Upload lora_chat.py with huggingface_hub

lora_chat.py

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+"""
+LUNA 100M — LoRA Adapter Chat
+Loads the base SFT model, injects LoRA, and applies an adapter checkpoint.
+
+Usage:
+    python lora_chat.py --adapter Base/out/sft/rag_mcp_lora/final/adapter_model.pt
+    python lora_chat.py --adapter Base/out/sft/rag_mcp_lora/step-001554/adapter_model.pt
+    python lora_chat.py --adapter /path/to/adapter_model.pt --max_new 300 --temp 0.8
+
+    # Use the full bundle (has rank/alpha/targets embedded):
+    python lora_chat.py --adapter Base/out/sft/rag_mcp_lora/final/adapter_bundle.pt --bundle
+"""
+
+import argparse
+import math
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pathlib import Path
+
+
+# ─── Model (matches sft_train.py exactly) ─────────────────────────────────────
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len=1024):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        t = torch.arange(max_seq_len).float()
+        freqs = torch.einsum("i,j->ij", t, inv_freq)
+        emb = torch.cat([freqs, freqs], dim=-1)
+        self.register_buffer("cos_cached", emb.cos())
+        self.register_buffer("sin_cached", emb.sin())
+
+    def forward(self, seq_len):
+        return self.cos_cached[:seq_len], self.sin_cached[:seq_len]
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat([-x2, x1], dim=-1)
+
+
+def apply_rotary(x, cos, sin):
+    c = cos.unsqueeze(0).unsqueeze(0)
+    s = sin.unsqueeze(0).unsqueeze(0)
+    return x * c + rotate_half(x) * s
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, n_embd, n_head, block_size, rotary_pct=0.25):
+        super().__init__()
+        self.n_head = n_head
+        self.head_dim = n_embd // n_head
+        self.rot_dim = int(self.head_dim * rotary_pct)
+        self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=True)
+        self.c_proj = nn.Linear(n_embd, n_embd, bias=True)
+        self.rotary = RotaryEmbedding(self.rot_dim, block_size)
+
+    def forward(self, x):
+        B, T, C = x.size()
+        qkv = self.c_attn(x).reshape(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        cos, sin = self.rotary(T)
+        q = torch.cat([apply_rotary(q[..., :self.rot_dim], cos, sin), q[..., self.rot_dim:]], dim=-1)
+        k = torch.cat([apply_rotary(k[..., :self.rot_dim], cos, sin), k[..., self.rot_dim:]], dim=-1)
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        return self.c_proj(y.transpose(1, 2).contiguous().view(B, T, C))
+
+
+class MLP(nn.Module):
+    def __init__(self, n_embd):
+        super().__init__()
+        self.fc   = nn.Linear(n_embd, 4 * n_embd, bias=True)
+        self.gelu = nn.GELU()
+        self.proj = nn.Linear(4 * n_embd, n_embd, bias=True)
+
+    def forward(self, x):
+        return self.proj(self.gelu(self.fc(x)))
+
+
+class Block(nn.Module):
+    def __init__(self, n_embd, n_head, block_size):
+        super().__init__()
+        self.ln1  = nn.LayerNorm(n_embd)
+        self.attn = CausalSelfAttention(n_embd, n_head, block_size)
+        self.ln2  = nn.LayerNorm(n_embd)
+        self.mlp  = MLP(n_embd)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class LUNAModel(nn.Module):
+    def __init__(self, vocab_size=50304, block_size=1024,
+                 n_layer=10, n_embd=768, n_head=12):
+        super().__init__()
+        self.block_size = block_size
+        self.wte     = nn.Embedding(vocab_size, n_embd)
+        self.blocks  = nn.ModuleList([Block(n_embd, n_head, block_size) for _ in range(n_layer)])
+        self.ln_f    = nn.LayerNorm(n_embd)
+        self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
+        self.lm_head.weight = self.wte.weight
+
+    def forward(self, idx):
+        x = self.wte(idx)
+        for block in self.blocks:
+            x = block(x)
+        return self.lm_head(self.ln_f(x))
+
+
+# ─── LoRA ─────────────────────────────────────────────────────────────────────
+
+class LoRALinear(nn.Module):
+    def __init__(self, base_layer, rank=16, alpha=32, dropout=0.0):
+        super().__init__()
+        self.base  = base_layer
+        self.scale = alpha / max(rank, 1)
+        self.dropout = nn.Dropout(dropout)
+        self.lora_a = nn.Linear(base_layer.in_features, rank, bias=False)
+        self.lora_b = nn.Linear(rank, base_layer.out_features, bias=False)
+        nn.init.kaiming_uniform_(self.lora_a.weight, a=math.sqrt(5))
+        nn.init.zeros_(self.lora_b.weight)
+        for p in self.base.parameters():
+            p.requires_grad = False
+
+    def forward(self, x):
+        return self.base(x) + self.lora_b(self.lora_a(self.dropout(x))) * self.scale
+
+
+def inject_lora(model, target_modules, rank, alpha):
+    for module_name, module in list(model.named_modules()):
+        if not isinstance(module, nn.Linear):
+            continue
+        if not any(module_name.endswith(t) for t in target_modules):
+            continue
+        parent_name, _, child_name = module_name.rpartition(".")
+        parent = model.get_submodule(parent_name) if parent_name else model
+        wrapped = LoRALinear(module, rank=rank, alpha=alpha)
+        wrapped = wrapped.to(device=module.weight.device, dtype=module.weight.dtype)
+        setattr(parent, child_name, wrapped)
+
+
+# ─── Generation ───────────────────────────────────────────────────────────────
+
+@torch.no_grad()
+def generate(model, input_ids, max_new=200, temperature=0.7,
+             top_p=0.9, top_k=50, rep_pen=1.1, device="cpu"):
+    ids = input_ids.to(device)
+    for _ in range(max_new):
+        logits = model(ids[:, -model.block_size:])[:, -1, :]
+        if rep_pen != 1.0:
+            for tid in set(ids[0].tolist()):
+                logits[0, tid] = logits[0, tid] / rep_pen if logits[0, tid] > 0 else logits[0, tid] * rep_pen
+        if temperature < 1e-6:
+            next_tok = logits.argmax(dim=-1, keepdim=True)
+        else:
+            logits = logits / temperature
+            probs  = F.softmax(logits, dim=-1)
+            if top_k > 0:
+                kv, _ = torch.topk(probs, min(top_k, probs.size(-1)))
+                probs[probs < kv[:, [-1]]] = 0.0
+                probs /= probs.sum()
+            if top_p < 1.0:
+                sp, si = torch.sort(probs, descending=True)
+                cum = torch.cumsum(sp, dim=-1)
+                sp[cum - sp > top_p] = 0.0
+                sp /= sp.sum()
+                next_tok = si[0, torch.multinomial(sp[0], 1)]
+            else:
+                next_tok = torch.multinomial(probs[0], 1)
+        ids = torch.cat([ids, next_tok.view(1, 1)], dim=1)
+        if next_tok.item() == 0:
+            break
+    return ids[0, input_ids.shape[1]:].tolist()
+
+
+def format_prompt(instruction):
+    return f"### Instruction:\n{instruction.strip()}\n\n### Response:\n"
+
+
+# ─── Main ─────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="LUNA 100M — LoRA Adapter Chat")
+    parser.add_argument("--adapter",  required=True,
+                        help="Path to adapter_model.pt or adapter_bundle.pt")
+    parser.add_argument("--bundle",   action="store_true",
+                        help="Adapter file is an adapter_bundle.pt (has config embedded)")
+    parser.add_argument("--base_ckpt", default=None,
+                        help="Path to base model .pth (auto-downloads from HF if not set)")
+    parser.add_argument("--tok_dir",  default="Base/checkpoints/EleutherAI/pythia-160m")
+    parser.add_argument("--rank",     type=int, default=16)
+    parser.add_argument("--alpha",    type=float, default=32.0)
+    parser.add_argument("--targets",  nargs="+",
+                        default=["attn.c_attn", "attn.c_proj", "mlp.fc", "mlp.proj"])
+    parser.add_argument("--max_new",  type=int,   default=200)
+    parser.add_argument("--temp",     type=float, default=0.7)
+    parser.add_argument("--top_p",    type=float, default=0.9)
+    parser.add_argument("--top_k",    type=int,   default=50)
+    parser.add_argument("--rep_pen",  type=float, default=1.1)
+    parser.add_argument("--device",   default="auto")
+    args = parser.parse_args()
+
+    # ── device ──
+    if args.device == "auto":
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device = args.device
+
+    # ── load adapter ──
+    adapter_path = Path(args.adapter)
+    if not adapter_path.exists():
+        raise FileNotFoundError(f"Adapter not found: {adapter_path}")
+
+    bundle = torch.load(adapter_path, map_location="cpu", weights_only=True)
+
+    if args.bundle and isinstance(bundle, dict) and "lora_rank" in bundle:
+        rank    = bundle["lora_rank"]
+        alpha   = bundle["lora_alpha"]
+        targets = bundle["target_modules"]
+        adapter_state = bundle["adapter"]
+        print(f"  Bundle config: rank={rank}, alpha={alpha}, targets={targets}")
+    else:
+        rank    = args.rank
+        alpha   = args.alpha
+        targets = args.targets
+        adapter_state = bundle
+
+    # ── resolve base checkpoint ──
+    base_ckpt = args.base_ckpt
+    if base_ckpt is None:
+        default = Path("Base/out/input_models/luna_sft_v1/sft_v1/final/model.pth")
+        if default.exists():
+            base_ckpt = str(default)
+        else:
+            print("  Base checkpoint not found locally — downloading from HF...")
+            from huggingface_hub import hf_hub_download
+            default.parent.mkdir(parents=True, exist_ok=True)
+            hf_hub_download(
+                repo_id="ASTERIZER/LUNA-100M",
+                filename="sft_v1/final/model.pth",
+                local_dir=str(default.parent.parent.parent),
+                token=os.environ.get("HF_TOKEN"),
+            )
+            base_ckpt = str(default)
+
+    # ── build and load base model ──
+    print(f"  Loading base: {base_ckpt}")
+    base_state = torch.load(base_ckpt, map_location="cpu", weights_only=True)
+    if isinstance(base_state, dict) and "model" in base_state:
+        base_state = base_state["model"]
+
+    model = LUNAModel()
+    model.load_state_dict(base_state, strict=True)
+    model = model.to(device)
+
+    # ── inject LoRA and load adapter weights ──
+    inject_lora(model, target_modules=targets, rank=rank, alpha=alpha)
+    missing, unexpected = model.load_state_dict(adapter_state, strict=False)
+    if unexpected:
+        print(f"  Warning: unexpected keys in adapter: {unexpected[:5]}")
+    lora_keys = [k for k in adapter_state if "lora" in k]
+    print(f"  Loaded {len(lora_keys)} LoRA weight tensors from {adapter_path.name}")
+
+    model.eval()
+
+    # ── tokenizer ──
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained(args.tok_dir)
+
+    # ── info ──
+    print(f"\n{'='*60}")
+    print(f"  LUNA 100M + LoRA Adapter")
+    print(f"  Adapter : {adapter_path}")
+    print(f"  Device  : {device}")
+    print(f"  max_new : {args.max_new}  temp: {args.temp}  top_p: {args.top_p}")
+    print(f"{'='*60}")
+    print("  Type your instruction and press Enter. Ctrl+C to quit.\n")
+
+    # ── REPL ──
+    while True:
+        try:
+            user_input = input("You: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nBye.")
+            break
+        if not user_input:
+            continue
+
+        prompt = format_prompt(user_input)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        tokens = generate(
+            model, input_ids,
+            max_new=args.max_new,
+            temperature=args.temp,
+            top_p=args.top_p,
+            top_k=args.top_k,
+            rep_pen=args.rep_pen,
+            device=device,
+        )
+        response = tokenizer.decode(tokens, skip_special_tokens=True)
+        print(f"\nLUNA: {response.strip()}\n")
+
+
+if __name__ == "__main__":
+    main()