def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
true_name = util.unique_fn_name(scope, "true")
false_name = util.unique_fn_name(scope, "false")
# 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
pred = ops.convert_to_tensor(pred)
if (tensor_util.is_tensor(pred)
and (pred.shape.dims is None or pred.shape.dims)):
pred = array_ops.squeeze_v2(pred)
true_graph = func_graph_module.func_graph_from_py_func(
true_name,
true_fn,
[],
{},
func_graph=util.CondBranchFuncGraph(
true_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
false_graph = func_graph_module.func_graph_from_py_func(
false_name,
false_fn,
[],
{},
func_graph=util.CondBranchFuncGraph(
false_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
verify_captures(_COND, [true_graph, false_graph])
return _build_cond(pred,
true_graph,
false_graph,
true_graph.external_captures,
false_graph.external_captures,
building_gradient=False,
name=scope)
def indexed_case(branch_index, branch_fns, name="indexed_case"):
"""Like conv_v2, except emits a Case op instead of an If."""
if isinstance(branch_index, int):
raise TypeError("branch_index must not be a Python int", branch_index)
with ops.name_scope(name) as scope:
branch_names = [
util.unique_fn_name(scope, "branch{}".format(b))
for b in range(len(branch_fns))
]
# 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
branch_index = ops.convert_to_tensor(branch_index, name="branch_index")
branch_graphs = []
for branch_name, branch_fn in zip(branch_names, branch_fns):
branch_graphs.append(
func_graph_module.func_graph_from_py_func(
branch_name,
branch_fn,
[],
{},
func_graph=util.CondBranchFuncGraph(
branch_name,
collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=branch_index))
verify_captures(_CASE, branch_graphs)
return _build_case(
branch_index,
branch_graphs, [g.external_captures for g in branch_graphs],
name=scope)
def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
true_name = util.unique_fn_name(scope, "true")
false_name = util.unique_fn_name(scope, "false")
# 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
pred = ops.convert_to_tensor(pred)
true_graph = func_graph_module.func_graph_from_py_func(
true_name,
true_fn,
[],
{},
func_graph=util.CondBranchFuncGraph(
true_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
false_graph = func_graph_module.func_graph_from_py_func(
false_name,
false_fn,
[],
{},
func_graph=util.CondBranchFuncGraph(
false_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
outputs = _build_cond(pred,
true_graph,
false_graph,
true_graph.external_captures,
false_graph.external_captures,
name=scope)
return func_graph_module.pack_sequence_as(
true_graph.structured_outputs, outputs)
def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
# Find the outer most graph for uniquing function names.
# TODO(jpienaar): Make this work in eager mode.
graph = ops.get_default_graph()
while isinstance(graph, function.FuncGraph):
graph = graph.outer_graph
true_name = graph.unique_name(("%strue" % scope).replace("/", "_"))
false_name = graph.unique_name(("%sfalse" % scope).replace("/", "_"))
# 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
pred = ops.convert_to_tensor(pred)
true_graph = func_graph_module.func_graph_from_py_func(
true_name,
true_fn, [], {},
func_graph=util.CondBranchFuncGraph(
true_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
false_graph = func_graph_module.func_graph_from_py_func(
false_name,
false_fn, [], {},
func_graph=util.CondBranchFuncGraph(
false_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
verify_captures(true_graph, false_graph)
return _build_cond(pred, true_graph, false_graph,
true_graph.external_captures,
false_graph.external_captures,
name=scope)
def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
true_name = util.unique_fn_name(scope, "true")
false_name = util.unique_fn_name(scope, "false")
# Automatic control dependencies are added in defuns, but not in v1
# graphs. Propagate that behavior here.
add_control_dependencies = util.in_defun()
pred = ops.convert_to_tensor(pred)
true_graph = func_graph_module.func_graph_from_py_func(
true_name,
true_fn, [], {},
func_graph=util.CondBranchFuncGraph(
true_name, read_only_collections=False),
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
false_graph = func_graph_module.func_graph_from_py_func(
false_name,
false_fn, [], {},
func_graph=util.CondBranchFuncGraph(
false_name, read_only_collections=False),
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
_check_same_outputs(true_graph, false_graph)
# Add inputs to true_graph and false_graph to make them match. Note that
# this modifies true_graph and false_graph.
cond_inputs = _make_inputs_match(true_graph, false_graph,
true_graph.external_captures,
false_graph.external_captures)
# Add all intermediate tensors as function outputs so they're available for
# the gradient computation.
true_intermediates = _get_intermediates(true_graph)
false_intermediates = _get_intermediates(false_graph)
# Save the original number of outputs to return to the caller.
num_cond_outputs = len(true_graph.outputs)
# Make the number/type of new intermediate outputs match.
extra_true_outputs, extra_false_outputs = _pad_params(
true_graph, false_graph, true_intermediates, false_intermediates)
true_graph.outputs.extend(extra_true_outputs)
false_graph.outputs.extend(extra_false_outputs)
# Create the If op.
tensors = gen_functional_ops._if( # pylint: disable=protected-access
pred,
cond_inputs, [t.dtype for t in true_graph.outputs],
util.create_new_tf_function(true_graph),
util.create_new_tf_function(false_graph),
output_shapes=_get_output_shapes(true_graph.outputs,
false_graph.outputs),
name=scope)
# TODO(b/110167197) this approach requires cond_v2 to have at least 1 output
util.maybe_set_lowering_attr(tensors[0].op)
# Return identities for each output of the If op, rather than the output of
# the If op directly. This makes pruning work if the output of cond() is
# fetched: the lowering pass converts the If outputs into IdentityN outputs,
# which if fetched will cause all ops in the taken branch to be run (since
# it takes all merge ops as input). After lowering, each output identity op
# will end up with only the appropriate merge op as input.
# TODO(b/79984175): this doesn't have to be a tuple once we covert to the
# correct output structure
tensors = tuple(array_ops.identity(t) for t in tensors)
return func_graph_module.pack_sequence_as(true_graph.structured_outputs,
tensors[:num_cond_outputs])
def _create_grad_func(func_graph, grads, name):
"""Returns the FuncGraph representation of _grad_fn."""
return func_graph_module.func_graph_from_py_func(
name,
lambda: _grad_fn(func_graph, grads), [], {},
func_graph=util.CondBranchFuncGraph(name, read_only_collections=False))
def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
# Find the outer most graph for uniquing function names.
# TODO(jpienaar): Make this work in eager mode.
graph = ops.get_default_graph()
while isinstance(graph, function.FuncGraph):
graph = graph.outer_graph
true_name = graph.unique_name(("%strue" % scope).replace("/", "_"))
false_name = graph.unique_name(
("%sfalse" % scope).replace("/", "_"))
true_graph = function.func_graph_from_py_func(
true_name,
true_fn, [], {},
func_graph=util.CondBranchFuncGraph(true_name))
false_graph = function.func_graph_from_py_func(
false_name,
false_fn, [], {},
func_graph=util.CondBranchFuncGraph(false_name))
_check_same_outputs(true_graph, false_graph)
# Add inputs to true_graph and false_graph to make them match. Note that
# this modifies true_graph and false_graph.
cond_inputs = _make_inputs_match(true_graph, false_graph,
true_graph.external_captures,
false_graph.external_captures)
# Add all intermediate tensors as function outputs so they're available for
# the gradient computation.
true_intermediates = _get_intermediates(true_graph)
false_intermediates = _get_intermediates(false_graph)
# Save the original number of outputs to return to the caller.
num_cond_outputs = len(true_graph.outputs)
# Make the number/type of new intermediate outputs match.
extra_true_outputs, extra_false_outputs = _pad_params(
true_graph, false_graph, true_intermediates, false_intermediates)
true_graph.outputs.extend(extra_true_outputs)
false_graph.outputs.extend(extra_false_outputs)
# Create the If op.
tensors = gen_functional_ops._if( # pylint: disable=protected-access
pred,
cond_inputs, [t.dtype for t in true_graph.outputs],
util.create_new_tf_function(true_graph),
util.create_new_tf_function(false_graph),
output_shapes=_get_output_shapes(true_graph.outputs,
false_graph.outputs),
name=scope)
# Set the flag to enable lowering on the `if` op if necessary
# Lowering allows cond_v2 to avoid some of the limitations of Functions,
# allowing users to specify devices & colocation inside of cond_v2 branches,
# and enabling non-strict evaluation & partial pruning of cond_v2 branches.
# This brings cond_v2 closer to feature parity with tf.cond.
#
# However, we do not lower `If` in the XLA context because it is easier for
# XLA to apply its own optimizations when dealing with un-lowered `If`
# operators than with lowered switch/merge control flow.
#
# TODO(b/110167197) this approach requires cond_v2 to have at least 1 output
if_op = tensors[0].op
if not control_flow_util.IsInXLAContext(if_op):
# pylint: disable=protected-access
if_op._set_attr("_lower_using_switch_merge",
attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
result = tuple(tensors[:num_cond_outputs])
if len(result) == 1:
return result[0]
else:
return result
def cond_v2(pred, true_fn, false_fn, name="cond"):
"""Like tf.cond, except emits a single If op."""
if isinstance(pred, bool):
raise TypeError("pred must not be a Python bool", pred)
if not name:
name = "cond"
with ops.name_scope(name) as scope:
true_name = util.unique_fn_name(scope, "true")
false_name = util.unique_fn_name(scope, "false")
# Automatic control dependencies are added in defuns, but not in v1
# graphs. Propagate that behavior here.
add_control_dependencies = util.in_defun()
pred = ops.convert_to_tensor(pred)
true_graph = func_graph_module.func_graph_from_py_func(
true_name,
true_fn, [], {},
func_graph=util.CondBranchFuncGraph(true_name,
read_only_collections=False),
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
false_graph = func_graph_module.func_graph_from_py_func(
false_name,
false_fn, [], {},
func_graph=util.CondBranchFuncGraph(false_name,
read_only_collections=False),
add_control_dependencies=add_control_dependencies,
op_return_value=pred)
_check_same_outputs(true_graph, false_graph)
# Add inputs to true_graph and false_graph to make them match. Note that
# this modifies true_graph and false_graph.
cond_inputs = _make_inputs_match(true_graph, false_graph,
true_graph.external_captures,
false_graph.external_captures)
# Add all intermediate tensors as function outputs so they're available for
# the gradient computation.
true_intermediates = _get_intermediates(true_graph)
false_intermediates = _get_intermediates(false_graph)
# Save the original number of outputs to return to the caller.
num_cond_outputs = len(true_graph.outputs)
# Make the number/type of new intermediate outputs match.
extra_true_outputs, extra_false_outputs = _pad_params(
true_graph, false_graph, true_intermediates, false_intermediates)
true_graph.outputs.extend(extra_true_outputs)
false_graph.outputs.extend(extra_false_outputs)
# Create the If op.
tensors = gen_functional_ops._if( # pylint: disable=protected-access
pred,
cond_inputs, [t.dtype for t in true_graph.outputs],
util.create_new_tf_function(true_graph),
util.create_new_tf_function(false_graph),
output_shapes=_get_output_shapes(true_graph.outputs,
false_graph.outputs),
name=scope)
# Set the flag to enable lowering on the `if` op if necessary
# Lowering allows cond_v2 to avoid some of the limitations of Functions,
# allowing users to specify devices & colocation inside of cond_v2 branches,
# and enabling non-strict evaluation & partial pruning of cond_v2 branches.
# This brings cond_v2 closer to feature parity with tf.cond.
#
# However, we do not lower `If` in the XLA context because it is easier for
# XLA to apply its own optimizations when dealing with un-lowered `If`
# operators than with lowered switch/merge control flow.
#
# TODO(b/110167197) this approach requires cond_v2 to have at least 1 output
if_op = tensors[0].op
if not control_flow_util.IsInXLAContext(if_op):
# pylint: disable=protected-access
if_op._set_attr("_lower_using_switch_merge",
attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
# Return identities for each output of the If op, rather than the output of
# the If op directly. This makes pruning work if the output of cond() is
# fetched: the lowering pass converts the If outputs into IdentityN outputs,
# which if fetched will cause all ops in the taken branch to be run (since
# it takes all merge ops as input). After lowering, each output identity op
# will end up with only the appropriate merge op as input.
# TODO(b/79984175): this doesn't have to be a tuple once we covert to the
# correct output structure
tensors = tuple(array_ops.identity(t) for t in tensors)
result = tuple(tensors[:num_cond_outputs])
if len(result) == 1:
return result[0]
else:
return result
