def testSlowPathExecute_VeryLargeOutputs(self):
split_dim = constant_op.constant(0, dtype=dtypes.int32)
value = [0, 1, 2, 3]
ctx = context.context()
ctx.ensure_initialized()
with self.assertRaises(core._FallbackException):
pywrap_tfe.TFE_Py_FastPathExecute(ctx, "Split", None, split_dim, value,
"num_split", 1000000000000)
value = constant_op.constant(value)
attrs = ("num_splits", 1000000000000)
with self.assertRaisesRegex(ValueError, "Number of outputs is too big"):
pywrap_tfe.TFE_Py_Execute(ctx._handle, None, "Split", [value], attrs,
1000000000000)
def quick_execute(op_name, num_outputs, inputs, attrs, ctx, name=None):
"""Execute a TensorFlow operation.
Args:
op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to
execute.
num_outputs: The number of outputs of the operation to fetch.
(Explicitly provided instead of being inferred for performance
reasons).
inputs: A list of inputs to the operation. Each entry should be a Tensor, or
a value which can be passed to the Tensor constructor to create one.
attrs: A tuple with alternating string attr names and attr values for this
operation.
ctx: The value of context.context().
name: Customized name for the operation.
Returns:
List of output Tensor objects. The list is empty if there are no outputs
Raises:
An exception on error.
"""
device_name = ctx.device_name
# pylint: disable=protected-access
try:
ctx.ensure_initialized()
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
inputs, attrs, num_outputs)
except core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
six.raise_from(core._status_to_exception(e.code, message), None)
except TypeError as e:
keras_symbolic_tensors = [
x for x in inputs if ops._is_keras_symbolic_tensor(x)
]
if keras_symbolic_tensors:
raise core._SymbolicException(
"Inputs to eager execution function cannot be Keras symbolic "
"tensors, but found {}".format(keras_symbolic_tensors))
raise e
# pylint: enable=protected-access
return tensors
def func():
pywrap_tfe.TFE_Py_Execute(ctx_handle, device, "MatMul", inputs,
attrs, 1)
def f():
pywrap_tfe.TFE_Py_Execute(ctx_handle, None, "Identity", inputs,
attrs, 1)
