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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from typing import Dict, Optional
import torch
# Substrings to ignore when processing state dicts
substrings_to_ignore = [
"_extra_state", # Extra states (BytesIO type) added by TransformerEngine for FP8 handling
]
def get_partial_state_dict(
state_dict: Dict[str, torch.Tensor],
prefix: str,
) -> Dict[str, torch.Tensor]:
"""
Get a partial state dict with keys starting with the given prefix
"""
return {k: v for k, v in state_dict.items() if k.startswith(prefix)}
def process_state_dict(
state_dict: Dict[str, torch.Tensor],
device: str = None,
dtype: torch.dtype = None,
prefix_to_remove: Optional[str] = None,
) -> Dict[str, torch.Tensor]:
"""
- Remove items with substring "_extra_state" in keys (TransformerEngine adds these for FP8)
- Move tensors to specified device and dtype if provided
Args:
state_dict (Dict[str, torch.Tensor]): The state dict to process
device (str, optional): The device to move tensors to. Defaults to None.
dtype (torch.dtype, optional): The dtype to move tensors to. Defaults to None.
prefix_to_remove (str, optional): The prefix to remove from the keys of the state dict. Defaults to None.
Returns:
Dict[str, torch.Tensor]: The processed state dict
"""
new_state_dict = {}
tensor_kwargs = {}
if device is not None:
tensor_kwargs["device"] = device
if dtype is not None:
tensor_kwargs["dtype"] = dtype
for key, value in state_dict.items():
# Check if any of the substrings to ignore are in the key
skip = False
for substr in substrings_to_ignore:
if substr in key:
skip = True
break
if skip:
continue
if len(tensor_kwargs) > 0:
value = value.to(**tensor_kwargs)
if prefix_to_remove is not None and key.startswith(prefix_to_remove):
key = key[len(prefix_to_remove) :]
new_state_dict[key] = value
return new_state_dict
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