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import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
class MultiheadAttention(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
super().__init__()
self.d_out = d_out
self.num_heads = num_heads
self.head_dim = d_out // num_heads
#step 3
self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
self.out_proj = nn.Linear(d_out, d_out)
self.dropout = nn.Dropout(dropout)
self.register_buffer("mask",torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_tokens, d_in = x.shape
#step 4
keys = self.W_key(x)
queries = self.W_query(x)
values = self.W_value(x)
#step 5
keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
values = values.view(b, num_tokens, self.num_heads, self.head_dim)
#step 6
keys = keys.transpose(1,2)
queries = queries.transpose(1,2)
values = values.transpose(1,2)
#step 7
attn_scores = queries @ keys.transpose(2,3)
#step 8
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
attn_scores.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
#step 9 - 11
ctx_vec = (attn_weights @ values).transpose(1, 2)
#step 12
ctx_vec = ctx_vec.contiguous().view(b, num_tokens, self.d_out)
ctx_vec = self.out_proj(ctx_vec)
return ctx_vec
#==========================================================================
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale = nn.Parameter(torch.ones(emb_dim))
self.shift = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm_x = (x - mean) / torch.sqrt(var + self.eps)
return self.scale * norm_x + self.shift
#==========================================================================
class GeLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(torch.sqrt(torch.tensor(2.0/torch.pi)) * (x + 0.044715 * torch.pow(x,3))))
#==========================================================================
class FeedForward(nn.Module):
def __init__(self, cfg):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(cfg.emb_dim, 4*cfg.emb_dim),
GeLU(),
nn.Linear(4*cfg.emb_dim, cfg.emb_dim)
)
def forward(self, x):
return self.layers(x)
#==========================================================================
class TransformerBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.att = MultiheadAttention(
d_in = cfg.emb_dim,
d_out = cfg.emb_dim,
context_length = cfg.context_length,
dropout = cfg.drop_rate,
num_heads = cfg.n_heads,
qkv_bias = cfg.qkv_bias
)
self.ff = FeedForward(cfg)
self.norm1 = LayerNorm(cfg.emb_dim)
self.norm2 = LayerNorm(cfg.emb_dim)
self.drop_shortcut = nn.Dropout(cfg.drop_rate)
def forward(self, x):
shortcut = x
x = self.norm1(x)
x = self.att(x)
x = self.drop_shortcut(x)
x = x + shortcut
shortcut = x
x = self.norm2(x)
x = self.ff(x)
x = self.drop_shortcut(x)
x = x + shortcut
return x
#=======================================================================
class TicketGPTConfig(PretrainedConfig):
model_type = "ticket_gpt" # Unique identifier for the AutoClass
def __init__(self, classes=8, context_length=1024, drop_rate=0.1, emb_dim=768, n_heads=12, n_layers=12, qkv_bias=True, vocab_size=50257, **kwargs):
super().__init__(**kwargs)
self.classes = classes
self.context_length = context_length
self.drop_rate = drop_rate
self.emb_dim = emb_dim
self.n_heads = n_heads
self.n_layers = n_layers
self.qkv_bias = qkv_bias
self.vocab_size = vocab_size
class TicketGPT(
PreTrainedModel,
):
config_class = TicketGPTConfig
def __init__(self, config):
super().__init__(config)
self.tok_emb = nn.Embedding(config.vocab_size, config.emb_dim)
self.pos_emb = nn.Embedding(config.context_length, config.emb_dim)
self.drop_emb = nn.Dropout(config.drop_rate)
self.trf_blocks = nn.Sequential(
*[TransformerBlock(config) for _ in range(config.n_layers)]
)
self.final_norm = LayerNorm(config.emb_dim)
self.out_head = nn.Linear(config.emb_dim, config.classes, bias=True)
def forward(self, x):
batch_size, seq_len = x.shape
tok_embeddings = self.tok_emb(x) #[2,4,768]
pos_embeddings = self.pos_emb(torch.arange(seq_len, device=x.device)) #[2,4,768]
x = tok_embeddings + pos_embeddings #[2,4,768]
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x) #[2,4,50257]
return logits
def predict(self, text, tokenizer, max_length=1024, pad_token_id=50256):
lookup = {
0:"Hardware",
1:"HR Support",
2:"Access",
3:"Miscellaneous",
4:"Storage",
5:"Purchase",
6:"Internal Project",
7:"Administrative rights"
}
current_device = next(self.parameters()).device
self.eval()
# Prepare inputs to the model
input_ids = tokenizer.encode(text)
supported_context_length = self.config.context_length
# Truncate sequences if they too long
input_ids = input_ids[:min(max_length, supported_context_length)]
# Pad sequences to the longest sequence
input_ids += [pad_token_id] * (max_length - len(input_ids))
input_tensor = torch.tensor(input_ids, device=current_device).unsqueeze(0) # add batch dimension
# Model inference
with torch.no_grad():
logits = self(input_tensor)[:, -1, :] # Logits of the last output token
predicted_label = torch.argmax(logits, dim=-1).item()
# Return the classified result
return lookup[predicted_label]
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