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refactor(ml-alpha): replace legacy attention_pool with MultiHorizonAttention [Stage 2]

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Single source of truth for the attention path. Deletes the legacy
single-Q `attention_pool.cu` and all `attn_*` fields from
`PerceptionTrainer`; wires `MultiHorizonAttention` (the bundle
introduced in Stage 1) into `step_batched` + `evaluate_batched` as
THE attention summary that seeds CfC's `h_old` at k=0.

Deletions:
  cuda/attention_pool.cu                      (244 lines)
  perception.rs::attn_q_d/attn_context_d/
    attn_weights_d/grad_attn_q_d/opt_attn_q/
    attn_fwd_fn/attn_bwd_fn/_attn_module/
    attn_grad_q_scratch_d                     (all struct fields)
  perception.rs::ATTENTION_POOL_CUBIN         (include_bytes constant)
  Their corresponding init + struct-construction lines.
  build.rs::KERNELS                           (drops "attention_pool")

New kernel + binding:
  cuda/horizon_mean_collapse.cu               (53 lines)
    - `horizon_mean_collapse_fwd/_bwd`: collapses [B, N_H, H] → [B, H]
      by averaging over the horizon axis. Single-pass, no reductions.
  src/horizon_mean_collapse.rs                 (host binding)

MHA additions:
  - `collapse` field + `ctx_mean_d` + `grad_ctx_mean_d` for the seed.
  - `grad_ctx_h_d` scratch (split from grad_horizon_tokens_scratch to
    avoid aliasing when MoE bwd writes d_ctx_h while pool bwd writes
    d_horizon_tokens).
  - `forward(ln_b_out)`: horizon-token pool → inverted pool → MoE
    dispatch → mean-collapse → ctx_mean_d.
  - `backward(ln_b_out, grad_ctx_mean, grad_ln_out)`: full reverse
    chain.
  - `apply_anchor()`: launches anchor_l2 on horizon_tokens, Q,
    experts_w.
  - `adamw_step()`: steps all 6 owned optimizer groups.

PerceptionTrainer integration:
  - Section 2d (forward): `self.mha.forward(&self.ln_out_d)` replaces
    the legacy attention_pool launch. CfC's h_old at k=0 now reads
    `self.mha.ctx_mean_d.device_ptr` (was `self.attn_context_d`).
  - Section 7c-pre (backward): `self.mha.backward(ln_out, grad_h_carry,
    grad_h_enriched_seq)` replaces the legacy attn_bwd_fn launch.
  - Four `reduce_axis0` launches collapse MHA's per-batch scratches
    into shared gradient buffers: grad_horizon_tokens, grad_q,
    grad_experts_w, grad_experts_b.
  - `self.mha.apply_anchor()` adds L2 anchor grad contributions.
  - Section 9 (AdamW): `self.mha.adamw_step()` replaces opt_attn_q.
  - `evaluate_batched`: `self.mha.forward` replaces the legacy fwd
    launch; h_old at k=0 reads `mha.ctx_mean_d`.
  - `self.mha.zero_grads()` at step start (capture-safe memset_zeros).

BUG CAUGHT DURING WIRING (NVIDIA-grade discipline): first wiring
attempt mis-sized the reduce_axis0 launches for the MoE
`grad_w_scratch_d` ([B, N_H, N_E, H, H]). Initial `n_tail = N_H * N_E
* H * H = 327680` would have made reduce_axis0 read 5× past the end
of the buffer → CUDA_ERROR_ILLEGAL_ADDRESS. Fix: `n_tail = N_E * H *
H = 65536` with `n_batch = B * N_H`, treating the leading two axes
together as the reduction dimension. Caught by stacked_trainer test
on RTX 3050; would have caused silent corruption then a hard fault
on L40S/H100 later.

LOCAL VERIFICATION (RTX 3050 sm_86):
  - ml-alpha builds clean (cuda feature).
  - All 38+ tests PASS serially with --test-threads=1:
      perception_overfit (8 tests incl. loss-shrinks)
      trunk_forward (5)
      stacked_loss_shrinks (multiple)
      bce_grad_finite_diff (4)
      snap_feature_assemble (9)
      ... (full suite green)
  - Numgrad parity for the 4 new MHA kernels (horizon_token, inv_attn,
    regime_moe_gate, anchor_l2) PASSES at 5e-2 rel.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-05-18 15:23:27 +02:00
parent 6a3f45d872
commit 9170d24fe3
7 changed files with 414 additions and 408 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
crates/ml-alpha/build.rs
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@@ -20,10 +20,10 @@ const KERNELS: &[&str] = &[
"horizon_lambda", // ISV-driven per-horizon gradient scaler (EMA + lambda)
"layer_norm", // Phase 1: trunk pre-CfC normalisation
"variable_selection", // Phase 2D: TFT-style per-feature gating
"attention_pool", // Legacy single-Q content summary at CfC k=0 (Stage 2 replaces with MHA)
"horizon_token_attention_pool", // Horizon-token K-prepend single-Q attention pool
"inverted_attention_pool", // Cross-variate (iTransformer-style) attention pool
"regime_moe_gate", // Top-1 regime MoE gate + expert dispatch + aux loss
"horizon_mean_collapse", // Mean over horizon axis (seeds CfC h_old at k=0)
"anchor_l2", // L2 anchor regularization toward init
"reduce_axis0", // Phase B: cross-batch param-grad reducer
];

244
crates/ml-alpha/cuda/attention_pool.cu
View File

@@ -1,244 +0,0 @@
// attention_pool.cu — Single-head attention pool over Mamba2 K-positions.
// Phase 3 (2026-05-17).
//
// Replaces the CfC's zero-initialised `h_old` at k=0 with a learned
// attention-pooled summary over all K LN_b output positions. The K-loop
// CfC keeps its recurrent semantics (h_old at k+1 = h_new at k); only
// the initial state changes from zero to a content-addressable lookup.
//
// Forward math (per sample b):
// scores[k] = Q · keys[b, k, :] # [K] — dot product over HIDDEN_DIM
// attn[k] = softmax_k(scores) # [K]
// context[h] = sum_k attn[k] * values[b, k, h] # [HIDDEN_DIM]
//
// For this attention pool, keys == values == LN_b output [B, K, HIDDEN_DIM].
// Single learned param: Q [HIDDEN_DIM].
//
// Saved for backward:
// attn_weights [B, K] (post-softmax)
//
// Block layout (forward):
// grid_dim = (B, 1, 1), block_dim = (HIDDEN_DIM=128, 1, 1).
// Thread tid handles dimension h of HIDDEN_DIM.
//
// Per `feedback_no_atomicadd.md`: block tree-reduce only, no atomics.
#define ATTN_HIDDEN_DIM 128
#define ATTN_BLOCK 128
#define ATTN_MAX_K 512 // safety cap on K; smoke uses K=16-64.
extern "C" __global__ void attention_pool_fwd(
const float* __restrict__ Q, // [HIDDEN_DIM]
const float* __restrict__ ln_out, // [B, K, HIDDEN_DIM]
int n_batch,
int k_seq,
float* __restrict__ context, // [B, HIDDEN_DIM]
float* __restrict__ attn_weights // [B, K] — saved post-softmax
) {
int b_idx = blockIdx.x;
int tid = threadIdx.x;
if (b_idx >= n_batch || tid >= ATTN_BLOCK) return;
extern __shared__ float smem[];
float* s_scores = smem; // [K]
float* s_red = smem + k_seq; // [BLOCK] — reduce scratch
float* s_context = smem + k_seq + ATTN_BLOCK; // [HIDDEN_DIM] — final output buffer
// Cache Q in shared mem (broadcast).
__shared__ float s_Q[ATTN_HIDDEN_DIM];
if (tid < ATTN_HIDDEN_DIM) s_Q[tid] = Q[tid];
__syncthreads();
// Pass 1: scores[k] = Q · ln_out[b, k, :].
// Each k is handled sequentially by ALL threads via tree-reduce
// over HIDDEN_DIM. K outer loop, threads tile HIDDEN_DIM inner.
// For HIDDEN_DIM=128 and ATTN_BLOCK=128 we have one-to-one.
const float* ln_b = ln_out + (long long)b_idx * k_seq * ATTN_HIDDEN_DIM;
for (int k = 0; k < k_seq; ++k) {
const float v = ln_b[k * ATTN_HIDDEN_DIM + tid] * s_Q[tid];
s_red[tid] = v;
__syncthreads();
for (int s = ATTN_BLOCK / 2; s > 0; s >>= 1) {
if (tid < s) s_red[tid] += s_red[tid + s];
__syncthreads();
}
if (tid == 0) s_scores[k] = s_red[0];
__syncthreads();
}
// Pass 2: softmax over K. Numerically stable: max-subtract + exp + sum.
// Block-wide max over s_scores[0..k_seq).
float my_max = -INFINITY;
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
const float v = s_scores[k];
if (v > my_max) my_max = v;
}
s_red[tid] = my_max;
__syncthreads();
for (int s = ATTN_BLOCK / 2; s > 0; s >>= 1) {
if (tid < s) {
const float a = s_red[tid];
const float b = s_red[tid + s];
s_red[tid] = (a > b) ? a : b;
}
__syncthreads();
}
__shared__ float s_max;
if (tid == 0) s_max = s_red[0];
__syncthreads();
// exp(score - max) + sum.
float my_sum = 0.0f;
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
const float e = expf(s_scores[k] - s_max);
s_scores[k] = e; // reuse as exp_shifted
my_sum += e;
}
s_red[tid] = my_sum;
__syncthreads();
for (int s = ATTN_BLOCK / 2; s > 0; s >>= 1) {
if (tid < s) s_red[tid] += s_red[tid + s];
__syncthreads();
}
__shared__ float s_sum;
if (tid == 0) s_sum = s_red[0];
__syncthreads();
// Pass 3: attn[k] = exp/sum; save; context[h] = sum_k attn[k] * ln_out[b, k, h].
// Save attn weights AND build the context output.
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
const float a = s_scores[k] / s_sum;
s_scores[k] = a; // overwrite again — now holds true attn weights
attn_weights[(long long)b_idx * k_seq + k] = a;
}
__syncthreads();
// Accumulate context[h=tid] = sum_k attn[k] * ln_out[b, k, h=tid].
if (tid < ATTN_HIDDEN_DIM) {
float c = 0.0f;
for (int k = 0; k < k_seq; ++k) {
c += s_scores[k] * ln_b[k * ATTN_HIDDEN_DIM + tid];
}
s_context[tid] = c;
context[(long long)b_idx * ATTN_HIDDEN_DIM + tid] = c;
}
}
// Attention pool backward — chain rule:
//
// d_attn[k] = sum_h grad_context[h] * values[b, k, h]
// + (this is from context = sum_k attn[k] * values[b, k, :])
//
// d_values[b, k, h] += grad_context[h] * attn[k]
// + d_scores[k] * Q[h]
//
// d_scores via softmax Jacobian:
// d_scores[k] = attn[k] * (d_attn[k] - sum_kp attn[kp] * d_attn[kp])
//
// d_Q[h] += sum_{b, k} d_scores[k] * values[b, k, h]
// (here values == ln_out)
//
// Single-writer discipline:
// - d_Q is [HIDDEN_DIM] shared across all (b, k). ONE block per
// launch, internal batch loop, tile over HIDDEN_DIM. += into d_Q.
// - d_values[b, k, h] is [B, K, HIDDEN_DIM] — one block per launch
// iterates over (b, k, h) sequentially; with block_dim = HIDDEN_DIM,
// thread h is sole writer of column h for ALL (b, k).
// Block-per-batch attn_pool bwd (Phase B commit 4).
// grid=(n_batch, 1, 1) block=(ATTN_BLOCK, 1, 1)
//
// Each block handles one batch's HIDDEN_DIM channels. grad_ln_out is
// per-batch indexed (already safe), so each block bi writes its
// [bi, :, :] slice via += onto whatever value grad_ln_out holds at
// kernel launch (the K-loop's contribution to LN_b output grad).
//
// grad_Q is a single [HIDDEN_DIM] shared across all batches → per-batch
// scratch [B, HIDDEN_DIM], reduced after the kernel returns.
extern "C" __global__ void attention_pool_bwd(
const float* __restrict__ Q, // [HIDDEN_DIM]
const float* __restrict__ ln_out, // [B, K, HIDDEN_DIM] (= values)
const float* __restrict__ attn_weights, // [B, K] from fwd
const float* __restrict__ grad_context, // [B, HIDDEN_DIM]
int n_batch,
int k_seq,
float* __restrict__ grad_Q_scratch, // [B, HIDDEN_DIM] (+=)
float* __restrict__ grad_ln_out // [B, K, HIDDEN_DIM] (+= chained with K-loop)
) {
int bi = blockIdx.x;
int tid = threadIdx.x;
if (bi >= n_batch || tid >= ATTN_BLOCK) return;
extern __shared__ float smem[];
float* s_dattn = smem; // [K]
float* s_dscores = smem + k_seq; // [K]
float* s_red = smem + 2 * k_seq; // [BLOCK]
__shared__ float s_Q[ATTN_HIDDEN_DIM];
__shared__ float s_attn[ATTN_MAX_K];
__shared__ float s_grad_ctx[ATTN_HIDDEN_DIM];
__shared__ float s_sum_attn_dattn;
if (tid < ATTN_HIDDEN_DIM) s_Q[tid] = Q[tid];
__syncthreads();
const float* ln_b = ln_out + (long long)bi * k_seq * ATTN_HIDDEN_DIM;
float* grad_ln_b = grad_ln_out + (long long)bi * k_seq * ATTN_HIDDEN_DIM;
// Cache attn + grad_context for this block's batch.
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
s_attn[k] = attn_weights[(long long)bi * k_seq + k];
}
if (tid < ATTN_HIDDEN_DIM) {
s_grad_ctx[tid] = grad_context[(long long)bi * ATTN_HIDDEN_DIM + tid];
}
__syncthreads();
// Pass 1: d_attn[k] = sum_h grad_context[h] * values[b, k, h].
for (int k = 0; k < k_seq; ++k) {
const float v = (tid < ATTN_HIDDEN_DIM)
? s_grad_ctx[tid] * ln_b[k * ATTN_HIDDEN_DIM + tid] : 0.0f;
s_red[tid] = v;
__syncthreads();
for (int s = ATTN_BLOCK / 2; s > 0; s >>= 1) {
if (tid < s) s_red[tid] += s_red[tid + s];
__syncthreads();
}
if (tid == 0) s_dattn[k] = s_red[0];
__syncthreads();
}
// Pass 2: sum_{kp} attn[kp] * d_attn[kp] (softmax Jacobian centring).
float my_sum = 0.0f;
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
my_sum += s_attn[k] * s_dattn[k];
}
s_red[tid] = my_sum;
__syncthreads();
for (int s = ATTN_BLOCK / 2; s > 0; s >>= 1) {
if (tid < s) s_red[tid] += s_red[tid + s];
__syncthreads();
}
if (tid == 0) s_sum_attn_dattn = s_red[0];
__syncthreads();
// Pass 3: d_scores[k] = attn[k] * (d_attn[k] - s_sum_attn_dattn).
for (int k = tid; k < k_seq; k += ATTN_BLOCK) {
s_dscores[k] = s_attn[k] * (s_dattn[k] - s_sum_attn_dattn);
}
__syncthreads();
// Pass 4: grad_Q_scratch[bi, h] += sum_k d_scores[k] * ln_out[b, k, h].
// d_ln_out[b, k, h] += grad_context[h] * attn[k] + d_scores[k] * Q[h].
// Thread h owns column h. Loops over k. grad_ln_out += chains the
// attn-path gradient on top of the K-loop's contribution.
if (tid < ATTN_HIDDEN_DIM) {
float dq_local = 0.0f;
for (int k = 0; k < k_seq; ++k) {
const float v = ln_b[k * ATTN_HIDDEN_DIM + tid];
dq_local += s_dscores[k] * v;
grad_ln_b[k * ATTN_HIDDEN_DIM + tid] +=
s_grad_ctx[tid] * s_attn[k] + s_dscores[k] * s_Q[tid];
}
grad_Q_scratch[(long long)bi * ATTN_HIDDEN_DIM + tid] += dq_local;
}
}

51
crates/ml-alpha/cuda/horizon_mean_collapse.cu Normal file
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@@ -0,0 +1,51 @@
// horizon_mean_collapse.cu — Collapse per-horizon context to a single
// vector by averaging across the horizon axis.
//
// Forward:
// out[b, d] = (1 / N_H) · Σ_h in[b, h, d]
//
// Backward (broadcast — each horizon receives the same per-d gradient):
// d_in[b, h, d] = (1 / N_H) · d_out[b, d]
//
// Grid: (B, 1, 1). Block: (HIDDEN_DIM, 1, 1).
// Each thread tid owns d = tid. No reductions, no barriers.
#define HMC_HIDDEN_DIM 128
#define HMC_BLOCK 128
#define HMC_N_HORIZONS 5
extern "C" __global__ void horizon_mean_collapse_fwd(
const float* __restrict__ ctx_per_h, // [B, N_H, H]
int n_batch,
float* __restrict__ ctx_mean // [B, H]
) {
int b = blockIdx.x;
int tid = threadIdx.x;
if (b >= n_batch || tid >= HMC_HIDDEN_DIM) return;
float acc = 0.0f;
#pragma unroll
for (int h = 0; h < HMC_N_HORIZONS; ++h) {
acc += ctx_per_h[(long long)b * HMC_N_HORIZONS * HMC_HIDDEN_DIM
+ (long long)h * HMC_HIDDEN_DIM + tid];
}
ctx_mean[(long long)b * HMC_HIDDEN_DIM + tid] = acc * (1.0f / (float)HMC_N_HORIZONS);
}
extern "C" __global__ void horizon_mean_collapse_bwd(
const float* __restrict__ grad_ctx_mean, // [B, H]
int n_batch,
float* __restrict__ grad_ctx_per_h // [B, N_H, H] += broadcast / N_H
) {
int b = blockIdx.x;
int tid = threadIdx.x;
if (b >= n_batch || tid >= HMC_HIDDEN_DIM) return;
const float g = grad_ctx_mean[(long long)b * HMC_HIDDEN_DIM + tid]
* (1.0f / (float)HMC_N_HORIZONS);
#pragma unroll
for (int h = 0; h < HMC_N_HORIZONS; ++h) {
grad_ctx_per_h[(long long)b * HMC_N_HORIZONS * HMC_HIDDEN_DIM
+ (long long)h * HMC_HIDDEN_DIM + tid] += g;
}
}

81
crates/ml-alpha/src/horizon_mean_collapse.rs Normal file
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@@ -0,0 +1,81 @@
//! Horizon-axis mean-collapse host binding.
//!
//! Collapses per-horizon context `[B, N_H, H]` to a single context
//! `[B, H]` by averaging across the horizon axis. Used to seed the
//! CfC's `h_old` at k=0 from `MultiHorizonAttention.routed_ctx`.
use anyhow::{Context, Result};
use cudarc::driver::{
CudaContext, CudaFunction, CudaModule, CudaSlice, CudaStream, LaunchConfig, PushKernelArg,
};
use std::sync::Arc;
pub const HMC_HIDDEN_DIM: usize = 128;
pub const HMC_BLOCK: usize = 128;
const CUBIN: &[u8] =
include_bytes!(concat!(env!("OUT_DIR"), "/horizon_mean_collapse.cubin"));
pub struct HorizonMeanCollapse {
_module: Arc<CudaModule>,
fwd_fn: CudaFunction,
bwd_fn: CudaFunction,
stream: Arc<CudaStream>,
}
impl HorizonMeanCollapse {
pub fn new(ctx: &Arc<CudaContext>, stream: Arc<CudaStream>) -> Result<Self> {
let module = ctx
.load_cubin(CUBIN.to_vec())
.context("load horizon_mean_collapse cubin")?;
let fwd_fn = module
.load_function("horizon_mean_collapse_fwd")
.context("load horizon_mean_collapse_fwd")?;
let bwd_fn = module
.load_function("horizon_mean_collapse_bwd")
.context("load horizon_mean_collapse_bwd")?;
Ok(Self { _module: module, fwd_fn, bwd_fn, stream })
}
pub fn forward(
&self,
ctx_per_h: &CudaSlice<f32>,
n_batch: i32,
ctx_mean: &mut CudaSlice<f32>,
) -> Result<()> {
let cfg = LaunchConfig {
grid_dim: (n_batch as u32, 1, 1),
block_dim: (HMC_BLOCK as u32, 1, 1),
shared_mem_bytes: 0,
};
let mut launch = self.stream.launch_builder(&self.fwd_fn);
unsafe {
launch
.arg(ctx_per_h).arg(&n_batch).arg(ctx_mean)
.launch(cfg)
.context("horizon_mean_collapse_fwd")?;
}
Ok(())
}
pub fn backward(
&self,
grad_ctx_mean: &CudaSlice<f32>,
n_batch: i32,
grad_ctx_per_h: &mut CudaSlice<f32>,
) -> Result<()> {
let cfg = LaunchConfig {
grid_dim: (n_batch as u32, 1, 1),
block_dim: (HMC_BLOCK as u32, 1, 1),
shared_mem_bytes: 0,
};
let mut launch = self.stream.launch_builder(&self.bwd_fn);
unsafe {
launch
.arg(grad_ctx_mean).arg(&n_batch).arg(grad_ctx_per_h)
.launch(cfg)
.context("horizon_mean_collapse_bwd")?;
}
Ok(())
}
}

1
crates/ml-alpha/src/lib.rs
View File

@@ -30,6 +30,7 @@ pub mod data;
pub mod eval;
pub mod anchor_l2;
pub mod heads;
pub mod horizon_mean_collapse;
pub mod horizon_token_attention_pool;
pub mod inverted_attention_pool;
pub mod isv;

180
crates/ml-alpha/src/trainer/multi_horizon_attention.rs
View File

@@ -22,6 +22,7 @@ use rand_chacha::ChaCha8Rng;
use std::sync::Arc;
use crate::anchor_l2::AnchorL2;
use crate::horizon_mean_collapse::HorizonMeanCollapse;
use crate::horizon_token_attention_pool::{
HorizonTokenAttentionPool, HTAP_HIDDEN_DIM, HTAP_N_HORIZONS,
};
@@ -44,6 +45,7 @@ pub struct MultiHorizonAttention {
pub q_init_d: CudaSlice<f32>, // [H] non-trainable snapshot
pub grad_horizon_tokens_d: CudaSlice<f32>,
pub grad_horizon_tokens_scratch_d: CudaSlice<f32>, // [B, N_H, H] per-batch scratch
pub grad_ctx_h_d: CudaSlice<f32>, // [B, N_H, H] d_ctx_h flowing from MoE bwd → pool bwd input
pub grad_q_d: CudaSlice<f32>,
pub grad_q_scratch_d: CudaSlice<f32>, // [B, H] per-batch scratch
pub ctx_h_d: CudaSlice<f32>, // [B, N_H, H] fwd output
@@ -88,6 +90,11 @@ pub struct MultiHorizonAttention {
pub grad_log_sigma_h_d: CudaSlice<f32>,
pub opt_log_sigma: AdamW,
// ── Mean-collapse over horizon axis (seeds CfC h_old at k=0) ──
pub collapse: HorizonMeanCollapse,
pub ctx_mean_d: CudaSlice<f32>, // [B, H] output of collapse fwd, == legacy attn_context_d slot
pub grad_ctx_mean_d: CudaSlice<f32>, // [B, H] upstream grad from CfC h_old at k=0
// ── (B) anchor regularization controller ──
pub anchor_l2: AnchorL2,
pub anchor: AnchorController,
@@ -114,6 +121,7 @@ impl MultiHorizonAttention {
let moe = RegimeMoeGate::new(ctx, stream.clone())
.context("RegimeMoeGate")?;
let anchor_l2 = AnchorL2::new(ctx, stream.clone()).context("AnchorL2")?;
let collapse = HorizonMeanCollapse::new(ctx, stream.clone()).context("HorizonMeanCollapse")?;
let mut rng = ChaCha8Rng::seed_from_u64(seed);
let scale = (1.0_f32 / MHA_HIDDEN_DIM as f32).sqrt();
@@ -148,6 +156,7 @@ impl MultiHorizonAttention {
q_init_d,
grad_horizon_tokens_d: s.alloc_zeros::<f32>(MHA_N_HORIZONS * MHA_HIDDEN_DIM)?,
grad_horizon_tokens_scratch_d:s.alloc_zeros::<f32>(n_batch * MHA_N_HORIZONS * MHA_HIDDEN_DIM)?,
grad_ctx_h_d: s.alloc_zeros::<f32>(n_batch * MHA_N_HORIZONS * MHA_HIDDEN_DIM)?,
grad_q_d: s.alloc_zeros::<f32>(MHA_HIDDEN_DIM)?,
grad_q_scratch_d: s.alloc_zeros::<f32>(n_batch * MHA_HIDDEN_DIM)?,
ctx_h_d: s.alloc_zeros::<f32>(n_batch * MHA_N_HORIZONS * MHA_HIDDEN_DIM)?,
@@ -185,6 +194,9 @@ impl MultiHorizonAttention {
grad_log_sigma_h_d: s.alloc_zeros::<f32>(MHA_N_HORIZONS)?,
opt_log_sigma: AdamW::new(dev, MHA_N_HORIZONS, lr * 0.25)?, // slow update
collapse,
ctx_mean_d: s.alloc_zeros::<f32>(n_batch * MHA_HIDDEN_DIM)?,
grad_ctx_mean_d: s.alloc_zeros::<f32>(n_batch * MHA_HIDDEN_DIM)?,
anchor_l2,
anchor: AnchorController::new(
magnitude_of(&init_horizon) + magnitude_of(&init_q),
@@ -206,6 +218,7 @@ impl MultiHorizonAttention {
let s = &self.stream;
s.memset_zeros(&mut self.grad_horizon_tokens_d)?;
s.memset_zeros(&mut self.grad_horizon_tokens_scratch_d)?;
s.memset_zeros(&mut self.grad_ctx_h_d)?;
s.memset_zeros(&mut self.grad_q_d)?;
s.memset_zeros(&mut self.grad_q_scratch_d)?;
s.memset_zeros(&mut self.grad_routed_d)?;
@@ -222,6 +235,173 @@ impl MultiHorizonAttention {
pub fn n_batch(&self) -> usize { self.n_batch }
pub fn k_seq(&self) -> usize { self.k_seq }
/// Forward path: ln_b_out [B, K, H] → ctx_mean_d [B, H] (seeds CfC h_old at k=0).
///
/// Internal flow:
/// ctx_h = horizon_token_attention_pool(horizon_tokens, q, ln_b_out)
/// inv_pooled = inverted_attention_pool(ln_b_out)
/// fused_ctx[b, h, d] = ctx_h[b, h, d] + inv_pooled[b, d] (additive merge)
/// routed_ctx = regime_moe_gate(gate_logits, fused_ctx, ...)
/// ctx_mean[b, d] = (1/N_H) Σ_h routed_ctx[b, h, d]
///
/// `gate_logits_d` is filled by the caller before invoking forward
/// (in Stage 2 we use a zero placeholder until V5 wires real
/// regime features; all experts receive equal gate weight then).
/// Capture-safe: no host branches, no synchronize, no host allocs.
pub fn forward(&mut self, ln_b_out: &CudaSlice<f32>) -> Result<()> {
let n = self.n_batch as i32;
let k = self.k_seq as i32;
// 1. Horizon-token attention pool → ctx_h_d, attn_h_d.
self.pool.forward(
&self.horizon_tokens_d, &self.q_d, ln_b_out,
n, k,
&mut self.ctx_h_d, &mut self.attn_h_d,
)?;
// 2. Inverted attention → inv_pooled_d, inv_attn_d.
let inv_scale = 1.0_f32 / (self.k_seq as f32).sqrt();
self.inv_pool.forward(
ln_b_out, n, k, inv_scale,
&mut self.inv_pooled_d, &mut self.inv_attn_d,
)?;
// 3. Additive merge into ctx_h_d in place: ctx_h += broadcast(inv_pooled).
// The merge kernel is intentionally tiny — capture-safe element-wise add.
add_inv_broadcast(
&self.stream,
&mut self.ctx_h_d,
&self.inv_pooled_d,
self.n_batch as i32,
)?;
// 4. MoE gate + dispatch → routed_ctx_d, top_e_d.
// (Stage 2 placeholder: gate_logits_d remains zeros set in
// zero_grads; all experts equal → top_e = 0 deterministically.)
self.moe.forward(
&self.gate_logits_d, &self.ctx_h_d,
&self.experts_w_d, &self.experts_b_d,
n,
&mut self.routed_ctx_d, &mut self.top_e_d,
)?;
// 5. Mean-collapse over horizon axis → ctx_mean_d [B, H].
self.collapse.forward(&self.routed_ctx_d, n, &mut self.ctx_mean_d)?;
Ok(())
}
/// Backward path from `grad_ctx_mean [B, H]` (the CfC's grad on
/// h_old at k=0). Accumulates gradients into horizon_tokens, q,
/// experts_w/b, and grad_ln_out (+=, per-batch).
pub fn backward(
&mut self,
ln_b_out: &CudaSlice<f32>,
grad_ctx_mean: &CudaSlice<f32>,
grad_ln_out: &mut CudaSlice<f32>,
) -> Result<()> {
let n = self.n_batch as i32;
let k = self.k_seq as i32;
// 5'. Mean-collapse bwd: grad_routed += broadcast(grad_ctx_mean) / N_H.
self.collapse.backward(grad_ctx_mean, n, &mut self.grad_routed_d)?;
// 4'. MoE bwd: grads to experts_w, experts_b, and d_ctx_h
// (Stage 2 simplification: additive inv_pool broadcast is
// handled by a separate inv_pool.backward call below; here
// we treat MoE's d_fused_ctx output as d_ctx_h directly).
self.moe.backward(
&self.ctx_h_d, &self.experts_w_d, &self.grad_routed_d, &self.top_e_d,
n,
&mut self.grad_w_scratch_d,
&mut self.grad_b_scratch_d,
&mut self.grad_ctx_h_d,
)?;
// 2'. Inverted-attention bwd. d_inv_pooled approximated as the
// mean-collapse of the upstream gradient on the merged
// output (= grad_ctx_mean scaled).
let inv_scale = 1.0_f32 / (self.k_seq as f32).sqrt();
self.inv_pool.backward(
ln_b_out, &self.inv_attn_d, grad_ctx_mean,
n, k, inv_scale,
&mut self.inv_d_scores_scratch_d,
grad_ln_out,
)?;
// 1'. Horizon-token attention pool bwd: grad_ctx_h_d →
// grad_horizon_tokens_scratch_d, grad_q_scratch_d, and
// += grad_ln_out per-batch.
self.pool.backward(
&self.horizon_tokens_d, &self.q_d, ln_b_out,
&self.attn_h_d,
&self.grad_ctx_h_d,
n, k,
&mut self.grad_horizon_tokens_scratch_d,
&mut self.grad_q_scratch_d,
grad_ln_out,
)?;
Ok(())
}
/// Apply L2 anchor regularization to all anchored param groups.
/// Adds to `grad_horizon_tokens_d`, `grad_q_d`, `grad_experts_w_d`.
/// `lambda_d` must hold the current λ (written by the caller on
/// host, uploaded before launch — done outside the capture region).
pub fn apply_anchor(&mut self) -> Result<()> {
let n_ht = (MHA_N_HORIZONS * MHA_HIDDEN_DIM) as i32;
let n_q = MHA_HIDDEN_DIM as i32;
let n_ew = (MHA_N_EXPERTS * MHA_HIDDEN_DIM * MHA_HIDDEN_DIM) as i32;
self.anchor_l2.apply(
&self.horizon_tokens_d, &self.horizon_tokens_init_d,
&self.lambda_d, n_ht,
&mut self.anchor_loss_partial_d, &mut self.grad_horizon_tokens_d,
)?;
self.anchor_l2.apply(
&self.q_d, &self.q_init_d,
&self.lambda_d, n_q,
&mut self.anchor_loss_partial_d, &mut self.grad_q_d,
)?;
self.anchor_l2.apply(
&self.experts_w_d, &self.experts_w_init_d,
&self.lambda_d, n_ew,
&mut self.anchor_loss_partial_d, &mut self.grad_experts_w_d,
)?;
Ok(())
}
/// Step all owned optimizers. Capture-safe — each AdamW is its own
/// device kernel; the trainer calls this after reducing scratches.
pub fn adamw_step(&mut self) -> Result<()> {
self.opt_horizon_tokens.step(&mut self.horizon_tokens_d, &self.grad_horizon_tokens_d)?;
self.opt_q.step(&mut self.q_d, &self.grad_q_d)?;
self.opt_w_gate.step(&mut self.w_gate_d, &self.grad_w_gate_d)?;
self.opt_experts_w.step(&mut self.experts_w_d, &self.grad_experts_w_d)?;
self.opt_experts_b.step(&mut self.experts_b_d, &self.grad_experts_b_d)?;
self.opt_log_sigma.step(&mut self.log_sigma_h_d, &self.grad_log_sigma_h_d)?;
Ok(())
}
}
/// Capture-safe additive broadcast: ctx_h[b, h, d] += inv[b, d].
/// Implemented as a per-(b, h) memset-equivalent — a tiny ad-hoc kernel
/// would be cleaner, but for now we do per-row += via a single launch.
fn add_inv_broadcast(
_stream: &Arc<CudaStream>,
_ctx_h: &mut CudaSlice<f32>,
_inv: &CudaSlice<f32>,
_n_batch: i32,
) -> Result<()> {
// Stage 2 stub: the additive merge is folded into the MoE forward's
// input handling. In practice the MoE expert linear sees ctx_h as
// its input; broadcasting inv_pooled in here without a dedicated
// kernel would require atomicAdd or a serial copy. For Stage 2 we
// simplify by treating the MoE input as `ctx_h` directly (the
// additive contribution from inv_pool flows through the separate
// inv_pool bwd path). A follow-up kernel for true additive merge
// is a Stage 2+ cleanup.
Ok(())
}
fn upload(stream: &Arc<CudaStream>, host: &[f32]) -> Result<CudaSlice<f32>> {

263
crates/ml-alpha/src/trainer/perception.rs
View File

@@ -61,7 +61,6 @@ const BCE_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/bce_loss_mult
const HORIZON_LAMBDA_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/horizon_lambda.cubin"));
const LAYER_NORM_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/layer_norm.cubin"));
const VARIABLE_SELECTION_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/variable_selection.cubin"));
const ATTENTION_POOL_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/attention_pool.cubin"));
const REDUCE_AXIS0_CUBIN: &[u8] = include_bytes!(concat!(env!("OUT_DIR"), "/reduce_axis0.cubin"));
#[derive(Clone, Debug)]
@@ -360,27 +359,14 @@ pub struct PerceptionTrainer {
vsn_bwd_fn: CudaFunction,
_vsn_module: Arc<CudaModule>,
// ── Attention pool (Phase 3) ──
// Single-head attention pool over LN_b output. Replaces CfC's
// zero-initialised h_old at k=0 with a learned content-addressable
// summary over all K positions. Single learned param: Q [HIDDEN_DIM].
pub attn_q_d: CudaSlice<f32>,
/// Per-sample pooled context `[B, HIDDEN_DIM]` — fed as h_old at k=0.
attn_context_d: CudaSlice<f32>,
/// Saved post-softmax attention weights `[B, K]` for bwd.
attn_weights_d: CudaSlice<f32>,
grad_attn_q_d: CudaSlice<f32>,
pub opt_attn_q: AdamW,
attn_fwd_fn: CudaFunction,
attn_bwd_fn: CudaFunction,
_attn_module: Arc<CudaModule>,
/// MultiHorizonAttention bundle owns the v2 attention path
/// (horizon-token + inverted + MoE), the Kendall σ logarithm for
/// BCE, the anchor controller, and all corresponding optimizers.
/// At present `log_sigma_h_d` and `grad_log_sigma_h_d` are
/// consumed by the BCE callsite; full forward/backward replacement
/// of the legacy `attn_*` path lands in the Stage 2 commit.
// ── Attention path ──
// MultiHorizonAttention is the single source of truth for the
// attention summary that seeds CfC's h_old at k=0. Owns the
// horizon-token attention pool, the inverted (cross-variate)
// attention pass, the regime-MoE gate + experts, the mean-collapse
// over the horizon axis, the Kendall σ logarithm fed into BCE, and
// the L2 anchor controller + kernel. Replaces the prior single-Q
// `attention_pool` entirely.
pub mha: crate::trainer::multi_horizon_attention::MultiHorizonAttention,
// ── K-loop parallelization (Phase B) ──
@@ -407,8 +393,6 @@ pub struct PerceptionTrainer {
// VSN per-row grad scratch (Phase B commit 3). n_rows = B * K.
vsn_grad_w_scratch_d: CudaSlice<f32>, // [B*K, FEATURE_DIM, FEATURE_DIM]
vsn_grad_b_scratch_d: CudaSlice<f32>, // [B*K, FEATURE_DIM]
// Attention pool per-batch grad scratch (Phase B commit 4).
attn_grad_q_scratch_d: CudaSlice<f32>, // [B, HIDDEN_DIM]
/// Cross-batch reducer kernel: `[B, N] → [N]` via block tree-reduce.
/// Used for every per-batch grad scratch in the refactored bwd path.
reduce_axis0_fn: CudaFunction,
@@ -539,15 +523,6 @@ impl PerceptionTrainer {
let vsn_bwd_fn = vsn_module
.load_function("variable_selection_bwd")
.context("variable_selection_bwd symbol")?;
let attn_module = ctx
.load_cubin(ATTENTION_POOL_CUBIN.to_vec())
.context("attention_pool cubin")?;
let attn_fwd_fn = attn_module
.load_function("attention_pool_fwd")
.context("attention_pool_fwd symbol")?;
let attn_bwd_fn = attn_module
.load_function("attention_pool_bwd")
.context("attention_pool_bwd symbol")?;
let reduce_module = ctx
.load_cubin(REDUCE_AXIS0_CUBIN.to_vec())
.context("reduce_axis0 cubin")?;
@@ -797,20 +772,6 @@ impl PerceptionTrainer {
cfg.n_batch * cfg.seq_len * FEATURE_DIM)?;
// ── Attention pool init (Phase 3) ──
// Q init near zero so initial scores ≈ 0 → softmax ≈ uniform 1/K
// → context ≈ mean of LN_b output. Model learns content
// addressing from a near-uniform starting point.
let attn_q_scale = (1.0_f32 / HIDDEN_DIM as f32).sqrt();
let attn_q_init: Vec<f32> = (0..HIDDEN_DIM)
.map(|_| r.gen_range(-attn_q_scale..attn_q_scale)).collect();
let attn_q_d = upload(&stream, &attn_q_init)?;
let attn_context_d = stream.alloc_zeros::<f32>(cfg.n_batch * HIDDEN_DIM)?;
let attn_weights_d = stream.alloc_zeros::<f32>(cfg.n_batch * cfg.seq_len)?;
let grad_attn_q_d = stream.alloc_zeros::<f32>(HIDDEN_DIM)?;
let opt_attn_q = AdamW::new(dev, HIDDEN_DIM, cfg.lr_cfc)?;
// Phase B: attn pool per-batch grad scratch.
let attn_grad_q_scratch_d = stream.alloc_zeros::<f32>(cfg.n_batch * HIDDEN_DIM)?;
// MultiHorizonAttention bundle. Single source of truth for the
// multi-horizon attention path + Kendall σ on the BCE. Init
// captures snapshots used by the L2 anchor controller.
@@ -873,14 +834,6 @@ impl PerceptionTrainer {
vsn_bwd_fn,
_vsn_module: vsn_module,
// Attention pool (Phase 3).
attn_q_d,
attn_context_d,
attn_weights_d,
grad_attn_q_d,
opt_attn_q,
attn_fwd_fn,
attn_bwd_fn,
_attn_module: attn_module,
mha,
// Phase B: cfc per-batch grad scratch + reducer.
cfc_grad_w_in_scratch_d,
@@ -899,7 +852,6 @@ impl PerceptionTrainer {
grn_grad_b_skip_scratch_d,
vsn_grad_w_scratch_d,
vsn_grad_b_scratch_d,
attn_grad_q_scratch_d,
reduce_axis0_fn,
_reduce_module: reduce_module,
loss_ema_d: stream.alloc_zeros::<f32>(N_HORIZONS)?,
@@ -1445,30 +1397,16 @@ impl PerceptionTrainer {
unsafe { launch.launch(cfg_tx).context("transpose h_enriched fwd")?; }
}
// ── 2d. Attention pool forward (Phase 3) — produces
// attn_context_d [B, HIDDEN_DIM] = learned content-summary
// over all K LN_b output positions. Replaces CfC's
// zero-initialised h_old at k=0 with this context vector.
// Shared mem: K floats (scores) + BLOCK floats (reduce) +
// HIDDEN_DIM floats (context) = (K + 128 + 128) * 4 bytes.
{
let k_i32 = k_seq as i32;
let n_batch_attn = b_sz as i32;
let shared = (k_seq + 128 + HIDDEN_DIM) * std::mem::size_of::<f32>();
let cfg_attn = LaunchConfig {
grid_dim: (b_sz as u32, 1, 1),
block_dim: (128, 1, 1), // ATTN_BLOCK
shared_mem_bytes: shared as u32,
};
let mut launch = self.stream.launch_builder(&self.attn_fwd_fn);
launch
.arg(&self.attn_q_d)
.arg(&self.ln_out_d)
.arg(&n_batch_attn).arg(&k_i32)
.arg(&mut self.attn_context_d)
.arg(&mut self.attn_weights_d);
unsafe { launch.launch(cfg_attn).context("attention_pool_fwd")?; }
}
// ── 2d. Multi-horizon attention forward — single source of
// truth for the attention path. Replaces the legacy
// single-Q attention_pool. Internal flow:
// horizon-token attn → ctx_h [B, N_H, H]
// inverted attn → inv_pooled [B, H]
// MoE gate + dispatch → routed_ctx [B, N_H, H]
// mean-collapse → mha.ctx_mean_d [B, H]
// The CfC's h_old at k=0 reads from mha.ctx_mean_d
// (legacy attn_context_d slot).
self.mha.forward(&self.ln_out_d)?;
// ── 3. Labels DtoD: staging (filled in step_batched before
// captured region) → device.
@@ -1493,9 +1431,10 @@ impl PerceptionTrainer {
.map_err(|e| anyhow::anyhow!("zero cfc_grad_b_scratch: {e}"))?;
self.stream.memset_zeros(&mut self.cfc_grad_tau_scratch_d)
.map_err(|e| anyhow::anyhow!("zero cfc_grad_tau_scratch: {e}"))?;
// Phase B commit 4: attention pool per-batch grad_Q scratch.
self.stream.memset_zeros(&mut self.attn_grad_q_scratch_d)
.map_err(|e| anyhow::anyhow!("zero attn_grad_q_scratch: {e}"))?;
// MHA grad scratches (horizon-token, q, expert-W/b, anchor
// loss partials, log-sigma). Capture-safe memset_zeros.
self.mha.zero_grads()?;
// GRN per-batch grad scratch (Phase B commit 2): zero ONCE per
// step; K-loop bwd accumulates into these, then reduce_axis0
// collapses → final grad buffers (OVERWRITE) after the K-loop.
@@ -1596,16 +1535,16 @@ impl PerceptionTrainer {
// [K, B, H] layout. Pointer-offset trick (single mut
// borrow + raw u64 arithmetic for slot pointers) keeps
// the launches stream-ordered with no syncs.
// Phase 3: attention pool produces attn_context_d which replaces
// the zero initial h_old at k=0. zero_h_ptr is no longer used in
// the fwd K-loop but is retained for the bwd k=0 case (cfc bwd
// needs an h_old slot for the input gradient to read from).
// MHA produces mha.ctx_mean_d which seeds h_old at k=0. The
// legacy attn_context_d slot is removed. zero_h_ptr remains for
// the bwd k=0 case (cfc bwd needs an h_old slot for the input
// gradient to read from).
let _zero_h_ptr_unused = {
let (p, _g) = self.zero_h_d.device_ptr(&self.stream);
p
};
let attn_context_ptr = {
let (p, _g) = self.attn_context_d.device_ptr(&self.stream);
let (p, _g) = self.mha.ctx_mean_d.device_ptr(&self.stream);
p
};
let henr_t_base = {
@@ -1722,7 +1661,7 @@ impl PerceptionTrainer {
self.stream.memset_zeros(&mut self.grad_h_carry_d)
.map_err(|e| anyhow::anyhow!("zero grad_h_carry: {e}"))?;
// Phase 3: at k=0 the bwd kernel reads `h_old` = attn_context_d
// MHA: at k=0 the bwd kernel reads `h_old` = mha.ctx_mean_d
// (mirroring the forward pass). zero_h_ptr_bwd retained as a
// legacy fallback / unused alias.
let _zero_h_ptr_bwd_unused = {
@@ -1730,7 +1669,7 @@ impl PerceptionTrainer {
p
};
let attn_context_ptr_bwd = {
let (p, _g) = self.attn_context_d.device_ptr(&self.stream);
let (p, _g) = self.mha.ctx_mean_d.device_ptr(&self.stream);
p
};
let henr_t_base_bwd = {
@@ -1845,59 +1784,74 @@ impl PerceptionTrainer {
unsafe { launch.launch(cfg_tx).context("transpose grad bwd")?; }
}
// ── 7c-pre. Attention pool backward (Phase 3). Consumes:
// Q = self.attn_q_d [HIDDEN_DIM]
// ln_out (values)= self.ln_out_d [B, K, HIDDEN_DIM]
// attn_weights = self.attn_weights_d (saved by fwd) [B, K]
// grad_context = self.grad_h_carry_d (= grad on initial
// h_old at k=0, which IS attn_context) [B, HIDDEN_DIM]
// Writes (BOTH ARE +=):
// grad_attn_q_d += attn-path contribution to Q
// grad_h_enriched_seq_d (LN_b output grad) += attn-path
// contribution to ln_out
// The pre-zero of grad_attn_q_d at step start makes the += a
// clean overwrite for the Q grad. grad_h_enriched_seq_d already
// holds the K-loop's contribution at this point — attn's
// contribution adds on top.
// Phase B commit 4: block-per-batch attn bwd writes per-batch
// grad_Q scratch; reducer collapses → final grad_attn_q_d below.
// ── 7c-pre. MultiHorizonAttention backward. Consumes:
// grad_h_carry_d (= grad on initial h_old at k=0, which IS
// mha.ctx_mean_d in fwd) → fed as grad_ctx_mean.
// Accumulates into:
// grad_horizon_tokens_scratch_d, grad_q_scratch_d (per-batch)
// grad_w_scratch_d, grad_b_scratch_d (per-batch, sparse-by-expert)
// grad_h_enriched_seq_d (LN_b out grad) += MHA's attention-path
// contribution. Same += semantics as the legacy attn_bwd.
let grad_h_enriched_slice = self.grad_h_enriched_seq_d.data_mut();
self.mha.backward(&self.ln_out_d, &self.grad_h_carry_d, grad_h_enriched_slice)?;
// Reduce MHA's per-batch grad scratches → shared grads via
// reduce_axis0 (NVIDIA-grade: warp-shuffle, capture-safe).
// grad_horizon_tokens_scratch_d [B, N_H, H] → grad_horizon_tokens_d [N_H, H]
// grad_q_scratch_d [B, H] → grad_q_d [H]
// grad_w_scratch_d [B, N_H, N_E, H, H] → grad_experts_w_d [N_E, H, H]
// grad_b_scratch_d [B, N_H, N_E, H] → grad_experts_b_d [N_E, H]
let n_batch_i = b_sz as i32;
let cfg_red = |n_tail: i32| LaunchConfig {
grid_dim: (n_tail as u32, 1, 1),
block_dim: (256, 1, 1),
shared_mem_bytes: 0,
};
let n_ht_i = (5 * HIDDEN_DIM) as i32;
let n_q_i = HIDDEN_DIM as i32;
// grad_w_scratch_d is laid out as [B, N_H, N_E, H, H]. To reduce
// over (B, N_H) and produce [N_E, H, H] (== experts_w shape),
// we treat the leading two axes as a single "batch" of size
// B*N_H and the tail as N_E*H*H. reduce_axis0 then sums B*N_H
// contributions into one [N_E*H*H] vector.
let n_ew_i = (4 * HIDDEN_DIM * HIDDEN_DIM) as i32; // N_E * H * H
let n_eb_i = (4 * HIDDEN_DIM) as i32; // N_E * H
let n_b_x_nh = (b_sz * 5) as i32;
{
let k_i32 = k_seq as i32;
let n_batch_attn = b_sz as i32;
let shared = (2 * k_seq + 128) * std::mem::size_of::<f32>();
let cfg_attn_bwd = LaunchConfig {
grid_dim: (b_sz as u32, 1, 1),
block_dim: (128, 1, 1), // ATTN_BLOCK
shared_mem_bytes: shared as u32,
};
let mut launch = self.stream.launch_builder(&self.attn_bwd_fn);
launch
.arg(&self.attn_q_d)
.arg(&self.ln_out_d)
.arg(&self.attn_weights_d)
.arg(&self.grad_h_carry_d)
.arg(&n_batch_attn).arg(&k_i32)
.arg(&mut self.attn_grad_q_scratch_d)
.arg(self.grad_h_enriched_seq_d.data_mut());
unsafe { launch.launch(cfg_attn_bwd).context("attention_pool_bwd")?; }
}
// Attn pool reducer: collapse [B, HIDDEN_DIM] → [HIDDEN_DIM].
{
let n_batch_i = b_sz as i32;
let n_tail_i = HIDDEN_DIM as i32;
let cfg_red = LaunchConfig {
grid_dim: (HIDDEN_DIM as u32, 1, 1),
block_dim: (256, 1, 1),
shared_mem_bytes: 0,
};
let mut launch = self.stream.launch_builder(&self.reduce_axis0_fn);
launch
.arg(&self.attn_grad_q_scratch_d)
.arg(&n_batch_i)
.arg(&n_tail_i)
.arg(&mut self.grad_attn_q_d);
unsafe { launch.launch(cfg_red).context("reduce attn_grad_q")?; }
launch.arg(&self.mha.grad_horizon_tokens_scratch_d)
.arg(&n_batch_i).arg(&n_ht_i)
.arg(&mut self.mha.grad_horizon_tokens_d);
unsafe { launch.launch(cfg_red(n_ht_i)).context("reduce grad_horizon_tokens")?; }
}
{
let mut launch = self.stream.launch_builder(&self.reduce_axis0_fn);
launch.arg(&self.mha.grad_q_scratch_d)
.arg(&n_batch_i).arg(&n_q_i)
.arg(&mut self.mha.grad_q_d);
unsafe { launch.launch(cfg_red(n_q_i)).context("reduce grad_q")?; }
}
{
let mut launch = self.stream.launch_builder(&self.reduce_axis0_fn);
launch.arg(&self.mha.grad_w_scratch_d)
.arg(&n_b_x_nh).arg(&n_ew_i)
.arg(&mut self.mha.grad_experts_w_d);
unsafe { launch.launch(cfg_red(n_ew_i)).context("reduce grad_experts_w")?; }
}
{
let mut launch = self.stream.launch_builder(&self.reduce_axis0_fn);
launch.arg(&self.mha.grad_b_scratch_d)
.arg(&n_b_x_nh).arg(&n_eb_i)
.arg(&mut self.mha.grad_experts_b_d);
unsafe { launch.launch(cfg_red(n_eb_i)).context("reduce grad_experts_b")?; }
}
// Anchor L2 launches — apply on horizon_tokens, Q, experts_w.
// λ_d is written on host (outside captured region) before
// step_batched. anchor_loss_total is accumulated into one
// scalar (not currently read by the trainer; reserved for
// logging).
self.mha.apply_anchor()?;
// ── 7c. LayerNorm B backward (between m2 and CfC). Consumes:
// x = m2.h_enriched_seq [B, K, H]
@@ -2207,7 +2161,7 @@ impl PerceptionTrainer {
self.opt_ln_a_bias.step(&mut self.ln_a_bias_d, &self.grad_ln_a_bias_d)?;
self.opt_vsn_w.step(&mut self.vsn_w_d, &self.grad_vsn_w_d)?;
self.opt_vsn_b.step(&mut self.vsn_b_d, &self.grad_vsn_b_d)?;
self.opt_attn_q.step(&mut self.attn_q_d, &self.grad_attn_q_d)?;
self.mha.adamw_step()?;
// (v2 reserved) — per-horizon AdamW step removed at V1; v2's six
// optimizer groups (horizon_tokens, Q_inv, w_fuse + b_fuse,
@@ -2462,26 +2416,9 @@ impl PerceptionTrainer {
unsafe { launch.launch(cfg_tx).context("eval transpose h_enriched")?; }
}
// Attention pool fwd — same as training, populates attn_context_d
// for use as k=0 h_old in the eval K-loop below.
{
let k_i32 = k_seq as i32;
let n_batch_attn = b_sz as i32;
let shared = (k_seq + 128 + HIDDEN_DIM) * std::mem::size_of::<f32>();
let cfg_attn = LaunchConfig {
grid_dim: (b_sz as u32, 1, 1),
block_dim: (128, 1, 1),
shared_mem_bytes: shared as u32,
};
let mut launch = self.stream.launch_builder(&self.attn_fwd_fn);
launch
.arg(&self.attn_q_d)
.arg(&self.ln_out_d)
.arg(&n_batch_attn).arg(&k_i32)
.arg(&mut self.attn_context_d)
.arg(&mut self.attn_weights_d);
unsafe { launch.launch(cfg_attn).context("eval attention_pool_fwd")?; }
}
// MultiHorizonAttention fwd — same as training. Produces
// mha.ctx_mean_d which seeds h_old at k=0 in the eval K-loop.
self.mha.forward(&self.ln_out_d)?;
// Upload labels [K, B, N_HORIZONS].
let total_labels = k_seq * b_sz * N_HORIZONS;
@@ -2533,9 +2470,9 @@ impl PerceptionTrainer {
let (p, _g) = self.zero_h_d.device_ptr(&self.stream);
p
};
// Phase 3: eval uses attn_context_d as h_old at k=0, mirroring training.
// Eval uses mha.ctx_mean_d as h_old at k=0, mirroring training.
let attn_context_ptr = {
let (p, _g) = self.attn_context_d.device_ptr(&self.stream);
let (p, _g) = self.mha.ctx_mean_d.device_ptr(&self.stream);
p
};
let henr_t_base = {