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double_check.py
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double_check.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class DecoderOnlyTransformer(nn.Module):
def __init__(self, embed_dim, num_heads, num_layers):
super(DecoderOnlyTransformer, self).__init__()
self.layers = nn.ModuleList([
DecoderLayer(embed_dim, num_heads) for _ in range(num_layers)
])
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class AttentionHead(nn.Module):
def __init__(self, embed_dim, head_dim):
super(AttentionHead, self).__init__()
self.query = nn.Linear(embed_dim, head_dim)
self.key = nn.Linear(embed_dim, head_dim)
self.value = nn.Linear(embed_dim, head_dim)
self.scale = head_dim ** -0.5
def forward(self, x):
q = self.query(x)
k = self.key(x)
v = self.value(x)
print(v)
attn_weights = torch.matmul(q, k.transpose(-2, -1))
attn_weights = F.softmax(attn_weights, dim=-1)
attn_output = torch.matmul(attn_weights, v)
return attn_output, attn_weights
torch.manual_seed(1133)
# Example usage:
embed_dim = 10
head_dim = 1
x = torch.arange(0, 10).view(1, 10).float()
attention_head = AttentionHead(embed_dim, head_dim)
output, weights = attention_head(x)
print(output)
import json
'''
json.dump(attention_head.query.weight.tolist(), open('query.json', 'w'))
json.dump(attention_head.key.weight.tolist(), open('key.json', 'w'))
json.dump(attention_head.value.weight.tolist(), open('value.json', 'w'))
json.dump(attention_head.query.bias.tolist(), open('query_bias.json', 'w'))
json.dump(attention_head.key.bias.tolist(), open('key_bias.json', 'w'))
json.dump(attention_head.value.bias.tolist(), open('value_bias.json', 'w'))
'''