Add model implementation files for trust_remote_code support

configuration_nanogpt.py

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+"""NanoGPT model configuration"""
+
+from transformers import PretrainedConfig
+
+class NanoGPTConfig(PretrainedConfig):
+    model_type = "nanogpt"
+    
+    def __init__(
+        self,
+        vocab_size=50257,
+        n_embd=768,
+        n_head=12,
+        n_layer=12,
+        block_size=1024,
+        bias=True,
+        dropout=0.0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_head = n_head
+        self.n_layer = n_layer
+        self.block_size = block_size
+        self.bias = bias
+        self.dropout = dropout

modeling_nanogpt.py

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+"""NanoGPT model implementation"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from .configuration_nanogpt import NanoGPTConfig
+
+class ExactNanoGPTAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        if not self.flash:
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                .view(1, 1, config.block_size, config.block_size))
+    
+    def forward(self, x):
+        B, T, C = x.size()
+        
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        
+        if self.flash:
+            y = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, 
+                attn_mask=None, 
+                dropout_p=self.dropout if self.training else 0, 
+                is_causal=True
+            )
+        else:
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v
+        
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+class ExactNanoGPTMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.gelu = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+    
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+class ExactNanoGPTBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias)
+        self.attn = ExactNanoGPTAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias)
+        self.mlp = ExactNanoGPTMLP(config)
+    
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+class NanoGPTModel(PreTrainedModel):
+    config_class = NanoGPTConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            drop = nn.Dropout(config.dropout),
+            h = nn.ModuleList([ExactNanoGPTBlock(config) for _ in range(config.n_layer)]),
+            ln_f = nn.LayerNorm(config.n_embd, bias=config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        self.post_init()
+    
+    def forward(self, input_ids, attention_mask=None, **kwargs):
+        device = input_ids.device
+        b, t = input_ids.size()
+        assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+        
+        tok_emb = self.transformer.wte(input_ids)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        
+        logits = self.lm_head(x)
+        
+        return CausalLMOutputWithCrossAttentions(logits=logits)
+    
+    def generate(self, input_ids, max_length=None, max_new_tokens=None, temperature=1.0, top_k=None, do_sample=True, top_p=None, **kwargs):
+        if max_new_tokens is None:
+            max_new_tokens = max_length - input_ids.shape[1] if max_length else 50
+        
+        for _ in range(max_new_tokens):
+            idx_cond = input_ids if input_ids.size(1) <= self.config.block_size else input_ids[:, -self.config.block_size:]
+            
+            with torch.no_grad():
+                outputs = self(idx_cond)
+                logits = outputs.logits
+            
+            logits = logits[:, -1, :] / temperature
+            
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            
+            if top_p is not None:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                sorted_indices_to_remove[..., 0] = 0
+                
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+            
+            probs = F.softmax(logits, dim=-1)
+            
+            if do_sample:
+                idx_next = torch.multinomial(probs, num_samples=1)
+            else:
+                _, idx_next = torch.topk(probs, k=1, dim=-1)
+            
+            input_ids = torch.cat((input_ids, idx_next), dim=1)
+        
+        return input_ids