Architecture hardening: autograd core, storage soundness, security, and module layout by neuralsorcerer · Pull Request #30 · neuralsorcerer/minitensor

neuralsorcerer · 2026-07-17T17:46:24Z

Summary

A staged, fully-validated refactor of the engine, bindings, and Python layers based on a complete architectural assessment. Every change is documented in docs/architecture_review.md (findings, target architecture, migration plan, measurements). Work was done in reviewable tranches, each independently green.

Priority order followed: correctness → security → maintainability → performance. Every performance claim is backed by an interleaved A/B measurement on release wheels; one optimization was rejected because it was measured slower, and one throughput optimization was reverted in favor of soundness (see below).

Correctness

view/reshape data corruption fixed. view re-strided unconditionally, so expand(...).reshape(...) produced a tensor whose shape claimed more elements than its storage held (reproduced). view now rejects non-contiguous tensors; reshape materializes a contiguous copy.
Backward pass now scopes to the traced subgraph instead of topologically sorting every node ever recorded on the thread, and no longer clones the full gradient map on every call.
Frozen-input gradient gating across the whole autograd layer — arithmetic, min/max/where, matmul, all seven loss functions, logaddexp, and concat skip gradient work for inputs that don't require it. Fixes get_gradient leaking gradients for constant tensors (e.g. loss targets) and speeds up graphs with frozen operands.
Per-tensor zero_grad (was wiping every gradient on the thread), chainable requires_grad_, overflow-checked Shape::numel/Strides.

Soundness (all identified UB removed)

Storage aliasing: Tensor::data_mut fabricated &mut TensorData from a shared Arc via raw-pointer cast (UB whenever storage was shared). Storage moved behind UnsafeCell; data_mut returns a DataMut token whose shared-parameter-update path has a documented aliasing contract.
Initializer kernels created &mut [bool]/&mut [T] over uninitialized memory (instant UB for bool). Rewritten to write through Vec::extend.
Op output buffers were allocated uninitialized and handed out as &mut [f32]/… — forming a reference to invalid values, i.e. UB. Now zero-initialized. Measured cost (A/B): ~25–35% on the allocation-bound element-wise microbenchmark, noise on training; the zero-cost MaybeUninit alternative is documented as opt-in future work. Also removed a vestigial manual refcount whose Drop leaked device buffers.

Security

Checked multiplication in Shape::numel/Strides::from_shape in all build profiles — a wrapped element count could under-allocate storage while stride-based kernels still trusted the dimensions (out-of-bounds access from an absurd shape).

API / features

Added no_grad() / enable_grad() / is_grad_enabled() / set_grad_enabled() (inference mode: no graph growth, results are detached leaves).
New engine-only breaking change: removed the dead operations::fusion module (623 lines, never called by any path). Migration note in the review doc.

Maintainability

include!→real-modules migration complete — 50 files across autograd, tensor, all operations clusters, the bindings, and the feature-gated backends/opencl pair. Two layout patterns (siblings + pub(crate) re-exports; children-of-core where impls touch private fields), preserving all field privacy.
Kernel dedup via macros — arithmetic, comparison, storage accessors, float-unary activations, and prod-reduction kernels; ~1000+ lines of copy-paste removed with behavior verified byte-identical.
Clippy enforced in CI (-D warnings); removed the crate-wide #[allow(clippy::all)]. Fixed real issues it surfaced, including latent bugs in never-compiled opencl test code.

Validation

639 Rust tests + 789 Python tests pass; clippy clean under -D warnings for both the default build and --features opencl.
Interleaved A/B benchmarks on release wheels at each performance-relevant step (numbers in the review doc).

Deliberately left to the maintainer

Removing the hardware/pooled-allocator subsystems (semver-major, still have in-repo consumers) and the optional MaybeUninit throughput recovery — both documented with rationale rather than forced.

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Generated by Claude Code

Architectural refactor based on a full review of the engine, bindings and Python layers (assessment and remaining migration plan documented in docs/architecture_review.md): Autograd: - Plan backward passes over the subgraph reachable from the loss tensor instead of topologically sorting every node recorded on the thread (ComputationGraph::plan_backward + execute_backward_plan). - Stop cloning the full gradient map on every backward call; backward now returns Result<()> and gradients are read via get_gradient. Tests and diagnostics that want the full map use the new backward_collect. - Make gradient-recording policy explicit with a thread-local grad mode (NoGradGuard / is_grad_enabled). Previously, node registration during gradient-kernel execution was suppressed only because add_to_graph used try_borrow_mut while the graph happened to be borrowed - a silent-failure invariant. add_to_graph is now gated on the grad mode and borrows loudly. - Release the tensors saved for backward as soon as a non-retaining backward() completes (release_saved_subgraph), instead of holding all activations until the next optimizer step; this also makes the "computation graph has been freed" error truthful. - DivBackward: compute both gradients with 4 kernels and no scratch ones tensor (was 6 kernels plus a ones allocation). Tensor/storage invariants: - Tensor::view now rejects non-contiguous tensors; Tensor::reshape and operations::shape_ops::reshape materialise a contiguous copy instead. Previously expand(...).reshape(...) silently produced a tensor whose shape claimed more elements than its storage held (masked at the Python boundary by a blanket copy in PyTensor::from_tensor, but corrupt for any engine-level consumer). squeeze/unsqueeze/flatten route through reshape. - Remove TensorData's vestigial manual reference count (inc_ref/dec_ref were never used outside their own tests) and make Drop unconditionally return raw device buffers to the allocator; sharing is Arc's job. - Tensor::data_mut takes the sound Arc::get_mut path whenever storage is uniquely owned; the intentional shared-parameter in-place update case is now documented at the call site. Kernels: - Replace the raw-pointer per-element parallel loops in add_inplace (gradient accumulation hot path) with safe chunked par_chunks_mut/zip loops, removing 10 unsafe blocks without changing semantics. Validation: full Rust workspace suite and Python pytest suite (778 passed) pass; interleaved A/B benchmarks on release wheels show no regression and a consistent ~15% improvement on gradient-accumulation-heavy backward passes (numbers in docs/architecture_review.md). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Second tranche of the architecture plan in docs/architecture_review.md: Gradient-recording mode: - Add user-facing no_grad()/enable_grad() context managers plus is_grad_enabled()/set_grad_enabled() in Python, backed by the engine's thread-local grad mode. Recording is gated centrally (Tensor::new, Tensor::set_grad_fn, add_to_graph): results inside no_grad are detached leaves, no operands are saved for backward, and inference no longer grows the autograd graph without bound. requires_grad_(True) remains an explicit, ungated opt-in. - Tensor::zero_grad now clears only that tensor's gradient instead of wiping every gradient on the thread (autograd::clear_gradient). - PyTensor.requires_grad_ returns self so calls chain like PyTorch. Correctness/security hardening: - Shape::numel and Strides::from_shape use checked multiplication in all build profiles. A wrapped product could under-allocate storage while stride-based kernels still trusted the dimensions, turning absurd shapes into out-of-bounds access; now they fail loudly. - nn/init.rs no longer creates &mut slices over uninitialized memory (instant UB for bool, unsound for all dtypes): all twelve initializer kernels write through Vec::extend. clone_data uses Vec::clone. - Op output buffers stay uninitialized behind one documented helper (owned_output_buffer) with an explicit write-before-read contract; full zero-initialization was implemented and measured at 30-35% slower on large element-wise ops (memset doubles output write traffic on reused allocations), so it was rejected in favor of the tracked MaybeUninit migration. Bool buffers are always zeroed. Lint debt: - Remove #![allow(clippy::all)] from the engine; fix or justify every warning (workspace is clean under -D warnings). Three style lints are allowed crate-wide with documented reasons (needless_range_loop, too_many_arguments, items_after_test_module). Device now implements std::str::FromStr. - Add a clippy job (-D warnings) to the lints workflow. Validation: 640 Rust tests and 789 Python tests pass (11 new grad-mode tests); interleaved A/B benchmarks on release wheels show performance parity with the previous commit. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Third tranche of the architecture plan in docs/architecture_review.md: Storage soundness: - TensorData's buffer now lives in an UnsafeCell, and Tensor::data_mut returns a DataMut access token instead of fabricating &mut TensorData from a shared Arc (undefined behavior whenever any other handle to the parameter existed - i.e. always, since the caller itself holds one). DataMut::Unique is ordinary exclusive access after copy-on-write; DataMut::Shared is the single documented exception - in-place parameter updates that must stay visible through every Arc handle (PyTorch semantics) - and goes through UnsafeCell-backed accessors with an explicit aliasing contract. Call sites are unchanged in shape: the token consumes itself and returns a slice borrowing from the tensor. - The ten hand-copied typed slice accessors (plus five new shared-mutation variants) are generated by one typed_slice_accessors! macro - the first application of the dtype-dispatch dedup planned for the ops layer. Autograd efficiency/correctness: - AddBackward/SubBackward/MulBackward/DivBackward now carry per-input requires_grad flags (like min/max/where/matmul already did) and skip the whole gradient chain for frozen inputs: no multiply, no broadcast reduction, no gradient map entry. This fixes get_gradient returning gradients for tensors that never required them (root cause, for arithmetic ops) and measurably speeds up graphs with frozen operands - scalar-broadcast backward work disappears, and the wide fan-out benchmark improved 15-30% in interleaved A/B runs. Validation: 641 Rust tests (new frozen-input regression test) and 789 Python tests pass; workspace clippy-clean under -D warnings; interleaved A/B release-wheel benchmarks show parity everywhere else. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Fourth tranche of the architecture plan in docs/architecture_review.md: - The eighteen hand-copied *_direct binary kernels (add/sub/mul/div across dtypes) are now generated by two macros (binary_kernel! / binary_kernel_simd!, the latter keeping the f32/f64 SIMD fast path), and neg's five dtype arms by one macro - ~440 lines of copy-paste removed. Interleaved A/B release-wheel benchmarks show exact performance parity, as expected for a purely mechanical expansion. - Remove operations/fusion.rs (623 lines): compiled and re-exported but never called by any execution path, the bindings, or the Python API. BREAKING (engine crate only; the Python package surface is unchanged): code depending on engine::operations::fusion should pin engine 0.2.1 or vendor the module from git history. Rationale and migration note in docs/architecture_review.md. Validation: 637 Rust tests pass (the 4 removed with fusion were its own unit tests) and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Fifth tranche of the architecture plan in docs/architecture_review.md: All seven loss backward functions (MSE, MAE, Huber, CrossEntropy, BCE, KLDiv, Focal) now carry per-input requires_grad flags, matching the arithmetic/minmax/matmul gradient functions. Previously MSE/MAE/Huber allocated and negated a full-size target gradient on every training step, and KLDiv ran a log/sub/add/mul chain for the target distribution - all discarded work whenever targets are constants, which is the overwhelmingly common case. Prediction gradients are likewise skipped when predictions are frozen (e.g. evaluating a loss against trainable targets only). Also fixes get_gradient reporting gradients for constant target tensors. New regression test: test_loss_targets_receive_no_gradient (frozen targets get no gradient; trainable targets still do). Validation: 638 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings; interleaved A/B release-wheel benchmarks show parity-to-slightly-better on the MLP training step (the saving scales with target size). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Sixth tranche of the architecture plan in docs/architecture_review.md: - LogAddExpBackward and ConcatBackward - the last multi-input gradient functions without per-input requires_grad flags - now skip work for frozen inputs. logaddexp previously ran an exp/sub/mul/broadcast-reduce chain per side unconditionally; concatenate extracted a gradient slice for every input including constants (e.g. constant features concatenated with trainable embeddings). PowBackward and LayerNormBackward already had flags; single-input gradient functions need none since they are only reached when their input requires grad. This completes the audit item from the plan, with a new regression test (test_concat_frozen_inputs_receive_no_gradient). - The five hand-copied cmp_* comparison kernels collapsed into one cmp_kernel! macro (511 -> 424 lines), continuing the dedup pattern established for storage accessors and arithmetic kernels. Validation: 639 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings; benchmarks within established ranges. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Seventh tranche of the architecture plan in docs/architecture_review.md: The autograd subsystem's seven include!-merged files are now proper submodules (core, arithmetic, linalg, shape, reduction, activation, tests) with their own imports, explicit pub(crate) boundaries for shared helpers, and glob re-exports in autograd/mod.rs keeping every crate::autograd::X path unchanged - no callers were touched. The merged namespace had been hiding real layout problems that the conversion surfaced and fixed: - the broadcast-reduction helper used by four gradient-function families lived in the tests include file (moved to core); - shared helpers had no visibility story at all (now explicit pub(crate): accumulate_grad, create_scalar_tensor, the softmax backward kernels, expand_reduction_grad, repeat_interleave_backward_impl); - per-file imports were unstated, resolving through whichever sibling happened to import them (each module now declares exactly what it uses, enforced by -D warnings). Also documents that PowBackward's trait impl lives in a different file than its struct - visible now that files are real modules. This is the template for converting the remaining include! clusters (tensor/mod, operations, bindings pytensor). Validation: 639 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Eighth tranche of the architecture plan in docs/architecture_review.md: The tensor subsystem's five include!-merged files are now proper modules. The method-impl files (ops, indexing, autograd, utils) become children of the module that declares the Tensor struct (children-of-core layout), so they retain access to its private fields - encapsulation is preserved exactly; no field had to widen to pub(crate). Public paths are unchanged via re-exports (crate::tensor::X still works; no callers touched). As with the autograd conversion, real module boundaries made the implicit explicit: the strided-copy helper shared between indexing and core now has a declared pub(super) visibility, and every file states its own imports (enforced by -D warnings) instead of resolving through whichever sibling happened to import a name. Remaining include! clusters (operations/*, bindings pytensor/*) follow the same two templates. Validation: 639 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Ninth tranche of the architecture plan in docs/architecture_review.md: All seven operations include! clusters (arithmetic, simd, shape_ops, loss, linalg, activation, reduction - 27 files) are now proper submodules with declared imports, following the autograd/tensor templates. Public paths are unchanged via re-exports; kernel-only submodules re-export at pub(crate) so internal helpers no longer leak into the crate's public API surface. As before, real module boundaries forced the implicit into the open: - ~190 cross-file helpers, layout structs, and macro-generated kernels now carry explicit pub(crate) visibility instead of resolving through the merged namespace by accident; - loss's private log/sign helpers shadowed the public activation ops with the same names - renamed (log_tensor/sign_tensor) after the ambiguity surfaced as an ambiguous-glob-reexport error; - every file declares its own imports, enforced by -D warnings. Engine-side include! conversion is complete. Remaining: the feature-gated backends/opencl pair (not compiled by default, so a conversion cannot be validated in this environment) and the bindings clusters. Validation: 639 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Tenth tranche of the architecture plan in docs/architecture_review.md. The bindings' two include! clusters are now real modules: - tensor.rs -> 19 files across pytensor/, python/, and creation/ become children of the `preamble` module (which declares PyTensor and the shared conversion helpers), so their `impl PyTensor` blocks keep access to the private `inner` field and the shared imports; - nn.rs -> layers.rs (pyclass wrappers + module registration) becomes a child of module.rs (pyclass structs + #[pyfunction]s), so `impl PyReLU` and the `wrap_pyfunction!` calls resolve against the definitions. The children-of-core layout (also used for tensor/ in the previous change) was necessary here because these files are one cohesive module split only for file size: siblings could not see each other's private fields, methods, or pyo3 macro-generated companion modules. Only genuinely cross-file items were widened to pub(crate) (helpers, a handful of PyTensor methods like is_leaf/eq_from_py, the FanInitKind enum, and the functional #[pyfunction]s). Public paths are unchanged via re-exports; no Python-facing behavior changed. With this, every include! in the default build is gone (only the feature-gated backends/opencl pair remains, untouched because it cannot be compiled/validated here). The crate-wide items_after_test_module allow, an artifact of the old layout, is removed - clippy passes without it. Validation: 639 Rust tests and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Eleventh tranche of the architecture plan in docs/architecture_review.md. The 34 uniform float-unary elementwise kernels in activation/hyperbolic.rs (exp/log/sinh/cosh/tanh/log1p/... - each a fetch-input, fetch-output, unary_apply(closure) wrapper) collapse into one float_unary_kernel! macro parameterized by the per-op mapping closure. ~300 lines removed, continuing the dedup pattern from the storage accessors, arithmetic, and comparison kernels. Each closure (including multi-line custom ones like log1p's -1/<-1 branch handling) was extracted verbatim by the conversion, so behavior is byte-identical. Verified by the full Rust and Python suites plus a numerical spot-check of exp/log/sqrt/sinh/cosh/tanh against numpy and log1p's special-value branches (-inf at -1, nan below -1). The non-uniform activation kernels (softplus/gelu/elu, which take extra scalar parameters) and the reduction kernels keep their per-dtype copies. Validation: 612 engine tests + full workspace suite and 789 Python tests pass; workspace clippy-clean under -D warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Twelfth tranche of the architecture plan in docs/architecture_review.md. The four prod_along_dim_{f32,f64,i32,i64} kernels are byte-identical apart from the element type and multiplicative identity; they collapse into one prod_along_dim_kernel! macro. The float/int sum_along_dim kernels are deliberately NOT touched - their float versions carry distinct SIMD-style paths that the int versions don't, so they are genuinely per-dtype, not duplication. Verified with the full Rust suite, workspace clippy under -D warnings, and a numerical spot-check of prod along every dim for f32/f64/i32/i64 against numpy; 789 Python tests pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

Thirteenth tranche of the architecture plan in docs/architecture_review.md. Addresses the two remaining validatable items. Soundness (finding 16, now fully fixed): Operation output buffers were allocated uninitialized and handed out as &mut [f32]/&mut [i64]/... by every kernel. Forming a &mut T reference to uninitialized memory is undefined behavior - the referent must be a valid T, and a float/int read from uninitialized memory is not a valid value even though every bit pattern is representable. Output buffers are now zero-initialized (owned_buffer_for_dtype always zeroes; the unsound owned_output_buffer helper is removed). Per the project's stated priority order (correctness before performance), soundness wins. The cost is measured honestly via interleaved A/B on release wheels: ~25-35% on the pure element-wise microbenchmark (add+sum over 2000x2000, where the extra memset raises output write traffic from ~3N to ~4N) and within noise on the matmul-dominated training step. The zero-cost alternative (MaybeUninit-typed kernel writes across the ~71 output sites) is documented as opt-in future work. Module layout (migration now complete): Converted the feature-gated backends/opencl include! pair to real modules (ops_impl as a child of context, matching the impl/struct split pattern), validated under --features opencl. This also surfaced and fixed pre-existing latent bugs in opencl code that CI never compiled (default features only): a private device_type field access, a missing CL_MEM_* import, an unused allocator parameter, and several clippy lints (div_ceil, collapsible if, non-Send/Sync Arc documented as intentional for the single-threaded backend). Every include! in the codebase is now gone. Validation: 639 Rust tests and 789 Python tests pass; clippy clean under -D warnings for both the default build and --features opencl. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SuYLZdiACFPJWs3v5vtboz

+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v7
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+      - uses: dtolnay/rust-toolchain@stable
+        with:
+          components: clippy
+      - uses: Swatinem/rust-cache@v2
+      - name: Run clippy (deny warnings)
+        run: cargo clippy --workspace --all-targets -- -D warnings


codecov · 2026-07-17T17:50:26Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.55%. Comparing base (1de795a) to head (e00bb17).

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #30   +/-   ##
=======================================
  Coverage   99.54%   99.55%           
=======================================
  Files          22       23    +1     
  Lines        5534     5623   +89     
  Branches      129      129           
=======================================
+ Hits         5509     5598   +89     
  Misses         19       19           
  Partials        6        6

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chatgpt-codex-connector · 2026-07-17T17:53:32Z

+        if grad_fn.is_some() && !autograd::is_grad_enabled() {
+            return;


Clear requires_grad when suppressing grad_fn

When grad mode is disabled this branch only skips attaching the grad function; it does not clear an already-true requires_grad flag. Any op that builds its result by cloning/reflagging an input instead of going through Tensor::new (for example with mt.no_grad(): y = x.reshape(x.shape) for a grad-requiring x, or the MAE/Huber paths that call requires_grad_(true)) can return requires_grad=True while add_to_graph also no-ops, leaving a tensor that looks differentiable but has no recorded graph and violating the new no_grad contract.

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claude added 13 commits July 16, 2026 17:15

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neuralsorcerer closed this Jul 17, 2026

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Architecture hardening: autograd core, storage soundness, security, and module layout - #30

Architecture hardening: autograd core, storage soundness, security, and module layout#30
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		if grad_fn.is_some() && !autograd::is_grad_enabled() {
		return;

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neuralsorcerer commented Jul 17, 2026

Summary

Correctness

Soundness (all identified UB removed)

Security

API / features

Maintainability

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Deliberately left to the maintainer

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