wehub-resource-sync
376 lines
12 KiB
Python
376 lines
12 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 math
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import unittest
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import numpy as np
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from op import Operator
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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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get_places,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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def adamgrad_wrapper(
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param,
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grad,
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moment,
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learning_rate,
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master_weight=None,
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epsilon=1e-8,
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multi_precision=False,
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):
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paddle._C_ops.adagrad_(
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param,
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grad,
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moment,
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learning_rate,
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master_weight,
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epsilon,
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multi_precision,
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)
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class TestAdagradOp1(OpTest):
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'''Test Adagrad operator with explicit attributes'''
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def setUp(self):
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self.op_type = "adagrad"
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self.python_api = adamgrad_wrapper
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self.python_out_sig = ['out']
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param = np.random.random((123, 321)).astype("float32")
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grad = np.random.random((123, 321)).astype("float32")
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moment = np.zeros((123, 321)).astype("float32")
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lr = 0.01
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epsilon = 1e-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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'LearningRate': np.array([lr]).astype("float32"),
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}
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self.attrs = {'epsilon': epsilon}
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moment_out = moment + grad * grad
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param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
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self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
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def test_check_output(self):
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self.check_output()
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class TestAdagradOp2(OpTest):
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'''Test Adagrad operator with default attributes'''
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def setUp(self):
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self.op_type = "adagrad"
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self.python_api = adamgrad_wrapper
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self.python_out_sig = ['out']
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param = np.random.random((123, 321)).astype("float32")
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grad = np.random.random((123, 321)).astype("float32")
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moment = np.zeros((123, 321)).astype("float32")
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lr = 0.01
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epsilon = 1e-6
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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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'LearningRate': np.array([lr]).astype("float32"),
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}
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self.attrs = {'epsilon': epsilon, "multi_precision": False}
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moment_out = moment + grad * grad
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param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon)
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self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
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def test_check_output(self):
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self.check_output()
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class TestSparseAdagradOp(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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# create and initialize Grad Variable
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height = 10
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rows = [0, 4, 7, 4]
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row_numel = 12
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(height)
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grad_selected_rows.set_rows(rows)
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np_array = np.ones((len(rows), row_numel)).astype("float32")
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np_array[0, 0] = 2.0
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np_array[2, 8] = 4.0
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(np_array, place)
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# create and initialize Param Variable
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param = scope.var('Param').get_tensor()
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param_array = np.full((height, row_numel), 5.0).astype("float32")
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param.set(param_array, place)
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# create and initialize LearningRate Variable
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lr = scope.var('LearningRate').get_tensor()
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lr_array = np.full((1), 2.0).astype("float32")
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lr.set(lr_array, place)
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# create and initialize moment Variable
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moment = scope.var('Moment').get_tensor()
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moment_np_array = np.full((height, row_numel), 2.0).astype("float32")
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moment.set(moment_np_array, place)
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adagrad_op = Operator(
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"adagrad",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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Moment='Moment',
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MomentOut='Moment',
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LearningRate='LearningRate',
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epsilon=2.0,
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)
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adagrad_op.run(scope, place)
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# get and compare moment result
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moment_result_array = np.array(moment)
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self.assertAlmostEqual(6.0, moment_result_array[rows[0], 0])
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self.assertAlmostEqual(3.0, moment_result_array[rows[0], 2])
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self.assertAlmostEqual(2.0, moment_result_array[1, 0])
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# 2.0 + (1.0 + 1.0)^2
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self.assertAlmostEqual(6.0, moment_result_array[rows[1], 10])
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self.assertAlmostEqual(6.0, moment_result_array[rows[3], 4])
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self.assertAlmostEqual(2.0, moment_result_array[5, 8])
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self.assertAlmostEqual(3.0, moment_result_array[rows[2], 1])
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self.assertAlmostEqual(18.0, moment_result_array[rows[2], 8])
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# get and compare param result
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result_array = np.array(param)
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def get_out(param, lr, grad, m, epsilon):
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return param - lr * grad / (math.sqrt(m) + epsilon)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 2.0, 6.0, 2.0), result_array[rows[0], 0], places=5
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)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 1.0, 3.0, 2.0), result_array[rows[0], 2], places=5
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)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 0.0, 2.0, 2.0), result_array[1, 0], places=5
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)
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# grad_merge = 1.0 + 1.0
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# m = 6.0
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 2.0, 6.0, 2.0),
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result_array[rows[1], 10],
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places=5,
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)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 0.0, 2.0, 2.0), result_array[5, 8], places=5
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)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 1.0, 3.0, 2.0), result_array[rows[2], 1], places=5
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)
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self.assertAlmostEqual(
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get_out(5.0, 2.0, 4.0, 18.0, 2.0),
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result_array[rows[2], 8],
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places=5,
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)
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def test_sparse_adagrad(self):
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for place in get_places():
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self.check_with_place(place)
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class TestAdagradOpMultiPrecision(unittest.TestCase):
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def _test_adagrad_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.Adagrad(0.1, parameters=model.parameters())
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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 test_main(self):
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for place in get_devices():
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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_adagrad_op_dygraph_place_amp(place, use_amp)
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class TestAdagradMultiPrecision2_0(unittest.TestCase):
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def dygraph_adagrad_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.Adagrad(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_adagrad_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.Adagrad(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_adagrad_mp(use_amp=True, mp=True)
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output2_dy, params2_dy = self.dygraph_adagrad_mp(
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use_amp=False, mp=False
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)
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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_adagrad_mp(use_amp=True, mp=True)
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output2_st = self.static_adagrad_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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paddle.enable_static()
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unittest.main()
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