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alternating design

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Andrej Karpathy 2026-01-16 23:52:12 +00:00
parent 9a88194c3f
commit e85db6b4a4
1 changed files with 33 additions and 27 deletions
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nanochat/gpt.py
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@ -45,6 +45,10 @@ def norm(x):
return F.rms_norm(x, (x.size(-1),))
def has_ve(layer_idx, n_layer):
"""Returns True if GPT layer should have Value Embedding (alternating, last layer always included)."""
return layer_idx % 2 == (n_layer - 1) % 2
def apply_rotary_emb(x, cos, sin):
assert x.ndim == 4 # multihead attention
d = x.shape[3] // 2
@ -67,8 +71,10 @@ class CausalSelfAttention(nn.Module):
self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
self.ve_gate_channels = 32
self.ve_gate = nn.Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None
def forward(self, x, cos_sin, window_size, kv_cache, v0, v0_lambda):
def forward(self, x, ve, cos_sin, window_size, kv_cache):
B, T, C = x.size()
# Project the input to get queries, keys, and values
@ -77,10 +83,11 @@ class CausalSelfAttention(nn.Module):
k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim)
v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim)
# Value residual (ResFormer): mix in projected initial embedding for later layers
if v0 is not None:
v0_reshaped = v0.view(B, T, self.n_kv_head, self.head_dim)
v = v + v0_lambda * v0_reshaped
# Value residual (ResFormer): mix in value embedding with input-dependent gate per head
if ve is not None:
ve = ve.view(B, T, self.n_kv_head, self.head_dim)
gate = 2 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels])) # (B, T, n_kv_head), range (0, 2)
v = v + gate.unsqueeze(-1) * ve
# Apply Rotary Embeddings to queries and keys to get relative positional encoding
cos, sin = cos_sin
@ -131,8 +138,8 @@ class Block(nn.Module):
self.attn = CausalSelfAttention(config, layer_idx)
self.mlp = MLP(config)
def forward(self, x, cos_sin, window_size, kv_cache, v0, v0_lambda):
x = x + self.attn(norm(x), cos_sin, window_size, kv_cache, v0, v0_lambda)
def forward(self, x, ve, cos_sin, window_size, kv_cache):
x = x + self.attn(norm(x), ve, cos_sin, window_size, kv_cache)
x = x + self.mlp(norm(x))
return x
@ -165,12 +172,10 @@ class GPT(nn.Module):
# Separate parameters so they can have different optimizer treatment
self.resid_lambdas = nn.Parameter(torch.ones(config.n_layer)) # fake init, real init in init_weights()
self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer)) # fake init, real init in init_weights()
# Value residual (ResFormer-style): every layer gets its own value embedding
# Paper: "Value Residual Learning" (arXiv:2410.17897) shows this improves information flow
# Value embeddings (ResFormer-style): alternating layers, last layer always included
head_dim = config.n_embd // config.n_head
kv_dim = config.n_kv_head * head_dim
self.value_embeds = nn.ModuleList([nn.Embedding(padded_vocab_size, kv_dim) for _ in range(config.n_layer)])
self.v0_lambdas = nn.Parameter(torch.zeros(config.n_layer)) # fake init, real init in init_weights()
self.value_embeds = nn.ModuleDict({str(i): nn.Embedding(padded_vocab_size, kv_dim) for i in range(config.n_layer) if has_ve(i, config.n_layer)})
# To support meta device initialization, we init the rotary embeddings here, but it's just "fake" meta tensors only.
# As for rotary_seq_len, these rotary embeddings are pretty small/cheap in memory,
# so let's just over-compute them by 10X, but assert fail if we ever reach that amount.
@ -181,6 +186,7 @@ class GPT(nn.Module):
self.register_buffer("cos", cos, persistent=False) # persistent=False means it's not saved to the checkpoint
self.register_buffer("sin", sin, persistent=False)
@torch.no_grad()
def init_weights(self):
"""
Initialize the full model in this one function for maximum clarity.
@ -212,15 +218,18 @@ class GPT(nn.Module):
torch.nn.init.zeros_(block.mlp.c_proj.weight)
# Per-layer scalars
with torch.no_grad():
self.resid_lambdas.fill_(1.0) # 1.0 => typical residual connections at init
self.x0_lambdas.fill_(0.0) # 0.0 => skip connection to input is disabled at init
self.v0_lambdas.fill_(0.0) # 0.0 => value residual is disabled at init
self.resid_lambdas.fill_(1.0) # 1.0 => typical residual connections at init
self.x0_lambdas.fill_(0.0) # 0.0 => skip connection to input is disabled at init
# Value embeddings (init like c_v: uniform with same std)
for ve in self.value_embeds:
for ve in self.value_embeds.values():
torch.nn.init.uniform_(ve.weight, -s, s)
# Gate weights init to zero so gates start at sigmoid(0) = 0.5, scaled by 2 -> 1.0 (neutral)
for block in self.transformer.h:
if block.attn.ve_gate is not None:
torch.nn.init.zeros_(block.attn.ve_gate.weight)
# Rotary embeddings
head_dim = self.config.n_embd // self.config.n_head
cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
@ -229,7 +238,7 @@ class GPT(nn.Module):
# Cast embeddings to bf16: optimizer can tolerate it and it saves memory
if self.transformer.wte.weight.device.type == "cuda":
self.transformer.wte.to(dtype=torch.bfloat16)
for ve in self.value_embeds:
for ve in self.value_embeds.values():
ve.to(dtype=torch.bfloat16)
def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None):
@ -295,9 +304,9 @@ class GPT(nn.Module):
"""
nparams = sum(p.numel() for p in self.parameters())
# Exclude non-matmul params: embeddings and per-layer scalars
value_embeds_numel = sum(ve.weight.numel() for ve in self.value_embeds)
value_embeds_numel = sum(ve.weight.numel() for ve in self.value_embeds.values())
nparams_exclude = (self.transformer.wte.weight.numel() + value_embeds_numel +
self.resid_lambdas.numel() + self.x0_lambdas.numel() + self.v0_lambdas.numel())
self.resid_lambdas.numel() + self.x0_lambdas.numel())
h, q, t = self.config.n_head, self.config.n_embd // self.config.n_head, self.config.sequence_len
# Sum attention FLOPs per layer, accounting for sliding window
attn_flops = 0
@ -323,15 +332,14 @@ class GPT(nn.Module):
def setup_optimizers(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):
model_dim = self.config.n_embd
ddp, rank, local_rank, world_size = get_dist_info()
# Separate out all parameters into groups (matrix, embedding, lm_head, value_embeds, resid_lambdas, x0_lambdas, v0_lambdas)
# Separate out all parameters into groups
matrix_params = list(self.transformer.h.parameters())
value_embeds_params = list(self.value_embeds.parameters())
embedding_params = list(self.transformer.wte.parameters())
lm_head_params = list(self.lm_head.parameters())
value_embeds_params = list(self.value_embeds.parameters())
resid_params = [self.resid_lambdas]
x0_params = [self.x0_lambdas]
v0_params = [self.v0_lambdas]
assert len(list(self.parameters())) == len(matrix_params) + len(embedding_params) + len(lm_head_params) + len(value_embeds_params) + len(resid_params) + len(x0_params) + len(v0_params)
assert len(list(self.parameters())) == len(matrix_params) + len(embedding_params) + len(lm_head_params) + len(value_embeds_params) + len(resid_params) + len(x0_params)
# Create the AdamW optimizer for the embedding, lm_head, and per-layer scalars
# Scale the LR for the AdamW parameters by ∝1/√dmodel (having tuned the LRs for 768 dim model)
dmodel_lr_scale = (model_dim / 768) ** -0.5
@ -342,7 +350,6 @@ class GPT(nn.Module):
dict(params=value_embeds_params, lr=embedding_lr * dmodel_lr_scale), # same LR as token embedding
dict(params=resid_params, lr=scalar_lr * 0.01), # these are a lot more sensitive because they accumulate in the residual stream
dict(params=x0_params, lr=scalar_lr),
dict(params=v0_params, lr=scalar_lr),
]
adamw_kwargs = dict(betas=adam_betas, eps=1e-10, weight_decay=0.0) # NOTE: weight decay is hardcoded to 0.0 for AdamW, only used in Muon
AdamWFactory = DistAdamW if ddp else partial(torch.optim.AdamW, fused=True)
@ -373,11 +380,10 @@ class GPT(nn.Module):
x = self.transformer.wte(idx)
x = norm(x)
x0 = x # save initial normalized embedding for x0 residual
# Value residual (ResFormer): every layer gets its own value embedding
v0s = [ve(idx) for ve in self.value_embeds] # n_layer x (B, T, kv_dim)
for i, block in enumerate(self.transformer.h):
x = self.resid_lambdas[i] * x + self.x0_lambdas[i] * x0
x = block(x, cos_sin, self.window_sizes[i], kv_cache, v0s[i], self.v0_lambdas[i])
ve = self.value_embeds[str(i)](idx) if str(i) in self.value_embeds else None
x = block(x, ve, cos_sin, self.window_sizes[i], kv_cache)
x = norm(x)
# Forward the lm_head (compute logits)