137 lines
5.3 KiB
Python
137 lines
5.3 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib.util
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import json
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import os
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import warnings
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from dataclasses import dataclass, field
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import torch
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from ..training_args import TrainingArguments
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from ..utils import cached_property, is_sagemaker_dp_enabled, logging
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logger = logging.get_logger(__name__)
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# TODO: should be moved to `utils` after refactoring of SageMakerTrainer
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def is_sagemaker_model_parallel_available():
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# Get the sagemaker specific mp parameters from smp_options variable.
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smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}")
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try:
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# Parse it and check the field "partitions" is included, it is required for model parallel.
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smp_options = json.loads(smp_options)
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if "partitions" not in smp_options:
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return False
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except json.JSONDecodeError:
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return False
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# Get the sagemaker specific framework parameters from mpi_options variable.
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mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
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try:
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# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
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mpi_options = json.loads(mpi_options)
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if not mpi_options.get("sagemaker_mpi_enabled", False):
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return False
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except json.JSONDecodeError:
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return False
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# Lastly, check if the `smdistributed` module is present.
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return importlib.util.find_spec("smdistributed") is not None
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if is_sagemaker_model_parallel_available():
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import smdistributed.modelparallel.torch as smp
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smp.init()
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@dataclass
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class SageMakerTrainingArguments(TrainingArguments):
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mp_parameters: str = field(
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default="",
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metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"},
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)
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def __post_init__(self):
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super().__post_init__()
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warnings.warn(
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"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
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"`TrainingArguments` instead.",
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FutureWarning,
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)
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@cached_property
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def _setup_devices(self) -> "torch.device":
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logger.info("PyTorch: setting up devices")
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if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
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logger.warning(
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"torch.distributed process group is initialized, but local_rank == -1. "
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"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
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)
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if self.no_cuda:
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device = torch.device("cpu")
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self._n_gpu = 0
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elif is_sagemaker_model_parallel_available():
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local_rank = smp.local_rank()
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device = torch.device("cuda", local_rank)
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self._n_gpu = 1
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elif is_sagemaker_dp_enabled():
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import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
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torch.distributed.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta)
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self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))
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device = torch.device("cuda", self.local_rank)
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self._n_gpu = 1
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elif self.local_rank == -1:
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# if n_gpu is > 1 we'll use nn.DataParallel.
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# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
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# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
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# trigger an error that a device index is missing. Index 0 takes into account the
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# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
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# will use the first GPU in that env, i.e. GPU#1
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
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# the default value.
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self._n_gpu = torch.cuda.device_count()
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else:
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# Here, we'll use torch.distributed.
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# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
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if not torch.distributed.is_initialized():
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torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta)
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device = torch.device("cuda", self.local_rank)
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self._n_gpu = 1
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if device.type == "cuda":
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torch.cuda.set_device(device)
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return device
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@property
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def world_size(self):
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if is_sagemaker_model_parallel_available():
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return smp.dp_size()
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return super().world_size
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@property
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def place_model_on_device(self):
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return not is_sagemaker_model_parallel_available()
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@property
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def _no_sync_in_gradient_accumulation(self):
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return False
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