Create beeper_model.py

beeper_model.py

@@ -0,0 +1,271 @@
+"""
+beeper_model.py - Core model module for Beeper
+Extracted from the training code for use in inference/apps
+"""
+
+import os
+import re
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional
+from safetensors.torch import load_file as load_safetensors
+
+# =========================================================================================
+# Model Components
+# =========================================================================================
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, n_heads: int, attn_dropout: float = 0.0):
+        super().__init__()
+        assert dim % n_heads == 0
+        self.nh = n_heads
+        self.hd = dim // n_heads
+        self.qkv = nn.Linear(dim, 3 * dim, bias=False)
+        self.proj = nn.Linear(dim, dim, bias=False)
+        self.attn_dropout = attn_dropout
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = self.qkv(x)
+        q, k, v = qkv.chunk(3, dim=-1)
+        q = q.view(B, T, self.nh, self.hd).transpose(1, 2)
+        k = k.view(B, T, self.nh, self.hd).transpose(1, 2)
+        v = v.view(B, T, self.nh, self.hd).transpose(1, 2)
+
+        # Use scaled_dot_product_attention when available
+        y = F.scaled_dot_product_attention(
+            q, k, v,
+            is_causal=True,
+            dropout_p=self.attn_dropout if self.training else 0.0,
+        )
+        
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_ratio=4.0, dropout=0.1):
+        super().__init__()
+        hidden = int(dim * mlp_ratio)
+        self.fc1 = nn.Linear(dim, hidden)
+        self.fc2 = nn.Linear(hidden, dim)
+        self.drop = nn.Dropout(dropout)
+    
+    def forward(self, x):
+        x = self.fc1(x)
+        x = F.gelu(x, approximate="tanh")
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class BeeperRoseGPT(nn.Module):
+    def __init__(self, cfg: dict):
+        super().__init__()
+        V = cfg.get("vocab_size", 8192)
+        D = cfg.get("dim", 512)
+        Ctx = cfg.get("context", 512)
+        H = cfg.get("n_heads", 8)
+        L = cfg.get("n_layers", 6)
+        MR = cfg.get("mlp_ratio", 4.0)
+        RD = cfg.get("resid_dropout", 0.1)
+        AD = cfg.get("dropout", 0.0)
+
+        self.vocab_size = V
+        self.context = Ctx
+        
+        # Core transformer components
+        self.token_emb = nn.Embedding(V, D)
+        self.pos_emb = nn.Parameter(torch.zeros(1, Ctx, D))
+        self.drop = nn.Dropout(RD)
+
+        self.blocks = nn.ModuleList([
+            nn.ModuleDict({
+                "norm1": nn.LayerNorm(D),
+                "attn": CausalSelfAttention(D, H, attn_dropout=AD),
+                "norm2": nn.LayerNorm(D),
+                "mlp": MLP(D, mlp_ratio=MR, dropout=RD),
+            }) for _ in range(L)
+        ])
+        
+        self.norm = nn.LayerNorm(D)
+        self.lm_head = nn.Linear(D, V, bias=False)
+        
+        # Weight tying
+        self.lm_head.weight = self.token_emb.weight
+
+        # Rose components (for compatibility, may not be used in inference)
+        self.rose_proj = nn.Linear(D, D, bias=False)
+        self.rose_anchors = nn.Parameter(torch.randn(3, D) / (D**0.5))
+
+        # Pentachora placeholders (not needed for inference but for weight compatibility)
+        self.register_buffer("pent_inited", torch.tensor(0, dtype=torch.uint8), persistent=False)
+        self.penta_coarse = None
+        self.penta_medium = None
+        self.penta_fine = None
+
+        self.apply(self._init)
+        self.grad_checkpoint = False
+
+    @staticmethod
+    def _init(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 _block_forward(self, blk, x):
+        x = x + blk["attn"](blk["norm1"](x))
+        x = x + blk["mlp"](blk["norm2"](x))
+        return x
+
+    def backbone(self, idx):
+        B, T = idx.shape
+        x = self.token_emb(idx) + self.pos_emb[:, :T, :]
+        x = self.drop(x)
+        
+        for blk in self.blocks:
+            x = self._block_forward(blk, x)
+        
+        return self.norm(x)
+
+    def forward(self, idx):
+        h = self.backbone(idx)
+        return self.lm_head(h)
+
+    def hidden_states(self, idx):
+        return self.backbone(idx)
+
+    def load_state_dict(self, state_dict, strict=False):
+        """Custom load that handles pentachora bank initialization gracefully"""
+        # Clean state dict keys
+        cleaned = {}
+        for k, v in state_dict.items():
+            if k.startswith("_orig_mod."):
+                k = k[10:]
+            if k.startswith("module."):
+                k = k[7:]
+            cleaned[k] = v
+        
+        # Initialize pentachora if present in checkpoint
+        if "penta_coarse" in cleaned:
+            self.penta_coarse = nn.Parameter(cleaned["penta_coarse"])
+        if "penta_medium" in cleaned:
+            self.penta_medium = nn.Parameter(cleaned["penta_medium"])
+        if "penta_fine" in cleaned:
+            self.penta_fine = nn.Parameter(cleaned["penta_fine"])
+        
+        return super().load_state_dict(cleaned, strict=strict)
+
+
+# =========================================================================================
+# Generation
+# =========================================================================================
+
+def _detokenize(text: str) -> str:
+    """Clean up tokenization artifacts"""
+    text = re.sub(r"\s+([,.;:!?%])", r"\1", text)
+    text = re.sub(r"\s+([\)\]\}])", r"\1", text)
+    text = re.sub(r"([\(\[\{])\s+", r"\1", text)
+    return text
+
+
+@torch.no_grad()
+def generate(
+    model: BeeperRoseGPT,
+    tok,  # Tokenizer
+    cfg: dict,
+    prompt: str,
+    max_new_tokens: int = 120,
+    temperature: float = None,
+    top_k: int = None,
+    top_p: float = None,
+    repetition_penalty: float = None,
+    presence_penalty: float = None,
+    frequency_penalty: float = None,
+    device: Optional[torch.device] = None,
+    detokenize: bool = True
+) -> str:
+    """
+    Generate text from Beeper model with various sampling strategies.
+    """
+    # Use defaults from config if not specified
+    temperature = temperature if temperature is not None else cfg.get("temperature", 0.9)
+    top_k = top_k if top_k is not None else cfg.get("top_k", 40)
+    top_p = top_p if top_p is not None else cfg.get("top_p", 0.9)
+    repetition_penalty = repetition_penalty if repetition_penalty is not None else cfg.get("repetition_penalty", 1.1)
+    presence_penalty = presence_penalty if presence_penalty is not None else cfg.get("presence_penalty", 0.6)
+    frequency_penalty = frequency_penalty if frequency_penalty is not None else cfg.get("frequency_penalty", 0.0)
+
+    device = device or next(model.parameters()).device
+    model.eval()
+    
+    # Encode prompt
+    ids = tok.encode(prompt).ids
+    x = torch.tensor([ids], dtype=torch.long, device=device)
+    
+    # Track token frequencies for penalties
+    vocab_size = cfg.get("vocab_size", 8192)
+    counts = torch.zeros(vocab_size, dtype=torch.int32, device=device)
+    for t in ids:
+        if 0 <= t < vocab_size:
+            counts[t] += 1
+
+    # Generate tokens
+    for _ in range(max_new_tokens):
+        # Get logits for next token
+        context_window = cfg.get("context", 512)
+        logits = model(x[:, -context_window:])
+        logits = logits[:, -1, :]
+
+        # Apply repetition penalty
+        if repetition_penalty and repetition_penalty != 1.0:
+            mask = counts > 0
+            if mask.any():
+                pos = logits[:, mask] > 0
+                logits[:, mask][pos] /= repetition_penalty
+                logits[:, mask][~pos] *= repetition_penalty
+
+        # Apply presence and frequency penalties
+        if presence_penalty or frequency_penalty:
+            pen = counts.float() * (frequency_penalty or 0.0) + (counts > 0).float() * (presence_penalty or 0.0)
+            logits = logits - pen.unsqueeze(0)
+
+        # Temperature scaling
+        logits = logits / max(1e-8, temperature)
+
+        # Top-k filtering
+        if top_k and top_k > 0:
+            k = min(top_k, logits.size(-1))
+            v, ix = torch.topk(logits, k, dim=-1)
+            filt = torch.full_like(logits, float("-inf"))
+            logits = filt.scatter_(-1, ix, v)
+
+        # Top-p (nucleus) filtering
+        if top_p and top_p < 1.0:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            probs = F.softmax(sorted_logits, dim=-1)
+            cumulative_probs = torch.cumsum(probs, dim=-1)
+            
+            # Find cutoff
+            cutoff_idx = (cumulative_probs > top_p).float().argmax(dim=-1)
+            mask = torch.arange(logits.size(-1), device=device).unsqueeze(0) > cutoff_idx.unsqueeze(-1)
+            sorted_logits = sorted_logits.masked_fill(mask, float("-inf"))
+            logits = torch.full_like(logits, float("-inf")).scatter(-1, sorted_indices, sorted_logits)
+
+        # Sample next token
+        probs = F.softmax(logits, dim=-1)
+        next_id = torch.multinomial(probs, num_samples=1)
+        
+        # Append to sequence
+        x = torch.cat([x, next_id], dim=1)
+        counts[next_id.item()] += 1
+
+    # Decode output
+    output = tok.decode(x[0].tolist())
+    return _detokenize(output) if detokenize else output