Upload EsmForMaskedLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "/shared/pretrained_models/overlap_multi_species_sh_gc/checkpoint-12000/",
+  "add_bias_fnn": false,
+  "architectures": [
+    "EsmForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "esm_config.EsmConfig",
+    "AutoModelForMaskedLM": "InstaDeepAI/nucleotide-transformer-v2-50m-multi-species--modeling_esm.EsmForMaskedLM",
+    "AutoModelForSequenceClassification": "InstaDeepAI/nucleotide-transformer-v2-50m-multi-species--modeling_esm.EsmForSequenceClassification",
+    "AutoModelForTokenClassification": "InstaDeepAI/nucleotide-transformer-v2-50m-multi-species--modeling_esm.EsmForTokenClassification"
+  },
+  "emb_layer_norm_before": false,
+  "esmfold_config": null,
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "is_folding_model": false,
+  "layer_norm_eps": 1e-12,
+  "mask_token_id": 2,
+  "max_position_embeddings": 2050,
+  "model_type": "esm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "rotary",
+  "tie_word_embeddings": false,
+  "token_dropout": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "use_cache": false,
+  "vocab_list": null,
+  "vocab_size": 4107
+}

esm_config.py

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+# 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.
+""" ESM model configuration"""
+
+from dataclasses import asdict, dataclass
+from typing import Optional
+
+from transformers import PretrainedConfig, logging
+
+logger = logging.get_logger(__name__)
+
+# TODO Update this
+ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
+    # See all ESM models at https://huggingface.co/models?filter=esm
+}
+
+
+class EsmConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM 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 ESM
+    [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) 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 ESM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ESMModel`].
+        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 ESM 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 EsmModel, EsmConfig
+
+    >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
+
+    >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
+
+    >>> # Accessing the model configuration >>> configuration = model.config
+    ```"""
+    model_type = "esm"
+
+    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,
+        esmfold_config=None,
+        vocab_list=None,
+        add_bias_fnn=True,
+        **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
+        # Arguments needed for Dalmatian
+        self.add_bias_fnn = add_bias_fnn
+        if is_folding_model:
+            if esmfold_config is None:
+                logger.info(
+                    "No esmfold_config supplied for folding model, using default values."
+                )
+                esmfold_config = EsmFoldConfig()
+            elif isinstance(esmfold_config, dict):
+                esmfold_config = EsmFoldConfig(**esmfold_config)
+            self.esmfold_config = esmfold_config
+            if vocab_list is None:
+                logger.warning(
+                    "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
+                )
+                self.vocab_list = get_default_vocab_list()
+            else:
+                self.vocab_list = vocab_list
+        else:
+            self.esmfold_config = None
+            self.vocab_list = None
+        if self.esmfold_config is not None and getattr(
+            self.esmfold_config, "use_esm_attn_map", False
+        ):
+            raise ValueError(
+                "The HuggingFace port of ESMFold does not support use_esm_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()
+        if isinstance(self.esmfold_config, EsmFoldConfig):
+            output["esmfold_config"] = self.esmfold_config.to_dict()
+        return output
+
+
+@dataclass
+class EsmFoldConfig:
+    esm_type: str = None
+    fp16_esm: bool = True
+    use_esm_attn_map: bool = False
+    esm_ablate_pairwise: bool = False
+    esm_ablate_sequence: bool = False
+    esm_input_dropout: float = 0
+
+    embed_aa: bool = True
+    bypass_lm: bool = False
+
+    lddt_head_hid_dim: int = 128
+    trunk: "TrunkConfig" = None
+
+    def __post_init__(self):
+        if self.trunk is None:
+            self.trunk = TrunkConfig()
+        elif isinstance(self.trunk, dict):
+            self.trunk = TrunkConfig(**self.trunk)
+
+    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["trunk"] = self.trunk.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>",
+        "L",
+        "A",
+        "G",
+        "V",
+        "S",
+        "E",
+        "R",
+        "T",
+        "I",
+        "D",
+        "P",
+        "K",
+        "Q",
+        "N",
+        "F",
+        "Y",
+        "M",
+        "H",
+        "W",
+        "C",
+        "X",
+        "B",
+        "U",
+        "Z",
+        "O",
+        ".",
+        "-",
+        "<null_1>",
+        "<mask>",
+    )

model.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:1cfd963e726f5fe2d272e93f6b52047acb1fb62946f27a521a3acc9f656eb2a9
+size 223642688