PyTorch 2.13.0 Release Notes

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

Highlights

FlexAttention lands on Apple Silicon (MPS), with up to ~12x speedup over SDPA on sparse patterns, and gains a deterministic backward path on CUDA for reproducible gradient computation.

CuTeDSL "Native DSL" backend gives Inductor a second high-performance code path (alongside Triton) for key GPU operations, with faster compilation. [Prototype]

nn.LinearCrossEntropyLoss combines the final prediction and loss computation to cut peak GPU memory by up to 4x for large-vocabulary language model training.

torchcomms, a new communications backend for PyTorch Distributed, improves fault tolerance, scalability, and debuggability for large-cluster training.

FSDP2 now overlaps reduce-scatter and all-gather communications via a dedicated process group (opt-in), increasing distributed training throughput.

Python 3.15 wheel support for PyTorch on Linux via the pytorch repository index, including builds compatible with free-threaded 3.15t.

Broader platform support: ROCm gains AOTriton 0.12b with native HIP CMake, Arm adds Armv9-A torch.compile targeting, and Intel XPU exposes new device telemetry APIs.

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

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

• Stop building CPython 3.13t (free-threaded) binaries (#182951)

  Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

  PyTorch 2.12:

  # cp313t (free-threaded 3.13) wheels were available
  python3.13t -m pip install torch
  

  PyTorch 2.13:

  # Use free-threaded Python 3.14t instead
  python3.14t -m pip install torch
  

• Bare PyObject is no longer allowed in operator schemas (#184209)

  Bare PyObject was accidentally accepted in operator schema strings in PyTorch 2.12. This was undocumented and is now rejected, since torch.compile does not support arbitrary PyObject inputs to custom ops. If you parse or register a schema with a bare PyObject argument or return type, you will now get a schema parse error.

  PyTorch 2.12:

  >>> from torch._C import parse_schema
  >>> parse_schema("foo(PyObject x) -> ()")  # accepted
  

  PyTorch 2.13:

  >>> from torch._C import parse_schema
  >>> parse_schema("foo(PyObject x) -> ()")  # raises a schema parse error
  

• Remove Bazel build support (#180883)

  The Bazel build was never broadly adopted and still depended on the antiquated Bazel 6, while the wider ecosystem has since moved to Bazel 9. All Bazel build files and CI jobs have been removed. Users building PyTorch with Bazel should migrate to the supported CMake/pip install build flow.

  PyTorch 2.12:

  # Build PyTorch with Bazel
  bazel build //:torch
  

  PyTorch 2.13:

  # Bazel build files have been removed; build from source with pip instead
  pip install --no-build-isolation -e .
  

• Enforce C20 minimum in header guards (#178150). In PyTorch 2.13, C20 is now required to import PyTorch headers.

• StorageImpl's built-in copy-on-write (COW) materialization is replaced by a pluggable materializer hook (#179063)

  StorageImpl no longer knows about COW directly. Its internal COW entry points StorageImpl::is_cow(), StorageImpl::maybe_materialize_cow(), and the friend cow::materialize_cow_storage() have been removed in favor of a single pluggable MaterializeFn hook (void(*)(StorageImpl*)) that a backend registers to run once, on the first mutable data-pointer access. COW is now just one consumer of this hook (c10::impl::cow::materialize_cow), and all COW behavior (lazy clone, refcounted shared data, copy-on-write) is unchanged. This also gives accelerator backends and eager-mode graph compilers a zero-fast-path-cost place to commit deferred allocations or materialize symbolic buffers on first mutation.

  This is a C++-only change. It affects out-of-tree backends/extensions that called the removed StorageImpl COW symbols directly; they will fail to compile against 2.13 with errors such as no member named 'is_cow' in 'c10::StorageImpl'. Migrate to the new hook API (set_materializer() / has_materializer() / clear_materializer()).

  PyTorch 2.12:

  // Detect a COW storage and force it to materialize.
  if (storage.is_cow()) {
    storage.maybe_materialize_cow();
  }
  

  PyTorch 2.13:

  // Register a one-shot materializer; it runs on the next mutable-data access
  // and then clears itself. COW registers c10::impl::cow::materialize_cow this way.
  storage.set_materializer(&my_backend_materialize);  // void(StorageImpl*)
  
  // `has_materializer()` replaces `is_cow()` for "is a deferred materialization pending?"
  if (storage.has_materializer()) { /* ... */ }
  

• Convert shared_ptr<Node> to intrusive_ptr<Node> (#181139). This changes the signature of Tensor::grad_fn. Accesses to Tensor.grad_fn() should change from std::shared_ptr<Node> to c10::intrusive_ptr<Node>. Similarly, construction of a C++ autograd function should change:

  PyTorch 2.12:

  std::shared_ptr<CustomCppNode> node(new CustomCppNode(), torch::autograd::deleteNode);
  

  PyTorch 2.13:

  auto node = c10::make_intrusive<CustomCppNode>();
  

• The minimum supported NCCL version when building from source is now 2.23 (#186292)

  PyTorch now requires NCCL >= 2.23 at compile time, and the preprocessor/runtime gates that guarded NCCL features introduced in 2.23 or earlier have been removed. Users who build PyTorch from source against a system NCCL older than 2.23 will hit compile errors against the dropped gates. Upgrade the NCCL installation to >= 2.23 to build. The prebuilt PyTorch wheels already bundle a compatible NCCL, so pip/conda users are unaffected.

• Remove named tensors (#173895)

  The named tensor feature (a long-deprecated prototype) has been fully removed to reduce overhead and code bloat. All associated Python and C++ APIs are gone, including Tensor.names, Tensor.rename(), Tensor.refine_names(), Tensor.align_to(), Tensor.align_as(), torch.align_tensors(), the names= keyword on factory functions (e.g. torch.zeros, torch.empty, torch.ones), and the C++ Dimname / DimnameList APIs. Code that previously relied on named dimensions must track dimension order positionally and avoid usage of any of these now-removed APIs or op overloads.

• The onednn::qconv2d_pointwise.binary and .binary_tensor operators no longer alias their input but rather return fresh tensors. Previously these ops mutated the qaccum input buffer and returned it directly, violating the PyTorch invariant that custom operator outputs must not alias inputs. This silently bypassed aliasing checks via the old -> Tensor(a!) schema and would become a hard error in a future PyTorch version (as mentioned in #182063), so the schema and implementation were corrected to return a fresh output. Most users are unaffected, only code that calls these ops directly and relies on the in-place mutation of qaccum must now read the returned tensor instead. (#177171)

Deprecations

• Custom operators that return an output aliasing one of their inputs are deprecated (#182063)

  When a custom operator returns an output that is the same tensor as (or otherwise aliases) one of its inputs under torch.compile, PyTorch now emits a UserWarning stating that this is deprecated and will become an error in a future version of PyTorch. Previously the warning stated the change would land in PyTorch 2.12; that timeline has been pushed back. To update your code, return a clone of the offending output instead of the input, or refactor the operator so it does not return the aliased tensor.

  Deprecated:

  @torch.library.custom_op("mylib::foo", mutates_args=())
  def foo(x: torch.Tensor) -> torch.Tensor:
      return x  # output aliases the input -- deprecated
  

  Updated:

  @torch.library.custom_op("mylib::foo", mutates_args=())
  def foo(x: torch.Tensor) -> torch.Tensor:
      return x.clone()  # return a clone instead
  

• Creating tensors with the quantized dtypes quint8, qint8, and qint32 is now deprecated and emits a warning. This covers both Python and C++ call sites; see #184982 for migration guidance (#184984)

  PyTorch 2.12:

  >>> x = torch.quantize_per_tensor(torch.randn(3), 0.1, 0, torch.quint8)
  

  PyTorch 2.13:

  >>> x = torch.quantize_per_tensor(torch.randn(3), 0.1, 0, torch.quint8)
  UserWarning: Creating tensors with quantized dtypes (quint8, qint8, qint32) is deprecated
  

• Rename distributed collective ops to the _single naming scheme and deprecate the old names (#186123, #186124, #186125, #186134, #186135, #186144)

  To align the public torch.distributed collective APIs with the naming used by torchcomms' TorchCommBackend, all_gather_into_tensor is renamed to all_gather_single and reduce_scatter_tensor to reduce_scatter_single. The previous names continue to work as thin wrappers that delegate to the new functions, but now emit a FutureWarning.

  PyTorch 2.12:

  dist.all_gather_into_tensor(output, input)
  dist.reduce_scatter_tensor(output, input)
  

  PyTorch 2.13:

  dist.all_gather_single(output, input)
  dist.reduce_scatter_single(output, input)
  

New Features

Python Frontend

• Add two new operator tags, torch.Tag.inplace and torch.Tag.out, that let an operator declare how it writes its result: inplace means it mutates a tensor in place, and out means it writes into a caller-provided output tensor. Native PyTorch operators are tagged automatically, and custom operators defined with torch.library can opt in by adding the tag. To be tagged inplace, an operator must take the tensor it mutates as its first positional argument (declared as Tensor(a!), and the only mutable argument) and return that same tensor. Tagging a custom operator this way improves its behavior under torch.compile: inplace ops now go through auto_functionalize, so the reinplacing pass can analyze clones and skip unnecessary copies, and both inplace and out ops get their fake/meta kernels generated for free. See the Python custom operators tutorial for how to author and tag custom operators. (#181100, #181099, #184199, #184200, #184201, #184202, #184203, #180851, #180852)
• Add const_data_ptr() Python binding to torch.Tensor for read-only data pointer access (#180382)
• Add an abbr property to torch.dtype that returns a dtype's short string abbreviation (e.g. torch.float32.abbr returns "f32") (#177296)
• Allow positional arguments to be passed as keyword arguments to autograd custom Functions (#182206)
• Expose rearrange in the torch.func namespace for einops-style tensor reshaping (#173183)

torch.nn

• Add nn.LinearCrossEntropyLoss, a fused linear-projection plus cross-entropy loss module that avoids materializing the full logits tensor (#181573, #185852, #172286, #186113)

Autograd

• Add torch.autograd.graph.region_activation_memory_budget (#185979)
• Support passing gradient inputs as a dict to torch.autograd.grad and torch.autograd.backward (#178140)

Distributed

• Add a registration API for symmetric memory arguments (lib.register_symm_mem_args()), letting operators (including out-of-tree ops) declare which arguments require symmetric-memory allocation (#173513)

• Remove NCCLSymmetricMemory's explicit dependency on ProcessGroupNCCL, enabling symmetric memory to work with out-of-tree backends such as torchcomms (#184260)

• Support accessing the ReduceOp.PREMUL_SUM factor from Python when implementing process group backends in Python (#185863)

• Expose the NCCL 2.30 maxP2pPeers config binding (#181686)

• Add rocSHMEM Triton integration for symmetric memory on ROCm (#178658)

• Support passing extra keyword arguments to the loss function in pipeline schedules via a new loss_kwargs parameter to step(), enabling loss functions that require arguments beyond (output, target) (such as chunked cross-entropy needing token counts for scaling) (#181057)

Distributed FSDP2

• Add FSDPModule.set_separate_reduce_scatter_group to give reduce-scatter its own NCCL communicator, enabling opt-in overlap of all-gather and reduce-scatter (#186335)
• Add set_reduce_scatter_max_input_buffers to keep multiple reduce-scatter input buffers in flight, so backward compute no longer stalls waiting to recycle a single reduce-scatter buffer (#186000)

Profiler

• Profiler/Kineto now emits channel metadata on CUDA backends (#185968)

Dynamo

• Add torch.compiler.set_default_backend to override the default torch.compile backend globally, so out-of-tree backend authors don't need to pass backend= at every call site (following the pattern of torch.set_default_dtype/torch.set_default_device). Explicit backend= arguments still take precedence (#178944)
• Add torch.compile(f, isolate_recompiles=True) to give each torch.compile call its own isolated cache bucket, preventing cross-compile interference in cache lookups and recompile-limit checks when multiple torch.compile calls target the same function (#178351)
• Add register_multi_grad_hook support to @leaf_function, allowing a backward hook to fire once per backward pass when all requires_grad inputs have their gradients computed (#179609)

Inductor

• Add flash-decoding support to the CPU FlexAttention template (chosen when query length is 1) with a new configurable PARTITION_SIZE kernel option (#159835)
• Add Triton convolution backward kernels (input and weight gradients) as an autotuning backend in place of the ATen-only fallback (#178945)
• Add an Inductor FX pass (decomp_comms) that eliminates all_gather for Gram-matrix optimizer patterns (Muon/Shampoo) under FSDP, gated by config.aten_distributed_optimizations.allow_comms_decompositions, yielding 1.25-1.95x training speedups (#184370)

Ahead-Of-Time Inductor (AOTI)

• Generated C shims for the AOTI stable ABI are now versioned and gated by TORCH_TARGET_VERSION, so shims introduced in newer releases are only exposed when the target version supports them (#181916)
• Triton CPU AOTI models now work end-to-end through the public torch._inductor.aoti_compile_and_package / aoti_load_package API, including packaging and loading of the multiple .so files emitted per kernel (#182251)
• Added stable C shim functions (torch_exception_get_what, torch_exception_get_what_without_backtrace, and STABLE_TORCH_ERROR_CODE_CHECK) so extensions built against the stable ABI can retrieve the original error message across the C API boundary (target version 2.13+) (#180135)
• Added a stable AOTI stream shim aoti_torch_stream_native_handle and torch::stable::accelerator::Stream::nativeHandle(), gated behind TORCH_FEATURE_VERSION >= 2.13, for retrieving a native stream handle from the stable ABI (#183930)

Release Engineering

• Add Python 3.15 wheel builds across Linux (CPU/CUDA), Triton, ROCm, and XPU (#182954, #184600, #185409, #184829, #184891, #184906, #185094)

CUDA

• Add CUDAGraph.get_graph_data() for graph topology introspection (#183165)
• Lightweight API to get private pool reserved memory bytes (#178240)

MPS

• Add FlexAttention support for MPS (#182552, #186215)
• Add support for torch.distributions.Dirichlet on MPS by adding _sample_dirichlet and _dirichlet_grad Metal implementations (#185458, #185854)
• Add grid_sampler_2d backward support on MPS (#179756)
• Add grid_sampler_3d backward support on MPS (#179388)
• Add lcm support on MPS via a new Metal kernel (#186279)
• Add complex support to c10/metal/reduction_utils.h (#180708) and a complex->bool specialization (#185938)

ROCm

• Enable external events in CUDA graphs (#178264)
• Enable GPU Address Sanitizer build (#183792, #176461)
• Improve Inductor GEMM search space performance using the Origami project (#172512)
• Use CMake native HIP language support, enable_language(HIP) (#180485)
• New Inductor benchmarker based on Torch Profiler (#175097)

XPU

• Add XPU device telemetry APIs for temperature, frequency, power draw, engine utilization, memory bandwidth usage, and used device memory through torch.xpu.* (#181082, #183427, #183428, #183429, #183430, #183431)
• Add FP8 blockwise scaling support for scaled_mm on XPU (#173630, #176043)

Improvements

Python Frontend

• Make it possible to load safetensors with torch.load (#170592)
• Make Storage.pin_memory / Storage.is_pinned device-agnostic (#186223)
• Add op_overloads to OpOverloadPacket to enumerate an operator's overloads (#182993)

torch.nn

• Expose num_splits in FlashAttention-2 and bump the flash-attention submodule (#179760)
• Support linear_bias in linear_cross_entropy on the reference and chunked paths (#185129, #185276)

Optimizer

• Fix SequentialLR wrong learning rate initialization when milestones contain 0 (#185986)

Autograd

• Implement autograd derivatives for torch.nextafter (#148820)
• Add torch.autograd.enforce_grad_layout_policy to control the memory layout policy for accumulated gradients (#180552)

Distributed

• When TorchComms is enabled, route new_group through split_group for subgroup creation, raising NotImplementedError for arguments split_group cannot honor (e.g. use_local_synchronization=True, sort_ranks=False) instead of silently falling back (#185416)

• Delegate dist.new_group to custom process group subclasses (#184262)

• Surface started-work metadata in NCCL watchdog timeouts (#183656)

• Add a health check endpoint to the distributed debug server (#179326)

• Make the DeviceMesh non-overlapping check stricter (#172343)

• Allow elastic_launch/launch_agent to accept a pre-created torchelastic health check server, so it can be started before rendezvous (#180543)

• Add an overlap_pp_comm flag to pipeline schedules (default True) that, when set to False, defers each pipeline RECV op to immediately before the compute op that consumes it, using rank-parity P2P ordering to avoid deadlock (helps platforms such as AMD ROCm where a pending RECV blocks unrelated compute) (#178815)

DTensor

• Migrate embedding and random ops to single-dim sharding strategies and increase op coverage (#180281, #180503)
• Add auto-infrastructure that derives single-dim sharding strategies for autogenerated op variants (.out, inplace, functional, and foreach), expanding strategy coverage to hundreds of additional ops (#185386)
• Register sharding strategies for additional ops: scatter, upsample/interpolation backward, anti-aliased upsample, batch norm backward, and aten.detach_.default (#186149, #180311, #184626, #182743, #181876)

Distributed FSDP2

• Support forward-mode automatic differentiation (torch.func.jvp) on models wrapped with fully_shard or replicate, including with mixed precision (#182732)

Linear Algebra Frontend

• Add Half and BFloat16 dispatch support for torch.trace on CPU (#184874)
• Improve heuristics for the cuSOLVER vs cuBLAS backend switch in torch.linalg.lu (#185344)

Profiler

• The memory viz tool now more accurately represents GPU footprint when impacted by fragmentation (#180515)
• The memory viz tool now aggregates stripes per-pool to improve visualization for large snapshots (#180613)
• Profiler now also exposes CUDA occupancy metadata as a nested dictionary in the .events() output (#180275)

FX

• split_module now supports torch.Size crossing graph split boundaries by decomposing size() calls into per-dimension sym_size nodes, and builds submodules lazily for faster inference graph splitting (#179839)

• CapabilityBasedPartitioner can now opt out of horizontal fusion via skip_horizontal_fusion=True, partitioning only through direct data dependencies (#184904)

• Enable rewriting of FX traces containing complex tensors during compilation (#169832)

Dynamo

• Implement additional Python operators in Dynamo: bitwise and (#184788), bitwise xor (#184789), left/right shift (#183462), floor division (#185652), true division (#185653), remainder (#185654), and divmod (#185655)
• Support tracing more constructs in Dynamo: einops 0.8.2 (#185619), record_function as a decorator (#184703), inference_mode retracing helpers (#185066), mark_dirty in the autograd Function HOP (#184267), warn_only deterministic toggles (#180373), and the _maybe_view_chunk_cat functional collective (#180389)
• Support item assignment and deletion (__setitem__/__delitem__) on more container types in Dynamo via sq_ass_item/mp_ass_subscript slots (#182862, #182996)
• Support torch.accelerator.device_index and torch.xpu.device in the device context manager (#181846, #181847)
• Improve Triton support under torch.compile: accept tl.constexpr values as kernel arguments (#181783) and handle capture_triton as a no-op during tracing (#183555)
• Improve dynamic shape specification: reduce verbosity in shape specs for the common case (#184271), add SeqSpec for list/tuple specs with better walk-spec errors (#185327), add ObjectSpec (#182764), pipe dynamic spec through torch.compile (#184501), and revisit guarding in mark_dynamic APIs (#181469)
• Improve torch.compile device mismatch errors with a dedicated FakeTensorDeviceMismatchError and actionable guidance to place inputs, parameters, and buffers on the same device (#185412)
• Improve error messages and diagnostics: clearer data-dependent errors for .any()/.all() (#180406), clearer torch._check tensor predicate errors (#185777), user-friendly reasons for skipped frames (#183596), carets in stack traces (#182393), and reporting why a symbol was created dynamically in symbolic_shapes logs (#168331)
• Make Dynamo exceptions pickleable (#185725)
• Inline decomposed quantization helpers in Dynamo (#185628)
• Make Dynamo debug/repro utilities device-agnostic (#184851)

Inductor

• Support pin_memory for torch.ones, torch.zeros and torch.full (#174595)
• Add a ROCm config flag to disable the pointer_range_32 optimization (#179604)
• Add a peak memory threshold config for combo kernels (#180578)
• Enable autotuning and a fast compensation path for CPU static/dynamic smooth-quant qlinear, with correct handling of 0D x_scale/x_zp and ReinterpretView strides (#181090)
• Add aot_inductor.autotune_per_kernel_alloc config to allocate-run-delete tensors per kernel during AOTI autotuning, avoiding OOM on large models (#181176)
• Bound AsyncCompile future waits with the compile_worker_wait_timeout setting (#181293)
• Add a100_default_flex_config entries for head_dim=192 (#181835)
• Add an explicit lowering (fallback) for aten.multinomial to avoid graph breaks (#182423)
• Add an Inductor lowering for _scaled_mm_v2 (#182527)
• Enable combo_kernel_autotune_grouping by default (#182567)
• Add cudagraph_partition_memory_budget config for partition reordering (#183569)
• Add ATen fallbacks for bincount, unique variants, and AMP scale ops so they compile without graph breaks (#183590)
• Unfuse the bias add from addmm when the bias is a narrowing dtype cast (fp32->bf16/fp16) to preserve bias precision in XPU AMP training (#183680)
• Emit a clearer diagnostic when a backward CUDAGraph output installed as a .grad buffer is invalidated on a later run (gradient accumulation) (#184003)
• Support split online softmax reductions in Inductor (#184069)
• Support fusing index_add-style atomic scatter mutations into Triton template epilogues behind a config flag (#184179)
• Add a cpp.march Inductor config knob so AOTInductor cpp-only builds can override or suppress the default CPU architecture flag (#184297)
• Improve the Triton cache directory guidance when loading shared objects from a noexec filesystem fails (#184362)
• Enable the Bert SDPA pattern rewrite on CUDA while keeping the original matmul/softmax math path (#184417)
• Improve the Triton launcher argument-mismatch error with a clearer message and a cached preflight check (#184522)
• Add broader CuteDSL op overrides (rsqrt, exp2, log2, log10, tan, acos, asin, atan, atan2, floor, logical_xor) (#184538)
• Add an optional fake_mode argument to standalone_compile (with dynamic_shapes="from_example_inputs") so it can reuse the caller's FakeTensorMode/ShapeEnv instead of always creating a fresh one (#184776)
• Support signbit on unsigned integer dtypes (#185985)
• Add a keep_static_cubin_raw config to retain cubin bytes in cached kernels so caches restored on another machine avoid recompilation (#186404)
• Extend BatchLinearLHSFusion's matcher to also match the inlined torch._C._nn.linear form so the (opt-in) fusion can fire on Dynamo-inlined linear (#186632)
• Add a clearer error message with install instructions when a compatible Flash Attention package is unavailable for flex attention (#186827)
• Add another anchor node to the batch-linear fusion pass so more small torch._C._nn.linear operations are grouped into a single batched kernel (#180477)
• Extend the batch fusion pass to support detach() method calls (#180513)
• Add a deterministic backward for the FlexAttention flash kernel (#174813)
• Use rand4x for Inductor Triton random number generation (#184377)
• Add a Quack-based CuTeDSL RMSNorm kernel (#182108)

Ahead-Of-Time Inductor (AOTI)

• Use fatbinary for multi-arch CUDA kernels (#184456)
• Support mixed-device constants in update_constant_buffer (#181114)
• Add FP8 header files in the AOTI shim.h (#178120)
• Add throttled cudaMemcpy for AOTI constant loading to reduce peak memory usage (#184823)
• Preserve AOTI proxy_executor error messages (#180884)
• Enable Triton kernels in AOTI C++ wrapper on CPU (#181068)
• Skip CPU vec ISA setup for device-only cpp_wrapper (#182089)
• Expose torchbind constants from AOTIModelPackageLoader (#182149)
• Improve AOTI error for Python custom ops (#186305)

Export

• Support serialization of opaque type constants in torch.export save/load (#181676)
• Make functorch JVP operator torch.export-able (#179686)
• Add UpdateConstantBufferFromCpu for host-to-device copy (#181637)

AOTAutograd

• Support CPU activation offloading in the rematerialization pass, including marking recomputed nodes for backward and adding a resize-to-0 deallocation op so offloaded tensors are freed after their host-to-device copy (#181937, #181938)
• Use FX node names in merge_view_inputs error messages, so non-differentiable view input mutation errors identify the specific offending inputs (#180424)

Composability

• Add fake tensor support for _transformer_encoder_layer_fwd so it traces under torch.compile (#183916)
• Enable Armv9-A target support for torch.compile on AArch64 (#184555)
• Functionalize in-place c10d collectives in standalone compile (#181836)

Foreach

• Fix/add empty check for _foreach_max (#173483)

ONNX

• Add adaptive_max_pool2d and adaptive_max_pool3d decompositions for ONNX export (#184396)

C++ Frontend

• Add stable ABI for set_python_module on torch::Library (#182720)
• Add == overloads for HeaderOnlyArrayRef (#185379)
• Add torch::stable::Generator (#186423)
• Add c10::layout typecaster for torch.layout (#179607)
• Add default-args support to def_static (#175644)
• Add support for controlling scientific notation in C++-side tensor printing (#173321)

Release Engineering

• Move the NCCL pin to 2.30 (#181313)
• Advance the Triton pin to 3.7.1 (#181001, #186792)
• Upgrade the XPU support package to 2026.0 (#182003)
• Add a configurable threshold to avoid power-of-two rounding for large pinned memory allocations (#171662)
• Move some pre-build steps from setup.py to CMake (#177641)

CUDA

• Debugging tool to verify that external inputs to a CUDA graph are alive before replay (#174649)
• Add get/set/reset functions for BLAS workspace sizes (#177912)
• Cleanup double import in BinaryDivFloorKernel.cu (#179260)
• Return supported CUDA arch list when no GPU is present but GPU is compiled (#180356)
• Detect and fix stale stream references in autograd during CUDA graph capture (#180090)
• Use opmath_t in i1 and i1e CUDA kernels (#183778)
• Support resize_ with address hint (#178215)
• Support bfloat16 in _embedding_bag_per_sample_weights_backward on CUDA (#185889)
• Align parsePerProcessMemoryFraction's return type with other parsers (#185139)
• Improve error message when cuda-bindings version is too old (#185990)
• Expose torch.cuda.current_solver_handle for cuSOLVER handle sharing (#176705)

cuDNN

• Add flag to select depthwise convolution backend (#176500)
• Upgrade cudnn_frontend submodule to 1.24 (#185554)

MPS

• Migrate many ops from MPSGraph to native Metal kernels for bernoulli (#182210), native_dropout (#182232), uniform/normal/randint (#182386), randperm (#182528), comparison ops eq/ne/lt/le/gt/ge (#183019), bitwise ops (#182839), scatter/gather (#184028), copy-cast (#184740), gelu/gelu_backward (#181451), replication pad (#183065), embedding backward (#185119), trace (#183627), count_nonzero (#180725), amax/amin/aminmax/all/any (#180752), and cumsum/cumprod (#185609), native_group_norm (#183830, native_group_norm_backward #184437), topk and kthvalue Metal kernels (#184106), single-block and multi-block sort, including a stable sort path (#180714, #182242, #181736)
• Support integer inputs to histc (#178624)
• Support out variants of unary ops (#184743)
• Improvements to SDPA Metal kernels: prefill attention kernels (#181575), GQA support (#183280), is_causal support (#181855), head dim 256 (#181852), float mask support (#183458), and a clearer error for causal + attn mask (#181856)
• Add an ILP variant for binary tensor iterators and replace unary VEC4 with a generic ILP-per-thread dense kernel (#182155, #181509, #183055)
• Make several Metal kernels stride-aware to avoid unnecessary .contiguous() calls: Col2Im (#181949), Im2Col (#182709), Repeat (#182718), LossOps (#182714), and HistogramKernel (#181951)
• Enable NDHWC+DHWIO fast path for Conv3d on channels_last_3d (#184612)
• Clear the MPSGraph cache in torch.mps.empty_cache() (#181485)
• Add a private API to get the host alias of Metal storage (#180961)
• Add input validation: stride > 0 in pool ops (#184875), F.fold (#182067), im2col (#183593), and bernoulli probabilities (#185065)
• Alert on non-deterministic algorithms on MPS (#185061)
• Raise a clear not-implemented error for dropout_p (#184126)
• Use proper precise log functions in unary kernels (#185381)

ROCm

• Additional cub::DeviceHistogram hipify mappings (#180433)
• SDPA improvements via AOTriton 0.12b: head_dim != head_dim_v, use_deterministic_algorithms, gfx1100 and gfx1151 promoted out of experimental, partial FAv3 support on gfx950 (#184288)

XPU

• Add last_level_cache_size and is_integrated_gpu to XPU device properties (#184499, #182624)
• Add XPU dispatch for _fused_adagrad_ (#185577)
• Support mixed-type operations between Nested and Dense tensors on XPU (#182654)
• Support torch.xpu.device in Dynamo device management (#181847)
• Recognize additional Intel BMG device IDs on XPU (#183414)
• Enable XPU device support for sparse Triton ops (#179805)
• Enable the bmm_outer_product Triton override on XPU (#180441)
• Improve test coverage for the XPU backend (#174370, #180881, #171154)
• Support non-blocking pinned device-to-host copies on XPU (#186224)
• Refactor the XPU oneDNN integration from the C API to the C++ API (#184486)

Functorch

• Add vmap batching rule for torch.unbind_copy (#178035)
• Add vmap batching rule for Tensor.view(dtype) (aten::view.dtype) (#180728)

Sparse Frontend

• Validate mat1/mat2 layouts in sspaddmm and clarify error messages (#179037)

Bug Fixes

Python Frontend

• Add opt_dtype validation to torch.nanmean() for consistent error handling (#172809)
• Route torch.nansum integer output dtype through nan_to_num + sum for correct results (#183808)
• Fix out-of-bounds read in CUDAStream::stream() (#184237)
• Align XPU logspace/linspace ref tests with upstream XFAIL state (#178734)

Dataloader Frontend

• Fix DataLoader file descriptor leak from atexit cleanup (#176607)

torch.nn

• Validate stride/padding/kernel_size length in slow_conv3d (#181063)
• Validate inputs in math_channel_shuffle (#181029)
• Validate delta type in nn.HuberLoss constructor (#184012)
• Lowercase the environment variable in torch/serialization.py so it matches the true values (#180959)
• Fix int32 overflow in layer_norm on CUDA for tensors with more than 2^32 elements (#181600)
• Reject NestedTensor inputs in flex_attention with a clear error instead of an unclear backend failure (#183516)
• Fix SDPA incorrect early return on 0 head dim qk with valid v (#184914)
• Fix reflection_pad1d backward CUDA launch for large batches (#185024)
• Fix lp_pool infinity norm handling (#183997)

Autograd

• Fix checkpoint context cleanup on forward errors (#184018)
• Fix torch.autograd.enforce_grad_layout_policy decorator state leak (#183868)

Distributed

• Fix NCCLComm::abort() to use the correct deregister API for window-registered handles (#181626)

• Fix FakeProcessGroup all_gather on tensors that require grad (#181790)

• Fix gather and allgather_coalesced on FakeProcessGroup to copy input to output (#182364)

• Fix the scatter and reduce_scatter family on FakeProcessGroup to copy input to output (#182365)

• Fix all_to_all on FakeProcessGroup and validate splits (#182366)

• Fix conflict between broadcast_buffers and init_sync in DDP (#178054)

• Fix gather on non-destination ranks for the TorchComms backend (#178533)

• Fix TCPStore compilation with Clang 20 (#185785)

• Fix NCCL symmetric memory mismatch by using an allocation-time counter instead of address for block ordering (#183489)

• Fix a symbol lookup issue with the symmetric memory __init__ (#186416)

• Fix the value returned by Work.exception() so the exception can be inspected from Python instead of being unusable (#184697)

• Fix false assertion errors in the flight recorder when using the ncclx, gloo, rccl, rcclx, mccl, or hccl backends (#179753)

• Fix a failure when creating a subgroup on a fake backend via new_group, which has no underlying communicator to split (#186172)

• Fix torch.compile of the _c10d_functional all_gather_tensor_out and reduce_scatter_tensor_out ops, which previously failed functionalization with "Found a custom (non-ATen) operator whose output has alias annotations" (#183597)

• Fix split_group on multi-backend process groups (e.g. init_process_group(backend="cpu:gloo,cuda:nccl")) to split only the relevant backend instead of every backend, avoiding spurious warnings, extra rendezvous overhead, and inconsistent process-group shapes (#182057)

• Fix FakeProcessGroup to reject rank >= world_size at construction time, which previously failed silently and only surfaced later when collectives indexed past world_size (#182363)

• Fix the torchelastic agent hanging indefinitely (and never exiting) when workers become stuck in an uninterruptible (D-state) process that SIGKILL cannot reap; the final proc.join()/proc.wait() in _close is now bounded by a timeout and the unkillable PID is logged (#185414)

• Fix pipelining producing incorrect results or cryptic runtime errors when a PipelineScheduleMulti topology communicates between non-adjacent stages (e.g. skip connections); this is now detected at initialization and raises a clear RuntimeError (#179293)

• Fix RuntimeError: only Tensors of floating point dtype can require gradients when building a pipeline for models with non-float intermediates (such as Hugging Face transformer models) (#183582)

• Make LocalTensorMode transparent to torch.compile so compilation proceeds as if the debugging mode were not active (#182667)

• Fix AssertionError in elastic c10d rendezvous when a node's rank changes across rendezvous rounds (e.g. a node becomes rank 0 after a peer leaves) (#182375)

• Fix shared-weight gradient double-counting in zero-bubble pipeline schedules (#181365)

• Fix None gradient handling in pipeline backward send/recv (#182182)

• Fix a pipelining crash when split_module interleaves get_attr nodes with placeholder nodes (#182644)

Distributed Checkpointing

• Fix optimizer state-dict save/load using the wrong process group for FSDP models initialized with a non-default process group, by forwarding the FSDP process group to FSDP.optim_state_dict and FSDP.optim_state_dict_to_load (#181261)
• Fix DefaultStager crash when reused (#183424)

DTensor

• Fix Context Parallel load balancing for short sequences that cannot be split into the required head/tail chunks, falling back to regular CP sharding for correctness (#183968)
• Fix squeeze leaving a DTensor's spec and local tensor out of sync (spec shape not matching the local tensor) by preventing squeeze from redistributing when strict_view is set (#175798)
• Fix .view() failures (e.g. in ColwiseParallel/RowwiseParallel) after an uneven Shard(dim>0)->Replicate redistribute by making the local tensor contiguous (#184443)
• Fix a device-side assert crash when tracing tensor-parallel models with make_fx, caused by sharding propagation recording dead global-shape shadow nodes into the graph (#185865)
• Avoid the native sharding cache for symbolic IValue arguments, fixing stale cached output shardings that confused global and local shard shapes for shape-sensitive ops under compiled autograd (#183246)
• Fix upsample backward crashes when the input is sharded (#182595)
• Fix Partial placement being lost during the autograd layout invariant (#180511)
• Fix OpSpec.mesh crash when specs contain None entries (#181541)
• Fix redistribute(backward_dtype=...) ignoring the backward dtype (#182032)
• Fix _StridedShard flag conflict during gradient accumulation (#183517)
• Fix reduction strategy linearity (#183794)
• Fix the cache key hashing for fake meshes (#184001)
• Fix FakeTensor device hint in sharding propagation (#183970)
• Fix group_norm scalar adjuster crash when weight=None (#184819)
• Fix to_local() dropping the _is_param marker that nn.Parameter sets on custom tensors (#184422)
• Fix pad_tensor/unpad_tensor creating unnecessary guards on symbolic pad sizes during tracing (#180887)
• Fix DTensor + activation checkpointing + torch.compile crash caused by an unbound inner symbol at the root tracer (#181797)
• Fix a DTensorSpec refcount leak in OpSchema._recompute_comparison_key (#181792)

Distributed FSDP2

• Fix stale post_accumulate_grad_hook results under CPUOffloadPolicy (#180666)
• Fix reduce-scatter of unused DTensor parameters that previously raised a mixed Tensor/DTensor error in chunk_cat (#183040)
• Fix IndexError for modules called with no forward inputs by preserving empty args/kwargs, matching FSDP1 behavior (#183943)
• Remove redundant stream waits (#183983)
• Fix a tensor-parallel + FSDP2 + mixed-precision bug where the unsharded compute tensor was wrapped back into a DTensor with a stale fp32 dtype, causing sharding propagation to fail in eager and torch.compile (#183805)
• Fix incorrectly reduced gradients when running a partial forward of a fully_shard([norm, head]) group for chunked loss, where the model forward runs norm only and head is called standalone per chunk; unshard/reshard previously relied on _modules_to_run_forward and produced wrong results for this pattern (#180428)
• Add a warning when a grad-requiring forward output is a view tensor, since in-place ops on the view silently drop the pre-backward hook and cause backward to skip the all-gather and fail (#181850)
• Fix incorrect clip_grad_norm results with multiple data-parallel shard axes (e.g. dp_shard + cp passed via DataParallelMeshDims), which exposed separate Shard axes with the wrong float32 reduction order and an incorrect Shard instead of _StridedShard; the axes are now flattened into a single shard axis in the sharding spec (#183629)
• Fix recomputed tensor metadata diverging from the original forward under activation checkpointing when cast_forward_inputs is enabled, by casting forward inputs during recompute (#182580)
• Fix incorrect resharding of full-DTensor parameters when tensor parallelism is also applied by using _StridedShard (#186126)

Linear Algebra Frontend

• Validate pivot range in torch.linalg.ldl_solve CPU kernel (#181032)
• Fix rocBLAS tunable GEMM solution handling in TunableOp (#182380)
• Route fp16 backward GEMMs to rocBLAS to preserve subnormals (#183766)
• Make collective bucketing tolerate hinted unbacked SymInts (#183544)

Profiler

• Fix an issue where profiler would issue a "Profiler clears events at the end of each cycle" warning even when no cycles are used in the schedule (#180387)
• Ensure that Profiler does not keep driving Kineto transitions even when GPU collection has stopped (#180698)

FX

• Preserve user runtime asserts in FX pass (#184608)

Dynamo

• Fix Python container and operator semantics in Dynamo to match CPython: list (#185425) and tuple (#185427) constructors, dict update (#185428), defaultdict inplace union (#185429), frozenset copy identity (#185430), sequence search (#185431), iand on bool constants (#184503), sequence * SymNode spurious graph break (#185260), and torch.Size tensor shape handling (#184613)
• Fix symbolic shape / fake tensor handling: float/bool + SymNode (#183362), PendingUnbackedSymbolNotFound for 0-d tensor Scalar args (#182660), GuardOnDataDependentSymNode on sparse tensors (#179616), and creating symbolic tensors from foreign fake tensors (#181794)
• Fix use-after-free issues in CUDAStream/Event tp_dealloc overrides (#183403) and Dynamo dict guard cleanup (#183753)
• Fix Dynamo crash when DeviceMesh is constructed inside torch.compile (#177201)
• Fix torch.compile crash when an unsupported type is passed to a tensor method inside try/except (#182106)
• Skip wrap_inline for exec'd Python functions (#181531)
• Fix tensor subclass construction under torch.compile (#183337)
• Preserve eager torch.full validation for nn.Parameter fill values in Dynamo (#183915)
• Prevent accuracy minifier repro recursion (#184077)
• Fix AOT export with flex attention BlockMask placeholders (#184611)
• Accept extra kwargs in CudagraphsBackend.__call__ (#182989)
• Avoid def forward(self, ..., self, ...) SyntaxError in dynamo_graph_capture_for_export (#185314)
• Fix scoped Chromium event reset (#184973)
• Fix Dynamo binding of overridden function defaults (#184852)
• Fix SAC context_fn clobbered by DDPOptimizer's propagate_metadata (#179496)
• Clear retained fake tensor CUDA constants (#184445)
• Fix Dynamo .grad reads for new in-graph parameters (#184972)
• Graph break on CUDA manual_seed in Dynamo so compiled random calls stay reproducible (#185761)
• Fix functional tensor to_dense no-op (#184586)
• Preserve original tensor strides across activation offload/reload (#186396)
• Graph break on duplicate autograd Function inputs (#184281)
• Raise IndexError in compile mode matching eager mode (#184856)
• Don't error on a skipped frame when fullgraph=True and a non-default stance is set (#183623)
• Set the is_compiling flag for the whole torch.compile session (#184614)
• Fix FxGraphCache pickling of opaque types with cyclic references (#180422)
• Handle missing Windows C++ compiler in shape guard fallback (#185447)

Inductor

• Fix max_autotune BMM correctness with dynamic OpenMP threads (#169128)
• Fix NaN output in CPU LayerNorm by guarding the Welford variance computation (#173989)
• Fix NameError from Python name mangling for user-defined Triton kernels whose names start with double underscores (#176100)
• Fix backward-pass shape mismatch in mix-order reduction for compiled models (#178098)
• Do not apply pointer_range_32 to user-defined Triton kernels on ROCm to avoid compilation crashes (#178541)
• Fix the e8m0_rceil_log2 pattern failing to register on any CUDA device (#178698)
• Fix cpp_wrapper backward compilation failing with a CudaKernelParamCache assertion when set via torch.compile options (#178847)
• Fix FxGraphCachePickler crash on unpicklable pybind11 extension types during cache key computation (#178853)
• Fix C++ compile error when indexing a transposed tensor with an indirect (tensor) index on the CPU backend (#178962)
• Fix TPU Pallas codegen to use subscript indexing for scalar and size-1 buffer accesses (#179099)
• Fix incorrect argmax/argmin indices in the CPP Tile2D vectorized kernel for transposed inputs (#179525)
• Fix user-kernel fusion to use mutation_outputs for the intermediate buffer and add epilogue source to the cache key (#179803)
• Fix missing mask on tl.atomic_add for constant-index stores (#179833)
• Add convolution input/weight dtype-mismatch check to match eager semantics (#179890)
• Raise a catchable error instead of crashing on integer divide-by-zero for fmod/remainder on CPU (#179923)
• Fix stride mismatch for user-visible reduction nodes caused by dislike_padding conflicting with contiguous storage layout (#180197)
• Fix check_bounds forward-reference C++ compile error in the CPU backend (#180212)
• Preserve Triton HIP compile options (waves_per_eu, matrix_instr_nonkdim, kpack) when combo kernels rewrite per-subkernel configs on ROCm (#180277)
• Fix torch.compile crash on cumsum with broadcast input when the scan dimension is 129 or larger (#180369)
• Fix AOTInductor while_loop codegen for the minimal-arrayref interface (wrap ArrayRefTensor inputs before assign) (#180370)
• Fix SM100 addmm CUTLASS codegen and relax addmm tolerances (#180432)
• Fix incorrect storage-offset propagation for as_strided (#180581)
• Filter CUTLASS kernels by fast-accum only on Hopper to avoid NoValidChoicesError for scaled_mm/scaled_grouped_mm with use_fast_accum=True on Blackwell (#180586)
• Make persistent-reduction config selection batch-invariant under deterministic mode (#180832)
• Fix UnicodeDecodeError on Windows when using icx-cl as the CXX compiler by decoding subprocess output with errors='replace' (#180853)
• Avoid emitting tl.float64 in Triton scalar-shape-math codegen on XPU devices without fp64 support (e.g. Intel Arc A770) (#180854)
• Fix silent incorrect results when adaptive_avg_pool2d is fused with downstream flatten + reduction for non-power-of-2 channel sizes (#180898)
• Fix CPU C++ fusion crash on GroupNorm + SDPA + bmm (#181015)
• Fix num_warps on AMD RDNA (wave32) GPUs so it is no longer incorrectly halved; only CDNA (warp_size 64) is halved (#181112)
• Fix unsupported index_expr/kindexpr error when using flex_attention with bias/index expressions (#181207)
• Fix unbacked-symbol bindings assertion in the slice lowering early-return path (#181219)
• Preserve program order of random ops in the scheduler to avoid numerical mismatch vs eager when fallback_random is enabled (#181245)
• Fix Inductor crash (NotImplementedError: View) when a flatten()-produced view is passed as the conv2d bias (#181363)
• Include per-device autocast dtype in the AOTAutograd cache key to prevent reusing a graph compiled under one autocast dtype (e.g. bfloat16) when running under another (e.g. float16) (#181564)
• Fix _to_copy decomposition so float64->float32 transfers to XPU devices without fp64 support no longer generate illegal fp64 buffers/crash (#181607)
• Prevent AutoHeuristic initialization from crashing in non-CUDA (e.g. XPU) environments (#181745)
• Fix AttributeError crash in remove_noop_ops when a node's meta val is a SymInt/SymFloat (auto dynamic shapes) instead of a Tensor (#181752)
• Fix ConcatKernel channels_last contiguous check to handle symbolic shapes, avoiding a data-dependent (DDE) LoweringException (#181845)
• Fix user-defined Triton kernel pointer args with no tl.load/tl.store being incorrectly eliminated (and not allocated) when epilogue fusion is enabled (#181868)
• Properly truncate (downcast then upcast) the accumulator for Triton GEMM epilogue fusions so numerics match eager (#181918)
• Skip non-tensor IR nodes (e.g. process groups) when realizing/marking mutated buffers in _CollectiveKernel.create_inplace (#181930)
• Pass the correct device to print_performance in generated benchmark_compiled_module so timing works on non-CUDA devices (e.g. XPU) (#181957)
• Serialize CompiledFxGraph._original_gm via GraphPickler to fix AOTAutograd/precompile cache serialization crashes for graphs with HOPs that have lifted buffers (e.g. flex_attention with a causal BlockMask) (#182088)
• Avoid raw stream name collisions in generated Inductor code (#182139)
• Disable the overlap-scheduling SPMD check by default to avoid an NCCL hang when ranks diverge on cache hit vs miss (#182281)
• Fix CUDA graphs to support self-overlapping inputs and unbacked shapes via a storage-copy fallback instead of erroring on complex memory overlap (#182524)
• Pass derived sym-int captures through maybe_realize in FlexAttention (#182610)
• Fix AutoHeuristic crash in non-CUDA environments (e.g. XPU and CPU-only builds) (#182614)
• Do not automatically move non-deterministic seed functions to GPU (#182748)
• Fix emulate_precision_casts on CPU C++ codegen so explicit/emulated fp32->fp16->fp32 precision barriers are no longer optimized away (#182882)
• Fix block-descriptor final shape expansion for discontiguous fused pointwise/reduction loads and stores (#182936)
• Fix crash in FLOP counting (count_flops_fx) for HigherOrderOperator targets such as flex_attention during overlap/bucketing (#182992)
• Raise device-mismatch errors for index_add/index_copy/index_reduce instead of silently succeeding under torch.compile (#183007)
• Fix Triton eager_input_vals propagation (#183334)
• Escape backslashes in user-defined Triton kernel source (#183421)
• Fix TMA config lowering block sizes below the heuristic choice, which could trigger an illegal memory access on large tensors (#183438)
• Fix CPU AOTInductor with user-defined Triton kernels when compile-time autotuning is disabled (#183463)
• Fix is_linear_add_bias crash when the bias node argument is a Python float (#183514)
• Fix convolution lowering and backward decomposition crash when a channel dimension is zero (#183539)
• Fix Inductor embedding negative index checks (#183636)
• Disable cudagraphs for input storage mutation (#183645)
• Fix torch.compile crash on cumprod backward by disallowing fusion of split-scan nodes with reductions (#183653)
• Fix CPU argmax logical index for transposed reductions (#183655)
• Fix while_loop backward expanded gradient strides (#183658)
• Fix the SDPA backward constraint for scalar gradient bases (#183662)
• Fix Inductor multi-stream codegen on XPU to emit torch.xpu stream APIs instead of CUDA-only calls (#183693)
• Fix BF16 Inductor failure on Neoverse V3 with GCC 12.4 (#183698)
• Fix the tail reduction suffix width (#183699)
• Support unused lifted SymInts in associative_scan lowering (#183706)
• Guard the fast Triton launcher to CUDA/HIP so XPU correctly falls back to the static launcher (#183707)
• Fix CPU C++ codegen crash from misaligned loop-split groups after fusion (#183715)
• Fix set_.source_Tensor lowering when the source is a view (#183724)
• Fix CPU GEMM k-slicing cache-block indexing (#183733)
• Skip cudagraph capture when the CUDA caching allocator is bypassed (#183780)
• Fix bytes-vs-elements mismatch in select_algorithm input storage check (#183791)
• Use libdevice log for strict Inductor numerics (#183844)
• Add meta registration and dtype check for div_ to match eager type-promotion semantics (#183859)
• Fix handle_synced_deallocation for XPU (#183865)
• Use hipModuleLoadData in StaticCudaLauncher on ROCm (#183926)
• Fix flex_attention autotune logging to use size hints for symbolic dims, avoiding lowering failures under dynamic shapes (#183933)
• Guard the SM100 E8M0 FP8 PTX lowering to NVIDIA so ROCm gfx11 uses the portable bit-manipulation fallback (#183949)
• Enable the Python dispatcher when re-running fake propagation for fallback kernels so custom/backend fake implementations supply correct output layout metadata (#183961)
• Fix a crash in tuned_addmm with max-autotune when the bias is an unrealized view (e.g. a transposed expression) (#183973)
• Compute gelu backward in opmath dtype for improved numerical accuracy (#183985)
• Cap Triton convolution warps for non-1x1 kernels to avoid miscompilation (#183989)
• Fix graph partition signatures to only drop buffers removed during that partition's codegen, preserving globally-removed buffers still needed by later partitions (#184004)
• Handle float8_e4m3fn dequantization on pre-sm89 CUDA devices to match eager bit patterns (#184008)
• Fix the native matmul config numel cap (#184011)
• Use CUDA libdevice for emulate-precision casts to match eager numerics (#184022)
• Fix Inductor compile-worker shutdown so the sidecar's nested worker pool is terminated and shutdown no longer hangs until the parent wait timeout (#184038)
• Fix bucketize/searchsorted on sliced or zero-stride lookup tensors (#184043)
• Fix scaled softmax non-finite semantics (#184046)
• Fix CPU FlexAttention aliased input lowering (#184071)
• Rewrite Triton floating-point self-subtraction to produce exact zero (#184093)
• Treat corrupt local autotune cache entries (bad JSON/invalid UTF-8) as cache misses instead of crashing compilation (#184096)
• Recurse through list/tuple arguments when applying needs_fixed_stride_order so fallback custom ops receive tensor-list inputs in the recorded stride order (#184098)
• Fix CPU Inductor vectorized asinh overflow (#184105)
• Treat low-precision saved-for-backward activations as precision barriers so forward math observes the same truncated value backward will load (#184110)
• Fix int to float8 casts in Inductor Triton codegen (#184115)
• Fix Inductor random fallback for RReLU (#184136)
• Fix scalar tensor lowering for torch.jagged layout to match eager and meta behavior (#184146)
• Preserve out_dtype for scaled_mm by propagating it to MMKernelInputs (#184168)
• Fix Inductor CUDA frexp exponent for non-finite inputs (#184176)
• Fix Inductor cache directory fallback for unmapped users (#184208)
• Guard dilation in convolution_backward lowering so dilated conv2d backward compiles under dynamic shapes (#184224)
• Force persistent reduction for sort in combo kernels (#184227)
• Fix as_strided storage offsets for graph input views (#184232)
• Guard dilation to ints in convolution_backward lowering so the Triton backward conv template no longer references undefined sympy symbols under dynamic shapes (#184255)
• Fix reinplacing through view index_put (#184263)
• Skip CUDA graphs for aten.topk on ROCm/HIP to avoid a rocPRIM memory fault on cudagraph-tree replays (MI350 / ROCm 7) (#184265)
• Fix module-table exhaustion during long exhaustive autotuning (#184285)
• Fix Inductor gather bounds for negative indices (#184287)
• Fix CUDA graph trees when used from Python-created threads (#184357)
• Fix Inductor compile pools hanging when running inside daemonic multiprocessing workers (#184472)
• Fix deterministic selection to correctly detect bound extern (ExternKernelCaller) choices (#184493)
• Fix CPU integer floor-division overflow so INT_MIN / -1 matches eager instead of trapping (#184497)
• Include default dtype in the Inductor FX cache key (#184500)
• Fix Triton compilation failure for scan/cumulative reductions (cumsum/cummax/cummin/cumprod) on unsigned integer dtypes (#184514)
• Fix Inductor fallback aliasing for boxed scalars (#184516)
• Extend convolution input/weight dtype-mismatch checks to all devices (#184518)
• Fall back to the standard lowering path for CUDA FP8 dtypes that Triton cannot compile for the active target (#184521)
• Check constant_offset alignment in TMACompatibilityChecker (#184564)
• Fix acc_type for fp8 dtypes in mm autotuning (use triton_type and accumulate in fp32) (#184591)
• Fix torch.cond subgraph buffer reuse by scoping EfficientPeakEstimate per subgraph during AOT codegen (#184623)
• Fix backward (gradient) support for addcmul_ and addcdiv_ under torch.compile (#184629)
• Fix Inductor loop indexing expression canonicalization (#184650)
• Fix ZeroTensor view with symbolic sizes (#184651)
• Fix invalid assert-message concatenation in CPU cpp_micro_gemm that broke AOTI compilation of AVX512 VNNI/WoQ-Int4 kernels (#184693)
• Refuse TMA for known-unaligned buffers and fall back to regular loads/stores (#184717)
• Record IR reads nested inside constant_args and kwargs in ExternKernel.get_read_writes() to prevent index producers from being incorrectly eliminated (#184751)
• Map float8 types correctly between torch and Triton for Triton template ops to unblock fp8 max-autotune (#184806)
• Fix Py_None reference counting in the C++ wrapper (#184869)
• Emit embedded Triton code and cubin files when loading saved cache artifacts so kernels are not needlessly recompiled (#184953)
• Fix Inductor subprocess compile pool leaking named semaphore resources when torch.compile runs inside a multiprocessing child process (#185070)
• Fix TorchInductor CPU codegen crash during vectorization tiling when an index expression contains relational sub-expressions (e.g. torch.compile(dynamic=True) with torch.func.grad) (#185080)
• Skip TMA codegen for dtypes lacking a tensor-map enum entry instead of crashing at runtime (#185223)
• Synchronize non-blocking device-to-host copies before a CPU read to avoid a race condition (#185252)
• Fix AOTInductor C++ codegen most-vexing-parse failure when a cached output is initialized from a constructor-call expression (#185257)
• Fix CuTeDSL grouped GEMM 'Invalid leading dimension' failure by using get_stride_order for assorted A/B layouts (#185437)
• Fix a NameError in combo kernels when TMA is enabled by handling DelayReplaceLine load expressions (#185514)
• Make MSVC compiler detection on Windows robust against non-zero exit codes and empty output to avoid crashes (#185523)
• Fix a cat lowering assertion error with 1-D statically-empty tensors (#185549)
• Accept interpreted Triton kernels in wrap_triton when TRITON_INTERPRET=1 is set (#185597)
• Fix wait_stream reordering by registering it among the synchronization ops that preserve control dependencies (#185627)
• Fix standalone_compile cache artifact save for grad-enabled graphs whose backward is lowered lazily (#185635)
• Preserve explicit lowp-fp (fp16/bf16) round-trip rounding on CPU under emulate_precision_casts to match eager (#185847)
• Separate value-producing symbolic expressions from indexing so codegen uses the correct dtype width (e.g. int64 arange) in tensor value computation (#185853)
• Gate the narrowing-cast bias-add unfuse on XPU to fix an accuracy regression on ROCm (#185856)
• Apply a layout constraint to the view.dtype lowering when falling back (#185879)
• Fix pickling of custom decomposition tables that broke FX graph caching (#185909)
• Fix FakeTensorUpdater handling of higher-order ops and their subgraphs (#185962)
• Implement get_stride for PermuteView to avoid a NotImplementedError (#185992)
• Preserve NaNs for CPU libm calls by avoiding fast-math rewrites in generated C++ (#186018)
• Fix Inductor crash when logging compute estimations that are SymFloat under dynamic shapes in overlap scheduling (#186054)
• Fix fake dtype inference for weighted torch.bincount so downstream codegen uses the correct dtype (#186077)
• Preserve tensor dtype in the scatter_upon_const_tensor rewrite for low-precision const tensors (#186481)
• Fix Inductor Triton compile/runtime crash from codegen emitting tl.broadcast_to(False, ...) by using tl.full instead (#186621)
• Fix diagonal_scatter backward under torch.compile (#185146)
• Fix a SymInt crash in the overlap scheduler's collective/compute node benchmarking (#186065)
• Allow generator placeholders through control deps (#183863)
• Fix torch.cat axis handling in Inductor pre-grad fusion (#183995)
• Fix stale backed-symbol references in AOTI deferred runtime asserts (#184624)
• Fix AOT FXIR parallel Triton kernel reload (#185134)
• Fix fp32->bf16->fp32 casts being dropped (#180575)
• Fix CUDA atan numerics in Inductor (#183984)
• Fix flip on 0-d tensors in the prims.rev lowering (#184104)
• Fix the softmax decomposition for symbolic empty dims (#184454)

Ahead-Of-Time Inductor (AOTI)

• Fix undefined identifier error in CppWrapper due to false-positive caching (#178147)
• Scale lazy TMA scratch by grid in cpp_wrapper (#182825)
• Fix folded constant offset indexing in AOTI constant buffer update (#179225)
• Add GPU stream synchronization after constant folding in AOTI (#181945)
• Fix use-after-free in pointer_to_optional_list (#183764)
• Fix Windows AOTI self-mmap size seek (#186386)
• Promote scalar literals to tensors for AOTI eager compilation (#185313)
• Use c10::make_scope_exit to avoid exception leaks (#184520)
• Fix deadlock in AOTInductorModelContainer::run() during concurrent constant folding (#181941)
• Track and unload CUmodule handles to prevent GPU code object leaks (#184860)
• Fix MSVC const pointer emission in cpp_wrapper temporary arrays (#179846)
• Fix MSVC path append in kernel context stack compression (#179857)
• Add explicit headers for cpp_wrapper to fix MSVC compilation (#180120)
• Fix inductor AOTI codegen for float('inf')/float('-inf') kernel args (#180297)
• Fix cond subgraph arrayref dispatch with generic lambda (#180558)
• Fix arrayref proxy executor tensor args (#182751)
• Defer Triton compile kickoff out of static init (#182824)
• Fix cpp_wrapper while loop carried mutations (#183657)
• Fix AOTI CUDA device copy allocation (#185634)
• Resolve relative TORCHINDUCTOR_CACHE_DIR (#185723)

Export

• Fix NaN float scalar input handling in export guard codegen and input constraint checks (#180399)
• Fix IndexError during decomposition by also excluding lifted tensor constants and custom objects when identifying user-input placeholders (#181179)
• Fix serialization of predispatch wrapper functions (JVP, vmap) in torch.export save/load (#181263)
• Fix Triton HOP argument packing to preserve kernel argument metadata (#182101)
• Simplify Min/Max of scaled symbolic terms (e.g. Min(128*s, 512*s) reduces to 128*s) so export no longer rejects valid branch guards (#185092)
• Clean up the buffer-registration hook on non-strict export trace failures to avoid a stray AssertionError on later eager buffer assignment (#184956)
• Fix export dynamic shapes for sparse COO inputs (#184993)
• Handle scalar tensor slice bounds in non-strict export (#184925)
• Fix torch.export.load GIL contention during tensor deserialization (#175983)

AOTAutograd

• Fix torch.compile crash with batched matmul in inference_mode (#181913)
• Fix expanded output tangent stride handling (#184519)
• Fix AOT synthetic-base out view returns (#185029)
• Preserve linalg error checks in AOT graphs (#184111)

Composability

• Fix data-dependent errors (unbacked SymInts) in pixel_shuffle, pdist, and reflection/replication padding ops (#183814)
• Add differentiable decomposition for max_pool2d/3d_with_indices (#179104)
• Add validation for invalid MaxUnpool output sizes in meta/decomposition kernels (#184706)
• Add validation for invalid conv2d kernel size in meta and symbolic-shape kernels (#180448)
• Add addmv decomposition dtype validation (#184140)
• Add fill_ meta value-tensor dimensionality validation (#179363)
• Add _weight_int8pack_mm meta inner-dims and scales validation (#179364)
• Fix torch.empty(..., out=...) shape validation under torch.compile (#182349)
• Fix torch.compile wrong output shape for norm() with a negative dim (#182405)
• Fix frac decomposition signed-zero handling (#183640)
• Fix pad_sequence mixed-dtype padding decomposition (#184173)
• Fix istft fake tensor length padding (#184532)
• Fix unfold_backward decomposition for overlapping windows (#183996)
• Fix split Tensor decomposition in Inductor (#184134)
• Fix embedding negative indices in Inductor (#184107)
• Fix flash SDPA activation dtype mismatch between meta and CPU implementations (#185573)
• Preserve 5D nearest upsample decomposition layout (#184553)
• Preserve aten.hardtanh meta semantics for export (#185298)
• Fix _fused_dropout decomposition at keep-probability zero (#184979)
• Fix addmm decomposition crash with out_dtype under FakeTensorMode (#179634)
• Fix torch.split decomposition for empty dim with nonzero split_size (#181493)
• Fix torch.distributions.Gamma under torch.compile (#174090)
• Fix miopen_batch_norm meta save_mean/save_var dtype (#179365)
• Fix reflection/replication pad stride mismatch under torch.compile (#179837)
• Fix symbolic float lp_pool2d compilation (#184000)
• Compare in opmath in hardtanh_backward decomposition (#185840)
• Use torch.sigmoid() in silu_backward decomposition (#185041)
• Fix index_copy decomposition shape checks (#184338)
• Fix LSTM export hidden state metadata (#185716)
• Fix private convolution fake symint handling (#185081)
• Preserve strides in meta zero (#185360)
• Fix runtime check for non_overlapping_and_dense (#186785)
• Update _cslt_sparse_mm meta registration for hipSPARSELt (#181609)
• Fix reflection/replication pad output memory format to match eager behavior on XPU (#184484)
• Handle unbacked dims in folded matmul under FakeTensor (#183397)
• Preserve unbacked batch dims in SDPA tracing under ProxyTensor (#183398)
• Fix mix_order_reduction over-fusion via load count check (#179494)
• Fix torch.compile crash from aten.lift functionalization on an already-functionalized tensor (e.g. randint followed by lift) (#185805)

Quantization

• Fix a segmentation fault when running fp8 qlinear on x86 CPU without AMX, caused by the qlinear primitive cache (#184317)

Foreach

• Fix _foreach_sub under compile (#184421)

ONNX

• Remove incorrect CastLike handling logic from OpRecorder (#182197)
• Fix _rotary_embedding_23_fake_impl stride drift for 3D and 4D inputs (#184854)
• Fix invoke_subgraph export with lifted tensor constants (#182230)
• Fix ONNX dynamic-shape RNN capture (#184872)
• Fix ONNX type promotion overload selection (#185005)

C++ Frontend

• Fix crash with invalid embedding bag mode (#186428)

Build Frontend

• Fix -Winconsistent-dllimport warning in tensor_numpy and tensor_new headers on Windows (#183703)
• Fix missing symbol exports for ValueError/NotImplementedError on Windows (#175340)

CUDA

• Workaround for nvrtcCompileProgram changing locale in CUDA < 12.6.2 (#180569)
• Zero total_weight before accumulating in nll_loss2d (#182082)
• Fix dtype promotion in max/min kernel (#181505)
• Round per-process memory fraction cap to avoid spurious OOM (#179444)
• Fix torch.cuda.ExternalStream(0) to wrap the NULL stream (#183258)
• Fix stream pool collision in conditional graph nodes (#185836)
• Preserve internal precision for native_group_norm in eager (#183946)

cuDNN

• Don't route to cuDNN SDPA for batch size or head dim > 65536 (#180718)

MPS

• Fix LSTM train/eval error (#180873) and LSTM dropout not being applied correctly (#185351)
• Fix multinomial SIGSEGV (#180493)
• Fix async copy failing (#181017)
• Work around a MetalPerformancePrimitives bug for F.linear on M5+ (#181466)
• Fix sliced sum reduction (#182688) and Metal unary operator behavior on large strided tensors (#183447)
• Fix uint32 offset overflow in scatter/gather kernels for strided views crossing 2^32 elements (#182054) and move col2im offset/stride to long to avoid overflow corruption (#185664)
• Fix NaN handling: relu (#183571), softshrink (#183710), hardsigmoid (#183939), cholesky (#184588), and fast::tanh overflow (#186286); return NaN for std/var on empty input (#184510)
• Fix layer_norm_backward silent correctness bug for frozen inputs (#183893)
• Fix Welford reduction codegen with dynamic shapes (#184206), a missing barrier in Welford reductions (#184328), and an Inductor undeclared identifier in multi-pass Welford reductions (#184502)
• Fix SDPA vector kernel mask offset for partially broadcast masks (#184180) and additive mask scaling in prefill attention (#184400)
• Fix _amp_foreach_non_finite_check_and_unscale_ zeroing fp16/bf16 grads (#184286) and stop ignoring grad scale and found_inf (#186360)
• Materialize neg bit in copy_kernel_mps (#184403)
• Fix sort returning out-of-bounds indices for bool/int-max/NaN inputs (#184620)
• Fix generator clone (#185002)
• Fix fill_ on byte-dtype views with misaligned storage offset (#183790)
• Fix complex exp family on the real axis using precise::sincos (#184749)
• Make deviceCount() consistent with Python to fix at::manual_seed() (#164571)
• Fix scale not being cached (#184122)
• Fix attention compilation on nightly (#186399)
• Fix the FFT warning (#183061)
• Disallow bitwise shifts for bool dtype (#186558)
• Fix bucketization speed/correctness (#185622)
• Make index copy fast (#185750)
• Enable and fix large-tensor tests on MPS (#182863)

ROCm

• Support TheRock wheel distribution in _find_rocm_home (#180723)

• Fix warpMergeSortTopK padding sentinel for integer dtypes (#182212)

• Guard ck_group_gemm on USE_ROCM_CK_GEMM (#182615)

• Fix large arange launch (#182657)

• Fix triu/tril for 64-bit indexing for large matrices (#179717)

• Drop dead CUDA/ROCm version gates from tests and helpers (#184879)

• Fix LayerNorm backward kernel for AMD Strix Halo GPUs (#183864)

• Decline CuteDSL scatter_add on ROCm (#185678)

• For HSTU, fix CK flash-attn GQA seqlen_q==1 garbage output (#186434)

• Inductor fixes:

  • Add config flag to disable pointer_range_32 optimization (#179604)
  • Fix maybe_hipify_code_wrapper for bare-token inputs (#183725)
  • Work around file handle limits in StaticCudaLauncher (#183926)
  • Preserve combo kernel HIP compile options (#180277)
  • lookup_device_info is now case-insensitive (#182284)

• Windows

  • Fix MIOpen CTC loss crash on Windows (#179264)
  • Apply per-config HIP optimization flags via CMAKE_HIP_FLAGS (#183856)
  • Fix inconsistent dllimport (#183690, #183324, #183282, #183694)
  • Remove redundant cuSPARSE/hipSPARSE error-string forward declarations (#180327)
  • Remove MSVC flags from CMAKE_HIP_FLAGS (#183365)
  • Don't set USE_ROCM_CK_SDPA on Windows (#183962)

XPU

• Avoid generating fp64 Triton code for XPU devices that do not support fp64 (#180854)
• Fix stream selection for XPU outputs in CurrentWorkStream (#179140)
• Fix reflection and replication padding on XPU to preserve eager-mode output memory format (#184484)
• Fix addmm shape handling and addmv_out stride preservation on XPU (#180985, #178498)
• Fix XPU deallocation handling and XPUPluggableAllocator registration (#183865, #179392)
• Fix numerical instability in logcumsumexp with complex inputs on XPU (#174492)
• Fix SyclExtension Windows builds for oneAPI 2025.3 and later (#170701)
• Fix getGlobalIdxFromDevice(-1) handling on XPU (#181361)

Benchmark

• Fix import-time device loading in benchmark timer (#181716)

TorchScript

• Fix OOB read in MemoryReadAdapter::read (#181193)
• Validate tensor sizes/strides/storage_offset in C++ Unpickler (#183381)
• Fix integer overflow in Unpickler storage size computation (#181310)
• Fix broadcast_shapes op missing in selective builds (#180860)
• Fix binary_cross_entropy SymInt error with dynamic shapes by registering aten::broadcast_shapes as a TorchScript builtin (#180583)
• Fix use-after-free in symbolic-shape runtime fusion guard (#183760)
• Apply bugfixes when enabling Link-Time Optimizations (#180868)

Performance

Autograd

• Use indexed storage for selective activation checkpointing (SAC) to avoid calling policy_fn during recompute (#176455)

Distributed

• Speed up store-based metadata exchange on TCPStore by using multiGet and a server-side barrier, reducing network round trips from 2*(world_size-1) to 1 (#182132)

• Coalesce the NCCL buffer and signal pad into a single symmetric-memory allocation so window registration runs only once (#183344)

• Fuse slice-cat TP collective patterns (#184911)

FX

• Skip GraphModule reconstruction in CSEPass when no common subexpressions were eliminated (#185479)

Dynamo

• Fast path guardless cache hits (#184683)
• Optimize jagged NestedTensor compile guards (#184053)
• Skip Dynamo graph break for scalar-only binary ops when tensorify is enabled (#183584)
• Avoid repr in Dynamo ID_MATCH guard text (#184796)
• Add a pinned memory pool for activation-offloading ao::offload ops to avoid per-tensor cudaHostAlloc overhead (gated by the pinned_memory_pool() context manager) (#186162)

Inductor

• Eliminate unnecessary clones for dtype views in auto_functionalize, improving FP8 KV-cache performance (#173177)
• Fix basic_gnn_sage fp32 single-thread performance regression (#177958)
• Add a tail-size heuristic for CPP tail-loop vectorization to avoid masking/cast overhead in unprofitable cases (#178243)
• Raise the split-reduction threshold to 524K on Blackwell+ to avoid over-splitting moderate reductions like softmax/entropy (#179729)
• Add a pass to replace NVLink collectives with CopyEngine collectives during overlap to remove SM contention with matmuls (#179937)
• Recalibrate the NCCL analytical model for comm/compute-overlap bucketing with auto-detected bandwidths and per-GPU-generation parameters, improving overlap (up to +14% MFU) (#180463)
• Add _FastCudaLauncher, a vectorcall C extension for pre-bound kernel launch that reduces per-launch overhead (#180507)
• Enable PadMM AutoHeuristics by default in deterministic mode, improving deterministic-mode inference speed (~14% on HuggingFace) (#181038)
• Skip catastrophically slow polynomial sympy.gcd on very wide shape expressions, cutting some backward compiles from over 50 minutes to about 6 (#181275)
• Add a 128x256x64 (num_stages=4) matmul config that speeds up large Hopper matmul shapes by ~1.3x and up (#181413)
• Run Inductor joint-graph passes on nested_compile_region (invoke_subgraph) subgraphs created with options, which were previously skipped (#181834)
• Use scaled persistent configs for the Blackwell scaled-mm TMA autotuning heuristic (#182009)
• Generate vectorized loads in score-mods for speedups on contiguous, aligned key/value-dimension loads (#183406)
• Enable reduction-broadcast vertical fusion for block-wise quantization (e.g. MXFP8/MXFP4) by reindexing pointwise nodes before can_fuse_vertical, reducing kernel count (#183521)
• Use int32 indices in grid_sampler_2d lowering on CUDA/XPU when sizes fit, avoiding unnecessary int64 arithmetic (#184269)
• Use the two-step variance path for small non-split CUDA reductions for better performance (#184383)
• Emit evict_first for coalesced last-use loads in persistent reductions (#184395)
• Use CPU-specific realization heuristic defaults to keep moderate pointwise bodies fused (#184411)
• Faster FlexAttention CuteDSL codegen via vectorized mask-mod loads and packed-interval span lowering (#184438)
• Avoid recomputing log in reused CPU pointwise so T5-style softmax bias inputs are materialized once (#184473)
• Use an Inductor-owned CUDA benchmarking path with an L2-sized cache buffer to avoid Triton's fixed 256MB autotune cache allocation (#184479)
• Improve compute/collective overlap scheduling by prioritizing collective prefetch candidates that fill more of the available overlap window (#185186)
• Fuse the decomposed _safe_softmax SDPA math path back into a native scaled_dot_product_attention call (new SFDP patterns 29/30) (#185574)
• Support CUTLASS EVT epilogue fusion through view/reshape between a GEMM and its pointwise consumer (#185796)
• Restore dense MKL-DNN pointwise convolution speed by keeping the forward_inference prop kind only for channels-last/MKLDNN-layout, fixing a ~2x dense-contiguous slowdown (#185997)
• Pre-bucket FSDP collectives in the compile overlap scheduler, merging many per-parameter all-gathers into bandwidth-saturating buckets (#179935)
• Decompose small dot-shaped batched matmuls (bmm) (#183911)

Ahead-Of-Time Inductor (AOTI)

• Parallelize tensor-to-bytes conversion for AOTI weight serialization (#181280)
• Enable shared model loading from a directory to avoid redundant unzipping (#172436)

Composability

• Use torch.var_mean to fuse paired var/mean reductions (#184843)
• Avoid a Triton sort compile-time cliff in create_block_mask (#182745)

C++ Frontend

• Fix reduced-precision rsqrt() double promotion (#181232)

Release Engineering

• Add an operator microbenchmark comparison workflow for PRs (#179476)
• Add a batch-invariant accuracy mode for benchmark perf tests (#180610)

CUDA

• Fix CUDA version check gating warp merge sort (#183527)
• Allow specifying nbits to radix sort in embedding_dense_backward_cuda (#183578) (#183578)
• Vectorize scatter_add with TMA bulk reduce on sm_90+ (Hopper) (#182675)

CPU (x86)

• Speed up random number generation for bfloat16 and half tensors on x86 (roughly 2x faster) by using AVX2/VSX vectorized instructions in the normal_/randn kernel (#179834)

CPU (AArch64)

• Improve bfloat16 transpose performance on AArch64 by adding a dedicated SVE-vectorized transpose_mxn implementation (#174097)
• Add SVE128 dispatch for Arm CPUs (#176256)

MPS

• Fully utilize Philox state in distribution kernels (#182247)
• 2D dispatch for strided unary kernels (#185291)
• Templatize Im2Col to regain performance when 32-bit indexing suffices (#185860)
• Faster norms (#186076)
• Flatten 5D tensors to 4D in batch_norm for performance (#180335)

ROCm

• Set MIOPEN_FIND_MODE=FAST in op benchmark CI to prevent cold-cache timeout (#179795)
• Fix FlexAttention fp16 default num_warps (8 -> 4) on AMD GPUs (#180720)
• Fix perf regression in index_add and index_reduce (#182533)
• Add no-fence optimization to the JIT reduce template (#176812)
• Add target-dependent FlexAttention default forward configs (#181283)

XPU

• Add oneDNN-backed nn.LSTM inference support on XPU, replacing the per-timestep fused-cell path with a sequence-level primitive (#185531)

Sparse Frontend

• Optimize the layernorm + sigmoid epilogue by providing an ideal input shape to layernorm after sparse matmul (#183472)

Documentation

Python Frontend

• Fix out_dtype signatures for bmm, mm, addmm, baddbmm (#179182)
• Add CUDA SDPA determinism section to randomness.rst (#182551)
• Convert stub docs pages to MyST Markdown (#183498)
• Expose nonzero_static docs (#185674)
• Note expand materialization costs (#185400)
• Fix torch.trapz documentation signature to match torch.trapezoid (#180571)
• Clarify that torch.normal does not support integer dtypes (#180580)
• Document actual keyword argument names for tensor_split (#182075)

Dataloader Frontend

• Document previously undocumented functions in the torch.utils.data documentation (#182682)

Optimizer

• Use \gamma consistently for lr in Adafactor math blocks (#184773)
• Clarify ReduceLROnPlateau threshold_mode behavior (#180638)

Distributed

• Improve the wording of the batch_isend_irecv documentation (#183022)

• Add documentation for 8 functions in distributed.md (#182544)

• Add TorchComms backend documentation to torch.distributed (#182711)

• Add a distributed training integration guide for out-of-tree accelerators (#182308)

• Convert the torchelastic elastic/quickstart.rst from reStructuredText to MyST Markdown (#182569)

• Convert the RPC rpc/rref.rst from reStructuredText to MyST Markdown (#182877)

• Clarify that --node-rank is only used with static rendezvous (#182374)

• Add API reference documentation for previously-undocumented functions in torch.distributed.rpc (#183393)

• Add API reference documentation for previously-undocumented functional optimizer APIs in torch.distributed.optim (#182871)

Distributed Checkpointing

• Document previously undocumented functions in the torch.distributed.checkpoint API reference (#182887)

DTensor

• Clarify default dtype behavior in the DTensor.redistribute docstring (#181671)
• Document undocumented functions in distributed.tensor.parallel.md (#182876)

Distributed FSDP2

• Document previously undocumented functions in distributed.fsdp.fully_shard (#182866)

Profiler

• Remove references to _KinetoProfile in public docs (#180672)

Export

• Fix typos in attention bias, activation, and dataloader docs (#184244)

ONNX

• Remove stale Caffe2 references from ONNX TorchScript exporter docs (#180498)

C++ Frontend

• Fix typos in export wrapper docstring and transformer module comment (#181972)

Developers

Optimizer

• Add optimizer reparameterization helper for non-strict tracing (#181643)

Autograd

• Expose the PrivateUse1 backend name as an alias in DeviceType (#184835)
• Show the forward op name instead of the backward node name in autograd anomaly/error messages (#180383)

Distributed

• Add a missing include to GlooDeviceFactory.cpp (#182800)
• Fix a missing #include <cuda.h> in CUDASymmetricMemoryTypes.hpp (#183704)

FX

• Add a fast path in GraphPickler.reducer_override for primitive types (#181602)

Inductor

• Enable the Inductor SYCL-TLA standalone runner on XPU (developer benchmarking tooling) (#174958)
• Add an optional Torch-Profiler-based Inductor autotuning benchmarker (off by default, env-gated) for more accurate timing of short-duration kernels (#175097)
• Add a dedicated custom-pass class for _pre_fusion_custom_pass to match its scheduler-node signature (#179050)
• Include _post_fusion_custom_pass in the FX graph cache hash (#179051)
• Share storage for aliased inputs in the generated benchmark_compiled_module repro code to avoid OOM (#181119)
• Enable frame pointers by default in AOTInductor shared libraries so profilers get accurate stacks (#181358)
• Fix decomposition and lowering debug info (overload names) for the compiler bisector, including across subprocesses (#181452)
• Add DOT graph dumps for Inductor debug traces (#184039)
• Fix the Inductor save_args debug/repro tooling to serialize fake tensors and symbolic metadata so it works with dynamic shapes (#184428)
• Fix the device argument in the generated combo-kernel benchmark script (#184868)

Ahead-Of-Time Inductor (AOTI)

• Add C-ABI-safe V2 interface for MinimalArrayref (#179483)
• Add C-ABI-safe V2 interface for UpdateConstantsMap (#180533)
• Add C-ABI-safe ExtractConstantsMapForEach (#183030)
• Add C-ABI-safe UpdateConstantBufferPairs (#183031)
• Add C-ABI-safe UpdateConstantBufferFromCpuPairs (#183032)
• Add C-ABI-safe UpdateInactiveConstantBufferPairs (#183033)
• Add C-ABI-safe AOTInductorModelCreateV2 (#185729)

Composability

• Change most HOPs to use @register_fake instead of py_impl(FakeTensorMode) (#186247)
• Genericize graphsafe RNG in aot_autograd to support non-CUDA device backends (#182391)
• Mark graphsafe_run_with_rng_state as cacheable for FxGraphCache (#185562)

Release Engineering

• Migrate the build to CMake / scikit-build-core: move NCCL checkout, source-file mirroring, header wrapping, compile_commands merging, and the torch._C extension/version.py build out of setup.py and into CMake (#181450, #177642, #177643, #177644, #180243)
• Drop the setuptools concat_license_files hook and adopt PEP 639 license-files; replace deprecated distutils usage (#180237, #182120)
• Install libaotriton_v2.so via cmake install for wheel packaging (#180242)
• Embed the macOS OpenMP runtime in PostBuildSteps (#180239)

XPU

• Respect MKLROOT, CMPLR_ROOT, and ONEAPI_ROOT from setvars.sh in FindMKL.cmake so custom oneAPI installs are detected correctly (#183506)

TorchScript

• Add torch._C._jit_replace_submodule to swap submodules in scripted modules while updating parent types and remapping referenced types across graphs (#180296)
• Use TORCH_CHECK instead of AT_ASSERT for single input/output node helpers, producing clearer error messages (#181282)
• Expose overlapsWithUsedNodes and getVmap from SubgraphRewriter (#183333)