Highlights

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

Windows wheel builds with CUDA 12.9.1 stack overflow during build (#156181)

Due to a bug introduced in CUDA 12.9.1, we are unable to complete full Windows wheel builds with this version, as compilation of torch.segment_reduce() crashes the build. Thus, we provide a wheel without torch.segment_reduce() included in order to sidestep the issue. If you need support for torch.segment_reduce(), please utilize a different version.

Backwards Incompatible Changes

CUDA Support

Removed support for Maxwell and Pascal architectures with CUDA 12.8 and 12.9 builds (#157517, #158478, #158744)

Due to binary size limitations, support for sm50 - sm60 architectures with CUDA 12.8 and 12.9 has been dropped for the 2.8.0 release. If you need support for these architectures, please utilize CUDA 12.6 instead.

Python Frontend

Calling an op with an input dtype that is unsupported now raises NotImplementedError instead of RuntimeError (#155470)

Please update exception handling logic to reflect this.

In 2.7.0

try:
    torch.nn.Hardshrink()(torch.randint(0, 5, (10,)))
except RuntimeError:
    ...

In 2.8.0

try:
    torch.nn.Hardshrink()(torch.randint(0, 5, (10,)))
except NotImplementedError:
    ...

Added missing in-place on view check to custom autograd.Function (#153094)

In 2.8.0, if a custom autograd.Function mutates a view of a leaf requiring grad, it now properly raises an error. Previously, it would silently leak memory.

   class Func(torch.autograd.Function):
        @staticmethod
        def forward(ctx, inp):
            inp.add_(1)
            ctx.mark_dirty(inp)
            return inp

        @staticmethod
        def backward(ctx, gO):
            pass

    a = torch.tensor([1.0, 2.0], requires_grad=True)
    b = a.view_as(a)
    Func.apply(b)

Output:

Version 2.7.0

Runs without error, but leaks memory

Version 2.8.0

RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation

An error is now properly thrown for the out variant of tensordot when called with a requires_grad=True tensor (#150270)

Please avoid passing an out tensor with requires_grad=True as gradients cannot be computed for this tensor.

In 2.7.0

a = torch.empty((4, 2), requires_grad=True)
b = torch.empty((2, 4), requires_grad=True)
c = torch.empty((2, 2), requires_grad=True)
# does not error, but gradients for c cannot be computed
torch.tensordot(a, b, dims=([1], [0]), out=c)

In 2.8.0

a = torch.empty((4, 2), requires_grad=True)
b = torch.empty((2, 4), requires_grad=True)
c = torch.empty((2, 2), requires_grad=True)
torch.tensordot(a, b, dims=([1], [0]), out=c)
# RuntimeError: tensordot(): the 'out' tensor was specified and requires gradients, and
# its shape does not match the expected result. Either remove the 'out' argument, ensure
# it does not require gradients, or make sure its shape matches the expected output.

torch.compile

Specialization of a tensor shape with mark_dynamic applied now correctly errors (#152661)

Prior to 2.8, it was possible for a guard on a symbolic shape to be incorrectly omitted if the symbolic shape evaluation was previously tested with guards suppressed (this often happens within the compiler itself). This has been fixed in 2.8 and usually will just silently "do the right thing" and add the correct guard. However, if the new guard causes a tensor marked with mark_dynamic to become specialized, this can result in an error. One workaround is to use maybe_mark_dynamic instead of mark_dynamic.

See the discussion in issue #157921 for more context.

Version 2.7.0

import torch

embed = torch.randn(2, 8192)
x = torch.zeros(8192)

torch._dynamo.mark_dynamic(x, 0)

@torch.compile
def f(embedding_indices, x):
    added_tokens_mask = torch.where(x > 10000, 1, 0)
    ei = torch.narrow(embedding_indices, 1, 0, x.size(0))
    return ei.clone()

f(embed, x)

Version 2.8.0

import torch

embed = torch.randn(2, 8192)
x = torch.zeros(8192)

torch._dynamo.maybe_mark_dynamic(x, 0)

@torch.compile
def f(embedding_indices, x):
    added_tokens_mask = torch.where(x > 10000, 1, 0)
    ei = torch.narrow(embedding_indices, 1, 0, x.size(0))
    return ei.clone()

f(embed, x)

Several config variables related to torch.compile have been renamed or removed

• Dynamo config variable enable_cpp_framelocals_guard_eval has changed to no longer have any effect (#151008).
• Inductor config variable rocm.n_max_profiling_configs is deprecated (#152341). Instead, use ck-tile based configs rocm.ck_max_profiling_configs and rocm.ck_tile_max_profiling_configs.
• Inductor config variable autotune_fallback_to_aten is deprecated (#154331). Inductor will no longer silently fall back to ATen. Please add "ATEN" to max_autotune_gemm_backends for the old behavior.
• Inductor config variables use_mixed_mm and mixed_mm_choice are deprecated (#152071). Inductor now supports prologue fusion, so there is no need for special cases now.
• Inductor config setting descriptive_names = False is deprecated (#151481). Please use one of the other available options: "torch", "original_aten", or "inductor_node".
• custom_op_default_layout_constraint has moved from inductor config to functorch config (#148104). Please reference it via torch._functorch.config.custom_op_default_layout_constraint instead of torch._inductor.config.custom_op_default_layout_constraint.
• AOTI config variable emit_current_arch_binary is deprecated (#155768).
• AOTI config variable aot_inductor.embed_cubin has been renamed to aot_inductor.embed_kernel_binary (#154412).
• AOTI config variable aot_inductor.compile_wrapper_with_O0 has been renamed to compile_wrapper_opt_level (#148714).

Added a stricter aliasing/mutation check for HigherOrderOperators (e.g. cond), which will explicitly error out if alias/mutation among inputs and outputs is unsupported (#148953, #146658).

For affected HigherOrderOperators, add .clone() to aliased outputs to address this.

Version 2.7.0

import torch

@torch.compile(backend="eager")
def fn(x):
    return torch.cond(x.sum() > 0, lambda x: x, lambda x: x + 1, [x])

fn(torch.ones(3))

Version 2.8.0

import torch

@torch.compile(backend="eager")
def fn(x):
    return torch.cond(x.sum() > 0, lambda x: x.clone(), lambda x: x + 1, [x])

fn(torch.ones(3))

guard_or_x and definitely_x have been consolidated (#152463)

We removed definitely_true / definitely_false and associated APIs, replacing them with guard_or_true / guard_or_false, which offer similar functionality and can be used to achieve the same effect. Please migrate to the latter.

Version 2.7.0

from torch.fx.experimental.symbolic_shapes import definitely_false, definitely_true

...
if definitely_true(x):
  ...

if definitely_false(y):
  ...

Version 2.8.0

from torch.fx.experimental.symbolic_shapes import guard_or_false, guard_or_true

...
if guard_or_false(x):
  ...

# alternatively: if guard_or_false(torch.sym_not(y))
if not guard_or_true(y):
  ...

torch.export

torch.export.export_for_inference has been removed in favor of torch.export.export_for_training().run_decompositions() (#149078)

Version 2.7.0

import torch

...
exported_program = torch.export.export_for_inference(mod, args, kwargs)

Version 2.8.0

import torch

...
exported_program = torch.export.export_for_training(
    mod, args, kwargs
).run_decompositions(decomp_table=decomp_table)

Switched default to strict=False in torch.export.export and export_for_training (#148790, #150941)

This differs from the previous release default of strict=True. To revert to the old default behavior, please explicitly pass strict=True.

Version 2.7.0

import torch

# default behavior is strict=True
torch.export.export(...)
torch.export.export_for_training(...)

Version 2.8.0

import torch

# strict=True must be explicitly passed to get the old behavior
torch.export.export(..., strict=True)
torch.export.export_for_training(..., strict=True)

ONNX

Default opset in torch.onnx.export is now 18 (#156023)

When dynamo=False, the default ONNX opset version has been updated from 17 to 18. Users can set opset_version to explicitly select an opset version.

Version 2.7

# opset_version=17
torch.onnx.export(...)

Version 2.8

# To preserve the original behavior
torch.onnx.export(..., opset_version=17)

# New: opset_version=18
torch.onnx.export(...)

The JitTraceConvertStrategy has been removed (#152556)

Support for JIT traced and scripted modules in the ONNX exporter when dynamo=True has been removed. You are encouraged to export an nn.Module directly, or create an ExportedProgram using torch.export before exporting to ONNX.

onnxscript>=0.3.1 is required for the dynamo=True option (#157017)

You must upgrade onnxscript to version 0.3.1 or higher for it to be compatible with PyTorch 2.8.

Build Frontend

Removed the torch/types.h include from Dispatcher.h (#149557)

This can cause build errors in C++ code that implicitly relies on this include (e.g. very old versions of torchvision).

Note that Dispatcher.h does not belong as an include from torch/types.h and was only present as a short-term hack to appease torchvision. If you run into torchvision build errors, please update to a more recent version of torchvision to resolve this.

Upgraded DLPack to 1.0 (#145000)

As part of the upgrade, some of the DLDeviceType enum values have been renamed. Please switch to the new names.

Version 2.7.0

from torch.utils.dlpack import DLDeviceType

d1 = DLDeviceType.kDLGPU
d2 = DLDeviceType.kDLCPUPinned
...

Version 2.8.0

from torch.utils.dlpack import DLDeviceType

d1 = DLDeviceType.kDLCUDA  # formerly kDLGPU
d2 = DLDeviceType.kDLCUDAHost  # formerly kDLCPUPinned
...

NVTX3 code has been moved from cmake/public/cuda.cmake to cmake/Dependencies.cmake (#151583)

This is a BC-breaking change for the build system interface. Downstream projects that previously got NVTX3 through cmake/public/cuda.cmake (i.e.. calling find_package(TORCH REQUIRED)) will now need to explicitly configure NVTX3 support in the library itself (i.e. use USE_SYSTEM_NVTX=1). The change is to fix the broken behavior where downstream projects couldn't find NVTX3 anyway due to the PROJECT_SOURCE_DIR mismatch.

Version 2.7.0:

• A downstream project using -DUSE_SYSTEM_NVTX would be able to find NVTX3 and torch::nvtx3 via PyTorch's cmake/public/cuda.cmake logic.
• A downstream project NOT using -DUSE_SYSTEM_NVTX would encounter build errors with CUDA 12.8 or above.

Version 2.8.0:

• A downstream project using -DUSE_SYSTEM_NVTX will not be able to find NVTX3 or torch::nvtx3 via PyTorch's cmake/public/cuda.cmake. The downstream project now needs to explicitly find NVTX3 and torch::nvtx3 by implementing the same logic in PyTorch's cmake/Dependences.cmake.
• A downstream project NOT using -DUSE_SYSTEM_NVTX will proceed building without NVTX unless another part of the build process re-enables NVTX.

Deprecations

MPS support for MacOS Ventura will be removed in 2.9

PyTorch 2.8 is the last release that will support GPU acceleration on MacOS Ventura. In the next release (2.9), MacOS Sonoma (released in Sept. 2023) or above will be required to use the MPS backend.

torch.ao.quantization is deprecated and will be removed in 2.10 (#153892)

To migrate:

• Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic)
  • Weight-only and dynamic quantization: use torchao eager mode quantize_.
  • Static quantization: use torchao PT2E quantization.
• FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx, torch.ao.quantization.quantize_fx.convert_fx): use torchao PT2E quantization (torchao.quantization.quantize_pt2e.prepare_pt2e, torchao.quantization.quantize_pt2e.convert_pt2e).

Note that PT2E quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e). See https://github.com/pytorch/ao/issues/2259 and https://docs.pytorch.org/ao/main/quick_start.html#pytorch-2-export-quantization for more details.

The dynamo=False (current default) option for torch.onnx.export is deprecated (#152478, #155580)

The default will be dynamo=True starting from PyTorch 2.9. You are encouraged to migrate to use the dynamo=True option in torch.onnx.export. This flag makes torch.export.export the default export path, replacing TorchScript.

To maintain the old behavior, set dynamo=False explicitly. You are encouraged to also experiment with the fallback=True option that will make the exporter fall back to the dynamo=False path if there are errors.

New Features

CUDA

• Support capture of event record and wait in CUDAGraphs for timing (#155372)

torch.compile

Dynamo

• Added support for hierarchical compilation via nested_compile_region (#156449)
• Allow guards to be dropped with custom filter functions via guard_filter_fn (#150936)
• Added dont_skip_tracing decorator to skip over most Dynamo skipfiles rules (#150586)

Inductor

• Added support for mapping a Dynamo graph to multiple different Inductor graphs, which can be optimized separately (#147648, #147038)

torch.export

• Introduced draft-export, an export variant designed to consistently produce a graph and generate a debugging report of issues encountered during tracing (#152637, #153219, #149465, #153627, #154190, #155744, #150876, #150948, #151051, #151065, #150809, #151797)

Ahead-Of-Time Inductor (AOTI)

• Added support for TorchBind objects (#150196, #154265)
• Added config variable aot_inductor.model_name_for_generated_files for specifying model name (#154129)

MPS

• MPSInductor: torch.compile for Apple GPUs (#150121, #149342, #151449, #151754, #149687, #149180, #149221, #153598, #152788, #153787, #152214, #151152, #155891, #154578, #151272, #151288, #153997, #151871, #153362, #156566, #150661, #153582)

ONNX

• Added new strategy draft_export (#147529, docs) to provide debugging information upon data-dependent / constraint errors when obtaining an ExportedProgram with torch.onnx.export

• Added support for symbolic operators in the dynamo=True export path (#148905, #149678, #150038, docs). Two operators torch.onnx.ops.symbolic and torch.onnx.ops.symbolic_multi_out are defined to allow you to create symbolic ONNX operators directly in your PyTorch models. You can use them in a forward method:

def forward(self, x: torch.Tensor) -> torch.Tensor:
    # Optionally use is_in_onnx_export to control the behavior during onnx export

    if torch.onnx.is_in_onnx_export():
        # Create a symbolic ONNX operator with the name "CustomOp" in the "custom_domain" domain.
        # The output tensor will have the specified dtype and shape
        return torch.onnx.ops.symbolic(
            "custom_domain::CustomOp",
            (x,),
            dict(attr_key="attr_value"),
            dtype=x.dtype,
            shape=x.shape,
            version=1,
        )
    else:
        return x

Python Frontend

• Added Generalized Pareto Distribution (GPD) (#135968)

Quantization

• Introduced torch.float4_e2m1fn_x2 dtype (#148791)

XPU

• Support Intel distributed backend (XCCL) (#141856)
• Support SYCL kernels through C++ extension (#132945)

Improvements

Build Frontend

• Removed outdated warning about TORCH_CUDA_ARCH_LIST (#152715, #155314)
• Made Eigen an optional build dependency (#155955)
• Updated CUTLASS to 3.9.2 (#152779)

Composability

• Enhanced custom op support with serializable op profiles and fake registration overrides (#151817, #150807, #150806)

C++ Frontend

• Exposed bicubic mode for torch::nn::functional::grid_sample (#150817)

CUDA

• Introduced no_implicit_headers mode for load_inline() on custom CUDA extensions (#149480)
• Support large batch sizes in SDPA memory-efficient attention backend (#154029, #154663)
• Fixed invalid indexing in SDPA memory-efficient attention backward (#155397)
• Support SDPA attention backends on sm121 (DGX Spark) (#152314)
• Added FP8 row-wise scaled-mm for sm12x (GeForce Blackwell) (#155991)

cuDNN

• Updated cuDNN frontend version to 1.12 (#153888)

Distributed

c10d

• Enhanced TCPStore with clone and queuing features (#150966, #151045, #150969, #151485)
• Added a collective time estimator for NCCL comms (#149343)
• Made getDefaultBackend more fault tolerant without relying on exceptions (#149152)
• Specified the default PyTorch Distributed backend for MPS (#149538)
• Supported masterListenFd in TCPStoreLibUvBackend (#150215)
• Used shared stores in gloo (#150230)
• Improved FR dump robustness with all watchdog broadcast wait, reduce dump timeout and shrinked mutex range (#150652, #151329, #155949)
• Added the record of each individual collective being coalesced in FR (#151238)
• Implemented safer book-keeping of NCCL communicators (#150681)
• Clarified behavior of TORCH_NCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK (#150682)
• Registered also future allocations in mempool with NCCL (#150684)
• Avoided computing global_rank when group_rank is used (#151373)
• Exposed NCCL communicator from ProcessGroupNCCL via an unsafe API (#152496)
• Added split sizes info dump for uneven all2all bw calculation (#151438)
• Made FR vendor neutral so that other backends can use it and integrated into gloo. (#152585, #152563, #154929, #152614)
• Added needs_contiguous_strides tag in functional collective (#153399, #153523)
• Allowed split_group to work with non-nccl backends (#152175)
• Simplified new_subgroups() by using new_subgroups_by_enumeration() (#153843)
• Made only current thread allocate to pool in ProcessGroupNCCL (#153990)
• Enabled using c10::Half for gloo (#153862)
• Released GIL in PG destructor (#154976)
• Enhanced get_process_group_ranks() to accept group=None (#154902)
• Skipped updating the default device distributed backend if already registered (#155320)
• Enabled querying the build and runtime NCCL versions (#156305)
• Disabled NCCL NVLS when using deterministic mode (#156381)
• Made init_process_group support index-only device id (#156214)
• Support enabling / disabling NaN detector per-ProcessGroup (#151723)
• Added support for reduce_scatter and ReduceOp::AVG in ProcessGroupGloo (#149781, #149869)
• Added FP8 support in ProcessGroupNCCL (#152706)
• Added ibverbs backend in gloo and enabled gloo CUDA when used with a backend that supports GPUDirect (#153015, #153425, #153406)

DeviceMesh

• Improved device selection logic (#150897)

DistributedDataParallel (DDP)

• Added one option to allow skipping all reduce unused parameters (#151503)
• Added check on received data to avoid segfault in the DDP reducer (#152143)
• Propagated use_python_reducer to C++ reducer (#152735) DistributedStateDict (DSD)
• Supported non-tensor-data write_size in planner write items (#149699)
• Skip popping meta device tensors (#153185)

DTensor

• Made StridedShard support uneven sharding (#150490)
• Added op support for torch.cumsum (#151071)
• Added DTensor redistribute fwd/bwd datatype conversion to enable SimpleFSDP mixed precision training (#150740)
• Added rich support to torch.distributed.tensor.debug.visualize_sharding (#152027)

FullyShardedDataParallel2 (FSDP2)

• Added PrivateUse1 backend in FSDP collectives and device type to pre forward hook (#147260, #149487)
• Added set_reshard_after_forward (#149103)
• Allowed different dtypes for no grad model params (#154103)
• Respected reshard_after_forward=True for root model and kept root unsharded when not specifying reshard_after_forward (#154704, #155319)
• Allowed forcing FSDP2 to always use SUM reductions (#155915)
• Made assert on all_reduce_event only if it's not CPU device (#150316)
• Enabled NCCL zero-copy (user buffer registration) for FSDP2 (#150564)

Pipeline Parallelism

• Added schedule visualizer (#150347)
• Allowed unused kwargs in ZB path (#153498)
• Added get_pipeline_order() for Gpipe and 1F1B (#155935)

ShardedTensor

• Added support for 0-size ShardedTensor and recalculated metadata from all_gather (#152583)

TensorParallel

• Added a ParallelStyle PrepareModuleInputOutput (#150372)

torchelastic

• No shutdown of rendezvous on leaving workers (#152525)

torch.compile

Dynamo

• Improved tracing support for python sets, tensor subclasses with __torch_function__, and namedtuple subclasses (#153150, #149792, #153982)
• Eliminated all Compiled Autograd dynamic shapes recompiles for compile time reduction (#151962, #152119, #151962, #149707, #149709, #148799, #148801)
• Added reason field to torch.compiler.disable (#150341)
• Removed lru_cache warnings for functions in the top-level torch namespace (#157718)

Inductor

• Added block sparse support for FlexAttention on CPU (#147196)
• Introduced new config settings:
  • aot_inductor.custom_ops_to_c_shims and aot_inductor.custom_op_libs: allow for specifying custom op C shim (#153968)
  • max_fusion_buffer_group_pairwise_attempts: limits fusions to specified node distance (#154688)
  • cuda.cutlass_enabled_ops: controls CUTLASS operation selection (#155770)
  • triton.cudagraph_capture_sizes: allows specifying certain shapes for which to capture CUDAGraphs; skips CUDAGraphs for other shapes (#156551)
  • use_static_cuda_launcher: enables launching compiled triton statically to improve cold start times (#148890)
  • assume_unaligned_fallback_output: allows inductor to track unaligned outputs (#150777)
  • cuda.cutlass_tma_only: controls whether or not to only use TMA-compatible kernels in CUTLASS (#152815)
  • static_launch_user_defined_triton_kernels: enables statically launching user defined triton kernels (#153725)
  • precompilation_timeout_seconds: controls the timeout on precompilation (#153788)
  • disable_decompose_k: disables new DecomposeK GEMM Kernels (#154421)
  • min_num_split: sets the minimum number of splits in a split reduction (#155941)
  • max_autotune_flex_search_space: allows specifying the size of the search space for flex attention autotuning (#156307)
• Introduced environment variable LOG_AUTOTUNE_RESULTS for autotune log (#156254)
• Improved numerical stability of CPU Welford reduction for normalizations (#145061)

torch.export

• Improved handling of builtin ops (min, max, math.pow) (#151348)
• Added min/max ranges for dim hints (#149590)
• Allow registering normal classes to pytree.register_dataclass (#147752)
• Allow specifying integer inputs as dynamic (#151842)
• Inline jit.scripted functions in export (#155180)
• Pretty printing for graph signature (#149710)

Ahead-Of-Time Inductor (AOTI)

• Support for device-side TMA (#157241)
• Added num_runners to AOTIModelPackageLoader (#149364)

FX

• Updated codegen compare op to == (#150611)
• Map names to operand indices when const folding submodules (#150692)
• Improved stacktrace when tracing (#151029, #155486)
• Support edge dialect ops in normalize_function (#143689)
• Fixed path naming in minifier (#153130)
• Added graph_code_verbose_log artifact for FX passes (#153775)
• Improved cache key graph printing performance (#151928)
• Added flag to fx.passes.split_module to normalize input names (#157793)

Linear Algebra Frontend

• Add tensor overlap check for cross (#154999)

MPS

• Added support for a number of torch.special operations as well as index_copy, hardshrink, rsub, col2im, and isin (#149174, #149203 #149123, #149368, #149378, #149563, #149687, #149705, #149783, #149407/#149680, #150279, #151754, #153786, #154326, #155304, #156263, #155382, #154010, #149816, #152282, #156090, #150060, #151600, #155002, #154671)
• Extended dtype support for:
  • index_put with half precision floats (#151869)
  • ConvTranspose3D with FP32 and complex (#154696)
  • log1p and sigmoid with int64 (#151791)
• Compute activation kernels at float precision (#155735)

Nested Tensor (NJT)

• Fixed contiguity in NJT string representation (#153529)

torch.nn

• Added warning for module full backward hook when no input requires gradient (#155339)
• Added Half support for weight_norm on CPU (#148878)

ONNX

• Updated ONNX to 1.18 (#152200)
• Added support for opsets (18-23) when dynamo=True (#149901, #154596)
• Added float4 support (#151069, #156353)
• Added support for ONNX operators Attention-23 and RotaryEmbedding-23 as native PyTorch ops (#156431, #156367, #154745)
• Added support for torch.scan (#154513)
• Added support for 0/1-sized example inputs on dynamic dimensions (#155717)
• Add group_norm support from opset 21 (#152138)
• Added asdict method to VerificationInfo class (#151024)
• Support running bfloat16 models with ONNX Runtime (#149646)
• Updated ONNX program doc formatting and improve robustness (#151623)
• Updated dynamic_shapes behavior to use torch.export.dim.DYNAMIC (#153065)
• Set the name of the producing node using the value name (#155413)
• Improved support for symbolic operators sym_float, sym_not, sym_min, sym_max (#153200, #152111, #152196)

Optimizer

• Added TensorLR variant for fused Adagrad on CPU (#153078)
• Convert tensor lr to 0-dim as needed for the optimizer to normally work (#145674)
• Added lr_lambda type check in MultiplicativeLR (#151973)

Profiler

• Added support for on-demand memory snapshot (#150559)
• Added PT2 compile context to visualizer (#152862)
• Added PT2 to memory snapshot (#152707)
• Added flag to toggle global and local callbacks for annotations (#154932)
• Pass overload names to Kineto (#149333)
• Set duration to -1 for unfinished CPU events (#150131)
• Start at index with most events (#154571)

Python Frontend

• Introduced torch.AcceleratorError (#152023)
• Implemented Size.__radd__() (#152554)
• Updated get_default_device() to also respect torch.device context manager (#148621)

Quantization

• Improved x86 PT2E quantization support with new uint8 ops (pointwise mul / add / add_relu and batch_norm2d), qconv1d-relu fusion, and lowering pass (#151112, #152411, #152811, #150751, #149708)
• Support boolean tensor for torch.fused_moving_avg_obs_fake_quant on CUDA (#153699)

Release Engineering

• Updated gcc11 to gcc13 in manylinux images (#152825, #152825, #150635, #158445)
• Updated to cmake 3.27.2 (#154783, #150549, #153380)

ROCm

• Allow user to override default flags for cpp_extension (#152432)
• Enabled support for sparse compressed mm/bmm/addmm (#153262)

Sparse Frontend

• Enabled sparse compressed tensor invariant checks for PrivateUse1 extension (#149374)

torch.func

• Add batching rules for ops: torch.Tensor.scatter_add_ (#150543), torch.matrix_exp (#155202)

XPU

• Support safe softmax, GQA, fp32 causal mask for SDP and increase maximum head dim from 256 to 576 on Intel GPU (#151999, #150992, #152091)
• Add memory reporting to Memory Profiler for Intel GPU (#152842)
• Support Intel GPU profiler toggle functionality (#155135)
• Support distributed memory tracker integration for Intel GPU (#150703)
• Improved error handling and reporting in Intel GPU CMake files (#149353)
• Support embed_cubin and multi_arch_kernel_binary options in AOTI for Intel GPU (#154514, #153924)
• Added generic and Intel GPU specific Stream and Event in UserDefineClass (#155787)
• Support int4 WOQ GEMM on Intel GPU (#137566)

Bug Fixes

Build Frontend

• Support builds with CMake-4.x (#150203)
• Fixed fbgemm build with gcc-12+ (#150847)
• Force build to conform to C++ standard on Windows by adding /permissive- flag (#149035)

Composability

• Fixed support for 1-element tuple returns from custom ops (#155447)
• Avoid overflow in torch.norm for scalar input (#144073)

CPU (x86)

• Fixed apparent copy-paste bug in log_softmax reduced-precision fp kernel (#156379)

CUDA

• Fixed deterministic indexing with broadcast (#154296)
• Fixed torch.backends.cuda.matmul.allow_fp16_accumulation crash when using cuBLASLt (#153083)
• Enable AsyncMM on Blackwell (#153519)
• Fixed torch.cuda.MemPool for multithreaded use-cases (#153356)
• Fix to avoid calling sum() on a default-constructed gamma / beta in layer_norm (#156600)
• Avoid hangs by erroring out for negative offsets or K=0 in grouped GEMMs (#153226)
• Don't error out in empty_cache under mempool context (#158180)

Distributed

c10d

• Fixed extra CUDA context created by barrier (#149144)
• Fixed the logic to use group rank instead of global rank when possible (#149488)
• Fixed ET trace collection of all_to_all (#149485)
• Disabled start event recording for coalesced col and improved profile title (#150863)
• Fixed connection reset in tcp store (#150987, #151052)
• Fixed unused group input argument in new_subgroups() (#152765, #153798)
• Fixed tcp init when using port 0 (#154156)
• Adopted a vector to temporarily keep the reference to future object to avoid blocking inside Flight Recorder (#156653)

Distributed Checkpointing (DCP)

• Fixed to use global coordinator rank in broadcast_object util function (#155912)

DistributedDataParallel (DDP)

• Fixed DDPOptimizer issue on static tensor index (#155746)

DTensor

• Fixed local_map with multi-threading (#149070)
• Fixed new_local_tensor in redistribute be None case (#152303)
• Fixed bug visualizing 1D Tensor using rich (#152871)

Pipeline Parallelism

• Optimized memory usage by releasing output memory earlier (#153383)

RPC

• Made torch importable if compiled without TensorPipe (#154382)

ShardedTensor

• Fixed sharded tensor gather when a local tensor on certain ranks has zero elements (#150914)

TensorParallel

• Turn async-TP applicability asserts back into silent skips (#158736)

torch.compile

Dynamo

• Eliminated silent incorrectness issues in the Compiled Autograd initial trace (#149014, #155521, #155289, #149336)
• Fixed various tracing errors involving einops, dict(mapping_proxy), and the FlexAttention HOP (#157754, #157515, #157519)
• Fixed unpack hook semantics for memory savings in checkpointing and offloading for Compiled Autograd (#147242, #153300)
• Fixed sources for dataclass defaults and the lru_cache method (#158689, #157308)
• Fixed spammy errors when an invalid TORCH_LOGS argument is passed (#151678)

Inductor

• Support special kwargs in AMD triton configs (#154605)
• Fixed minifier when one has multiple Python runtimes (#155918)
• Bug fix for int8 GEMM compensation epilogue (#152408)

torch.export

• Fixed tracing of the following: aten.is_nonzero (#149637), torch.bincount() (#152497), aten.div (#150874) slicing (#150104), and attn_mask (#158618), aten.to (#153972), scalar tensor construction (#154661)
• Fixed dynamic_shapes spec for kwargs (#148772, #149528, #150103)
• Fixed input bugs in unflattener (#149206, #153474, #153000)
• Fix nonstrict tracing of functools.partial (#153408), and higher order ops (#149295)
• Fixed serialization/deserialization of None inputs (#150515), math module (#154643), call_torchbind (#155647), and enums (#154821)
• Fixed state dict modification in run_decompositions (#151436)
• Fixed subclass access custom op bug (#149698)

Ahead-Of-Time Inductor (AOTI)

• Fixed AOTI update_constant_buffer issue (#149243)
• Fixed a memory leak in model_package_loader (#152334)
• Don't alloc weights in AOTIModel if they don't exist (#152692)
• Fixed state of ConstantFolding (#153152)
• Fixed index offset for optional tensor return (#155073)
• Fixed float8 type printing for min/max value printing (#154466)

Linear Algebra Frontend

• Fix to workaround LAPACK workspace size being returned as a floating point value (#149682)
• Fixed the accumulation type for dot and gemv (#152676)
• Fixed torch.lobpcg to compute same largest eigenvalue as scipy and np.linalg.eig (#152789)
• Fixed 32-bit indexing overflows in ReducedPrecisionGemV (#150949)

MPS

• Fixed various op support issues: unary/binary ops with 2**32+ element inputs, binary ops with inputs with different dtypes, ops with complex scalar inputs, cholesky decomp, floor_divide type promotion, index_kernel with large inputs, lerp with complex inputs, logit with half/bfloat16 inputs, SDPA memory leak, torch.special.entr, tri[ul], matrix inversion with N>1024, and where with non-contiguous cond (#152479, #155183, #149233, #151176, #151282, #158239, #152371, #149974, #158237, #146754, #158867, #155184, #152204)

torch.nn

• Fixed load_state_dict behavior for nn.LazyLinear (#147599)

ONNX

• Fixed bfloat16 support in onnx_program callable (#151121)
• Produce correct dtypes for bf16/f8 in IR TorchTensor (#151259)
• Preserve all legacy exporter params in fallback (#156659)
• Fixed 4D tensor conversion for SDPA (#157509)

Optimizer

• Fixed bug where lr_scheduler unexpectedly calls step() when init argument last_epoch > -1 (#149312)
• Fixed CosineAnnealingWarmRestarts resetting T_cur (#151289)

Profiler

• Fixed empty C call queue in python tracer (#150370)
• Removed decref from python context in python tracer (#151625)
• Enable all configured activities in CUPTI Range Profiler mode (#154749)

Python Frontend

• Fixed segfault during numpy string tensor conversion (#155364)
• Added checks for empty tensor list (#155383)
• Fixed sample validation for MixtureSameFamily distribution (#151317)
• Fixed bug where creating a second Wishart or Uniform distribution modifies constraints on the first (#154361)
• Fix to properly export torch::utils::tensor_to_numpy symbol (#154178)
• Fixed torch.[con]cat[enate] to avoid crashing on empty inputs (#155460)
• Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#158655)

Release Engineering

• Checkout optional submodules when publishing a release tarball (#156615)
• Fixed MacOS MP hang in Python-3.12+ (#155698)
• Fixed static functions when using module in MSVC (#148675)
• Fixed VS2022-caused AVX512 illegal instruction issue (#153480)

ROCm

• Fixed build error for opportunistic fastatomics with newer compilers (#152841)

TunableOp

• More TF32 support (#149088)
• Fixed offline tuning for ScaledGEMM (#149677)
• Fixed row-wise ScaledGEMM (#152403)
• Support submatrices in offline tuning for ROCm (#151138)

Vulkan

• Fixed torch.is_vulkan_available() on Mac (#155595)

XPU

• Fixed matmul accuracy when offset > 0 (#154495)
• Fixed torch.xpu.is_bf16_supported to correctly report presence of Intel GPU (#152317)
• Fixed AOT compilation in SYCL C++ extension (#156364)

Performance

Autograd

• Improved autograd streams synchronization (#151079, #157914)

CPU (AArch64)

• Compute ELU(0) with the cheaper definition (#155765)

CUDA

• Improved performance of cat and index_select (#150233, #152380, #151715)

Dataloader Frontend

• Reduced memory usage of SubsetRandomSampler by iterating over list instead of tensor (#149126)

torch.compile

Inductor

• Improved performance of GEMMs (#147315, #151530, #149373, #156174, #155444)
• Added a config option cpp.use_small_dequant_buffer to use a small dequant buffer for WOQ int4 GEMM (#156395)
• Support graph partitioning on custom ops (#149782)
• Optimized the heuristics of parallel reduction on CPU (#149614)

torch.export

• Cache unflattened graph module (#150030)

JIT

• Improved Dead Code Elimination (DCE) compile times for large graphs (#153645)

Linear Algebra Frontend

• Introduced fast path for torch.dot with float16/bfloat16 (#152799)

MPS

• Improved performance of LayerNorm, mm / bmm, sum / prod reductions, arithmetic ops, binary kernels, SDPA, linear, and cumsum / cumprod (#152010, #150541, #150566, #147644, #149730, #152781, #152210, #157494)

Python Frontend

• Optimized SVE embedding performance (#150176)
• Improved performance for torch.tensordot when contracting to a scalar (#145936)

ROCm

• Improved performance of softmax, NLLLoss, in-place sum, max pooling backward / reductions on NHWC inputs, max pooling, multi-dimensional reductions, and non-vectorized elementwise kernels (#149076, #149779, #149548, #151230, #152267, #154522, #154619, #155806, #153184)
• Improved scatter add performance on MI250X (#151724)
• Extended vectorized elementwise kernel to more heterogenous tensor types (#149738)
• Use HipSparseLT to further accelerate semi-structured (e.g. 2:4) sparsity (#150578)

Sparse Frontend

• Skip sparse tensor invariant validation when loading sparse Tensors from external storage (#154610, #154759, #154638)

XPU

• Enabled post-op fusion for oneDNN convolution on Intel GPU (#150287)
• Reduced host overhead for Intel GPU by eliminating meaningless API calls (#151111)
• Improved INT4 WOQ GEMM for Intel GPU by introducing a cache mechanism to reduce the oneDNN integration overhead further (#147693)
• Improved scalar tensor case handling in addmm, baddmm to reduce oneDNN integration overhead on Intel GPU (#153051)

Documentation

Autograd

• Added more details on why ctx.save_for_backward is important in note about extending autograd (#153005)
• Updated docs of torch.autograd.graph.saved_tensors_hooks to avoid refcycle (#153049)
• Updated gradient behavior note in torch.amin and torch.amax (#155071)

CUDA

• Fixed deprecated amp APIs in docs (#154553)
• Documented device memory apis in correct module (#155126)
• Documented non-pytorch CUDA memory allocation and how to query it (#150880)

Distributed

c10d

• Documented object collectives limitations (#150815)
• Updated NCCLConfig with QOS variable (#151821)
• Documented get_default_backend_for_device (#158236)

FullyShardedDataParallel2 (FSDP2)

• Updated ignored_params docstring and added unit tests (#149074)
• Added pointer to torchtitan (#153079)
• Added warning for incorrected grad results at world size 1 (#154928)

torch.export

• Added mini tutorial for provenance tracking (#152211)
• Updated docs for Dims and ExportGraphSignature (#156262, #156244)

Linear Algebra Frontend

• Addressed ambiguity in docs for torch.linalg.norm()'s ord argument of +2 & -2 (#155148)

torch.nn

• Improved documentation for transformer-related layers, nn.RNN, nn.functional loss functions, interpolate saturate cast behavior, ConvTranspose2d stride / output_size arguments, and register_full_backward_hook (#155123, #153620, #148436, #151304, #150819, #150609, #151785)
• Fixed examples for nn.Sequential and nn.LazyModuleMixin (#147304, #150596)
• Documented padding size limitations in nn.modules.padding and AvgPoolND (#155618, #152680)

ONNX

• Convert .rst doc files to markdown (#155228, #155556)
• Improved docstring of ONNX symbolic ops (#149668)
• Added note for attention op symbolic function (#156441)
• Added ONNX Dynamo metadata documentation (#155816)

Optimizer

• Added scripts to generate plots of LRSchedulers (#149189)
• Included other accelerators in capturable docstr for optimizers (#149770)
• Updated SGD documentation to match implementation and document that dampening is skipped in SGD first step (#149884, #152833)
• Fixed doc for CosineAnnealingLR to accurately reflect its recursive learning rate schedule (#152936)
• Fixed incorrect citation of authors in Adafactor documentation (#145209)
• Added load_state_dict hint doc about invoke order work with lr_scheduler (#149942)

Python Frontend

• Make torch.Library's kind have no default value to be consistent with the code (#149390)
• Added 32-bit complex to the list of dtypes (#144590)
• Clarified behavior when integer dtype is used with requires_grad=True in tensor.to() (#150913)
• Optimized cdist param description (#151178)
• Updated serialization docs (#153631)
• Render Example: and not Example:: in docs (#153978)
• Added docstring indicating undefined behavior for converting inf to int (#154781)
• Updated as_strided() docs (#149146)
• Fixed keepdim param optional description (#151197)
• Clarify that x and dx are mutually exclusive in torch.trapezoid docs (#151190)
• Documented out_dtype arg for torch GEMM operations (#151704)
• Fixed the basic description of torch.min(), torch.max(), torch.all(), and torch.any() (#152658)
• Added torch.triu_indices, torch.tril_indices dtype description (#150749)
• Optimized torch.equal description (#149618)

Quantization

• Fixed incorrect get_default_qat_qconfig in prepare_qat_fx docs (#155100)

Release Engineering

• Migrated to new theme (#149331)

XPU

• Improved "Getting Started on Intel GPU" hardware requirements and notes (#151886)

Developers

Distributed

c10d

• Added param recording for uniqueID broadcasting and allgather (#149166)
• Added logger config and more loggings, e.g. nccl_version and thread name/id, for flight record in PGNCCL (#150356, #150513, #151048, #152648, #155142, #155754)
• Surfaced error type when we unlink and create named pipe for DumpPipe (#150648)
• Improved the logs on remote shutdown of tcpstore (#153586)
• Enhanced Error Logging in new_subgroups() for Non-Divisible World Sizes (#154124)
• Added a logger for all nccl collectives with its time duration when completed (#156008)
• Updated error message in get_backend() with more details (#141796)

FullyShardedDataParallel (FSDP1)

• Print FQNs when debugging FlatParamHandle (#151336)

FullyShardedDataParallel2 (FSDP2)

• Added FSDP2 logging (#155826)

RPC

• Correctly pass exceptions raised from rpc_init to CPython (#154325)

torchelastic

• Added the logging of start of torch elastic workers (#150849)
• Passed event log handler to record function calls (#155457)
• Added torch.distributed.run option to provide destination for event logging (#155268)

torch.export

• Add TracingContext (#149294)
• Monkeypatch fake mode so it errors on invalid custom ops (#149410)
• Fixed torch export docs for preserve_module_call_signature (#151140)
• Improved error message for deserializing custom triton op (#152029)
• Better type annotation for lift_constants_pass (#152072)
• Fixed bug in detect_attr_assignment (#151824)

Ahead-Of-Time Inductor (AOTI)

• Refactor AOTInductor runtime API for Intel GPU (#153929)
• Improve stable library APIs (#152040)
• Add a basic shim and stable::Tensor is_contiguous API (#156228)

FX

• Gracefully exit minimizer when there is no discrepancy in block mode (#154076)

Optimizer

• Improve decorator typing for Optimizer subclasses (#153374)
• Optimize typing in lr_scheduler.py (#151219)
• Fixed the type hint of step() with default value (#153367)

Release Engineering

• Added support for CUDA 12.9 in CI/CD (#154980, #156630, #155895, #155799, #155496, #155340, #155819, #156108)
• Added support for ROCm 6.4 in CI/CD (#151236, #151345, #151355, #153253, #156112)
• Moved CI from ubuntu 20.04 images to ubuntu 22.04 and 24.04 (#154437, #154153, #149142)
• Moved CI to CUDA 12.8 (#154004, #152810, #155087, #148963)
• Enabled CI on MI300 (#150667, #152133, #148394, #153134)
• Enabled CI on H100 (#153900, #154562, #153170, #155861, #155719, #156429)
• Enabled CD for Windows Arm64 (#150310, #152109, #149850, #152099)
• Enabled testing of binary Docker builds in CI/CD (#151483, #151488, #151489, #151706)
• Added smoke test to validate NCCL and cuDNN versions in PyPI packages (#149885, #150194)
• Enabled monitoring for performance tests (#153452, #153453, #153454, #153456)
• Improved benchmarking and performance testing on MacOS (#151721, #151747, #151748, #153897, #155493, #153897, #155493)
• Use setup-python from for Mac tests (#155698)
• Removed CUDA 11.8 and 12.4 support in CI/CD (#155509, #154169, #152362, #155555, #154893)
• Removed Anaconda support in CI/CD (#147789, #152338, #152431, #152377, #152433, #147476, #151035, #152860, #152702, #154303, #154309)