diff --git "a/modeling_esm_plusplus.py" "b/modeling_esm_plusplus.py" new file mode 100644--- /dev/null +++ "b/modeling_esm_plusplus.py" @@ -0,0 +1,3182 @@ +from __future__ import annotations + +import torch +import torch._inductor.config as inductor_config +import torch._dynamo as dynamo + +# Enable TensorFloat32 tensor cores for float32 matmul (Ampere+ GPUs) +# Provides significant speedup with minimal precision loss +torch.set_float32_matmul_precision('high') + +# Enable TF32 for matrix multiplications and cuDNN operations +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True + +# Enable cuDNN autotuner - finds fastest algorithms for your hardware +# Best when input sizes are consistent; may slow down first iterations +torch.backends.cudnn.benchmark = True + +# Deterministic operations off for speed (set True if reproducibility needed) +torch.backends.cudnn.deterministic = False +inductor_config.max_autotune_gemm_backends = "ATEN,CUTLASS,FBGEMM" + +dynamo.config.capture_scalar_outputs = True +torch._dynamo.config.recompile_limit = 16 + +import io +import os +import queue +import sqlite3 +import struct +import threading +import time + +import networkx as nx +import numpy as np +import torch +from tqdm.auto import tqdm +from typing import Any, Callable, Dict, Iterator, List, Optional, Set, Tuple +from torch.utils.data import DataLoader +from torch.utils.data import Dataset as TorchDataset +from transformers import PreTrainedTokenizerBase + + +# SQLite stores tensors as compact blobs. Keep this header format compatible +# with Protify readers that share the same dtype/version codes. +_COMPACT_VERSION = 0x01 +_DTYPE_TO_CODE = {torch.float16: 0, torch.bfloat16: 1, torch.float32: 2} +_CODE_TO_DTYPE = {0: torch.float16, 1: torch.bfloat16, 2: torch.float32} +_CODE_TO_NP_DTYPE = {0: np.float16, 1: np.float16, 2: np.float32} + + +def tensor_to_embedding_blob(tensor: torch.Tensor) -> bytes: + """Serialize a tensor to compact binary format for SQLite blob storage. + + Format: [version:1][dtype_code:1][ndim:4][shape:4*ndim][raw_bytes] + bfloat16 tensors are stored as float16 bytes (numpy lacks bfloat16) + but tagged with dtype_code=1 so they can be cast back on read. + Falls back to torch.save for unsupported dtypes. + """ + t = tensor.cpu() + if t.dtype not in _DTYPE_TO_CODE: + buffer = io.BytesIO() + torch.save(t, buffer) + return buffer.getvalue() + dtype_code = _DTYPE_TO_CODE[t.dtype] + + if t.dtype == torch.bfloat16: + raw = t.half().numpy().tobytes() + else: + raw = t.numpy().tobytes() + + shape = t.shape + header = struct.pack(f' bytes: + """Build just the compact header for a given dtype and shape.""" + dtype_code = _DTYPE_TO_CODE[dtype] + return struct.pack(f' List[bytes]: + """Serialize a batch of same-shape tensors to compact blobs (fast path for vectors). + + Builds the header once and slices raw bytes per row. Much faster than + per-row tensor_to_embedding_blob calls for uniform-shape batches. + """ + assert batch.ndim >= 2, f"Expected batch with >= 2 dims, got {batch.ndim}" + t = batch.cpu() + store_dtype = t.dtype + if t.dtype not in _DTYPE_TO_CODE: + return [tensor_to_embedding_blob(t[i]) for i in range(t.shape[0])] + + if t.dtype == torch.bfloat16: + arr = t.half().numpy() + store_dtype = torch.bfloat16 + else: + arr = t.numpy() + + row_shape = tuple(t.shape[1:]) + header = _compact_header(store_dtype, row_shape) + raw = arr.tobytes() + stride = len(raw) // t.shape[0] + return [header + raw[i * stride:(i + 1) * stride] for i in range(t.shape[0])] + + +def embedding_blob_to_tensor(blob: bytes, fallback_shape: Optional[Tuple[int, ...]] = None) -> torch.Tensor: + """Deserialize a blob back to a tensor. Auto-detects compact vs legacy formats.""" + if len(blob) >= 6 and blob[0] == _COMPACT_VERSION: + dtype_code = blob[1] + ndim = struct.unpack_from(' torch.Tensor: + assert isinstance(hidden_state_index, int), "hidden_state_index must be an integer." + if store_all_hidden_states: + assert hidden_states is not None, "store_all_hidden_states requires output_hidden_states=True." + assert len(hidden_states) > 0, "Model returned no hidden states." + return torch.stack(tuple(hidden_states), dim=1) + if hidden_state_index == -1: + return last_hidden_state + assert hidden_states is not None, "hidden_state_index selection requires output_hidden_states=True." + return hidden_states[hidden_state_index] + + +def _trim_full_embedding(embedding: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + mask = attention_mask.bool() + if embedding.ndim == 2: + return embedding[mask].reshape(-1, embedding.shape[-1]) + if embedding.ndim == 3: + return embedding[:, mask, :].reshape(embedding.shape[0], -1, embedding.shape[-1]) + raise AssertionError(f"Expected full embedding tensor with 2 or 3 dims, got {embedding.ndim}.") + + +def pool_embeddings( + embeddings: Dict[str, torch.Tensor], + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, +) -> Dict[str, torch.Tensor]: + pooler = Pooler(pooling_types) + pooled: Dict[str, torch.Tensor] = {} + for sequence, embedding in embeddings.items(): + assert isinstance(sequence, str), "Expected embedding dictionary keys to be sequences (str)." + assert isinstance(embedding, torch.Tensor), "Expected embedding dictionary values to be tensors." + if embedding.ndim == 1: + pooled[sequence] = embedding.cpu() + continue + if embedding.ndim == 3: + embedding = embedding[hidden_state_index] + assert embedding.ndim == 2, f"Expected token-wise embedding with 2 dims, got {embedding.ndim}." + pooled[sequence] = pooler(embedding.unsqueeze(0)).squeeze(0).cpu() + return pooled + + +def load_pooled_embeddings_from_pth( + save_path: str, + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, +) -> 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." + return pool_embeddings(payload, pooling_types=pooling_types, hidden_state_index=hidden_state_index) + + +def load_pooled_embeddings_from_db( + db_path: str, + sequences: Optional[List[str]] = None, + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, +) -> 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, timeout=30) as conn: + cursor = conn.cursor() + if sequences is None: + cursor.execute("SELECT sequence, embedding FROM embeddings") + else: + if len(sequences) == 0: + return loaded + placeholders = ",".join(["?"] * len(sequences)) + cursor.execute( + f"SELECT sequence, embedding FROM embeddings WHERE sequence IN ({placeholders})", + tuple(sequences), + ) + for sequence, embedding_bytes in cursor.fetchall(): + loaded[sequence] = embedding_blob_to_tensor(embedding_bytes) + return pool_embeddings(loaded, pooling_types=pooling_types, hidden_state_index=hidden_state_index) + + +def maybe_compile(model: torch.nn.Module, dynamic: bool = False) -> torch.nn.Module: + """Compile model with torch.compile if possible. + + Skips compilation when dynamic=True (padding='longest') because + flex attention's create_block_mask is incompatible with dynamic shapes + under torch.compile, causing CUDA illegal memory access. + """ + if dynamic: + print("Skipping torch.compile (dynamic shapes + flex attention incompatible)") + return model + try: + model = torch.compile(model) + print("Model compiled") + except Exception as e: + print(f"Skipping torch.compile: {e}") + return model + + +def build_collator( + tokenizer: PreTrainedTokenizerBase, + padding: str = 'max_length', + max_length: int = 512, +) -> Callable[[List[str]], Dict[str, torch.Tensor]]: + def _collate_fn(sequences: List[str]) -> Dict[str, torch.Tensor]: + kwargs: Dict[str, Any] = dict( + return_tensors="pt", padding=padding, truncation=True, max_length=max_length, + ) + if padding != 'max_length': + kwargs['pad_to_multiple_of'] = 8 + return tokenizer(sequences, **kwargs) + return _collate_fn + + +def _make_embedding_progress( + dataloader: DataLoader, + padding: str, + n_warmup: int = 3, + n_calibration: int = 5, +) -> Iterator[Tuple[int, Any]]: + """Progress-bar wrapper for embedding loops. Drop-in replacement for enumerate(dataloader). + + When padding='max_length', all batches have uniform cost so plain tqdm works. + When padding='longest' (sorted longest-first), batch times vary dramatically. + In that case: yield warmup batches first (compiler warmup + OOM check on longest + sequences), then time mid-length calibration batches to estimate total ETA. + + Keep in sync with protify/embedder.py and core/atlas/precomputed.py. + """ + total = len(dataloader) + if padding == 'max_length' or total <= n_warmup + n_calibration: + for i, batch in tqdm(enumerate(dataloader), total=total, desc='Embedding batches'): + yield i, batch + return + + dl_iter = iter(dataloader) + + # Warm up on the longest batches first; sorted inputs make these the OOM-risk + # and compile-stabilization cases. + warmup_bar = tqdm(range(n_warmup), desc='Warmup (longest batches)', leave=False) + for i in warmup_bar: + batch = next(dl_iter) + yield i, batch + warmup_bar.close() + + # Move toward mid-length batches for ETA calibration, yielding every real + # batch on the way so no sequences are skipped. + mid_start = total // 2 + intermediate_bar = tqdm( + range(n_warmup, mid_start), desc='Embedding batches', leave=False, + ) + for i in intermediate_bar: + batch = next(dl_iter) + yield i, batch + intermediate_bar.close() + + # Mid-length batches give a better remaining-time estimate than the longest + # warmup batches. + calibration_times: List[float] = [] + cal_bar = tqdm(range(n_calibration), desc='Calibrating ETA', leave=False) + for j in cal_bar: + t0 = time.perf_counter() + batch = next(dl_iter) + yield mid_start + j, batch + calibration_times.append(time.perf_counter() - t0) + cal_bar.close() + + avg_time = sum(calibration_times) / len(calibration_times) + remaining_start = mid_start + n_calibration + remaining_count = total - remaining_start + estimated_total_seconds = avg_time * remaining_count + + # Finish the tail with the calibrated ETA shown in the progress bar. + main_bar = tqdm( + range(remaining_count), + desc='Embedding batches', + bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]', + ) + main_bar.set_postfix_str(f'ETA ~{estimated_total_seconds:.0f}s (calibrated)') + for k in main_bar: + batch = next(dl_iter) + yield remaining_start + k, batch + main_bar.close() + + +class _SQLWriter: + """Context manager for async SQL embedding writes. Matches core/embed/storage.SQLEmbeddingWriter.""" + + def __init__(self, conn: sqlite3.Connection, queue_maxsize: int = 4) -> None: + self._conn = conn + self._queue: queue.Queue = queue.Queue(maxsize=queue_maxsize) + self._thread: Optional[threading.Thread] = None + + def __enter__(self) -> "_SQLWriter": + self._thread = threading.Thread(target=self._writer_loop, daemon=True) + self._thread.start() + return self + + def write_batch(self, rows: List[Tuple[str, bytes]]) -> None: + self._queue.put(rows) + + def _writer_loop(self) -> None: + cursor = self._conn.cursor() + while True: + item = self._queue.get() + if item is None: + break + cursor.executemany("INSERT OR REPLACE INTO embeddings VALUES (?, ?)", item) + if self._queue.qsize() == 0: + self._conn.commit() + self._conn.commit() + + def __exit__(self, *exc) -> None: + if self._thread is not None: + self._queue.put(None) + self._thread.join() + self._thread = None + + +class Pooler: + def __init__(self, pooling_types: List[str]) -> None: + self.pooling_types = pooling_types + self.pooling_options: Dict[str, Callable] = { + '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: + assert isinstance(attentions, torch.Tensor) + maxed_attentions = torch.max(attentions, dim=1)[0] + return maxed_attentions + + def _page_rank(self, attention_matrix: np.ndarray, personalization: Optional[dict] = None, nstart: Optional[dict] = None, prune_type: str = "top_k_outdegree") -> Dict[int, float]: + 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: np.ndarray) -> nx.DiGraph: + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + return G + + def _calculate_importance_weights(self, dict_importance: Dict[int, float], attention_mask: Optional[torch.Tensor] = None) -> np.ndarray: + if attention_mask is not None: + for k in list(dict_importance.keys()): + if attention_mask[k] == 0: + del dict_importance[k] + + 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) -> torch.Tensor: + maxed_attentions = self._create_pooled_matrices_across_layers(attentions).numpy() + 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) -> torch.Tensor: + 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) -> torch.Tensor: + if attention_mask is None: + return emb.max(dim=1).values + else: + mask = attention_mask.unsqueeze(-1).bool() + return emb.masked_fill(~mask, float('-inf')).max(dim=1).values + + def norm_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: + 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) -> torch.Tensor: + if attention_mask is None: + return emb.median(dim=1).values + else: + mask = attention_mask.unsqueeze(-1).bool() + return emb.masked_fill(~mask, float('nan')).nanmedian(dim=1).values + + def std_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: + if attention_mask is None: + return emb.std(dim=1) + else: + 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) -> torch.Tensor: + if attention_mask is None: + return emb.var(dim=1) + else: + attention_mask = attention_mask.unsqueeze(-1) + mean = (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) + mean = mean.unsqueeze(1) + squared_diff = (emb - mean) ** 2 + var = (squared_diff * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) + return var + + def cls_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: + return emb[:, 0, :] + + def __call__( + self, + emb: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + attentions: Optional[torch.Tensor] = None + ) -> torch.Tensor: + if attention_mask is not None: + assert attention_mask.sum(dim=-1).min() > 0, ( + "Pooler received samples with all-zero attention masks. " + "This causes NaN from division by zero. Filter empty inputs before pooling." + ) + final_emb: List[torch.Tensor] = [] + for pooling_type in self.pooling_types: + final_emb.append(self.pooling_options[pooling_type](emb=emb, attention_mask=attention_mask, attentions=attentions)) + return torch.cat(final_emb, dim=-1) + + +class ProteinDataset(TorchDataset): + """Simple dataset for protein sequences.""" + def __init__(self, sequences: List[str]) -> None: + self.sequences = sequences + + def __len__(self) -> int: + return len(self.sequences) + + def __getitem__(self, idx: int) -> str: + return self.sequences[idx] + + +def parse_fasta(fasta_path: str) -> List[str]: + assert os.path.exists(fasta_path), f"FASTA file does not exist: {fasta_path}" + sequences = [] + current_seq = [] + with open(fasta_path, 'r') as f: + for line in f: + line = line.strip() + if not line: + continue + if line.startswith('>'): + if current_seq: + sequences.append(''.join(current_seq)) + current_seq = [] + else: + current_seq.append(line) + if current_seq: + sequences.append(''.join(current_seq)) + return sequences + + +class EmbeddingMixin: + def _embed( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + ) -> 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.""" + with sqlite3.connect(db_path, timeout=30) as conn: + c = conn.cursor() + c.execute("SELECT sequence FROM embeddings") + return {row[0] for row in c.fetchall()} + + 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" + ")" + ) + 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, timeout=30) as conn: + self._ensure_embeddings_table(conn) + cursor = conn.cursor() + if sequences is None: + cursor.execute("SELECT sequence, embedding FROM embeddings") + else: + if len(sequences) == 0: + return loaded + placeholders = ",".join(["?"] * len(sequences)) + cursor.execute( + f"SELECT sequence, embedding FROM embeddings WHERE sequence IN ({placeholders})", + tuple(sequences), + ) + + rows = cursor.fetchall() + for row in rows: + sequence = row[0] + embedding_bytes = row[1] + loaded[sequence] = embedding_blob_to_tensor(embedding_bytes) + return loaded + + def pool_embeddings( + self, + embeddings: Dict[str, torch.Tensor], + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, + ) -> Dict[str, torch.Tensor]: + return pool_embeddings(embeddings, pooling_types=pooling_types, hidden_state_index=hidden_state_index) + + def load_pooled_embeddings_from_pth( + self, + save_path: str, + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, + ) -> Dict[str, torch.Tensor]: + return load_pooled_embeddings_from_pth( + save_path, + pooling_types=pooling_types, + hidden_state_index=hidden_state_index, + ) + + def load_pooled_embeddings_from_db( + self, + db_path: str, + sequences: Optional[List[str]] = None, + pooling_types: List[str] = ['mean'], + hidden_state_index: int = -1, + ) -> Dict[str, torch.Tensor]: + return load_pooled_embeddings_from_db( + db_path, + sequences=sequences, + pooling_types=pooling_types, + hidden_state_index=hidden_state_index, + ) + + def embed_dataset( + self, + sequences: Optional[List[str]] = None, + 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', + fasta_path: Optional[str] = None, + padding: str = 'max_length', + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + **kwargs, + ) -> Optional[Dict[str, torch.Tensor]]: + """ + Embed a dataset of protein sequences. + + Supports two modes: + - Tokenizer mode (ESM2/ESM++): provide `tokenizer` or use `self.tokenizer`. + - Sequence mode (E1): pass `tokenizer=None`, `_embed(sequences, return_attention_mask=True, **kwargs)` is used. + + Sequences can be supplied as a list via `sequences`, parsed from a FASTA file via + `fasta_path`, or both (the two sources are combined). At least one must be provided. + """ + if fasta_path is not None: + fasta_sequences = parse_fasta(fasta_path) + sequences = list(sequences or []) + fasta_sequences + assert sequences is not None and len(sequences) > 0, \ + "Must provide at least one sequence via `sequences` or `fasta_path`." + assert isinstance(hidden_state_index, int), "hidden_state_index must be an integer." + assert full_embeddings or not store_all_hidden_states, \ + "store_all_hidden_states=True requires full_embeddings=True." + sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences])) + sequences = sorted(sequences, key=len, reverse=True) + pooler = Pooler(pooling_types) if not full_embeddings else None + if tokenizer is None and self.config.model_type != "E1": + tokenizer = self.tokenizer + tokenizer_mode = tokenizer is not None + + # Resolve padding and compilation + dynamic = padding == 'longest' + compiled_model = maybe_compile(self, dynamic=dynamic) + + if tokenizer_mode: + collate_fn = build_collator(tokenizer, padding=padding, max_length=max_len) + device = self.device + else: + collate_fn = None + device = None + + def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + assert isinstance(residue_embeddings, 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, + prefetch_factor=2 if num_workers > 0 else None, + collate_fn=collate_fn, + shuffle=False, + pin_memory=True, + ) + for i, batch in _make_embedding_progress(dataloader, padding): + 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 = compiled_model._embed( + input_ids, + attention_mask, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + ) + 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 = compiled_model._embed( + seqs, + return_attention_mask=True, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + **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: + # Resume safely: skip sequences already present in the SQLite table. + conn = sqlite3.connect(sql_db_path, timeout=30, check_same_thread=False) + conn.execute('PRAGMA journal_mode=WAL') + conn.execute('PRAGMA busy_timeout=30000') + conn.execute('PRAGMA synchronous=OFF') + conn.execute('PRAGMA cache_size=-64000') + self._ensure_embeddings_table(conn) + 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: + # Embed batches synchronously; serialize/write them on the SQL writer thread. + with _SQLWriter(conn) as writer: + with torch.inference_mode(): + for seqs, residue_embeddings, attention_mask in iter_batches(to_embed): + embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) + if full_embeddings: + batch_rows = [] + for seq, emb, mask in zip(seqs, embeddings, attention_mask): + batch_rows.append((seq, tensor_to_embedding_blob(_trim_full_embedding(emb, mask)))) + else: + blobs = batch_tensor_to_blobs(embeddings) + batch_rows = list(zip(seqs, blobs)) + writer.write_batch(batch_rows) + 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.inference_mode(): + 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 = _trim_full_embedding(emb, mask) + embeddings_dict[seq] = emb.cpu() + + if save: + torch.save(embeddings_dict, save_path) + + return embeddings_dict + + +if __name__ == "__main__": + # Manual smoke test for pooling shape behavior. + pooler = Pooler(pooling_types=['max', 'parti']) + batch_size = 8 + seq_len = 64 + hidden_size = 128 + num_layers = 12 + emb = torch.randn(batch_size, seq_len, hidden_size) + attentions = torch.randn(batch_size, num_layers, seq_len, seq_len) + attention_mask = torch.ones(batch_size, seq_len) + y = pooler(emb=emb, attention_mask=attention_mask, attentions=attentions) + print(y.shape) + +"""Shared attention infrastructure for all FastPLMs models. + +Contains: AttentionBackend enum, backend resolution, mask creation, +flex attention helpers, flash kernel detection/dispatch, and pad/unpad utilities. +""" +from enum import Enum +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from torch.nn import functional as F +from einops import rearrange + +try: + from torch.nn.attention.flex_attention import create_block_mask, flex_attention, BlockMask +except ImportError: + create_block_mask = None + flex_attention = None + BlockMask = None + +_compiled_flex_attention = None + + +def _get_flex_attention_fn(): + """Return flex_attention callable: compiled (fused kernel) by default, or eager when debug flag is set.""" + global _compiled_flex_attention + if flex_attention is None: + return None + flex_mod = torch.nn.attention.flex_attention + if getattr(flex_mod, "_FLEX_ATTENTION_DISABLE_COMPILE_DEBUG", False): + return flex_attention + if _compiled_flex_attention is None: + _compiled_flex_attention = torch.compile( + flex_attention, + dynamic=False, + ) + return _compiled_flex_attention + + +# HuggingFace `kernels` exposes slightly different APIs for Flash Attention 2 +# and 3. Detect the loaded variant once so every caller uses the same dispatch. +def _infer_kernels_flash_variant(kernel) -> Optional[str]: + if hasattr(kernel, "fwd") and hasattr(kernel, "varlen_fwd"): + return "flash_attn2" + if hasattr(kernel, "flash_attn_func") and hasattr(kernel, "flash_attn_varlen_func"): + return "flash_attn3" + return None + + +def _try_get_kernels_flash(): + try: + from kernels import get_kernel + except ImportError: + return None, None + + flash_kernel = None + flash_kernel_variant = None + try: + flash_kernel = get_kernel("kernels-community/flash-attn3") + flash_kernel_variant = _infer_kernels_flash_variant(flash_kernel) + assert flash_kernel_variant is not None, "Loaded flash-attn3 kernel does not expose a supported API." + except Exception: + try: + flash_kernel = get_kernel("kernels-community/flash-attn2") + flash_kernel_variant = _infer_kernels_flash_variant(flash_kernel) + assert flash_kernel_variant is not None, "Loaded flash-attn2 kernel does not expose a supported API." + except Exception: + flash_kernel = None + flash_kernel_variant = None + return flash_kernel, flash_kernel_variant + + +_FLASH_KERNELS_LOADED = False +FLASH_KERNEL = None +FLASH_KERNEL_VARIANT = None + + +def _ensure_flash_kernels_loaded(): + global _FLASH_KERNELS_LOADED, FLASH_KERNEL, FLASH_KERNEL_VARIANT + if _FLASH_KERNELS_LOADED: + return + _FLASH_KERNELS_LOADED = True + FLASH_KERNEL, FLASH_KERNEL_VARIANT = _try_get_kernels_flash() + + +def _kernels_flash_forward( + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + causal: bool = False, + softmax_scale: Optional[float] = None, +) -> torch.Tensor: + """Flash-attention forward, optionally overriding the softmax scale. + + When `softmax_scale is None`, the flash kernel applies its default + `1 / sqrt(head_dim)`. Pass `softmax_scale=1.0` if the caller has already + pre-scaled Q (the convention used by ESM2, DPLM, DPLM2, E1, ESMFold). + Failing to override when Q is pre-scaled applies the scale twice. On + DPLM-150M, that produced pooled-embedding cosine around -0.12 and argmax + agreement around 0.27 vs SDPA. + """ + assert FLASH_KERNEL is not None, "Kernel Flash Attention is not available in this environment." + if FLASH_KERNEL_VARIANT == "flash_attn2": + return FLASH_KERNEL.fwd( + q=query_states, k=key_states, v=value_states, + softmax_scale=softmax_scale, is_causal=causal, + )[0] + if FLASH_KERNEL_VARIANT == "flash_attn3": + try: + output = FLASH_KERNEL.flash_attn_func( + q=query_states, k=key_states, v=value_states, + softmax_scale=softmax_scale, causal=causal, + ) + except TypeError: + output = FLASH_KERNEL.flash_attn_func( + query_states, key_states, value_states, + 0.0, softmax_scale, causal, + ) + if isinstance(output, tuple): + return output[0] + return output + raise AssertionError(f"Unsupported kernels flash attention variant: {FLASH_KERNEL_VARIANT}") + + +def _kernels_flash_varlen_forward( + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_in_batch_q: int, + max_seqlen_in_batch_k: int, + causal: bool = False, + softmax_scale: Optional[float] = None, +) -> torch.Tensor: + """Varlen flash-attention forward, optionally overriding the softmax scale. + + See `_kernels_flash_forward` docstring for why `softmax_scale=1.0` must be + passed when Q has been pre-scaled by the caller. + """ + assert FLASH_KERNEL is not None, "Kernel Flash Attention is not available in this environment." + if FLASH_KERNEL_VARIANT == "flash_attn2": + return FLASH_KERNEL.varlen_fwd( + q=query_states, k=key_states, v=value_states, + cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, + softmax_scale=softmax_scale, is_causal=causal, + )[0] + if FLASH_KERNEL_VARIANT == "flash_attn3": + try: + output = FLASH_KERNEL.flash_attn_varlen_func( + q=query_states, k=key_states, v=value_states, + cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, + softmax_scale=softmax_scale, causal=causal, + ) + except TypeError: + output = FLASH_KERNEL.flash_attn_varlen_func( + query_states, key_states, value_states, + cu_seqlens_q, cu_seqlens_k, + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + 0.0, softmax_scale, causal, + ) + if isinstance(output, tuple): + return output[0] + return output + raise AssertionError(f"Unsupported kernels flash attention variant: {FLASH_KERNEL_VARIANT}") + + +# Varlen flash attention runs only on real tokens. These helpers remove padding +# before the kernel call and restore the original padded batch shape afterward. +class IndexFirstAxis(torch.autograd.Function): + @staticmethod + def forward(ctx, input, indices) -> torch.Tensor: + ctx.save_for_backward(indices) + assert input.ndim >= 2 + ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:] + second_dim = other_shape.numel() + return torch.gather( + rearrange(input, "b ... -> b (...)"), 0, indices.unsqueeze(1).expand(-1, second_dim) + ).reshape(-1, *other_shape) + + @staticmethod + def backward(ctx, grad_output) -> Tuple[torch.Tensor, None]: + (indices,) = ctx.saved_tensors + assert grad_output.ndim >= 2 + other_shape = grad_output.shape[1:] + grad_output = rearrange(grad_output, "b ... -> b (...)") + grad_input = torch.zeros( + [ctx.first_axis_dim, grad_output.shape[1]], device=grad_output.device, dtype=grad_output.dtype + ) + grad_input.scatter_(0, indices.unsqueeze(1).expand(-1, grad_output.shape[1]), grad_output) + return grad_input.reshape(ctx.first_axis_dim, *other_shape), None + + +class IndexPutFirstAxis(torch.autograd.Function): + @staticmethod + def forward(ctx, values, indices, first_axis_dim) -> torch.Tensor: + ctx.save_for_backward(indices) + assert indices.ndim == 1 + assert values.ndim >= 2 + output = torch.zeros(first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype) + output[indices] = values + return output + + @staticmethod + def backward(ctx, grad_output) -> Tuple[torch.Tensor, None, None]: + (indices,) = ctx.saved_tensors + return grad_output[indices], None, None + + +index_first_axis = IndexFirstAxis.apply +index_put_first_axis = IndexPutFirstAxis.apply + + +def pad_input(hidden_states: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor: + output = index_put_first_axis(hidden_states, indices, batch * seqlen) + return rearrange(output, "(b s) ... -> b s ...", b=batch) + + +def _unpad_input( + query_layer: torch.Tensor, + key_layer: torch.Tensor, + value_layer: torch.Tensor, + attention_mask_2d: torch.Tensor, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[torch.Tensor, torch.Tensor], Tuple[int, int]]: + batch_size, seq_len, num_heads, head_dim = query_layer.shape + seqlens = attention_mask_2d.sum(dim=1).int() + cu_seqlens = F.pad(seqlens.cumsum(0, dtype=torch.int32), (1, 0)) + max_seqlen = int(seqlens.max().item()) + indices = attention_mask_2d.flatten().nonzero(as_tuple=False).flatten() + query_layer = index_first_axis(query_layer.reshape(batch_size * seq_len, num_heads, head_dim), indices) + key_layer = index_first_axis(key_layer.reshape(batch_size * seq_len, num_heads, head_dim), indices) + value_layer = index_first_axis(value_layer.reshape(batch_size * seq_len, num_heads, head_dim), indices) + return query_layer, key_layer, value_layer, indices, (cu_seqlens, cu_seqlens), (max_seqlen, max_seqlen) + + +def kernels_flash_attention_func( + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + attention_mask_2d: Optional[torch.Tensor] = None, + causal: bool = False, + softmax_scale: Optional[float] = None, +) -> torch.Tensor: + """Public flash-attention entry point with optional padding handling. + + `softmax_scale`: + None -> kernel applies its default `1 / sqrt(head_dim)`. + float -> kernel uses the given scale (pass 1.0 when Q is pre-scaled + by the caller). + + Caller contract: if a model family pre-scales Q by `1/sqrt(head_dim)` + before calling this function (ESM2, DPLM, DPLM2, E1, and ESMFold do), pass + `softmax_scale=1.0`. Otherwise the flash kernel applies its default scale + again, yielding an effective `1/head_dim` scale that drifts across layers. + """ + assert FLASH_KERNEL is not None, "Kernel Flash Attention is not available in this environment." + if not causal and attention_mask_2d is not None: + batch_size, q_len = query_states.shape[:2] + ( + query_states, key_states, value_states, + indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k), + ) = _unpad_input(query_states, key_states, value_states, attention_mask_2d) + attn_output_unpad = _kernels_flash_varlen_forward( + query_states=query_states, key_states=key_states, value_states=value_states, + cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, + max_seqlen_in_batch_q=max_seqlen_q, max_seqlen_in_batch_k=max_seqlen_k, + softmax_scale=softmax_scale, + ) + return pad_input(attn_output_unpad, indices_q, batch_size, q_len) + else: + return _kernels_flash_forward( + query_states=query_states, key_states=key_states, value_states=value_states, + causal=causal, softmax_scale=softmax_scale, + ) + + +# User-facing backend strings resolve to this enum before attention dispatch. +class AttentionBackend(Enum): + AUTO = "auto" + KERNELS_FLASH = "kernels_flash" + FLEX = "flex" + SDPA = "sdpa" + + +VALID_ATTENTION_BACKENDS = tuple(b.value for b in AttentionBackend) + + +_BACKEND_CONFIRMED = False + + +def resolve_attention_backend(requested_backend: str) -> AttentionBackend: + global _BACKEND_CONFIRMED + assert requested_backend in VALID_ATTENTION_BACKENDS, ( + f"Unsupported attention backend: {requested_backend}. Expected one of {VALID_ATTENTION_BACKENDS}." + ) + if requested_backend in (AttentionBackend.AUTO.value, AttentionBackend.KERNELS_FLASH.value): + _ensure_flash_kernels_loaded() + if requested_backend == AttentionBackend.AUTO.value: + if FLASH_KERNEL is not None: + resolved = AttentionBackend.KERNELS_FLASH + elif flex_attention is not None: + resolved = AttentionBackend.FLEX + else: + resolved = AttentionBackend.SDPA + elif requested_backend == AttentionBackend.KERNELS_FLASH.value: + assert FLASH_KERNEL is not None, "Kernels Flash Attention is not available in this environment." + resolved = AttentionBackend.KERNELS_FLASH + elif requested_backend == AttentionBackend.FLEX.value: + assert flex_attention is not None, "Flex Attention is not available in this environment." + resolved = AttentionBackend.FLEX + elif requested_backend == AttentionBackend.SDPA.value: + resolved = AttentionBackend.SDPA + else: + raise AssertionError(f"Unsupported attention backend: {requested_backend}") + if not _BACKEND_CONFIRMED: + print(f"Attention backend: config='{requested_backend}' -> resolved='{resolved.value}'") + _BACKEND_CONFIRMED = True + return resolved + + +@torch.compiler.disable +def get_attention_mask( + effective_backend: AttentionBackend, + batch_size: int, + seq_len: int, + device: torch.device, + attention_mask: Optional[torch.Tensor] = None, +) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[BlockMask]]: + """Build padding masks once for all encoder layers. + + Returns (attention_mask_2d, attention_mask_4d, flex_block_mask). + """ + if attention_mask is None: + return None, None, None + + attention_mask_2d = attention_mask.bool() + + if effective_backend == AttentionBackend.KERNELS_FLASH: + return attention_mask_2d, None, None + + if effective_backend == AttentionBackend.FLEX: + assert create_block_mask is not None, "Flex attention backend requested but torch.create_block_mask is unavailable." + valid_lens = attention_mask_2d.sum(dim=-1) + + def mask_mod(batch_idx, head_idx, q_idx, kv_idx): + return (q_idx < valid_lens[batch_idx]) & (kv_idx < valid_lens[batch_idx]) + + flex_block_mask = create_block_mask(mask_mod, batch_size, 1, seq_len, seq_len, device=device) + return attention_mask_2d, None, flex_block_mask + + # SDPA/manual masks only keys. Padding queries still attend to real keys, so + # their outputs stay finite instead of softmaxing over all -inf scores. + attention_mask_4d = attention_mask_2d[:, None, None, :] + return attention_mask_2d, attention_mask_4d, None + + +def bool_to_additive_mask( + bool_mask: torch.Tensor, + dtype: torch.dtype, +) -> torch.Tensor: + """Convert a bool mask (True = valid) to a float additive mask (0.0 valid, -inf invalid). + + Why this exists: calling `bool_mask.masked_fill(bool_mask.logical_not(), float('-inf'))` + directly on a bool tensor returns a bool tensor because `-inf` casts to `True`. + That silently drops the mask. Always allocate a float tensor first, then fill it. + This helper is the sanctioned way to build an SDPA additive mask from a bool validity mask. + """ + assert bool_mask.dtype == torch.bool, ( + f"bool_to_additive_mask requires a bool tensor, got dtype={bool_mask.dtype}" + ) + additive = torch.zeros_like(bool_mask, dtype=dtype) + additive.masked_fill_(bool_mask.logical_not(), float("-inf")) + return additive + +import typing as T +from dataclasses import dataclass, fields + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +@dataclass +class TTTConfig: + lr: float = 4e-4 + steps: int = 30 + ags: int = 16 + batch_size: int = 2 + mask_ratio: float = 0.15 + crop_size: int = 1024 + bert_leave_prob: float = 0.1 + bert_replace_prob: float = 0.1 + optimizer: str = "sgd" + momentum: float = 0.0 + weight_decay: float = 0.0 + seed: int | None = 0 + lora_rank: int = 8 + lora_alpha: float = 32.0 + lora_target_replace_module: str | None = None + lora_target_modules: tuple[str, ...] | None = None + initial_state_reset: bool = True + automatic_best_state_reset: bool = False + eval_each_step: bool = False + gradient_clip: bool = False + gradient_clip_max_norm: float = 1.0 + + @classmethod + def from_kwargs(cls, **kwargs: T.Any) -> "TTTConfig": + valid_names = {field.name for field in fields(cls)} + unknown_names = set(kwargs) - valid_names + assert len(unknown_names) == 0, f"Unknown TTTConfig fields: {sorted(unknown_names)}" + return cls(**kwargs) + + def merged(self, overrides: T.Mapping[str, T.Any] | "TTTConfig" | None) -> "TTTConfig": + if overrides is None: + return self + if isinstance(overrides, TTTConfig): + return overrides + values = {field.name: self.__dict__[field.name] for field in fields(self)} + for name, value in overrides.items(): + assert name in values, f"Unknown TTTConfig field: {name}" + values[name] = value + return TTTConfig(**values) + + def verify(self) -> None: + assert self.lr > 0.0, "TTT learning rate must be positive." + assert self.steps >= 1, "TTT steps must be >= 1." + assert self.ags >= 1, "TTT gradient accumulation steps must be >= 1." + assert self.batch_size >= 1, "TTT batch_size must be >= 1." + assert 0.0 < self.mask_ratio <= 1.0, "TTT mask_ratio must be in (0, 1]." + assert self.crop_size >= 1, "TTT crop_size must be >= 1." + assert self.lora_rank >= 1, "TTT v1 is LoRA-only, so lora_rank must be >= 1." + assert self.lora_alpha > 0.0, "TTT lora_alpha must be positive." + assert self.optimizer in {"adamw", "sgd"}, "TTT optimizer must be 'adamw' or 'sgd'." + assert 0.0 <= self.bert_leave_prob <= 1.0, "bert_leave_prob must be in [0, 1]." + assert 0.0 <= self.bert_replace_prob <= 1.0, "bert_replace_prob must be in [0, 1]." + assert self.bert_leave_prob + self.bert_replace_prob <= 1.0, ( + "bert_leave_prob + bert_replace_prob must be <= 1." + ) + if self.gradient_clip: + assert self.gradient_clip_max_norm > 0.0, "gradient_clip_max_norm must be positive." + + +class LoraInjectedLinear(nn.Module): + def __init__(self, linear: nn.Module, rank: int, alpha: float) -> None: + super().__init__() + weight = linear._parameters["weight"] + assert weight.ndim == 2, "LoRA can only wrap 2D linear weights." + self.linear = linear + self.linear.requires_grad_(False) + self.rank = rank + self.scale = alpha + in_features = weight.shape[1] + out_features = weight.shape[0] + self.lora_down = nn.Linear(in_features, rank, bias=False, dtype=torch.float32) + self.lora_up = nn.Linear(rank, out_features, bias=False, dtype=torch.float32) + self.lora_down.to(device=weight.device) + self.lora_up.to(device=weight.device) + nn.init.normal_(self.lora_down.weight, std=1.0 / rank) + nn.init.zeros_(self.lora_up.weight) + + @property + def weight(self) -> torch.Tensor: + return self.linear._parameters["weight"] + + @property + def bias(self) -> torch.Tensor | None: + return self.linear._parameters["bias"] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + base = self.linear(x) + delta = self.lora_up(self.lora_down(x.to(dtype=torch.float32))) * self.scale + return base + delta.to(dtype=base.dtype) + + +class FastPLMTestTimeTrainingMixin: + def init_ttt(self, ttt_config: TTTConfig | T.Mapping[str, T.Any] | None = None) -> None: + base_config = TTTConfig() + self._ttt_cfg = base_config.merged(ttt_config) + self._ttt_cfg.verify() + self._ttt_initialized = False + self._ttt_initial_state: list[dict[str, torch.Tensor]] | None = None + + @property + def ttt_config(self) -> TTTConfig: + if "_ttt_cfg" not in self.__dict__: + self.init_ttt() + return self._ttt_cfg + + def _ttt_get_trainable_modules(self) -> list[nn.Module]: + return [self] + + def _ttt_get_frozen_modules(self) -> list[nn.Module]: + return [] + + def _ttt_tokenize( + self, + seq: str | list[str] | None = None, + input_ids: torch.Tensor | None = None, + **kwargs: T.Any, + ) -> torch.Tensor | dict[str, torch.Tensor]: + del kwargs + if input_ids is not None: + return input_ids + assert seq is not None, "Pass either seq or input_ids for TTT." + tokenized = self.tokenizer(seq, return_tensors="pt", padding=True) + return tokenized["input_ids"] + + def _ttt_mask_token(self) -> int: + return int(self.tokenizer.mask_token_id) + + def _ttt_padding_token(self) -> int: + return int(self.tokenizer.pad_token_id) + + def _ttt_replacement_tokens(self, input_ids: torch.Tensor) -> torch.Tensor: + tokenizer = self.tokenizer + special_ids = set(tokenizer.all_special_ids) + vocab_size = int(self.config.vocab_size) + ids = [idx for idx in range(vocab_size) if idx not in special_ids] + assert len(ids) > 0, "TTT replacement token set is empty." + return torch.tensor(ids, device=input_ids.device, dtype=input_ids.dtype) + + def _ttt_predict_logits( + self, + batch: torch.Tensor | dict[str, torch.Tensor], + **kwargs: T.Any, + ) -> torch.Tensor: + del kwargs + if isinstance(batch, dict): + output = self(**batch) + return output.logits + attention_mask = batch.ne(self._ttt_padding_token()) + output = self(input_ids=batch, attention_mask=attention_mask) + return output.logits + + def _ttt_eval_step( + self, + step: int, + loss: float, + seq: str | list[str] | None = None, + input_ids: torch.Tensor | None = None, + **kwargs: T.Any, + ) -> tuple[dict[str, T.Any], float | None]: + del step, loss, seq, input_ids, kwargs + return {}, None + + def _ttt_is_lora_target( + self, + name: str, + full_name: str, + module: nn.Module, + active: bool, + target_modules: tuple[str, ...] | None, + ) -> bool: + if not active: + return False + if isinstance(module, LoraInjectedLinear): + return False + if ( + target_modules is not None + and name not in target_modules + and full_name not in target_modules + ): + return False + if isinstance(module, nn.Linear): + return True + if "weight" not in module._parameters: + return False + weight = module._parameters["weight"] + if weight is None or weight.ndim != 2: + return False + return "Linear" in module.__class__.__name__ + + def _ttt_inject_lora(self) -> int: + cfg = self.ttt_config + cfg.verify() + target_class = cfg.lora_target_replace_module + target_modules = cfg.lora_target_modules + wrapped = 0 + + def inject(module: nn.Module, prefix: str, active: bool) -> None: + nonlocal wrapped + for name, child in list(module.named_children()): + full_name = f"{prefix}.{name}" if prefix else name + child_active = active + if target_class is not None: + child_active = active or child.__class__.__name__ == target_class + if self._ttt_is_lora_target(name, full_name, child, child_active, target_modules): + setattr( + module, + name, + LoraInjectedLinear(child, rank=cfg.lora_rank, alpha=cfg.lora_alpha), + ) + wrapped += 1 + continue + inject(child, full_name, child_active) + + for trainable_module in self._ttt_get_trainable_modules(): + inject(trainable_module, "", target_class is None) + assert wrapped > 0, "TTT LoRA injection did not find any target modules." + return wrapped + + def _ttt_lora_modules(self) -> list[LoraInjectedLinear]: + return [module for module in self.modules() if isinstance(module, LoraInjectedLinear)] + + def _ttt_lora_parameters(self) -> list[nn.Parameter]: + params: list[nn.Parameter] = [] + for module in self._ttt_lora_modules(): + params.extend(module.lora_down.parameters()) + params.extend(module.lora_up.parameters()) + assert len(params) > 0, "TTT has no LoRA parameters." + return params + + def _ttt_snapshot_lora_state(self) -> list[dict[str, torch.Tensor]]: + snapshot = [] + for module in self._ttt_lora_modules(): + snapshot.append( + { + "lora_down.weight": module.lora_down.weight.detach().clone(), + "lora_up.weight": module.lora_up.weight.detach().clone(), + } + ) + assert len(snapshot) > 0, "TTT has no LoRA state to snapshot." + return snapshot + + def _ttt_restore_lora_state(self, state: list[dict[str, torch.Tensor]]) -> None: + modules = self._ttt_lora_modules() + assert len(modules) == len(state), "TTT LoRA state/module count mismatch." + with torch.no_grad(): + for module, module_state in zip(modules, state): + module.lora_down.weight.copy_(module_state["lora_down.weight"]) + module.lora_up.weight.copy_(module_state["lora_up.weight"]) + + def _ttt_ensure_initialized(self) -> None: + if "_ttt_cfg" not in self.__dict__: + self.init_ttt() + if self._ttt_initialized: + return + self._ttt_inject_lora() + self._ttt_initial_state = self._ttt_snapshot_lora_state() + self._ttt_initialized = True + + def ttt_reset(self) -> None: + self._ttt_ensure_initialized() + assert self._ttt_initial_state is not None, "TTT initial state is not available." + self._ttt_restore_lora_state(self._ttt_initial_state) + + def _ttt_make_optimizer(self) -> torch.optim.Optimizer: + cfg = self.ttt_config + params = self._ttt_lora_parameters() + if cfg.optimizer == "sgd": + return torch.optim.SGD( + params, + lr=cfg.lr, + momentum=cfg.momentum, + weight_decay=cfg.weight_decay, + ) + return torch.optim.AdamW(params, lr=cfg.lr, weight_decay=cfg.weight_decay) + + def _ttt_to_device( + self, + batch: torch.Tensor | dict[str, torch.Tensor], + device: torch.device, + ) -> torch.Tensor | dict[str, torch.Tensor]: + if isinstance(batch, dict): + return {name: tensor.to(device) for name, tensor in batch.items()} + return batch.to(device) + + def _ttt_input_ids_from_batch( + self, + batch: torch.Tensor | dict[str, torch.Tensor], + ) -> torch.Tensor: + if isinstance(batch, dict): + return batch["input_ids"] + return batch + + def _ttt_set_input_ids( + self, + batch: torch.Tensor | dict[str, torch.Tensor], + input_ids: torch.Tensor, + ) -> torch.Tensor | dict[str, torch.Tensor]: + if isinstance(batch, dict): + updated = dict(batch) + updated["input_ids"] = input_ids + return updated + return input_ids + + def _ttt_non_special_mask(self, input_ids: torch.Tensor) -> torch.Tensor: + pad_token = self._ttt_padding_token() + mask = input_ids.ne(pad_token) + special_ids = set(self.tokenizer.all_special_ids) + for special_id in special_ids: + mask = mask & input_ids.ne(int(special_id)) + return mask + + def _ttt_sample_crop( + self, + batch: torch.Tensor | dict[str, torch.Tensor], + generator: torch.Generator, + ) -> torch.Tensor | dict[str, torch.Tensor]: + input_ids = self._ttt_input_ids_from_batch(batch) + cfg = self.ttt_config + if input_ids.shape[1] <= cfg.crop_size: + return batch + high = input_ids.shape[1] - cfg.crop_size + 1 + start = int( + torch.randint( + high, + (1,), + generator=generator, + device=input_ids.device, + ).item() + ) + end = start + cfg.crop_size + if isinstance(batch, dict): + cropped = {} + for name, tensor in batch.items(): + if tensor.ndim >= 2 and tensor.shape[1] == input_ids.shape[1]: + cropped[name] = tensor[:, start:end] + else: + cropped[name] = tensor + return cropped + return input_ids[:, start:end] + + def _ttt_sample_batch( + self, + tokenized: torch.Tensor | dict[str, torch.Tensor], + generator: torch.Generator, + ) -> tuple[torch.Tensor | dict[str, torch.Tensor], torch.Tensor]: + cfg = self.ttt_config + batch = self._ttt_sample_crop(tokenized, generator) + input_ids = self._ttt_input_ids_from_batch(batch) + rows = torch.randint( + input_ids.shape[0], + (cfg.batch_size,), + generator=generator, + device=input_ids.device, + ) + if isinstance(batch, dict): + sampled: torch.Tensor | dict[str, torch.Tensor] = {} + for name, tensor in batch.items(): + if tensor.ndim >= 1 and tensor.shape[0] == input_ids.shape[0]: + sampled[name] = tensor.index_select(0, rows) + else: + sampled[name] = tensor + else: + sampled = input_ids.index_select(0, rows) + + sampled_ids = self._ttt_input_ids_from_batch(sampled) + labels = sampled_ids.clone() + non_special = self._ttt_non_special_mask(sampled_ids) + label_mask = torch.zeros_like(non_special) + for row_idx in range(sampled_ids.shape[0]): + candidate_positions = torch.where(non_special[row_idx])[0] + if candidate_positions.numel() == 0: + continue + num_mask = max(1, int(round(candidate_positions.numel() * cfg.mask_ratio))) + order = torch.randperm( + candidate_positions.numel(), + generator=generator, + device=sampled_ids.device, + ) + chosen = candidate_positions[order[:num_mask]] + label_mask[row_idx, chosen] = True + labels = labels.masked_fill(~label_mask, -100) + + masked_ids = sampled_ids.clone() + chosen_positions = torch.where(label_mask) + if chosen_positions[0].numel() > 0: + random_values = torch.rand( + chosen_positions[0].shape, + generator=generator, + device=sampled_ids.device, + ) + leave = random_values < cfg.bert_leave_prob + replace = (random_values >= cfg.bert_leave_prob) & ( + random_values < cfg.bert_leave_prob + cfg.bert_replace_prob + ) + mask = ~(leave | replace) + if mask.any(): + masked_ids[ + chosen_positions[0][mask], + chosen_positions[1][mask], + ] = self._ttt_mask_token() + if replace.any(): + replacement_tokens = self._ttt_replacement_tokens(sampled_ids) + replacement_idx = torch.randint( + replacement_tokens.shape[0], + (int(replace.sum().item()),), + generator=generator, + device=sampled_ids.device, + ) + masked_ids[ + chosen_positions[0][replace], + chosen_positions[1][replace], + ] = replacement_tokens[replacement_idx] + + return self._ttt_set_input_ids(sampled, masked_ids), labels + + def ttt( + self, + seq: str | list[str] | None = None, + input_ids: torch.Tensor | None = None, + ttt_config: TTTConfig | T.Mapping[str, T.Any] | None = None, + **kwargs: T.Any, + ) -> dict[str, T.Any]: + if ttt_config is not None: + if "_ttt_initialized" in self.__dict__ and self._ttt_initialized: + next_cfg = self.ttt_config.merged(ttt_config) + assert next_cfg.lora_rank == self.ttt_config.lora_rank, ( + "Changing lora_rank after TTT initialization is not supported." + ) + assert next_cfg.lora_alpha == self.ttt_config.lora_alpha, ( + "Changing lora_alpha after TTT initialization is not supported." + ) + assert ( + next_cfg.lora_target_replace_module + == self.ttt_config.lora_target_replace_module + ), "Changing LoRA target class after TTT initialization is not supported." + assert next_cfg.lora_target_modules == self.ttt_config.lora_target_modules, ( + "Changing LoRA target modules after TTT initialization is not supported." + ) + self._ttt_cfg = next_cfg + else: + self.init_ttt(ttt_config) + + self._ttt_ensure_initialized() + cfg = self.ttt_config + if cfg.initial_state_reset: + self.ttt_reset() + + device = next(self.parameters()).device + tokenized = self._ttt_tokenize(seq=seq, input_ids=input_ids, **kwargs) + tokenized = self._ttt_to_device(tokenized, device) + generator_device = device if device.type == "cuda" else torch.device("cpu") + generator = torch.Generator(device=generator_device) + if cfg.seed is not None: + generator.manual_seed(cfg.seed) + + module_modes = {module: module.training for module in self.modules()} + requires_grad = {param: param.requires_grad for param in self.parameters()} + losses: list[float] = [] + step_metrics: list[dict[str, T.Any]] = [] + best_state: list[dict[str, torch.Tensor]] | None = None + best_metric: float | None = None + best_step = 0 + + try: + self.train() + for param in self.parameters(): + param.requires_grad_(False) + for param in self._ttt_lora_parameters(): + param.requires_grad_(True) + + optimizer = self._ttt_make_optimizer() + optimizer.zero_grad(set_to_none=True) + total_micro_steps = cfg.steps * cfg.ags + for micro_step in range(total_micro_steps): + batch, labels = self._ttt_sample_batch(tokenized, generator) + logits = self._ttt_predict_logits(batch, **kwargs) + labels = labels.to(device=logits.device) + loss = F.cross_entropy( + logits.reshape(-1, logits.shape[-1]), + labels.reshape(-1), + ignore_index=-100, + ) + (loss / cfg.ags).backward() + if (micro_step + 1) % cfg.ags != 0: + continue + + if cfg.gradient_clip: + torch.nn.utils.clip_grad_norm_( + self._ttt_lora_parameters(), + cfg.gradient_clip_max_norm, + ) + optimizer.step() + optimizer.zero_grad(set_to_none=True) + step = (micro_step + 1) // cfg.ags + loss_value = float(loss.detach().item()) + losses.append(loss_value) + if cfg.eval_each_step: + metrics, metric = self._ttt_eval_step( + step=step, + loss=loss_value, + seq=seq, + input_ids=input_ids, + **kwargs, + ) + if len(metrics) > 0: + step_metrics.append(metrics) + if metric is not None and ( + best_metric is None or metric > best_metric + ): + best_metric = metric + best_step = step + best_state = self._ttt_snapshot_lora_state() + + if cfg.automatic_best_state_reset and best_state is not None: + self._ttt_restore_lora_state(best_state) + finally: + for param, value in requires_grad.items(): + param.requires_grad_(value) + for module, training in module_modes.items(): + module.train(training) + + return { + "losses": losses, + "step_metrics": step_metrics, + "best_step": best_step, + "best_metric": best_metric, + } + +""" +ESM++ model implementation. + +ESM++ is a faithful implementation of ESMC that allows for batching and standard Huggingface compatibility +The ESM Python package is not required + +Modified from https://github.com/Biohub/esm +License: https://github.com/Biohub/esm/blob/main/LICENSE.md +""" + +import math +import os +import json +import torch +import torch.nn as nn +import torch.nn.functional as F +from dataclasses import dataclass +from functools import cache, partial +from pathlib import Path +from typing import Optional, Tuple, Union, List +from einops import rearrange, repeat +from huggingface_hub import snapshot_download +from safetensors.torch import load_file as load_safetensors_file +from tokenizers import Tokenizer +from tokenizers.models import BPE +from tokenizers.processors import TemplateProcessing +from transformers import PreTrainedModel, PreTrainedTokenizerFast, PretrainedConfig +from transformers.modeling_outputs import ModelOutput + + + +class ESMplusplusConfig(PretrainedConfig): + """Configuration class for ESM++ model. + + Args: + vocab_size: Size of the vocabulary + hidden_size: Dimension of hidden layers + num_attention_heads: Number of attention heads + num_hidden_layers: Number of transformer layers + num_labels: Number of output labels for classification + problem_type: Type of problem - regression, single/multi label classification + """ + model_type = "ESMplusplus" + def __init__( + self, + vocab_size: int = 64, + hidden_size: int = 960, + num_attention_heads: int = 15, + num_hidden_layers: int = 30, + num_labels: int = 2, + problem_type: Optional[str] = None, + dropout: float = 0.0, + initializer_range: float = 0.02, + attn_backend: str = "sdpa", + **kwargs, + ): + super().__init__(**kwargs) + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.num_hidden_layers = num_hidden_layers + self.num_labels = num_labels + self.problem_type = problem_type + self.dropout = dropout + self.initializer_range = initializer_range + self.tie_word_embeddings = False + self.attn_backend = attn_backend + + +### Rotary Embeddings +def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor: + """Rotates half the hidden dims of the input.""" + if not interleaved: + x1, x2 = x.chunk(2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + else: + x1, x2 = x[..., ::2], x[..., 1::2] + return rearrange( + torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2 + ) + + +def apply_rotary_emb_torch( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + interleaved: bool = False, + _inplace: bool = False, +) -> torch.Tensor: + """Apply rotary embeddings to input based on cos and sin.""" + ro_dim = cos.shape[-1] * 2 + assert ro_dim <= x.shape[-1] + seqlen = x.size(1) + cos = cos[:seqlen] + sin = sin[:seqlen] + cos = repeat(cos, "s d -> s 1 (2 d)") + sin = repeat(sin, "s d -> s 1 (2 d)") + return torch.cat( + [ + x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin, + x[..., ro_dim:], + ], + dim=-1, + ) + + +class RotaryEmbedding(torch.nn.Module): + """Rotary position embeddings. + + Based on the paper "RoFormer: Enhanced Transformer with Rotary Position Embedding" + + Args: + dim: Dimension of the embedding + base: Base for computing angular frequencies + interleaved: Whether to use interleaved rotations + scale_base: Base for scaling + scaling_factor: Factor for scaling positions + pos_idx_in_fp32: Whether to compute position indices in fp32 + device: Computation device + """ + def __init__( + self, + dim: int, + base: float = 10000.0, + interleaved: bool = False, + scale_base: Optional[float] = None, + scaling_factor: float = 1.0, + pos_idx_in_fp32: bool = True, + device: Optional[torch.device] = None, + ): + super().__init__() + self.dim = dim + self.base = float(base) + self.pos_idx_in_fp32 = pos_idx_in_fp32 + self.interleaved = interleaved + self.scale_base = scale_base + self.scaling_factor = scaling_factor + self.device = device + + self._seq_len_cached = 0 + self._cos_cached = None + self._sin_cached = None + self._cos_k_cached = None + self._sin_k_cached = None + self.reset_parameters() + + def reset_parameters(self): + """Reset the parameters of the embedding.""" + if "inv_freq" in self._buffers and isinstance(self._buffers["inv_freq"], torch.Tensor): + buffer_device = self._buffers["inv_freq"].device + else: + buffer_device = self.device + inv_freq = self._compute_inv_freq(buffer_device) + self._seq_len_cached = 0 + self._cos_cached = None + self._sin_cached = None + self._cos_k_cached = None + self._sin_k_cached = None + self.register_buffer("inv_freq", inv_freq, persistent=False) + arange = torch.arange(0, self.dim, 2, device=buffer_device, dtype=torch.float32) + scale = ( + (arange + 0.4 * self.dim) / (1.4 * self.dim) + if self.scale_base is not None + else None + ) + self.register_buffer("scale", scale) + + def _compute_inv_freq(self, device: Optional[torch.device] = None) -> torch.Tensor: + """Compute inverse frequency bands. + + Always computes on CPU then moves to the requested device. This matches + native Biohub ESMC, which computes inv_freq on CPU at + `__init__` and migrates via `.to(device)`. Computing directly on GPU + gives a ~3.7e-9 bit-level difference in inv_freq (fp32 transcendental + precision differs between CPU and GPU), which compounds through the 30 + attention layers to ~1e-3 mse divergence from native at + `hidden_states[-2]`. See testing/parity_debug_rotary.py. + """ + cpu_inv_freq = 1 / ( + self.base + ** ( + torch.arange(0, self.dim, 2, device="cpu", dtype=torch.float32) + / self.dim + ) + ) + if device is not None and torch.device(device).type != "cpu": + return cpu_inv_freq.to(device) + return cpu_inv_freq + + def _update_cos_sin_cache(self, seqlen: int, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None): + """Update the cached cosine and sine values.""" + if ( + seqlen > self._seq_len_cached + or self._cos_cached is None + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + or (self.training and self._cos_cached.is_inference()) + ): + self._seq_len_cached = seqlen + if self.pos_idx_in_fp32: + t = torch.arange(seqlen, device=device, dtype=torch.float32) + t /= self.scaling_factor + if self.inv_freq.dtype != torch.float32: + inv_freq = self.inv_freq.to(torch.float32) + else: + inv_freq = self.inv_freq + else: + t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) + t /= self.scaling_factor + inv_freq = self.inv_freq + freqs = torch.outer(t, inv_freq) + + if self.scale is None: + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + else: + power = ( + torch.arange( + seqlen, dtype=self.scale.dtype, device=self.scale.device + ) + - seqlen // 2 + ) / self.scale_base + scale = self.scale.to(device=power.device) ** power.unsqueeze(-1) + self._cos_cached = (torch.cos(freqs) * scale).to(dtype) + self._sin_cached = (torch.sin(freqs) * scale).to(dtype) + self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype) + self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype) + + def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply rotary embeddings to queries and keys. + + Args: + q: Query tensor of shape (batch, seqlen, nheads, headdim) + k: Key tensor of shape (batch, seqlen, nheads, headdim) + + Returns: + Tuple of rotated query and key tensors + """ + # NOTE: do NOT recompute inv_freq here if device has changed. The native + # ESMC implementation computes inv_freq once on CPU at __init__ and + # relies on PyTorch's `.to(device)` to migrate the buffer. Recomputing + # the values directly on GPU gives a ~3.7e-9 bit-level difference vs the + # CPU-computed-then-moved values due to fp32 transcendental precision, + # which compounds through 30 attention layers to ~1e-3 mse divergence + # from native at `hidden_states[-2]`. See testing/parity_debug_rotary.py. + self._update_cos_sin_cache(q.shape[1], device=q.device, dtype=q.dtype) + assert self._cos_cached is not None + assert self._sin_cached is not None + if self.scale is None: + return ( + apply_rotary_emb_torch( + q, + self._cos_cached, + self._sin_cached, + self.interleaved, + True, # inplace=True + ), + apply_rotary_emb_torch( + k, + self._cos_cached, + self._sin_cached, + self.interleaved, + True, # inplace=True + ), + ) # type: ignore + else: + assert False + + +### Feedforward Network Components +def swiglu_correction_fn(expansion_ratio: float, d_model: int) -> int: + """Compute corrected dimension for SwiGLU.""" + return int(((expansion_ratio * d_model) + 255) // 256 * 256) + + +class SwiGLU(nn.Module): + """SwiGLU activation function.""" + def __init__(self): + super(SwiGLU, self).__init__() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x1, x2 = x.chunk(2, dim=-1) + return F.silu(x1) * x2 + + +def swiglu_ln_ffn(d_model: int, expansion_ratio: float) -> nn.Sequential: + """Create SwiGLU feedforward network with layer normalization.""" + return nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear( + d_model, swiglu_correction_fn(expansion_ratio, d_model) * 2, bias=False + ), + SwiGLU(), + nn.Linear(swiglu_correction_fn(expansion_ratio, d_model), d_model, bias=False), + ) + + +### Attention +class MultiHeadAttention(nn.Module): + """Multi-head attention with rotary embeddings and configurable backend. + + Args: + d_model: Model dimension + n_heads: Number of attention heads + attn_backend: One of "auto", "kernels_flash", "flex", "sdpa" + """ + def __init__( + self, + d_model: int, + n_heads: int, + attn_backend: str = "sdpa", + ): + super().__init__() + self.d_model = d_model + self.n_heads = n_heads + self.d_head = self.d_model // self.n_heads + self.scale = 1.0 / math.sqrt(self.d_head) + self.attn_backend = resolve_attention_backend(attn_backend) + self.layernorm_qkv = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, d_model * 3, bias=False) + ) + self.out_proj = nn.Linear(d_model, d_model, bias=False) + self.q_ln = nn.LayerNorm(d_model, bias=False) + self.k_ln = nn.LayerNorm(d_model, bias=False) + self.reshaper = partial(rearrange, pattern="b s (h d) -> b h s d", h=n_heads) + self.rotary = RotaryEmbedding(d_model // n_heads) + + def _apply_rotary(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + q = q.unflatten(-1, (self.n_heads, self.d_head)) + k = k.unflatten(-1, (self.n_heads, self.d_head)) + q, k = self.rotary(q, k) + q = q.flatten(-2, -1) + k = k.flatten(-2, -1) + return q, k + + def forward( + self, + x: torch.Tensor, + attention_mask_2d: Optional[torch.Tensor] = None, + attention_mask_4d: Optional[torch.Tensor] = None, + flex_block_mask: Optional[BlockMask] = None, + output_attentions: bool = False, + output_s_max: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[torch.Tensor]]]: + qkv_BLD3 = self.layernorm_qkv(x) + query_BLD, key_BLD, value_BLD = torch.chunk(qkv_BLD3, 3, dim=-1) + query_BLD, key_BLD = ( + self.q_ln(query_BLD).to(query_BLD.dtype), + self.k_ln(key_BLD).to(query_BLD.dtype), + ) + query_BLD, key_BLD = self._apply_rotary(query_BLD, key_BLD) + query_BHLD, key_BHLD, value_BHLD = map(self.reshaper, (query_BLD, key_BLD, value_BLD)) + + attn_output, attn_weights, s_max = self._attn( + query_BHLD, key_BHLD, value_BHLD, + attention_mask_2d=attention_mask_2d, + attention_mask_4d=attention_mask_4d, + flex_block_mask=flex_block_mask, + output_attentions=output_attentions, + output_s_max=output_s_max, + ) + + output = self.out_proj(attn_output) + return output, attn_weights, s_max + + def _attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_2d: Optional[torch.Tensor] = None, + attention_mask_4d: Optional[torch.Tensor] = None, + flex_block_mask: Optional[BlockMask] = None, + output_attentions: bool = False, + output_s_max: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[torch.Tensor]]]: + if output_attentions: + return self._manual_attn(query_BHLD, key_BHLD, value_BHLD, attention_mask_4d, output_s_max) + + if self.attn_backend == AttentionBackend.KERNELS_FLASH: + attn_output, attn_weights = self._kernels_flash_attn(query_BHLD, key_BHLD, value_BHLD, attention_mask_2d) + elif self.attn_backend == AttentionBackend.FLEX: + attn_output, attn_weights = self._flex_attn(query_BHLD, key_BHLD, value_BHLD, flex_block_mask) + elif self.attn_backend == AttentionBackend.SDPA: + attn_output, attn_weights = self._sdpa_attn(query_BHLD, key_BHLD, value_BHLD, attention_mask_4d) + else: + raise AssertionError(f"Unsupported resolved backend: {self.attn_backend}") + + s_max = self._compute_s_max(query_BHLD, key_BHLD) if output_s_max else None + return attn_output, attn_weights, s_max + + @torch.no_grad() + def _compute_s_max(self, query_BHLD: torch.Tensor, key_BHLD: torch.Tensor) -> List[torch.Tensor]: + q_norm = torch.linalg.vector_norm(query_BHLD, dim=-1) + k_norm = torch.linalg.vector_norm(key_BHLD, dim=-1) + s_max_bound = (q_norm.max(dim=-1).values * k_norm.max(dim=-1).values).max(dim=0).values * self.scale + return [s_max_bound[h] for h in range(self.n_heads)] + + def _manual_attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_4d: Optional[torch.Tensor] = None, + output_s_max: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[List[torch.Tensor]]]: + attn_weights = torch.matmul(query_BHLD, key_BHLD.transpose(-2, -1)) * self.scale + if attention_mask_4d is not None: + attn_weights = attn_weights.masked_fill(attention_mask_4d.logical_not(), float("-inf")) + attn_weights = F.softmax(attn_weights, dim=-1) + context_BHLD = torch.matmul(attn_weights, value_BHLD) + attn_output = rearrange(context_BHLD, "b h s d -> b s (h d)") + s_max = self._compute_s_max(query_BHLD, key_BHLD) if output_s_max else None + return attn_output, attn_weights, s_max + + def _kernels_flash_attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_2d: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, None]: + query_BLHD = query_BHLD.transpose(1, 2).contiguous() + key_BLHD = key_BHLD.transpose(1, 2).contiguous() + value_BLHD = value_BHLD.transpose(1, 2).contiguous() + attn_output = kernels_flash_attention_func( + query_states=query_BLHD, key_states=key_BLHD, value_states=value_BLHD, + attention_mask_2d=attention_mask_2d, causal=False, + ) + return rearrange(attn_output, "b s h d -> b s (h d)"), None + + def _flex_attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + flex_block_mask: Optional[BlockMask] = None, + ) -> Tuple[torch.Tensor, None]: + assert flex_attention is not None, "Flex attention is not available in this environment." + fn = _get_flex_attention_fn() + context_BHLD = fn(query_BHLD, key_BHLD, value_BHLD, block_mask=flex_block_mask, scale=self.scale) + return rearrange(context_BHLD, "b h s d -> b s (h d)"), None + + def _sdpa_attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_4d: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, None]: + context_BHLD = F.scaled_dot_product_attention( + query_BHLD, key_BHLD, value_BHLD, attn_mask=attention_mask_4d, scale=self.scale, + ) + return rearrange(context_BHLD, "b h s d -> b s (h d)"), None + + +### Regression Head +def RegressionHead(d_model: int, output_dim: int, hidden_dim: Optional[int] = None) -> nn.Module: + """Create a regression head with optional hidden dimension. + + Args: + d_model: Input dimension + output_dim: Output dimension + hidden_dim: Optional hidden dimension (defaults to d_model) + """ + hidden_dim = hidden_dim if hidden_dim is not None else d_model + return nn.Sequential( + nn.Linear(d_model, hidden_dim), + nn.GELU(), + nn.LayerNorm(hidden_dim), + nn.Linear(hidden_dim, output_dim), + ) + + +### Transformer Block +class UnifiedTransformerBlock(nn.Module): + """Transformer block with attention and feedforward layers.""" + def __init__( + self, + d_model: int, + n_heads: int, + residue_scaling_factor: float = 1, + expansion_ratio: float = 8 / 3, + dropout: float = 0.0, + attn_backend: str = "sdpa", + ): + super().__init__() + self.attn = MultiHeadAttention(d_model=d_model, n_heads=n_heads, attn_backend=attn_backend) + self.ffn = swiglu_ln_ffn(d_model, expansion_ratio) + self.scaling_factor = residue_scaling_factor + self.dropout = nn.Dropout(dropout) + + def forward( + self, + x: torch.Tensor, + attention_mask_2d: Optional[torch.Tensor] = None, + attention_mask_4d: Optional[torch.Tensor] = None, + flex_block_mask: Optional[BlockMask] = None, + output_attentions: bool = False, + output_s_max: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[torch.Tensor]]]: + attn_output, attn_weights, s_max = self.attn( + x, + attention_mask_2d=attention_mask_2d, + attention_mask_4d=attention_mask_4d, + flex_block_mask=flex_block_mask, + output_attentions=output_attentions, + output_s_max=output_s_max, + ) + x = x + self.dropout(attn_output) / self.scaling_factor + x = x + self.dropout(self.ffn(x)) / self.scaling_factor + return x, attn_weights, s_max + + +### Model Outputs +@dataclass +class TransformerOutput(ModelOutput): + """Output type for transformer encoder.""" + last_hidden_state: Optional[torch.Tensor] = None + hidden_states: Optional[Tuple[torch.Tensor]] = None + attentions: Optional[Tuple[torch.Tensor]] = None + s_max: Optional[Tuple[List[torch.Tensor], ...]] = None + + +@dataclass +class ESMplusplusOutput(ModelOutput): + """Output type for ESM++ models.""" + 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 + s_max: Optional[Tuple[List[torch.Tensor], ...]] = None + + +### Transformer Stack +class TransformerStack(nn.Module): + """Stack of transformer blocks.""" + def __init__( + self, + d_model: int, + n_heads: int, + n_layers: int, + dropout: float = 0.0, + attn_backend: str = "sdpa", + ): + super().__init__() + self.attention_backend = resolve_attention_backend(attn_backend) + self.blocks = nn.ModuleList( + [ + UnifiedTransformerBlock( + d_model, + n_heads, + residue_scaling_factor=math.sqrt(n_layers / 36), + dropout=dropout, + attn_backend=attn_backend, + ) + for i in range(n_layers) + ] + ) + self.norm = nn.LayerNorm(d_model, bias=False) + self.gradient_checkpointing = False + + @property + def attn_backend(self) -> AttentionBackend: + return self.attention_backend + + @attn_backend.setter + def attn_backend(self, backend: str) -> None: + resolved = resolve_attention_backend(backend) + self.attention_backend = resolved + for block in self.blocks: + block.attn.attn_backend = resolved + + def forward( + self, + x: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + sequence_id: Optional[torch.Tensor] = None, + output_hidden_states: Optional[bool] = False, + output_attentions: Optional[bool] = False, + output_s_max: Optional[bool] = False, + esmfold2_hidden_states: bool = False, + ) -> TransformerOutput: + hidden_states = () if output_hidden_states else None + attentions = () if output_attentions else None + full_s_max = () if output_s_max else None + + if sequence_id is None: + attention_mask_2d, attention_mask_4d, flex_block_mask = get_attention_mask( + effective_backend=self.attention_backend, + batch_size=x.shape[0], + seq_len=x.shape[1], + device=x.device, + attention_mask=attention_mask, + ) + else: + attention_mask_2d, attention_mask_4d, flex_block_mask = self._sequence_id_attention_masks( + sequence_id=sequence_id, + batch_size=x.shape[0], + seq_len=x.shape[1], + device=x.device, + ) + + if output_hidden_states and esmfold2_hidden_states: + assert hidden_states is not None + hidden_states += (x,) + + for block_index, block in enumerate(self.blocks): + if self.gradient_checkpointing and self.training: + x, attn_weights, s_max = self._gradient_checkpointing_func( + block.__call__, + x=x, + attention_mask_2d=attention_mask_2d, + attention_mask_4d=attention_mask_4d, + flex_block_mask=flex_block_mask, + output_attentions=output_attentions, + output_s_max=output_s_max, + ) + else: + x, attn_weights, s_max = block( + x=x, + attention_mask_2d=attention_mask_2d, + attention_mask_4d=attention_mask_4d, + flex_block_mask=flex_block_mask, + output_attentions=output_attentions, + output_s_max=output_s_max, + ) + + if attentions is not None: + attentions += (attn_weights,) + if output_hidden_states: + assert hidden_states is not None + if not esmfold2_hidden_states or block_index < len(self.blocks) - 1: + hidden_states += (x,) + if full_s_max is not None: + full_s_max += (s_max,) + + last_hidden_state = self.norm(x) + if output_hidden_states: + hidden_states += (last_hidden_state,) + + return TransformerOutput( + last_hidden_state=last_hidden_state, + hidden_states=hidden_states, + attentions=attentions, + s_max=full_s_max, + ) + + def _sequence_id_attention_masks( + self, + sequence_id: torch.Tensor, + batch_size: int, + seq_len: int, + device: torch.device, + ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[BlockMask]]: + assert sequence_id.shape == (batch_size, seq_len), ( + f"sequence_id shape must be {(batch_size, seq_len)}, got {tuple(sequence_id.shape)}" + ) + if sequence_id.dtype == torch.bool: + attention_mask_2d = sequence_id + attention_mask_4d = ( + attention_mask_2d[:, None, :, None] + & attention_mask_2d[:, None, None, :] + ) + else: + attention_mask_2d = sequence_id != -1 + attention_mask_4d = ( + attention_mask_2d[:, None, :, None] + & attention_mask_2d[:, None, None, :] + & (sequence_id.unsqueeze(-1) == sequence_id.unsqueeze(-2)).unsqueeze(1) + ) + + if self.attention_backend == AttentionBackend.KERNELS_FLASH: + assert sequence_id.dtype == torch.bool, ( + "ESM++ kernels_flash only supports boolean sequence_id padding masks. " + "Use sdpa or flex for chain-aware integer sequence_id masks." + ) + return attention_mask_2d, attention_mask_4d, None + + if self.attention_backend == AttentionBackend.FLEX: + assert create_block_mask is not None, ( + "Flex attention backend requested but torch.create_block_mask is unavailable." + ) + + if sequence_id.dtype == torch.bool: + + def mask_mod(batch_idx, head_idx, q_idx, kv_idx): + return ( + sequence_id[batch_idx, q_idx] + & sequence_id[batch_idx, kv_idx] + ) + + else: + + def mask_mod(batch_idx, head_idx, q_idx, kv_idx): + q_id = sequence_id[batch_idx, q_idx] + kv_id = sequence_id[batch_idx, kv_idx] + return (q_id != -1) & (q_id == kv_id) + + flex_block_mask = create_block_mask( + mask_mod, + batch_size, + 1, + seq_len, + seq_len, + device=device, + ) + return attention_mask_2d, attention_mask_4d, flex_block_mask + + return attention_mask_2d, attention_mask_4d, None + + +class PreTrainedESMplusplusModel(PreTrainedModel): + """ + init weights for ESM++ models + """ + config_class = ESMplusplusConfig + base_model_prefix = "esm++" + supports_gradient_checkpointing = True + all_tied_weights_keys = {} + + @classmethod + def is_remote_code(cls) -> bool: + # Prevent post-load reinitialization of tensors already loaded from checkpoints. + return True + + def _init_weights(self, module): + """Initialize the weights""" + # HF from_pretrained marks loaded parameters with `_is_hf_initialized`. + # Skip this module if any local parameter is already marked as loaded. + for parameter in module.parameters(recurse=False): + if "_is_hf_initialized" in parameter.__dict__ and parameter.__dict__["_is_hf_initialized"]: + return + + if isinstance(module, nn.Linear): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + with torch.no_grad(): + module.weight[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + nn.init.zeros_(module.bias) + nn.init.ones_(module.weight) + + @property + def attn_backend(self) -> str: + return self.config.attn_backend + + @attn_backend.setter + def attn_backend(self, backend: str) -> None: + assert backend in VALID_ATTENTION_BACKENDS, f"Unsupported attn_backend: {backend}. Expected one of {VALID_ATTENTION_BACKENDS}." + self.config.attn_backend = backend + for module in self.modules(): + if isinstance(module, TransformerStack): + module.attn_backend = backend + + def _reset_rotary_embeddings(self): + """Refresh non-persistent rotary buffers after checkpoint loading.""" + for module in self.modules(): + if isinstance(module, RotaryEmbedding): + module.reset_parameters() + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + output_loading_info = bool(kwargs["output_loading_info"]) if "output_loading_info" in kwargs else False + loaded = super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + if output_loading_info: + model, loading_info = loaded + model._reset_rotary_embeddings() + return model, loading_info + loaded._reset_rotary_embeddings() + return loaded + + @classmethod + def from_pretrained_esm( + cls, + model_name: str, + device: Union[torch.device, str] = "cpu", + ): + """Load a pretrained ESM++ model.""" + key = _resolve_esmc_checkpoint_key(model_name) + if key == "esmc-300": + return ESMplusplus_300M(device=device) + if key == "esmc-600": + return ESMplusplus_600M(device=device) + if key == "esmc-6b": + return ESMplusplus_6B(device=device) + raise ValueError(f"Invalid model name: {model_name}") + + +### ESM++ Models +class ESMplusplusModel(PreTrainedESMplusplusModel, EmbeddingMixin): + """ + ESM++ model. transformer model with no heads + """ + config_class = ESMplusplusConfig + def __init__(self, config: ESMplusplusConfig, **kwargs): + PreTrainedESMplusplusModel.__init__(self, config, **kwargs) + self.config = config + self.vocab_size = config.vocab_size + self.embed = nn.Embedding(self.vocab_size, config.hidden_size) + self.transformer = TransformerStack( + d_model=config.hidden_size, + n_heads=config.num_attention_heads, + n_layers=config.num_hidden_layers, + dropout=config.dropout, + attn_backend=config.attn_backend, + ) + self.tokenizer = EsmSequenceTokenizer() + self.init_weights() + + def get_input_embeddings(self): + return self.embed + + def set_input_embeddings(self, value): + self.embed = value + + def _embed( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + ) -> torch.Tensor: + x = self.embed(input_ids) + output_hidden_states = store_all_hidden_states or hidden_state_index != -1 + output = self.transformer( + x=x, + attention_mask=attention_mask, + output_hidden_states=output_hidden_states, + output_attentions=False, + ) + return select_hidden_state_embeddings( + output.last_hidden_state, + output.hidden_states, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + ) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + sequence_id: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + esmfold2_hidden_states: bool = False, + return_dict: Optional[bool] = None, + **kwargs, + ) -> ESMplusplusOutput: + assert input_ids is not None or inputs_embeds is not None, "You have to specify either input_ids or inputs_embeds" + assert not (input_ids is not None and inputs_embeds is not None), "You cannot specify both input_ids and inputs_embeds at the same time" + + if inputs_embeds is None: + x = self.embed(input_ids) + else: + x = inputs_embeds + + transformer_output = self.transformer( + x=x, + attention_mask=attention_mask, + sequence_id=sequence_id, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + output_s_max=output_s_max, + esmfold2_hidden_states=esmfold2_hidden_states, + ) + return ESMplusplusOutput( + last_hidden_state=transformer_output.last_hidden_state, + hidden_states=transformer_output.hidden_states, + attentions=transformer_output.attentions, + s_max=transformer_output.s_max, + ) + +class ESMplusplusForMaskedLM(FastPLMTestTimeTrainingMixin, PreTrainedESMplusplusModel, EmbeddingMixin): + """ + ESM++ model for masked language modeling. + Implements the base ESM++ architecture with a masked language modeling head. + """ + config_class = ESMplusplusConfig + def __init__(self, config: ESMplusplusConfig, **kwargs): + PreTrainedESMplusplusModel.__init__(self, config, **kwargs) + self.config = config + self.vocab_size = config.vocab_size + self.embed = nn.Embedding(self.vocab_size, config.hidden_size) + self.transformer = TransformerStack( + d_model=config.hidden_size, + n_heads=config.num_attention_heads, + n_layers=config.num_hidden_layers, + dropout=config.dropout, + attn_backend=config.attn_backend, + ) + self.sequence_head = RegressionHead(config.hidden_size, self.vocab_size) + self.ce_loss = nn.CrossEntropyLoss() + self.tokenizer = EsmSequenceTokenizer() + self.init_weights() + self.init_ttt({"lora_target_replace_module": "MultiHeadAttention"}) + + def get_input_embeddings(self): + return self.embed + + def set_input_embeddings(self, value): + self.embed = value + + def get_output_embeddings(self): + return self.sequence_head[-1] + + def set_output_embeddings(self, new_embeddings): + self.sequence_head[-1] = new_embeddings + + def _embed( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + ) -> torch.Tensor: + x = self.embed(input_ids) + output_hidden_states = store_all_hidden_states or hidden_state_index != -1 + output = self.transformer( + x=x, + attention_mask=attention_mask, + output_hidden_states=output_hidden_states, + output_attentions=False, + ) + return select_hidden_state_embeddings( + output.last_hidden_state, + output.hidden_states, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + ) + + def _ttt_get_trainable_modules(self) -> list[nn.Module]: + return [self.transformer] + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + sequence_id: 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, + output_s_max: Optional[bool] = False, + esmfold2_hidden_states: bool = False, + return_dict: Optional[bool] = None, + compute_logits: bool = True, + **kwargs, + ) -> ESMplusplusOutput: + if inputs_embeds is None: + x = self.embed(input_ids) + else: + x = inputs_embeds + + output = self.transformer( + x=x, + attention_mask=attention_mask, + sequence_id=sequence_id, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + output_s_max=output_s_max, + esmfold2_hidden_states=esmfold2_hidden_states, + ) + + last_hidden_state = output.last_hidden_state + logits = self.sequence_head(last_hidden_state) if compute_logits else None + loss = None + if labels is not None: + assert logits is not None, "labels require compute_logits=True." + loss = self.ce_loss(logits.view(-1, self.vocab_size), labels.view(-1)) + + return ESMplusplusOutput( + loss=loss, + logits=logits, + last_hidden_state=last_hidden_state, + hidden_states=output.hidden_states, + attentions=output.attentions, + s_max=output.s_max, + ) + + +class ESMplusplusForSequenceClassification(ESMplusplusForMaskedLM, EmbeddingMixin): + """ + ESM++ model for sequence classification. + Extends the base ESM++ model with a classification head. + """ + def __init__(self, config: ESMplusplusConfig, **kwargs): + ESMplusplusForMaskedLM.__init__(self, config, **kwargs) + self.config = config + self.num_labels = config.num_labels + self.classifier = RegressionHead(config.hidden_size * 2, config.num_labels, config.hidden_size * 4) + # Large intermediate projections help with sequence classification tasks (*4) + self.mse = nn.MSELoss() + self.ce = nn.CrossEntropyLoss() + self.bce = nn.BCEWithLogitsLoss() + # if kwargs has pooling_types, use them, otherwise use ['cls', 'mean'] + if 'pooling_types' in kwargs and isinstance(kwargs['pooling_types'], List[str]) and len(kwargs['pooling_types']) > 0: + pooling_types = kwargs['pooling_types'] + else: + pooling_types = ['mean', 'var'] + self.pooler = Pooler(pooling_types) + self.init_weights() + + def _embed( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + ) -> torch.Tensor: + x = self.embed(input_ids) + output_hidden_states = store_all_hidden_states or hidden_state_index != -1 + output = self.transformer( + x=x, + attention_mask=attention_mask, + output_hidden_states=output_hidden_states, + output_attentions=False, + ) + return select_hidden_state_embeddings( + output.last_hidden_state, + output.hidden_states, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + ) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + sequence_id: 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, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + **kwargs, + ) -> ESMplusplusOutput: + output = super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + sequence_id=sequence_id, + inputs_embeds=inputs_embeds, + labels=None, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + ) + + last_hidden_state = output.last_hidden_state + features = self.pooler(last_hidden_state, attention_mask) + logits = self.classifier(features) + + 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.flatten(), labels.flatten()) + 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 ESMplusplusOutput( + loss=loss, + logits=logits, + last_hidden_state=last_hidden_state, + hidden_states=output.hidden_states, + attentions=output.attentions, + s_max=output.s_max, + ) + + +class ESMplusplusForTokenClassification(ESMplusplusForMaskedLM, EmbeddingMixin): + """ + ESM++ model for token classification. + Extends the base ESM++ model with a token classification head. + """ + def __init__(self, config: ESMplusplusConfig, **kwargs): + ESMplusplusForMaskedLM.__init__(self, config, **kwargs) + self.config = config + self.num_labels = config.num_labels + self.classifier = RegressionHead(config.hidden_size, config.num_labels, config.hidden_size * 4) + # Large intermediate projections help with sequence classification tasks (*4) + self.loss_fct = nn.CrossEntropyLoss() + self.init_weights() + + def _embed( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + hidden_state_index: int = -1, + store_all_hidden_states: bool = False, + ) -> torch.Tensor: + x = self.embed(input_ids) + output_hidden_states = store_all_hidden_states or hidden_state_index != -1 + output = self.transformer( + x, + attention_mask, + output_hidden_states=output_hidden_states, + output_attentions=False, + ) + return select_hidden_state_embeddings( + output.last_hidden_state, + output.hidden_states, + hidden_state_index=hidden_state_index, + store_all_hidden_states=store_all_hidden_states, + ) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + sequence_id: 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, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + **kwargs, + ) -> ESMplusplusOutput: + output = super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + sequence_id=sequence_id, + inputs_embeds=inputs_embeds, + labels=None, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + ) + + last_hidden_state = output.last_hidden_state + logits = self.classifier(last_hidden_state) + loss = None + if labels is not None: + loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return ESMplusplusOutput( + loss=loss, + logits=logits, + last_hidden_state=last_hidden_state, + hidden_states=output.hidden_states, + attentions=output.attentions, + s_max=output.s_max, + ) + + +### Loading from Biohub +_ESMC_CHECKPOINT_SPECS = { + "esmc-300": { + "repo_id": "biohub/ESMC-300M", + "hidden_size": 960, + "num_attention_heads": 15, + "num_hidden_layers": 30, + }, + "esmc-600": { + "repo_id": "biohub/ESMC-600M", + "hidden_size": 1152, + "num_attention_heads": 18, + "num_hidden_layers": 36, + }, + "esmc-6b": { + "repo_id": "biohub/ESMC-6B", + "hidden_size": 2560, + "num_attention_heads": 40, + "num_hidden_layers": 80, + }, +} + + +def _resolve_esmc_checkpoint_key(model: str) -> str: + normalized = model.lower().replace("_", "-") + if "300" in normalized: + return "esmc-300" + if "600" in normalized: + return "esmc-600" + if "6b" in normalized: + return "esmc-6b" + raise ValueError(f"{model=} is an invalid ESMC model name.") + + +@staticmethod +@cache +def data_root(model: str): + if "INFRA_PROVIDER" in os.environ: + return Path("") + key = _resolve_esmc_checkpoint_key(model) + return Path(snapshot_download(repo_id=_ESMC_CHECKPOINT_SPECS[key]["repo_id"])) + + +def get_esmc_checkpoint_path(model: str) -> Path: + key = _resolve_esmc_checkpoint_key(model) + spec = _ESMC_CHECKPOINT_SPECS[key] + if "weights_relpath" in spec: + return data_root(key) / spec["weights_relpath"] + checkpoint_dir = data_root(key) + if (checkpoint_dir / "model.safetensors").exists(): + return checkpoint_dir / "model.safetensors" + if (checkpoint_dir / "model.safetensors.index.json").exists(): + return checkpoint_dir / "model.safetensors.index.json" + raise FileNotFoundError(f"No ESMC checkpoint found under {checkpoint_dir}.") + + +def _normalize_esmc_state_key(key: str) -> Optional[str]: + if key.endswith("._extra_state"): + return None + if key.startswith("esmc."): + key = key[len("esmc."):] + if key.startswith("lm_head."): + key = f"sequence_head.{key[len('lm_head.'):]}" + replacements = ( + (".attn.layernorm_qkv.layer_norm_bias", ".attn.layernorm_qkv.0.bias"), + (".attn.layernorm_qkv.layer_norm_weight", ".attn.layernorm_qkv.0.weight"), + (".attn.layernorm_qkv.weight", ".attn.layernorm_qkv.1.weight"), + (".ffn.layer_norm_bias", ".ffn.0.bias"), + (".ffn.layer_norm_weight", ".ffn.0.weight"), + (".ffn.fc1_weight", ".ffn.1.weight"), + (".ffn.fc2_weight", ".ffn.3.weight"), + ) + for old, new in replacements: + key = key.replace(old, new) + return key + + +def _normalize_esmc_state_dict(state_dict: dict) -> dict: + normalized = {} + for key, tensor in state_dict.items(): + normalized_key = _normalize_esmc_state_key(key) + if normalized_key is None: + continue + normalized[normalized_key] = tensor + return normalized + + +def _safetensors_checkpoint_files(checkpoint_path: Path) -> List[Path]: + if checkpoint_path.name == "model.safetensors": + return [checkpoint_path] + with checkpoint_path.open("r", encoding="utf-8") as f: + index = json.load(f) + return [ + checkpoint_path.parent / filename + for filename in sorted(set(index["weight_map"].values())) + ] + + +def _load_safetensors_state_dict( + model_obj: ESMplusplusForMaskedLM, + checkpoint_path: Path, + device: Union[torch.device, str], +) -> None: + expected_keys = set(model_obj.state_dict().keys()) + loaded_keys = set() + device_string = str(torch.device(device)) + for shard_path in _safetensors_checkpoint_files(checkpoint_path): + shard_state_dict = load_safetensors_file(shard_path, device=device_string) + normalized = _normalize_esmc_state_dict(shard_state_dict) + unexpected = set(normalized.keys()) - expected_keys + assert len(unexpected) == 0, ( + f"Unexpected ESMC checkpoint keys in {shard_path.name}: " + f"{sorted(unexpected)[:10]}" + ) + model_obj.load_state_dict(normalized, strict=False) + loaded_keys.update(normalized.keys()) + + missing = expected_keys - loaded_keys + assert len(missing) == 0, ( + f"ESMC checkpoint did not provide all expected keys: {sorted(missing)[:10]}" + ) + + +def _load_esmc_checkpoint_model( + config: ESMplusplusConfig, + model: str, + device: Union[torch.device, str] = "cpu", +) -> ESMplusplusForMaskedLM: + key = _resolve_esmc_checkpoint_key(model) + spec = _ESMC_CHECKPOINT_SPECS[key] + assert config.hidden_size == spec["hidden_size"], ( + f"ESMC loader expected hidden_size={spec['hidden_size']} for {key}, " + f"but got {config.hidden_size}." + ) + assert config.num_attention_heads == spec["num_attention_heads"], ( + f"ESMC loader expected num_attention_heads={spec['num_attention_heads']} for {key}, " + f"but got {config.num_attention_heads}." + ) + assert config.num_hidden_layers == spec["num_hidden_layers"], ( + f"ESMC loader expected num_hidden_layers={spec['num_hidden_layers']} for {key}, " + f"but got {config.num_hidden_layers}." + ) + with torch.device(device): + model_obj = ESMplusplusForMaskedLM(config) + checkpoint_path = get_esmc_checkpoint_path(key) + if checkpoint_path.suffix == ".safetensors" or checkpoint_path.name == "model.safetensors.index.json": + _load_safetensors_state_dict( + model_obj=model_obj, + checkpoint_path=checkpoint_path, + device=device, + ) + else: + state_dict = torch.load(checkpoint_path, map_location=device) + model_obj.load_state_dict(_normalize_esmc_state_dict(state_dict)) + return model_obj + + +def ESMplusplus_300M(device: Union[torch.device, str] = "cpu"): + config = ESMplusplusConfig( + hidden_size=960, + num_attention_heads=15, + num_hidden_layers=30, + ) + return _load_esmc_checkpoint_model(config=config, model="esmc-300", device=device) + + +def ESMplusplus_600M(device: Union[torch.device, str] = "cpu"): + config = ESMplusplusConfig( + hidden_size=1152, + num_attention_heads=18, + num_hidden_layers=36, + ) + return _load_esmc_checkpoint_model(config=config, model="esmc-600", device=device) + + +def ESMplusplus_6B(device: Union[torch.device, str] = "cpu"): + config = ESMplusplusConfig( + hidden_size=2560, + num_attention_heads=40, + num_hidden_layers=80, + ) + return _load_esmc_checkpoint_model(config=config, model="esmc-6b", device=device) + + +### Tokenization +SEQUENCE_VOCAB = [ + "", "", "", "", + "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", + "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", + "O", ".", "-", "|", + "", +] + +class EsmSequenceTokenizer(PreTrainedTokenizerFast): + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + unk_token="", + cls_token="", + pad_token="", + mask_token="", + eos_token="", + chain_break_token="|", + **kwargs, + ): + all_tokens = SEQUENCE_VOCAB + token_to_id = {tok: ind for ind, tok in enumerate(all_tokens)} + + # a character-level tokenizer is the same as BPE with no token merges + bpe = BPE(token_to_id, merges=[], unk_token=unk_token) + tokenizer = Tokenizer(bpe) + special_tokens = [ + cls_token, + pad_token, + mask_token, + eos_token, + chain_break_token, + ] + self.cb_token = chain_break_token + additional_special_tokens = [chain_break_token] + + tokenizer.add_special_tokens(special_tokens) + + # This is where we configure the automatic addition of special tokens when we call + # tokenizer(text, add_special_tokens=True). Note that you can also configure how two + # sequences are merged if you want. + tokenizer.post_processor = TemplateProcessing( # type: ignore + single=" $A ", + pair=":0 $A:0 :0 $B:1 :1", + special_tokens=[ + ("", tokenizer.token_to_id("")), + ("", tokenizer.token_to_id("")), + ], + ) + super().__init__( + tokenizer_object=tokenizer, + unk_token=unk_token, + cls_token=cls_token, + pad_token=pad_token, + mask_token=mask_token, + eos_token=eos_token, + additional_special_tokens=additional_special_tokens, + **kwargs, + ) + + # These are a footgun, we never use the `bos` token anywhere so we're just overriding it here. + @property + def bos_token(self): + return self.cls_token + + @property + def bos_token_id(self): + return self.cls_token_id + + @property + def chain_break_token(self): + return self.cb_token + + @property + def chain_break_token_id(self): + return self.convert_tokens_to_ids(self.chain_break_token) + + @property + def all_token_ids(self): + return list(range(self.vocab_size)) + + @property + def special_token_ids(self): + return self.all_special_ids + + +if __name__ == "__main__": + import random + + import torch + + from torch import Tensor + + def print_tensor_shapes(prefix: str, obj): + if isinstance(obj, Tensor): + print(f"{prefix}{obj.shape}") + elif isinstance(obj, dict): + for name, value in obj.items(): + print_tensor_shapes(f"{prefix}{name}.", value) + elif isinstance(obj, list): + for idx, value in enumerate(obj): + print_tensor_shapes(f"{prefix}[{idx}].", value) + elif isinstance(obj, tuple): + for idx, value in enumerate(obj): + print_tensor_shapes(f"{prefix}[{idx}].", value) + elif hasattr(obj, "__dict__"): + for name, value in vars(obj).items(): + if name.startswith("_"): + continue + print_tensor_shapes(f"{prefix}{name}.", value) + else: + print(f"{prefix}{type(obj)}") + + random.seed(0) + torch.manual_seed(0) + + tokenizer = EsmSequenceTokenizer() + num_attention_heads = random.choice([2, 4]) + config = ESMplusplusConfig( + vocab_size=tokenizer.vocab_size, + hidden_size=16 * num_attention_heads, + num_attention_heads=num_attention_heads, + num_hidden_layers=random.choice([1, 2]), + num_labels=2, + dropout=0.0, + ) + + batch = tokenizer(["ACDEFG", "MKTW"], return_tensors="pt", padding=True) + batch["labels"] = batch["input_ids"].clone() + model = ESMplusplusForMaskedLM(config=config).eval() + + with torch.no_grad(): + output = model(**batch, return_dict=True) + + print("Batch shape:") + print_tensor_shapes("", batch) + print("Output shape:") + print_tensor_shapes("", output)