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fix(critical): eliminate bf16 remnants from CUDA reduction kernels

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Root cause of 61 test failures: bf16→f32 migration missed two sites:

1. reduction_kernels.cu: atomicCAS loops used `unsigned short` (2 bytes = bf16)
   on float* data. With f32 (4 bytes), the 2-byte CAS corrupted adjacent memory
   → CUDA_ERROR_ILLEGAL_ADDRESS. Fixed: use `unsigned int` + __float_as_uint().

2. reductions.rs: shared memory allocated at `threads * 2` (bf16 = 2 bytes)
   instead of `threads * 4` (f32 = 4 bytes) in 5 kernel launch sites.
   Kernels wrote past shared memory → undefined behavior.

Also fix evaluation engine tests for 9-level ExposureLevel mapping
(Long50 = 0.25 not 0.5, Short50 = -1.0 = Short×Full).

Result: 287/287 ml-dqn tests pass (was 226/287).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-04-11 00:42:26 +02:00
parent ee6088eb53
commit 82f64551e6
3 changed files with 40 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
crates/ml-core/src/cuda_autograd/reduction_kernels.cu
View File

@@ -87,37 +87,37 @@ void fused_stats_reduce(const float* __restrict__ data,
w_cnt = warp_reduce_sum_bf16(w_cnt);
if (threadIdx.x == 0) {
// atomicMin for bf16 via CAS loop
// atomicMin for f32 via CAS loop (unsigned int = 4 bytes = sizeof(float))
{
unsigned short* addr_us = (unsigned short*)&result[0];
unsigned short old_bits = *addr_us;
unsigned short new_bits = *(unsigned short*)&w_min;
while (*(float*)&old_bits > w_min) {
unsigned short assumed = old_bits;
old_bits = atomicCAS(addr_us, assumed, new_bits);
unsigned int* addr_ui = (unsigned int*)&result[0];
unsigned int old_bits = *addr_ui;
unsigned int new_bits = __float_as_uint(w_min);
while (__uint_as_float(old_bits) > w_min) {
unsigned int assumed = old_bits;
old_bits = atomicCAS(addr_ui, assumed, new_bits);
if (old_bits == assumed) break;
float cur = *(float*)&old_bits;
float new_val = bf16_fmin(cur, w_min);
new_bits = *(unsigned short*)&new_val;
float cur = __uint_as_float(old_bits);
float new_val = fminf(cur, w_min);
new_bits = __float_as_uint(new_val);
}
}
// atomicMax for bf16 via CAS loop
// atomicMax for f32 via CAS loop
{
unsigned short* addr_us = (unsigned short*)&result[1];
unsigned short old_bits = *addr_us;
unsigned short new_bits = *(unsigned short*)&w_max;
while (*(float*)&old_bits < w_max) {
unsigned short assumed = old_bits;
old_bits = atomicCAS(addr_us, assumed, new_bits);
unsigned int* addr_ui = (unsigned int*)&result[1];
unsigned int old_bits = *addr_ui;
unsigned int new_bits = __float_as_uint(w_max);
while (__uint_as_float(old_bits) < w_max) {
unsigned int assumed = old_bits;
old_bits = atomicCAS(addr_ui, assumed, new_bits);
if (old_bits == assumed) break;
float cur = *(float*)&old_bits;
float new_val = bf16_fmax(cur, w_max);
new_bits = *(unsigned short*)&new_val;
float cur = __uint_as_float(old_bits);
float new_val = fmaxf(cur, w_max);
new_bits = __float_as_uint(new_val);
}
}
atomicAddBF16(&result[2], w_sum);
atomicAddBF16(&result[3], w_ssq);
atomicAddBF16(&result[4], w_cnt);
atomicAdd(&result[2], w_sum);
atomicAdd(&result[3], w_ssq);
atomicAdd(&result[4], w_cnt);
}
}
}
@@ -187,14 +187,14 @@ void argmax_flat(const float* __restrict__ data,
if (threadIdx.x == 0) {
// CAS loop to merge block results into global output
// result[0] = best val (as bf16), result[1] = best idx (as bf16)
unsigned short* val_addr = (unsigned short*)&result[0];
unsigned short old_bits = *val_addr;
while (*(float*)&old_bits < w_val) {
unsigned short assumed = old_bits;
old_bits = atomicCAS(val_addr, assumed, *(unsigned short*)&w_val);
// result[0] = best val (as f32), result[1] = best idx (as f32)
unsigned int* val_addr = (unsigned int*)&result[0];
unsigned int old_bits = *val_addr;
while (__uint_as_float(old_bits) < w_val) {
unsigned int assumed = old_bits;
old_bits = atomicCAS(val_addr, assumed, __float_as_uint(w_val));
if (old_bits == assumed) {
result[1] = bf16((int)w_idx);
result[1] = (float)w_idx;
break;
}
}

14
crates/ml-core/src/cuda_autograd/reductions.rs
View File

@@ -94,7 +94,7 @@ impl ReductionKernels {
// Cap blocks to avoid excessive atomic contention
let blocks = blocks.min(1024);
let n_i32 = n as i32;
let shared_bytes = threads * 5 * 2; // 5 f32 per thread for shared reduction (min, max, sum, sum_sq, count)
let shared_bytes = threads * 5 * 4; // 5 f32 per thread for shared reduction (min, max, sum, sum_sq, count)
let cfg = LaunchConfig {
grid_dim: (blocks, 1, 1),
@@ -167,8 +167,8 @@ impl ReductionKernels {
let blocks = ((n as u32) + threads - 1) / threads;
let blocks = blocks.min(1024);
let n_i32 = n as i32;
// Shared: [B f32 values (B*2 bytes)][B int indices (B*4 bytes)]
let shared_bytes = threads * 2 + threads * 4;
// Shared: [B f32 values (B*4 bytes)][B int indices (B*4 bytes)]
let shared_bytes = threads * 4 + threads * 4;
let cfg = LaunchConfig {
grid_dim: (blocks, 1, 1),
@@ -216,8 +216,8 @@ impl ReductionKernels {
let threads = 256_u32.min(cols as u32).max(64);
let rows_i32 = rows as i32;
let cols_i32 = cols as i32;
// Shared: [B f32 values (B*2 bytes)][B int indices (B*4 bytes)]
let shared_bytes = threads * 2 + threads * 4;
// Shared: [B f32 values (B*4 bytes)][B int indices (B*4 bytes)]
let shared_bytes = threads * 4 + threads * 4;
let cfg = LaunchConfig {
grid_dim: (rows as u32, 1, 1),
@@ -267,7 +267,7 @@ impl ReductionKernels {
let blocks = ((n as u32) + threads - 1) / threads;
let blocks = blocks.min(1024);
let n_i32 = n as i32;
let shared_bytes = threads * 2; // 1 f32 per thread
let shared_bytes = threads * 4; // 1 f32 (4 bytes) per thread
let cfg = LaunchConfig {
grid_dim: (blocks, 1, 1),
@@ -315,7 +315,7 @@ impl ReductionKernels {
let threads = 256_u32.min(rows as u32);
let rows_i32 = rows as i32;
let cols_i32 = cols as i32;
let shared_bytes = threads * 2; // 1 f32 per thread
let shared_bytes = threads * 4; // 1 f32 (4 bytes) per thread
let cfg = LaunchConfig {
grid_dim: (cols as u32, 1, 1),

5
crates/ml-dqn/src/evaluation/engine.rs
View File

@@ -448,18 +448,19 @@ mod tests {
engine.trades.first().map_or(false, |t| t.pnl > 0.0),
"Price went up on long = profit"
);
assert!((engine.current_exposure - 0.5).abs() < 1e-6);
assert!((engine.current_exposure - 0.25).abs() < 1e-6, "Long50 = Long×Small = 0.25 exposure");
}
#[test]
fn factored_reversal_generates_trade() {
let mut engine = EvaluationEngine::new(10000.0);
engine.process_bar_factored(0, &bar(100.0), &market_action(ExposureLevel::Long100));
engine.process_bar_factored(1, &bar(105.0), &market_action(ExposureLevel::Short100));
engine.process_bar_factored(1, &bar(105.0), &market_action(ExposureLevel::Short50));
assert!(
!engine.trades.is_empty(),
"Reversal should generate at least 1 trade"
);
// Short50 = Short×Full = -1.0 exposure
assert!((engine.current_exposure - (-1.0)).abs() < 1e-6);
}