diff --git "a/modeling_dplm2.py" "b/modeling_dplm2.py" --- "a/modeling_dplm2.py" +++ "b/modeling_dplm2.py" @@ -1,1505 +1,1515 @@ -### Embedding Mixin + Pooler -import os -import sqlite3 -import networkx as nx -import numpy as np -import torch -from tqdm.auto import tqdm -from typing import Callable, List, Optional -from torch.utils.data import DataLoader -from torch.utils.data import Dataset as TorchDataset -from transformers import PreTrainedTokenizerBase - - -class Pooler: - def __init__(self, pooling_types: List[str]): - self.pooling_types = pooling_types - self.pooling_options = { - 'mean': self.mean_pooling, - 'max': self.max_pooling, - 'norm': self.norm_pooling, - 'median': self.median_pooling, - 'std': self.std_pooling, - 'var': self.var_pooling, - 'cls': self.cls_pooling, - 'parti': self._pool_parti, - } - - def _create_pooled_matrices_across_layers(self, attentions: torch.Tensor) -> torch.Tensor: - maxed_attentions = torch.max(attentions, dim=1)[0] - return maxed_attentions - - def _page_rank(self, attention_matrix, personalization=None, nstart=None, prune_type="top_k_outdegree"): - # Run PageRank on the attention matrix converted to a graph. - # Raises exceptions if the graph doesn't match the token sequence or has no edges. - # Returns the PageRank scores for each token node. - G = self._convert_to_graph(attention_matrix) - if G.number_of_nodes() != attention_matrix.shape[0]: - raise Exception( - f"The number of nodes in the graph should be equal to the number of tokens in sequence! You have {G.number_of_nodes()} nodes for {attention_matrix.shape[0]} tokens.") - if G.number_of_edges() == 0: - raise Exception(f"You don't seem to have any attention edges left in the graph.") - - return nx.pagerank(G, alpha=0.85, tol=1e-06, weight='weight', personalization=personalization, nstart=nstart, max_iter=100) - - def _convert_to_graph(self, matrix): - # Convert a matrix (e.g., attention scores) to a directed graph using networkx. - # Each element in the matrix represents a directed edge with a weight. - G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) - return G - - def _calculate_importance_weights(self, dict_importance, attention_mask: Optional[torch.Tensor] = None): - # Remove keys where attention_mask is 0 - if attention_mask is not None: - for k in list(dict_importance.keys()): - if attention_mask[k] == 0: - del dict_importance[k] - - #dict_importance[0] # remove cls - #dict_importance[-1] # remove eos - total = sum(dict_importance.values()) - return np.array([v / total for _, v in dict_importance.items()]) - - def _pool_parti(self, emb: torch.Tensor, attentions: torch.Tensor, attention_mask: Optional[torch.Tensor] = None): # (b, L, d) -> (b, d) - maxed_attentions = self._create_pooled_matrices_across_layers(attentions).numpy() - # emb is (b, L, d), maxed_attentions is (b, L, L) - emb_pooled = [] - for e, a, mask in zip(emb, maxed_attentions, attention_mask): - dict_importance = self._page_rank(a) - importance_weights = self._calculate_importance_weights(dict_importance, mask) - num_tokens = int(mask.sum().item()) - emb_pooled.append(np.average(e[:num_tokens], weights=importance_weights, axis=0)) - pooled = torch.tensor(np.array(emb_pooled)) - return pooled - - def mean_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.mean(dim=1) - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) - - def max_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.max(dim=1).values - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).max(dim=1).values - - def norm_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.norm(dim=1, p=2) - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).norm(dim=1, p=2) - - def median_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.median(dim=1).values - else: - attention_mask = attention_mask.unsqueeze(-1) - return (emb * attention_mask).median(dim=1).values - - def std_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.std(dim=1) - else: - # Compute variance correctly over non-masked positions, then take sqrt - var = self.var_pooling(emb, attention_mask, **kwargs) - return torch.sqrt(var) - - def var_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - if attention_mask is None: - return emb.var(dim=1) - else: - # Correctly compute variance over only non-masked positions - attention_mask = attention_mask.unsqueeze(-1) # (b, L, 1) - # Compute mean over non-masked positions - mean = (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) - mean = mean.unsqueeze(1) # (b, 1, d) - # Compute squared differences from mean, only over non-masked positions - squared_diff = (emb - mean) ** 2 # (b, L, d) - # Sum squared differences over non-masked positions and divide by count - var = (squared_diff * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) - return var - - def cls_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) - return emb[:, 0, :] - - def __call__( - self, - emb: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - attentions: Optional[torch.Tensor] = None - ): # [mean, max] - final_emb = [] - for pooling_type in self.pooling_types: - final_emb.append(self.pooling_options[pooling_type](emb=emb, attention_mask=attention_mask, attentions=attentions)) # (b, d) - return torch.cat(final_emb, dim=-1) # (b, n_pooling_types * d) - - -class ProteinDataset(TorchDataset): - """Simple dataset for protein sequences.""" - def __init__(self, sequences: list[str]): - self.sequences = sequences - - def __len__(self) -> int: - return len(self.sequences) - - def __getitem__(self, idx: int) -> str: - return self.sequences[idx] - - -def build_collator(tokenizer: PreTrainedTokenizerBase) -> Callable[[list[str]], dict[str, torch.Tensor]]: - def _collate_fn(sequences: list[str]) -> dict[str, torch.Tensor]: - return tokenizer(sequences, return_tensors="pt", padding='longest') - return _collate_fn - - -class EmbeddingMixin: - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - raise NotImplementedError - - @property - def device(self) -> torch.device: - """Get the device of the model.""" - return next(self.parameters()).device - - def _read_sequences_from_db(self, db_path: str) -> set[str]: - """Read sequences from SQLite database.""" - sequences = [] - with sqlite3.connect(db_path) as conn: - c = conn.cursor() - c.execute("SELECT sequence FROM embeddings") - while True: - row = c.fetchone() - if row is None: - break - sequences.append(row[0]) - return set(sequences) - - def _ensure_embeddings_table(self, conn: sqlite3.Connection) -> None: - cursor = conn.cursor() - cursor.execute( - "CREATE TABLE IF NOT EXISTS embeddings (" - "sequence TEXT PRIMARY KEY, " - "embedding BLOB NOT NULL, " - "shape TEXT, " - "dtype TEXT" - ")" - ) - cursor.execute("PRAGMA table_info(embeddings)") - rows = cursor.fetchall() - column_names = [row[1] for row in rows] - if "shape" not in column_names: - cursor.execute("ALTER TABLE embeddings ADD COLUMN shape TEXT") - if "dtype" not in column_names: - cursor.execute("ALTER TABLE embeddings ADD COLUMN dtype TEXT") - conn.commit() - - def load_embeddings_from_pth(self, save_path: str) -> dict[str, torch.Tensor]: - assert os.path.exists(save_path), f"Embedding file does not exist: {save_path}" - payload = torch.load(save_path, map_location="cpu", weights_only=True) - assert isinstance(payload, dict), "Expected .pth embeddings file to contain a dictionary." - for sequence, tensor in payload.items(): - assert isinstance(sequence, str), "Expected embedding dictionary keys to be sequences (str)." - assert isinstance(tensor, torch.Tensor), "Expected embedding dictionary values to be tensors." - return payload - - def load_embeddings_from_db(self, db_path: str, sequences: Optional[List[str]] = None) -> dict[str, torch.Tensor]: - assert os.path.exists(db_path), f"Embedding database does not exist: {db_path}" - loaded: dict[str, torch.Tensor] = {} - with sqlite3.connect(db_path) as conn: - self._ensure_embeddings_table(conn) - cursor = conn.cursor() - if sequences is None: - cursor.execute("SELECT sequence, embedding, shape, dtype FROM embeddings") - else: - if len(sequences) == 0: - return loaded - placeholders = ",".join(["?"] * len(sequences)) - cursor.execute( - f"SELECT sequence, embedding, shape, dtype FROM embeddings WHERE sequence IN ({placeholders})", - tuple(sequences), - ) - - rows = cursor.fetchall() - for row in rows: - sequence = row[0] - embedding_bytes = row[1] - shape_text = row[2] - dtype_text = row[3] - assert shape_text is not None, "Missing shape metadata in embeddings table." - assert dtype_text is not None, "Missing dtype metadata in embeddings table." - shape_values = [int(value) for value in shape_text.split(",") if len(value) > 0] - assert len(shape_values) > 0, f"Invalid shape metadata for sequence: {sequence}" - expected_size = int(np.prod(shape_values)) - np_dtype = np.dtype(dtype_text) - array = np.frombuffer(embedding_bytes, dtype=np_dtype) - assert array.size == expected_size, f"Shape mismatch while reading sequence: {sequence}" - reshaped = array.copy().reshape(tuple(shape_values)) - loaded[sequence] = torch.from_numpy(reshaped) - return loaded - - def embed_dataset( - self, - sequences: List[str], - tokenizer: Optional[PreTrainedTokenizerBase] = None, - batch_size: int = 2, - max_len: int = 512, - truncate: bool = True, - full_embeddings: bool = False, - embed_dtype: torch.dtype = torch.float32, - pooling_types: List[str] = ['mean'], - num_workers: int = 0, - sql: bool = False, - save: bool = True, - sql_db_path: str = 'embeddings.db', - save_path: str = 'embeddings.pth', - **kwargs, - ) -> Optional[dict[str, torch.Tensor]]: - """ - Embed a dataset of protein sequences. - - Supports two modes: - - Tokenizer mode (ESM2/ESM++): provide `tokenizer`, `_embed(input_ids, attention_mask)` is used. - - Sequence mode (E1): pass `tokenizer=None`, `_embed(sequences, return_attention_mask=True, **kwargs)` is used. - """ - sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences])) - sequences = sorted(sequences, key=len, reverse=True) - hidden_size = self.config.hidden_size - pooler = Pooler(pooling_types) if not full_embeddings else None - tokenizer_mode = tokenizer is not None - if tokenizer_mode: - collate_fn = build_collator(tokenizer) - device = self.device - else: - collate_fn = None - device = None - - def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - if full_embeddings or residue_embeddings.ndim == 2: - return residue_embeddings - return pooler(residue_embeddings, attention_mask) - - def iter_batches(to_embed: List[str]): - if tokenizer_mode: - assert collate_fn is not None - assert device is not None - dataset = ProteinDataset(to_embed) - dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False) - for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'): - seqs = to_embed[i * batch_size:(i + 1) * batch_size] - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - residue_embeddings = self._embed(input_ids, attention_mask) - yield seqs, residue_embeddings, attention_mask - else: - for batch_start in tqdm(range(0, len(to_embed), batch_size), desc='Embedding batches'): - seqs = to_embed[batch_start:batch_start + batch_size] - batch_output = self._embed(seqs, return_attention_mask=True, **kwargs) - assert isinstance(batch_output, tuple), "Sequence mode _embed must return (last_hidden_state, attention_mask)." - assert len(batch_output) == 2, "Sequence mode _embed must return exactly two values." - residue_embeddings, attention_mask = batch_output - assert isinstance(attention_mask, torch.Tensor), "Sequence mode _embed must return attention_mask as a torch.Tensor." - yield seqs, residue_embeddings, attention_mask - - if sql: - conn = sqlite3.connect(sql_db_path) - self._ensure_embeddings_table(conn) - c = conn.cursor() - already_embedded = self._read_sequences_from_db(sql_db_path) - to_embed = [seq for seq in sequences if seq not in already_embedded] - print(f"Found {len(already_embedded)} already embedded sequences in {sql_db_path}") - print(f"Embedding {len(to_embed)} new sequences") - if len(to_embed) > 0: - with torch.no_grad(): - for i, (seqs, residue_embeddings, attention_mask) in enumerate(iter_batches(to_embed)): - embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) - for seq, emb, mask in zip(seqs, embeddings, attention_mask): - if full_embeddings: - emb = emb[mask.bool()].reshape(-1, hidden_size) - emb_np = emb.cpu().numpy() - emb_shape = ",".join([str(dim) for dim in emb_np.shape]) - emb_dtype = str(emb_np.dtype) - c.execute( - "INSERT OR REPLACE INTO embeddings (sequence, embedding, shape, dtype) VALUES (?, ?, ?, ?)", - (seq, emb_np.tobytes(), emb_shape, emb_dtype), - ) - if tokenizer_mode and (i + 1) % 100 == 0: - conn.commit() - conn.commit() - conn.close() - return None - - embeddings_dict = {} - if os.path.exists(save_path): - embeddings_dict = self.load_embeddings_from_pth(save_path) - to_embed = [seq for seq in sequences if seq not in embeddings_dict] - print(f"Found {len(embeddings_dict)} already embedded sequences in {save_path}") - print(f"Embedding {len(to_embed)} new sequences") - else: - to_embed = sequences - print(f"Embedding {len(to_embed)} new sequences") - - if len(to_embed) > 0: - with torch.no_grad(): - for seqs, residue_embeddings, attention_mask in iter_batches(to_embed): - embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) - for seq, emb, mask in zip(seqs, embeddings, attention_mask): - if full_embeddings: - emb = emb[mask.bool()].reshape(-1, hidden_size) - embeddings_dict[seq] = emb.cpu() - - if save: - torch.save(embeddings_dict, save_path) - - return embeddings_dict - - -""" -FastPLMs-compatible DPLM2 implementation. -""" - -import torch -import torch.nn as nn -from torch.nn import functional as F -from dataclasses import dataclass -from einops import rearrange -from enum import Enum -from typing import List, Optional, Tuple, Union - -from transformers import EsmTokenizer -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - ModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.models.esm.configuration_esm import EsmConfig -from transformers.models.esm.modeling_esm import ( - EsmAttention, - EsmClassificationHead, - EsmEmbeddings, - EsmEncoder, - EsmIntermediate, - EsmLayer, - EsmLMHead, - EsmOutput, - EsmPooler, - EsmPreTrainedModel, - EsmSelfAttention, - EsmSelfOutput, - RotaryEmbedding, - apply_rotary_pos_emb, -) - -try: - from torch.nn.attention.flex_attention import create_block_mask, flex_attention, BlockMask -except (ImportError, AttributeError): - create_block_mask = None - flex_attention = None - BlockMask = None - - -### Kernels Flash Attention Detection -def _infer_kernels_flash_variant(kernel) -> str | None: - 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_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, -) -> torch.Tensor: - 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, 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, causal=causal) - except TypeError: - output = FLASH_KERNEL.flash_attn_func(query_states, key_states, value_states, 0.0, None, 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, -) -> torch.Tensor: - 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, - 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, - 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, None, causal, - ) - if isinstance(output, tuple): - return output[0] - return output - raise AssertionError(f"Unsupported kernels flash attention variant: {FLASH_KERNEL_VARIANT}") - - -### Unpad / Pad helpers for varlen flash attention -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: torch.Tensor | None = None, - causal: bool = False, -) -> torch.Tensor: - 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, - ) - 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, - ) - - -### Attention Backend Enum & Resolution -class AttentionBackend(Enum): - AUTO = "auto" - KERNELS_FLASH = "kernels_flash" - FLEX = "flex" - SDPA = "sdpa" - - -VALID_ATTENTION_BACKENDS = tuple(b.value for b in AttentionBackend) - - -def resolve_attention_backend(requested_backend: str) -> AttentionBackend: - assert requested_backend in VALID_ATTENTION_BACKENDS, ( - f"Unsupported attention backend: {requested_backend}. Expected one of {VALID_ATTENTION_BACKENDS}." - ) - if requested_backend == AttentionBackend.AUTO.value: - if FLASH_KERNEL is not None: - return AttentionBackend.KERNELS_FLASH - if flex_attention is not None: - return AttentionBackend.FLEX - return AttentionBackend.SDPA - if requested_backend == AttentionBackend.KERNELS_FLASH.value: - assert FLASH_KERNEL is not None, "Kernels Flash Attention is not available in this environment." - return AttentionBackend.KERNELS_FLASH - if requested_backend == AttentionBackend.FLEX.value: - assert flex_attention is not None, "Flex Attention is not available in this environment." - return AttentionBackend.FLEX - if requested_backend == AttentionBackend.SDPA.value: - return AttentionBackend.SDPA - raise AssertionError(f"Unsupported attention backend: {requested_backend}") - - -from transformers import PreTrainedTokenizerBase - - -class BaseSequenceTokenizer: - def __init__(self, tokenizer: PreTrainedTokenizerBase): - self.tokenizer = tokenizer - - def __call__(self, sequences, **kwargs): - raise NotImplementedError - - -def get_attention_mask( - effective_backend: AttentionBackend, - batch_size: int, - seq_len: int, - device: torch.device, - attention_mask: Optional[torch.Tensor] = None, -) -> tuple[torch.Tensor | None, torch.Tensor | None, "BlockMask | None"]: - 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 - attention_mask_4d = attention_mask_2d[:, None, :, None] & attention_mask_2d[:, None, None, :] - return attention_mask_2d, attention_mask_4d, None - - -def _infer_modality_type(input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - input_mask = attention_mask.bool() - modality_type = ((input_ids < 33) & input_mask).int() - modality_type[~input_mask] = 2 - return modality_type - - -@dataclass -class DPLM2MaskedLMOutput(ModelOutput): - loss: Optional[torch.Tensor] = None - logits: Optional[torch.Tensor] = None - last_hidden_state: Optional[torch.Tensor] = None - hidden_states: Optional[Tuple[torch.Tensor, ...]] = None - attentions: Optional[Tuple[torch.Tensor, ...]] = None - s_max: Optional[Tuple[list[torch.Tensor], ...]] = None - - -@dataclass -class DPLM2EncoderOutput(ModelOutput): - 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 - - -class DPLM2Config(EsmConfig): - model_type = "dplm2" - - def __init__( - self, - attn_backend: str = "auto", - aa_type: int = 1, - struct_type: int = 0, - pad_type: int = 2, - **kwargs, - ): - super().__init__(**kwargs) - self.attn_backend = attn_backend - self.aa_type = aa_type - self.struct_type = struct_type - self.pad_type = pad_type - self.tie_word_embeddings = False - - -class DPLM2PreTrainedModel(EsmPreTrainedModel): - config_class = DPLM2Config - base_model_prefix = "dplm2" - supports_gradient_checkpointing = True - tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") - all_tied_weights_keys = {} - - @classmethod - def is_remote_code(cls) -> bool: - # Prevent post-load reinitialization of tensors already loaded from checkpoints. - return True - - @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 - resolved = resolve_attention_backend(backend) - for module in self.modules(): - if isinstance(module, ModifiedEsmEncoder): - module.attention_backend = resolved - elif isinstance(module, ModifiedEsmSelfAttention): - module.attn_backend = resolved - - - -class ModifiedRotaryEmbedding(RotaryEmbedding): - def __init__(self, dim: int, aa_type: int, struct_type: int): - super().__init__(dim) - self.aa_type = aa_type - self.struct_type = struct_type - - def _has_multimodal_tokens(self, type_ids: Optional[torch.Tensor]) -> bool: - if type_ids is None: - return False - aa_present = (type_ids == self.aa_type).any() - struct_present = (type_ids == self.struct_type).any() - return bool(aa_present and struct_present) - - def _update_cos_sin_tables( - self, - x: torch.Tensor, - type_ids: Optional[torch.Tensor], - seq_dimension: int = 2, - ) -> Tuple[torch.Tensor, torch.Tensor]: - seq_len = x.shape[seq_dimension] - if self._has_multimodal_tokens(type_ids): - seq_len = seq_len // 2 - - cache_is_stale = ( - self._cos_cached is None - or self._sin_cached is None - or seq_len != self._seq_len_cached - or self._cos_cached.device != x.device - ) - if cache_is_stale: - self._seq_len_cached = seq_len - t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq) - freqs = torch.outer(t, self.inv_freq) - emb = torch.cat((freqs, freqs), dim=-1).to(x.device) - self._cos_cached = emb.cos()[None, None, :, :] - self._sin_cached = emb.sin()[None, None, :, :] - - return self._cos_cached, self._sin_cached - - def forward( - self, - q: torch.Tensor, - k: torch.Tensor, - type_ids: Optional[torch.Tensor], - ) -> Tuple[torch.Tensor, torch.Tensor]: - self._cos_cached, self._sin_cached = self._update_cos_sin_tables( - k, - type_ids=type_ids, - seq_dimension=-2, - ) - - if self._has_multimodal_tokens(type_ids): - q_1, q_2 = q.chunk(2, dim=-2) - k_1, k_2 = k.chunk(2, dim=-2) - q_1 = apply_rotary_pos_emb(q_1, self._cos_cached, self._sin_cached) - q_2 = apply_rotary_pos_emb(q_2, self._cos_cached, self._sin_cached) - k_1 = apply_rotary_pos_emb(k_1, self._cos_cached, self._sin_cached) - k_2 = apply_rotary_pos_emb(k_2, self._cos_cached, self._sin_cached) - return torch.cat((q_1, q_2), dim=-2), torch.cat((k_1, k_2), dim=-2) - - return ( - apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached), - apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached), - ) - - -class ModifiedEsmSelfAttention(EsmSelfAttention): - def __init__(self, config, position_embedding_type=None): - super().__init__(config, position_embedding_type) - self.config = config - self.scale = self.attention_head_size**-0.5 - self.dropout_prob = config.attention_probs_dropout_prob - self.attn_backend = resolve_attention_backend(config.attn_backend) - self.rotary_embeddings = ModifiedRotaryEmbedding( - dim=self.attention_head_size, - aa_type=config.aa_type, - struct_type=config.struct_type, - ) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask_2d: torch.Tensor | None = None, - attention_mask_4d: torch.Tensor | None = None, - flex_block_mask: "BlockMask | None" = None, - output_attentions: bool = False, - output_s_max: bool = False, - type_ids: Optional[torch.Tensor] = None, - ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: - batch_size, seq_length = hidden_states.shape[:-1] - hidden_shape = (batch_size, seq_length, -1, self.attention_head_size) - query_BHLD = self.query(hidden_states).view(hidden_shape).transpose(1, 2) - key_BHLD = self.key(hidden_states).view(hidden_shape).transpose(1, 2) - value_BHLD = self.value(hidden_states).view(hidden_shape).transpose(1, 2) - - query_BHLD = query_BHLD * self.scale - - if self.position_embedding_type == "rotary": - query_BHLD, key_BHLD = self.rotary_embeddings(query_BHLD, key_BHLD, type_ids) - - 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, - ) - return attn_output, attn_weights, s_max - - def _attn( - self, - query_BHLD: torch.Tensor, - key_BHLD: torch.Tensor, - value_BHLD: torch.Tensor, - attention_mask_2d: torch.Tensor | None = None, - attention_mask_4d: torch.Tensor | None = None, - flex_block_mask: "BlockMask | None" = None, - output_attentions: bool = False, - output_s_max: bool = False, - ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: - 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 - return [s_max_bound[h] for h in range(self.num_attention_heads)] - - def _manual_attn( - self, - query_BHLD: torch.Tensor, - key_BHLD: torch.Tensor, - value_BHLD: torch.Tensor, - attention_mask_4d: torch.Tensor | None = None, - output_s_max: bool = False, - ) -> tuple[torch.Tensor, torch.Tensor, list[torch.Tensor] | None]: - attn_weights = torch.matmul(query_BHLD, key_BHLD.transpose(-1, -2)) - 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) - if self.dropout_prob > 0 and self.training: - attn_weights = F.dropout(attn_weights, p=self.dropout_prob, training=self.training) - 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: torch.Tensor | None = 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: "BlockMask | None" = None, - ) -> tuple[torch.Tensor, None]: - assert flex_attention is not None, "Flex attention is not available in this environment." - assert query_BHLD.dtype in (torch.float16, torch.bfloat16), ( - f"Flex attention requires float16 or bfloat16, got {query_BHLD.dtype}." - ) - context_BHLD = flex_attention(query_BHLD, key_BHLD, value_BHLD, block_mask=flex_block_mask, scale=1.0) - 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: torch.Tensor | None = None, - ) -> tuple[torch.Tensor, None]: - context_BHLD = F.scaled_dot_product_attention( - query_BHLD, key_BHLD, value_BHLD, - attn_mask=attention_mask_4d, - dropout_p=self.dropout_prob if self.training else 0.0, - scale=1.0, - ) - return rearrange(context_BHLD, "b h s d -> b s (h d)"), None - - -class ModifiedEsmAttention(EsmAttention): - def __init__(self, config): - nn.Module.__init__(self) - self.self = ModifiedEsmSelfAttention(config) - self.output = EsmSelfOutput(config) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask_2d: torch.Tensor | None = None, - attention_mask_4d: torch.Tensor | None = None, - flex_block_mask: "BlockMask | None" = None, - output_attentions: bool = False, - output_s_max: bool = False, - type_ids: Optional[torch.Tensor] = None, - ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: - hidden_states_ln = self.LayerNorm(hidden_states) - attn_output, attn_weights, s_max = self.self( - hidden_states_ln, - 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, - type_ids=type_ids, - ) - attention_output = self.output(attn_output, hidden_states) - return attention_output, attn_weights, s_max - - -class ModifiedEsmLayer(EsmLayer): - def __init__(self, config): - nn.Module.__init__(self) - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = ModifiedEsmAttention(config) - self.intermediate = EsmIntermediate(config) - self.output = EsmOutput(config) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask_2d: torch.Tensor | None = None, - attention_mask_4d: torch.Tensor | None = None, - flex_block_mask: "BlockMask | None" = None, - output_attentions: bool = False, - output_s_max: bool = False, - type_ids: Optional[torch.Tensor] = None, - ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: - attention_output, attn_weights, s_max = self.attention( - hidden_states, - 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, - type_ids=type_ids, - ) - layer_output = self.feed_forward_chunk(attention_output) - return layer_output, attn_weights, s_max - - -class ModifiedEsmEncoder(EsmEncoder): - def __init__(self, config): - nn.Module.__init__(self) - self.config = config - self.attention_backend = resolve_attention_backend(config.attn_backend) - self.layer = nn.ModuleList([ModifiedEsmLayer(config) for _ in range(config.num_hidden_layers)]) - self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - output_hidden_states: bool = False, - output_attentions: bool = False, - output_s_max: bool = False, - type_ids: Optional[torch.Tensor] = None, - ) -> DPLM2EncoderOutput: - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - full_s_max = () if output_s_max else None - - attention_mask_2d, attention_mask_4d, flex_block_mask = get_attention_mask( - effective_backend=self.attention_backend, - batch_size=hidden_states.shape[0], - seq_len=hidden_states.shape[1], - device=hidden_states.device, - attention_mask=attention_mask, - ) - - for layer_module in self.layer: - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - hidden_states, attn_weights, s_max = self._gradient_checkpointing_func( - layer_module.__call__, - hidden_states, - attention_mask_2d, - attention_mask_4d, - flex_block_mask, - output_attentions, - output_s_max, - type_ids, - ) - else: - hidden_states, attn_weights, s_max = layer_module( - hidden_states, - 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, - type_ids=type_ids, - ) - - if all_attentions is not None: - all_attentions = all_attentions + (attn_weights,) - if full_s_max is not None: - full_s_max = full_s_max + (s_max,) - - if self.emb_layer_norm_after: - hidden_states = self.emb_layer_norm_after(hidden_states) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - return DPLM2EncoderOutput( - last_hidden_state=hidden_states, - hidden_states=all_hidden_states, - attentions=all_attentions, - s_max=full_s_max, - ) - - -class FAST_DPLM2_ENCODER(DPLM2PreTrainedModel, EmbeddingMixin): - """Inner encoder class that holds the actual ESM-style weights (embeddings, encoder) - so that the weight keys are prefixed with 'esm.' in the outer DPLM2Model, - matching pretrained DPLM2 checkpoints.""" - - def __init__(self, config, **kwargs): - DPLM2PreTrainedModel.__init__(self, config, **kwargs) - self.config = config - self.embeddings = EsmEmbeddings(config) - self.encoder = ModifiedEsmEncoder(config) - self.post_init() - - def get_input_embeddings(self) -> nn.Module: - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - if attention_mask is None: - attention_mask = input_ids.ne(self.config.pad_token_id) - type_ids = _infer_modality_type(input_ids, attention_mask) - token_embedding_output = self.embeddings(input_ids, attention_mask=attention_mask) - encoder_outputs = self.encoder( - token_embedding_output, - attention_mask=attention_mask, - output_hidden_states=False, - output_attentions=False, - type_ids=type_ids, - ) - return encoder_outputs.last_hidden_state - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - output_s_max: Optional[bool] = False, - return_dict: Optional[bool] = None, - type_ids: Optional[torch.Tensor] = None, - ) -> DPLM2EncoderOutput: - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - pass - elif inputs_embeds is None: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - token_embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - attention_mask=attention_mask, - inputs_embeds=inputs_embeds, - ) - encoder_outputs = self.encoder( - token_embedding_output, - attention_mask=attention_mask, - output_hidden_states=output_hidden_states, - output_attentions=output_attentions, - output_s_max=output_s_max, - type_ids=type_ids, - ) - - return DPLM2EncoderOutput( - last_hidden_state=encoder_outputs.last_hidden_state, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - s_max=encoder_outputs.s_max, - ) - - -class DPLM2Model(DPLM2PreTrainedModel, EmbeddingMixin): - config_class = DPLM2Config - def __init__(self, config, add_pooling_layer=True): - DPLM2PreTrainedModel.__init__(self, config) - self.config = config - self.esm = FAST_DPLM2_ENCODER(config) - self.pooler = EsmPooler(config) if add_pooling_layer else None - self.post_init() - - def get_input_embeddings(self) -> nn.Module: - return self.esm.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.esm.embeddings.word_embeddings = value - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - output_s_max: Optional[bool] = False, - return_dict: Optional[bool] = None, - type_ids: Optional[torch.Tensor] = None, - ) -> DPLM2EncoderOutput: - outputs = self.esm( - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - output_s_max=output_s_max, - type_ids=type_ids, - ) - sequence_output = outputs.last_hidden_state - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - return DPLM2EncoderOutput( - last_hidden_state=sequence_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - s_max=outputs.s_max, - ) - - -class DPLM2ForMaskedLM(DPLM2PreTrainedModel, EmbeddingMixin): - config_class = DPLM2Config - def __init__(self, config, dropout: float = 0.1, vocab_size: Optional[int] = None): - config.hidden_dropout_prob = dropout - config.tie_word_embeddings = False - if vocab_size is not None: - config.vocab_size = vocab_size - DPLM2PreTrainedModel.__init__(self, config) - self.esm = FAST_DPLM2_ENCODER(config) - self.lm_head = EsmLMHead(config) - self.loss_fct = nn.CrossEntropyLoss() - self.post_init() - self.pad_id = config.pad_token_id - self.tokenizer = self.__class__.tokenizer - if isinstance(config._name_or_path, str) and len(config._name_or_path) > 0: - self.tokenizer = EsmTokenizer.from_pretrained(config._name_or_path) - - def get_input_embeddings(self) -> nn.Module: - return self.esm.get_input_embeddings() - - def get_output_embeddings(self): - return self.lm_head.decoder - - def set_output_embeddings(self, new_embeddings): - self.lm_head.decoder = new_embeddings - - def _get_modality_type(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - return _infer_modality_type(input_ids, attention_mask) - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - if attention_mask is None: - attention_mask = input_ids.ne(self.pad_id) - type_ids = self._get_modality_type(input_ids, attention_mask) - outputs = self.esm( - input_ids=input_ids, - attention_mask=attention_mask, - type_ids=type_ids, - output_attentions=False, - output_hidden_states=False, - ) - return outputs.last_hidden_state - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - type_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - output_s_max: Optional[bool] = False, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], DPLM2MaskedLMOutput]: - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if attention_mask is None: - assert input_ids is not None - attention_mask = input_ids.ne(self.pad_id) - - if type_ids is None: - assert input_ids is not None - type_ids = self._get_modality_type(input_ids, attention_mask) - - outputs = self.esm( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - output_s_max=output_s_max, - type_ids=type_ids, - ) - - sequence_output = outputs.last_hidden_state - logits = self.lm_head(sequence_output) - loss = None - if labels is not None: - labels = labels.to(logits.device) - loss = self.loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) - - if return_dict is False: - output = (logits, sequence_output, outputs.hidden_states, outputs.attentions) - if loss is not None: - return (loss,) + output - return output - - return DPLM2MaskedLMOutput( - loss=loss, - logits=logits, - last_hidden_state=sequence_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - s_max=outputs.s_max, - ) - - -class DPLM2ForSequenceClassification(DPLM2PreTrainedModel, EmbeddingMixin): - config_class = DPLM2Config - - def __init__(self, config): - DPLM2PreTrainedModel.__init__(self, config) - self.num_labels = config.num_labels - self.esm = FAST_DPLM2_ENCODER(config) - self.classifier = EsmClassificationHead(config) - self.mse = nn.MSELoss() - self.ce = nn.CrossEntropyLoss() - self.bce = nn.BCEWithLogitsLoss() - self.post_init() - - def get_input_embeddings(self) -> nn.Module: - return self.esm.get_input_embeddings() - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - type_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - output_s_max: Optional[bool] = False, - return_dict: Optional[bool] = None, - **kwargs, - ) -> DPLM2MaskedLMOutput: - if type_ids is None and input_ids is not None: - if attention_mask is None: - attention_mask = input_ids.ne(self.config.pad_token_id) - type_ids = _infer_modality_type(input_ids, attention_mask) - - outputs = self.esm( - input_ids=input_ids, - attention_mask=attention_mask, - type_ids=type_ids, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - output_s_max=output_s_max, - ) - sequence_output = outputs.last_hidden_state - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - if self.num_labels == 1: - loss = self.mse(logits.squeeze(), labels.squeeze()) - else: - loss = self.mse(logits, labels) - elif self.config.problem_type == "single_label_classification": - loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss = self.bce(logits, labels) - - return DPLM2MaskedLMOutput( - loss=loss, - logits=logits, - last_hidden_state=sequence_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - s_max=outputs.s_max, - ) - - -class DPLM2ForTokenClassification(DPLM2PreTrainedModel, EmbeddingMixin): - config_class = DPLM2Config - - def __init__(self, config): - DPLM2PreTrainedModel.__init__(self, config) - self.num_labels = config.num_labels - self.esm = FAST_DPLM2_ENCODER(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, config.num_labels) - self.loss_fct = nn.CrossEntropyLoss() - self.post_init() - - def get_input_embeddings(self) -> nn.Module: - return self.esm.get_input_embeddings() - - def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - return self.esm._embed(input_ids, attention_mask) - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - type_ids: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - output_s_max: Optional[bool] = False, - return_dict: Optional[bool] = None, - **kwargs, - ) -> DPLM2MaskedLMOutput: - if type_ids is None and input_ids is not None: - if attention_mask is None: - attention_mask = input_ids.ne(self.config.pad_token_id) - type_ids = _infer_modality_type(input_ids, attention_mask) - - outputs = self.esm( - input_ids=input_ids, - attention_mask=attention_mask, - type_ids=type_ids, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - output_s_max=output_s_max, - ) - sequence_output = self.dropout(outputs.last_hidden_state) - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - return DPLM2MaskedLMOutput( - loss=loss, - logits=logits, - last_hidden_state=sequence_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - s_max=outputs.s_max, - ) - - -if __name__ == "__main__": - import random - - import torch - - from torch import Tensor - from transformers import EsmTokenizer - - 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) - - num_attention_heads = random.choice([2, 4]) - config = DPLM2Config( - hidden_size=16 * num_attention_heads, - num_attention_heads=num_attention_heads, - num_hidden_layers=random.choice([1, 2]), - attention_probs_dropout_prob=0.0, - hidden_dropout_prob=0.0, - attn_backend="sdpa", - ) - tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") - batch = tokenizer(["ACDEFGH", "MKTW"], return_tensors="pt", padding="longest") - batch["labels"] = batch["input_ids"].clone() - model = DPLM2ForMaskedLM(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) +### Embedding Mixin + Pooler +import os +import sqlite3 +import networkx as nx +import numpy as np +import torch +from tqdm.auto import tqdm +from typing import Callable, List, Optional +from torch.utils.data import DataLoader +from torch.utils.data import Dataset as TorchDataset +from transformers import PreTrainedTokenizerBase + + +class Pooler: + def __init__(self, pooling_types: List[str]): + self.pooling_types = pooling_types + self.pooling_options = { + 'mean': self.mean_pooling, + 'max': self.max_pooling, + 'norm': self.norm_pooling, + 'median': self.median_pooling, + 'std': self.std_pooling, + 'var': self.var_pooling, + 'cls': self.cls_pooling, + 'parti': self._pool_parti, + } + + def _create_pooled_matrices_across_layers(self, attentions: torch.Tensor) -> torch.Tensor: + maxed_attentions = torch.max(attentions, dim=1)[0] + return maxed_attentions + + def _page_rank(self, attention_matrix, personalization=None, nstart=None, prune_type="top_k_outdegree"): + # Run PageRank on the attention matrix converted to a graph. + # Raises exceptions if the graph doesn't match the token sequence or has no edges. + # Returns the PageRank scores for each token node. + G = self._convert_to_graph(attention_matrix) + if G.number_of_nodes() != attention_matrix.shape[0]: + raise Exception( + f"The number of nodes in the graph should be equal to the number of tokens in sequence! You have {G.number_of_nodes()} nodes for {attention_matrix.shape[0]} tokens.") + if G.number_of_edges() == 0: + raise Exception(f"You don't seem to have any attention edges left in the graph.") + + return nx.pagerank(G, alpha=0.85, tol=1e-06, weight='weight', personalization=personalization, nstart=nstart, max_iter=100) + + def _convert_to_graph(self, matrix): + # Convert a matrix (e.g., attention scores) to a directed graph using networkx. + # Each element in the matrix represents a directed edge with a weight. + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + return G + + def _calculate_importance_weights(self, dict_importance, attention_mask: Optional[torch.Tensor] = None): + # Remove keys where attention_mask is 0 + if attention_mask is not None: + for k in list(dict_importance.keys()): + if attention_mask[k] == 0: + del dict_importance[k] + + #dict_importance[0] # remove cls + #dict_importance[-1] # remove eos + total = sum(dict_importance.values()) + return np.array([v / total for _, v in dict_importance.items()]) + + def _pool_parti(self, emb: torch.Tensor, attentions: torch.Tensor, attention_mask: Optional[torch.Tensor] = None): # (b, L, d) -> (b, d) + maxed_attentions = self._create_pooled_matrices_across_layers(attentions).numpy() + # emb is (b, L, d), maxed_attentions is (b, L, L) + emb_pooled = [] + for e, a, mask in zip(emb, maxed_attentions, attention_mask): + dict_importance = self._page_rank(a) + importance_weights = self._calculate_importance_weights(dict_importance, mask) + num_tokens = int(mask.sum().item()) + emb_pooled.append(np.average(e[:num_tokens], weights=importance_weights, axis=0)) + pooled = torch.tensor(np.array(emb_pooled)) + return pooled + + def mean_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.mean(dim=1) + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) + + def max_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.max(dim=1).values + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).max(dim=1).values + + def norm_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.norm(dim=1, p=2) + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).norm(dim=1, p=2) + + def median_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.median(dim=1).values + else: + attention_mask = attention_mask.unsqueeze(-1) + return (emb * attention_mask).median(dim=1).values + + def std_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.std(dim=1) + else: + # Compute variance correctly over non-masked positions, then take sqrt + var = self.var_pooling(emb, attention_mask, **kwargs) + return torch.sqrt(var) + + def var_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + if attention_mask is None: + return emb.var(dim=1) + else: + # Correctly compute variance over only non-masked positions + attention_mask = attention_mask.unsqueeze(-1) # (b, L, 1) + # Compute mean over non-masked positions + mean = (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) + mean = mean.unsqueeze(1) # (b, 1, d) + # Compute squared differences from mean, only over non-masked positions + squared_diff = (emb - mean) ** 2 # (b, L, d) + # Sum squared differences over non-masked positions and divide by count + var = (squared_diff * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d) + return var + + def cls_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs): # (b, L, d) -> (b, d) + return emb[:, 0, :] + + def __call__( + self, + emb: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + attentions: Optional[torch.Tensor] = None + ): # [mean, max] + final_emb = [] + for pooling_type in self.pooling_types: + final_emb.append(self.pooling_options[pooling_type](emb=emb, attention_mask=attention_mask, attentions=attentions)) # (b, d) + return torch.cat(final_emb, dim=-1) # (b, n_pooling_types * d) + + +class ProteinDataset(TorchDataset): + """Simple dataset for protein sequences.""" + def __init__(self, sequences: list[str]): + self.sequences = sequences + + def __len__(self) -> int: + return len(self.sequences) + + def __getitem__(self, idx: int) -> str: + return self.sequences[idx] + + +def build_collator(tokenizer: PreTrainedTokenizerBase) -> Callable[[list[str]], dict[str, torch.Tensor]]: + def _collate_fn(sequences: list[str]) -> dict[str, torch.Tensor]: + return tokenizer(sequences, return_tensors="pt", padding='longest') + return _collate_fn + + +class EmbeddingMixin: + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + raise NotImplementedError + + @property + def device(self) -> torch.device: + """Get the device of the model.""" + return next(self.parameters()).device + + def _read_sequences_from_db(self, db_path: str) -> set[str]: + """Read sequences from SQLite database.""" + sequences = [] + with sqlite3.connect(db_path) as conn: + c = conn.cursor() + c.execute("SELECT sequence FROM embeddings") + while True: + row = c.fetchone() + if row is None: + break + sequences.append(row[0]) + return set(sequences) + + def _ensure_embeddings_table(self, conn: sqlite3.Connection) -> None: + cursor = conn.cursor() + cursor.execute( + "CREATE TABLE IF NOT EXISTS embeddings (" + "sequence TEXT PRIMARY KEY, " + "embedding BLOB NOT NULL, " + "shape TEXT, " + "dtype TEXT" + ")" + ) + cursor.execute("PRAGMA table_info(embeddings)") + rows = cursor.fetchall() + column_names = [row[1] for row in rows] + if "shape" not in column_names: + cursor.execute("ALTER TABLE embeddings ADD COLUMN shape TEXT") + if "dtype" not in column_names: + cursor.execute("ALTER TABLE embeddings ADD COLUMN dtype TEXT") + conn.commit() + + def load_embeddings_from_pth(self, save_path: str) -> dict[str, torch.Tensor]: + assert os.path.exists(save_path), f"Embedding file does not exist: {save_path}" + payload = torch.load(save_path, map_location="cpu", weights_only=True) + assert isinstance(payload, dict), "Expected .pth embeddings file to contain a dictionary." + for sequence, tensor in payload.items(): + assert isinstance(sequence, str), "Expected embedding dictionary keys to be sequences (str)." + assert isinstance(tensor, torch.Tensor), "Expected embedding dictionary values to be tensors." + return payload + + def load_embeddings_from_db(self, db_path: str, sequences: Optional[List[str]] = None) -> dict[str, torch.Tensor]: + assert os.path.exists(db_path), f"Embedding database does not exist: {db_path}" + loaded: dict[str, torch.Tensor] = {} + with sqlite3.connect(db_path) as conn: + self._ensure_embeddings_table(conn) + cursor = conn.cursor() + if sequences is None: + cursor.execute("SELECT sequence, embedding, shape, dtype FROM embeddings") + else: + if len(sequences) == 0: + return loaded + placeholders = ",".join(["?"] * len(sequences)) + cursor.execute( + f"SELECT sequence, embedding, shape, dtype FROM embeddings WHERE sequence IN ({placeholders})", + tuple(sequences), + ) + + rows = cursor.fetchall() + for row in rows: + sequence = row[0] + embedding_bytes = row[1] + shape_text = row[2] + dtype_text = row[3] + assert shape_text is not None, "Missing shape metadata in embeddings table." + assert dtype_text is not None, "Missing dtype metadata in embeddings table." + shape_values = [int(value) for value in shape_text.split(",") if len(value) > 0] + assert len(shape_values) > 0, f"Invalid shape metadata for sequence: {sequence}" + expected_size = int(np.prod(shape_values)) + np_dtype = np.dtype(dtype_text) + array = np.frombuffer(embedding_bytes, dtype=np_dtype) + assert array.size == expected_size, f"Shape mismatch while reading sequence: {sequence}" + reshaped = array.copy().reshape(tuple(shape_values)) + loaded[sequence] = torch.from_numpy(reshaped) + return loaded + + def embed_dataset( + self, + sequences: List[str], + tokenizer: Optional[PreTrainedTokenizerBase] = None, + batch_size: int = 2, + max_len: int = 512, + truncate: bool = True, + full_embeddings: bool = False, + embed_dtype: torch.dtype = torch.float32, + pooling_types: List[str] = ['mean'], + num_workers: int = 0, + sql: bool = False, + save: bool = True, + sql_db_path: str = 'embeddings.db', + save_path: str = 'embeddings.pth', + **kwargs, + ) -> Optional[dict[str, torch.Tensor]]: + """ + Embed a dataset of protein sequences. + + Supports two modes: + - Tokenizer mode (ESM2/ESM++): provide `tokenizer`, `_embed(input_ids, attention_mask)` is used. + - Sequence mode (E1): pass `tokenizer=None`, `_embed(sequences, return_attention_mask=True, **kwargs)` is used. + """ + sequences = list(set([seq[:max_len] if truncate else seq for seq in sequences])) + sequences = sorted(sequences, key=len, reverse=True) + hidden_size = self.config.hidden_size + pooler = Pooler(pooling_types) if not full_embeddings else None + tokenizer_mode = tokenizer is not None + if tokenizer_mode: + collate_fn = build_collator(tokenizer) + device = self.device + else: + collate_fn = None + device = None + + def get_embeddings(residue_embeddings: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if full_embeddings or residue_embeddings.ndim == 2: + return residue_embeddings + return pooler(residue_embeddings, attention_mask) + + def iter_batches(to_embed: List[str]): + if tokenizer_mode: + assert collate_fn is not None + assert device is not None + dataset = ProteinDataset(to_embed) + dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, collate_fn=collate_fn, shuffle=False) + for i, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc='Embedding batches'): + seqs = to_embed[i * batch_size:(i + 1) * batch_size] + input_ids = batch['input_ids'].to(device) + attention_mask = batch['attention_mask'].to(device) + residue_embeddings = self._embed(input_ids, attention_mask) + yield seqs, residue_embeddings, attention_mask + else: + for batch_start in tqdm(range(0, len(to_embed), batch_size), desc='Embedding batches'): + seqs = to_embed[batch_start:batch_start + batch_size] + batch_output = self._embed(seqs, return_attention_mask=True, **kwargs) + assert isinstance(batch_output, tuple), "Sequence mode _embed must return (last_hidden_state, attention_mask)." + assert len(batch_output) == 2, "Sequence mode _embed must return exactly two values." + residue_embeddings, attention_mask = batch_output + assert isinstance(attention_mask, torch.Tensor), "Sequence mode _embed must return attention_mask as a torch.Tensor." + yield seqs, residue_embeddings, attention_mask + + if sql: + conn = sqlite3.connect(sql_db_path) + self._ensure_embeddings_table(conn) + c = conn.cursor() + already_embedded = self._read_sequences_from_db(sql_db_path) + to_embed = [seq for seq in sequences if seq not in already_embedded] + print(f"Found {len(already_embedded)} already embedded sequences in {sql_db_path}") + print(f"Embedding {len(to_embed)} new sequences") + if len(to_embed) > 0: + with torch.no_grad(): + for i, (seqs, residue_embeddings, attention_mask) in enumerate(iter_batches(to_embed)): + embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) + for seq, emb, mask in zip(seqs, embeddings, attention_mask): + if full_embeddings: + emb = emb[mask.bool()].reshape(-1, hidden_size) + emb_np = emb.cpu().numpy() + emb_shape = ",".join([str(dim) for dim in emb_np.shape]) + emb_dtype = str(emb_np.dtype) + c.execute( + "INSERT OR REPLACE INTO embeddings (sequence, embedding, shape, dtype) VALUES (?, ?, ?, ?)", + (seq, emb_np.tobytes(), emb_shape, emb_dtype), + ) + if tokenizer_mode and (i + 1) % 100 == 0: + conn.commit() + conn.commit() + conn.close() + return None + + embeddings_dict = {} + if os.path.exists(save_path): + embeddings_dict = self.load_embeddings_from_pth(save_path) + to_embed = [seq for seq in sequences if seq not in embeddings_dict] + print(f"Found {len(embeddings_dict)} already embedded sequences in {save_path}") + print(f"Embedding {len(to_embed)} new sequences") + else: + to_embed = sequences + print(f"Embedding {len(to_embed)} new sequences") + + if len(to_embed) > 0: + with torch.no_grad(): + for seqs, residue_embeddings, attention_mask in iter_batches(to_embed): + embeddings = get_embeddings(residue_embeddings, attention_mask).to(embed_dtype) + for seq, emb, mask in zip(seqs, embeddings, attention_mask): + if full_embeddings: + emb = emb[mask.bool()].reshape(-1, hidden_size) + embeddings_dict[seq] = emb.cpu() + + if save: + torch.save(embeddings_dict, save_path) + + return embeddings_dict + + +""" +FastPLMs-compatible DPLM2 implementation. +""" + +import torch +import torch.nn as nn +from torch.nn import functional as F +from dataclasses import dataclass +from einops import rearrange +from enum import Enum +from typing import List, Optional, Tuple, Union + +from transformers import EsmTokenizer +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + ModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.models.esm.configuration_esm import EsmConfig +from transformers.models.esm.modeling_esm import ( + EsmAttention, + EsmClassificationHead, + EsmEmbeddings, + EsmEncoder, + EsmIntermediate, + EsmLayer, + EsmLMHead, + EsmOutput, + EsmPooler, + EsmPreTrainedModel, + EsmSelfAttention, + EsmSelfOutput, + RotaryEmbedding, + apply_rotary_pos_emb, +) + +try: + from torch.nn.attention.flex_attention import create_block_mask, flex_attention, BlockMask +except (ImportError, AttributeError): + create_block_mask = None + flex_attention = None + BlockMask = None + + +### Kernels Flash Attention Detection +def _infer_kernels_flash_variant(kernel) -> str | None: + 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_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, +) -> torch.Tensor: + 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, 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, causal=causal) + except TypeError: + output = FLASH_KERNEL.flash_attn_func(query_states, key_states, value_states, 0.0, None, 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, +) -> torch.Tensor: + 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, + 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, + 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, None, causal, + ) + if isinstance(output, tuple): + return output[0] + return output + raise AssertionError(f"Unsupported kernels flash attention variant: {FLASH_KERNEL_VARIANT}") + + +### Unpad / Pad helpers for varlen flash attention +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: torch.Tensor | None = None, + causal: bool = False, +) -> torch.Tensor: + 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, + ) + 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, + ) + + +### Attention Backend Enum & Resolution +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 == 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 + + +from transformers import PreTrainedTokenizerBase + + +class BaseSequenceTokenizer: + def __init__(self, tokenizer: PreTrainedTokenizerBase): + self.tokenizer = tokenizer + + def __call__(self, sequences, **kwargs): + raise NotImplementedError + + +def get_attention_mask( + effective_backend: AttentionBackend, + batch_size: int, + seq_len: int, + device: torch.device, + attention_mask: Optional[torch.Tensor] = None, +) -> tuple[torch.Tensor | None, torch.Tensor | None, "BlockMask | None"]: + 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 + attention_mask_4d = attention_mask_2d[:, None, :, None] & attention_mask_2d[:, None, None, :] + return attention_mask_2d, attention_mask_4d, None + + +def _infer_modality_type(input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + input_mask = attention_mask.bool() + modality_type = ((input_ids < 33) & input_mask).int() + modality_type[~input_mask] = 2 + return modality_type + + +@dataclass +class DPLM2MaskedLMOutput(ModelOutput): + loss: Optional[torch.Tensor] = None + logits: Optional[torch.Tensor] = None + last_hidden_state: Optional[torch.Tensor] = None + hidden_states: Optional[Tuple[torch.Tensor, ...]] = None + attentions: Optional[Tuple[torch.Tensor, ...]] = None + s_max: Optional[Tuple[list[torch.Tensor], ...]] = None + + +@dataclass +class DPLM2EncoderOutput(ModelOutput): + 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 + + +class DPLM2Config(EsmConfig): + model_type = "dplm2" + + def __init__( + self, + attn_backend: str = "sdpa", + aa_type: int = 1, + struct_type: int = 0, + pad_type: int = 2, + **kwargs, + ): + super().__init__(**kwargs) + self.attn_backend = attn_backend + self.aa_type = aa_type + self.struct_type = struct_type + self.pad_type = pad_type + self.tie_word_embeddings = False + + +class DPLM2PreTrainedModel(EsmPreTrainedModel): + config_class = DPLM2Config + base_model_prefix = "dplm2" + supports_gradient_checkpointing = True + tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") + all_tied_weights_keys = {} + + @classmethod + def is_remote_code(cls) -> bool: + # Prevent post-load reinitialization of tensors already loaded from checkpoints. + return True + + @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 + resolved = resolve_attention_backend(backend) + for module in self.modules(): + if isinstance(module, ModifiedEsmEncoder): + module.attention_backend = resolved + elif isinstance(module, ModifiedEsmSelfAttention): + module.attn_backend = resolved + + + +class ModifiedRotaryEmbedding(RotaryEmbedding): + def __init__(self, dim: int, aa_type: int, struct_type: int): + super().__init__(dim) + self.aa_type = aa_type + self.struct_type = struct_type + + def _has_multimodal_tokens(self, type_ids: Optional[torch.Tensor]) -> bool: + if type_ids is None: + return False + aa_present = (type_ids == self.aa_type).any() + struct_present = (type_ids == self.struct_type).any() + return bool(aa_present and struct_present) + + def _update_cos_sin_tables( + self, + x: torch.Tensor, + type_ids: Optional[torch.Tensor], + seq_dimension: int = 2, + ) -> Tuple[torch.Tensor, torch.Tensor]: + seq_len = x.shape[seq_dimension] + if self._has_multimodal_tokens(type_ids): + seq_len = seq_len // 2 + + cache_is_stale = ( + self._cos_cached is None + or self._sin_cached is None + or seq_len != self._seq_len_cached + or self._cos_cached.device != x.device + ) + if cache_is_stale: + self._seq_len_cached = seq_len + t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq) + freqs = torch.outer(t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + self._cos_cached = emb.cos()[None, None, :, :] + self._sin_cached = emb.sin()[None, None, :, :] + + return self._cos_cached, self._sin_cached + + def forward( + self, + q: torch.Tensor, + k: torch.Tensor, + type_ids: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + self._cos_cached, self._sin_cached = self._update_cos_sin_tables( + k, + type_ids=type_ids, + seq_dimension=-2, + ) + + if self._has_multimodal_tokens(type_ids): + q_1, q_2 = q.chunk(2, dim=-2) + k_1, k_2 = k.chunk(2, dim=-2) + q_1 = apply_rotary_pos_emb(q_1, self._cos_cached, self._sin_cached) + q_2 = apply_rotary_pos_emb(q_2, self._cos_cached, self._sin_cached) + k_1 = apply_rotary_pos_emb(k_1, self._cos_cached, self._sin_cached) + k_2 = apply_rotary_pos_emb(k_2, self._cos_cached, self._sin_cached) + return torch.cat((q_1, q_2), dim=-2), torch.cat((k_1, k_2), dim=-2) + + return ( + apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached), + apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached), + ) + + +class ModifiedEsmSelfAttention(EsmSelfAttention): + def __init__(self, config, position_embedding_type=None): + super().__init__(config, position_embedding_type) + self.config = config + self.scale = self.attention_head_size**-0.5 + self.dropout_prob = config.attention_probs_dropout_prob + self.attn_backend = resolve_attention_backend(config.attn_backend) + self.rotary_embeddings = ModifiedRotaryEmbedding( + dim=self.attention_head_size, + aa_type=config.aa_type, + struct_type=config.struct_type, + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask_2d: torch.Tensor | None = None, + attention_mask_4d: torch.Tensor | None = None, + flex_block_mask: "BlockMask | None" = None, + output_attentions: bool = False, + output_s_max: bool = False, + type_ids: Optional[torch.Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: + batch_size, seq_length = hidden_states.shape[:-1] + hidden_shape = (batch_size, seq_length, -1, self.attention_head_size) + query_BHLD = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + key_BHLD = self.key(hidden_states).view(hidden_shape).transpose(1, 2) + value_BHLD = self.value(hidden_states).view(hidden_shape).transpose(1, 2) + + query_BHLD = query_BHLD * self.scale + + if self.position_embedding_type == "rotary": + query_BHLD, key_BHLD = self.rotary_embeddings(query_BHLD, key_BHLD, type_ids) + + 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, + ) + return attn_output, attn_weights, s_max + + def _attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_2d: torch.Tensor | None = None, + attention_mask_4d: torch.Tensor | None = None, + flex_block_mask: "BlockMask | None" = None, + output_attentions: bool = False, + output_s_max: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: + 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 + return [s_max_bound[h] for h in range(self.num_attention_heads)] + + def _manual_attn( + self, + query_BHLD: torch.Tensor, + key_BHLD: torch.Tensor, + value_BHLD: torch.Tensor, + attention_mask_4d: torch.Tensor | None = None, + output_s_max: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor, list[torch.Tensor] | None]: + attn_weights = torch.matmul(query_BHLD, key_BHLD.transpose(-1, -2)) + 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) + if self.dropout_prob > 0 and self.training: + attn_weights = F.dropout(attn_weights, p=self.dropout_prob, training=self.training) + 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: torch.Tensor | None = 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: "BlockMask | None" = None, + ) -> tuple[torch.Tensor, None]: + assert flex_attention is not None, "Flex attention is not available in this environment." + assert query_BHLD.dtype in (torch.float16, torch.bfloat16), ( + f"Flex attention requires float16 or bfloat16, got {query_BHLD.dtype}." + ) + context_BHLD = flex_attention(query_BHLD, key_BHLD, value_BHLD, block_mask=flex_block_mask, scale=1.0) + 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: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, None]: + context_BHLD = F.scaled_dot_product_attention( + query_BHLD, key_BHLD, value_BHLD, + attn_mask=attention_mask_4d, + dropout_p=self.dropout_prob if self.training else 0.0, + scale=1.0, + ) + return rearrange(context_BHLD, "b h s d -> b s (h d)"), None + + +class ModifiedEsmAttention(EsmAttention): + def __init__(self, config): + nn.Module.__init__(self) + self.self = ModifiedEsmSelfAttention(config) + self.output = EsmSelfOutput(config) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask_2d: torch.Tensor | None = None, + attention_mask_4d: torch.Tensor | None = None, + flex_block_mask: "BlockMask | None" = None, + output_attentions: bool = False, + output_s_max: bool = False, + type_ids: Optional[torch.Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: + hidden_states_ln = self.LayerNorm(hidden_states) + attn_output, attn_weights, s_max = self.self( + hidden_states_ln, + 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, + type_ids=type_ids, + ) + attention_output = self.output(attn_output, hidden_states) + return attention_output, attn_weights, s_max + + +class ModifiedEsmLayer(EsmLayer): + def __init__(self, config): + nn.Module.__init__(self) + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ModifiedEsmAttention(config) + self.intermediate = EsmIntermediate(config) + self.output = EsmOutput(config) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask_2d: torch.Tensor | None = None, + attention_mask_4d: torch.Tensor | None = None, + flex_block_mask: "BlockMask | None" = None, + output_attentions: bool = False, + output_s_max: bool = False, + type_ids: Optional[torch.Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor] | None]: + attention_output, attn_weights, s_max = self.attention( + hidden_states, + 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, + type_ids=type_ids, + ) + layer_output = self.feed_forward_chunk(attention_output) + return layer_output, attn_weights, s_max + + +class ModifiedEsmEncoder(EsmEncoder): + def __init__(self, config): + nn.Module.__init__(self) + self.config = config + self.attention_backend = resolve_attention_backend(config.attn_backend) + self.layer = nn.ModuleList([ModifiedEsmLayer(config) for _ in range(config.num_hidden_layers)]) + self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_hidden_states: bool = False, + output_attentions: bool = False, + output_s_max: bool = False, + type_ids: Optional[torch.Tensor] = None, + ) -> DPLM2EncoderOutput: + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + full_s_max = () if output_s_max else None + + attention_mask_2d, attention_mask_4d, flex_block_mask = get_attention_mask( + effective_backend=self.attention_backend, + batch_size=hidden_states.shape[0], + seq_len=hidden_states.shape[1], + device=hidden_states.device, + attention_mask=attention_mask, + ) + + for layer_module in self.layer: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + hidden_states, attn_weights, s_max = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_mask_2d, + attention_mask_4d, + flex_block_mask, + output_attentions, + output_s_max, + type_ids, + ) + else: + hidden_states, attn_weights, s_max = layer_module( + hidden_states, + 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, + type_ids=type_ids, + ) + + if all_attentions is not None: + all_attentions = all_attentions + (attn_weights,) + if full_s_max is not None: + full_s_max = full_s_max + (s_max,) + + if self.emb_layer_norm_after: + hidden_states = self.emb_layer_norm_after(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + return DPLM2EncoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_attentions, + s_max=full_s_max, + ) + + +class FAST_DPLM2_ENCODER(DPLM2PreTrainedModel, EmbeddingMixin): + """Inner encoder class that holds the actual ESM-style weights (embeddings, encoder) + so that the weight keys are prefixed with 'esm.' in the outer DPLM2Model, + matching pretrained DPLM2 checkpoints.""" + + def __init__(self, config, **kwargs): + DPLM2PreTrainedModel.__init__(self, config, **kwargs) + self.config = config + self.embeddings = EsmEmbeddings(config) + self.encoder = ModifiedEsmEncoder(config) + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if attention_mask is None: + attention_mask = input_ids.ne(self.config.pad_token_id) + type_ids = _infer_modality_type(input_ids, attention_mask) + token_embedding_output = self.embeddings(input_ids, attention_mask=attention_mask) + encoder_outputs = self.encoder( + token_embedding_output, + attention_mask=attention_mask, + output_hidden_states=False, + output_attentions=False, + type_ids=type_ids, + ) + return encoder_outputs.last_hidden_state + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + type_ids: Optional[torch.Tensor] = None, + ) -> DPLM2EncoderOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + pass + elif inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + token_embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + ) + encoder_outputs = self.encoder( + token_embedding_output, + attention_mask=attention_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + output_s_max=output_s_max, + type_ids=type_ids, + ) + + return DPLM2EncoderOutput( + last_hidden_state=encoder_outputs.last_hidden_state, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + s_max=encoder_outputs.s_max, + ) + + +class DPLM2Model(DPLM2PreTrainedModel, EmbeddingMixin): + config_class = DPLM2Config + def __init__(self, config, add_pooling_layer=True): + DPLM2PreTrainedModel.__init__(self, config) + self.config = config + self.esm = FAST_DPLM2_ENCODER(config) + self.pooler = EsmPooler(config) if add_pooling_layer else None + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.esm.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.esm.embeddings.word_embeddings = value + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + type_ids: Optional[torch.Tensor] = None, + ) -> DPLM2EncoderOutput: + outputs = self.esm( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + type_ids=type_ids, + ) + sequence_output = outputs.last_hidden_state + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return DPLM2EncoderOutput( + last_hidden_state=sequence_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + s_max=outputs.s_max, + ) + + +class DPLM2ForMaskedLM(DPLM2PreTrainedModel, EmbeddingMixin): + config_class = DPLM2Config + def __init__(self, config, dropout: float = 0.1, vocab_size: Optional[int] = None): + config.hidden_dropout_prob = dropout + config.tie_word_embeddings = False + if vocab_size is not None: + config.vocab_size = vocab_size + DPLM2PreTrainedModel.__init__(self, config) + self.esm = FAST_DPLM2_ENCODER(config) + self.lm_head = EsmLMHead(config) + self.loss_fct = nn.CrossEntropyLoss() + self.post_init() + self.pad_id = config.pad_token_id + self.tokenizer = self.__class__.tokenizer + if isinstance(config._name_or_path, str) and len(config._name_or_path) > 0: + self.tokenizer = EsmTokenizer.from_pretrained(config._name_or_path) + + def get_input_embeddings(self) -> nn.Module: + return self.esm.get_input_embeddings() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + def _get_modality_type(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + return _infer_modality_type(input_ids, attention_mask) + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if attention_mask is None: + attention_mask = input_ids.ne(self.pad_id) + type_ids = self._get_modality_type(input_ids, attention_mask) + outputs = self.esm( + input_ids=input_ids, + attention_mask=attention_mask, + type_ids=type_ids, + output_attentions=False, + output_hidden_states=False, + ) + return outputs.last_hidden_state + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + type_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], DPLM2MaskedLMOutput]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if attention_mask is None: + assert input_ids is not None + attention_mask = input_ids.ne(self.pad_id) + + if type_ids is None: + assert input_ids is not None + type_ids = self._get_modality_type(input_ids, attention_mask) + + outputs = self.esm( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + type_ids=type_ids, + ) + + sequence_output = outputs.last_hidden_state + logits = self.lm_head(sequence_output) + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss = self.loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if return_dict is False: + output = (logits, sequence_output, outputs.hidden_states, outputs.attentions) + if loss is not None: + return (loss,) + output + return output + + return DPLM2MaskedLMOutput( + loss=loss, + logits=logits, + last_hidden_state=sequence_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + s_max=outputs.s_max, + ) + + +class DPLM2ForSequenceClassification(DPLM2PreTrainedModel, EmbeddingMixin): + config_class = DPLM2Config + + def __init__(self, config): + DPLM2PreTrainedModel.__init__(self, config) + self.num_labels = config.num_labels + self.esm = FAST_DPLM2_ENCODER(config) + self.classifier = EsmClassificationHead(config) + self.mse = nn.MSELoss() + self.ce = nn.CrossEntropyLoss() + self.bce = nn.BCEWithLogitsLoss() + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.esm.get_input_embeddings() + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + type_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + **kwargs, + ) -> DPLM2MaskedLMOutput: + if type_ids is None and input_ids is not None: + if attention_mask is None: + attention_mask = input_ids.ne(self.config.pad_token_id) + type_ids = _infer_modality_type(input_ids, attention_mask) + + outputs = self.esm( + input_ids=input_ids, + attention_mask=attention_mask, + type_ids=type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + ) + sequence_output = outputs.last_hidden_state + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + if self.num_labels == 1: + loss = self.mse(logits.squeeze(), labels.squeeze()) + else: + loss = self.mse(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss = self.bce(logits, labels) + + return DPLM2MaskedLMOutput( + loss=loss, + logits=logits, + last_hidden_state=sequence_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + s_max=outputs.s_max, + ) + + +class DPLM2ForTokenClassification(DPLM2PreTrainedModel, EmbeddingMixin): + config_class = DPLM2Config + + def __init__(self, config): + DPLM2PreTrainedModel.__init__(self, config) + self.num_labels = config.num_labels + self.esm = FAST_DPLM2_ENCODER(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + self.loss_fct = nn.CrossEntropyLoss() + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.esm.get_input_embeddings() + + def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + return self.esm._embed(input_ids, attention_mask) + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + type_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_s_max: Optional[bool] = False, + return_dict: Optional[bool] = None, + **kwargs, + ) -> DPLM2MaskedLMOutput: + if type_ids is None and input_ids is not None: + if attention_mask is None: + attention_mask = input_ids.ne(self.config.pad_token_id) + type_ids = _infer_modality_type(input_ids, attention_mask) + + outputs = self.esm( + input_ids=input_ids, + attention_mask=attention_mask, + type_ids=type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_s_max=output_s_max, + ) + sequence_output = self.dropout(outputs.last_hidden_state) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return DPLM2MaskedLMOutput( + loss=loss, + logits=logits, + last_hidden_state=sequence_output, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + s_max=outputs.s_max, + ) + + +if __name__ == "__main__": + import random + + import torch + + from torch import Tensor + from transformers import EsmTokenizer + + 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) + + num_attention_heads = random.choice([2, 4]) + config = DPLM2Config( + hidden_size=16 * num_attention_heads, + num_attention_heads=num_attention_heads, + num_hidden_layers=random.choice([1, 2]), + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.0, + attn_backend="sdpa", + ) + tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") + batch = tokenizer(["ACDEFGH", "MKTW"], return_tensors="pt", padding="longest") + batch["labels"] = batch["input_ids"].clone() + model = DPLM2ForMaskedLM(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)