wehub-resource-sync
490 lines
18 KiB
Python
490 lines
18 KiB
Python
# Copyright (c) 2022 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 copy
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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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check_run_big_shape_test,
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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 import OpTest, skip_check_grad_ci
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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 XPUTestElementwiseAddOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'elementwise_add'
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self.use_dynamic_create_class = False
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class TestElementwiseAddOp(XPUOpTest):
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def setUp(self):
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self.op_type = "elementwise_add"
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self.init_dtype()
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self.init_input_output()
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self.init_axis()
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self.init_max_relative_error()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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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_normal(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['X', 'Y'],
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'Out',
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max_relative_error=self.max_relative_error,
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)
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def test_check_grad_ignore_x(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['Y'],
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'Out',
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no_grad_set=set("X"),
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max_relative_error=self.max_relative_error,
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)
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def test_check_grad_ignore_y(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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no_grad_set=set("Y"),
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max_relative_error=self.max_relative_error,
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)
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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def init_dtype(self):
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self.dtype = self.in_type
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def init_axis(self):
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self.axis = -1
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def init_max_relative_error(self):
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self.max_relative_error = 0.006
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class TestElementwiseAddOp_ZeroDim1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.uniform(-1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(-1, 1, []).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroDim2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.uniform(-1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(-1, 1, [13, 17]).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroDim3(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.uniform(-1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(-1, 1, []).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroSize1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(0, 3, 4).astype(self.dtype)
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self.y = np.random.rand(1).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroSize2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(0, 3, 4).astype(self.dtype)
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self.y = np.random.rand(0, 3, 4).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroSize3(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(3, 0, 4).astype(self.dtype)
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self.y = np.random.rand(1, 1, 4).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_ZeroSize4(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(3, 0, 4).astype(self.dtype)
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self.y = np.random.rand(1, 0, 4).astype(self.dtype)
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self.out = self.x + self.y
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1) to test broadcast."
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)
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class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 3, 4).astype(self.dtype)
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self.y = np.random.rand(1).astype(self.dtype)
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self.out = self.x + self.y
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1,1) to test broadcast."
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)
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class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 3, 4).astype(self.dtype)
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self.y = np.random.rand(1, 1).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.random((100,)).astype(self.dtype)
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self.y = np.random.random((100,)).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(100, 2, 3).astype(self.dtype)
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self.y = np.random.rand(100).astype(self.dtype)
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self.out = self.x + self.y.reshape(100, 1, 1)
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def init_axis(self):
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self.axis = 0
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class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 100, 3).astype(self.dtype)
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self.y = np.random.rand(100).astype(self.dtype)
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self.out = self.x + self.y.reshape(1, 100, 1)
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def init_axis(self):
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self.axis = 1
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class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 3, 100).astype(self.dtype)
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self.y = np.random.rand(100).astype(self.dtype)
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self.out = self.x + self.y.reshape(1, 1, 100)
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class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
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self.y = np.random.rand(10, 12).astype(self.dtype)
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self.out = self.x + self.y.reshape(1, 10, 12, 1)
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def init_axis(self):
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self.axis = 1
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class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(100, 2, 3, 4).astype(self.dtype)
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self.y = np.random.rand(100, 1).astype(self.dtype)
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self.out = self.x + self.y.reshape(100, 1, 1, 1)
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def init_axis(self):
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self.axis = 0
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class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(10, 3, 12).astype(self.dtype)
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self.y = np.random.rand(10, 1, 12).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
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self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
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self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
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self.out = self.x + self.y
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class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 10, 12).astype(self.dtype)
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self.y = np.random.rand(10, 12).astype(self.dtype)
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self.out = self.x + self.y.reshape(1, 10, 12)
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def init_axis(self):
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self.axis = 1
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1) to test broadcast."
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)
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class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(100, 1).astype(self.dtype)
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self.y = np.random.rand(1).astype(self.dtype)
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self.out = self.x + self.y.reshape(1, 1)
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def init_axis(self):
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self.axis = 1
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class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(100, 2, 3).astype(self.dtype)
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self.y = np.random.rand(100, 1, 1).astype(self.dtype)
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self.out = self.x + self.y
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def init_axis(self):
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self.axis = -1
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class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(2, 3, 100).astype(self.dtype)
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self.y = np.random.rand(1, 1, 100).astype(self.dtype)
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self.out = self.x + self.y
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def init_axis(self):
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self.axis = -1
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class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
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self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
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self.out = self.x + self.y
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def init_axis(self):
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self.axis = -1
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class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(10, 12).astype(self.dtype)
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self.y = np.random.rand(2, 3, 10, 12).astype(self.dtype)
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self.out = self.x + self.y
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def init_axis(self):
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self.axis = 2
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@check_run_big_shape_test()
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class TestElementwiseAddOpLargeShape1(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(8192, 1920).astype(self.dtype)
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self.y = np.random.rand(1920).astype(self.dtype)
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self.out = self.x + self.y
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@check_run_big_shape_test()
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class TestElementwiseAddOpLargeShape2(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(1, 8192, 5, 128).astype(self.dtype)
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self.y = np.random.rand(1, 8192, 5, 128).astype(self.dtype)
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self.out = self.x + self.y
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@check_run_big_shape_test()
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class TestElementwiseAddOpLargeShape3(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(1024, 5120).astype(self.dtype)
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self.y = np.random.rand(5120).astype(self.dtype)
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self.out = self.x + self.y
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@check_run_big_shape_test()
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class TestElementwiseAddOpLargeShape4(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(8192, 3456).astype(self.dtype)
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self.y = np.random.rand(3456).astype(self.dtype)
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self.out = self.x + self.y
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@check_run_big_shape_test()
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class TestElementwiseAddOpLargeShape5(TestElementwiseAddOp):
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def init_input_output(self):
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self.x = np.random.rand(1, 8192, 31776).astype(self.dtype)
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self.y = np.random.rand(31776).astype(self.dtype)
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self.out = self.x + self.y
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class TestAddOp(unittest.TestCase):
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def test_name(self):
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with base.program_guard(base.Program()):
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x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
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y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
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y_1 = paddle.add(x, y, name='add_res')
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if not paddle.framework.use_pir_api():
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self.assertEqual(('add_res' in y_1.name), True)
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def test_declarative(self):
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with base.program_guard(base.Program()):
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def gen_data():
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return {
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"x": np.array([2, 3, 4]).astype('float32'),
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"y": np.array([1, 5, 2]).astype('float32'),
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}
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x = paddle.static.data(name="x", shape=[3], dtype='float32')
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y = paddle.static.data(name="y", shape=[3], dtype='float32')
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z = paddle.add(x, y)
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place = base.XPUPlace(0)
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exe = base.Executor(place)
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z_value = exe.run(feed=gen_data(), fetch_list=[z])
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z_expected = np.array([3.0, 8.0, 6.0])
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self.assertEqual((z_value == z_expected).all(), True)
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def test_dygraph(self):
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with base.dygraph.guard():
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np_x = np.array([2, 3, 4]).astype('float32')
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np_y = np.array([1, 5, 2]).astype('float32')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.add(x, y)
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np_z = z.numpy()
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z_expected = np.array([3.0, 8.0, 6.0])
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self.assertEqual((np_z == z_expected).all(), True)
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support_types = get_xpu_op_support_types('elementwise_add')
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real_types = [t for t in support_types if t != 'complex64']
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for stype in real_types:
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create_test_class(globals(), XPUTestElementwiseAddOp, stype)
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if 'complex64' in support_types:
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class TestElementwiseAddOpComplex(OpTest):
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def setUp(self):
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self.op_type = "elementwise_add"
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self.python_api = paddle.add
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self.init_dtype()
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self.init_input_output()
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self.init_axis()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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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_normal(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['X', 'Y'],
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'Out',
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)
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def test_check_grad_ignore_x(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['Y'],
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'Out',
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no_grad_set=set("X"),
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)
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def test_check_grad_ignore_y(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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no_grad_set=set("Y"),
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)
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def init_input_output(self):
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self.x = (
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np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4)
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).astype(self.dtype)
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self.y = (
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np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4)
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).astype(self.dtype)
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self.out = self.x + self.y
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def init_dtype(self):
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self.dtype = np.complex64
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def init_axis(self):
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self.axis = -1
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class TestElementwiseAddOpComplex2(TestElementwiseAddOpComplex):
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def init_input_output(self):
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self.x = (
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np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4)
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).astype(self.dtype)
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self.y = (np.random.rand(1, 1) + 1j * np.random.rand(1, 1)).astype(
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self.dtype
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)
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self.out = self.x + self.y
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class TestElementwiseAddOpComplex3(TestElementwiseAddOpComplex):
|
|
def init_input_output(self):
|
|
self.x = (
|
|
np.random.rand(10, 2, 3) + 1j * np.random.rand(10, 2, 3)
|
|
).astype(self.dtype)
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|
self.y = (np.random.rand(1, 1) + 1j * np.random.rand(1, 1)).astype(
|
|
self.dtype
|
|
)
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|
self.out = self.x + self.y
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.get_xpu_device_version(0) != core.XPUVersion.XPU3,
|
|
"only supported on XPU3",
|
|
)
|
|
class TestTensorFloat32Bfloat16OrFloat16Add(unittest.TestCase):
|
|
def _float32_bfloat16_or_float16_add(self, y_dtype):
|
|
paddle.disable_static()
|
|
test_num = 5
|
|
val_range = 10000
|
|
shapes = []
|
|
for i in range(test_num):
|
|
shape = [
|
|
np.random.randint(1, val_range),
|
|
np.random.randint(1, val_range),
|
|
]
|
|
shapes.append(shape)
|
|
|
|
for i, shape in enumerate(shapes):
|
|
x = paddle.randn(list(shape), dtype=paddle.float32)
|
|
x_copy = copy.deepcopy(x)
|
|
y = paddle.randn(list(shape), dtype=y_dtype)
|
|
x.add_(y)
|
|
x_copy.add_(paddle.cast(y, paddle.float32))
|
|
np.testing.assert_equal(x.numpy(), x_copy.numpy())
|
|
del x, x_copy
|
|
|
|
def test_float32_bfloat16_add(self):
|
|
self._float32_bfloat16_or_float16_add(y_dtype=paddle.bfloat16)
|
|
|
|
def test_float32_float16_add(self):
|
|
self._float32_bfloat16_or_float16_add(y_dtype=paddle.float16)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|