def testBatchFunctionOpWithInputError(self):
"""Tests that batch_function op works with error in the inputs."""
if context.executing_eagerly():
return
with self.cached_session() as sess:
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
@function.Defun(dtypes.int32, dtypes.int32)
def computation(in0, in1):
return in0 + in1
result = gen_batch_ops.batch_function(
[inp], # computation actually expects 2 inputs.
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000, # 100ms
batching_queue="",
f=computation,
captured_tensors=computation.captured_inputs,
Tout=[
o.type for o in computation.definition.signature.output_arg
])
with self.assertRaisesRegex(
InvalidArgumentError,
r"Function takes 2 argument\(s\) but 1 argument\(s\) were passed"
):
sess.run([result], feed_dict={inp: [2]})
def testBatchFunctionOp(self):
"""Tests that the batch_function op works."""
with self.test_session() as sess:
@function.Defun(dtypes.int32)
def computation(in_t):
return in_t + 1
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
result = gen_batch_ops.batch_function(
[inp],
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000,
Tout=[dtypes.int32],
f=computation,
captured_tensors=computation.captured_inputs)
thread_results = []
def worker():
thread_results.extend(sess.run([result], feed_dict={inp: [1]}))
worker_thread = threading.Thread(target=worker)
worker_thread.start()
main_results = sess.run([result], feed_dict={inp: [2]})
worker_thread.join()
self.assertEqual(thread_results[0], [2])
self.assertEqual(main_results[0], [3])
def testBatchFunctionOp(self):
"""Tests that the batch_function op works."""
if context.executing_eagerly():
return
with self.cached_session() as sess:
@function.Defun(dtypes.int32)
def computation(in_t):
return in_t + 1
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
result = gen_batch_ops.batch_function(
[inp],
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000,
Tout=[dtypes.int32],
f=computation,
captured_tensors=computation.captured_inputs)
thread_results = []
def worker():
thread_results.extend(sess.run([result], feed_dict={inp: [1]}))
worker_thread = threading.Thread(target=worker)
worker_thread.start()
main_results = sess.run([result], feed_dict={inp: [2]})
worker_thread.join()
self.assertEqual(thread_results[0], [2])
self.assertEqual(main_results[0], [3])
def testBatchFunctionOpWithCapturedInput(self):
"""Tests that batch_function op works with captured input."""
with self.test_session() as sess:
captured_inp0 = array_ops.placeholder_with_default(2, shape=[])
captured_inp1 = array_ops.placeholder_with_default(1, shape=[])
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
@function.Defun(dtypes.int32)
def computation(inp):
return inp + captured_inp0 - captured_inp1
result = gen_batch_ops.batch_function(
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000, # 100ms
allowed_batch_sizes=[3, 10],
batching_queue="",
f=computation,
in_tensors=[inp],
captured_tensors=computation.captured_inputs,
Tout=[o.type for o in computation.definition.signature.output_arg])
thread_results = []
def worker():
thread_results.extend(sess.run([result], feed_dict={inp: [1]}))
worker_thread = threading.Thread(target=worker)
worker_thread.start()
main_results = sess.run([result], feed_dict={inp: [2]})
worker_thread.join()
self.assertEqual(thread_results[0], [2])
self.assertEqual(main_results[0], [3])
def testBatchFunctionOpWithCapturedInput(self):
"""Tests that batch_function op works with captured input."""
with self.cached_session() as sess:
captured_inp0 = array_ops.placeholder_with_default(2, shape=[])
captured_inp1 = array_ops.placeholder_with_default(1, shape=[])
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
@function.Defun(dtypes.int32)
def computation(inp):
return inp + captured_inp0 - captured_inp1
result = gen_batch_ops.batch_function(
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000, # 100ms
allowed_batch_sizes=[3, 10],
batching_queue="",
f=computation,
in_tensors=[inp],
captured_tensors=computation.captured_inputs,
Tout=[
o.type for o in computation.definition.signature.output_arg
])
thread_results = []
def worker():
thread_results.extend(sess.run([result], feed_dict={inp: [1]}))
worker_thread = threading.Thread(target=worker)
worker_thread.start()
main_results = sess.run([result], feed_dict={inp: [2]})
worker_thread.join()
self.assertEqual(thread_results[0], [2])
self.assertEqual(main_results[0], [3])
def decorated(*args): # pylint: disable=missing-docstring
@function.defun(autograph=autograph)
def computation(*computation_args):
return fn(*computation_args)
computation = computation.get_concrete_function(
*[tensor_spec.TensorSpec(dtype=x.dtype, shape=x.shape, name=str(i))
for i, x in enumerate(args)])
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError("All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
return gen_batch_ops.batch_function(
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
allowed_batch_sizes=allowed_batch_sizes,
max_enqueued_batches=max_enqueued_batches,
shared_name=name,
f=computation,
in_tensors=list(args),
captured_tensors=computation.captured_inputs,
Tout=[o.dtype for o in computation.outputs])
def decorated(*args): # pylint: disable=missing-docstring
@function.defun()
def computation(*computation_args):
return fn(*computation_args)
computation = computation.get_concrete_function(
*[tensor_spec.TensorSpec(dtype=x.dtype, shape=x.shape, name=str(i))
for i, x in enumerate(args)])
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError("All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
return gen_batch_ops.batch_function(
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
allowed_batch_sizes=allowed_batch_sizes,
max_enqueued_batches=max_enqueued_batches,
shared_name=name,
f=computation,
in_tensors=list(args),
captured_tensors=computation.captured_inputs,
Tout=[o.dtype for o in computation.outputs])
def decorated(*args): # pylint: disable=missing-docstring
types = [arg.dtype for arg in args]
@function.Defun(*types)
def computation(*computation_args):
return fn(*computation_args)
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError(
"All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
return gen_batch_ops.batch_function(
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
allowed_batch_sizes=allowed_batch_sizes,
max_enqueued_batches=max_enqueued_batches,
shared_name=name,
f=computation,
in_tensors=list(args),
captured_tensors=computation.captured_inputs,
Tout=[
o.type
for o in computation.definition.signature.output_arg
])
def decorated(*args): # pylint: disable=missing-docstring
types = [arg.dtype for arg in args]
@function.Defun(*types)
def computation(*computation_args):
return fn(*computation_args)
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError("All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
for inp in computation.captured_inputs:
print("inp: %s" % inp)
for op in inp.consumers():
print("op: %s" % op)
return gen_batch_ops.batch_function(
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
allowed_batch_sizes=allowed_batch_sizes,
max_enqueued_batches=max_enqueued_batches,
shared_name=name,
f=computation,
in_tensors=list(args),
captured_tensors=computation.captured_inputs,
Tout=[o.type for o in computation.definition.signature.output_arg])
def testBatchFunctionOpWithLargeBatchSplitted(self):
"""Tests that the batch_function op works with large batch splitted."""
if context.executing_eagerly():
return
with self.cached_session() as sess:
@function.Defun(dtypes.int32)
def computation(in_t):
return in_t + 3
inp = array_ops.placeholder(dtype=dtypes.int32)
result = gen_batch_ops.batch_function(
[inp],
num_batch_threads=2,
# enable_large_batch_splitting is True, so it's valid as long as
# max('allowed_batch_sizes') <= 'max_batch_size'.
allowed_batch_sizes=[1, 2],
max_batch_size=5,
batch_timeout_micros=100000, # 100ms
Tout=[dtypes.int32],
enable_large_batch_splitting=True,
f=computation,
captured_tensors=computation.captured_inputs)
thread1_results = []
thread2_results = []
# Input sizes of worker1 and main thread are larger than
# max(allowed_batch_sizes), while input size of worker2 is smaller.
def worker1():
thread1_results.extend(
sess.run([result], feed_dict={inp: [5, 6, 7, 8, 9]}))
worker_thread1 = threading.Thread(target=worker1)
worker_thread1.start()
def worker2():
thread2_results.extend(
sess.run([result], feed_dict={inp: [10]}))
worker_thread2 = threading.Thread(target=worker2)
worker_thread2.start()
main_results = sess.run([result], feed_dict={inp: [2, 3, 4]})
worker_thread1.join()
worker_thread2.join()
self.assertTrue(
np.all(
np.equal(thread2_results[0], np.array([13],
dtype=np.int32))))
self.assertTrue(
np.all(
np.equal(thread1_results[0],
np.array([8, 9, 10, 11, 12], dtype=np.int32))))
self.assertTrue(
np.all(
np.equal(main_results[0],
np.array([5, 6, 7], dtype=np.int32))))
def testBatchFunctionOpWithInputError(self):
"""Tests that batch_function op works with error in the inputs."""
with self.test_session() as sess:
inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
@function.Defun(dtypes.int32, dtypes.int32)
def computation(in0, in1):
return in0 + in1
result = gen_batch_ops.batch_function(
[inp], # computation actually expects 2 inputs.
num_batch_threads=1,
max_batch_size=10,
batch_timeout_micros=100000, # 100ms
batching_queue="",
f=computation,
captured_tensors=computation.captured_inputs,
Tout=[o.type for o in computation.definition.signature.output_arg])
with self.assertRaisesRegexp(InvalidArgumentError,
".*2 arguments.*but 1.*"):
sess.run([result], feed_dict={inp: [2]})
