def testConcatWithNonFullyDefinedElementShape(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[None, 2])
l = list_ops.tensor_list_push_back(l, [[0., 1.]])
l = list_ops.tensor_list_push_back(l, [[2., 3.], [4., 5.]])
t = list_ops.tensor_list_concat(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[0., 1.], [2., 3.], [4., 5.]])
def testStack(self):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=scalar_shape())
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [1.0, 2.0])
def testUnknownShape(self):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=-1)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant([1.0, 2.0]))
_, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e, [1.0, 2.0])
def testPushInFullListFails(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[], max_num_elements=1)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Tried to push item into a full list"):
l = list_ops.tensor_list_push_back(l, 2.)
self.evaluate(l)
def _testStack(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=scalar_shape(),
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [1.0, 2.0])
def testPushInEmptyListWithUnknownElementShape(self):
with self.cached_session(), self.test_scope():
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=None, max_num_elements=2)
l = list_ops.tensor_list_push_back(l, [3.0, 4.0])
# Pushing an element with a different shape should raise an error.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "Shape"):
l = list_ops.tensor_list_push_back(l, 5.)
self.evaluate(
list_ops.tensor_list_stack(l, element_dtype=dtypes.float32))
def testSetDoesNotUpdatePushIndex(self):
with self.cached_session(), self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=[], element_dtype=dtypes.float32, max_num_elements=2)
# SetItem should not change the push index.
l = list_ops.tensor_list_set_item(l, 1, 3.)
l = list_ops.tensor_list_push_back(l, 5.)
l = list_ops.tensor_list_push_back(l, 7.)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [5., 7.])
def testConcatWithMismatchingTensorShapesFails(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=None)
l = list_ops.tensor_list_push_back(l, [[0., 1.]])
l = list_ops.tensor_list_push_back(l, [[2.], [4.]])
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
r"Tried to concat tensors with unequal shapes: "
r"\[2\] vs \[1\]"):
t = list_ops.tensor_list_concat(l, element_dtype=dtypes.float32)
self.evaluate(t)
def _testStack(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=[],
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
if not context.executing_eagerly():
self.assertAllEqual(t.shape.as_list(), [None])
self.assertAllEqual(self.evaluate(t), [1.0, 2.0])
def testGatherGrad(self):
with backprop.GradientTape() as tape:
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=scalar_shape())
c0 = constant_op.constant(1.0)
tape.watch(c0)
l = list_ops.tensor_list_push_back(l, c0)
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
s = (t[0] + t[1]) * (t[0] + t[1])
dt = tape.gradient(s, c0)
self.assertAllEqual(self.evaluate(dt), 6.0)
def testStack(self):
with self.cached_session(), self.test_scope():
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=[],
max_num_elements=2)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e, 1.0)
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t.shape.as_list(), [None])
self.assertAllEqual(t, [1.0, 2.0])
def testPushPop(self):
with self.cached_session() as sess, self.test_scope():
num = array_ops.placeholder(dtypes.int32)
l = list_ops.tensor_list_reserve(
element_shape=(7, 15), num_elements=num, element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(7, 15)))
l = list_ops.tensor_list_push_back(
l, constant_op.constant(2.0, shape=(7, 15)))
l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
_, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e2, {num: 10}), 2.0 * np.ones((7, 15)))
self.assertAllEqual(sess.run(e1, {num: 10}), 1.0 * np.ones((7, 15)))
def testPushPop(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=(7, 15),
element_dtype=dtypes.float32,
max_num_elements=10)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(7, 15)))
l = list_ops.tensor_list_push_back(
l, constant_op.constant(2.0, shape=(7, 15)))
l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
_, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e2), 2.0 * np.ones((7, 15)))
self.assertAllEqual(sess.run(e1), 1.0 * np.ones((7, 15)))
def testStackWithPartiallyDefinedElementShape(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[-1])
l = list_ops.tensor_list_push_back(l, constant_op.constant([1.0]))
l = list_ops.tensor_list_push_back(l, constant_op.constant([2.0]))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[1.0], [2.0]])
# Should raise an error when the element tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
l = list_ops.tensor_list_push_back(l, constant_op.constant([2.0, 3.0]))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.evaluate(t)
def _WhileGrad(op, *grads): # pylint: disable=invalid-name
"""The gradient of a While op produced by while_loop."""
body_graph = _get_body_graph(op)
# Replace None gradients with zeros. This is needed because `grads` could have
# None incoming gradients for the TensorLists. If we pass None's through, the
# custom gradient of TensorListPopBack will create an EmptyTensorList inside
# the FuncGraph which is undesirable.
# TODO(b/80444525): There might be an issue with treating no gradient as zero
# gradient in certain cases. Consider replacing None gradients with Zeros
# for accumulators only.
grads = [
g if g is not None else array_ops.zeros_like(output)
for g, output in zip(grads, op.outputs)
]
body_grad_graph, args = _create_grad_func(
body_graph, grads,
util.unique_grad_fn_name(body_graph.name), op)
intermediate_tensors = _get_intermediates(body_grad_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=_get_tensor_convertible_shape(intermediate_tensor.shape))
with body_grad_graph.as_default():
tensor_list_ph = body_grad_graph.capture(tensor_list, whitelisted=True)
# Push the intermediate tensor to the tensor list.
appended_tensor_list = list_ops.tensor_list_push_back(tensor_list_ph,
intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_grad_graph.outputs.append(appended_tensor_list)
def grad_cond(counter, max_iters, *unused_args):
return counter < max_iters
loop_vars = args + body_grad_graph.external_captures
grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name)
cond_grad_graph = func_graph_module.func_graph_from_py_func(
grad_cond_name, grad_cond, loop_vars, {},
func_graph=util.WhileCondFuncGraph(grad_cond_name))
assert len(loop_vars) == len(body_grad_graph.inputs)
assert len(loop_vars) == len(body_grad_graph.outputs)
assert len(loop_vars) == len(cond_grad_graph.inputs)
outputs = gen_functional_ops._while(
loop_vars,
util.create_new_tf_function(cond_grad_graph),
util.create_new_tf_function(body_grad_graph),
output_shapes=[t.shape for t in body_grad_graph.outputs],
name="%s_grad" % op.name)
_copy_handle_data(body_grad_graph.outputs, outputs)
_maybe_set_lowering_attr(outputs[0].op)
# outputs[0] is the loop counter.
# outputs[1] is the total number of loop iterations.
return outputs[2:2 + len(op.inputs)]
def testDefaultGradYs(self):
with ops.Graph().as_default():
tl = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=ops.convert_to_tensor([], dtype=dtypes.int32))
a = constant(1.0)
tl = list_ops.tensor_list_push_back(tl, a)
grad_tl = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=ops.convert_to_tensor([], dtype=dtypes.int32))
grad_tl = list_ops.tensor_list_push_back(tl, constant(5.0))
grad = gradients.gradients(tl, a, grad_ys=grad_tl)[0]
with self.cached_session() as sess:
self.assertEquals(self.evaluate(grad), 5.)
def testConcatListWithScalarElementsFails(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=None)
l1 = list_ops.tensor_list_push_back(l, 1.)
with self.assertRaisesRegexp(
errors.InvalidArgumentError, "Concat saw a scalar shape at index 0"
" but requires at least vectors"):
t = list_ops.tensor_list_concat(l1, element_dtype=dtypes.float32)
self.evaluate(t)
l1 = list_ops.tensor_list_push_back(l, [1.])
l1 = list_ops.tensor_list_push_back(l1, 2.)
with self.assertRaisesRegexp(
errors.InvalidArgumentError, "Concat saw a scalar shape at index 1"
" but requires at least vectors"):
t = list_ops.tensor_list_concat(l1, element_dtype=dtypes.float32)
self.evaluate(t)
def testPushPopSeparateLists(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=[],
element_dtype=dtypes.float32,
max_num_elements=20)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l2 = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l3 = list_ops.tensor_list_push_back(l, constant_op.constant(3.0))
_, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
l2, e21 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l2, e22 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l3, e31 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
l3, e32 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
result = sess.run([e11, [e21, e22], [e31, e32]])
self.assertEqual(result, [1.0, [2.0, 1.0], [3.0, 1.0]])
def _testPushPop(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=[],
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 1.0)
def testStackWithUnknownElementShape(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=None,
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [1.0, 2.0])
# Should raise an error when the element tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
l = list_ops.tensor_list_push_back(l, constant_op.constant([3.0, 4.0]))
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.evaluate(t)
def body(i, m, t1):
t1 = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(t1), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: t1)
t1 = list_ops.tensor_list_push_back(t1, m * i)
i += 1.0
return i, m, t1
def testPushPopSeparateLists(self):
with self.cached_session() as sess, self.test_scope():
num = array_ops.placeholder(dtypes.int32)
l = list_ops.tensor_list_reserve(
element_shape=scalar_shape(),
num_elements=num,
element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l2 = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l3 = list_ops.tensor_list_push_back(l, constant_op.constant(3.0))
_, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
l2, e21 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l2, e22 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l3, e31 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
l3, e32 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
result = sess.run([e11, [e21, e22], [e31, e32]], {num: 20})
self.assertEqual(result, [1.0, [2.0, 1.0], [3.0, 1.0]])
def tf_tensor_list_new(elements, element_dtype=None, element_shape=None):
"""Overload of new_list that stages a Tensor list creation."""
if tensor_util.is_tensor(elements):
if element_shape is not None:
raise ValueError(
'element shape may not be specified when creating list from tensor')
element_shape = array_ops.shape(elements)[1:]
l = list_ops.tensor_list_from_tensor(elements, element_shape=element_shape)
return l
elements = tuple(ops.convert_to_tensor(el) for el in elements)
all_dtypes = set(el.dtype for el in elements)
if len(all_dtypes) == 1:
inferred_dtype = tuple(all_dtypes)[0]
if element_dtype is not None and element_dtype != inferred_dtype:
raise ValueError(
'incompatible dtype; specified: {}, inferred from {}: {}'.format(
element_dtype, elements, inferred_dtype))
elif all_dtypes:
# Heterogeneous lists are ok.
if element_dtype is not None:
raise ValueError(
'specified dtype {} is inconsistent with that of elements {}'.format(
element_dtype, elements))
inferred_dtype = dtypes.variant
else:
inferred_dtype = dtypes.variant
all_shapes = set(tuple(el.shape.as_list()) for el in elements)
if len(all_shapes) == 1:
inferred_shape = array_ops.shape(elements[0])
if element_shape is not None and element_shape != inferred_shape:
raise ValueError(
'incompatible shape; specified: {}, inferred from {}: {}'.format(
element_shape, elements, inferred_shape))
elif all_shapes:
# Heterogeneous lists are ok.
if element_shape is not None:
raise ValueError(
'specified shape {} is inconsistent with that of elements {}'.format(
element_shape, elements))
inferred_shape = constant_op.constant(-1) # unknown shape, by convention
else:
inferred_shape = constant_op.constant(-1) # unknown shape, by convention
if element_dtype is None:
element_dtype = inferred_dtype
if element_shape is None:
element_shape = inferred_shape
element_shape = ops.convert_to_tensor(element_shape, dtype=dtypes.int32)
l = list_ops.empty_tensor_list(
element_shape=element_shape, element_dtype=element_dtype)
for el in elements:
l = list_ops.tensor_list_push_back(l, el)
return l
def testDoNotConstantFoldVariants(self):
with self.cached_session() as sess, self.test_scope():
val = array_ops.placeholder(dtype=dtypes.float32)
l = list_ops.empty_tensor_list(
element_shape=(7, 15),
element_dtype=dtypes.float32,
max_num_elements=10)
# Note: Pushing a Placeholder will force the constant folding code
# to build a Const node with a DT_VARIANT output. This tests that XLA
# passes a cf_consider_fn which prevent folding such nodes.
l = list_ops.tensor_list_push_back(
l, array_ops.fill(value=val, dims=(7, 15)))
l = list_ops.tensor_list_push_back(
l, constant_op.constant(2.0, shape=(7, 15)))
l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
_, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e2, {val: 1.0}), 2.0 * np.ones((7, 15)))
self.assertAllEqual(sess.run(e1, {val: 1.0}), 1.0 * np.ones((7, 15)))
def testGatherWithUnknownElementShape(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=-1)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant([3.0, 4.0]))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
t = list_ops.tensor_list_gather(l, [2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[3.0, 4.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2], element_dtype=dtypes.float32)
self.evaluate(t)
def testGraphStack(self):
with context.graph_mode(), self.test_session():
tl = list_ops.empty_tensor_list(
element_shape=constant_op.constant([1], dtype=dtypes.int32),
element_dtype=dtypes.int32)
tl = list_ops.tensor_list_push_back(tl, [1])
self.assertAllEqual(
list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32).eval(),
[[1]])
def testEmptyTensorListMax(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=(10, 15), element_dtype=dtypes.float32,
max_num_elements=2)
l = list_ops.tensor_list_push_back(
l, array_ops.fill(value=3.0, dims=(10, 15)))
_, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e), 3.0 * np.ones((10, 15)))
def testEmptyTensorListNoMax(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=(7, 15), element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(7, 15)))
_, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Set the max number of elements"):
self.assertAllEqual(sess.run(e), 1.0 * np.ones((7, 15)))
def testPushPopGradients(self):
with backprop.GradientTape() as tape:
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[])
c = constant_op.constant(1.0)
tape.watch(c)
l = list_ops.tensor_list_push_back(l, c)
l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
e = 2 * e
self.assertAllEqual(self.evaluate(tape.gradient(e, [c])[0]), 2.0)
def testGraphStack(self):
with self.cached_session():
tl = list_ops.empty_tensor_list(
element_shape=constant_op.constant([1], dtype=dtypes.int32),
element_dtype=dtypes.int32)
tl = list_ops.tensor_list_push_back(tl, [1])
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32)),
[[1]])
def testDoNotConstantFoldVariants(self):
with self.session() as sess, self.test_scope():
val = array_ops.placeholder(dtype=dtypes.float32)
l = list_ops.empty_tensor_list(element_shape=(7, 15),
element_dtype=dtypes.float32,
max_num_elements=10)
# Note: Pushing a Placeholder will force the constant folding code
# to build a Const node with a DT_VARIANT output. This tests that XLA
# passes a cf_consider_fn which prevent folding such nodes.
l = list_ops.tensor_list_push_back(
l, array_ops.fill(value=val, dims=(7, 15)))
l = list_ops.tensor_list_push_back(
l, constant_op.constant(2.0, shape=(7, 15)))
l, e2 = list_ops.tensor_list_pop_back(l,
element_dtype=dtypes.float32)
_, e1 = list_ops.tensor_list_pop_back(l,
element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e2, {val: 1.0}), 2.0 * np.ones(
(7, 15)))
self.assertAllEqual(sess.run(e1, {val: 1.0}), 1.0 * np.ones(
(7, 15)))
def testSerializeListWithMaxNumElements(self):
if context.num_gpus():
# TODO(b/119151861): Enable on GPU.
return
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
l = list_ops.empty_tensor_list(element_shape=None,
element_dtype=dtypes.float32,
max_num_elements=2)
l = list_ops.tensor_list_push_back(l, 1.)
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps = list_ops.tensor_list_push_back(l_ps, 2.)
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Tried to push item into a full list"):
with ops.device("/job:worker"):
l_worker = array_ops.identity(l_ps)
l_worker = list_ops.tensor_list_push_back(l_worker, 3.0)
self.evaluate(l_worker)
def testEmptyTensorList(self):
dim = 7
with self.cached_session() as sess, self.test_scope():
p = array_ops.placeholder(dtypes.int32)
l = list_ops.empty_tensor_list(
element_shape=(p, 15), element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(dim, 15)))
_, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Use TensorListReserve instead"):
self.assertEqual(sess.run(e, {p: dim}), 1.0 * np.ones((dim, 15)))
def testGatherWithUnknownElementShape(self):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=-1)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant([3.0, 4.0]))
t = list_ops.tensor_list_gather(l, [1, 0],
element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
t = list_ops.tensor_list_gather(l, [2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[3.0, 4.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2],
element_dtype=dtypes.float32)
self.evaluate(t)
def testZerosLikeVariant(self):
for dtype in (dtypes.uint8, dtypes.uint16, dtypes.int8, dtypes.int16,
dtypes.int32, dtypes.int64, dtypes.float16,
dtypes.float32, dtypes.float64, dtypes.complex64,
dtypes.complex128, dtypes.bool):
l = list_ops.empty_tensor_list(element_dtype=dtypes.variant,
element_shape=scalar_shape())
sub_l = list_ops.empty_tensor_list(element_dtype=dtype,
element_shape=scalar_shape())
l = list_ops.tensor_list_push_back(l, sub_l)
sub_l = list_ops.tensor_list_push_back(
sub_l, math_ops.cast(1, dtype=dtype))
l = list_ops.tensor_list_push_back(l, sub_l)
sub_l = list_ops.tensor_list_push_back(
sub_l, math_ops.cast(2, dtype=dtype))
l = list_ops.tensor_list_push_back(l, sub_l)
# l : [[],
# [1],
# [1, 2]]
#
# l_zeros : [[],
# [0],
# [0, 0]]
l_zeros = array_ops.zeros_like(l)
outputs = []
for _ in range(3):
l_zeros, out = list_ops.tensor_list_pop_back(
l_zeros, element_dtype=dtypes.variant)
outputs.append(
list_ops.tensor_list_stack(out, element_dtype=dtype))
# Note: `outputs` contains popped values so the order is reversed.
self.assertAllEqual(self.evaluate(outputs[2]), [])
self.assertAllEqual(self.evaluate(outputs[1]),
np.zeros((1, ), dtype=dtype.as_numpy_dtype))
self.assertAllEqual(self.evaluate(outputs[0]),
np.zeros((2, ), dtype=dtype.as_numpy_dtype))
def testGatherWithPartiallyDefinedElementShape(self, max_num_elements):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=[-1],
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant([1.0]))
l = list_ops.tensor_list_push_back(l, constant_op.constant([2.0, 3.0]))
l = list_ops.tensor_list_push_back(l, constant_op.constant([4.0, 5.0]))
t = list_ops.tensor_list_gather(l, [0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[1.0]])
t = list_ops.tensor_list_gather(l, [1, 2],
element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[2.0, 3.0], [4.0, 5.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2],
element_dtype=dtypes.float32)
self.evaluate(t)
def testAddNNestedList(self):
l1 = list_ops.tensor_list_from_tensor([1.0, 2.0], element_shape=[])
l2 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
l3 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
l4 = list_ops.tensor_list_from_tensor([7.0, 8.0], element_shape=[])
a = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
a = list_ops.tensor_list_push_back(a, l1)
a = list_ops.tensor_list_push_back(a, l2)
b = list_ops.empty_tensor_list(
element_dtype=dtypes.variant, element_shape=[])
b = list_ops.tensor_list_push_back(b, l3)
b = list_ops.tensor_list_push_back(b, l4)
result = math_ops.add_n((a, b))
result_0 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 0, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
result_1 = list_ops.tensor_list_stack(
list_ops.tensor_list_get_item(result, 1, element_dtype=dtypes.variant),
element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(result_0), [6., 8.])
self.assertAllEqual(self.evaluate(result_1), [10., 12.])
def testZerosLike(self):
for dtype in (dtypes.uint8, dtypes.uint16, dtypes.int8, dtypes.int16,
dtypes.int32, dtypes.int64, dtypes.float16,
dtypes.float32, dtypes.float64, dtypes.complex64,
dtypes.complex128, dtypes.bool):
l_empty = list_ops.empty_tensor_list(element_dtype=dtype,
element_shape=scalar_shape())
l_empty_zeros = array_ops.zeros_like(l_empty)
t_empty_zeros = list_ops.tensor_list_stack(l_empty_zeros,
element_dtype=dtype)
l_full = list_ops.tensor_list_push_back(
l_empty, math_ops.cast(0, dtype=dtype))
l_full = list_ops.tensor_list_push_back(
l_full, math_ops.cast(1, dtype=dtype))
l_full_zeros = array_ops.zeros_like(l_full)
t_full_zeros = list_ops.tensor_list_stack(l_full_zeros,
element_dtype=dtype)
self.assertAllEqual(self.evaluate(t_empty_zeros), [])
self.assertAllEqual(self.evaluate(t_full_zeros),
np.zeros((2, ), dtype=dtype.as_numpy_dtype))
def _tf_tensor_list_append(list_, x):
"""Overload of list_append that stages a Tensor list write."""
def empty_list_of_elements_like_x():
tensor_x = ops.convert_to_tensor(x)
return list_ops.empty_tensor_list(
element_shape=array_ops.shape(tensor_x),
element_dtype=tensor_x.dtype)
list_ = control_flow_ops.cond(
list_ops.tensor_list_length(list_) > 0,
lambda: list_,
empty_list_of_elements_like_x,
)
return list_ops.tensor_list_push_back(list_, x)
def dynamic_list_append(target, element):
"""Converts a list append call inline."""
if isinstance(target, tensor_array_ops.TensorArray):
return target.write(target.size(), element)
# TODO(mdan): What's the right way to check this?
# TODO(mdan): We may not need this branch.
# It may be possible to use TensorList alone if the loop body will not
# require wrapping it, although we'd have to think about an autoboxing
# mechanism for lists received as parameter.
if isinstance(target, ops.Tensor):
return list_ops.tensor_list_push_back(target, element)
# Python targets (including TensorList): fallback to their original append.
target.append(element)
return target
def tf_tensor_list_new(elements, element_dtype=None, element_shape=None):
"""Overload of new_list that stages a Tensor list creation."""
elements = tuple(ops.convert_to_tensor(el) for el in elements)
all_dtypes = set(el.dtype for el in elements)
if len(all_dtypes) == 1:
inferred_dtype = tuple(all_dtypes)[0]
if element_dtype is not None and element_dtype != inferred_dtype:
raise ValueError(
'incompatible dtype; specified: {}, inferred from {}: {}'.
format(element_dtype, elements, inferred_dtype))
else:
# Heterogeneous lists are ok.
if element_dtype is not None:
raise ValueError(
'specified dtype {} is inconsistent with that of elements {}'.
format(element_dtype, elements))
inferred_dtype = dtypes.variant
all_shapes = set(tuple(el.shape.as_list()) for el in elements)
if len(all_shapes) == 1:
inferred_shape = array_ops.shape(elements[0])
if element_shape is not None and element_shape != inferred_shape:
raise ValueError(
'incompatible shape; specified: {}, inferred from {}: {}'.
format(element_shape, elements, inferred_shape))
else:
# Heterogeneous lists are ok.
if element_shape is not None:
raise ValueError(
'specified shape {} is inconsistent with that of elements {}'.
format(element_shape, elements))
inferred_shape = constant_op.constant(
-1) # unknown shape, by convention
if element_dtype is None:
element_dtype = inferred_dtype
if element_shape is None:
element_shape = inferred_shape
l = list_ops.empty_tensor_list(element_shape=element_shape,
element_dtype=element_dtype)
for el in elements:
l = list_ops.tensor_list_push_back(l, el)
return l
def _tf_tensor_list_new(elements):
"""Overload of new_list that stages a Tensor list creation."""
elements = tuple(ops.convert_to_tensor(el) for el in elements)
all_dtypes = set(el.dtype for el in elements)
if len(all_dtypes) == 1:
element_dtype = tuple(all_dtypes)[0]
else:
# Heterogeneous lists are ok.
element_dtype = dtypes.variant
# TODO(mdan): This may fail for elements of variable shapes.
all_shapes = set(tuple(el.shape.as_list()) for el in elements)
if len(all_shapes) == 1:
element_shape = array_ops.shape(elements[0])
else:
# Heterogeneous lists are ok.
element_shape = constant_op.constant(-1) # unknown shape, by convention
l = list_ops.empty_tensor_list(
element_shape=element_shape, element_dtype=element_dtype)
for el in elements:
l = list_ops.tensor_list_push_back(l, el)
return l
def testCPUGPUCopyNested(self):
if not context.num_gpus():
return
t = constant_op.constant([1.0, 2.0])
child_l = list_ops.tensor_list_from_tensor(t, element_shape=[])
l = list_ops.empty_tensor_list(
element_shape=constant_op.constant([], dtype=dtypes.int32),
element_dtype=dtypes.variant)
l = list_ops.tensor_list_push_back(l, child_l)
with context.device("gpu:0"):
l_gpu = array_ops.identity(l)
_, child_l_gpu = list_ops.tensor_list_pop_back(
l_gpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_gpu, element_dtype=dtypes.float32)[1]), 2.0)
l_cpu = array_ops.identity(l_gpu)
_, child_l_cpu = list_ops.tensor_list_pop_back(
l_cpu, element_dtype=dtypes.variant)
self.assertAllEqual(
self.evaluate(
list_ops.tensor_list_pop_back(
child_l_cpu, element_dtype=dtypes.float32)[1]), 2.0)
def Body(x, tl):
return x + 1, list_ops.tensor_list_push_back(tl, x)
def InnerBody(inner_x, outer_x, tl):
return inner_x + 1, outer_x + 1, list_ops.tensor_list_push_back(
tl, x)
def body(list_, m):
list_ = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(list_), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: list_)
list_ = list_ops.tensor_list_push_back(list_, m)
return list_, m
def Body(x, tl):
# There is an accumulator in the loop already so we should not add
# another.
tl = list_ops.tensor_list_push_back(tl, x)
return x**2., tl
def testPushPop(self):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=scalar_shape())
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 1.0)
def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph, self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(ops.tensor_id(tensor))
if captured_tensor is not None:
return captured_tensor
# Do not accumulate loop invariants.
if (any(tensor is t for t in self._forward_graph.inputs) and
any(tensor is t for t in self._forward_graph.outputs)):
captured_tensor = super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
# Add to `popped_tensor_lists` so that this gets added to the list of
# outputs.
# TODO(srbs): Rename popped_tensor_lists.
self.popped_tensor_lists[ops.tensor_id(captured_tensor)] = captured_tensor
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
return captured_tensor
# Do not accumulate Const nodes. Instead copy them directly in the backward
# graph.
# TODO(srbs): This just checks for `Const` nodes. Consider checking for
# graph compile time consts in general.
# TODO(srbs): Consider making this a loop input.
if constant_op.is_constant(tensor):
real_value = constant_op.constant(
tensor_util.constant_value(tensor), dtype=tensor.dtype)
self._indirect_captures[ops.tensor_id(tensor)] = real_value
return real_value
# Resource tensors are not accumulated and handled specially.
if tensor.dtype == dtypes.resource:
return self._resource_capture_helper(tensor)
# No need to accumulate loop invariants. Capture them directly.
# The captured tensor gets resolved to the corresponding while output in
# `_resolve_grad_captures`.
if _is_loop_invariant(tensor, self._forward_graph_inputs,
self._forward_graph_outputs):
captured_tensor = super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
return captured_tensor
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
#
# Note: We clear the control dependencies to avoid a cycle in case a
# control tensor has an input path to an output of the forward While.
#
# E.g.:
# x = tf.while_loop(...)
# y = f(x)
# with tf.control_dependencies([y]):
# tf.gradients(y, x)
#
# Since the EmptyTensorList is fed back into the forward While, not
# removing the control edge would cause a cycle.
with self._forward_graph.outer_graph.as_default():
with util.clear_control_inputs():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations,
name=_build_accumulator_name(tensor))
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph, self)._capture_helper(
accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
self.popped_tensor_lists[ops.tensor_id(
captured_accumulator)] = new_tensor_list
return captured_tensor
def append(self, value): self.list_ = list_ops.tensor_list_push_back(self.list_, value)
def while_loop(cond,
body,
loop_vars,
shape_invariants=None,
maximum_iterations=None,
name=None):
"""Like tf.while_loop, except emits a single While op."""
maximum_iterations = _validate_and_convert_to_tensor(maximum_iterations)
# Keep the original loop_vars around to know which args were TensorArrays.
orig_loop_vars = loop_vars
# Cache its length since we use it at multiple places below.
len_orig_loop_vars = len(orig_loop_vars)
# Convert TensorArrays to their flow variables. These get converted back to
# TensorArrays before calling `cond` and `body`. See `wrapped_cond` and
# `wrapped_body` below.
loop_vars = list(_tensor_array_to_flow(orig_loop_vars))
loop_vars = nest.map_structure(
ops.internal_convert_to_tensor_or_indexed_slices, loop_vars)
if shape_invariants is not None:
nest.assert_same_structure(orig_loop_vars, shape_invariants)
else:
shape_invariants = nest.map_structure(lambda t: t.shape, loop_vars)
if not name:
name = "while"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
cond_name = util.unique_fn_name(scope, "cond")
body_name = util.unique_fn_name(scope, "body")
loop_counter = constant_op.constant(
0,
dtype=maximum_iterations.dtype
if maximum_iterations is not None else None,
name="loop_counter")
# Add loop counter needed for computing gradients.
loop_vars = [loop_counter] + loop_vars
shape_invariants = type(shape_invariants)([tensor_shape.scalar()
]) + shape_invariants
# Automatic control dependencies are added in defuns, but not in v1
# graphs. Propagate that behavior here.
add_control_dependencies = util.in_defun()
# Build a `cond` wrapper that can handle the extra counter loop_var.
def wrapped_cond(loop_counter, *args):
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
if maximum_iterations is None:
return cond(*_pack_sequence_as(orig_loop_vars, args))
else:
return math_ops.logical_and(
loop_counter < maximum_iterations,
cond(*_pack_sequence_as(orig_loop_vars, args)))
cond_graph = func_graph_module.func_graph_from_py_func(
cond_name,
wrapped_cond,
loop_vars, {},
signature=_build_signature(loop_vars, shape_invariants),
func_graph=util.WhileCondFuncGraph(cond_name),
add_control_dependencies=add_control_dependencies)
# Add external_captures of cond to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
loop_vars = loop_vars + cond_graph.external_captures
shape_invariants = shape_invariants + type(shape_invariants)(
[t.shape for t in cond_graph.external_captures])
def wrapped_body(loop_counter, *args):
"""Loop body augmented with counter update.
Args:
loop_counter: Loop counter which needs to be incremented in the body.
*args: List of args
args[:len_orig_loop_vars] - Args for the original loop body.
args[len_orig_loop_vars:] - External captures of cond. These get
passed through as is.
Returns:
A list of tensors the same length as args.
"""
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
outputs = body(
*_pack_sequence_as(orig_loop_vars, args[:len_orig_loop_vars]))
if not nest.is_sequence(outputs):
outputs = [outputs]
# Compare the structure of input and output of body converting the
# top-level tuples to list to be compatible with legacy while_loop.
nest.assert_same_structure(list(outputs), list(orig_loop_vars))
outputs = _tensor_array_to_flow(outputs)
# Return the external_captures of cond_graph as is, i.e., treat them as
# loop invariants.
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
return [loop_counter + 1] + list(outputs) + list(
args[len_orig_loop_vars:])
body_graph = func_graph_module.func_graph_from_py_func(
body_name,
wrapped_body,
loop_vars, {},
signature=_build_signature(loop_vars, shape_invariants),
func_graph=util.WhileBodyFuncGraph(body_name),
add_control_dependencies=add_control_dependencies)
# Add external captures of body to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
loop_vars = loop_vars + body_graph.external_captures
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
body_graph.outputs.extend(body_graph.internal_captures)
# Capture `external_captures` of `body_graph` in `cond_graph` so that it
# expects to receive those as arguments.
# TODO(b/118457764): Dedup tensors that are captured in both the cond and
# body. This logic already exists in cond_v2.
with cond_graph.as_default():
for external_capture in body_graph.external_captures:
assert external_capture not in cond_graph.captures, (
"Looks like both cond and body are capturing the same tensor %s. "
"This is not supported yet. For now consider passing,"
" this as a loop variable." % str(external_capture))
cond_graph.capture(external_capture)
# Export all tensors in the loop body that may be needed for gradient
# computation. We do this by accumulating the intermediate values in
# TensorLists.
intermediate_tensors = _get_intermediates(body_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=intermediate_tensor.shape,
max_num_elements=maximum_iterations)
loop_vars.append(tensor_list)
with cond_graph.as_default():
# Add a placeholder to cond_graph's inputs corresponding to the
# tensor_list.
cond_graph.capture(tensor_list)
with body_graph.as_default():
# Push the intermediate tensor to the tensor list. This captures the
# `tensor_list` as well.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_graph.outputs.append(appended_tensor_list)
# Make sure that the shapes of the loop outputs are compatible with the
# shape invariants, or the shapes of the loop vars if the invariants are not
# specified.
num_flattened_outputs = len(nest.flatten(orig_loop_vars))
_check_shapes_compat(
body_graph.outputs[1:1 + num_flattened_outputs],
nest.flatten(shape_invariants[1:1 + len_orig_loop_vars]),
nest.flatten(loop_vars[1:1 + len_orig_loop_vars]))
flattened_loop_vars = nest.flatten(loop_vars)
_check_num_inputs_outputs(cond_graph, body_graph,
len(flattened_loop_vars))
outputs = gen_functional_ops._while(
flattened_loop_vars,
util.create_new_tf_function(cond_graph),
util.create_new_tf_function(body_graph),
output_shapes=[t.shape for t in body_graph.outputs],
name=scope)
_copy_handle_data(body_graph.outputs, outputs)
util.maybe_set_lowering_attr(outputs[0].op)
_maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations)
# Return identities for each output of the While op, rather than the output
# of the While op directly. This makes pruning work if the output of
# while_loop() is fetched: the lowering pass converts the While outputs into
# IdentityN outputs, which if fetched will cause all ops in the body to be
# run (since it takes all exit ops as input). After lowering, each output
# identity op will end up with only the appropriate exit op as input.
outputs = tuple(array_ops.identity(t) for t in outputs)
# First var is loop counter.
outputs = _pack_sequence_as(orig_loop_vars,
outputs[1:1 + num_flattened_outputs])
flattened_outputs = nest.flatten(outputs)
if len(flattened_outputs) == 1:
return flattened_outputs[0]
else:
return outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name
"""The gradient of a While op produced by while_loop."""
body_graph = _get_body_graph(op)
# Set the incoming gradient of TensorArray handles to None. The gradient
# implementation currently assumes all resource tensors correspond to float32
# ResourceVariables, which can lead to runtime shape errors when used with a
# TensorArray. This is a workaround until TensorArrays are reimplemented with
# TensorLists instead of resources.
# Also set the incoming gradient of non-trainable inputs to None. It is
# possible that we receive non-None gradients for non-trainable types in
# nested while loops because we accumulate outputs of the inner while as
# variant tensors which are trainable and hence receive zeros_like tensors in
# the gradient pass. The non-trainable tensors then receive the popped zeros
# tensor from this zeros variant. The gradient for the loop vars corresponding
# to these tensors is None or zeros (this happens only if the loop var is
# accumulated as well) in _grad_fn so we reset these.
# TODO(b/118712257): Remove the IsTrainable filter once we can handle None
# output grads in _grad_fn.
grads = [
None if _is_tensor_array_handle(output)
or not gradients_impl.IsTrainable(output) else grad
for grad, output in zip(grads, op.outputs)
]
# Ensure that all non-resource trainable outputs have incoming gradients.
assert all(g is not None or o.dtype == dtypes.resource
or not gradients_impl.IsTrainable(o)
for o, g in zip(op.outputs, grads)
), "All trainable loop vars must receive incoming gradients."
# We compute the gradient for the sub-graph between trainable ys and xs
# with non-None incoming gradients. We later pad the None's to the list of
# outputs.
ys, xs, non_none_grads = zip(
*[(y, x, grad)
for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads)
if grad is not None])
body_grad_graph, args = _create_grad_func(
ys, xs, non_none_grads, body_graph,
util.unique_grad_fn_name(body_graph.name), op)
intermediate_tensors = _get_intermediates(body_grad_graph)
maximum_iterations = op.get_attr(
"_maximum_iterations") if _is_in_xla_context() else None
assert not _is_in_xla_context() or maximum_iterations is not None
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=intermediate_tensor.shape,
max_num_elements=maximum_iterations)
with body_grad_graph.as_default():
tensor_list_ph = body_grad_graph.capture(tensor_list,
whitelisted=True)
# Push the intermediate tensor to the tensor list.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list_ph, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_grad_graph.outputs.append(appended_tensor_list)
def grad_cond(counter, max_iters, *unused_args):
return counter < max_iters
loop_vars = args + body_grad_graph.external_captures
grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name)
cond_grad_graph = func_graph_module.func_graph_from_py_func(
grad_cond_name,
grad_cond,
loop_vars, {},
func_graph=util.WhileCondFuncGraph(grad_cond_name))
_check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars))
outputs = gen_functional_ops._while(
loop_vars,
util.create_new_tf_function(cond_grad_graph),
util.create_new_tf_function(body_grad_graph),
output_shapes=[t.shape for t in body_grad_graph.outputs],
name="%s_grad" % op.name)
_copy_handle_data(body_grad_graph.outputs, outputs)
util.maybe_set_lowering_attr(outputs[0].op)
_maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations)
# See comment in while_loop.
outputs = [array_ops.identity(t) for t in outputs]
# Set None as the output gradient for tensors with None input gradient
# e.g. TensorArray handles.
# outputs[0] is the loop counter.
# outputs[1] is the total number of loop iterations.
index = 2
none_padded_outputs = []
for g in grads:
if g is None:
none_padded_outputs.append(None)
else:
none_padded_outputs.append(outputs[index])
index += 1
return none_padded_outputs
def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
if tensor.dtype == dtypes.resource:
# Resource-type tensors are not accumulated.
# If a resource tensor exists in the loop body it must either be a loop
# input or an output of a nested While op inside the loop body which
# had captured the external resource.
if tensor in self._forward_graph.inputs:
index = self._forward_graph.inputs.index(tensor)
elif tensor.op.type == "While":
# Captured resources occur at the same index in the lists of inputs and
# outputs of a while op. So we lookup the input of `tensor.op` at the
# same index as the index of `tensor` in the `tensor.op.outputs`.
index = self._forward_graph.inputs.index(
tensor.op.inputs[tensor.value_index])
else:
raise ValueError(
"Taking gradient of a while loop which creates"
" a resource in its body is not supported: %s" %
str(tensor))
# This must be a loop invariant.
assert self._forward_graph.inputs[
index] == self._forward_graph.outputs[
index], "Resource tensors must be loop invariants %s." % str(
self._forward_graph._while.inputs[index])
tensor_in_outer_graph = self._forward_graph._while.inputs[index]
self._indirect_captures[tensor] = self.capture(
tensor_in_outer_graph, whitelisted=True)
return self._indirect_captures[tensor]
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
with self._forward_graph.outer_graph.as_default():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations)
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(
tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph,
self)._capture_helper(accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[tensor] = captured_tensor
self.popped_tensor_lists[captured_accumulator] = new_tensor_list
return captured_tensor
def while_loop(cond, body, loop_vars, shape_invariants=None, name=None):
"""Like tf.while_loop, except emits a single While op."""
flattened_loop_vars = nest.flatten(loop_vars)
if shape_invariants is not None:
nest.assert_same_structure(loop_vars, shape_invariants)
flattened_shapes = nest.flatten(shape_invariants)
else:
flattened_shapes = [t.shape for t in flattened_loop_vars]
del shape_invariants
if not name:
name = "while"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
cond_name = util.unique_fn_name(scope, "cond")
body_name = util.unique_fn_name(scope, "body")
num_outputs = len(flattened_loop_vars)
# Add loop counter needed for computing gradients.
flattened_loop_vars = [constant_op.constant(0., name="loop_counter")
] + flattened_loop_vars
flattened_shapes = [tensor_shape.scalar()] + flattened_shapes
# Build a `cond` wrapper that can handle the extra counter loop_var.
def wrapped_cond(unused_loop_counter, *loop_vars):
return cond(*loop_vars)
signature = [
tensor_spec.TensorSpec(shape, t.dtype)
for shape, t in zip(flattened_shapes, flattened_loop_vars)
]
cond_graph = function.func_graph_from_py_func(
cond_name,
wrapped_cond,
flattened_loop_vars, {},
signature=signature,
func_graph=util.WhileCondFuncGraph(cond_name))
# Add external_captures of cond to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
flattened_loop_vars = flattened_loop_vars + cond_graph.external_captures
flattened_shapes = flattened_shapes + [
t.shape for t in cond_graph.external_captures
]
def wrapped_body(loop_counter, *args):
"""Loop body augmented with counter update.
Args:
loop_counter: Loop counter which needs to be incremented in the body.
*args: List of args
args[:num_outputs] - Args for the original loop body.
args[num_outputs:] - External captures of cond. These get passed
through as is.
Returns:
A list of tensors the same length as args.
"""
outputs = body(*args[:num_outputs])
if not isinstance(outputs, collections.Sequence):
outputs = [outputs]
# Return the external_captures of cond_graph as is, i.e., treat them as
# loop invariants.
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
return [loop_counter + 1] + list(outputs) + list(
args[num_outputs:])
signature = [
tensor_spec.TensorSpec(shape, t.dtype)
for shape, t in zip(flattened_shapes, flattened_loop_vars)
]
body_graph = function.func_graph_from_py_func(
body_name,
wrapped_body,
flattened_loop_vars, {},
signature=signature,
func_graph=util.WhileBodyFuncGraph(body_name))
# Add external captures of body to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
flattened_loop_vars = flattened_loop_vars + body_graph.external_captures
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
body_graph.outputs.extend(body_graph.internal_captures)
# Capture `external_captures` of `body_graph` in `cond_graph` so that it
# expects to receive those as arguments.
# TODO(srbs): Dedup tensors that are captured in both the cond and body.
# This logic already exists in cond_v2.
with cond_graph.as_default():
for external_capture in body_graph.external_captures:
cond_graph.capture(external_capture)
# Export all tensors in the loop body that may be needed for gradient
# computation. We do this by accumulating the intermediate values in
# TensorLists.
intermediate_tensors = _get_intermediates(body_graph)
for intermediate_tensor in intermediate_tensors:
# TODO(srbs): Cache and re-use empty tensor lists.
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=_get_tensor_convertible_shape(
intermediate_tensor.shape))
flattened_loop_vars.append(tensor_list)
with cond_graph.as_default():
# Add a placeholder to cond_graph's inputs corresponding to the
# tensor_list.
cond_graph.capture(tensor_list)
with body_graph.as_default():
# Push the intermediate tensor to the tensor list. This captures the
# `tensor_list` as well.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_graph.outputs.append(appended_tensor_list)
# Make sure that the shapes of the loop outputs are compatible with the
# shape invariants, or the shapes of the loop vars if the invariants are not
# specified.
_check_shapes_compat(body_graph.outputs[1:1 + num_outputs],
flattened_shapes[1:1 + num_outputs],
flattened_loop_vars[1:1 + num_outputs])
outputs = gen_functional_ops._while(
flattened_loop_vars,
util.create_new_tf_function(cond_graph),
util.create_new_tf_function(body_graph),
output_shapes=[t.shape for t in body_graph.outputs],
name=scope)
_copy_handle_data(body_graph.outputs, outputs)
_maybe_set_lowering_attr(outputs[0].op)
# First var is loop counter.
if num_outputs == 1:
return outputs[1]
else:
return nest.pack_sequence_as(loop_vars, outputs[1:1 + num_outputs])
def while_loop(cond,
body,
loop_vars,
shape_invariants=None,
parallel_iterations=10,
maximum_iterations=None,
name=None,
return_same_structure=True,
back_prop=True):
"""Like tf.while_loop, except emits a single While op."""
# Keep the original loop_vars around to know which args were TensorArrays.
orig_loop_vars = loop_vars
# Cache its length since we use it at multiple places below.
len_orig_loop_vars = len(orig_loop_vars)
# Convert TensorArrays to their flow variables. These get converted back to
# TensorArrays before calling `cond` and `body`. See `wrapped_cond` and
# `wrapped_body` below.
loop_vars = list(_tensor_array_to_flow(orig_loop_vars))
loop_vars = nest.map_structure(
ops.internal_convert_to_tensor_or_indexed_slices, loop_vars,
expand_composites=True)
if shape_invariants is not None:
nest.assert_same_structure(orig_loop_vars, shape_invariants,
expand_composites=False)
signature = nest.map_structure(
control_flow_ops._shape_invariant_to_type_spec, loop_vars,
list(shape_invariants), expand_composites=False)
shape_invariants = nest.map_structure(
control_flow_ops._get_shape_invariant, loop_vars,
list(shape_invariants), expand_composites=False)
else:
signature = nest.map_structure(
type_spec.type_spec_from_value, loop_vars, expand_composites=False)
shape_invariants = nest.map_structure(
control_flow_ops._get_shape_invariant, loop_vars,
expand_composites=False)
if not name:
name = "while"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
cond_name = util.unique_fn_name(scope, "cond")
body_name = util.unique_fn_name(scope, "body")
maximum_iterations_loop_var = _build_maximum_iterations_loop_var(
maximum_iterations)
loop_counter = constant_op.constant(
0,
dtype=maximum_iterations_loop_var.dtype
if maximum_iterations is not None else None,
name="loop_counter")
# Add loop counter needed for computing gradients.
loop_vars = [loop_counter, maximum_iterations_loop_var] + loop_vars
shape_invariants = [tensor_shape.TensorShape([])] * 2 + shape_invariants
signature = (
[tensor_spec.TensorSpec.from_tensor(loop_counter),
tensor_spec.TensorSpec.from_tensor(maximum_iterations_loop_var)] +
signature)
# Automatic control dependencies are added in defuns, but not in v1
# graphs. Propagate that behavior here.
add_control_dependencies = ops.get_default_graph()._add_control_dependencies
def wrapped_cond(loop_counter, maximum_iterations_arg, *args):
"""Extra `cond` wrapper that can handle the extra counter loop_var."""
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
pred = cond(*_pack_sequence_as(orig_loop_vars, args))
if (tensor_util.is_tensor(pred) and
(pred.shape.dims is None or pred.shape.dims)):
pred = array_ops.squeeze_v2(pred)
if maximum_iterations is None:
return pred
else:
return math_ops.logical_and(
loop_counter < maximum_iterations_arg, pred)
# NOTE(skyewm): we set collections to the outer graph's collections for
# compatibility with TPUEstimator.
cond_graph = func_graph_module.func_graph_from_py_func(
cond_name,
wrapped_cond,
[], # We provide signature instead of args.
{},
signature=signature,
func_graph=util.WhileCondFuncGraph(
cond_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies)
def wrapped_body(loop_counter, maximum_iterations_arg, *args):
"""Loop body augmented with counter update.
Args:
loop_counter: Loop counter which needs to be incremented in the body.
maximum_iterations_arg: Maximum iterations of the loop.
*args: List of args
Returns:
A list of tensors the same length as args.
"""
# Capture the tensors already captured in cond_graph so that they appear
# in the same order in body_graph.external_captures.
for t in cond_graph.external_captures:
ops.get_default_graph().capture(t)
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
outputs = body(*_pack_sequence_as(orig_loop_vars, args))
if not nest.is_sequence_or_composite(outputs):
outputs = [outputs]
# Compare the structure of input and output of body converting the
# top-level tuples to list to be compatible with legacy while_loop.
nest.assert_same_structure(list(outputs), list(orig_loop_vars),
expand_composites=True)
outputs = _tensor_array_to_flow(outputs)
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
return [loop_counter + 1, maximum_iterations_arg] + list(outputs)
body_graph = func_graph_module.func_graph_from_py_func(
body_name,
wrapped_body,
[], # We provide signature instead of args.
{},
signature=signature,
func_graph=util.WhileBodyFuncGraph(
body_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies)
# Add external captures of body to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
loop_vars = loop_vars + body_graph.external_captures
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
body_graph.outputs.extend(body_graph.internal_captures)
# Capture the extra `external_captures` of `body_graph` in `cond_graph` so
# that it expects to receive those as arguments.
with cond_graph.as_default():
num_cond_captures = len(cond_graph.external_captures)
assert (cond_graph.external_captures ==
body_graph.external_captures[:num_cond_captures])
cond_graph_captures = object_identity.ObjectIdentitySet(
cond_graph.external_captures)
for body_capture in body_graph.external_captures[num_cond_captures:]:
assert body_capture not in cond_graph_captures
cond_graph.capture(body_capture)
# Make sure that the shapes of the loop outputs are compatible with the
# shape invariants, or the shapes of the loop vars if the invariants are not
# specified.
num_flattened_outputs = len(nest.flatten(orig_loop_vars,
expand_composites=True))
# First var is loop counter and second var is maximum_iterations.
first_loop_var_index = 2
_check_shapes_compat(
body_graph.outputs[first_loop_var_index:first_loop_var_index +
num_flattened_outputs],
nest.flatten(
shape_invariants[first_loop_var_index:first_loop_var_index +
len_orig_loop_vars], expand_composites=True),
nest.flatten(loop_vars[first_loop_var_index:first_loop_var_index +
len_orig_loop_vars], expand_composites=True))
num_original_outputs = len(body_graph.outputs)
if back_prop and util.output_all_intermediates():
# Export all tensors in the loop body that may be needed for gradient
# computation. We do this by accumulating the intermediate values in
# TensorLists.
intermediate_tensors = _get_intermediates(body_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=intermediate_tensor.shape,
max_num_elements=maximum_iterations)
loop_vars.append(tensor_list)
with cond_graph.as_default():
# Add a placeholder to cond_graph's inputs corresponding to the
# tensor_list.
cond_graph.capture(tensor_list)
with body_graph.as_default():
# Push the intermediate tensor to the tensor list. This captures the
# `tensor_list` as well.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_graph.outputs.append(appended_tensor_list)
flattened_loop_vars = nest.flatten(loop_vars, expand_composites=True)
_check_num_inputs_outputs(cond_graph, body_graph,
len(flattened_loop_vars))
_check_inputs_outputs_types_match(body_graph, flattened_loop_vars)
with ops.control_dependencies(
list(cond_graph.control_captures) + list(body_graph.control_captures)):
output_shapes = [t.shape for t in body_graph.outputs]
orig_loop_vars_range = slice(first_loop_var_index,
first_loop_var_index + num_flattened_outputs)
output_shapes[orig_loop_vars_range] = nest.flatten(
shape_invariants, expand_composites=True)[orig_loop_vars_range]
cond_stateful_ops = [
op for op in cond_graph.get_operations() if op._is_stateful
]
body_stateful_ops = [
op for op in body_graph.get_operations() if op._is_stateful
]
if (cond_stateful_ops or body_stateful_ops):
op_fn = gen_functional_ops._while
else:
op_fn = gen_functional_ops.stateless_while
outputs = op_fn(
flattened_loop_vars,
util.create_new_tf_function(cond_graph),
util.create_new_tf_function(body_graph),
output_shapes=output_shapes,
parallel_iterations=parallel_iterations,
name=scope)
# This is needed so we do not compute derivative wrt these extra outputs.
outputs[0].op._set_attr("_num_original_outputs",
attr_value_pb2.AttrValue(i=num_original_outputs))
_copy_handle_data(body_graph.outputs, outputs)
util.maybe_set_lowering_attr(outputs[0].op)
util.maybe_propagate_compile_time_consts_in_xla(outputs[0].op)
# Return identities for each output of the While op, rather than the output
# of the While op directly. This makes pruning work if the output of
# while_loop() is fetched: the lowering pass converts the While outputs into
# IdentityN outputs, which if fetched will cause all ops in the body to be
# run (since it takes all exit ops as input). After lowering, each output
# identity op will end up with only the appropriate exit op as input.
outputs = tuple(array_ops.identity(t) for t in outputs)
outputs = _pack_sequence_as(
orig_loop_vars, outputs[first_loop_var_index:first_loop_var_index +
num_flattened_outputs])
if return_same_structure:
return outputs
flattened_outputs = nest.flatten(outputs, expand_composites=True)
if len(flattened_outputs) == 1:
return flattened_outputs[0]
else:
return outputs
def Body(x, tl):
tl = list_ops.tensor_list_push_back(tl, x)
tl = list_ops.tensor_list_push_back(tl, constant_op.constant(100.))
return x**2., tl
def body(i, t1):
t1 = list_ops.tensor_list_push_back(t1, i)
i += 1
return i, t1
def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# Resource tensors are not accumulated and handled specially.
if tensor.dtype == dtypes.resource:
return self._resource_capture_helper(tensor)
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
#
# Note: We clear the control dependencies to avoid a cycle in case a
# control tensor has an input path to an output of the forward While.
#
# E.g.:
# x = tf.while_loop(...)
# y = f(x)
# with tf.control_dependencies([y]):
# tf.gradients(y, x)
#
# Since the EmptyTensorList is fed back into the forward While, not
# removing the control edge would cause a cycle.
with self._forward_graph.outer_graph.as_default():
with util.clear_control_inputs():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations,
name=_build_accumulator_name(tensor))
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(
tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph,
self)._capture_helper(accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[tensor] = captured_tensor
self.popped_tensor_lists[captured_accumulator] = new_tensor_list
return captured_tensor
def _WhileGrad(op, *grads): # pylint: disable=invalid-name
"""The gradient of a While op produced by while_loop."""
body_graph = _get_body_graph(op)
# Set the incoming gradient of TensorArray handle to None.
# TODO(b/118164915): We need a way of distinguising b/w TensorArray resource
# handles and ResourceVariables and set the default gradient of only the
# TensorArray handle to None.
grads = [
None if output.dtype == dtypes.resource else g
for g, output in zip(grads, op.outputs)
]
# Ensure that all non-resource trainable outputs have incoming gradients.
assert all(g is not None or o.dtype == dtypes.resource
or not gradients_impl.IsTrainable(o)
for o, g in zip(op.outputs, grads)
), "All trainable loop vars must receive incoming gradients."
# We compute the gradient for the sub-graph between trainable ys and xs
# with non-None incoming gradients. We later pad the None's to the list of
# outputs.
ys, xs, non_none_grads = zip(
*[(y, x, grad)
for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads)
if grad is not None])
body_grad_graph, args = _create_grad_func(
ys, xs, non_none_grads, body_graph,
util.unique_grad_fn_name(body_graph.name), op)
intermediate_tensors = _get_intermediates(body_grad_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=_get_tensor_convertible_shape(
intermediate_tensor.shape))
with body_grad_graph.as_default():
tensor_list_ph = body_grad_graph.capture(tensor_list,
whitelisted=True)
# Push the intermediate tensor to the tensor list.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list_ph, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_grad_graph.outputs.append(appended_tensor_list)
def grad_cond(counter, max_iters, *unused_args):
return counter < max_iters
loop_vars = args + body_grad_graph.external_captures
grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name)
cond_grad_graph = func_graph_module.func_graph_from_py_func(
grad_cond_name,
grad_cond,
loop_vars, {},
func_graph=util.WhileCondFuncGraph(grad_cond_name))
_check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars))
outputs = gen_functional_ops._while(
loop_vars,
util.create_new_tf_function(cond_grad_graph),
util.create_new_tf_function(body_grad_graph),
output_shapes=[t.shape for t in body_grad_graph.outputs],
name="%s_grad" % op.name)
_copy_handle_data(body_grad_graph.outputs, outputs)
_maybe_set_lowering_attr(outputs[0].op)
# Set None as the output gradient for tensors with None input gradient
# e.g. TensorArray handles.
# outputs[0] is the loop counter.
# outputs[1] is the total number of loop iterations.
index = 2
none_padded_outputs = []
for g in grads:
if g is None:
none_padded_outputs.append(None)
else:
none_padded_outputs.append(outputs[index])
index += 1
return none_padded_outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name
"""The gradient of a While op produced by while_loop."""
body_graph = _get_body_graph(op)
# Replace None gradients with zeros. This is needed because `grads` could have
# None incoming gradients for the TensorLists. If we pass None's through, the
# custom gradient of TensorListPopBack will create an EmptyTensorList inside
# the FuncGraph which is undesirable.
# TODO(b/80444525): There might be an issue with treating no gradient as zero
# gradient in certain cases. Consider replacing None gradients with Zeros
# for accumulators only.
grads = [
g if g is not None else array_ops.zeros_like(output)
for g, output in zip(grads, op.outputs)
]
body_grad_graph, args = _create_grad_func(
body_graph, grads, util.unique_grad_fn_name(body_graph.name), op)
intermediate_tensors = _get_intermediates(body_grad_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=_get_tensor_convertible_shape(
intermediate_tensor.shape))
with body_grad_graph.as_default():
tensor_list_ph = body_grad_graph.capture(tensor_list,
whitelisted=True)
# Push the intermediate tensor to the tensor list.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list_ph, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_grad_graph.outputs.append(appended_tensor_list)
def grad_cond(counter, max_iters, *unused_args):
return counter < max_iters
loop_vars = args + body_grad_graph.external_captures
grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name)
cond_grad_graph = function.func_graph_from_py_func(
grad_cond_name,
grad_cond,
loop_vars, {},
func_graph=util.WhileCondFuncGraph(grad_cond_name))
assert len(loop_vars) == len(body_grad_graph.inputs)
assert len(loop_vars) == len(body_grad_graph.outputs)
assert len(loop_vars) == len(cond_grad_graph.inputs)
outputs = gen_functional_ops._while(
loop_vars,
util.create_new_tf_function(cond_grad_graph),
util.create_new_tf_function(body_grad_graph),
output_shapes=[t.shape for t in body_grad_graph.outputs],
name="%s_grad" % op.name)
_copy_handle_data(body_grad_graph.outputs, outputs)
_maybe_set_lowering_attr(outputs[0].op)
# outputs[0] is the loop counter.
# outputs[1] is the total number of loop iterations.
return outputs[2:2 + len(op.inputs)]
