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2bfd19c | 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 | # Copyright (c) 2025 SandAI. All Rights Reserved.
#
# 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
from datetime import timedelta
import torch
import inference.infra.distributed.parallel_state as mpu
from inference.common import print_rank_0
from inference.infra.parallelism.pipeline_parallel import init_pp_scheduler
from . import parallel_state as mpu
def dist_init(config):
"""Initialize torch.distributed and core model parallel."""
assert torch.cuda.is_available()
device_count = torch.cuda.device_count()
if torch.distributed.is_initialized():
print_rank_0("Torch distribution already initialized, skipping initialization ...")
else:
rank = int(os.getenv("RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1"))
# Manually set the device ids.
if device_count > 0:
device = rank % device_count
torch.cuda.set_device(device)
# Call the init process
torch.distributed.init_process_group(
backend=config.engine_config.distributed_backend,
world_size=world_size,
rank=rank,
timeout=timedelta(minutes=config.engine_config.distributed_timeout_minutes),
)
assert config.engine_config.cp_size * config.engine_config.pp_size == torch.distributed.get_world_size()
if device_count > 0:
if mpu.model_parallel_is_initialized():
print_rank_0("Model parallel is already initialized")
else:
mpu.initialize_model_parallel(
cp_size=config.engine_config.cp_size,
pp_size=config.engine_config.pp_size,
nccl_communicator_config_path=None,
distributed_timeout_minutes=config.engine_config.distributed_timeout_minutes,
order="tp-cp-pp-dp",
)
if mpu.get_pp_world_size() > 1:
init_pp_scheduler()
print_rank_0("Initialize torch distribution and model parallel successfully")
def is_last_rank():
return torch.distributed.get_rank() == (torch.distributed.get_world_size() - 1)
def is_last_tp_cp_rank():
return mpu.get_tp_rank(with_context_parallel=True) == mpu.get_tp_world_size(with_context_parallel=True) - 1
def get_world_size():
if torch.distributed.is_available() and torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
else:
world_size = 1
return world_size
def get_device(local_rank=None):
backend = torch.distributed.get_backend()
if backend == "nccl":
if local_rank is None:
device = torch.device("cuda")
else:
device = torch.device(f"cuda:{local_rank}")
elif backend == "gloo":
device = torch.device("cpu")
else:
raise RuntimeError
return device
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