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import torch
import os
import mini3di
import numpy as np
import torch.nn.functional as F
from rdkit import Chem
from rdkit.Chem import AllChem
from src.data.protein_dataset import dynamic_pad
from transformers import AutoTokenizer, AutoModelForMaskedLM, LlamaForCausalLM, LlamaTokenizer, T5Tokenizer, T5EncoderModel, AutoModelForCausalLM, AutoModel
from src.data.esm.sdk.api import LogitsConfig
from model_zoom.procyon.model.model_unified import UnifiedProCyon
from model_zoom.progen2.modeling_progen import ProGenForCausalLM
from tokenizers import Tokenizer
from model_zoom.GearNet.data.protein import Protein
from model_zoom.GearNet.data.transform import ProteinView
from model_zoom.GearNet.data.transform import Compose
from model_zoom.GearNet.data.geo_graph import GraphConstruction
from model_zoom.GearNet.data.function import AlphaCarbonNode, SpatialEdge, KNNEdge, SequentialEdge
from model_zoom.GearNet.gearnet import GeometryAwareRelationalGraphNeuralNetwork
from model_zoom.esm.utils.sampling import _BatchedESMProteinTensor
from model_zoom.ProTrek.model.ProTrek.protrek_trimodal_model import ProTrekTrimodalModel
from vplm import TransformerForMaskedLM, TransformerConfig
from vplm import VPLMTokenizer
from peft import TaskType, get_peft_model
from peft import IA3Config, AdaLoraConfig, LoraConfig
from vplm import VPLMTokenizer
MODEL_ZOOM_PATH = '/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/model_zoom'
class BaseProteinModel(nn.Module):
def __init__(self, device, **kwargs):
super().__init__()
self.device = device
def construct_batch(self, data, batch_size, task_name=None):
raise NotImplementedError
def setup_peft(self, peft_type):
raise NotImplementedError
def get_tokenizer(self):
raise NotImplementedError
def forward(self, batch):
raise NotImplementedError
class UtilsModel:
def __init__(self):
super().__init__()
def post_process_cpu(self, batch, embeddings, attention_masks, start, ends, task_type='binary_classification'):
# sparse return
results = []
for i, end in enumerate(ends):
end = int(end.item())
embedding = embeddings[i][start:end].cpu()
name = batch['name'][i]
attention_mask = attention_masks[i][start:end].cpu()
label = torch.tensor(batch['label'][i])
results.append({'name': name,
'embedding': embedding,
'attention_mask': attention_mask.bool(),
'label': label} )
return results
def pad_data(self, data, dim=0, pad_value=0, max_length=1022):
if data.shape[dim] < max_length:
data = self.dynamic_pad(data, [0, max_length-data.shape[dim]], dim=dim, pad_value=pad_value)
else:
# start = torch.randint(0, data.shape[0]-self.max_length+1, (1,)).item()
# start = torch.randint(0, data.shape[0]-self.max_length+1, (1,)).item()
start = 0
end = start + max_length
# 构建切片列表，对其他维度用 slice(None)，目标维度用 slice(start, end)
slices = [slice(None)] * data.ndim
slices[dim] = slice(start, end)
data = data[tuple(slices)] # 正确应用多维切片
return data
def dynamic_pad(self, tensor, pad_size, dim=0, pad_value=0):
# 获取原始形状
shape = list(tensor.shape)
num_dims = len(shape)
# 生成 padding 参数
pad = [0] * (2 * num_dims)
prev_pad_size, post_pad_size = pad_size
pad_index = 2 * (num_dims - dim - 1)
pad[pad_index] = prev_pad_size # 前面 padding
pad[pad_index + 1] = post_pad_size # 后面 padding
# 应用 padding
padded_tensor = F.pad(tensor, pad, mode="constant", value=pad_value)
return padded_tensor
class ESM2Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, model_path = 'esm2_650m',**kwargs):
super().__init__(device)
from transformers import AutoTokenizer, AutoModelForMaskedLM
self.model = AutoModelForMaskedLM.from_pretrained(f"{MODEL_ZOOM_PATH}/{model_path}").to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/{model_path}")
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def setup_peft(self, peft_type="lora", **kwargs):
if peft_type == "freeze":
for param in self.model.parameters():
param.requires_grad = False
else:
if peft_type == "lora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=False,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["query", "value"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "ia3":
peft_config = IA3Config(
task_type=TaskType.FEATURE_EXTRACTION,
target_modules=["query", "value", "dense"], # 应用 IA³ 的模块
feedforward_modules=["dense"], # 在 MLP 层加 IA³
)
elif peft_type == "dora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
use_dora=True,
inference_mode=False,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["query", "value"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "adalora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = AdaLoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
r=lora_r,
lora_alpha=lora_alpha,
target_r=4,
init_r=12,
beta1=0.85, beta2=0.85,
tinit=200,
tfinal=1000,
deltaT=10,
target_modules=["query", "value"],
)
self.model = get_peft_model(self.model, peft_config)
def construct_batch(self, batch):
MAXLEN = self.max_length
max_length_batch = min(max([len(sample['seq']) for sample in batch]) + 2, self.max_length + 2) # +2 for <s> and </s>
result = {
'name': [],
'seq': [],
'attention_mask': [],
'label': []
}
for sample in batch:
seq_token = torch.tensor(self.tokenizer.encode(sample['seq']))[:MAXLEN]
attention_mask = torch.zeros(max_length_batch)
attention_mask[:len(seq_token)] = 1
seq_token = self.pad_data(seq_token, dim=0, max_length=max_length_batch)
result['name'].append(sample['name'])
result['seq'].append(seq_token)
result['attention_mask'].append(attention_mask)
result['label'].append(sample['label'])
result['seq'] = torch.stack(result['seq'], dim=0).to(self.device)
result['attention_mask'] = torch.stack(result['attention_mask'], dim=0).to(self.device)
return result
def forward(self, batch, post_process=True, task_type='binary_classification', return_prob=False, return_logits=False, **kwargs):
attention_mask = batch['attention_mask']
outputs = self.model.esm(
batch['seq'],
attention_mask=attention_mask,
return_dict=True,
)
if return_logits:
logits = self.model.lm_head(outputs.last_hidden_state)
return logits
if return_prob:
logits = self.model.lm_head(outputs.last_hidden_state)
probs = F.softmax(logits, dim=-1)
return probs
embeddings = outputs.last_hidden_state
ends = attention_mask.sum(dim=-1)-1
start = 1
if post_process:
result = self.post_process_cpu(batch, embeddings, attention_mask, start, ends, task_type=task_type)
else:
result = embeddings
return result
class SmilesModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, **kwargs):
super().__init__(device)
def construct_batch(self, batch):
result = {'smiles': []}
for sample in batch:
mol = Chem.MolFromSmiles(sample['smiles'])
if mol is not None:
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
smiles = torch.tensor([int(ele) for ele in list(fp.ToBitString())]).float()
else:
smiles = torch.tensor([0]*2048).float()
result['smiles'].append(smiles)
return result
def forward(self, batch, post_process=True, task_type='binary_classification'):
return batch
class ESM3Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, sequence_only=False):
super().__init__(device)
from model_zoom.esm.models.esm3 import ESM3
self.model = ESM3.from_pretrained("esm3-sm-open-v1").to(self.device)
self.sequence_only = sequence_only
self.max_length = max_length
def get_tokenizer(self):
return self.model.tokenizers.sequence
def construct_batch(self, batch):
from model_zoom.esm.utils import encoding
from model_zoom.esm.utils.misc import stack_variable_length_tensors
addBOS = 1
pad_id = self.model.tokenizers.sequence.pad_token_id
max_len = min(max([len(s['seq']) for s in batch]) + 2*addBOS, self.max_length + 2*addBOS)
names, prot_tensors, labels, masks = [], [], [], []
sequence_list, coordinates_list, structure_tokens_batch = [], [], []
for sample in batch:
seq, coords = sample['seq'], sample['X']
seq_tokenizer = self.model.tokenizers.sequence
struct_tokenizer = self.model.tokenizers.structure
# tokenize
seq_tok = encoding.tokenize_sequence(seq, seq_tokenizer, add_special_tokens=True)
with torch.no_grad():
coords_tok, _plddt, struct_tok = encoding.tokenize_structure(
np.array(coords),
self.model.get_structure_encoder(),
struct_tokenizer,
add_special_tokens=True
)
coords_tok, struct_tok = torch.tensor(coords_tok), torch.tensor(struct_tok)
mask = torch.zeros(max_len)
mask[:seq_tok.shape[0]] = 1
seq_tok = self.pad_data(seq_tok, dim=0, pad_value=pad_id, max_length=max_len)
struct_tok = self.pad_data(struct_tok, dim=0, pad_value=pad_id, max_length=max_len)
coords_tok = dynamic_pad(coords_tok, [addBOS, addBOS], dim=0, pad_value=0) # 坐标需要和seq一样加上BOS, EOS
coords_tok = self.pad_data(coords_tok, dim=0, max_length=max_len)
sequence_list.append(seq_tok)
coordinates_list.append(coords_tok)
structure_tokens_batch.append(struct_tok)
names.append(sample['name'])
masks.append(mask)
labels.append(sample['label'])
sequence_tokens = stack_variable_length_tensors(
sequence_list,
constant_value=pad_id,
).to(self.device)
structure_tokens_batch = stack_variable_length_tensors(
structure_tokens_batch,
constant_value=pad_id,
).to(self.device)
coordinates_batch = stack_variable_length_tensors(
coordinates_list,
constant_value=pad_id,
).to(self.device)
protein_tensor = _BatchedESMProteinTensor(sequence=sequence_tokens,
structure=structure_tokens_batch, coordinates=coordinates_batch).to(self.device)
return {
'name': names,
'seq': protein_tensor,
'attention_mask': torch.stack([m.bool() for m in masks]).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
tens, mask = batch['seq'], batch['attention_mask']
if return_logits:
out = self.model.logits(
tens, LogitsConfig(
sequence=True, structure=False, secondary_structure=False,
sasa=False, function=False, residue_annotations=False, return_embeddings=True
)
)
logits = out.logits.sequence
return logits
out = self.model.logits(
tens, LogitsConfig(
sequence=True, structure=True, secondary_structure=True,
sasa=True, function=True, residue_annotations=True, return_embeddings=True
)
)
embeddings = out.embeddings
ends = mask.sum(dim=-1) - 1
start = 1
if post_process:
return self.post_process_cpu(batch, embeddings, mask, start, ends, task_type)
return embeddings
class ESMC600MModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
from model_zoom.esm.models.esmc import ESMC
self.model = ESMC.from_pretrained("esmc_600m").to(self.device)
self.max_length = max_length
def get_tokenizer(self):
return self.model.tokenizer
def construct_batch(self, batch):
from model_zoom.esm.utils.misc import stack_variable_length_tensors
addBOS = 1
pad_id = self.model.tokenizer.pad_token_id
max_len = min(max([self.model._tokenize([s['seq']]).shape[1]-2 for s in batch]) + 2*addBOS, self.max_length+2*addBOS)
names, prots, masks, labels = [], [], [], []
token_ids_list = []
for sample in batch:
seq = sample['seq']
token_ids = self.model._tokenize([seq]).flatten()
mask = torch.zeros(max_len)
mask[:len(token_ids)] = 1
token_ids = self.pad_data(token_ids, dim=0, pad_value=pad_id, max_length=max_len)
token_ids_list.append(token_ids)
names.append(sample['name'])
masks.append(mask)
labels.append(sample['label'])
sequence_tokens = stack_variable_length_tensors(
token_ids_list,
constant_value=self.model.tokenizer.pad_token_id,
)
protein_tensor = _BatchedESMProteinTensor(sequence=sequence_tokens).to(self.device)
return {
'name': names,
'seq': protein_tensor,
'attention_mask': torch.stack([m.bool() for m in masks]).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
tens, mask = batch['seq'], batch['attention_mask']
outputs = self.model.logits(tens, LogitsConfig(sequence=True, return_embeddings=True))
if return_logits:
return outputs.logits.sequence
embeddings = outputs.embeddings
ends = mask.sum(dim=-1) - 1
start = 1
if post_process:
return self.post_process_cpu(batch, embeddings, mask, start, ends, task_type)
return embeddings
# ProCyon
class ProCyonModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, sequence_only=False):
super().__init__(device)
protein_view_transform = ProteinView(view='residue')
self.transform = Compose([protein_view_transform])
self.graph_construction_model = GraphConstruction(
node_layers=[AlphaCarbonNode()],
edge_layers=[SpatialEdge(radius=10.0, min_distance=5),
KNNEdge(k=10, min_distance=5),
SequentialEdge(max_distance=2)],
edge_feature="gearnet"
)
self.gearnet_edge = GeometryAwareRelationalGraphNeuralNetwork(input_dim=21, hidden_dims=[512, 512, 512, 512, 512, 512],
num_relation=7, edge_input_dim=59, num_angle_bin=8,
batch_norm=True, concat_hidden=True, short_cut=True, readout="sum"
)
ckpt = torch.load("/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/model_zoom/GearNet/mc_gearnet_edge.pth")
self.gearnet_edge.load_state_dict(ckpt)
self.gearnet_edge = self.gearnet_edge.to(self.device)
self.gearnet_edge.eval()
os.environ["HOME_DIR"] = MODEL_ZOOM_PATH
os.environ["DATA_DIR"] = "/nfs_beijing/wanghao/2025-onesystem/vllm/ProCyon-Instruct"
os.environ["LLAMA3_PATH"] = "/nfs_beijing/wanghao/2025-onesystem/vllm/Meta-Llama-3-8B"
procyon_ckpt = '/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/model_zoom/procyon/model_weights/ProCyon-Full'
self.esm_pretrain_model = AutoModelForMaskedLM.from_pretrained(f"{MODEL_ZOOM_PATH}/esm2_3b").to(self.device)
self.esm_pretrain_model.eval()
self.esm_tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/esm2_3b")
# procyon initialization
self.model, _ = UnifiedProCyon.from_pretrained(
pretrained_weights_dir=procyon_ckpt,
checkpoint_dir=procyon_ckpt
)
self.model = self.model.to(self.device)
self.max_length = max_length
self.sequence_only = sequence_only
def get_tokenizer(self):
return self.esm_tokenizer
def construct_batch(self, batch):
names, seqs, structs, labels = [], [], [], []
for sample in batch:
try:
seqs_list = sample['seq'] if isinstance(sample['seq'], list) else [sample['seq']]
pdbs = sample['pdb_path'] if isinstance(sample['pdb_path'], list) else [sample['pdb_path']]
seq_embs, struct_embs = [], []
for s, p in zip(seqs_list, pdbs):
toks = self.esm_tokenizer([s], return_tensors='pt', padding=True, max_length=self.max_length, truncation=True)
out = self.esm_pretrain_model.esm(toks.input_ids.to(self.device), attention_mask=toks.attention_mask.to(self.device), return_dict=True)
seq_embs.append(out.last_hidden_state.squeeze(0).mean(0))
prot = Protein.from_pdb(p, bond_feature="length", residue_feature="symbol")
prot = self.transform({"graph": prot})["graph"]
packed = Protein.pack([prot])
protein = self.graph_construction_model(packed).to(self.device)
with torch.no_grad():
gea = self.gearnet_edge(protein, protein.node_feature.float())
struct_embs.append(gea["graph_feature"].flatten())
names.append(sample['name'])
seqs.append(torch.cat(seq_embs, dim=-1))
structs.append(torch.cat(struct_embs, dim=-1))
labels.append(sample['label'])
except Exception as e:
print(f"Error processing sample {sample['name']}: {e}")
continue
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'X': torch.stack(structs).unsqueeze(1).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
seq_emb, struct_emb = batch['seq'], batch['X']
aaseq = self.model.token_projectors['aaseq'](seq_emb)
struct_proj = self.model.token_projectors['prot_structure'](struct_emb)
B = aaseq.shape[0]
instr = ["Describe the following protein with functions: <|protein|> <|struct|>"] * B
input_ids, attn = self.model._prepare_text_inputs_and_tokenize(instr, [[]]*B, no_pad=True)
input_ids, attn = input_ids.to(self.device), attn.to(self.device)
if self.sequence_only:
embeds, _ = self.model._prepare_input_embeddings(input_ids, protein_soft_tokens=aaseq)
else:
embeds, _ = self.model._prepare_input_embeddings(input_ids, protein_soft_tokens=aaseq, protein_struct_tokens=struct_proj)
mask = ~(input_ids == self.model.tokenizer.pad_token_id)
out = self.model.text_encoder(input_embeds=embeds, attn_masks=attn)
h = out.hidden_states[-1]
ends = mask.sum(dim=-1)
start = 0
if post_process:
return self.post_process_cpu(batch, h, mask, start, ends, task_type)
return h
# GearNet
class GearNetModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
pv = ProteinView(view='residue')
self.transform = Compose([pv])
self.graph_construction = GraphConstruction(
node_layers=[AlphaCarbonNode()], edge_layers=[SpatialEdge(radius=10.0, min_distance=5), KNNEdge(k=10, min_distance=5), SequentialEdge(max_distance=2)], edge_feature="gearnet"
)
self.gearnet = GeometryAwareRelationalGraphNeuralNetwork(
input_dim=21, hidden_dims=[512]*6, num_relation=7, edge_input_dim=59, num_angle_bin=8,
batch_norm=True, concat_hidden=True, short_cut=True, readout="sum"
)
ckpt = torch.load(f"{MODEL_ZOOM_PATH}/GearNet/mc_gearnet_edge.pth")
self.gearnet.load_state_dict(ckpt)
self.gearnet = self.gearnet.to(self.device).eval()
self.max_length = max_length
def get_tokenizer(self):
return None
def construct_batch(self, batch):
names, embeddings, attention_masks, labels = [], [], [], []
for sample in batch:
try:
pdbs = sample['pdb_path'] if isinstance(sample['pdb_path'], list) else [sample['pdb_path']]
prots = []
for p in pdbs:
pr = Protein.from_pdb(p, bond_feature="length", residue_feature="symbol")
prots.append(self.transform({"graph": pr})["graph"])
pack = Protein.pack(prots)
max_res = pack.num_residues.max().item()
gc = self.graph_construction(pack.to(self.device))
node = self.gearnet(gc.to(self.device), gc.node_feature.float().to(self.device))["node_feature"]
splits = torch.cumsum(F.pad(pack.num_residues, (1,0)), dim=0)
attention_mask = torch.zeros(len(splits)-1, max_res).to(self.device)
embeddings_temp = []
for i in range(len(splits)-1):
start, end = splits[i], splits[i+1]
embedding = node[start:end]
attention_mask[i, :embedding.shape[0]] = 1
embedding = self.pad_data(embedding, dim=0, max_length=max_res)
embeddings_temp.append(embedding)
embeddings_temp = torch.stack(embeddings_temp)
embeddings.append(embeddings_temp)
attention_masks.append(attention_mask)
labels.append(sample['label'])
names.append(sample['name'])
except Exception as e:
print(f"Error processing sample {sample['name']}: {e}")
continue
max_len = max([one.shape[1] for one in embeddings])
embeddings = torch.stack([F.pad(one[0], (0,0,0, max_len-one.shape[1])) for one in embeddings], dim=0)
attention_masks = torch.stack([F.pad(one[0], (0, max_len-one.shape[1])) for one in attention_masks], dim=0)
return {
'name': names,
'X': embeddings,
'attention_mask': attention_masks,
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
emb = batch['X']; mask = batch['attention_mask']
ends = mask.sum(dim=-1)
start = 0
if post_process:
return self.post_process_cpu(batch, emb, mask, start, ends, task_type)
return emb
# ProLLAMA
class ProLLAMAModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
llama_path = f"{MODEL_ZOOM_PATH}/ProLLaMA"
self.model = LlamaForCausalLM.from_pretrained(llama_path).to(self.device)
self.tokenizer = LlamaTokenizer.from_pretrained(llama_path)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = min(
max(len(s) for sample in batch for s in (sample['seq'] if isinstance(sample['seq'], list) else [sample['seq']])) + 2,
self.max_length + 2
)
names, seqs, masks, labels = [], [], [], []
for sample in batch:
seqs_list = sample['seq'] if isinstance(sample['seq'], list) else [sample['seq']]
tok_ids, m = [], []
for s in seqs_list:
s2 = f"[Determine superfamily] Seq=<{s}>"
tid = torch.tensor(self.tokenizer.encode(s2))
mask = torch.zeros(max_len, dtype=torch.bool); mask[:len(tid)] = True
tid = self.pad_data(tid, dim=0, max_length=max_len)
tok_ids.append(tid); m.append(mask)
names.append(sample['name']); seqs.append(torch.hstack(tok_ids)); masks.append(torch.hstack(m)); labels.append(sample['label'])
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
seq, mask = batch['seq'], batch['attention_mask']
out = self.model(input_ids=seq, attention_mask=mask, output_hidden_states=True)
emb = out.hidden_states[-1].float()
ends = mask.sum(dim=-1) ; start = 0
if post_process:
return self.post_process_cpu(batch, emb, mask, start, ends, task_type)
return emb
# ProST
class ProSTModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
self.prost = AutoModel.from_pretrained(
f"{MODEL_ZOOM_PATH}/protst",
trust_remote_code=True,
torch_dtype=torch.bfloat16
).to(self.device)
self.model = self.prost.protein_model
self.tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/esm1b_650m")
self.max_length = max_length
# self.alphabet = self.tokenizer.all_tokens
# self.logit_scale = self.prost.logit_scale
# alphabet_tokens = self.tokenizer(self.alphabet)
# with torch.no_grad():
# alphabet_seqs = torch.tensor(alphabet_tokens["input_ids"]).to(self.device)
# alphabet_ams = torch.tensor(alphabet_tokens["attention_mask"]).to(self.device)
# self.label_features = self.model(
# input_ids=alphabet_seqs,
# attention_mask=alphabet_ams,
# return_dict=True
# ).residue_feature[:,1:-1,:].squeeze(1).unsqueeze(0).contiguous() # (33, 512)
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
names, seqs, masks, labels = [], [], [], []
max_length_batch = min(
max([len(self.tokenizer.encode(sample['seq'], add_special_tokens=False)) for sample in batch]) + 2, # +2 for <s> and </s>
self.max_length + 2
)
for sample in batch:
seq = sample['seq'][:max_length_batch]
tid = torch.tensor(self.tokenizer.encode(seq))
mask = torch.zeros(max_length_batch, dtype=torch.bool); mask[:len(tid)] = True
tid = self.pad_data(tid, dim=0, max_length=max_length_batch)
names.append(sample['name']); seqs.append(tid); masks.append(mask); labels.append(sample['label'])
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
out = self.model(input_ids=batch['seq'], attention_mask=batch['attention_mask'], return_dict=True)
emb = out.residue_feature
if return_logits:
# emb_feature = emb / emb.norm(dim=-1, keepdim=True)
# label_features = self.label_features.expand((emb.shape[0], -1, -1))
# logits = self.logit_scale * emb @ label_features.permute(0, 2, 1)
return emb
ends = batch['attention_mask'].sum(dim=-1) - 1; start = 1
if post_process:
return self.post_process_cpu(batch, emb, batch['attention_mask'], start, ends, task_type)
return emb
# ProGen2
class ProGen2Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
self.model = ProGenForCausalLM.from_pretrained(f"{MODEL_ZOOM_PATH}/progen2").to(self.device)
def create_tokenizer_custom(file):
with open(file, 'r') as f:
return Tokenizer.from_str(f.read())
self.tokenizer = create_tokenizer_custom(file=f"{MODEL_ZOOM_PATH}/progen2/tokenizer.json")
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = max(len(self.tokenizer.encode(s).ids) for sample in batch for s in ([sample['seq']] if not isinstance(sample['seq'], list) else sample['seq']))
names, seqs, masks, labels = [], [], [], []
for sample in batch:
seqs_list = sample['seq'] if isinstance(sample['seq'], list) else [sample['seq']]
tids, m = [], []
for s in seqs_list:
tok = torch.tensor(self.tokenizer.encode(s).ids)
mask = torch.zeros(max_len, dtype=torch.bool); mask[:len(tok)] = True
tok = self.pad_data(tok, dim=0, max_length=max_len)
mask = self.pad_data(mask, dim=0, max_length=max_len)
tids.append(tok); m.append(mask)
stacked = torch.hstack(tids)[:self.max_length]; mstack = torch.hstack(m)[:self.max_length]
names.append(sample['name']); seqs.append(stacked); masks.append(mstack); labels.append(sample['label'])
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
out = self.model.transformer(batch['seq'], return_dict=True)
emb = out.last_hidden_state
ends = batch['attention_mask'].sum(dim=-1) - 1; start = 0
if post_process:
return self.post_process_cpu(batch, emb, batch['attention_mask'], start, ends, task_type)
return emb
# ProstT5
class ProstT5Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, sequence_only=False):
super().__init__(device)
self.tokenizer = T5Tokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/ProstT5", do_lower_case=False, legacy=False)
self.model = T5EncoderModel.from_pretrained(f"{MODEL_ZOOM_PATH}/ProstT5").to(self.device)
self.encoder_3di = mini3di.Encoder()
self.max_length = max_length
self.sequence_only = sequence_only
def get_tokenizer(self):
return self.tokenizer
def setup_peft(self, peft_type="lora", **kwargs):
if peft_type == "freeze":
for param in self.model.parameters():
param.requires_grad = False
else:
if peft_type == "lora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=False,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["q", "v"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "ia3":
peft_config = IA3Config(
task_type=TaskType.FEATURE_EXTRACTION,
target_modules=["q", "v", "wi", "wo"], # 应用 IA³ 的模块
feedforward_modules=["wi", "wo"], # 在 MLP 层加 IA³
)
elif peft_type == "dora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
use_dora=True,
inference_mode=False,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["q", "v"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "adalora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = AdaLoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
r=lora_r,
lora_alpha=lora_alpha,
target_r=4,
init_r=12,
beta1=0.85, beta2=0.85,
tinit=200,
tfinal=1000,
deltaT=10,
target_modules=["q", "v"],
)
self.model = get_peft_model(self.model, peft_config)
def construct_batch(self, batch):
import re
max_length_batch = min(
max([len(sample['seq']) for sample in batch]) + 2,
self.max_length + 2
)
names, seqs, masks, labels = [], [], [], []
seq_tokens, attention_masks = [], []
for sample in batch:
seq = sample['seq']; X = sample['X']
N, CA, C, CB = X[:,0], X[:,1], X[:,2], X[:,3]
attention_mask = torch.zeros(2, max_length_batch, device=self.device)
states = self.encoder_3di.encode_atoms(
ca=CA.float().cpu().numpy(),
cb=CB.float().cpu().numpy(),
n=N.float().cpu().numpy(),
c=C.float().cpu().numpy(),
)
struct_seq = self.encoder_3di.build_sequence(states).lower()
if self.sequence_only:
sequence_examples = [seq, seq]
sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", sequence))) for sequence in sequence_examples]
sequence_examples = [ "<AA2fold>" + " " + s if s.isupper() else "<fold2AA>" + " " + s # this expects 3Di sequences to be already lower-case
for s in sequence_examples
]
else:
sequence_examples = [seq, struct_seq]
sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", sequence))) for sequence in sequence_examples]
sequence_examples = [ "<AA2fold>" + " " + s if s.isupper() else "<fold2AA>" + " " + s # this expects 3Di sequences to be already lower-case
for s in sequence_examples
]
seq_token = self.tokenizer.batch_encode_plus(sequence_examples,
add_special_tokens=True,
padding="longest",
return_tensors='pt').to(self.device)
attention_mask[:, :seq_token.input_ids.shape[1]] = 1
seq_token = self.pad_data(seq_token.input_ids, dim=1, max_length=max_length_batch)
attention_mask = self.pad_data(attention_mask, dim=1, max_length=max_length_batch)
seq_tokens.append(seq_token)
attention_masks.append(attention_mask)
names.append(sample['name'])
labels.append(sample['label'])
return {
'name': names,
'seq': torch.cat(seq_tokens, dim=0),
'attention_mask': torch.cat(attention_masks, dim=0),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
seq, attention_mask = batch['seq'], batch['attention_mask']
embedding_repr = self.model(
seq,
attention_mask=attention_mask
)
last = embedding_repr.last_hidden_state
# embeddings = embeddings.reshape(embeddings.shape[0]//2, 2, embeddings.shape[1], embeddings.shape[2])
# embeddings = torch.cat([embeddings[:,0], embeddings[:,1]], dim=-1)
# reshape back [B,2,L,H] -> concat axes
B2, L, H = last.size()
b = len(batch['name'])
last = last.view(b, 2, L, H)
emb = torch.cat([last[:,0], last[:,1]], dim=-1)
if return_logits:
return emb
mask = batch['attention_mask'][::2] # use every two rows
ends = mask.sum(dim=-1) - 1; start = 1
if post_process:
return self.post_process_cpu(batch, emb, mask, start, ends, task_type)
return emb
# ProtGPT2 -gzy
class ProtGPT2Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
self.tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/ProtGPT2")
self.model = AutoModelForCausalLM.from_pretrained(f"{MODEL_ZOOM_PATH}/ProtGPT2").to(self.device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = max(len(self.tokenizer.encode(s)) for sample in batch for s in ([sample['seq']] if not isinstance(sample['seq'], list) else sample['seq']))
names, seqs, masks, labels = [], [], [], []
for sample in batch:
seqs_list = sample['seq'] if isinstance(sample['seq'], list) else [sample['seq']]
tids, m = [], []
for s in seqs_list:
tok = torch.tensor(self.tokenizer.encode(s))
mask = torch.zeros(max_len, dtype=torch.bool); mask[:len(tok)] = True
tok = self.pad_data(tok, dim=0, max_length=max_len)
mask = self.pad_data(mask, dim=0, max_length=max_len)
tids.append(tok); m.append(mask)
names.append(sample['name']); seqs.append(torch.hstack(tids)); masks.append(torch.hstack(m)); labels.append(sample['label'])
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
out = self.model(input_ids=batch['seq'], attention_mask=batch['attention_mask'], output_hidden_states=True)
emb = out.hidden_states[-1]
ends = batch['attention_mask'].sum(dim=-1); start = 0
if post_process:
return self.post_process_cpu(batch, emb, batch['attention_mask'], start, ends, task_type)
return emb
# ProTrek
class ProTrekModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, model_path='protrek_650m'):
super().__init__(device)
if model_path == 'protrek_650m':
config = {
"protein_config": f"{MODEL_ZOOM_PATH}/ProTrek/ProTrek_650M_UniRef50/esm2_t33_650M_UR50D",
"text_config": f"{MODEL_ZOOM_PATH}/ProTrek/ProTrek_650M_UniRef50/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
"structure_config": f"{MODEL_ZOOM_PATH}/ProTrek/ProTrek_650M_UniRef50/foldseek_t30_150M",
"load_protein_pretrained": False,
"load_text_pretrained": False,
"from_checkpoint": f"{MODEL_ZOOM_PATH}/ProTrek/ProTrek_650M_UniRef50/ProTrek_650M_UniRef50.pt"
}
if model_path == 'protrek_35m':
config = {
"protein_config": f"{MODEL_ZOOM_PATH}/protrek_35m/esm2_t12_35M_UR50D",
"text_config": f"{MODEL_ZOOM_PATH}/protrek_35m/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
"structure_config": f"{MODEL_ZOOM_PATH}/protrek_35m/foldseek_t12_35M",
"load_protein_pretrained": False,
"load_text_pretrained": False,
"from_checkpoint": f"{MODEL_ZOOM_PATH}/protrek_35m/ProTrek_35M_UniRef50.pt"
}
self.model = ProTrekTrimodalModel(**config).to(self.device)
self.encoder_3di = mini3di.Encoder()
self.max_length = max_length
def get_tokenizer(self):
return self.model.protein_encoder.tokenizer
def setup_peft(self, peft_type="lora", **kwargs):
if peft_type == "freeze":
for param in self.model.parameters():
param.requires_grad = False
else:
if peft_type == "lora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=False,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["query", "value"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "ia3":
peft_config = IA3Config(
task_type=TaskType.FEATURE_EXTRACTION,
target_modules=["query", "value", "dense"], # 应用 IA³ 的模块
feedforward_modules=["dense"], # 在 MLP 层加 IA³
)
elif peft_type == "dora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=False,
use_dora=True,
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=["query", "value"], # 仅调整 Attention 的 query 和 value
)
elif peft_type == "adalora":
lora_r, lora_alpha, lora_dropout = kwargs.get("lora_r", 8), \
kwargs.get("lora_alpha", 16), \
kwargs.get("lora_dropout", 0.1)
peft_config = AdaLoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
r=lora_r,
lora_alpha=lora_alpha,
target_r=4,
init_r=12,
beta1=0.85, beta2=0.85,
tinit=200,
tfinal=1000,
deltaT=10,
target_modules=["query", "value"],
)
protein_encoder = self.model.protein_encoder.model.esm
structure_encoder = self.model.structure_encoder.model.esm
protein_encoder = get_peft_model(protein_encoder, peft_config)
structure_encoder = get_peft_model(structure_encoder, peft_config)
self.model.protein_encoder.model.esm = protein_encoder
self.model.structure_encoder.model.esm = structure_encoder
# self.model = get_peft_model(self.model, peft_config)
def construct_batch(self, batch):
names, seqs, structs, masks, labels = [], [], [], [], []
max_length_batch = min(
max([len(sample['seq']) for sample in batch]) + 2,
self.max_length + 2
)
for sample in batch:
seq = sample['seq']; X = sample['X']
if X is None: continue
N, CA, C, CB = X[:,0], X[:,1], X[:,2], X[:,3]
states = self.encoder_3di.encode_atoms(
ca=CA.float().cpu().numpy(),
cb=CB.float().cpu().numpy(),
n=N.float().cpu().numpy(),
c=C.float().cpu().numpy(),
)
struct_seq = self.encoder_3di.build_sequence(states).lower()
# merged sequence
mask = torch.zeros(max_length_batch, dtype=torch.bool)
mask[:len(seq)+2] = True
names.append(sample['name'])
seqs.append(seq)
structs.append(struct_seq)
masks.append(mask)
labels.append(sample['label'])
return {
'name': names,
'seq': seqs,
'struct': structs,
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
# get representations
if return_logits:
seq_tokens = self.model.protein_encoder.tokenizer.batch_encode_plus(batch['seq'], return_tensors="pt", padding=True)
seq_tokens["input_ids"], seq_tokens["attention_mask"] = seq_tokens["input_ids"].to(self.device), seq_tokens["attention_mask"].to(self.device)
seq_logits = self.model.protein_encoder(seq_tokens, get_mask_logits=True)[-1]
# structure_tokens = self.model.structure_encoder.tokenizer.batch_encode_plus(batch['struct'], return_tensors="pt", padding=True)
# structure_tokens["input_ids"], structure_tokens["attention_mask"] = structure_tokens["input_ids"].to(self.device), structure_tokens["attention_mask"].to(self.device)
# struct_logits = self.model.structure_encoder(structure_tokens, get_mask_logits=True)[-1]
return seq_logits
prot = self.model.get_protein_repr(batch['seq'])
struct = self.model.get_structure_repr(batch['struct'])
emb = torch.cat([prot, struct], dim=-1)
mask = batch['attention_mask']
ends = mask.sum(dim=-1) - 1; start = 0
if post_process:
return self.post_process_cpu(batch, emb, mask, start, ends, task_type)
return emb
# SaPort - seq + struct
class SaPortModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, model_path="SaPort/ckpt", sequence_only=False):
super().__init__(device)
from transformers import EsmTokenizer, EsmForMaskedLM
self.encoder_3di = mini3di.Encoder()
self.tokenizer = EsmTokenizer.from_pretrained(f'{MODEL_ZOOM_PATH}/{model_path}')
self.model = EsmForMaskedLM.from_pretrained(f'{MODEL_ZOOM_PATH}/{model_path}').to(self.device)
self.max_length = max_length
self.sequence_only = sequence_only
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
names, seqs, masks, labels = [], [], [], []
max_len = min(
max([len(s['seq']) for s in batch]) + 2,
self.max_length + 2
)
for sample in batch:
seq, X = sample['seq'], sample['X']
N, CA, C, CB = X[:,0], X[:,1], X[:,2], X[:,3]
states = self.encoder_3di.encode_atoms(
ca=CA.float().cpu().numpy(),
cb=CB.float().cpu().numpy(),
n=N.float().cpu().numpy(),
c=C.float().cpu().numpy(),
)
struct_seq = self.encoder_3di.build_sequence(states).lower()
merged = ''.join(a + b.lower() for a, b in zip(seq, struct_seq))
tid = torch.tensor(self.tokenizer(merged, return_tensors='pt').input_ids[0])
mask = torch.zeros(max_len, dtype=torch.bool)
mask[:len(tid)] = True
tid = self.pad_data(tid, dim=0, max_length=max_len)
mask = self.pad_data(mask, dim=0, max_length=max_len)
names.append(sample['name'])
seqs.append(tid)
masks.append(mask)
labels.append(torch.tensor(sample['label']))
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels,
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
seq, mask = batch['seq'], batch['attention_mask']
if return_logits:
out = self.model(input_ids=seq, attention_mask=mask, return_dict=True)
return out.logits
out = self.model.esm(input_ids=seq, attention_mask=mask, return_dict=True)
emb = out.last_hidden_state
start = 0
ends = mask.sum(dim=-1) - 1
if post_process:
return self.post_process_cpu(batch, emb, mask, start, ends, task_type)
return emb
# VenusPLM: seq only
class VenusPLMModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
config = TransformerConfig.from_pretrained(MODEL_ZOOM_PATH + '/venusplm', attn_impl="sdpa")
self.model = TransformerForMaskedLM.from_pretrained(MODEL_ZOOM_PATH + '/venusplm', config=config).to(self.device)
self.tokenizer = VPLMTokenizer.from_pretrained(MODEL_ZOOM_PATH + '/venusplm')
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
names, seqs, masks, labels = [], [], [], []
max_len = min(
max([len(s['seq']) for s in batch]) + 2,
self.max_length + 2
)
for sample in batch:
seq = sample['seq']
seq_tokens = torch.tensor(self.tokenizer.encode(seq))[:self.max_length]
attention_mask = torch.zeros(max_len, dtype=torch.bool)
attention_mask[:len(seq_tokens)] = True
seq_tokens = self.pad_data(seq_tokens, dim=0, max_length=max_len)
seq_tokens = self.pad_data(seq_tokens, dim=0, max_length=max_len)
names.append(sample['name'])
seqs.append(seq_tokens)
masks.append(attention_mask)
labels.append(torch.tensor(sample['label']))
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
out = self.model(input_ids=batch['seq'], attention_mask=batch['attention_mask'], output_hidden_states=True)
if return_logits:
return out.logits
emb = out.hidden_states[-1]
start = 0
ends = batch['attention_mask'].sum(dim=-1) - 1
if post_process:
return self.post_process_cpu(batch, emb, batch['attention_mask'], start, ends, task_type)
return emb
# ProSST-2048 seq + struct
class ProSST2048Model(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
from model_zoom.ProSST.prosst.structure.quantizer import PdbQuantizer
weight_path = f"{MODEL_ZOOM_PATH}/ProSST/prosst_2048_weight"
self.tokenizer = AutoTokenizer.from_pretrained(weight_path, trust_remote_code=True)
self.quantizer = PdbQuantizer(structure_vocab_size=2048)
self.model = AutoModelForMaskedLM.from_pretrained(weight_path, trust_remote_code=True).to(self.device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = min(max([len(s['seq']) for s in batch]) + 2, self.max_length + 2)
names, seqs, xs, masks, labels = [], [], [], [], []
for sample in batch:
seq = sample['seq']
pdb_path = sample['pdb_path']
pdb_name = os.path.basename(pdb_path)
seq_tokens = torch.tensor(self.tokenizer.encode(seq))[:self.max_length]
struct = self.quantizer(pdb_path, return_residue_seq=False)['2048'][pdb_name]["struct"]
struct_seq = [i + 3 for i in struct]
struct_seq = [1] + struct_seq + [2]
struct_tokens = torch.tensor(struct_seq)[:self.max_length]
attention_mask = torch.zeros(max_len, dtype=torch.bool)
attention_mask[:len(seq_tokens)] = True
seq_tokens = self.pad_data(seq_tokens, dim=0, max_length=max_len)
struct_tokens = self.pad_data(struct_tokens, dim=0, max_length=max_len)
names.append(sample['name'])
seqs.append(seq_tokens)
xs.append(struct_tokens)
masks.append(attention_mask)
labels.append(torch.tensor(sample['label']))
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'X': torch.stack(xs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', **kwargs):
outputs = self.model(
input_ids=batch['seq'],
attention_mask=batch['attention_mask'],
output_hidden_states=True,
ss_input_ids=batch['X']
)
embeddings = outputs.hidden_states[-1]
ends = batch['attention_mask'].sum(dim=-1) - 1
start = 1
if post_process:
return self.post_process_cpu(batch, embeddings, batch['attention_mask'], start, ends, task_type)
return embeddings
# ProtTrans https://github.com/agemagician/ProtTrans
class ProtT5(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
from transformers import T5Tokenizer, T5EncoderModel
weight_path = f"{MODEL_ZOOM_PATH}/ProtT5"
self.tokenizer = T5Tokenizer.from_pretrained(weight_path, do_lower_case=False)
self.model = T5EncoderModel.from_pretrained(weight_path).to(device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = min(max([len(s['seq']) for s in batch]) + 2, self.max_length + 2)
# max_len = min(max([len(s['seq']) for s in batch]), self.max_length) + 2
names, seqs, masks, labels = [], [], [], []
for sample in batch:
seq = " ".join(list(sample['seq'][:self.max_length]))
seq_tokens = torch.tensor(self.tokenizer.encode(seq, add_special_tokens=False))[:self.max_length]
attention_mask = torch.zeros(max_len, dtype=torch.bool)
attention_mask[:len(seq_tokens)] = True
seq_tokens = self.pad_data(seq_tokens, dim=0, max_length=max_len)
names.append(sample['name'])
seqs.append(seq_tokens)
masks.append(attention_mask)
labels.append(torch.tensor(sample['label']))
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
embedding_repr = self.model(
input_ids=batch['seq'],
attention_mask=batch['attention_mask'],
)
embeddings = embedding_repr.last_hidden_state
if return_logits:
return embeddings
ends = batch['attention_mask'].sum(dim=-1) - 1
start = 1
if post_process:
return self.post_process_cpu(batch, embeddings, batch['attention_mask'], start, ends, task_type)
return embeddings
class DPLMModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, model_path="dplm_650m", **kwargs):
super().__init__(device)
from transformers import AutoTokenizer, AutoModelForMaskedLM
self.model = AutoModelForMaskedLM.from_pretrained(f"{MODEL_ZOOM_PATH}/esm2_650m").to(self.device)
params = torch.load(f'{MODEL_ZOOM_PATH}/{model_path}/pytorch_model.bin')
self.model.load_state_dict(params, strict=True)
self.tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/esm2_650m")
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
MAXLEN = self.max_length
max_length_batch = min(
max([len(sample['seq']) for sample in batch]) + 2, # +2 for <s> and </s>
self.max_length + 2
)
result = {
'name': [],
'seq': [],
'attention_mask': [],
'label': []
}
for sample in batch:
seq_token = torch.tensor(self.tokenizer.encode(sample['seq']))[:MAXLEN]
attention_mask = torch.zeros(max_length_batch)
attention_mask[:len(seq_token)] = 1
seq_token = self.pad_data(seq_token, dim=0, max_length=max_length_batch)
result['name'].append(sample['name'])
result['seq'].append(seq_token)
result['attention_mask'].append(attention_mask)
result['label'].append(sample['label'])
result['seq'] = torch.stack(result['seq'], dim=0).to(self.device)
result['attention_mask'] = torch.stack(result['attention_mask'], dim=0).to(self.device)
return result
def forward(self, batch, post_process=True, task_type='binary_classification', return_prob=False, return_logits=False, **kwargs):
attention_mask = batch['attention_mask']
outputs = self.model.esm(
batch['seq'],
attention_mask=attention_mask,
return_dict=True,
)
if return_prob or return_logits:
if return_prob and return_logits: return_logits = False
logits = self.model.lm_head(outputs.last_hidden_state)
if return_logits:
return logits
probs = F.softmax(logits, dim=-1)
return probs
embeddings = outputs.last_hidden_state
ends = attention_mask.sum(dim=-1)-1
start = 1
if post_process:
result = self.post_process_cpu(batch, embeddings, attention_mask, start, ends, task_type=task_type)
else:
result = embeddings
return result
class OntoProteinModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, **kwargs):
super().__init__(device)
from transformers import AutoTokenizer, AutoModelForMaskedLM
self.tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_ZOOM_PATH}/OntoProtein")
self.model = AutoModelForMaskedLM.from_pretrained(f"{MODEL_ZOOM_PATH}/OntoProtein").to(self.device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
import re
MAXLEN = self.max_length
max_length_batch = min(
max([len(sample['seq']) for sample in batch]) + 2, # +2 for <s> and </s>
MAXLEN + 2
)
result = {
'name': [],
'seq': [],
'attention_mask': [],
'token_type_ids': [],
'label': []
}
for sample in batch:
sequence_Example = ' '.join(sample['seq'])
sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example)
encoded_input = self.tokenizer(sequence_Example, return_tensors='pt')
input_ids = self.pad_data(encoded_input['input_ids'][0], dim=0, max_length=max_length_batch)
attention_mask = self.pad_data(encoded_input['attention_mask'][0], dim=0, max_length=max_length_batch)
token_type_ids = self.pad_data(encoded_input['token_type_ids'][0], dim=0, max_length=max_length_batch)
result['name'].append(sample['name'])
result['seq'].append(input_ids)
result['attention_mask'].append(attention_mask)
result['token_type_ids'].append(token_type_ids)
result['label'].append(sample['label'])
result['seq'] = torch.stack(result['seq'], dim=0).to(self.device)
result['attention_mask'] = torch.stack(result['attention_mask'], dim=0).to(self.device)
result['token_type_ids'] = torch.stack(result['token_type_ids'], dim=0).to(self.device)
return result
def forward(self, batch, post_process=True, task_type='binary_classification', return_prob=False, **kwargs):
output = self.model.bert(
input_ids=batch['seq'],
attention_mask=batch['attention_mask'],
token_type_ids=batch['token_type_ids'],
)
if return_prob:
logits = self.model.cls(output.last_hidden_state)
probs = F.softmax(logits, dim=-1)
return probs
attention_mask = batch['attention_mask']
embeddings = output.last_hidden_state
ends = attention_mask.sum(dim=-1)-1
start = 1
if post_process:
result = self.post_process_cpu(batch, embeddings, attention_mask, start, ends, task_type=task_type)
else:
result = embeddings
return result
class ANKHBase(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022):
super().__init__(device)
from transformers import AutoTokenizer, T5EncoderModel
weight_path = f"{MODEL_ZOOM_PATH}/ankh_base"
self.tokenizer = AutoTokenizer.from_pretrained(weight_path)
self.model = T5EncoderModel.from_pretrained(weight_path).to(device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
max_len = min(max([len(s['seq']) for s in batch]) + 2, self.max_length + 2)
names, seqs, masks, labels = [], [], [], []
for sample in batch:
seq = sample['seq'][:1022]
seq_tokens = torch.tensor(self.tokenizer.encode(seq, add_special_tokens=False))[:self.max_length]
attention_mask = torch.zeros(max_len, dtype=torch.bool)
attention_mask[:len(seq_tokens)] = True
seq_tokens = self.pad_data(seq_tokens, dim=0, max_length=max_len)
names.append(sample['name'])
seqs.append(seq_tokens)
masks.append(attention_mask)
labels.append(torch.tensor(sample['label']))
return {
'name': names,
'seq': torch.stack(seqs).to(self.device),
'attention_mask': torch.stack(masks).to(self.device),
'label': labels
}
def forward(self, batch, post_process=True, task_type='binary_classification', return_logits=False, **kwargs):
embedding_repr = self.model(
input_ids=batch['seq'],
attention_mask=batch['attention_mask'],
)
embeddings = embedding_repr.last_hidden_state
if return_logits:
return embeddings
ends = batch['attention_mask'].sum(dim=-1) - 1
start = 1
if post_process:
return self.post_process_cpu(batch, embeddings, batch['attention_mask'], start, ends, task_type)
return embeddings
class PGLMModel(BaseProteinModel, UtilsModel):
def __init__(self, device, max_length=1022, model_path="proteinglm-1b-mlm", **kwargs):
super().__init__(device)
from transformers import AutoTokenizer, AutoModelForMaskedLM
self.tokenizer = AutoTokenizer.from_pretrained(
f"{MODEL_ZOOM_PATH}/{model_path}",
trust_remote_code=True,
local_files_only=True,
use_fast=True
)
self.model = AutoModelForMaskedLM.from_pretrained(
f"{MODEL_ZOOM_PATH}/{model_path}",
trust_remote_code=True,
local_files_only=True
).to(self.device)
self.max_length = max_length
def get_tokenizer(self):
return self.tokenizer
def construct_batch(self, batch):
MAXLEN = self.max_length
max_length_batch = min(
max([len(self.tokenizer.encode(sample['seq'], add_special_tokens=False)) for sample in batch]) + 2, # +2 for <s> and </s>
self.max_length + 2
)
result = {
'name': [],
'seq': [],
'attention_mask': [],
'label': []
}
for sample in batch:
output = self.tokenizer(sample['seq'], add_special_tokens=True, return_tensors='pt')
seq_token = output['input_ids'][0]
attention_mask = torch.zeros(max_length_batch)
attention_mask[:len(seq_token)] = 1
seq_token = self.pad_data(seq_token, dim=0, max_length=max_length_batch)
result['name'].append(sample['name'])
result['seq'].append(seq_token)
result['attention_mask'].append(attention_mask)
result['label'].append(sample['label'])
result['seq'] = torch.stack(result['seq'], dim=0).to(self.device)
result['attention_mask'] = torch.stack(result['attention_mask'], dim=0).to(self.device)
return result
def forward(self, batch, post_process=True, task_type='binary_classification', return_prob=False, return_logits=False, **kwargs):
attention_mask = batch['attention_mask']
outputs = self.model(
batch['seq'],
attention_mask=batch['attention_mask'],
output_hidden_states=True, return_last_hidden_state=True
)
if return_prob or return_logits:
if return_prob and return_logits: return_logits = False
if return_logits:
return outputs.logits
probs = F.softmax(logits, dim=-1)
return probs
embeddings = outputs.hidden_states.permute(1,0,2)
ends = attention_mask.sum(dim=-1)-1
start = 1
if post_process:
result = self.post_process_cpu(batch, embeddings, attention_mask, start, ends, task_type=task_type)
else:
result = embeddings
return result
if __name__ == "__main__":
import torch
import sys; sys.path.append("/nfs_beijing/kubeflow-user/wanghao/workspace/ai4sci/protein_benchmark_new/protein_benchmark")
sys.path.append('/nfs_beijing/kubeflow-user/wanghao/workspace/ai4sci/protein_benchmark_new/protein_benchmark/model_zoom')
from src.data.esm.sdk.api import ESMProtein
model_name = "saprot"
# this is a unit test for models' logits
pdb_path = "/nfs_beijing/kubeflow-user/wanghao/workspace/ai4sci/protein_benchmark_new/protein_benchmark/datasets/DMS_ProteinGym_substitutions/ProteinGym_AF2_structures/A0A1I9GEU1_NEIME.pdb"
structure = ESMProtein.from_pdb(pdb_path)
sequence = structure.sequence
coordinates = structure.coordinates
print(f"length of sequence: {len(sequence)}")
ori_batch = [
{
"seq": sequence,
"X": coordinates,
"name": "unknown",
"label": 1.0
}
]
# ======= VenusPLM: Seq-only =======
if model_name == "esm2_650m":
model = ESM2Model(device="cuda:0")
if model_name == "esmc_600m":
model = ESMC600MModel(device="cuda:0")
if model_name == "esm3_1.4b":
model = ESM3Model(device="cuda:0")
if model_name == "venusplm":
model = VenusPLMModel(device="cuda:0")
if model_name == "protst":
model = ProSTModel(device="cuda:0")
if model_name == "prostt5":
model = ProstT5Model(device="cuda:0")
if model_name == "protrek":
model = ProTrekModel(device="cuda:0")
if model_name == "saprot":
model = SaPortModel(device="cuda:0")
if model_name == "prott5":
model = ProtT5(device="cuda:0")
if model_name == "dplm":
model = DPLMModel(device="cuda:0")
if model_name == "dplm_150m":
model = DPLMModel(device="cuda:0", model_path="dplm_150m")
if model_name == "dplm_3b":
model = DPLMModel(device="cuda:0", model_path="dplm_3b")
if model_name == "dplm":
model = DPLMModel(device="cuda:0")
if model_name == "pglm":
model = PGLMModel(device="cuda:0")
# seq_tokens = torch.tensor(model.tokenizer.encode(sequence)).unsqueeze(0).to(model.device)
# attention_mask = torch.ones(seq_tokens.shape[1], dtype=torch.bool).unsqueeze(0).to(model.device)
# print(f"sequence shape: {seq_tokens.shape}")
input_batch = model.construct_batch(ori_batch)
logits = model.forward(batch=input_batch, return_logits=True)
print(logits.shape)
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