# coding=utf-8 # Copyright 2023 the Falcon authors and HuggingFace Inc. 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. """ Falcon configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from transformers import AutoConfig logger = logging.get_logger(__name__) class MAELMConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 65024): Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FalconModel`] hidden_size (`int`, *optional*, defaults to 4544): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 71): Number of attention heads for each attention layer in the Transformer encoder. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for MLP layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for attention layers. num_kv_heads (`int`, *optional*): Number of key-value heads to use per attention layer. If unset, defaults to the same value as `num_attention_heads`. alibi (`bool`, *optional*, defaults to `False`): Whether to use ALiBi positional biases during self-attention. new_decoder_architecture (`bool`, *optional*, defaults to `False`): Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn` arguments are ignored, as the new decoder always uses parallel attention. multi_query (`bool`, *optional*, defaults to `True`): Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`. parallel_attn (`bool`, *optional*, defaults to `True`): Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`. bias (`bool`, *optional*, defaults to `False`): Whether to use bias on Linear layers. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained Falcon models with RoPE support up to 2048 tokens. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. bos_token_id (`int`, *optional*, defaults to 11): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 11): The id of the "end-of-sequence" token. """ model_type = "MAELM" def __init__( self, seed=42, cache_dir=None, do_train=True, do_eval=False, do_test=False, dataset_name=None, spect_len=2992, train_dataset_list=[{'train_file': '/mnt/bn/music-nas-dxj1/datasets/MCC_AIGC/mccaigc_train_1w.csv', \ 'train_tokenized_data': None, 'train_data_root': '/mnt/bn/music-nas-dxj1/datasets/MCC_AIGC/logmel',}], per_device_eval_batch_size=32, preprocessing_num_workers=64, overwrite_cache=True, output_dir='/mnt/bn/music-nas-dxj1/VWork/ckpts_vault/cap_lynx-apm_umg_PT-mccaigc1w_FT', save_interval_steps=1000, overwrite_output_dir=True, gradient_accumulation_steps=1, num_train_epochs=50, per_device_train_batch_size=12, learning_rate=0.00005, lm_lr_ratio=0.1, tokenizer_name='meta-llama/Llama-2-7b-hf', resume_from_checkpoint=None, resume_from_pth='epoch_4-step_8639-allstep_60000.pth', backbone={'name': 'MAEViT', 'arch': 'b', 'patch_size': 16, 'mask_ratio': 0.0, 'img_size': [80, 2992], \ 'ckpt': 'epoch_20.pth'}, neck={'name': 'LMDecoder', 'patch_size': 16, 'img_size': [80, 2992], 'in_chans': 3, 'embed_dim': 768, \ 'decoder_embed_dim': 4544, 'freeze_decoder': True, 'decoder_type': 'meta-llama/Llama-2-7b-hf'}, wandb={'proj': 'ATRena_cap', 'expname': 'cap_lynx_apmPT_mccaigc1wFT'}, **kwargs, ): self.backbone = backbone self.neck = neck self.tokenizer_name = tokenizer_name self._name_or_path = None self.resume_from_checkpoint = resume_from_checkpoint self.resume_from_pth = resume_from_pth self.auto_map = {"AutoConfig": "configuration_maelm.MAELMConfig", "AutoModel": "modeling_maelm.MAEForCausalLM"}