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test_model.py

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+import torch
+import torch.nn as nn
+import os
+import networkx as nx
+from torch.nn import functional as F
+from torch.utils.data import Dataset as TorchDataset
+from torch.utils.data import DataLoader as DataLoader
+from typing import Optional, Tuple, Union, Callable, List, Dict, Any
+from einops import rearrange
+from dataclasses import dataclass
+from transformers import PreTrainedModel, PretrainedConfig, EsmTokenizer, PreTrainedTokenizerBase
+from transformers.modeling_outputs import (
+    ModelOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    SequenceClassifierOutput,
+    TokenClassifierOutput
+)
+from transformers.models.esm.modeling_esm import (
+    EsmIntermediate,
+    EsmOutput,
+    EsmPooler,
+    EsmLMHead,
+    EsmSelfOutput,
+    EsmClassificationHead,
+)
+from tqdm.auto import tqdm
+
+from pooler import Pooler
+
+
+@dataclass
+class EsmMaskedLMOutput(ModelOutput):
+    loss: Optional[torch.Tensor] = None
+    logits: Optional[torch.Tensor] = None
+    last_hidden_state: Optional[torch.Tensor] = None
+    hidden_states: Optional[Tuple[torch.Tensor, ...]] = None
+    attentions: Optional[Tuple[torch.Tensor, ...]] = None
+
+
+class FastEsmConfig(PretrainedConfig):
+    model_type = "fast_esm"
+    def __init__(
+        self,
+        vocab_size: int = None,
+        mask_token_id: int = None,
+        pad_token_id: int = None,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 1026,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        position_embedding_type: str = "absolute",
+        emb_layer_norm_before: bool = None,
+        token_dropout: bool = 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.emb_layer_norm_before = emb_layer_norm_before
+        self.tie_word_embeddings = False
+        self.token_dropout = token_dropout
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionar y of all the attributes that make up this configuration instance,
+        """
+        output = super().to_dict()
+        return output
+
+
+def rotate_half(x: torch.Tensor) -> torch.Tensor:
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+def symmetrize(x: torch.Tensor) -> torch.Tensor:
+    "Make layer symmetric in final two dimensions, used for contact prediction."
+    return x + x.transpose(-1, -2)
+
+
+def average_product_correct(x: torch.Tensor) -> torch.Tensor:
+    "Perform average product correct, used for contact prediction."
+    a1 = x.sum(-1, keepdims=True)
+    a2 = x.sum(-2, keepdims=True)
+    a12 = x.sum((-1, -2), keepdims=True)
+
+    avg = a1 * a2
+    avg.div_(a12)  # in-place to reduce memory
+    normalized = x - avg
+    return normalized
+
+
+class EsmContactPredictionHead(nn.Module):
+    """Performs symmetrization, apc, and computes a logistic regression on the output features"""
+
+    def __init__(
+        self,
+        in_features: int,
+        bias: bool = True,
+        eos_idx: int = 2,
+    ):
+        super().__init__()
+        self.in_features = in_features
+        self.eos_idx = eos_idx
+        self.regression = nn.Linear(in_features, 1, bias=bias)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, input_ids: torch.Tensor, attentions: torch.Tensor) -> torch.Tensor:
+        # remove eos token attentions
+        eos_mask = input_ids.ne(self.eos_idx).to(attentions)
+        eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
+        attentions = attentions * eos_mask[:, None, None, :, :]
+        attentions = attentions[..., :-1, :-1]
+        # remove cls token attentions
+        attentions = attentions[..., 1:, 1:]
+        batch_size, layers, heads, seqlen, _ = attentions.size()
+        attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
+
+        # features: batch x channels x tokens x tokens (symmetric)
+        attentions = attentions.to(
+            self.regression.weight.device
+        )  # attentions always float32, may need to convert to float16
+        attentions = average_product_correct(symmetrize(attentions))
+        attentions = attentions.permute(0, 2, 3, 1)
+        return self.activation(self.regression(attentions).squeeze(3))
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Rotary position embeddings based on those in
+    [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
+    matrices which depend on their relative positions.
+    """
+
+    def __init__(self, dim: int):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
+        inv_freq = inv_freq
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_tables(self, x: torch.Tensor, seq_dimension: int = 2) -> Tuple[torch.Tensor, torch.Tensor]:
+        seq_len = x.shape[seq_dimension]
+
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+            self._seq_len_cached = seq_len
+            t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+            self._cos_cached = emb.cos()[None, None, :, :].to(x.dtype)
+            self._sin_cached = emb.sin()[None, None, :, :].to(x.dtype)
+
+        return self._cos_cached, self._sin_cached
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+class EsmEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        if config.emb_layer_norm_before:
+            self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        else:
+            self.layer_norm = None
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+        )
+        self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        past_key_values_length: Optional[int] = 0,
+    ):
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+
+        if self.token_dropout:
+            embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0)
+            mask_ratio_train = 0.15 * 0.8
+            src_lengths = attention_mask.sum(-1)
+            mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
+            embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
+                embeddings.dtype
+            )
+
+        if self.layer_norm is not None:
+            embeddings = self.layer_norm(embeddings)
+        if attention_mask is not None:
+            embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
+        return embeddings
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        """
+        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+        Args:
+            inputs_embeds: torch.Tensor
+
+        Returns: torch.Tensor
+        """
+        input_shape = inputs_embeds.size()[:-1]
+        sequence_length = input_shape[1]
+
+        position_ids = torch.arange(
+            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+        )
+        return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class EsmSelfAttention(nn.Module):
+    def __init__(self, config, position_embedding_type: Optional[str] = None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+        self.scale = self.attention_head_size**-0.5
+
+        self.dropout_prob = config.attention_probs_dropout_prob
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.rotary_embeddings = None
+        if self.position_embedding_type == "rotary":
+            self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        return rearrange(x, 'b s (h d) -> b h s d', h=self.num_attention_heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Forward pass for self attention.
+        
+        Args:
+            hidden_states: Input tensor
+            attention_mask: Optional attention mask
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            Output tensor and optionally attention weights
+        """
+        query_layer = self.transpose_for_scores(self.query(hidden_states)) * self.scale
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        if self.position_embedding_type == "rotary":
+            query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+        if output_attentions:
+            # Manual attention computation - apply scaling here
+            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) * self.scale
+            if attention_mask is not None:
+                attention_scores = attention_scores + attention_mask
+            attention_probs = F.softmax(attention_scores, dim=-1)
+            if self.dropout_prob > 0:
+                attention_probs = F.dropout(attention_probs, p=self.dropout_prob, training=self.training)
+            context_layer = torch.matmul(attention_probs, value_layer)
+            context_layer = rearrange(context_layer, 'b h s d -> b s (h d)')
+            return context_layer, attention_probs
+        else:
+            context_layer = F.scaled_dot_product_attention(
+                query_layer,
+                key_layer,
+                value_layer,
+                attn_mask=attention_mask,
+                dropout_p=self.dropout_prob,
+                scale=1.0
+            )
+            context_layer = rearrange(context_layer, 'b h s d -> b s (h d)')
+            return context_layer
+        
+
+class EsmAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = EsmSelfAttention(config)
+        self.output = EsmSelfOutput(config)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Forward pass for attention layer.
+        
+        Args:
+            hidden_states: Input tensor
+            attention_mask: Optional attention mask
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            Output tensor and optionally attention weights
+        """
+        hidden_states_ln = self.LayerNorm(hidden_states)
+        self_outputs = self.self(
+            hidden_states_ln,
+            attention_mask,
+            output_attentions,
+        )
+        if output_attentions:
+            attention_output, attention_weights = self_outputs
+            attention_output = self.output(attention_output, hidden_states)
+            return attention_output, attention_weights
+        else:
+            attention_output = self_outputs
+            return self.output(attention_output, hidden_states)
+
+
+class EsmLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = EsmAttention(config)
+        self.intermediate = EsmIntermediate(config)
+        self.output = EsmOutput(config)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Forward pass for transformer layer.
+        
+        Args:
+            hidden_states: Input tensor
+            attention_mask: Optional attention mask
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            Output tensor and optionally attention weights
+        """
+        attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            output_attentions,
+        )
+        if output_attentions:
+            attention_output, attention_weights = attention_outputs
+        else:
+            attention_output = attention_outputs
+            attention_weights = None
+
+        layer_output = self.feed_forward_chunk(attention_output)
+        
+        if output_attentions:
+            return layer_output, attention_weights
+        return layer_output
+
+    def feed_forward_chunk(self, attention_output):
+        attention_output_ln = self.LayerNorm(attention_output)
+        intermediate_output = self.intermediate(attention_output_ln)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class EsmEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([EsmLayer(config) for _ in range(config.num_hidden_layers)])
+        self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ) -> BaseModelOutputWithPastAndCrossAttentions:
+        """Forward pass for transformer encoder.
+        
+        Args:
+            hidden_states: Input tensor
+            attention_mask: Optional attention mask
+            output_hidden_states: Whether to return all hidden states
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            BaseModelOutputWithPastAndCrossAttentions containing model outputs
+        """
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        for layer_module in self.layer:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+
+            if output_attentions:
+                hidden_states, attention_weights = layer_outputs
+                all_attentions = all_attentions + (attention_weights,)
+            else:
+                hidden_states = layer_outputs
+
+        if self.emb_layer_norm_after:
+            hidden_states = self.emb_layer_norm_after(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+class ProteinDataset(TorchDataset):
+    """Simple dataset for protein sequences."""
+    def __init__(self, sequences: list[str]):
+        self.sequences = sequences
+
+    def __len__(self) -> int:
+        return len(self.sequences)
+
+    def __getitem__(self, idx: int) -> str:
+        return self.sequences[idx]
+
+
+def build_collator(tokenizer) -> Callable[[list[str]], tuple[torch.Tensor, torch.Tensor]]:
+    def _collate_fn(sequences: list[str]) -> tuple[torch.Tensor, torch.Tensor]:
+        """Collate function for batching sequences."""
+        return tokenizer(sequences, return_tensors="pt", padding='longest')
+    return _collate_fn
+
+
+class EmbeddingMixin:
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        raise NotImplementedError
+
+    @property
+    def device(self) -> torch.device:
+        """Get the device of the model."""
+        return next(self.parameters()).device
+
+    def _read_sequences_from_db(self, db_path: str) -> set[str]:
+        """Read sequences from SQLite database."""
+        import sqlite3
+        sequences = []
+        with sqlite3.connect(db_path) as conn:
+            c = conn.cursor()
+            c.execute("SELECT sequence FROM embeddings")
+            while True:
+                row = c.fetchone()
+                if row is None:
+                    break
+                sequences.append(row[0])
+        return set(sequences)
+
+    def embed_dataset(
+        self,
+        sequences: List[str],
+        tokenizer: PreTrainedTokenizerBase,
+        batch_size: int = 2,
+        max_len: int = 512,
+        truncate: bool = True,
+        full_embeddings: bool = False,
+        embed_dtype: torch.dtype = torch.float32,
+        pooling_types: List[str] = ['mean'],
+        num_workers: int = 0,
+        sql: bool = False,
+        save: bool = True,
+        sql_db_path: str = 'embeddings.db',
+        save_path: str = 'embeddings.pth',
+    ) -> Optional[dict[str, torch.Tensor]]:
+        """Embed a dataset of protein sequences.
+        
+        Args:
+            sequences: List of protein sequences
+            batch_size: Batch size for processing
+            max_len: Maximum sequence length
+            full_embeddings: Whether to return full residue-wise (True) embeddings or pooled (False)
+            pooling_type: Type of pooling ('mean' or 'cls')
+            num_workers: Number of workers for data loading, 0 for the main process
+            sql: Whether to store embeddings in SQLite database - will be stored in float32
+            sql_db_path: Path to SQLite database
+            
+        Returns:
+            Dictionary mapping sequences to embeddings, or None if sql=True
+
+        Note:
+            - If sql=True, embeddings can only be stored in float32
+            - sql is ideal if you need to stream a very large dataset for training in real-time
+            - save=True is ideal if you can store the entire embedding dictionary in RAM
+            - sql will be used if it is True and save is True or False
+            - If your sql database or .pth file is already present, they will be scanned first for already embedded sequences
+            - Sequences will be truncated to max_len and sorted by length in descending order for faster processing
+
+        Example:
+            >>> embedder = EmbeddingMixin()
+            >>> embedding_dict = embedder.embed_dataset(
+                sequences=[
+                    'MALWMRLLPLLALLALWGPDPAAA', ... # list of protein sequences
+                ],
+                batch_size=2, # adjust for your GPU memory
+                max_len=512, # adjust for your needs
+                full_embeddings=False, # if True, no pooling is performed
+                embed_dtype=torch.float32, # cast to what dtype you want
+                pooling_type=['mean', 'cls'], # more than one pooling type will be concatenated together
+                num_workers=0, # if you have many cpu cores, we find that num_workers = 4 is fast for large datasets
+                sql=False, # if True, embeddings will be stored in SQLite database
+                sql_db_path='embeddings.db',
+                save=True, # if True, embeddings will be saved as a .pth file
+                save_path='embeddings.pth',
+            )
+            >>> # embedding_dict is a dictionary mapping sequences to their embeddings as tensors for .pth or numpy arrays for sql
+        """
+        sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences]))
+        sequences = sorted(sequences, key=len, reverse=True)
+        hidden_size = self.config.hidden_size
+        collate_fn = build_collator(tokenizer)
+        device = self.device
+        pooler = Pooler(pooling_types) if not full_embeddings else None
+
+        def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+            if full_embeddings or residue_embeddings.ndim == 2: # if already pooled or want residue-wise embeddings
+                return residue_embeddings
+            else:
+                return pooler(residue_embeddings, attention_mask)
+
+        if sql:
+            import sqlite3
+            conn = sqlite3.connect(sql_db_path)
+            c = conn.cursor()
+            c.execute('CREATE TABLE IF NOT EXISTS embeddings (sequence text PRIMARY KEY, embedding blob)')
+            already_embedded = self._read_sequences_from_db(sql_db_path)
+            to_embed = [seq for seq in sequences if seq not in already_embedded]
+            print(f"Found {len(already_embedded)} already embedded sequences in {sql_db_path}")
+            print(f"Embedding {len(to_embed)} new sequences")
+            if len(to_embed) > 0:
+                dataset = ProteinDataset(to_embed)
+                dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False)
+                with torch.no_grad():
+                    for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
+                        seqs = to_embed[i * batch_size:(i + 1) * batch_size]
+                        input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
+                        residue_embeddings = self._embed(input_ids, attention_mask).float() # sql requires float32
+                        embeddings = get_embeddings(residue_embeddings, attention_mask)
+                        for seq, emb, mask in zip(seqs, embeddings, attention_mask):
+                            if full_embeddings:
+                                emb = emb[mask.bool()].reshape(-1, hidden_size)
+                            c.execute("INSERT OR REPLACE INTO embeddings VALUES (?, ?)", 
+                                    (seq, emb.cpu().numpy().tobytes()))
+                        
+                        if (i + 1) % 100 == 0:
+                            conn.commit()
+            
+                conn.commit()
+            conn.close()
+            return None
+
+        embeddings_dict = {}
+        if os.path.exists(save_path):
+            embeddings_dict = torch.load(save_path, map_location='cpu', weights_only=True)
+            to_embed = [seq for seq in sequences if seq not in embeddings_dict]
+            print(f"Found {len(embeddings_dict)} already embedded sequences in {save_path}")
+            print(f"Embedding {len(to_embed)} new sequences")
+        else:
+            to_embed = sequences
+            print(f"Embedding {len(to_embed)} new sequences")
+
+        if len(to_embed) > 0:
+            dataset = ProteinDataset(to_embed)
+            dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False)
+            with torch.no_grad():
+                for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
+                    seqs = to_embed[i * batch_size:(i + 1) * batch_size]
+                    input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
+                    residue_embeddings = self._embed(input_ids, attention_mask)
+                    embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype)
+                    for seq, emb, mask in zip(seqs, embeddings, attention_mask):
+                        if full_embeddings:
+                            emb = emb[mask.bool()].reshape(-1, hidden_size)
+                        embeddings_dict[seq] = emb.cpu()
+
+        if save:
+            torch.save(embeddings_dict, save_path)
+
+        return embeddings_dict
+
+
+class FastEsmPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+    config_class = FastEsmConfig
+    base_model_prefix = "fastesm"
+    supports_gradient_checkpointing = True
+    tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def get_input_embeddings(self) -> nn.Module:
+        try:
+            return self.embeddings.word_embeddings
+        except AttributeError:
+            return self.esm.embeddings.word_embeddings
+
+
+class FAST_ESM_ENCODER(FastEsmPreTrainedModel, EmbeddingMixin):
+    def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs):
+        FastEsmPreTrainedModel.__init__(self, config, **kwargs)
+        self.config = config
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = EsmEncoder(config)
+        self.contact_head = EsmContactPredictionHead(
+            in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
+        )
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        token_embedding_output = self.embeddings(input_ids, attention_mask=attention_mask)
+        batch_size, seq_length = input_ids.shape
+        if attention_mask is not None:
+            extended_attention_mask = attention_mask[:, None, None, :].expand(
+                batch_size, 1, seq_length, seq_length
+            ).bool()
+        else:
+            extended_attention_mask = None
+        encoder_outputs = self.encoder(
+            token_embedding_output,
+            attention_mask=extended_attention_mask,
+            output_hidden_states=False,
+            output_attentions=False,
+        )
+        return encoder_outputs.last_hidden_state
+
+    def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        attns = self(input_ids, attention_mask=attention_mask, output_attentions=True).attentions
+        attns = torch.stack(attns, dim=1)
+        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
+        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
+        return self.contact_head(input_ids, attns)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None, # to play nice with HF adjacent packages
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        """Forward pass for base model.
+        
+        Args:
+            input_ids: Input token IDs
+            attention_mask: Optional attention mask
+            position_ids: Optional position IDs
+            inputs_embeds: Optional input embeddings
+            output_hidden_states: Whether to return all hidden states
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            Model outputs including hidden states and optionally attention weights
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        token_embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+
+        if attention_mask is not None:
+            extended_attention_mask = attention_mask[:, None, None, :].expand(
+                batch_size, 1, seq_length, seq_length
+            ).bool()
+        else:
+            extended_attention_mask = None
+
+        encoder_outputs = self.encoder(
+            token_embedding_output,
+            attention_mask=extended_attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        sequence_output = encoder_outputs.last_hidden_state
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class FastEsmModel(FastEsmPreTrainedModel, EmbeddingMixin):
+    def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs):
+        FastEsmPreTrainedModel.__init__(self, config, **kwargs)
+        self.config = config
+        self.esm = FAST_ESM_ENCODER(config)
+        self.pooler = EsmPooler(config) if add_pooling_layer else None
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        return self.esm.predict_contacts(input_ids, attention_mask=attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None, # to play nice with HF adjacent packages
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        """Forward pass for base model.
+        
+        Args:
+            input_ids: Input token IDs
+            attention_mask: Optional attention mask
+            position_ids: Optional position IDs
+            inputs_embeds: Optional input embeddings
+            output_hidden_states: Whether to return all hidden states
+            output_attentions: Whether to return attention weights
+            
+        Returns:
+            Model outputs including hidden states and optionally attention weights
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        sequence_output = outputs.last_hidden_state
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FastEsmForMaskedLM(FastEsmPreTrainedModel, EmbeddingMixin):
+    def __init__(self, config, **kwargs):
+        FastEsmPreTrainedModel.__init__(self, config, **kwargs)
+        self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False)
+        self.lm_head = EsmLMHead(config)
+        self.loss_fct = nn.CrossEntropyLoss()
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        return self.esm.predict_contacts(input_ids, attention_mask=attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None, # to play nice with HF adjacent packages
+        **kwargs,
+    ) -> Union[Tuple, EsmMaskedLMOutput]:
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        sequence_output = outputs.last_hidden_state
+        prediction_scores = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(prediction_scores.device)
+            loss = self.loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        return EsmMaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            last_hidden_state=sequence_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FastEsmForSequenceClassification(FastEsmPreTrainedModel, EmbeddingMixin):
+    def __init__(self, config, **kwargs):
+        FastEsmPreTrainedModel.__init__(self, config, **kwargs)
+        self.num_labels = config.num_labels
+        self.config = config
+        self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False)
+        self.classifier = EsmClassificationHead(config)
+        self.mse = nn.MSELoss()
+        self.ce = nn.CrossEntropyLoss()
+        self.bce = nn.BCEWithLogitsLoss()
+        self.init_weights()
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        return self.esm.predict_contacts(input_ids, attention_mask=attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs.last_hidden_state
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                if self.num_labels == 1:
+                    loss = self.mse(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = self.mse(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss = self.bce(logits, labels)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FastEsmForTokenClassification(FastEsmPreTrainedModel, EmbeddingMixin):
+    def __init__(self, config, **kwargs):
+        FastEsmPreTrainedModel.__init__(self, config, **kwargs)
+        self.num_labels = config.num_labels
+        self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        self.loss_fct = nn.CrossEntropyLoss()
+        self.init_weights()
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        return self.esm.predict_contacts(input_ids, attention_mask=attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs.last_hidden_state
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+if __name__ == "__main__":
+    """
+    Test the hidden state differences between the FastEsmModel and the HF EsmModel.
+    In full precision, the differences are very very small, but nonzero due to floating point issues with F.scaled_dot_product_attention.
+    In Pytorch 2.5+ (and linux kernel), this implementation is very fast and uses less memory than the HF implementation.
+    """
+    import random
+    from transformers import EsmForMaskedLM as TransformersEsmModel, EsmTokenizer
+
+    model_paths = [
+        "facebook/esm2_t6_8M_UR50D",
+        "facebook/esm2_t12_35M_UR50D",
+        #"facebook/esm2_t30_150M_UR50D",
+        #"facebook/esm2_t33_650M_UR50D",
+    ]
+    canonical_amino_acids = "ACDEFGHIKLMNPQRSTVWY"
+    length = 64
+    seq_count = 100
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    tolerances = [1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8]
+
+    def generate_random_sequence(length: int) -> str:
+        return 'M' + "".join(random.choices(canonical_amino_acids, k=length))
+
+    print("Percentage of hidden states that are within the tolerance:")
+    for model_path in model_paths:
+        print(f"Testing {model_path}...")
+        tokenizer = EsmTokenizer.from_pretrained(model_path)
+        config = FastEsmConfig.from_pretrained(model_path)
+        fast_model = FastEsmForMaskedLM(config).from_pretrained(model_path).to(device)
+        print('fast model')
+        print(fast_model)
+        model = TransformersEsmModel.from_pretrained(model_path, token_dropout=False).to(device)
+        print('transformers model')
+        print(model)
+
+        counts = [0] * len(tolerances)
+        for _ in range(seq_count):
+            example_seq = generate_random_sequence(length)
+            fast_tokens = tokenizer(example_seq, return_tensors="pt").input_ids.to(device)
+            fast_output = fast_model(fast_tokens, output_hidden_states=True).hidden_states[-1].detach().cpu()
+
+            model_tokens = tokenizer(example_seq, return_tensors="pt").input_ids.to(device)
+            model_output = model(model_tokens, output_hidden_states=True).hidden_states[-1].detach().cpu()
+
+            for i, atol in enumerate(tolerances):
+                if torch.allclose(fast_output, model_output, atol=atol):
+                    counts[i] += 1
+
+        print(f"{model_path}:")
+        for i, atol in enumerate(tolerances):
+            print(f"    tolerance={atol}: {counts[i] / seq_count * 100}%")
+    
+        model.cpu()
+        fast_model.cpu()
+        del model
+        del fast_model
+        torch.cuda.empty_cache()