# Copyright 2025 The HuggingFace Team. All rights reserved. # # 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 Optional import torch from ..models.attention import AttentionModuleMixin, FeedForward, LuminaFeedForward from ..models.attention_processor import Attention, MochiAttention _ATTENTION_CLASSES = (Attention, MochiAttention, AttentionModuleMixin) _FEEDFORWARD_CLASSES = (FeedForward, LuminaFeedForward) _SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "single_transformer_blocks", "layers") _TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",) _CROSS_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "layers") _ALL_TRANSFORMER_BLOCK_IDENTIFIERS = tuple( { *_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS, *_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS, *_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS, } ) # Layers supported for group offloading and layerwise casting _GO_LC_SUPPORTED_PYTORCH_LAYERS = ( torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d, torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d, torch.nn.Linear, # TODO(aryan): look into torch.nn.LayerNorm, torch.nn.GroupNorm later, seems to be causing some issues with CogVideoX # because of double invocation of the same norm layer in CogVideoXLayerNorm ) def _get_submodule_from_fqn(module: torch.nn.Module, fqn: str) -> Optional[torch.nn.Module]: for submodule_name, submodule in module.named_modules(): if submodule_name == fqn: return submodule return None