PyTorch 2.7.0 Release Notes

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

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

NCCL init hits CUDA failure 'invalid argument' on 12.2 driver

Some users with 12.2 CUDA driver (535 version) report seeing "CUDA driver error: invalid argument" during NCCL or Symmetric Memory initialization. This issue is currently under investigation, see #150852. If you use PyTorch from source, a known workaround is to rebuild PyTorch with CUDA 12.2 toolkit. Otherwise, you can try upgrading the CUDA driver on your system.

Backwards Incompatible Changes

Dropped support for Triton < 2.2.0. Removed Support for CUDA 12.4, Anaconda in CI/CD.

• Removed CUDA 12.4 support in CI/CD in favor of 12.8 (#148895, #142856, #144118, #145566, #145844, #148602, #143076, #148717)
• Removed Anaconda support in CI/CD (#144870, #145015, #147792)
• Dropped support for Triton < 2.2.0 (versions without ASTSource) (#143817)

C++ Extensions py_limited_api=True is now built with -DPy_LIMITED_API (#145764)

We formally began respecting the py_limited_api=True kwarg in 2.6 and stopped linking libtorch_python.so when the flag was specified, as libtorch_python.so does not guarantee using APIs from from the stable Python limited API. In 2.7, we go further by specifying the -DPy_LIMITED_API flag which will enforce that the extension is buildable with the limited API. As a result of this enforcement, custom extensions that set py_limited_api=True but do not abide by the limited API may fail to build. For an example, see #152243.

This is strictly better behavior as it is sketchy to claim CPython agnosticism without enforcing with the flag. If you run into this issue, please ensure that the extension you are building does not use any APIs which are outside of the Python limited API, e.g., pybind.

Change torch.Tensor.new_tensor() to be on the given Tensor's device by default (#144958)

This function was always creating the new Tensor on the "cpu" device and will now use the same device as the current Tensor object. This behavior is now consistent with other .new_* methods.

Use Manylinux 2.28 and CXX11_ABI=1 for future released Linux wheel builds.

With Migration to manylinux_2_28 (AlmaLinux 8 based), we can no longer support OS distros with glibc2_26. These include popular Amazon Linux 2 and CentOS 7. (#143423, #146200, #148028, #148135, #148195, #148129)

torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)

Users using the torch.onnx.dynamo_export API may see some ExportOptions become
unsupported due to an internal switch to use torch.onnx.export(..., dynamo=True): diagnostic_options, fake_context and onnx_registry are removed/ignored by ExportOptions. Only dynamic_shapes is retained.

Users should move to use the dynamo=True option on torch.onnx.export as
torch.onnx.dynamo_export is now deprecated. Leverage the dynamic_shapes argument in torch.onnx.export for specifying dynamic shapes on the model.

Version 2.6.0

torch.onnx.dynamo_export(model, *args, **kwargs)

Version 2.7.0

torch.onnx.export(model, args, kwargs=kwargs, dynamo=True)

Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg to the LRScheduler constructor. (#147301)

Both APIs have been deprecated since 2.2. Please use LRScheduler.get_last_lr() to access the learning rate instead.print_lr and verbose were confusing, not properly documented and were little used, as described in #99270, so we deprecated them in 2.2. Now, we complete the deprecation by removing them completely. To access and print the learning rate of a LRScheduler:

Version 2.6.0

optim = ...
lrsched = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, verbose=True)
// lrsched will internally call print_lr() and print the learning rate      

Version 2.7.0

optim = ...
lrsched = torch.optim.lr_scheduler.ReduceLROnPlateau(optim)
print(lrsched.get_last_lr())

libtorch_python.so symbols are now invisible by default on all platforms except Apple (#142214)

Previously, the symbols in libtorch_python.so were exposed with default visibility. We have transitioned to being more intentional about what we expose as public symbols for our python API in C++. After #142214, public symbols will be marked explicitly while everything else will be hidden. Some extensions using private symbols will see linker failures with this change.

Please use torch.export.export instead of capture_pre_autograd_graph to export the model for pytorch 2 export quantization (#139505)

capture_pre_autograd_graph was a temporary API in torch.export. Since now we have a better longer term API: export available, we can deprecate it.

Version 2.6.0

from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantize_pt2e import prepare_pt2e
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer().set_global(
    get_symmetric_quantization_config()
)
m = capture_pre_autograd_graph(m, *example_inputs)
m = prepare_pt2e(m, quantizer)

Version 2.7.0

from torch.export import export
from torch.ao.quantization.quantize_pt2e import prepare_pt2e
# please get xnnpack quantizer from executorch (https://github.com/pytorch/executorch/)
from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer().set_global(
    get_symmetric_quantization_config()
)
m = export(m, *example_inputs)
m = prepare_pt2e(m, quantizer)

New interface for torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable Node Level provenance tracking (#144277)

We now track a mapping between the nodes in the pre-grad and post-grad graph. See the issue for an example frontend to visualize the transformations. To update your GraphTransformObserver subclasses, instead of overriding on_node_creation and on_node_erase, there are new functions get_node_creation_hook, get_node_erase_hook, get_node_replace_hook and get_deepcopy_hook. These are registered on the GraphModule member of the GraphTransformObserver upon entry and exit of a with block

Version 2.6.0

class MyPrintObserver(GraphTransformObserver):
    def on_node_creation(self, node: torch.fx.Node):
        print(node)

Version 2.7.0

class MyPrintObserver(GraphTransformObserver):
    def get_node_creation_hook(self):
        def hook(node: torch.fx.Node):
            print(node)
        return hook

torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no longer public (#141612)

We are planning to make all functions under torch.ao.quantization.pt2e.graph_utils private. This update marks get_control_flow_submodules as a private API. If you have to or want to continue using get_control_flow_submodules, please make a private call by using _get_control_flow_submodules.

Example:
Version 2.6:

>>> from torch.ao.quantization.pt2e.graph_utils import get_control_flow_submodules

Version 2.7:

>>> from torch.ao.quantization.pt2e.graph_utils import get_control_flow_submodules
ImportError: cannot import name 'get_control_flow_submodules' from 'torch.ao.quantization.pt2e.graph_utils'
>>> from torch.ao.quantization.pt2e.graph_utils import _get_control_flow_submodules  # Note: Use _get_control_flow_submodules for private access

Deprecations

torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)

Users should use the dynamo=True option on torch.onnx.export.

Version 2.6.0

torch.onnx.dynamo_export(model, *args, **kwargs)

Version 2.7.0

torch.onnx.export(model, args, kwargs=kwargs, dynamo=True)

XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)

XNNPACKQuantizer is a quantizer for xnnpack that was added into pytorch/pytorch for initial development. However, as it is not related to our core quantization workflow, we have moved it to ExecuTorch instead. Please use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead of torch.ao.quantization.quantizer.xnnpack_quantizer.

Version 2.6.0

from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantize_pt2e import prepare_pt2e
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer().set_global(
    get_symmetric_quantization_config()
)
m = capture_pre_autograd_graph(m, *example_inputs)
m = prepare_pt2e(m, quantizer)

Version 2.7.0

# we also updated the export call
from torch.export import export
from torch.ao.quantization.quantize_pt2e import prepare_pt2e
# please get xnnpack quantizer from executorch (https://github.com/pytorch/executorch/)
from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer().set_global(
    get_symmetric_quantization_config()
)
m = export(m, *example_inputs)
m = prepare_pt2e(m, quantizer)

New features

Release Engineering

• Added support for CUDA 12.8 in CI/CD (#145567, #145789, #145792, #145765, #146019, #146378, #146957, #147037, #146265, #147607, #148000, #149584)
• Added Python 3.13 and 3.13t support in CI/CD (#144698, #143078, #144697, #143074, #141806, #146614)
• Added aarch64 support for pytorch-triton package (#148768, #148705)
• Added support Windows XPU CI/CD (#148755, #147637, #148313, #143185, #148319, #144316, #144644, #144034, #145255)
• Added support for ROCm MI300 CI/CD (#143673, #145504, #146675, #147904, #145398, #145621, #145829, #145790, #144594)
• Added support for PEP585, Type Hinting Generics In Standard Collections (#145707, #145177, #145708, #145342, #145101)
• Added Windows Arm64 Nightly Builds (#139760)

Python Frontend

• Introduce a new torch.utils.serialization.config namespace for all serialization related configurations (#143324)
• Add torch.serialization.config.save.use_pinned_memory_for_d2h to speed up torch.save when passed gpu devices (#143342)
• Add torch.utils.serialization.config.load.calculate_storage_offsets to reduce random reads and significantly improve performance for storage with bad random access performance (#143880)
• Add support for __torch_function__ handler on dtype arguments, similar to subclass objects (#145085)

C++ Extensions

• Support libtorch-agnostic extensions with stable torch ABI (#148892, #148832, #148124, #149208, #149052)

Distributed

Context Parallel

• We provided a Context Parallel API (#131351) for users to parallelize torch.nn.functional.scaled_dot_product_attention over the sequence dimension. We implemented
  Ring Attention (#131351) and an AllGather-based approach (#132820) where the all-gather is issued before the first local SDPA
  and the subsequent local SDPAs will have to wait until the all-gather completes, and offered a user API (#142093) to select the desired approach. The implementation
  currently supports three SDPA kernels: SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION, and SDPBackend.CUDNN_ATTENTION (#148537). We also
  verified that our Context Parallel implementation is compatible with other parallelisms and torch.compile.

c10d

• Implemented ncclCommInitRankScalable (merging #136789) (#144794)

Distributed Checkpoint (DCP)

• Cache save plans: to mitigate overhead from planning steps (#147116, #147343)
• Build a storage reader/writer to write checkpoints in HF format (#148089)

CUDA

• Blackwell support added across native kernels, CUDA math libraries, and torch.compile (#145270)
• Make torch.cuda.gds APIs public (#147120)

MPS

• Prototype of torch.compile for Metal (#143893)
• Provide Metal kernel authoring via Python (#148972)

ROCm

• CK Memory-Efficient Attention (attention bias support) (#147778)
• CK Flash Attention Backend (#143695)
• Enhanced Windows support for PyTorch on ROCm (#148563, #144098)
• Support for gfx1102 arch (Navi33) in wheel builds (#147761)
• hipblaslt rowwise f8 gemm (#144432)

XPU

• Add AOT Inductor support for Intel GPU (#140269, #140664, #149175)
• Support torch.compile on Windows Platform for XPU (#147637, #144316, #149511)
• Support SYCL with torch.utils.cpp_extension APIs (#132945)
• Enhance Intel GPU performance on PyTorch 2 Export Post Training Quantization (#136753, #135465,#135337, #135189)
• Enable windows Kineto profiler(#148319)
• Enable TF32 support for XPU based on oneDNN backend (#137570)

torch.compile

Dynamo

• Support tracing contextlib.contextmanager in Dynamo (#136033)
• nonstrict_trace escape hatch to apply non-strict tracing to difficult-to-compile code (#146367)
• Delayed compile for dynamic shapes (#147983)
• Support tracing generators (#141055)
• Whitelist of source files to apply dynamic shapes to (#147979)
• Support tracing list subclasses (#146819)

Inductor

• Enable non power-of-2 head_dim for FlexAttention (#133495).
• Add FlexAttention kernel parameter tuning options: num_warps and num_stages (#139639).
• Support vectorization for score and mask in FlexAttention CPU (#143638).
• ConfigFuzzer: a new debugging tool designed to fuzz Torch compile configurations. Given a test function, it will identify combinations of configs that throw errors during compilation and execution (#139736) (#145565).
• Support fusion of pointwise ops into Template Prologues. TORCHINDUCTOR_PROLOGUE_FUSION enables this feature (#147008).
• Add instantiation level for generating configs in the CUTLASS backend. Set TORCHINDUCTOR_CUTLASS_INSTANTIATION_LEVEL. Consult config.py for information (#146230).
• Add L2 Swizzle config for CUTLASS backend: cuda.cutlass_max_profiling_swizzle_options (#146088).
• Emit a CMakeLists.txt when package_cpp_only is specified in AOTI (#143352).
• One Dynamo graph can now map to multiple inductor graphs with different graph_partition functions. Set the graph_partition in inductor config to enable (#147038).

Profiler

• Add overload names to profiler (#143114)
• Enable profiling on all threads via experimentalConfig (#143659)

Quantization

• Enables kernel from KleidAI to run model that was quantized such that weights are in int4 (with symmetric quantization either using channel-wise or group-wise, with the group size being a multiple of 32), while at runtime the activations are dynamically quantized from fp32 to int8 and weights are upcast from int4 to int8 so that int8 matrix multiplication is executed. This dynamic quantization of activations and matrix multiplication is performed inside of function torch.ops.aten._dyn_quant_matmul_4bit, while the weights, scaled and optional bias are packed in torch.ops.aten._dyn_quant_pack_4bit_weight. To use it on your model you can quantize it using the following example that leverages torchao:

from torchao.dtypes import PlainLayout
from torchao.experimental.packed_linear_int8_dynamic_activation_intx_weight_layout import (
    PackedLinearInt8DynamicActivationIntxWeightLayout,
)
from torchao.experimental.quant_api import (
    int8_dynamic_activation_intx_weight,
)
from torchao.quantization.granularity import (
    PerGroup,
    PerRow,
)
from torchao.quantization.quant_api import quantize_
from torchao.quantization.quant_primitives import MappingType
my_model = Model()
quantize_(
    my_model,
    int8_dynamic_activation_intx_weight(
        weight_dtype=torch.int4,
        granularity=PerGroup(32), # PerRow() is also supported
        has_weight_zeros=True, # Should be True
        weight_mapping_type=MappingType.SYMMETRIC_NO_CLIPPING_ERR # MappingType.SYMMETRIC can also be used but increases error
        layout=PackedLinearInt8DynamicActivationIntxWeightLayout(target="aten"),
    ),
)

ONNX

torch.onnx.verification.verify_onnx_program (#148396, #148706, #148730, #148707)

A new verification API torch.onnx.verification.verify_onnx_program can now be used to verify numerical accuracy of the exported ONNX model. Users can use the compare_intermediates option to identify any operator that causes numerical discrepancies in intermediate tensors. It is possible to use a tool like model-explorer to visualize the verification results.

• Support custom axis name through dynamic_shapes (#146321)
• torch.onnx.export(dynamo=True) now optimizes the output model by default (#146187)

Improvements

Release Engineering

• Added TorchCache Benchmark tests (#147641, #147688, #147782, #147780, #147781, #147783, #147546)
• Upgrade CD to 6.3 for ROCm (#142152, #142151, #143613)
• Add cufile to a dependency list for CUDA 12.x builds and enable use by default (#145748, #148465, #148137)
• Add support for gfx1102 and gfx12 to ROCm wheel and libtorch builds (#147761, #148562)

Python Frontend

• Add support for CPU scalar in torch.addcmul (#143264)
• Set -DPy_LIMITED_API flag for py_limited_api=True cpp_extensions (#145764)
• Add support for serialization for uintx/intx in weights_only (#147500)
• Add warning to torch.jit.load (#143403)
• Make record/storage alignment in torch.save configurable (#147788)
• Support with statement on torch.Stream (#140138)

Autograd

• Allow torch.autograd.graph.GradientEdge as torch.autograd.backward outputs #144744
• Implement gradient for the residuals of torch.linalg.lstsq #148526
• Add deterministic kernel for reflection_pad2d_backward (#136241)
• Improve softmax backward pass native CUDA implementation (#145866)
• Improve Pareto frontier plot for AutoAC (#148678)

Dataloader

• Dataloader distributes tasks to workers as they become available when in_order is False (#142324)
• Update pin memory related APIs to not pass device argument. device and pin_memory_device are discouraged and will be deprecated in the future. (#131858)

Linear Algebra

• Improve dim argument validation for empty inputs for torch.cum{min,max}. (#143920)
• Properly throw an error when trying to sort complex numbers. (#144113)

Nested Tensor (NJT)

• Support NJT chunk() backward on batch dim (#144584)
• Support remaining *_like factory functions for NJT (#144889)
• Improve matmul with NJTs via backward support and composition with dense tensors (#144587, #146405)

torch.nn

• Add strict kwarg to nn.Module.set_submodule and fix bug for non dot-delineated strings (#143455)
• Improve input dimensions check for reflection_pad1d, reflection_pad2d and reflection_pad3d (#141670)

torch.optim

• Refactor AdamW to subclass Adam (#143710, #144972)
• Add support for differentiable LR and weight_decay in SGD, Adam(W) (#143510, #143679, #143726)

Build Frontend

• Make PyTorch with HomeBrew installed OpenMP (#145870)
• Enable onednn in pytorch for ppc64le architecture (#143743)
• Enable build for Blackwell GPU family (#145436)
• Fix OOM whle building on RasberryPi by sharding codegenerated files (#144364)

C++ Frontend

• Introduce a new API isAcceleratorExcluded (#144959)

Distributed

c10d

• Simplified abort and shutdown by adding both to Backend and ProcessGroup objects (#148798)
• Used new_group instead of split_group on non-CUDA device (#141469)
• Removed call_guard in pybind object init of c10d (#143598)
• Enabled coalescing path on XPU and dispatch to XPU tensor barrier if XCCL backend is specified. (#143735)
• Preserved PyWork's Python reference counting when used in functional collectives (#146376)
• Enabled soft fail bind when agent store active inside TCPStore (#147465)
• Made getDefaultBackend more fault tolerant (#148596)

DistributedDataParallel (DDP)

• Added init_sync option to control collectives during initialization (#142824)
• Decoupled python reducer from compilation mode (#147123)

FullyShardedDataParallel2 (FSDP2)

• Clamp reduce_dtype in lazy init (#143297)
• Enabled FSDP2 on XPU device (#143737)
• Made post-backward condition more robust (#144781)
• Enabled MTIA device in FSDP2 library code (#145842)
• Avoided resetting version counter of all_gather_output in inference_mode (#146709)
• Supported ignoring parameters in FSDP2 (#146631)
• Enabled FSDP tests on XPU device (#147518)
• Enabled FSDP2 on HPU device (#148667)

DTensor

• Added aten.amin/amax to linear_reduction_strategy (#143747)
• Added src_data_rank to distribute_tensor API (#143883)
• Added strategy for _scaled_mm (#143760)
• Added aten.view.dtype op support (#144404)
• Enabled sharding prop to handle cross mesh computation (#147869)
• Added CuDNN SDPA op support to DTensor (#148537)
• Optimized shard_dim_alltoall to use alltoall_single (#148868)
• Deprecated _shard_tensor to use src_data_rank=None (#144171)
• Added pointwise ops strategy for aten.minimum (#145816)

TensorParallel

• Propagated src_data_rank kwarg in TP API (#144005)

Torch Elastic

• Added kill logic for current process when killing a worker (#141060)
• Made etcd_rendezvous publicly importable (#145396)
• Exposed the rendezvous keepalive arguments (#145228)

Pipelining

• Added generate_stage_to_rank_mapping utility (#146193)
• Removed stage_index_to_group_rank from schedule (#146217)

CPU

General

• Implement blend operation for float, double, int in VEC ATen backend for SVE (#146479)
• Upgrade submodule oneDNN to v3.7.1 (#148293)

x86

• Add support for int8 brgemm (#143384)

CUDA

• Refine CUDA Stream priority (#143849)
• Expose sharedMemPerMultiprocessor device property to python (#143119)
• Expose remaining sharedMem cudaDeviceProps to python (#143226)
• Add range check for embedding_bag on input index >= 0 of cuda device (#140791)
• Fix linter warnings (#147386)
• Change behavior of pinning memory so it does not init a cuda context if one is not already present (#145752, #149033)
• Add cutlass kernel for rowwise scaled mm on SM 10.0 (blackwell) (#148421)
• Add get_stream_from_external API for CUDA backend (#143799)
• Update cuDNN-frontend submodule to 1.10.0, used by cuDNN convolution and SDPA integrations (#145780)

MPS

• Adding support to MPS for operators: angle, entr, spherical_bessel_j0,xlog1py, sinc,round.decimals, linalg.det, cholesky.ex, bilineard2d_aa,linalg.solve, zeta, cholesky, fused_rms_norm, lu_unpack, lu_factor_ex, slogdet and logdet (#143449, #147948, #146818, #147687, #146539, #147266, #146279, #146799, #145526, #146531, #146465, #145701, #145301, #146681, #144651, #145341, #146771, #147914)
• Extending data type support for angle and atan2 for long type, torch.special.sinc to complex, torch.mm / torch.bmm to integral types (#149017, #146648, #145809, #147526)
• Support torch.accelerator.synchronize() on MPS (#143171)
• Add error checking when dispatching kernel (#146458)
• For MPSInductor
  • Fix index generation for transpose (#143973)
  • Fix multi rangevar kernel invocation (#144050)
  • Better error when kernel fails to compile (#144649)
  • Fix large prod and sum reductions (#148975)
  • Adding support to MPSInductor for operators: gamma, zeta, sinc, spherical_bessel_j0, entr (#145341, #146465, #146539, #147650, #148128)

ROCm

• Fix TunableOp UTs: Rotating Buffer (#143172)
• Enable *_load_dwordx4 ISA for BFloat16 and Half. (#141397)
• Fix condition for small tensor tuning (#144087)

XPU

• Enable FP64 GEMM (#140677)
• Enable Sparse CSR support (#144722)
• Improve XPU Stream implemenation(#141123,#141119,#142347)
• Enable XPU for Inductor MM Triton Kernel Benchmark (#148237)
• Align XPU convolution_backward output layout between fake tensor and real output tensor (#146880)
• Improve error handling and reporting in CMake files (#149353)
• Refine torch.xpu.get_device_properties API error message (#144379)
• Enable nested_layer_norm support for XPU (#148593)
• Generalize is_big_gpu() check in Inductor (#143491)
• Allow XPU device in sparse compressed tensor factory functions (#147306)

Profiler

• Add optional flag to profiler to toggle external correlations (#143314)
• Add delimeter in memory vizualizer to show where allocation addr begins (#147461)
• Add last entry to truncated values in Kineto args (#148576)
• Add profiler activity for HPU devices (#148182)
• Add HPU availabilities to profiler (#149115)

torch.compile

Dynamo

• Better tracing support for user-defined dict subclasses (#143548)
• Improved graph break messages for some common graph break sites (#146525)
• Improved tracing of exceptions (#146492)
• Remove a number of builtin and third-party modules from trace_rules.py skipfiles (#145856)
• Remove some specialized variables for specific third-party classes (e.g. transformers ModelOutput) (#143567)
• Compiled Autograd dropped annotation requirements for custom autograd functions (#146229, #146720)

AOTDispatcher

• Fix a quadratic compile time edge case during training when you have long parallel chains of compute (#145082)
• handle compiling mutations on tangents in custom autograd.Functions (#141131)
• handle compiling buffer input mutations of the form buffer.copy_(int) (#141161)
• Fix handling of mutable custom operators in compile when used with torch.inference_mode (#147925)

Dynamic Shapes

• Better unbacked symint handling for topk (#147017)
• dynamic shape support for interpolate(antialias=True) backward (#141198)
• Better unbacked symint handling in the partitioner (#143877)
• Support dynamic shape inputs to nonzer_static (#146006)
• Improve logging in the symbolic shapes framework (provenance tracking, error messages) (#143378, #146625, #146583, #146532, #145354, #146858, #146939, #146955, #147240,#146413m #145848, #147836, #146298)
• Simplify and speed up _compute_symbolic_stride() (#138844)
• Add max kwarg to torch._check (#144471)
• Apply hints to symbol not expr when materializing unbacked tensor intermediates in the partitioner (#144097)
• Add backed_size_oblivious config (#148696)
• Add mark_unbacked strict mode (#147333, #147342)

Decompositions, FakeTensor and meta tensors

Several operator decomps received improvements/bugfixes:

• torch._refs.tensor (#143461)
• torch._refs.mean (#147188)
• linspace (#147997)
• addmv (#143792)
  New meta tensor implementations for a few pytorch operators:
• nonzero (#144727)
• silu, sigmoid, _softmax, embedding (#147862)
  New fake tensor implementation for a few pytorch operators:
• unique_consecutive (#145649)
  Several general FakeTensor improvements
• force UntypedStorage.from_buffer(buf) to return meta storage under FakeTensorMode (#146642)
• support meta_tensor.to(device='cpu') under fake_mode (#146729)

Inductor

• Add profiling support for codegened CPU FlexAttention kernels (#145894).
• Other FlexAttention improvements: (#147765) (#147435) (#147010) (#146657) (#145059) (#144938) (#143299) (#142281) (#147918) (#148857).
• Add Inductor support for non-power-of-2 cooperative RSPLIT (#145689).
• Remove runtime dependency on packaging (#149125)
• Add Cutlass support for runtime param choices, starting with swizzle (#147223).
• Make Inductor cpp backend enable_floating_point_contract_flag take string. Previously, the only options were "on" or "off". Now the value of INDUCTOR_CPP_ENABLE_FLOATING_POINT_CONTRACT_FLAG will be passed to ffp-contract (#143450).
• Add upcasting FP16/BF16 math reductions to FP32 in Triton (#141052).
• Support for more types of async_compile pools. Set variable TORCHINDUCTOR_WORKER_START to one of "subprocess", "fork", or "spawn" (#144491).
• Create a new benchmarker to replace Triton's do_bench (#133058).
• Inplace-padding support for cpp-wrapper (#145325).
• New environment variables for emulate_precision_casts: TORCHINDUCTOR_EMULATE_PRECISION_CASTS (#145948).
• New environment variables to filter cutlass kernels: TORCHINDUCTOR_CUTLASS_ALLOWLIST and TORCHINDUCTOR_CUTLASS_DENYLIST (#148161).
• Add option to disable runtime scalar assertions: TORCHINDUCTOR_SCALAR_ASSERTS (#146462).
• Add new inductor configs to compiler bisector: layout_optimization and comprehensive_padding (#148450).
• Add an option to skip optimizing generated wrapper code. Set AOT_INDUCTOR_COMPILE_WRAPPER_WITH_O0=1 (#144866).
• Support dynamic shape constraints in Export (#146044).
• Handle MLIR scf.yield more accurately in user Triton code (#147762).
• Support Triton 3.3: add a global_scratch arg, fix cpp_wrapper (#148051, #149973).
• Removed an unnecessarily struct runtime alignment assertion, allowing more flexible use cases of AOTI (#143236).
• Support _int_mm in AOTI (#144571).
• Support AOTI + CUDAGraphs when calling from Python (#148601).
• New post grad pass to remove torch.ops.aten._assert_tensor_metadata.default for AOTI (#145028).
• Support basic TorchBind in aot_compile and aoti_compile_and_package (#148506).
• Add top level tlparse logging for AOTI (#147760)
• Added Inductor dashboard benchmarks (#144427, #145791, #145654, #145655, #146449, #145683, #141371, #143223)
• Add AOTI shim for _weight_int4pack_mm_cpu_tensor (#149031)

torch.fx

• Fix subgraph rewriter to support matched pattern with no users (#143842)
• Improve error message to include entire GraphModule (#146197, #148090)
• Allow overriding of ShapeProp (#148784)

torch.export

serialization

• Add float8 support in serialization schema (#143343)
• Allow pickle protocol overriding for serialization (#142253)
• Add serialization support for SymInt inputs in higher-order op subgraphs (#142284)
• Unify single-output and multi-output serialization schemas for higher-order op subgraphs (#143227)
• Add "+export" logging to de/serialization process (#145283)
• Sync model container types to serialization schema (#145959)
• Serialize pytree namedtuple field names in input spec (#145956)
• Replace builtins.getattr with serializable higher-order-op for tensor subclasses (#145772)

dynamic shapes

• Support slice operations with SymInt indices in non-strict export (#143217)
• Export with automatic dynamic shapes (Dim.AUTO) for TorchScript -> Export Converter (#138273)
• Support partially specifying dimensions in ShapesCollection (#147534)

draft export

• Report frequency of data-dependent errors in draft export (#145030)
• Report LOC for data-dependent errors in draft export (#145443)
• Add tlparse for draft export (#145810)
• Deduplicate expression_created logging in draft export (#146859)
• Remove report as return output for draft export, attached as ep._report (#147558)

miscellaneous

• Don't decompose custom triton ops when exporting (#144284)
• Handle input/buffer mutations for joint-graph export (#144806)
• Allow builtin bitshift ops in verifier (#145802)
• Introduce aoti_call_delegate higher-order-op for eager-mode runnability (#145630)
• Include tensor subclass buffers in parametrization rules (#145991)
• Expose pytree namedtuple metadata to FlatArgsAdapter (#146107)
• Implement OSS-only model runner (#146440)
• Exclude core ATen ops upsample_bilinear2d.vec, nearest2d.vec from default decomposition table (#147153)
• Improve error message for unsupported input types (#147532)
• Initial support for exporting methods (#147573)

Quantization

• Add an option keep_original_weights in _lower_to_native_backend (#141049)
• Handle meta tensors in FX quantization (#144726)
• Add fp8 support to index_cuda (#144747)
• Add the torch.float8_e8m0fnu dtype to PyTorch (#147466)
• Improve the performance of 8 bit quantized linear and addition operation on AArch64 by leveraging operations from Arm Compute Library (#148585)
• Enables int8 linear operations to use mkl-dnn when activations, weights and accumulation are all signed 8-bit integers (#139887)

ONNX

• Dynamic shapes support is improved (#144801)
• Automatically convert dynamic_axes to dynamic_shapes with torch.export.Dim.AUTO (#143158)
• Fix bug for exporting torch.cdist into onnx and support 'compute_mode' (#144213)
• Remove LegacyDynamoStrategy (#145442)
• Set warning stacklevel so it appears at the torch.onnx call site (#147165)
• Pick up missing types in dynamic_shapes renaming (#147407)
• Update saved exported program in debugging report if the exporting passes run_decomposition() (#148617)
• Use torch export to get dynamic_shapes for JIT convert strategy (#148627)
• Use torch.export.Dim.AUTO in dynamo_export (#144356)
• Support complex comparison when verify=True (#148619)

JIT

• Relax type-checks for empty dicts (#147167)

Lazy Tensor

• Introduce cache clearing APIs for the lazy graph executor (#144489)

torch.package

• Add support for UntypedStorage tensors (#143930)

Bug fixes

Python Frontend

• Fix torch.lerp type promotion (#141117)
• Fix memory leak on torch.Tensor when both slots and python gc are used (#143203)
• Fix torch.bfloat16 support for __cuda_array_interface__. (#143042)
• Fix rare dispatcher bug for inplace operations that would make the returned torch.Tensor incorrect. (#145530)
• Stop using MKL for randomness generation on CPU (#146174)
• Move accelerator detection to use build time (#146098)
• Fix torch.load under FakeTensorMode to create FakeTensor with correct devices (for plain Tensors) (#147786)
• Fix torch.acos, torch.asin, torch.atan, torch.exp, torch.sigmoid, torch.div, for torch.complex datatypes on CPU (#134838, #140358, #140391, #140375, #144749)

Autograd

• Fix torch.autograd.graph.allow_mutation_on_saved_tensors for inplace foreach ops #145520
• Fix boundary conditions for hardswish backward (#143899)
• Use float data type for Half sum in fallback implementation of batchnorm backward on CPU (#147353)
• Fix torch.compile + ddp + non-reentrant AC pack hook firing count (#144271)

Linear Algebra

• Fix workarea compute in eigh (#146456)

Nested Tensor (NJT)

• Fix NJT min / max backward() for non-ragged reductions (#144583)
• Fix NJT frexp() to handle both outputs (#144585)
• Fix NJT fill.Scalar for contiguous inputs (#144586)
• Fix inference mode for composite implicit ops without nested-specific kernel (#146633)
• Fix flop counter for SDPA and test (#147032)

torch.nn

• Fix broken meta function for flex-attention backwards (#146563)

Build Frontend

• Fix unbalanced #pragma diagnostic pop in VecLib (#148354)
• Fix atomic operation compatibility for ARMv8-A (Raspberry Pi 4) by adjusting compilation flags (#148070)
• Make PyTorch buildable by CMake-4.x (#150203)

C++ Frontend

• Fix Apple Clang ICE when building with -march=armv8.6a (#142879)
• Fix inductor regression on aarch64 neoverse-v1 with gcc10.2 by disabling tree vectorization (#148489)

Distributed

Distibuted Checkpoint (DCP)

• fix dcp gather_object/scatter_object_list (#147675)

Distributed (c10d)

• Fixed CudaEventCache for dangling references (#144496)
• Make all-reduce input contiguous in distributed.nn.all_reduce (#144267)
• Removed Alltoallv specialization for PyTorch generic all_to_all (#145045)
• Added a handle case when remote peer closes connection for TCPStore (#145757)
• Fixed memory leak on shutdown (#145507)
• Fixed an issue where functional collectives don't force fx stride on inputs when compiled (#146467)
• Associated tensor allocation support with NCCL version (#146842)
• Modified API to get device string from device with torch.device (#146290)
• Fixed dist.init_process_group on windows (#148266)
• Fixed capturability of isend and irecv (#148462)

DistributedStateDict (DSD)

• Fixed strict=False case for DDP (#143038)
• Fixed issue when there is a PG without parameters (#147730)
• Fixed the shared parameter mismatch for optimizer state_dict when flattening FQNs are used (#148825)

FullyShardedDataParallel2 (FSDP2)

• Rooted fix for FP8 tensor (#143248)
• Added workaround to fix buffer_dtype without root parameters (#143989)
• Supported custom all reduce hook across FSDP units (#147114)
• Fixed bug in FSDP wrapped module with zero argument (#147771)

DTensor

• Fixed torch.distributed._functional_collectives.AsyncCollectiveTensor for aten.to. (#134661)
• Deferred DTensor RNG state sync until first random op call or manual_seed call to support more flexible OffsetBasedRNGTracker init (#147025)
• Fixed _scaled_dot_product_flash_attention sharding (#148125)
• Fixed redistribution cost for all-reduce (#148761)

Pipelining

• Fixed backward_one_chunk when the output of the model is a view (#142237)
• Threw error with ZB and compile (#143599)
• Fixed FSDP+PP stream sync bug (#144535)
• Fixed PP grad scaling (#144352)
• No allowing for num_microbatches > num_stages for single stage schedules (#144702)
• Fixed shape_inference for V-schedules (#147000)

CPU

General

• Use sleef implementation for CPP backend asinh codegen (#142360)

x86

• Constrain the shape of other tensor for Conv/Linear + broadcast add fusion (#141759)

CUDA

• Let PYTORCH_NO_CUDA_MEMORY_CACHING has effect only when value is 1 (#145905)
• Fix race condition in cuda initialization (#143238)
• Fix a few 64-bit indexing issues, account for number of threads in complex128 scan (#143401)
• Fix acquire pattern (correctness with respect to memory model) in topk (#144945)
• Int64 indexing fix for UpSampleNearest3D (#144865)
• Fix printing of the number of GPUs when certain asserts are raised (#146838)
• Update the number of threads in avg_pool2d backward for SM 10.0 to prevent runtime crash (#145669)
• Only use f8f8bf16 rowwise scaled matmul to SM 9.0 (precedes #148421 adding of kernel) (#145728)
• Fix 64-bit indexing for Upsample2D (#141923)
• Fix path lookup in _preload_cuda_deps (#149808)
• Help support Blackwell: Fix backward launch bounds again for sm100, sm120 (#150640)

MPS

• Workaround for gather_out in MPS backend (#135543)
• Fix fmin/fmax for scalar argument (#143934)
• Fix crash when mm is invoked with mixed dtypes (#143948)
• Fix torch.add(x,y, alpha=2) crash (#143949)
• Fix nllnd_loss_backward crash with different dtypes (#144170)
• Make sure that MPSStream is usable from C++ (#144559)
• Make MPSProfiler usable from C++ (#144560)
• Fix regression in con-contiguous bitwise ops (#146085)
• Fix lu factor for large tensors with bs>1 (#146753)
• Ensure 4d input in _scaled_dot_product_attention_math_mps (#146623)
• Fix cholesky_ex for empty inputs (#147159)
• Fix attention for >4d tensors (#147545)
• Fix empty placeholder error for smooth l1 loss (#148133)
• Fix sqrt and other for torch.chalf (#148285)
• Fix unary_kernel_strided logic (#148512)
• Fix scalar to tensors bitshifts (#148686)
• Fix multinomial sampling for non-contiguous tensors (#141515)
• Fix triangular for >3D tensors (#144545)
• Fix missing autorelease in lstm_mps causing leaked memory (#145503)
• Fix missing autoreleasepool around runUniqueGraph to prevent leaks (#145512)
• Workaround rng bug for 5D tensors (#147667)
• Fix Wreorder-init-list (#148839)
• Fix invalid format string in libfmt calls (#148855)
• Fix c10::metal::log_gamma correctness on M4 (#145740)
• Fix lu factor for non contiguous tensors (#146279)
• Fix attention enable_gqa crash on MPS (#149147)
• Fix dot/mm for conj_tensors (#150157)
• Fix tril op not handling infs correctly (#149866)
• In MPSInductor:
  • Fix min/max reductions over large dims (#149004)
  • Fix argmin/max signatures (#149020)
  • Fix masked/where for inf values (#144500)
  • Move threadfence to before first read from shared memory, not after (#149437)

ROCm

• TunableOp use thread-safe getenv functions (#142274)
• fix torch.layer_norm invalid configuration problem when input is large tensor (#144007)
• [Inductor][CK] hackfix for segfault in addmm op (#144519)
• Fix torch.layer_norm invalid configuration when input is large tensor (#144007)
• Fix isnan integer overload errors on MicroSoft STL (#146605)
• Fixes and improvements to CUDA->HIP flag conversion for CPP extensions (#149245)

XPU

• Fix SDPA dummy log_sum_exmp output to match meta function (#148652)
• Fix memory leak in deconv backward (#144385)
• Add XPU support to torch.utils._content_store to accelerate XPU tensor hashing for tensor serialization (#147785)
• Enabling XPU in OffsetBasedRNGTracker to unbreak torch.distributed (#148360)
• torch.backends.mkldnn.flags() CM should not warn (#150358)

Profiler

• Hide Kineto step() for iterative on-demand tracking behind environment variable (#144494)
• Enable CUPTI on Windows (#141454)
• Fix device setting error of other backends in torch.profiler (#144237)
• Fix assertion failure in PyTorch profiler (#143940)

torch.compile

• Do not depend on numpy during torch._functorch import (#149683)

Dynamo

• Guard on global autocast state (#143592)
• Fix some internal crashes involving undefined names (#144784)
• Multiple silent incorrectness fixes for Compiled Autograd (#144707)
• Fix graph break in FlexAttention when using Compiled Autograd (#144533)

Inductor

• Fix a bug where the options dictionary on torch.compile calls was ignored (#145131).
• Inductor now supports nanj in cpp wrapper CPU (#144064).
• Fix a bug in the fractional_max_pool lowering in Inductor (#144395).
• FlexAttention: Fix a few more symbolic shape issues (#142816).
• Fix a bug in associative_scan (#143048).
• Fix the Index Put lowering with same input of self and values (#139366).
• Fix a bug in torch.polygamma(n) when n == 0 (#144058).
• Fix bug in integer avg_pool that was causing 0 rounding (#144059).
• Change avg_pool with uint to match eager (#144313).
• Fix bug in max-autotune on smaller GPUs (<68 SMs) (#145133).
• Fix bug in torch.logit decomposition (#145576).
• Fix bug in the strides when lowering custom op (#148367).
• Update triton support to account for changes in AttrsDescriptor (#145051) (#145348) (#145575) (#145583) (#145515).
• Fix bug where the benchmark_harness isn't generated, but is called in some cases (#145532).
• Make sure not using cpp wrapper when setting nvtx training annotation (#145538).
• Fix bug where SVE256 features were run on SVE128 systems (#146207).
• Fix an unaligned memory access issue in mm_template (#146293).
• Fix intermediate debug information with cpp_wrapper (#145527).
• Fix bug where inductor was codegen-ing wrong shapes for bucketize when it was fused as an epilogue (#148769).
• Fix bug in AOTI one-pass codegen when max-autotune is turned on (#143098).
• Fix a memory leak in package AOTIModelPackageLoaderPybind::boxed_run (#146100).
• Fix None and equal_to_1 arguments issue in Triton kernel generated by AOTI (#148102)
• Fix backwards compatibility for AOTIModelPackageLoader() constructor defaults (#149082)
• Fix blank space break windows file path (#149388)
• Fix inductor windows linker error (#150256)

torch.fx

• Fix get_source_partitions when weights are tied (#142446)
• Prevent DCE of ATen rng nodes (#144319)
• Fix incorrect type comparison (#145449)
• Fix DCE of setitem node (#145714)
• Fix pytree.register_constant to be usable in export (#147533)
• Fix edge case in translation validation bisector (#145414)

torch.export

serialization

• Rewrite the export schema format to archive without BC-breakage (#142511)
• Serialize all dataclass fields, including default-valued members, in export schema (#142286)
• Fix SymBool incorrectly serialized as bools (#144295)
• Fix serialization roundtrippability for nodes with default arguments (#144686)
• Fix deserializing bool graph outputs (#144791)
• Fix deserialization for and_ operator (#145506)
• Explicitly serialize unbacked_bindings (#144894)
• Relax serialization assertion to warning for unbacked_bindings keys (#145777)
• Avoid always printing GraphModule in de/serialization logging (#145857)
• Bump ShapeEnv unbacked symbol counters for unbacked_bindings in deserialization (#145882)
• Fix serialization for nested terms in nn_module_stack (#145901)
• Fix typo in SymFloat serialization (#146112)
• Fix deserialization for .requires_grad field (#146351)
• Support math.trunc ops for serialization (#146715)
• Serialize math.inf and NaN as strings (#146490)
• Loosen SymInt input serialization for Inductor (#147237)

draft export

• Fix dense-in-memory check for fake-kernel inference, for draft export (#145653)
• Fix lazy_trace_handler bug in draft export logging (#146106)
• Only clear pending unbacked symbols for overwritten fake-kernels for draft export (#147427)
• Ignore when real-tensor fallback fails in draft export (#147779)

miscellaneous

• Fix dynamic shape constraint checking when non-strict retracing (#143442)
• Fix ._modules corner case for nn_module_stack metadata in strict-mode (#142823)
• Fix placeholder name ordering for kwargs in non-strict mode (#144278)
• Extend support for distributed ops (all_reduce, all_gather, all_gather_into_tensor, all_to_all_single, reduce_scatter_tensor) in non-strict mode (#147133, #147417)
• Fix error with unflattener submodule reordering (#146181)
• Make stack_trace field optional in insert_custom_op_guards pass (#146438)
• Differentiate ScriptModules and ScriptObjects for TorchBind (#147399)
• Restore lost input mutations with export_tracepoint (#148709)
• Symintify transpose_ (#149057)

ONNX

• Support subgraphs with 1+ outputs (#145860)
• Delete rename_dynamic_shapes_with_model_inputs (#146002)
• Handle number of outputs in builder (#147164)
• Fix missed None type support in dynamic_shapes string cases (#148025)

Performance

Release Engineering

• Add perf testing on H100 (#146868, #147947)

Sparse Frontend

• Remove unnecessary tensor clones throughout codebase (#148159)

Distributed

Distributed Checkpoint (DCP)

• Introduce process based async checkpointing (#147039)

c10d

• Changed ALLOC_BUFFER_SIZE from 4000 to 4096 to be a power of 2 for TCPStore (#145759)
• Improved IPC tensor release performance by releasing the IpcMutex when deleting the ExpandableSegments object and the GIL in WorkNCCL destructor (#148805)

CPU

General

• Simplify vec128 bfloat16/half fmadds (#144486)
• Parallelize sort (#142391)

x86

• Set prop_kind to forward_inference when grad is not needed for mkldnn_convolution_pointwise (#142855)
• Support reduce ops for add and max (#144065)
• use zero-point to decide conv src zp mask (#149473)

CUDA

• Let PYTORCH_NO_CUDA_MEMORY_CACHING has effect only when value is 1 (#145905)
• Fix race condition in cuda initialization (#143238)
• Fix a few 64-bit indexing issues, account for number of threads in complex128 scan (#143401)
• Fix acquire pattern (correctness with respect to memory model) in topk (#144945)
• Int64 indexing fix for UpSampleNearest3D (#144865)
• Fix printing of the number of GPUs when certain asserts are raised (#146838)
• Update the number of threads in avg_pool2d backward for SM 10.0 to prevent runtime crash (#145669)
• Only use f8f8bf16 rowwise scaled matmul to SM 9.0 (precedes #148421 adding of kernel) (#145728)
• Fix 64-bit indexing for Upsample2D (#141923)

MPS

• Faster integer batched matmul (#147877)
• Implement linear1d as shader (#148154)
• Metal unary kernel for sqrt (#148272)
• Faster unary operations for strided tensors (#148350)
• Introduce strides unary op (#148468)
• Implemented masked_fill_scalar as shader (#147369)
• Implement bilineard2d as shader (#145581)
• Optimize Cholesky (#145722)
• Speedup interpolation (#148277)

ROCm

• Improve backwards indexing when stride is not one (#147630)
• Improvements for vectorized elementwise kernels (#143269)
• Skip L1 cache for single-use buffers in tl.load (#143115)
• Improve performance of reduce sum for 3D shapes (#143137)
• Enable _load_dwordx4 ISA for BFloat16 and Half (#141397)
• Improve reduce sum calculation for low CU count (#141378)
• Tune 3d tensor sums when not using fastest dimension (#146170)
• Optimize the stride one indexing backwards kernel (#146420)
• Use IPT=8 for block radix sort (#147657)
• Improve performance of reduce sum for 3D shapes (#143137)
• change preferred blas lib defaults (#150212)

XPU

• Optimize SDPA Inference Performance for XPU (#147614, #147612)
• Improve zero-point memory creation (#148640)
• Avoid unnecessary copy when the destination tensor of Matmul is non-contiguous or input is broadcasted (#144759, #143784)

torch.compile

Dynamo

• Implement dynamic shape guards in C++ (#139899)
• Directly access Python frame locals in guard checks (#140063)
• Misc. Dynamo tracing time improvements (#143066)

Inductor

• Support for Arm Neon and SVE support for FP32 Gemm Wrapper (#144327).
• New GEMM kernel: persistent_tma (#142101).
• Enable CPP Grouped GEMM Template (#143796).
• Auto-tuning support for i8 x i8 -> i32 GEMM kernel on AMX ISA (#143187).
• Add new GEMM templates for CPU AVX512: _weight_int4pack_mm_for_cpu (#146756).
• Fuse SmoothQuant int8 linear pattern (#142036).
• Add torchao da8w8 pattern with symmetric quantized activations and weights (#142110).
• Support tiling reduction dimensions: Instead of having a single reduction dimension called "r", we can now support 2D reductions with "r0_" and "r1_" dimensions. 2D reductions generate two nested loops, with different block pointer advancements in each loop body (#137243).
• New config to skip L1 cache for single-use buffers in triton codegen (#143115).
• Implement max_pool2d_with_indices as a reduction for large window sizes (#147876).
• Optimize the heuristics of outer loop fusion in Inductor CPU backend (#147523).
• Support parallel reduction for GroupNorm in Inductor CPU backend (#144020).
• Add support for online softmax. Online softmax uses a customized reduction to compute max and sum at the same time by accessing the data in one pass (#127011).
• Add ROCm specific matmul tuning parameters (#148437).

torch.fx

• Micro-optimization in Graph.nodes.__iter__ (#144631)
• Micro-optimization in map_aggregate(immutable_dict) (#147691)
• Move DCE rand check to import time (#145118)

Quantization

• Enable fast qlinear static/dynamic path for AArch64 through ACL directly (#148585)
• Improve KleidiAI 4 bit kernel performance (#146476)
• Add NEON implementation for 8 bit quantized embedding bag on AArch64 to improve performance by ~5.5x on Neoverse V1 cores (#147322)

Documentation

Python Frontend

• Fix description of input in torch.addbmm() (#146664)
• fix numpy docs reference (#147697)
• Add torch.cat type promotion documentation (#141339)
• Add details torch.topk indices stability when duplicate values (#143736)
• Add overloads to torch.diagonal documentation (#144214)
• remove incorrect warnings from torch.{min,max} documentation (#146725)
• Update addbmm, addmm, addmv and baddbmm description (#146689)
• Fix torch.max optional args dim, keepdim description (#147177)
• Update torch.bucketize documentaion (#148400)
• Fix docs recommending inefficient tensor op order (#144270)

Autograd

• Suppress vmap warning from torch.autograd.gradcheck #144287

Nested Tensor (NJT)

• Update OSS nested tensor docs to focus on NJT (#145402)

torch.nn

• Add clarification for target types in CrossEntropyLoss doc (#145444)

torch.optim

• Clarify what we mean by decoupled weight decay in the *AdamWs (#144101, #144984)
• Corrected description of AMSGrad algorithm (#142351)

Build Frontend

• Removing doc references to PRE_CXX11_ABI. (#149756)

Distributed

FullyShardedDataParallel2 (FSDP2)

• Highlighted equivalence of set_requires_gradient_sync and no_sync (#148715)

Distributed (c10d)

• Updated docs for wait() (#143305)
• Added comments to the end of Macro for better readability (#144789)

DTensor

• Added some documentation for from_group API and add a 2D test (#146364)
• Expose the __create_chunk_list__ in the doc (#144100)

DistributedStateDict (DSD)

• Updated the document to mention the limitation of set_optimizer_state_dict (#148918)

Torch Elastic

• Replaced incorrect .. note:: invocations (#142868)
• Fixed the doc string for record (#146968)

Pipelining

• Updated tutorials and documentation (#143045)

CUDA

• Correct docs for clock_rate to MHz, fixes #147098 (#147393)

XPU

• Improve "Getting Started on Intel GPU" hardware requirements and notes(#147802, #148168, #150397)
• Improve SYCL extension, source build and AOT Inductor documentation (#147988, #143476, #149299)
• Update Doc for Intel XPU Profiling (#134515)
• Update CMAKE_PREFIX_PATH for XPU windows README (#148863)

torch.compile

Dynamo

• Remove the suggestion to use suppress_errors on compiler error (#146553)
• Automatically generated Dynamo docs (#146736)

Inductor

• Spruce up docs for emulate_precision_casts (#145579).
• Minor fixes to export and AOTI docs (#144513).
• Update AOTI tutorial (#143390).
• inductor.config.descriptive_names = False is no longer a suggested option (#145523).

torch.fx

• Improve logging for splitter (#143771)
• Update literal typing for torch/fx/graph nodelist (#144650)
• Improve typing for torch/fx/_pytree.py and torch/utils/_pytree.py (#145173)
• Fix minor mistake in docstring of replace_pattern (#147611)

torch.export

• Export Programming Model: #143546
• Update dynamic shapes docs for dims() and suggested fixes parser: #142510
• Clean up docstring for torch.export.load(): #141490

Quantization

• Add torchao docs link to PyTorch libraries (#145412)

ONNX

• Update TorchDynamo-based ONNX Exporter memory usage example code. (#144139)
• Deprecation message follow up (#147005)
• Expose verification utilities (#148603)

Developers

Python Frontend

• Collect packages with importlib in collect_env (#144616)
• added __add__ and __mul__ hints to torch.Size (#144322)

Distributed

FullyShardedDataParallel2 (FSDP2)

• Enabled the typing of fully_shard so that the return value can be chained with typing enabled (#147489)

Distributed (c10d)

• Improved the dump mechanism for flight recorder (#143446)
• Added log trace capture enabled or not in flight recorder (#143865)
• Added file flush in file based dumper of flight recorder (#145458)
• Caught c10 error and log message inside monitoring thread (#145413)
• Added an API to get the status/error code at the PG level (#144498)
• Moved record param for init to the right place (#148571)
• Enabled testing generelization for multiple accelerator devices (#139749)

TensorParallel

• Added warning when module is distributed twice (#147006)

Pipelining

• Improved shape inference debug logging (#144929)

MPS

• Support includes in metal objects (#145087)
• Context manager to capture Metal commands for debugging/profiling (#144561)

XPU

• Reduce the binary size of the XPU Windows package (#148313)
• Add Python 3.13 build for XPU (#146614)
• Make XPU Triton build supports manylinux 2.28 (#148195)
• Fix XPU builds inside venv (#150300)

Benchmark

• Remove old ONNX benchmarks from operator benchmarks (#146325)
• Add option to write operator benchmark output to a JSON (#142809)
• Improve operator benchmark results parsing (#144297)
• Add more operators {add_, addcmul, arange, baddbmm, bmm, clamp, div, div_, gelu, index_add, logical_and, mul_, sub_, topk, where} to operator benchmark (#145625)
• Add cachebench to operator benchmarks for PT2 caching (#147537)

torch.compile

Dynamo

• New internal graph break API that enforces better error messages (#146525)
• Replace internal calls to torch._dynamo.optimize() with torch.compile() (#142451)

Inductor

• Support for export to unwrap/wrap subclasses AOT, resolves UX issue in torchao where users had to manually unwrap their subclasses before calling export (#141941).
• Autotuning logs will now show up in TORCH_LOGs under the name "autotuning" (#147222).
• Replace set by OrderedSet: only use OrderedSet in the Inductor codebase (#138466).
• Now MPS is considered a GPU_TYPE (#143634).
• Separate unary post op fusion and lowering for qlinear (#143903).
• New classes to help with kernel memory analysis in heuristics (#142026).
• Move ir_pre_fusion.txt and ir_post_fusion.txt from TORCH_COMPILE_DEBUG to TORCH_LOGS. For example, TORCH_LOGS="+ir_pre_fusion" (#147248).
• Implement deepcopy for AOTICompiledModel (#145423)

torch.fx

• Downgrade some logs (#147538, #145075)
• Refactor immutable collections implementation (#144640)
• Make fx.node.map_arg() and .map_aggregate() generic (#146248)