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[megatron] feat: use yaml to manage mbridge args #4584

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[megatron] feat: use yaml to manage mbridge args #4584

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6a98545
add mbridge config
Kite0011 Dec 18, 2025
7ca6247
use general inferface for mbridge save_weights
Kite0011 Dec 18, 2025
3e7b555
fint fix
Kite0011 Dec 18, 2025
0611191
add distributed_filesystem arg
Kite0011 Dec 18, 2025
0079597
add default value
Kite0011 Dec 18, 2025
2c6c967
update memory_efficient default value
Kite0011 Dec 18, 2025
0eb6a9d
Update verl/utils/checkpoint/megatron_checkpoint_manager.py
Kite0011 Dec 18, 2025
a704639
Update verl/utils/checkpoint/megatron_checkpoint_manager.py
Kite0011 Dec 18, 2025
59d46f9
Update verl/trainer/config/config.py
Kite0011 Dec 18, 2025
e85bd9a
fix lint
Kite0011 Dec 18, 2025
63c19a3
update mbridge setting
Kite0011 Dec 19, 2025
71bc169
fix bug
Kite0011 Dec 19, 2025
87565d0
fix lint
Kite0011 Dec 19, 2025
ec9aa98
update code
Kite0011 Dec 25, 2025
e2b1e06
fix ci
Kite0011 Dec 31, 2025
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4 changes: 4 additions & 0 deletions verl/trainer/config/actor/actor.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -122,6 +122,10 @@ checkpoint:

# Whether to save checkpoints asynchronously. Only effective for Megatron as of now.
async_save: False

# Mbridge config extension.
mbridge_config:
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@ISEEKYAN ISEEKYAN Dec 19, 2025

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please don't add these yaml configs.

memory_efficient: False

# optimizer configs
optim:
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1 change: 1 addition & 0 deletions verl/trainer/config/config.py
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Expand Up @@ -36,6 +36,7 @@ class CheckpointConfig(BaseConfig):
save_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"])
load_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"])
async_save: bool = False
mbridge_config: Optional[dict[str, Any]] = field(default_factory=dict)


@dataclass
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4 changes: 4 additions & 0 deletions verl/trainer/config/critic/critic.yaml
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Expand Up @@ -98,6 +98,10 @@ checkpoint:
# Whether to save checkpoints asynchronously. Only effective for Megatron as of now.
async_save: False

# Mbridge config extension.
mbridge_config:
memory_efficient: False

# profile the critic model in `update_critic`
profiler:

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3 changes: 3 additions & 0 deletions verl/trainer/config/sft_trainer_engine.yaml
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Expand Up @@ -52,6 +52,9 @@ checkpoint:

# For more flexibility, you can specify the contents to load from the checkpoint.
load_contents: ${checkpoint.save_contents}
# Mbridge config extension.
mbridge_config:
memory_efficient: False

trainer:
default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
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4 changes: 2 additions & 2 deletions verl/utils/checkpoint/megatron_checkpoint_manager.py
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Expand Up @@ -500,7 +500,7 @@ def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: i
hf_ckpt_path = get_hf_model_checkpoint_path(local_path)
if self.vanilla_bridge:
self.bridge.save_weights(
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@ISEEKYAN ISEEKYAN Dec 19, 2025

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recommend to modify like this:


import inspect
extended_args = {}
if "distributed_filesystem" in inspect.signature(self.bridge.save_weights):
    extended_args["distributed_filesystem"] = True
    extended_args["memory_efficient"] = True
self.bridge.save_weights(self.model, hf_ckpt_path, **extended_args)

self.model, hf_ckpt_path, distributed_filesystem=True, memory_efficient=True
self.model, hf_ckpt_path, **self.checkpoint_config.mbridge_config
)
else:
self.bridge.save_hf_weights(self.model, hf_ckpt_path)
Expand Down Expand Up @@ -572,7 +572,7 @@ def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: i
hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path)
if self.vanilla_bridge:
self.bridge.save_weights(
self.model, hf_model_ckpt_path, distributed_filesystem=True, memory_efficient=True
self.model, hf_model_ckpt_path, **self.checkpoint_config.mbridge_config
)
else:
self.bridge.save_hf_weights(self.model, hf_model_ckpt_path)
Expand Down