wehub-resource-sync
515 lines
17 KiB
Python
515 lines
17 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 unittest
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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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get_places,
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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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def pow_grad(x, y, dout):
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dx = dout * y * np.power(x, (y - 1))
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dy = dout * np.log(x) * np.power(x, y)
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return dx, dy
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class TestElementwisePowOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, [20, 5]).astype("float64"),
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'Y': np.random.uniform(1, 2, [20, 5]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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if hasattr(self, 'attrs'):
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self.check_output(check_dygraph=False)
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else:
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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if hasattr(self, 'attrs'):
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self.check_grad(
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['X', 'Y'], 'Out', check_prim=True, check_dygraph=False
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)
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else:
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self.check_grad(
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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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class TestElementwisePowOp_ZeroBaseNumber1(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.randint(-100, -1, size=[20, 5]).astype("int32"),
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'Y': np.random.randint(-200, 0, size=[20, 5], dtype="int32"),
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}
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self.outputs = {'Out': np.zeros([20, 5]).astype("int32")}
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def test_check_grad_normal(self):
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pass
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class TestElementwisePowOp_ZeroBaseNumber2(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.randint(2, 100, size=[20, 5]).astype("int32"),
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'Y': np.random.randint(-200, 0, size=[20, 5], dtype="int32"),
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}
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self.outputs = {'Out': np.zeros([20, 5]).astype("int32")}
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def test_check_grad_normal(self):
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pass
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class TestElementwisePowOp_ZeroBaseNumber3(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.asarray([-1, 0, 1]),
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'Y': np.asarray([-1, -1, -1]),
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}
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self.outputs = {'Out': np.asarray([-1, 0, 1])}
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def test_check_grad_normal(self):
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pass
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class TestElementwisePowOp_ZeroDim1(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, []).astype("float64"),
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'Y': np.random.uniform(1, 2, []).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_ZeroDim2(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, [20, 5]).astype("float64"),
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'Y': np.random.uniform(1, 2, []).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_ZeroDim3(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, []).astype("float64"),
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'Y': np.random.uniform(1, 2, [20, 5]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_big_shape_1(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, [10, 10]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [10, 10]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_big_shape_2(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, [10, 10]).astype("float64"),
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'Y': np.random.uniform(0.2, 2, [10, 10]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['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 TestElementwisePowOp_scalar(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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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, [3, 3, 4]).astype(np.float64),
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'Y': np.random.uniform(0.1, 1, [1]).astype(np.float64),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_tensor(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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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, [100]).astype("float64"),
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'Y': np.random.uniform(1, 3, [100]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_broadcast_0(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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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, 1, 100]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [100]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOp_broadcast_4(TestElementwisePowOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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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, 10, 3, 5]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [2, 10, 1, 5]).astype("float64"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOpInt(OpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {'X': np.asarray([1, 3, 6]), 'Y': np.asarray([1, 1, 1])}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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if hasattr(self, 'attrs'):
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self.check_output(check_dygraph=False)
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else:
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestElementwisePowGradOpInt(unittest.TestCase):
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def setUp(self):
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self.x = np.asarray([1, 3, 6])
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self.y = np.asarray([1, 1, 1])
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self.res = self.x**self.y
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# dout = 1
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self.grad_res = np.asarray([1, 1, 1])
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# dx = dout * y * pow(x, y-1)
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self.grad_x = (
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self.grad_res * self.y * (self.x ** (self.y - 1)).astype("int")
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)
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# dy = dout * log(x) * pow(x, y)
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self.grad_y = (
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self.grad_res * np.log(self.x) * (self.x**self.y)
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).astype("int")
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def test_grad(self):
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for place in get_places():
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with base.dygraph.guard(place):
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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x.stop_gradient = False
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y.stop_gradient = False
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res = x**y
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res.retain_grads()
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res.backward()
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np.testing.assert_array_equal(res.gradient(), self.grad_res)
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np.testing.assert_array_equal(x.gradient(), self.grad_x)
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np.testing.assert_array_equal(y.gradient(), self.grad_y)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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self.inputs = {
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'X': np.array([1 + 2j, 3 + 4j, 5 + 6j], dtype=np.complex128),
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'Y': np.array([2.0, 3.0, 4.0], dtype=np.float64),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def _get_places(self):
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places = [base.CPUPlace()]
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if core.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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return places
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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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_pir=True,
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)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp1(TestElementwisePowComplexOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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real_part = np.random.uniform(-5, 5, size=(3, 4))
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imag_part = np.random.uniform(-5, 5, size=(3, 4))
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self.inputs = {
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'X': real_part + 1j * imag_part,
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'Y': np.random.uniform(1, 5, size=(3, 4)),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp2(TestElementwisePowComplexOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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real_part = np.random.uniform(-5, 5, size=(20, 50))
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imag_part = np.random.uniform(-5, 5, size=(20, 50))
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self.inputs = {
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'X': real_part + 1j * imag_part,
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'Y': np.random.uniform(1, 5, size=(20, 50)),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp3(TestElementwisePowComplexOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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real_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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imag_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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self.inputs = {
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'X': real_part + 1j * imag_part,
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'Y': np.random.uniform(1, 5, size=(3, 5, 3)),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp4(TestElementwisePowComplexOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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real_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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imag_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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self.inputs = {
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'X': real_part + 1j * imag_part,
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'Y': real_part + 1j * imag_part,
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for complex dtype is not fully supported",
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)
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class TestElementwisePowComplexOp5(TestElementwisePowComplexOp):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self._check_cinn = True
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x_real_part = np.random.uniform(-5, 5, size=(5, 3))
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x_imag_part = np.random.uniform(-5, 5, size=(5, 3))
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y_real_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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y_imag_part = np.random.uniform(-5, 5, size=(3, 5, 3))
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self.inputs = {
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'X': x_real_part + 1j * x_imag_part,
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'Y': y_real_part + 1j * y_imag_part,
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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class TestElementwisePowOpFP16(OpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.dtype = np.float16
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self.python_api = paddle.pow
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self.public_python_api = paddle.pow
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self.prim_op_type = "prim"
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self.inputs = {
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'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
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'Y': np.random.uniform(1, 2, [20, 5]).astype("float16"),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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if hasattr(self, 'attrs'):
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self.check_output(check_dygraph=False)
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else:
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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user_defined_grads=pow_grad(
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self.inputs['X'], self.inputs['Y'], 1 / self.inputs['X'].size
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),
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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 (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm(),
|
|
"BFP16 test runs only on CUDA",
|
|
)
|
|
class TestElementwisePowBF16Op(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_pow"
|
|
self.prim_op_type = "prim"
|
|
self.dtype = np.uint16
|
|
self.python_api = paddle.pow
|
|
self.public_python_api = paddle.pow
|
|
|
|
x = np.random.uniform(0, 1, [20, 5]).astype(np.float32)
|
|
y = np.random.uniform(0, 1, [20, 5]).astype(np.float32)
|
|
out = np.power(x, y)
|
|
self.inputs = {
|
|
'X': convert_float_to_uint16(x),
|
|
'Y': convert_float_to_uint16(y),
|
|
}
|
|
self.outputs = {'Out': convert_float_to_uint16(out)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True, check_symbol_infer=False)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X', 'Y'], 'Out')
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
self.check_grad_with_place(
|
|
get_device_place(),
|
|
['X', 'Y'],
|
|
'Out',
|
|
check_prim=True,
|
|
only_check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|