diff --git "a/modeling_fastesm.py" "b/modeling_fastesm.py" --- "a/modeling_fastesm.py" +++ "b/modeling_fastesm.py" @@ -1,1146 +1,1135 @@ -### Embedding Mixin + Pooler -import os -import sqlite3 -import networkx as nx -import numpy as np -import torch -from tqdm.auto import tqdm -from typing import Callable, List, Optional -from torch.utils.data import DataLoader -from torch.utils.data import Dataset as TorchDataset -from transformers import PreTrainedTokenizerBase - - -class Pooler: - def __init__(self, pooling_types: List[str]): - self.pooling_types = pooling_types - self.pooling_options = { - 'mean': self.mean_pooling, - 'max': self.max_pooling, - 'norm': self.norm_pooling, - 'median': self.median_pooling, - 'std': self.std_pooling, - 'var': self.var_pooling, - 'cls': self.cls_pooling, - 'parti': self._pool_parti, - } - - def _create_pooled_matrices_across_layers(self, attentions: torch.Tensor) -> torch.Tensor: - maxed_attentions = torch.max(attentions, dim=1)[0] - return maxed_attentions - - def _page_rank(self, attention_matrix, personalization=None, nstart=None, prune_type="top_k_outdegree"): - # Run PageRank on the attention matrix converted to a graph. - # Raises exceptions if the graph doesn't match the token sequence or has no edges. - # Returns the PageRank scores for each token node. - G = self._convert_to_graph(attention_matrix) - if G.number_of_nodes() != attention_matrix.shape[0]: - raise Exception( - f"The number of nodes in the graph should be equal to the number of tokens in sequence! You have {G.number_of_nodes()} nodes for {attention_matrix.shape[0]} tokens.") - if G.number_of_edges() == 0: - raise Exception(f"You don't seem to have any attention edges left in the graph.") - - return nx.pagerank(G, alpha=0.85, tol=1e-06, weight='weight', personalization=personalization, nstart=nstart, max_iter=100) - - def _convert_to_graph(self, matrix): - # Convert a matrix (e.g., attention scores) to a directed graph using networkx. - # Each element in the matrix represents a directed edge with a weight. - G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) - return G - - def _calculate_importance_weights(self, dict_importance, attention_mask: Optional[torch.Tensor] = None): - # Remove keys where attention_mask is 0 - if attention_mask is not None: - for k in list(dict_importance.keys()): - if attention_mask[k] == 0: - del dict_importance[k] - - #dict_importance[0] # remove cls - #dict_importance[-1] # remove eos - total = sum(dict_importance.values()) - return np.array([v / total for _, v in dict_importance.items()]) - - def _pool_parti(self, emb: torch.Tensor, attentions: torch.Tensor, attention_mask: Optional[torch.Tensor] = None): # (b, L, d) -> (b, d) - maxed_attentions = self._create_pooled_matrices_across_layers(attentions).numpy() - # emb is (b, L, d), maxed_attentions is (b, L, L) - emb_pooled = [] - for e, a, mask in zip(emb, maxed_attentions, attention_mask): - dict_importance = self._page_rank(a) - importance_weights = self._calculate_importance_weights(dict_importance, mask) - num_tokens = int(mask.sum().item()) - emb_pooled.append(np.average(e[:num_tokens], weights=importance_weights, axis=0)) - pooled = torch.tensor(np.array(emb_pooled)) - return pooled - - def mean_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.mean(dim=1) - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) - - def max_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.max(dim=1).values - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).max(dim=1).values - - def norm_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.norm(dim=1, p=2) - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).norm(dim=1, p=2) - - def median_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.median(dim=1).values - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).median(dim=1).values - - def std_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.std(dim=1) - else: - # Compute variance correctly over non-masked positions, then take sqrt - var = self.var_pooling(emb, attention_mask, **kwargs) - return torch.sqrt(var) - - def var_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.var(dim=1) - else: - # Correctly compute variance over only non-masked positions - attention_mask = attention_mask.unsqueeze(-1) # (b, L, 1) - # Compute mean over non-masked positions - mean = (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) - mean = mean.unsqueeze(1) # (b, 1, d) - # Compute squared differences from mean, only over non-masked positions - squared_diff = (emb - mean) ** 2 # (b, L, d) - # Sum squared differences over non-masked positions and divide by count - var = (squared_diff * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) - return var - - def cls_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - return emb[:, 0, :] - - def __call__( - self, - emb: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - attentions: Optional[torch.Tensor] = None - ): # [mean, max] - final_emb = [] - for pooling_type in self.pooling_types: - final_emb.append(self.pooling_options[pooling_type](emb=emb, attention_mask=attention_mask, attentions=attentions)) # (b, d) - return torch.cat(final_emb, dim=-1) # (b, n_pooling_types * d) - - -class ProteinDataset(TorchDataset): - """Simple dataset for protein sequences.""" - def __init__(self, sequences: list[str]): - self.sequences = sequences - - def __len__(self) -> int: - return len(self.sequences) - - def __getitem__(self, idx: int) -> str: - return self.sequences[idx] - - -def build_collator(tokenizer: PreTrainedTokenizerBase) -> Callable[[list[str]], dict[str, torch.Tensor]]: - def _collate_fn(sequences: list[str]) -> dict[str, torch.Tensor]: - return tokenizer(sequences, return_tensors="pt", padding='longest') - return _collate_fn - - -class EmbeddingMixin: - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - raise NotImplementedError - - @property - def device(self) -> torch.device: - """Get the device of the model.""" - return next(self.parameters()).device - - def _read_sequences_from_db(self, db_path: str) -> set[str]: - """Read sequences from SQLite database.""" - 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 _ensure_embeddings_table(self, conn: sqlite3.Connection) -> None: - cursor = conn.cursor() - cursor.execute( - "CREATE TABLE IF NOT EXISTS embeddings (" - "sequence TEXT PRIMARY KEY, " - "embedding BLOB NOT NULL, " - "shape TEXT, " - "dtype TEXT" - ")" - ) - cursor.execute("PRAGMA table_info(embeddings)") - rows = cursor.fetchall() - column_names = [row[1] for row in rows] - if "shape" not in column_names: - cursor.execute("ALTER TABLE embeddings ADD COLUMN shape TEXT") - if "dtype" not in column_names: - cursor.execute("ALTER TABLE embeddings ADD COLUMN dtype TEXT") - conn.commit() - - def load_embeddings_from_pth(self, save_path: str) -> dict[str, torch.Tensor]: - assert os.path.exists(save_path), f"Embedding file does not exist: {save_path}" - payload = torch.load(save_path, map_location="cpu", weights_only=True) - assert isinstance(payload, dict), "Expected .pth embeddings file to contain a dictionary." - for sequence, tensor in payload.items(): - assert isinstance(sequence, str), "Expected embedding dictionary keys to be sequences (str)." - assert isinstance(tensor, torch.Tensor), "Expected embedding dictionary values to be tensors." - return payload - - def load_embeddings_from_db(self, db_path: str, sequences: Optional[List[str]] = None) -> dict[str, torch.Tensor]: - assert os.path.exists(db_path), f"Embedding database does not exist: {db_path}" - loaded: dict[str, torch.Tensor] = {} - with sqlite3.connect(db_path) as conn: - self._ensure_embeddings_table(conn) - cursor = conn.cursor() - if sequences is None: - cursor.execute("SELECT sequence, embedding, shape, dtype FROM embeddings") - else: - if len(sequences) == 0: - return loaded - placeholders = ",".join(["?"] * len(sequences)) - cursor.execute( - f"SELECT sequence, embedding, shape, dtype FROM embeddings WHERE sequence IN ({placeholders})", - tuple(sequences), - ) - - rows = cursor.fetchall() - for row in rows: - sequence = row[0] - embedding_bytes = row[1] - shape_text = row[2] - dtype_text = row[3] - assert shape_text is not None, "Missing shape metadata in embeddings table." - assert dtype_text is not None, "Missing dtype metadata in embeddings table." - shape_values = [int(value) for value in shape_text.split(",") if len(value) > 0] - assert len(shape_values) > 0, f"Invalid shape metadata for sequence: {sequence}" - expected_size = int(np.prod(shape_values)) - np_dtype = np.dtype(dtype_text) - array = np.frombuffer(embedding_bytes, dtype=np_dtype) - assert array.size == expected_size, f"Shape mismatch while reading sequence: {sequence}" - reshaped = array.copy().reshape(tuple(shape_values)) - loaded[sequence] = torch.from_numpy(reshaped) - return loaded - - def embed_dataset( - self, - sequences: List[str], - tokenizer: Optional[PreTrainedTokenizerBase] = None, - batch_size: int = 2, - max_len: int = 512, - truncate: bool = True, - full_embeddings: bool = False, - embed_dtype: torch.dtype = torch.float32, - pooling_types: List[str] = ['mean'], - num_workers: int = 0, - sql: bool = False, - save: bool = True, - sql_db_path: str = 'embeddings.db', - save_path: str = 'embeddings.pth', - **kwargs, - ) -> Optional[dict[str, torch.Tensor]]: - """ - Embed a dataset of protein sequences. - - Supports two modes: - - Tokenizer mode (ESM2/ESM++): provide `tokenizer`, `_embed(input_ids, attention_mask)` is used. - - Sequence mode (E1): pass `tokenizer=None`, `_embed(sequences, return_attention_mask=True, **kwargs)` is used. - """ - sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences])) - sequences = sorted(sequences, key=len, reverse=True) - hidden_size = self.config.hidden_size - pooler = Pooler(pooling_types) if not full_embeddings else None - tokenizer_mode = tokenizer is not None - if tokenizer_mode: - collate_fn = build_collator(tokenizer) - device = self.device - else: - collate_fn = None - device = None - - def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - if full_embeddings or residue_embeddings.ndim == 2: - return residue_embeddings - return pooler(residue_embeddings, attention_mask) - - def iter_batches(to_embed: List[str]): - if tokenizer_mode: - assert collate_fn is not None - assert device is not None - dataset = ProteinDataset(to_embed) - dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False) - for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'): - seqs = to_embed[i * batch_size:(i + 1) * batch_size] - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - residue_embeddings = self._embed(input_ids, attention_mask) - yield seqs, residue_embeddings, attention_mask - else: - for batch_start in tqdm(range(0, len(to_embed), batch_size), desc='Embedding batches'): - seqs = to_embed[batch_start:batch_start + batch_size] - batch_output = self._embed(seqs, return_attention_mask=True, **kwargs) - assert isinstance(batch_output, tuple), "Sequence mode _embed must return (last_hidden_state, attention_mask)." - assert len(batch_output) == 2, "Sequence mode _embed must return exactly two values." - residue_embeddings, attention_mask = batch_output - assert isinstance(attention_mask, torch.Tensor), "Sequence mode _embed must return attention_mask as a torch.Tensor." - yield seqs, residue_embeddings, attention_mask - - if sql: - conn = sqlite3.connect(sql_db_path) - self._ensure_embeddings_table(conn) - c = conn.cursor() - 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, (seqs, residue_embeddings, attention_mask) in enumerate(iter_batches(to_embed)): - embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) - for seq, emb, mask in zip(seqs, embeddings, attention_mask): - if full_embeddings: - emb = emb[mask.bool()].reshape(-1, hidden_size) - emb_np = emb.cpu().numpy() - emb_shape = ",".join([str(dim) for dim in emb_np.shape]) - emb_dtype = str(emb_np.dtype) - c.execute( - "INSERT OR REPLACE INTO embeddings (sequence, embedding, shape, dtype) VALUES (?, ?, ?, ?)", - (seq, emb_np.tobytes(), emb_shape, emb_dtype), - ) - if tokenizer_mode and (i + 1) % 100 == 0: - conn.commit() - conn.commit() - conn.close() - return None - - embeddings_dict = {} - if os.path.exists(save_path): - embeddings_dict = self.load_embeddings_from_pth(save_path) - to_embed = [seq for seq in sequences if seq not in embeddings_dict] - print(f"Found {len(embeddings_dict)} already embedded sequences in {save_path}") - print(f"Embedding {len(to_embed)} new sequences") - else: - to_embed = sequences - print(f"Embedding {len(to_embed)} new sequences") - - if len(to_embed) > 0: - with torch.no_grad(): - for seqs, residue_embeddings, attention_mask in iter_batches(to_embed): - embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) - for seq, emb, mask in zip(seqs, embeddings, attention_mask): - if full_embeddings: - emb = emb[mask.bool()].reshape(-1, hidden_size) - embeddings_dict[seq] = emb.cpu() - - if save: - torch.save(embeddings_dict, save_path) - - return embeddings_dict - - -import torch -import torch.nn as nn -from torch.nn import functional as F -from typing import Optional, Tuple, Union, Dict, Any -from einops import rearrange -from dataclasses import dataclass -from transformers import PreTrainedModel, PretrainedConfig, EsmTokenizer -from transformers import initialization as init -from transformers.modeling_outputs import ( - ModelOutput, - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - SequenceClassifierOutput, - TokenClassifierOutput -) -from transformers.models.esm.modeling_esm import ( - EsmIntermediate, - EsmOutput, - EsmPooler, - EsmLMHead, - EsmSelfOutput, - EsmClassificationHead, - EsmContactPredictionHead, - EsmEmbeddings, - RotaryEmbedding, -) -try: - from torch.nn.attention.flex_attention import create_block_mask - from torch.nn.attention.flex_attention import flex_attention -except ImportError: - create_block_mask = None - flex_attention = None - - -def _create_pad_block_mask(attention_mask_2d: torch.Tensor): - assert create_block_mask is not None, "Flex attention block mask requires create_block_mask." - token_valid = attention_mask_2d.bool() - batch_size, seq_len = token_valid.shape - - def mask_mod(batch_idx, head_idx, q_idx, kv_idx): - return token_valid[batch_idx, q_idx] & token_valid[batch_idx, kv_idx] - - return create_block_mask( - mask_mod, - batch_size, - 1, - seq_len, - seq_len, - device=attention_mask_2d.device, - ) - - -@dataclass -class EsmMaskedLMOutput(ModelOutput): - loss: Optional[torch.Tensor] = None - logits: Optional[torch.Tensor] = None - last_hidden_state: Optional[torch.Tensor] = None - hidden_states: Optional[Tuple[torch.Tensor, ...]] = None - attentions: Optional[Tuple[torch.Tensor, ...]] = None - - -class FastEsmConfig(PretrainedConfig): - model_type = "fast_esm" - def __init__( - self, - vocab_size: int = None, - mask_token_id: int = None, - pad_token_id: int = None, - hidden_size: int = 768, - num_hidden_layers: int = 12, - num_attention_heads: int = 12, - intermediate_size: int = 3072, - hidden_dropout_prob: float = 0.1, - attention_probs_dropout_prob: float = 0.1, - max_position_embeddings: int = 1026, - initializer_range: float = 0.02, - layer_norm_eps: float = 1e-12, - position_embedding_type: str = "rotary", - emb_layer_norm_before: bool = None, - token_dropout: bool = True, - attn_backend: str = "sdpa", - **kwargs, - ): - super().__init__( - pad_token_id=pad_token_id, - mask_token_id=mask_token_id, - **kwargs, - ) - - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.intermediate_size = intermediate_size - self.hidden_dropout_prob = hidden_dropout_prob - self.attention_probs_dropout_prob = attention_probs_dropout_prob - self.max_position_embeddings = max_position_embeddings - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.position_embedding_type = position_embedding_type - self.emb_layer_norm_before = emb_layer_norm_before - self.tie_word_embeddings = False - self.token_dropout = token_dropout - self.attn_backend = attn_backend - - def to_dict(self) -> Dict[str, Any]: - """ - Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. - - Returns: - `Dict[str, any]`: Dictionar y of all the attributes that make up this configuration instance, - """ - output = super().to_dict() - return output - - -class EsmSelfAttention(nn.Module): - def __init__(self, config, position_embedding_type: Optional[str] = None): - super().__init__() - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " - f"heads ({config.num_attention_heads})" - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - self.scale = self.attention_head_size**-0.5 - - self.dropout_prob = config.attention_probs_dropout_prob - self.attn_backend = config.attn_backend - 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 forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - flex_block_mask: Optional[object] = None, - output_attentions: Optional[bool] = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: - """Forward pass for self attention. - - Args: - hidden_states: Input tensor - attention_mask: Optional attention mask - output_attentions: Whether to return attention weights - - Returns: - Output tensor and optionally attention weights - """ - batch_size, seq_length = hidden_states.shape[:-1] - hidden_shape = (batch_size, seq_length, -1, self.attention_head_size) - query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) - key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2) - value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2) - - query_layer = query_layer * self.scale - - if self.position_embedding_type == "rotary": - query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer) - - if output_attentions: - # Manual attention computation - apply scaling here - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - if attention_mask is not None: - attention_scores = attention_scores.masked_fill(attention_mask.logical_not(), float("-inf")) - 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: - if self.attn_backend == "flex": - assert flex_attention is not None, "Flex attention backend requested but torch.flex_attention is unavailable." - assert query_layer.dtype in (torch.float16, torch.bfloat16), f"Flex attention backend requires float16 or bfloat16, got {query_layer.dtype}." - assert flex_block_mask is not None, "Flex attention backend requires a block mask" - context_layer = flex_attention( - query_layer, - key_layer, - value_layer, - block_mask=flex_block_mask, - scale=1.0, # applied before rotary - ) - else: - context_layer = F.scaled_dot_product_attention( - query_layer, - key_layer, - value_layer, - attn_mask=attention_mask, - dropout_p=self.dropout_prob if self.training else 0.0, - scale=1.0 # applied before rotary - ) - context_layer = rearrange(context_layer, 'b h s d -> b s (h d)') - return context_layer - - -class EsmAttention(nn.Module): - def __init__(self, config): - super().__init__() - self.self = EsmSelfAttention(config) - self.output = EsmSelfOutput(config) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - flex_block_mask: Optional[object] = None, - output_attentions: Optional[bool] = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: - """Forward pass for attention layer. - - Args: - hidden_states: Input tensor - attention_mask: Optional attention mask - output_attentions: Whether to return attention weights - - Returns: - Output tensor and optionally attention weights - """ - hidden_states_ln = self.LayerNorm(hidden_states) - self_outputs = self.self( - hidden_states_ln, - attention_mask, - flex_block_mask, - output_attentions, - ) - if output_attentions: - attention_output, attention_weights = self_outputs - attention_output = self.output(attention_output, hidden_states) - return attention_output, attention_weights - else: - attention_output = self_outputs - return self.output(attention_output, hidden_states) - - -class EsmLayer(nn.Module): - def __init__(self, config): - super().__init__() - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = EsmAttention(config) - self.intermediate = EsmIntermediate(config) - self.output = EsmOutput(config) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - flex_block_mask: Optional[object] = None, - output_attentions: Optional[bool] = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: - """Forward pass for transformer layer. - - Args: - hidden_states: Input tensor - attention_mask: Optional attention mask - output_attentions: Whether to return attention weights - - Returns: - Output tensor and optionally attention weights - """ - attention_outputs = self.attention( - hidden_states, - attention_mask, - flex_block_mask, - output_attentions, - ) - if output_attentions: - attention_output, attention_weights = attention_outputs - else: - attention_output = attention_outputs - attention_weights = None - - layer_output = self.feed_forward_chunk(attention_output) - - if output_attentions: - return layer_output, attention_weights - return layer_output - - def feed_forward_chunk(self, attention_output): - attention_output_ln = self.LayerNorm(attention_output) - intermediate_output = self.intermediate(attention_output_ln) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class EsmEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([EsmLayer(config) for _ in range(config.num_hidden_layers)]) - self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - flex_block_mask: Optional[object] = None, - output_hidden_states: Optional[bool] = False, - output_attentions: Optional[bool] = False, - ) -> BaseModelOutputWithPastAndCrossAttentions: - """Forward pass for transformer encoder. - - Args: - hidden_states: Input tensor - attention_mask: Optional attention mask - output_hidden_states: Whether to return all hidden states - output_attentions: Whether to return attention weights - - Returns: - BaseModelOutputWithPastAndCrossAttentions containing model outputs - """ - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - - for layer_module in self.layer: - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - layer_outputs = self._gradient_checkpointing_func( - layer_module.__call__, - hidden_states, - attention_mask, - flex_block_mask, - output_attentions, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - flex_block_mask, - output_attentions, - ) - - if output_attentions: - hidden_states, attention_weights = layer_outputs - all_attentions = all_attentions + (attention_weights,) - else: - hidden_states = layer_outputs - - if self.emb_layer_norm_after: - hidden_states = self.emb_layer_norm_after(hidden_states) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - hidden_states=all_hidden_states, - attentions=all_attentions, - ) - - -class FastEsmPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - config_class = FastEsmConfig - base_model_prefix = "fastesm" - supports_gradient_checkpointing = True - tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") - all_tied_weights_keys = {} - - @torch.no_grad() - def _init_weights(self, module): - """Initialize the weights""" - super()._init_weights(module) - if isinstance(module, EsmLMHead): - init.zeros_(module.bias) - elif isinstance(module, EsmEmbeddings): - init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) - elif isinstance(module, RotaryEmbedding): - inv_freq = 1.0 / (10000 ** (torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim)) - init.copy_(module.inv_freq, inv_freq) - - def get_output_embeddings(self): - # NOTE: get_output_embeddings() must return None to prevent accidental weight tying. - # See e.g. https://github.com/huggingface/transformers/pull/39339#discussion_r2219126400 - return None - - -class FAST_ESM_ENCODER(FastEsmPreTrainedModel, EmbeddingMixin): - def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs): - FastEsmPreTrainedModel.__init__(self, config, **kwargs) - self.config = config - self.embeddings = EsmEmbeddings(config) - self.encoder = EsmEncoder(config) - self.contact_head = EsmContactPredictionHead( - in_features=config.num_hidden_layers * config.num_attention_heads, bias=True - ) - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - token_embedding_output = self.embeddings(input_ids, attention_mask=attention_mask) - batch_size, seq_length = input_ids.shape - flex_block_mask = None - if attention_mask is not None: - token_attention_mask = attention_mask.bool() - if self.config.attn_backend == "flex": - assert create_block_mask is not None, ( - "Flex attention backend requested but torch.create_block_mask is unavailable." - ) - flex_block_mask = _create_pad_block_mask(token_attention_mask) - extended_attention_mask = None - else: - extended_attention_mask = token_attention_mask[:, None, None, :].expand( - batch_size, 1, seq_length, seq_length - ) - else: - extended_attention_mask = None - encoder_outputs = self.encoder( - token_embedding_output, - attention_mask=extended_attention_mask, - flex_block_mask=flex_block_mask, - output_hidden_states=False, - output_attentions=False, - ) - return encoder_outputs.last_hidden_state - - def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - attns = self(input_ids, attention_mask=attention_mask, output_attentions=True).attentions - attns = torch.stack(attns, dim=1) - attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3) - attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4) - return self.contact_head(input_ids, attns) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, # to play nice with HF adjacent packages - ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: - """Forward pass for base model. - - Args: - input_ids: Input token IDs - attention_mask: Optional attention mask - position_ids: Optional position IDs - inputs_embeds: Optional input embeddings - output_hidden_states: Whether to return all hidden states - output_attentions: Whether to return attention weights - - Returns: - Model outputs including hidden states and optionally attention weights - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - batch_size, seq_length = input_shape - token_embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - attention_mask=attention_mask, - inputs_embeds=inputs_embeds, - ) - - if attention_mask is None: - token_attention_mask = torch.ones((batch_size, seq_length), device=input_ids.device).bool() - else: - token_attention_mask = attention_mask.bool() - - if self.config.attn_backend == "flex" and not output_attentions: - assert create_block_mask is not None, "Flex attention backend requested but torch.create_block_mask is unavailable." - flex_block_mask = _create_pad_block_mask(token_attention_mask) - extended_attention_mask = None - else: - flex_block_mask = None - extended_attention_mask = token_attention_mask[:, None, None, :].expand(batch_size, 1, seq_length, seq_length) - - encoder_outputs = self.encoder( - token_embedding_output, - attention_mask=extended_attention_mask, - flex_block_mask=flex_block_mask, - output_hidden_states=output_hidden_states, - output_attentions=output_attentions, - ) - sequence_output = encoder_outputs.last_hidden_state - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - ) - - -class FastEsmModel(FastEsmPreTrainedModel, EmbeddingMixin): - def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs): - FastEsmPreTrainedModel.__init__(self, config, **kwargs) - self.config = config - self.esm = FAST_ESM_ENCODER(config) - self.pooler = EsmPooler(config) if add_pooling_layer else None - self.post_init() - - def get_input_embeddings(self): - return self.esm.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.esm.embeddings.word_embeddings = value - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, # to play nice with HF adjacent packages - **kwargs, - ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: - """Forward pass for base model. - - Args: - input_ids: Input token IDs - attention_mask: Optional attention mask - position_ids: Optional position IDs - inputs_embeds: Optional input embeddings - output_hidden_states: Whether to return all hidden states - output_attentions: Whether to return attention weights - - Returns: - Model outputs including hidden states and optionally attention weights - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - outputs = self.esm( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - output_hidden_states=output_hidden_states, - output_attentions=output_attentions, - ) - sequence_output = outputs.last_hidden_state - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -class FastEsmForMaskedLM(FastEsmPreTrainedModel, EmbeddingMixin): - def __init__(self, config, **kwargs): - FastEsmPreTrainedModel.__init__(self, config, **kwargs) - self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) - self.lm_head = EsmLMHead(config) - self.loss_fct = nn.CrossEntropyLoss() - self.post_init() - - def get_input_embeddings(self): - return self.esm.embeddings.word_embeddings - - def get_output_embeddings(self): - return self.lm_head.decoder - - def set_output_embeddings(self, new_embeddings): - self.lm_head.decoder = new_embeddings - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, # to play nice with HF adjacent packages - **kwargs, - ) -> Union[Tuple, EsmMaskedLMOutput]: - outputs = self.esm( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - output_hidden_states=output_hidden_states, - output_attentions=output_attentions, - ) - sequence_output = outputs.last_hidden_state - prediction_scores = self.lm_head(sequence_output) - - loss = None - if labels is not None: - labels = labels.to(prediction_scores.device) - loss = self.loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - return EsmMaskedLMOutput( - loss=loss, - logits=prediction_scores, - last_hidden_state=sequence_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -class FastEsmForSequenceClassification(FastEsmPreTrainedModel, EmbeddingMixin): - def __init__(self, config, **kwargs): - FastEsmPreTrainedModel.__init__(self, config, **kwargs) - self.num_labels = config.num_labels - self.config = config - self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) - self.classifier = EsmClassificationHead(config) - self.mse = nn.MSELoss() - self.ce = nn.CrossEntropyLoss() - self.bce = nn.BCEWithLogitsLoss() - self.post_init() - - def get_input_embeddings(self): - return self.esm.embeddings.word_embeddings - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - **kwargs, - ) -> Union[Tuple, SequenceClassifierOutput]: - outputs = self.esm( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - ) - sequence_output = outputs.last_hidden_state - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - if self.num_labels == 1: - loss = self.mse(logits.squeeze(), labels.squeeze()) - else: - loss = self.mse(logits, labels) - elif self.config.problem_type == "single_label_classification": - loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss = self.bce(logits, labels) - - return SequenceClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -class FastEsmForTokenClassification(FastEsmPreTrainedModel, EmbeddingMixin): - def __init__(self, config, **kwargs): - FastEsmPreTrainedModel.__init__(self, config, **kwargs) - self.num_labels = config.num_labels - self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, config.num_labels) - self.loss_fct = nn.CrossEntropyLoss() - self.post_init() - - def get_input_embeddings(self): - return self.esm.embeddings.word_embeddings - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - **kwargs, - ) -> Union[Tuple, TokenClassifierOutput]: - outputs = self.esm( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - ) - sequence_output = outputs.last_hidden_state - sequence_output = self.dropout(sequence_output) - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - +### Embedding Mixin + Pooler +import os +import sqlite3 +import networkx as nx +import numpy as np +import torch +from tqdm.auto import tqdm +from typing import Callable, List, Optional +from torch.utils.data import DataLoader +from torch.utils.data import Dataset as TorchDataset +from transformers import PreTrainedTokenizerBase + + +class Pooler: + def __init__(self, pooling_types: List[str]): + self.pooling_types = pooling_types + self.pooling_options = { + 'mean': self.mean_pooling, + 'max': self.max_pooling, + 'norm': self.norm_pooling, + 'median': self.median_pooling, + 'std': self.std_pooling, + 'var': self.var_pooling, + 'cls': self.cls_pooling, + 'parti': self._pool_parti, + } + + def _create_pooled_matrices_across_layers(self, attentions: torch.Tensor) -> torch.Tensor: + maxed_attentions = torch.max(attentions, dim=1)[0] + return maxed_attentions + + def _page_rank(self, attention_matrix, personalization=None, nstart=None, prune_type="top_k_outdegree"): + # Run PageRank on the attention matrix converted to a graph. + # Raises exceptions if the graph doesn't match the token sequence or has no edges. + # Returns the PageRank scores for each token node. + G = self._convert_to_graph(attention_matrix) + if G.number_of_nodes() != attention_matrix.shape[0]: + raise Exception( + f"The number of nodes in the graph should be equal to the number of tokens in sequence! You have {G.number_of_nodes()} nodes for {attention_matrix.shape[0]} tokens.") + if G.number_of_edges() == 0: + raise Exception(f"You don't seem to have any attention edges left in the graph.") + + return nx.pagerank(G, alpha=0.85, tol=1e-06, weight='weight', personalization=personalization, nstart=nstart, max_iter=100) + + def _convert_to_graph(self, matrix): + # Convert a matrix (e.g., attention scores) to a directed graph using networkx. + # Each element in the matrix represents a directed edge with a weight. + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + return G + + def _calculate_importance_weights(self, dict_importance, attention_mask: Optional[torch.Tensor] = None): + # Remove keys where attention_mask is 0 + if attention_mask is not None: + for k in list(dict_importance.keys()): + if attention_mask[k] == 0: + del dict_importance[k] + + #dict_importance[0] # remove cls + #dict_importance[-1] # remove eos + total = sum(dict_importance.values()) + return np.array([v / total for _, v in dict_importance.items()]) + + def _pool_parti(self, emb: torch.Tensor, attentions: torch.Tensor, attention_mask: Optional[torch.Tensor] = None): # (b, L, d) -> (b, d) + maxed_attentions = self._create_pooled_matrices_across_layers(attentions).numpy() + # emb is (b, L, d), maxed_attentions is (b, L, L) + emb_pooled = [] + for e, a, mask in zip(emb, maxed_attentions, attention_mask): + dict_importance = self._page_rank(a) + importance_weights = self._calculate_importance_weights(dict_importance, mask) + num_tokens = int(mask.sum().item()) + emb_pooled.append(np.average(e[:num_tokens], weights=importance_weights, axis=0)) + pooled = torch.tensor(np.array(emb_pooled)) + return pooled + + def mean_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.mean(dim=1) + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) + + def max_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.max(dim=1).values + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).max(dim=1).values + + def norm_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.norm(dim=1, p=2) + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).norm(dim=1, p=2) + + def median_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.median(dim=1).values + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).median(dim=1).values + + def std_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.std(dim=1) + else: + # Compute variance correctly over non-masked positions, then take sqrt + var = self.var_pooling(emb, attention_mask, **kwargs) + return torch.sqrt(var) + + def var_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.var(dim=1) + else: + # Correctly compute variance over only non-masked positions + attention_mask = attention_mask.unsqueeze(-1) # (b, L, 1) + # Compute mean over non-masked positions + mean = (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) + mean = mean.unsqueeze(1) # (b, 1, d) + # Compute squared differences from mean, only over non-masked positions + squared_diff = (emb - mean) ** 2 # (b, L, d) + # Sum squared differences over non-masked positions and divide by count + var = (squared_diff * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) + return var + + def cls_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + return emb[:, 0, :] + + def __call__( + self, + emb: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + attentions: Optional[torch.Tensor] = None + ): # [mean, max] + final_emb = [] + for pooling_type in self.pooling_types: + final_emb.append(self.pooling_options[pooling_type](emb=emb, attention_mask=attention_mask, attentions=attentions)) # (b, d) + return torch.cat(final_emb, dim=-1) # (b, n_pooling_types * d) + + +class ProteinDataset(TorchDataset): + """Simple dataset for protein sequences.""" + def __init__(self, sequences: list[str]): + self.sequences = sequences + + def __len__(self) -> int: + return len(self.sequences) + + def __getitem__(self, idx: int) -> str: + return self.sequences[idx] + + +def build_collator(tokenizer: PreTrainedTokenizerBase) -> Callable[[list[str]], dict[str, torch.Tensor]]: + def _collate_fn(sequences: list[str]) -> dict[str, torch.Tensor]: + return tokenizer(sequences, return_tensors="pt", padding='longest') + return _collate_fn + + +class EmbeddingMixin: + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + raise NotImplementedError + + @property + def device(self) -> torch.device: + """Get the device of the model.""" + return next(self.parameters()).device + + def _read_sequences_from_db(self, db_path: str) -> set[str]: + """Read sequences from SQLite database.""" + 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 _ensure_embeddings_table(self, conn: sqlite3.Connection) -> None: + cursor = conn.cursor() + cursor.execute( + "CREATE TABLE IF NOT EXISTS embeddings (" + "sequence TEXT PRIMARY KEY, " + "embedding BLOB NOT NULL, " + "shape TEXT, " + "dtype TEXT" + ")" + ) + cursor.execute("PRAGMA table_info(embeddings)") + rows = cursor.fetchall() + column_names = [row[1] for row in rows] + if "shape" not in column_names: + cursor.execute("ALTER TABLE embeddings ADD COLUMN shape TEXT") + if "dtype" not in column_names: + cursor.execute("ALTER TABLE embeddings ADD COLUMN dtype TEXT") + conn.commit() + + def load_embeddings_from_pth(self, save_path: str) -> dict[str, torch.Tensor]: + assert os.path.exists(save_path), f"Embedding file does not exist: {save_path}" + payload = torch.load(save_path, map_location="cpu", weights_only=True) + assert isinstance(payload, dict), "Expected .pth embeddings file to contain a dictionary." + for sequence, tensor in payload.items(): + assert isinstance(sequence, str), "Expected embedding dictionary keys to be sequences (str)." + assert isinstance(tensor, torch.Tensor), "Expected embedding dictionary values to be tensors." + return payload + + def load_embeddings_from_db(self, db_path: str, sequences: Optional[List[str]] = None) -> dict[str, torch.Tensor]: + assert os.path.exists(db_path), f"Embedding database does not exist: {db_path}" + loaded: dict[str, torch.Tensor] = {} + with sqlite3.connect(db_path) as conn: + self._ensure_embeddings_table(conn) + cursor = conn.cursor() + if sequences is None: + cursor.execute("SELECT sequence, embedding, shape, dtype FROM embeddings") + else: + if len(sequences) == 0: + return loaded + placeholders = ",".join(["?"] * len(sequences)) + cursor.execute( + f"SELECT sequence, embedding, shape, dtype FROM embeddings WHERE sequence IN ({placeholders})", + tuple(sequences), + ) + + rows = cursor.fetchall() + for row in rows: + sequence = row[0] + embedding_bytes = row[1] + shape_text = row[2] + dtype_text = row[3] + assert shape_text is not None, "Missing shape metadata in embeddings table." + assert dtype_text is not None, "Missing dtype metadata in embeddings table." + shape_values = [int(value) for value in shape_text.split(",") if len(value) > 0] + assert len(shape_values) > 0, f"Invalid shape metadata for sequence: {sequence}" + expected_size = int(np.prod(shape_values)) + np_dtype = np.dtype(dtype_text) + array = np.frombuffer(embedding_bytes, dtype=np_dtype) + assert array.size == expected_size, f"Shape mismatch while reading sequence: {sequence}" + reshaped = array.copy().reshape(tuple(shape_values)) + loaded[sequence] = torch.from_numpy(reshaped) + return loaded + + def embed_dataset( + self, + sequences: List[str], + tokenizer: Optional[PreTrainedTokenizerBase] = None, + batch_size: int = 2, + max_len: int = 512, + truncate: bool = True, + full_embeddings: bool = False, + embed_dtype: torch.dtype = torch.float32, + pooling_types: List[str] = ['mean'], + num_workers: int = 0, + sql: bool = False, + save: bool = True, + sql_db_path: str = 'embeddings.db', + save_path: str = 'embeddings.pth', + **kwargs, + ) -> Optional[dict[str, torch.Tensor]]: + """ + Embed a dataset of protein sequences. + + Supports two modes: + - Tokenizer mode (ESM2/ESM++): provide `tokenizer`, `_embed(input_ids, attention_mask)` is used. + - Sequence mode (E1): pass `tokenizer=None`, `_embed(sequences, return_attention_mask=True, **kwargs)` is used. + """ + sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences])) + sequences = sorted(sequences, key=len, reverse=True) + hidden_size = self.config.hidden_size + pooler = Pooler(pooling_types) if not full_embeddings else None + tokenizer_mode = tokenizer is not None + if tokenizer_mode: + collate_fn = build_collator(tokenizer) + device = self.device + else: + collate_fn = None + device = None + + def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if full_embeddings or residue_embeddings.ndim == 2: + return residue_embeddings + return pooler(residue_embeddings, attention_mask) + + def iter_batches(to_embed: List[str]): + if tokenizer_mode: + assert collate_fn is not None + assert device is not None + dataset = ProteinDataset(to_embed) + dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False) + for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'): + seqs = to_embed[i * batch_size:(i + 1) * batch_size] + input_ids = batch['input_ids'].to(device) + attention_mask = batch['attention_mask'].to(device) + residue_embeddings = self._embed(input_ids, attention_mask) + yield seqs, residue_embeddings, attention_mask + else: + for batch_start in tqdm(range(0, len(to_embed), batch_size), desc='Embedding batches'): + seqs = to_embed[batch_start:batch_start + batch_size] + batch_output = self._embed(seqs, return_attention_mask=True, **kwargs) + assert isinstance(batch_output, tuple), "Sequence mode _embed must return (last_hidden_state, attention_mask)." + assert len(batch_output) == 2, "Sequence mode _embed must return exactly two values." + residue_embeddings, attention_mask = batch_output + assert isinstance(attention_mask, torch.Tensor), "Sequence mode _embed must return attention_mask as a torch.Tensor." + yield seqs, residue_embeddings, attention_mask + + if sql: + conn = sqlite3.connect(sql_db_path) + self._ensure_embeddings_table(conn) + c = conn.cursor() + 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, (seqs, residue_embeddings, attention_mask) in enumerate(iter_batches(to_embed)): + embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) + for seq, emb, mask in zip(seqs, embeddings, attention_mask): + if full_embeddings: + emb = emb[mask.bool()].reshape(-1, hidden_size) + emb_np = emb.cpu().numpy() + emb_shape = ",".join([str(dim) for dim in emb_np.shape]) + emb_dtype = str(emb_np.dtype) + c.execute( + "INSERT OR REPLACE INTO embeddings (sequence, embedding, shape, dtype) VALUES (?, ?, ?, ?)", + (seq, emb_np.tobytes(), emb_shape, emb_dtype), + ) + if tokenizer_mode and (i + 1) % 100 == 0: + conn.commit() + conn.commit() + conn.close() + return None + + embeddings_dict = {} + if os.path.exists(save_path): + embeddings_dict = self.load_embeddings_from_pth(save_path) + to_embed = [seq for seq in sequences if seq not in embeddings_dict] + print(f"Found {len(embeddings_dict)} already embedded sequences in {save_path}") + print(f"Embedding {len(to_embed)} new sequences") + else: + to_embed = sequences + print(f"Embedding {len(to_embed)} new sequences") + + if len(to_embed) > 0: + with torch.no_grad(): + for seqs, residue_embeddings, attention_mask in iter_batches(to_embed): + embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) + for seq, emb, mask in zip(seqs, embeddings, attention_mask): + if full_embeddings: + emb = emb[mask.bool()].reshape(-1, hidden_size) + embeddings_dict[seq] = emb.cpu() + + if save: + torch.save(embeddings_dict, save_path) + + return embeddings_dict + + +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Optional, Tuple, Union, Dict, Any +from einops import rearrange +from dataclasses import dataclass +from transformers import PreTrainedModel, PretrainedConfig, EsmTokenizer +from transformers import initialization as init +from transformers.modeling_outputs import ( + ModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + SequenceClassifierOutput, + TokenClassifierOutput +) +from transformers.models.esm.modeling_esm import ( + EsmIntermediate, + EsmOutput, + EsmPooler, + EsmLMHead, + EsmSelfOutput, + EsmClassificationHead, + EsmContactPredictionHead, + EsmEmbeddings, + RotaryEmbedding, +) +try: + from torch.nn.attention.flex_attention import create_block_mask + from torch.nn.attention.flex_attention import flex_attention +except ImportError: + create_block_mask = None + flex_attention = None + + +def get_attention_mask( + attn_backend: str, + batch_size: int, + seq_len: int, + device: torch.device, + attention_mask: Optional[torch.Tensor] = None +) -> torch.Tensor: + if attention_mask is None: + token_attention_mask = torch.ones((batch_size, seq_len), device=device).bool() + else: + token_attention_mask = attention_mask.bool() + + if attn_backend == "flex": + assert create_block_mask is not None, "Flex attention backend requested but torch.create_block_mask is unavailable." + + def mask_mod(batch_idx, head_idx, q_idx, kv_idx): + return token_attention_mask[batch_idx, q_idx] & token_attention_mask[batch_idx, kv_idx] + + flex_block_mask = create_block_mask( + mask_mod, + batch_size, + 1, + seq_len, + seq_len, + device=device, + ) + extended_attention_mask = None + else: + flex_block_mask = None + extended_attention_mask = token_attention_mask[:, None, :, None] & token_attention_mask[:, None, None, :] + + return extended_attention_mask, flex_block_mask + + +@dataclass +class EsmMaskedLMOutput(ModelOutput): + loss: Optional[torch.Tensor] = None + logits: Optional[torch.Tensor] = None + last_hidden_state: Optional[torch.Tensor] = None + hidden_states: Optional[Tuple[torch.Tensor, ...]] = None + attentions: Optional[Tuple[torch.Tensor, ...]] = None + + +class FastEsmConfig(PretrainedConfig): + model_type = "fast_esm" + def __init__( + self, + vocab_size: int = None, + mask_token_id: int = None, + pad_token_id: int = None, + hidden_size: int = 768, + num_hidden_layers: int = 12, + num_attention_heads: int = 12, + intermediate_size: int = 3072, + hidden_dropout_prob: float = 0.1, + attention_probs_dropout_prob: float = 0.1, + max_position_embeddings: int = 1026, + initializer_range: float = 0.02, + layer_norm_eps: float = 1e-12, + position_embedding_type: str = "rotary", + emb_layer_norm_before: bool = None, + token_dropout: bool = True, + attn_backend: str = "sdpa", + **kwargs, + ): + super().__init__( + pad_token_id=pad_token_id, + mask_token_id=mask_token_id, + **kwargs, + ) + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.position_embedding_type = position_embedding_type + self.emb_layer_norm_before = emb_layer_norm_before + self.tie_word_embeddings = False + self.token_dropout = token_dropout + self.attn_backend = attn_backend + + def to_dict(self) -> Dict[str, Any]: + """ + Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. + + Returns: + `Dict[str, any]`: Dictionar y of all the attributes that make up this configuration instance, + """ + output = super().to_dict() + return output + + +class EsmSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type: Optional[str] = None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + self.scale = self.attention_head_size**-0.5 + + self.dropout_prob = config.attention_probs_dropout_prob + self.attn_backend = config.attn_backend + 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 forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + flex_block_mask: object, + output_attentions: Optional[bool] = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """Forward pass for self attention. + + Args: + hidden_states: Input tensor + attention_mask: 4D attention mask + flex_block_mask: Flex attention block mask + output_attentions: Whether to return attention weights + + Returns: + Output tensor and optionally attention weights + """ + batch_size, seq_length = hidden_states.shape[:-1] + hidden_shape = (batch_size, seq_length, -1, self.attention_head_size) + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2) + + query_layer = query_layer * self.scale + + if self.position_embedding_type == "rotary": + query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer) + + if output_attentions: + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) # (b, h, l, l) + attention_scores = attention_scores.masked_fill(attention_mask.logical_not(), float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + if self.dropout_prob > 0 and self.training: + 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: + if self.attn_backend == "flex": + assert flex_attention is not None, "Flex attention backend requested but torch.flex_attention is unavailable." + assert query_layer.dtype in (torch.float16, torch.bfloat16), f"Flex attention backend requires float16 or bfloat16, got {query_layer.dtype}." + assert flex_block_mask is not None, "Flex attention backend requires a block mask" + context_layer = flex_attention( + query_layer, + key_layer, + value_layer, + block_mask=flex_block_mask, + scale=1.0, # applied before rotary + ) + else: + context_layer = F.scaled_dot_product_attention( + query_layer, + key_layer, + value_layer, + attn_mask=attention_mask, + dropout_p=self.dropout_prob if self.training else 0.0, + scale=1.0 # applied before rotary + ) + 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: torch.Tensor, + flex_block_mask: object, + output_attentions: Optional[bool] = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """Forward pass for attention layer. + + Args: + hidden_states: Input tensor + attention_mask: 4D attention mask + flex_block_mask: Flex attention block 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, + flex_block_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: torch.Tensor, + flex_block_mask: object, + output_attentions: Optional[bool] = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """Forward pass for transformer layer. + + Args: + hidden_states: Input tensor + attention_mask: 4D attention mask + flex_block_mask: Flex attention block mask + output_attentions: Whether to return attention weights + + Returns: + Output tensor and optionally attention weights + """ + attention_outputs = self.attention( + hidden_states, + attention_mask, + flex_block_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: torch.Tensor, + flex_block_mask: object, + output_hidden_states: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ) -> BaseModelOutputWithPastAndCrossAttentions: + """Forward pass for transformer encoder. + + Args: + hidden_states: Input tensor + attention_mask: 4D attention mask + flex_block_mask: Flex attention block 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, + flex_block_mask, + output_attentions, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + flex_block_mask, + output_attentions, + ) + + if output_attentions: + hidden_states, attention_weights = layer_outputs + all_attentions = all_attentions + (attention_weights,) + else: + hidden_states = layer_outputs + + if self.emb_layer_norm_after: + hidden_states = self.emb_layer_norm_after(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_attentions, + ) + + +class FastEsmPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + config_class = FastEsmConfig + base_model_prefix = "fastesm" + supports_gradient_checkpointing = True + tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") + all_tied_weights_keys = {} + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, EsmLMHead): + init.zeros_(module.bias) + elif isinstance(module, EsmEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, RotaryEmbedding): + inv_freq = 1.0 / (10000 ** (torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + def get_output_embeddings(self): + # NOTE: get_output_embeddings() must return None to prevent accidental weight tying. + # See e.g. https://github.com/huggingface/transformers/pull/39339#discussion_r2219126400 + return None + + +class FAST_ESM_ENCODER(FastEsmPreTrainedModel, EmbeddingMixin): + def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs): + FastEsmPreTrainedModel.__init__(self, config, **kwargs) + self.config = config + self.embeddings = EsmEmbeddings(config) + self.encoder = EsmEncoder(config) + self.contact_head = EsmContactPredictionHead( + in_features=config.num_hidden_layers * config.num_attention_heads, bias=True + ) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + token_embedding_output = self.embeddings(input_ids, attention_mask=attention_mask) + attention_mask, flex_block_mask = get_attention_mask( + attn_backend=self.config.attn_backend, + batch_size=input_ids.shape[0], + seq_len=input_ids.shape[1], + device=input_ids.device, + attention_mask=attention_mask, + ) + encoder_outputs = self.encoder( + token_embedding_output, + attention_mask=attention_mask, + flex_block_mask=flex_block_mask, + output_hidden_states=False, + output_attentions=False, + ) + return encoder_outputs.last_hidden_state + + def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + attns = self(input_ids, attention_mask=attention_mask, output_attentions=True).attentions + attns = torch.stack(attns, dim=1) + attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3) + attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4) + return self.contact_head(input_ids, attns) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, # to play nice with HF adjacent packages + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + """Forward pass for base model. + + Args: + input_ids: Input token IDs + attention_mask: Optional attention mask + position_ids: Optional position IDs + inputs_embeds: Optional input embeddings + output_hidden_states: Whether to return all hidden states + output_attentions: Whether to return attention weights + + Returns: + Model outputs including hidden states and optionally attention weights + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + token_embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + ) + attention_mask, flex_block_mask = get_attention_mask( + attn_backend=self.config.attn_backend, + batch_size=input_ids.shape[0], + seq_len=input_ids.shape[1], + device=input_ids.device, + attention_mask=attention_mask, + ) + encoder_outputs = self.encoder( + token_embedding_output, + attention_mask=attention_mask, + flex_block_mask=flex_block_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + ) + sequence_output = encoder_outputs.last_hidden_state + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class FastEsmModel(FastEsmPreTrainedModel, EmbeddingMixin): + def __init__(self, config, add_pooling_layer: Optional[bool] = True, **kwargs): + FastEsmPreTrainedModel.__init__(self, config, **kwargs) + self.config = config + self.esm = FAST_ESM_ENCODER(config) + self.pooler = EsmPooler(config) if add_pooling_layer else None + self.post_init() + + def get_input_embeddings(self): + return self.esm.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.esm.embeddings.word_embeddings = value + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, # to play nice with HF adjacent packages + **kwargs, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + """Forward pass for base model. + + Args: + input_ids: Input token IDs + attention_mask: Optional attention mask + position_ids: Optional position IDs + inputs_embeds: Optional input embeddings + output_hidden_states: Whether to return all hidden states + output_attentions: Whether to return attention weights + + Returns: + Model outputs including hidden states and optionally attention weights + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + + outputs = self.esm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + ) + sequence_output = outputs.last_hidden_state + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class FastEsmForMaskedLM(FastEsmPreTrainedModel, EmbeddingMixin): + def __init__(self, config, **kwargs): + FastEsmPreTrainedModel.__init__(self, config, **kwargs) + self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) + self.lm_head = EsmLMHead(config) + self.loss_fct = nn.CrossEntropyLoss() + self.post_init() + + def get_input_embeddings(self): + return self.esm.embeddings.word_embeddings + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, # to play nice with HF adjacent packages + **kwargs, + ) -> Union[Tuple, EsmMaskedLMOutput]: + outputs = self.esm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + ) + sequence_output = outputs.last_hidden_state + prediction_scores = self.lm_head(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(prediction_scores.device) + loss = self.loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return EsmMaskedLMOutput( + loss=loss, + logits=prediction_scores, + last_hidden_state=sequence_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class FastEsmForSequenceClassification(FastEsmPreTrainedModel, EmbeddingMixin): + def __init__(self, config, **kwargs): + FastEsmPreTrainedModel.__init__(self, config, **kwargs) + self.num_labels = config.num_labels + self.config = config + self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) + self.classifier = EsmClassificationHead(config) + self.mse = nn.MSELoss() + self.ce = nn.CrossEntropyLoss() + self.bce = nn.BCEWithLogitsLoss() + self.post_init() + + def get_input_embeddings(self): + return self.esm.embeddings.word_embeddings + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple, SequenceClassifierOutput]: + outputs = self.esm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + ) + sequence_output = outputs.last_hidden_state + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + if self.num_labels == 1: + loss = self.mse(logits.squeeze(), labels.squeeze()) + else: + loss = self.mse(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss = self.bce(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class FastEsmForTokenClassification(FastEsmPreTrainedModel, EmbeddingMixin): + def __init__(self, config, **kwargs): + FastEsmPreTrainedModel.__init__(self, config, **kwargs) + self.num_labels = config.num_labels + self.esm = FAST_ESM_ENCODER(config, add_pooling_layer=False) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + self.loss_fct = nn.CrossEntropyLoss() + self.post_init() + + def get_input_embeddings(self): + return self.esm.embeddings.word_embeddings + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def predict_contacts(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + return self.esm.predict_contacts(input_ids, attention_mask=attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple, TokenClassifierOutput]: + outputs = self.esm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + ) + sequence_output = outputs.last_hidden_state + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + )