mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-11-08 09:03:20 +08:00
Keval Morabia
cb8f9efa50
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 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 socket
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
import torch.nn as nn
|
|
|
|
|
|
def get_free_port():
|
|
sock = socket.socket()
|
|
sock.bind(("", 0))
|
|
port = sock.getsockname()[1]
|
|
return port
|
|
|
|
|
|
def init_process(rank, size, job=None, backend="gloo", port=None):
|
|
"""Initialize the distributed environment."""
|
|
|
|
os.environ["MASTER_ADDR"] = "localhost"
|
|
|
|
port = str(get_free_port()) if port is None else str(port)
|
|
|
|
# We need to use a different port for each tests to avoid conflicts
|
|
os.environ["MASTER_PORT"] = port
|
|
|
|
dist.init_process_group(backend, rank=rank, world_size=size)
|
|
if backend == "nccl" and torch.cuda.is_available():
|
|
torch.cuda.set_device(rank)
|
|
torch.manual_seed(1234)
|
|
if job is not None:
|
|
job(rank, size)
|
|
|
|
|
|
def spawn_multiprocess_job(size, job, backend="gloo"):
|
|
port = get_free_port()
|
|
|
|
processes = []
|
|
mp.set_start_method("spawn", force=True)
|
|
for rank in range(size):
|
|
p = mp.Process(target=init_process, args=(rank, size, job, backend, port))
|
|
p.start()
|
|
processes.append(p)
|
|
|
|
for p in processes:
|
|
p.join()
|
|
|
|
# Ensure that all processes have exited successfully
|
|
assert not p.exitcode
|
|
|
|
|
|
def get_device_counts():
|
|
num_gpus = torch.cuda.device_count()
|
|
return [
|
|
1,
|
|
pytest.param(2, marks=pytest.mark.skipif(num_gpus < 2, reason="need 2 GPUs!")),
|
|
]
|
|
|
|
|
|
def synchronize_state_dict(model: nn.Module):
|
|
state_dict = model.state_dict()
|
|
for v in state_dict.values():
|
|
dist.all_reduce(v, op=dist.ReduceOp.SUM)
|
|
model.load_state_dict(state_dict)
|