Emerging-Optimizers/tests/test_distributed_soap_utils_cpu.py at main · NVIDIA-NeMo/Emerging-Optimizers · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
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
79
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
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys

import torch
from _comparison import assert_equal
from absl import flags, logging
from absl.testing import absltest, parameterized

from emerging_optimizers.legacy_soap import soap, tp_rekls
from emerging_optimizers.utils import get_pg_rank, get_pg_size


flags.DEFINE_enum("device", "cpu", ["cpu", "cuda"], "Device to run tests on")
flags.DEFINE_integer("seed", None, "Random seed for reproducible tests")
FLAGS = flags.FLAGS


def setUpModule() -> None:
    if FLAGS.seed is not None:
        logging.info("Setting random seed to %d", FLAGS.seed)
        torch.manual_seed(FLAGS.seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(FLAGS.seed)


def tearDownModule() -> None:
    torch.distributed.destroy_process_group()


class AllGatherGradAndKroneckerFactorsTpCpuTest(parameterized.TestCase):
    def setUp(self):
        super().setUp()
        self.tp_group = torch.distributed.group.WORLD
        self.world_size = get_pg_size(self.tp_group)
        self.rank = get_pg_rank(self.tp_group)

    @parameterized.product(
        shape=((16, 32), (32, 16), (96, 200)),
        partition_dim=(0, 1),
    )
    def test_matches_non_distributed(self, shape, partition_dim):
        m, n = shape
        # Grad is sharded along partition_dim; both kronecker factors are sharded along dim 0,
        # so m (rows of L) and n (rows of R) must each be divisible by the group size.
        assert m % self.world_size == 0 and n % self.world_size == 0, "shape must be divisible by world size"
        full_grad = torch.randint(-5, 5, shape)
        full_l = torch.randint(-5, 5, (m, m))
        full_r = torch.randint(-5, 5, (n, n))
        # All-reduce ensures that every rank starts from the same tensors.
        torch.distributed.all_reduce(full_grad, group=self.tp_group)
        torch.distributed.all_reduce(full_l, group=self.tp_group)
        torch.distributed.all_reduce(full_r, group=self.tp_group)

        local_grad = full_grad.chunk(self.world_size, dim=partition_dim)[self.rank].contiguous()
        local_l = full_l.chunk(self.world_size, dim=0)[self.rank].contiguous()
        local_r = full_r.chunk(self.world_size, dim=0)[self.rank].contiguous()

        gathered_grad, gathered_factors = tp_rekls.all_gather_grad_and_kronecker_factors_tp(
            kronecker_factor_list=[local_l, local_r],
            grad=local_grad,
            partition_dim=partition_dim,
            tp_group=self.tp_group,
        )

        assert_equal(gathered_grad, full_grad)
        assert_equal(gathered_factors[0], full_l)
        assert_equal(gathered_factors[1], full_r)

    @parameterized.product(
        shape=((16, 32), (32, 16), (96, 200)),
        partition_dim=(0, 1),
    )
    def test_updated_factors_match_non_distributed(self, shape, partition_dim):
        m, n = shape
        assert m % self.world_size == 0 and n % self.world_size == 0, "shape must be divisible by world size"
        # lerp_ requires a floating-point dtype, but values stay integer-valued so both paths
        # produce bit-identical results.
        full_grad = torch.randint(-5, 5, shape, dtype=torch.float32)
        full_l = torch.randint(-5, 5, (m, m), dtype=torch.float32)
        full_r = torch.randint(-5, 5, (n, n), dtype=torch.float32)
        torch.distributed.all_reduce(full_grad, group=self.tp_group)
        torch.distributed.all_reduce(full_l, group=self.tp_group)
        torch.distributed.all_reduce(full_r, group=self.tp_group)

        shampoo_beta = 0.95

        ref_l = full_l.clone()
        ref_r = full_r.clone()
        soap.update_kronecker_factors([ref_l, ref_r], full_grad, shampoo_beta=shampoo_beta)

        local_grad = full_grad.chunk(self.world_size, dim=partition_dim)[self.rank].contiguous()
        local_l = full_l.chunk(self.world_size, dim=0)[self.rank].contiguous()
        local_r = full_r.chunk(self.world_size, dim=0)[self.rank].contiguous()

        gathered_grad, gathered_factors = tp_rekls.all_gather_grad_and_kronecker_factors_tp(
            kronecker_factor_list=[local_l, local_r],
            grad=local_grad,
            partition_dim=partition_dim,
            tp_group=self.tp_group,
        )
        soap.update_kronecker_factors(gathered_factors, gathered_grad, shampoo_beta=shampoo_beta)

        assert_equal(gathered_factors[0], ref_l)
        assert_equal(gathered_factors[1], ref_r)


if __name__ == "__main__":
    torch.distributed.init_process_group(backend="gloo")
    torch.set_float32_matmul_precision("highest")

    rank = get_pg_rank(torch.distributed.group.WORLD)

    for i, arg in enumerate(sys.argv):
        if arg.startswith("--xml_output_file="):
            base, ext = os.path.splitext(arg)

            # Attach rank to the output file name
            sys.argv[i] = f"{base}_rank{rank}{ext}"
            break

    absltest.main()