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https://github.com/karpathy/nanochat.git
synced 2026-03-19 03:13:15 +00:00
All of these improvements were developed by Claude running autonomously over ~2 days using autoresearch. I didn't touch anything - incredible. All tuning was done on d12 but generalized easily to larger models (e.g. d24 in particular). This means we will also get a new "Time to GPT-2" Leaderboard entry, which I will push separately.
Optimizer & schedule changes: - Increase unembedding LR 0.004 -> 0.008, weight decay 0.2 -> 0.28 - Per-group Adam betas and weight decay (instead of shared global betas) - Muon beta2 0.95 -> 0.9, momentum warmup target 0.95 -> 0.97 over 400 steps - Warmup: ratio-based -> absolute steps (default 40) - Warmdown ratio 0.5 -> 0.65, final LR fraction 0.0 -> 0.05 - Weight decay schedule: linear -> cosine decay - Polar express norm factor 1.02 -> 1.01 Architecture & init changes: - VE gate: channels 32 -> 12, scale range 2x -> 3x, init small positive - Add post-QK-norm scaling (q,k *= 1.15) for sharper attention - Embedding init std 1.0 -> 0.8, MLP c_fc init 0.5x smaller - RoPE base theta 10K -> 100K - Short attention window: seq_len/2 -> ~seq_len/3 (ceil to 128 tile) - Logit softcap 20 -> 15
This commit is contained in:
Andrej Karpathy
parent
1076f97059
commit
6ed7d1d82c
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@ -76,7 +76,7 @@ class CausalSelfAttention(nn.Module):
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self.c_k = Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
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self.c_v = Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
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self.c_proj = Linear(self.n_embd, self.n_embd, bias=False)
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self.ve_gate_channels = 32
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self.ve_gate_channels = 12
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self.ve_gate = Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None
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def forward(self, x, ve, cos_sin, window_size, kv_cache):
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@ -91,13 +91,15 @@ class CausalSelfAttention(nn.Module):
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# Value residual (ResFormer): mix in value embedding with input-dependent gate per head
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if ve is not None:
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ve = ve.view(B, T, self.n_kv_head, self.head_dim)
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gate = 2 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels])) # (B, T, n_kv_head), range (0, 2)
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gate = 3 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels])) # (B, T, n_kv_head), range (0, 3)
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v = v + gate.unsqueeze(-1) * ve
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# Apply Rotary Embeddings to queries and keys to get relative positional encoding
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cos, sin = cos_sin
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q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
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q, k = norm(q), norm(k) # QK norm
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q = q * 1.15 # sharper attention (split scale between Q and K), TODO think through better
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k = k * 1.15
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# Flash Attention (FA3 on Hopper+, PyTorch SDPA fallback elsewhere)
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# window_size is (left, right) tuple: (N, 0) for causal, (-1, 0) for full context
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@ -208,7 +210,7 @@ class GPT(nn.Module):
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"""
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# Embedding and unembedding
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torch.nn.init.normal_(self.transformer.wte.weight, mean=0.0, std=1.0)
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torch.nn.init.normal_(self.transformer.wte.weight, mean=0.0, std=0.8)
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torch.nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001)
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# Transformer blocks: uniform init with bound = sqrt(3) * std (same standard deviation as normal)
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@ -219,7 +221,7 @@ class GPT(nn.Module):
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torch.nn.init.uniform_(block.attn.c_k.weight, -s, s)
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torch.nn.init.uniform_(block.attn.c_v.weight, -s, s)
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torch.nn.init.zeros_(block.attn.c_proj.weight) # projections are zero
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torch.nn.init.uniform_(block.mlp.c_fc.weight, -s, s)
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torch.nn.init.uniform_(block.mlp.c_fc.weight, -s * 0.5, s * 0.5) # 0.5x init scale for c_fc
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torch.nn.init.zeros_(block.mlp.c_proj.weight)
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# Per-layer scalars
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@ -230,10 +232,10 @@ class GPT(nn.Module):
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for ve in self.value_embeds.values():
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torch.nn.init.uniform_(ve.weight, -s, s)
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# Gate weights init to zero so gates start at sigmoid(0) = 0.5, scaled by 2 -> 1.0 (neutral)
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# Gate weights init with small positive values so gates start slightly above neutral
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for block in self.transformer.h:
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if block.attn.ve_gate is not None:
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torch.nn.init.zeros_(block.attn.ve_gate.weight)
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torch.nn.init.uniform_(block.attn.ve_gate.weight, 0.0, 0.02)
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# Rotary embeddings
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head_dim = self.config.n_embd // self.config.n_head
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@ -248,7 +250,7 @@ class GPT(nn.Module):
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for ve in self.value_embeds.values():
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ve.to(dtype=COMPUTE_DTYPE)
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def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None):
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def _precompute_rotary_embeddings(self, seq_len, head_dim, base=100000, device=None):
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# TODO: bump base theta more? e.g. 100K is more common more recently
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# autodetect the device from model embeddings
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if device is None:
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@ -280,7 +282,7 @@ class GPT(nn.Module):
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assert all(c in "SL" for c in pattern), f"Invalid window_pattern: {pattern}. Use only S and L."
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# Map characters to window sizes
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long_window = config.sequence_len
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short_window = long_window // 2
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short_window = -(-long_window // 3 // 128) * 128 # ceil to FA3 tile size (2048 -> 768)
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char_to_window = {
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"L": (long_window, 0),
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"S": (short_window, 0),
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@ -353,7 +355,7 @@ class GPT(nn.Module):
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'total': total,
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}
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def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, adam_betas=(0.8, 0.95), scalar_lr=0.5):
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def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, scalar_lr=0.5):
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model_dim = self.config.n_embd
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ddp, rank, local_rank, world_size = get_dist_info()
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@ -373,10 +375,10 @@ class GPT(nn.Module):
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# Build param_groups with all required fields explicit
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param_groups = [
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# AdamW groups (embeddings, lm_head, scalars)
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dict(kind='adamw', params=lm_head_params, lr=unembedding_lr * dmodel_lr_scale, betas=adam_betas, eps=1e-10, weight_decay=0.0),
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dict(kind='adamw', params=embedding_params, lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, eps=1e-10, weight_decay=0.0),
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dict(kind='adamw', params=value_embeds_params, lr=embedding_lr * dmodel_lr_scale, betas=adam_betas, eps=1e-10, weight_decay=0.0),
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dict(kind='adamw', params=resid_params, lr=scalar_lr * 0.01, betas=adam_betas, eps=1e-10, weight_decay=0.0),
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dict(kind='adamw', params=lm_head_params, lr=unembedding_lr * dmodel_lr_scale, betas=(0.8, 0.96), eps=1e-10, weight_decay=0.01),
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dict(kind='adamw', params=embedding_params, lr=embedding_lr * dmodel_lr_scale, betas=(0.8, 0.995), eps=1e-10, weight_decay=0.001),
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dict(kind='adamw', params=value_embeds_params, lr=embedding_lr * dmodel_lr_scale * 0.5, betas=(0.8, 0.995), eps=1e-10, weight_decay=0.01),
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dict(kind='adamw', params=resid_params, lr=scalar_lr * 0.01, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.05),
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dict(kind='adamw', params=x0_params, lr=scalar_lr, betas=(0.96, 0.95), eps=1e-10, weight_decay=0.0), # higher beta1 for x0
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]
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# Muon groups (matrix params, grouped by shape for stacking)
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@ -384,7 +386,7 @@ class GPT(nn.Module):
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group_params = [p for p in matrix_params if p.shape == shape]
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param_groups.append(dict(
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kind='muon', params=group_params, lr=matrix_lr,
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momentum=0.95, ns_steps=5, beta2=0.95, weight_decay=weight_decay,
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momentum=0.95, ns_steps=5, beta2=0.9, weight_decay=weight_decay,
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))
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Factory = DistMuonAdamW if ddp else MuonAdamW
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@ -416,7 +418,7 @@ class GPT(nn.Module):
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x = norm(x)
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# Forward the lm_head (compute logits)
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softcap = 20 # smoothly cap the logits to the range [-softcap, softcap]
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softcap = 15 # smoothly cap the logits to the range [-softcap, softcap]
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logits = self.lm_head(x) # (B, T, padded_vocab_size) <- very big tensor, large amount of memory
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logits = logits[..., :self.config.vocab_size] # slice to remove padding
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logits = logits.float() # switch to fp32 for logit softcap and loss computation
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@ -113,7 +113,7 @@ def muon_step_fused(
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# Polar express
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X = g.bfloat16()
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X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6)
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X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.01 + 1e-6)
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if g.size(-2) > g.size(-1): # Tall matrix
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for a, b, c in polar_express_coeffs[:ns_steps]:
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A = X.mT @ X
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@ -60,15 +60,13 @@ parser.add_argument("--target-param-data-ratio", type=float, default=10.5, help=
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parser.add_argument("--device-batch-size", type=int, default=32, help="per-device batch size. good number to reduce to 16,8,4,... if you OOM on VRAM.")
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parser.add_argument("--total-batch-size", type=int, default=-1, help="total batch size in tokens. decent numbers are e.g. 524288. (-1 = auto-compute optimal)")
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parser.add_argument("--embedding-lr", type=float, default=0.3, help="learning rate for embedding parameters (Adam)")
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parser.add_argument("--unembedding-lr", type=float, default=0.004, help="learning rate for unembedding parameters (Adam)")
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parser.add_argument("--weight-decay", type=float, default=0.2, help="cautious weight decay for the Muon optimizer (for weights)")
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parser.add_argument("--unembedding-lr", type=float, default=0.008, help="learning rate for unembedding parameters (Adam)")
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parser.add_argument("--weight-decay", type=float, default=0.28, help="cautious weight decay for the Muon optimizer (for weights)")
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parser.add_argument("--matrix-lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
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parser.add_argument("--scalar-lr", type=float, default=0.5, help="learning rate for scalars (resid_lambdas, x0_lambdas)")
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parser.add_argument("--adam-beta1", type=float, default=0.8, help="Adam beta1 for embedding/unembedding")
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parser.add_argument("--adam-beta2", type=float, default=0.95, help="Adam beta2 for embedding/unembedding")
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parser.add_argument("--warmup-ratio", type=float, default=0.0, help="ratio of iterations for LR warmup")
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parser.add_argument("--warmdown-ratio", type=float, default=0.5, help="ratio of iterations for LR warmdown")
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parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR as fraction of initial LR")
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parser.add_argument("--warmup-steps", type=int, default=40, help="number of steps for LR warmup")
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parser.add_argument("--warmdown-ratio", type=float, default=0.65, help="ratio of iterations for LR warmdown")
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parser.add_argument("--final-lr-frac", type=float, default=0.05, help="final LR as fraction of initial LR")
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parser.add_argument("--resume-from-step", type=int, default=-1, help="resume training from this step (-1 = disable)")
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# Evaluation
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parser.add_argument("--eval-every", type=int, default=250, help="evaluate val bpb every N steps (-1 = disable)")
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@ -311,7 +309,6 @@ optimizer = model.setup_optimizer(
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unembedding_lr=args.unembedding_lr * batch_lr_scale,
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embedding_lr=args.embedding_lr * batch_lr_scale,
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scalar_lr=args.scalar_lr * batch_lr_scale,
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adam_betas=(args.adam_beta1, args.adam_beta2),
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# Muon hyperparameters
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matrix_lr=args.matrix_lr * batch_lr_scale,
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weight_decay=weight_decay_scaled,
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@ -360,7 +357,7 @@ print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
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# Learning rate schedule (linear warmup, constant, linear warmdown)
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def get_lr_multiplier(it):
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warmup_iters = round(args.warmup_ratio * num_iterations)
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warmup_iters = args.warmup_steps
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warmdown_iters = round(args.warmdown_ratio * num_iterations)
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if it < warmup_iters:
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return (it + 1) / warmup_iters
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@ -370,15 +367,15 @@ def get_lr_multiplier(it):
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progress = (num_iterations - it) / warmdown_iters
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return progress * 1.0 + (1 - progress) * args.final_lr_frac
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# Momentum scheduler for Muon optimizer (warms up to 0.95 over the first 300 steps)
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# Momentum scheduler for Muon optimizer (warms up to 0.97 over the first 400 steps)
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def get_muon_momentum(it):
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frac = min(it / 300, 1)
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momentum = (1 - frac) * 0.85 + frac * 0.95
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frac = min(it / 400, 1)
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momentum = (1 - frac) * 0.85 + frac * 0.97
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return momentum
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# Weight decay scheduler for Muon optimizer (linearly decays to zero over the course of training)
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# Weight decay scheduler for Muon optimizer (cosine decay to zero over the course of training)
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def get_weight_decay(it):
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return weight_decay_scaled * (1 - it / num_iterations)
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return weight_decay_scaled * 0.5 * (1 + math.cos(math.pi * it / num_iterations))
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# -----------------------------------------------------------------------------
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# Training loop
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@ -605,7 +602,7 @@ get_report().log(section="Base model training", data=[
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"Number of training tokens": total_tokens,
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"Tokens : Scaling params ratio": total_batch_size * num_iterations / num_scaling_params,
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"DDP world size": ddp_world_size,
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"warmup_ratio": args.warmup_ratio,
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"warmup_steps": args.warmup_steps,
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"warmdown_ratio": args.warmdown_ratio,
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"final_lr_frac": args.final_lr_frac,
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},
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