def testRefIdentityShape(self):
with self.test_session():
shape = [2, 3]
tensor = tf.Variable(
tf.constant([[1, 2, 3], [6, 5, 4]], dtype=tf.int32))
self.assertEquals(shape, tensor.get_shape())
self.assertEquals(shape,
gen_array_ops._ref_identity(tensor).get_shape())
def testColocationContraints(self):
with ops.Graph().as_default() as g:
c = constant_op.constant([10])
v = variables.Variable([3], dtype=dtypes.int32)
i = gen_array_ops._ref_identity(v)
a = state_ops.assign(i, c)
train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
train_op.append(a)
mg = meta_graph.create_meta_graph_def(graph=g)
grappler_item = item.Item(mg)
groups = grappler_item.GetColocationGroups()
self.assertEqual(len(groups), 1)
self.assertItemsEqual(
groups[0], ['Assign', 'RefIdentity', 'Variable', 'Variable/Assign'])
def _Identity(data, name=None):
"""Return a tensor with the same shape and contents as the input tensor.
Args:
data: A Tensor.
name: A name for this operation (optional).
Returns:
A Tensor with the same type and value as the input Tensor.
"""
if not data.dtype.is_ref_dtype:
return array_ops.identity(data, name=name)
else:
return gen_array_ops._ref_identity(data, name=name)
def testColocationContraints(self):
with ops.Graph().as_default() as g:
c = constant_op.constant([10])
v = variables.Variable([3], dtype=dtypes.int32)
i = gen_array_ops._ref_identity(v)
a = state_ops.assign(i, c)
train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
train_op.append(a)
mg = meta_graph.create_meta_graph_def(graph=g)
grappler_item = item.Item(mg)
groups = grappler_item.GetColocationGroups()
self.assertEqual(len(groups), 1)
self.assertEqual(
sorted(groups[0]),
['Assign', 'RefIdentity', 'Variable', 'Variable/Assign'])
def create_op(self, *args, **kwargs):
"""Creates an `Operation`.
For operations of the following form
orig_value = op(*args, **kwargs)
this function constructs the following subgraph :
v = Variable()
if v is not initialized:
orig_value = op(*args, **kwargs)
v.assign(orig_value) # Initializes v
return orig_value
else:
return v
The above transformation is not performed and the original op is returned
as is if any of the following is true:
* `_return_as_is` flag is set to true.
* op_type is listed in _PASS_THROUGH_OPS
* op has no outputs.
* One of the op's return value has a ref type.
Args:
*args: Arguments for create_op()
**kwargs: Keyword arguments for create_op(). Refer to
tensorflow.python.framework.ops.Graph.create_op() for the mandatory
and optional arguments.
Returns:
An Operation.
Raises:
UnimplementedError: if output type is a reference and the op's type
is not one of the supported types in `_REF_OPS_WHITELIST`.
"""
op_type = kwargs['op_type'] if 'op_type' in kwargs else args[0]
output_dtypes = kwargs['dtypes'] if 'dtypes' in kwargs else args[2]
output_dtypes = [dtypes.as_dtype(d) for d in output_dtypes]
if self._return_as_is or op_type in _PASS_THROUGH_OPS:
return self._wrap(
super(ImperativeGraph, self).create_op(*args, **kwargs))
if not output_dtypes:
return self._wrap(
super(ImperativeGraph, self).create_op(*args, **kwargs))
output_has_ref = any([dtype._is_ref_dtype for dtype in output_dtypes]) # pylint: disable=protected-access
if output_has_ref:
if op_type not in _REF_OPS_WHITELIST:
raise errors.UnimplementedError(
None, None, op_type + ' op not supported in '
'imperative graph')
ret = super(ImperativeGraph, self).create_op(*args, **kwargs)
if self._in_variable_creation:
if op_type == 'Assign':
self.add_pending_init(ret)
return self._wrap(ret)
with self.return_as_is():
# Declares the variables to hold the output values of this op.
op_output_var = [
state_ops.variable_op_v2(tensor_shape.TensorShape(None),
dtype,
container=self._name)
for dtype in output_dtypes
]
# Ops to free the resources used by the temporary cache variables.
# The following two ops are created for each cache variable,
# having no control dependencies on any other ops :
# var_handle_op ----> destroy_resource_op
for dtype, v in zip(output_dtypes, op_output_var):
with ops.control_dependencies(None):
self._variable_cleanup_ops += [
gen_resource_variable_ops.destroy_resource_op(
gen_resource_variable_ops.var_handle_op(
dtype,
tensor_shape.TensorShape(None),
container=self._name,
shared_name=v.op.name),
ignore_lookup_error=True)
]
# Create the conditional to run the original op only when the variable
# corresponding to the first output is not initialized.
inited = state_ops.is_variable_initialized(op_output_var[0])
v_f, v_t = control_flow_ops.ref_switch(op_output_var[0], inited)
# pylint: disable=protected-access
v_f_op = gen_array_ops._ref_identity(v_f)
v_t_op = gen_array_ops._ref_identity(v_t)
# pylint: enable=protected-access
with ops.control_dependencies([v_f_op.op]):
# Create the original op
orig_op = self._wrap(
super(ImperativeGraph, self).create_op(*args, **kwargs))
shapes = [val.get_shape() for val in orig_op.outputs]
controls = []
for var, val in zip(op_output_var, orig_op.outputs):
if (not val.get_shape().is_fully_defined()
or val.get_shape().num_elements() > 0):
assign_op = state_ops.assign(var,
val,
validate_shape=False)
assign_op.set_shape(val.get_shape())
controls.append(assign_op)
values = []
if len(controls) > 1:
if control_flow_ops.IsSwitch(orig_op):
# pylint: disable=protected-access
controls = gen_control_flow_ops._ref_merge(controls)
# pylint: enable=protected-access
else:
controls = control_flow_ops.tuple(controls)
for var, val in zip(op_output_var, orig_op.outputs):
with ops.control_dependencies(controls):
with self.colocate_with(v_f_op):
real_val = array_ops.identity(val)
with ops.control_dependencies([v_t_op.op]):
with self.colocate_with(v_t_op):
stored_val = array_ops.identity(var)
stored_val.set_shape(val.get_shape())
real_val, _ = control_flow_ops.merge(
[real_val, stored_val])
real_val.op.node_def.attr['_gradient_op_type'].CopyFrom(
attr_value_pb2.AttrValue(
s=compat.as_bytes(self._merge_op_type)))
values.append(real_val)
for i, _ in enumerate(shapes):
values[i].set_shape(shapes[i])
self._outputs_map[orig_op.name] = values
try:
self._gradient_function_map[
orig_op.name] = ops.get_gradient_function(orig_op)
except (KeyError, LookupError):
pass
else:
orig_op.node_def.attr['_gradient_op_type'].CopyFrom(
attr_value_pb2.AttrValue(
s=compat.as_bytes(self._imperative_op_type)))
return MultiOutputOperation(values)
def create_op(self, *args, **kwargs):
"""Creates an `Operation`.
For operations of the following form
orig_value = op(*args, **kwargs)
this function constructs the following subgraph :
v = Variable()
if v is not initialized:
orig_value = op(*args, **kwargs)
v.assign(orig_value) # Initializes v
return orig_value
else:
return v
The above transformation is not performed and the original op is returned
as is if any of the following is true:
* `_return_as_is` flag is set to true.
* op_type is listed in _PASS_THROUGH_OPS
* op has no outputs.
* One of the op's return value has a ref type.
Args:
*args: Arguments for create_op()
**kwargs: Keyword arguments for create_op(). Refer to
tensorflow.python.framework.ops.Graph.create_op() for the mandatory
and optional arguments.
Returns:
An Operation.
Raises:
UnimplementedError: if output type is a reference and the op's type
is not one of the supported types in `_REF_OPS_WHITELIST`.
"""
op_type = kwargs['op_type'] if 'op_type' in kwargs else args[0]
output_dtypes = kwargs['dtypes'] if 'dtypes' in kwargs else args[2]
output_dtypes = [dtypes.as_dtype(d) for d in output_dtypes]
if self._return_as_is or op_type in _PASS_THROUGH_OPS:
return self._wrap(super(ImperativeGraph, self).create_op(*args, **kwargs))
if not output_dtypes:
return self._wrap(
super(ImperativeGraph, self).create_op(*args, **kwargs))
output_has_ref = any([dtype._is_ref_dtype for dtype in output_dtypes]) # pylint: disable=protected-access
if output_has_ref:
if op_type not in _REF_OPS_WHITELIST:
raise errors.UnimplementedError(None, None,
op_type + ' op not supported in '
'imperative graph')
ret = super(ImperativeGraph, self).create_op(*args, **kwargs)
if self._in_variable_creation:
if op_type == 'Assign':
self.add_pending_init(ret)
return self._wrap(ret)
with self.return_as_is():
# Declares the variables to hold the output values of this op.
op_output_var = [state_ops.variable_op_v2(
tensor_shape.TensorShape(None), dtype, container=self._name)
for dtype in output_dtypes]
# Ops to free the resources used by the temporary cache variables.
# The following two ops are created for each cache variable,
# having no control dependencies on any other ops :
# var_handle_op ----> destroy_resource_op
for dtype, v in zip(output_dtypes, op_output_var):
with ops.control_dependencies(None):
self._variable_cleanup_ops += [
gen_resource_variable_ops.destroy_resource_op(
gen_resource_variable_ops.var_handle_op(
dtype, tensor_shape.TensorShape(None),
container=self._name, shared_name=v.op.name),
ignore_lookup_error=True)]
# Create the conditional to run the original op only when the variable
# corresponding to the first output is not initialized.
inited = state_ops.is_variable_initialized(op_output_var[0])
v_f, v_t = control_flow_ops.ref_switch(op_output_var[0], inited)
# pylint: disable=protected-access
v_f_op = gen_array_ops._ref_identity(v_f)
v_t_op = gen_array_ops._ref_identity(v_t)
# pylint: enable=protected-access
with ops.control_dependencies([v_f_op.op]):
# Create the original op
orig_op = self._wrap(
super(ImperativeGraph, self).create_op(*args, **kwargs))
shapes = [val.get_shape() for val in orig_op.outputs]
controls = []
for var, val in zip(op_output_var, orig_op.outputs):
if (not val.get_shape().is_fully_defined() or
val.get_shape().num_elements() > 0):
assign_op = state_ops.assign(var, val, validate_shape=False)
assign_op.set_shape(val.get_shape())
controls.append(assign_op)
values = []
if len(controls) > 1:
if control_flow_ops.IsSwitch(orig_op):
# pylint: disable=protected-access
controls = gen_control_flow_ops._ref_merge(controls)
# pylint: enable=protected-access
else:
controls = control_flow_ops.tuple(controls)
for var, val in zip(op_output_var, orig_op.outputs):
with ops.control_dependencies(controls):
with self.colocate_with(v_f_op):
real_val = array_ops.identity(val)
with ops.control_dependencies([v_t_op.op]):
with self.colocate_with(v_t_op):
stored_val = array_ops.identity(var)
stored_val.set_shape(val.get_shape())
real_val, _ = control_flow_ops.merge([real_val, stored_val])
real_val.op.node_def.attr['_gradient_op_type'].CopyFrom(
attr_value_pb2.AttrValue(s=compat.as_bytes(self._merge_op_type)))
values.append(real_val)
for i, _ in enumerate(shapes):
values[i].set_shape(shapes[i])
self._outputs_map[orig_op.name] = values
try:
self._gradient_function_map[orig_op.name] = ops.get_gradient_function(
orig_op)
except (KeyError, LookupError):
pass
else:
orig_op.node_def.attr['_gradient_op_type'].CopyFrom(
attr_value_pb2.AttrValue(
s=compat.as_bytes(self._imperative_op_type)))
return MultiOutputOperation(values, orig_op)
def testRefIdentityShape(self):
with self.test_session():
shape = [2, 3]
tensor = tf.Variable(tf.constant([[1, 2, 3], [6, 5, 4]], dtype=tf.int32))
self.assertEquals(shape, tensor.get_shape())
self.assertEquals(shape, gen_array_ops._ref_identity(tensor).get_shape())