wehub-resource-sync
1380 lines
46 KiB
Python
1380 lines
46 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 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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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 TestElementwiseOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.init_inputs()
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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self.if_enable_cinn()
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def init_inputs(self):
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype),
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}
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def init_dtype(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_prim=self.check_prim,
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check_prim_pir=self.check_prim_pir,
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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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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set("X"),
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check_prim=self.check_prim,
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check_prim_pir=self.check_prim_pir,
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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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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set('Y'),
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check_prim=self.check_prim,
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check_prim_pir=self.check_prim_pir,
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check_pir=True,
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)
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def if_check_prim(self):
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self.check_prim = True
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self.check_prim_pir = True
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def if_enable_cinn(self):
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pass
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class TestElementwiseFP16OP(TestElementwiseOp):
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def init_dtype(self):
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self.dtype = np.float16
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class TestElementwiseSubOp_ZeroSize1(TestElementwiseOp):
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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.subtract(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 TestElementwiseSubOp_ZeroSize2(TestElementwiseSubOp_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.subtract(self.x, self.y)
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class TestElementwiseSubOp_ZeroSize3(TestElementwiseSubOp_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.subtract(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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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestElementwiseBF16OP(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32),
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'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.inputs = {
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'X': convert_float_to_uint16(self.inputs['X']),
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'Y': convert_float_to_uint16(self.inputs['Y']),
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}
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self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
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self.if_check_prim()
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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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, ['X', 'Y'], 'Out', max_relative_error=0.1
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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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max_relative_error=0.1,
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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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max_relative_error=0.1,
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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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class TestElementwiseSubOp_ZeroDim1(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.inputs = {
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'X': np.random.uniform(0.1, 1, []).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, []).astype(self.dtype),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestElementwiseSubFP16OP_ZeroDim1(TestElementwiseSubOp_ZeroDim1):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestElementwiseSubBF16OP_ZeroDim1(TestElementwiseBF16OP):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.dtype = np.uint16
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, []).astype(np.float32),
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'Y': np.random.uniform(0.1, 1, []).astype(np.float32),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.inputs = {
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'X': convert_float_to_uint16(self.inputs['X']),
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'Y': convert_float_to_uint16(self.inputs['Y']),
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}
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self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
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self.if_check_prim()
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self.if_enable_cinn()
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class TestElementwiseSubOp_ZeroDim2(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, []).astype(self.dtype),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestElementwiseSubFP16OP_ZeroDim2(TestElementwiseSubOp_ZeroDim2):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestElementwiseSubBF16OP_ZeroDim2(TestElementwiseBF16OP):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.dtype = np.uint16
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32),
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'Y': np.random.uniform(0.1, 1, []).astype(np.float32),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.inputs = {
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'X': convert_float_to_uint16(self.inputs['X']),
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'Y': convert_float_to_uint16(self.inputs['Y']),
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}
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self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
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self.if_check_prim()
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self.if_enable_cinn()
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class TestElementwiseSubOp_ZeroDim3(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.inputs = {
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'X': np.random.uniform(0.1, 1, []).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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class TestElementwiseSubFP16OP_ZeroDim3(TestElementwiseSubOp_ZeroDim3):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestElementwiseBF16OP_ZeroDim3(TestElementwiseBF16OP):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.dtype = np.uint16
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, []).astype(np.float32),
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'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.inputs = {
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'X': convert_float_to_uint16(self.inputs['X']),
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'Y': convert_float_to_uint16(self.inputs['Y']),
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}
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self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
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self.if_check_prim()
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self.if_enable_cinn()
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestBF16ElementwiseOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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out = x - y
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self.inputs = {
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'X': convert_float_to_uint16(x),
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'Y': convert_float_to_uint16(y),
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}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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self.if_check_prim()
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self.if_enable_cinn()
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def if_check_prim(self):
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self.check_prim = True
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def if_enable_cinn(self):
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self.enable_cinn = False
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_prim=self.check_prim)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'], 'Out', no_grad_set=set("X"), check_prim=self.check_prim
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)
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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 TestElementwiseSubOp_scalar(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.inputs = {
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'X': np.random.rand(10, 3, 4).astype(self.dtype),
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'Y': np.random.rand(1).astype(self.dtype),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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class TestElementwiseSubOp_Vector(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.public_python_api = paddle.subtract
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self.prim_op_type = "prim"
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self.init_dtype()
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self.inputs = {
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'X': np.random.random((100,)).astype(self.dtype),
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'Y': np.random.random((100,)).astype(self.dtype),
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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self.if_check_prim()
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class TestElementwiseSubOp_broadcast_0(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.python_api = paddle.subtract
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self.init_dtype()
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self.inputs = {
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'X': np.random.rand(100, 3, 2).astype(self.dtype),
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'Y': np.random.rand(100).astype(self.dtype),
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}
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self.attrs = {'axis': 0}
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self.outputs = {
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'Out': self.inputs['X'] - self.inputs['Y'].reshape(100, 1, 1)
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}
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def test_check_output(self):
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self.check_output(check_dygraph=False, check_pir=False)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_dygraph=False, check_pir=False)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set("X"),
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check_dygraph=False,
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check_pir=False,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.005,
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no_grad_set=set('Y'),
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check_dygraph=False,
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check_pir=False,
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)
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class TestElementwiseSubFP16OP_broadcast_0(TestElementwiseSubOp_broadcast_0):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestElementwiseBF16OP_broadcast_0(TestElementwiseBF16OP):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.dtype = np.uint16
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self.python_api = paddle.subtract
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self.inputs = {
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'X': np.random.rand(100, 3, 2).astype(np.float32),
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'Y': np.random.rand(100).astype(np.float32),
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}
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self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(100, 1, 1)
|
|
}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.attrs = {'axis': 0}
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_output_with_place(
|
|
place, check_dygraph=False, check_pir=False
|
|
)
|
|
|
|
def test_check_grad_normal(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place, ['X', 'Y'], 'Out', check_dygraph=False, check_pir=False
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
check_dygraph=False,
|
|
check_pir=False,
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
check_dygraph=False,
|
|
check_pir=False,
|
|
)
|
|
|
|
|
|
class TestElementwiseSubOp_broadcast_1(TestElementwiseSubOp_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 100, 3).astype(self.dtype),
|
|
'Y': np.random.rand(100).astype(self.dtype),
|
|
}
|
|
|
|
self.attrs = {'axis': 1}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 100, 1)
|
|
}
|
|
|
|
|
|
class TestElementwiseSubFP16OP_broadcast_1(TestElementwiseSubOp_broadcast_1):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_broadcast_1(TestElementwiseBF16OP_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 100, 3).astype(np.float32),
|
|
'Y': np.random.rand(100).astype(np.float32),
|
|
}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 100, 1)
|
|
}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.attrs = {'axis': 1}
|
|
|
|
|
|
class TestElementwiseSubOp_broadcast_2(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(self.dtype),
|
|
'Y': np.random.rand(100).astype(self.dtype),
|
|
}
|
|
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 100)
|
|
}
|
|
self.if_check_prim()
|
|
|
|
|
|
class TestElementwiseSubFP16OP_broadcast_2(TestElementwiseSubOp_broadcast_2):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_broadcast_2(TestElementwiseBF16OP_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(np.float32),
|
|
'Y': np.random.rand(100).astype(np.float32),
|
|
}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 100)
|
|
}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.if_check_prim()
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_broadcast_3(TestElementwiseBF16OP_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 10, 12, 3).astype(np.float32),
|
|
'Y': np.random.rand(10, 12).astype(np.float32),
|
|
}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 10, 12, 1)
|
|
}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.attrs = {'axis': 1}
|
|
|
|
|
|
class TestElementwiseSubOp_broadcast_3(TestElementwiseSubOp_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 10, 12, 3).astype(self.dtype),
|
|
'Y': np.random.rand(10, 12).astype(self.dtype),
|
|
}
|
|
|
|
self.attrs = {'axis': 1}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 10, 12, 1)
|
|
}
|
|
|
|
|
|
class TestElementwiseSubFP16OP_broadcast_3(TestElementwiseSubOp_broadcast_3):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
class TestElementwiseSubOp_broadcast_4(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 5, 3, 12).astype(self.dtype),
|
|
'Y': np.random.rand(2, 5, 1, 12).astype(self.dtype),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_broadcast_4(TestElementwiseBF16OP_broadcast_0):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 5, 3, 12).astype(np.float32),
|
|
'Y': np.random.rand(2, 5, 1, 12).astype(np.float32),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.if_check_prim()
|
|
|
|
|
|
class TestElementwiseSubFP16OP_broadcast_4(TestElementwiseSubOp_broadcast_4):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
class TestElementwiseSubOp_commonuse_1(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(self.dtype),
|
|
'Y': np.random.rand(1, 1, 100).astype(self.dtype),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.if_check_prim()
|
|
|
|
|
|
class TestElementwiseSubFP16OP_commonuse_1(TestElementwiseSubOp_commonuse_1):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_commonuse_1(TestElementwiseBF16OP):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(np.float32),
|
|
'Y': np.random.rand(1, 1, 100).astype(np.float32),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
|
|
class TestElementwiseSubOp_commonuse_2(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 3, 1, 4).astype(self.dtype),
|
|
'Y': np.random.rand(10, 1, 12, 1).astype(self.dtype),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.if_check_prim()
|
|
|
|
|
|
class TestElementwiseSubFP16OP_commonuse_2(TestElementwiseSubOp_commonuse_2):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_commonuse_2(TestElementwiseBF16OP):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 3, 1, 4).astype(np.float32),
|
|
'Y': np.random.rand(10, 1, 12, 1).astype(np.float32),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
|
|
class TestElementwiseSubOp_xsize_lessthan_ysize(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.init_dtype()
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 12).astype(self.dtype),
|
|
'Y': np.random.rand(2, 3, 10, 12).astype(self.dtype),
|
|
}
|
|
self.attrs = {'axis': 2}
|
|
|
|
self.outputs = {
|
|
'Out': self.inputs['X'].reshape(1, 1, 10, 12) - self.inputs['Y']
|
|
}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
|
|
class TestElementwiseSubFP16OP_xsize_lessthan_ysize(
|
|
TestElementwiseSubOp_xsize_lessthan_ysize
|
|
):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestElementwiseBF16OP_xsize_lessthan_ysize(TestElementwiseBF16OP):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 12).astype(np.float32),
|
|
'Y': np.random.rand(2, 3, 10, 12).astype(np.float32),
|
|
}
|
|
self.attrs = {'axis': 2}
|
|
self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(self.inputs['X']),
|
|
'Y': convert_float_to_uint16(self.inputs['Y']),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
|
|
class TestComplexElementwiseSubOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.prim_op_type = "prim"
|
|
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}
|
|
self.if_check_prim()
|
|
self.if_enable_cinn()
|
|
|
|
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=False)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(
|
|
['X', 'Y'], 'Out', check_prim=self.check_prim, check_pir=False
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
self.check_grad(
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
check_prim=self.check_prim,
|
|
check_pir=False,
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
check_prim=self.check_prim,
|
|
check_pir=False,
|
|
)
|
|
|
|
def if_enable_cinn(self):
|
|
self.enable_cinn = False
|
|
|
|
def if_check_prim(self):
|
|
self.check_prim = False
|
|
|
|
|
|
class TestRealComplexElementwiseSubOp(TestComplexElementwiseSubOp):
|
|
def init_input_output(self):
|
|
self.x = 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 if_enable_cinn(self):
|
|
self.enable_cinn = False
|
|
|
|
def if_check_prim(self):
|
|
self.check_prim = False
|
|
|
|
|
|
class TestSubtractApi(unittest.TestCase):
|
|
def _executed_api(self, x, y, name=None):
|
|
return paddle.subtract(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=np.float32)
|
|
|
|
y_1 = self._executed_api(x, y, name='subtract_res')
|
|
self.assertEqual(('subtract_res' in y_1.name), True)
|
|
|
|
def test_declarative(self):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
|
|
def gen_data():
|
|
return {
|
|
"x": np.array([2, 3, 4]).astype(np.float32),
|
|
"y": np.array([1, 5, 2]).astype(np.float32),
|
|
}
|
|
|
|
x = paddle.static.data(name="x", shape=[3], dtype=np.float32)
|
|
y = paddle.static.data(name="y", shape=[3], dtype=np.float32)
|
|
z = self._executed_api(x, y)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
if paddle.framework.in_pir_mode():
|
|
z_value = exe.run(feed=gen_data(), fetch_list=[z])
|
|
else:
|
|
z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
|
|
z_expected = np.array([1.0, -2.0, 2.0])
|
|
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(False)
|
|
z_expected = np.array([1.0, -2.0, 2.0])
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
class TestSubtractApiZeroSize(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.subtract(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.subtract(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.subtract(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z, np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
|
|
class TestSubtractApiZeroSize2(TestSubtractApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(3).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3).astype('float32')
|
|
|
|
|
|
class TestSubtractApiZeroSize3(TestSubtractApiZeroSize):
|
|
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 TestSubtractApiZeroSize4(TestSubtractApiZeroSize):
|
|
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 TestSubtractInplaceApi(TestSubtractApi):
|
|
def _executed_api(self, x, y, name=None):
|
|
return x.subtract_(y, name)
|
|
|
|
|
|
class TestSubtractInplaceBroadcastSuccess(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.subtract_(y)
|
|
numpy_result = self.x_numpy - self.y_numpy
|
|
self.assertEqual(
|
|
(inplace_result.numpy() == numpy_result).all(), True
|
|
)
|
|
|
|
|
|
class TestSubtractInplaceBroadcastSuccess2(TestSubtractInplaceBroadcastSuccess):
|
|
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 TestSubtractInplaceBroadcastSuccess3(TestSubtractInplaceBroadcastSuccess):
|
|
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 TestSubtractInplaceBroadcastError(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.subtract_(y)
|
|
|
|
self.assertRaises(ValueError, broadcast_shape_error)
|
|
|
|
|
|
class TestSubtractInplaceBroadcastError2(TestSubtractInplaceBroadcastError):
|
|
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 TestSubtractInplaceBroadcastError3(TestSubtractInplaceBroadcastError):
|
|
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 TestFloatElementwiseSubop(unittest.TestCase):
|
|
def test_dygraph_sub(self):
|
|
with paddle.base.dygraph.guard():
|
|
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, rtol=1e-07, atol=1e-07
|
|
)
|
|
|
|
# normal case: tensor - scalar
|
|
expect_out = np_a - 1
|
|
actual_out = tensor_a - 1
|
|
np.testing.assert_allclose(
|
|
actual_out, expect_out, rtol=1e-07, atol=1e-07
|
|
)
|
|
|
|
# normal case: scalar - tenor
|
|
expect_out = 1 - np_a
|
|
actual_out = 1 - tensor_a
|
|
np.testing.assert_allclose(
|
|
actual_out, expect_out, rtol=1e-07, atol=1e-07
|
|
)
|
|
|
|
|
|
class TestFloatElementwiseSubop1(unittest.TestCase):
|
|
def test_dygraph_sub(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, rtol=1e-07, atol=1e-07
|
|
)
|
|
|
|
# normal case: tenor - nparray
|
|
actual_out = tensor_a - np_b
|
|
np.testing.assert_allclose(
|
|
actual_out, expect_out, rtol=1e-07, atol=1e-07
|
|
)
|
|
|
|
|
|
class TestElementwiseOpZeroSize(TestElementwiseOp):
|
|
def init_inputs(self):
|
|
self.attrs = {'enable_check_eager_comp': False}
|
|
self.inputs = {
|
|
'X': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype),
|
|
'Y': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype),
|
|
}
|
|
|
|
def if_check_prim(self):
|
|
self.check_prim = False
|
|
self.check_prim_pir = False
|
|
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseOpZeroSize2(TestElementwiseOpZeroSize):
|
|
def init_inputs(self):
|
|
self.inputs = {
|
|
'X': np.random.uniform(0.1, 1, [2, 1, 4, 5]).astype(self.dtype),
|
|
'Y': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype),
|
|
}
|
|
|
|
|
|
class TestElementwiseOpZeroSize3(TestElementwiseOpZeroSize):
|
|
def init_inputs(self):
|
|
self.inputs = {
|
|
'X': np.random.uniform(0.1, 1, [2, 1, 0, 5]).astype(self.dtype),
|
|
'Y': np.random.uniform(0.1, 1, [2, 1, 1, 5]).astype(self.dtype),
|
|
}
|
|
|
|
|
|
class TestTensorSubAPIWarnings(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_sub")
|
|
data = paddle.static.data(
|
|
name='data', shape=[None, 3, 32, 32], dtype=np.float32
|
|
)
|
|
out = helper.create_variable_for_type_inference(dtype=data.dtype)
|
|
os.environ['FLAGS_print_extra_attrs'] = "1"
|
|
helper.append_op(
|
|
type="elementwise_sub",
|
|
inputs={'X': data, 'Y': data},
|
|
outputs={'Out': out},
|
|
attrs={'axis': 1, 'use_onednn': False},
|
|
)
|
|
self.assertTrue(
|
|
"op elementwise_sub's attr axis = 1 is not the default value: -1"
|
|
in str(context[-1].message)
|
|
)
|
|
os.environ['FLAGS_print_extra_attrs'] = "0"
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseSubOp_Stride(TestElementwiseOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_sub"
|
|
self.python_api = paddle.subtract
|
|
self.public_python_api = paddle.subtract
|
|
self.transpose_api = paddle.transpose
|
|
self.as_stride_api = paddle.as_strided
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
|
|
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.subtract(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 TestElementwiseSubOp_Stride1(TestElementwiseSubOp_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.subtract(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseSubOp_Stride2(TestElementwiseSubOp_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.subtract(self.x, self.y)
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseSubOp_Stride3(TestElementwiseSubOp_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.subtract(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseSubOp_Stride4(TestElementwiseSubOp_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.subtract(self.x, self.y)
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseSubOp_Stride5(TestElementwiseSubOp_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.subtract(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 TestElementwiseSubOp_Stride_ZeroDim1(TestElementwiseSubOp_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.subtract(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseSubOp_Stride_ZeroSize1(TestElementwiseSubOp_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.subtract(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()
|