Random in-place operations (normal_, uniform_, exponential_, bernoulli_, random_) were silently failing on non-contiguous tensors on macOS < 15.0.
* Added needsGather check and scatter-back logic to handle non-contiguous output tensors, following the pattern used in PointwiseOps.
* Adds test to confirm these now work
* Remove pre-macOS15 xfail for test_Dropout
Fixes#165257 and #124029
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165267
Approved by: https://github.com/kulinseth, https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Summary: To fix https://github.com/pytorch/pytorch/issues/159400. Currently, test_aoti_abi_check and test_aoti_inference need to be built in two passes, first build pytorch using the regular `pythonsetup.py develop` and then build with `CMAKE_FRESH=1 BUILD_AOT_INDUCTOR_TEST=1 python setup.py devleop`. This is cumbersome. Fix by rewriting CMakeLists.txt for test_aoti_inference to one-pass build which runs AOTI to compile models at the test time. Also update CI test script to get rid of two-pass build. For test_aoti_abi_check, it is not AOTI specific, so we make it not guarded by BUILD_AOT_INDUCTOR_TEST.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164277
Approved by: https://github.com/janeyx99
Pull replace_node function out of Scheduler.finalize_multi_template_buffers(). This is needed by the next PR (#163369). As part of this also pull the _replace_operation_buffer() up to top-level since it needed no self references.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163368
Approved by: https://github.com/PaulZhang12
### Summary
Improves validation of `torch.linalg.eig` results by verifying the eigen decomposition identity **A v − v λ = 0**.
### Motivation
Eigenvectors are not unique, and numerical differences between backends (cuSOLVER, MAGMA, CPU)
can cause false test failures. This PR replaces direct elementwise comparisons with a mathematical
identity check, improving robustness across devices.
### Details
- Introduces `fulfills_eigen_decomposition_identity()` in `test_eig_compare_backends()` to validate the eigen equation.
- Uses CPU matmul for high-precision verification.
- Handles zero-sized matrices explicitly.
- Tolerances derived from numerical comparisons between cuSOLVER and NumPy.
See discussion: [dev-discuss.pytorch.org link](https://dev-discuss.pytorch.org/t/cusolver-dnxgeev-faster-cuda-eigenvalue-calculations/3248/6)
### Impact
- Improves test stability and correctness across eig backends.
- No change to public API.
- All tests pass; lintrunner reports no issues.
- Enables introduction of new eig backends without false test failures.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166322
Approved by: https://github.com/lezcano
This PR refactors the autocast context manager in autocast_mode.py to simplify and centralize the logic for checking supported dtypes for each device. The previous implementation repeated similar checks for multiple device types. Now, a single mapping device_supported_dtypes is used to associate device types with their supported dtypes, and the validation logic is unified.
**The former PR #163446 was merged but reverted due to failed CI test on `openreg` related tests.**
This RR additionally slightly modified some test assertions for passing the CI tests. CI failed due to assertion for the exactly same error message. For example:
```
File "/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/tests/test_autocast.py", line 9, in test_autocast_with_unsupported_type
with self.assertWarnsRegex(
AssertionError: "In openreg autocast, but the target dtype torch.float32 is not supported." does not match "In openreg autocast, but the target dtype is not supported. Disabling autocast."
```
Sorry for the inconvenience again.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165221
Approved by: https://github.com/albanD
RMS/Layer norm backward would generated 2 kind of reductions:
- the reduction computing dx which reduce across the hidden dimension (in the context of transformer)
- the reduction computing dw/db which reduce across the BxT (batch size , sequence length) dimension.
These 2 set of reductions have common input buffers but inductor can not fuse them because of different loop orders.
There are multiple sources of custom kernels that implement fused version of such kernel (Liger-Kernel, quack, Paul Zhang's internal post). This PR enable Inductor to generate such kernels automatically.
The generated kernel is very similar to 33924d20b6/src/liger_kernel/ops/rms_norm.py (L114) .
To make the implementation simple and performing, we enable such fusion only if the inner reduction (computing dx) is a persistent reduction. This should be true for representative inputs. Persistent reduction is critical for the perf here to make sure a loaded tensor does not need to be reload.
To make sure the inner reduction (computing dx) and outer reductions (computing dw/db) being fusible, the PR does the following:
1. convert the outer reductions to pointwise by replacing 'reduction' & 'store_reduction' node with a new type of node 'parital_accumulate'. The new node will collect the reduction type, buffer name, input of reduction etc, which is essential for proper codegening.
2. do loop reordering (rely on the earlier loop ordering after fusion work) to reorder the loops of the converted pointwise so it can be fused with the inner reduction
3. there can be epilogues that need to be added in the end. E.g. the outer reduction may be followed by a division for mean , or followed by a down cast if dw/db is in low precision (fp16/bf16).
Some early benchmarking on H100 shows about 2X speedup for both RMSNorm and LayerNorm backward for shape (1152 * 500, 384 ) used in some internal model. Note that, I manually disable split reduction in this benchmarking since otherwise the fusion will be skipped right now. The next PR will make the mix-order-reduction compose better with split reduction
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165370
Approved by: https://github.com/jansel
ghstack dependencies: #166204
Summary: In this diff, we add a event pool so that we dont have to create/destroy events all the time, instead re-use the events from the pool.
Test Plan: contbuild
Differential Revision: D84685495
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165647
Approved by: https://github.com/bbus
Fixes#166068
Use the ROCm version string that does not contain a hash. The string is set in LoadHIP.cmake.
Tested on repro provided by reporter.
For a ROCm 7.0 docker container, we get `7.0.0`.
For a ROCm 7.0.2 docker container, we get `7.0.2`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166336
Approved by: https://github.com/jeffdaily
Summary:
Support deepseek-style scaling in Inductor Triton for FP8 GEMMs. DeepSeek-style scaling is a colloquial term for a fine-grained mixed precision framework using FP8 to train [Deepseek-V3](https://arxiv.org/pdf/2412.19437), DeepSeek AI's recent MoE (Mixture of Experts) model. DeepSeek-style scaling effectively extends the dynamic range of FP8 by mitigating dequantization overhead under increased-precision accumulation, which is key to achieving more accurate FP8 GEMM results.
DeepSeek-style scaling on matmul `A @ B` leverages two different types of scaling strategies to preserve a balance between numerical stability and training efficiency:
- Activations (input tensor `A`): tile-wise (1x128 across shape `(M, K)`)
- Weights (input tensor `B`): block-wise (128x128 across shape `(N, K)`)
This diff enables Inductor users to replicate past successes with deepseek-style scaling and achieve higher numerical stability while increasing training efficiency.
NOTE: Block-wise 128x128 scaling is only supported in CUDA 12.9+; therefore, deepseek-style scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run deepseek-style scaling.
NOTE: Accuracy for FP8 is unstable, even with high tolerances, which is why TritonBench benchmarks are unlikely to be accurate against a `torch` implementation.
Test Plan:
In OSS PyTorch, run
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 python run.py --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 4096 --n 768 --k 512 --output="{output_dir}/deepseek_bench.csv" --scaling_deepseek --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/deepseek_style/deepseek_bench.log
```
Differential Revision: D83609850
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164404
Approved by: https://github.com/slayton58
Previously, we would completely skip building and calling any resume function if the leaf frame's resume instruction was RETURN_VALUE/RETURN_CONST. Now, we only skip building/calling resume functions for frames that are resuming on RETURN_VALUE.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165808
Approved by: https://github.com/Lucaskabela
ghstack dependencies: #166013, #166015
This `patch.dict(counters, ...` appears to be ancient code that doesn't really seem to be doing anything? It causes issues in nested graph breaks because the patch cleanup clears out the record of the nested graph break. Removing the patch to see if it's even needed in the first place.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166015
Approved by: https://github.com/Lucaskabela
ghstack dependencies: #166013
Summary:
as title
- Some IR nodes are created during `finalize_multi_template_buffers()` in Scheduler. This PR adds provenance (`origin_node` and `origins`) for those nodes.
- Extract `assign_origin_node` function
Test Plan:
```
buck run mode/opt fbcode//caffe2/test/inductor:provenance_tracing -- -r test_deferred_triton_kernels
```
Differential Revision: D83979975
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164746
Approved by: https://github.com/mlazos
Summary:
Add a gflag to allow us skip empty constant named parameter during
dense loading. In [vm_parameters.py](https://fburl.com/code/7xr9ihwy), there is
a constant _empty_tensor parameter used for the model. This constant parameter
is skipped in XL weights during model publish because it is empty. This will
break model inplace update later because it will be reported by the AOTI
container but cannot be found from the model merge weights. This diff will
allow us to solve the problem.
Test Plan: Verified inplace update in job https://www.internalfb.com/vanguard/serving_test_cases/1165842932095688
Reviewed By: muchulee8, joannec3634
Differential Revision: D85082330
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166037
Approved by: https://github.com/muchulee8, https://github.com/jcwchen
Namely, error out rather than crash when out dtype is of an unexpected type
Resize output tensor to the expected size in `_out` operation, to prevent crash when tensor of an unexpected size is passed.
Preserve symbolic shapes whenever possible
Test plan: Run `python test_ops.py -v -k test_out_warning_fft_hfft_mps` for MPS device, without this change it crashes with `Error: Invalid KernelDAG, equalShape for destination failed'`, run `python ../test/test_ops.py -v -k test_dtypes_stft_mps`, without this change it crashes with `A complex mlir::Type does not have a corresponding complex MPSDataType"`, when input dtype is bfloat16
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166272
Approved by: https://github.com/kulinseth
Fixes#163929
Fixes argmin/argmax operations to return correct logical indices instead of physical memory offsets when applied to transposed/permuted tensors. When `argmin()` or `argmax()` is called on a transposed tensor, Inductor was returning physical memory indices instead of logical row-major indices. This caused incorrect results that don't match eager mode behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165983
Approved by: https://github.com/shunting314
