wehub-resource-sync
262 lines
8.9 KiB
Python
262 lines
8.9 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import (
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convert_float_to_uint16,
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skip_check_grad_ci,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle import base
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paddle.enable_static()
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INT_GROUP = [np.int32, np.int64]
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class XPUTestElementwiseDivOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'elementwise_div'
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self.use_dynamic_create_class = False
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class ElementwiseDivOp(XPUOpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.dtype = self.in_type
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self.init_dtype()
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self.use_xpu = True
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self.init_shape()
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self.init_input_output()
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""" Warning
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CPU gradient check error!
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'X': np.random.random((32,84)).astype("float32"),
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'Y': np.random.random((32,84)).astype("float32")
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"""
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def gen_data_depend_on_dtype(self, shape):
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if self.dtype in INT_GROUP:
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return np.random.randint(1, 100, size=shape)
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else:
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return np.random.uniform(-1, 1, size=shape)
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def reshape_y_depend_on_x(self):
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if len(self.x_shape) <= len(self.y_shape) or self.y_shape == ():
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return self.y
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reshape_dims = [
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1 if i not in self.y_shape else i for i in self.x_shape
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]
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return np.reshape(self.y, reshape_dims)
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def init_input_output(self):
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self.x = self.gen_data_depend_on_dtype(self.x_shape)
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self.y = self.gen_data_depend_on_dtype(self.y_shape)
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reshaped_y = self.reshape_y_depend_on_x()
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if self.dtype == np.uint16:
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self.outputs = {'Out': np.divide(self.x, reshaped_y)}
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self.inputs = {
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'X': convert_float_to_uint16(self.x),
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'Y': convert_float_to_uint16(self.y),
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}
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else:
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self.inputs = {
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'X': self.x.astype(self.dtype),
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'Y': self.y.astype(self.dtype),
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}
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reshaped_y.astype(self.dtype)
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self.outputs = {
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'Out': (
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self.inputs['X'] // reshaped_y
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if self.dtype in INT_GROUP
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else np.divide(self.inputs['X'], reshaped_y)
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)
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}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad_normal(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place, ['X', 'Y'], 'Out', max_relative_error=0.05
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)
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def test_check_grad_ignore_x(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['Y'],
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'Out',
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max_relative_error=0.05,
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no_grad_set=set("X"),
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)
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def test_check_grad_ignore_y(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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max_relative_error=0.05,
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no_grad_set=set('Y'),
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)
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def init_dtype(self):
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pass
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def init_shape(self):
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self.x_shape = [13, 17]
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self.y_shape = [13, 17]
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class TestElementwiseDivOp_ZeroDim1(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = []
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class TestElementwiseDivOp_ZeroDim2(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [13, 17]
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self.y_shape = []
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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 TestElementwiseDivOp_scalar(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [20, 3, 4]
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self.y_shape = [1]
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class TestElementwiseDivOp_Vector(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [100]
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self.y_shape = [100]
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class TestElementwiseDivOp_broadcast_0(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [100, 3, 4]
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self.y_shape = [100]
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self.attrs = {'axis': 0}
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class TestElementwiseDivOp_broadcast_1(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 100, 4]
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self.y_shape = [100]
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self.attrs = {'axis': 1}
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class TestElementwiseDivOp_broadcast_2(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 100]
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self.y_shape = [100]
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class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 10, 12, 5]
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self.y_shape = [10, 12]
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self.attrs = {'axis': 1}
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class TestElementwiseDivOp_broadcast_4(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 50]
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self.y_shape = [2, 1, 50]
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class TestElementwiseDivOp_broadcast_5(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 4, 20]
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self.y_shape = [2, 3, 1, 20]
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class TestElementwiseDivOp_commonuse_1(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 100]
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self.y_shape = [1, 1, 100]
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class TestElementwiseDivOp_commonuse_2(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [30, 3, 1, 5]
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self.y_shape = [30, 1, 4, 1]
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class TestElementwiseDivOp_xsize_lessthan_ysize(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [10, 12]
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self.y_shape = [2, 3, 10, 12]
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self.attrs = {'axis': 2}
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class TestElementwiseDivBroadcast(unittest.TestCase):
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def test_shape_with_batch_sizes(self):
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with base.program_guard(base.Program()):
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x_var = paddle.static.data(
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name='x', dtype='float32', shape=[None, 3, None, None]
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)
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one = 2.0
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out = one / x_var
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exe = base.Executor(base.XPUPlace(0))
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x = np.random.uniform(0.1, 0.6, (1, 3, 32, 32)).astype(
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'float32'
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)
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(out_result,) = exe.run(feed={'x': x}, fetch_list=[out])
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self.assertEqual((out_result == (2 / x)).all(), True)
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class TestElementwiseDivBroadcastZeroSize(unittest.TestCase):
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def test_rtruediv_with_scalar(self):
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main_prog = base.Program()
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startup_prog = base.Program()
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with base.program_guard(main_prog, startup_prog):
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x = paddle.static.data(
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name='x', dtype='float32', shape=[0, 1358]
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)
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x.stop_gradient = False
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scalar = 1.0
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out = scalar / x
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loss = paddle.sum(out)
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x_grad = paddle.static.gradients([loss], [x])[0]
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exe = base.Executor(base.XPUPlace(0))
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exe.run(startup_prog)
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x_np = np.random.uniform(0.1, 100.0, size=(0, 1358)).astype(
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'float32'
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)
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out_np, x_grad_np = exe.run(
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main_prog,
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feed={'x': x_np},
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fetch_list=[out, x_grad],
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)
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self.assertEqual(out_np.shape, (0, 1358))
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self.assertEqual(out_np.size, 0)
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np.testing.assert_allclose(out_np, 1.0 / x_np, rtol=1e-06, atol=0.0)
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self.assertEqual(x_grad_np.shape, (0, 1358))
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self.assertEqual(x_grad_np.size, 0)
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np.testing.assert_allclose(
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x_grad_np, -1.0 / (x_np * x_np), rtol=1e-06, atol=0.0
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)
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support_types = get_xpu_op_support_types('elementwise_div')
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for stype in support_types:
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create_test_class(globals(), XPUTestElementwiseDivOp, stype)
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if __name__ == '__main__':
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unittest.main()
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