self.assertEqual(text, new_text)
self.assertEqual(errors, ["test.py:1: %s requires manual check." % decay])
self.assertIn("%s has been changed" % decay, report)
def testEstimatorLossReductionChangege(self):
text = "tf.estimator.LinearClassifier(a, b)\n"
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(text, new_text)
self.assertEqual(errors, ["test.py:1: %s requires manual check."
% "tf.estimator.LinearClassifier"])
self.assertIn("loss_reduction has been changed", report)
class TestUpgradeFiles(test_util.TensorFlowTestCase):
def testInplace(self):
"""Check to make sure we don't have a file system race."""
temp_file = tempfile.NamedTemporaryFile("w", delete=False)
original = "tf.conj(a)\n"
upgraded = "tf.math.conj(a)\n"
temp_file.write(original)
temp_file.close()
upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
upgrader.process_file(temp_file.name, temp_file.name)
self.assertAllEqual(open(temp_file.name).read(), upgraded)
os.unlink(temp_file.name)
if __name__ == "__main__":
test_lib.main()
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
opts['dump_to_file'] = os.path.join(test.get_temp_dir(), 'dump')
opts['account_type_regexes'] = ['.*']
opts['select'] = [
'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device', 'op_types'
]
with session.Session() as sess, ops.device('/cpu:0'):
x = self._BuildSmallModel()
sess.run(variables.global_variables_initializer())
run_meta = config_pb2.RunMetadata()
_ = sess.run(x,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE),
run_metadata=run_meta)
model_analyzer.print_model_analysis(
sess.graph, run_meta, tfprof_options=opts)
with gfile.Open(opts['dump_to_file'], 'r') as f:
# pylint: disable=line-too-long
self.assertEqual(
'_TFProfRoot (0/450 params, 0/10.44k flops, 0B/5.28KB, _kTFScopeParent)\n Conv2D (0/0 params, 5.83k/5.83k flops, 432B/432B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D)\n Conv2D_1 (0/0 params, 4.61k/4.61k flops, 384B/384B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D)\n DW (3x3x3x6, 162/162 params, 0/0 flops, 648B/1.30KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables)\n DW/Assign (0/0 params, 0/0 flops, 0B/0B, /device:CPU:0, /device:CPU:0|Assign)\n DW/Initializer (0/0 params, 0/0 flops, 0B/0B, _kTFScopeParent)\n DW/Initializer/random_normal (0/0 params, 0/0 flops, 0B/0B, Add)\n DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, 0B/0B, RandomStandardNormal)\n DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, 0B/0B, Const)\n DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, 0B/0B, Mul)\n DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, 0B/0B, Const)\n DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, 0B/0B, Const)\n DW/read (0/0 params, 0/0 flops, 648B/648B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity)\n DW2 (2x2x6x12, 288/288 params, 0/0 flops, 1.15KB/2.30KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables)\n DW2/Assign (0/0 params, 0/0 flops, 0B/0B, /device:CPU:0, /device:CPU:0|Assign)\n DW2/Initializer (0/0 params, 0/0 flops, 0B/0B, _kTFScopeParent)\n DW2/Initializer/random_normal (0/0 params, 0/0 flops, 0B/0B, Add)\n DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, 0B/0B, RandomStandardNormal)\n DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, 0B/0B, Const)\n DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, 0B/0B, Mul)\n DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, 0B/0B, Const)\n DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, 0B/0B, Const)\n DW2/read (0/0 params, 0/0 flops, 1.15KB/1.15KB, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity)\n init (0/0 params, 0/0 flops, 0B/0B, /device:CPU:0, /device:CPU:0|NoOp)\n zeros (0/0 params, 0/0 flops, 864B/864B, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const)\n',
f.read())
# pylint: enable=line-too-long
if __name__ == '__main__':
test.main()
def main(argv=None): # pylint: disable=function-redefined _ops.enable_eager_execution() _test.main(argv)
parser.add_argument(
'--num_inputs',
type=int,
default=1024,
help='Dimension of inputs that are fed into each LSTM cell.'
)
parser.add_argument(
'--num_nodes',
type=int,
default=1024,
help='Number of nodes in each LSTM cell.'
)
parser.add_argument(
'--device',
type=str,
default='gpu',
help="""\
TensorFlow device to assign ops to, e.g. "gpu", "cpu". For details see
documentation for tf.Graph.device.\
"""
)
parser.add_argument(
'--dump_graph_dir',
type=str,
default='',
help='If non-empty, dump graphs in *.pbtxt format to this directory.'
)
global FLAGS # pylint:disable=global-at-module-level
FLAGS, unparsed = parser.parse_known_args()
test.main(argv=[sys.argv[0]] + unparsed)
def main(argv=None): _context.enable_eager_execution() _test.main(argv)
node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: "/device:GPU:0" }
node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: "/device:GPU:0" }
node { name: "Variable_1" op: "VariableV2" device: "/device:GPU:0" }
node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: "/device:GPU:0" }
node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: "/device:GPU:0" }
node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: "/device:GPU:0" }
node { name: "group_deps" op: "NoOp" input: "^MatMul" device: "/device:GPU:0" }
""", self._StripGraph(gd))
def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
benchmark_instance = matmul_benchmark.MatmulBenchmark()
duration = benchmark_instance.run_graph("gpu", n, m, k, transpose_a,
transpose_b, 1, dtype)
self.assertTrue(duration > 1e-6)
if __name__ == "__main__":
dtypes = [np.float32, np.float64]
index = 0
for _dtype in dtypes:
for _n, _m, (_transpose_a, _transpose_b) in itertools.product(
[512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False,
True)]):
_k = _n
setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
index += 1
googletest.main()
def main(argv=None): _ops.enable_eager_execution() _test.main(argv)
with ops.device("cpu:0"):
x1 = array_ops.ones([2, 2])
x2 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x2)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
# `y` is placed on the local CPU as expected.
self.assertEqual(y.device,
"/job:%s/replica:0/task:0/device:CPU:0" % JOB_NAME)
class RemoteExecutionWithoutLazyRemoteInputsCopyTest(RemoteExecutionTest):
@classmethod
def setUpClass(cls):
super(RemoteExecutionWithoutLazyRemoteInputsCopyTest, cls).setUpClass()
context._reset_context()
context.context().lazy_remote_inputs_copy = False
@classmethod
def tearDownClass(cls):
super(RemoteExecutionWithoutLazyRemoteInputsCopyTest, cls).tearDownClass()
context._reset_context()
context.context().lazy_remote_inputs_copy = True
if __name__ == "__main__":
ops.enable_eager_execution()
test.main()
b_np.imag = np.random.normal(
-5, 5, k * n).astype(dtype).reshape([k, n])
for adjoint_a, transpose_a in trans_options:
for adjoint_b, transpose_b in trans_options:
name = "%s_%s_%s_%s_%s_%s_%s_%s_%s" % (
use_static_shape, dtype.__name__, m, n, k,
adjoint_a, transpose_a, adjoint_b,
transpose_b)
_AddTest(
MatMulTest, "MatMulTest", name,
_GetMatMulTest(a_np,
b_np,
use_static_shape,
adjoint_a=adjoint_a,
transpose_a=transpose_a,
adjoint_b=adjoint_b,
transpose_b=transpose_b))
_AddTest(
MatMulGradientTest, "MatMulGradientTest",
name,
_GetMatMulGradientTest(
a_np,
b_np,
use_static_shape,
adjoint_a=adjoint_a,
transpose_a=transpose_a,
adjoint_b=adjoint_b,
transpose_b=transpose_b))
test_lib.main()
