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import os
import argparse
import torch
from accelerate import DeepSpeedPlugin, Accelerator
from .utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
def add_deepspeed_arguments(parser: argparse.ArgumentParser):
# DeepSpeed Arguments. https://huggingface.co/docs/accelerate/usage_guides/deepspeed
parser.add_argument("--deepspeed", action="store_true", help="enable deepspeed training")
parser.add_argument("--zero_stage", type=int, default=2, choices=[0, 1, 2, 3], help="Possible options are 0,1,2,3.")
parser.add_argument(
"--offload_optimizer_device",
type=str,
default=None,
choices=[None, "cpu", "nvme"],
help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stages 2 and 3.",
)
parser.add_argument(
"--offload_optimizer_nvme_path",
type=str,
default=None,
help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.",
)
parser.add_argument(
"--offload_param_device",
type=str,
default=None,
choices=[None, "cpu", "nvme"],
help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stage 3.",
)
parser.add_argument(
"--offload_param_nvme_path",
type=str,
default=None,
help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.",
)
parser.add_argument(
"--zero3_init_flag",
action="store_true",
help="Flag to indicate whether to enable `deepspeed.zero.Init` for constructing massive models."
"Only applicable with ZeRO Stage-3.",
)
parser.add_argument(
"--zero3_save_16bit_model",
action="store_true",
help="Flag to indicate whether to save 16-bit model. Only applicable with ZeRO Stage-3.",
)
parser.add_argument(
"--fp16_master_weights_and_gradients",
action="store_true",
help="fp16_master_and_gradients requires optimizer to support keeping fp16 master and gradients while keeping the optimizer states in fp32.",
)
def prepare_deepspeed_args(args: argparse.Namespace):
if not args.deepspeed:
return
# To avoid RuntimeError: DataLoader worker exited unexpectedly with exit code 1.
args.max_data_loader_n_workers = 1
def prepare_deepspeed_plugin(args: argparse.Namespace):
if not args.deepspeed:
return None
try:
import deepspeed
except ImportError as e:
logger.error(
"deepspeed is not installed. please install deepspeed in your environment with following command. DS_BUILD_OPS=0 pip install deepspeed"
)
exit(1)
deepspeed_plugin = DeepSpeedPlugin(
zero_stage=args.zero_stage,
gradient_accumulation_steps=args.gradient_accumulation_steps,
gradient_clipping=args.max_grad_norm,
offload_optimizer_device=args.offload_optimizer_device,
offload_optimizer_nvme_path=args.offload_optimizer_nvme_path,
offload_param_device=args.offload_param_device,
offload_param_nvme_path=args.offload_param_nvme_path,
zero3_init_flag=args.zero3_init_flag,
zero3_save_16bit_model=args.zero3_save_16bit_model,
)
deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = args.train_batch_size
deepspeed_plugin.deepspeed_config["train_batch_size"] = (
args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"])
)
deepspeed_plugin.set_mixed_precision(args.mixed_precision)
if args.mixed_precision.lower() == "fp16":
deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0 # preventing overflow.
if args.full_fp16 or args.fp16_master_weights_and_gradients:
if args.offload_optimizer_device == "cpu" and args.zero_stage == 2:
deepspeed_plugin.deepspeed_config["fp16"]["fp16_master_weights_and_grads"] = True
logger.info("[DeepSpeed] full fp16 enable.")
else:
logger.info(
"[DeepSpeed]full fp16, fp16_master_weights_and_grads currently only supported using ZeRO-Offload with DeepSpeedCPUAdam on ZeRO-2 stage."
)
if args.offload_optimizer_device is not None:
logger.info("[DeepSpeed] start to manually build cpu_adam.")
deepspeed.ops.op_builder.CPUAdamBuilder().load()
logger.info("[DeepSpeed] building cpu_adam done.")
return deepspeed_plugin
# Accelerate library does not support multiple models for deepspeed. So, we need to wrap multiple models into a single model.
def prepare_deepspeed_model(args: argparse.Namespace, **models):
# remove None from models
models = {k: v for k, v in models.items() if v is not None}
class DeepSpeedWrapper(torch.nn.Module):
def __init__(self, **kw_models) -> None:
super().__init__()
self.models = torch.nn.ModuleDict()
for key, model in kw_models.items():
if isinstance(model, list):
model = torch.nn.ModuleList(model)
assert isinstance(
model, torch.nn.Module
), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}"
self.models.update(torch.nn.ModuleDict({key: model}))
def get_models(self):
return self.models
ds_model = DeepSpeedWrapper(**models)
return ds_model
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