# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import re import urllib import warnings from argparse import Namespace from pathlib import Path import torch import esm from esm.model.esm2 import ESM2 def _has_regression_weights(model_name): """Return whether we expect / require regression weights; Right now that is all models except ESM-1v and ESM-IF""" return not ("esm1v" in model_name or "esm_if" in model_name) def load_model_and_alphabet(model_name): if model_name.endswith(".pt"): # treat as filepath return load_model_and_alphabet_local(model_name) else: return load_model_and_alphabet_hub(model_name) def load_hub_workaround(url): try: data = torch.hub.load_state_dict_from_url(url, progress=False, map_location="cpu") except RuntimeError: # Pytorch version issue - see https://github.com/pytorch/pytorch/issues/43106 fn = Path(url).name data = torch.load( f"{torch.hub.get_dir()}/checkpoints/{fn}", map_location="cpu", ) except urllib.error.HTTPError as e: raise Exception(f"Could not load {url}, check if you specified a correct model name?") return data def load_regression_hub(model_name): url = f"https://dl.fbaipublicfiles.com/fair-esm/regression/{model_name}-contact-regression.pt" regression_data = load_hub_workaround(url) return regression_data def _download_model_and_regression_data(model_name): url = f"https://dl.fbaipublicfiles.com/fair-esm/models/{model_name}.pt" model_data = load_hub_workaround(url) if _has_regression_weights(model_name): regression_data = load_regression_hub(model_name) else: regression_data = None return model_data, regression_data def load_model_and_alphabet_hub(model_name): model_data, regression_data = _download_model_and_regression_data(model_name) return load_model_and_alphabet_core(model_name, model_data, regression_data) def load_model_and_alphabet_local(model_location): """Load from local path. The regression weights need to be co-located""" model_location = Path(model_location) model_data = torch.load(str(model_location), map_location="cpu") model_name = model_location.stem if _has_regression_weights(model_name): regression_location = str(model_location.with_suffix("")) + "-contact-regression.pt" regression_data = torch.load(regression_location, map_location="cpu") else: regression_data = None return load_model_and_alphabet_core(model_name, model_data, regression_data) def has_emb_layer_norm_before(model_state): """Determine whether layer norm needs to be applied before the encoder""" return any(k.startswith("emb_layer_norm_before") for k, param in model_state.items()) def _load_model_and_alphabet_core_v1(model_data): import esm # since esm.inverse_folding is imported below, you actually have to re-import esm here alphabet = esm.Alphabet.from_architecture(model_data["args"].arch) if model_data["args"].arch == "roberta_large": # upgrade state dict pra = lambda s: "".join(s.split("encoder_")[1:] if "encoder" in s else s) prs1 = lambda s: "".join(s.split("encoder.")[1:] if "encoder" in s else s) prs2 = lambda s: "".join( s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s ) model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} model_state = {prs1(prs2(arg[0])): arg[1] for arg in model_data["model"].items()} model_state["embed_tokens.weight"][alphabet.mask_idx].zero_() # For token drop model_args["emb_layer_norm_before"] = has_emb_layer_norm_before(model_state) model_type = esm.ProteinBertModel elif model_data["args"].arch == "protein_bert_base": # upgrade state dict pra = lambda s: "".join(s.split("decoder_")[1:] if "decoder" in s else s) prs = lambda s: "".join(s.split("decoder.")[1:] if "decoder" in s else s) model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} model_state = {prs(arg[0]): arg[1] for arg in model_data["model"].items()} model_type = esm.ProteinBertModel elif model_data["args"].arch == "msa_transformer": # upgrade state dict pra = lambda s: "".join(s.split("encoder_")[1:] if "encoder" in s else s) prs1 = lambda s: "".join(s.split("encoder.")[1:] if "encoder" in s else s) prs2 = lambda s: "".join( s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s ) prs3 = lambda s: s.replace("row", "column") if "row" in s else s.replace("column", "row") model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} model_state = {prs1(prs2(prs3(arg[0]))): arg[1] for arg in model_data["model"].items()} if model_args.get("embed_positions_msa", False): emb_dim = model_state["msa_position_embedding"].size(-1) model_args["embed_positions_msa_dim"] = emb_dim # initial release, bug: emb_dim==1 model_type = esm.MSATransformer elif "invariant_gvp" in model_data["args"].arch: import esm.inverse_folding model_type = esm.inverse_folding.gvp_transformer.GVPTransformerModel model_args = vars(model_data["args"]) # convert Namespace -> dict def update_name(s): # Map the module names in checkpoints trained with internal code to # the updated module names in open source code s = s.replace("W_v", "embed_graph.embed_node") s = s.replace("W_e", "embed_graph.embed_edge") s = s.replace("embed_scores.0", "embed_confidence") s = s.replace("embed_score.", "embed_graph.embed_confidence.") s = s.replace("seq_logits_projection.", "") s = s.replace("embed_ingraham_features", "embed_dihedrals") s = s.replace("embed_gvp_in_local_frame.0", "embed_gvp_output") s = s.replace("embed_features_in_local_frame.0", "embed_gvp_input_features") return s model_state = { update_name(sname): svalue for sname, svalue in model_data["model"].items() if "version" not in sname } else: raise ValueError("Unknown architecture selected") model = model_type( Namespace(**model_args), alphabet, ) return model, alphabet, model_state def _load_model_and_alphabet_core_v2(model_data): def upgrade_state_dict(state_dict): """Removes prefixes 'model.encoder.sentence_encoder.' and 'model.encoder.'.""" prefixes = ["encoder.sentence_encoder.", "encoder."] pattern = re.compile("^" + "|".join(prefixes)) state_dict = {pattern.sub("", name): param for name, param in state_dict.items()} return state_dict cfg = model_data["cfg"]["model"] state_dict = model_data["model"] state_dict = upgrade_state_dict(state_dict) alphabet = esm.data.Alphabet.from_architecture("ESM-1b") model = ESM2( num_layers=cfg.encoder_layers, embed_dim=cfg.encoder_embed_dim, attention_heads=cfg.encoder_attention_heads, alphabet=alphabet, token_dropout=cfg.token_dropout, ) return model, alphabet, state_dict def load_model_and_alphabet_core(model_name, model_data, regression_data=None): if regression_data is not None: model_data["model"].update(regression_data["model"]) if model_name.startswith("esm2"): model, alphabet, model_state = _load_model_and_alphabet_core_v2(model_data) else: model, alphabet, model_state = _load_model_and_alphabet_core_v1(model_data) expected_keys = set(model.state_dict().keys()) found_keys = set(model_state.keys()) if regression_data is None: expected_missing = {"contact_head.regression.weight", "contact_head.regression.bias"} error_msgs = [] missing = (expected_keys - found_keys) - expected_missing if missing: error_msgs.append(f"Missing key(s) in state_dict: {missing}.") unexpected = found_keys - expected_keys if unexpected: error_msgs.append(f"Unexpected key(s) in state_dict: {unexpected}.") if error_msgs: raise RuntimeError( "Error(s) in loading state_dict for {}:\n\t{}".format( model.__class__.__name__, "\n\t".join(error_msgs) ) ) if expected_missing - found_keys: warnings.warn( "Regression weights not found, predicting contacts will not produce correct results." ) model.load_state_dict(model_state, strict=regression_data is not None) return model, alphabet def esm1_t34_670M_UR50S(): """34 layer transformer model with 670M params, trained on Uniref50 Sparse. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1_t34_670M_UR50S") def esm1_t34_670M_UR50D(): """34 layer transformer model with 670M params, trained on Uniref50 Dense. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1_t34_670M_UR50D") def esm1_t34_670M_UR100(): """34 layer transformer model with 670M params, trained on Uniref100. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1_t34_670M_UR100") def esm1_t12_85M_UR50S(): """12 layer transformer model with 85M params, trained on Uniref50 Sparse. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1_t12_85M_UR50S") def esm1_t6_43M_UR50S(): """6 layer transformer model with 43M params, trained on Uniref50 Sparse. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1_t6_43M_UR50S") def esm1b_t33_650M_UR50S(): """33 layer transformer model with 650M params, trained on Uniref50 Sparse. This is our best performing model, which will be described in a future publication. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1b_t33_650M_UR50S") def esm_msa1_t12_100M_UR50S(): warnings.warn( "This model had a minor bug in the positional embeddings, " "please use ESM-MSA-1b: esm.pretrained.esm_msa1b_t12_100M_UR50S()", ) return load_model_and_alphabet_hub("esm_msa1_t12_100M_UR50S") def esm_msa1b_t12_100M_UR50S(): return load_model_and_alphabet_hub("esm_msa1b_t12_100M_UR50S") def esm1v_t33_650M_UR90S(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 1 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_1") def esm1v_t33_650M_UR90S_1(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 1 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_1") def esm1v_t33_650M_UR90S_2(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 2 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_2") def esm1v_t33_650M_UR90S_3(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 3 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_3") def esm1v_t33_650M_UR90S_4(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 4 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_4") def esm1v_t33_650M_UR90S_5(): """33 layer transformer model with 650M params, trained on Uniref90. This is model 5 of a 5 model ensemble. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_5") def esm_if1_gvp4_t16_142M_UR50(): """Inverse folding model with 142M params, with 4 GVP-GNN layers, 8 Transformer encoder layers, and 8 Transformer decoder layers, trained on CATH structures and 12 million alphafold2 predicted structures from UniRef50 sequences. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm_if1_gvp4_t16_142M_UR50") def esm2_t6_8M_UR50D(): """6 layer ESM-2 model with 8M params, trained on UniRef50. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t6_8M_UR50D") def esm2_t12_35M_UR50D(): """12 layer ESM-2 model with 35M params, trained on UniRef50. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t12_35M_UR50D") def esm2_t30_150M_UR50D(): """30 layer ESM-2 model with 150M params, trained on UniRef50. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t30_150M_UR50D") def esm2_t33_650M_UR50D(): """33 layer ESM-2 model with 650M params, trained on UniRef50. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t33_650M_UR50D") def esm2_t36_3B_UR50D(): """36 layer ESM-2 model with 3B params, trained on UniRef50. Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t36_3B_UR50D") def esm2_t48_15B_UR50D(): """48 layer ESM-2 model with 15B params, trained on UniRef50. If you have OOM while loading this model, please refer to README on how to employ FSDP and ZeRO CPU offloading Returns a tuple of (Model, Alphabet). """ return load_model_and_alphabet_hub("esm2_t48_15B_UR50D")