362 lines
14 KiB
Python
362 lines
14 KiB
Python
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# coding=utf-8
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# Copyright 2022 Meta and 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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""" ESM model configuration"""
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from dataclasses import asdict, dataclass
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from typing import Optional
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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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# TODO Update this
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from ..deprecated._archive_maps import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class EsmConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
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according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the ESM
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[facebook/esm-1b](https://huggingface.co/facebook/esm-1b) 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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Args:
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vocab_size (`int`, *optional*):
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Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ESMModel`].
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mask_token_id (`int`, *optional*):
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The index of the mask token in the vocabulary. This must be included in the config because of the
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"mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
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pad_token_id (`int`, *optional*):
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The index of the padding token in the vocabulary. This must be included in the config because certain parts
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of the ESM code use this instead of the attention mask.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 1026):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
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For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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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). Only
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relevant if `config.is_decoder=True`.
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emb_layer_norm_before (`bool`, *optional*):
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Whether to apply layer normalization after embeddings but before the main stem of the network.
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token_dropout (`bool`, defaults to `False`):
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When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
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Examples:
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```python
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>>> from transformers import EsmModel, EsmConfig
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>>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
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>>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
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>>> # Accessing the model configuration >>> configuration = model.config
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```"""
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model_type = "esm"
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def __init__(
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self,
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vocab_size=None,
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mask_token_id=None,
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pad_token_id=None,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=1026,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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position_embedding_type="absolute",
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use_cache=True,
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emb_layer_norm_before=None,
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token_dropout=False,
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is_folding_model=False,
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esmfold_config=None,
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vocab_list=None,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.emb_layer_norm_before = emb_layer_norm_before
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self.token_dropout = token_dropout
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self.is_folding_model = is_folding_model
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if is_folding_model:
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if esmfold_config is None:
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logger.info("No esmfold_config supplied for folding model, using default values.")
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esmfold_config = EsmFoldConfig()
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elif isinstance(esmfold_config, dict):
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esmfold_config = EsmFoldConfig(**esmfold_config)
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self.esmfold_config = esmfold_config
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if vocab_list is None:
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logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!")
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self.vocab_list = get_default_vocab_list()
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else:
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self.vocab_list = vocab_list
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else:
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self.esmfold_config = None
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self.vocab_list = None
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if self.esmfold_config is not None and getattr(self.esmfold_config, "use_esm_attn_map", False):
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raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!")
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
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Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = super().to_dict()
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if isinstance(self.esmfold_config, EsmFoldConfig):
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output["esmfold_config"] = self.esmfold_config.to_dict()
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return output
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@dataclass
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class EsmFoldConfig:
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esm_type: str = None
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fp16_esm: bool = True
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use_esm_attn_map: bool = False
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esm_ablate_pairwise: bool = False
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esm_ablate_sequence: bool = False
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esm_input_dropout: float = 0
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embed_aa: bool = True
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bypass_lm: bool = False
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lddt_head_hid_dim: int = 128
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trunk: "TrunkConfig" = None
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def __post_init__(self):
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if self.trunk is None:
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self.trunk = TrunkConfig()
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elif isinstance(self.trunk, dict):
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self.trunk = TrunkConfig(**self.trunk)
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
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Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = asdict(self)
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output["trunk"] = self.trunk.to_dict()
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return output
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@dataclass
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class TrunkConfig:
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num_blocks: int = 48
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sequence_state_dim: int = 1024
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pairwise_state_dim: int = 128
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sequence_head_width: int = 32
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pairwise_head_width: int = 32
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position_bins: int = 32
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dropout: float = 0
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layer_drop: float = 0
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cpu_grad_checkpoint: bool = False
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max_recycles: int = 4
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chunk_size: Optional[int] = 128
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structure_module: "StructureModuleConfig" = None
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def __post_init__(self):
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if self.structure_module is None:
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self.structure_module = StructureModuleConfig()
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elif isinstance(self.structure_module, dict):
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self.structure_module = StructureModuleConfig(**self.structure_module)
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if self.max_recycles <= 0:
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raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}.")
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if self.sequence_state_dim % self.sequence_state_dim != 0:
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raise ValueError(
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"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
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f" {self.sequence_state_dim} and {self.sequence_state_dim}."
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)
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if self.pairwise_state_dim % self.pairwise_state_dim != 0:
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raise ValueError(
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"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
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f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
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)
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sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
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pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
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if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
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raise ValueError(
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"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
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f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
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)
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if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
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raise ValueError(
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"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
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f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
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)
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if self.pairwise_state_dim % 2 != 0:
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raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.")
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if self.dropout >= 0.4:
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raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}.")
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
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Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = asdict(self)
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output["structure_module"] = self.structure_module.to_dict()
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return output
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@dataclass
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class StructureModuleConfig:
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"""
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Args:
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sequence_dim:
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Single representation channel dimension
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pairwise_dim:
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Pair representation channel dimension
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ipa_dim:
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IPA hidden channel dimension
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resnet_dim:
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Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
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num_heads_ipa:
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Number of IPA heads
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num_qk_points:
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Number of query/key points to generate during IPA
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num_v_points:
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Number of value points to generate during IPA
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dropout_rate:
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Dropout rate used throughout the layer
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num_blocks:
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Number of structure module blocks
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num_transition_layers:
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Number of layers in the single representation transition (Alg. 23 lines 8-9)
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num_resnet_blocks:
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Number of blocks in the angle resnet
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num_angles:
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Number of angles to generate in the angle resnet
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trans_scale_factor:
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Scale of single representation transition hidden dimension
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epsilon:
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Small number used in angle resnet normalization
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inf:
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Large number used for attention masking
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"""
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sequence_dim: int = 384
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pairwise_dim: int = 128
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ipa_dim: int = 16
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resnet_dim: int = 128
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num_heads_ipa: int = 12
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num_qk_points: int = 4
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num_v_points: int = 8
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dropout_rate: float = 0.1
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num_blocks: int = 8
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num_transition_layers: int = 1
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num_resnet_blocks: int = 2
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num_angles: int = 7
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trans_scale_factor: int = 10
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epsilon: float = 1e-8
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inf: float = 1e5
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def to_dict(self):
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return asdict(self)
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def get_default_vocab_list():
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return (
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"<cls>",
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"<pad>",
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"<eos>",
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"<unk>",
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"L",
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"A",
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"G",
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"V",
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"S",
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"E",
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"R",
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"T",
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"I",
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"D",
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"P",
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"K",
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"Q",
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"N",
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"F",
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"Y",
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"M",
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"H",
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"W",
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"C",
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|
|
"X",
|
||
|
|
"B",
|
||
|
|
"U",
|
||
|
|
"Z",
|
||
|
|
"O",
|
||
|
|
".",
|
||
|
|
"-",
|
||
|
|
"<null_1>",
|
||
|
|
"<mask>",
|
||
|
|
)
|