Delete configuration_omnigenome.py

configuration_omnigenome.py

@@ -1,307 +0,0 @@
-# coding=utf-8
-# Copyright 2022 Meta 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.
-""" OmniGenome model configuration"""
-
-from dataclasses import asdict, dataclass
-from typing import Optional
-
-from transformers import PretrainedConfig
-
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-# TODO Update this
-OmniGenome_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "yangheng/OmniGenome-52M": "https://huggingface.co/yangheng/OmniGenome-52M/resolve/main/config.json",
-    "yangheng/OmniGenome-186M": "https://huggingface.co/yangheng/OmniGenome-186M/resolve/main/config.json",
-    # See all OmniGenome models at https://huggingface.co/models?filter=OmniGenome
-}
-
-
-class OmniGenomeConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`OmniGenomeModel`]. It is used to instantiate a OmniGenome 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 OmniGenome
-    [yangheng/OmniGenome-52M](https://huggingface.co/yangheng/OmniGenome-52M) 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*):
-            Vocabulary size of the OmniGenome model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`OmniGenomeModel`].
-        mask_token_id (`int`, *optional*):
-            The index of the mask token in the vocabulary. This must be included in the config because of the
-            "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
-        pad_token_id (`int`, *optional*):
-            The index of the padding token in the vocabulary. This must be included in the config because certain parts
-            of the OmniGenome code use this instead of the attention mask.
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 1026):
-            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).
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
-            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
-            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
-            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
-            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
-            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
-        is_decoder (`bool`, *optional*, defaults to `False`):
-            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        emb_layer_norm_before (`bool`, *optional*):
-            Whether to apply layer normalization after embeddings but before the main stem of the network.
-        token_dropout (`bool`, defaults to `False`):
-            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
-
-    Examples:
-
-    ```python
-    # >>> from transformers import OmniGenomeModel, OmniGenomeConfig
-    #
-    # >>> # Initializing a OmniGenome yangheng/OmniGenome-52M style configuration >>> configuration = OmniGenomeConfig()
-    #
-    # >>> # Initializing a model from the configuration >>> model = OmniGenomeModel(configuration)
-    #
-    # >>> # Accessing the model configuration >>> configuration = model.config
-    ```"""
-
-    model_type = "mprna"
-
-    def __init__(
-        self,
-        vocab_size=None,
-        mask_token_id=None,
-        pad_token_id=None,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=1026,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        position_embedding_type="absolute",
-        use_cache=True,
-        emb_layer_norm_before=None,
-        token_dropout=False,
-        is_folding_model=False,
-        OmniGenomefold_config=None,
-        vocab_list=None,
-        **kwargs,
-    ):
-        super().__init__(
-            pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
-        )
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
-        self.emb_layer_norm_before = emb_layer_norm_before
-        self.token_dropout = token_dropout
-        self.is_folding_model = is_folding_model
-        self.OmniGenomefold_config = None
-        self.vocab_list = None
-        if self.OmniGenomefold_config is not None and getattr(
-            self.OmniGenomefold_config, "use_OmniGenome_attn_map", False
-        ):
-            raise ValueError(
-                "The HuggingFace port of OmniGenomeFold does not support use_OmniGenome_attn_map at this time!"
-            )
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = super().to_dict()
-        return output
-
-
-@dataclass
-class TrunkConfig:
-    num_blocks: int = 48
-    sequence_state_dim: int = 1024
-    pairwise_state_dim: int = 128
-    sequence_head_width: int = 32
-    pairwise_head_width: int = 32
-    position_bins: int = 32
-    dropout: float = 0
-    layer_drop: float = 0
-    cpu_grad_checkpoint: bool = False
-    max_recycles: int = 4
-    chunk_size: Optional[int] = 128
-    structure_module: "StructureModuleConfig" = None
-
-    def __post_init__(self):
-        if self.structure_module is None:
-            self.structure_module = StructureModuleConfig()
-        elif isinstance(self.structure_module, dict):
-            self.structure_module = StructureModuleConfig(**self.structure_module)
-
-        if self.max_recycles <= 0:
-            raise ValueError(
-                f"`max_recycles` should be positive, got {self.max_recycles}."
-            )
-        if self.sequence_state_dim % self.sequence_state_dim != 0:
-            raise ValueError(
-                "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
-                f" {self.sequence_state_dim} and {self.sequence_state_dim}."
-            )
-        if self.pairwise_state_dim % self.pairwise_state_dim != 0:
-            raise ValueError(
-                "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
-                f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
-            )
-
-        sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
-        pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
-
-        if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
-            raise ValueError(
-                "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
-                f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
-            )
-        if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
-            raise ValueError(
-                "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
-                f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
-            )
-        if self.pairwise_state_dim % 2 != 0:
-            raise ValueError(
-                f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
-            )
-
-        if self.dropout >= 0.4:
-            raise ValueError(
-                f"`dropout` should not be greater than 0.4, got {self.dropout}."
-            )
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = asdict(self)
-        output["structure_module"] = self.structure_module.to_dict()
-        return output
-
-
-@dataclass
-class StructureModuleConfig:
-    """
-    Args:
-        sequence_dim:
-            Single representation channel dimension
-        pairwise_dim:
-            Pair representation channel dimension
-        ipa_dim:
-            IPA hidden channel dimension
-        resnet_dim:
-            Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
-        num_heads_ipa:
-            Number of IPA heads
-        num_qk_points:
-            Number of query/key points to generate during IPA
-        num_v_points:
-            Number of value points to generate during IPA
-        dropout_rate:
-            Dropout rate used throughout the layer
-        num_blocks:
-            Number of structure module blocks
-        num_transition_layers:
-            Number of layers in the single representation transition (Alg. 23 lines 8-9)
-        num_resnet_blocks:
-            Number of blocks in the angle resnet
-        num_angles:
-            Number of angles to generate in the angle resnet
-        trans_scale_factor:
-            Scale of single representation transition hidden dimension
-        epsilon:
-            Small number used in angle resnet normalization
-        inf:
-            Large number used for attention masking
-    """
-
-    sequence_dim: int = 384
-    pairwise_dim: int = 128
-    ipa_dim: int = 16
-    resnet_dim: int = 128
-    num_heads_ipa: int = 12
-    num_qk_points: int = 4
-    num_v_points: int = 8
-    dropout_rate: float = 0.1
-    num_blocks: int = 8
-    num_transition_layers: int = 1
-    num_resnet_blocks: int = 2
-    num_angles: int = 7
-    trans_scale_factor: int = 10
-    epsilon: float = 1e-8
-    inf: float = 1e5
-
-    def to_dict(self):
-        return asdict(self)
-
-
-def get_default_vocab_list():
-    return (
-        "<cls>",
-        "<pad>",
-        "<eos>",
-        "<unk>",
-        "A",
-        "C",
-        "G",
-        "T",
-        "U",
-        "N",
-        " ",
-        "<mask>",
-    )