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()