wehub-resource-sync
459 lines
14 KiB
Python
459 lines
14 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,
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get_device_place,
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is_custom_device,
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)
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from utils import dygraph_guard
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import paddle
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from paddle.base import core
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paddle.enable_static()
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def asgd_wrapper(
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param,
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grad,
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learning_rate,
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d,
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y,
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n,
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master_param=None,
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multi_precision=False,
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):
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paddle._C_ops.asgd_(
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param,
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grad,
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learning_rate,
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d,
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y,
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n,
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None,
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False,
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)
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class TestASGDOpMixin:
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def setUp(self):
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self.init_basic_info()
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self.init_input()
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self.update_input_dtype()
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self.init_output()
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self.update_output_dtype()
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self.inputs = {
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"param": self.params,
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"grad": self.grads,
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"learning_rate": self.learning_rate,
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"d": self.ds,
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"y": self.ys,
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"n": self.n,
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}
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self.outputs = {
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"param_out": self.params_out,
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"d_out": self.ds_out,
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"y_out": self.ys_out,
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}
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def init_basic_info(self):
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self.op_type = "asgd"
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self.python_api = asgd_wrapper
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self.python_out_sig = ['Out']
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self.h = 102
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self.w = 105
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def init_input(self):
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self.params = np.random.random((self.h, self.w))
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self.learning_rate = np.array([0.001])
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self.n = np.array([1000])
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self.grads = np.random.random((self.h, self.w))
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self.ds = np.random.random((self.h, self.w))
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self.ys = np.random.random((self.h, self.w))
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def init_output(self):
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self.ds_out = self.ds - self.ys + self.grads
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self.ys_out = self.grads.copy()
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self.params_out = (
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self.params - (self.learning_rate / self.n) * self.ds_out
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)
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def update_input_dtype(self):
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pass
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def update_output_dtype(self):
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pass
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestASGDOp(TestASGDOpMixin, OpTest):
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pass
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class TestCase1(TestASGDOp):
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def update_input_dtype(self):
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self.params = self.params.astype("float32")
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self.learning_rate = self.learning_rate.astype("float32")
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self.n = self.n.astype("float32")
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self.grads = self.grads.astype("float32")
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self.ds = self.ds.astype("float32")
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self.ys = self.ys.astype("float32")
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class TestCase2(TestASGDOp):
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def update_input_dtype(self):
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self.params = self.params.astype("float16")
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self.learning_rate = self.learning_rate.astype("float16")
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self.n = self.n.astype("float16")
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self.grads = self.grads.astype("float16")
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self.ds = self.ds.astype("float16")
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self.ys = self.ys.astype("float16")
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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class TestCase3(TestASGDOp):
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def update_input_dtype(self):
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self.params = convert_float_to_uint16(self.params)
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self.learning_rate = convert_float_to_uint16(self.learning_rate)
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self.n = convert_float_to_uint16(self.n)
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self.grads = convert_float_to_uint16(self.grads)
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self.ds = convert_float_to_uint16(self.ds)
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self.ys = convert_float_to_uint16(self.ys)
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def update_output_dtype(self):
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self.ds_out = convert_float_to_uint16(self.ds_out)
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self.ys_out = convert_float_to_uint16(self.ys_out)
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self.params_out = convert_float_to_uint16(self.params_out)
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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class TestCase4(TestASGDOp):
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def init_input(self):
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self.params = np.random.random((self.h, self.w))
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self.learning_rate = np.array([0.001])
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self.n = np.array([1])
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self.grads = np.random.random((self.h, self.w))
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self.ds = np.random.random((self.h, self.w))
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self.ys = np.random.random((self.h, self.w))
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class TestASGDV2(unittest.TestCase):
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def test_asgd_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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asgd = paddle.optimizer.ASGD(
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learning_rate=0.001,
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batch_num=2,
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parameters=linear.parameters(),
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)
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out = linear(a)
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out.backward()
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asgd.step()
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asgd.clear_gradients()
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def test_raise_error(self):
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self.assertRaises(
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ValueError,
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paddle.optimizer.ASGD,
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batch_num=2,
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learning_rate=None,
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)
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self.assertRaises(
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ValueError,
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paddle.optimizer.ASGD,
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batch_num=None,
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)
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self.assertRaises(
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ValueError,
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paddle.optimizer.ASGD,
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batch_num=-2,
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)
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def test_asgd_group_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear_1 = paddle.nn.Linear(13, 5)
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linear_2 = paddle.nn.Linear(5, 3)
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asgd = paddle.optimizer.ASGD(
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learning_rate=0.001,
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batch_num=2,
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parameters=[
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{'params': linear_1.parameters()},
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{
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'params': linear_2.parameters(),
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'learning_rate': 0.0001,
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},
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],
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)
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out = linear_1(a)
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out = linear_2(out)
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out.backward()
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asgd.step()
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asgd.clear_gradients()
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class TestASGDV2WeightDecay(unittest.TestCase):
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def test_weight_decay_int(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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asgd = paddle.optimizer.ASGD(
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learning_rate=0.001,
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batch_num=2,
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parameters=linear.parameters(),
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weight_decay=1,
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)
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out = linear(a)
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out.backward()
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asgd.step()
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asgd.clear_gradients()
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class TestASGDMultiPrecision(unittest.TestCase):
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def dygraph_asgd_mp(self, mp):
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paddle.disable_static()
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paddle.seed(10)
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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.ASGD(
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batch_num=2, parameters=model.parameters(), multi_precision=mp
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)
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if mp:
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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 mp:
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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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optimizer.step()
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optimizer.clear_grad()
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return output, model.parameters()
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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_asgd_mp(mp=True)
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output2_dy, params2_dy = self.dygraph_asgd_mp(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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class TestASGDSimple(unittest.TestCase):
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def setUp(self) -> None:
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self.data = np.random.random(size=(2, 2)).astype('float32')
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def run_static(self):
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with paddle.pir_utils.IrGuard():
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paddle.seed(10)
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np.random.seed(10)
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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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with paddle.static.program_guard(train_program, startup_program):
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input = paddle.static.data(
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shape=[2, 2], name='input', dtype='float32'
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)
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model = paddle.nn.Linear(2, 2)
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output = model(input)
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loss = paddle.mean(output)
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optimizer = paddle.optimizer.ASGD(
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batch_num=3,
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)
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optimizer.minimize(loss)
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exe.run(startup_program)
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out = []
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for _ in range(10):
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(loss_data,) = exe.run(
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train_program, feed={"input": self.data}, 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 run_dygraph(self):
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with dygraph_guard():
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paddle.seed(10)
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np.random.seed(10)
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out = []
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model = paddle.nn.Linear(2, 2)
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optimizer = paddle.optimizer.ASGD(
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batch_num=3, parameters=model.parameters()
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)
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for _ in range(10):
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output = model(paddle.to_tensor(self.data))
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loss = paddle.mean(output)
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out.append(loss.numpy())
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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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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out1 = self.run_dygraph()
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out2 = self.run_static()
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np.testing.assert_allclose(out1, out2)
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class TestASGDValidation:
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def setUp(self) -> None:
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self.init_all_size()
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self.init_batch_size()
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self.init_batch_num()
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self.data = np.random.random(size=(self.all_size, 2)).astype('float32')
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def init_all_size(self):
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self.all_size = 64
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def init_batch_size(self):
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self.batch_size = 8
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def init_batch_num(self):
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self.batch_num = (int)(self.all_size / self.batch_size)
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def run_validation(self) -> None:
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with dygraph_guard():
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paddle.seed(10)
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np.random.seed(10)
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param_validation = {}
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grad_validation = {}
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lr_validation = {}
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d_validation = {}
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ys_validation = {}
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y_validation = {}
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n_validation = {}
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model = paddle.nn.Linear(2, 2)
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optimizer = paddle.optimizer.ASGD(
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batch_num=self.batch_num, parameters=model.parameters()
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)
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for param in model.parameters():
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d_validation[param.name] = np.zeros(param.shape)
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ys_validation[param.name] = np.zeros(
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[self.batch_num, *param.shape]
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)
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for i in range(5):
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data_start = i * self.batch_size % self.all_size
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data_end = data_start + self.batch_size
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cur_data = self.data[data_start:data_end]
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output = model(paddle.to_tensor(cur_data))
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loss = paddle.mean(output)
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loss = output
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loss.backward()
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for param in model.parameters():
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param_validation[param.name] = param.numpy()
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optimizer.step()
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for param in model.parameters():
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grad_validation[param.name] = param.grad.numpy()
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lr_validation[param.name] = optimizer.get_lr()
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y_validation[param.name] = ys_validation[param.name][
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i % self.batch_num
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]
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d_validation[param.name] = (
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d_validation[param.name]
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- y_validation[param.name]
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+ grad_validation[param.name]
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)
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ys_validation[param.name][i % self.batch_num] = (
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grad_validation[param.name]
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)
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n_validation[param.name] = min(i + 1, self.batch_num)
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param_validation[param.name] = (
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param_validation[param.name]
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- lr_validation[param.name]
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* d_validation[param.name]
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/ n_validation[param.name]
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)
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np.testing.assert_allclose(
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param.numpy(),
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param_validation[param.name],
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)
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optimizer.clear_grad()
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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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self.run_validation()
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class TestASGDValidationCase1(TestASGDValidation, unittest.TestCase):
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pass
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class TestASGDValidationCase2(TestASGDValidationCase1):
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def init_batch_num(self):
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self.batch_num = 2
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if __name__ == "__main__":
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unittest.main()
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