Update modeling_fastesm.py

modeling_fastesm.py

@@ -1,528 +1,553 @@
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from typing import Optional, Tuple, Union
-from einops import rearrange
-from transformers.modeling_outputs import (
-    MaskedLMOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    SequenceClassifierOutput,
-    TokenClassifierOutput
-)
-from transformers.models.esm.modeling_esm import (
-    RotaryEmbedding,
-    EsmContactPredictionHead,
-    EsmIntermediate,
-    EsmOutput,
-    EsmPooler,
-    EsmLMHead,
-    EsmSelfOutput,
-    EsmClassificationHead,
-    EsmPreTrainedModel,
-    create_position_ids_from_input_ids,
-    gelu
-)
-
-
-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.padding_idx = config.pad_token_id
-        self.position_embeddings = nn.Embedding(
-            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
-        )
-        # Token dropout does not work correctly so we disable it
-        # self.token_dropout = config.token_dropout
-        self.mask_token_id = config.mask_token_id
-
-    def forward(
-        self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if position_ids is None:
-            if input_ids is not None:
-                # Create the position ids from the input token ids. Any padded tokens remain padded.
-                position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
-            else:
-                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings = embeddings + position_embeddings
-
-        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,
-    ) -> Tuple[torch.Tensor]:
-        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)
-
-        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
-        )
-        return rearrange(context_layer, 'b h s d -> b s (h d)')
-        
-
-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,
-        attention_mask=None,
-    ):
-        hidden_states_ln = self.LayerNorm(hidden_states)
-        attention_output = self.self(
-            hidden_states_ln,
-            attention_mask,
-        )
-        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,
-        attention_mask=None,
-    ):
-        attention_output = self.attention(
-            hidden_states,
-            attention_mask,
-        )
-        layer_output = self.feed_forward_chunk(attention_output)
-        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,
-        attention_mask=None,
-        output_hidden_states=False,
-    ):
-        all_hidden_states = () if output_hidden_states 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:
-                hidden_states = self._gradient_checkpointing_func(
-                    layer_module.__call__,
-                    hidden_states,
-                    attention_mask,
-                )
-            else:
-                hidden_states = layer_module(
-                    hidden_states,
-                    attention_mask,
-                )
-
-        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,
-        )
-
-
-class FastEsmModel(EsmPreTrainedModel):
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-        self.embeddings = EsmEmbeddings(config)
-        self.encoder = EsmEncoder(config)
-        self.pooler = EsmPooler(config) if add_pooling_layer else None
-        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 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_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
-        if output_attentions is not None:
-            raise ValueError("output_attentions is not supported by F.scaled_dot_product_attention")
-        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,
-        )
-        # Prepare attention mask
-        if attention_mask is not None:
-            # attention_mask shape should be (batch_size, 1, 1, seq_length)
-            # Expand to (batch_size, 1, seq_length, seq_length)
-            extended_attention_mask = attention_mask[:, None, None, :].expand(
-                batch_size, 1, seq_length, seq_length
-            )
-            # Convert mask to float with 0.0 for positions to keep and -inf for masked positions
-            attention_mask = attention_mask.to(dtype=embedding_output.dtype)  # fp16 compatibility
-            attention_mask = (1.0 - attention_mask) * torch.finfo(embedding_output.dtype).min
-        else:
-            extended_attention_mask = None
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            output_hidden_states=output_hidden_states,
-        )
-        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,
-        )
-
-
-class FastEsmForMaskedLM(EsmPreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.esm = FastEsmModel(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,
-    ) -> 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,
-        )
-
-    def predict_contacts(self, tokens, attention_mask):
-        raise NotImplementedError("predict_contacts is not supported by F.scaled_dot_product_attention")
-
-
-class FastEsmForSequenceClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-        self.esm = FastEsmModel(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,
-        )
-
-
-class FastEsmForTokenClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.esm = FastEsmModel(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,
-        )
-
-
-if __name__ == "__main__":
-    """
-    Test the hidden state differences between the FastEsmModel and the HF EsmModel.
-    In full precision, the differences are 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 EsmModel 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)
-        fast_model = FastEsmModel.from_pretrained(model_path, token_dropout=False).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).last_hidden_state.detach().cpu()
-
-            model_tokens = tokenizer(example_seq, return_tensors="pt").input_ids.to(device)
-            model_output = model(model_tokens).last_hidden_state.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 typing import Optional, Tuple, Union
+from einops import rearrange
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    SequenceClassifierOutput,
+    TokenClassifierOutput
+)
+from transformers.models.esm.modeling_esm import (
+    RotaryEmbedding,
+    EsmContactPredictionHead,
+    EsmIntermediate,
+    EsmOutput,
+    EsmPooler,
+    EsmLMHead,
+    EsmSelfOutput,
+    EsmClassificationHead,
+    create_position_ids_from_input_ids,
+)
+from .config_fastesm import FastEsmConfig
+
+
+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.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+        )
+        # Token dropout does not work correctly so we disable it
+        # self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(
+        self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if position_ids is None:
+            if input_ids is not None:
+                # Create the position ids from the input token ids. Any padded tokens remain padded.
+                position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
+            else:
+                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = embeddings + position_embeddings
+
+        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,
+    ) -> Tuple[torch.Tensor]:
+        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)
+
+        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
+        )
+        return rearrange(context_layer, 'b h s d -> b s (h d)')
+        
+
+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,
+        attention_mask=None,
+    ):
+        hidden_states_ln = self.LayerNorm(hidden_states)
+        attention_output = self.self(
+            hidden_states_ln,
+            attention_mask,
+        )
+        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,
+        attention_mask=None,
+    ):
+        attention_output = self.attention(
+            hidden_states,
+            attention_mask,
+        )
+        layer_output = self.feed_forward_chunk(attention_output)
+        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,
+        attention_mask=None,
+        output_hidden_states=False,
+    ):
+        all_hidden_states = () if output_hidden_states 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:
+                hidden_states = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_mask,
+                )
+            else:
+                hidden_states = layer_module(
+                    hidden_states,
+                    attention_mask,
+                )
+
+        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,
+        )
+
+
+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
+    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
+
+
+class FastEsmModel(FastEsmPreTrainedModel):
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = EsmEncoder(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.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        if output_attentions is not None:
+            raise ValueError("output_attentions is not supported by F.scaled_dot_product_attention")
+        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,
+        )
+        # Prepare attention mask
+        if attention_mask is not None:
+            # attention_mask shape should be (batch_size, 1, 1, seq_length)
+            # Expand to (batch_size, 1, seq_length, seq_length)
+            extended_attention_mask = attention_mask[:, None, None, :].expand(
+                batch_size, 1, seq_length, seq_length
+            )
+            # Convert mask to float with 0.0 for positions to keep and -inf for masked positions
+            attention_mask = attention_mask.to(dtype=embedding_output.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * torch.finfo(embedding_output.dtype).min
+        else:
+            extended_attention_mask = None
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_hidden_states=output_hidden_states,
+        )
+        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,
+        )
+
+
+class FastEsmForMaskedLM(FastEsmPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.esm = FastEsmModel(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,
+    ) -> 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,
+        )
+
+    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 = FastEsmModel(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,
+        )
+
+
+class FastEsmForTokenClassification(FastEsmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.esm = FastEsmModel(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,
+        )
+
+
+if __name__ == "__main__":
+    """
+    Test the hidden state differences between the FastEsmModel and the HF EsmModel.
+    In full precision, the differences are 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 EsmModel 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)
+        fast_model = FastEsmModel.from_pretrained(model_path, token_dropout=False).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).last_hidden_state.detach().cpu()
+
+            model_tokens = tokenizer(example_seq, return_tensors="pt").input_ids.to(device)
+            model_output = model(model_tokens).last_hidden_state.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()