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1faccd4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device
from verl.utils.torch_functional import (
distributed_masked_mean,
distributed_mean_max_min_std,
expand_as_nested,
masked_mean,
)
def _worker_mean(rank: int, world_size: int, rendezvous_file: str):
# 1) set GPU and init NCCL
get_torch_device().set_device(rank)
dist.init_process_group(
backend=get_nccl_backend(),
init_method=f"file://{rendezvous_file}",
rank=rank,
world_size=world_size,
)
# each rank holds tensor [rank+1]
local = torch.tensor([float(rank + 1)], device=f"{get_device_name()}:{rank}")
mean, gmax, gmin, gstd = distributed_mean_max_min_std(local, True, True, True)
values = [float(i + 1) for i in range(world_size)]
exp_mean = sum(values) / len(values)
exp_max = max(values)
exp_min = min(values)
var = sum((x - exp_mean) ** 2 for x in values) / (len(values) - 1)
exp_std = var**0.5
# all ranks should see the same result
assert torch.allclose(mean.cpu(), torch.tensor(exp_mean)), f"mean@{rank}"
assert torch.allclose(gmax.cpu(), torch.tensor(exp_max)), f"max@{rank}"
assert torch.allclose(gmin.cpu(), torch.tensor(exp_min)), f"min@{rank}"
assert torch.allclose(gstd.cpu(), torch.tensor(exp_std)), f"std@{rank}"
dist.destroy_process_group()
@pytest.mark.parametrize(
"value,mask,gt",
[
([1.0, 2.0, 3.0, 4.0], [1, 0, 0, 1], 2.5),
([1.0, 2.0, float("nan"), 4.0], [1, 0, 0, 1], 2.5),
([1.0, 2.0, float("nan"), 4.0], [1, 0, 1, 0], float("nan")),
],
)
def test_masked_mean(value, mask, gt):
res = masked_mean(torch.tensor(value), torch.tensor(mask))
gt = torch.tensor(gt)
assert torch.allclose(res, gt) or (torch.isnan(res) and torch.isnan(gt))
@pytest.mark.parametrize("world_size", [2, 4])
def test_distributed_mean_max_min_std(world_size, tmp_path):
rendezvous_file = str(tmp_path / "rdzv_mean")
os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True)
mp.spawn(
fn=_worker_mean,
args=(world_size, rendezvous_file),
nprocs=world_size,
join=True,
)
def _worker_mask(rank: int, world_size: int, rendezvous_file: str):
get_torch_device().set_device(rank)
dist.init_process_group(
backend=get_nccl_backend(),
init_method=f"file://{rendezvous_file}",
rank=rank,
world_size=world_size,
)
# build per‐rank tensor and mask
local_tensor = torch.tensor([rank * 2 + 1.0, rank * 2 + 2.0], device=f"{get_device_name()}:{rank}")
if rank == 0:
mask = torch.tensor([1, 0], device=f"{get_device_name()}:{rank}", dtype=torch.float32)
else:
mask = torch.tensor([0, 1], device=f"{get_device_name()}:{rank}", dtype=torch.float32)
gmean = distributed_masked_mean(local_tensor, mask)
valid_values = [1.0] + [2 * i + 2.0 for i in range(1, world_size)]
expected_mean = sum(valid_values) / len(valid_values)
assert torch.allclose(gmean.cpu(), torch.tensor(expected_mean)), f"masked_mean@{rank}"
dist.destroy_process_group()
@pytest.mark.parametrize("world_size", [2, 4])
def test_distributed_masked_mean(world_size, tmp_path):
rendezvous_file = str(tmp_path / "rdzv_mask")
os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True)
mp.spawn(
fn=_worker_mask,
args=(world_size, rendezvous_file),
nprocs=world_size,
join=True,
)
def test_expand_as_nested():
a = torch.randn(2)
b = torch.randn(3)
c = torch.randn(4)
nested_tensor = torch.nested.as_nested_tensor([a, b, c], layout=torch.jagged)
tensor = torch.tensor([1, 2, 3])
output = expand_as_nested(tensor, nested_tensor)
assert output.values().tolist() == [1, 1, 2, 2, 2, 3, 3, 3, 3]
assert torch.all(output.offsets() == nested_tensor.offsets()).item()
# test exceptions
with pytest.raises(AssertionError):
expand_as_nested(tensor, tensor)
other_tensor = torch.tensor([1, 2, 3, 4])
with pytest.raises(AssertionError):
expand_as_nested(other_tensor, nested_tensor)
other_tensor = torch.tensor([[1, 2, 3]])
with pytest.raises(AssertionError):
expand_as_nested(other_tensor, nested_tensor)
with pytest.raises(AssertionError):
expand_as_nested(tensor, nested_tensor.unsqueeze(-1))
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