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simply one VE per layer, works best

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Andrej Karpathy
2026-01-16 22:08:52 +00:00
parent 0b58d70e99
commit 9a88194c3f
1 changed files with 17 additions and 17 deletions
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nanochat/gpt.py
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@@ -165,14 +165,12 @@ 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): separate embedding for values, mixed into later layers
# Value residual (ResFormer-style): every layer gets its own value embedding
# Paper: "Value Residual Learning" (arXiv:2410.17897) shows this improves information flow
# We apply to last 1/4 of layers as the paper shows later layers benefit most
head_dim = config.n_embd // config.n_head
kv_dim = config.n_kv_head * head_dim
self.value_embed = nn.Embedding(padded_vocab_size, kv_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_residual_start = config.n_layer - config.n_layer // 4 # last 1/4 of layers
# 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.
@@ -219,8 +217,9 @@ class GPT(nn.Module):
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
# Value embedding (init like c_v: uniform with same std)
torch.nn.init.uniform_(self.value_embed.weight, -s, s)
# Value embeddings (init like c_v: uniform with same std)
for ve in self.value_embeds:
torch.nn.init.uniform_(ve.weight, -s, s)
# Rotary embeddings
head_dim = self.config.n_embd // self.config.n_head
@@ -230,7 +229,8 @@ 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)
self.value_embed.to(dtype=torch.bfloat16)
for ve in self.value_embeds:
ve.to(dtype=torch.bfloat16)
def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None):
# TODO: bump base theta more? e.g. 100K is more common more recently
@@ -295,7 +295,8 @@ class GPT(nn.Module):
"""
nparams = sum(p.numel() for p in self.parameters())
# Exclude non-matmul params: embeddings and per-layer scalars
nparams_exclude = (self.transformer.wte.weight.numel() + self.value_embed.weight.numel() +
value_embeds_numel = sum(ve.weight.numel() for ve in self.value_embeds)
nparams_exclude = (self.transformer.wte.weight.numel() + value_embeds_numel +
self.resid_lambdas.numel() + self.x0_lambdas.numel() + self.v0_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
@@ -322,15 +323,15 @@ 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_embed, resid_lambdas, x0_lambdas, v0_lambdas)
# Separate out all parameters into groups (matrix, embedding, lm_head, value_embeds, resid_lambdas, x0_lambdas, v0_lambdas)
matrix_params = list(self.transformer.h.parameters())
embedding_params = list(self.transformer.wte.parameters())
lm_head_params = list(self.lm_head.parameters())
value_embed_params = list(self.value_embed.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_embed_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) + len(v0_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
@@ -338,7 +339,7 @@ class GPT(nn.Module):
adam_groups = [
dict(params=lm_head_params, lr=unembedding_lr * dmodel_lr_scale),
dict(params=embedding_params, lr=embedding_lr * dmodel_lr_scale),
dict(params=value_embed_params, lr=embedding_lr * dmodel_lr_scale), # same LR as token embedding
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),
@@ -372,12 +373,11 @@ class GPT(nn.Module):
x = self.transformer.wte(idx)
x = norm(x)
x0 = x # save initial normalized embedding for x0 residual
# Value residual (ResFormer): separate value embedding for later layers
v0 = self.value_embed(idx) # (B, T, kv_dim)
# 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
v0_for_layer = v0 if i >= self.value_residual_start else None
x = block(x, cos_sin, self.window_sizes[i], kv_cache, v0_for_layer, self.v0_lambdas[i])
x = block(x, cos_sin, self.window_sizes[i], kv_cache, v0s[i], self.v0_lambdas[i])
x = norm(x)
# Forward the lm_head (compute logits)