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A100
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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
# Copyright 2024 NVIDIA CORPORATION & 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.
#
# SPDX-License-Identifier: Apache-2.0
import re
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
class SoundMultimodalProjectorConfig(PretrainedConfig):
model_type = "sound_mm_projector"
def __init__(self, sound_mm_projector_type: str = None, **kwargs):
super().__init__()
self.sound_mm_projector_type = sound_mm_projector_type
class SoundMultimodalProjector(PreTrainedModel):
config_class = SoundMultimodalProjectorConfig
def __init__(self, sound_mm_projector_cfg: SoundMultimodalProjectorConfig, config: PretrainedConfig):
super().__init__(sound_mm_projector_cfg)
# sound_mm_projector_type = sound_mm_projector_cfg.sound_mm_projector_type
sound_mm_projector_type = "mlp"
if sound_mm_projector_type == "mlp":
self.layers = nn.Sequential(
nn.Linear(config.sound_hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.hidden_size),
)
else:
raise ValueError(f"Unknown projector type: {sound_mm_projector_type}")
def forward(self, x, *args, **kwargs):
return self.layers(x)
AutoConfig.register("sound_mm_projector", SoundMultimodalProjectorConfig)
AutoModel.register(SoundMultimodalProjectorConfig, SoundMultimodalProjector)
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