# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 Optional, Union
import torch
from ..._utils import torch_dtype_to_str
from ...logger import logger
from ...mapping import Mapping
from ..modeling_utils import PretrainedConfig, QuantConfig
[docs]
class LlavaNextVisionConfig(PretrainedConfig):
def __init__(self,
*,
image_size: int,
patch_size: int,
text_hidden_size: int,
projector_hidden_act: str = 'gelu',
num_channels: int = 3,
vision_model_type: str = 'clip_vision_model',
**kwargs):
self.image_size = image_size
self.patch_size = patch_size
self.text_hidden_size = text_hidden_size
self.num_channels = num_channels
self.projector_hidden_act = projector_hidden_act
self.vision_model_type = vision_model_type
super().__init__(**kwargs)
[docs]
@classmethod
def from_hugging_face(
cls,
hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
import transformers
if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
hf_config = hf_config_or_dir
else:
hf_config_dir = str(hf_config_or_dir)
hf_config = transformers.AutoConfig.from_pretrained(
hf_config_dir, trust_remote_code=True)
if hf_config.model_type == "llava_next":
from transformers import LlavaNextConfig
hf_config = LlavaNextConfig.from_pretrained(hf_config_dir)
else:
logger.error("Provided model type is not llava_next.")
text_hidden_size = hf_config.text_config.hidden_size
# Extract only the vision config
llava_next_vision_config = hf_config.vision_config
# llava-next uses the second last layer as vision output
num_feature_layers = llava_next_vision_config.num_hidden_layers + hf_config.vision_feature_layer + 1
vision_model_type = getattr(llava_next_vision_config,
"vision_model_type", "clip_vision_model")
num_key_value_heads = getattr(
llava_next_vision_config, "num_key_value_heads",
llava_next_vision_config.num_attention_heads)
# Default configs from HF
hidden_act = 'quick_gelu'
norm_epsilon = 1e-5
head_size = llava_next_vision_config.hidden_size // llava_next_vision_config.num_attention_heads
if dtype == 'auto':
dtype = getattr(hf_config, 'torch_dtype', None)
if dtype is None:
dtype = 'float16'
if isinstance(dtype, torch.dtype):
dtype = torch_dtype_to_str(dtype)
if dtype == 'float32':
dtype = 'float16'
return cls(
image_size=llava_next_vision_config.image_size,
patch_size=llava_next_vision_config.patch_size,
text_hidden_size=text_hidden_size,
projector_hidden_act=hf_config.projector_hidden_act,
vision_model_type=vision_model_type,
architecture=hf_config.architectures[0],
dtype=dtype,
num_hidden_layers=num_feature_layers,
num_attention_heads=llava_next_vision_config.num_attention_heads,
hidden_size=llava_next_vision_config.hidden_size,
intermediate_size=llava_next_vision_config.intermediate_size,
num_key_value_heads=num_key_value_heads,
head_size=head_size,
vocab_size=llava_next_vision_config.vocab_size,
hidden_act=hidden_act,
norm_epsilon=norm_epsilon,
mapping=mapping,
quantization=quant_config,
**kwargs)
