# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import OpTest import paddle from paddle import base class TestRepeatInterleaveOp(OpTest): def setUp(self): self.op_type = "repeat_interleave" self.python_api = paddle.repeat_interleave self.init_dtype_type() index_np = np.random.randint( low=0, high=3, size=self.index_size ).astype(self.index_type) x_np = np.random.random(self.x_shape).astype(self.x_type) self.inputs = {'X': x_np, 'RepeatsTensor': index_np} self.attrs = {'dim': self.dim, 'output_size': -1} outer_loop = np.prod(self.x_shape[: self.dim]) x_reshape = [outer_loop, *self.x_shape[self.dim :]] x_np_reshape = np.reshape(x_np, tuple(x_reshape)) out_list = [] for i in range(outer_loop): for j in range(self.index_size): for k in range(index_np[j]): out_list.append(x_np_reshape[i, j]) self.out_shape = list(self.x_shape) self.out_shape[self.dim] = np.sum(index_np) self.out_shape = tuple(self.out_shape) out = np.reshape(out_list, self.out_shape) self.outputs = {'Out': out} def init_dtype_type(self): self.dim = 1 self.x_type = np.float64 self.index_type = np.int64 self.x_shape = (8, 4, 5) self.index_size = self.x_shape[self.dim] def test_check_output(self): self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad(['X'], 'Out', check_pir=True) class TestRepeatInterleaveOp2(OpTest): def setUp(self): self.op_type = "repeat_interleave" self.python_api = paddle.repeat_interleave self.init_dtype_type() index_np = 2 x_np = np.random.random(self.x_shape).astype(self.x_type) self.inputs = {'X': x_np} # , 'RepeatsTensor': None} self.attrs = {'dim': self.dim, 'Repeats': index_np, 'output_size': -1} outer_loop = np.prod(self.x_shape[: self.dim]) x_reshape = [outer_loop, *self.x_shape[self.dim :]] x_np_reshape = np.reshape(x_np, tuple(x_reshape)) out_list = [] for i in range(outer_loop): for j in range(self.index_size): for k in range(index_np): out_list.append(x_np_reshape[i, j]) self.out_shape = list(self.x_shape) self.out_shape[self.dim] = index_np * self.index_size self.out_shape = tuple(self.out_shape) out = np.reshape(out_list, self.out_shape) self.outputs = {'Out': out} def init_dtype_type(self): self.dim = 1 self.x_type = np.float64 self.x_shape = (8, 4, 5) self.index_size = self.x_shape[self.dim] def test_check_output(self): self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad(['X'], 'Out', check_pir=True) class TestRepeatInterleaveOpWithOutputSize1(TestRepeatInterleaveOp): def setUp(self): super().setUp() self.attrs['output_size'] = self.out_shape[self.dim] class TestRepeatInterleaveOpWithOutputSize2(TestRepeatInterleaveOp): def setUp(self): super().setUp() self.attrs['output_size'] = -1 class TestRepeatInterleaveOp2WithOutputSize1(TestRepeatInterleaveOp2): def setUp(self): super().setUp() self.attrs['output_size'] = self.out_shape[self.dim] class TestRepeatInterleaveOp2WithOutputSize2(TestRepeatInterleaveOp2): def setUp(self): super().setUp() self.attrs['output_size'] = -1 class TestRepeatInterleaveOp_ZeroSize(TestRepeatInterleaveOp2): def init_dtype_type(self): self.dim = 1 self.x_type = np.float64 self.x_shape = (8, 0, 5) self.index_size = self.x_shape[self.dim] class TestIndexSelectAPI(unittest.TestCase): def input_data(self): self.data_zero_dim_x = np.array(0.5).astype('float32') self.data_x = np.array( [ [1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], ] ).astype('float32') self.data_zero_dim_index = np.array(2) self.data_index = np.array([0, 1, 2, 1]).astype('int32') self.data_index_output_size = np.array([2, 1, 3]).astype('int32') def test_repeat_interleave_api(self): paddle.enable_static() self.input_data() # case 1: with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32') index = paddle.static.data( name='repeats_', shape=[4], dtype='int32', ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) x.stop_gradient = False index.stop_gradient = False z = paddle.repeat_interleave(x, index, axis=1) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={'x': self.data_x, 'repeats_': self.data_index}, fetch_list=[z], return_numpy=False, ) expect_out = np.repeat(self.data_x, self.data_index, axis=1) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) # case 2: repeats = np.array([1, 2, 1]).astype('int32') with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 4], dtype="float32") index = paddle.static.data( name='repeats_', shape=[3], dtype='int32', ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=0) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={ 'x': self.data_x, 'repeats_': repeats, }, fetch_list=[z], return_numpy=False, ) expect_out = np.repeat(self.data_x, repeats, axis=0) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) repeats = 2 with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32') z = paddle.repeat_interleave(x, repeats, axis=0) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={'x': self.data_x}, fetch_list=[z], return_numpy=False ) expect_out = np.repeat(self.data_x, repeats, axis=0) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) # case 3 zero_dim: if not paddle.framework.in_pir_mode(): with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1], dtype="float32") if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, repeats) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={'x': self.data_zero_dim_x}, fetch_list=[z], return_numpy=False, ) expect_out = np.repeat(self.data_zero_dim_x, repeats) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) # case 4 negative axis: with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32') index = paddle.static.data( name='repeats_', shape=[4], dtype='int32', ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=-1) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={'x': self.data_x, 'repeats_': self.data_index}, fetch_list=[z], return_numpy=False, ) expect_out = np.repeat(self.data_x, self.data_index, axis=-1) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) # case 5 output_size: with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int32', ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=1, output_size=6) exe = base.Executor(base.CPUPlace()) (res,) = exe.run( feed={ 'x': self.data_x[:, :3], 'repeats_': self.data_index_output_size, }, fetch_list=[z], ) expect_out = np.repeat( self.data_x[:, :3], self.data_index_output_size, axis=1 ) np.testing.assert_allclose(expect_out, res, rtol=1e-05) # case 6 output_size = -1 with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int32', ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z2 = paddle.repeat_interleave(x, index, axis=1, output_size=-1) exe = base.Executor(base.CPUPlace()) (res2,) = exe.run( feed={ 'x': self.data_x[:, :3], 'repeats_': self.data_index_output_size, }, fetch_list=[z2], ) np.testing.assert_allclose(expect_out, res2, rtol=1e-05) # case 7 output_size error with ( self.assertRaises(ValueError), paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ), ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int32', ) z = paddle.repeat_interleave(x, index, axis=1, output_size=5) exe = base.Executor(base.CPUPlace()) exe.run( feed={ 'x': self.data_x[:, :3], 'repeats_': self.data_index_output_size, }, fetch_list=[z], ) # case 8 repeats is int, output_size provided and correct with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, 2, axis=1, output_size=6) exe = base.Executor(base.CPUPlace()) (res3,) = exe.run( feed={'x': self.data_x[:, :3]}, fetch_list=[z], ) expect_out3 = np.repeat(self.data_x[:, :3], 2, axis=1) np.testing.assert_allclose(expect_out3, res3, rtol=1e-05) # case 9: x.numel = 0, repeats is tensor, output_size = -1 with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[0, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int32' ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=1, output_size=-1) exe = base.Executor(base.CPUPlace()) (res4,) = exe.run( feed={ 'x': np.zeros((0, 3), dtype='float32'), 'repeats_': self.data_index_output_size, }, fetch_list=[z], ) expect_out4 = np.repeat( np.zeros((0, 3), dtype='float32'), self.data_index_output_size, axis=1, ) np.testing.assert_allclose(expect_out4, res4, rtol=1e-05) # case 10: x.numel = 0, repeats is tensor, output_size = actual value with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[0, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int32' ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) output_size_actual = int(self.data_index_output_size.sum()) z = paddle.repeat_interleave( x, index, axis=1, output_size=output_size_actual ) exe = base.Executor(base.CPUPlace()) (res4b,) = exe.run( feed={ 'x': np.zeros((0, 3), dtype='float32'), 'repeats_': self.data_index_output_size, }, fetch_list=[z], ) expect_out4b = np.repeat( np.zeros((0, 3), dtype='float32'), self.data_index_output_size, axis=1, ) np.testing.assert_allclose(expect_out4b, res4b, rtol=1e-05) # case 11: repeats tensor dtype = int64, output_size = -1 with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int64' ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=1, output_size=-1) exe = base.Executor(base.CPUPlace()) (res5,) = exe.run( feed={ 'x': self.data_x[:, :3], 'repeats_': self.data_index_output_size.astype('int64'), }, fetch_list=[z], ) expect_out5 = np.repeat( self.data_x[:, :3], self.data_index_output_size, axis=1 ) np.testing.assert_allclose(expect_out5, res5, rtol=1e-05) # case 11: repeats tensor dtype = int64, output_size = actual value with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32') index = paddle.static.data( name='repeats_', shape=[3], dtype='int64' ) if not paddle.framework.in_pir_mode(): x.desc.set_need_check_feed(False) index.desc.set_need_check_feed(False) z = paddle.repeat_interleave(x, index, axis=1, output_size=6) exe = base.Executor(base.CPUPlace()) (res6,) = exe.run( feed={ 'x': self.data_x[:, :3], 'repeats_': self.data_index_output_size.astype('int64'), }, fetch_list=[z], ) np.testing.assert_allclose(expect_out5, res6, rtol=1e-05) def test_dygraph_api(self): self.input_data() # case axis none input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('int32') index_x = np.array([1, 1, 2, 1, 2, 2]).astype('int32') with base.dygraph.guard(): x = paddle.to_tensor(input_x) index = paddle.to_tensor(index_x) z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat(input_x, index_x, axis=None) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case repeats int with base.dygraph.guard(): x = paddle.to_tensor(input_x) index = 2 z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat(input_x, index, axis=None) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case input dtype is bfloat16 input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('uint16') with base.dygraph.guard(): x = paddle.to_tensor(input_x) index = paddle.to_tensor(index_x) z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat(input_x, index_x, axis=None) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) with base.dygraph.guard(): x = paddle.to_tensor(input_x) index = 2 z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat(input_x, index, axis=None) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 1: with base.dygraph.guard(): x = paddle.to_tensor(self.data_x) index = paddle.to_tensor(self.data_index) z = paddle.repeat_interleave(x, index, -1) np_z = z.numpy() expect_out = np.repeat(self.data_x, self.data_index, axis=-1) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) with base.dygraph.guard(): x = paddle.to_tensor(self.data_x) index = paddle.to_tensor(self.data_index) z = paddle.repeat_interleave(x, index, 1) np_z = z.numpy() expect_out = np.repeat(self.data_x, self.data_index, axis=1) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 2: index_x = np.array([1, 2, 1]).astype('int32') with base.dygraph.guard(): x = paddle.to_tensor(self.data_x) index = paddle.to_tensor(index_x) z = paddle.repeat_interleave(x, index, axis=0) np_z = z.numpy() expect_out = np.repeat(self.data_x, index, axis=0) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 3 zero_dim: with base.dygraph.guard(): x = paddle.to_tensor(self.data_zero_dim_x) index = 2 z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat(self.data_zero_dim_x, index, axis=None) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 4 zero_dim_index with base.dygraph.guard(): x = paddle.to_tensor(self.data_zero_dim_x) index = paddle.to_tensor(self.data_zero_dim_index) z = paddle.repeat_interleave(x, index, None) np_z = z.numpy() expect_out = np.repeat( self.data_zero_dim_x, self.data_zero_dim_index, axis=None ) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 5 repeat_interleave_with_tensor_double_grad with base.dygraph.guard(): x_pd = paddle.randn([40, 50]) x_pd.stop_gradient = False axis = 1 repeats_pd = paddle.randint(1, 50, [x_pd.shape[axis]]) y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis) dy_pd = paddle.randn_like(y_pd) dy_pd.stop_gradient = False g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0] ddx_pd = paddle.randn_like(x_pd) gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0] np.testing.assert_allclose( gg_pd.numpy(), paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(), 1e-5, 1e-5, ) # case 6 repeat_interleave_double_grad with base.dygraph.guard(): x_pd = paddle.randn([40, 50]) x_pd.stop_gradient = False axis = 1 repeats_pd = 4 y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis) dy_pd = paddle.randn_like(y_pd) dy_pd.stop_gradient = False g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0] ddx_pd = paddle.randn_like(x_pd) gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0] np.testing.assert_allclose( gg_pd.numpy(), paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(), 1e-5, 1e-5, ) # case 7 repeat_interleave_with_i64_tensor_double_grad with base.dygraph.guard(): x_pd = paddle.randn([40, 50]) x_pd.stop_gradient = False axis = 1 repeats_pd = paddle.randint( 1, 50, [x_pd.shape[axis]], dtype="int64" ) y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis) dy_pd = paddle.randn_like(y_pd) dy_pd.stop_gradient = False g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0] ddx_pd = paddle.randn_like(x_pd) gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0] np.testing.assert_allclose( gg_pd.numpy(), paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(), 1e-5, 1e-5, ) # case 8 0-size_repeat_interleave_with_i64_tensor_double_grad with base.dygraph.guard(): x_pd = paddle.randn([0, 50]) x_pd.stop_gradient = False axis = 1 repeats_pd = paddle.randint( 1, 50, [x_pd.shape[axis]], dtype="int64" ) y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis) dy_pd = paddle.randn_like(y_pd) dy_pd.stop_gradient = False g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0] ddx_pd = paddle.randn_like(x_pd) gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0] np.testing.assert_allclose( gg_pd.numpy(), paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(), 1e-5, 1e-5, ) # case 9 0-size_repeat_interleave_with_i32_tensor_double_grad with base.dygraph.guard(): x_pd = paddle.randn([0, 50]) x_pd.stop_gradient = False axis = 1 repeats_pd = paddle.randint( 1, 50, [x_pd.shape[axis]], dtype="int32" ) y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis) dy_pd = paddle.randn_like(y_pd) dy_pd.stop_gradient = False g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0] ddx_pd = paddle.randn_like(x_pd) gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0] np.testing.assert_allclose( gg_pd.numpy(), paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(), 1e-5, 1e-5, ) # case 10 output_size: with base.dygraph.guard(): x = paddle.to_tensor(self.data_x[:, :3]) index = paddle.to_tensor(self.data_index_output_size) z = paddle.repeat_interleave(x, index, axis=1, output_size=6) np_z = z.numpy() expect_out = np.repeat( self.data_x[:, :3], self.data_index_output_size, axis=1 ) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) with base.dygraph.guard(): x = paddle.to_tensor(self.data_x[:, :3]) index = paddle.to_tensor(self.data_index_output_size) z = x.repeat_interleave(index, axis=1, output_size=6) np_z = z.numpy() np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) with base.dygraph.guard(): x_np = np.array([[1.0, 2.0], [3.0, 4.0]]).astype('float32') index_np = np.array([2, 1]).astype('int32') x = paddle.to_tensor(x_np, stop_gradient=False) index = paddle.to_tensor(index_np) z = paddle.repeat_interleave(x, index, axis=1, output_size=3) z.backward() expected_grad = np.array([[2.0, 1.0], [2.0, 1.0]]) np.testing.assert_allclose( x.grad.numpy(), expected_grad, rtol=1e-05 ) x = paddle.to_tensor(x_np, stop_gradient=False) z = x.repeat_interleave(index, axis=1, output_size=3) z.backward() np.testing.assert_allclose( x.grad.numpy(), expected_grad, rtol=1e-05 ) with base.dygraph.guard(): x = paddle.to_tensor(self.data_x[:, :3]) index = paddle.to_tensor(self.data_index_output_size) with self.assertRaises(ValueError): z = paddle.repeat_interleave(x, index, axis=1, output_size=5) class TestRepeatInterleave_ZeroSizeRepeatsTensor(unittest.TestCase): """Cover the `repeats.dims()[0] == 0` branch added to paddle/phi/kernels/funcs/repeat_tensor2index_tensor.{cc,cu}. The functor short-circuits when the repeats tensor itself is 0-sized (i.e. x.shape[axis] == 0 with a Tensor-typed repeats argument). """ def _check(self, repeats_dtype): # InferMeta only allows 0-size repeats tensor when x is 1-D shape [0]. x_np = np.zeros([0], dtype="float32") repeats_np = np.zeros([0], dtype=repeats_dtype) paddle.disable_static() x = paddle.to_tensor(x_np) x.stop_gradient = False repeats = paddle.to_tensor(repeats_np) out = paddle.repeat_interleave(x, repeats, axis=0) np.testing.assert_equal(out.numpy().shape, (0,)) # Also exercise the backward path through the functor. loss = paddle.sum(out) loss.backward() np.testing.assert_equal(x.grad.shape, [0]) def test_zero_size_repeats_int32(self): self._check(repeats_dtype="int32") def test_zero_size_repeats_int64(self): self._check(repeats_dtype="int64") if __name__ == '__main__': unittest.main()