# Copyright 2022 Alibaba Group and The 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. """ MMSpeech model configuration""" import warnings from transformers import PretrainedConfig from transformers.utils import logging logger = logging.get_logger() class MMSpeechConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`~OFAModel`]. It is used to instantiate an OFA 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 OFA [ofa-base](https://huggingface.co/ofa-base) 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 50265): Vocabulary size of the OFA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~OFAModel`] or [`~TFOFAModel`]. d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether or not the model is used as an encoder/decoder. scale_embedding (`bool`, *optional*, defaults to `False`): Whether or not to scale the embedding. If True, embedding = Math.qrt(d_model) * embedding pad_token_id (`int`, *optional*, defaults to `1`): The id of the _padding_ token. bos_token_id (`int`, *optional*, defaults to `0`): The id of the _beginning-of-stream_ token. decoder_start_token_id (`int`, *optional*, defaults to `0`): If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token. eos_token_id (`int`, *optional*, defaults to `2`): The id of the _end-of-stream_ token. forced_eos_token_id (`int`, *optional*, defaults to `2`): The id of the token to force as the last generated token when `max_length` is reached. encoder_normalize_before (`bool`, *optional*, defaults to `True`): Whether or not to use layer normalization before the self attention and fc layer within encoder layer decoder_normalize_before (`bool`, *optional*, defaults to `True`): Whether or not to use layer normalization before the self attention and fc layer within decoder layer normformer (`bool`, *optional*, defaults to `True`): Whether or not to use layer normalization between the self attention layers and fc layer within encoder&decoder layer encoder_drop_path_rate (`float`, *optional*, defaults to `0.0`): The drop path rate using in the encoder. see more about drop path [drop path](https://arxiv.org/abs/1605.07648) decoder_drop_path_rate (`float`, *optional*, defaults to `0.0`): The drop path rate using in the decoder. see more about drop path [drop path](https://arxiv.org/abs/1605.07648) layernorm_embedding (`bool`, *optional*, defaults to `True`): Whether or not to use layer normalization for text input embedding in encoder and decoder. patch_layernorm_embedding (`bool`, *optional*, defaults to `True`): Whether or not to use layer normalization for image patch input embedding in encoder and decoder. entangle_position_embedding (`bool`, *optional*, defaults to `False`): Whether or not to entangle position embedding to input embedding. resnet_type (`str`, *optional*, defaults to `"resnet101"`): The image encoder's type in OFA, only works when use_ofasys=False. `"resnet18"`, `"resnet34"`, `"resnet50"`, `"resnet101"` and `"resnet152"` are supported. resnet_model_path (`str`, *optional*, defaults to `None`): The path where can load resnet model. If None, will use random initialized weights. resnet_drop_path_rate The drop path rate using in resnet for image encoding, see more about drop path [drop path](https://arxiv.org/abs/1605.07648) token_bucket_size (`int`, *optional*, defaults to `256`): The number of token buckets to use for each attention layer. image_bucket_size (`int`, *optional*, defaults to `42`): The number of image buckets to use for each attention layer. add_type_embedding (`bool`, *optional*, defaults to `True`): Whether or not to add type embedding to the input while encoding. So far, type means the type of modality, and only Text&Image modalities is supported, `0`=Text, `1`=Image share_decoder_input_output_embed (`bool`, *optional*, defaults to `True`): Whether or not to share the input embedding table as the weights the output projection in decoder. If False, using a new linear projection. attn_scale_factor (`float`, *optional*, defaults to `2.0`): The position embedding scaling factor. If it works, position_embedding = position_embedding * float(d_model / num_attention_heads * attn_scale_factor)**-0.5 code_layernorm_embedding (`bool`, *optional*, defaults to `True`): Whether or not to user layer normalization for code generation code_image_size (`int`, *optional*, defaults to `128`): Image size of generated images. Also used in calculating the image's position id for attention bias. interpolate_position (`bool`, *optional*, defaults to `False`): Deprecated now, will be deleted in next version. orig_patch_image_size (`int`, *optional*, defaults to `224`): Deprecated now, will be deleted in next version. share_attn_bias (`bool`, *optional*, defaults to `False`): Whether or not to share attn_bias cross transformer layers use_image_feature (`bool`, *optional*, defaults to `True`): Whether or not the model have image modality. disable_entangle (`bool`, *optional*, defaults to `False`): Whether or not to disable the entangle relative configs. use_ofasys (`bool`, *optional*, defaults to `False`): Whether or not the model is come from OFA-Sys. If True, the model structure will be some differences from OFA vit_type (`str`, *optional*, defaults to `"vit_base"`): The image encoder's type in OFA-Sys, only works when use_ofasys=True. `"vit_base"`, `"vit_large"`, `"vit_large_336"` and `"vit_huge"` are supported. vit_drop_path_rate The drop path rate using the image encoder vit. see more about drop path [drop path](https://arxiv.org/abs/1605.07648) """ # noqa model_type = 'ofa' keys_to_ignore_at_inference = ['past_key_values'] attribute_map = { 'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model' } def __init__(self, vocab_size=59457, max_position_embeddings=1024, encoder_layers=4, encoder_ffn_dim=512 * 4, encoder_attention_heads=8, decoder_layers=4, decoder_ffn_dim=512 * 4, decoder_attention_heads=8, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='gelu', d_model=512, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, pad_token_id=1, bos_token_id=0, decoder_start_token_id=0, eos_token_id=2, forced_eos_token_id=2, encoder_normalize_before=True, decoder_normalize_before=True, normformer=True, encoder_drop_path_rate=0.0, decoder_drop_path_rate=0.0, layernorm_embedding=True, patch_layernorm_embedding=True, resnet_type='resnet101', resnet_model_path=None, resnet_drop_path_rate=0.0, token_bucket_size=256, image_bucket_size=42, add_type_embedding=True, share_decoder_input_output_embed=True, attn_scale_factor=2., code_layernorm_embedding=False, code_image_size=128, entangle_position_embedding=False, interpolate_position=False, orig_patch_image_size=224, share_attn_bias=False, use_image_feature=True, disable_entangle=False, use_ofasys=False, vit_type='vit_base', vit_drop_path_rate=0.0, use_gamma_feature=False, gamma=1.0, exclude_mlp=True, temperature_init_value=None, remove_decoder_type_embedding=False, mlp_dim=512, required_seq_len_multiple=2, encoder_pos_conv_depth=5, encoder_conv_pos=95, encoder_conv_pos_groups=16, encoder_max_positions=100000, phone_vocab_size=141, audio_mask_prob=0.65, audio_mask_selection='static', audio_mask_other=0, audio_mask_length=10, audio_no_mask_overlap=False, audio_mask_min_space=1, audio_mask_channel_prob=0.0, audio_mask_channel_before=False, audio_mask_channel_selection='static', audio_mask_channel_other=0, audio_mask_channel_length=10, audio_no_mask_channel_overlap=False, audio_mask_channel_min_space=1, encoder_dropout_input=0.0, encoder_dropout_features=0.0, phone_dict_size=124, **kwargs): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.encoder_normalize_before = encoder_normalize_before self.decoder_normalize_before = decoder_normalize_before self.normformer = normformer self.encoder_drop_path_rate = encoder_drop_path_rate self.decoder_drop_path_rate = decoder_drop_path_rate self.layernorm_embedding = layernorm_embedding self.patch_layernorm_embedding = patch_layernorm_embedding self.resnet_type = resnet_type self.resnet_model_path = resnet_model_path self.resnet_drop_path_rate = resnet_drop_path_rate self.token_bucket_size = token_bucket_size self.image_bucket_size = image_bucket_size self.add_type_embedding = add_type_embedding self.share_decoder_input_output_embed = share_decoder_input_output_embed self.attn_scale_factor = attn_scale_factor self.code_layernorm_embedding = code_layernorm_embedding self.code_image_size = code_image_size self.entangle_position_embedding = entangle_position_embedding self.interpolate_position = interpolate_position self.orig_patch_image_size = orig_patch_image_size self.share_attn_bias = share_attn_bias self.use_image_feature = use_image_feature self.disable_entangle = disable_entangle self.use_ofasys = use_ofasys self.vit_type = vit_type self.vit_drop_path_rate = vit_drop_path_rate self.use_gamma_feature = use_gamma_feature # add some new features from ofa self.use_gamma_feature = use_gamma_feature self.gamma = gamma self.exclude_mlp = exclude_mlp self.temperature_init_value = temperature_init_value self.remove_decoder_type_embedding = remove_decoder_type_embedding self.mlp_dim = mlp_dim # FP16 optimization self.required_seq_len_multiple = required_seq_len_multiple # encoder_pos_conv self.encoder_pos_conv_depth = encoder_pos_conv_depth self.encoder_conv_pos = encoder_conv_pos self.encoder_conv_pos_groups = encoder_conv_pos_groups self.encoder_max_positions = encoder_max_positions # phone self.phone_vocab_size = phone_vocab_size # audio_mask self.audio_mask_prob = audio_mask_prob self.audio_mask_selection = audio_mask_selection self.audio_mask_other = audio_mask_other self.audio_mask_length = audio_mask_length self.audio_no_mask_overlap = audio_no_mask_overlap self.audio_mask_min_space = audio_mask_min_space self.audio_mask_channel_prob = audio_mask_channel_prob self.audio_mask_channel_before = audio_mask_channel_before self.audio_mask_channel_selection = audio_mask_channel_selection self.audio_mask_channel_other = audio_mask_channel_other self.audio_mask_channel_length = audio_mask_channel_length self.audio_no_mask_channel_overlap = audio_no_mask_channel_overlap self.audio_mask_channel_min_space = audio_mask_channel_min_space # audio encoder self.encoder_dropout_input = encoder_dropout_input self.encoder_dropout_features = encoder_dropout_features self.phone_dict_size = phone_dict_size super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) # ensure backward compatibility for BART CNN models if self.forced_bos_token_id is None and kwargs.get( 'force_bos_token_to_be_generated', False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. ' 'The config can simply be saved and uploaded again to be fixed.' )