[bridge] Support GPTBridge callback

src/mcore_bridge/bridge/gpt_bridge.py

@@ -5,13 +5,12 @@
 import torch
 import torch.distributed as dist
 import torch.nn.functional as F
-import transformers
 from megatron.core import mpu
 from packaging import version
 from peft import PeftModel
 from peft.utils import ModulesToSaveWrapper
 from tqdm import tqdm
-from typing import List, Optional, Union
+from typing import Callable, List, Optional, Union
 
 from mcore_bridge.tuners import LoraParallelLinear
 from mcore_bridge.utils import (MxFp4Dequantizer, SafetensorLazyLoader, StreamingSafetensorSaver, deep_getattr,
@@ -66,7 +65,6 @@ def __init__(self, config):
         self.pp_group = mpu.get_pipeline_model_parallel_group()
         self.etp_group = mpu.get_expert_tensor_parallel_group()
         self.ep_group = mpu.get_expert_model_parallel_group()
-        self.is_transformers_5 = version.parse(transformers.__version__) >= version.parse('5.0.0.dev')
         self.tp_rank = mpu.get_tensor_model_parallel_rank()
         self.pp_rank = mpu.get_pipeline_model_parallel_rank()
         self.etp_rank = mpu.get_expert_tensor_parallel_rank()
@@ -1615,7 +1613,14 @@ def _convert_mtp_layer(self, lm_model, hf_state_dict, hf_prefix: str, layer_idx:
             hf_state_dict.update(origin_hf_state_dict)
         return hf_state_dict
 
-    def load_weights(self, mg_models, hf_model_dir: str, peft_format: bool = False, adapter_name: str = 'default'):
+    def load_weights(
+        self,
+        mg_models,
+        hf_model_dir: str,
+        peft_format: bool = False,
+        adapter_name: str = 'default',
+        converter: Optional[Callable] = None,
+    ):
         """Load weights from safetensors (HuggingFace) format into Megatron model.
 
         Args:
@@ -1624,24 +1629,38 @@ def load_weights(self, mg_models, hf_model_dir: str, peft_format: bool = False,
             peft_format: Whether the weights are in PEFT (LoRA, etc.) format. Defaults to False.
                 If True, loads LoRA delta weights. If False, loads the full model weights.
             adapter_name: Name of the adapter for PEFT models. Defaults to 'default'.
+            converter: Used to perform key-value conversion on the newly loaded state_dict.
         """
         self._peft_format = peft_format
         self._adapter_name = adapter_name
         mg_models = unwrap_model(mg_models)
         self._disable_tqdm = False
         with torch.no_grad(), SafetensorLazyLoader(hf_model_dir, peft_format=peft_format) as loader:
             state_dict = loader.get_state_dict()
+            if converter:
+                new_state_dict = {}
+                for k, v in state_dict.items():
+                    kv = converter(k, v)
+                    if kv is None:
+                        continue
+                    k, v = kv
+                    new_state_dict[k] = v
+                state_dict = new_state_dict
             hf_prefix = 'base_model.model.' if peft_format else ''
             for mg_model in mg_models:
                 list(self._convert([mg_model], state_dict, hf_prefix, True, 'Loading: '))
 
-    def export_weights(self,
-                       mg_models,
-                       target_device=None,
-                       only_master_rank: bool = False,
-                       peft_format: bool = False,
-                       tqdm_desc: str = 'Exporting: ',
-                       disable_tqdm: bool = True):
+    def export_weights(
+        self,
+        mg_models,
+        target_device=None,
+        only_master_rank: bool = False,
+        peft_format: bool = False,
+        adapter_name: str = 'default',
+        converter: Optional[Callable] = None,
+        tqdm_desc: str = 'Exporting: ',
+        disable_tqdm: bool = True,
+    ):
         """Export Megatron model weights to safetensors (HuggingFace) format as a generator.
 
         This method yields weight tensors one by one for streaming save operations or RL weight synchronization,
@@ -1654,6 +1673,8 @@ def export_weights(self,
             peft_format: Whether to export in PEFT (LoRA, etc.) format. Defaults to False.
                 - If True, exports only LoRA delta weights. If False, exports the complete model weights
                 (e.g., after merge-lora or full-parameter fine-tuning).
+            adapter_name: Name of the adapter for PEFT models. Defaults to 'default'.
+            converter: Used to perform key-value conversion on the newly exported state_dict.
             tqdm_desc: Description text for the progress bar. Defaults to 'Exporting: '.
             disable_tqdm: Whether to disable the tqdm progress bar. Defaults to True.
 
@@ -1663,8 +1684,8 @@ def export_weights(self,
         self._target_device = target_device
         self._only_master_rank = only_master_rank
         self._peft_format = peft_format
+        self._adapter_name = adapter_name
         self._disable_tqdm = disable_tqdm
-        self._adapter_name = 'default'
         self._peft_target_modules = set()
         self._peft_modules_to_save = set()
         hf_prefix = 'base_model.model.' if peft_format else ''
@@ -1674,13 +1695,21 @@ def export_weights(self,
                 mg_models[i] = mg_model.model
         self.config = mg_models[0].config
         with torch.no_grad():
-            yield from self._convert(mg_models, {}, hf_prefix, False, tqdm_desc=tqdm_desc)
+            for k, v in self._convert(mg_models, {}, hf_prefix, False, tqdm_desc=tqdm_desc):
+                if converter:
+                    kv = converter(k, v)
+                    if kv is None:
+                        continue
+                    k, v = kv
+                yield k, v
 
     def save_weights(
         self,
         mg_models,
         output_dir: str,
         peft_format: bool = False,
+        adapter_name: str = 'default',
+        converter: Optional[Callable] = None,
         max_shard_size: str = '5GB',
     ) -> None:
         """Save Megatron model checkpoint in safetensors (HuggingFace) format.
@@ -1695,6 +1724,8 @@ def save_weights(
             peft_format: Whether to save in PEFT (LoRA, etc.) format. Defaults to False.
                 If True, saves LoRA delta weights. If False, saves the complete model weights
                 (e.g., after merge-lora or full-parameter fine-tuning).
+            adapter_name: Name of the adapter for PEFT models. Defaults to 'default'.
+            converter: Used to perform key-value conversion on the newly exported state_dict.
             max_shard_size: Maximum size of a single storage file, default is '5GB'.
         """
         gc_collect()
@@ -1705,6 +1736,8 @@ def save_weights(
                 target_device='cpu',
                 only_master_rank=True,
                 peft_format=peft_format,
+                adapter_name=adapter_name,
+                converter=converter,
                 tqdm_desc='Saving: ',
                 disable_tqdm=False):
             saver.add_tensor(k, v)