wehub-resource-sync
327 lines
10 KiB
Python
327 lines
10 KiB
Python
# Copyright (c) 2021 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 op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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def cal_kthvalue(x, k, axis, keepdim=False):
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if axis < 0:
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axis = len(x.shape) + axis
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indices = np.argsort(x, axis=axis)
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value = np.sort(x, axis=axis)
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indices = indices.take(indices=k - 1, axis=axis)
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value = value.take(indices=k - 1, axis=axis)
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if keepdim:
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indices = np.expand_dims(indices, axis)
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value = np.expand_dims(value, axis)
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return value, indices
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class TestKthvalueOp(OpTest):
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def init_args(self):
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self.k = 5
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self.axis = -1
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def init_dtype(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = [2, 1, 2, 4, 10]
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def setUp(self):
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self.op_type = "kthvalue"
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self.prim_op_type = "prim"
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self.python_api = paddle.kthvalue
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self.public_python_api = paddle.kthvalue
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self.init_dtype()
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self.init_shape()
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self.input_data = np.random.random(self.shape).astype(self.dtype)
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self.init_args()
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self.inputs = {'X': self.input_data}
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self.attrs = {'k': self.k, 'axis': self.axis}
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output, indices = cal_kthvalue(
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self.input_data, k=self.k, axis=self.axis
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)
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self.outputs = {'Out': output, 'Indices': indices}
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def test_check_output(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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self.check_grad(
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['X'],
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'Out',
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check_pir=True,
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check_prim_pir=True,
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)
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class TestKthvalueOpFp16(TestKthvalueOp):
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def init_dtype(self):
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self.dtype = np.float16
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class TestKthvalueOp_ZeroSize(TestKthvalueOp):
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def init_shape(self):
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self.shape = [2, 1, 0, 4, 10]
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class TestKthvalueOpWithKeepdim(OpTest):
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def init_args(self):
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self.k = 2
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self.axis = 1
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def init_dtype(self):
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self.dtype = np.float64
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def setUp(self):
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self.init_args()
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self.init_dtype()
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self.op_type = "kthvalue"
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self.prim_op_type = "prim"
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self.python_api = paddle.kthvalue
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self.public_python_api = paddle.kthvalue
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self.input_data = np.random.random([1, 3, 2, 4, 10]).astype(self.dtype)
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self.inputs = {'X': self.input_data}
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self.attrs = {'k': self.k, 'axis': self.axis, 'keepdim': True}
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output, indices = cal_kthvalue(
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self.input_data, k=self.k, axis=self.axis, keepdim=True
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)
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self.outputs = {'Out': output, 'Indices': indices}
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def test_check_output(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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self.check_grad(
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['X'],
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'Out',
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check_pir=True,
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check_prim_pir=True,
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)
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class TestKthvalueOpWithKeepdimFp16(TestKthvalueOpWithKeepdim):
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def init_dtype(self):
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self.dtype = np.float16
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class TestKthvalueOpKernels(unittest.TestCase):
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def setUp(self):
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self.axes = [2, -1]
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def test_kthvalue_op(self):
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paddle.disable_static()
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def test_cpu_kernel():
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shape = (2, 128, 10)
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k = 2
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paddle.set_device('cpu')
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inputs = np.random.random(shape)
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tensor = paddle.to_tensor(inputs)
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for axis in self.axes:
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value_expect, indice_expect = cal_kthvalue(inputs, k, axis)
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v, inds = paddle.kthvalue(tensor, k, axis)
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np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)
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np.testing.assert_allclose(
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inds.numpy(), indice_expect, rtol=1e-05
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)
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def test_gpu_kernel():
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shape = (2, 30, 250)
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k = 244
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paddle.set_device(get_device())
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inputs = np.random.random(shape)
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tensor = paddle.to_tensor(inputs)
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for axis in self.axes:
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value_expect, indice_expect = cal_kthvalue(inputs, k, axis)
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v, inds = paddle.kthvalue(tensor, k, axis)
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np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)
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np.testing.assert_allclose(
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inds.numpy(), indice_expect, rtol=1e-05
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)
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test_cpu_kernel()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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test_gpu_kernel()
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class TestKthvalueOpWithNaN(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x = paddle.uniform([2, 200, 10], dtype='float32')
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def test_errors(self):
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def test_nan_in_cpu_kernel():
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paddle.set_device('cpu')
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nan_position = 100
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self.x[0, nan_position, 2] = float('nan')
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v, inds = self.x.kthvalue(k=200, axis=1)
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self.assertTrue(np.isnan(v[0, 2].numpy()))
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self.assertEqual(inds[0, 2].numpy(), nan_position)
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def test_nan_in_gpu_kernel():
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paddle.set_device(get_device())
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nan_position = 100
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self.x[0, nan_position, 2] = float('nan')
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v, inds = self.x.kthvalue(k=200, axis=1)
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self.assertTrue(np.isnan(v[0, 2].numpy()))
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self.assertEqual(inds[0, 2].numpy(), nan_position)
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test_nan_in_cpu_kernel()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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test_nan_in_gpu_kernel()
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class TestKthvalueOpErrors(unittest.TestCase):
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def setUp(self):
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self.x = paddle.uniform([2, 10, 20, 25], dtype='float32')
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def test_errors(self):
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paddle.disable_static()
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def test_k_lowrange_error():
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self.x.kthvalue(k=0, axis=2)
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self.assertRaises(ValueError, test_k_lowrange_error)
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def test_k_uprange_error():
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self.x.kthvalue(k=500, axis=2)
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self.assertRaises(ValueError, test_k_uprange_error)
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def test_dim_range_error():
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self.x.kthvalue(k=10, axis=5)
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self.assertRaises(ValueError, test_dim_range_error)
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def test_k_error_0_dim_input():
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x_0d = paddle.full([], 1)
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x_0d.kthvalue(k=8)
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self.assertRaises(ValueError, test_k_error_0_dim_input)
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class TestModeOpInStatic(unittest.TestCase):
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def setUp(self):
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np.random.seed(666)
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self.input_data = np.random.random((2, 20, 1, 2, 80)).astype(np.float64)
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self.k = 10
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def test_run_static(self):
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paddle.enable_static()
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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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input_tensor = paddle.static.data(
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name="x", shape=[2, 20, 1, 2, 80], dtype="float64"
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)
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result = paddle.kthvalue(input_tensor, self.k, axis=1)
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expect_value = cal_kthvalue(self.input_data, self.k, axis=1)[0]
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exe = paddle.static.Executor(paddle.CPUPlace())
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paddle_result = exe.run(
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feed={"x": self.input_data}, fetch_list=[result]
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)[0]
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np.testing.assert_allclose(paddle_result, expect_value, rtol=1e-05)
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class TestKthvalueFP16Op(OpTest):
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def init_args(self):
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self.k = 5
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self.axis = -1
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self.keepdim = False
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self.input_data = np.random.random((2, 1, 2, 4, 10))
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self.dtype = np.float16
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def setUp(self):
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self.op_type = "kthvalue"
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self.python_api = paddle.kthvalue
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self.init_args()
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self.inputs = {'X': self.input_data}
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self.attrs = {'k': self.k, 'axis': self.axis, 'keepdim': self.keepdim}
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output, indices = cal_kthvalue(
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self.input_data, k=self.k, axis=self.axis, keepdim=self.keepdim
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)
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self.outputs = {'Out': output, 'Indices': indices}
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def test_check_output(self):
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paddle.enable_static()
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self.check_output(check_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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self.check_grad({'X'}, 'Out', check_pir=True)
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class TestKthvalueWithKeepdimFP16Op(TestKthvalueFP16Op):
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def init_args(self):
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self.k = 2
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self.axis = 1
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self.keepdim = True
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self.input_data = np.random.random((1, 3, 2, 4, 10))
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestKthvalueBF16Op(OpTest):
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def init_args(self):
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self.k = 2
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self.axis = 1
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def setUp(self):
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self.init_args()
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self.op_type = 'kthvalue'
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self.python_api = paddle.kthvalue
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self.dtype = np.uint16
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x = np.random.random((1, 3, 2, 4, 10))
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {'k': self.k, 'axis': self.axis, 'keepdim': True}
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out, indices = cal_kthvalue(x, k=self.k, axis=self.axis, keepdim=True)
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self.outputs = {'Out': convert_float_to_uint16(out), 'Indices': indices}
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def test_check_output(self):
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paddle.enable_static()
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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place = get_device_place()
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self.check_grad_with_place(place, {'X'}, 'Out', check_pir=True)
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if __name__ == '__main__':
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unittest.main()
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