| """
|
| ArmGPT Model - A modern GPT with RMSNorm, SwiGLU, and RoPE.
|
|
|
| Architecture:
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| 1. Token Embedding: convert token IDs to vectors
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| 2. RoPE: rotary position embeddings (no learned position table)
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| 3. Transformer Blocks: RMSNorm + Attention + SwiGLU MLP
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| 4. Output Head: predict the next token
|
| """
|
|
|
| import math
|
| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
|
|
| class RMSNorm(nn.Module):
|
| """Root Mean Square Layer Normalization — faster than LayerNorm, no bias."""
|
|
|
| def __init__(self, dim, eps=1e-6):
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| super().__init__()
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| self.eps = eps
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| self.weight = nn.Parameter(torch.ones(dim))
|
|
|
| def forward(self, x):
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| rms = torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
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| return (x.float() * rms).type_as(x) * self.weight
|
|
|
|
|
| def precompute_rope(dim, max_seq_len, theta=10000.0):
|
| """Precompute rotary position embedding frequencies."""
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| freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
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| t = torch.arange(max_seq_len).float()
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| freqs = torch.outer(t, freqs)
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| cos = freqs.cos()
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| sin = freqs.sin()
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| return cos, sin
|
|
|
|
|
| def apply_rope(x, cos, sin):
|
| """Apply rotary position embeddings to query/key tensors."""
|
| B, n_head, T, head_dim = x.shape
|
| cos = cos[:T].unsqueeze(0).unsqueeze(0)
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| sin = sin[:T].unsqueeze(0).unsqueeze(0)
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|
|
| x1 = x[..., :head_dim // 2]
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| x2 = x[..., head_dim // 2:]
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| return torch.cat([x1 * cos - x2 * sin, x2 * cos + x1 * sin], dim=-1)
|
|
|
|
|
| class CausalSelfAttention(nn.Module):
|
| """Self-attention with RoPE (no causal mask buffer needed — using F.scaled_dot_product_attention)."""
|
|
|
| def __init__(self, n_embd, n_head, block_size, dropout):
|
| super().__init__()
|
| assert n_embd % n_head == 0
|
| self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=False)
|
| self.c_proj = nn.Linear(n_embd, n_embd, bias=False)
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| self.n_head = n_head
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| self.n_embd = n_embd
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| self.head_dim = n_embd // n_head
|
| self.dropout = dropout
|
|
|
| cos, sin = precompute_rope(self.head_dim, block_size)
|
| self.register_buffer("rope_cos", cos)
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| self.register_buffer("rope_sin", sin)
|
|
|
| def forward(self, x):
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| B, T, C = x.size()
|
| qkv = self.c_attn(x)
|
| q, k, v = qkv.split(self.n_embd, dim=2)
|
|
|
| q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
|
| k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
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| v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
|
|
|
|
|
| q = apply_rope(q, self.rope_cos, self.rope_sin)
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| k = apply_rope(k, self.rope_cos, self.rope_sin)
|
|
|
|
|
| y = F.scaled_dot_product_attention(
|
| q, k, v,
|
| is_causal=True,
|
| dropout_p=self.dropout if self.training else 0.0,
|
| )
|
|
|
| y = y.transpose(1, 2).contiguous().view(B, T, C)
|
| y = self.c_proj(y)
|
| return y
|
|
|
|
|
| class SwiGLUMLP(nn.Module):
|
| """SwiGLU feed-forward network — better than GELU, used by LLaMA/Mistral."""
|
|
|
| def __init__(self, n_embd, dropout):
|
| super().__init__()
|
|
|
| hidden = int(8 / 3 * n_embd)
|
| hidden = ((hidden + 63) // 64) * 64
|
| self.w1 = nn.Linear(n_embd, hidden, bias=False)
|
| self.w2 = nn.Linear(hidden, n_embd, bias=False)
|
| self.w3 = nn.Linear(n_embd, hidden, bias=False)
|
| self.dropout = nn.Dropout(dropout)
|
|
|
| def forward(self, x):
|
| return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
|
|
|
|
|
| class Block(nn.Module):
|
| """Transformer block: RMSNorm + Attention + SwiGLU MLP."""
|
|
|
| def __init__(self, n_embd, n_head, block_size, dropout):
|
| super().__init__()
|
| self.ln_1 = RMSNorm(n_embd)
|
| self.attn = CausalSelfAttention(n_embd, n_head, block_size, dropout)
|
| self.ln_2 = RMSNorm(n_embd)
|
| self.mlp = SwiGLUMLP(n_embd, dropout)
|
|
|
| def forward(self, x):
|
| x = x + self.attn(self.ln_1(x))
|
| x = x + self.mlp(self.ln_2(x))
|
| return x
|
|
|
|
|
| class GPT(nn.Module):
|
| """GPT language model with RMSNorm, RoPE, and SwiGLU."""
|
|
|
| def __init__(self, vocab_size, n_layer, n_head, n_embd, block_size, dropout):
|
| super().__init__()
|
| self.block_size = block_size
|
|
|
| self.transformer = nn.ModuleDict(dict(
|
| wte=nn.Embedding(vocab_size, n_embd),
|
| drop=nn.Dropout(dropout),
|
| blocks=nn.ModuleList([
|
| Block(n_embd, n_head, block_size, dropout)
|
| for _ in range(n_layer)
|
| ]),
|
| ln_f=RMSNorm(n_embd),
|
| ))
|
| self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
|
| self.transformer.wte.weight = self.lm_head.weight
|
|
|
| self.apply(self._init_weights)
|
| n_params = sum(p.numel() for p in self.parameters())
|
| print(f"GPT model initialized: {n_params:,} parameters")
|
|
|
| def _init_weights(self, module):
|
| if isinstance(module, nn.Linear):
|
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
|
| if module.bias is not None:
|
| torch.nn.init.zeros_(module.bias)
|
| elif isinstance(module, nn.Embedding):
|
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
|
|
|
| def forward(self, idx, targets=None):
|
| B, T = idx.size()
|
| assert T <= self.block_size, f"Sequence length {T} exceeds block_size {self.block_size}"
|
|
|
|
|
| x = self.transformer.drop(self.transformer.wte(idx))
|
|
|
| for block in self.transformer.blocks:
|
| x = block(x)
|
|
|
| x = self.transformer.ln_f(x)
|
| logits = self.lm_head(x)
|
|
|
| loss = None
|
| if targets is not None:
|
| loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
|
|
|
| return logits, loss
|
|
|
| @torch.no_grad()
|
| def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None,
|
| stop_tokens=None, repetition_penalty=1.0):
|
| """Generate tokens autoregressively.
|
|
|
| Args:
|
| repetition_penalty: 1.0 = no penalty (off). >1.0 discourages
|
| repeating tokens already in the context (CTRL-style penalty).
|
| Typical values: 1.1–1.3. Helps small LMs escape repetition loops.
|
| """
|
| for _ in range(max_new_tokens):
|
| idx_cond = idx[:, -self.block_size:]
|
| logits, _ = self(idx_cond)
|
| logits = logits[:, -1, :]
|
|
|
|
|
|
|
|
|
| if repetition_penalty != 1.0:
|
| seen = torch.unique(idx_cond)
|
| seen_logits = logits[:, seen]
|
| seen_logits = torch.where(
|
| seen_logits > 0,
|
| seen_logits / repetition_penalty,
|
| seen_logits * repetition_penalty,
|
| )
|
| logits[:, seen] = seen_logits
|
|
|
| logits = logits / 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)
|
| if stop_tokens and idx_next.item() in stop_tokens:
|
| break
|
| return idx
|
|
|
