PyTorch 2.12.0 Release Notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation
• Developers
• Security

Highlights

Batched linalg.eigh on CUDA is up to 100x faster due to updated cuSolver backend selection.

New torch.accelerator.Graph API unifies graph capture and replay across CUDA, XPU, and out-of-tree backends.

torch.export.save now supports Microscaling (MX) quantization formats, enabling full export of aggressively compressed models.

Adagrad now supports fused=True, joining Adam, AdamW, and SGD with a single-kernel optimizer implementation.

torch.cond control flow can now be captured and replayed inside CUDA Graphs.

ROCm users gain expandable memory segments, rocSHMEM symmetric memory collectives, and FlexAttention pipelining.

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Build Frontend

• Strengthened SVE compile checks in FindARM.cmake, which may reject previously accepted but incorrect SVE configurations (#176646)

Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build.

• Updated the minimum CUDA version required to build PyTorch from source to CUDA 12.6 (#178925)

Building PyTorch from source with CUDA versions older than 12.6 is no longer supported. Users building custom binaries should install CUDA 12.6 or newer and make sure CUDA_HOME points to that installation.

Version 2.11: bash CUDA_HOME=/usr/local/cuda-12.4 python setup.py develop

Version 2.12: bash CUDA_HOME=/usr/local/cuda-12.6 python setup.py develop

• Enforced a C++20 minimum in CMake build files (#178662)

Source builds now require a compiler and build configuration that support C++20. If you maintain custom build scripts or downstream extensions that build PyTorch from source, update the compiler and remove assumptions that PyTorch can be built as C++17.

Distributed

• torch.distributed.nn.functional ops now raise RuntimeError under torch.compile (#177342)

All ops in torch.distributed.nn.functional (e.g., broadcast, all_reduce, all_gather, reduce_scatter, all_to_all_single) now raise RuntimeError when called inside torch.compile. Users should migrate to the functional collectives API in torch.distributed._functional_collectives.

Version 2.11: python @torch.compile def my_func(x): return torch.distributed.nn.functional.all_reduce(x, op=ReduceOp.SUM)

Version 2.12: python @torch.compile def my_func(x): return torch.distributed._functional_collectives.all_reduce(x, reduceOp="sum", group=group)

TorchElastic

• torchrun now defaults to an OS-assigned free port for single-node training instead of port 29500 (#175699)

When running torchrun --nproc-per-node=N script.py without specifying --master-port or --standalone, the default behavior now automatically uses an OS-assigned free port via the c10d rendezvous backend. This eliminates "Address already in use" errors when running multiple training jobs concurrently. Multi-node training, explicit --master-port, PET_MASTER_PORT env var, and --standalone are unchanged.

Version 2.11: bash # Used static rendezvous on port 29500 by default torchrun --nproc-per-node=4 train.py

Version 2.12: ```bash # Uses OS-assigned free port by default torchrun --nproc-per-node=4 train.py

# To explicitly use a fixed port: torchrun --nproc-per-node=4 --master-port=29500 train.py ```

MPS

• All MPS tensors are now allocated in unified memory (#175818)

Previously, MPS tensors could be allocated in either device-only or unified memory. Now all MPS tensors use unified memory unconditionally. This simplifies memory management and enables CPU access to MPS tensor data without explicit copies. Code that relied on device-only memory placement may observe different performance characteristics.

Inductor

• The max_autotune layout-constraint deferral introduced in 2.11 is now opt-in (#175330)

In 2.11, Inductor deferred layout freezing for max_autotune templates to expose more fusion opportunities. This caused a regional-inductor failure mode, so the default in 2.12 reverts to immediate layout freezing. Users who relied on the deferred behavior for fusion opportunities should opt in explicitly via torch._inductor.config.max_autotune_defer_layout_freezing or TORCHINDUCTOR_MAX_AUTOTUNE_DEFER_LAYOUT_FREEZING=1.

Version 2.11: python # Deferred layout freezing was the default torch.compile(model, mode="max-autotune")

Version 2.12: python import torch._inductor.config as cfg cfg.max_autotune_defer_layout_freezing = True # or set TORCHINDUCTOR_MAX_AUTOTUNE_DEFER_LAYOUT_FREEZING=1 torch.compile(model, mode="max-autotune")

Deprecations

Release Engineering

• Deprecate CUDA 12.8 builds in favor of CUDA 13.0 (#179072)

CUDA 12.8 binaries have been removed from the PyTorch binary build matrix. CUDA 13.0 is now the stable default and CUDA 12.6 remains available for users on older drivers. Users explicitly pinning the cu128 index URL will need to switch to cu130 (recommended) or cu126.

Version 2.11: bash pip install torch --index-url https://download.pytorch.org/whl/cu128

Version 2.12: bash # Use CUDA 13.0 (default on PyPI): pip install torch # Or explicitly: pip install torch --index-url https://download.pytorch.org/whl/cu130 # Older driver fallback: pip install torch --index-url https://download.pytorch.org/whl/cu126

• Compatibility with CMake < 3.10 will be removed in a future release (#166259)

Source builds against CMake versions older than 3.10 now emit a deprecation warning. A future release will require CMake 3.10 or newer; please upgrade CMake before then.

Linear Algebra

• Several CUDA linear algebra operators no longer use the MAGMA backend and now dispatch to cuSolver or cuBLAS unconditionally:
• torch.linalg.eigh now dispatches to cuSolver (#174619)
• torch.linalg.lu_solve now dispatches to cuSolver/cuBLAS (#174248)
• torch.linalg.cholesky_inverse now dispatches to cuSolver (#174681)
• torch.linalg.cholesky_solve now dispatches to cuSolver (#174769)

User code calling these APIs does not need to change. The practical impact is for users who depended on MAGMA-specific numerical behavior, performance characteristics, or debugging. Those calls now use the cuSolver/cuBLAS implementations on CUDA.

FullyShardedDataParallel2 (FSDP2)

• Compiling through FSDP2 hooks without graph breaks is no longer supported (#174863, #174906). If you use compiled autograd with FSDP2, update your code to allow graph breaks around FSDP2 hooks or disable compiled autograd for the FSDP2 training step.

Version 2.11: python with torch._dynamo.config.patch(compiled_autograd=True): compiled_model = torch.compile(fsdp_model, fullgraph=True) loss = compiled_model(input).sum() loss.backward()

Version 2.12: ```python # Either run FSDP2 backward without fullgraph. compiled_model = torch.compile(fsdp_model, fullgraph=False) loss = compiled_model(input).sum() loss.backward()

# Or apply compile before applying FSDP. compiled_model_pre_fsdp = torch.compile(model, fullgraph=True) compiled_model = fully_shard(compiled_model_pre_fsdp, ...) loss = compiled_model(input).sum() loss.backward() ```

Profiler

• Profiler's metadata_json field is now deprecated; use event_metadata instead (#179417)

Version 2.11: python metadata = event.metadata_json

Version 2.12: python metadata = event.event_metadata

Dynamo

• torch.compile(fullgraph=True) now warns when a call runs no compiled code; will error in 2.13 (#181940)

Previously fullgraph=True was only validated once Dynamo actually compiled and ran the function. If Dynamo was bypassed at call time (e.g. under a user-defined TorchDispatchMode), the annotation silently had no effect. 2.12 emits a warning; 2.13 will raise. For graph-break errors without fullgraph's stronger guarantees, use torch._dynamo.error_on_graph_break.

Version 2.12: ```python from torch.utils._python_dispatch import TorchDispatchMode

class LoggingMode(TorchDispatchMode): def torch_dispatch(self, func, types, args=(), kwargs=None): return func(args, *(kwargs or {}))

@torch.compile(fullgraph=True) def model(x): return x.sin() + 1

# A user-defined TorchDispatchMode is active, so Dynamo skips the frame # and no compiled code runs — emits a warning in 2.12, will raise in 2.13. with LoggingMode(): # Remove this to fix warning model(torch.randn(3, 4))

```

• The inline_inbuilt_nn_modules Dynamo config is deprecated (#177489, #178205)

Inlining of in-built nn.Module instances is now the default; setting the flag emits a deprecation warning and it will be removed in a future release.

Version 2.11: python import torch._dynamo.config as cfg cfg.inline_inbuilt_nn_modules = True # was a tunable knob

Version 2.12: python # No action needed — inlining is on by default. # Remove any explicit references to torch._dynamo.config.inline_inbuilt_nn_modules.

• Added a deprecation framework to the torch.compile config module so individual options can be marked deprecated (#169837)

New Features

Release Engineering

• Add Claude-powered autorevert AI advisor workflow (#177404, #178810)
• Add torchtitan tests to PyTorch CI (#175901, #176774, #179749, #177572)
• Add Pallas TPU CI configuration (#173870, #174532, #175650, #175590)
• Add downloadable profiler traces and TLParse output from CI runs (#178488)
• Enable full AArch64 unit testing for pull requests, with periodic m7g coverage maintained on trunk (#178270)

Python Frontend

• Introduced torch.accelerator.Graph as a unified frontend Graph interface (#171285)

Foreach

• Add _foreach_clone operator, with a fast path for CUDA utilizing _foreach_copy_ (#177421)

Distributed

• Add Store::barrier API and TCPStore client BARRIER support, reducing synchronization round trips compared to the existing ADD+WAIT pattern (#174920)
• Add NCCL communicator suspend(), resume(), and memory_stats() APIs for managing communicator memory lifecycle (#176300)
• Add all_to_all support in the Gloo backend (#165435)
• Add reduce_scatter_offset to symmetric memory, supporting variable-sized block reductions with NVLink multicast or LSA fallback (#177791)
• Enable batch_isend_irecv to work under torch.compile (#161213)
• Add torch.distributed.symmetric_memory.is_symm_mem_tensor() API to check if a tensor is a symmetric memory tensor (#178947)
• Convert NanCheck to a standalone op (torch.ops.c10d.check_for_nan) usable outside of ProcessGroupNCCL (#174990)

DTensor

• Add support for twice-differentiable DTensor redistribution (#160509)
• DeviceMesh is now traceable by torch.compile. Make DeviceMesh opaque (#176661), Make placements opaque (#171482).
• Add grad_placements parameter to DTensor.from_local(), allowing explicit control over gradient placements in the backward pass (#175867)

FullyShardedDataParallel2 (FSDP2)

• Support per-parameter meshes in FSDP2, enabling different parameter groups to shard over different meshes (#173509)
• Support fully_shard with DTensors on a full SPMD mesh via DataParallelMeshDims (#176334)
• Add FSDP2 support for non-floating-point parameters by excluding non-float parameters from reduce-scatter while still sharding and all-gathering them as needed (#177948)

TorchElastic

• Add configurable --shutdown-timeout to torchrun for controlling the SIGTERM-to-SIGKILL timeout during worker shutdown (#172596)

CPU x86

• Expose a CPUBlas brgemm API for fp8 (e4m3 & e5m2) GEMM, backed by oneDNN (#172548)

CUDA

• Added support for torch.cond with CUDA graphs, using conditional graph nodes (CUDA 12.4+) so data-dependent control flow can be captured entirely inside a single CUDA graph. Works with the eager and cudagraphs torch.compile backends (no Inductor support yet). (#168912)

MPS

• Implemented linalg_qr for MPS (#172536)
• Added cholesky_solve support on MPS (#176703)
• Added index_reduce on MPS (#174936)
• Implemented torch.distributions.Gamma (forward + backward) on MPS (#179228)
• Enabled mvlgamma on MPS (#178914)
• Added nonzero_static implementation on MPS (#179589) (from miscategorized)

ROCm

• Enable expandable segments (#173330, #177974, #179930, #179781)
• hipSPARSELt
• Enable for ROCm >= 7.12 (#170852, #178285)
• Enable FP8 semi-structured sparsity (#179310)
• amdgcnspirv is now a supported offload target, not enabled by default (#172559)

XPU

• Support torch.accelerator.Graph on XPU (#176421)
• Added memory_clock_rate and memory_bus_width to XPU device properties (#171967)
• Enable split_group API when TorchComms is used as a backend for TorchTitan on XPU (#178236)

Profiler

• Profiler's Activity selection allows for fine-grained activity type selection. (#176351)
• Memory visualize has a new tab to show private pool memory view (#177289)

Dynamo

• Made torch._dynamo.aot_compile public, with aot_eager and inductor backend support and docs (#179917, #180008)
• Added a recompile_limit keyword argument to torch.compile to override the per-function recompile cap without touching global config (#177936)
• Added min/max bounds to torch._dynamo.mark_unbacked for communicating value ranges to the symbolic shape system (#176313)
• Added bdb, a pdb-style debugger for stepping through nested frames during Dynamo tracing (n, u, d, r, bt), plus a user-callable breakpoint() that auto-starts it (#174626, #174746, #175200)

Inductor

• Added user-defined stream support to torch.compile. Inductor now codegens stream context managers (enter/exit) and record_stream calls in the wrapper, enabling user streams to flow through compiled regions with proper synchronization, scheduler integration, and cross-stream dependency tracking (#165390, #165391, #165504, #165505, #174223, #176700, #177694)
• Added ao::offload, ao::reload, and ao::wait ops for asynchronous activation offloading. These ops encapsulate async CPU offloading stream management following the same async 2-op pattern as c10d functional collectives, reducing IR size from 7 nodes (offload) and 5 nodes (reload) down to 2 nodes each (#177621)
• Added user-defined Triton kernel unary epilogue fusion. Inductor can now fuse user Triton kernels with downstream pointwise epilogues (e.g. relu()), parsing the user kernel source via AST and inlining the epilogue into the tl.store expression (#173662)
• Added out-variant discovery and lowering for custom ops. When a custom op registers both functional and .out overloads, Inductor automatically lowers single-output and multi-output functional ops to their .out variants as ExternKernelOut, enabling memory planner buffer reuse (#175116, #176117)
• max_autotune now extends to combo kernels. The autotuning pipeline generates and benchmarks per-sub-kernel block-size phase configs, with chained sequential autotuning and per-sub-kernel reduction hints (#177715, #178936, #179317)
• Added non-TMA persistent Triton templates for mm and addmm for max-autotune, enabling persistent kernels on hardware without TMA (#177781, #179095)
• Added CUTLASS backend support for torch.float8_e5m2 dtype, including registration for FP8 GEMM autotuning (#171176)
• Added XPU CUTLASS GEMM kernel codegen and codecache to max-autotune-gemm, allowing CUTLASS-style GEMM templates to target Intel GPUs (#161938, #161939)
• Added MTIA Triton codegen for sort, median, and mode operations (#178525)
• Added a Triton template for depthwise conv1d (#175280)
• Added AVX10.2 fp32↔fp8 intrinsics in at::vec::convert for the Inductor C++ x86 backend (#172309)
• Pallas backend: added scalar prefetch and indirect access support (#177212)
• Added a disable_welford_reduction config flag to opt out of Welford reduction in codegen (#175778)

Ahead-Of-Time Inductor (AOTI)

• Add MXFP4 dtype support (float8_e8m0fnu and float4_e2m1fn_x2) to the AOTInductor C shim layer, enabling MXFP4 quantization (e.g., for AMD MI350) (#176496)
• Add a compile backend registry and custom device support for AOTI Eager, letting out-of-tree device backends plug into the AOTI eager compile/load flow without modifying upstream code (#175605)

torch.fx

• Add tuple_return option to split_module that wraps submodule outputs in a tuple (#179007)
• Add ignore_raw_node option to GraphPickler (#176939)
• Add _merge_overlapping_fusions() method to FxNetSplitter which detects and merges overlapping fusion groups (#177099)

torch.export

• Add serialization support for float8_e8m0fnu dtype (#176270)
• Add serialization support for torch.uint32 and torch.uint64 dtypes (#179434)
• Add serialization support for nested float lists (List[List[float]]) (#178081)

JIT

• Added input-independent graph optimization API (#179393)

Improvements

Release Engineering

• Add support for CUDA 13.2 in CI/CD including binary builds, magma builds, and Windows CD workflows; update binaries to CUDA 13.2.1 (#177083, #177197, #177918, #178660, #180288, #177975, #177567, #180293)
• Upgrade Triton to 3.7 (#174896, #178821, #179586, #179971, #177364, #177723)
• Upgrade NCCL to 2.29.7 (#176299)
• Upgrade cusparseLt to 0.8.1 for CUDA 12.9 / 13.0 builds (#177456)
• Migrate clang15 CI jobs to clang18 and consolidate ASAN/ONNX images (#178801, #178803, #178928)
• Bump MACOSX_DEPLOYMENT_TARGET to 14.0 (#179083)
• Bump numpy pin to 2.3.4 for Python 3.14 builds (#179720)
• Add macOS wheel platform tag vs dylib minos validation (#177609, #177993)
• Enable Metal-4 shaders offline compilation (#179378)
• Migrate lint and other workflows from EC2 to k8s ARC runners (OSDC) (#177431, #177899, #177950, #177953, #177954, #178585, #178973, #179058)
• Add XPU client docker image and CI tests (#174188, #177831, #178380, #178383, #178143, #179786)
• Merge majority of libtorch builds into wheel CD builds (#174753, #177802)
• Enable R2/S3 dual upload for torch nightly packages (#175352, #175570)
• Make CUDA 13.0 cross-compilation work (#181287)

Python Frontend

• Used compiler wrapper when building C++ extensions (#175696)
• Updated uniform and normal sampling on CPU to improve fp16/bf16 results (#175988)
• Changed requires_grad to Optional[bool] in torch.asarray (#170897)

Autograd

• Implemented narrow_copy derivative (#175609)
• Implemented higher-order derivatives for grid_sample (#177487)
• Implemented backward and forward AD for torch.aminmax (#175215)
• Exposed num_splits in varlen attention to allow disabling split_kv (#176905)
• Added user AutoNamingMode support in Selective Activation Checkpointing (#175348)
• Refactored torch.utils.checkpoint to no longer use autograd.Function for saving inputs (#174327)

Dataloader

• Added a thread-safe RNG utility function (#175375)

Linear Algebra

• Added _int_mm unsigned int8 × signed int8 (u8s8) support on CPU (#168226)
• Added FP64 support for TunableOp on ROCm via hipBLASLt (#178195)

Nested Tensor (NJT)

• Added nested tensor deserialization support (#174843)

torch.nn

• Added bias argument to nn normalization methods (LayerNorm, GroupNorm, RMSNorm, etc.) (#176573)
• Improved MultiMarginLoss error message for inconsistent target size (#174072)
• Added enable_gqa flag to varlen_attn (#179468)
• Allowed eps=0 in batch_norm during eval mode (#175508)
• Added meta device support in trunc_normal_ initialization (#176240)
• Split onehot checks for CPU and accelerators (#181211)

Sparse

• Implemented clone operator for semi-structured sparse tensors (#174991)
• Allowed semi-structured sparse tensors to be instantiated with alg_id (#178659)
• Enabled FP8 semi-structured sparsity on ROCm via hipSPARSELt (#179310)

Build Frontend

• Simplified SVE256 detection (#176247)
• Removed ARMv7 checks (#176645)

C++ Frontend

• Upgraded cpp_extension and cpp_builder to C++20 (#176659)
• Reland at::Tag header-only changes and add a library.def override for tags (#181608)

Distributed

• Add configurable worker timeout and partial data support to the distributed debug server (#176058)
• Add timeout parameter to torch.distributed.barrier() (#174974)
• Add reduce_scatter_tensor_coalesced support to ProcessGroupWrapper (#168961)
• Functional collectives API now automatically handles non-contiguous inputs instead of asserting (#177965)
• DDP: Add batched_grad_copy option to reduce per-parameter kernel launches to 2 kernels per bucket (#176638)
• DDP: Refactor bucket capacity config into BucketCapacityConfig dataclass (#175217)
• Add signal name to ChildFailedError exitcode output for better debugging (#175254)
• Add CUDA-aware detection for Cray MPICH (#178323)
• Support dist.broadcast for FP8 tensors on GPUs older than SM90 (#175884)
• Add __torch_function__ handlers for distributed functions (#176376)
• Enable split_group API for TorchComms on XPU (#178236)
• Make py-spy dumps nonblocking by default (#178312)
• Add ncclx and gloo to FlightRecorder trace analyzer backend allowlist (#180268)
• Improve error message on symmetric memory handle exchange (#178989)
• SymmMem: Add thread safety to NCCL and NVSHMEM backends (#176551)
• Check NCCL terminate signal more frequently when exiting from heartbeat monitor (#170000)
• Implement missing methods in ProcessGroupWrapper (#178779)
• Add compute_estimator option for overlap scheduling (#175204)
• [local_tensor] Add standalone rank_map/tensor_map functions (#174795)

Distributed Checkpoint (DCP)

• DCP: Improve save plan validation error messaging (#176728)
• DCP: Preserve original exception in metadata read failure for better debuggability (#177739)

DTensor

• Support DTensor view ops (flatten/unflatten) with _StridedSharding for full nn.Linear(DTensor) compatibility (#166483)
• Add Dijkstra-based single-dim strategy search for DTensor sharding propagation, avoiding exponential enumeration of strategy combinations (#169438)
• DTensor: Add is_pinned() support (#177235)
• DTensor: Add print() HOP support (#175222)
• DTensor: Emit zero paddings for uneven shardings to enable SPMD compilation (#177758)
• DTensor: Make run_dtensor_rng_op compatible with compile_on_one_rank (#177447)
• DTensor: Lenient handling of view redistributes in decomposition flow (#175194)
• DTensor: Redistribute from/to _StridedShard through Replicate (#179059)
• DTensor: Raise clearer error for unsupported Split(Flatten) sharding propagation (#179632)
• DTensor: Unbacked-safe view_groups (#174629)
• DTensor: Expanded sharding strategy coverage for index_select, index, index_fill, index_reduce, roll, fft, constant_pad_nd, squeeze.dims, interpolate, linalg ops, LayerNorm/RMSNorm FW/BW, foreach/fused ops, and einsum linearity (#176037, #176038, #178456, #175463, #175656, #173563, #176991, #176955, #179173, #177186, #177187, #176150, #174830)

FullyShardedDataParallel (FSDP)

• Remove mixed-dtype rejection from FSDP clip_grad_norm to match the documented behavior (#173641)

FullyShardedDataParallel2 (FSDP2)

• Allow ModuleList/ModuleDict subclasses that implement forward() (#175033)
• FSDP2: Support dataclass args/kwargs output without memory leakage (#174692)
• Share more implementation code between replicate and FSDP2 fully_shard (#173580)
• Consolidate FSDP2 shard_mesh and shard_mesh_from_root handling (#174107)

Distributed Pipeline

• DTensor metadata foundation for Pipeline Parallelism with DTensor-aware stage and schedule refactoring (#177727, #177728)
• Pipeline Parallel: Dispatch homogeneous P2P ops individually to avoid stream serialization (#175712)

TorchElastic

• [elastic] Add Windows support for stdout/stderr redirects (#176789)
• torchelastic: Keep health check alive during exit barrier (#178197)

CUDA

• [CUDA] Fix offset_t operators to be __host__ __device__ in SortStable.cu (#175997)
• [CUDA] [Green Context] Add support for workqueue limit (#177242)
• Remove dead avg_pool3d backward shape-check variables in CUDA (#178893)
• [BE] add missing assert on cuda device synchronize in ATen tests (#174966)
• [reland 2][pytorch] Preemptive OOM rejection using per_process_memory_fraction + throw_on_cudamalloc_oom (#179473) (#179473)
• [cuda graphs] Add enable_annotations kwarg totorch.cuda.graph` (#179867)
• [CUDA/ROCm] avoid double casting in ReduceLogicKernel (#176132)
• Nit fix: Align state_step tensor max to param tensor max (#178913)

cuDNN

• Upgrade 12.8, 13.0 (and 12.9) wheels to cuDNN 9.20.0.48 (#177321)

MPS

• Fixed abs complex overflow/underflow on MPS (#174346)
• Migrated index_fill_ to native Metal (#175822)
• Extended histogram to float/bfloat types on MPS (#176913)
• Extended unfold_backward to torch.complex64 on MPS (#177274)
• Added complex input support to scatter, gather, repeat, cumsum, logcumsumexp, cumprod, and nn.functional.linear on MPS (#177794, #178198, #178328, #178411, #178436, #178799)
• Migrated lerp, eye, relu, silu, fill_, xlogy, norm to native Metal kernels (#177093, #178683, #178866, #179071, #176101, #177749, #177328)
• Registered DeviceCapability for MPS backend (#178180)
• Switched exponential distribution to native Metal (#174277)
• Add enable_gqa parameter to SDPA MPS meta registration (#181550)

ROCm

• CPP extensions only compile for user's detected arch (#168998)
• Remove obsolete HIP NaN handling workarounds; remove technical debt (#171104)

XPU

• Support half precision FFT on XPU backend (#171231)
• Add proper float64 handling for addmv, addmm, and baddbmm on XPU (#174590)
• Enable FMA-based addcdiv lowering for XPU (#176163)
• Enable bmm_outer_product Triton override for XPU (#180816)
• Use version check for XPU fallback registration in Inductor (#174679)
• Catch Intel Triton compilation/runtime errors as IntelGPUError in Inductor (#169167)
• Improve Inductor UT coverage for XPU (#174053, #174054, #174055, #174056, #174057, #174058)
• Added Uint16/Uint32/Uint64/FP8 support to XPU device capability reporting (#178467)

Profiler

• Profiler's events() method now has parity with information returned in export_chrome_trace(). (#177662, #177888, #178168, #178597, #178901, #179714)
• Restructure Memviz and add tests (#179488)

Dynamo

• Broader Python tracing: enum.Enum iteration, nn.Module.__getattribute__, _enter_autocast/_exit_autocast and other context managers, next() on itertools.count, itertools.takewhile, bool(OrderedDict), NamedTuple.__eq__(tuple), numpy ndarray.flat, and locals()/vars() (#175176, #175527, #173877, #176521, #178818, #177876, #175394, #176729, #175787, #179595)
• CPython nb_index/nb_bool/nb_float slots so Dynamo can trace operator.index(tensor), bool(...), and float(...); graph-break on torch.Generator methods (#178921, #178931, #179114, #180198, #178519)
• Higher-order ops & subgraphs: cond supports aliases and mutations under no_grad, autogradable leaf modules support pytree outputs, nonstrict_trace accepts nn.Module inputs, and invoke_subgraph supports subgraph reuse (#172836, #172152, #175010, #172372, #176644)
• Streams & Triton: current-stream handling via torch.cuda.stream, sync barriers via a dependency HOP, triton.set_allocator inside torch.compile, and reuse of tracked objects for Triton prune_configs_by (#177610, #168894, #177470, #177874)

Inductor

• Unified OUT_DTYPE, ACC_TYPE, and INDEX_DTYPE codegen flow in Triton templates (#179453)
• Enabled cudagraph w/o partition for cpp-wrapper (#179249)
• Added FMA-based addcdiv lowering for CUDA parity with eager and matching _foreach_addcdiv to _foreach_addcmul (#174912, #175309, #175310, #175839, #176237)
• Added lerp decompositions for bitwise parity with eager (#176804)
• Added outer-product decomposition (#176552)
• Enabled padding fusion with torch.cat and avoided duplicate computation in cat/pad when inputs have multiple consumers (#175729)
• Lowered functional symmetric memory ops to ExternKernelOut for output buffer reuse, and added symm_mem planning for graph inputs and fallback regions (#174856, #175449)
• Modified addmm template call to support hipblaslt bias-fused kernels on ROCm (#177130)
• Newly trained PadMM AutoHeuristics for A100 and H200, plus support for pad_mm AutoHeuristics in deterministic mode (#176186, #179826)
• Propagate metadata in pattern matcher and add validation (#179113)
• FlexAttention: raise a clear NotImplementedError when return_aux=AuxRequest(max_scores=True) is requested with BACKEND='FLASH' instead of failing later with an opaque error (#177434)
• Migrated Inductor internals from legacy allow_tf32 to fp32_precision to avoid divergence with the new TF32 API (#176098)
• Pallas backend: enabled element-wise ops, native TPU OOB DMA masking via aligned block specs, and generalized N-D transpose permutation detection (#174743, #175458, #176952)
• Registered lowerings for prims.scalar_tensor and aten.arange.start_step (#179017, #179028)
• Added SDPA pattern matching support for visformer (#177826)
• Relaxed concat-linear fusion to support GQA QKV (#178523)
• Allowed subgraphs to be benchmarked with async pipelined autotuning (#175455)
• Added convert_element_type lowering to emulate PyTorch eager numerics (#176781)
• Added GEMM configs to XPU autotuning heuristic (#177647)
• Added kpack Triton compile options on ROCm (#173179)
• ROCm: enabled exhaustive autotuning for FP8 (#177797)
• Override decomposition for aten.index_add (#179486)
• Drop tile_k from nvMatmulHeuristics matching (#176845)

Ahead-Of-Time Inductor (AOTI)

• Add aten._grouped_mm to AOTInductor fallback ops, enabling cpp-wrapper mode for grouped_mm (#177307)
• Support lazy Triton kernel compilation for cpp-wrapper on XPU (#179239)
• Add dynamic shapes support to AOTI Eager via AOTIPythonKernelHolder, allowing a single compiled kernel to serve multiple input shapes (#176018)
• Support multi-return ops in AOTI Eager (e.g., native_layer_norm, aminmax) (#176019)
• Allow custom ops with Optional[List[T]] arguments in cpp wrapper (#174460)
• Add lazy Triton kernel compilation for cpp-wrapper (#175416)
• Add TMA support for lazy Triton kernel compilation (#175548)
• Call latest c_shim version for versioned fallback ops (#181548)
• Add BC-safe c_shim v2 for _scaled_dot_product_attention_math_for_mps enable_gqa (#181549)

torch.fx

• Update get_source_partitioner to parse nn_module_stack metadata for improved source-based graph partitioning (#175788)
• split_module now uses _make_graph_module to support lazy recompile (#177907)
• Fix fuser_utils.topo_sort to produce a stable ordering (#175378)
• Fix GraphPickler to support nodes with slice() arguments (#175996)

Composability

• Added DynamicInt __pow__ and __rpow__ methods (#179868)
• Added scaled_mm_v2 CPU implementation (#176266)

Bug fixes

Release Engineering

• Fix periodic inductor CI silently skipping all tests (#177695)
• Fix python docs build hanging in CI (#180177)
• Avoid installing test dll into Windows wheel and fix libuv copy path (#179024)
• Fix aarch64 build-osdc using x86 runner on ARC (#179783)
• Fix stale PYTORCH_RELEASES_CODE_CC dict (#182369)

Python Frontend

• Fixed torch.isclose broadcast failure with equal_nan=True (#175244)

Autograd

• Fixed torch.trace backward for non-square matrices (#175068)
• Added explicit error when layer_norm computes 3rd order derivatives (#176234)
• Fixed _wrap_sync_node to replace deps in output node's nested args (#178471)
• Fixed thread-unsafe lazy init of setup_context cache (#179475)

Linear Algebra

• Fixed addmv backward pass failure (#165777)
• Fixed determinant gradient for 1×1 matrices (#171225)
• Fixed linalg.det backward for 0-dimensional inputs (#177498)
• Fixed cholesky(upper=True) on macOS for matrices larger than block size (#179154)
• Revert [#172340] (#181364)

torch.nn

• Fixed trunc_normal_ low precision issue when used with half-precision dtypes (#174997)
• Added dtype check to nll_loss meta function to prevent invalid input types (#175151)
• Fixed numerical inconsistency in Conv3d.reset_parameters for channels_last format (#175990)
• Fixed MSELoss failing to compute gradients when inputs have different dtypes (#175743)
• Fixed GroupNorm backward correctness bug on AMD wavefront-64 GPUs (#178872)
• Fixed nn.functional.pad compile crash with deterministic mode and replication padding (#177166)
• Fixed issue [#110505] (#176559)
• Fix FA4 integration for varlen (#177675)

Sparse

• Fixed torch.bmm(COO, Dense) memory misalignment on CUDA (#175347)

Build Frontend

• Fixed TORCH_BUILD_VERSION not updating when version.txt changes (#176167)

Distributed

• Fix _CoalescingManager not passing Opts to allgather_into_tensor_coalesced() (#175379)
• Fix _CoalescingManager to raise exception when ops in the coalesced list are not the same type (#175573)
• Fix getenv/setenv race condition causing segfault during NCCL initialization with heartbeat thread (#167523)
• Fix AsyncCollectiveTensor inputs leaking into compiled regions, causing RuntimeError or silent data corruption in TP + compile workflows (#179849)
• Fix potential infinite loop in FlightRecorder when multiple ProcessGroups run into barrier (#179449)
• Fix two forward passes of DDP-wrapped BatchNorm raising error (#175851)
• Fix USE_RCOM typo to USE_ROCM in intra_node_comm.cpp (#175078)
• Fix HPU backend mapping issue (#174764)
• Fix NCCL symmetric memory mismatch by using allocation-time counter for Block ordering (#178362)
• Fix NCCLPeerAllocInfo destructor to properly deregister windows and free resources (#177459)
• Fix nested DDP causing _active_ddp_module cleared by inner _inside_ddp_module() (#178364)
• Fix extra deps mapping and cycles after bucketing (#177688)
• Add proper skips for FP8 on sm < 89 (#170528)
• Fix cross type bucketing (#175150)

Distributed Checkpoint (DCP)

• DCP: Fix save plan caching bug during validation failures (#176289)
• DCP: Fix Metadata.storage_meta regression from dataclasses.replace() (#178001)
• DCP: Fix unpicklable FrameSummary._code on Python 3.13+ (#177754)

DTensor

• Fix DTensor subclass __torch_dispatch__ bypass (#177878)
• Fix symbolic shape handling by copying symbolic shapes as needed (#178210)
• Fix _StridedShard not in safe globals for checkpoint loading (#178560)
• Fix DTensor stack dim normalization (#174640)
• Fix DTensor view_as_complex with P(max)/P(min) placements (#173935)
• Fix DTensor get_mesh_from_args when first arg is not a tensor (#169265)
• Fix DTensor tp_conv rejecting batch-dim-only sharding for valid configs (#176448)
• Fix DTensor compute_local_stride for unevenly-sharded tensors (#177174)
• Fix DTensor scaled_mm sharding strategy (#177234)
• Fix DTensor double-shard validation in propagate_shape_and_sharding (#177973)
• Fix DTensor Dijkstra sharding search shardability checks and graceful fallback (#177167)
• Fix DTensor index_put sharding strategy for None indices (#179217)
• Fix DTensor precision loss in NestedRedistribute backward dtype handling (#179495)
• Fix DTensor backward for value-selecting reductions (topk, sort, min, etc.) (#178668)
• Fix DTensor InputDim.__eq__ type guard to prevent int comparison bugs (#178599)
• Fix None IValue == DTensorSpec, cache key collision, and move op_strategy_context (#178442)
• Fix DeviceMesh.__getitem__ by disabling proxy tensor handling (#176007)

FullyShardedDataParallel (FSDP)

• Fix FSDP _unshard() passing a CUDA stream where an event was expected (#170525)
• Fix symbolic context reuse for gradients when creating meta tensors (#176864)
• Fix HSDP sync_module_states broadcast order for buffers with meta-device initialization (#178569)
• Fix activation checkpointing crash when passing BlockMask as an argument (#179215)
• Revert a FSDP1 all-gather prefetching regression (#181667)
• Revert PR 178223 to bring back all-gather prefetching (#181669)

FullyShardedDataParallel2 (FSDP2)

• Fix FSDP2 split_with_sizes_copy() missing dim argument (#169173)
• Fix mixed DTensor errors with nested FSDP and activation checkpointing (#171779)
• Fix tied weights with uneven sharding across separate FSDP groups (#176225)
• Revert FSDP2 communication-op FQN annotations due to async_op=True profiler trace issues (#182100)
• Revert "[FSDP2] add fqn to communication ops" (#182157)

Distributed Pipeline

• Fix stage_backward_weight with multi-output intermediate in pipeline parallelism (#175705)

CPU aarch64

• Add vectorized BF16 transpose_mxn specialization for AArch64 SVE, providing a deterministic vectorized BF16 transpose implementation independent of fixed vector widths (#174097)

CUDA

• Fix cuda torch.topk index bug for inputs over 32-bit length (#176095)
• Fix test/inductor/test_fp8.py hang on sm89 (#177573)
• Use fp8 conversion intrinsics on Hopper+ to work around ptxas codegen bug (#177870)
• [CUDA] Fix wrong non-atomic handling in AdaptiveMaxPooling2d.cu (#179261)
• [CUDA] Fix wrong ComplexTransform const kTransformB in fpA_intB_gemm.h (#179271)
• [CUDA] Fix wrong LayoutB in fpA_intB_gemm.h (#179269)
• Back out "[CUDA][cuBLASLt] set cuBLASLt as a default BLAS backend when available (#174594)" (#177703) (#177703)
• Fix CUTLASS illegal memory access via subprocess isolation (#172123)

cuDNN

• [cuDNN][SDPA] Don't route to cuDNN SDPA when dropout probability is not a multiple of 1/16 (#174245)
• Fix cuDNN SDPA with zero-stride (broadcast) Q/K/V inputs (#175764)
• [cuDNN][SDPA] Fix attn-mask conversion constant (#177868)

MPS

• Fixed AvgPool for channels_last + offset inputs (#175235)
• Fixed linalg_solve to return pivots (#175284)
• Fixed lu_solve for broadcasted bias (#175332)
• Fixed addmm/mm to return zero-filled matrix when an input is empty (#175905)
• Fixed index_reduce atomic misalignment for sub-32-bit types (#176009)
• Fixed masked_fill for non-contiguous outputs (#176171)
• Fixed layer_norm with noncontiguous bias (#176238)
• Added unsigned int types to Metal cast operations (#176343)
• Fixed solve_triangular for noncontiguous inputs (#176335)
• Fixed histogram/histogramdd with noncontiguous weight (#175906)
• Fixed MPS memory leak in getStridedMPSNDArray (#176648)
• Added error checking for bmm on MPS (#176771)
• Fixed half-precision type mismatches in Metal shader codegen (#176436)
• Fixed SDPA output shape when value head dim differs (#176843)
• Added error when creating torch.cdouble tensor on MPS (#176985)
• Fixed _copy_from_and_resize logic (#177606)
• Fixed linear backward crash with channels_last grad (#178278)
• Fixed mm padding overflow and incorrect alignment conditions (#178203)
• Fixed nested ops.masked variable name collisions in Metal codegen (#178304)
• Fixed in-place self.add_(other, alpha) RuntimeErrors with type promotion (#178724)
• Fixed BatchNorm with mixed input/weight dtypes (#178775)
• Fixed hi/lo swap typo in Metal Philox RNG (#179227)
• Allowed getMPSScalar construction for uint64 (#179230)
• Fixed mm with stride-0 inputs on macOS < 26.4 (#180236)
• Fixed masked_scatter side-effect and aligned behavior with CPU (#175622)
• Fixed lgamma/digamma/polygamma noncontiguous behavior (#175603)
• Fixed masked_scatter to preserve scalar tensor shape (#174381)
• Fix sliced channels_last tensor handling (#181107)
• Fix SDPA wrong output for permuted q/k/v with B > 1 (#181886)
• Fix bool mask handling in the 1-pass SDPA decode kernel (#182311)

ROCm

• Fix SDPA build error when USE_FLASH_ATTENTION=0 USE_MEM_EFF_ATTENTION=1 (#177552)
• Fix _get_amdsmi_device_index to return devices in correct order (#178398)
• Fix scaled_mm incorrectly validating unsupported swizzle (#178688)
• Move rocblas.h include out of anonymous namespace (#178767)
• Don't crash for MHA backward with head dim > 192, fall back to CK tile (#178946)
• Don't fail torch.cuda.device_count() if pynvml is installed (#175077)
• Fix hipblaslt GEMMs executing concurrently on multiple HIP streams (#179053)
• Windows
• Fix linker failure caused by missing DLL export directives via native headers (#179138)
• Fix int4mm std::memcpy build error (#175410)
• Fix Windows access violation in MIOpen CTC loss dispatch (#178284)
• Fix Windows DLL linkage for batch norm (-Winconsistent-dllimport) (#179706)
• Workaround hipGraph event query errors in NCCL watchdog (#175377)
• Fix linker error for aotriton when USE_MEM_EFF_ATTENTION=ON but USE_FLASH_ATTENTION=OFF (#175079)
• Fix build_amd.py (hipify) failure when MSLK submodule is missing (#175180)
• Hipify CUdeviceptr in lazy scratch allocation codegen (#179978)

XPU

• Fix torch.compile graph break inside torch.autocast('xpu') causing dtype mismatch (#180309)
• Fix conv2d incorrect results and alignment errors for non-64-byte-aligned tensors on XPU (#177956)
• Fix nn.Embedding module failures on XPU (#178987)
• Fix XPU OneDNN symbol leak (#172437)
• Fix meta kernel for _scaled_dot_product_fused_attention_overrideable to preserve query layout (#178986)
• Fix tensorwise scaling settings on XPU (#177810)
• Fix DeviceOpOverrides registered incorrectly on XPU (#178959)
• Fix SyclExtension Windows build for oneAPI 2025.3+ breaking change (#170701)

Dynamo

• Guard correctness: missing source annotation on float guard after recompile, missing guards on class attribute access for literals/enums, guard on constant function __defaults__, guard tensor-method fallthrough against unknown methods, and closure-hash in CodeId so factory functions don't reuse stale graphs (#177103, #177191, #178420, #177737, #173512)
• Tracing: @property setters bypassed by torch.compile, AttributeError swallowed by try/except on tensor attributes, torch.Size dict lookups with tensor-backed keys, graph break on enum members with class values, detach_ autograd metadata, allow_in_graph crash inside compiled functions, preserve original exception in GuardOnDataDependentSymNode, and einops 0.6.1 backwards patch (#176624, #175611, #177313, #177439, #177875, #178524, #176016, #177165)
• Nested graph breaks: global-scope bug in nested closures, decorators in the compiled region, graph break in contextlib.contextmanager init, and parent-stack corruption in step_graph_break (#176906, #177090, #177195, #177408)
• Compile-state & integration: torch.compiler.is_exporting() returning True during torch.compile, activation-checkpoint metadata loss through custom autograd.Function, mixed-dtype bmm/matmul, vjp_fn under torch.compile, _extract_distributed_info crash on FX-Node group_name, nested_compile_region cache keyed on fn.__code__, and reverting allow_in_graph deprecation warn-spam (#176499, #177396, #177696, #173883, #178108, #179148, #178340)
• Fix cuda_stream pointer extraction for generic torch.Stream (#181019)
• Warn instead of erroring on fullgraph=True fallback to eager (#181940)

AOTDispatcher

• Fixed AOTAutograd crash on no_grad views of differentiable intermediates (#175673)
• Fixed inplace checks in autograd backward functions during functionalization (#177213)

Inductor

• Fix floordiv Inductor lowering for mixed signedness (Triton workaround) (#175168)
• Use Sm100CollectiveEpilogue on SM100 (#175305)
• Fix aten.resize on overlapping-stride views (#176651)
• Fix ConstructorMoverPass replacing CPU placeholder in graph output and creating mixed-device pointwise ops (#176164, #177646)
• Use VecMask::from for scalar masks in CPU codegen (#178148)
• Fix triton_main_loop_scaled_mm template to use correct scale recipe (#178005)
• Fix cpp_wrapper lazy compile stale state across fresh_cache resets (#178162)
• Fix int64 indexing with >65k M/N size (#172925)
• Fix BMM Triton template grid_y overflow for large batch dims and i32 overflow in template kernel signature for large storage offsets (#178617, #179333)
• Fix remove_no_ops incorrectly eliminating ops on mutated values (#174938)
• Fix negative-zero constant codegen for the Triton backend (#176035)
• Fix coordinate descent tuner incorrectly re-running on warm cache (#173391)
• Fix BF16/FP16 scalar comparison to match eager (#175807)
• Defensively check name is in buffer_read_counts before access (#171245)
• Fix cpp-wrapper SyntaxError when Triton kernel has docstrings (#176796)
• Fix fallback_random dropout stride mismatch (#177077)
• Fix AssertionError in ForeachKernelSchedulerNode loop reordering after fusion (#176849)
• Fix incorrect rounding of floordiv (#177926)
• Fix issue in AMD persistent_mm_template selection (#178178)
• Pointwise configs with max_autotune must include pointwise configs with max_autotune_pointwise (#177995)
• Fix num_warps when max_autotune is enabled on HIP (#178023)
• Fix nn.Dropout accuracy discrepancies between Triton and torch implementations (#178843)
• Fix eager/compiled mismatch for integer floor_divide with zero divisor (#178016)
• Accept 1D bias in addmm ATen heuristic on ROCm (#179087)
• Fix randn_like inconsistency between eager and compile with fallback_random=True (#177994)
• Fix MetalScheduling constructor in MPSInductor (#179646)
• Prevent cross-stream inplace and memory-planning buffer reuse for user-streams (#178548, #178549)
• Fix argmax/argmin returning incorrect indices for boolean tensors on CUDA (#174076)
• Fix block-pointer advancement for broadcasted tensors (#175008)
• Fix masked vectorization for the Inductor C++ backend (#174648)
• Fix bucketize NaN handling in Triton codegen and Pallas sign to match PyTorch NaN semantics (#176579, #176814)
• Fix UnicodeDecodeError in Triton depthwise conv template (#176484)
• Handle 0-sized dimensions in Pallas codegen (#176813)
• Fix torch._check divisibility propagation to Triton tt.divisibility (#175755)
• Fix SIGSEGV on AMD RDNA (Wave32) from removed reduction masks in persistent kernels (#176269)
• Pallas: fix non-stride-1 reductions and prevent incompatible reduction fusion (#176489)
• Fix block_ptr store dtype for inplace-mutated buffers (#177860)
• Fix constants handling for Triton constexpr (#172354)
• Fix Inductor reinplace bool shadowing ('bool' object not callable) (#176090)
• Fix Inductor _split_iteration_ranges silently dropping dimensions (#177673)
• Fix sym_sum lowering to accept varargs (#178661)
• Fix bias_addmm for AMD (#178929)
• Define unbacked slice size symbol when bounds become provable after tracing (#178897)
• Add isinf() to Float8_e4m3fn to fix nan_asserts crash with fp8 inputs (#160641)
• Fix division by zero in the Triton kernel launcher when Grid2DWithYZOverflow (#178878)
• Prevent user-kernel fusion with non-unary epilogues (#179735)
• Preserve order of torch.cond (#179457)
• Fix performance regression caused by user kernel epilogue fusion (#176772)
• Fix torch.compile performance regression for cumprod backward (#170388)
• Include lazy_triton_compile.h in the XPU cpp_wrapper header (#180815)
• Fix cudagraphs compatibility with the current stream (#180916)
• Revert native API stamp-out for BMM outer product (#181658)
• Fix dynamic shape tile issue (#181795)
• Avoid raw stream name collisions in Inductor (#182178)

Ahead-Of-Time Inductor (AOTI)

• Fix AOTI incorrect loads from bool tensor pointers in user-defined Triton kernels (#176353)
• Fix lazy compile kernel state collisions across modules by making it per-module instead of global (#178163)
• Fix expression-nesting limit in cpp-wrapper when combo kernel gets too large (#180217)
• Fix const folding in run_single_threaded (#174998)
• Fix AOTI Eager caching to populate the in-memory cache after first compilation, avoiding repeated disk round-trips on every dispatch (#176017)
• Fix SIGPE by adding additional check logics in the codegen (#170669)
• Fix scratch size for TMA in C++ wrapper (#175385)
• Emit int64_t type declaration for kernel numel variables (#176922)
• Fix CPP wrapper lazy compile for scalar tensor args (#178478)
• Fix Triton kernel stream for user stream contexts (#178547)

torch.fx

• Fix edge case in get_source_partitioner (#175935)
• Fix make_fx handling of value types for opaque objects so values are inlined into the graph consistently with dynamo behavior (#178036)
• Fix meta["val"] not being populated for reconstructed opaque nodes, resolving partitioner classification issues (#179660)
• Fix repeat_interleave fx graph to be runnable (#177909)
• Fix fuse_by_partitions crash when partition has no external outputs (#175203)
• Fix set_stack_trace (#177332)
• Handle weakref objects during graph serialization in GraphPickler (#178190)
• Fix horizontal fusion bug and add partition tests for regional inductor (#178421)

torch.export

• Fix export serialization to handle unused List[Tensor] outputs (#176677)
• Fix MPS SDPA meta kernel to avoid data-dependent branching (#177620)
• Fix torch.export.unflatten KeyError when module FQNs contain @N suffixes (#179278)
• Fix run_decompositions() failure for custom ops with List[List[Tensor]] arguments (#176355)
• Fix torch.quantile DataDependentOutputException failure (#174859)
• Fix isin decomposition with using symbolic shapes (#178076)

Quantization

• Fix activation quantization creating duplicate backward placeholders (#180287)

JIT

• Silenced CPython 3.13.8 inspect.getsourcelines() bug (#179066)
• Fixed data race in opaque type registry (#175694)
• Fixed xplat build: use PyObjectType::get() directly (#178786)

Functorch

• Fixed grad/vjp/jvp returning zeros under inference_mode (#177596)
• Fixed vmap batch rules for group_norm backward operator (#176272)
• [functorch] Fix double-pop in popDynamicLayerStackToDepth (#177585)

torch.func

• Fixed scatter error messages for inplace operations (#179420)

Composability

• Fixed stride handling in FFT meta registrations (#175731)
• Fixed exception messages displaying as tuples instead of formatted strings (#175957)
• Fixed one_hot runtime error (#177160)
• Fixed wrong bool-to-int conversion in symbolic tracing (#177178)
• Preserved scalar item() semantics for size-1 tensors (#177270)
• Fixed _build_proxy_for_sym_expr for n-ary sympy.Add by mapping to torch.sym_sum (#175398)

FX

• Fix the MetaProxy error caused by skipping dispatch (#181170)
• Preserve FakeScriptObject for value-type opaques (#181454)

Performance

Release Engineering

• Add deterministic mode for benchmark perf tests (#178233)
• Fix subprocess benchmark crash for addmm with input_reorder (#177930)
• Add unbacked perf testing to inductor periodic (#177034)

Autograd

• Used non-blocking copy in save_on_cpu pack hook for faster activation offloading (#175421)

Linear Algebra

• Improved torch.cholesky_solve performance for batched inputs on CUDA via cuSolver (#175898)

torch.nn

• Added NEON implementation of interpolate for bilinear/bicubic with antialias on ChannelsLast RGB images on ARM (#176217)
• Parallelized upsample_bicubic2d across batch/channel dimensions — 4-43x speedup for VLM position embedding resizing (#174578)
• Freed q, k, v early in multi_head_attention_forward to reduce peak memory usage (#178452)

Foreach

• Increase kernel argument size 4KB -> 32KB for CUDA 13+ (#178641)
• Add multi-tensor fast path for p=0 norm (#179869)

Sparse

• Reduced CPU overhead in sparse operations for improved performance (#179193)
• Minor performance improvements for torch.bmm(COO, Dense) (#175347)

Distributed

• Improve AsyncMM.cu performance by avoiding redundant IO/compute via ElementC void type (#178653)
• Improve Context Parallel head-tail load balancer indices creation performance (up to 1555x speedup for 1M sequence length) (#178199)
• Improve tensor-to-allocation lookup in NCCL Symmetric Memory (#176744)
• Avoid two probes when inserting handle into SymmMem cache (#177463)

Distributed Checkpoint (DCP)

• Optimize DCP consolidation I/O for remote mounts (from ~2h to 35s) (#175762)

DTensor

• Improve DTensor performance for torch.cat and pytree ops (#174879)
• Skip unnecessary all-reduce of total_weight in DTensor nll_loss_backward for reduction='sum' (#177233)
• Cache DecompStrategy and fake mesh in DTensor (#175205)

FullyShardedDataParallel (FSDP)

• Use SymmMem for reduce-scatter in FSDP (#177111)

FullyShardedDataParallel2 (FSDP2)

• Improve FSDP2 parameter FQN lookup from quadratic to linear complexity (#174675)
• Overlap FSDP2 reduce-scatter with compute for per-parameter meshes (#177319)
• Cache FSDP2 shard_mesh to avoid repeated submesh creation (#179655)

CPU aarch64

• Add TLS stack_bounds to avoid expensive reads (#181137)

CUDA

• Update eigh CUDA heuristics (#175403)
• [CUBLAS][Blackwell] Enable 32MiB cuBLAS workspaces on Blackwell (#175344)
• [CUDA] [PERFORMANCE] Improve performance for RowwiseScaledMM.cu by avoiding redundant IO/compute via indicating that indicating that ElementC type is void (#178644)
• [CUDA] [PERFORMANCE] Improve performance for ScaledGroupMM.cu by avoiding redundant IO/compute via indicating that indicating that ElementC type is void (#178325)
• [CUDA][TensorIterator] Improve vectorized elementwise kernel: instruction cache (#175336)
• [pt] Reland vec8 vectorization (#176352)
• Use aminmax instead of min and max kernels in histc (#178011)

MPS

• Reimplemented cross as single-stage Metal kernel (#175498)
• Replaced MPSGraph nonzero with native Metal prefix-sum + scatter (#178484)
• Sped up RMSNorm on MPS (#180173)
• Removed .item() sync in _amp_non_finite_check_and_unscale_mps (#180267)

ROCm

• Directly access GPU scalars if largeBar is enabled, avoiding D2H copy (#177023)
• TopK operator performance improvement via RadixSelect prefetching (#174897, #177149, #178188, #174837)
• Improved kernel loop unrolling by leveraging compiler (#177697)
• Remove need for expensive fence in normalization kernel (#175286)
• Avoid double casting in ReduceLogicKernel (#176132)
• In group_gemm, use new kernel for all K equal cases (#173502)
• Use BFloat16 native hardware type casting (#178814)
• Use optimized tiled kernel for LayerNorm gamma beta backward (#179019)
• Tune Flex-Attention occupancy on gfx942 for head_dim 64/128/256 (#176261)

XPU

• Remove unnecessary device-to-host synchronization in torch.nn.functional.one_hot for XPU by skipping boundary validation checks only needed on CPU (#179831)
• Add GEMM configs to XPU autotuning heuristic (#177647)

Dynamo

• Faster guards: GUARD_VALUE_DISPATCH dispatch table, fast-path requires_grad, DICT_NOT_CONTAINS/SET_NOT_CONTAINS, PyType_GetDict on Python ≥3.12, thread-safe dict-version tracking, no recompiles on hoistable opaque objects, and a general guard-optimization pass (#176033, #177158, #176053, #179170, #178703, #176643, #175006)
• Faster tracing: tree_map_with_path, constant-folding elementwise_dtypes, inline_invoke_subgraph post-tracing pass, emit subgraph output intermediates only on observed side effects, drop unnecessary realize_all in speculate_subgraph, skip SymInt copies in TENSOR_SUBCLASS_METADATA_MATCH, and avoid closure refcycle in _empty_create_subclass (#174146, #177743, #176082, #177368, #176742, #175596, #175660)

AOTAutograd

• Skipped expensive debug_lines computation in AOT autograd cache (#179733)
• Sped up ConfigModule._get_dict by avoiding unnecessary work (#179734)

Inductor

• Add donate_graph_module option to standalone_compile to avoid extra graph module copies (#179910)
• Rewrite multi-consumer F.pad as torch.cat for zero-copy (#177216)
• ROCm: enable pipelining for FlexAttention (#176676)
• CPU: make remove_identity in-place for inference to align with pre_grad_passes (#177805)
• CPU: fuse round and to in quant (#171699)
• Fix hardcoded OMP thread counts in torch.compile (#170585)

Ahead-Of-Time Inductor (AOTI)

• Batch cubin-to-obj conversion using .incbin assembly, dramatically reducing AOTI compile time for models with many Triton kernels (e.g., ~640x speedup on the cubin embedding phase for a 4-layer MoE with 661 cubins) (#177864)
• Parallelize PTX-to-fatbin compilation when emit_multi_arch_kernel is enabled, saving several minutes on AOTI export for large models (#177904)

torch.fx

• Fix quadratic name generation in _NamespaceBase.create_name, significantly improving performance for graphs with many nodes (#176515, #177217)
• Propagate custom annotations to runtime asserts (#170796)

torch.export

• Skip duplicate tensor serialization when saving multiple exported programs (#176880)

ONNX

• Optimized _jit_pass_onnx_deduplicate_initializers from O(N^2) to O(N) (#175888)

JIT

• Cached can_compile_class and short-circuited type inference for ProcessGroup (#179396)

Functorch

• [FUNCTORCH] Use [] instead of list() for improved performance (#175491)

Composability

• Decomposed mm/addmm to pointwise multiply when K==1, yielding up to 1.55x speedup for outer-product-like matrix multiplications (#175825)
• Improved tracing speed via the aggressive_guard_free_semantics config flag (#174654)
• Reduced threshold for calling sympy.factor to 50, avoiding expensive symbolic simplification on large expressions (#177779)
• Added per-SymNode expression cache, reducing redundant symbolic computation (#175353)

Documentation

Release Engineering

• Auto-detect missing doc redirects for moved/deleted files (#173805)
• Add .nojekyll file creation in CPP doc push script (#179721)
• Simplify condition for linux-docs job (#180391)
• Skip llms-full.txt during Sphinx builds and generate it in nightly push (#181070)
• Disable llms-full.txt (#181141)
• Fix link to C++ torch stable docs (#181613)
• Use full clone for docs build to fix nightly hang (#181661)
• Add checkout-mode input to setup-linux action (#181702)
• Make docs build behave the same for push=true and push=false (#181921)
• Reduce sidebar navigation size for generated API pages (#181943)

Python Frontend

• Documented get_device_capability and clarified supported dtypes (#178397)
• Fixed typo in index_copy doc (#175843)
• Updated amax doc (#175863)
• Updated take_along_dim doc (#175844)

torch.nn

• Fixed varlen attention docstring (#175261)
• Fixed device mismatch in scaled_dot_product_attention docstring example (#178684)
• Fixed incorrect attn_mask shape in scaled_dot_product_attention docs (#177999)
• Clarified RMSNorm eps parameter default behavior (#173887)
• Improved Conv2d docs: clarified math variable to parameter mapping and fixed cross-correlation link (#178965)

torch.optim

• Add minimal usage example to Muon optimizer docstring (#177029) (#177262)

C++ Frontend

• Improved stable ABI docs for header-only dispatch macros and STD_TORCH_CHECK (#177996, #177997)
• Restructured C++ docs (#174096)

Distributed

• Document FSDP2 communication grouping and scheduling semantics (#176318)

CUDA

• [Typo] ot -> to (#179265)
• [BE] Tesor -> Tensor (#175061)
• [Typo] Quiet -> Quite (#179266)
• Document public APIs using a Claude Skill (#175578)

Dynamo

• Improve nonstrict_trace documentation (#172395)

AOTDispatcher

• Documented supported input and output dtypes for custom ops (#175452)

Inductor

• Fix incorrect remediation instructions in cudagraph pending-backward warning (#176865)

torch.fx

• Update and correct the documentation for Cond operator (#175419)

Developers

Release Engineering

• Update CXX_STANDARD to C++20 across build targets (#178343)
• Remove legacy_nvidia_driver code (#175363)
• Change default CUDA arch list to sm_7.5 (#175574)
• Move binary build scripts from .circleci/ to .ci/pytorch/ and clean up old copies (#175930, #175915, #175917)
• Add ARC runner label mapping config and experiment support to runner determinator (#177803, #177804)
• Use sccache when available for faster builds (#175556)
• Standardize ninja installation to PyPI (#179508)
• Remove ancient OpenSSL 1.1.1k build (#179513)
• Remove stale CentOS-7 references (#179507)
• Remove UCC/UCX from Docker builds (#175607)

Autograd

• Registered numel, dim, get_device, storage_offset, is_contiguous in native_functions.yaml (#177200)

torch.nn

• Added flop registration to varlen attention (#179500)

Sparse

• Included thrust/pair.h in each translation unit where thrust::pair is used (#169267)
• Implemented branch-free and guard-free padding+mul operator for semi-structured sparsity (#177699)

Build Frontend

• Included CMAKE_CUDA_FLAGS in build settings report (#175236)
• Enforced C++20 for XPU SYCL device compilation (#179497)

Distributed

• Add profiling name to NCCL collectives (#173837)
• Add NCCL collective sequence number (seq_num) to Kineto profiler traces (#177148)
• Add RECORD_PARAM_COMMS to symmetric memory CUDA ops for ProcessGroup metadata in profiler traces (#178571)
• Capture async flag of collectives in PyTorch execution trace (#169416)
• TorchComms: Flight Recorder debug server integration and hook support (#175270, #175561, #178359)
• Refactor NCCLDevCommManager API design (#177380)
• ROCm: Use amdsmi instead of rocmsmi for intra-node communication (#176506)
• SymmMem: Improve CUDA hygiene (#175616)
• SymmMem: Use host API to get NCCL peer pointer (#176570)
• Split _BackendWrapper import to torchcomms._backend_wrapper module (#177157, #178352)
• ROCm: Enable cpp/c10d unit tests (#169063)
• Fix FR script for coalesced collective not scheduled (#177076)

DTensor

• Add _dtensor::mesh_get_process_group custom op (#178116)
• Add custom op for flattened submesh lookup during compile_on_one_rank tracing (#178889)

FullyShardedDataParallel2 (FSDP2)

• Fix fully_shard argument type hints for better type-checking consistency (#171574)

MPS

• Standardized Metal kernel compilation around AsyncCompile (#179838)
• Removed pre-MacOS14 check from MpsDeviceInterface (#175804) (from miscategorized)

XPU

• Enforce C++20 for XPU SYCL device compilation (#179497, #179613)

Dynamo

• Repro/debug: after_aot repro generator in fx_graph_runnable, dumping resumption-frame bytecode in tlparse, and running the early pre-grad pass before prepare_aot_module_simplified (#179657, #166940, #178394)
• Export plumbing: drop an unneeded flag from dynamo_graph_capture_for_export and support torch._ops.Overload (#175646, #175647)

AOTDispatcher

• Codegen AOTDispatchSubclassWrapper (#176741)
• Added UUID-based cache key support for pre-grad custom passes (#177403) (from miscategorized)

Inductor

• Refactor autotune_select_algorithm to always return a (node, choice) tuple (#177181)
• Add min/max input distance metric to scheduler nodes (#175730)
• Refactor autoheuristic_use/collect to facilitate configuring/defaulting AH for different ops (#180276)

torch.fx

• Add type annotations to core torch/fx types (#179590)
• Add type annotations to torch/fx operator_schemas and subgraph_rewriter (#179761)
• Add type annotations to torch/fx/experimental/migrate_gradual_types (#180070)
• Add type annotations to torch/fx proxy, interpreter, symbol (#179718)
• Add type annotations to torch/fx/experimental proxy_tensor (#179864)
• Improve consistency between variable name and typing annotation (#176291)
• Add timing to GraphPickler.loads method (#175440)
• Apply up007 and up045 to torch/fx (#176308)
• Remove more undocumented functions (#175663)

JIT

• Removed torch/csrc/utils/six.h (#179110)
• Upgraded runtime JIT/Inductor codegen to C++20 (#176502) (from miscategorized)

Composability

• Deleted size_vars / size_hint API (#175365)
• Changed symbolic expressions to use FloorDiv and Mod instead of // and % on SymPy exprs (#177051)
• Tagged backward nodes via _patch_autograd_grad and updated remat pass (#179105)
• Added fast_bind support in normalize_function for FakeTensor (#175740)

Caffe2

• Enabled hipsparselt in caffe2 HIP builds for ROCm (#175810)
• Fixed pybind11 3.0.3 ambiguous return type deduction in caffe2 (#179277)

Developer Experience

• Added clean command to spin config (#167550)
• Moved environment variable handling from setup.py to CMake (#177640)

Security

Release Engineering

• Pin third-party GitHub Actions to SHA and extract unsafe expressions (#178638)