debug_gradients.GradientsDebugger()
with grad_debugger_2.watch_gradients_by_tensors(self.sess.graph, y):
gradient_descent.GradientDescentOptimizer(0.1).minimize(z2)
dz1_dy = grad_debugger_1.gradient_tensor(y)
dz2_dy = grad_debugger_2.gradient_tensor(y)
self.assertIsInstance(dz1_dy, ops.Tensor)
self.assertIsInstance(dz2_dy, ops.Tensor)
self.assertIsNot(dz1_dy, dz2_dy)
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(5.0**2, self.sess.run(z1))
self.assertAllClose(5.0**0.5, self.sess.run(z2))
self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy))
self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy))
def testWatchGradientsWorksOnRefTensor(self):
y = math_ops.add(self.w, -1.0, name="y")
grad_debugger = debug_gradients.GradientsDebugger()
with grad_debugger.watch_gradients_by_tensor_names(self.sess.graph, "u:0$"):
grads = gradients_impl.gradients(y, [self.u, self.v])
self.assertEqual(2, len(grads))
u_grad = grads[0]
v_grad = grads[1]
self.assertIs(u_grad, grad_debugger.gradient_tensor("u:0"))
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(3.0, self.sess.run(u_grad))
self.assertAllClose(2.0, self.sess.run(v_grad))
self.assertAllClose(
3.0, self.sess.run(grad_debugger.gradient_tensor("u:0")))
def testWatchGradientsByXTensorsWorks(self):
y = math_ops.add(self.w, -1.0, name="foo/y")
z = math_ops.square(y, name="foo/z")
# The constructrion of the forward graph has completed.
# But we can still get the gradient tensors by using
# watch_gradients_by_x_tensors().
grad_debugger = debug_gradients.GradientsDebugger()
with grad_debugger.watch_gradients_by_tensors(self.sess.graph,
[self.w, self.u, y]):
gradient_descent.GradientDescentOptimizer(0.1).minimize(z)
self.assertEqual(3, len(grad_debugger.gradient_tensors()))
u_grad = grad_debugger.gradient_tensor(self.u)
w_grad = grad_debugger.gradient_tensor(self.w)
y_grad = grad_debugger.gradient_tensor(y)
self.sess.run(variables.global_variables_initializer())
self.assertAllClose(10.0, self.sess.run(y_grad))
self.assertAllClose(10.0, self.sess.run(w_grad))
self.assertAllClose(30.0, self.sess.run(u_grad))