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1ebe45d | 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 | import torch
import torch.nn as nn
import torch
import torch.nn as nn
def get_rotary_position_encoding(input: torch.Tensor, base= 10000, device = "cpu"):
context_length, dimension = input.shape
assert dimension % 2 == 0, "dimension must be even"
half_dimension = dimension // 2
freqs_indices = torch.arange(0, half_dimension, device = device, dtype = torch.float32)
freqs = 1.0 / (base ** (freqs_indices / dimension))
positions = torch.arange(0, context_length, device = device, dtype = torch.float32).unsqueeze(1)
angles = positions * freqs
sin_angles = torch.sin(angles)
cos_angles = torch.cos(angles)
input_even = input[:, :dimension // 2]
input_odd = input[:, dimension // 2:]
input_even_rotated = input_even * cos_angles - input_odd * sin_angles
input_odd_rotated = input_even * sin_angles + input_odd * cos_angles
input_rotated = torch.empty_like(input)
input_rotated[:, :dimension //2] = input_even_rotated
input_rotated[:, dimension // 2:] = input_odd_rotated
return input_rotated
class UstaEmbedding(nn.Module):
def __init__(self, vocab_size, embedding_dim, context_length):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.get_pos = get_rotary_position_encoding # Burada atadık ✅
def forward(self, x):
x = self.embedding(x)
x = self.get_pos(x) # ✅ Düzeltilmiş satır
return x
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