Update model_class.py

model_class.py

@@ -1,187 +1,210 @@
-import torch
-import torch.nn as nn
-from transformers import PreTrainedModel, PretrainedConfig
-# from huggingface_hub import PyTorchModelHubMixin
-
-
-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
-
-#=======================================================================
-
-# "config": {
-#     "classes": 8,
-#     "context_length": 1024,
-#     "drop_rate": 0.1,
-#     "emb_dim": 768,
-#     "n_heads": 12,
-#     "n_layers": 12,
-#     "qkv_bias": true,
-#     "vocab_size": 50257
-#   },
-
-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
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+# from huggingface_hub import PyTorchModelHubMixin
+
+
+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 classify_review(text, model, tokenizer, max_length=None, 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
+        model.eval()
+    
+        # Prepare inputs to the model
+        input_ids = tokenizer.encode(text)
+        supported_context_length = model.pos_emb.weight.shape[0]
+    
+        # 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 = model(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]