Update model.py

model.py

@@ -1,163 +1,158 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from dataclasses import dataclass
-import numpy as np
-from tqdm.auto import tqdm
-from contextlib import nullcontext
-import os
-
-class LayerNorm(nn.Module):
-    def __init__(self, ndim, bias):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(ndim))
-        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
-    def forward(self, x):
-        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
-
-class CausalSelfAttention(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.flash = hasattr(F, '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 = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.attn_dropout.p if self.training else 0.0, 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 MLP(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):
-        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
-
-class Block(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.ln1 = LayerNorm(config.n_embd, config.bias)
-        self.attn = CausalSelfAttention(config)
-        self.ln2 = LayerNorm(config.n_embd, config.bias)
-        self.mlp = MLP(config)
-    def forward(self, x):
-        x = x + self.attn(self.ln1(x))
-        x = x + self.mlp(self.ln2(x))
-        return x
-
-@dataclass
-class GPTConfig:
-    block_size: int
-    vocab_size: int
-    n_layer: int
-    n_head: int
-    n_embd: int
-    dropout: float = 0.0
-    bias: bool = True
-
-class GPT(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        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([Block(config) for _ in range(config.n_layer)]),
-            ln_f=LayerNorm(config.n_embd, config.bias),
-        ))
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        self.transformer.wte.weight = self.lm_head.weight  # weight tying
-
-        self.apply(self._init_weights)
-        for pn, p in self.named_parameters():
-            if pn.endswith('c_proj.weight'):
-                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
-
-    def _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-            if module.bias is not None:
-                nn.init.zeros_(module.bias)
-        elif isinstance(module, nn.Embedding):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-
-    def forward(self, idx, targets=None):
-        device = idx.device
-        b, t = idx.size()
-        assert t <= self.config.block_size
-        pos = torch.arange(0, t, dtype=torch.long, device=device)
-
-        tok_emb = self.transformer.wte(idx)
-        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)
-
-        if targets is not None:
-            logits = self.lm_head(x)
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
-            return logits, loss
-        else:
-            logits = self.lm_head(x[:, [-1], :])
-            return logits, None
-
-    @torch.no_grad()
-    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
-        """
-        Generate tokens given a conditioning sequence.
-        idx: Tensor of shape (B, T)
-        """
-        for _ in range(max_new_tokens):
-            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
-            logits, _ = self(idx_cond)
-            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')
-            probs = F.softmax(logits, dim=-1)
-            idx_next = torch.multinomial(probs, num_samples=1)
-            idx = torch.cat((idx, idx_next), dim=1)
-        return idx
-
-
-config = GPTConfig(
-    vocab_size=50257,     # use the tokenizer's vocab size
-    block_size=128,       # or whatever context size you're training with
-    n_layer=6,
-    n_head=6,
-    n_embd=384,
-    dropout=0.1,
-    bias=True
-)
-
-model = GPT(config)
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from transformers import PreTrainedModel, PretrainedConfig
+
+# ------------------------------
+# Hugging Face-Compatible Config
+# ------------------------------
+class GPTConfig(PretrainedConfig):
+    model_type = "custom-gpt"
+
+    def __init__(self, vocab_size, block_size, n_layer, n_head, n_embd, dropout=0.0, bias=True, **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.block_size = block_size
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.dropout = dropout
+        self.bias = bias
+
+# ------------------------------
+# GPT Model with HF Integration
+# ------------------------------
+class GPT(PreTrainedModel):
+    config_class = GPTConfig
+
+    def __init__(self, config):
+        super().__init__(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([Block(config) for _ in range(config.n_layer)]),
+            ln_f=LayerNorm(config.n_embd, config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight  # weight tying
+
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.config.block_size
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+
+        tok_emb = self.transformer.wte(idx)
+        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)
+
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+            return logits, loss
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            return logits, None
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            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')
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+# ------------------------------
+# Building Blocks
+# ------------------------------
+class LayerNorm(nn.Module):
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+    def forward(self, x):
+        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
+
+class CausalSelfAttention(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.flash = hasattr(F, '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 = F.scaled_dot_product_attention(q, k, v, attn_mask=None,
+                                               dropout_p=self.attn_dropout.p if self.training else 0.0,
+                                               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 MLP(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):
+        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = LayerNorm(config.n_embd, config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln2 = LayerNorm(config.n_embd, config.bias)
+        self.mlp = MLP(config)
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+