Create model.py

model.py

@@ -0,0 +1,192 @@
+"""
+model.py -- standalone architecture definition for GTM-3-base.
+
+This is a plain PyTorch nanoGPT-style GPT model with RoPE (rotary position
+embeddings), NOT a HuggingFace `transformers` AutoModel. To load the
+released weights:
+
+    pip install torch safetensors tiktoken
+
+    import json, torch
+    from safetensors.torch import load_file
+    from model import GPT, GPTConfig
+
+    with open("config.json") as f:
+        config = GPTConfig(**json.load(f))
+    model = GPT(config)
+    state_dict = load_file("model.safetensors")
+    model.load_state_dict(state_dict)
+    model.eval()
+
+    import tiktoken
+    enc = tiktoken.get_encoding("gpt2")
+    ids = enc.encode_ordinary("Once upon a time,")
+    x = torch.tensor([ids], dtype=torch.long)
+    out = model.generate(x, max_new_tokens=100, temperature=0.8, top_k=50,
+                          eot_token=enc.eot_token, repetition_penalty=1.3)
+    print(enc.decode(out[0].tolist()))
+"""
+
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+@dataclass
+class GPTConfig:
+    vocab_size: int = 50257
+    block_size: int = 1024
+    n_layer: int = 10
+    n_head: int = 8
+    n_embd: int = 608
+    dropout: float = 0.0
+    bias: bool = True
+    rope_theta: float = 10000.0  # standard RoPE base frequency
+
+
+def precompute_rope_freqs(head_dim, max_seq_len, theta=10000.0, device="cpu"):
+    """Precompute the complex rotation frequencies used by RoPE, one pair
+    per (position, frequency-band). Standard formula: theta_i = theta^(-2i/dim)."""
+    assert head_dim % 2 == 0, "RoPE requires an even head_dim"
+    freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
+    t = torch.arange(max_seq_len, device=device).float()
+    freqs = torch.outer(t, freqs)  # (max_seq_len, head_dim/2)
+    return torch.polar(torch.ones_like(freqs), freqs)  # complex64, (max_seq_len, head_dim/2)
+
+
+def apply_rope(x, freqs_cis):
+    """Apply rotary position embeddings to a (B, n_head, T, head_dim) tensor."""
+    B, n_head, T, head_dim = x.shape
+    x_complex = torch.view_as_complex(x.float().reshape(B, n_head, T, head_dim // 2, 2))
+    freqs_cis = freqs_cis[:T].view(1, 1, T, head_dim // 2)
+    x_rotated = x_complex * freqs_cis
+    x_out = torch.view_as_real(x_rotated).reshape(B, n_head, T, head_dim)
+    return x_out.type_as(x)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.head_dim = config.n_embd // config.n_head
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.dropout = config.dropout
+
+    def forward(self, x, freqs_cis):
+        B, T, C = x.shape
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        # RoPE is applied to queries and keys only, not values -- this is what
+        # makes attention scores depend on *relative* position between tokens
+        q = apply_rope(q, freqs_cis)
+        k = apply_rope(k, freqs_cis)
+        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)
+        return self.resid_dropout(self.c_proj(y))
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.gelu = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
+
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x, freqs_cis):
+        x = x + self.attn(self.ln_1(x), freqs_cis)
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.n_embd),
+            # NOTE: no wpe (learned position embedding) -- RoPE replaces it entirely,
+            # applied inside attention rather than added to the input embeddings
+            drop=nn.Dropout(config.dropout),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f=nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight
+        head_dim = config.n_embd // config.n_head
+        freqs_cis = precompute_rope_freqs(head_dim, config.block_size, theta=config.rope_theta)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith("c_proj.weight"):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        assert T <= self.config.block_size, "sequence longer than block_size"
+        x = self.transformer.drop(self.transformer.wte(idx))
+        freqs_cis = self.freqs_cis.to(x.device)
+        for block in self.transformer.h:
+            x = block(x, freqs_cis)
+        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), ignore_index=-1)
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None, eot_token=None,
+                 repetition_penalty=1.0):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if repetition_penalty != 1.0:
+                for seen_id in set(idx[0].tolist()):
+                    if logits[0, seen_id] > 0:
+                        logits[0, seen_id] /= repetition_penalty
+                    else:
+                        logits[0, seen_id] *= repetition_penalty
+            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 eot_token is not None and idx_next.item() == eot_token:
+                break
+        return idx