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Summary: Attempt 3 to check numel and nbytes overflow. This time we defer checking dynamic sized inputs until their size is realized. Reviewed By: lucylq Differential Revision: D98148157
…inear (pytorch#19117) ## Summary The bilinear grid_sampler_2d portable kernel computes interpolation weights via subtractions like `(ix_se - ix)` where both operands are close integer-valued coordinates in pixel space. In fp16 (10 bits of mantissa) that's classic catastrophic cancellation — the result has only a handful of significant bits. The downstream weighted-sum accumulation then loses further precision. Measured on a unit test exercising interior grid points with fp16 inputs, the kernel drifts by ~0.1 absolute from an fp32 reference. That's visible as incorrect depth / flow output near non-integer sample points, which is most of them. ## Fix An `AccType<CTYPE>` trait mapping `Half` and `BFloat16` to `float`, leaving every other dtype unchanged. Used for intermediate coordinate, weight computation, and `out_val` accumulation. Loads cast `CTYPE -> ACC`; the final store casts `ACC -> CTYPE` once. Only internal math is promoted; memory layout / public API / tensor dtypes are unchanged. ```cpp template <typename CTYPE> using AccType = std::conditional_t< std::is_same_v<CTYPE, executorch::aten::Half> || std::is_same_v<CTYPE, executorch::aten::BFloat16>, float, CTYPE>; ``` ## Effects - **fp32 / Int / any non-half dtype**: `AccType<T>` is `T`, so the generated code is byte-identical. No behavior change. - **Half / BFloat16**: `max_abs` vs an fp32 reference drops from **~0.1 to 0** on the shapes I tested (N=1..2, C=7..64, H/W up to 96, both `align_corners` values). - **Perf**: a handful of fp16↔fp32 conversions per output element. Not measurable at op level; well within the portable kernel's scalar cost envelope. ## Scope Only touches the bilinear interpolation path. The nearest-mode path doesn't do weighted-sum accumulation and doesn't have the cancellation issue — left alone in this change. ## Test plan - [x] Builds clean for Android arm64 and host (Apple Clang 21). - [x] Verified numerically via a standalone harness that runs the kernel with matched fp32 / fp16 inputs and compares against an fp32-then-downcast reference. All shapes pass within a single fp16 ULP (or are bit-exact). fp32 tests remain bit-identical to the pre-change kernel. - [x] Existing `kernels/test/op_grid_sampler_2d_test.cpp` unit tests continue to pass (both fp32 shapes that were previously tested, and the fp16 path I'm specifically fixing). Happy to add an fp16-specific test case to `op_grid_sampler_2d_test.cpp` if useful for CI coverage here — just let me know the preferred approach. cc @larryliu0820 @manuelcandales
Differential Revision: D101728720 Pull Request resolved: pytorch#19052
### Summary Fixes pytorch#18924 Extends `aten.bitwise_not` support in the MLX delegate to handle integer tensors, not just boolean tensors. Previously the handler only dispatched to `LogicalNotNode` for `bool` and raised `NotImplementedError` for all other dtypes. This adds a dedicated `BitwiseInvertNode` backed by `mlx::core::bitwise_invert`, and updates the handler to dispatch based on dtype: - `bool` → `LogicalNotNode` (unchanged) - `int32`, `int64` → `BitwiseInvertNode` #### Changes: - `serialization/schema.fbs`: add `BitwiseInvertNode` table and append to `OpNode` union - `runtime/MLXInterpreter.h`: add `exec_bitwise_invert()` and dispatch case - `ops.py`: update `_bitwise_not_handler` to dispatch to `BitwiseInvertNode` for integers - `test/test_ops.py`: add `bitwise_not_int` test for `int32` and `int64` ### Test plan All tests were ran on a machine with an Apple M1 Pro CPU, macOS 26.4.1. - `python3 -m py_compile backends/mlx/ops.py backends/mlx/test/test_ops.py` - `python3 backends/mlx/serialization/generate.py` - `python3 -m executorch.backends.mlx.test.run_all_tests bitwise_not_int` ### Test output ``` ============================================================ TEST SUMMARY ============================================================ Passed: 6 Failed: 0 ============================================================ ``` cc @metascroy --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Co-authored-by: Scott Roy <161522778+metascroy@users.noreply.github.com>
Differential Revision: D102070375 Pull Request resolved: pytorch#19074
pytorch#19075 failed to cp to main
Add Closeable interface so Module can be used with try-with-resources. close() delegates to destroy(). Also make destroy() idempotent by checking mHybridData.isValid() before calling resetNative(), satisfying the Closeable contract. This commit was authored with the help of Claude.
TrainingModule: implement Closeable, replace Log.e + silent empty returns with IllegalStateException throws. Add checkNotDestroyed() guard on all public methods. SGD: throw IllegalStateException instead of bare RuntimeException when optimizer is destroyed. AsrModule: throw ExecutorchRuntimeException instead of bare RuntimeException on transcription failure. ExecuTorchRuntime.validateFilePath: throw IllegalArgumentException instead of bare RuntimeException, with descriptive message. JNI constructors: wrap ExecuTorchJni and ExecuTorchLlmJni constructor bodies in try-catch so C++ exceptions become ExecutorchRuntimeException instead of generic RuntimeException. This commit was authored with the help of Claude. --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Differential Revision: D102425537 Pull Request resolved: pytorch#19128
…_nhwc operator + tests Differential Revision: D96507563 Pull Request resolved: pytorch#18479
…ch#19028 (pytorch#19133) ## Summary Reverts the following Android PRs: - pytorch#19099 — Android: consistent error types across all modules - pytorch#19124 — Android: Module implements Closeable - pytorch#19092 — Android: improve error diagnostics for LlmModule and exceptions - pytorch#19028 — Ignored Module tests: provide required input tensor Authored with Claude.
Differential Revision: D102488314 Pull Request resolved: pytorch#19134
Differential Revision: D102385104 Pull Request resolved: pytorch#19122
Differential Revision: D102493794 Pull Request resolved: pytorch#19136
…rch#19137) Differential Revision: D102493838 Pull Request resolved: pytorch#19137
…ualcomm/README.md
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Summary
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Test plan
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