Add minimal sparse d24 recipe

nanochat/engine.py

@@ -102,11 +102,14 @@ class KVCache:
         self.cache_seqlens = torch.zeros(batch_size, dtype=torch.int32, device=device)
         # Previous token's normalized embedding for smear (set by model forward pass)
         self.prev_embedding = None
+        # Previous token ids for bigram hash features during decoding
+        self.prev_token_ids = None
 
     def reset(self):
         """Reset cache to empty state."""
         self.cache_seqlens.zero_()
         self.prev_embedding = None
+        self.prev_token_ids = None
 
     def get_pos(self):
         """Get current position (assumes all batch elements at same position)."""
@@ -135,6 +138,8 @@ class KVCache:
         # Copy smear state: expand batch=1 prev_embedding to num_samples
         if other.prev_embedding is not None:
             self.prev_embedding = other.prev_embedding.expand(self.batch_size, -1, -1).clone()
+        if other.prev_token_ids is not None:
+            self.prev_token_ids = other.prev_token_ids.expand(self.batch_size).clone()
 
 # -----------------------------------------------------------------------------
 @torch.inference_mode()

nanochat/gpt.py

@@ -37,6 +37,37 @@ class GPTConfig:
     # Characters: L=long (full context), S=short (quarter context)
     # Examples: "L"=all full context, "SL"=alternating, "SSL"=two short then one long
     window_pattern: str = "SSSL"
+    # Sparse funnel attention: local layers use a short window, global layers use full context.
+    use_sparse_funnel: bool = True
+    n_global: int = 0              # number of full-context (global) layers, 0 = auto depth//4
+    chirp_gamma: float = 0.7       # chirp exponent for global placement (deeper-biased)
+    local_window: int = 128        # local attention window size
+    global_layer_override: tuple = ()  # explicit global layer indices (overrides chirp)
+    rope_base: int = 200000        # RoPE base frequency
+    use_smear: bool = True         # cheap bigram-like embedding mixing
+    smear_channels: int = 24       # number of early embedding channels used to predict smear gate
+    bigram_vocab_size: int = 4096  # hashed bigram embedding buckets (0 disables)
+    bigram_dim: int = 128          # bigram embedding dim before projection
+    use_gated_attn: bool = True    # learn a lightweight per-head gate on attention output
+    attn_gate_channels: int = 12   # channels used to predict attention gate
+    ve_layers: tuple = ()          # explicit layer indices for value residuals (empty = auto global layers under sparse funnel)
+
+
+def default_sparse_n_global(n_layer):
+    return max(1, n_layer // 4)
+
+
+def compute_sparse_global_layers(n_layer, n_global, chirp_gamma, global_layer_override=()):
+    if global_layer_override:
+        global_layers = set(global_layer_override)
+    else:
+        G = n_global if n_global > 0 else default_sparse_n_global(n_layer)
+        global_layers = set()
+        for i in range(1, G + 1):
+            idx = max(0, min(n_layer - 1, int(n_layer * (i / G) ** chirp_gamma) - 1))
+            global_layers.add(idx)
+    global_layers.add(n_layer - 1)
+    return tuple(sorted(global_layers))
 
 
 def norm(x):
@@ -47,11 +78,58 @@ class Linear(nn.Linear):
     Replaces autocast: master weights stay fp32 for optimizer precision,
     but matmuls run in the activation dtype (typically bf16 from embeddings)."""
     def forward(self, x):
-        return F.linear(x, self.weight.to(dtype=x.dtype))
+        w = self.weight
+        if w.dtype != x.dtype:
+            w = w.to(dtype=x.dtype)
+        return F.linear(x, w)
 
 
-def has_ve(layer_idx, n_layer):
-    """Returns True if GPT layer should have Value Embedding (alternating, last layer always included)."""
+class EmbeddingLinear(nn.Module):
+    """Lightweight linear layer for lm_head without redundant dtype casting."""
+    def __init__(self, in_features, out_features, bias=False, device=None, dtype=None):
+        super().__init__()
+        assert not bias
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = nn.Parameter(torch.empty(out_features, in_features, device=device, dtype=dtype))
+    def forward(self, x):
+        return F.linear(x, self.weight)
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash causal token pairs into a compact learned embedding."""
+    def __init__(self, bigram_vocab_size, bigram_dim, model_dim):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        self.proj = Linear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens, prev_tokens=None):
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.full_like(t, mod)
+        if prev_tokens is None:
+            if t.size(1) > 1:
+                out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        else:
+            prev = prev_tokens.to(torch.int32).view(t.size(0), 1)
+            out[..., 0] = torch.bitwise_xor(36313 * t[..., 0], 27191 * prev[..., 0]) % mod
+            if t.size(1) > 1:
+                out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids, prev_tokens=None):
+        h = self.embed(self.bigram_hash(token_ids, prev_tokens=prev_tokens))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+def has_ve(layer_idx, n_layer, layer_set=()):
+    """Returns True if GPT layer should have Value Embedding."""
+    if layer_set:
+        return layer_idx in layer_set
     return layer_idx % 2 == (n_layer - 1) % 2
 
 def apply_rotary_emb(x, cos, sin):
@@ -77,7 +155,13 @@ class CausalSelfAttention(nn.Module):
         self.c_v = Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
         self.c_proj = Linear(self.n_embd, self.n_embd, bias=False)
         self.ve_gate_channels = 12
-        self.ve_gate = Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None
+        self.ve_gate = Linear(
+            self.ve_gate_channels,
+            self.n_kv_head,
+            bias=False,
+        ) if has_ve(layer_idx, config.n_layer, layer_set=config.ve_layers) else None
+        self.attn_gate_channels = config.attn_gate_channels
+        self.attn_gate = Linear(self.attn_gate_channels, self.n_head, bias=False) if config.use_gated_attn else None
 
     def forward(self, x, ve, cos_sin, window_size, kv_cache):
         B, T, C = x.size()
@@ -91,7 +175,7 @@ class CausalSelfAttention(nn.Module):
         # 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 = 3 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels]))  # (B, T, n_kv_head), range (0, 3)
+            gate = 3 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels]))
             v = v + gate.unsqueeze(-1) * ve
 
         # Apply Rotary Embeddings to queries and keys to get relative positional encoding
@@ -120,6 +204,10 @@ class CausalSelfAttention(nn.Module):
             if self.layer_idx == kv_cache.n_layers - 1:
                 kv_cache.advance(T)
 
+        if self.attn_gate is not None:
+            gate = 2 * torch.sigmoid(self.attn_gate(x[..., :self.attn_gate_channels]))
+            y = y * gate.unsqueeze(-1).to(dtype=y.dtype)
+
         # Re-assemble the heads and project back to residual stream
         y = y.contiguous().view(B, T, -1)
         y = self.c_proj(y)
@@ -160,11 +248,16 @@ class GPT(nn.Module):
         """
         super().__init__()
         self.config = config
+        if config.use_sparse_funnel and config.n_global <= 0:
+            config.n_global = default_sparse_n_global(config.n_layer)
+        if config.use_sparse_funnel and not config.ve_layers:
+            config.ve_layers = compute_sparse_global_layers(
+                config.n_layer, config.n_global, config.chirp_gamma, config.global_layer_override
+            )
         # Compute per-layer window sizes for sliding window attention
         # window_size is (left, right) tuple: (-1, 0) for full context, (N, 0) for sliding window
         self.window_sizes = self._compute_window_sizes(config)
         # Pad vocab for efficiency (DDP, tensor cores). This is just an optimization - outputs are cropped in forward().
-        # https://huggingface.co/docs/transformers/main_classes/model#transformers.PreTrainedModel.resize_token_embeddings
         padded_vocab_size = ((config.vocab_size + pad_vocab_size_to - 1) // pad_vocab_size_to) * pad_vocab_size_to
         if padded_vocab_size != config.vocab_size:
             print0(f"Padding vocab_size from {config.vocab_size} to {padded_vocab_size} for efficiency")
@@ -172,22 +265,31 @@ class GPT(nn.Module):
             "wte": nn.Embedding(padded_vocab_size, config.n_embd),
             "h": nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.n_layer)]),
         })
-        self.lm_head = Linear(config.n_embd, padded_vocab_size, bias=False)
+        self.bigram = BigramHashEmbedding(config.bigram_vocab_size, config.bigram_dim, config.n_embd) if config.bigram_vocab_size > 0 else None
+        self.lm_head = EmbeddingLinear(config.n_embd, padded_vocab_size, bias=False)
         # Per-layer learnable scalars (inspired by modded-nanogpt)
         # resid_lambdas: scales the residual stream at each layer (init 1.0 = neutral)
         # x0_lambdas: blends initial embedding back in at each layer (init 0.0 = disabled)
         # 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()
-        # Smear: mix previous token's embedding into current token (cheap bigram-like info)
-        self.smear_gate = Linear(24, 1, bias=False)
-        self.smear_lambda = nn.Parameter(torch.zeros(1))
+        # Optional smear: mix previous token's embedding into current token before the trunk
+        if config.use_smear:
+            self.smear_gate = Linear(config.smear_channels, 1, bias=False)
+            self.smear_lambda = nn.Parameter(torch.zeros(1))
+        else:
+            self.smear_gate = None
+            self.smear_lambda = None
         # Backout: subtract cached mid-layer residual before final norm to remove low-level features
         self.backout_lambda = nn.Parameter(0.2 * torch.ones(1))
         # 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.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.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, layer_set=config.ve_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.
@@ -217,31 +319,41 @@ class GPT(nn.Module):
         # Embedding and unembedding
         torch.nn.init.normal_(self.transformer.wte.weight, mean=0.0, std=0.8)
         torch.nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001)
+        if self.bigram is not None:
+            torch.nn.init.zeros_(self.bigram.embed.weight)
+            if self.bigram.proj is not None:
+                bigram_s = 3**0.5 * self.bigram.embed.embedding_dim ** -0.5
+                torch.nn.init.uniform_(self.bigram.proj.weight, -bigram_s, bigram_s)
+            self.bigram.scale.data.fill_(0.05)
+        if self.smear_gate is not None:
+            torch.nn.init.zeros_(self.smear_gate.weight)
+            self.smear_lambda.data.zero_()
+        self.backout_lambda.data.fill_(0.2)
 
         # Transformer blocks: uniform init with bound = sqrt(3) * std (same standard deviation as normal)
         n_embd = self.config.n_embd
         s = 3**0.5 * n_embd**-0.5 # sqrt(3) multiplier makes sure Uniform achieves the same std as Normal
         for block in self.transformer.h:
-            torch.nn.init.uniform_(block.attn.c_q.weight, -s, s) # weights use Uniform to avoid outliers
+            torch.nn.init.uniform_(block.attn.c_q.weight, -s, s)
             torch.nn.init.uniform_(block.attn.c_k.weight, -s, s)
-            torch.nn.init.uniform_(block.attn.c_v.weight, -s, s)
-            torch.nn.init.zeros_(block.attn.c_proj.weight) # projections are zero
-            torch.nn.init.uniform_(block.mlp.c_fc.weight, -s * 0.4, s * 0.4)  # 0.4x init scale for c_fc
+            torch.nn.init.uniform_(block.attn.c_v.weight, -0.85 * s, 0.85 * s)
+            torch.nn.init.uniform_(block.attn.c_proj.weight, -0.008, 0.008)
+            torch.nn.init.uniform_(block.mlp.c_fc.weight, -s * 0.4, s * 0.4)
             torch.nn.init.zeros_(block.mlp.c_proj.weight)
 
         # Per-layer scalars
-        # Per-layer resid init: stronger residual at early layers, weaker at deep layers
+        import math
         n_layer = self.config.n_layer
+        resid_start, resid_end = 1.18, 1.06
+        resid_decay = math.log(resid_start / resid_end) / max(n_layer - 1, 1)
+        half_depth = max(1, n_layer // 2)
         for i in range(n_layer):
-            self.resid_lambdas.data[i] = 1.15 - (0.10 * i / max(n_layer - 1, 1))
-        # Decaying x0 init: earlier layers get more input embedding blending
-        for i in range(n_layer):
-            self.x0_lambdas.data[i] = 0.20 - (0.15 * i / max(n_layer - 1, 1))
-
-        # Smear/backout scalars and smear gate must be explicitly initialized 
-        torch.nn.init.zeros_(self.smear_lambda)
-        torch.nn.init.constant_(self.backout_lambda, 0.2)
-        torch.nn.init.uniform_(self.smear_gate.weight, 0.0, 0.02)
+            self.resid_lambdas.data[i] = resid_start * math.exp(-resid_decay * i)
+            if i < half_depth:
+                frac = i / max(half_depth - 1, 1)
+                self.x0_lambdas.data[i] = 0.24 * (1.0 - frac) + 0.08 * frac
+            else:
+                self.x0_lambdas.data[i] = 0.0
 
         # Value embeddings (init like c_v: uniform with same std)
         for ve in self.value_embeds.values():
@@ -251,6 +363,8 @@ class GPT(nn.Module):
         for block in self.transformer.h:
             if block.attn.ve_gate is not None:
                 torch.nn.init.uniform_(block.attn.ve_gate.weight, 0.0, 0.02)
+            if block.attn.attn_gate is not None:
+                torch.nn.init.uniform_(block.attn.attn_gate.weight, 0.0, 0.02)
 
         # Rotary embeddings
         head_dim = self.config.n_embd // self.config.n_head
@@ -262,11 +376,17 @@ class GPT(nn.Module):
         # because GradScaler cannot unscale fp16 gradients.
         if COMPUTE_DTYPE != torch.float16:
             self.transformer.wte.to(dtype=COMPUTE_DTYPE)
+            self.lm_head.to(dtype=COMPUTE_DTYPE)
             for ve in self.value_embeds.values():
                 ve.to(dtype=COMPUTE_DTYPE)
+            if self.bigram is not None:
+                self.bigram.embed.to(dtype=COMPUTE_DTYPE)
+                if self.bigram.proj is not None:
+                    self.bigram.proj.to(dtype=COMPUTE_DTYPE)
 
-    def _precompute_rotary_embeddings(self, seq_len, head_dim, base=100000, device=None):
-        # TODO: bump base theta more? e.g. 100K is more common more recently
+    def _precompute_rotary_embeddings(self, seq_len, head_dim, base=None, device=None):
+        if base is None:
+            base = self.config.rope_base
         # autodetect the device from model embeddings
         if device is None:
             device = self.transformer.wte.weight.device
@@ -285,31 +405,42 @@ class GPT(nn.Module):
     def _compute_window_sizes(self, config):
         """
         Compute per-layer window sizes for sliding window attention.
-
-        Returns list of (left, right) tuples for FA3's window_size parameter:
-        - left: how many tokens before current position to attend to (-1 = unlimited)
-        - right: how many tokens after current position to attend to (0 for causal)
-
-        Pattern string is tiled across layers. Final layer always gets L (full context).
-        Characters: L=long (full context), S=short (quarter context)
+        Supports two modes:
+        1. Pattern-based (original): window_pattern string tiled across layers
+        2. Sparse funnel: chirped global placement + fixed local window
         """
-        pattern = config.window_pattern.upper()
-        assert all(c in "SL" for c in pattern), f"Invalid window_pattern: {pattern}. Use only S and L."
-        # Map characters to window sizes
-        long_window = config.sequence_len
-        short_window = -(-long_window // 4 // 128) * 128  # ceil to FA3 tile size (2048 -> 768)
-        char_to_window = {
-            "L": (long_window, 0),
-            "S": (short_window, 0),
-        }
-        # Tile pattern across layers
-        window_sizes = []
-        for layer_idx in range(config.n_layer):
-            char = pattern[layer_idx % len(pattern)]
-            window_sizes.append(char_to_window[char])
-        # Final layer always gets full context
-        window_sizes[-1] = (long_window, 0)
-        return window_sizes
+        L = config.n_layer
+        full = config.sequence_len
+
+        if config.use_sparse_funnel:
+            # Sparse funnel: chirped global placement
+            global_layers = set(compute_sparse_global_layers(
+                L, config.n_global, config.chirp_gamma, config.global_layer_override
+            ))
+            self._global_layers = sorted(global_layers)
+
+            w = config.local_window
+            window_sizes = []
+            for layer_idx in range(L):
+                if layer_idx in global_layers:
+                    window_sizes.append((full, 0))
+                else:
+                    window_sizes.append((w, 0))
+            return window_sizes
+        else:
+            # Original pattern-based mode
+            self._global_layers = set()
+            pattern = config.window_pattern.upper()
+            assert all(c in "SL" for c in pattern), f"Invalid window_pattern: {pattern}. Use only S and L."
+            long_window = full
+            short_window = -(-long_window // 4 // 128) * 128
+            char_to_window = {"L": (long_window, 0), "S": (short_window, 0)}
+            window_sizes = []
+            for layer_idx in range(L):
+                char = pattern[layer_idx % len(pattern)]
+                window_sizes.append(char_to_window[char])
+            window_sizes[-1] = (long_window, 0)
+            return window_sizes
 
     def get_device(self):
         return self.transformer.wte.weight.device
@@ -329,9 +460,14 @@ 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.values())
+        bigram_embed_numel = 0 if self.bigram is None else self.bigram.embed.weight.numel() + self.bigram.scale.numel()
+        smear_numel = 0
+        if self.smear_gate is not None:
+            smear_numel += self.smear_gate.weight.numel() + self.smear_lambda.numel()
         nparams_exclude = (self.transformer.wte.weight.numel() + value_embeds_numel +
+                          bigram_embed_numel +
                           self.resid_lambdas.numel() + self.x0_lambdas.numel() +
-                          self.smear_gate.weight.numel() + self.smear_lambda.numel() + self.backout_lambda.numel())
+                          smear_numel + self.backout_lambda.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
@@ -356,14 +492,18 @@ class GPT(nn.Module):
         """
         # Count each group separately (mirrors the grouping in setup_optimizers)
         wte = sum(p.numel() for p in self.transformer.wte.parameters())
+        bigram_hash = sum(p.numel() for p in self.bigram.parameters()) if self.bigram is not None else 0
         value_embeds = sum(p.numel() for p in self.value_embeds.parameters())
         lm_head = sum(p.numel() for p in self.lm_head.parameters())
         transformer_matrices = sum(p.numel() for p in self.transformer.h.parameters())
-        scalars = self.resid_lambdas.numel() + self.x0_lambdas.numel() + self.smear_gate.weight.numel() + self.smear_lambda.numel() + self.backout_lambda.numel()
-        total = wte + value_embeds + lm_head + transformer_matrices + scalars
+        scalars = self.resid_lambdas.numel() + self.x0_lambdas.numel() + self.backout_lambda.numel()
+        if self.smear_gate is not None:
+            scalars += self.smear_gate.weight.numel() + self.smear_lambda.numel()
+        total = wte + bigram_hash + value_embeds + lm_head + transformer_matrices + scalars
         assert total == sum(p.numel() for p in self.parameters()), "Parameter count mismatch"
         return {
             'wte': wte,
+            'bigram_hash': bigram_hash,
             'value_embeds': value_embeds,
             'lm_head': lm_head,
             'transformer_matrices': transformer_matrices,
@@ -376,14 +516,28 @@ class GPT(nn.Module):
         ddp, rank, local_rank, world_size = get_dist_info()
 
         # Separate out all parameters into groups
-        matrix_params = list(self.transformer.h.parameters())
+        gated_attn_params = [block.attn.attn_gate.weight for block in self.transformer.h if block.attn.attn_gate is not None]
+        matrix_params = [p for p in self.transformer.h.parameters() if all(p is not gp for gp in gated_attn_params)]
         value_embeds_params = list(self.value_embeds.parameters())
         embedding_params = list(self.transformer.wte.parameters())
+        bigram_embed_params = []
+        bigram_matrix_params = []
+        bigram_scalar_params = []
+        if self.bigram is not None:
+            bigram_embed_params.append(self.bigram.embed.weight)
+            if self.bigram.proj is not None:
+                bigram_matrix_params.append(self.bigram.proj.weight)
+            bigram_scalar_params.append(self.bigram.scale)
         lm_head_params = list(self.lm_head.parameters())
         resid_params = [self.resid_lambdas]
         x0_params = [self.x0_lambdas]
-        smear_params = [self.smear_gate.weight, self.smear_lambda, self.backout_lambda]
-        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(smear_params)
+        smear_params = []
+        if self.smear_gate is not None:
+            smear_params.extend([self.smear_gate.weight, self.smear_lambda])
+        backout_params = [self.backout_lambda]
+        matrix_params.extend(bigram_matrix_params)
+        all_grouped = matrix_params + gated_attn_params + value_embeds_params + embedding_params + bigram_embed_params + bigram_scalar_params + lm_head_params + resid_params + x0_params + smear_params + backout_params
+        assert len(list(self.parameters())) == len(all_grouped)
 
         # Scale the LR for the AdamW parameters by ∝1/√dmodel (tuned for 768 dim model)
         dmodel_lr_scale = (model_dim / 768) ** -0.5
@@ -397,8 +551,16 @@ class GPT(nn.Module):
             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),
             dict(kind='adamw', params=resid_params, lr=scalar_lr * 0.01, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.05),
             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
-            dict(kind='adamw', params=smear_params, lr=0.2, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0),
         ]
+        if bigram_embed_params:
+            param_groups.append(dict(kind='adamw', params=bigram_embed_params, lr=embedding_lr * dmodel_lr_scale, betas=(0.8, 0.995), eps=1e-10, weight_decay=0.001))
+        if bigram_scalar_params:
+            param_groups.append(dict(kind='adamw', params=bigram_scalar_params, lr=0.1, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0))
+        if gated_attn_params:
+            param_groups.append(dict(kind='adamw', params=gated_attn_params, lr=0.15, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0))
+        if smear_params:
+            param_groups.append(dict(kind='adamw', params=smear_params, lr=0.2, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0))
+        param_groups.append(dict(kind='adamw', params=backout_params, lr=0.15, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0))
         # Muon groups (matrix params, grouped by shape for stacking)
         for shape in sorted({p.shape for p in matrix_params}):
             group_params = [p for p in matrix_params if p.shape == shape]
@@ -427,26 +589,33 @@ class GPT(nn.Module):
         # Embed the tokens
         x = self.transformer.wte(idx) # embed current token
         x = x.to(COMPUTE_DTYPE) # ensure activations are in compute dtype (no-op usually, but active for fp16 code path)
+        if self.bigram is not None:
+            prev_tokens = None if kv_cache is None else kv_cache.prev_token_ids
+            x = x + self.bigram(idx, prev_tokens=prev_tokens).to(x.dtype)
+            if kv_cache is not None:
+                kv_cache.prev_token_ids = idx[:, -1].clone()
         x = norm(x)
+        x_base = x
 
-        # Smear: mix previous token's embedding into current position (cheap bigram info)
-        if kv_cache is None:
-            # Training / naive generate: full sequence available, use fast slice
-            assert T > 1, "Training forward pass should have T > 1"
-            gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, 1:, :24]))
-            x = torch.cat([x[:, :1], x[:, 1:] + gate * x[:, :-1]], dim=1)
-        else:
-            # KV cache inference: read prev embedding from cache, store current for next step
-            x_pre_smear = kv_cache.prev_embedding
-            kv_cache.prev_embedding = x[:, -1:, :]
-            if T > 1:
-                # Prefill: apply smear to positions 1+, same as training
-                gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, 1:, :24]))
-                x = torch.cat([x[:, :1], x[:, 1:] + gate * x[:, :-1]], dim=1)
-            elif x_pre_smear is not None:
-                # Decode: single token, use cached prev embedding
-                gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, :, :24]))
-                x = x + gate * x_pre_smear
+        # Smear: inject a gated copy of the previous token embedding as cheap bigram context.
+        if self.smear_gate is not None:
+            gate_channels = self.config.smear_channels
+            if kv_cache is None:
+                assert T > 1, "Training forward pass should have T > 1"
+                x = x_base.clone()
+                gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x_base[:, 1:, :gate_channels]))
+                x[:, 1:] = x[:, 1:] + gate * x_base[:, :-1]
+            else:
+                prev1 = kv_cache.prev_embedding
+                kv_cache.prev_embedding = x_base[:, -1:, :]
+                x = x_base.clone()
+
+                if prev1 is not None:
+                    gate_first = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x_base[:, :1, :gate_channels]))
+                    x[:, :1] = x[:, :1] + gate_first * prev1
+                if T > 1:
+                    gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x_base[:, 1:, :gate_channels]))
+                    x[:, 1:] = x[:, 1:] + gate * x_base[:, :-1]
 
         # Forward the trunk of the Transformer
         x0 = x  # save initial normalized embedding for x0 residual

runs/speedrun.sh

@@ -69,8 +69,8 @@ python -m scripts.tok_eval
 echo "Waiting for dataset download to complete..."
 wait $DATASET_DOWNLOAD_PID
 
-# d24 model (slightly undertrained to beat GPT-2 => decrease data:params ratio from compute optimal 10.5 (default) to 8)
-torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=24 --target-param-data-ratio=8 --device-batch-size=16 --fp8 --run=$WANDB_RUN
+# d24 sparse recipe (baked into the branch defaults), using the recorded 5,318-step horizon
+torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=24 --target-param-data-ratio=8 --num-iterations=5318 --device-batch-size=16 --fp8 --run=$WANDB_RUN
 # evaluate the model: CORE metric, BPB on train/val, and draw samples
 torchrun --standalone --nproc_per_node=8 -m scripts.base_eval -- --device-batch-size=16
 

scripts/base_train.py

@@ -25,7 +25,7 @@ import wandb
 import torch
 import torch.distributed as dist
 
-from nanochat.gpt import GPT, GPTConfig, Linear
+from nanochat.gpt import GPT, GPTConfig, Linear, default_sparse_n_global, compute_sparse_global_layers
 from nanochat.dataloader import tokenizing_distributed_data_loader_bos_bestfit, tokenizing_distributed_data_loader_with_state_bos_bestfit
 from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, autodetect_device_type, get_peak_flops, COMPUTE_DTYPE, COMPUTE_DTYPE_REASON, is_ddp_initialized
 from nanochat.tokenizer import get_tokenizer, get_token_bytes
@@ -52,6 +52,20 @@ parser.add_argument("--aspect-ratio", type=int, default=64, help="model_dim = de
 parser.add_argument("--head-dim", type=int, default=128, help="target head dimension for attention")
 parser.add_argument("--max-seq-len", type=int, default=2048, help="max context length")
 parser.add_argument("--window-pattern", type=str, default="SSSL", help="sliding window pattern tiled across layers: L=full, S=half context (e.g. 'SSL')")
+# Sparse funnel architecture
+parser.add_argument("--sparse-funnel", action=argparse.BooleanOptionalAction, default=True, help="use sparse funnel architecture instead of window pattern")
+parser.add_argument("--n-global", type=int, default=0, help="number of global (full-context) layers, 0 = auto depth//4")
+parser.add_argument("--chirp-gamma", type=float, default=0.7, help="chirp exponent for global layer placement")
+parser.add_argument("--local-window", type=int, default=128, help="local attention window size")
+parser.add_argument("--global-layers", type=str, default="", help="explicit global layer indices, comma-separated (overrides chirp)")
+parser.add_argument("--rope-base", type=int, default=200000, help="RoPE base frequency")
+parser.add_argument("--smear", action=argparse.BooleanOptionalAction, default=True, help="restore cheap bigram-like embedding mixing before the transformer trunk")
+parser.add_argument("--smear-channels", type=int, default=24, help="number of embedding channels used to predict smear gate")
+parser.add_argument("--bigram-vocab-size", type=int, default=4096, help="hashed bigram embedding buckets (0 disables)")
+parser.add_argument("--bigram-dim", type=int, default=128, help="bigram embedding dim before projection")
+parser.add_argument("--gated-attn", action=argparse.BooleanOptionalAction, default=True, help="learn lightweight per-head attention output gates")
+parser.add_argument("--attn-gate-channels", type=int, default=12, help="embedding channels used to predict attention gates")
+parser.add_argument("--ve-layers", type=str, default="", help="explicit value-residual layer indices, comma-separated")
 # Training horizon (only one used, in order of precedence)
 parser.add_argument("--num-iterations", type=int, default=-1, help="explicit number of optimization steps (-1 = disable)")
 parser.add_argument("--target-flops", type=float, default=-1.0, help="calculate num_iterations to reach target_flops (-1 = disable)")
@@ -78,6 +92,14 @@ parser.add_argument("--save-every", type=int, default=-1, help="save checkpoints
 # Output
 parser.add_argument("--model-tag", type=str, default=None, help="override model tag for checkpoint directory name")
 args = parser.parse_args()
+
+if args.sparse_funnel and args.n_global <= 0:
+    args.n_global = default_sparse_n_global(args.depth)
+if args.sparse_funnel and not args.ve_layers:
+    args.ve_layers = ",".join(
+        str(x) for x in compute_sparse_global_layers(args.depth, args.n_global, args.chirp_gamma)
+    )
+
 user_config = vars(args).copy()  # for logging
 # -----------------------------------------------------------------------------
 # Compute init and wandb logging
@@ -133,10 +155,21 @@ def build_model_meta(depth):
     base_dim = depth * args.aspect_ratio
     model_dim = ((base_dim + args.head_dim - 1) // args.head_dim) * args.head_dim
     num_heads = model_dim // args.head_dim
+    # Parse explicit layer lists
+    global_layer_override = tuple(int(x) for x in args.global_layers.split(",") if x.strip()) if args.global_layers else ()
+    ve_layers_parsed = tuple(int(x) for x in args.ve_layers.split(",") if x.strip()) if args.ve_layers else ()
     config = GPTConfig(
         sequence_len=args.max_seq_len, vocab_size=vocab_size,
         n_layer=depth, n_head=num_heads, n_kv_head=num_heads, n_embd=model_dim,
         window_pattern=args.window_pattern,
+        use_sparse_funnel=args.sparse_funnel,
+        n_global=args.n_global, chirp_gamma=args.chirp_gamma,
+        local_window=args.local_window, global_layer_override=global_layer_override,
+        rope_base=args.rope_base,
+        use_smear=args.smear, smear_channels=args.smear_channels,
+        bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim,
+        use_gated_attn=args.gated_attn, attn_gate_channels=args.attn_gate_channels,
+        ve_layers=ve_layers_parsed,
     )
     with torch.device("meta"):
         model_meta = GPT(config)