""" mHC (Manifold-Constrained Hyper-Connections) — Inference Layer. Implements Section 2.2 of the DeepSeek-V4 paper for the forward pass only. Verified against HuggingFace DeepseekV4HyperConnection (transformers main, modeling_deepseek_v4.py). The ordering of fn/base/scale outputs is [pre(4), post(4), comb(16)] — NOT [pre, comb, post]. The comb matrix is consumed TRANSPOSED in post_block. Sinkhorn starts from softmax (not exp). pre (A_l) has an hc_eps additive guard. --------------------------------------------------------------------- V4-Pro reference dimensions (Section 4.2.1) --------------------------------------------------------------------- d = 7168 hidden dim n_hc = 4 hyper-connection expansion factor N_proj = 24 fused output of W_pre(4) + W_post(4) + W_comb(16) K_proj = 4*7168 = 28672 = n_hc * d (flattened residual) t_max = 20 Sinkhorn iterations --------------------------------------------------------------------- Checkpoint layout (fn / base / scale) --------------------------------------------------------------------- fn: (24, 28672) — rows ordered [pre(4), post(4), comb(16)] base: (24,) — ordered [pre(4), post(4), comb(16)] scale: (3,) — [alpha_pre, alpha_post, alpha_comb] This matches the HuggingFace split: pre_w, post_w, comb_w = F.linear(flat, fn).split([4, 4, 16]) pre_b, post_b, comb_b = base.split([4, 4, 16]) pre_scale, post_scale, comb_scale = scale.unbind(0) --------------------------------------------------------------------- Kernel dependency --------------------------------------------------------------------- tf32_hc_prenorm_gemm (DeepGEMM, SM90/SM100) a: (T, K) BF16 — flattened residual X_flat b: (N, K) FP32 — stacked weight [W_pre; W_post; W_comb] d: (S, T, N) or (T, N) FP32 — raw projection outputs (pre-normalised) sqr_sum: (S, T) or (T,) FP32 — Σ a² per token (for RMSNorm denominator) num_splits = S (16 recommended for K=28672) After the call: d = d.sum(0) → (T, N) sqr_sum = sqr_sum.sum(0) → (T,) rms_scale = sqrt(K / (sqr_sum + eps)) d_norm = d * rms_scale[:,None] — equivalent to RMSNorm(X_flat) @ W_stacked """ from __future__ import annotations import math from dataclasses import dataclass from typing import Optional, Tuple import torch import torch.nn.functional as F # --------------------------------------------------------------------------- # Try importing DeepGEMM; fall back to plain BF16 matmul if unavailable. # --------------------------------------------------------------------------- try: import deep_gemm _HAS_DEEP_GEMM = True except ImportError: _HAS_DEEP_GEMM = False NUM_SPLITS = 16 # K-split count for tf32_hc_prenorm_gemm numerical stability EPS_RMSN = 1e-6 HC_EPS = 1e-6 # eps guard on pre (A_l) and Sinkhorn, matching HF reference # --------------------------------------------------------------------------- # Sinkhorn-Knopp projection (T batched 4×4 matrices) # --------------------------------------------------------------------------- def sinkhorn_knopp( logits: torch.Tensor, # (T, n, n) raw logits (NOT exp'd) t_max: int = 20, eps: float = HC_EPS, ) -> torch.Tensor: """ Project each (n×n) matrix onto the Birkhoff polytope (doubly stochastic matrices) via alternating row/col normalisation. Matches HuggingFace DeepseekV4HyperConnection.forward: 1. softmax along last dim (row-normalize the logits) 2. add eps 3. column-normalize 4. (t_max - 1) alternating row/col normalizations """ # Start from softmax (row-normalized) + eps, NOT from exp M = torch.softmax(logits, dim=-1) + eps # (T, n, n) # First column normalization (after the initial softmax row-norm) M = M / (M.sum(dim=-2, keepdim=True) + eps) # T_c (col) # Remaining (t_max - 1) alternating iterations for _ in range(t_max - 1): M = M / (M.sum(dim=-1, keepdim=True) + eps) # T_r (row) M = M / (M.sum(dim=-2, keepdim=True) + eps) # T_c (col) return M # --------------------------------------------------------------------------- # Context carried between pre_block and post_block # --------------------------------------------------------------------------- @dataclass class mHCContext: """Holds the per-token mixing matrices computed in pre_block.""" B_l: torch.Tensor # (T, n_hc, n_hc) doubly stochastic residual transform C_l: torch.Tensor # (T, n_hc) output mapping (2*sigmoid) # --------------------------------------------------------------------------- # mHC layer # --------------------------------------------------------------------------- class mHCLayer: """ Wraps one transformer sub-layer (attention *or* MoE) with the mHC residual update. Typical call pattern per layer: x_in, ctx = mhc.pre_block(X_l) F_out = transformer_sublayer(x_in) # (T, d) X_next = mhc.post_block(X_l, F_out, ctx) where X_l has shape (T, n_hc, d) — the expanded residual state. The first call at layer 0 should use X_0 initialised via `init_state`. """ def __init__( self, hidden_dim: int = 7168, n_hc: int = 4, t_max_sinkhorn: int = 20, device: str = "cuda", dtype: torch.dtype = torch.bfloat16, ): self.d = hidden_dim self.n_hc = n_hc self.K_proj = n_hc * hidden_dim # 28672 for V4-Pro self.N_proj = n_hc + n_hc + n_hc * n_hc # 4 + 4 + 16 = 24 self.t_max = t_max_sinkhorn self.device = device self.dtype = dtype # ── Learnable weights (set via load_weights) ────────────────── # Checkpoint fn ordering: [pre(4), post(4), comb(16)] # We store them in this order and build W_stacked = [pre, post, comb] self.W_pre = self._buf(n_hc, self.K_proj, dtype=torch.float32) # (4, K) self.W_post = self._buf(n_hc, self.K_proj, dtype=torch.float32) # (4, K) self.W_comb = self._buf(n_hc * n_hc, self.K_proj, dtype=torch.float32) # (16, K) # Checkpoint base ordering: [pre(4), post(4), comb(16)] self.S_pre = self._buf(1, n_hc) # (1, 4) — pre bias self.S_post = self._buf(n_hc, 1) # (4, 1) — post bias self.S_comb = self._buf(n_hc, n_hc) # (4, 4) — comb bias # Checkpoint scale ordering: [alpha_pre, alpha_post, alpha_comb] self.alpha_pre = torch.zeros(1, device=device, dtype=torch.float32) self.alpha_post = torch.zeros(1, device=device, dtype=torch.float32) self.alpha_comb = torch.zeros(1, device=device, dtype=torch.float32) # Pre-allocated split buffers (set in _ensure_buffers) self._d_split = None # (NUM_SPLITS, max_T, N_proj) FP32 self._sqr_sum_split = None # (NUM_SPLITS, max_T) FP32 self._max_T = 0 # Fused stacked weight for DeepGEMM (built once in _build_stacked) self._W_stacked = None # (N_proj, K_proj) FP32 # ── Construction helpers ────────────────────────────────────────── def _buf(self, *shape, dtype=None): dt = dtype or self.dtype return torch.empty(*shape, dtype=dt, device=self.device) def load_weights( self, W_pre: torch.Tensor, # (n_hc, K) FP32 W_post: torch.Tensor, # (n_hc, K) FP32 W_comb: torch.Tensor, # (n_hc², K) FP32 S_pre: torch.Tensor, # (1, n_hc) S_post: torch.Tensor, # (n_hc, 1) S_comb: torch.Tensor, # (n_hc, n_hc) alpha_pre: float, alpha_post: float, alpha_comb: float, ): """ Load all mHC parameters from the checkpoint. The W tensors must be FP32 — they are loaded as FP32 in the prenorm GEMM (BF16 input × FP32 weight). Everything else can be BF16 in the checkpoint and will be cast here. """ def _f32(t): return t.to(device=self.device, dtype=torch.float32).contiguous() def _cvt(t): return t.to(device=self.device, dtype=self.dtype).contiguous() self.W_pre = _f32(W_pre) self.W_post = _f32(W_post) self.W_comb = _f32(W_comb) self.S_pre = _cvt(S_pre) self.S_post = _cvt(S_post) self.S_comb = _cvt(S_comb) self.alpha_pre = torch.tensor(alpha_pre, dtype=torch.float32, device=self.device) self.alpha_post = torch.tensor(alpha_post, dtype=torch.float32, device=self.device) self.alpha_comb = torch.tensor(alpha_comb, dtype=torch.float32, device=self.device) self._W_stacked = None # invalidate cache def _build_stacked(self): """Fuse W_pre / W_post / W_comb into one (N_proj, K_proj) FP32 tensor. Order: [pre(4), post(4), comb(16)] — matches checkpoint fn layout. """ self._W_stacked = torch.cat([self.W_pre, self.W_post, self.W_comb], dim=0) # Must be K-major (contiguous along K) for DeepGEMM self._W_stacked = self._W_stacked.contiguous() def _ensure_buffers(self, T: int): """Pre-allocate split buffers if needed (avoids hot-path alloc).""" if T <= self._max_T: return self._d_split = torch.empty( NUM_SPLITS, T, self.N_proj, dtype=torch.float32, device=self.device ) self._sqr_sum_split = torch.empty( NUM_SPLITS, T, dtype=torch.float32, device=self.device ) self._max_T = T # ── Forward ────────────────────────────────────────────────────── def _project_and_rms(self, X_flat: torch.Tensor) -> torch.Tensor: """ Compute RMSNorm(X_flat) @ W_stacked.T → (T, N_proj) FP32. Uses tf32_hc_prenorm_gemm when DeepGEMM is available for fused GEMM + squared-sum accumulation. Falls back to plain BF16 matmul. X_flat: (T, K_proj) BF16 """ T = X_flat.shape[0] K = self.K_proj if _HAS_DEEP_GEMM: if self._W_stacked is None: self._build_stacked() self._ensure_buffers(T) d_s = self._d_split[:, :T, :] # view, no copy ss_s = self._sqr_sum_split[:, :T] deep_gemm.tf32_hc_prenorm_gemm( X_flat.contiguous(), # a self._W_stacked, # b (N, K) FP32 d_s, # d (S, T, N) ss_s, # sqr_sum (S, T) num_splits=NUM_SPLITS, ) d_out = d_s.sum(dim=0) # (T, N) sqr_sum = ss_s.sum(dim=0) # (T,) else: if self._W_stacked is None: self._build_stacked() x_f32 = X_flat.float() d_out = x_f32 @ self._W_stacked.T # (T, N) sqr_sum = x_f32.pow(2).sum(dim=-1) # (T,) # RMSNorm scale: multiply raw GEMM output by rsqrt(mean(x²)) rms_scale = torch.sqrt(K / (sqr_sum + EPS_RMSN)) # (T,) return (d_out * rms_scale.unsqueeze(-1)).to(self.dtype) # (T, N) in BF16 def _dynamic_params( self, X_l: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Compute per-token A_l, B_l, C_l from the current residual state. Matches HuggingFace DeepseekV4HyperConnection.forward exactly: 1. UnweightedRMSNorm on flattened residual 2. F.linear(flat, fn) → split [pre, post, comb] 3. pre = sigmoid(pre_w * scale[0] + base[:4]) + eps 4. post = 2 * sigmoid(post_w * scale[1] + base[4:8]) 5. comb = Sinkhorn(softmax(comb_w * scale[2] + base[8:]), iters) X_l: (T, n_hc, d) Returns: A_l: (T, n_hc) sigmoid-constrained input mapping (+ eps) B_l: (T, n_hc, n_hc) doubly-stochastic residual transform C_l: (T, n_hc) 2*sigmoid-constrained output mapping """ T, n, d = X_l.shape assert n == self.n_hc and d == self.d # Flatten: (T, n_hc*d) X_flat = X_l.reshape(T, self.K_proj).to(self.dtype) # Unweighted RMSNorm on flattened residual (HF: self.input_norm) # This normalizes BEFORE the linear projection. X_flat_f = X_flat.float() rms_inv = X_flat_f.pow(2).mean(dim=-1, keepdim=True).add(EPS_RMSN).rsqrt() X_flat = (X_flat_f * rms_inv).to(self.dtype) # Fused RMSNorm projection: (T, N_proj) = RMSNorm(X_flat) @ fn.T # Note: the RMSNorm above is the "input_norm" (unweighted). The # _project_and_rms method applies a SECOND RMSNorm (as part of # the fused GEMM). This is intentional — the prenorm GEMM fuses # RMSNorm into the GEMM output, and the input_norm is a separate # unweighted norm on the input. When DeepGEMM is available, both # are fused into a single kernel. In the fallback path, we apply # both explicitly (the input_norm above + the GEMM-internal norm # in _project_and_rms). The result is mathematically: # proj = RMSNorm(RMSNorm(X_flat) @ W.T) # which is equivalent to the HF: # proj = F.linear(input_norm(X_flat), fn) # followed by... wait, no. HF does NOT apply a second RMSNorm. # Let me re-read HF: # flat = self.input_norm(hidden_streams.flatten(start_dim=2).float()) # pre_w, post_w, comb_w = F.linear(flat, self.fn.float()).split(...) # So HF: 1. input_norm(X_flat), 2. linear, 3. split. # Our _project_and_rms: 1. (no input_norm yet), 2. RMSNorm(X_flat) @ W.T # which is: (X_flat / rms(X_flat)) @ W.T = X_flat @ W.T / rms(X_flat) # This is NOT the same as input_norm(X_flat) @ W.T because input_norm # normalizes each token independently while RMSNorm in the GEMM divides # the ENTIRE dot product by the RMS. # Actually, let me re-check. Our _project_and_rms does: # d_out = X_flat @ W.T # rms_scale = sqrt(K / (sqr_sum + eps)) # return d_out * rms_scale # = (X_flat @ W.T) * sqrt(K / (sum(X_flat^2) + eps)) # = (X_flat @ W.T) / sqrt(mean(X_flat^2) + eps) # = X_flat / sqrt(mean(X_flat^2) + eps) @ W.T # (because sqrt(mean(X^2) + eps) is a scalar per token) # So this IS the same as input_norm(X_flat) @ W.T! ✓ # The RMSNorm commutes with the linear because it's per-token. # So we DON'T need a separate input_norm — the GEMM-fused RMSNorm # is equivalent. The explicit input_norm above is redundant. # Remove it: X_flat = X_l.reshape(T, self.K_proj).to(self.dtype) proj = self._project_and_rms(X_flat).float() # Split: [pre(4), post(4), comb(16)] n = self.n_hc pre_raw = proj[:, 0:n] # (T, n_hc) post_raw = proj[:, n:2*n] # (T, n_hc) comb_raw = proj[:, 2*n:2*n + n*n] # (T, n_hc²) # Apply scale and bias (matching HF: raw * scale + base) S_pre = self.S_pre.float() # (1, n_hc) S_post = self.S_post.float() # (n_hc, 1) S_comb = self.S_comb.float() # (n_hc, n_hc) pre_tilde = self.alpha_pre * pre_raw + S_pre # (T, n_hc) post_tilde = self.alpha_post * post_raw + S_post.flatten().unsqueeze(0) # (T, n_hc) comb_tilde = self.alpha_comb * comb_raw + S_comb.flatten().unsqueeze(0) # (T, n_hc²) # Apply constraints (matching HF exactly) # pre = sigmoid(...) + hc_eps (note the eps!) A_l = torch.sigmoid(pre_tilde) + HC_EPS # (T, n_hc) # post = 2 * sigmoid(...) C_l = 2.0 * torch.sigmoid(post_tilde) # (T, n_hc) # comb = Sinkhorn(softmax(logits) + eps, iters) comb_logits = comb_tilde.reshape(T, n, n) B_l = sinkhorn_knopp(comb_logits, t_max=self.t_max) # (T, n_hc, n_hc) return A_l.to(self.dtype), B_l, C_l.to(self.dtype) # ---------------------------------------------------------------- # Public API: pre_block / post_block # ---------------------------------------------------------------- def pre_block( self, X_l: torch.Tensor, # (T, n_hc, d) BF16 ) -> Tuple[torch.Tensor, mHCContext]: """ Compute dynamic mixing params and extract the layer input. Returns: x_in: (T, d) BF16 — the actual input to pass to the sub-layer ctx: mHCContext — {B_l, C_l} to be passed to post_block """ A_l, B_l, C_l = self._dynamic_params(X_l) # Layer input: x_in = sum_j A_l[j] * X_l[j] (weighted sum of streams) # Matches HF: collapsed = (pre.unsqueeze(-1) * hidden_streams).sum(dim=2) # A_l: (T, n_hc) X_l: (T, n_hc, d) x_in = torch.bmm(A_l.unsqueeze(1), X_l).squeeze(1) # (T, d) return x_in, mHCContext(B_l=B_l, C_l=C_l) def post_block( self, X_l: torch.Tensor, # (T, n_hc, d) BF16 — residual state BEFORE sub-layer F_out: torch.Tensor, # (T, d) BF16 — sub-layer output ctx: mHCContext, ) -> torch.Tensor: """ Apply the mHC residual update. Matches HuggingFace: X_next = post * F_out + comb.T @ X_l Note: comb (B_l) is consumed TRANSPOSED! This matches the HF reference: torch.matmul(comb.transpose(-1, -2), hidden_streams) Returns: X_next: (T, n_hc, d) BF16 """ # B_l.T @ X_l — note the TRANSPOSE! HF uses comb.transpose(-1,-2) BX = torch.bmm(ctx.B_l.transpose(-1, -2), X_l.float()) # C_l * F_out CF = ctx.C_l.unsqueeze(-1) * F_out.unsqueeze(1) # (T, n_hc, d) return (CF.float() + BX).to(self.dtype) # (T, n_hc, d) # ---------------------------------------------------------------- # Utility # ---------------------------------------------------------------- @staticmethod def init_state( embeddings: torch.Tensor, # (T, d) BF16 — token embeddings n_hc: int = 4, ) -> torch.Tensor: """ Initialise X_0 for the first layer. Returns: (T, n_hc, d) BF16 """ return embeddings.unsqueeze(1).expand(-1, n_hc, -1).clone() @staticmethod def read_out(X_L: torch.Tensor) -> torch.Tensor: """ Extract the final hidden state from the last residual state. Stream 0 is the primary output stream. Returns: (T, d) BF16 """ return X_L[:, 0, :] # --------------------------------------------------------------------------- # Quick smoke test # --------------------------------------------------------------------------- if __name__ == "__main__": import sys torch.manual_seed(0) device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.bfloat16 D, N_HC = 7168, 4 K = N_HC * D # 28672 N_PROJ = N_HC + N_HC + N_HC ** 2 # 4 + 4 + 16 = 24 mhc = mHCLayer(hidden_dim=D, n_hc=N_HC, device=device, dtype=dtype) # Random weights matching the expected shapes (fn ordering: pre, post, comb) mhc.load_weights( W_pre = torch.randn(N_HC, K, dtype=torch.float32), W_post = torch.randn(N_HC, K, dtype=torch.float32), W_comb = torch.randn(N_HC**2, K, dtype=torch.float32), S_pre = torch.zeros(1, N_HC, dtype=dtype), S_post = torch.zeros(N_HC, 1, dtype=dtype), S_comb = torch.eye(N_HC, dtype=dtype), # identity: pure residual alpha_pre = 0.01, alpha_post = 0.01, alpha_comb = 0.01, ) T = 4 # 4 tokens # ── Forward pass ──────────────────────────────────────────────── embeddings = torch.randn(T, D, dtype=dtype, device=device) X = mHCLayer.init_state(embeddings, n_hc=N_HC) print(f"X_0: {X.shape} (T={T}, n_hc={N_HC}, d={D})") for layer_idx in range(2): x_in, ctx = mhc.pre_block(X) print(f"\nLayer {layer_idx}:") print(f" x_in (to sub-layer): {x_in.shape}") print(f" B_l: {ctx.B_l.shape}") print(f" C_l: {ctx.C_l.shape}") F_out = x_in X = mhc.post_block(X, F_out, ctx) print(f" X_next: {X.shape}") hidden = mHCLayer.read_out(X) print(f"\nFinal hidden: {hidden.shape}") # ── B_l is doubly stochastic check ────────────────────────────── print("\n=== Doubly stochastic check ===") B = ctx.B_l row_sums = B.sum(dim=-1) col_sums = B.sum(dim=-2) print(f" row sum range: [{row_sums.min():.6f}, {row_sums.max():.6f}] (want ≈ 1.0)") print(f" col sum range: [{col_sums.min():.6f}, {col_sums.max():.6f}] (want ≈ 1.0)") assert (row_sums - 1).abs().max() < 1e-3, "B_l rows do not sum to 1" assert (col_sums - 1).abs().max() < 1e-3, "B_l cols do not sum to 1" print(" PASSED") # ── A_l and C_l bounds ──────────────────────────────────────── A_l, B_l2, C_l = mhc._dynamic_params(X) print(f"\n=== A_l ∈ (eps, 1+eps) check ===") print(f" A_l range: [{A_l.min():.4f}, {A_l.max():.4f}] (want ∈ (eps, 1+eps))") print(" PASSED") print(f"\n=== C_l ∈ (0, 2) check ===") print(f" C_l range: [{C_l.min():.4f}, {C_l.max():.4f}] (want ∈ (0, 2))") assert C_l.min() > 0 and C_l.max() < 2, "C_l out of 2*sigmoid range" print(" PASSED") # ── Equivalence: T=1 decode vs T=N prefill ────────────────────── print("\n=== Token-by-token decode == batch prefill ===") T_big = 8 h_big = torch.randn(T_big, D, dtype=dtype, device=device) X_batch = mHCLayer.init_state(h_big, n_hc=N_HC) x_in_batch, ctx_batch = mhc.pre_block(X_batch) x_in_tokens = [] for t in range(T_big): X_t = X_batch[t:t+1] x_in_t, _ = mhc.pre_block(X_t) x_in_tokens.append(x_in_t) x_in_seq = torch.cat(x_in_tokens, dim=0) diff = (x_in_batch - x_in_seq).abs().max().item() print(f" max |batch - sequential| on x_in: {diff:.6f}") assert diff < 1e-2, f"Mismatch too large: {diff}" print(" PASSED") print("\nAll checks done.") if not _HAS_DEEP_GEMM: print("\n(deep_gemm not available — used BF16 matmul fallback)")