Upload configuration_maelm.py

configuration_maelm.py

@@ -0,0 +1,131 @@
+# 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='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': '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
+
+AutoConfig.register("MAELM", MAELMConfig)