wehub-resource-sync
1309 lines
42 KiB
Python
1309 lines
42 KiB
Python
# Copyright (c) 2018 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 os
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import unittest
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import warnings
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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_place,
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is_custom_device,
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skip_check_grad_ci,
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)
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import paddle
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import paddle.distributed as dist
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from paddle import base
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from paddle.base import core
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from paddle.base.layer_helper import LayerHelper
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class TestElementwiseAddOp(OpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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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.public_python_api = paddle.add
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self.prim_op_type = "prim"
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.if_check_prim()
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self.if_enable_cinn()
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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 check_dygraph(self):
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return not self.use_onednn and self.axis == -1
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def test_check_output(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_output(
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check_dygraph=self.check_dygraph(),
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check_pir=self.check_dygraph(),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_normal(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if self.dtype == np.float16:
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return
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_dygraph=self.check_dygraph(),
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check_prim=self.check_prim,
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check_prim_pir=self.check_dygraph(),
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check_pir=self.check_dygraph(),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_x(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if self.dtype == np.float16:
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return
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=self.check_dygraph(),
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check_prim=self.check_prim,
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check_prim_pir=self.check_dygraph(),
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check_pir=self.check_dygraph(),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_y(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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if self.dtype == np.float16:
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return
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=self.check_dygraph(),
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check_prim=self.check_prim,
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check_prim_pir=self.check_dygraph(),
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check_pir=self.check_dygraph(),
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check_pir_onednn=self.check_pir_onednn,
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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 = np.float64
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def init_axis(self):
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self.axis = -1
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def if_check_prim(self):
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self.check_prim = self.axis == -1
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def if_enable_cinn(self):
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pass
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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(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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class TestElementwiseAddOp_ZeroDim2(TestElementwiseAddOp_ZeroDim1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, []).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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class TestElementwiseAddOp_ZeroDim3(TestElementwiseAddOp_ZeroDim1):
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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, []).astype(self.dtype)
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self.out = np.add(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.uniform(0.1, 1, [3]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3]).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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def test_check_grad_ignore_y(self):
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pass
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class TestElementwiseAddOp_ZeroSize2(TestElementwiseAddOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 3, 4]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3, 4]).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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class TestElementwiseAddOp_ZeroSize3(TestElementwiseAddOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 0, 2]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [3, 0, 1]).astype(self.dtype)
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self.out = np.add(self.x, self.y)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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place = get_device_place()
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self.check_output_with_place(
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place,
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atol=1e-3,
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check_dygraph=self.check_dygraph(),
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check_pir=self.check_dygraph(),
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)
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def test_check_grad_normal(self):
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place = get_device_place()
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self.check_grad_with_place(place, ['X', 'Y'], 'Out', check_prim=True)
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def test_check_grad_ignore_x(self):
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place = get_device_place()
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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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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def test_check_grad_ignore_y(self):
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place = get_device_place()
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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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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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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 core.cudnn_version() < 8100
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or paddle.device.cuda.get_device_capability()[0] < 8,
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"only support compiled with CUDA and cudnn version need larger than 8.1.0 and device's compute capability is at least 8.0",
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)
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class TestBF16ElementwiseAddOp(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.public_python_api = paddle.add
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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self.out = np.add(self.x, self.y)
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self.axis = -1
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(convert_float_to_uint16(self.x)),
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'Y': OpTest.np_dtype_to_base_dtype(convert_float_to_uint16(self.y)),
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}
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self.attrs = {'axis': self.axis, 'use_onednn': False}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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self.if_enable_cinn()
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def test_check_output(self):
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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_normal(self):
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place = get_device_place()
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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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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def test_check_grad_ignore_x(self):
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place = get_device_place()
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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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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def test_check_grad_ignore_y(self):
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place = get_device_place()
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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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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def if_enable_cinn(self):
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self.enable_cinn = False
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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) to test broadcast."
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)
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class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
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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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@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 TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
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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 TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
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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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = 0
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def if_check_prim(self):
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self.check_prim = False
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@skip_check_grad_ci(
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reason="the numerical method is not accurate enough on fp16"
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)
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class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
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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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = 0
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# In paddle2.0 api we don't have axis parameter in add,
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# so we can't check prim when axis is not -1 by default.
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def if_check_prim(self):
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self.check_prim = self.axis == -1
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# Because the numerical method is not accurate enough on fp16,
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# so we do not test the grad on fp16
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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def test_check_grad_ignore_y(self):
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pass
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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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = 1
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def if_check_prim(self):
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self.check_prim = False
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class TestFP16ElementwiseAddOp_broadcast_1(
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TestFP16ElementwiseAddOp_broadcast_0
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):
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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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self.python_api = paddle.add
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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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self.python_api = paddle.add
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class TestFP16ElementwiseAddOp_broadcast_2(
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TestFP16ElementwiseAddOp_broadcast_0
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):
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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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = -1
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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, 1).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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = 1
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class TestFP16ElementwiseAddOp_broadcast_3(
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TestFP16ElementwiseAddOp_broadcast_0
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):
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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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self.python_api = paddle.add
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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, 1, 2).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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self.python_api = paddle.add
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def init_axis(self):
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self.axis = 0
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class TestFP16ElementwiseAddOp_broadcast_4(
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TestFP16ElementwiseAddOp_broadcast_0
|
|
):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype)
|
|
self.y = np.random.rand(100, 1).astype(self.dtype)
|
|
self.out = self.x + self.y.reshape(100, 1, 1, 1)
|
|
self.python_api = paddle.add
|
|
|
|
def init_axis(self):
|
|
self.axis = 0
|
|
|
|
|
|
class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 3, 12).astype(self.dtype)
|
|
self.y = np.random.rand(10, 1, 12).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestFP16ElementwiseAddOp_broadcast_5(
|
|
TestFP16ElementwiseAddOp_broadcast_0
|
|
):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 3, 12).astype(self.dtype)
|
|
self.y = np.random.rand(10, 1, 12).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
|
|
self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
|
|
self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestFP16ElementwiseAddOp_broadcast_6(
|
|
TestFP16ElementwiseAddOp_broadcast_0
|
|
):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
|
|
self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 10, 12).astype(self.dtype)
|
|
self.y = np.random.rand(10, 12).astype(self.dtype)
|
|
self.out = self.x + self.y.reshape(1, 10, 12)
|
|
|
|
def init_axis(self):
|
|
self.axis = 1
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="the numerical method is not accurate enough on fp16."
|
|
)
|
|
class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 10, 12).astype(self.dtype)
|
|
self.y = np.random.rand(10, 12).astype(self.dtype)
|
|
self.out = self.x + self.y.reshape(1, 10, 12)
|
|
|
|
def init_axis(self):
|
|
self.axis = 1
|
|
|
|
# Because the numerical method is not accurate enough on fp16,
|
|
# so we do not test the grad on fp16
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 100, 1).astype(self.dtype)
|
|
self.y = np.random.rand(100, 1).astype(self.dtype)
|
|
self.out = self.x + self.y.reshape(1, 100, 1)
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="[skip shape check] Use y_shape(1) to test broadcast."
|
|
)
|
|
class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(100, 1).astype(self.dtype)
|
|
self.y = np.random.rand(1).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
|
|
self.y = np.random.rand(100, 1, 1).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestFP16ElementwiseAddOp_channelwise_add(TestFP16ElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
|
|
self.y = np.random.rand(100, 1, 1).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
|
|
self.y = np.random.rand(1, 1, 100).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestElementwiseFP16AddOp_commonuse_add1(TestFP16ElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
|
|
self.y = np.random.rand(1, 1, 100).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
|
|
self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 12).astype(self.dtype)
|
|
self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = 2
|
|
|
|
|
|
class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 1, 12).astype(self.dtype)
|
|
self.y = np.random.rand(10, 2, 12).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def init_axis(self):
|
|
self.axis = 0
|
|
|
|
|
|
class TestAddApi(unittest.TestCase):
|
|
def _executed_api(self, x, y, name=None):
|
|
return paddle.add(x, y, name)
|
|
|
|
def test_name(self):
|
|
with (
|
|
paddle.pir_utils.OldIrGuard(),
|
|
base.program_guard(base.Program()),
|
|
):
|
|
x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
|
|
y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
|
|
|
|
y_1 = self._executed_api(x, y, name='add_res')
|
|
self.assertEqual(('add_res' in y_1.name), True)
|
|
|
|
def test_declarative(self):
|
|
with base.program_guard(base.Program()):
|
|
|
|
def gen_data():
|
|
return {
|
|
"x": np.array([2, 3, 4]).astype('float32'),
|
|
"y": np.array([1, 5, 2]).astype('float32'),
|
|
}
|
|
|
|
x = paddle.static.data(name="x", shape=[3], dtype='float32')
|
|
y = paddle.static.data(name="y", shape=[3], dtype='float32')
|
|
z = self._executed_api(x, y)
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
z_value = exe.run(feed=gen_data(), fetch_list=[z])
|
|
z_expected = np.array([3.0, 8.0, 6.0])
|
|
self.assertEqual((z_value == z_expected).all(), True)
|
|
|
|
def test_dygraph(self):
|
|
with base.dygraph.guard():
|
|
np_x = np.array([2, 3, 4]).astype('float64')
|
|
np_y = np.array([1, 5, 2]).astype('float64')
|
|
x = paddle.to_tensor(np_x)
|
|
y = paddle.to_tensor(np_y)
|
|
z = self._executed_api(x, y)
|
|
np_z = z.numpy()
|
|
z_expected = np.array([3.0, 8.0, 6.0])
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
class TestAddApiZeroSize(unittest.TestCase):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 3, 4).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3, 4).astype('float32')
|
|
|
|
def _executed_api(self, x, y, name=None):
|
|
return paddle.add(x, y, name)
|
|
|
|
def test_declarative(self):
|
|
self.init_data()
|
|
with base.program_guard(base.Program()):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.x_numpy.shape, dtype=self.x_numpy.dtype
|
|
)
|
|
y = paddle.static.data(
|
|
name="y", shape=self.y_numpy.shape, dtype=self.y_numpy.dtype
|
|
)
|
|
z = self._executed_api(x, y)
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
z_value = exe.run(
|
|
feed={"x": self.x_numpy, "y": self.y_numpy}, fetch_list=[z]
|
|
)
|
|
np_z = np.add(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z_value[0], np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
def test_dygraph(self):
|
|
self.init_data()
|
|
places = (
|
|
[paddle.CPUPlace(), get_device_place()]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [paddle.CPUPlace()]
|
|
)
|
|
for place in places:
|
|
with base.dygraph.guard(place):
|
|
x = paddle.to_tensor(self.x_numpy)
|
|
y = paddle.to_tensor(self.y_numpy)
|
|
z = self._executed_api(x, y)
|
|
np_z = np.add(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z, np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
|
|
class TestAddApiZeroSize2(TestAddApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(3).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3).astype('float32')
|
|
|
|
|
|
class TestAddApiZeroSize3(TestAddApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 0).astype('float32')
|
|
self.y_numpy = np.random.rand(1, 0).astype('float32')
|
|
|
|
|
|
class TestAddApiZeroSize4(TestAddApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y_numpy = np.random.rand(3, 0, 1).astype('float32')
|
|
|
|
|
|
class TestAddInplaceApi(TestAddApi):
|
|
def _executed_api(self, x, y, name=None):
|
|
return x.add_(y, name)
|
|
|
|
|
|
class TestAddInplaceBroadcastSuccess(unittest.TestCase):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 3, 4).astype('float')
|
|
self.y_numpy = np.random.rand(3, 4).astype('float')
|
|
|
|
def test_broadcast_success(self):
|
|
with paddle.base.dygraph.guard():
|
|
self.init_data()
|
|
x = paddle.to_tensor(self.x_numpy)
|
|
y = paddle.to_tensor(self.y_numpy)
|
|
inplace_result = x.add_(y)
|
|
numpy_result = self.x_numpy + self.y_numpy
|
|
self.assertEqual(
|
|
(inplace_result.numpy() == numpy_result).all(), True
|
|
)
|
|
|
|
|
|
class TestAddInplaceBroadcastSuccess2(TestAddInplaceBroadcastSuccess):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float')
|
|
self.y_numpy = np.random.rand(3, 1).astype('float')
|
|
|
|
|
|
class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float')
|
|
self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float')
|
|
|
|
|
|
class TestAddInplaceBroadcastError(unittest.TestCase):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(3, 4).astype('float')
|
|
self.y_numpy = np.random.rand(2, 3, 4).astype('float')
|
|
|
|
def test_broadcast_errors(self):
|
|
with paddle.base.dygraph.guard():
|
|
self.init_data()
|
|
x = paddle.to_tensor(self.x_numpy)
|
|
y = paddle.to_tensor(self.y_numpy)
|
|
|
|
def broadcast_shape_error():
|
|
x.add_(y)
|
|
|
|
self.assertRaises(ValueError, broadcast_shape_error)
|
|
|
|
|
|
class TestAddInplaceBroadcastError2(TestAddInplaceBroadcastError):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 1, 4).astype('float')
|
|
self.y_numpy = np.random.rand(2, 3, 4).astype('float')
|
|
|
|
|
|
class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float')
|
|
self.y_numpy = np.random.rand(2, 3, 4).astype('float')
|
|
|
|
|
|
class TestComplexElementwiseAddOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_add"
|
|
self.python_api = paddle.add
|
|
self.dtype = np.complex128
|
|
self.shape = (2, 3, 4, 5)
|
|
self.init_input_output()
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
self.attrs = {'axis': -1, 'use_onednn': False}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def init_base_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.random(self.shape).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random(self.shape).astype(self.dtype)
|
|
self.y = np.random.random(self.shape).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random(self.shape).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(['X', 'Y'], 'Out', check_pir=True)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
self.check_grad(['Y'], 'Out', no_grad_set=set("X"), check_pir=True)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
self.check_grad(['X'], 'Out', no_grad_set=set('Y'), check_pir=True)
|
|
|
|
|
|
class TestRealComplexElementwiseAddOp(TestComplexElementwiseAddOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.random(self.shape).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random(self.shape).astype(self.dtype)
|
|
self.y = np.random.random(self.shape).astype(self.dtype)
|
|
self.out = self.x + self.y
|
|
|
|
|
|
class TestBoolAddFloatElementwiseAddop(unittest.TestCase):
|
|
def test_static_add(self):
|
|
paddle.enable_static()
|
|
a = 1.5
|
|
b = paddle.full([4, 5, 6], True, dtype='bool')
|
|
c = a + b
|
|
self.assertTrue(c.dtype == paddle.float32)
|
|
with paddle.pir_utils.IrGuard():
|
|
a = 1.5
|
|
b = paddle.full([4, 5, 6], True, dtype='bool')
|
|
c = a + b
|
|
self.assertTrue(c.dtype == core.DataType.FLOAT32)
|
|
|
|
def test_dygraph_add(self):
|
|
with paddle.base.dygraph.guard():
|
|
a = 1.5
|
|
b = paddle.full([2], True, dtype='bool')
|
|
# special case: scalar + tensor(bool)
|
|
c = a + b
|
|
self.assertTrue(c.dtype == paddle.float32)
|
|
|
|
np_a = np.random.random((2, 3, 4)).astype(np.float64)
|
|
np_b = np.random.random((2, 3, 4)).astype(np.float64)
|
|
|
|
tensor_a = paddle.to_tensor(np_a, dtype="float32")
|
|
tensor_b = paddle.to_tensor(np_b, dtype="float32")
|
|
|
|
# normal case: tensor + tensor
|
|
expect_out = np_a + np_b
|
|
actual_out = tensor_a + tensor_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
# normal case: tensor + scalar
|
|
expect_out = np_a + 1
|
|
actual_out = tensor_a + 1
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
# normal case: scalar + tenor
|
|
expect_out = 1 + np_a
|
|
actual_out = 1 + tensor_a
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
|
|
class TestElementwiseAddop1(unittest.TestCase):
|
|
def test_dygraph_add(self):
|
|
with paddle.base.dygraph.guard():
|
|
np_a = np.random.random((2, 3, 4)).astype(np.float32)
|
|
np_b = np.random.random((2, 3, 4)).astype(np.float32)
|
|
|
|
tensor_a = paddle.to_tensor(np_a, dtype="float32")
|
|
tensor_b = paddle.to_tensor(np_b, dtype="float32")
|
|
|
|
# normal case: nparray + tenor
|
|
expect_out = np_a + np_b
|
|
actual_out = np_a + tensor_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
# normal case: tensor + nparray
|
|
actual_out = tensor_a + np_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
|
|
class TestTensorAddNumpyScalar(unittest.TestCase):
|
|
def test_float32_add(self):
|
|
paddle.disable_static()
|
|
a = paddle.full([4, 5, 6], 1.5, dtype='float32')
|
|
b = np.array([1.5], dtype='float32')[0]
|
|
c = a + b
|
|
self.assertTrue(c.dtype == paddle.float32)
|
|
|
|
def test_float16_add(self):
|
|
if not (core.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
paddle.disable_static()
|
|
a = paddle.full([4, 5, 6], 1.5, dtype='float16')
|
|
b = np.array([1.5], dtype='float16')[0]
|
|
c = a + b
|
|
self.assertTrue(c.dtype == paddle.float16)
|
|
|
|
|
|
class TestTensorAddAPIWarnings(unittest.TestCase):
|
|
def test_warnings(self):
|
|
with (
|
|
paddle.pir_utils.OldIrGuard(),
|
|
warnings.catch_warnings(record=True) as context,
|
|
):
|
|
warnings.simplefilter("always")
|
|
|
|
paddle.enable_static()
|
|
helper = LayerHelper("elementwise_add")
|
|
data = paddle.static.data(
|
|
name='data', shape=[None, 3, 32, 32], dtype='float32'
|
|
)
|
|
out = helper.create_variable_for_type_inference(dtype=data.dtype)
|
|
os.environ['FLAGS_print_extra_attrs'] = "1"
|
|
helper.append_op(
|
|
type="elementwise_add",
|
|
inputs={'X': data, 'Y': data},
|
|
outputs={'Out': out},
|
|
attrs={'axis': 1, 'use_onednn': False},
|
|
)
|
|
self.assertTrue(
|
|
"op elementwise_add's attr axis = 1 is not the default value: -1"
|
|
in str(context[-1].message)
|
|
)
|
|
os.environ['FLAGS_print_extra_attrs'] = "0"
|
|
|
|
|
|
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(val_range), np.random.randint(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
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or core.cudnn_version() < 8100
|
|
or paddle.device.cuda.get_device_capability()[0] < 8,
|
|
"only support compiled with CUDA and cudnn version need larger than 8.1.0 and device's compute capability is at least 8.0",
|
|
)
|
|
class TestTensorFloat32Bfloat16Add(TestTensorFloat32Bfloat16OrFloat16Add):
|
|
def test_float32_bfloat16_add(self):
|
|
place = get_device_place()
|
|
with base.dygraph.base.guard(place=place):
|
|
self._float32_bfloat16_or_float16_add(y_dtype=paddle.bfloat16)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or core.cudnn_version() < 8100,
|
|
"only support compiled with CUDA and cudnn version need larger than 8.1.0",
|
|
)
|
|
class TestTensorFloat32Float16Add(TestTensorFloat32Bfloat16OrFloat16Add):
|
|
def test_float32_float16_add(self):
|
|
place = get_device_place()
|
|
with base.dygraph.base.guard(place=place):
|
|
self._float32_bfloat16_or_float16_add(y_dtype=paddle.float16)
|
|
|
|
|
|
class TestElementwiseAddOpAutoParallel(OpTest):
|
|
def init_kernel_type(self):
|
|
self.use_onednn = False
|
|
|
|
def setUp(self):
|
|
self.op_type = "elementwise_add"
|
|
self.python_api = paddle.add
|
|
self.public_python_api = paddle.add
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
self.init_kernel_type()
|
|
self.init_axis()
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
self.init_placements()
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
|
|
self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def check_dygraph(self):
|
|
return not self.use_onednn and self.axis == -1
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
['X', 'Y'],
|
|
'Out',
|
|
check_auto_parallel=True,
|
|
)
|
|
|
|
def init_placements(self):
|
|
self.placements = {
|
|
"X": [dist.Shard(0)],
|
|
"Y": [dist.Replicate()],
|
|
}
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.uniform(0.1, 1, [16, 32]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [16, 32]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
|
|
def init_axis(self):
|
|
self.axis = -1
|
|
|
|
def if_check_prim(self):
|
|
self.check_prim = self.axis == -1
|
|
|
|
def if_enable_cinn(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseAddOpAutoParallelXShardBroadcast(
|
|
TestElementwiseAddOpAutoParallel
|
|
):
|
|
def init_placements(self):
|
|
self.placements = {
|
|
"X": [dist.Shard(0)],
|
|
"Y": [dist.Replicate()],
|
|
}
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.uniform(0.1, 1, [8, 16]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [2, 8, 16]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseAddOpAutoParallelXYShard(TestElementwiseAddOpAutoParallel):
|
|
def init_placements(self):
|
|
self.placements = {
|
|
"X": [dist.Shard(0)],
|
|
"Y": [dist.Shard(1)],
|
|
}
|
|
|
|
def test_check_grad(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place, ['X', 'Y'], 'Out', check_auto_parallel=True
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.uniform(0.1, 1, [16, 32]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [16, 32]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
|
|
|
|
class TestElementwiseAddOpAutoParallelXYShardBroadcast(
|
|
TestElementwiseAddOpAutoParallelXYShard
|
|
):
|
|
def init_placements(self):
|
|
self.placements = {
|
|
"X": [dist.Shard(0)],
|
|
"Y": [dist.Replicate()],
|
|
}
|
|
|
|
def test_check_grad(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place, ['X', 'Y'], 'Out', check_auto_parallel=True
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.uniform(0.1, 1, [8, 16]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [2, 8, 16]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseAddOp_Stride(TestElementwiseAddOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_add"
|
|
self.python_api = paddle.add
|
|
self.public_python_api = paddle.add
|
|
self.transpose_api = paddle.transpose
|
|
self.as_stride_api = paddle.as_strided
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
self.init_kernel_type()
|
|
self.init_axis()
|
|
|
|
self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
|
|
|
|
self.inputs_stride = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
|
|
}
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_strided_forward = True
|
|
self.check_output(
|
|
place,
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.test_stride_backward = True
|
|
place = get_device_place()
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad_with_place(
|
|
place,
|
|
['X', 'Y'],
|
|
'Out',
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
self.test_stride_backward = True
|
|
place = get_device_place()
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
self.test_stride_backward = True
|
|
place = get_device_place()
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad_with_place(
|
|
place,
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride1(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride2(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride3(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride4(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride5(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.out = np.add(self.x, self.y)
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseAddOp_Stride_ZeroDim1(TestElementwiseAddOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseAddOp_Stride_ZeroSize1(TestElementwiseAddOp_Stride):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y = np.random.rand(3, 0, 1).astype('float32')
|
|
self.out = np.add(self.x, self.y)
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|