alternating design

nanochat/gpt.py

@@ -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
+        # Value residual (ResFormer): mix in value embedding with input-dependent gate per head
-        if v0 is not None:
+        if ve is not None:
-            v0_reshaped = v0.view(B, T, self.n_kv_head, self.head_dim)
+            ve = ve.view(B, T, self.n_kv_head, self.head_dim)
-            v = v + v0_lambda * v0_reshaped
+            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):
+    def forward(self, x, ve, cos_sin, window_size, kv_cache):
-        x = x + self.attn(norm(x), cos_sin, window_size, kv_cache, v0, v0_lambda)
+        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
+        # Value embeddings (ResFormer-style): alternating layers, last layer always included
-        # Paper: "Value Residual Learning" (arXiv:2410.17897) shows this improves information flow
         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.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)})
-        self.v0_lambdas = nn.Parameter(torch.zeros(config.n_layer))   # fake init, real init in init_weights()
         # 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
 
         # 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)
-        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
@@ -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)