wehub-resource-sync
407 lines
13 KiB
Python
407 lines
13 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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get_device,
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get_device_place,
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get_devices,
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is_custom_device,
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)
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import paddle
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def adamx_wrapper(
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param,
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grad,
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lr,
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moment,
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inf_norm,
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beta1_pow=None,
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master_weight=None,
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beta1=0.78,
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beta2=0.899,
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epsilon=1e-5,
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find_master=False,
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):
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return paddle._C_ops.adamax_(
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param,
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grad,
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lr,
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moment,
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inf_norm,
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beta1_pow,
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master_weight,
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beta1,
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beta2,
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epsilon,
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find_master,
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)
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class TestAdamaxOp1(OpTest):
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def setUp(self):
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'''Test Adamax Operator with supplied attributes'''
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self.op_type = "adamax"
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self.python_api = adamx_wrapper
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self.python_out_sig = ['Out']
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The infinity norm is positive
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inf_norm = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.002
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beta1 = 0.78
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beta2 = 0.899
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epsilon = 1e-5
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beta1_pow = beta1**10
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment': moment,
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'InfNorm': inf_norm,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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}
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self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
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param_out, moment_out, inf_norm_out = adamax_step(
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self.inputs, self.attrs
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)
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self.outputs = {
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'ParamOut': param_out,
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'MomentOut': moment_out,
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'InfNormOut': inf_norm_out,
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}
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def test_check_output(self):
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self.check_output()
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class TestAdamaxOp2(OpTest):
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'''Test Adamax Operator with default attributes'''
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def setUp(self):
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self.op_type = "adamax"
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self.python_api = adamx_wrapper
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self.python_out_sig = ['Out']
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The infinity norm is positive
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inf_norm = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.002
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beta1 = 0.9
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beta2 = 0.999
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epsilon = 1e-8
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beta1_pow = beta1**8
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment': moment,
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'InfNorm': inf_norm,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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}
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attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
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param_out, moment_out, inf_norm_out = adamax_step(self.inputs, attrs)
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self.outputs = {
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'ParamOut': param_out,
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'MomentOut': moment_out,
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'InfNormOut': inf_norm_out,
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}
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def test_check_output(self):
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self.check_output()
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class TestAdamaxOpMultipleSteps(OpTest):
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def setUp(self):
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'''Test Adamax Operator with supplied attributes'''
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self.op_type = "adamax"
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self.python_api = adamx_wrapper
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self.python_out_sig = ['Out']
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self.num_steps = 10
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The infinity norm is positive
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inf_norm = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.002
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beta1 = 0.8
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beta2 = 0.99
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epsilon = 1e-5
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beta1_pow = 1
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment': moment,
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'InfNorm': inf_norm,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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}
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self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
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def test_check_output(self):
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for _ in range(self.num_steps):
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param_out, moment_out, inf_norm_out = adamax_step(
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self.inputs, self.attrs
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)
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self.outputs = {
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'ParamOut': param_out,
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'MomentOut': moment_out,
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'InfNormOut': inf_norm_out,
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}
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# Verify output for this step
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self.check_output()
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# Output of this step becomes input for next step
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self.inputs['Param'] = param_out
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self.inputs['Moment'] = moment_out
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self.inputs['InfNorm'] = inf_norm_out
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# Update Beta1 Power accumulator for next step
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self.inputs['Beta1Pow'] *= self.attrs['beta1']
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# Randomize gradient for next step
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self.inputs['Grad'] = np.random.uniform(-1, 1, (102, 105)).astype(
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"float32"
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)
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def adamax_step(inputs, attributes):
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'''
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Simulate one step of the adamax optimizer
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:param inputs: dict of inputs
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:param attributes: dict of attributes
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:return tuple: tuple of output param, moment, inf_norm and
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beta1 power accumulator
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'''
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param = inputs['Param']
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grad = inputs['Grad']
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moment = inputs['Moment']
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inf_norm = inputs['InfNorm']
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lr = inputs['LearningRate']
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beta1_pow = inputs['Beta1Pow']
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beta1 = attributes['beta1']
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beta2 = attributes['beta2']
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epsilon = attributes['epsilon']
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moment_out = beta1 * moment + (1 - beta1) * grad
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inf_norm_out = np.maximum(beta2 * inf_norm + epsilon, np.abs(grad))
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lr_t = lr / (1 - beta1_pow)
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param_out = param - lr_t * np.divide(moment_out, inf_norm_out)
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return param_out, moment_out, inf_norm_out
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class TestAdamaxOpV2(unittest.TestCase):
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def test_adamax_op_invalid_input(self):
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import paddle
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paddle.disable_static()
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linear = paddle.nn.Linear(10, 10)
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with self.assertRaises(ValueError):
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adam = paddle.optimizer.Adamax(
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0.1, beta1=-1, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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adam = paddle.optimizer.Adamax(
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0.1, beta2=-1, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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adam = paddle.optimizer.Adamax(
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0.1, epsilon=-1, parameters=linear.parameters()
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)
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class TestAdamaxOpMultiPrecision(unittest.TestCase):
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def _test_adamax_op_dygraph_place_amp(self, place, use_amp=False):
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import paddle
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paddle.disable_static()
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paddle.seed(10)
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paddle.set_device(place)
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input = paddle.randn((5, 5))
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model = paddle.nn.Linear(5, 5)
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optimizer = paddle.optimizer.Adamax(
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0.1, beta1=0.1, parameters=model.parameters()
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)
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optimizer._multi_precision = use_amp
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for idx in range(2):
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if place == get_device() and use_amp:
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model = paddle.amp.decorate(models=model, level='O2')
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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if place == get_device() and use_amp:
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with paddle.amp.auto_cast(level='O2'):
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output = model(input)
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loss = paddle.mean(output)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.step(optimizer)
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optimizer.clear_grad()
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else:
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output = model(input)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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paddle.enable_static()
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def _get_places(self):
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return get_devices()
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def test_main(self):
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for place in self._get_places():
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use_amp_list = [True, False]
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for use_amp in use_amp_list:
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self._test_adamax_op_dygraph_place_amp(place, use_amp)
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class TestAdamaxMultiPrecision2_0(unittest.TestCase):
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def dygraph_adamax_mp(self, mp, use_amp):
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paddle.disable_static()
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paddle.seed(100)
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paddle.set_device(get_device())
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input = paddle.randn((2, 2))
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model = paddle.nn.Linear(2, 2)
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optimizer = paddle.optimizer.Adamax(0.5, parameters=model.parameters())
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optimizer._multi_precision = mp
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if use_amp:
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model = paddle.amp.decorate(models=model, level='O2')
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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for idx in range(5):
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if use_amp:
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with paddle.amp.auto_cast(level='O2'):
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output = model(input)
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loss = paddle.mean(output)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.minimize(optimizer, scaled)
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optimizer.clear_grad()
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else:
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output = model(input)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return output, model.parameters()
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def static_adamax_mp(self, mp, use_amp):
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paddle.enable_static()
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paddle.seed(100)
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np.random.seed(100)
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exe = paddle.static.Executor(get_device_place())
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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optimizer = paddle.optimizer.Adamax(0.1)
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with paddle.static.program_guard(train_program, startup_program):
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if use_amp:
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data = paddle.static.data(
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shape=[2, 2], name='X', dtype='float16'
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)
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else:
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data = paddle.static.data(
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shape=[2, 2], name='X', dtype='float32'
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)
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hidden_layer = paddle.nn.Linear(2, 10)
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if use_amp:
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hidden_layer, optimizer = paddle.amp.decorate(
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models=hidden_layer,
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optimizers=optimizer,
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level='O2',
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master_weight=True,
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master_grad=False,
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)
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with paddle.amp.auto_cast(
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level='O2', dtype='float16', use_promote=True
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):
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hidden = hidden_layer(data)
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loss = paddle.mean(hidden)
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else:
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hidden = hidden_layer(data)
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loss = paddle.mean(hidden)
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optimizer.minimize(loss)
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exe.run(startup_program)
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if use_amp:
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x = np.random.random(size=(2, 2)).astype('float16')
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else:
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x = np.random.random(size=(2, 2)).astype('float32')
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out = []
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for idx in range(5):
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(loss_data,) = exe.run(
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train_program, feed={"X": x}, fetch_list=[loss]
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)
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out.append(loss_data)
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return out
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def test_main(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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"Test dygraph mode"
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output1_dy, params1_dy = self.dygraph_adamax_mp(use_amp=True, mp=True)
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output2_dy, params2_dy = self.dygraph_adamax_mp(use_amp=False, mp=False)
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np.testing.assert_allclose(
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output1_dy.astype('float32').numpy(),
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output2_dy.astype('float32').numpy(),
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rtol=1e-05,
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atol=0.1,
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)
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for idx in range(len(params1_dy)):
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np.testing.assert_allclose(
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params1_dy[idx].astype('float32').numpy(),
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params2_dy[idx].astype('float32').numpy(),
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rtol=1e-05,
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atol=0.1,
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)
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"Test static mode"
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with paddle.pir_utils.IrGuard():
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output1_st = self.static_adamax_mp(use_amp=True, mp=True)
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output2_st = self.static_adamax_mp(use_amp=False, mp=False)
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for idx in range(len(output1_st)):
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np.testing.assert_allclose(
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output1_st[idx].astype('float32'),
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output2_st[idx].astype('float32'),
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rtol=1e-05,
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atol=0.1,
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)
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if __name__ == "__main__":
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unittest.main()
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