Per pearl_build_rs_rerun_if_env_changed, every std::env::var() must be
paired with cargo:rerun-if-env-changed. Task #321 fixed crates/ml/build.rs
(commit e3d082968) but missed 6 sibling build.rs files. Each reads
CUDA_COMPUTE_CAP without registering the rerun directive, so cargo
sees no env-change between e.g. SM 89 (L40S) → SM 90 (H100) workflow
re-submissions and cache-hits the wrong-arch cubin from the previous
run. At runtime, the H100 fails to load the SM 89 cubin with
CUDA_ERROR_NO_BINARY_FOR_GPU on rmsnorm — exactly what killed
train-multi-seed-vlv8c (commit 0371d6a76, post-Class-B chain).
Files (all add `println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP")`
just before the env::var() read):
- crates/ml-dqn/build.rs (rmsnorm — root cause of vlv8c failure)
- crates/ml-ensemble/build.rs
- crates/ml-explainability/build.rs
- crates/ml-ppo/build.rs
- crates/ml-supervised/build.rs
- crates/ml-core/build.rs
Atomic single commit per feedback_no_partial_refactor.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Implements the CUDA kernels (`interpolate_input`, `perturb_dimension`)
and wires `compute_gpu` in `integrated_gradients.rs` to run the full
IG algorithm on-device. Replaces the stub that previously returned
`MLError::ModelError("IG GPU kernels not available: cubins not yet
wired")` and fell back to CPU.
Algorithm: for each of `num_steps` interpolation points along the
baseline->input path, compute central-finite-difference gradients for
all `num_features` dimensions via two GPU forward passes per feature.
Single cubin (`ig_kernels.cubin`) compiled via build.rs (following the
crates/ml/build.rs pattern — nvcc, `-arch=sm_\${CUDA_COMPUTE_CAP}`, O3,
f32) and embedded with `include_bytes!`. The `forward_fn` consumers
operate on `GpuTensor`, so no dtoh round-trips inside the inner loop —
only the scalar `[1]` tensor output of each forward pass is pulled to
host (via `to_scalar`) per gradient sample. The three scratch buffers
(`interpolated`, `x_plus`, `x_minus`) are allocated once outside the
step loop and reused across all steps and features. No atomicAdd —
the kernels are trivial 1-D element-wise writes.
Tests: existing CPU tests pass unchanged. Added GPU smoke test
`test_ig_compute_gpu_linear_model` (gated `#[cfg(feature = \"cuda\")]`
+ `#[ignore]`) that builds a linear model as a `GpuTensor`-native
forward (elementwise mul + mean), verifies the completeness axiom
within 1%, and cross-checks GPU attributions against the CPU path
within 5%. Passes locally on RTX 3050 Ti (sm_86).
Removes 3 TODO markers and the embedded `_IG_CUDA_SRC` const that
were awaiting this work, along with the `#[allow(unused_variables)]`
stub attribute on `compute_gpu`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
