perf(ml-alpha): full zero-alloc training step (#3 foundation)
Eliminates ALL per-step allocations from the training hot path —
foundation for CUDA Graph capture (next commit). Before this commit,
each step_batched call allocated:
Mamba2 forward: input_2d view, x, a_proj, b_proj, h_s2, h_enriched_seq
Mamba2 backward: d_a_per_channel/d_b_per_channel/d_w_c/d_h_s2 (#2 covered)
d_a_proj_flat, d_b_proj_flat, dw_c
LinearGrads.{dw,db,dx} × 3 projections (cuBLAS internal)
d_x_from_a + d_x_from_b + d_x (elementwise add)
dw_out, db_out (zero-init shells)
Trainer wrapper: window_tensor, h_enriched_seq_t, grad_h_enriched_seq_t,
grad_h_enriched_seq
~20-25 cudaMalloc / GpuTensor::zeros calls per step × 2000 steps/epoch =
40-50K allocations per epoch.
This commit adds zero-alloc `_into` variants throughout the chain:
ml-core/cuda_autograd/linear.rs:
OwnedGpuLinear::forward_with_slices_into
OwnedGpuLinear::backward_with_slices_into
reduce_sum_axis0_into
ml-core/cuda_autograd/elementwise.rs + gpu_tensor.rs:
ElementwiseKernels::binary_into
GpuTensor::add_into
ml-alpha/mamba2_block.rs:
Mamba2BlockForwardScratch (pre-allocated forward cache)
Mamba2BackwardGradsBuffers (pre-allocated backward outputs)
Mamba2Block::forward_train_seq_into (zero-alloc forward)
Mamba2Block::backward_from_h_enriched_seq_full_into (zero-alloc backward)
Mamba2AdamW::step_from_buffers (reads grads_buffers directly)
ml-alpha/trainer/perception.rs:
PerceptionTrainer pre-allocates: window_tensor_d, h_enriched_seq_t_d,
grad_h_enriched_seq_t_d, grad_h_enriched_seq_d, mamba2_fwd_scratch,
mamba2_grads_buffers
step_batched + evaluate_batched fully wired through _into variants
Original `forward_with_slices` / `backward_with_slices` / `binary` / `add` /
`backward_from_h_enriched_seq` paths preserved unchanged — Phase E.3
ml/examples callers unaffected.
The captured-graph commit (next) only needs to wrap this zero-alloc
training step in cuGraph capture/replay; no further refactoring of
buffer management.
77 ml-alpha tests pass. Synthetic overfit converges identically
(0.29 → 0.0007 in 250 steps) — gradients are bit-identical to the
allocating path.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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