mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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from ..._utils import torch_dtype_to_str
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from ...logger import logger
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from ...mapping import Mapping
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from ..modeling_utils import PretrainedConfig, QuantConfig
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class LlavaNextVisionConfig(PretrainedConfig):
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def __init__(self,
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*,
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image_size: int,
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patch_size: int,
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text_hidden_size: int,
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projector_hidden_act: str = 'gelu',
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num_channels: int = 3,
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vision_model_type: str = 'clip_vision_model',
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**kwargs):
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self.image_size = image_size
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self.patch_size = patch_size
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self.text_hidden_size = text_hidden_size
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self.num_channels = num_channels
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self.projector_hidden_act = projector_hidden_act
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self.vision_model_type = vision_model_type
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super().__init__(**kwargs)
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@classmethod
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def from_hugging_face(
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cls,
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hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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import transformers
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if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
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hf_config = hf_config_or_dir
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else:
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hf_config_dir = str(hf_config_or_dir)
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hf_config = transformers.AutoConfig.from_pretrained(
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hf_config_dir, trust_remote_code=True)
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if hf_config.model_type == "llava_next":
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from transformers import LlavaNextConfig
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hf_config = LlavaNextConfig.from_pretrained(hf_config_dir)
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else:
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logger.error("Provided model type is not llava_next.")
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text_hidden_size = hf_config.text_config.hidden_size
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# Extract only the vision config
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llava_next_vision_config = hf_config.vision_config
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# llava-next uses the second last layer as vision output
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num_feature_layers = llava_next_vision_config.num_hidden_layers + hf_config.vision_feature_layer + 1
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vision_model_type = getattr(llava_next_vision_config,
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"vision_model_type", "clip_vision_model")
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num_key_value_heads = getattr(
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llava_next_vision_config, "num_key_value_heads",
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llava_next_vision_config.num_attention_heads)
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# Default configs from HF
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hidden_act = 'quick_gelu'
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norm_epsilon = 1e-5
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head_size = llava_next_vision_config.hidden_size // llava_next_vision_config.num_attention_heads
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if dtype == 'auto':
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dtype = getattr(hf_config, 'torch_dtype', None)
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if dtype is None:
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dtype = 'float16'
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if isinstance(dtype, torch.dtype):
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dtype = torch_dtype_to_str(dtype)
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if dtype == 'float32':
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dtype = 'float16'
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return cls(
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image_size=llava_next_vision_config.image_size,
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patch_size=llava_next_vision_config.patch_size,
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text_hidden_size=text_hidden_size,
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projector_hidden_act=hf_config.projector_hidden_act,
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vision_model_type=vision_model_type,
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architecture=hf_config.architectures[0],
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dtype=dtype,
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num_hidden_layers=num_feature_layers,
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num_attention_heads=llava_next_vision_config.num_attention_heads,
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hidden_size=llava_next_vision_config.hidden_size,
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intermediate_size=llava_next_vision_config.intermediate_size,
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num_key_value_heads=num_key_value_heads,
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head_size=head_size,
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vocab_size=llava_next_vision_config.vocab_size,
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hidden_act=hidden_act,
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norm_epsilon=norm_epsilon,
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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