[AlexanderSoftCode]

Context

After PR #14, a breakdown of the CIFAR-10 step on the RX 9070 XT showed conv at ~40 % of GPU time and BatchNorm + ReLU at ~42 %. ReLU's backward stood out: it took 0.90 ms, 3× its forward (0.28 ms), though both should cost about one pass over memory. This PR moves the backward into a single elementwise kernel. One commit, e5f2787. It also strips trailing whitespace in activations.py, so review with git diff -w.

ReLU backward

dvalues * (self.output > 0) ran as two kernels. The compare wrote a 1-byte bool mask, and the multiply read it back as a mixed fp32 × bool operand. Timed alone at 256×32×32×32 fp32 (RX 9070 XT):

	ms
forward maximum	0.105
backward as written	0.380
↳ out > 0	0.100
↳ d * bool_mask	0.263
d * fp32_mask, for comparison	0.170
fused output > 0 ? d : 0	0.171

Nothing in it branches, so warp divergence wasn't the cost. The time went to the extra pass and the slow bool operand. The fused kernel reaches ~590 GB/s for its 12 B per element, close to the card's bandwidth (the forward reaches ~640 GB/s).

aether/custom_kernels/relu_kernel.py adds the kernel in the same form as loss_kernel.py: a module-level ElementwiseKernel, an is_gpu_relu_backward_available() check and a launch function. There's no kernel cache, because there's nothing to key it on. CuPy already caches the binary per dtype pair. dvalues and output are typed separately (T, U), so an fp16 output with an fp32 gradient doesn't raise. ReLU._compile_for_device binds it as _backward_gpu and keeps the multiply as _backward_fallback. Forward has no GPU kernel, since maximum already runs at bandwidth. It's bound as _forward_fallback, like the other pointer-swap layers, which test_device_migration expects.

RX 9070 XT, benchmarks/cifar10_breakdown.py (synthetic data, batch 256):

	ms/step	ReLU backward (5 layers)
main	7.26	0.90 ms
this PR	6.78	0.45 ms

That's −6.6 % per step. A second run gave 6.81 ms.

Testing

• python3 -m unittest discover tests: 1142 tests pass on NumPy and CuPy (65 skipped).
• test_backward_mixed_dtypes_and_nan: an fp16 output with fp32 dvalues keeps the gradient dtype, and the NaN case has hand-written expected values for each path. On CuPy it fails on main (NaN where 0 is expected).
• test_device_migration.test_pointer_swap_binds_gpu_paths_after_to_cupy now covers ReLU too.
• GPU: tests and benchmark ran on an RX 9070 XT (gfx1201, ROCm) with CuPy 14.2.

Follow-ups (out of scope)

• Not measured on the RTX 3050. The kernel is a plain ElementwiseKernel with nothing vendor-specific, but the speedup there is unconfirmed.
• Fuse ReLU into BatchNorm's forward and backward. Together they're still ~3.3 ms of the 9070 XT step, mostly separate full passes over the activations.