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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
try:
from safetensors.torch import save_file, load_file
_HAS_SAFETENSORS = True
except ImportError:
_HAS_SAFETENSORS = False
PHYSICOCHEMICAL_FEATURES = {
'A': [1.8, 0.0, 0.0, 89.0, 0.360, 0.76, 0.83, 0.37, 1.0, 0.0, 0.0, 0.0],
'R': [-4.5, 1.0, 1.0, 174.0, 0.293, 0.76, 0.93, 0.58, 0.0, 0.0, 1.0, 0.0],
'N': [-3.5, 0.0, 0.0, 132.0, 0.337, 0.79, 0.89, 0.54, 0.0, 0.0, 1.0, 0.0],
'D': [-3.5, -1.0, 0.0, 133.0, 0.281, 0.74, 0.72, 0.54, 0.0, 0.0, 1.0, 0.0],
'C': [2.5, 0.0, 0.0, 121.0, 0.160, 0.81, 0.87, 0.39, 1.0, 0.0, 0.0, 0.0],
'Q': [-3.5, 0.0, 0.0, 146.0, 0.316, 0.80, 0.91, 0.57, 0.0, 0.0, 1.0, 0.0],
'E': [-3.5, -1.0, 0.0, 147.0, 0.282, 0.77, 0.73, 0.60, 0.0, 0.0, 1.0, 0.0],
'G': [-0.4, 0.0, 0.0, 75.0, 0.360, 0.69, 0.75, 0.39, 1.0, 0.0, 0.0, 0.0],
'H': [-3.2, 0.1, 1.0, 155.0, 0.290, 0.80, 0.80, 0.49, 0.0, 0.0, 1.0, 0.0],
'I': [4.5, 0.0, 0.0, 131.0, 0.321, 0.79, 0.83, 0.24, 1.0, 0.0, 0.0, 0.0],
'L': [3.8, 0.0, 0.0, 131.0, 0.313, 0.78, 0.85, 0.22, 1.0, 0.0, 0.0, 0.0],
'K': [-3.9, 1.0, 0.0, 146.0, 0.329, 0.73, 0.85, 0.58, 0.0, 0.0, 1.0, 0.0],
'M': [1.9, 0.0, 0.0, 149.0, 0.302, 0.80, 0.85, 0.34, 1.0, 0.0, 0.0, 0.0],
'F': [2.8, 0.0, 0.0, 165.0, 0.287, 0.77, 0.82, 0.22, 1.0, 0.0, 0.0, 0.0],
'P': [-1.6, 0.0, 0.0, 115.0, 0.314, 0.64, 0.63, 0.42, 1.0, 0.0, 0.0, 0.0],
'S': [-0.8, 0.0, 0.0, 105.0, 0.384, 0.74, 0.74, 0.47, 0.0, 0.0, 1.0, 0.0],
'T': [-0.7, 0.0, 0.0, 119.0, 0.360, 0.76, 0.77, 0.45, 0.0, 0.0, 1.0, 0.0],
'W': [-0.9, 0.0, 0.0, 204.0, 0.277, 0.75, 0.82, 0.27, 1.0, 0.0, 0.0, 0.0],
'Y': [-1.3, 0.0, 0.0, 181.0, 0.292, 0.77, 0.79, 0.34, 0.0, 0.0, 1.0, 0.0],
'V': [4.2, 0.0, 0.0, 117.0, 0.329, 0.76, 0.85, 0.24, 1.0, 0.0, 0.0, 0.0],
}
AA_ORDER = 'ARNDCQEGHILKMFPSTWYV'
def get_physicochem_matrix():
mat = []
for aa in AA_ORDER:
mat.append(PHYSICOCHEMICAL_FEATURES[aa])
return torch.tensor(mat, dtype=torch.float32)
class SwiGLU(nn.Module):
def __init__(self, dim, hidden_dim):
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(dim, hidden_dim, bias=False)
self.w3 = nn.Linear(hidden_dim, dim, bias=False)
def forward(self, x):
return self.w3(F.silu(self.w1(x)) * self.w2(x))
class ConvBlock(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, dilation=1, dropout=0.1):
super().__init__()
pad = dilation * (kernel_size - 1) // 2
self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, padding=pad,
dilation=dilation, bias=False)
self.bn = nn.BatchNorm1d(out_dim)
self.act = nn.GELU()
self.drop = nn.Dropout(dropout)
def forward(self, x):
r = x
x = x.transpose(1, 2)
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.drop(x)
x = x.transpose(1, 2)
if r.shape[-1] != x.shape[-1]:
return x
return x + r
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_model, n_heads, dropout=0.1):
super().__init__()
assert d_model % n_heads == 0
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(d_model, d_model * 3, bias=False)
self.out = nn.Linear(d_model, d_model)
self.drop = nn.Dropout(dropout)
self.pos_enc = nn.Parameter(torch.randn(1, 512, d_model) * 0.02)
def forward(self, x):
b, l, _ = x.shape
x = x + self.pos_enc[:, :l, :]
qkv = self.qkv(x).reshape(b, l, 3, self.n_heads, self.head_dim)
qkv = qkv.permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = F.softmax(attn, dim=-1)
attn = self.drop(attn)
out = (attn @ v).transpose(1, 2).reshape(b, l, self.d_model)
return self.out(out)
class TransformerLayer(nn.Module):
def __init__(self, d_model, n_heads, ff_dim, dropout=0.1, sd_prob=0.0):
super().__init__()
self.sd_prob = sd_prob
self.norm1 = nn.LayerNorm(d_model)
self.attn = MultiHeadSelfAttention(d_model, n_heads, dropout)
self.norm2 = nn.LayerNorm(d_model)
self.ff = SwiGLU(d_model, ff_dim)
self.drop = nn.Dropout(dropout)
def forward(self, x):
if self.training and self.sd_prob > 0:
if torch.rand(1).item() < self.sd_prob:
return x
x = x + self.drop(self.attn(self.norm1(x)))
x = x + self.drop(self.ff(self.norm2(x)))
return x
class PeptEdgeV2(nn.Module):
def __init__(self, vocab_size=21, max_len=200, d_model=192, n_heads=6,
num_layers=4, ff_dim=384, num_classes=2, dropout=0.15, sd_prob=0.05):
super().__init__()
self.config = {
'vocab_size': vocab_size,
'max_len': max_len,
'd_model': d_model,
'n_heads': n_heads,
'num_layers': num_layers,
'ff_dim': ff_dim,
'num_classes': num_classes,
'dropout': dropout,
'sd_prob': sd_prob,
'architectures': ['PeptEdgeV2'],
'model_type': 'peptedgev2',
}
phys_mat = get_physicochem_matrix()
self.register_buffer('phys_mat', phys_mat)
self.token_embed = nn.Embedding(vocab_size, d_model - 64, padding_idx=0)
self.phys_proj = nn.Sequential(
nn.Linear(12, 64),
nn.LayerNorm(64),
)
self.input_drop = nn.Dropout(dropout)
self.conv_layers = nn.ModuleList([
ConvBlock(d_model, d_model, 3, 1, dropout),
ConvBlock(d_model, d_model, 5, 1, dropout),
ConvBlock(d_model, d_model, 7, 2, dropout),
ConvBlock(d_model, d_model, 11, 1, dropout),
])
self.conv_norm = nn.LayerNorm(d_model)
self.transformer_layers = nn.ModuleList([
TransformerLayer(d_model, n_heads, ff_dim, dropout, sd_prob if i > 0 else 0.0)
for i in range(num_layers)
])
self.norm = nn.LayerNorm(d_model)
self.attn_pool_q = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)
self.classifier = nn.Sequential(
nn.Linear(d_model * 3, d_model),
nn.LayerNorm(d_model),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(d_model, d_model // 2),
nn.GELU(),
nn.Dropout(dropout * 0.5),
nn.Linear(d_model // 2, num_classes),
)
self._init_weights()
def _init_weights(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.kaiming_normal_(p, mode='fan_out', nonlinearity='relu')
def to_dict(self):
return dict(self.config)
@classmethod
def from_dict(cls, config):
return cls(
vocab_size=config['vocab_size'],
max_len=config['max_len'],
d_model=config['d_model'],
n_heads=config['n_heads'],
num_layers=config['num_layers'],
ff_dim=config['ff_dim'],
num_classes=config['num_classes'],
dropout=config.get('dropout', 0.15),
sd_prob=config.get('sd_prob', 0.05),
)
def save_pretrained(self, save_directory, safe_serialization=True):
os.makedirs(save_directory, exist_ok=True)
config = self.to_dict()
with open(os.path.join(save_directory, 'config.json'), 'w') as f:
json.dump(config, f, indent=2)
if safe_serialization and _HAS_SAFETENSORS:
save_file(self.state_dict(), os.path.join(save_directory, 'model.safetensors'))
else:
torch.save({'model_state_dict': self.state_dict()},
os.path.join(save_directory, 'pytorch_model.bin'))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, map_location=None, **kwargs):
pt_dir = pretrained_model_name_or_path
if not os.path.isdir(pretrained_model_name_or_path):
import huggingface_hub
from huggingface_hub import snapshot_download
pt_dir = snapshot_download(
repo_id=pretrained_model_name_or_path,
allow_patterns=['*.json', '*.safetensors', '*.bin'],
**kwargs,
)
with open(os.path.join(pt_dir, 'config.json')) as f:
config = json.load(f)
model = cls.from_dict(config)
st_dir = os.path.join(pt_dir, 'model.safetensors')
if os.path.exists(st_dir):
if not _HAS_SAFETENSORS:
raise ImportError('safetensors is required to load model.safetensors')
state = load_file(st_dir)
else:
ckpt = torch.load(os.path.join(pt_dir, 'pytorch_model.bin'), map_location=map_location)
state = ckpt.get('model_state_dict', ckpt)
model.load_state_dict(state)
return model
def forward(self, x, return_embeddings=False):
b, l = x.shape
aa_idx = torch.clamp(x, 0, 19).long()
phys = F.embedding(aa_idx, self.phys_mat)
phys = self.phys_proj(phys)
tok = self.token_embed(x)
h = torch.cat([tok, phys], dim=-1)
h = self.input_drop(h)
for conv in self.conv_layers:
h = conv(h)
h = self.conv_norm(h)
for tf in self.transformer_layers:
h = tf(h)
h = self.norm(h)
mean_pool = h.mean(dim=1)
max_pool = h.max(dim=1)[0]
attn_scores = torch.matmul(h, self.attn_pool_q.transpose(1, 2))
attn_weights = F.softmax(attn_scores.squeeze(-1), dim=1).unsqueeze(1)
attn_pool = torch.matmul(attn_weights, h).squeeze(1)
h_pool = torch.cat([mean_pool, max_pool, attn_pool], dim=-1)
if return_embeddings:
return h_pool
logits = self.classifier(h_pool)
return logits
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
if __name__ == '__main__':
model = PeptEdgeV2(vocab_size=21, max_len=200, num_classes=2)
total = count_parameters(model)
print(f'PeptEdgeV2 params: {total:,}')
x = torch.randint(0, 20, (4, 100))
out = model(x)
print(f'Input: {x.shape}, Output: {out.shape}')
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