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import torch
import torch.nn as nn
import torch.nn.functional as F
class TransformerClassifier(nn.Module):
def __init__(self, vocab_size, embed_dim, num_heads, num_layers, num_classes, max_seq_len):
super(TransformerClassifier, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.pos_encoder = nn.Parameter(torch.zeros(1, max_seq_len, embed_dim))
encoder_layers = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, batch_first=True, dim_feedforward=embed_dim*4)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)
self.fc = nn.Linear(embed_dim, num_classes)
def forward(self, x):
padding_mask = (x == 0)
x = self.embedding(x) + self.pos_encoder
x = self.transformer_encoder(x, src_key_padding_mask=padding_mask)
x = x.mean(dim=1)
x = self.fc(x)
return x
class LSTMClassifier(nn.Module):
def __init__(self, vocab_size, embed_dim, hidden_dim, num_layers, num_classes, dropout):
super(LSTMClassifier, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.lstm = nn.LSTM(
input_size=embed_dim, hidden_size=hidden_dim, num_layers=num_layers,
batch_first=True, bidirectional=True, dropout=dropout if num_layers > 1 else 0
)
self.fc = nn.Linear(hidden_dim * 2, num_classes)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
_, (hidden, cell) = self.lstm(embedded)
hidden_cat = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)
output = self.fc(self.dropout(hidden_cat))
return output
class SimpleMambaBlock(nn.Module):
"""
Логика: Проекция -> 1D Свертка -> Активация -> Селективный SSM -> Выходная проекция
"""
def __init__(self, d_model, d_state, d_conv, expand=2):
super().__init__()
self.d_model = d_model
self.d_state = d_state
self.d_conv = d_conv
self.expand = expand
d_inner = int(self.expand * self.d_model)
self.in_proj = nn.Linear(d_model, d_inner * 2, bias=False)
self.conv1d = nn.Conv1d(
in_channels=d_inner, out_channels=d_inner,
kernel_size=d_conv, padding=d_conv - 1,
groups=d_inner, bias=True
)
self.x_proj = nn.Linear(d_inner, self.d_state + self.d_state + 1, bias=False)
self.dt_proj = nn.Linear(1, d_inner, bias=True)
A = torch.arange(1, d_state + 1, dtype=torch.float32).repeat(d_inner, 1)
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(d_inner))
self.out_proj = nn.Linear(d_inner, d_model, bias=False)
def forward(self, x):
B, L, D = x.shape
xz = self.in_proj(x)
x, z = xz.chunk(2, dim=-1)
x = x.transpose(1, 2)
x = self.conv1d(x)[:, :, :L]
x = x.transpose(1, 2)
x = F.silu(x)
y = self.ssm(x)
y = y * F.silu(z)
y = self.out_proj(y)
return y
def ssm(self, x):
batch_size, seq_len, d_inner = x.shape
A = -torch.exp(self.A_log.float())
D = self.D.float()
x_dbl = self.x_proj(x)
delta, B_param, C_param = torch.split(x_dbl, [1, self.d_state, self.d_state], dim=-1)
delta = F.softplus(self.dt_proj(delta))
h = torch.zeros(batch_size, d_inner, self.d_state, device=x.device)
ys = []
for i in range(seq_len):
delta_i = delta[:, i, :]
A_i = torch.exp(delta_i.unsqueeze(-1) * A)
B_i = delta_i.unsqueeze(-1) * B_param[:, i, :].unsqueeze(1)
h = A_i * h + B_i * x[:, i, :].unsqueeze(-1)
y_i = (h @ C_param[:, i, :].unsqueeze(-1)).squeeze(-1)
ys.append(y_i)
y = torch.stack(ys, dim=1)
y = y + x * D
return y
class CustomMambaClassifier(nn.Module):
def __init__(self, vocab_size, d_model, d_state, d_conv, num_layers, num_classes):
super().__init__()
self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=0)
self.layers = nn.ModuleList(
[SimpleMambaBlock(d_model, d_state, d_conv) for _ in range(num_layers)]
)
self.fc = nn.Linear(d_model, num_classes)
def forward(self, x):
x = self.embedding(x)
for layer in self.layers:
x = layer(x)
pooled_output = x.mean(dim=1)
return self.fc(pooled_output)
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