129 lines
5.9 KiB
Python
129 lines
5.9 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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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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""" Pop2Piano model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import POP2PIANO_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class Pop2PianoConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Pop2PianoForConditionalGeneration`]. It is used
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to instantiate a Pop2PianoForConditionalGeneration model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the
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Pop2Piano [sweetcocoa/pop2piano](https://huggingface.co/sweetcocoa/pop2piano) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Arguments:
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vocab_size (`int`, *optional*, defaults to 2400):
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Vocabulary size of the `Pop2PianoForConditionalGeneration` model. Defines the number of different tokens
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that can be represented by the `inputs_ids` passed when calling [`Pop2PianoForConditionalGeneration`].
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composer_vocab_size (`int`, *optional*, defaults to 21):
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Denotes the number of composers.
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d_model (`int`, *optional*, defaults to 512):
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Size of the encoder layers and the pooler layer.
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d_kv (`int`, *optional*, defaults to 64):
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Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will
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be defined as `num_heads * d_kv`.
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d_ff (`int`, *optional*, defaults to 2048):
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Size of the intermediate feed forward layer in each `Pop2PianoBlock`.
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num_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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num_decoder_layers (`int`, *optional*):
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Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
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num_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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relative_attention_num_buckets (`int`, *optional*, defaults to 32):
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The number of buckets to use for each attention layer.
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relative_attention_max_distance (`int`, *optional*, defaults to 128):
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The maximum distance of the longer sequences for the bucket separation.
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dropout_rate (`float`, *optional*, defaults to 0.1):
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The ratio for all dropout layers.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
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testing).
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feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`):
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Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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dense_act_fn (`string`, *optional*, defaults to `"relu"`):
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Type of Activation Function to be used in `Pop2PianoDenseActDense` and in `Pop2PianoDenseGatedActDense`.
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"""
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model_type = "pop2piano"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=2400,
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composer_vocab_size=21,
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d_model=512,
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d_kv=64,
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d_ff=2048,
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num_layers=6,
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num_decoder_layers=None,
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num_heads=8,
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relative_attention_num_buckets=32,
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relative_attention_max_distance=128,
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dropout_rate=0.1,
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layer_norm_epsilon=1e-6,
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initializer_factor=1.0,
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feed_forward_proj="gated-gelu", # noqa
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is_encoder_decoder=True,
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use_cache=True,
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pad_token_id=0,
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eos_token_id=1,
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dense_act_fn="relu",
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.composer_vocab_size = composer_vocab_size
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self.d_model = d_model
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self.d_kv = d_kv
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self.d_ff = d_ff
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self.num_layers = num_layers
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self.num_decoder_layers = num_decoder_layers if num_decoder_layers is not None else self.num_layers
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self.num_heads = num_heads
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.relative_attention_max_distance = relative_attention_max_distance
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self.dropout_rate = dropout_rate
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_factor = initializer_factor
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self.feed_forward_proj = feed_forward_proj
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self.use_cache = use_cache
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self.dense_act_fn = dense_act_fn
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self.is_gated_act = self.feed_forward_proj.split("-")[0] == "gated"
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self.hidden_size = self.d_model
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self.num_attention_heads = num_heads
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self.num_hidden_layers = num_layers
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super().__init__(
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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**kwargs,
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)
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