mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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116 lines
5.2 KiB
Python
116 lines
5.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2025 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 Any, Dict, Optional, Sequence
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from ...mapping import Mapping
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from ..modeling_utils import PretrainedConfig, QuantConfig
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class STDiTModelConfig(PretrainedConfig):
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def __init__(self,
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architecture: str = 'STDiT3',
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checkpoint_path: str = 'pretrained_ckpt/model.safetensors',
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vae_type: str = "hpcai-tech/OpenSora-VAE-v1.2",
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text_encoder_type: str = "DeepFloyd/t5-v1_1-xxl",
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caption_channels: int = 4096,
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num_hidden_layers: int = 28,
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hidden_size: int = 1152,
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width: int = 640,
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height: int = 360,
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num_frames: int = 102,
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latent_size: Sequence[int] = [30, 45, 80],
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stdit_patch_size: Sequence[int] = [1, 2, 2],
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spatial_patch_size: Sequence[int] = [1, 8, 8],
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temporal_patch_size: Sequence[int] = [4, 1, 1],
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in_channels: int = 4,
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input_sq_size: int = 512,
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num_attention_heads: int = 16,
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mlp_ratio: float = 4.0,
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class_dropout_prob: float = 0.1,
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model_max_length: int = 300,
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learn_sigma: bool = True,
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qk_norm: bool = True,
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skip_y_embedder: bool = False,
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dtype: Optional[str] = None,
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mapping: Mapping = Mapping(),
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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kwargs.update({
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'architecture': architecture,
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'num_hidden_layers': num_hidden_layers,
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'num_attention_heads': num_attention_heads,
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'hidden_size': hidden_size,
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'dtype': dtype
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})
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super().__init__(**kwargs)
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self.checkpoint_path = checkpoint_path
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self.vae_type = vae_type
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self.text_encoder_type = text_encoder_type
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self.caption_channels = caption_channels
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self.width = width
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self.height = height
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self.num_frames = num_frames
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self.latent_size = latent_size
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self.stdit_patch_size = stdit_patch_size
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self.spatial_patch_size = spatial_patch_size
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self.temporal_patch_size = temporal_patch_size
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self.in_channels = in_channels
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self.input_sq_size = input_sq_size
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self.mlp_ratio = mlp_ratio
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self.class_dropout_prob = class_dropout_prob
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self.model_max_length = model_max_length
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self.learn_sigma = learn_sigma
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self.qk_norm = qk_norm
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self.skip_y_embedder = skip_y_embedder
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self.mapping = mapping
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self.quant_config = quant_config
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@classmethod
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def from_input_config(cls,
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input_config: Dict[str, Any],
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dtype: str = 'auto',
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mapping: Mapping = Mapping(),
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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return cls(architecture=input_config['architecture'],
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checkpoint_path=input_config['checkpoint_path'],
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vae_type=input_config['vae_type'],
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text_encoder_type=input_config['text_encoder_type'],
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caption_channels=input_config['caption_channels'],
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num_hidden_layers=input_config['num_hidden_layers'],
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width=input_config['width'],
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height=input_config['height'],
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num_frames=input_config['num_frames'],
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latent_size=input_config['latent_size'],
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hidden_size=input_config['hidden_size'],
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stdit_patch_size=input_config['stdit_patch_size'],
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spatial_patch_size=input_config['spatial_patch_size'],
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temporal_patch_size=input_config['temporal_patch_size'],
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in_channels=input_config['in_channels'],
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input_sq_size=input_config['input_sq_size'],
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num_attention_heads=input_config['num_attention_heads'],
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mlp_ratio=input_config['mlp_ratio'],
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class_dropout_prob=input_config['class_dropout_prob'],
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model_max_length=input_config['model_max_length'],
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learn_sigma=input_config['learn_sigma'],
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qk_norm=input_config['qk_norm'],
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skip_y_embedder=input_config['skip_y_embedder'],
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dtype=dtype,
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mapping=mapping,
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quant_config=quant_config,
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**kwargs)
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