File size: 2,636 Bytes
3b2d368 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FMLP(nn.Module):
def __init__(self, config):
super().__init__()
# Check if LoRA is enabled for ICL MLP
use_lora = getattr(config, 'use_lora_icl_mlp', False)
if use_lora:
from .lora import LoRALinear
lora_rank = getattr(config, 'lora_rank', 8)
lora_alpha = getattr(config, 'lora_alpha', 16)
lora_dropout = getattr(config, 'lora_dropout', 0.0)
self.fc_1 = LoRALinear(
config.embed_dim_f, config.mlp_dim_f, bias=True,
lora_rank=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout
)
self.fc_2 = LoRALinear(
config.mlp_dim_f, config.embed_dim_f, bias=True,
lora_rank=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout
)
else:
self.fc_1 = nn.Linear(config.embed_dim_f, config.mlp_dim_f, bias=True)
self.fc_2 = nn.Linear(config.mlp_dim_f, config.embed_dim_f, bias=True)
self.activation = nn.GELU()
self.dropout = nn.Dropout(0.1)
def forward(self, x):
x = self.fc_1(x)
x = self.activation(x)
x = self.dropout(x)
x = self.fc_2(x)
return x
class PhiMLP(nn.Module):
def __init__(self, config):
super().__init__()
# Check if LoRA is enabled for Phi MLP
use_lora = getattr(config, 'use_lora_phi_mlp', False)
if use_lora:
from .lora import LoRALinear
lora_rank = getattr(config, 'lora_rank', 8)
lora_alpha = getattr(config, 'lora_alpha', 16)
lora_dropout = getattr(config, 'lora_dropout', 0.0)
self.fc_1 = LoRALinear(
config.embed_dim_phi, config.mlp_dim_phi, bias=True,
lora_rank=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout
)
self.fc_2 = LoRALinear(
config.mlp_dim_phi, config.embed_dim_phi, bias=True,
lora_rank=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout
)
else:
self.fc_1 = nn.Linear(config.embed_dim_phi, config.mlp_dim_phi, bias=True)
self.fc_2 = nn.Linear(config.mlp_dim_phi, config.embed_dim_phi, bias=True)
self.activation = nn.GELU()
self.dropout = nn.Dropout(0.1)
def forward(self, x):
x = self.fc_1(x)
x = self.activation(x)
x = self.dropout(x)
x = self.fc_2(x)
return x |