wehub-resource-sync
323 lines
12 KiB
Python
323 lines
12 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle import base
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def ref_repeat_interleave(x_np, index_np, axis):
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x_shape = x_np.shape
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if axis < 0:
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axis += len(x_shape)
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index_size = x_shape[axis]
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if not isinstance(index_np, np.ndarray):
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index_np = np.full([index_size], index_np, dtype=np.int32)
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outer_loop = np.prod(x_shape[:axis])
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x_reshape = [outer_loop, *x_shape[axis:]]
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x_np_reshape = np.reshape(x_np, tuple(x_reshape))
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out_list = []
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for i in range(outer_loop):
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for j in range(index_size):
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for k in range(index_np[j]):
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out_list.append(x_np_reshape[i, j])
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out_shape = list(x_shape)
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out_shape[axis] = np.sum(index_np)
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out_shape = tuple(out_shape)
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out = np.reshape(out_list, out_shape)
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return out
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class XPUTestRepeatInterleaveOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = "repeat_interleave"
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class TestRepeatInterleaveOp(XPUOpTest):
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def setUp(self):
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self.op_type = "repeat_interleave"
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self.python_api = paddle.repeat_interleave
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self.init_case()
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x_np = np.random.random(self.x_shape).astype(self.x_type)
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self.inputs = {'X': x_np}
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self.attrs = {'dim': self.dim}
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if hasattr(self, "index") and self.index is not None:
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index_np = self.index
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self.attrs['Repeats'] = index_np
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else:
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index_np = np.random.randint(
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low=0, high=5, size=self.x_shape[self.dim]
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).astype(self.index_type)
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self.inputs['RepeatsTensor'] = index_np
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out = ref_repeat_interleave(x_np, index_np, self.dim)
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self.outputs = {'Out': out}
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def init_case(self):
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self.dim = 1
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self.x_type = self.in_type
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self.index_type = np.int64
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self.x_shape = (8, 4, 5)
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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place = paddle.XPUPlace(0)
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self.check_grad(place, ['X'], 'Out')
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class TestRepeatInterleaveOp2(TestRepeatInterleaveOp):
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def init_case(self):
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self.dim = 1
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self.x_type = self.in_type
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self.x_shape = (8, 4, 5)
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self.index = 2
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support_types = get_xpu_op_support_types('repeat_interleave')
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for stype in support_types:
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create_test_class(globals(), XPUTestRepeatInterleaveOp, stype)
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class TestRepeatInterleaveAPI(unittest.TestCase):
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def input_data(self):
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self.data_zero_dim_x = np.array(0.5).astype('float32')
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self.data_x = np.array(
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[
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[1.0, 2.0, 3.0, 4.0],
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[5.0, 6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0, 12.0],
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]
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).astype('float32')
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self.data_zero_dim_index = np.array(2)
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self.data_index = np.array([0, 1, 2, 1]).astype('int32')
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def test_repeat_interleave_api(self):
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paddle.enable_static()
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self.input_data()
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# case 1:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
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index = paddle.static.data(
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name='repeats_',
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shape=[4],
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dtype='int32',
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)
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if not paddle.framework.in_pir_mode():
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x.desc.set_need_check_feed(False)
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index.desc.set_need_check_feed(False)
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x.stop_gradient = False
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index.stop_gradient = False
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z = paddle.repeat_interleave(x, index, axis=1)
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exe = base.Executor(base.XPUPlace(0))
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(res,) = exe.run(
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feed={'x': self.data_x, 'repeats_': self.data_index},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.repeat(self.data_x, self.data_index, axis=1)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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# case 2:
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repeats = np.array([1, 2, 1]).astype('int32')
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype="float32")
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index = paddle.static.data(
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name='repeats_',
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shape=[3],
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dtype='int32',
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)
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if not paddle.framework.in_pir_mode():
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x.desc.set_need_check_feed(False)
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index.desc.set_need_check_feed(False)
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z = paddle.repeat_interleave(x, index, axis=0)
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exe = base.Executor(base.XPUPlace(0))
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(res,) = exe.run(
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feed={
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'x': self.data_x,
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'repeats_': repeats,
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},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.repeat(self.data_x, repeats, axis=0)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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repeats = 2
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
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z = paddle.repeat_interleave(x, repeats, axis=0)
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if not paddle.framework.in_pir_mode():
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x.desc.set_need_check_feed(False)
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exe = base.Executor(base.XPUPlace(0))
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(res,) = exe.run(
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feed={'x': self.data_x}, fetch_list=[z], return_numpy=False
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)
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expect_out = np.repeat(self.data_x, repeats, axis=0)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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# case 3 zero_dim:
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if not paddle.framework.in_pir_mode():
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1], dtype="float32")
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if not paddle.framework.in_pir_mode():
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x.desc.set_need_check_feed(False)
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z = paddle.repeat_interleave(x, repeats)
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exe = base.Executor(base.XPUPlace(0))
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(res,) = exe.run(
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feed={'x': self.data_zero_dim_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.repeat(self.data_zero_dim_x, repeats)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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# case 4 negative axis:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
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index = paddle.static.data(
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name='repeats_',
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shape=[4],
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dtype='int32',
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)
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if not paddle.framework.in_pir_mode():
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x.desc.set_need_check_feed(False)
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index.desc.set_need_check_feed(False)
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z = paddle.repeat_interleave(x, index, axis=-1)
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exe = base.Executor(base.XPUPlace(0))
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(res,) = exe.run(
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feed={'x': self.data_x, 'repeats_': self.data_index},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.repeat(self.data_x, self.data_index, axis=-1)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_dygraph_api(self):
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self.input_data()
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# case axis none
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input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('int32')
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index_x = np.array([1, 1, 2, 1, 2, 2]).astype('int32')
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with base.dygraph.guard():
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x = paddle.to_tensor(input_x)
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index = paddle.to_tensor(index_x)
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(input_x, index_x, axis=None)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case repeats int
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with base.dygraph.guard():
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x = paddle.to_tensor(input_x)
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index = 2
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(input_x, index, axis=None)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case input dtype is bfloat16
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input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('uint16')
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with base.dygraph.guard():
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x = paddle.to_tensor(input_x)
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index = paddle.to_tensor(index_x)
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(input_x, index_x, axis=None)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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with base.dygraph.guard():
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x = paddle.to_tensor(input_x)
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index = 2
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(input_x, index, axis=None)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 1:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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index = paddle.to_tensor(self.data_index)
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z = paddle.repeat_interleave(x, index, -1)
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np_z = z.numpy()
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expect_out = np.repeat(self.data_x, self.data_index, axis=-1)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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index = paddle.to_tensor(self.data_index)
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z = paddle.repeat_interleave(x, index, 1)
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np_z = z.numpy()
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expect_out = np.repeat(self.data_x, self.data_index, axis=1)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 2:
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index_x = np.array([1, 2, 1]).astype('int32')
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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index = paddle.to_tensor(index_x)
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z = paddle.repeat_interleave(x, index, axis=0)
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np_z = z.numpy()
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expect_out = np.repeat(self.data_x, index, axis=0)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 3 zero_dim:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_zero_dim_x)
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index = 2
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(self.data_zero_dim_x, index, axis=None)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 4 zero_dim_index
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_zero_dim_x)
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index = paddle.to_tensor(self.data_zero_dim_index)
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z = paddle.repeat_interleave(x, index, None)
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np_z = z.numpy()
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expect_out = np.repeat(
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self.data_zero_dim_x, self.data_zero_dim_index, axis=None
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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