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4ca4e4c | 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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .transformer import SimplePyTorchTFLayer, SimpleHandmadeTFLayer
from .ssm import SimpleSSMLayer
from .mlp import SimpleMLPLayer
# Sine Positional Encodings, if desired
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=20):
super().__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(1).float()
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-torch.log(torch.tensor(10000.0)) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.pe = pe.unsqueeze(0)
def forward(self, x):
return self.pe[:, :x.size(1)].to(x.device)
class HybridModel(nn.Module):
def __init__(self, args):
super().__init__()
# Token Embedding
self.embedding = nn.Embedding(args.vocab_size, args.embed_dim)
# Choice of positional encodings. For these small models, learned seems better
self.positional_encoding = args.positional_encoding
if args.positional_encoding == "sine":
self.pos_encoder = PositionalEncoding(args.embed_dim, args.sequence_len)
if args.positional_encoding == "learned":
p = torch.zeros((args.sequence_len, args.embed_dim))
torch.nn.init.xavier_uniform_(p)
self.pos_encoder = nn.Parameter(p)
self.layers = []
for layer in args.layers:
# Transformer Layers
if layer == "TF":
if "do_norm" not in vars(args).keys(): args.do_norm = True
if args.pytorch_transformer:
self.layers.append(SimplePyTorchTFLayer(args.embed_dim, args.num_heads,
causal=True))
else:
self.layers.append(SimpleHandmadeTFLayer(args.embed_dim, args.num_heads,
causal=True, do_norm=args.do_norm))
# Transformer (non-causal) Layers
if layer == "TF-nC":
if "do_norm" not in vars(args).keys(): args.do_norm = True
if args.pytorch_transformer:
self.layers.append(SimplePyTorchTFLayer(args.embed_dim, args.num_heads,
causal=False))
else:
self.layers.append(SimpleHandmadeTFLayer(args.embed_dim, args.num_heads,
causal=False, do_norm=args.do_norm))
# MLP layers (already included in transformer layers)
if layer == "MLP":
self.layers.append(SimpleMLPLayer(args.embed_dim, args.embed_dim, args.embed_dim))
# Mamba layers
if layer == "SSM":
if "d_conv" not in vars(args).keys(): args.d_conv = 4
if "expand" not in vars(args).keys(): args.expand = 2
if not args.d_conv: args.d_conv = 4
if not args.expand: args.expand = 2
self.layers.append(SimpleSSMLayer(args.embed_dim, args.state_dim,
args.d_conv, args.expand))
self.layers = nn.ModuleList(self.layers)
self.decoder = nn.Linear(args.embed_dim, args.vocab_size)
def forward(self, x, mask):
x = self.embedding(x) * (self.embedding.embedding_dim ** 0.5)
if self.positional_encoding == "sine":
x = x + self.pos_encoder(x) # For the sinusoidal positional encoding class
if self.positional_encoding == "learned":
x = x + self.pos_encoder # For the learned positional encodings
for layer in self.layers:
# x = x + layer(x, mask)
x = layer(x, mask)
return self.decoder(x) |