wehub-resource-sync
272 lines
8.1 KiB
Python
272 lines
8.1 KiB
Python
# Copyright (c) 2020 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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from paddle.base import core
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paddle.enable_static()
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class XPUTestSumOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'sum'
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self.use_dynamic_create_class = False
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class TestSumOp(XPUOpTest):
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def setUp(self):
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self.init_dtype()
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self.set_xpu()
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self.op_type = "sum"
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self.place = paddle.XPUPlace(0)
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self.set_shape()
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x0 = np.random.random(self.shape).astype(self.dtype)
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x1 = np.random.random(self.shape).astype(self.dtype)
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x2 = np.random.random(self.shape).astype(self.dtype)
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self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
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y = x0 + x1 + x2
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self.outputs = {'Out': y}
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def init_dtype(self):
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self.dtype = self.in_type
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def set_xpu(self):
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self.__class__.use_xpu = True
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self.__class__.no_need_check_grad = True
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self.__class__.op_type = self.dtype
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def set_shape(self):
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self.shape = (3, 10)
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ['x0'], 'Out')
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class TestSumOp1(TestSumOp):
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def set_shape(self):
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self.shape = 5
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class TestSumOp2(TestSumOp):
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def set_shape(self):
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self.shape = (1, 1, 1, 1, 1)
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class TestSumOp3(TestSumOp):
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def set_shape(self):
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self.shape = (10, 5, 7)
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class TestSumOp4(TestSumOp):
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def set_shape(self):
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self.shape = (2, 2, 3, 3)
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def create_test_sum_fp16_class(parent):
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class TestSumFp16Case(parent):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_w_is_selected_rows(self):
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place = core.XPUPlace(0)
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# if core.is_float16_supported(place):
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for inplace in [True, False]:
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self.check_with_place(place, inplace)
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cls_name = "{}_{}".format(parent.__name__, "SumFp16Test")
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TestSumFp16Case.__name__ = cls_name
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globals()[cls_name] = TestSumFp16Case
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class API_Test_Add_n(unittest.TestCase):
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def test_api(self):
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with base.program_guard(base.Program(), base.Program()):
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input0 = paddle.tensor.fill_constant(
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shape=[2, 3], dtype='int64', value=5
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)
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input1 = paddle.tensor.fill_constant(
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shape=[2, 3], dtype='int64', value=3
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)
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expected_result = np.empty((2, 3))
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expected_result.fill(8)
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sum_value = paddle.add_n([input0, input1])
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exe = base.Executor(base.XPUPlace(0))
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result = exe.run(fetch_list=[sum_value])
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self.assertEqual((result == expected_result).all(), True)
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with base.dygraph.guard():
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input0 = paddle.ones(shape=[2, 3], dtype='float32')
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expected_result = np.empty((2, 3))
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expected_result.fill(2)
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sum_value = paddle.add_n([input0, input0])
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self.assertEqual((sum_value.numpy() == expected_result).all(), True)
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class TestRaiseSumError(unittest.TestCase):
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def test_errors(self):
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def test_type():
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paddle.add_n([11, 22])
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self.assertRaises(TypeError, test_type)
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def test_dtype():
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data1 = paddle.static.data(name="input1", shape=[10], dtype="int8")
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data2 = paddle.static.data(name="input2", shape=[10], dtype="int8")
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paddle.add_n([data1, data2])
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self.assertRaises(TypeError, test_dtype)
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def test_dtype1():
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data1 = paddle.static.data(name="input1", shape=[10], dtype="int8")
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paddle.add_n(data1)
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self.assertRaises(TypeError, test_dtype1)
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class TestRaiseSumsError(unittest.TestCase):
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def test_errors(self):
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def test_type():
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paddle.add_n([11, 22])
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self.assertRaises(TypeError, test_type)
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def test_dtype():
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data1 = paddle.static.data(name="input1", shape=[10], dtype="int8")
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data2 = paddle.static.data(name="input2", shape=[10], dtype="int8")
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paddle.add_n([data1, data2])
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self.assertRaises(TypeError, test_dtype)
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def test_dtype1():
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data1 = paddle.static.data(name="input3", shape=[10], dtype="int8")
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paddle.add_n(data1)
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self.assertRaises(TypeError, test_dtype1)
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class TestSumOpError(unittest.TestCase):
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def test_errors(self):
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def test_empty_list_input():
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with base.dygraph.guard():
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paddle._C_ops.sum([])
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def test_list_of_none_input():
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with base.dygraph.guard():
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paddle._C_ops.sum([None])
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self.assertRaises(ValueError, test_empty_list_input)
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self.assertRaises(ValueError, test_list_of_none_input)
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class TestDenseTensorAndSelectedRowsOp(unittest.TestCase):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 3, 4, 5, 6]
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self.dtype = np.float32
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self.init_kernel_type()
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def check_with_place(self, place, inplace):
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self.check_input_and_output(place, inplace, True, True, True)
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def init_kernel_type(self):
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pass
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def _get_array(self, rows, row_numel):
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array = np.ones((len(rows), row_numel)).astype(self.dtype)
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for i in range(len(rows)):
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array[i] *= rows[i]
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return array
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def check_input_and_output(
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self,
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place,
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inplace,
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w1_has_data=False,
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w2_has_data=False,
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w3_has_data=False,
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):
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paddle.disable_static()
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w1 = self.create_lod_tensor(place)
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w2 = self.create_selected_rows(place, w2_has_data)
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x = [w1, w2]
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out = paddle.add_n(x)
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result = np.ones((1, self.height)).astype(np.int32).tolist()[0]
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for ele in self.rows:
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result[ele] += 1
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out_t = np.array(out)
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self.assertEqual(out_t.shape[0], self.height)
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np.testing.assert_array_equal(
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out_t,
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self._get_array(list(range(self.height)), self.row_numel)
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* np.tile(np.array(result).reshape(self.height, 1), self.row_numel),
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)
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paddle.enable_static()
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def create_selected_rows(self, place, has_data):
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# create and initialize W Variable
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if has_data:
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rows = self.rows
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else:
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rows = []
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w_array = self._get_array(self.rows, self.row_numel)
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var = core.eager.Tensor(
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core.VarDesc.VarType.FP32,
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w_array.shape,
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"selected_rows",
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core.VarDesc.VarType.SELECTED_ROWS,
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True,
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)
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w_selected_rows = var.value().get_selected_rows()
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w_selected_rows.set_height(self.height)
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w_selected_rows.set_rows(rows)
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w_tensor = w_selected_rows.get_tensor()
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w_tensor.set(w_array, place)
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return var
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def create_lod_tensor(self, place):
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w_array = self._get_array(list(range(self.height)), self.row_numel)
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return paddle.to_tensor(w_array)
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def test_w_is_selected_rows(self):
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places = [core.XPUPlace(0)]
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for place in places:
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self.check_with_place(place, True)
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support_types = get_xpu_op_support_types('sum')
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for stype in support_types:
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create_test_class(globals(), XPUTestSumOp, stype)
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if __name__ == "__main__":
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unittest.main()
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