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# Copyright 2024 ByteDance 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 inspect
import torch
import torch.distributed as dist
def get_adamw(
model: torch.nn.Module,
weight_decay: float,
learning_rate: float,
betas: tuple[float, float],
device_type: str,
) -> torch.optim.AdamW:
"""
Create an AdamW optimizer for the given model with specified parameters.
Args:
model (torch.nn.Module): The model for which the optimizer is created.
weight_decay (float): The weight decay (L2 penalty) for the optimizer.
learning_rate (float): The learning rate for the optimizer.
betas (tuple): Coefficients used for computing running averages of gradient and its square.
device_type (str): The device type ('cuda' or 'cpu') on which the optimizer will operate.
Returns:
torch.optim.AdamW: The AdamW optimizer configured with the specified parameters.
"""
# start with all of the candidate parameters
param_dict = {pn: p for pn, p in model.named_parameters()}
# filter out those that do not require grad
param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
optim_groups = [
{"params": decay_params, "weight_decay": weight_decay},
{"params": nodecay_params, "weight_decay": 0.0},
]
num_decay_params = sum(p.numel() for p in decay_params)
num_nodecay_params = sum(p.numel() for p in nodecay_params)
print(
f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters"
)
print(
f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters"
)
# Create AdamW optimizer and use the fused version if it is available
fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == "cuda"
extra_args = dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(
optim_groups, lr=learning_rate, betas=betas, **extra_args
)
print(f"using fused AdamW: {use_fused}")
return optimizer
def get_optimizer(configs, model: torch.nn.Module) -> torch.optim.Optimizer:
if configs.adam.use_adamw:
optimizer = get_adamw(
model=model,
weight_decay=configs.adam.weight_decay,
learning_rate=configs.adam.lr,
betas=(configs.adam.beta1, configs.adam.beta2),
device_type="cuda" if torch.cuda.is_available() else "cpu",
)
else:
optimizer = torch.optim.Adam(
model.parameters(),
lr=configs.adam.lr,
weight_decay=configs.adam.weight_decay,
betas=(configs.adam.beta1, configs.adam.beta2),
)
return optimizer
def is_loss_nan_check(loss: torch.Tensor) -> bool:
"""check the validness of the current loss
Args:
loss: the loss from the model
Returns:
bool: if True, loss is not nan or inf
"""
def is_nan(x):
return torch.isnan(x).any() or torch.isinf(x).any()
def all_reduce_tensor(tensor, op=dist.ReduceOp.SUM):
if dist.is_initialized():
dist.all_reduce(tensor, op=op)
return tensor
nan_flag = torch.tensor(
1.0 if is_nan(loss) else 0.0,
device=loss.device if torch.cuda.is_available() else None,
) # support cpu
# avoid "Watchdog caught collective operation timeout" error
all_reduce_tensor(nan_flag)
if nan_flag.item() > 0.0:
return True
return False
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