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model.py

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+"""GPT char-level minúsculo (estilo nanoGPT), pra treinar em CPU.
+Não é o modelo final do projeto — é o brinquedo que prova a esteira toda de graça.
+O modelo "de verdade" (BPE, maior) roda na GPU alugada depois."""
+from __future__ import annotations
+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+@dataclass
+class GPTConfig:
+    vocab_size: int
+    block_size: int = 256
+    n_layer: int = 6
+    n_head: int = 6
+    n_embd: int = 192
+    dropout: float = 0.1
+
+
+class CausalSelfAttention(nn.Module):
+    """Atencao causal com SDPA e QKV fundido.
+
+    Substitui nn.MultiheadAttention, que desliga o fast-path assim que recebe um
+    attn_mask explicito e cai no caminho lento — parte do motivo da MFU ter ficado em
+    ~11% na H200. F.scaled_dot_product_attention com is_causal=True usa o kernel
+    fundido e dispensa materializar a mascara TxT.
+
+    Os PARAMETROS mantem de proposito os nomes de nn.MultiheadAttention
+    (in_proj_weight, in_proj_bias, out_proj.weight, out_proj.bias): o state_dict fica
+    identico, entao checkpoint antigo — inclusive o de 219M do run pocket-h200-v2 —
+    carrega sem conversao nenhuma.
+
+    Bonus pra fase 3: o export pra NPU ja exigia SDPA decomposto no lugar do
+    nn.MultiheadAttention, entao treino e export passam a usar o mesmo caminho.
+    """
+
+    def __init__(self, c: GPTConfig):
+        super().__init__()
+        assert c.n_embd % c.n_head == 0
+        self.n_head = c.n_head
+        self.n_embd = c.n_embd
+        self.p_drop = c.dropout
+        self.in_proj_weight = nn.Parameter(torch.empty(3 * c.n_embd, c.n_embd))
+        self.in_proj_bias = nn.Parameter(torch.zeros(3 * c.n_embd))
+        self.out_proj = nn.Linear(c.n_embd, c.n_embd)
+        nn.init.xavier_uniform_(self.in_proj_weight)   # mesmo init do MultiheadAttention
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = F.linear(x, self.in_proj_weight, self.in_proj_bias)
+        q, k, v = qkv.chunk(3, dim=-1)
+        hs = C // self.n_head
+        q = q.view(B, T, self.n_head, hs).transpose(1, 2)
+        k = k.view(B, T, self.n_head, hs).transpose(1, 2)
+        v = v.view(B, T, self.n_head, hs).transpose(1, 2)
+        y = F.scaled_dot_product_attention(
+            q, k, v, is_causal=True,
+            dropout_p=self.p_drop if self.training else 0.0)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        return self.out_proj(y)
+
+
+class Block(nn.Module):
+    def __init__(self, c: GPTConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(c.n_embd)
+        self.attn = CausalSelfAttention(c)
+        self.ln2 = nn.LayerNorm(c.n_embd)
+        self.mlp = nn.Sequential(
+            nn.Linear(c.n_embd, 4 * c.n_embd),
+            nn.GELU(),
+            nn.Linear(4 * c.n_embd, c.n_embd),
+            nn.Dropout(c.dropout),
+        )
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, c: GPTConfig):
+        super().__init__()
+        self.c = c
+        self.tok = nn.Embedding(c.vocab_size, c.n_embd)
+        self.pos = nn.Embedding(c.block_size, c.n_embd)
+        self.drop = nn.Dropout(c.dropout)
+        self.blocks = nn.ModuleList([Block(c) for _ in range(c.n_layer)])
+        self.lnf = nn.LayerNorm(c.n_embd)
+        self.head = nn.Linear(c.n_embd, c.vocab_size, bias=False)
+        self.tok.weight = self.head.weight  # weight tying
+        self.apply(self._init)
+
+    def _init(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def num_params(self) -> int:
+        return sum(p.numel() for p in self.parameters())
+
+    def forward(self, idx, targets=None):
+        T = idx.size(1)
+        pos = torch.arange(T, device=idx.device)
+        x = self.drop(self.tok(idx) + self.pos(pos))
+        for b in self.blocks:
+            x = b(x)
+        x = self.lnf(x)
+        logits = self.head(x)
+        loss = None
+        if targets is not None:
+            # reshape, nao view: o chamador costuma passar uma FATIA (lote[:, 1:]),
+            # que nao e contigua, e view falha nesse caso
+            loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)),
+                                   targets.reshape(-1))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=0.8, top_k=40):
+        self.eval()
+        for _ in range(max_new_tokens):
+            cond = idx[:, -self.c.block_size:]
+            logits, _ = self(cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float("inf")
+            probs = F.softmax(logits, dim=-1)
+            nxt = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, nxt), dim=1)
+        return idx