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chat.py

@@ -1,258 +1,253 @@
-"""
-LUNA SFT — Interactive Chat
-Loads the SFT fine-tuned model once, then lets you chat continuously.
-
-Usage:
-    python chat.py
-    python chat.py --ckpt "D:\\ASTERIZER 2026\\LUNA\\Base\\out\\sft\\model.pth"
-    python chat.py --max_new 300 --temp 0.7
-"""
-
-import sys, argparse, torch
-import torch.nn as nn
-import torch.nn.functional as F
-from pathlib import Path
-
-
-# ─── Model (must match train.py) ─────────────────────────────────────────────
-
-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))
-
-
-# ─── Generation ───────────────────────────────────────────────────────────────
-
-@torch.no_grad()
-def generate(model, input_ids, max_new=200, temperature=0.7,
-             top_p=0.9, top_k=50, repetition_penalty=1.1, device="cpu"):
-    ids = input_ids.to(device)
-    generated = []
-
-    for _ in range(max_new):
-        logits = model(ids[:, -model.block_size:])[:, -1, :]
-
-        # Repetition penalty
-        if repetition_penalty != 1.0:
-            for tok_id in set(ids[0].tolist()):
-                if logits[0, tok_id] > 0:
-                    logits[0, tok_id] /= repetition_penalty
-                else:
-                    logits[0, tok_id] *= repetition_penalty
-
-        if temperature < 1e-6:
-            next_token = logits.argmax(dim=-1, keepdim=True)
-        else:
-            logits = logits / temperature
-            probs = F.softmax(logits, dim=-1)
-
-            # Top-k
-            if top_k > 0:
-                kval = min(top_k, probs.size(-1))
-                topk_vals, _ = torch.topk(probs, kval)
-                probs[probs < topk_vals[:, [-1]]] = 0.0
-                probs /= probs.sum()
-
-            # Top-p
-            if top_p < 1.0:
-                sorted_probs, sorted_idx = torch.sort(probs, descending=True)
-                cumsum = torch.cumsum(sorted_probs, dim=-1)
-                mask = cumsum - sorted_probs > top_p
-                sorted_probs[mask] = 0.0
-                sorted_probs /= sorted_probs.sum()
-                next_token = sorted_idx[0, torch.multinomial(sorted_probs[0], 1)]
-            else:
-                next_token = torch.multinomial(probs[0], 1)
-
-        ids = torch.cat([ids, next_token.view(1, 1)], dim=1)
-        generated.append(next_token.item())
-
-        if next_token.item() == 50276:  # EOS
-            break
-
-    return generated
-
-
-# ─── Alpaca prompt template ───────────────────────────────────────────────────
-
-ALPACA_TEMPLATE = (
-    "Below is an instruction that describes a task. "
-    "Write a response that appropriately completes the request.\n\n"
-    "### Instruction:\n{instruction}\n\n### Response:\n"
-)
-
-ALPACA_TEMPLATE_INPUT = (
-    "Below is an instruction that describes a task, paired with further context. "
-    "Write a response that appropriately completes the request.\n\n"
-    "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
-)
-
-
-def format_prompt(instruction, context=""):
-    if context.strip():
-        return ALPACA_TEMPLATE_INPUT.format(instruction=instruction, input=context)
-    return ALPACA_TEMPLATE.format(instruction=instruction)
-
-
-# ─── Main ─────────────────────────────────────────────────────────────────────
-
-def main():
-    parser = argparse.ArgumentParser(description="LUNA SFT — Interactive Chat")
-    parser.add_argument("--ckpt", default=r"D:\ASTERIZER 2026\LUNA\Base\out\sft\model.pth")
-    parser.add_argument("--tok_dir", default="Base/checkpoints/EleutherAI/pythia-160m")
-    parser.add_argument("--max_new", type=int, default=256)
-    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 = "cuda" if args.device == "auto" and torch.cuda.is_available() else args.device
-    if device == "auto":
-        device = "cpu"
-    print(f"\nDevice: {device}")
-
-    # Load model
-    print(f"Loading: {args.ckpt}")
-    ckpt = torch.load(args.ckpt, map_location="cpu", weights_only=True)
-    state = ckpt["model"] if "model" in ckpt else ckpt
-    model = LUNAModel()
-    model.load_state_dict(state, strict=True)
-    model = model.to(device).eval()
-    params = sum(p.numel() for p in model.parameters())
-    print(f"  Model loaded: {params:,} parameters")
-
-    # Load tokenizer
-    from transformers import AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained(args.tok_dir)
-    print(f"  Tokenizer: {args.tok_dir} (vocab {tokenizer.vocab_size})")
-
-    # Chat loop
-    print(f"\n{'='*60}")
-    print("  LUNA — Interactive Chat")
-    print(f"  max_new={args.max_new}  temp={args.temp}  top_p={args.top_p}  top_k={args.top_k}")
-    print(f"  Type your message and press Enter. Type 'quit' to exit.")
-    print(f"{'='*60}\n")
-
-    while True:
-        try:
-            user_input = input("You: ").strip()
-        except (EOFError, KeyboardInterrupt):
-            print("\nBye!")
-            break
-
-        if not user_input:
-            continue
-        if user_input.lower() in ("quit", "exit", "q"):
-            print("Bye!")
-            break
-
-        prompt = format_prompt(user_input)
-        ids = tokenizer.encode(prompt, return_tensors="pt")
-
-        tokens = generate(
-            model, ids,
-            max_new=args.max_new,
-            temperature=args.temp,
-            top_p=args.top_p,
-            top_k=args.top_k,
-            repetition_penalty=args.rep_pen,
-            device=device,
-        )
-
-        response = tokenizer.decode(tokens, skip_special_tokens=True).strip()
-        # Cut at any trailing ### if model generates next template
-        if "### " in response:
-            response = response.split("### ")[0].strip()
-
-        print(f"\nLUNA: {response}\n")
-
-
-if __name__ == "__main__":
-    main()
+"""
+LUNA SFT — Interactive Chat
+Loads the SFT fine-tuned model once, then lets you chat continuously.
+
+Usage:
+    python chat.py
+    python chat.py --ckpt "D:\\ASTERIZER 2026\\LUNA\\Base\\out\\sft\\model.pth"
+    python chat.py --max_new 300 --temp 0.7
+"""
+
+import sys, argparse, torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pathlib import Path
+
+
+# ─── Model (must match train.py) ─────────────────────────────────────────────
+
+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))
+
+
+# ─── Generation ───────────────────────────────────────────────────────────────
+
+@torch.no_grad()
+def generate(model, input_ids, max_new=200, temperature=0.7,
+             top_p=0.9, top_k=50, repetition_penalty=1.1, eos_token_id=0, device="cpu"):
+    ids = input_ids.to(device)
+    generated = []
+
+    for _ in range(max_new):
+        logits = model(ids[:, -model.block_size:])[:, -1, :]
+
+        # Repetition penalty
+        if repetition_penalty != 1.0:
+            for tok_id in set(ids[0].tolist()):
+                if logits[0, tok_id] > 0:
+                    logits[0, tok_id] /= repetition_penalty
+                else:
+                    logits[0, tok_id] *= repetition_penalty
+
+        if temperature < 1e-6:
+            next_token = logits.argmax(dim=-1, keepdim=True)
+        else:
+            logits = logits / temperature
+            probs = F.softmax(logits, dim=-1)
+
+            # Top-k
+            if top_k > 0:
+                kval = min(top_k, probs.size(-1))
+                topk_vals, _ = torch.topk(probs, kval)
+                probs[probs < topk_vals[:, [-1]]] = 0.0
+                probs /= probs.sum()
+
+            # Top-p
+            if top_p < 1.0:
+                sorted_probs, sorted_idx = torch.sort(probs, descending=True)
+                cumsum = torch.cumsum(sorted_probs, dim=-1)
+                mask = cumsum - sorted_probs > top_p
+                sorted_probs[mask] = 0.0
+                sorted_probs /= sorted_probs.sum()
+                next_token = sorted_idx[0, torch.multinomial(sorted_probs[0], 1)]
+            else:
+                next_token = torch.multinomial(probs[0], 1)
+
+        ids = torch.cat([ids, next_token.view(1, 1)], dim=1)
+        generated.append(next_token.item())
+
+        if next_token.item() == eos_token_id:
+            break
+
+    return generated
+
+
+# ─── Alpaca prompt template ───────────────────────────────────────────────────
+
+# Prompt format matching sft_train.py exactly (no preamble)
+def format_prompt(instruction, context=""):
+    inst = instruction.strip()
+    ctx = context.strip()
+    if inst and ctx:
+        return f"### Instruction:\n{inst}\n\n### Input:\n{ctx}\n\n### Response:\n"
+    elif inst:
+        return f"### Instruction:\n{inst}\n\n### Response:\n"
+    else:
+        return f"### Input:\n{ctx}\n\n### Response:\n"
+
+
+# ─── Main ─────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="LUNA SFT — Interactive Chat")
+    parser.add_argument("--ckpt", default=r"D:\ASTERIZER 2026\LUNA\Base\out\sft\model.pth")
+    parser.add_argument("--tok_dir", default="Base/checkpoints/EleutherAI/pythia-160m")
+    parser.add_argument("--max_new", type=int, default=150)
+    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=40)
+    parser.add_argument("--rep_pen", type=float, default=1.0)
+    parser.add_argument("--device", default="auto")
+    args = parser.parse_args()
+
+    device = "cuda" if args.device == "auto" and torch.cuda.is_available() else args.device
+    if device == "auto":
+        device = "cpu"
+    print(f"\nDevice: {device}")
+
+    # Load model
+    print(f"Loading: {args.ckpt}")
+    ckpt = torch.load(args.ckpt, map_location="cpu", weights_only=True)
+    state = ckpt["model"] if "model" in ckpt else ckpt
+    model = LUNAModel()
+    model.load_state_dict(state, strict=True)
+    model = model.to(device).eval()
+    params = sum(p.numel() for p in model.parameters())
+    print(f"  Model loaded: {params:,} parameters")
+
+    # Load tokenizer
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained(args.tok_dir)
+    print(f"  Tokenizer: {args.tok_dir} (vocab {tokenizer.vocab_size})")
+    print(f"  EOS: {tokenizer.eos_token} (id {tokenizer.eos_token_id})")
+
+    # Chat loop
+    print(f"\n{'='*60}")
+    print("  LUNA — Interactive Chat")
+    print(f"  max_new={args.max_new}  temp={args.temp}  top_p={args.top_p}  top_k={args.top_k}")
+    print(f"  Type your message and press Enter. Type 'quit' to exit.")
+    print(f"{'='*60}\n")
+
+    while True:
+        try:
+            user_input = input("You: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nBye!")
+            break
+
+        if not user_input:
+            continue
+        if user_input.lower() in ("quit", "exit", "q"):
+            print("Bye!")
+            break
+
+        prompt = format_prompt(user_input)
+        ids = tokenizer.encode(prompt, return_tensors="pt")
+
+        tokens = generate(
+            model, ids,
+            max_new=args.max_new,
+            temperature=args.temp,
+            top_p=args.top_p,
+            top_k=args.top_k,
+            repetition_penalty=args.rep_pen,
+            eos_token_id=tokenizer.eos_token_id or 0,
+            device=device,
+        )
+
+        response = tokenizer.decode(tokens, skip_special_tokens=True).strip()
+        # Cut at any trailing ### if model generates next template
+        if "### " in response:
+            response = response.split("### ")[0].strip()
+
+        print(f"\nLUNA: {response}\n")
+
+
+if __name__ == "__main__":
+    main()