Upload modeling_fastesm.py with huggingface_hub

modeling_fastesm.py

@@ -1,998 +1,998 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.data import Dataset, DataLoader
-from typing import Optional, Tuple, Union
-from einops import rearrange
-from transformers import PreTrainedModel, PretrainedConfig, EsmTokenizer
-from transformers.modeling_outputs import (
-    MaskedLMOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    SequenceClassifierOutput,
-    TokenClassifierOutput
-)
-from transformers.models.esm.modeling_esm import (
-    EsmIntermediate,
-    EsmOutput,
-    EsmPooler,
-    EsmLMHead,
-    EsmSelfOutput,
-    EsmClassificationHead,
-)
-from tqdm.auto import tqdm
-
-
-class FastEsmConfig(PretrainedConfig):
-    model_type = "fast_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",
-        emb_layer_norm_before=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.emb_layer_norm_before = emb_layer_norm_before
-
-    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
-
-
-def rotate_half(x):
-    x1, x2 = x.chunk(2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(x, cos, sin):
-    cos = cos[:, :, : x.shape[-2], :]
-    sin = sin[:, :, : x.shape[-2], :]
-
-    return (x * cos) + (rotate_half(x) * sin)
-
-
-def symmetrize(x):
-    "Make layer symmetric in final two dimensions, used for contact prediction."
-    return x + x.transpose(-1, -2)
-
-
-def average_product_correct(x):
-    "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=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)
-        self.activation = nn.Sigmoid()
-
-    def forward(self, tokens, attentions):
-        # remove eos token attentions
-        eos_mask = tokens.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, seq_dimension=2):
-        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
-        )
-
-    def forward(
-        self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        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=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.FloatTensor] = None,
-        output_attentions: 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 to get attention weights
-            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-            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.FloatTensor] = None,
-        output_attentions: 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.FloatTensor] = None,
-        output_attentions: 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.FloatTensor] = None,
-        output_hidden_states: bool = False,
-        output_attentions: 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,
-        )
-
-
-### Dataset for Embedding
-class ProteinDataset(Dataset):
-    """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]
-
-
-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_t33_650M_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):
-            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
-
-    @property
-    def device(self) -> torch.device:
-        """Get the device of the model."""
-        return next(self.parameters()).device
-
-    def mean_pooling(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Apply mean pooling to sequence outputs."""
-        if attention_mask is None:
-            return x.mean(dim=1)
-        else:
-            attention_mask = attention_mask.unsqueeze(-1)
-            return (x * attention_mask).sum(dim=1) / attention_mask.sum(dim=1)
-
-    def _collate_fn(self, sequences: list[str]) -> tuple[torch.Tensor, torch.Tensor]:
-        """Collate function for batching sequences."""
-        return self.tokenizer(sequences, return_tensors="pt", padding='longest', pad_to_multiple_of=8)
-
-    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],
-        batch_size: int = 2,
-        max_len: int = 512,
-        full_embeddings: bool = False,
-        full_precision: bool = False,
-        pooling_type: str = 'mean',
-        num_workers: int = 0,
-        sql: bool = False,
-        sql_db_path: str = 'embeddings.db',
-    ) -> 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)
-            full_precision: Whether to cast to full precision (float32) before storage - relevant for dict storage
-            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
-        """
-        sequences = list(set([seq[:max_len] for seq in sequences]))
-        sequences = sorted(sequences, key=len, reverse=True)
-        dataset = ProteinDataset(sequences)
-        dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=self._collate_fn)
-        device = self.device
-
-        def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-            if full_embeddings:
-                return residue_embeddings
-            elif pooling_type == 'mean':
-                return self.mean_pooling(residue_embeddings, attention_mask)
-            else:
-                return residue_embeddings[:, 0, :]
-
-        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:
-                with torch.no_grad():
-                    for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
-                        seqs = sequences[i * batch_size:(i + 1) * batch_size]
-                        input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
-                        residue_embeddings = self.forward(input_ids, attention_mask, output_hidden_states=True).hidden_states[-1].detach().float() # required for sql
-                        embeddings = get_embeddings(residue_embeddings, attention_mask).cpu()
-
-                        for seq, emb in zip(seqs, embeddings):
-                            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 = {}
-        with torch.no_grad():
-            for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
-                seqs = sequences[i * batch_size:(i + 1) * batch_size]
-                input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
-                residue_embeddings = self.forward(input_ids, attention_mask, output_hidden_states=True).hidden_states[-1].detach().float()
-                if full_precision:
-                    residue_embeddings = residue_embeddings.float()
-                embeddings = get_embeddings(residue_embeddings, attention_mask).cpu()
-                for seq, emb in zip(seqs, embeddings):
-                    embeddings_dict[seq] = emb
-                    
-        return embeddings_dict
-
-
-class FAST_ESM_ENCODER(FastEsmPreTrainedModel):
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-        self.embeddings = EsmEmbeddings(config)
-        self.encoder = EsmEncoder(config)
-        # 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 forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = 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
-        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(
-            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):
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        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 forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = 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
-        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(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-        )
-        sequence_output = encoder_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=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class FastEsmForMaskedLM(FastEsmPreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        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 forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = 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, MaskedLMOutput]:
-        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 MaskedLMOutput(
-            loss=loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def predict_contacts(self, tokens, attention_mask):
-        raise NotImplementedError("predict_contacts is not supported by F.scaled_dot_product_attention")
-
-
-class FastEsmForSequenceClassification(FastEsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        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 forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> 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):
-    def __init__(self, config):
-        super().__init__(config)
-        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 forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> 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)
-        model = TransformersEsmModel.from_pretrained(model_path, token_dropout=False).to(device)
-
-        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()
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils.data import Dataset, DataLoader
+from typing import Optional, Tuple, Union
+from einops import rearrange
+from transformers import PreTrainedModel, PretrainedConfig, EsmTokenizer
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    SequenceClassifierOutput,
+    TokenClassifierOutput
+)
+from transformers.models.esm.modeling_esm import (
+    EsmIntermediate,
+    EsmOutput,
+    EsmPooler,
+    EsmLMHead,
+    EsmSelfOutput,
+    EsmClassificationHead,
+)
+from tqdm.auto import tqdm
+
+
+class FastEsmConfig(PretrainedConfig):
+    model_type = "fast_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",
+        emb_layer_norm_before=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.emb_layer_norm_before = emb_layer_norm_before
+
+    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
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+def symmetrize(x):
+    "Make layer symmetric in final two dimensions, used for contact prediction."
+    return x + x.transpose(-1, -2)
+
+
+def average_product_correct(x):
+    "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=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)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, tokens, attentions):
+        # remove eos token attentions
+        eos_mask = tokens.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, seq_dimension=2):
+        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
+        )
+
+    def forward(
+        self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        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=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.FloatTensor] = None,
+        output_attentions: 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 to get attention weights
+            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+            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.FloatTensor] = None,
+        output_attentions: 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.FloatTensor] = None,
+        output_attentions: 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.FloatTensor] = None,
+        output_hidden_states: bool = False,
+        output_attentions: 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,
+        )
+
+
+### Dataset for Embedding
+class ProteinDataset(Dataset):
+    """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]
+
+
+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_t33_650M_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):
+            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
+
+    @property
+    def device(self) -> torch.device:
+        """Get the device of the model."""
+        return next(self.parameters()).device
+
+    def mean_pooling(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """Apply mean pooling to sequence outputs."""
+        if attention_mask is None:
+            return x.mean(dim=1)
+        else:
+            attention_mask = attention_mask.unsqueeze(-1)
+            return (x * attention_mask).sum(dim=1) / attention_mask.sum(dim=1)
+
+    def _collate_fn(self, sequences: list[str]) -> tuple[torch.Tensor, torch.Tensor]:
+        """Collate function for batching sequences."""
+        return self.tokenizer(sequences, return_tensors="pt", padding='longest', pad_to_multiple_of=8)
+
+    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],
+        batch_size: int = 2,
+        max_len: int = 512,
+        full_embeddings: bool = False,
+        full_precision: bool = False,
+        pooling_type: str = 'mean',
+        num_workers: int = 0,
+        sql: bool = False,
+        sql_db_path: str = 'embeddings.db',
+    ) -> 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)
+            full_precision: Whether to cast to full precision (float32) before storage - relevant for dict storage
+            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
+        """
+        sequences = list(set([seq[:max_len] for seq in sequences]))
+        sequences = sorted(sequences, key=len, reverse=True)
+        dataset = ProteinDataset(sequences)
+        dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=self._collate_fn)
+        device = self.device
+
+        def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+            if full_embeddings:
+                return residue_embeddings
+            elif pooling_type == 'mean':
+                return self.mean_pooling(residue_embeddings, attention_mask)
+            else:
+                return residue_embeddings[:, 0, :]
+
+        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:
+                with torch.no_grad():
+                    for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
+                        seqs = sequences[i * batch_size:(i + 1) * batch_size]
+                        input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
+                        residue_embeddings = self.forward(input_ids, attention_mask, output_hidden_states=True).hidden_states[-1].detach().float() # required for sql
+                        embeddings = get_embeddings(residue_embeddings, attention_mask).cpu()
+
+                        for seq, emb in zip(seqs, embeddings):
+                            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 = {}
+        with torch.no_grad():
+            for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'):
+                seqs = sequences[i * batch_size:(i + 1) * batch_size]
+                input_ids, attention_mask = batch['input_ids'].to(device), batch['attention_mask'].to(device)
+                residue_embeddings = self.forward(input_ids, attention_mask, output_hidden_states=True).hidden_states[-1].detach().float()
+                if full_precision:
+                    residue_embeddings = residue_embeddings.float()
+                embeddings = get_embeddings(residue_embeddings, attention_mask).cpu()
+                for seq, emb in zip(seqs, embeddings):
+                    embeddings_dict[seq] = emb
+                    
+        return embeddings_dict
+
+
+class FAST_ESM_ENCODER(FastEsmPreTrainedModel):
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = EsmEncoder(config)
+        # 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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = 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
+        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(
+            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):
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = 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
+        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(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        sequence_output = encoder_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=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class FastEsmForMaskedLM(FastEsmPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = 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, MaskedLMOutput]:
+        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 MaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def predict_contacts(self, tokens, attention_mask):
+        raise NotImplementedError("predict_contacts is not supported by F.scaled_dot_product_attention")
+
+
+class FastEsmForSequenceClassification(FastEsmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> 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):
+    def __init__(self, config):
+        super().__init__(config)
+        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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> 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)
+        model = TransformersEsmModel.from_pretrained(model_path, token_dropout=False).to(device)
+
+        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()