Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "82c6a50b61a29c9b71d3845b7c1fa626361cf96c"
Switch branch/tag
Find file Normal viewHistoryPermalink
test_other_optimizer.py 4.64 KB
Newer Older
aiss's avatar
update v0.9.2  
aiss committed
1
2
3
4 
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team
aiss's avatar
push dsv0.8.2 version  
aiss committed
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19 

import deepspeed
from deepspeed.ops.op_builder import FusedLambBuilder

from unit.common import DistributedTest
from unit.simple_model import *

from unit.checkpoint.common import checkpoint_correctness_verification

import pytest


class TestOtherOptimizerCheckpoint(DistributedTest):
    world_size = 2
aiss's avatar
update v0.9.2  
aiss committed
20 
    @pytest.mark.skipif(not deepspeed.ops.__compatible_ops__[FusedLambBuilder.NAME], reason="lamb is not compatible")
aiss's avatar
push dsv0.8.2 version  
aiss committed
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78 
    def test_checkpoint_unfused_optimizer(self, tmpdir):
        config_dict = {
            "train_batch_size": 2,
            "steps_per_print": 1,
            "optimizer": {
                "type": "Lamb",
                "params": {
                    "lr": 0.00015
                }
            },
            "gradient_clipping": 1.0,
            "fp16": {
                "enabled": True
            },
            "scheduler": {
                "type": "OneCycle",
                "params": {
                    "cycle_first_step_size": 1000,
                    "cycle_first_stair_count": 500,
                    "cycle_second_step_size": 1000,
                    "cycle_second_stair_count": 500,
                    "decay_step_size": 1000,
                    "cycle_min_lr": 0.0001,
                    "cycle_max_lr": 0.0010,
                    "decay_lr_rate": 0.001,
                    "cycle_min_mom": 0.85,
                    "cycle_max_mom": 0.99,
                    "decay_mom_rate": 0.0
                }
            }
        }

        args = args_from_dict(tmpdir, config_dict)
        hidden_dim = 10
        models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]

        # Load & verify optimizer states
        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            load_optimizer_states=True)

        # Ignore optimizer states
        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            load_optimizer_states=False)

    def test_checkpoint_fused_optimizer(self, tmpdir):
        config_dict = {
            "train_batch_size": 2,
            "steps_per_print": 1,
            "optimizer": {
                "type": "Adam",
                "params": {
                    "lr": 0.00015,
aiss's avatar
update v0.9.2  
aiss committed
79 
                    "betas": [0.8, 0.999],
aiss's avatar
push dsv0.8.2 version  
aiss committed
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114 
                    "eps": 1e-8,
                    "weight_decay": 3e-7
                }
            },
            "fp16": {
                "enabled": True
            }
        }

        args = args_from_dict(tmpdir, config_dict)
        hidden_dim = 10
        models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]

        # Load & verify optimizer states
        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            load_optimizer_states=True)

        # Ignore optimizer states
        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            load_optimizer_states=False)

    def test_checkpoint_fp32_optimizer(self, tmpdir):
        config_dict = {
            "train_batch_size": 2,
            "steps_per_print": 1,
            "optimizer": {
                "type": "Adam",
                "params": {
                    "lr": 0.00015,
aiss's avatar
update v0.9.2  
aiss committed
115 
                    "betas": [0.8, 0.999],
aiss's avatar
push dsv0.8.2 version  
aiss committed
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132 
                    "eps": 1e-8,
                    "weight_decay": 3e-7
                }
            },
            "fp16": {
                "enabled": False
            }
        }

        args = args_from_dict(tmpdir, config_dict)
        hidden_dim = 10
        models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]
        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            fp16=False)