From f8932a44bc2cd9845d64c685347acb81697a530b Mon Sep 17 00:00:00 2001 From: HGuillemet Date: Sun, 19 May 2024 22:29:21 +0200 Subject: [PATCH] * Upgrade presets for PyTorch 2.3.0 (pull #1498) --- .github/actions/deploy-ubuntu/action.yml | 16 +- CHANGELOG.md | 2 +- platform/pom.xml | 2 +- pytorch/README.md | 6 +- pytorch/cppbuild.sh | 6 +- pytorch/include_list.pl | 8 +- pytorch/platform/gpu/pom.xml | 2 +- pytorch/platform/pom.xml | 2 +- pytorch/pom.xml | 2 +- pytorch/samples/pom.xml | 4 +- .../pytorch/AOTIModelContainerRunner.java | 48 + .../pytorch/AOTIModelContainerRunnerCpu.java | 48 + .../pytorch/AcceleratorHooksInterface.java | 37 + .../java/org/bytedeco/pytorch/Allocator.java | 26 +- .../java/org/bytedeco/pytorch/AnyModule.java | 2 + .../org/bytedeco/pytorch/AutogradMeta.java | 121 - .../java/org/bytedeco/pytorch/AwaitPtr.java | 10 +- .../org/bytedeco/pytorch/BackendMetaRef.java | 10 +- .../bytedeco/pytorch/CUDAHooksInterface.java | 2 +- .../bytedeco/pytorch/ChunkRecordIterator.java | 1 + .../gen/java/org/bytedeco/pytorch/Code.java | 1 + .../bytedeco/pytorch/ConstantStringPtr.java | 10 +- .../java/org/bytedeco/pytorch/Context.java | 14 +- .../org/bytedeco/pytorch/DataPtrVector.java | 4 +- .../pytorch/DeviceGuardImplInterface.java | 2 +- .../bytedeco/pytorch/DeviceTypeOptional.java | 35 + .../pytorch/DifferentiableViewMeta.java | 49 - .../org/bytedeco/pytorch/DispatchKeySet.java | 21 +- .../{DLDevice_.java => DynamicLibrary.java} | 12 +- .../org/bytedeco/pytorch/EnumHolderPtr.java | 10 +- .../org/bytedeco/pytorch/Float8_e5m2fnuz.java | 1 + .../java/org/bytedeco/pytorch/FuturePtr.java | 10 +- .../bytedeco/pytorch/GeneratorImplPtr.java | 10 +- .../bytedeco/pytorch/HIPHooksInterface.java | 2 +- .../gen/java/org/bytedeco/pytorch/Half.java | 3 + .../org/bytedeco/pytorch/LPPool3dImpl.java | 49 + .../bytedeco/pytorch/LPPool3dImplBase.java | 37 + .../pytorch/LPPool3dImplCloneable.java | 37 + .../java/org/bytedeco/pytorch/Library.java | 3 + .../bytedeco/pytorch/LongSmallVectorImpl.java | 2 +- .../bytedeco/pytorch/MPSHooksInterface.java | 8 +- .../bytedeco/pytorch/MTIAHooksInterface.java | 5 +- .../gen/java/org/bytedeco/pytorch/Method.java | 3 + .../gen/java/org/bytedeco/pytorch/Module.java | 5 + .../bytedeco/pytorch/NestedTensorImpl.java | 12 +- .../gen/java/org/bytedeco/pytorch/Node.java | 5 +- .../gen/java/org/bytedeco/pytorch/ObjPtr.java | 10 +- .../org/bytedeco/pytorch/OperandInfo.java | 2 + .../bytedeco/pytorch/OptimizerParamState.java | 5 +- .../pytorch/PlacementDeleteContext.java | 10 +- .../pytorch/PostAccumulateGradHook.java | 4 +- .../pytorch/PrivateUse1HooksInterface.java | 12 +- .../bytedeco/pytorch/PyObjectHolderPtr.java | 10 +- .../org/bytedeco/pytorch/QuantizerPtr.java | 10 +- .../pytorch/ROCmBackwardPassGuard.java | 39 + .../bytedeco/pytorch/RRefInterfacePtr.java | 10 +- .../pytorch/RecordFunctionCallbacksEntry.java | 4 +- .../gen/java/org/bytedeco/pytorch/Scalar.java | 14 + .../org/bytedeco/pytorch/SequentialImpl.java | 3 + .../org/bytedeco/pytorch/SizesAndStrides.java | 2 + .../java/org/bytedeco/pytorch/Storage.java | 26 +- .../org/bytedeco/pytorch/StorageImpl.java | 14 +- .../org/bytedeco/pytorch/StorageImplPtr.java | 10 +- .../org/bytedeco/pytorch/StringIntMap.java | 48 + .../java/org/bytedeco/pytorch/SymBool.java | 2 + .../pytorch/SymIntSmallVectorImpl.java | 2 +- .../java/org/bytedeco/pytorch/SymNode.java | 10 +- .../org/bytedeco/pytorch/SymNodeImpl.java | 7 +- .../bytedeco/pytorch/T_DataPtrSizeT_T.java | 4 +- .../gen/java/org/bytedeco/pytorch/Tensor.java | 5 + .../java/org/bytedeco/pytorch/TensorArg.java | 2 + .../java/org/bytedeco/pytorch/TensorImpl.java | 7 +- .../org/bytedeco/pytorch/TensorImplPtr.java | 10 +- .../bytedeco/pytorch/TensorIteratorBase.java | 42 +- .../pytorch/TensorIteratorConfig.java | 5 + .../org/bytedeco/pytorch/TensorMaker.java | 5 +- .../org/bytedeco/pytorch/TensorNames.java | 2 +- .../org/bytedeco/pytorch/TensorOptions.java | 6 +- .../java/org/bytedeco/pytorch/TreeRef.java | 10 +- .../java/org/bytedeco/pytorch/TuplePtr.java | 10 +- .../org/bytedeco/pytorch/VariableInfo.java | 6 +- .../org/bytedeco/pytorch/WeakStorage.java | 4 +- .../bytedeco/pytorch/XPUHooksInterface.java | 17 +- .../cuda/AOTIModelContainerRunnerCuda.java | 68 + .../pytorch/cuda/AllocatorConfigInfo.java | 51 + .../pytorch/cuda/CTCLossDescriptor.java | 2 - .../bytedeco/pytorch/cuda/CUDAAllocator.java | 34 +- .../pytorch/cuda/DeviceAssertionData.java | 5 + .../pytorch/cuda/DeviceAssertionsData.java | 1 + .../bytedeco/pytorch/cuda/DeviceStats.java | 11 + .../pytorch/cuda/RNNDataDescriptor.java | 56 + .../bytedeco/pytorch/cuda/RNNDescriptor.java | 5 +- .../bytedeco/pytorch/cuda/SegmentInfo.java | 2 +- .../bytedeco/pytorch/cuda/SnapshotInfo.java | 1 + .../pytorch/cuda/TensorDescriptor.java | 10 - .../org/bytedeco/pytorch/cuda/TraceEntry.java | 18 +- .../org/bytedeco/pytorch/global/torch.java | 3529 ++++++++--------- .../bytedeco/pytorch/global/torch_cuda.java | 158 +- .../pytorch/cuda/functions/StreamFilter.java | 29 + .../functions/StorageImplCreateHelper.java | 34 - .../org/bytedeco/pytorch/presets/torch.java | 106 +- .../bytedeco/pytorch/presets/torch_cuda.java | 22 +- .../pytorch/presets/torch_cuda_include.h | 5 +- .../bytedeco/pytorch/presets/torch_include.h | 22 +- 104 files changed, 2878 insertions(+), 2385 deletions(-) create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunner.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunnerCpu.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/AcceleratorHooksInterface.java delete mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/AutogradMeta.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/DeviceTypeOptional.java delete mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/DifferentiableViewMeta.java rename pytorch/src/gen/java/org/bytedeco/pytorch/{DLDevice_.java => DynamicLibrary.java} (61%) create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImpl.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplBase.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplCloneable.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/ROCmBackwardPassGuard.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/StringIntMap.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AOTIModelContainerRunnerCuda.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AllocatorConfigInfo.java create mode 100644 pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDataDescriptor.java create mode 100644 pytorch/src/main/java/org/bytedeco/pytorch/cuda/functions/StreamFilter.java delete mode 100644 pytorch/src/main/java/org/bytedeco/pytorch/functions/StorageImplCreateHelper.java diff --git a/.github/actions/deploy-ubuntu/action.yml b/.github/actions/deploy-ubuntu/action.yml index 108bea62600..b5bf8f9351a 100644 --- a/.github/actions/deploy-ubuntu/action.yml +++ b/.github/actions/deploy-ubuntu/action.yml @@ -16,6 +16,16 @@ runs: fi cd $HOME + # Free disk space by removing some unused system components + $SUDO rm -rf /usr/local/lib/android + $SUDO rm -rf /usr/share/dotnet + + # Allocate a swapfile on Linux as it's not enabled by default. Needed for pytorch and mkl. + $SUDO fallocate -l 4GB /swapfile + $SUDO chmod 600 /swapfile + $SUDO mkswap /swapfile + $SUDO swapon /swapfile + mkdir -p .ccache echo "max_size = 2.0G" > .ccache/ccache.conf echo "hash_dir = false" >> .ccache/ccache.conf @@ -230,12 +240,6 @@ runs: curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/86d6a4c1-c998-4c6b-9fff-ca004e9f7455/l_onemkl_p_2024.0.0.49673_offline.sh $SUDO bash l_onemkl_p_2024.0.0.49673_offline.sh -s -a -s --eula accept export MAVEN_OPTIONS="-Djavacpp.platform.compiler=clang++" - - # Allocate a swapfile on Linux as it's not enabled by default - $SUDO fallocate -l 4GB /swapfile - $SUDO chmod 600 /swapfile - $SUDO mkswap /swapfile - $SUDO swapon /swapfile fi if [[ "$CI_DEPLOY_PLATFORM" == "linux-armhf" ]] && [[ "$CI_DEPLOY_MODULE" == "flycapture" ]]; then diff --git a/CHANGELOG.md b/CHANGELOG.md index 9d25a69fe48..be1f22d8437 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,7 @@ * Build FFmpeg with zimg to enable zscale filter ([pull #1481](https://github.com/bytedeco/javacpp-presets/pull/1481)) * Enable PulseAudio support for FFmpeg on Linux ([pull #1472](https://github.com/bytedeco/javacpp-presets/pull/1472)) * Virtualize `btCollisionWorld`, `btOverlapFilterCallback`, `btOverlapCallback` from Bullet Physics SDK ([pull #1475](https://github.com/bytedeco/javacpp-presets/pull/1475)) - * Upgrade presets for FFmpeg 7.0, DNNL 3.4.1, OpenBLAS 0.3.27, CPython 3.12.3, NumPy 1.26.4, SciPy 1.13.0, LLVM 18.1.4, libffi 3.4.6, PyTorch 2.2.2 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.16.1, TensorRT 10.0.1.6, Triton Inference Server 2.44.0, ONNX 1.16.0, ONNX Runtime 1.17.3, TVM 0.16.0, and their dependencies + * Upgrade presets for FFmpeg 7.0, DNNL 3.4.1, OpenBLAS 0.3.27, CPython 3.12.3, NumPy 1.26.4, SciPy 1.13.0, LLVM 18.1.4, libffi 3.4.6, PyTorch 2.3.0 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.16.1, TensorRT 10.0.1.6, Triton Inference Server 2.44.0, ONNX 1.16.0, ONNX Runtime 1.17.3, TVM 0.16.0, and their dependencies ### January 29, 2024 version 1.5.10 * Introduce `macosx-arm64` builds for PyTorch ([pull #1463](https://github.com/bytedeco/javacpp-presets/pull/1463)) diff --git a/platform/pom.xml b/platform/pom.xml index a77f9880be6..7ee2032b15a 100644 --- a/platform/pom.xml +++ b/platform/pom.xml @@ -292,7 +292,7 @@ org.bytedeco pytorch-platform - 2.2.2-${project.version} + 2.3.0-${project.version} org.bytedeco diff --git a/pytorch/README.md b/pytorch/README.md index 8d9c6b055fb..98899c6fde8 100644 --- a/pytorch/README.md +++ b/pytorch/README.md @@ -9,7 +9,7 @@ Introduction ------------ This directory contains the JavaCPP Presets module for: - * PyTorch 2.2.2 https://pytorch.org/ + * PyTorch 2.3.0 https://pytorch.org/ Please refer to the parent README.md file for more detailed information about the JavaCPP Presets. @@ -48,14 +48,14 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic org.bytedeco pytorch-platform - 2.2.2-1.5.11-SNAPSHOT + 2.3.0-1.5.11-SNAPSHOT org.bytedeco pytorch-platform-gpu - 2.2.2-1.5.11-SNAPSHOT + 2.3.0-1.5.11-SNAPSHOT diff --git a/pytorch/cppbuild.sh b/pytorch/cppbuild.sh index 3e229e73b81..523a8785296 100755 --- a/pytorch/cppbuild.sh +++ b/pytorch/cppbuild.sh @@ -35,7 +35,7 @@ if [[ $PLATFORM == windows* ]]; then export PYTHON_BIN_PATH=$(which python.exe) fi -PYTORCH_VERSION=2.2.2 +PYTORCH_VERSION=2.3.0 export PYTORCH_BUILD_VERSION="$PYTORCH_VERSION" export PYTORCH_BUILD_NUMBER=1 @@ -53,10 +53,6 @@ git checkout v$PYTORCH_VERSION git submodule update --init --recursive git submodule foreach --recursive 'git reset --hard' -# Fix version of this submodule to allow compilation on windows. -# Probably could be removed when we upgrade to 2.3 -(cd third_party/pocketfft; git checkout 9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe) - CPYTHON_HOST_PATH="$INSTALL_PATH/../../../cpython/cppbuild/$PLATFORM/host/" CPYTHON_PATH="$INSTALL_PATH/../../../cpython/cppbuild/$PLATFORM/" OPENBLAS_PATH="$INSTALL_PATH/../../../openblas/cppbuild/$PLATFORM/" diff --git a/pytorch/include_list.pl b/pytorch/include_list.pl index a5153bb3f1d..a91ad04e216 100644 --- a/pytorch/include_list.pl +++ b/pytorch/include_list.pl @@ -52,9 +52,7 @@ sub go { chdir "cppbuild/linux-x86_64-gpu/pytorch/torch/include"; -# Doesn't compile on Windows. Waiting for 2.2.1. -#go('torch/csrc/api/include/torch/torch.h', 'torch/script.h', 'torch/csrc/inductor/aoti_model_container_runner.h'); -go('torch/csrc/api/include/torch/torch.h', 'torch/script.h'); +go('torch/csrc/api/include/torch/torch.h', 'torch/script.h', 'torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h'); print <org.bytedeco pytorch-platform-gpu - 2.2.2-${project.parent.version} + 2.3.0-${project.parent.version} JavaCPP Presets Platform GPU for PyTorch diff --git a/pytorch/platform/pom.xml b/pytorch/platform/pom.xml index ae94ac9a268..ee51db3b7af 100644 --- a/pytorch/platform/pom.xml +++ b/pytorch/platform/pom.xml @@ -12,7 +12,7 @@ org.bytedeco pytorch-platform - 2.2.2-${project.parent.version} + 2.3.0-${project.parent.version} JavaCPP Presets Platform for PyTorch diff --git a/pytorch/pom.xml b/pytorch/pom.xml index 6fe7e46a00f..aa4f8e569bc 100644 --- a/pytorch/pom.xml +++ b/pytorch/pom.xml @@ -11,7 +11,7 @@ org.bytedeco pytorch - 2.2.2-${project.parent.version} + 2.3.0-${project.parent.version} JavaCPP Presets for PyTorch diff --git a/pytorch/samples/pom.xml b/pytorch/samples/pom.xml index 3cf94d51c70..b3b4c34acef 100644 --- a/pytorch/samples/pom.xml +++ b/pytorch/samples/pom.xml @@ -12,14 +12,14 @@ org.bytedeco pytorch-platform - 2.2.2-1.5.11-SNAPSHOT + 2.3.0-1.5.11-SNAPSHOT org.bytedeco pytorch-platform-gpu - 2.2.2-1.5.11-SNAPSHOT + 2.3.0-1.5.11-SNAPSHOT diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunner.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunner.java new file mode 100644 index 00000000000..315a3bb11ad --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunner.java @@ -0,0 +1,48 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + + +@Namespace("torch::inductor") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class AOTIModelContainerRunner extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public AOTIModelContainerRunner(Pointer p) { super(p); } + + + + + + + + public native @ByVal TensorVector run( + @ByRef TensorVector inputs); + + public native @ByVal ExtraFilesMap getConstantNamesToOriginalFQNs(); + public native @ByVal StringIntMap getConstantNamesToDtypes(); + public native void update_inactive_constant_buffer(@Cast("const torch::inductor::TensorConstantMap*") @ByRef HashAliasedIValueMap const_map); + public native void update_constant_buffer( + @Cast("const torch::inductor::TensorConstantMap*") @ByRef HashAliasedIValueMap const_map, + @Cast("bool") boolean use_inactive, + @Cast("bool") boolean validate_full_updates); + public native void run_const_fold( + @Cast("bool") boolean use_inactive); + public native void swap_constant_buffer(); + + public native @ByVal StringVector get_call_spec(); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunnerCpu.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunnerCpu.java new file mode 100644 index 00000000000..245736a92bb --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/AOTIModelContainerRunnerCpu.java @@ -0,0 +1,48 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + +@Namespace("torch::inductor") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class AOTIModelContainerRunnerCpu extends AOTIModelContainerRunner { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public AOTIModelContainerRunnerCpu(Pointer p) { super(p); } + + public AOTIModelContainerRunnerCpu( + @StdString BytePointer model_so_path, + @Cast("size_t") long num_models/*=1*/) { super((Pointer)null); allocate(model_so_path, num_models); } + private native void allocate( + @StdString BytePointer model_so_path, + @Cast("size_t") long num_models/*=1*/); + public AOTIModelContainerRunnerCpu( + @StdString BytePointer model_so_path) { super((Pointer)null); allocate(model_so_path); } + private native void allocate( + @StdString BytePointer model_so_path); + public AOTIModelContainerRunnerCpu( + @StdString String model_so_path, + @Cast("size_t") long num_models/*=1*/) { super((Pointer)null); allocate(model_so_path, num_models); } + private native void allocate( + @StdString String model_so_path, + @Cast("size_t") long num_models/*=1*/); + public AOTIModelContainerRunnerCpu( + @StdString String model_so_path) { super((Pointer)null); allocate(model_so_path); } + private native void allocate( + @StdString String model_so_path); + + public native @ByVal TensorVector run(@ByRef TensorVector inputs); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AcceleratorHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AcceleratorHooksInterface.java new file mode 100644 index 00000000000..ba443287ab4 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/AcceleratorHooksInterface.java @@ -0,0 +1,37 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + + +// AcceleratorHooksInterface is a shared interface provided by all +// accelerators to allow generic code. +// This inferface is hook-based as it corresponds to all the functions +// that are going to be called in a generic way from the CPU code. + +@Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class AcceleratorHooksInterface extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public AcceleratorHooksInterface(Pointer p) { super(p); } + + // This should never actually be implemented, but it is used to + // squelch -Werror=non-virtual-dtor + + // Whether the device at device_index is fully initialized or not. + public native @Cast("bool") boolean hasPrimaryContext(@Cast("c10::DeviceIndex") byte device_index); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Allocator.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Allocator.java index db9d39b70e3..f6dc7870f9d 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Allocator.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Allocator.java @@ -46,7 +46,22 @@ public class Allocator extends Pointer { public Allocator(Pointer p) { super(p); } - public native @Name("allocate") @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr _allocate(@Cast("size_t") long n); + public native @Name("allocate") @StdMove DataPtr _allocate(@Cast("size_t") long n); + + // Clones an allocation that came from this allocator. + // + // To perform the copy, this function calls `copy_data`, which + // must be implemented by derived classes. + // + // Note that this explicitly ignores any context that may have been + // attached to the input data. + // + // Requires: input data was allocated by the same allocator. + public native @StdMove DataPtr clone(@Const Pointer data, @Cast("std::size_t") long n); + + // Checks if DataPtr has a simple context, not wrapped with any out of the + // ordinary contexts. + public native @Cast("bool") boolean is_simple_data_ptr(@StdMove DataPtr data_ptr); // If this returns a non nullptr, it means that allocate() // is guaranteed to return a unique_ptr with this deleter attached; @@ -55,4 +70,13 @@ public class Allocator extends Pointer { public native PointerConsumer raw_deleter(); public native Pointer raw_allocate(@Cast("size_t") long n); public native void raw_deallocate(Pointer ptr); + + // Copies data from one allocation to another. + // Pure virtual, so derived classes must define behavior. + // Derived class implementation can simply call `default_copy_data` + // to use `std::memcpy`. + // + // Requires: src and dest were allocated by this allocator + // Requires: src and dest both have length >= count + public native void copy_data(Pointer dest, @Const Pointer src, @Cast("std::size_t") long count); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AnyModule.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AnyModule.java index f5d68f0fad4..3f97cc39eec 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/AnyModule.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/AnyModule.java @@ -283,6 +283,8 @@ public class AnyModule extends Pointer { private native void allocate(@SharedPtr @Cast({"", "std::shared_ptr"}) MaxUnpool3dImpl module); public AnyModule(FractionalMaxPool3dImpl module) { super((Pointer)null); allocate(module); } private native void allocate(@SharedPtr @Cast({"", "std::shared_ptr"}) FractionalMaxPool3dImpl module); + public AnyModule(LPPool3dImpl module) { super((Pointer)null); allocate(module); } + private native void allocate(@SharedPtr @Cast({"", "std::shared_ptr"}) LPPool3dImpl module); public AnyModule(RNNImpl module) { super((Pointer)null); allocate(module); } private native void allocate(@SharedPtr @Cast({"", "std::shared_ptr"}) RNNImpl module); public AnyModule(LSTMImpl module) { super((Pointer)null); allocate(module); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AutogradMeta.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AutogradMeta.java deleted file mode 100644 index 788de523b3f..00000000000 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/AutogradMeta.java +++ /dev/null @@ -1,121 +0,0 @@ -// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE - -package org.bytedeco.pytorch; - -import org.bytedeco.pytorch.Allocator; -import org.bytedeco.pytorch.Function; -import org.bytedeco.pytorch.functions.*; -import org.bytedeco.pytorch.Module; -import org.bytedeco.javacpp.annotation.Cast; -import java.nio.*; -import org.bytedeco.javacpp.*; -import org.bytedeco.javacpp.annotation.*; - -import static org.bytedeco.javacpp.presets.javacpp.*; -import static org.bytedeco.openblas.global.openblas_nolapack.*; -import static org.bytedeco.openblas.global.openblas.*; - -import static org.bytedeco.pytorch.global.torch.*; - // namespace impl - -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -// AutogradMeta -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -/** Each {@code Variable} has one unique {@code AutogradMeta} struct, which stores autograd - * metadata fields that are necessary for tracking the Variable's autograd - * history. As an optimization, a Variable may store a nullptr, in lieu of a - * default constructed AutogradMeta. */ - -@Namespace("torch::autograd") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class AutogradMeta extends AutogradMetaInterface { - static { Loader.load(); } - /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ - public AutogradMeta(Pointer p) { super(p); } - - public native @StdString BytePointer name_(); public native AutogradMeta name_(BytePointer setter); - - public native @ByRef @Cast("torch::autograd::Variable*") Tensor grad_(); public native AutogradMeta grad_(Tensor setter); - public native @SharedPtr Node grad_fn_(); public native AutogradMeta grad_fn_(Node setter); - - // This field is used to store all the forward AD gradients - // associated with this AutogradMeta (and the Tensor it corresponds to) - // There is a semantic 1:1 correspondence between AutogradMeta and - // ForwardGrad but: - // - This field is lazily populated. - // - This field is a shared_ptr but it must never be - // shared by multiple Tensors. See Note [ Using ForwardGrad ] - // Any transition from not_initialized to initialized - // must be protected by mutex_ - public native @SharedPtr ForwardGrad fw_grad_(); public native AutogradMeta fw_grad_(ForwardGrad setter); - - // The hooks_ field is actually reused by both python and cpp logic - // For both cases, we have a data structure, cpp_hooks_list_ (cpp) - // or dict (python) which is the canonical copy. - // Then, for both cases, we always register a single hook to - // hooks_ which wraps all the hooks in the list/dict. - // And, again in both cases, if the grad_fn exists on that tensor - // we will additionally register a single hook to the grad_fn. - // - // Note that the cpp and python use cases aren't actually aware of - // each other, so using both is not defined behavior. - - - - // The post_acc_grad_hooks_ field stores only Python hooks - // (PyFunctionTensorPostAccGradHooks) that are called after the - // .grad field has been accumulated into. This is less complicated - // than the hooks_ field, which encapsulates a lot more. - public native @UniquePtr @Cast({"", "", "std::unique_ptr&&"}) PostAccumulateGradHook post_acc_grad_hooks_(); public native AutogradMeta post_acc_grad_hooks_(PostAccumulateGradHook setter); - - // Only meaningful on leaf variables (must be false otherwise) - public native @Cast("bool") boolean requires_grad_(); public native AutogradMeta requires_grad_(boolean setter); - - // Only meaningful on non-leaf variables (must be false otherwise) - public native @Cast("bool") boolean retains_grad_(); public native AutogradMeta retains_grad_(boolean setter); - - public native @Cast("bool") boolean is_view_(); public native AutogradMeta is_view_(boolean setter); - - // The "output number" of this variable; e.g., if this variable - // was the second output of a function, then output_nr == 1. - // We use this to make sure we can setup the backwards trace - // correctly when this variable is passed to another function. - public native @Cast("uint32_t") int output_nr_(); public native AutogradMeta output_nr_(int setter); - - // Mutex to ensure that concurrent read operations that modify internal - // state are still thread-safe. Used by grad_fn(), grad_accumulator(), - // fw_grad() and set_fw_grad() - // This is mutable because we need to be able to acquire this from const - // version of this class for the functions above - - /** Sets the {@code requires_grad} property of {@code Variable}. This should be true for - * leaf variables that want to accumulate gradients, and false for all other - * variables. */ - public native void set_requires_grad(@Cast("bool") boolean requires_grad, TensorImpl self_impl); - - public native @Cast("bool") boolean requires_grad(); - - /** Accesses the gradient {@code Variable} of this {@code Variable}. */ - public native @Cast("torch::autograd::Variable*") @ByRef Tensor mutable_grad(); - - public native @Cast("const torch::autograd::Variable*") @ByRef Tensor grad(); - - public native @Cast("const torch::autograd::Variable*") @ByRef Tensor fw_grad(@Cast("uint64_t") long level, @Const @ByRef TensorBase self); - - public native void set_fw_grad( - @Const @ByRef TensorBase new_grad, - @Const @ByRef TensorBase self, - @Cast("uint64_t") long level, - @Cast("bool") boolean is_inplace_op); - - public AutogradMeta( - TensorImpl self_impl/*=nullptr*/, - @Cast("bool") boolean requires_grad/*=false*/, - @ByVal(nullValue = "torch::autograd::Edge()") Edge gradient_edge) { super((Pointer)null); allocate(self_impl, requires_grad, gradient_edge); } - @UniquePtr @Name("std::make_unique") private native void allocate( - TensorImpl self_impl/*=nullptr*/, - @Cast("bool") boolean requires_grad/*=false*/, - @ByVal(nullValue = "torch::autograd::Edge()") Edge gradient_edge); - public AutogradMeta() { super((Pointer)null); allocate(); } - @UniquePtr @Name("std::make_unique") private native void allocate(); -} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/AwaitPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/AwaitPtr.java index 2aabc38bebb..23152f289e3 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/AwaitPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/AwaitPtr.java @@ -53,6 +53,10 @@ public class AwaitPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) AwaitPtr put(@ByRef(true) AwaitPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Await get(); public native @ByRef @Name("operator *") @NoException(true) Await multiply(); @@ -68,9 +72,9 @@ public class AwaitPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class AwaitPtr extends Pointer { */ public static native @ByVal AwaitPtr unsafe_adapt_non_heap_allocated( Await raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/BackendMetaRef.java b/pytorch/src/gen/java/org/bytedeco/pytorch/BackendMetaRef.java index 3846c176118..25a75ec8ca8 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/BackendMetaRef.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/BackendMetaRef.java @@ -53,6 +53,10 @@ public class BackendMetaRef extends Pointer { public native @ByRef @Name("operator =") @NoException(true) BackendMetaRef put(@ByRef(true) BackendMetaRef rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) BackendMeta get(); public native @ByRef @Name("operator *") @NoException(true) BackendMeta multiply(); @@ -68,9 +72,9 @@ public class BackendMetaRef extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class BackendMetaRef extends Pointer { */ public static native @ByVal BackendMetaRef unsafe_adapt_non_heap_allocated( BackendMeta raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/CUDAHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/CUDAHooksInterface.java index a446e505655..3663700ec5d 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/CUDAHooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/CUDAHooksInterface.java @@ -36,7 +36,7 @@ // never forgets to implement each virtual function in the real implementation // in CUDAHooks. This probably doesn't buy us much though. @Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class CUDAHooksInterface extends Pointer { +public class CUDAHooksInterface extends AcceleratorHooksInterface { static { Loader.load(); } /** Default native constructor. */ public CUDAHooksInterface() { super((Pointer)null); allocate(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/ChunkRecordIterator.java b/pytorch/src/gen/java/org/bytedeco/pytorch/ChunkRecordIterator.java index f144159f573..aa22654d8d7 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/ChunkRecordIterator.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/ChunkRecordIterator.java @@ -27,4 +27,5 @@ public class ChunkRecordIterator extends Pointer { // Read at most `chunkSize` into `buf`. Return the number of actual bytes read. public native @Cast("size_t") long next(Pointer buf); + public native @Cast("size_t") long recordSize(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Code.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Code.java index 9c5c559676b..b779f88056b 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Code.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Code.java @@ -83,4 +83,5 @@ private native void allocate( public native @Cast("torch::jit::Node**") @StdVector PointerPointer instructions_source(); public native void request_bailout(@Cast("size_t") long index); public native @Cast("size_t") long register_size(); + public native @SharedPtr("torch::jit::Graph") @ByVal Graph graph(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/ConstantStringPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/ConstantStringPtr.java index 96fa79756dc..5953da42afc 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/ConstantStringPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/ConstantStringPtr.java @@ -53,6 +53,10 @@ public class ConstantStringPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) ConstantStringPtr put(@ByRef(true) ConstantStringPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) ConstantString get(); public native @ByRef @Name("operator *") @NoException(true) ConstantString multiply(); @@ -68,9 +72,9 @@ public class ConstantStringPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class ConstantStringPtr extends Pointer { */ public static native @ByVal ConstantStringPtr unsafe_adapt_non_heap_allocated( ConstantString raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Context.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Context.java index a55f2aa984d..0a261ba302a 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Context.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Context.java @@ -37,6 +37,9 @@ public class Context extends Pointer { private native void allocate(); public native @Const @ByRef Generator defaultGenerator(@ByVal Device device); + public native @Const @ByRef AcceleratorHooksInterface getAcceleratorHooksInterface( + @ByVal(nullValue = "c10::optional(c10::nullopt)") DeviceTypeOptional opt_device_type); + public native @Const @ByRef AcceleratorHooksInterface getAcceleratorHooksInterface(); public native @ByVal Device getDeviceFromPtr(Pointer data, DeviceType device_type); public native @ByVal Device getDeviceFromPtr(Pointer data, @Cast("c10::DeviceType") byte device_type); public static native @Cast("bool") boolean isPinnedPtr(@Const Pointer data); @@ -63,6 +66,8 @@ public class Context extends Pointer { // call_once check. getNonVariableType is called fairly frequently public native void lazyInitCUDA(); public native void lazyInitHIP(); + public native void lazyInitXPU(); + public native void lazyInitPrivateUse1(); public static native @Cast("const at::cuda::NVRTC*") @ByRef Pointer getNVRTC(); public static native @Cast("bool") boolean setFlushDenormal(@Cast("bool") boolean on); @@ -81,6 +86,8 @@ public class Context extends Pointer { public native void setBenchmarkLimitCuDNN(int arg0); public native @Cast("bool") boolean deterministicCuDNN(); public native void setDeterministicCuDNN(@Cast("bool") boolean arg0); + public native @Cast("bool") boolean userEnabledNNPACK(); + public native void setUserEnabledNNPACK(@Cast("bool") boolean e); // Note [Disabling Fused SDP Kernels] // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -102,6 +109,9 @@ public class Context extends Pointer { public native void setSDPUseMath(@Cast("bool") boolean arg0); public native @Cast("bool") boolean userEnabledMathSDP(); + public native void setSDPUseCuDNN(@Cast("bool") boolean arg0); + public native @Cast("bool") boolean userEnabledCuDNNSDP(); + public native LinalgBackend linalgPreferredBackend(); public native void setLinalgPreferredBackend(LinalgBackend arg0); public native void setLinalgPreferredBackend(@Cast("at::LinalgBackend") byte arg0); @@ -187,7 +197,7 @@ public class Context extends Pointer { // Throws an error if `Context::deterministicAlgorithms()` is true, CUDA // >= 10.2, and CUBLAS_WORKSPACE_CONFIG is not set to either ":16:8" or // ":4096:8". For more details: - // https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility + // https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility public native void alertCuBLASConfigNotDeterministic(); public native void setFloat32MatmulPrecision(@StdString BytePointer s); @@ -220,4 +230,6 @@ public class Context extends Pointer { public native void setDefaultMobileCPUAllocator(); public native void unsetDefaultMobileCPUAllocator(); + public native @Cast("bool") boolean allowFP16ReductionCPU(); + public native void setAllowFP16ReductionCPU(@Cast("bool") boolean arg0); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DataPtrVector.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DataPtrVector.java index e88111330e8..cd4bddf0dd1 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/DataPtrVector.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/DataPtrVector.java @@ -31,7 +31,7 @@ public class DataPtrVector extends Pointer { public DataPtr front() { return get(0); } public DataPtr back() { return get(size() - 1); } - @Index(function = "at") public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr get(@Cast("size_t") long i); + @Index(function = "at") public native @StdMove DataPtr get(@Cast("size_t") long i); public native @ByVal Iterator begin(); public native @ByVal Iterator end(); @@ -41,7 +41,7 @@ public Iterator() { } public native @Name("operator ++") @ByRef Iterator increment(); public native @Name("operator ==") boolean equals(@ByRef Iterator it); - public native @Name("operator *") @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr get(); + public native @Name("operator *") @StdMove DataPtr get(); } } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceGuardImplInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceGuardImplInterface.java index dbb16d8d7b3..30ba04ce7ae 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceGuardImplInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceGuardImplInterface.java @@ -173,7 +173,7 @@ public native void record( * being used on the given stream, and that it should thus avoid recycling the * DataPtr until all work on that stream is done. */ - public native void recordDataPtrOnStream(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr arg0, @Const @ByRef Stream arg1); + public native void recordDataPtrOnStream(@StdMove DataPtr arg0, @Const @ByRef Stream arg1); /** * Intended use of this class is to leak the DeviceGuardImpl at program end. diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceTypeOptional.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceTypeOptional.java new file mode 100644 index 00000000000..fe724392c22 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/DeviceTypeOptional.java @@ -0,0 +1,35 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + +@NoOffset @Name("c10::optional") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class DeviceTypeOptional extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DeviceTypeOptional(Pointer p) { super(p); } + public DeviceTypeOptional(DeviceType value) { this(); put(value); } + public DeviceTypeOptional() { allocate(); } + private native void allocate(); + public native @Name("operator =") @ByRef DeviceTypeOptional put(@ByRef DeviceTypeOptional x); + + public native boolean has_value(); + public native void reset(); + public native @Name("value") @ByRef DeviceType get(); + @ValueSetter public native DeviceTypeOptional put(@ByRef DeviceType value); +} + diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DifferentiableViewMeta.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DifferentiableViewMeta.java deleted file mode 100644 index 148bb86e524..00000000000 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/DifferentiableViewMeta.java +++ /dev/null @@ -1,49 +0,0 @@ -// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE - -package org.bytedeco.pytorch; - -import org.bytedeco.pytorch.Allocator; -import org.bytedeco.pytorch.Function; -import org.bytedeco.pytorch.functions.*; -import org.bytedeco.pytorch.Module; -import org.bytedeco.javacpp.annotation.Cast; -import java.nio.*; -import org.bytedeco.javacpp.*; -import org.bytedeco.javacpp.annotation.*; - -import static org.bytedeco.javacpp.presets.javacpp.*; -import static org.bytedeco.openblas.global.openblas_nolapack.*; -import static org.bytedeco.openblas.global.openblas.*; - -import static org.bytedeco.pytorch.global.torch.*; - - -@Namespace("torch::autograd") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class DifferentiableViewMeta extends AutogradMeta { - static { Loader.load(); } - /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ - public DifferentiableViewMeta(Pointer p) { super(p); } - - /** requires_grad is a backward AD field so we only use the view specific - * logic for backward differentiable views */ - public native @Cast("bool") boolean requires_grad(); - - public native @Cast("bool") boolean shared_view_info(); - - public native @Cast("bool") boolean has_bw_view(); - - public native @Cast("const torch::autograd::ViewInfo*") @ByRef Pointer get_backward_view(); - - public native @Cast("uint32_t") int get_attr_version(); - - public native void set_attr_version(@Cast("uint32_t") int new_attr_version); - - public native CreationMeta get_creation_meta(); - - public native void set_creation_meta(CreationMeta new_creation_meta); - public native void set_creation_meta(@Cast("torch::autograd::CreationMeta") byte new_creation_meta); - - public native @Cast("bool") boolean has_fw_view(); - - public native @Cast("const torch::autograd::ViewInfo*") @ByRef Pointer get_forward_view(); -} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DispatchKeySet.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DispatchKeySet.java index adaeeae62cd..9c6dc5d6605 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/DispatchKeySet.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/DispatchKeySet.java @@ -52,6 +52,7 @@ // we have: // - "Dense": CPU, CUDA, XLA, ... (~12 keys) // - "Sparse": SparseCPU, SparseCUDA, ... +// - "SparseCsr": SparseCsrCPU, SparseCsrCUDA, ... // - "Quantized": QuantizedCPU, QuantizedCUDA, QuantizedXLA, ... // - "Autograd": AutogradCPU, AutogradCUDA, Autograd XLA, ... // The problem is that total number of keys grows quadratically with [# @@ -65,7 +66,7 @@ // (1) "Building block" keys // (a) backends: Everything in the BackendComponent enum (e.g. CPUBit, // CUDABit) (b) functionalities: (per-backend) functionality-bit DispatchKeys -// (e.g. AutogradFunctionality, Sparse, Dense) +// (e.g. AutogradFunctionality, SparseCsr, Sparse, Dense) // (2) "Runtime" keys // (a) "non-customizable backends" (e.g. FPGA) // (b) "non-customizable functionalities" (e.g. Functionalize) @@ -89,14 +90,16 @@ // Backend keys and functionality keys that count as "building blocks" will // contribute to a full cross product of functionality that can be overriden. // -// For example, right now we have at least 12 "backend" building blocks (CPU, -// CUDA, XLA, ...) and at least 4 "functionality" building blocks (Dense, -// Sparse, Quantized, AutogradFunctionality, ...). These keys together allow -// every dispatcher operator to be customized in up to 12*4 different ways. Each -// of those requires a slot in the operator table of every dispatcher operator. -// Not every piece of functionality necessarily needs to be customizable -// per-backend, and not every backend necessarily needs to be able to customize -// every type of functionality. +// For example, right now we have at least 12 "backend" building +// blocks (CPU, CUDA, XLA, ...) and at least 5 "functionality" +// building blocks (Dense, Sparse, SparseCsr, Quantized, +// AutogradFunctionality, ...). These keys together allow every +// dispatcher operator to be customized in up to 12*4 different +// ways. Each of those requires a slot in the operator table of every +// dispatcher operator. Not every piece of functionality necessarily +// needs to be customizable per-backend, and not every backend +// necessarily needs to be able to customize every type of +// functionality. // // // (2) Every runtime key corresponds directly to a slot in an operator's runtime diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/DLDevice_.java b/pytorch/src/gen/java/org/bytedeco/pytorch/DynamicLibrary.java similarity index 61% rename from pytorch/src/gen/java/org/bytedeco/pytorch/DLDevice_.java rename to pytorch/src/gen/java/org/bytedeco/pytorch/DynamicLibrary.java index 2d9bd766d75..a7b641d7013 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/DLDevice_.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/DynamicLibrary.java @@ -18,13 +18,11 @@ import static org.bytedeco.pytorch.global.torch.*; -// We use forward declaration here instead of #include to avoid -// leaking DLPack implementation detail to every project that includes `ATen/Context.h`, which in turn -// would lead to a conflict when linked with another project using DLPack (for example TVM) -@Opaque @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class DLDevice_ extends Pointer { +// Forward declare DynamicLibrary +@Namespace("at") @Opaque @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class DynamicLibrary extends Pointer { /** Empty constructor. Calls {@code super((Pointer)null)}. */ - public DLDevice_() { super((Pointer)null); } + public DynamicLibrary() { super((Pointer)null); } /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ - public DLDevice_(Pointer p) { super(p); } + public DynamicLibrary(Pointer p) { super(p); } } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/EnumHolderPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/EnumHolderPtr.java index 8744b022f2d..69763f3177b 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/EnumHolderPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/EnumHolderPtr.java @@ -53,6 +53,10 @@ public class EnumHolderPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) EnumHolderPtr put(@ByRef(true) EnumHolderPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) EnumHolder get(); public native @ByRef @Name("operator *") @NoException(true) EnumHolder multiply(); @@ -68,9 +72,9 @@ public class EnumHolderPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class EnumHolderPtr extends Pointer { */ public static native @ByVal EnumHolderPtr unsafe_adapt_non_heap_allocated( EnumHolder raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Float8_e5m2fnuz.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Float8_e5m2fnuz.java index 1b7382cbf63..8d7862155bb 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Float8_e5m2fnuz.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Float8_e5m2fnuz.java @@ -43,4 +43,5 @@ public class Float8_e5m2fnuz extends Pointer { private native void allocate(float value); public native @Name("operator float") float asFloat(); public native @Cast("bool") boolean isnan(); + public native @Cast("bool") boolean isinf(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/FuturePtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/FuturePtr.java index 17f7e70c8a0..e1e3298ceb1 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/FuturePtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/FuturePtr.java @@ -53,6 +53,10 @@ public class FuturePtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) FuturePtr put(@ByRef(true) FuturePtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Future get(); public native @ByRef @Name("operator *") @NoException(true) Future multiply(); @@ -68,9 +72,9 @@ public class FuturePtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class FuturePtr extends Pointer { */ public static native @ByVal FuturePtr unsafe_adapt_non_heap_allocated( Future raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/GeneratorImplPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/GeneratorImplPtr.java index c193fb90c30..72996e68657 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/GeneratorImplPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/GeneratorImplPtr.java @@ -53,6 +53,10 @@ public class GeneratorImplPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) GeneratorImplPtr put(@ByRef(true) GeneratorImplPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) GeneratorImpl get(); public native @ByRef @Name("operator *") @NoException(true) GeneratorImpl multiply(); @@ -68,9 +72,9 @@ public class GeneratorImplPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class GeneratorImplPtr extends Pointer { */ public static native @ByVal GeneratorImplPtr unsafe_adapt_non_heap_allocated( GeneratorImpl raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/HIPHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/HIPHooksInterface.java index af34f7bc116..4069d8ea30c 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/HIPHooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/HIPHooksInterface.java @@ -50,7 +50,7 @@ public class HIPHooksInterface extends Pointer { public native @Cast("bool") boolean hasHIP(); - public native @Cast("int64_t") long current_device(); + public native @Cast("c10::DeviceIndex") byte current_device(); public native Allocator getPinnedMemoryAllocator(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Half.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Half.java index f82960077e1..3447c5fca84 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Half.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Half.java @@ -43,9 +43,12 @@ public class Half extends Pointer { public Half(@Cast("unsigned short") short bits, @ByVal from_bits_t arg1) { super((Pointer)null); allocate(bits, arg1); } private native void allocate(@Cast("unsigned short") short bits, @ByVal from_bits_t arg1); +// #if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) +// #else public Half(float value) { super((Pointer)null); allocate(value); } private native void allocate(float value); public native @Name("operator float") float asFloat(); +// #endif // #if defined(__CUDACC__) || defined(__HIPCC__) // #endif diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImpl.java new file mode 100644 index 00000000000..461a6d50ebd --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImpl.java @@ -0,0 +1,49 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + + +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LPPool3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +/** Applies the LPPool3d function element-wise. + * See https://pytorch.org/docs/master/nn.html#torch.nn.LPPool3d to learn + * about the exact behavior of this module. + * + * See the documentation for {@code torch::nn::LPPool3dOptions} class to learn what + * constructor arguments are supported for this module. + * + * Example: + * 
{@code
+ *  LPPool3d model(LPPool3dOptions(1, std::vector({3, 4, 5})).stride(
+ *  {5, 6, 7}).ceil_mode(true));
+ *  }
*/ +@Namespace("torch::nn") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class LPPool3dImpl extends LPPool3dImplBase { + static { Loader.load(); } + + + public LPPool3dImpl(double norm_type, @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size) { super((Pointer)null); allocate(norm_type, kernel_size); } + private native void allocate(double norm_type, @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size); + public LPPool3dImpl(@Const @ByRef LPPool3dOptions options_) { super((Pointer)null); allocate(options_); } + private native void allocate(@Const @ByRef LPPool3dOptions options_); + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public LPPool3dImpl(Pointer p) { super(p); } + + + public native @ByVal Tensor forward(@Const @ByRef Tensor input); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplBase.java b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplBase.java new file mode 100644 index 00000000000..f96c6ef80ff --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplBase.java @@ -0,0 +1,37 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + +@Name("torch::nn::LPPoolImpl<3,torch::nn::LPPool3dImpl>") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class LPPool3dImplBase extends LPPool3dImplCloneable { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public LPPool3dImplBase(Pointer p) { super(p); } + + public LPPool3dImplBase(double norm_type, @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size) { super((Pointer)null); allocate(norm_type, kernel_size); } + private native void allocate(double norm_type, @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size); + public LPPool3dImplBase(@Const @ByRef LPPool3dOptions options_) { super((Pointer)null); allocate(options_); } + private native void allocate(@Const @ByRef LPPool3dOptions options_); + + public native void reset(); + + /** Pretty prints the {@code LPPool{1,2}d} module into the given {@code stream}. */ + public native void pretty_print(@Cast("std::ostream*") @ByRef Pointer stream); + + public native @ByRef LPPool3dOptions options(); public native LPPool3dImplBase options(LPPool3dOptions setter); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplCloneable.java b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplCloneable.java new file mode 100644 index 00000000000..f312956c232 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/LPPool3dImplCloneable.java @@ -0,0 +1,37 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + +@Name("torch::nn::Cloneable") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class LPPool3dImplCloneable extends Module { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public LPPool3dImplCloneable(Pointer p) { super(p); } + + + /** {@code reset()} must perform initialization of all members with reference + * semantics, most importantly parameters, buffers and submodules. */ + public native void reset(); + + /** Performs a recursive "deep copy" of the {@code Module}, such that all parameters + * and submodules in the cloned module are different from those in the + * original module. */ + public native @SharedPtr("torch::nn::Module") @ByVal Module clone( + @Const @ByRef(nullValue = "c10::optional(c10::nullopt)") DeviceOptional device); + public native @SharedPtr("torch::nn::Module") @ByVal Module clone(); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Library.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Library.java index 0c021da8d99..bba6dc0b4e8 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Library.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Library.java @@ -261,4 +261,7 @@ private native void allocate( // within a library. The API is the same as before with 1 minor change. // Instead of m.class_("foo") you instead do // m.class_(TORCH_SELECTIVE_CLASS("foo")) + + // De-registers all registrations created with this Library + public native void reset(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/LongSmallVectorImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/LongSmallVectorImpl.java index 0854b6d714f..fe099d243b2 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/LongSmallVectorImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/LongSmallVectorImpl.java @@ -39,7 +39,7 @@ public class LongSmallVectorImpl extends LongSmallVectorBase { public native @Cast("int64_t") long pop_back_val(); - public native void swap(@ByRef LongSmallVectorImpl RHS); + public native @NoException(true) void swap(@ByRef LongSmallVectorImpl RHS); /** Add the specified range to the end of the SmallVector. */ diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/MPSHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/MPSHooksInterface.java index 2044cfaa3af..f53deee0912 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/MPSHooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/MPSHooksInterface.java @@ -19,7 +19,7 @@ @Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class MPSHooksInterface extends Pointer { +public class MPSHooksInterface extends AcceleratorHooksInterface { static { Loader.load(); } /** Default native constructor. */ public MPSHooksInterface() { super((Pointer)null); allocate(); } @@ -44,8 +44,8 @@ public class MPSHooksInterface extends Pointer { // Initialize the MPS library state public native void initMPS(); public native @Cast("bool") boolean hasMPS(); - public native @Cast("bool") boolean isOnMacOS13orNewer(@Cast("unsigned") int minor/*=0*/); - public native @Cast("bool") boolean isOnMacOS13orNewer(); + public native @Cast("bool") boolean isOnMacOSorNewer(@Cast("unsigned") int major/*=13*/, @Cast("unsigned") int minor/*=0*/); + public native @Cast("bool") boolean isOnMacOSorNewer(); public native @Const @ByRef Generator getDefaultMPSGenerator(); public native Allocator getMPSDeviceAllocator(); public native void deviceSynchronize(); @@ -66,6 +66,6 @@ public class MPSHooksInterface extends Pointer { public native void synchronizeEvent(@Cast("uint32_t") int event_id); public native @Cast("bool") boolean queryEvent(@Cast("uint32_t") int event_id); public native double elapsedTimeOfEvents(@Cast("uint32_t") int start_event_id, @Cast("uint32_t") int end_event_id); - + public native @Cast("bool") boolean hasPrimaryContext(@Cast("c10::DeviceIndex") byte device_index); // #undef FAIL_MPSHOOKS_FUNC } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/MTIAHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/MTIAHooksInterface.java index 7486498dfe7..6c314249e46 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/MTIAHooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/MTIAHooksInterface.java @@ -19,7 +19,7 @@ @Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class MTIAHooksInterface extends Pointer { +public class MTIAHooksInterface extends AcceleratorHooksInterface { static { Loader.load(); } /** Default native constructor. */ public MTIAHooksInterface() { super((Pointer)null); allocate(); } @@ -42,4 +42,7 @@ public class MTIAHooksInterface extends Pointer { public native @Cast("bool") boolean hasMTIA(); public native @StdString BytePointer showConfig(); + + public native @Cast("bool") boolean hasPrimaryContext(@Cast("c10::DeviceIndex") byte device_index); + } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Method.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Method.java index c8c04f8a1ad..c2d5a49764b 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Method.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Method.java @@ -35,6 +35,9 @@ public class Method extends IMethod { // the module that contains this method. public native @ByVal JitModule owner(); + // the raw objectptr that owns this method, for when the method is owned by a + // torchbind object. + public native @ByVal @Cast("torch::jit::ObjectPtr*") ObjPtr raw_owner(); public native void run(@ByRef IValueVector stack); public native @ByVal @Name("operator ()") IValue apply( diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Module.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Module.java index 063755fac57..57b4f4903ca 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Module.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Module.java @@ -819,6 +819,11 @@ public native void apply( /// public native @Name("as") @NoException(true) FractionalMaxPool3dImpl asFractionalMaxPool3d(); + /// + /// + /// + public native @Name("as") @NoException(true) LPPool3dImpl asLPPool3d(); + /// /// /// diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/NestedTensorImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/NestedTensorImpl.java index d5bda553fad..5ac5cdda20b 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/NestedTensorImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/NestedTensorImpl.java @@ -40,19 +40,23 @@ private native void allocate( @ByVal Tensor storage_offsets); public NestedTensorImpl( - @ByVal Tensor buffer, + @Const @ByRef Tensor buffer, @ByVal Tensor nested_sizes, @ByVal Tensor nested_strides, @ByVal Tensor storage_offsets) { super((Pointer)null); allocate(buffer, nested_sizes, nested_strides, storage_offsets); } private native void allocate( - @ByVal Tensor buffer, + @Const @ByRef Tensor buffer, @ByVal Tensor nested_sizes, @ByVal Tensor nested_strides, @ByVal Tensor storage_offsets); // assume contiguous, `nested_strides` and `offsets` // can be infered from `nested_sizes` - public NestedTensorImpl(@ByVal Tensor buffer, @ByVal Tensor nested_sizes) { super((Pointer)null); allocate(buffer, nested_sizes); } - private native void allocate(@ByVal Tensor buffer, @ByVal Tensor nested_sizes); + public NestedTensorImpl( + @Const @ByRef Tensor buffer, + @Const @ByRef Tensor nested_sizes) { super((Pointer)null); allocate(buffer, nested_sizes); } + private native void allocate( + @Const @ByRef Tensor buffer, + @Const @ByRef Tensor nested_sizes); // This constructor is used creating view tensors from nested tensors public NestedTensorImpl( diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Node.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Node.java index 2f95bdc593d..1e5d82a6afc 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Node.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Node.java @@ -25,7 +25,7 @@ // or more input `Variable`s and producing zero or more output `Variable`s. All // functions in PyTorch's autograd machinery derive from this class and // override its `apply` method. Instances of such subclasses will then be -// invokeable via the call operator. +// invokable via the call operator. // // Nodes in the Autograd Graph //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -127,8 +127,7 @@ public class Node extends Pointer { * elements are on different devices (across multiple GPUs, for example) * they may have different streams. */ - public native @ByVal StreamOptional stream(DeviceType device_type); - public native @ByVal StreamOptional stream(@Cast("c10::DeviceType") byte device_type); + public native @ByVal StreamOptional stream(); public native void clear_input_metadata(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/ObjPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/ObjPtr.java index 9fa6a8d2d33..5e99b8a7a68 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/ObjPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/ObjPtr.java @@ -53,6 +53,10 @@ public class ObjPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) ObjPtr put(@ByRef(true) ObjPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Object get(); public native @ByRef @Name("operator *") @NoException(true) Object multiply(); @@ -68,9 +72,9 @@ public class ObjPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class ObjPtr extends Pointer { */ public static native @ByVal ObjPtr unsafe_adapt_non_heap_allocated( Object raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/OperandInfo.java b/pytorch/src/gen/java/org/bytedeco/pytorch/OperandInfo.java index 053afe6bfb7..2e02db50df5 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/OperandInfo.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/OperandInfo.java @@ -74,6 +74,8 @@ public class OperandInfo extends Pointer { public native @Cast("bool") boolean is_read_write(); public native OperandInfo is_read_write(boolean setter); + public native @Cast("bool") boolean is_const(); public native OperandInfo is_const(boolean setter); + public native void validate(); /** The tensor operand. Note that the strides, data pointer, and diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/OptimizerParamState.java b/pytorch/src/gen/java/org/bytedeco/pytorch/OptimizerParamState.java index d0ae5d9353a..6d1c1350059 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/OptimizerParamState.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/OptimizerParamState.java @@ -16,7 +16,10 @@ import static org.bytedeco.openblas.global.openblas.*; import static org.bytedeco.pytorch.global.torch.*; - // namespace torch + + +// Forward declarations confuse Doxygen +// #ifndef DOXYGEN_SHOULD_SKIP_THIS // #endif // DOXYGEN_SHOULD_SKIP_THIS @Namespace("torch::optim") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/PlacementDeleteContext.java b/pytorch/src/gen/java/org/bytedeco/pytorch/PlacementDeleteContext.java index 991c61bca49..0ce81f3f9ff 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/PlacementDeleteContext.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/PlacementDeleteContext.java @@ -33,19 +33,19 @@ public class PlacementDeleteContext extends Pointer { /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ public PlacementDeleteContext(Pointer p) { super(p); } - public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr_(); public native PlacementDeleteContext data_ptr_(DataPtr setter); + public native @StdMove DataPtr data_ptr_(); public native PlacementDeleteContext data_ptr_(DataPtr setter); public native PlacementConsumer placement_dtor_(); public native PlacementDeleteContext placement_dtor_(PlacementConsumer setter); public native @Cast("size_t") long size_(); public native PlacementDeleteContext size_(long setter); public PlacementDeleteContext( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, PlacementConsumer placement_dtor, @Cast("size_t") long size) { super((Pointer)null); allocate(data_ptr, placement_dtor, size); } private native void allocate( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, PlacementConsumer placement_dtor, @Cast("size_t") long size); - public static native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr makeDataPtr( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + public static native @StdMove DataPtr makeDataPtr( + @StdMove DataPtr data_ptr, PlacementConsumer placement_dtor, @Cast("size_t") long size, @ByVal Device device); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/PostAccumulateGradHook.java b/pytorch/src/gen/java/org/bytedeco/pytorch/PostAccumulateGradHook.java index 7159d95e57a..2e4e9de3363 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/PostAccumulateGradHook.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/PostAccumulateGradHook.java @@ -24,12 +24,12 @@ public class PostAccumulateGradHook extends Pointer { /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ public PostAccumulateGradHook(Pointer p) { super(p); } - public native @Name("operator ()") void apply(@Cast("const torch::autograd::Variable*") @ByRef Tensor tensor); + public native @Name("operator ()") void apply(@Const @ByRef Tensor tensor); // only implemented for python hooks on nodes, registers hook with compiled // autograd public native void compiled_args(@ByRef CompiledNodeArgs args); public native void apply_with_saved( - @Cast("torch::autograd::Variable*") @ByRef Tensor arg0, + @ByRef Tensor arg0, @ByRef SwapSavedVariables arg1); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/PrivateUse1HooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/PrivateUse1HooksInterface.java index 77289c3c8ae..a5c4e290442 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/PrivateUse1HooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/PrivateUse1HooksInterface.java @@ -19,7 +19,7 @@ @Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class PrivateUse1HooksInterface extends Pointer { +public class PrivateUse1HooksInterface extends AcceleratorHooksInterface { static { Loader.load(); } /** Default native constructor. */ public PrivateUse1HooksInterface() { super((Pointer)null); allocate(); } @@ -36,7 +36,15 @@ public class PrivateUse1HooksInterface extends Pointer { return new PrivateUse1HooksInterface((Pointer)this).offsetAddress(i); } - public native @Const @ByRef Generator getDefaultGenerator(@Cast("c10::DeviceIndex") byte device_index); + public native @Const @ByRef Generator getDefaultGenerator( + @Cast("c10::DeviceIndex") byte device_index); public native @ByVal Device getDeviceFromPtr(Pointer data); + + public native Allocator getPinnedMemoryAllocator(); + + public native @Cast("bool") boolean hasPrimaryContext(@Cast("c10::DeviceIndex") byte device_index); + + public native void initPrivateUse1(); + public native void resizePrivateUse1Bytes(@Const @ByRef Storage storage, @Cast("size_t") long newsize); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/PyObjectHolderPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/PyObjectHolderPtr.java index 80491b17fd2..3c6e5c71da5 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/PyObjectHolderPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/PyObjectHolderPtr.java @@ -53,6 +53,10 @@ public class PyObjectHolderPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) PyObjectHolderPtr put(@ByRef(true) PyObjectHolderPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) PyObjectHolder get(); public native @ByRef @Name("operator *") @NoException(true) PyObjectHolder multiply(); @@ -68,9 +72,9 @@ public class PyObjectHolderPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class PyObjectHolderPtr extends Pointer { */ public static native @ByVal PyObjectHolderPtr unsafe_adapt_non_heap_allocated( PyObjectHolder raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/QuantizerPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/QuantizerPtr.java index e90a7ec2907..fa1f8539ca3 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/QuantizerPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/QuantizerPtr.java @@ -53,6 +53,10 @@ public class QuantizerPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) QuantizerPtr put(@ByRef(true) QuantizerPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Quantizer get(); public native @ByRef @Name("operator *") @NoException(true) Quantizer multiply(); @@ -68,9 +72,9 @@ public class QuantizerPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class QuantizerPtr extends Pointer { */ public static native @ByVal QuantizerPtr unsafe_adapt_non_heap_allocated( Quantizer raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/ROCmBackwardPassGuard.java b/pytorch/src/gen/java/org/bytedeco/pytorch/ROCmBackwardPassGuard.java new file mode 100644 index 00000000000..be8c1517090 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/ROCmBackwardPassGuard.java @@ -0,0 +1,39 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + + +@Namespace("at") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class ROCmBackwardPassGuard extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ROCmBackwardPassGuard(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ROCmBackwardPassGuard(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ROCmBackwardPassGuard position(long position) { + return (ROCmBackwardPassGuard)super.position(position); + } + @Override public ROCmBackwardPassGuard getPointer(long i) { + return new ROCmBackwardPassGuard((Pointer)this).offsetAddress(i); + } + + public ROCmBackwardPassGuard() { super((Pointer)null); allocate(); } + private native void allocate(); + public static native @Cast("bool") boolean is_backward_pass(); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/RRefInterfacePtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/RRefInterfacePtr.java index 487eb30858a..4a230f04d3c 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/RRefInterfacePtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/RRefInterfacePtr.java @@ -53,6 +53,10 @@ public class RRefInterfacePtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) RRefInterfacePtr put(@ByRef(true) RRefInterfacePtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) RRefInterface get(); public native @ByRef @Name("operator *") @NoException(true) RRefInterface multiply(); @@ -68,9 +72,9 @@ public class RRefInterfacePtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class RRefInterfacePtr extends Pointer { */ public static native @ByVal RRefInterfacePtr unsafe_adapt_non_heap_allocated( RRefInterface raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/RecordFunctionCallbacksEntry.java b/pytorch/src/gen/java/org/bytedeco/pytorch/RecordFunctionCallbacksEntry.java index 77034bfa6ce..a82b1646554 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/RecordFunctionCallbacksEntry.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/RecordFunctionCallbacksEntry.java @@ -27,8 +27,8 @@ public class RecordFunctionCallbacksEntry extends Pointer { /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ public RecordFunctionCallbacksEntry(Pointer p) { super(p); } - public RecordFunctionCallbacksEntry(@Cast("at::RecordFunctionCallback*") @ByRef(true) Pointer cb, @Cast("at::CallbackHandle") long h) { super((Pointer)null); allocate(cb, h); } - private native void allocate(@Cast("at::RecordFunctionCallback*") @ByRef(true) Pointer cb, @Cast("at::CallbackHandle") long h); + public RecordFunctionCallbacksEntry(@ByVal @Cast("at::RecordFunctionCallback*") Pointer cb, @Cast("at::CallbackHandle") long h) { super((Pointer)null); allocate(cb, h); } + private native void allocate(@ByVal @Cast("at::RecordFunctionCallback*") Pointer cb, @Cast("at::CallbackHandle") long h); public native @ByRef @Cast("at::RecordFunctionCallback*") Pointer callback_(); public native RecordFunctionCallbacksEntry callback_(Pointer setter); public native @Cast("bool") boolean enabled_(); public native RecordFunctionCallbacksEntry enabled_(boolean setter); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Scalar.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Scalar.java index f9c108d5216..ff1e5629149 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Scalar.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Scalar.java @@ -80,6 +80,15 @@ public class Scalar extends Pointer { public Scalar(@ByVal DoubleComplex vv) { super((Pointer)null); allocate(vv); } private native void allocate(@ByVal DoubleComplex vv); + // Helper constructors to allow Scalar creation from long and long long types + // As std::is_same_v is false(except Android), one needs to + // provide a constructor from either long or long long in addition to one from + // int64_t +// #if defined(__APPLE__) || defined(__MACOSX) +// #endif +// #if defined(__linux__) && !defined(__ANDROID__) +// #endif + // #undef DEFINE_IMPLICIT_CTOR // Value* is both implicitly convertible to SymbolicVariable and bool which @@ -97,6 +106,8 @@ public class Scalar extends Pointer { // return checked_convert(v.i, #type); // } else if (Tag::HAS_i == tag) { // return checked_convert(v.i, #type); +// } else if (Tag::HAS_u == tag) { +// return checked_convert(v., #type); // } else if (Tag::HAS_si == tag) { // return checked_convert( // toSymInt().guard_int(__FILE__, __LINE__), #type); @@ -128,6 +139,9 @@ public class Scalar extends Pointer { public native @ByVal Float8_e4m3fn toFloat8_e4m3fn(); public native @ByVal Float8_e5m2fnuz toFloat8_e5m2fnuz(); public native @ByVal Float8_e4m3fnuz toFloat8_e4m3fnuz(); + public native @Cast("uint16_t") short toUInt16(); + public native @Cast("uint32_t") int toUInt32(); + public native @Cast("uint64_t") long toUInt64(); // #undef DEFINE_ACCESSOR diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SequentialImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SequentialImpl.java index 329dab60ef1..0dd66bed09f 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SequentialImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SequentialImpl.java @@ -256,6 +256,7 @@ public SequentialImpl( public native @Name("push_back") void push_back(@SharedPtr AdaptiveMaxPool3dImpl module_ptr); public native @Name("push_back") void push_back(@SharedPtr MaxUnpool3dImpl module_ptr); public native @Name("push_back") void push_back(@SharedPtr FractionalMaxPool3dImpl module_ptr); + public native @Name("push_back") void push_back(@SharedPtr LPPool3dImpl module_ptr); public native @Name("push_back") void push_back(@SharedPtr RNNImpl module_ptr); public native @Name("push_back") void push_back(@SharedPtr LSTMImpl module_ptr); public native @Name("push_back") void push_back(@SharedPtr GRUImpl module_ptr); @@ -463,6 +464,8 @@ public SequentialImpl( public native @Name("push_back") void push_back(@StdString String name, @SharedPtr MaxUnpool3dImpl module_ptr); public native @Name("push_back") void push_back(@StdString BytePointer name, @SharedPtr FractionalMaxPool3dImpl module_ptr); public native @Name("push_back") void push_back(@StdString String name, @SharedPtr FractionalMaxPool3dImpl module_ptr); + public native @Name("push_back") void push_back(@StdString BytePointer name, @SharedPtr LPPool3dImpl module_ptr); + public native @Name("push_back") void push_back(@StdString String name, @SharedPtr LPPool3dImpl module_ptr); public native @Name("push_back") void push_back(@StdString BytePointer name, @SharedPtr RNNImpl module_ptr); public native @Name("push_back") void push_back(@StdString String name, @SharedPtr RNNImpl module_ptr); public native @Name("push_back") void push_back(@StdString BytePointer name, @SharedPtr LSTMImpl module_ptr); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SizesAndStrides.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SizesAndStrides.java index fec178b15ab..41e93f17f5b 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SizesAndStrides.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SizesAndStrides.java @@ -45,9 +45,11 @@ public class SizesAndStrides extends Pointer { // TODO: different iterator types for sizes & strides to prevent // mixing the two accidentally. + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init) public SizesAndStrides() { super((Pointer)null); allocate(); } private native void allocate(); + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init) public SizesAndStrides(@Const @ByRef SizesAndStrides rhs) { super((Pointer)null); allocate(rhs); } private native void allocate(@Const @ByRef SizesAndStrides rhs); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Storage.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Storage.java index 50220dbdd1d..8bdd697a348 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Storage.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Storage.java @@ -65,20 +65,20 @@ public static class unsafe_borrow_t extends Pointer { // Allocates memory buffer using given allocator and creates a storage with it public Storage( @ByVal use_byte_size_t arg0, - @ByVal SymInt size_bytes, + @Const @ByRef SymInt size_bytes, Allocator allocator/*=nullptr*/, @Cast("bool") boolean resizable/*=false*/) { super((Pointer)null); allocate(arg0, size_bytes, allocator, resizable); } private native void allocate( @ByVal use_byte_size_t arg0, - @ByVal SymInt size_bytes, + @Const @ByRef SymInt size_bytes, Allocator allocator/*=nullptr*/, @Cast("bool") boolean resizable/*=false*/); public Storage( @ByVal use_byte_size_t arg0, - @ByVal SymInt size_bytes) { super((Pointer)null); allocate(arg0, size_bytes); } + @Const @ByRef SymInt size_bytes) { super((Pointer)null); allocate(arg0, size_bytes); } private native void allocate( @ByVal use_byte_size_t arg0, - @ByVal SymInt size_bytes); + @Const @ByRef SymInt size_bytes); // Creates storage with pre-allocated memory buffer. Allocator is given for // potential future reallocations, however it can be nullptr if the storage @@ -86,23 +86,23 @@ private native void allocate( public Storage( @ByVal use_byte_size_t arg0, @Cast("size_t") long size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, Allocator allocator/*=nullptr*/, @Cast("bool") boolean resizable/*=false*/) { super((Pointer)null); allocate(arg0, size_bytes, data_ptr, allocator, resizable); } private native void allocate( @ByVal use_byte_size_t arg0, @Cast("size_t") long size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, Allocator allocator/*=nullptr*/, @Cast("bool") boolean resizable/*=false*/); public Storage( @ByVal use_byte_size_t arg0, @Cast("size_t") long size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr) { super((Pointer)null); allocate(arg0, size_bytes, data_ptr); } + @StdMove DataPtr data_ptr) { super((Pointer)null); allocate(arg0, size_bytes, data_ptr); } private native void allocate( @ByVal use_byte_size_t arg0, @Cast("size_t") long size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr); + @StdMove DataPtr data_ptr); // Legacy constructor for partially initialized (dtype or memory) storages // that can be temporarily created with Caffe2 APIs. See the note on top of // TensorImpl.h for details. @@ -129,12 +129,12 @@ private native void allocate( public native @ByRef DataPtr mutable_data_ptr(); - public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr(); + public native @StdMove DataPtr data_ptr(); // Returns the previous data_ptr - public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr set_data_ptr(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr); + public native @StdMove DataPtr set_data_ptr(@StdMove DataPtr data_ptr); - public native void set_data_ptr_noswap(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr); + public native void set_data_ptr_noswap(@StdMove DataPtr data_ptr); public native DeviceType device_type(); @@ -164,7 +164,5 @@ public native void UniqueStorageShareExternalPointer( Pointer src, @Cast("size_t") long _capacity); - public native void UniqueStorageShareExternalPointer( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, - @Cast("size_t") long _capacity); + public native void UniqueStorageShareExternalPointer(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, @Cast("size_t") long size_bytes); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImpl.java index a0832d8183d..08dcc314221 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImpl.java @@ -57,13 +57,13 @@ public class StorageImpl extends Pointer { public StorageImpl( @ByVal use_byte_size_t arg0, @ByVal SymInt size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, Allocator allocator, @Cast("bool") boolean resizable) { super((Pointer)null); allocate(arg0, size_bytes, data_ptr, allocator, resizable); } private native void allocate( @ByVal use_byte_size_t arg0, @ByVal SymInt size_bytes, - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, Allocator allocator, @Cast("bool") boolean resizable); @@ -103,12 +103,12 @@ private native void allocate( public native @ByRef DataPtr mutable_data_ptr(); - public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr(); + public native @StdMove DataPtr data_ptr(); // Returns the previous data_ptr - public native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr set_data_ptr(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr); + public native @StdMove DataPtr set_data_ptr(@StdMove DataPtr data_ptr); - public native void set_data_ptr_noswap(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr); + public native void set_data_ptr_noswap(@StdMove DataPtr data_ptr); public native @Const Pointer data(); @@ -142,9 +142,7 @@ public native void UniqueStorageShareExternalPointer( /** * Can only be called when use_count is 1 */ - public native void UniqueStorageShareExternalPointer( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, - @Cast("size_t") long size_bytes); + public native void UniqueStorageShareExternalPointer(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, @Cast("size_t") long size_bytes); // This method can be used only after storage construction and cannot be used // to modify storage status diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImplPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImplPtr.java index 38d5937fdcb..777055f6ad6 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImplPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/StorageImplPtr.java @@ -53,6 +53,10 @@ public class StorageImplPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) StorageImplPtr put(@ByRef(true) StorageImplPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) StorageImpl get(); public native @ByRef @Name("operator *") @NoException(true) StorageImpl multiply(); @@ -68,9 +72,9 @@ public class StorageImplPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class StorageImplPtr extends Pointer { */ public static native @ByVal StorageImplPtr unsafe_adapt_non_heap_allocated( StorageImpl raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/StringIntMap.java b/pytorch/src/gen/java/org/bytedeco/pytorch/StringIntMap.java new file mode 100644 index 00000000000..06c99b20c37 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/StringIntMap.java @@ -0,0 +1,48 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch; + +import org.bytedeco.pytorch.Allocator; +import org.bytedeco.pytorch.Function; +import org.bytedeco.pytorch.functions.*; +import org.bytedeco.pytorch.Module; +import org.bytedeco.javacpp.annotation.Cast; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; + +import static org.bytedeco.pytorch.global.torch.*; + +@Name("std::unordered_map") @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) +public class StringIntMap extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public StringIntMap(Pointer p) { super(p); } + public StringIntMap() { allocate(); } + private native void allocate(); + public native @Name("operator =") @ByRef StringIntMap put(@ByRef StringIntMap x); + + public boolean empty() { return size() == 0; } + public native long size(); + + @Index public native int get(@StdString BytePointer i); + public native StringIntMap put(@StdString BytePointer i, int value); + + public native void erase(@ByVal Iterator pos); + public native @ByVal Iterator begin(); + public native @ByVal Iterator end(); + @NoOffset @Name("iterator") public static class Iterator extends Pointer { + public Iterator(Pointer p) { super(p); } + public Iterator() { } + + public native @Name("operator ++") @ByRef Iterator increment(); + public native @Name("operator ==") boolean equals(@ByRef Iterator it); + public native @Name("operator *().first") @MemberGetter @StdString BytePointer first(); + public native @Name("operator *().second") @MemberGetter int second(); + } +} + diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SymBool.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SymBool.java index 7f281dd3ea2..3a2aeea918d 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SymBool.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SymBool.java @@ -67,6 +67,8 @@ public class SymBool extends Pointer { public native @Cast("bool") boolean guard_bool(String file, @Cast("int64_t") long line); public native @Cast("bool") boolean expect_true(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); public native @Cast("bool") boolean expect_true(String file, @Cast("int64_t") long line); + public native @Cast("bool") boolean guard_size_oblivious(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); + public native @Cast("bool") boolean guard_size_oblivious(String file, @Cast("int64_t") long line); public native @Cast("bool") boolean has_hint(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SymIntSmallVectorImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SymIntSmallVectorImpl.java index 5a4ece68c12..373f2a582b2 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SymIntSmallVectorImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SymIntSmallVectorImpl.java @@ -42,7 +42,7 @@ public class SymIntSmallVectorImpl extends SymIntSmallVectorBase { public native @ByVal SymInt pop_back_val(); - public native void swap(@ByRef SymIntSmallVectorImpl RHS); + public native @NoException(true) void swap(@ByRef SymIntSmallVectorImpl RHS); /** Add the specified range to the end of the SmallVector. */ diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SymNode.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SymNode.java index 3e7d3f7dd21..8f9b11012c7 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SymNode.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SymNode.java @@ -53,6 +53,10 @@ public class SymNode extends Pointer { public native @ByRef @Name("operator =") @NoException(true) SymNode put(@ByRef(true) SymNode rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) SymNodeImpl get(); public native @ByRef @Name("operator *") @NoException(true) SymNodeImpl multiply(); @@ -68,9 +72,9 @@ public class SymNode extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class SymNode extends Pointer { */ public static native @ByVal SymNode unsafe_adapt_non_heap_allocated( SymNodeImpl raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/SymNodeImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/SymNodeImpl.java index 3da1ca11317..c10e46fd3f6 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/SymNodeImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/SymNodeImpl.java @@ -45,6 +45,7 @@ public class SymNodeImpl extends Pointer { public native @Cast("bool") boolean is_int(); public native @Cast("bool") boolean is_bool(); public native @Cast("bool") boolean is_float(); + public native @Cast("bool") boolean is_nested_int(); public native @ByVal SymNode add(@Const @ByRef SymNode other); public native @ByVal SymNode sub(@Const @ByRef SymNode other); public native @ByVal SymNode mul(@Const @ByRef SymNode other); @@ -97,6 +98,8 @@ public class SymNodeImpl extends Pointer { public native @Cast("bool") boolean guard_bool(String file, @Cast("int64_t") long line); public native double guard_float(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); public native double guard_float(String file, @Cast("int64_t") long line); + public native @Cast("bool") boolean guard_size_oblivious(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); + public native @Cast("bool") boolean guard_size_oblivious(String file, @Cast("int64_t") long line); public native @Cast("bool") boolean expect_true(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); public native @Cast("bool") boolean expect_true(String file, @Cast("int64_t") long line); public native @Cast("bool") boolean expect_size(@Cast("const char*") BytePointer file, @Cast("int64_t") long line); @@ -105,8 +108,8 @@ public class SymNodeImpl extends Pointer { public native @Cast("bool") boolean bool_(); public native @Cast("bool") boolean has_hint(); public native @StdString BytePointer str(); - public native @ByVal LongOptional singleton_int(); - public native @ByVal LongOptional singleton_coeff(); + public native @ByVal LongOptional nested_int(); + public native @ByVal LongOptional nested_int_coeff(); public native @ByVal LongOptional constant_int(); public native @ByVal BoolOptional constant_bool(); public native @ByVal LongOptional maybe_as_int(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/T_DataPtrSizeT_T.java b/pytorch/src/gen/java/org/bytedeco/pytorch/T_DataPtrSizeT_T.java index b7a301a1f5d..095c067bae1 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/T_DataPtrSizeT_T.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/T_DataPtrSizeT_T.java @@ -26,8 +26,8 @@ public class T_DataPtrSizeT_T extends Pointer { private native void allocate(); - public @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr get0() { return get0(this); } - @Namespace @Name("std::get<0>") public static native @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr get0(@ByRef T_DataPtrSizeT_T container); + public @StdMove DataPtr get0() { return get0(this); } + @Namespace @Name("std::get<0>") public static native @StdMove DataPtr get0(@ByRef T_DataPtrSizeT_T container); public @Cast("size_t") long get1() { return get1(this); } @Namespace @Name("std::get<1>") public static native @Cast("size_t") long get1(@ByRef T_DataPtrSizeT_T container); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/Tensor.java b/pytorch/src/gen/java/org/bytedeco/pytorch/Tensor.java index f7cbc6eba3d..47faee6e12e 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/Tensor.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/Tensor.java @@ -489,6 +489,7 @@ private native void allocate( public native @ByRef Tensor copysign_(@Const @ByRef Tensor other); public native @ByVal Tensor copysign(@Const @ByRef Scalar other); public native @ByRef Tensor copysign_(@Const @ByRef Scalar other); + public native @ByVal Tensor _lazy_clone(); public native @ByVal Tensor logical_not(); public native @ByRef Tensor logical_not_(); public native @ByVal Tensor logical_xor(@Const @ByRef Tensor other); @@ -915,6 +916,10 @@ private native void allocate( public native @ByVal Tensor slice(); public native @ByVal Tensor slice_symint(@Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional end, @ByVal(nullValue = "c10::SymInt(1)") SymInt step); public native @ByVal Tensor slice_symint(); + public native @ByVal Tensor slice_inverse(@Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional end, @Cast("int64_t") long step/*=1*/); + public native @ByVal Tensor slice_inverse(@Const @ByRef Tensor src); + public native @ByVal Tensor slice_inverse_symint(@Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional end, @ByVal(nullValue = "c10::SymInt(1)") SymInt step); + public native @ByVal Tensor slice_inverse_symint(@Const @ByRef Tensor src); public native @ByVal Tensor slice_scatter(@Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional end, @Cast("int64_t") long step/*=1*/); public native @ByVal Tensor slice_scatter(@Const @ByRef Tensor src); public native @ByVal Tensor slice_scatter_symint(@Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional end, @ByVal(nullValue = "c10::SymInt(1)") SymInt step); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorArg.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorArg.java index c67e4e97f25..4c973e312e4 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorArg.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorArg.java @@ -28,6 +28,7 @@ public class TensorArg extends Pointer { /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ public TensorArg(Pointer p) { super(p); } + // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members) @MemberGetter public native @Const @ByRef Tensor tensor(); public native @Cast("const char*") BytePointer name(); public native TensorArg name(BytePointer setter); public native int pos(); public native TensorArg pos(int setter); // 1-indexed @@ -36,6 +37,7 @@ public class TensorArg extends Pointer { public TensorArg(@Const @ByRef Tensor tensor, String name, int pos) { super((Pointer)null); allocate(tensor, name, pos); } private native void allocate(@Const @ByRef Tensor tensor, String name, int pos); // Try to mitigate any possibility of dangling reference to temporaries. + // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved) public native @Const @Name("operator ->") Tensor access(); public native @Const @ByRef @Name("operator *") Tensor multiply(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImpl.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImpl.java index 1996b4d0733..f6b903ec3cd 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImpl.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImpl.java @@ -415,6 +415,9 @@ public enum SizesStridesPolicy { // Whether a tensor is sparse CSR or not. public native @Cast("bool") boolean is_sparse_csr(); + // Whether a tensor is sparse CSR/CSC/BSR/BSC or not. + public native @Cast("bool") boolean is_sparse_compressed(); + public native @Cast("bool") boolean is_quantized(); public native @Cast("bool") boolean is_meta(); @@ -465,7 +468,7 @@ public enum SizesStridesPolicy { // Inference tensor has version_counter_.enabled() == false public native @Cast("bool") boolean is_inference(); - public native @Cast("int64_t") long get_device(); + public native @Cast("c10::DeviceIndex") byte get_device(); public native @ByVal Device device(); public native Layout layout(); @@ -956,7 +959,7 @@ public native void set_named_tensor_meta( public native void ShareData(@Const @ByRef TensorImpl src); public native void ShareExternalPointer( - @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr data_ptr, + @StdMove DataPtr data_ptr, @Const @ByVal TypeMeta data_type, @Cast("size_t") long size_bytes); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImplPtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImplPtr.java index 0eae33a9c78..1292b6f9bf3 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImplPtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorImplPtr.java @@ -53,6 +53,10 @@ public class TensorImplPtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) TensorImplPtr put(@ByRef(true) TensorImplPtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) TensorImpl get(); public native @ByRef @Name("operator *") @NoException(true) TensorImpl multiply(); @@ -68,9 +72,9 @@ public class TensorImplPtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class TensorImplPtr extends Pointer { */ public static native @ByVal TensorImplPtr unsafe_adapt_non_heap_allocated( TensorImpl raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorBase.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorBase.java index 6e8a4567ca8..5a4dbcd14dc 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorBase.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorBase.java @@ -61,33 +61,33 @@ public class TensorIteratorBase extends MetaBase { public native @Cast("bool") boolean is_dim_reduced(int dim); /** Accessors for each operand */ - public native @ByVal LongArrayRef strides(int arg); - public native Pointer data_ptr(int arg); - public native ScalarType dtype(int arg/*=0*/); + public native @ByVal LongArrayRef strides(@Cast("int64_t") long arg); + public native Pointer data_ptr(@Cast("int64_t") long arg); + public native ScalarType dtype(@Cast("int64_t") long arg/*=0*/); public native ScalarType dtype(); public native ScalarType common_dtype(); - public native ScalarType input_dtype(int arg/*=0*/); + public native ScalarType input_dtype(@Cast("int64_t") long arg/*=0*/); public native ScalarType input_dtype(); - public native @ByVal Device device(int arg/*=0*/); + public native @ByVal Device device(@Cast("int64_t") long arg/*=0*/); public native @ByVal Device device(); - public native DeviceType device_type(int arg/*=0*/); + public native DeviceType device_type(@Cast("int64_t") long arg/*=0*/); public native DeviceType device_type(); - public native @Cast("int64_t") long element_size(int arg); - public native @Cast("bool") boolean is_scalar(int arg); - public native @Cast("bool") boolean is_cpu_scalar(int arg); + public native @Cast("int64_t") long element_size(@Cast("int64_t") long arg); + public native @Cast("bool") boolean is_scalar(@Cast("int64_t") long arg); + public native @Cast("bool") boolean is_cpu_scalar(@Cast("int64_t") long arg); - public native @Const @ByRef TensorBase tensor_base(int arg); - public native @Const @ByRef Tensor tensor(int arg); + public native @Const @ByRef TensorBase tensor_base(@Cast("int64_t") long arg); + public native @Const @ByRef Tensor tensor(@Cast("int64_t") long arg); - public native @Const @ByRef TensorBase output_base(int arg/*=0*/); + public native @Const @ByRef TensorBase output_base(@Cast("int64_t") long arg/*=0*/); public native @Const @ByRef TensorBase output_base(); - public native @Const @ByRef Tensor output(int arg/*=0*/); + public native @Const @ByRef Tensor output(@Cast("int64_t") long arg/*=0*/); public native @Const @ByRef Tensor output(); - public native @Const @ByRef TensorBase input_base(int arg/*=0*/); + public native @Const @ByRef TensorBase input_base(@Cast("int64_t") long arg/*=0*/); public native @Const @ByRef TensorBase input_base(); - public native @Const @ByRef Tensor input(int arg/*=0*/); + public native @Const @ByRef Tensor input(@Cast("int64_t") long arg/*=0*/); public native @Const @ByRef Tensor input(); // Copies from temporary outputs back to the original outputs @@ -95,7 +95,7 @@ public class TensorIteratorBase extends MetaBase { public native void cast_outputs(); /** Removes an operand from this iterator */ - public native void remove_operand(int arg); + public native void remove_operand(@Cast("int64_t") long arg); /** Shrinks an iterated dimension */ public native void narrow(int dim, @Cast("int64_t") long start, @Cast("int64_t") long size); /** Narrows every dim after and including {@code start_dim} to size one. */ @@ -104,7 +104,7 @@ public class TensorIteratorBase extends MetaBase { /** Replaces the data pointer for the operand at index {@code arg}. * The new pointer should have the same sizes, strides and dtype as the * original */ - public native void unsafe_replace_operand(int arg, Pointer data); + public native void unsafe_replace_operand(@Cast("int64_t") long arg, Pointer data); /** Splits this TensorIterator into two iterators. Together they iterate over * the entire operation. Used by {@code with_32bit_indexing()}. */ @@ -130,7 +130,7 @@ public class TensorIteratorBase extends MetaBase { /** Create a strides array for a Tensor with shape of this iterator. The * parameter {@code element_size} specifies the size of Tensor's data type in * bytes (e.g. {@code 4} for {@code float}) */ - public native @ByVal @Cast("at::TensorIteratorBase::StrideVector*") SymDimVector compatible_stride(int element_size); + public native @ByVal @Cast("at::TensorIteratorBase::StrideVector*") SymDimVector compatible_stride(@Cast("int64_t") long element_size); /** Inverts the re-ordering done by reorder_dimensions. This can only be * called *before* coalesce_dimensions() is called. */ @@ -148,9 +148,9 @@ public class TensorIteratorBase extends MetaBase { public native @ByVal @Cast("at::TensorIteratorBase::PtrVector*") SymDimVector get_base_ptrs(); // Helper functions for advanced stride manipulations (e.g. torch.flip) - public native void _unsafe_set_arg_strides(int arg, @ByVal LongArrayRef strides); - public native void _unsafe_set_arg_strides(int arg, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... strides); - public native void _unsafe_set_arg_data(int arg, Pointer data); + public native void _unsafe_set_arg_strides(@Cast("const int64_t") long arg, @ByVal LongArrayRef strides); + public native void _unsafe_set_arg_strides(@Cast("const int64_t") long arg, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... strides); + public native void _unsafe_set_arg_data(@Cast("const int64_t") long arg, Pointer data); /** true if the stride computation can use 32-bit arithmetic. Used by GPU * kernels */ diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorConfig.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorConfig.java index 98bf03c4a18..b4311f0b508 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorConfig.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorIteratorConfig.java @@ -45,10 +45,12 @@ public class TensorIteratorConfig extends Pointer { // Important: the outputs have to be added before the inputs. public native @ByRef TensorIteratorConfig add_output(@Const @ByRef TensorBase output); public native @ByRef TensorIteratorConfig add_input(@Const @ByRef TensorBase input); + public native @ByRef TensorIteratorConfig add_const_input(@Const @ByRef TensorBase input); // Borrowing from temporaries is unlikely to go well. + // Stores input/output Tensors while incrementing the reference count. // Note that add_{in,out}put are nearly always what you @@ -56,6 +58,7 @@ public class TensorIteratorConfig extends Pointer { // compile. public native @ByRef TensorIteratorConfig add_owned_output(@Const @ByRef TensorBase output); public native @ByRef TensorIteratorConfig add_owned_input(@Const @ByRef TensorBase input); + public native @ByRef TensorIteratorConfig add_owned_const_input(@Const @ByRef TensorBase input); // Advanced API: stores input/output Tensors without incrementing // the reference count. The caller must ensure that these Tensors @@ -64,10 +67,12 @@ public class TensorIteratorConfig extends Pointer { // Important: the outputs have to be added before the inputs. public native @ByRef TensorIteratorConfig add_borrowed_output(@Const @ByRef TensorBase output); public native @ByRef TensorIteratorConfig add_borrowed_input(@Const @ByRef TensorBase input); + public native @ByRef TensorIteratorConfig add_borrowed_const_input(@Const @ByRef TensorBase input); // Borrowing from temporaries is unlikely to go well. + // Sets the check_mem_overlap_ flag, which is true by default. // If true, inputs are checked for partial overlap with the outputs and diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorMaker.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorMaker.java index dfb22fa1529..5a82150042d 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorMaker.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorMaker.java @@ -25,9 +25,8 @@ * * at::Tensor tensor = at::for_blob(data, sizes) * .strides(strides) - * .context(context, [](void *ctx) { delete static_cast(ctx); }) - * .options(...) - * .make_tensor(); + * .context(context, [](void *ctx) { delete static_cast(ctx); + * }) .options(...) .make_tensor(); * */ @Namespace("at") @NoOffset @Properties(inherit = org.bytedeco.pytorch.presets.torch.class) public class TensorMaker extends Pointer { diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorNames.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorNames.java index 5ba13495d98..b94b4f48a69 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorNames.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorNames.java @@ -49,6 +49,6 @@ public class TensorNames extends Pointer { public native void checkUnique(@Cast("const char*") BytePointer op_name); public native void checkUnique(String op_name); - public native void append(@ByRef(true) TensorName name); + public native void append(@ByVal TensorName name); public native @ByVal DimnameVector toDimnameVec(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorOptions.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorOptions.java index e46c3e9ddc7..fb3796f2af8 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TensorOptions.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TensorOptions.java @@ -199,7 +199,7 @@ public class TensorOptions extends Pointer { public native @ByVal @NoException(true) DeviceOptional device_opt(); /** Returns the device index of the {@code TensorOptions}. */ - public native @NoException(true) int device_index(); + public native @Cast("c10::DeviceIndex") @NoException(true) byte device_index(); /** Returns the dtype of the {@code TensorOptions}. */ public native @ByVal @NoException(true) TypeMeta dtype(); @@ -240,8 +240,12 @@ public class TensorOptions extends Pointer { /** Returns if the layout is sparse */ public native @Cast("bool") boolean is_sparse(); + /** Returns if the layout is sparse CSR, deprecated, use + * is_sparse_compressed() instead */ public native @Cast("bool") boolean is_sparse_csr(); + public native @Cast("bool") boolean is_sparse_compressed(); + // For compatibility with legacy tensor.type() comparisons public native @Cast("bool") boolean type_equal(@Const @ByRef TensorOptions other); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TreeRef.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TreeRef.java index f65c9a9a987..a827f9811ae 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TreeRef.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TreeRef.java @@ -53,6 +53,10 @@ public class TreeRef extends Pointer { public native @ByRef @Name("operator =") @NoException(true) TreeRef put(@ByRef(true) TreeRef rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Tree get(); public native @ByRef @Name("operator *") @NoException(true) Tree multiply(); @@ -68,9 +72,9 @@ public class TreeRef extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class TreeRef extends Pointer { */ public static native @ByVal TreeRef unsafe_adapt_non_heap_allocated( Tree raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/TuplePtr.java b/pytorch/src/gen/java/org/bytedeco/pytorch/TuplePtr.java index 57480dba941..8cd45265936 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/TuplePtr.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/TuplePtr.java @@ -53,6 +53,10 @@ public class TuplePtr extends Pointer { public native @ByRef @Name("operator =") @NoException(true) TuplePtr put(@ByRef(true) TuplePtr rhs); + // Assignment is implemented using copy and swap. That's safe for self + // assignment. + // NOLINTNEXTLINE(bugprone-unhandled-self-assignment) + public native @NoException(true) Tuple get(); public native @ByRef @Name("operator *") @NoException(true) Tuple multiply(); @@ -68,9 +72,9 @@ public class TuplePtr extends Pointer { // We do a lot of null-pointer checks in our code, good to have this be cheap. public native @Cast("bool") @NoException(true) boolean defined(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean unique(); @@ -134,7 +138,7 @@ public class TuplePtr extends Pointer { */ public static native @ByVal TuplePtr unsafe_adapt_non_heap_allocated( Tuple raw_ptr, - @Cast("size_t") long expected_decrefs); + @Cast("uint32_t") int expected_decrefs); /** * Turn a **non-owning raw pointer** to an intrusive_ptr. It is diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/VariableInfo.java b/pytorch/src/gen/java/org/bytedeco/pytorch/VariableInfo.java index 0b7432be9d4..a7e0b27fc0f 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/VariableInfo.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/VariableInfo.java @@ -35,10 +35,10 @@ public class VariableInfo extends Pointer { public VariableInfo() { super((Pointer)null); allocate(); } private native void allocate(); - public VariableInfo(@Cast("const torch::autograd::Variable*") @ByRef Tensor var) { super((Pointer)null); allocate(var); } - private native void allocate(@Cast("const torch::autograd::Variable*") @ByRef Tensor var); + public VariableInfo(@Const @ByRef Tensor var) { super((Pointer)null); allocate(var); } + private native void allocate(@Const @ByRef Tensor var); - public native @ByVal @Cast("torch::autograd::Variable*") Tensor zeros(@ByRef OptionalDeviceGuard device_guard); + public native @ByVal Tensor zeros(@ByRef OptionalDeviceGuard device_guard); public native @ByRef Layout layout(); public native VariableInfo layout(Layout setter); public native @ByRef Device device(); public native VariableInfo device(Device setter); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/WeakStorage.java b/pytorch/src/gen/java/org/bytedeco/pytorch/WeakStorage.java index 087d4dd4dcc..f0b7bfa9d7c 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/WeakStorage.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/WeakStorage.java @@ -63,9 +63,9 @@ public class WeakStorage extends Pointer { // public native @NoException(true) StorageImpl _unsafe_get_target(); - public native @Cast("size_t") @NoException(true) long use_count(); + public native @Cast("uint32_t") @NoException(true) int use_count(); - public native @Cast("size_t") @NoException(true) long weak_use_count(); + public native @Cast("uint32_t") @NoException(true) int weak_use_count(); public native @Cast("bool") @NoException(true) boolean expired(); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/XPUHooksInterface.java b/pytorch/src/gen/java/org/bytedeco/pytorch/XPUHooksInterface.java index 3d4a031af0f..1fcb4111d48 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/XPUHooksInterface.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/XPUHooksInterface.java @@ -43,14 +43,7 @@ public class XPUHooksInterface extends Pointer { public native @StdString BytePointer showConfig(); - public native @ByVal Device getATenDeviceFromDLPackDevice( - @Const @ByRef DLDevice_ dl_device, - Pointer data); - - public native @ByRef DLDevice_ getDLPackDeviceFromATenDevice( - @ByRef DLDevice_ dl_device, - @Const @ByRef Device aten_device, - Pointer data); + public native int getGlobalIdxFromDevice(@Const @ByRef Device device); public native @ByVal Generator getXPUGenerator(@Cast("c10::DeviceIndex") byte device_index/*=-1*/); public native @ByVal Generator getXPUGenerator(); @@ -58,5 +51,11 @@ public class XPUHooksInterface extends Pointer { public native @Const @ByRef Generator getDefaultXPUGenerator(@Cast("c10::DeviceIndex") byte device_index/*=-1*/); public native @Const @ByRef Generator getDefaultXPUGenerator(); - public native int getNumGPUs(); + public native @Cast("c10::DeviceIndex") byte getNumGPUs(); + + public native @Cast("c10::DeviceIndex") byte current_device(); + + public native @ByVal Device getDeviceFromPtr(Pointer arg0); + + public native void deviceSynchronize(@Cast("c10::DeviceIndex") byte arg0); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AOTIModelContainerRunnerCuda.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AOTIModelContainerRunnerCuda.java new file mode 100644 index 00000000000..b99c61cbf15 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AOTIModelContainerRunnerCuda.java @@ -0,0 +1,68 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch.cuda; + +import org.bytedeco.pytorch.*; +import org.bytedeco.pytorch.cuda.functions.*; +import org.bytedeco.pytorch.Error; +import org.bytedeco.pytorch.global.torch.DeviceType; +import org.bytedeco.pytorch.global.torch.ScalarType; +import org.bytedeco.pytorch.global.torch.MemoryFormat; +import org.bytedeco.pytorch.Allocator; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; +import org.bytedeco.pytorch.*; +import static org.bytedeco.pytorch.global.torch.*; + +import static org.bytedeco.pytorch.global.torch_cuda.*; + + +// NOTICE: Following APIs are subject to change due to active development +// We provide NO BC guarantee for these APIs +@Namespace("torch::inductor") @Properties(inherit = org.bytedeco.pytorch.presets.torch_cuda.class) +public class AOTIModelContainerRunnerCuda extends AOTIModelContainerRunner { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public AOTIModelContainerRunnerCuda(Pointer p) { super(p); } + + // @param device_str: cuda device string, e.g. "cuda", "cuda:0" + public AOTIModelContainerRunnerCuda( + @StdString BytePointer model_so_path, + @Cast("size_t") long num_models/*=1*/, + @StdString BytePointer device_str/*="cuda"*/, + @StdString BytePointer cubin_dir/*=""*/) { super((Pointer)null); allocate(model_so_path, num_models, device_str, cubin_dir); } + private native void allocate( + @StdString BytePointer model_so_path, + @Cast("size_t") long num_models/*=1*/, + @StdString BytePointer device_str/*="cuda"*/, + @StdString BytePointer cubin_dir/*=""*/); + public AOTIModelContainerRunnerCuda( + @StdString BytePointer model_so_path) { super((Pointer)null); allocate(model_so_path); } + private native void allocate( + @StdString BytePointer model_so_path); + public AOTIModelContainerRunnerCuda( + @StdString String model_so_path, + @Cast("size_t") long num_models/*=1*/, + @StdString String device_str/*="cuda"*/, + @StdString String cubin_dir/*=""*/) { super((Pointer)null); allocate(model_so_path, num_models, device_str, cubin_dir); } + private native void allocate( + @StdString String model_so_path, + @Cast("size_t") long num_models/*=1*/, + @StdString String device_str/*="cuda"*/, + @StdString String cubin_dir/*=""*/); + public AOTIModelContainerRunnerCuda( + @StdString String model_so_path) { super((Pointer)null); allocate(model_so_path); } + private native void allocate( + @StdString String model_so_path); + + public native @ByVal TensorVector run(@ByRef TensorVector inputs); + + public native @ByVal TensorVector run_with_cuda_stream( + @ByRef TensorVector inputs, + @ByVal CUDAStream cuda_stream); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AllocatorConfigInfo.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AllocatorConfigInfo.java new file mode 100644 index 00000000000..e1cd6e5c4c7 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/AllocatorConfigInfo.java @@ -0,0 +1,51 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch.cuda; + +import org.bytedeco.pytorch.*; +import org.bytedeco.pytorch.cuda.functions.*; +import org.bytedeco.pytorch.Error; +import org.bytedeco.pytorch.global.torch.DeviceType; +import org.bytedeco.pytorch.global.torch.ScalarType; +import org.bytedeco.pytorch.global.torch.MemoryFormat; +import org.bytedeco.pytorch.Allocator; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; +import org.bytedeco.pytorch.*; +import static org.bytedeco.pytorch.global.torch.*; + +import static org.bytedeco.pytorch.global.torch_cuda.*; + + +@Namespace("c10::cuda::CUDACachingAllocator") @Properties(inherit = org.bytedeco.pytorch.presets.torch_cuda.class) +public class AllocatorConfigInfo extends Pointer { + static { Loader.load(); } + /** Default native constructor. */ + public AllocatorConfigInfo() { super((Pointer)null); allocate(); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public AllocatorConfigInfo(long size) { super((Pointer)null); allocateArray(size); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public AllocatorConfigInfo(Pointer p) { super(p); } + private native void allocate(); + private native void allocateArray(long size); + @Override public AllocatorConfigInfo position(long position) { + return (AllocatorConfigInfo)super.position(position); + } + @Override public AllocatorConfigInfo getPointer(long i) { + return new AllocatorConfigInfo((Pointer)this).offsetAddress(i); + } + + public native double garbage_collection_threshold(); public native AllocatorConfigInfo garbage_collection_threshold(double setter); + public native @Cast("size_t") long max_split_size(); public native AllocatorConfigInfo max_split_size(long setter); + public native @Cast("size_t") long pinned_num_register_threads(); public native AllocatorConfigInfo pinned_num_register_threads(long setter); + public native @Cast("bool") boolean expandable_segments(); public native AllocatorConfigInfo expandable_segments(boolean setter); + public native @Cast("bool") boolean release_lock_on_malloc(); public native AllocatorConfigInfo release_lock_on_malloc(boolean setter); + public native @Cast("bool") boolean pinned_use_host_register(); public native AllocatorConfigInfo pinned_use_host_register(boolean setter); + public native @StdString BytePointer last_allocator_settings(); public native AllocatorConfigInfo last_allocator_settings(BytePointer setter); + public native @ByRef @Cast("std::vector*") SizeTVector roundup_power2_divisions(); public native AllocatorConfigInfo roundup_power2_divisions(SizeTVector setter); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CTCLossDescriptor.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CTCLossDescriptor.java index 587ebac4a1a..2d72e4c6270 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CTCLossDescriptor.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CTCLossDescriptor.java @@ -41,10 +41,8 @@ public class CTCLossDescriptor extends Pointer { } public native void set(@Cast("cudnnDataType_t") int datatype); -// #if CUDNN_VERSION >= 7600 public native void setEx( @Cast("cudnnDataType_t") int datatype, @Cast("cudnnLossNormalizationMode_t") int normMode, @Cast("cudnnNanPropagation_t") int gradMode); -// #endif } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CUDAAllocator.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CUDAAllocator.java index 8ead036744f..845775aa5dc 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CUDAAllocator.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/CUDAAllocator.java @@ -33,24 +33,26 @@ public class CUDAAllocator extends Allocator { public native void raw_delete(Pointer ptr); public native void init(int device_count); public native @Cast("bool") boolean initialized(); - public native void setMemoryFraction(double fraction, int device); + public native void setMemoryFraction(double fraction, byte device); public native void emptyCache(); - public native void cacheInfo(int dev_id, @Cast("size_t*") SizeTPointer largestBlock); + public native void cacheInfo(byte device, @Cast("size_t*") SizeTPointer largestBlock); public native Pointer getBaseAllocation(Pointer ptr, @Cast("size_t*") SizeTPointer size); - public native void recordStream(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr arg0, @ByVal CUDAStream stream); - public native @ByVal DeviceStats getDeviceStats(int device); - public native void resetAccumulatedStats(int device); - public native void resetPeakStats(int device); + public native void recordStream(@StdMove DataPtr arg0, @ByVal CUDAStream stream); + public native @ByVal DeviceStats getDeviceStats(byte device); + public native void resetAccumulatedStats(byte device); + public native void resetPeakStats(byte device); public native @ByVal SnapshotInfo snapshot(); - public native void beginAllocateStreamToPool( - int device, - @Cast("cudaStream_t") Pointer stream, + public native void beginAllocateToPool( + byte device, + @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id, + @ByVal StreamFilter filter); + public native void endAllocateToPool( + byte device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id); - public native void endAllocateStreamToPool(int device, @Cast("cudaStream_t") Pointer stream); - public native void releasePool(int device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id); + public native void releasePool(byte device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id); // returns true if the allocated blocks are equal to expected live allocations public native @Cast("bool") boolean checkPoolLiveAllocations( - int device, + byte device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id, @Const @ByRef PointerSet expected_live_allocations); public native @SharedPtr Pointer getIpcDevPtr(@StdString BytePointer handle); @@ -76,7 +78,9 @@ public native void recordHistory( // callback. public native void attachAllocatorTraceTracker(@ByVal AllocatorTraceTracker tracker); - public native void enablePeerAccess(int dev, int dev_to_access); + public native void enablePeerAccess( + byte dev, + byte dev_to_access); // memory not allocated from cudaMalloc cannot be copied // across devices using cudaMemcpyAsync if peer to peer access is disabled. @@ -98,10 +102,10 @@ public native void recordHistory( @Cast("cudaStream_t") Pointer stream, @Cast("bool") boolean p2p_enabled); public native @SharedPtr("c10::cuda::CUDACachingAllocator::AllocatorState") @ByVal AllocatorState getCheckpointState( - int device, + byte device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair id); public native @ByVal CheckpointDelta setCheckpointPoolState( - int device, + byte device, @SharedPtr("c10::cuda::CUDACachingAllocator::AllocatorState") @ByVal AllocatorState pps); public native @StdString BytePointer name(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionData.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionData.java index cc8973cf24f..13d813e1177 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionData.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionData.java @@ -43,12 +43,15 @@ public class DeviceAssertionData extends Pointer { } /** Stringification of the assertion */ + // NOLINTNEXTLINE(*-c-arrays) public native @Cast("char") byte assertion_msg(int i); public native DeviceAssertionData assertion_msg(int i, byte setter); @MemberGetter public native @Cast("char*") BytePointer assertion_msg(); /** File the assertion was in */ + // NOLINTNEXTLINE(*-c-arrays) public native @Cast("char") byte filename(int i); public native DeviceAssertionData filename(int i, byte setter); @MemberGetter public native @Cast("char*") BytePointer filename(); /** Name of the function the assertion was in */ + // NOLINTNEXTLINE(*-c-arrays) public native @Cast("char") byte function_name(int i); public native DeviceAssertionData function_name(int i, byte setter); @MemberGetter public native @Cast("char*") BytePointer function_name(); /** Line number the assertion was at */ @@ -56,9 +59,11 @@ public class DeviceAssertionData extends Pointer { /** Number uniquely identifying the kernel launch that triggered the assertion */ public native @Cast("uint32_t") int caller(); public native DeviceAssertionData caller(int setter); /** block_id of the thread that failed the assertion */ + // NOLINTNEXTLINE(*-c-arrays) public native int block_id(int i); public native DeviceAssertionData block_id(int i, int setter); @MemberGetter public native IntPointer block_id(); /** third_id of the thread that failed the assertion */ + // NOLINTNEXTLINE(*-c-arrays) public native int thread_id(int i); public native DeviceAssertionData thread_id(int i, int setter); @MemberGetter public native IntPointer thread_id(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionsData.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionsData.java index c6a38b4110a..c7a9ee2b638 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionsData.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceAssertionsData.java @@ -46,6 +46,7 @@ public class DeviceAssertionsData extends Pointer { * in {@code assertions} */ public native int assertion_count(); public native DeviceAssertionsData assertion_count(int setter); /** An array of assertions that will be written to in a race-free manner */ + // NOLINTNEXTLINE(*-c-arrays) public native @ByRef DeviceAssertionData assertions(int i); public native DeviceAssertionsData assertions(int i, DeviceAssertionData setter); @MemberGetter public native DeviceAssertionData assertions(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceStats.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceStats.java index 1122dcfeef0..ab61f5fa58c 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceStats.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/DeviceStats.java @@ -75,6 +75,17 @@ public class DeviceStats extends Pointer { // COUNT: total number of oversize blocks requiring malloc public native @ByRef Stat oversize_segments(); public native DeviceStats oversize_segments(Stat setter); + // COUNT: total number of synchronize_and_free_events() calls + public native @Cast("int64_t") long num_sync_all_streams(); public native DeviceStats num_sync_all_streams(long setter); + + // COUNT: total number of CUDA allocation calls. This includes both cuMemMap + // and cudaMalloc. + public native @Cast("int64_t") long num_device_alloc(); public native DeviceStats num_device_alloc(long setter); + + // COUNT: total number of CUDA free calls. This includes both cuMemUnmap + // and cudaFree. + public native @Cast("int64_t") long num_device_free(); public native DeviceStats num_device_free(long setter); + // SIZE: maximum block size that is allowed to be split. public native @Cast("int64_t") long max_split_size(); public native DeviceStats max_split_size(long setter); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDataDescriptor.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDataDescriptor.java new file mode 100644 index 00000000000..a42bc32c9e9 --- /dev/null +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDataDescriptor.java @@ -0,0 +1,56 @@ +// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.bytedeco.pytorch.cuda; + +import org.bytedeco.pytorch.*; +import org.bytedeco.pytorch.cuda.functions.*; +import org.bytedeco.pytorch.Error; +import org.bytedeco.pytorch.global.torch.DeviceType; +import org.bytedeco.pytorch.global.torch.ScalarType; +import org.bytedeco.pytorch.global.torch.MemoryFormat; +import org.bytedeco.pytorch.Allocator; +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +import static org.bytedeco.javacpp.presets.javacpp.*; +import static org.bytedeco.openblas.global.openblas_nolapack.*; +import static org.bytedeco.openblas.global.openblas.*; +import org.bytedeco.pytorch.*; +import static org.bytedeco.pytorch.global.torch.*; + +import static org.bytedeco.pytorch.global.torch_cuda.*; + + +// A generic class for wrapping cuDNN descriptor types. All you need +// is to give the underlying type the Descriptor_t points to (usually, +// if it's cudnnTensorDescriptor_t it points to cudnnTensorStruct), +// the constructor and the destructor. Subclasses are responsible +// for defining a set() function to actually set the descriptor. +// +// Descriptors default construct to a nullptr, and have a descriptor +// initialized the first time you call set() or any other initializing +// function. + +@Namespace("at::native") @Properties(inherit = org.bytedeco.pytorch.presets.torch_cuda.class) +public class RNNDataDescriptor extends Pointer { + static { Loader.load(); } + /** Default native constructor. */ + public RNNDataDescriptor() { super((Pointer)null); allocate(); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public RNNDataDescriptor(long size) { super((Pointer)null); allocateArray(size); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public RNNDataDescriptor(Pointer p) { super(p); } + private native void allocate(); + private native void allocateArray(long size); + @Override public RNNDataDescriptor position(long position) { + return (RNNDataDescriptor)super.position(position); + } + @Override public RNNDataDescriptor getPointer(long i) { + return new RNNDataDescriptor((Pointer)this).offsetAddress(i); + } + + public native void set(@Const @ByRef Tensor t, @Cast("cudnnRNNDataLayout_t") int layout, int maxSeqLength, int batchSize, int vectorSize, @Const IntPointer seqLengthArray); + public native void set(@Const @ByRef Tensor t, @Cast("cudnnRNNDataLayout_t") int layout, int maxSeqLength, int batchSize, int vectorSize, @Const IntBuffer seqLengthArray); + public native void set(@Const @ByRef Tensor t, @Cast("cudnnRNNDataLayout_t") int layout, int maxSeqLength, int batchSize, int vectorSize, @Const int[] seqLengthArray); +} diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDescriptor.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDescriptor.java index 831e24dc315..74596dae641 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDescriptor.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/RNNDescriptor.java @@ -41,7 +41,10 @@ public class RNNDescriptor extends Pointer { } - public native void set(@Cast("cudnnHandle_t") Pointer handle, int hidden_size, int proj_size, int num_layers, @ByRef(true) DropoutDescriptor dropout_desc, + public native void set(@Cast("cudnnHandle_t") Pointer handle, + int input_size, + @Cast("bool") boolean packed, + int hidden_size, int proj_size, int num_layers, @ByRef(true) DropoutDescriptor dropout_desc, @Cast("cudnnRNNInputMode_t") int input_mode, @Cast("cudnnDirectionMode_t") int bidirectional, @Cast("cudnnRNNMode_t") int mode, @Cast("cudnnDataType_t") int datatype, @Cast("cudnnDataType_t") int input_type, @Cast("cudnnRNNAlgo_t") int algo, @Cast("bool") boolean allow_tf32); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SegmentInfo.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SegmentInfo.java index b22f2ccbc03..deac68b1f95 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SegmentInfo.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SegmentInfo.java @@ -41,7 +41,7 @@ public class SegmentInfo extends Pointer { return new SegmentInfo((Pointer)this).offsetAddress(i); } - public native @Cast("int64_t") long device(); public native SegmentInfo device(long setter); + public native byte device(); public native SegmentInfo device(byte setter); public native @Cast("int64_t") @Name("address") long _address(); public native SegmentInfo _address(long setter); public native @Cast("int64_t") long total_size(); public native SegmentInfo total_size(long setter); public native @Cast("int64_t") long requested_size(); public native SegmentInfo requested_size(long setter); // unrounded, actually requested size diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SnapshotInfo.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SnapshotInfo.java index 19af7311a7e..bdb993045d9 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SnapshotInfo.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/SnapshotInfo.java @@ -42,4 +42,5 @@ public class SnapshotInfo extends Pointer { public native @StdVector SegmentInfo segments(); public native SnapshotInfo segments(SegmentInfo setter); public native @StdVector TraceEntryVector device_traces(); public native SnapshotInfo device_traces(TraceEntryVector setter); + public native @ByRef AllocatorConfigInfo config_metadata(); public native SnapshotInfo config_metadata(AllocatorConfigInfo setter); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TensorDescriptor.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TensorDescriptor.java index a602c344567..c1b7a56ab77 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TensorDescriptor.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TensorDescriptor.java @@ -22,16 +22,6 @@ import static org.bytedeco.pytorch.global.torch_cuda.*; -// A generic class for wrapping cuDNN descriptor types. All you need -// is to give the underlying type the Descriptor_t points to (usually, -// if it's cudnnTensorDescriptor_t it points to cudnnTensorStruct), -// the constructor and the destructor. Subclasses are responsible -// for defining a set() function to actually set the descriptor. -// -// Descriptors default construct to a nullptr, and have a descriptor -// initialized the first time you call set() or any other initializing -// function. - @Namespace("at::native") @Properties(inherit = org.bytedeco.pytorch.presets.torch_cuda.class) public class TensorDescriptor extends Pointer { static { Loader.load(); } diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TraceEntry.java b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TraceEntry.java index 4077a77c580..c1c219d3134 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TraceEntry.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/cuda/TraceEntry.java @@ -52,7 +52,7 @@ public enum Action { } public TraceEntry( Action action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @@ -60,7 +60,7 @@ public TraceEntry( @SharedPtr GatheredContext context/*=nullptr*/) { super((Pointer)null); allocate(action, device, addr, size, stream, time, context); } private native void allocate( Action action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @@ -68,21 +68,21 @@ private native void allocate( @SharedPtr GatheredContext context/*=nullptr*/); public TraceEntry( Action action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @Cast("c10::approx_time_t") long time) { super((Pointer)null); allocate(action, device, addr, size, stream, time); } private native void allocate( Action action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @Cast("c10::approx_time_t") long time); public TraceEntry( @Cast("c10::cuda::CUDACachingAllocator::TraceEntry::Action") int action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @@ -90,7 +90,7 @@ public TraceEntry( @SharedPtr GatheredContext context/*=nullptr*/) { super((Pointer)null); allocate(action, device, addr, size, stream, time, context); } private native void allocate( @Cast("c10::cuda::CUDACachingAllocator::TraceEntry::Action") int action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @@ -98,20 +98,20 @@ private native void allocate( @SharedPtr GatheredContext context/*=nullptr*/); public TraceEntry( @Cast("c10::cuda::CUDACachingAllocator::TraceEntry::Action") int action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @Cast("c10::approx_time_t") long time) { super((Pointer)null); allocate(action, device, addr, size, stream, time); } private native void allocate( @Cast("c10::cuda::CUDACachingAllocator::TraceEntry::Action") int action, - int device, + byte device, @Cast("int64_t") long addr, @Cast("size_t") long size, @Cast("cudaStream_t") Pointer stream, @Cast("c10::approx_time_t") long time); public native Action action_(); public native TraceEntry action_(Action setter); - public native int device_(); public native TraceEntry device_(int setter); + public native byte device_(); public native TraceEntry device_(byte setter); public native @Cast("int64_t") long addr_(); public native TraceEntry addr_(long setter); // for OOM, this is the amount of free bytes reported by cuda public native @SharedPtr GatheredContext context_(); public native TraceEntry context_(GatheredContext setter); public native @Cast("cudaStream_t") Pointer stream_(); public native TraceEntry stream_(Pointer setter); diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch.java b/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch.java index a64491b5db0..cd5d30c8eff 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch.java @@ -77,6 +77,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Targeting ../DeviceOptional.java +// Targeting ../DeviceTypeOptional.java + + // Targeting ../LongArrayRefOptional.java @@ -527,6 +530,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Targeting ../TreeRefStringMap.java +// Targeting ../StringIntMap.java + + // Targeting ../StringSet.java @@ -759,6 +765,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define TORCH_HIP_API C10_IMPORT // #endif +// #if defined(TORCH_XPU_BUILD_MAIN_LIB) +// #define TORCH_XPU_API C10_EXPORT +// #else +// #define TORCH_XPU_API C10_IMPORT +// #endif + // Enums only need to be exported on windows for non-CUDA files // #if defined(_WIN32) && defined(__CUDACC__) // #define C10_API_ENUM C10_API @@ -934,11 +946,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Simply define the namespace, in case a dependent library want to refer to // the c10 namespace but not any nontrivial files. - // namespace c10 - // namespace c10 - // namespace c10 + + // Since C10 is the core library for caffe2 (and aten), we will simply reroute // all abstractions defined in c10 to be available in caffe2 as well. @@ -946,8 +957,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // c10 namespace where possible. - - // namespace at + // namespace at::cuda // WARNING!!! THIS IS A GIANT HACK!!! // This line means you cannot simultaneously include c10/hip @@ -957,8 +967,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // from at::cuda. This namespace makes that happen. When // HIPIFY is no longer out-of-place, we can switch the cuda // here to hip and everyone is happy. - - // namespace at + // namespace at::cuda + // namespace at::xpu // C10_LIKELY/C10_UNLIKELY // @@ -1006,13 +1016,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define C10_ERASE C10_ALWAYS_INLINE C10_ATTR_VISIBILITY_HIDDEN -// C10_FALLTHROUGH - Annotate fallthrough to the next case in a switch. -// #if C10_HAS_CPP_ATTRIBUTE(fallthrough) -// #define C10_FALLTHROUGH [[fallthrough]] -// #else -// #define C10_FALLTHROUGH -// #endif - // #include // #ifdef __HIPCC__ @@ -1090,16 +1093,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define C10_ALWAYS_INLINE_UNLESS_MOBILE C10_ALWAYS_INLINE // #endif -// Portable determination of whether type T is trivially copyable. -// Warning: __has_trivial_copy for GCC may not always detect the non-POD -// correctly. For example, T = std::unique_ptr may evaluate to true and be -// treated as POD. This can cause unexpected behavior. -// #if defined(__GNUG__) && __GNUC__ < 5 && !defined(__clang__) -// #define C10_IS_TRIVIALLY_COPYABLE(T) __has_trivial_copy(T) -// #else -// #define C10_IS_TRIVIALLY_COPYABLE(T) std::is_trivially_copyable::value -// #endif - // #if defined(__CUDA_ARCH__) // #if defined(_MSC_VER) && defined(__CUDACC__) // #define CONSTEXPR_EXCEPT_WIN_CUDA const @@ -1225,8 +1218,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include +// #include +// #include // #include // #include +// #include // These contains all device types that also have a BackendComponent // and therefore participate in per-backend functionality dispatch keys. @@ -1318,9 +1314,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10") public static native @StdString BytePointer get_privateuse1_backend(@Cast("bool") boolean lower_case/*=true*/); @Namespace("c10") public static native @StdString BytePointer get_privateuse1_backend(); +@Namespace("c10") public static native @Cast("bool") boolean is_privateuse1_backend_registered(); + // namespace c10 // namespace std - +// NOLINTNEXTLINE(misc-unused-using-decls) + // namespace torch // Parsed from c10/util/Deprecated.h @@ -1414,7 +1413,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // #if C10_CLANG_HAS_WARNING("-Wshorten-64-to-32") // #endif @@ -1433,6 +1431,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { +// Overloads of _str for wide types; forces narrowing. + + + + @@ -1473,10 +1476,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #ifndef C10_UTIL_EXCEPTION_H_ // #define C10_UTIL_EXCEPTION_H_ +// #include // #include // #include -// #include +// #include // #include // #include // #include @@ -1722,8 +1726,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10::detail") public static native String torchCheckMsgImpl( String arg0, String args); - // namespace detail - // namespace c10 + // namespace c10::detail // #define TORCH_CHECK_MSG(cond, type, ...) // (::c10::detail::torchCheckMsgImpl( @@ -1779,8 +1782,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { String condMsg, @ByVal CompileTimeEmptyString arg4); - // namespace detail - // namespace c10 + // namespace c10::detail // #ifdef STRIP_ERROR_MESSAGES // #define TORCH_CHECK(cond, ...) @@ -1934,8 +1936,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { */ - // namespace detail - // namespace c10 + // namespace c10::detail // Deprecated alias; this alias was deprecated because people kept mistakenly // using it for user error checking. Use TORCH_INTERNAL_ASSERT or TORCH_CHECK @@ -1983,6 +1984,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #include // #include @@ -2007,7 +2009,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include +// #include // #include // #include @@ -2058,6 +2062,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // _(Dense, ) // _(Quantized, Quantized) // _(Sparse, Sparse) +// _(SparseCsr, SparseCsr) // _(NestedTensor, NestedTensor) // _(AutogradFunctionality, Autograd) @@ -2236,36 +2241,34 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // See [Note: Per-Backend Functionality Dispatch Keys] Sparse((short)(Undefined.value + 9)), - // TODO: Make SparseCsr a functionality key - SparseCsrCPU((short)(Undefined.value + 10)), - SparseCsrCUDA((short)(Undefined.value + 11)), + SparseCsr((short)(Undefined.value + 10)), - NestedTensor((short)(Undefined.value + 12)), + NestedTensor((short)(Undefined.value + 11)), // In some situations, it is not immediately obvious what the correct // backend for function is, because the function in question doesn't // have any "tensor" arguments. In this case, a BackendSelect function // can be registered to implement the custom determination of the // correct backend. - BackendSelect((short)(Undefined.value + 13)), + BackendSelect((short)(Undefined.value + 12)), - Python((short)(Undefined.value + 14)), + Python((short)(Undefined.value + 13)), // Out-of-core key for Fake Tensor in torchdistx. // See https://pytorch.org/torchdistx/latest/fake_tensor.html // TODO: delete this in favor of Python-implemented fake tensor - Fake((short)(Undefined.value + 15)), + Fake((short)(Undefined.value + 14)), // See Note [Out-of-tree vmap+grad prototype]. The purpose of this key // is to insert code after the "autograd subsystem" runs, so this key should // be directly after ADInplaceOrView and all of the autograd keys. - FuncTorchDynamicLayerBackMode((short)(Undefined.value + 16)), + FuncTorchDynamicLayerBackMode((short)(Undefined.value + 15)), // Alias and mutation removal. // If some backends want to opt into only alias removal or only mutation // removal, // we can consider adding separate keys dedicated to those individual passes. // See Note [Functionalization Pass In Core] for details. - Functionalize((short)(Undefined.value + 17)), + Functionalize((short)(Undefined.value + 16)), // The named dispatch key is set for any tensors with named dimensions. // Although we have a dispatch key for named tensors, for historical reasons, @@ -2280,19 +2283,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // has named dimension propagation that doesn't match that of its // constituent parts. // TODO: delete this once torchdim lands in functorch - Named((short)(Undefined.value + 18)), + Named((short)(Undefined.value + 17)), // The Conjugate dispatch key is set for any tensors that need to perform // conjugation // This is implemented at a dispatch level right before any backends run - Conjugate((short)(Undefined.value + 19)), + Conjugate((short)(Undefined.value + 18)), // The Negative dispatch key is set for any tensors that need to perform // negation // This is implemented at a dispatch level right before any backends run - Negative((short)(Undefined.value + 20)), + Negative((short)(Undefined.value + 19)), - ZeroTensor((short)(Undefined.value + 21)), // registered at build/aten/src/ATen/RegisterZeroTensor.cpp + ZeroTensor((short)(Undefined.value + 20)), // registered at build/aten/src/ATen/RegisterZeroTensor.cpp // Note [ADInplaceOrView key] // ADInplaceOrView key is used by inplace or view ops to register a kernel @@ -2329,7 +2332,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // `at::AutoDispatchBelowADInplaceOrView` guard of functional ops // up to the `VariableType` kernel. Thus we only add the extra dispatch // to view/inplace ops to minimize its perf impact to real models. - ADInplaceOrView((short)(Undefined.value + 22)), + ADInplaceOrView((short)(Undefined.value + 21)), // Note [Alias Dispatch Key : Autograd] // All backends are oblivious to autograd; autograd is handled as a // layer which happens on top of all backends. It inspects the autograd @@ -2355,10 +2358,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // reserved user-defined backends. All other in-tree backends share the // AutogradOther key. We can add specific autograd key for those backends // upon request. - AutogradOther((short)(Undefined.value + 23)), + AutogradOther((short)(Undefined.value + 22)), // See [Note: Per-Backend Functionality Dispatch Keys] - AutogradFunctionality((short)(Undefined.value + 24)), + AutogradFunctionality((short)(Undefined.value + 23)), // NestedTensor is an example of something that isn't a "real backend" // (because it mostly consists of redispatching kernels) @@ -2367,64 +2370,64 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // exclusively for handling autograd for NestedTensor. // lives out of tree at // https://github.com/pytorch/nestedtensor - AutogradNestedTensor((short)(Undefined.value + 25)), + AutogradNestedTensor((short)(Undefined.value + 24)), - Tracer((short)(Undefined.value + 26)), + Tracer((short)(Undefined.value + 25)), // TODO: make Autocast a functionality key // Autocasting precedes VariableTypeId, to ensure casts are autograd-exposed // and inputs are saved for backward in the post-autocast type. - AutocastCPU((short)(Undefined.value + 27)), - AutocastXPU((short)(Undefined.value + 28)), - AutocastIPU((short)(Undefined.value + 29)), - AutocastHPU((short)(Undefined.value + 30)), - AutocastXLA((short)(Undefined.value + 31)), + AutocastCPU((short)(Undefined.value + 26)), + AutocastXPU((short)(Undefined.value + 27)), + AutocastIPU((short)(Undefined.value + 28)), + AutocastHPU((short)(Undefined.value + 29)), + AutocastXLA((short)(Undefined.value + 30)), // AutocastXLA is only being used for TPUs. XLA GPUs continue to use // AutocastCUDA. - AutocastCUDA((short)(Undefined.value + 32)), - AutocastPrivateUse1((short)(Undefined.value + 33)), + AutocastCUDA((short)(Undefined.value + 31)), + AutocastPrivateUse1((short)(Undefined.value + 32)), // ~~~~~~~~~~~~~~~~~~~~~~~~~~~ WRAPPERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ // // There are a number of alternative modes which may want to handle before // autograd; for example, error checking, tracing, profiling or vmap. They // go here. - FuncTorchBatched((short)(Undefined.value + 34)), // See Note [Out-of-tree vmap+grad prototype] + FuncTorchBatched((short)(Undefined.value + 33)), // See Note [Out-of-tree vmap+grad prototype] // Dispatch key for BatchedTensorImpl wrapping a nested tensor. - BatchedNestedTensor((short)(Undefined.value + 35)), + BatchedNestedTensor((short)(Undefined.value + 34)), - FuncTorchVmapMode((short)(Undefined.value + 36)), // See Note [Out-of-tree vmap+grad prototype] + FuncTorchVmapMode((short)(Undefined.value + 35)), // See Note [Out-of-tree vmap+grad prototype] // This is the dispatch key for BatchedTensorImpl, which is used to implement // batching rules for vmap. - Batched((short)(Undefined.value + 37)), + Batched((short)(Undefined.value + 36)), // When we are inside a vmap, all tensors dispatch on this key. // See Note: [DispatchKey::VmapMode usage] for more details. - VmapMode((short)(Undefined.value + 38)), + VmapMode((short)(Undefined.value + 37)), - FuncTorchGradWrapper((short)(Undefined.value + 39)), // See Note [Out-of-tree vmap+grad prototype] + FuncTorchGradWrapper((short)(Undefined.value + 38)), // See Note [Out-of-tree vmap+grad prototype] // Out-of-core key for Deferred Module Initialization in torchdistx. // See https://pytorch.org/torchdistx/latest/deferred_init.html - DeferredInit((short)(Undefined.value + 40)), + DeferredInit((short)(Undefined.value + 39)), // Used by Python key logic to know the set of tls on entry to the dispatcher // This kernel assumes it is the top-most non-functorch-related DispatchKey. // If you add a key above, make sure to update the fallback implementation for // this. - PythonTLSSnapshot((short)(Undefined.value + 41)), + PythonTLSSnapshot((short)(Undefined.value + 40)), // This key should be at the very top of the dispatcher - FuncTorchDynamicLayerFrontMode((short)(Undefined.value + 42)), // See Note [Out-of-tree vmap+grad prototype] + FuncTorchDynamicLayerFrontMode((short)(Undefined.value + 41)), // See Note [Out-of-tree vmap+grad prototype] // TESTING: This is intended to be a generic testing tensor type id. // Don't use it for anything real; its only acceptable use is within a single // process test. Use it by creating a TensorImpl with this DispatchKey, and // then registering operators to operate on this type id. See // aten/src/ATen/core/dispatch/backend_fallback_test.cpp for a usage example. - TESTING_ONLY_GenericWrapper((short)(Undefined.value + 43)), + TESTING_ONLY_GenericWrapper((short)(Undefined.value + 42)), // TESTING: This is intended to be a generic testing tensor type id. // Don't use it for anything real; its only acceptable use is within a ingle @@ -2433,51 +2436,51 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // to operate on this type id. See // aten/src/ATen/core/dispatch/backend_fallback_test.cpp // for a usage example - TESTING_ONLY_GenericMode((short)(Undefined.value + 44)), + TESTING_ONLY_GenericMode((short)(Undefined.value + 43)), // This key is used for pre-dispatch tracing in make_fx. // It has lower priority than the PythonDispatcher key // because we use the PythonDispatcher to intercept the key from python, // and avoid having to implement it in C++. - PreDispatch((short)(Undefined.value + 45)), + PreDispatch((short)(Undefined.value + 44)), // This is a bypass that allows you to skip running the C++ dispatcher // entirely - PythonDispatcher((short)(Undefined.value + 46)), + PythonDispatcher((short)(Undefined.value + 45)), // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FIN ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ // - EndOfFunctionalityKeys((short)(Undefined.value + 47)), + EndOfFunctionalityKeys((short)(Undefined.value + 46)), - StartOfDenseBackends((short)(Undefined.value + 48)), - CPU((short)(Undefined.value + 49)), + StartOfDenseBackends((short)(Undefined.value + 47)), + CPU((short)(Undefined.value + 48)), - CUDA((short)(Undefined.value + 50)), + CUDA((short)(Undefined.value + 49)), - HIP((short)(Undefined.value + 51)), + HIP((short)(Undefined.value + 50)), - XLA((short)(Undefined.value + 52)), + XLA((short)(Undefined.value + 51)), - MPS((short)(Undefined.value + 53)), + MPS((short)(Undefined.value + 52)), - IPU((short)(Undefined.value + 54)), + IPU((short)(Undefined.value + 53)), - XPU((short)(Undefined.value + 55)), + XPU((short)(Undefined.value + 54)), - HPU((short)(Undefined.value + 56)), + HPU((short)(Undefined.value + 55)), - VE((short)(Undefined.value + 57)), + VE((short)(Undefined.value + 56)), - Lazy((short)(Undefined.value + 58)), + Lazy((short)(Undefined.value + 57)), - MTIA((short)(Undefined.value + 59)), + MTIA((short)(Undefined.value + 58)), - PrivateUse1((short)(Undefined.value + 60)), + PrivateUse1((short)(Undefined.value + 59)), - PrivateUse2((short)(Undefined.value + 61)), + PrivateUse2((short)(Undefined.value + 60)), - PrivateUse3((short)(Undefined.value + 62)), + PrivateUse3((short)(Undefined.value + 61)), - Meta((short)(Undefined.value + 63)), + Meta((short)(Undefined.value + 62)), EndOfDenseBackends((short)(0)), StartOfQuantizedBackends((short)(1)), QuantizedCPU((short)(2)), @@ -2541,36 +2544,67 @@ public class torch extends org.bytedeco.pytorch.presets.torch { SparseMeta((short)( QuantizedMeta.value + 16)), EndOfSparseBackends((short)( SparseMeta.value)), - StartOfNestedTensorBackends((short)( SparseMeta.value + 1)), - NestedTensorCPU((short)( SparseMeta.value + 2)), + StartOfSparseCsrBackends((short)( SparseMeta.value + 1)), + SparseCsrCPU((short)( SparseMeta.value + 2)), + + SparseCsrCUDA((short)( SparseMeta.value + 3)), + + SparseCsrHIP((short)( SparseMeta.value + 4)), - NestedTensorCUDA((short)( SparseMeta.value + 3)), + SparseCsrXLA((short)( SparseMeta.value + 5)), - NestedTensorHIP((short)( SparseMeta.value + 4)), + SparseCsrMPS((short)( SparseMeta.value + 6)), - NestedTensorXLA((short)( SparseMeta.value + 5)), + SparseCsrIPU((short)( SparseMeta.value + 7)), - NestedTensorMPS((short)( SparseMeta.value + 6)), + SparseCsrXPU((short)( SparseMeta.value + 8)), - NestedTensorIPU((short)( SparseMeta.value + 7)), + SparseCsrHPU((short)( SparseMeta.value + 9)), - NestedTensorXPU((short)( SparseMeta.value + 8)), + SparseCsrVE((short)( SparseMeta.value + 10)), - NestedTensorHPU((short)( SparseMeta.value + 9)), + SparseCsrLazy((short)( SparseMeta.value + 11)), - NestedTensorVE((short)( SparseMeta.value + 10)), + SparseCsrMTIA((short)( SparseMeta.value + 12)), - NestedTensorLazy((short)( SparseMeta.value + 11)), + SparseCsrPrivateUse1((short)( SparseMeta.value + 13)), - NestedTensorMTIA((short)( SparseMeta.value + 12)), + SparseCsrPrivateUse2((short)( SparseMeta.value + 14)), - NestedTensorPrivateUse1((short)( SparseMeta.value + 13)), + SparseCsrPrivateUse3((short)( SparseMeta.value + 15)), - NestedTensorPrivateUse2((short)( SparseMeta.value + 14)), + SparseCsrMeta((short)( SparseMeta.value + 16)), + EndOfSparseCsrBackends((short)( SparseCsrMeta.value)), + StartOfNestedTensorBackends((short)( SparseCsrMeta.value + 1)), + NestedTensorCPU((short)( SparseCsrMeta.value + 2)), - NestedTensorPrivateUse3((short)( SparseMeta.value + 15)), + NestedTensorCUDA((short)( SparseCsrMeta.value + 3)), - NestedTensorMeta((short)( SparseMeta.value + 16)), + NestedTensorHIP((short)( SparseCsrMeta.value + 4)), + + NestedTensorXLA((short)( SparseCsrMeta.value + 5)), + + NestedTensorMPS((short)( SparseCsrMeta.value + 6)), + + NestedTensorIPU((short)( SparseCsrMeta.value + 7)), + + NestedTensorXPU((short)( SparseCsrMeta.value + 8)), + + NestedTensorHPU((short)( SparseCsrMeta.value + 9)), + + NestedTensorVE((short)( SparseCsrMeta.value + 10)), + + NestedTensorLazy((short)( SparseCsrMeta.value + 11)), + + NestedTensorMTIA((short)( SparseCsrMeta.value + 12)), + + NestedTensorPrivateUse1((short)( SparseCsrMeta.value + 13)), + + NestedTensorPrivateUse2((short)( SparseCsrMeta.value + 14)), + + NestedTensorPrivateUse3((short)( SparseCsrMeta.value + 15)), + + NestedTensorMeta((short)( SparseCsrMeta.value + 16)), EndOfNestedTensorBackends((short)( NestedTensorMeta.value)), StartOfAutogradFunctionalityBackends((short)( NestedTensorMeta.value + 1)), AutogradCPU((short)( NestedTensorMeta.value + 2)), @@ -2778,6 +2812,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace c10 // Expose the constant, but not the TYPE (DispatchKey is an implementation // detail!) +// NOLINTNEXTLINE(misc-unused-using-decls) // namespace torch // NB: You really shouldn't use this instance; this enum is guaranteed @@ -2789,7 +2824,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include +// #include +// #include /** * is_equality_comparable is true_type iff the equality operator is defined @@ -2850,17 +2886,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * // true We define it here to resolve a MSVC bug. See * https://github.com/pytorch/pytorch/issues/30932 for details. */ - // namespace guts - // namespace c10 + // namespace c10::guts // Parsed from c10/util/TypeList.h // #pragma once -// #include // #include // #include +// #include +// #include +// #include +// #include /** * Type holding a list of types for compile time type computations @@ -3022,8 +3060,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace detail // namespace typelist - // namespace guts - // namespace c10 + // namespace c10::guts // Parsed from c10/util/bit_cast.h @@ -3047,11 +3084,20 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include // #include // #include +// #include // #include // #include +// #include +// #include +// #include +// #include // #include +// #include +// #include // Targeting ../FunctionalityOffsetAndMask.java @@ -3229,23 +3275,27 @@ public class torch extends org.bytedeco.pytorch.presets.torch { SparseVE(12), SparseXPU(13), SparsePrivateUse1(14), - ORT(15), - XLA(16), - Vulkan(17), - Metal(18), - Meta(19), - QuantizedCPU(20), - QuantizedCUDA(21), - QuantizedXPU(22), - QuantizedPrivateUse1(23), - Undefined(24), - MkldnnCPU(25), - MPS(26), - HPU(27), - Lazy(28), - MTIA(29), - PrivateUse1(30), - NumOptions(31); + SparseCsrHIP(15), + SparseCsrVE(16), + SparseCsrXPU(17), + SparseCsrPrivateUse1(18), + ORT(19), + XLA(20), + Vulkan(21), + Metal(22), + Meta(23), + QuantizedCPU(24), + QuantizedCUDA(25), + QuantizedXPU(26), + QuantizedPrivateUse1(27), + Undefined(28), + MkldnnCPU(29), + MPS(30), + HPU(31), + Lazy(32), + MTIA(33), + PrivateUse1(34), + NumOptions(35); public final int value; private Backend(int v) { this.value = v; } @@ -3283,6 +3333,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include @Namespace("c10") public enum Layout { Strided((byte)(0)), @@ -3419,7 +3470,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // #include // #include // #include @@ -3526,12 +3576,18 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include // #include // #include // #include // #include +// #include +// #include +// #include // #include +// #include +// #include // #include // Targeting ../ArgumentArrayRef.java @@ -3675,11 +3731,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include // #include // #include +// #include // #include +// #include // Memory format is not the property of a Tensor. It is the way to tell an // operator how the result should be organized in memory and nothing more. That @@ -3815,8 +3872,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include // #include +// #include +// #include /** * QScheme is an enum that specifies the type of quantization. This has a one @@ -3850,6 +3908,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include /** An index representing a specific stream. A StreamId is not independently * meaningful without knowing the Device it is associated with; try to @@ -3888,6 +3953,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #if defined(__CUDACC__) && !defined(USE_ROCM) @@ -4114,6 +4180,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include // #include @Namespace("c10::detail") public static native float fp32_from_bits(@Cast("uint32_t") int w); @@ -4267,7 +4334,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * and inspired by Half implementation from pytorch/c10/util/Half.h */ // #include -// #include // #include // #include // #include @@ -4323,11 +4389,29 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // IWYU pragma: keep +// Parsed from c10/util/Float8_fnuz_cvt.h + +// #pragma once + +// #include + +// #include + +/* + * Convert a 8-bit floating-point number in either f8 E4M3FNUZ or bf8 E5M2FNUZ + * format, in bit representation, to a 32-bit floating-point number. + */ + + // namespace c10::detail + + // Parsed from c10/util/Float8_e4m3fnuz-inl.h // #pragma once // #include +// #include +// #include // #include // #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") @@ -4345,6 +4429,100 @@ public class torch extends org.bytedeco.pytorch.presets.torch { +/** Arithmetic */ + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e4m3fnuz add(@Const @ByRef Float8_e4m3fnuz a, @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(@Const @ByRef Float8_e4m3fnuz a, @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e4m3fnuz multiply(@Const @ByRef Float8_e4m3fnuz a, @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e4m3fnuz divide( + @Const @ByRef Float8_e4m3fnuz a, + @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(@Const @ByRef Float8_e4m3fnuz a); + +@Namespace("c10") public static native @ByRef @Name("operator +=") Float8_e4m3fnuz addPut( + @ByRef Float8_e4m3fnuz a, + @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator -=") Float8_e4m3fnuz subtractPut( + @ByRef Float8_e4m3fnuz a, + @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator *=") Float8_e4m3fnuz multiplyPut( + @ByRef Float8_e4m3fnuz a, + @Const @ByRef Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator /=") Float8_e4m3fnuz dividePut( + @ByRef Float8_e4m3fnuz a, + @Const @ByRef Float8_e4m3fnuz b); + +/** Arithmetic with floats */ + +@Namespace("c10") public static native @Name("operator +") float add(@ByVal Float8_e4m3fnuz a, float b); +@Namespace("c10") public static native @Name("operator -") float subtract(@ByVal Float8_e4m3fnuz a, float b); +@Namespace("c10") public static native @Name("operator *") float multiply(@ByVal Float8_e4m3fnuz a, float b); +@Namespace("c10") public static native @Name("operator /") float divide(@ByVal Float8_e4m3fnuz a, float b); + +@Namespace("c10") public static native @Name("operator +") float add(float a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator -") float subtract(float a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator *") float multiply(float a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator /") float divide(float a, @ByVal Float8_e4m3fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator +=") FloatPointer addPut(@ByRef FloatPointer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator +=") FloatBuffer addPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator +=") float[] addPut(@ByRef float[] a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") FloatPointer subtractPut(@ByRef FloatPointer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") FloatBuffer subtractPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") float[] subtractPut(@ByRef float[] a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") FloatPointer multiplyPut(@ByRef FloatPointer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") FloatBuffer multiplyPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") float[] multiplyPut(@ByRef float[] a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") FloatPointer dividePut(@ByRef FloatPointer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") FloatBuffer dividePut(@ByRef FloatBuffer a, @Const @ByRef Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") float[] dividePut(@ByRef float[] a, @Const @ByRef Float8_e4m3fnuz b); + +/** Arithmetic with doubles */ + +@Namespace("c10") public static native @Name("operator +") double add(@ByVal Float8_e4m3fnuz a, double b); +@Namespace("c10") public static native @Name("operator -") double subtract(@ByVal Float8_e4m3fnuz a, double b); +@Namespace("c10") public static native @Name("operator *") double multiply(@ByVal Float8_e4m3fnuz a, double b); +@Namespace("c10") public static native @Name("operator /") double divide(@ByVal Float8_e4m3fnuz a, double b); + +@Namespace("c10") public static native @Name("operator +") double add(double a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator -") double subtract(double a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator *") double multiply(double a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @Name("operator /") double divide(double a, @ByVal Float8_e4m3fnuz b); + +/** Arithmetic with ints */ + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e4m3fnuz add(@ByVal Float8_e4m3fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(@ByVal Float8_e4m3fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e4m3fnuz multiply(@ByVal Float8_e4m3fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e4m3fnuz divide(@ByVal Float8_e4m3fnuz a, int b); + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e4m3fnuz add(int a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(int a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e4m3fnuz multiply(int a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e4m3fnuz divide(int a, @ByVal Float8_e4m3fnuz b); + +//// Arithmetic with int64_t + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e4m3fnuz add(@ByVal Float8_e4m3fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(@ByVal Float8_e4m3fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e4m3fnuz multiply(@ByVal Float8_e4m3fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e4m3fnuz divide(@ByVal Float8_e4m3fnuz a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e4m3fnuz add(@Cast("int64_t") long a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e4m3fnuz subtract(@Cast("int64_t") long a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e4m3fnuz multiply(@Cast("int64_t") long a, @ByVal Float8_e4m3fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e4m3fnuz divide(@Cast("int64_t") long a, @ByVal Float8_e4m3fnuz b); + +/** NOTE: we do not define comparisons directly and instead rely on the implicit + * conversion from c10::Float8_e4m3fnuz to float. */ + // namespace c10 // namespace std @@ -4354,8 +4532,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Parsed from c10/util/Float8_e4m3fnuz.h -/// -/// /// // #pragma once @@ -4363,13 +4539,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * conversions to standard C types and basic arithmetic operations. Note that * arithmetic operations are implemented by converting to floating point and * performing the operation in float32. - * * Binary configuration remains the same as Float8_e4m3fn: * s eeee mmm * 1 sign bit * 4 exponent bits * 3 mantissa bits - * * The key differences versus Float8_e4m3fn are: * bias = 8 * no infinities or negative zero @@ -4379,9 +4553,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * the existing Float8_e4m3fn implementation. */ // #include -// #include // #include // #include +// #include // #if defined(__cplusplus) && (__cplusplus >= 201103L) // #include @@ -4393,18 +4567,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -/* - * Convert a 8-bit floating-point number in fp8 E4M3FNUZ format, in bit - * representation, to a 32-bit floating-point number in IEEE single-precision - * format, in bit representation. - * - * @note The implementation doesn't use any floating-point operations. - */ -// #if defined(__CUDA_ARCH__) || defined(__HIP__) -@Namespace("c10::detail") public static native float fp8e4m3fnuz_to_fp32_value(@Cast("uint8_t") byte arg0); -// #else -// #endif - /* * Convert a 32-bit floating-point number in IEEE single-precision format to a * 8-bit floating-point number in fp8 E4M3FNUZ format, in bit representation. @@ -4617,7 +4779,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // makes the code both less verbose and potentially more efficient. // #define COMPLEX_INTEGER_OP_TEMPLATE_CONDITION // typename std::enable_if_t< -// std::is_floating_point::value && std::is_integral::value, +// std::is_floating_point_v && std::is_integral_v, // int> = 0 // #undef COMPLEX_INTEGER_OP_TEMPLATE_CONDITION @@ -4694,7 +4856,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") // #endif -/** Constructors */ +// #if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) +// #else @@ -4702,6 +4865,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { +// #endif /* !defined(__aarch64__) || defined(C10_MOBILE) || defined(__CUDACC__) \ +// */ + // #if defined(__CUDACC__) || defined(__HIPCC__) // #endif @@ -4818,8 +4984,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * If you are writing a compute bound kernel, you can use the CUDA half * intrinsics directly on the Half type from device code. */ +// #include // #include -// #include // #include // #include // #include @@ -4827,25 +4993,18 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #if defined(__cplusplus) && (__cplusplus >= 201103L) // #include -// #include // #elif !defined(__OPENCL_VERSION__) // #include -// #include // #endif // #ifdef _MSC_VER // #include // #endif -// #include // #include // #include // #include // #include -// #include -// #include -// #include -// #include // #ifdef __CUDACC__ // #include @@ -4861,7 +5020,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // for SYCL 2020 // #endif -// #include // operator typeid +// #if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) +// #endif /* * Convert a 16-bit floating-point number in IEEE half-precision format, in bit @@ -4894,6 +5054,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { */ @Namespace("c10::detail") public static native @Cast("uint16_t") short fp16_ieee_from_fp32_value(float f); +// #if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) +// #endif + // Targeting ../Half.java @@ -5119,6 +5282,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include // #include // #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") @@ -5136,6 +5301,102 @@ public class torch extends org.bytedeco.pytorch.presets.torch { + + +/** Arithmetic */ + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e5m2fnuz add(@Const @ByRef Float8_e5m2fnuz a, @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(@Const @ByRef Float8_e5m2fnuz a, @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e5m2fnuz multiply(@Const @ByRef Float8_e5m2fnuz a, @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e5m2fnuz divide( + @Const @ByRef Float8_e5m2fnuz a, + @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(@Const @ByRef Float8_e5m2fnuz a); + +@Namespace("c10") public static native @ByRef @Name("operator +=") Float8_e5m2fnuz addPut( + @ByRef Float8_e5m2fnuz a, + @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator -=") Float8_e5m2fnuz subtractPut( + @ByRef Float8_e5m2fnuz a, + @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator *=") Float8_e5m2fnuz multiplyPut( + @ByRef Float8_e5m2fnuz a, + @Const @ByRef Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator /=") Float8_e5m2fnuz dividePut( + @ByRef Float8_e5m2fnuz a, + @Const @ByRef Float8_e5m2fnuz b); + +/** Arithmetic with floats */ + +@Namespace("c10") public static native @Name("operator +") float add(@ByVal Float8_e5m2fnuz a, float b); +@Namespace("c10") public static native @Name("operator -") float subtract(@ByVal Float8_e5m2fnuz a, float b); +@Namespace("c10") public static native @Name("operator *") float multiply(@ByVal Float8_e5m2fnuz a, float b); +@Namespace("c10") public static native @Name("operator /") float divide(@ByVal Float8_e5m2fnuz a, float b); + +@Namespace("c10") public static native @Name("operator +") float add(float a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator -") float subtract(float a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator *") float multiply(float a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator /") float divide(float a, @ByVal Float8_e5m2fnuz b); + +@Namespace("c10") public static native @ByRef @Name("operator +=") FloatPointer addPut(@ByRef FloatPointer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator +=") FloatBuffer addPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator +=") float[] addPut(@ByRef float[] a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") FloatPointer subtractPut(@ByRef FloatPointer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") FloatBuffer subtractPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator -=") float[] subtractPut(@ByRef float[] a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") FloatPointer multiplyPut(@ByRef FloatPointer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") FloatBuffer multiplyPut(@ByRef FloatBuffer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator *=") float[] multiplyPut(@ByRef float[] a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") FloatPointer dividePut(@ByRef FloatPointer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") FloatBuffer dividePut(@ByRef FloatBuffer a, @Const @ByRef Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByRef @Name("operator /=") float[] dividePut(@ByRef float[] a, @Const @ByRef Float8_e5m2fnuz b); + +/** Arithmetic with doubles */ + +@Namespace("c10") public static native @Name("operator +") double add(@ByVal Float8_e5m2fnuz a, double b); +@Namespace("c10") public static native @Name("operator -") double subtract(@ByVal Float8_e5m2fnuz a, double b); +@Namespace("c10") public static native @Name("operator *") double multiply(@ByVal Float8_e5m2fnuz a, double b); +@Namespace("c10") public static native @Name("operator /") double divide(@ByVal Float8_e5m2fnuz a, double b); + +@Namespace("c10") public static native @Name("operator +") double add(double a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator -") double subtract(double a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator *") double multiply(double a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @Name("operator /") double divide(double a, @ByVal Float8_e5m2fnuz b); + +/** Arithmetic with ints */ + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e5m2fnuz add(@ByVal Float8_e5m2fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(@ByVal Float8_e5m2fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e5m2fnuz multiply(@ByVal Float8_e5m2fnuz a, int b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e5m2fnuz divide(@ByVal Float8_e5m2fnuz a, int b); + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e5m2fnuz add(int a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(int a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e5m2fnuz multiply(int a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e5m2fnuz divide(int a, @ByVal Float8_e5m2fnuz b); + +//// Arithmetic with int64_t + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e5m2fnuz add(@ByVal Float8_e5m2fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(@ByVal Float8_e5m2fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e5m2fnuz multiply(@ByVal Float8_e5m2fnuz a, @Cast("int64_t") long b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e5m2fnuz divide(@ByVal Float8_e5m2fnuz a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal @Name("operator +") Float8_e5m2fnuz add(@Cast("int64_t") long a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator -") Float8_e5m2fnuz subtract(@Cast("int64_t") long a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator *") Float8_e5m2fnuz multiply(@Cast("int64_t") long a, @ByVal Float8_e5m2fnuz b); +@Namespace("c10") public static native @ByVal @Name("operator /") Float8_e5m2fnuz divide(@Cast("int64_t") long a, @ByVal Float8_e5m2fnuz b); + +/** NOTE: we do not define comparisons directly and instead rely on the implicit + * conversion from c10::Float8_e5m2fnuz to float. */ + // namespace c10 // namespace std @@ -5145,8 +5406,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Parsed from c10/util/Float8_e5m2fnuz.h -/// -/// /// // #pragma once @@ -5154,13 +5413,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * conversions to standard C types and basic arithmetic operations. Note that * arithmetic operations are implemented by converting to floating point and * performing the operation in float32. - * * Binary configuration remains the same as e5m2: * s eeeee mm * 1 sign bit * 5 exponent bits * 2 mantissa bits - * * The key differences that e5m2fnuz brings are: * bias = 16 * no infinities or negative zero @@ -5170,7 +5427,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * the existing Float8_e4m3fn implementation. */ // #include -// #include // #include // #include @@ -5184,18 +5440,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -/* - * Convert a 8-bit floating-point number in fp8 E5M2FNUZ format, in bit - * representation, to a 32-bit floating-point number in IEEE single-precision - * format, in bit representation. - * - * @note The implementation doesn't use any floating-point operations. - */ -// #if defined(__CUDA_ARCH__) || defined(__HIP__) -@Namespace("c10::detail") public static native float fp8e5m2fnuz_to_fp32_value(@Cast("uint8_t") byte arg0); -// #else -// #endif - /* * Convert a 32-bit floating-point number in IEEE single-precision format to a * 8-bit floating-point number in fp8 E5M2 format, in bit representation. @@ -5327,16 +5571,34 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -// #include +// #include // #include +// #include // #include +// #include + +// dummy struct for uint1 to uint7, actual functionality +// of these dtypes will be implemented in python with Tensor subclass // For the macros below: -// NB: If you want to macro some code for all non-QInt scalar types (i.e. types -// with complete information, you probably want one of the -// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND -// macros below, which are designed to behave similarly to the Dispatch macros -// with the same name. +// +// For users: If you want to macro some code for all non-QInt scalar types +// (i.e. types with complete information, you probably want one of the +// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND macros below, which are +// designed to behave similarly to the Dispatch macros with the same name. +// +// For adding a new dtype: In the beginning, we had an idea that there was a +// list of all scalar types, and you could use AT_FORALL_SCALAR_TYPES to +// iterate over them. But over the years we added weird types which couldn't +// be handled uniformly everywhere and so in the end we ended up with some +// mish-mosh of some helper macros, but mostly use sites making a call about +// what dtypes they can or can't support. So if you want to add a new dtype, +// the preferred resolution is to find a dtype similar to what you want, +// grep for it and edit all the sites you find this way. If you need to add +// a completely new kind of dtype, you're going to have to laboriously audit +// all of the sites everywhere to figure out how it should work. Consulting +// some old PRs where we added new dtypes (check history of this file) can +// help give you an idea where to start. // NB: Order matters for this macro; it is relied upon in // _promoteTypesLookup and the serialization format. @@ -5368,10 +5630,24 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // _(c10::Float8_e4m3fn, Float8_e4m3fn) /* 24 */ // _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz) /* 25 */ // _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz) /* 26 */ +// _(uint16_t, UInt16) /* 27 */ +// _(uint32_t, UInt32) /* 28 */ +// _(uint64_t, UInt64) /* 29 */ +// _(c10::dummy_uint1_7_t<1>, UInt1) /* 30 */ +// _(c10::dummy_uint1_7_t<2>, UInt2) /* 31 */ +// _(c10::dummy_uint1_7_t<3>, UInt3) /* 32 */ +// _(c10::dummy_uint1_7_t<4>, UInt4) /* 33 */ +// _(c10::dummy_uint1_7_t<5>, UInt5) /* 34 */ +// _(c10::dummy_uint1_7_t<6>, UInt6) /* 35 */ +// _(c10::dummy_uint1_7_t<7>, UInt7) /* 36 */ // If you want to support ComplexHalf for real, add ComplexHalf // into this macro (and change the name). But beware: convert() // doesn't work for all the conversions you need... +// +// TODO: To add unsigned int types here, we must define accumulate type. +// But uint8 currently accumulates into int64, so we would have to make +// an inconsistent choice for the larger types. Difficult. // #define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(_) // _(uint8_t, Byte) // _(int8_t, Char) @@ -5388,6 +5664,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // _(at::Float8_e5m2, Float8_e5m2) // _(at::Float8_e4m3fn, Float8_e4m3fn) +// This macro controls many of our C++ APIs, including constructors +// for Scalar as well as the data() and item() accessors on Tensor // #define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(_) // _(uint8_t, Byte) // _(int8_t, Char) @@ -5434,9 +5712,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { Float8_e5m2((byte)(23)), /* 23 */ Float8_e4m3fn((byte)(24)), /* 24 */ Float8_e5m2fnuz((byte)(25)), /* 25 */ - Float8_e4m3fnuz((byte)(26)), - Undefined((byte)(27)), - NumOptions((byte)(28)); + Float8_e4m3fnuz((byte)(26)), /* 26 */ + UInt16((byte)(27)), /* 27 */ + UInt32((byte)(28)), /* 28 */ + UInt64((byte)(29)), /* 29 */ + UInt1((byte)(30)), /* 30 */ + UInt2((byte)(31)), /* 31 */ + UInt3((byte)(32)), /* 32 */ + UInt4((byte)(33)), /* 33 */ + UInt5((byte)(34)), /* 34 */ + UInt6((byte)(35)), /* 35 */ + UInt7((byte)(36)), + Undefined((byte)(37)), + NumOptions((byte)(38)); public final byte value; private ScalarType(byte v) { this.value = v; } @@ -5462,7 +5750,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // /* https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba */ // /* TODO: remove once the bug is fixed. */ // static type t; -// }; /* 0 */ /* 1 */ /* 2 */ /* 3 */ /* 4 */ /* 5 */ /* 6 */ /* 7 */ /* 8 */ /* 9 */ /* 10 */ /* 11 */ /* 12 */ /* 13 */ /* 14 */ /* 15 */ /* 16 */ /* 17 */ /* 18 */ /* 19 */ /* 20 */ /* 21 */ /* 22 */ /* 23 */ /* 24 */ /* 25 */ /* 26 */ +// }; /* 0 */ /* 1 */ /* 2 */ /* 3 */ /* 4 */ /* 5 */ /* 6 */ /* 7 */ /* 8 */ /* 9 */ /* 10 */ /* 11 */ /* 12 */ /* 13 */ /* 14 */ /* 15 */ /* 16 */ /* 17 */ /* 18 */ /* 19 */ /* 20 */ /* 21 */ /* 22 */ /* 23 */ /* 24 */ /* 25 */ /* 26 */ /* 27 */ /* 28 */ /* 29 */ /* 30 */ /* 31 */ /* 32 */ /* 33 */ /* 34 */ /* 35 */ /* 36 */ // #undef SPECIALIZE_ScalarTypeToCPPType @@ -5473,10 +5761,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // struct CppTypeToScalarType // : std:: // integral_constant { -// }; /* 0 */ /* 1 */ /* 2 */ /* 3 */ /* 4 */ /* 5 */ /* 6 */ /* 7 */ /* 8 */ /* 9 */ /* 10 */ /* 11 */ /* 12 */ /* 13 */ /* 14 */ /* 15 */ /* 16 */ /* 17 */ /* 18 */ /* 19 */ /* 20 */ /* 21 */ /* 22 */ /* 23 */ /* 24 */ /* 25 */ /* 26 */ +// }; /* 0 */ /* 1 */ /* 2 */ /* 3 */ /* 4 */ /* 5 */ /* 6 */ /* 7 */ /* 8 */ /* 9 */ /* 10 */ /* 11 */ /* 12 */ /* 13 */ /* 14 */ /* 15 */ /* 16 */ /* 17 */ /* 18 */ /* 19 */ /* 20 */ /* 21 */ /* 22 */ /* 23 */ /* 24 */ /* 25 */ /* 26 */ /* 27 */ /* 28 */ /* 29 */ /* 30 */ /* 31 */ /* 32 */ /* 33 */ /* 34 */ /* 35 */ /* 36 */ // #undef SPECIALIZE_CppTypeToScalarType +// NB: despite its generic sounding name, the macros that don't take _AND +// are mostly only used by tensorexpr // #define AT_FORALL_INT_TYPES(_) // _(uint8_t, Byte) // _(int8_t, Char) @@ -5493,6 +5783,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // _(float, Float) // _(double, Double) +// These macros are often controlling how many template instantiations we +// create for kernels. It is typically inappropriate to add new dtypes here, +// instead, new types should be added to use sites on a case-by-case basis. +// We generally are not accepting new dtypes due to binary size concerns. + // #define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) // _(uint8_t, Byte) // _(int8_t, Char) @@ -5671,6 +5966,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define DEFINE_CONSTANT(_, name) // constexpr ScalarType k##name = ScalarType::name; +// NOLINTNEXTLINE(clang-diagnostic-unused-const-variable) @Namespace("c10") @MemberGetter public static native ScalarType kByte(); /* 0 */ @Namespace("c10") @MemberGetter public static native ScalarType kChar(); /* 1 */ @Namespace("c10") @MemberGetter public static native ScalarType kShort(); /* 2 */ @@ -5698,6 +5994,16 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10") @MemberGetter public static native ScalarType kFloat8_e4m3fn(); /* 24 */ @Namespace("c10") @MemberGetter public static native ScalarType kFloat8_e5m2fnuz(); /* 25 */ @Namespace("c10") @MemberGetter public static native ScalarType kFloat8_e4m3fnuz(); /* 26 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt16(); /* 27 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt32(); /* 28 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt64(); /* 29 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt1(); /* 30 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt2(); /* 31 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt3(); /* 32 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt4(); /* 33 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt5(); /* 34 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt6(); /* 35 */ + @Namespace("c10") @MemberGetter public static native ScalarType kUInt7(); /* 36 */ // #undef DEFINE_CONSTANT @Namespace("c10") public static native @Cast("const char*") BytePointer toString(ScalarType t); @@ -5720,6 +6026,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10") public static native @Cast("bool") boolean isBitsType(ScalarType t); +@Namespace("c10") public static native @Cast("bool") boolean isBarebonesUnsignedType(ScalarType t); + @Namespace("c10") public static native ScalarType toQIntType(ScalarType t); @Namespace("c10") public static native ScalarType toUnderlying(ScalarType t); @@ -5745,25 +6053,16 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace c10 -// Parsed from c10/util/in_place.h - -// #pragma once - -// #include - - // namespace c10 - - // Parsed from c10/util/MaybeOwned.h // #pragma once // #include // #include -// #include // #include // #include +// #include /** MaybeOwnedTraits describes how to borrow from T. Here is how we * can implement borrowing from an arbitrary type T using a raw @@ -5794,6 +6093,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include +// #include // Targeting ../SymNodeImpl.java @@ -5807,11 +6109,15 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #include // #include +// #include // #include +// #include +// #include // Targeting ../SymFloat.java @@ -5825,9 +6131,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include -// #include +// #include // #include +// #include // #include +// #include +// #include +// #include // Targeting ../SymBool.java @@ -5839,6 +6149,21 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define TORCH_SYM_INTERNAL_ASSERT(cond, ...) // TORCH_INTERNAL_ASSERT((cond).expect_true(__FILE__, __LINE__), __VA_ARGS__) +@Namespace("c10") public static native @Cast("bool") boolean guard_size_oblivious(@Cast("bool") boolean b, @Cast("const char*") BytePointer file, @Cast("int64_t") long line); +@Namespace("c10") public static native @Cast("bool") boolean guard_size_oblivious(@Cast("bool") boolean b, String file, @Cast("int64_t") long line); + +@Namespace("c10") public static native @Cast("bool") boolean guard_size_oblivious( + @Const @ByRef SymBool b, + @Cast("const char*") BytePointer file, + @Cast("int64_t") long line); +@Namespace("c10") public static native @Cast("bool") boolean guard_size_oblivious( + @Const @ByRef SymBool b, + String file, + @Cast("int64_t") long line); + +// #define TORCH_GUARD_SIZE_OBLIVIOUS(cond) +// c10::guard_size_oblivious((cond), __FILE__, __LINE__) + // namespace c10 @@ -5848,11 +6173,15 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #include // #include +// #include +// #include // #include +// #include // #include // Targeting ../SymInt.java @@ -5980,6 +6309,40 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10") public static native @Cast("std::ostream*") @ByRef @Name("operator <<") Pointer shiftLeft(@Cast("std::ostream*") @ByRef Pointer os, @Const @ByRef SymInt s); @Namespace("c10") public static native @ByVal @Name("operator -") SymInt subtract(@Const @ByRef SymInt s); + +@Namespace("c10") public static native @Cast("bool") boolean sym_eq(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_eq(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean sym_ne(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_ne(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean sym_lt(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_lt(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean sym_le(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_le(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean sym_gt(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_gt(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean sym_ge(@Cast("int64_t") long a, @Cast("int64_t") long b); + +@Namespace("c10") public static native @ByVal SymBool sym_ge(@Const @ByRef SymInt a, @Const @ByRef SymInt b); + +@Namespace("c10") public static native @Cast("bool") boolean definitely_true( + @Const @ByRef SymBool b, + @Cast("const char*") BytePointer file, + @Cast("int64_t") long line); +@Namespace("c10") public static native @Cast("bool") boolean definitely_true( + @Const @ByRef SymBool b, + String file, + @Cast("int64_t") long line); + // namespace c10 @@ -5993,6 +6356,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include @@ -6029,23 +6393,25 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include +// #include // #include // #include // #include // #include // #include +// #include // #include // #include +// #include +// #include // #include +// #include // #include // #include // #include +// #include // #include - -// #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") -// #endif // Targeting ../Scalar.java @@ -6073,17 +6439,18 @@ public class torch extends org.bytedeco.pytorch.presets.torch { + + + // #undef DEFINE_TO // namespace c10 - // Parsed from c10/util/IdWrapper.h // #pragma once -// #include // #include // #include // #include @@ -6150,6 +6517,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// NOLINTNEXTLINE(*c-arrays*) @Namespace("c10::util::detail") @MemberGetter public static native @Cast("const uint64_t") long crc64_table(int i); @Namespace("c10::util::detail") @MemberGetter public static native @Cast("const uint64_t*") LongPointer crc64_table(); @@ -6166,8 +6534,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10::util") public static native @Const @ByVal crc64_t crc64(@StringView BytePointer str); @Namespace("c10::util") public static native @Const @ByVal crc64_t crc64(@StringView String str); - // namespace util - // namespace c10 + // namespace c10::util // Allow usage of crc64_t in std::unordered_set @@ -6177,12 +6544,14 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include // #include // #include // #include -// #include -// #include +// #include +// #include +// #include +// #include +// #include // TODO Make it work for more compilers @@ -6246,8 +6615,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // resolved to `basic_string` either in `std` namespace or in // `std::__cxx11` one (`__cxx11` is an inline namespace) // #endif - // namespace util - // namespace c10 + // namespace c10::util @@ -6261,8 +6629,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include -// #include // #include // namespace detail @@ -6282,21 +6650,28 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include // #include -// #include +// #include +// #include // #include // #include +// #include +// #include // #include // #include +// #include // #include // #include +// #include // #include // #include // #include +// #include +// #include // #include -// #include /* * TypeIdentifier is a small type containing an id. @@ -6318,8 +6693,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // later. So the namespace is not fixed at the moment. // Make at::Half a fundamental type. - // namespace guts - // namespace c10 + // namespace c10::guts // Targeting ../TypeIdentifier.java @@ -6372,8 +6746,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // // item sizes for TypeMeta::itemsize() fast path -@Namespace("caffe2") @MemberGetter public static native @Cast("const uint8_t") byte scalarTypeItemSizes(int i); -@Namespace("caffe2") @MemberGetter public static native @Cast("const uint8_t*") BytePointer scalarTypeItemSizes(); +@Namespace("caffe2") @MemberGetter public static native @ByRef @Cast("const std::array*") BytePointer scalarTypeItemSizes(); // Targeting ../TypeMeta.java @@ -6412,6 +6785,16 @@ public class torch extends org.bytedeco.pytorch.presets.torch { /* 24 */ /* 25 */ /* 26 */ + /* 27 */ + /* 28 */ + /* 29 */ + /* 30 */ + /* 31 */ + /* 32 */ + /* 33 */ + /* 34 */ + /* 35 */ + /* 36 */ // #undef DEFINE_SCALAR_METADATA_INSTANCE @@ -6495,10 +6878,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { /* namespace detail */ - @Namespace("caffe2::detail") @MemberGetter public static native @Cast("const uint16_t") short uint16_t_metadata_index(); - /* namespace detail */ - - @Namespace("caffe2::detail") @MemberGetter public static native @Cast("const uint16_t") short char_metadata_index(); /* namespace detail */ @@ -6615,8 +6994,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include +// #include // #include -// #include @Namespace("c10") public enum DebugInfoKind { PRODUCER_INFO((byte)(0)), @@ -6650,8 +7029,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Parsed from c10/util/UniqueVoidPtr.h // #pragma once +// #include // #include +// #include +// #include // #include // Does not delete anything @@ -6691,10 +7073,16 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include +// #include +// #include +// #include // #include +// #include // #include +// #include +// #include +// #include // #include // #include // #include @@ -6705,10 +7093,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // NB: Device is NOT tested for here; a CUDA nullptr is as much a nullptr as a // CPU nullptr -@Namespace("c10") public static native @Cast("bool") @Name("operator ==") @NoException(true) boolean equals(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr dp, @ByVal @Cast("std::nullptr_t*") PointerPointer arg1); -@Namespace("c10") public static native @Cast("bool") @Name("operator ==") @NoException(true) boolean equals(@ByVal @Cast("std::nullptr_t*") PointerPointer arg0, @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr dp); -@Namespace("c10") public static native @Cast("bool") @Name("operator !=") @NoException(true) boolean notEquals(@Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr dp, @ByVal @Cast("std::nullptr_t*") PointerPointer arg1); -@Namespace("c10") public static native @Cast("bool") @Name("operator !=") @NoException(true) boolean notEquals(@ByVal @Cast("std::nullptr_t*") PointerPointer arg0, @Cast({"", "c10::DataPtr&&"}) @StdMove DataPtr dp); +@Namespace("c10") public static native @Cast("bool") @Name("operator ==") @NoException(true) boolean equals(@StdMove DataPtr dp, @ByVal @Cast("std::nullptr_t*") PointerPointer arg1); +@Namespace("c10") public static native @Cast("bool") @Name("operator ==") @NoException(true) boolean equals(@ByVal @Cast("std::nullptr_t*") PointerPointer arg0, @StdMove DataPtr dp); +@Namespace("c10") public static native @Cast("bool") @Name("operator !=") @NoException(true) boolean notEquals(@StdMove DataPtr dp, @ByVal @Cast("std::nullptr_t*") PointerPointer arg1); +@Namespace("c10") public static native @Cast("bool") @Name("operator !=") @NoException(true) boolean notEquals(@ByVal @Cast("std::nullptr_t*") PointerPointer arg0, @StdMove DataPtr dp); // Targeting ../Allocator.java @@ -6773,6 +7161,36 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace c10 +// Parsed from c10/core/impl/COW.h + +// #pragma once + +// #include +// #include + // namespace c10 + +// Creates a Copy-on-write (COW) clone of the given storage. This will also +// convert the given storage into a COW storage if it is not COW already. +// +// Converting the storage into a COW storage will not be successful if the +// storage's DataPtr has some context (`DataPtr::get_context()`) which is not +// equal to the data pointer (`DataPtr::get()`). In this case, a nullptr is +// returned. +@Namespace("c10::impl::cow") public static native @ByVal StorageImplPtr lazy_clone_storage( + @ByRef StorageImpl storage); + +// Check if a storage has a simple DataPtr with no abnormal context +@Namespace("c10::impl::cow") public static native @Cast("bool") boolean has_simple_data_ptr(@Const @ByRef StorageImpl storage); + +// Check if a DataPtr is COW +@Namespace("c10::impl::cow") public static native @Cast("bool") boolean is_cow_data_ptr(@StdMove DataPtr data_ptr); + +// Eagerly copies a COW storage's data, turning it into a non-COW storage. +@Namespace("c10::impl::cow") public static native void materialize_cow_storage(@ByRef StorageImpl storage); + + // namespace c10::impl::cow + + // Parsed from c10/util/python_stub.h // #pragma once @@ -6783,21 +7201,35 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include // #include +// #include +// #include // #include - +// #include +// #include +// #include // #include +// #include +// #include // Targeting ../StorageImpl.java // Declare StorageImpl create function pointer types. -@Namespace("c10") public static native void SetStorageImplCreate(DeviceType t, @Cast("c10::StorageImplCreateHelper") StorageImplPtr fptr); -@Namespace("c10") public static native void SetStorageImplCreate(@Cast("c10::DeviceType") byte t, @Cast("c10::StorageImplCreateHelper") StorageImplPtr fptr); -@Namespace("c10") public static native @Cast("c10::StorageImplCreateHelper") StorageImplPtr GetStorageImplCreate(DeviceType t); -@Namespace("c10") public static native @Cast("c10::StorageImplCreateHelper") StorageImplPtr GetStorageImplCreate(@Cast("c10::DeviceType") byte t); + + + +@Namespace("c10") public static native @ByVal StorageImplPtr make_storage_impl( + @ByVal StorageImpl.use_byte_size_t use_byte_size, + @ByVal SymInt size_bytes, + @StdMove DataPtr data_ptr, + Allocator allocator, + @Cast("bool") boolean resizable, + @ByVal DeviceOptional device_opt); // namespace c10 @@ -6806,7 +7238,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include +// #include // See example implementation in TensorBase.h and TensorBody.h. // Synopsis: @@ -6862,8 +7294,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include +// #include +// #include // #include +// #include +// #include +// #include // #include +// #include +// #include +// #include +// #include +// #include @Namespace("c10") public static native @Cast("bool") boolean isSharedStorageAlias( @Const @ByRef Storage storage0, @@ -6900,7 +7343,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // NB: This Registry works poorly when you have other namespaces. // Make all macro invocations from inside the at namespace. -// #include // #include // #include // #include @@ -6911,6 +7353,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #include @@ -7108,9 +7551,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * general - that will allow Python to run without wrong flags. */ +// #include // #include -// #include // #include /** * Sets the usage message when a commandline tool is called with "--help". @@ -7353,8 +7796,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10::impl") public static native @Cast("bool") boolean tls_is_dispatch_keyset_excluded(@ByVal DispatchKeySet ks); @Namespace("c10::impl") public static native @Cast("bool") boolean tls_is_dispatch_keyset_included(@ByVal DispatchKeySet ks); - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/InferenceMode.h @@ -7362,6 +7804,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include // #include // #include // Targeting ../InferenceMode.java @@ -7378,6 +7822,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include @Namespace("c10") public static native @ByVal LongArrayRef asIntArrayRefUnchecked(@ByVal SymIntArrayRef ar); @@ -7429,15 +7874,22 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include // #include // #include // #include // #include +// #include // #include +// #include // #include +// #include // #include +// #include +// #include // #include @Namespace("c10") public static native DispatchKey computeDispatchKey( @@ -7499,7 +7951,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { /** Convenience function that returns a {@code TensorOptions} object with the * {@code device} set to CUDA and the {@code device_index} set to the given one. */ -@Namespace("c10") public static native @ByVal TensorOptions device_index(short device_index); +@Namespace("c10") public static native @ByVal TensorOptions device_index(@Cast("c10::DeviceIndex") byte device_index); /** Convenience function that returns a {@code TensorOptions} object with the * {@code requires_grad} set to the given one. */ @@ -7544,8 +7996,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/impl/PyInterpreter.h @@ -7553,6 +8004,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include // #include // #include // #include @@ -7566,7 +8018,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Forward declarations // namespace c10 - // namespace torch // Actual implementation // Targeting ../PyInterpreterVTable.java @@ -7602,8 +8053,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Override public String toString() { return intern().name(); } } - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/impl/PyObjectSlot.h @@ -7617,8 +8067,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/impl/SizesAndStrides.h @@ -7637,8 +8086,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/util/DimVector.h @@ -7648,6 +8096,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include @Namespace("c10") @MemberGetter public static native @Cast("const size_t") long kDimVectorStaticSize(); @@ -7662,10 +8111,14 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include // #include +// #include +// #include // #include // #include +// #include // #include +// #include // Targeting ../SymbolicShapeMeta.java @@ -7678,6 +8131,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include +// #include +// #include // This template can only be specialized at int64_t and c10::SymInt; // you'll get linker errors otherwise // namespace detail @@ -7927,22 +8384,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * user-provided input. */ -// GCC7 is getting an internal compiler error on the new -// implementation, so keep the old one (which evaluates the error -// message eagerly and therefore is undesirable for general use -// compared to the new one) around for it. -// #if defined(__GNUG__) && __GNUC__ <= 7 && !defined(__clang__) - -// #define CAFFE_ENFORCE_THAT_IMPL(op, lhs, rhs, expr, ...) -// ::c10::enforce_detail::enforceThatImpl( -// op, lhs, rhs, __FILE__, __LINE__, expr, nullptr, ##__VA_ARGS__) - -// #define CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(op, lhs, rhs, expr, ...) -// ::c10::enforce_detail::enforceThatImpl( -// op, (lhs), (rhs), __FILE__, __LINE__, expr, this, ##__VA_ARGS__) - -// #else - // #define CAFFE_ENFORCE_THAT_IMPL(op, lhs, rhs, expr, ...) // ::c10::enforce_detail::enforceThatImpl( // op, @@ -7970,7 +8411,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // return ::c10::enforce_detail::enforceFailMsgImpl( // arg1, arg2, ##__VA_ARGS__); // }) -// #endif // namespace enforce_detail @@ -8071,11 +8511,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include - +// #include +// #include +// #include // #include // #include // #include +// #include /** Sum of a list of integers; accumulates into the int64_t datatype */ @@ -8103,11 +8545,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include -// #include -// #include -// #include -// #include +// #include // GCC has __builtin_mul_overflow from before it supported __has_builtin // #ifdef _MSC_VER @@ -8133,18 +8572,27 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include +// #include +// #include +// #include // #include // #include +// #include // #include +// #include // #include // #include // #include +// #include // #include // #include // #include // #include // #include +// #include // #include +// #include // #include // #include // #include @@ -8157,10 +8605,14 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include +// #include // #include // #include +// #include // #include // #include +// #include // A global boolean variable to control whether we free memory when a Tensor // is shrunk to a smaller size. As a result, a Tensor is always going to @@ -8176,9 +8628,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // is larger than this flag in bytes. This only applies to functions which // respect caffe2_keep_on_shrink. - -// #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") -// #endif // namespace at /** @@ -8357,12 +8806,16 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace c10 - // Parsed from c10/core/UndefinedTensorImpl.h // #pragma once +// #include +// #include // #include +// #include +// #include +// #include // Targeting ../UndefinedTensorImpl.java @@ -8459,7 +8912,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -// #include // #include // #include // #include @@ -8606,7 +9058,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -// #include // Targeting ../NamedTensorMeta.java @@ -8729,6 +9180,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include // #include // Shared ExclusivelyOwnedTraits implementation between caffe2::Tensor and @@ -8803,6 +9255,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -8885,6 +9338,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -9220,6 +9674,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -9630,6 +10085,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // aten::copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) +// aten::_lazy_clone(Tensor self) -> Tensor + + // aten::logical_not(Tensor self) -> Tensor @@ -10512,6 +10970,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) +// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + + +// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + + // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor @@ -11894,8 +12358,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/cpp_hook.h @@ -11905,17 +12368,23 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from c10/util/hash.h // #pragma once +// #include +// #include // #include // #include +// #include // #include +// #include +// #include +// #include +// #include // #include // #include @@ -11959,6 +12428,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // const auto hash_code = sha1_hash.str(); // TODO: Compare vs OpenSSL and/or CryptoPP implementations +@Namespace("c10") public static native @Cast("const uint64_t") @NoException(true) long twang_mix64(@Cast("uint64_t") long key); + //////////////////////////////////////////////////////////////////////////////// // c10::hash implementation //////////////////////////////////////////////////////////////////////////////// @@ -12006,8 +12477,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Targeting ../Edge.java - // namespace autograd - // namespace torch + // namespace torch::autograd // The idiomatic way of enabling use of a custom type as the key of hash // containers in C++11. This method removes the requirement of having to pass @@ -12121,8 +12591,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from ATen/NamedTensor.h @@ -12469,6 +12938,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once +// #include // #include // #include @@ -12622,6 +13092,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // - make sherwood_v3_table::convertible_to_iterator public because GCC5 seems // to have issues with it otherwise // - fix compiler warnings in operator templated_iterator +// - make use of 'if constexpr' and eliminate AssignIfTrue template // Copyright Malte Skarupke 2017. // Distributed under the Boost Software License, Version 1.0. @@ -12632,19 +13103,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include // #include +// #include // #include // #include // #include // #include +// #include // #include +// #include +// #include // #include // #include -// #if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion") -// #endif - // #ifdef _MSC_VER // #define SKA_NOINLINE(...) __declspec(noinline) __VA_ARGS__ // #else @@ -12664,7 +13135,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace ska_ordered - // Parsed from ATen/core/Dict_inl.h // #pragma once @@ -13227,6 +13697,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // in a Meyer singleton), it implies that you must *leak* objects when // putting them in the registry. This is done by deleting the destructor // on DeviceGuardImplInterface. +// NOLINTNEXTLINE(*c-arrays*) // Targeting ../DeviceGuardImplRegistrar.java @@ -13257,8 +13728,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * to virtual dispatch on the DeviceGuardImpl registry. */ - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/impl/InlineDeviceGuard.h @@ -13269,10 +13739,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // InlineOptionalDeviceGuard. // #include +// #include // #include // #include -// #include +// #include // #include +// #include +// #include /** * A DeviceGuard is an RAII class that sets a device to some value @@ -13323,15 +13796,18 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * use this. See OptionalDeviceGuard for user-oriented usage notes. */ - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/DeviceGuard.h // #pragma once +// #include +// #include // #include +// #include +// #include /** RAII guard that sets a certain default device in its constructor, and * changes it back to the device that was originally active upon destruction. @@ -13375,14 +13851,17 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/Event.h // #pragma once +// #include +// #include +// #include +// #include // #include // #include @@ -13444,15 +13923,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * See InlineOptionalDeviceGuard for more guidance on how to use this class. */ - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/StreamGuard.h // #pragma once +// #include +// #include // #include +// #include +// #include +// #include /** * A StreamGuard is an RAII class that changes the current device @@ -13529,6 +14012,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include @Namespace("c10::raw::weak_intrusive_ptr") public static native void incref(@Cast("c10::intrusive_ptr_target*") Pointer self); @@ -13537,6 +14021,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace raw +@Namespace("c10::detail") @MemberGetter public static native @Cast("const uint32_t") int kImpracticallyHugeReferenceCount(); + // namespace detail + /** * intrusive_ptr is an alternative to shared_ptr that has better * performance because it does the refcounting intrusively @@ -13567,17 +14054,17 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Increment needs to be acquire-release to make use_count() and // unique() reliable. -@Namespace("c10::detail") public static native @Cast("size_t") long atomic_refcount_increment(@Cast("std::atomic*") @ByRef LongPointer refcount); +@Namespace("c10::detail") public static native @Cast("uint32_t") int atomic_refcount_increment(@Cast("std::atomic*") @ByRef IntPointer refcount); // weak_use_count() is only used for testing, so we don't need it to // be reliable. Relaxed should be fine. -@Namespace("c10::detail") public static native @Cast("size_t") long atomic_weakcount_increment(@Cast("std::atomic*") @ByRef LongPointer weakcount); +@Namespace("c10::detail") public static native @Cast("uint32_t") int atomic_weakcount_increment(@Cast("std::atomic*") @ByRef IntPointer weakcount); // Both decrements need to be acquire-release for correctness. See // e.g. std::shared_ptr implementation. -@Namespace("c10::detail") public static native @Cast("size_t") long atomic_refcount_decrement(@Cast("std::atomic*") @ByRef LongPointer refcount); +@Namespace("c10::detail") public static native @Cast("uint32_t") int atomic_refcount_decrement(@Cast("std::atomic*") @ByRef IntPointer refcount); -@Namespace("c10::detail") public static native @Cast("size_t") long atomic_weakcount_decrement(@Cast("std::atomic*") @ByRef LongPointer weakcount); +@Namespace("c10::detail") public static native @Cast("uint32_t") int atomic_weakcount_decrement(@Cast("std::atomic*") @ByRef IntPointer weakcount); // Targeting ../QuantizerPtr.java @@ -13656,7 +14143,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // NullType::singleton to this function @Namespace("c10::raw::intrusive_ptr") public static native void decref(@Cast("c10::intrusive_ptr_target*") Pointer self); -@Namespace("c10::raw::intrusive_ptr") public static native @Cast("size_t") long use_count(@Cast("c10::intrusive_ptr_target*") Pointer self); +@Namespace("c10::raw::intrusive_ptr") public static native @Cast("uint32_t") int use_count(@Cast("c10::intrusive_ptr_target*") Pointer self); // namespace intrusive_ptr @@ -14015,6 +14502,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { + + + + @@ -14043,7 +14534,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // #include // #include // #include @@ -15230,7 +15720,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // #include // #include // #include @@ -15243,8 +15732,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * compatibility with external user's legacy C++ frontend code. Our intention * is to eliminate the {@code Variable} class in the near future. */ - // namespace autograd - // namespace torch + // namespace torch::autograd // The following are all internal APIs and should not be shown in libtorch docs. // Therefore, we wrap the following code with `#ifndef DOXYGEN_SHOULD_SKIP_THIS @@ -15252,470 +15740,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #ifndef DOXYGEN_SHOULD_SKIP_THIS -/** Check if this type is supported by the autograd engine. - * If you change this, update the doc at the top of the - * torch/autograd/__init__.py file and - * "test_set_requires_grad_only_for_continuous_types" in test/test_autograd.py */ -@Namespace("torch::autograd") public static native @Cast("bool") boolean isDifferentiableType(ScalarType t); - -/**~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * Variable - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * A {@code Variable} augments a {@code Tensor} with the ability to interact in our - * autograd machinery. Conceptually, {@code Variable}s travel along {@code Edge}s between - * {@code Node}s in the autograd graph. A {@code Variable} can either be a leaf, like a - * weight in a neural network, or an interior variable, when it is the result - * of an operation between variables. Every {@code Variable} also stores another - * {@code Variable} called its {@code grad} (gradient). If the variable is a leaf, its - * gradient will be accumulated into this variable. - * - * Every Tensor is a Variable, but sometimes we colloquially refer to Variables - * that don't require gradients as Tensors (since none of the autograd - * machinery for Variables applies). Historically, Variables and Tensors - * were separate concepts, but now they are exactly the same (i.e. we have - * {@code using Variable = at::Tensor}). - * - * Gradient Edges - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * Furthermore, {@code Variable}s have the notion of a {@code gradient_edge}, which is the - * edge in the autograd graph that connects the variable to a particular input - * of the gradient function that will be invoked with the variable during the - * backward pass. More precisely, this gradient function can be one of two - * things: - * 1. A {@code grad_fn}, if the variable is in the interior of the graph. This is the - * gradient of the function that produced the variable. - * 2. A {@code grad_accumulator}, if the variable is a leaf, which accumulates a - * scalar gradient value into its {@code grad} variable. - * - * Versioning - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * Another major feature of {@code Variable}s are *versions*. Versions are - * incremented when an in-place mutation of a variable occurs. Versions are - * useful when constructing {@code SavedVariable}s, which take a snapshot of a - * {@code Variable} at a certain version. You can retrieve a {@code Variable}'s version - * through its {@code current_version()} method. - * - * Views - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * It is possible for a {@code Variable} to be a *view* of another {@code Variable}, in - * which case it tracks that {@code Variable}'s data and autograd history. Beyond - * construction, the interface of a view is identical to that of a regular - * {@code Variable}. You can determine whether {@code Variable} is in fact a view by - * probing its {@code is_view()} method. Note that the *view* semantics are only - * meaningful for {@code Variable} relations that are relevant to autograd. - * See NOTE [ Autograd View Variables ] for more details. - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ - -// Private-ish functions for manipulating variables; we don't want to put them -// on Tensor proper - -// WARNING: This may return a nullptr. If you require AutogradMeta to return -// a materialized structure, use materialize_autograd_meta instead. -@Namespace("torch::autograd::impl") public static native AutogradMeta get_autograd_meta(@Const @ByRef TensorBase arg0); - -// WARNING: This will return a nullptr if the Tensor is not a view. -@Namespace("torch::autograd::impl") public static native DifferentiableViewMeta get_view_autograd_meta(@Const @ByRef TensorBase arg0); - -// Returns the current autograd meta, materializing it if it was previously -// none. This counts as a *mutating* operation, so do not call it on -// "read-only" operators; in particular, this is NOT thread safe -@Namespace("torch::autograd::impl") public static native AutogradMeta materialize_autograd_meta(@Const @ByRef TensorBase arg0); - -/** Set the gradient accumulator of the {@code Variable}. This is only applicable to - * leaf variables. Interior variables should call {@code set_gradient_edge()}. */ - -/** Attempts to get a pointer to the gradient accumulator of the {@code Variable}, - * if it still exists. If the gradient accumulator function has been - * destroyed, returns a {@code nullptr}. */ -@Namespace("torch::autograd::impl") public static native @SharedPtr Node try_get_grad_accumulator(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -/** Gets the gradient accumulator of the {@code Variable} if it has one, or else - * create one on the fly and return it. */ -@Namespace("torch::autograd::impl") public static native @SharedPtr Node grad_accumulator(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -/** Returns the "canonical" gradient edge of this {@code Variable}, i.e. either the - * gradient function if this is an interior {@code Variable}, or the gradient - * accumulator otherwise. If the {@code Variable} is interior, the returned {@code Edge} - * will store the input index of the {@code Node} to which this variable is - * connected in its {@code input_nr} field. For leaves, the {@code input_nr} is always - * zero. Note that {@code set_gradient_edge} and {@code gradient_edge} are not - * symmetric. You must use {@code set_gradient_edge} to set the {@code grad_fn} and - * {@code set_grad_accumulator} to set the accumulator. */ -@Namespace("torch::autograd::impl") public static native @ByVal Edge gradient_edge(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -/** Set the gradient edge -- i.e. {@code grad_fn} and {@code input_nr} -- of the - * {@code Variable}. - * NOTE: This will always set the {@code grad_fn}, even if this is a leaf variable, - * and never the {@code grad_accumulator}. For the latter, use - * {@code set_grad_accumulator}. This allows late construction of an interior - * {@code Variable}. */ - -/// -@Namespace("torch::autograd::impl") public static native void set_gradient_edge(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0, @ByVal Edge edge); - -// Autograd Graph Interaction -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -/** Update the {@code grad_fn} of an existing Variable. Called after in-place - * modifications. - * - * For View Variables: - * Called after in-place modifications. Modifies the grad_fn of the base - * Variable. */ -@Namespace("torch::autograd::impl") public static native void rebase_history(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0, @ByVal Edge gradient_edge); - -/** Gets the raw gradient function pointer, whatever it currently is. */ -@Namespace("torch::autograd::impl") public static native Node grad_fn_unsafe(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -/** Increments the version count of this {@code Variable}. */ -@Namespace("torch::autograd::impl") public static native void bump_version(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); -@Namespace("torch::autograd::impl") public static native void set_version_counter( - @Cast("const torch::autograd::Variable*") @ByRef Tensor arg0, - @Const @ByRef VariableVersion version_counter); - -/** Retrieves this {@code Variable}s version counter. */ -@Namespace("torch::autograd::impl") public static native @Const @ByRef VariableVersion version_counter(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -@Namespace("torch::autograd::impl") public static native void set_name(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0, @StdString BytePointer name); -@Namespace("torch::autograd::impl") public static native void set_name(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0, @StdString String name); - -@Namespace("torch::autograd::impl") public static native void add_hook( - @Const @ByRef TensorBase arg0, - @UniquePtr @ByVal FunctionPreHook hook); -@Namespace("torch::autograd::impl") public static native @ByRef FunctionPreHookVector hooks(@Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); -@Namespace("torch::autograd::impl") public static native void clear_hooks(@Const @ByRef TensorBase arg0); - -@Namespace("torch::autograd::impl") public static native void set_post_acc_grad_hooks( - @Const @ByRef TensorBase arg0, - @UniquePtr PostAccumulateGradHook dict); -@Namespace("torch::autograd::impl") public static native @UniquePtr PostAccumulateGradHook post_acc_grad_hooks( - @Cast("const torch::autograd::Variable*") @ByRef Tensor arg0); - -@Namespace("torch::autograd::impl") public static native void create_cpp_hook( - @Const @ByRef TensorBase arg0, - @Cast("bool") boolean is_retains_grad_hooks/*=false*/); -@Namespace("torch::autograd::impl") public static native void create_cpp_hook( - @Const @ByRef TensorBase arg0); - -// Targeting ../AutogradMeta.java - - - -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -// DifferentiableViewMeta -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -/** NOTE [ Autograd View Variables ] - * - * Many operations return Variable that shares storage with an input Variable. - * The returned Variable is called a **view** Variable on the input **base** - * Variable. - * - * In PyTorch, we have two types of views: differentiable views, and - * non-differentiable views. In either type, to support proper version - * checking, the base and view Variables must always share the same - * version_counter. - * - * - * Differentiable Views - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * This class allows to track both forward and backward AD differentiable - * views. These views can have different base as non-differentiable view for - * forward and backward mode AD are not the same. - * - * Most function are either both forward and backward differentiable views (for - * example: view, select, narrow, transpose, etc) or both not forward and not - * backward differentiable views (for example: indices, values, eq, lt, etc). - * But there are also functions that are forward but not backward - * differentiable views (only detach for now) or functions that are backward - * but not forward differentiable view (only make_dual and unpack dual for - * now). - * - * A concrete example of two views with different bases is as follow: - * - * # Have: - * # dual is a dual Tensor that is neither a forward or backward view - * detached_dual = dual.detach() - * view = detached_dual.view_as(dual) - * # The forward base of view is dual - * # The backward base of view is detached_dual - * - * - Backward Mode View - * Differentiable views are the view variables where you want gradients to flow - * back to the base variables. Out-of-place operations on views are quite - * straightforward, but in-place ones are very tricky. Even if the base - * variable may not require grad when we create the view, we still need to - * track the view relation because future in-place ops may require back-proping - * through it. For example, we need to support - * - * (1) in-place operation on view, e.g., - * - * # Have: - * # base.requires_grad = False - * # var.requires_grad = True - * base[1] = var # i.e., base[1].copy_(var) - * torch.autograd.grad(base.sum(), var) <- should return an all ones - * tensor - * - * (2) in-place operation on base after view is created, e.g., - * - * # Have: - * # base.requires_grad = False - * # var.requires_grad = True - * view = base[1] - * base.copy_(var) - * torch.autograd.grad(view.sum(), var) <- should return a tensor with - * var[1] filled with all ones and - * zeros everywhere else - * - * - Forward Mode View - * Forward differentiable views follow the same semantic as backward ones but - * show up differently as they are computed along with the forward evaluation. - * The hard examples above are thus very similar - * - * (1) in-place operation on view, e.g., - * - * # Have: - * # base is a regular Tensor - * # var is a dual Tensor whose tangent is all ones - * base[1] = var # i.e., base[1].copy_(var) - * # Now, base is a dual Tensor - * _, fw_grad = fwAD.unpack_dual(base) <- fw_grad should be a tensor with - * fw_grad[1] filled with all ones - * and zeros everywhere else - * - * (2) in-place operation on base after view is created, e.g., - * - * # Have: - * # base is a regular Tensor - * # var is a dual Tensor whose tangent is all ones - * view = base[1] - * base.copy_(var) - * _, fw_grad = fwAD.unpack_dual(view) <- fw_grad should be an all ones - * tensor - * - * See Note [Forward Grad View/inplace] for more details on how we handle these - * hard cases. - * - * - * DifferentiableViewMeta is created to support gradient tracking of - * such **in-place** operations. In particular, - * + if an in-place op is done on base, the grad_fn field of the view may - * become stale. So accesses should always go through grad_fn(), which - * reconstructs an updated grad_fn if the version_counter has incremented. - * All other fields are always valid. - * + if an in-place op is done on view, in rebase_history() of view, which is - * called after every in-place op in VariableType.cpp, the grad_fn of base - * is updated. - * + if a single autograd Node returns multiple differentiable views, if any - * output is modified by an inplace operation, the autograd engine will - * make an equivalent graph (corresponding to the view operations) without - * using equivalent graph, where each output is treated as if it were - * produced by a distinct view operation. This discards the original (e.g., - * user provided) grad_fn. If the provided grad_fn does more than the - * backward of the view, then the DifferentiableViewMeta must be created - * with creation_meta= CreationMeta::MULTI_OUTPUT_NODE to prevent the - * engine from ignoring the provided grad_fn. - * - * Interaction with GradMode: - * The particular case that we consider here is: - * - * # Have: - * # base.requires_grad = True or False - * with torch.no_grad(): - * view = base[1] - * base.requires_grad_() - * view.copy_(var) - * torch.autograd.grad(base.sum(), var) <- what should it return? - * - * Given that this particular code example is ambiguous and can easily be - * replace by either moving both inside the no_grad block or both outside, we - * explicitly forbid it. For now, it is deprecated by a warning. This is - * achieved by setting creation_meta=CreationMeta::NO_GRAD_MODE for all - * differentiable views created in no_grad mode. - * - * See Note [View + Inplace update for base tensor] - * and Note [View + Inplace update for view tensor] for the details how - * autograd handles inplace update with view ops. - * - * Non-Differentiable Views - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - * In certain cases, although function outputs share storage with inputs, they - * will **never** require gradient history tracking. Instead of registering the - * view relation via DifferentiableViewMeta in autograd, the views will be - * using usual AutogradMeta and just share the version counters with the base - * Variables. - * Such views include: - * 1. Views created from .detach() - * 2. Views that are non-differentiable by its nature. - * E.g., {@code sparse_tensor.indices()} is a integral view on a (possibly) - * floating point tensor. - * See top of {@code derivatives.yaml} on how to specify that outputs of a - * function are non-differentiable. - * These are called non-differentiable views as the gradients do not flow - * through the view relation. - * - * Relevant logic for both differentiable and non-differentiable views is - * implemented in make_variable_(non_)differentiable_view below, and - * wrap_output of gen_variable_type.py. -

- * NOTE [ View + Inplace detection ] - * - * We want to detect views followed by inplace as they are often forbidden to - * ensure correctness of the computed gradients. But since we want to only - * notify the user when both happen, we tag the DifferentiableViewMeta when the - * view is created via the {@code make_variable_*_view()} functions. This tag is then - * checked by the {@code check_inplace()} function from {@code VariableTypeUtils.h} that - * should be called before every inplace operation and to detect cases where - * other views are modified and this one is rebased by side effect, we also - * check in the {@code VariableHooks::grad_fn()}. -

- * Flag that gives more information about when this view was created: - * - IN_CUSTOM_FUNCTION should be set when the view is created inside a custom - * autograd Function is returned. - * - NO_GRAD_MODE should be set when a view in created when GradMode is - * disabled - * - MULTI_OUTPUT_NODE should be set when a Node created by codegen code - * returns - * multiple differentiable views - * - Inference_MODE should be set when a view of normal tensor is created in - * InferenceMode. - * - DEFAULT is for all other cases */ -@Namespace("torch::autograd") public enum CreationMeta { - DEFAULT((byte)(0)), - IN_CUSTOM_FUNCTION((byte)(1)), - MULTI_OUTPUT_NODE((byte)(2)), - NO_GRAD_MODE((byte)(3)), - INFERENCE_MODE((byte)(4)); - - public final byte value; - private CreationMeta(byte v) { this.value = v; } - private CreationMeta(CreationMeta e) { this.value = e.value; } - public CreationMeta intern() { for (CreationMeta e : values()) if (e.value == value) return e; return this; } - @Override public String toString() { return intern().name(); } -} - -/** Handles correctly propagating CreationMeta when a new view is created from a - * previous view. In general, we don't want the new view to be _less_ - * restrictive than the previous view (it's okay to be _more_ restrictive). A - * CreationMeta value of DEFAULT is currently the least restrictive, as the - * behavior for all other CreationMeta values is to error out for in-place ops. - * A CreationMeta value of INFERENCE_MODE is currently the most restrictive, so - * it takes precedence in propagation. If this changes, the logic here will - * need to be updated to properly handle the new semantics. */ -@Namespace("torch::autograd") public static native CreationMeta propagate_creation_meta( - CreationMeta prev_view_creation_meta, - CreationMeta new_view_creation_meta); -@Namespace("torch::autograd") public static native @Cast("torch::autograd::CreationMeta") byte propagate_creation_meta( - @Cast("torch::autograd::CreationMeta") byte prev_view_creation_meta, - @Cast("torch::autograd::CreationMeta") byte new_view_creation_meta); - -/** Unified function to handle error checking when rebase happens - * indirect=true means that the caller is not doing the inplace, but the - * inplace happened somewhere else. */ -@Namespace("torch::autograd") public static native void handle_view_on_rebase( - DifferentiableViewMeta diff_view_meta, - @Cast("bool") boolean indirect/*=false*/); -@Namespace("torch::autograd") public static native void handle_view_on_rebase( - DifferentiableViewMeta diff_view_meta); -// Targeting ../DifferentiableViewMeta.java - - - -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -// Variable Implementation -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -// Factory Functions -//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -/** Creates a {@code Variable} that is a *view* of another (*base*) variable. - * The {@code gradient_edge} is an optional (gradient_function, input_number) pair. - * {@code is_differentiable} is a bool that specifies whether this view is - * differentiable, i.e., whether the relation should be tracked by autograd. - * See NOTE [ Autograd View Variables ] for details. -

- * NOTE: {@code allow_tensor_metadata_change} is set to true by default, because - * there are a lot of call sites to these factory functions that need to change - * the variable's size or storage afterwards, and they don't expect the - * original tensor (where the variable is created from) to be updated. Setting - * {@code allow_tensor_metadata_change_} to false by default would unnecessarily - * prevent those changes from happening and is undesirable. */ - -// See NOTE [ Autograd View Variables ] for details. -// Differentiable view. Track history with DifferentiableViewMeta. -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_differentiable_view( - @Const @ByRef Tensor data, - @ByVal @Cast("c10::optional*") Pointer backward_info, - @ByVal @Cast("c10::optional*") Pointer forward_info, - @Cast("bool") boolean shared_view_info, - CreationMeta creation_meta, - @Cast("bool") boolean allow_tensor_metadata_change/*=true*/); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_differentiable_view( - @Const @ByRef Tensor data, - @ByVal @Cast("c10::optional*") Pointer backward_info, - @ByVal @Cast("c10::optional*") Pointer forward_info, - @Cast("bool") boolean shared_view_info, - CreationMeta creation_meta); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_differentiable_view( - @Const @ByRef Tensor data, - @ByVal @Cast("c10::optional*") Pointer backward_info, - @ByVal @Cast("c10::optional*") Pointer forward_info, - @Cast("bool") boolean shared_view_info, - @Cast("torch::autograd::CreationMeta") byte creation_meta, - @Cast("bool") boolean allow_tensor_metadata_change/*=true*/); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_differentiable_view( - @Const @ByRef Tensor data, - @ByVal @Cast("c10::optional*") Pointer backward_info, - @ByVal @Cast("c10::optional*") Pointer forward_info, - @Cast("bool") boolean shared_view_info, - @Cast("torch::autograd::CreationMeta") byte creation_meta); - -// See NOTE [ Autograd View Variables ] for details. -// Non-differentiable view. Just share version counter. - -/// -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_non_differentiable_view( - @Cast("const torch::autograd::Variable*") @ByRef Tensor base, - @Const @ByRef Tensor data, - @Cast("bool") boolean allow_tensor_metadata_change/*=true*/); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable_non_differentiable_view( - @Cast("const torch::autograd::Variable*") @ByRef Tensor base, - @Const @ByRef Tensor data); - -/** Creates a {@code Variable} from the given {@code Tensor}, copying its underlying - * {@code TensorImpl}. {@code requires_grad} should be set only for leaves, and determines - * whether the {@code Variable} will accumulate gradients. NOTE: {@code data} must *not* be - * a {@code Variable} already. Its dynamic type *must* be {@code Tensor}. - * - * TODO: Eliminate this function as much as possible, as it can be expressed - * more clearly as detach() or a no-op in most call sites (especially when - * there is only one use of the variable). */ -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable( - @ByVal Tensor data, - @Cast("bool") boolean requires_grad/*=false*/, - @Cast("bool") boolean allow_tensor_metadata_change/*=true*/); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable( - @ByVal Tensor data); - -/** Creates a {@code Variable} from the given {@code Tensor}, copying its underlying - * {@code TensorImpl}. {@code gradient_edge} should be a (function, input_nr) pair - * specifying the function in the autograd graph, and what particular input of - * that function, this variable is connected to. */ -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable( - @Const @ByRef Tensor data, - @ByVal Edge gradient_edge, - @Cast("bool") boolean allow_tensor_metadata_change/*=true*/); -@Namespace("torch::autograd") public static native @ByVal @Cast("torch::autograd::Variable*") Tensor make_variable( - @Const @ByRef Tensor data, - @ByVal Edge gradient_edge); - -@Namespace("torch::autograd::utils") public static native @Cast("bool") boolean has_same_meta(@Cast("const torch::autograd::Variable*") @ByRef Tensor base, @Cast("const torch::autograd::Variable*") @ByRef Tensor other); - - // namespace utils - // namespace autograd - // namespace torch - // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ @@ -15828,8 +15852,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("torch::autograd::forward_ad") public static native void exit_dual_level(@Cast("uint64_t") long level); // namespace forward_ad - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from ATen/core/alias_info.h @@ -16019,7 +16042,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { */ // #include -// #include // #include // Targeting ../ArgumentDef.java @@ -16386,6 +16408,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // // utils @@ -16623,6 +16646,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include /** * Represent a function pointer as a C++ type. @@ -16700,7 +16724,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // TODO Instead of this, move torch::jit::Stack to the c10 namespace. +// #include +// #include // TODO Instead of this, move torch::jit::Stack to the c10 namespace. // Targeting ../KernelFunction.java @@ -16717,6 +16742,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include +// #include + @@ -16780,6 +16808,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // - make sherwood_v3_table::convertible_to_iterator public because GCC5 seems // to have issues with it otherwise // - fix compiler warnings in operator templated_iterator +// - make use of 'if constexpr' and eliminate AssignIfTrue template // Copyright Malte Skarupke 2017. // Distributed under the Boost Software License, Version 1.0. @@ -16825,29 +16854,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #endif -// Parsed from c10/util/either.h - -// Originally taken from -// https://github.com/cryfs/cryfs/blob/14ad22570ddacef22d5ff139cdff68a54fc8234d/src/cpp-utils/either.h - -// #pragma once - -// #include -// #include -// #include -/** - * either is a tagged union that holds either an object of type A - * or an object of type B. - */ - // namespace c10 - - // Parsed from c10/core/PyHandleCache.h // #pragma once // #include // #include +// #include // #include // #include @@ -16889,6 +16902,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // Targeting ../SafePyObject.java @@ -16903,9 +16917,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include -// #include -// #include +// #include // #if defined(_MSC_VER) // #endif // Targeting ../bitset.java @@ -16914,8 +16926,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10::utils") public static native @Cast("bool") @Name("operator !=") @NoException(true) boolean notEquals(@ByVal bitset lhs, @ByVal bitset rhs); - // namespace utils - // namespace c10 + // namespace c10::utils // Parsed from ATen/core/Variadic.h @@ -16989,7 +17000,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// #include // #include // #include // #include @@ -17041,7 +17051,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -// #include // #include // #include // #include @@ -17314,8 +17323,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include -// #include - /** * A very simple Synchronization class for error-free use of data * in a multi-threaded context. See folly/docs/Synchronized.md for @@ -17491,11 +17498,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { dynamic_output_shape(2), generated(3), inplace_view(4), - nondeterministic_bitwise(5), - nondeterministic_seeded(6), - pointwise(7), - pt2_compliant_tag(8), - view_copy(9); + needs_fixed_stride_order(5), + nondeterministic_bitwise(6), + nondeterministic_seeded(7), + pointwise(8), + pt2_compliant_tag(9), + view_copy(10); public final int value; private Tag(int v) { this.value = v; } @@ -18042,8 +18050,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("torch::autograd") public static native void setAutogradFallbackMode(@Cast("torch::autograd::AutogradFallbackMode") int mode); @Namespace("torch::autograd") public static native AutogradFallbackMode getAutogradFallbackMode(); - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/anomaly_mode.h @@ -18063,8 +18070,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/grad_mode.h @@ -18074,8 +18080,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from ATen/FuncTorchTLS.h @@ -18175,8 +18180,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/core/impl/TorchDispatchModeTLS.h @@ -18207,16 +18211,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("c10::impl") public static native @StdString BytePointer to_string(TorchDispatchModeKey mode_key); @Namespace("c10::impl") public static native @StdString String to_string(@Cast("c10::impl::TorchDispatchModeKey") byte mode_key); - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from ATen/ThreadLocalState.h // #pragma once -// #include - // #include // #include // #include @@ -18334,7 +18335,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // values in-place (adding an input twice will accumulate the result). // This behaviour is needed and used only in backward graphs. -// #include // #include // #include @@ -18342,8 +18342,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/utils/warnings.h @@ -18378,8 +18377,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("torch::autograd") @MemberGetter public static native int CPU_DEVICE(); public static final int CPU_DEVICE = CPU_DEVICE(); - - // GraphTask holds metadata needed for a single execution of backward() // The guard that sets and restores current_graph_task. @@ -18391,8 +18388,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("torch::autograd") public static native int get_current_graph_task_id(); - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from ATen/core/MT19937RNGEngine.h @@ -18449,6 +18445,72 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace at +// Parsed from ATen/detail/AcceleratorHooksInterface.h + +// #pragma once + +// #include +// Targeting ../AcceleratorHooksInterface.java + + + + // namespace at + + +// Parsed from ATen/detail/MTIAHooksInterface.h + +// #pragma once + +// #include + +// #include + +// #include + +// #include + + +@Namespace("at") @MemberGetter public static native @Cast("const char*") BytePointer MTIA_HELP(); +// Targeting ../MTIAHooksInterface.java + + +// #define REGISTER_MTIA_HOOKS(clsname) +// C10_REGISTER_CLASS(MTIAHooksRegistry, clsname, clsname) +@Namespace("at::detail") public static native @Const @ByRef MTIAHooksInterface getMTIAHooks(); + // namespace detail + // namespace at + + +// Parsed from ATen/DeviceAccelerator.h + +// #pragma once + +// #include +// #include + +// #include +// #include + +// This file defines the top level Accelerator concept for PyTorch. +// A device is an accelerator per the definition here if: +// - It is mutually exclusive with all other accelerators +// - It performs asynchronous compute via a Stream/Event system +// - It provides a set of common APIs as defined by AcceleratorHooksInterface +// +// As of today, accelerator devices are (in no particular order): +// CUDA, MTIA, PrivateUse1 +// We want to add once all the proper APIs are supported and tested: +// HIP, MPS, XPU + +// Ensures that only one accelerator is available (at +// compile time if possible) and return it. +// When checked is true, the returned optional always has a value. +@Namespace("at") public static native @Optional @Cast("c10::DeviceType*") BytePointer getAccelerator(@Cast("bool") boolean checked/*=false*/); +@Namespace("at") public static native @Optional @Cast("c10::DeviceType*") BytePointer getAccelerator(); + + // namespace at + + // Parsed from ATen/LinalgBackend.h // #pragma once @@ -18520,6 +18582,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include + // Forward-declares at::Generator and at::cuda::NVRTC // namespace cuda // namespace at @@ -18600,6 +18664,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include +// #include // #include // #include @@ -18618,28 +18683,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // namespace at -// Parsed from ATen/detail/MTIAHooksInterface.h - -// #pragma once - -// #include - -// #include - -// #include - - -@Namespace("at") @MemberGetter public static native @Cast("const char*") BytePointer MTIA_HELP(); -// Targeting ../MTIAHooksInterface.java - - -// #define REGISTER_MTIA_HOOKS(clsname) -// C10_REGISTER_CLASS(MTIAHooksRegistry, clsname, clsname) -@Namespace("at::detail") public static native @Const @ByRef MTIAHooksInterface getMTIAHooks(); - // namespace detail - // namespace at - - // Parsed from ATen/detail/ORTHooksInterface.h // #pragma once @@ -18667,7 +18710,10 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include +// #include // #include +// #include // #include // Targeting ../PrivateUse1HooksInterface.java @@ -18676,11 +18722,18 @@ public class torch extends org.bytedeco.pytorch.presets.torch { -@Namespace("at") public static native void RegisterPrivateUse1HooksInterface(PrivateUse1HooksInterface hook_); +@Namespace("at") public static native void RegisterPrivateUse1HooksInterface( + PrivateUse1HooksInterface hook_); @Namespace("at") public static native PrivateUse1HooksInterface GetPrivateUse1HooksInterface(); +@Namespace("at") public static native @Cast("bool") boolean isPrivateUse1HooksRegistered(); + +@Namespace("at::detail") public static native @Const @ByRef PrivateUse1HooksInterface getPrivateUse1Hooks(); + + // namespace detail + // namespace at // Parsed from ATen/detail/XPUHooksInterface.h @@ -18695,9 +18748,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include -// Targeting ../DLDevice_.java - - @Namespace("at") @MemberGetter public static native @Cast("const char*") BytePointer XPU_HELP(); // Targeting ../XPUHooksInterface.java @@ -18717,9 +18767,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once -// #include -// #include // #include +// #include +// #include /** * QEngine is an enum that is used to select the engine to run quantized ops. @@ -18768,8 +18818,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include -// #include +// #include // #include +// #include // Reads an environment variable and returns // - optional, if set equal to "1" // - optional, if set equal to "0" @@ -18777,10 +18828,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // // NB: // Issues a warning if the value of the environment variable is not 0 or 1. -@Namespace("c10::utils") public static native @ByVal BoolOptional check_env(@Cast("const char*") BytePointer name); -@Namespace("c10::utils") public static native @ByVal BoolOptional check_env(String name); - // namespace utils - // namespace c10 +@Namespace("c10::utils") public static native @Cast("bool*") @Optional BoolPointer check_env(@Cast("const char*") BytePointer name); +@Namespace("c10::utils") public static native @Cast("bool*") @Optional boolean[] check_env(String name); + // namespace c10::utils // Parsed from ATen/Context.h @@ -18788,11 +18838,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include +// #include // #include // #include // #include // #include // #include +// #include // #include // #include // #include @@ -18809,7 +18861,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include -// #include // #include @Namespace("at") public enum Float32MatmulPrecision { HIGHEST(0), HIGH(1), MEDIUM(2); @@ -18863,9 +18914,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Targeting ../NoTF32Guard.java +// Targeting ../ROCmBackwardPassGuard.java + -// #ifdef USE_ROCM -// #endif // namespace at @@ -19312,7 +19363,9 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // not TensorGeometryArg, because the Tensor to TensorGeometry // conversion will blow up if you have undefined tensors. -@Namespace("at") public static native @Cast("std::ostream*") @ByRef @Name("operator <<") Pointer shiftLeft(@Cast("std::ostream*") @ByRef Pointer out, @ByVal TensorGeometryArg t); +@Namespace("at") public static native @Cast("std::ostream*") @ByRef @Name("operator <<") Pointer shiftLeft( + @Cast("std::ostream*") @ByRef Pointer out, + @Const @ByRef TensorGeometryArg t); @Namespace("at") public static native void checkDim( @Cast("at::CheckedFrom") BytePointer c, @Const @ByRef Tensor tensor, @@ -19388,12 +19441,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Cast("at::CheckedFrom") BytePointer c, @Const @ByRef TensorGeometryArg t, @Cast("int64_t") long dim, - @ByVal SymInt size); + @Const @ByRef SymInt size); @Namespace("at") public static native void checkSize_symint( @Cast("at::CheckedFrom") String c, @Const @ByRef TensorGeometryArg t, @Cast("int64_t") long dim, - @ByVal SymInt size); + @Const @ByRef SymInt size); @Namespace("at") public static native void checkNumel( @Cast("at::CheckedFrom") BytePointer c, @Const @ByRef TensorGeometryArg t, @@ -31381,7 +31434,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { Pointer data, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... sizes); - // namespace at + // namespace at // Parsed from ATen/ops/from_file.h @@ -49460,6 +49513,43 @@ public class torch extends org.bytedeco.pytorch.presets.torch { +// Parsed from ATen/ops/slice_inverse.h + +// #pragma once + +// @generated by torchgen/gen.py from Function.h + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + + + +// #include + + +// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) +@Namespace("at") public static native @ByVal Tensor slice_inverse(@Const @ByRef Tensor self, @Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") LongOptional end, @Cast("int64_t") long step/*=1*/); +@Namespace("at") public static native @ByVal Tensor slice_inverse(@Const @ByRef Tensor self, @Const @ByRef Tensor src); + + +// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) +@Namespace("at") public static native @ByVal Tensor slice_inverse_symint(@Const @ByRef Tensor self, @Const @ByRef Tensor src, @Cast("int64_t") long dim/*=0*/, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional start, @ByVal(nullValue = "c10::optional(c10::nullopt)") SymIntOptional end, @ByVal(nullValue = "c10::SymInt(1)") SymInt step); +@Namespace("at") public static native @ByVal Tensor slice_inverse_symint(@Const @ByRef Tensor self, @Const @ByRef Tensor src); + + + + + // Parsed from ATen/ops/slice_scatter.h // #pragma once @@ -50432,13 +50522,24 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include -// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor +// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor @Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal LongArrayRef size, @ByVal TensorOptions options); @Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] size, @ByVal TensorOptions options); -// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + + +// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor @Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal LongArrayRef size, @ByVal ScalarTypeOptional dtype, @ByVal LayoutOptional layout, @ByVal DeviceOptional device, @ByVal BoolOptional pin_memory); @Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] size, @ByVal ScalarTypeOptional dtype, @ByVal LayoutOptional layout, @ByVal DeviceOptional device, @ByVal BoolOptional pin_memory); + +// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor +@Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor_symint(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal SymIntArrayRef size, @ByVal TensorOptions options); + + +// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor +@Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor_symint(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal SymIntArrayRef size, @ByVal ScalarTypeOptional dtype, @ByVal LayoutOptional layout, @ByVal DeviceOptional device, @ByVal BoolOptional pin_memory); + + // aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor @Namespace("at") public static native @ByVal Tensor sparse_compressed_tensor(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal TensorOptions options); // aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor @@ -58179,6 +58280,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58197,6 +58299,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58214,6 +58317,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58301,6 +58405,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58308,6 +58413,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58325,9 +58431,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include +// #include // #include // #include // #include @@ -58357,6 +58465,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include +// #include +// #include +// #include +// #include // #include // #include // #include @@ -58368,6 +58482,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include // #include // #include // #include @@ -58382,6 +58498,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58393,10 +58510,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include // #include +// #include +// #include // #include // #include // #include @@ -58446,6 +58566,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -58499,6 +58620,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -59226,6 +59348,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -59810,9 +59933,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #endif -// #include -// #include - /** * Records TensorOptions, shape of the tensor, whether or not the Python * dispatch key is set (tensor subclass), and, where applicable, the stream the @@ -59821,8 +59941,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * If is_valid() is false, then the corresponding input is not used and may be * an undefined tensor. */ - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/saved_variable_hooks.h @@ -59834,8 +59953,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/autograd/saved_variable.h @@ -59855,8 +59973,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { /** A snapshot of a variable at a certain version. A {@code SavedVariable} stores * enough information to reconstruct a variable from a certain point in time. */ - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/utils/variadic.h @@ -59956,7 +60073,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { * If you don't want the {@code Node}'s {@code num_inputs} to be incremented, use * {@code set_gradient_edge} directly. */ @Namespace("torch::autograd") public static native void create_gradient_edge( - @Cast("torch::autograd::Variable*") @ByRef Tensor variable, + @ByRef Tensor variable, @SharedPtr Node function); /** Return true if any of the variables in the list require a gradient. */ @@ -59964,8 +60081,19 @@ public class torch extends org.bytedeco.pytorch.presets.torch { /** Return the next edges of all the given variables, or tuples of variables. */ - // namespace autograd - // namespace torch + // namespace torch::autograd + + +// Parsed from torch/csrc/autograd/variable_info.h + +// #pragma once + +// #include +// Targeting ../VariableInfo.java + + + + // namespace torch::autograd // Parsed from torch/csrc/autograd/custom_function.h @@ -59978,6 +60106,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include @@ -60004,15 +60133,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // Targeting ../AutogradContext.java -// Targeting ../VariableInfo.java - - // CppNode is the Node in the autograd graph that represents the user defined // backward function for Function. Calls to CppNode::apply are forward to // T::backward(). -@Namespace("torch::autograd") public static native @ByVal TensorOptionalVector to_optional(@Cast("torch::autograd::Variable*") @ByRef Tensor output); +@Namespace("torch::autograd") public static native @ByVal TensorOptionalVector to_optional(@ByRef Tensor output); @Namespace("torch::autograd") public static native @ByVal TensorOptionalVector to_optional(@ByRef TensorVector output); @@ -60028,8 +60154,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { - // namespace autograd - // namespace torch + // namespace torch::autograd // Parsed from torch/csrc/api/include/torch/autograd.h @@ -60081,11 +60206,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #define TORCH_ARG(T, name) // public: -// inline auto name(const T& new_##name)->decltype(*this) { /* NOLINT */ +// inline auto name(const T& new_##name) -> decltype(*this) { /* NOLINT */ // this->name##_ = new_##name; // return *this; // } -// inline auto name(T&& new_##name)->decltype(*this) { /* NOLINT */ +// inline auto name(T&& new_##name) -> decltype(*this) { /* NOLINT */ // this->name##_ = std::move(new_##name); // return *this; // } @@ -60945,6 +61070,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #pragma once // #include // #include +// #include // Computes the contiguous strides of a tensor, given its sizes. @Namespace("c10") public static native @ByVal @Cast("c10::DimVector*") SymDimVector contiguous_strides(@Const @ByVal LongArrayRef sizes); @@ -61443,6 +61569,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61461,6 +61588,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61478,6 +61606,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61565,6 +61694,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61572,6 +61702,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61589,9 +61720,11 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include +// #include // #include // #include // #include @@ -61621,6 +61754,12 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include +// #include +// #include +// #include +// #include // #include // #include // #include @@ -61632,6 +61771,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include +// #include // #include // #include // #include @@ -61646,6 +61787,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61657,10 +61799,13 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include // #include +// #include +// #include // #include // #include // #include @@ -61710,6 +61855,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -61763,6 +61909,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -62490,6 +62637,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #include // #include +// #include // #include // #include // #include @@ -63001,9 +63149,6 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // #include // #endif -// #include -// #include - // #define AT_FORALL_BINARY_OPS(_) // _(+, x.add(y), y.add(x)) // _(*, x.mul(y), y.mul(x)) @@ -63660,6 +63805,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { @Namespace("torch") public static native @ByVal @Name("_sparse_compressed_tensor_unsafe") Tensor torch__sparse_compressed_tensor_unsafe(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal LongArrayRef size); @Namespace("torch") public static native @ByVal @Name("_sparse_compressed_tensor_unsafe") Tensor torch__sparse_compressed_tensor_unsafe(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] size, @ByVal(nullValue = "at::TensorOptions{}") TensorOptions options); @Namespace("torch") public static native @ByVal @Name("_sparse_compressed_tensor_unsafe") Tensor torch__sparse_compressed_tensor_unsafe(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... size); +@Namespace("torch") public static native @ByVal Tensor _sparse_compressed_tensor_unsafe_symint(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal SymIntArrayRef size, @ByVal(nullValue = "at::TensorOptions{}") TensorOptions options); +@Namespace("torch") public static native @ByVal Tensor _sparse_compressed_tensor_unsafe_symint(@Const @ByRef Tensor compressed_indices, @Const @ByRef Tensor plain_indices, @Const @ByRef Tensor values, @ByVal SymIntArrayRef size); @Namespace("torch") public static native @ByVal @Name("_sparse_csr_tensor_unsafe") Tensor torch__sparse_csr_tensor_unsafe(@Const @ByRef Tensor crow_indices, @Const @ByRef Tensor col_indices, @Const @ByRef Tensor values, @ByVal LongArrayRef size, @ByVal(nullValue = "at::TensorOptions{}") TensorOptions options); @Namespace("torch") public static native @ByVal @Name("_sparse_csr_tensor_unsafe") Tensor torch__sparse_csr_tensor_unsafe(@Const @ByRef Tensor crow_indices, @Const @ByRef Tensor col_indices, @Const @ByRef Tensor values, @ByVal LongArrayRef size); @Namespace("torch") public static native @ByVal @Name("_sparse_csr_tensor_unsafe") Tensor torch__sparse_csr_tensor_unsafe(@Const @ByRef Tensor crow_indices, @Const @ByRef Tensor col_indices, @Const @ByRef Tensor values, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] size, @ByVal(nullValue = "at::TensorOptions{}") TensorOptions options); @@ -64541,8 +64688,7 @@ public class torch extends org.bytedeco.pytorch.presets.torch { // A pair of (byte offset, SourceRange) describing a specific segment // of the output stream - // namespace jit - // namespace torch + // namespace torch::jit // namespace std @@ -65147,6 +65293,8 @@ public class torch extends org.bytedeco.pytorch.presets.torch { + + @Namespace("torch::jit") public enum ExecutorExecutionMode { SIMPLE(0), PROFILING(1); @@ -65768,7 +65916,7 @@ The list of (type, depth) pairs controls the type of specializations and the num * torch::nn::Linear model(3, 4); * torch::save(model, "model.pt"); * - * torch::optim::SGD sgd(/*lr=* /0.9); + * torch::optim::SGD sgd(model->parameters(), 0.9); // 0.9 is learning rate * std::ostringstream stream; * // Note that the same stream cannot be used in multiple torch::save(...) * // invocations, otherwise the header will be corrupted. @@ -65828,7 +65976,7 @@ The list of (type, depth) pairs controls the type of specializations and the num * torch::nn::Linear model(3, 4); * torch::load(model, "model.pt"); * - * torch::optim::SGD sgd(/*lr=* /0.9); + * torch::optim::SGD sgd(model->parameters(), 0.9); // 0.9 is learning rate * std::istringstream stream("..."); * torch::load(sgd, stream); * @@ -66981,116 +67129,199 @@ The list of (type, depth) pairs controls the type of specializations and the num // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS +// #endif /* DOXYGEN_SHOULD_SKIP_THIS */ + +/** Cholesky decomposition +/** +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.cholesky +/** +/** Example: +/** 

{@code
+/** auto A = torch::randn({4, 4});
+/** auto A = torch::matmul(A, A.t());
+/** auto L = torch::linalg::cholesky(A);
+/** assert(torch::allclose(torch::matmul(L, L.t()), A));
+/** }
*/ + +// C10_DEPRECATED_MESSAGE("linalg_det is deprecated, use det instead.") + +/** See the documentation of torch.linalg.det */ + +/** Computes the sign and (natural) logarithm of the determinant + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.slogdet */ + +/** Computes eigenvalues and eigenvectors of non-symmetric/non-hermitian + * matrices + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eig */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T eig(@Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T eig(@Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer eig_out( +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer eig_out( @ByRef Tensor eigvals, @ByRef Tensor eigvecs, @Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor eigvals(@Const @ByRef Tensor self); +/** Computes eigenvalues of non-symmetric/non-hermitian matrices + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigvals */ +@Namespace("torch::linalg") public static native @ByVal Tensor eigvals(@Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor eigvals_out(@ByRef Tensor result, @Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T eigh( +/// +@Namespace("torch::linalg") public static native @ByRef Tensor eigvals_out(@ByRef Tensor result, @Const @ByRef Tensor self); + +/** Computes eigenvalues and eigenvectors + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigh */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T eigh( @Const @ByRef Tensor self, @StringView BytePointer uplo); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T eigh( +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T eigh( @Const @ByRef Tensor self, @StringView String uplo); -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer eigh_out( + +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer eigh_out( @ByRef Tensor eigvals, @ByRef Tensor eigvecs, @Const @ByRef Tensor self, @StringView BytePointer uplo); -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer eigh_out( +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer eigh_out( @ByRef Tensor eigvals, @ByRef Tensor eigvecs, @Const @ByRef Tensor self, @StringView String uplo); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor eigvalsh(@Const @ByRef Tensor self, @StringView BytePointer uplo); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor eigvalsh(@Const @ByRef Tensor self, @StringView String uplo); +/** Computes eigenvalues + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigvalsh */ +@Namespace("torch::linalg") public static native @ByVal Tensor eigvalsh(@Const @ByRef Tensor self, @StringView BytePointer uplo); +@Namespace("torch::linalg") public static native @ByVal Tensor eigvalsh(@Const @ByRef Tensor self, @StringView String uplo); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor eigvalsh_out( + +/// +@Namespace("torch::linalg") public static native @ByRef Tensor eigvalsh_out( @ByRef Tensor result, @Const @ByRef Tensor self, @StringView BytePointer uplo); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor eigvalsh_out( +@Namespace("torch::linalg") public static native @ByRef Tensor eigvalsh_out( @ByRef Tensor result, @Const @ByRef Tensor self, @StringView String uplo); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor householder_product(@Const @ByRef Tensor input, @Const @ByRef Tensor tau); +/** Computes the product of Householder matrices + * + * See + * https://pytorch.org/docs/master/linalg.html#torch.linalg.householder_product */ +@Namespace("torch::linalg") public static native @ByVal Tensor householder_product(@Const @ByRef Tensor input, @Const @ByRef Tensor tau); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor householder_product_out( +@Namespace("torch::linalg") public static native @ByRef Tensor householder_product_out( @ByRef Tensor result, @Const @ByRef Tensor input, @Const @ByRef Tensor tau); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T lu_factor( + +/// +@Namespace("torch::linalg") public static native @ByVal T_TensorTensorTensorTensor_T lstsq( @Const @ByRef Tensor self, - @Cast("const bool") boolean pivot); + @Const @ByRef Tensor b, + @ByVal DoubleOptional cond, + @ByVal StringViewOptional driver); + +/** Computes the matrix exponential + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_exp */ + +// C10_DEPRECATED_MESSAGE("linalg_norm is deprecated, use norm instead.") -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_factor_out( +// C10_DEPRECATED_MESSAGE("linalg_norm is deprecated, use norm instead.") + +// C10_DEPRECATED_MESSAGE("linalg_norm_out is deprecated, use norm_out +// instead.") + +// C10_DEPRECATED_MESSAGE("linalg_norm_out is deprecated, use norm_out +// instead.") + +/** Computes the LU factorization with partial pivoting + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.lu_factor */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T lu_factor( + @Const @ByRef Tensor input, + @Cast("const bool") boolean pivot/*=true*/); +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T lu_factor( + @Const @ByRef Tensor input); + + +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_factor_out( @ByRef Tensor LU, @ByRef Tensor pivots, @Const @ByRef Tensor self, - @Cast("const bool") boolean pivot); + @Cast("const bool") boolean pivot/*=true*/); +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_factor_out( + @ByRef Tensor LU, + @ByRef Tensor pivots, + @Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensorTensor_T lu( - @Const @ByRef Tensor self, - @Cast("const bool") boolean pivot); +/** Computes the LU factorization with partial pivoting + * + * See https://pytorch.org/docs/master/linalg.html#torch.linalg.lu */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensorTensor_T lu( + @Const @ByRef Tensor input, + @Cast("const bool") boolean pivot/*=true*/); +@Namespace("torch::linalg") public static native @ByVal T_TensorTensorTensor_T lu( + @Const @ByRef Tensor input); -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_out( +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_out( @ByRef Tensor P, @ByRef Tensor L, @ByRef Tensor U, @Const @ByRef Tensor self, - @Cast("const bool") boolean pivot); - -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensorTensorTensor_T lstsq( - @Const @ByRef Tensor self, - @Const @ByRef Tensor b, - @ByVal DoubleOptional cond, - @ByVal StringViewOptional driver); + @Cast("const bool") boolean pivot/*=true*/); +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_out( + @ByRef Tensor P, + @ByRef Tensor L, + @ByRef Tensor U, + @Const @ByRef Tensor self); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor norm( +@Namespace("torch::linalg") public static native @ByVal Tensor norm( @Const @ByRef Tensor self, @Const @ByRef ScalarOptional opt_ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor norm( +@Namespace("torch::linalg") public static native @ByVal Tensor norm( @Const @ByRef Tensor self, @Const @ByRef ScalarOptional opt_ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long[] opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor norm( +@Namespace("torch::linalg") public static native @ByVal Tensor norm( @Const @ByRef Tensor self, - @StringView BytePointer ord, + @StdString BytePointer ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor norm( +@Namespace("torch::linalg") public static native @ByVal Tensor norm( @Const @ByRef Tensor self, - @StringView String ord, + @StdString String ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long[] opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, @Const @ByRef ScalarOptional opt_ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, @Const @ByRef ScalarOptional opt_ord, @@ -67098,42 +67329,43 @@ The list of (type, depth) pairs controls the type of specializations and the num @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, - @StringView BytePointer ord, + @StdString BytePointer ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, - @StringView String ord, + @StdString String ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long[] opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor vector_norm( +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.vector_norm */ +@Namespace("torch::linalg") public static native @ByVal Tensor vector_norm( @Const @ByRef Tensor self, @ByVal Scalar ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor vector_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor vector_norm( @Const @ByRef Tensor self, @ByVal Scalar ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long[] opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor vector_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor vector_norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, @ByVal Scalar ord, @ByVal LongArrayRefOptional opt_dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor vector_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor vector_norm_out( @ByRef Tensor result, @Const @ByRef Tensor self, @ByVal Scalar ord, @@ -67141,27 +67373,28 @@ The list of (type, depth) pairs controls the type of specializations and the num @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional opt_dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_norm */ +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @Const @ByRef Scalar ord, @ByVal LongArrayRef dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @Const @ByRef Scalar ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @Const @ByRef Scalar ord, @ByVal LongArrayRef dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @Const @ByRef Scalar ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @@ -67169,53 +67402,53 @@ The list of (type, depth) pairs controls the type of specializations and the num @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @StdString BytePointer ord, @ByVal LongArrayRef dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @StdString String ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @StdString BytePointer ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_norm( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_norm( @Const @ByRef Tensor self, @StdString String ord, @ByVal LongArrayRef dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @StdString BytePointer ord, @ByVal LongArrayRef dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @StdString String ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @StdString BytePointer ord, @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] dim, @Cast("bool") boolean keepdim, @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_norm_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_norm_out( @Const @ByRef Tensor self, @StdString String ord, @ByVal LongArrayRef dim, @@ -67223,274 +67456,82 @@ The list of (type, depth) pairs controls the type of specializations and the num @ByVal ScalarTypeOptional dtype, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_power_out(@Const @ByRef Tensor self, @Cast("int64_t") long n, @ByRef Tensor result); +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_power */ + +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_power_out(@Const @ByRef Tensor self, @Cast("int64_t") long n, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_rank(@Const @ByRef Tensor input, double tol, @Cast("bool") boolean hermitian); +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_rank */ +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_rank(@Const @ByRef Tensor input, double tol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_rank( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_rank( @Const @ByRef Tensor input, @Const @ByRef Tensor tol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_rank( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_rank( @Const @ByRef Tensor input, @ByVal DoubleOptional atol, @ByVal DoubleOptional rtol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor matrix_rank( +@Namespace("torch::linalg") public static native @ByVal Tensor matrix_rank( @Const @ByRef Tensor input, @Const @ByRef TensorOptional atol, @Const @ByRef TensorOptional rtol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_rank_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_rank_out( @ByRef Tensor result, @Const @ByRef Tensor input, double tol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_rank_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_rank_out( @ByRef Tensor result, @Const @ByRef Tensor input, @Const @ByRef Tensor tol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_rank_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_rank_out( @ByRef Tensor result, @Const @ByRef Tensor input, @ByVal DoubleOptional atol, @ByVal DoubleOptional rtol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor matrix_rank_out( +@Namespace("torch::linalg") public static native @ByRef Tensor matrix_rank_out( @ByRef Tensor result, @Const @ByRef Tensor input, @Const @ByRef TensorOptional atol, @Const @ByRef TensorOptional rtol, @Cast("bool") boolean hermitian); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor multi_dot(@ByVal TensorArrayRef tensors); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor multi_dot(@ByVal TensorVector tensors); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor multi_dot_out(@ByVal TensorArrayRef tensors, @ByRef Tensor result); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor multi_dot_out(@ByVal TensorVector tensors, @ByRef Tensor result); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor pinv(@Const @ByRef Tensor input, double rcond, @Cast("bool") boolean hermitian); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor pinv_out( - @ByRef Tensor result, - @Const @ByRef Tensor input, - double rcond, - @Cast("bool") boolean hermitian); - -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T qr( - @Const @ByRef Tensor input, - @StringView BytePointer mode); -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T qr( - @Const @ByRef Tensor input, - @StringView String mode); - -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer qr_out( - @ByRef Tensor Q, - @ByRef Tensor R, - @Const @ByRef Tensor input, - @StringView BytePointer mode); -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer qr_out( - @ByRef Tensor Q, - @ByRef Tensor R, - @Const @ByRef Tensor input, - @StringView String mode); - -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensor_T solve_ex( - @Const @ByRef Tensor input, - @Const @ByRef Tensor other, - @Cast("bool") boolean left, - @Cast("bool") boolean check_errors); - -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer solve_ex_out( - @ByRef Tensor result, - @ByRef Tensor info, - @Const @ByRef Tensor input, - @Const @ByRef Tensor other, - @Cast("bool") boolean left, - @Cast("bool") boolean check_errors); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor solve(@Const @ByRef Tensor input, @Const @ByRef Tensor other, @Cast("bool") boolean left); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor solve_out( - @ByRef Tensor result, - @Const @ByRef Tensor input, - @Const @ByRef Tensor other, - @Cast("bool") boolean left); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor solve_triangular( - @Const @ByRef Tensor input, - @Const @ByRef Tensor other, - @Cast("bool") boolean upper, - @Cast("bool") boolean left, - @Cast("bool") boolean unitriangular); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor solve_triangular_out( - @ByRef Tensor result, - @Const @ByRef Tensor input, - @Const @ByRef Tensor other, - @Cast("bool") boolean upper, - @Cast("bool") boolean left, - @Cast("bool") boolean unitriangular); - -@Namespace("torch::linalg::detail") public static native @ByVal T_TensorTensorTensor_T svd( - @Const @ByRef Tensor input, - @Cast("bool") boolean full_matrices, - @ByVal StringViewOptional driver); - -@Namespace("torch::linalg::detail") public static native @ByVal @Cast("std::tuple*") PointerPointer svd_out( - @ByRef Tensor U, - @ByRef Tensor S, - @ByRef Tensor Vh, - @Const @ByRef Tensor input, - @Cast("bool") boolean full_matrices, - @ByVal StringViewOptional driver); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor svdvals( - @Const @ByRef Tensor input, - @ByVal StringViewOptional driver); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor svdvals_out( - @ByRef Tensor result, - @Const @ByRef Tensor input, - @ByVal StringViewOptional driver); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor tensorinv(@Const @ByRef Tensor self, @Cast("int64_t") long ind); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor tensorinv_out(@ByRef Tensor result, @Const @ByRef Tensor self, @Cast("int64_t") long ind); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor tensorsolve( - @Const @ByRef Tensor self, - @Const @ByRef Tensor other, - @ByVal LongArrayRefOptional dims); -@Namespace("torch::linalg::detail") public static native @ByVal Tensor tensorsolve( - @Const @ByRef Tensor self, - @Const @ByRef Tensor other, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long... dims); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor tensorsolve_out( - @ByRef Tensor result, - @Const @ByRef Tensor self, - @Const @ByRef Tensor other, - @ByVal LongArrayRefOptional dims); -@Namespace("torch::linalg::detail") public static native @ByRef Tensor tensorsolve_out( - @ByRef Tensor result, - @Const @ByRef Tensor self, - @Const @ByRef Tensor other, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long... dims); - -@Namespace("torch::linalg::detail") public static native @ByVal Tensor inv(@Const @ByRef Tensor input); - -@Namespace("torch::linalg::detail") public static native @ByRef Tensor inv_out(@ByRef Tensor result, @Const @ByRef Tensor input); - - // namespace detail -// #endif /* DOXYGEN_SHOULD_SKIP_THIS */ - -/** Cholesky decomposition -/** -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.cholesky -/** -/** Example: -/** 
{@code
-/** auto A = torch::randn({4, 4});
-/** auto A = torch::matmul(A, A.t());
-/** auto L = torch::linalg::cholesky(A);
-/** assert(torch::allclose(torch::matmul(L, L.t()), A));
-/** }
*/ - -// C10_DEPRECATED_MESSAGE("linalg_det is deprecated, use det instead.") - -/** See the documentation of torch.linalg.det */ - -/** Computes the sign and (natural) logarithm of the determinant - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.slogdet */ - -/** Computes eigenvalues and eigenvectors of non-symmetric/non-hermitian - * matrices - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eig */ - -/** Computes eigenvalues of non-symmetric/non-hermitian matrices - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigvals */ - -/** Computes eigenvalues and eigenvectors - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigh */ - -/** Computes eigenvalues - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.eigvalsh */ - -/** Computes the product of Householder matrices - * - * See - * https://pytorch.org/docs/master/linalg.html#torch.linalg.householder_product */ - -/** Computes the matrix exponential - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_exp */ - -// C10_DEPRECATED_MESSAGE("linalg_norm is deprecated, use norm instead.") - -// C10_DEPRECATED_MESSAGE("linalg_norm is deprecated, use norm instead.") - -// C10_DEPRECATED_MESSAGE("linalg_norm_out is deprecated, use norm_out -// instead.") - -// C10_DEPRECATED_MESSAGE("linalg_norm_out is deprecated, use norm_out -// instead.") - -/** Computes the LU factorization with partial pivoting - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.lu_factor */ -@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T lu_factor( - @Const @ByRef Tensor input); +/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.multi_dot */ +@Namespace("torch::linalg") public static native @ByVal Tensor multi_dot(@ByVal TensorArrayRef tensors); +@Namespace("torch::linalg") public static native @ByVal Tensor multi_dot(@ByVal TensorVector tensors); /// -@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_factor_out( - @ByRef Tensor LU, - @ByRef Tensor pivots, - @Const @ByRef Tensor self); - -/** Computes the LU factorization with partial pivoting - * - * See https://pytorch.org/docs/master/linalg.html#torch.linalg.lu */ -@Namespace("torch::linalg") public static native @ByVal T_TensorTensorTensor_T lu( - @Const @ByRef Tensor input); - -@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer lu_out( - @ByRef Tensor P, - @ByRef Tensor L, - @ByRef Tensor U, - @Const @ByRef Tensor self); - -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.vector_norm */ - -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_norm */ - -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_power */ - -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.matrix_rank */ - -/** See https://pytorch.org/docs/master/linalg.html#torch.linalg.multi_dot */ +@Namespace("torch::linalg") public static native @ByRef Tensor multi_dot_out(@ByVal TensorArrayRef tensors, @ByRef Tensor result); +@Namespace("torch::linalg") public static native @ByRef Tensor multi_dot_out(@ByVal TensorVector tensors, @ByRef Tensor result); /** Computes the pseudo-inverse * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.pinv */ +@Namespace("torch::linalg") public static native @ByVal Tensor pinv( + @Const @ByRef Tensor input, + double rcond/*=1e-15*/, + @Cast("bool") boolean hermitian/*=false*/); @Namespace("torch::linalg") public static native @ByVal Tensor pinv( @Const @ByRef Tensor input); /// +@Namespace("torch::linalg") public static native @ByRef Tensor pinv_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + double rcond/*=1e-15*/, + @Cast("bool") boolean hermitian/*=false*/); @Namespace("torch::linalg") public static native @ByRef Tensor pinv_out( @ByRef Tensor result, @Const @ByRef Tensor input); @@ -67498,6 +67539,25 @@ The list of (type, depth) pairs controls the type of specializations and the num /** Computes the QR decomposition * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.qr */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T qr( + @Const @ByRef Tensor input, + @StringView BytePointer mode/*="reduced"*/); +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T qr( + @Const @ByRef Tensor input, + @StringView String mode/*="reduced"*/); + + +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer qr_out( + @ByRef Tensor Q, + @ByRef Tensor R, + @Const @ByRef Tensor input, + @StringView BytePointer mode/*="reduced"*/); +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer qr_out( + @ByRef Tensor Q, + @ByRef Tensor R, + @Const @ByRef Tensor input, + @StringView String mode/*="reduced"*/); /** Computes the LDL decomposition * @@ -67538,10 +67598,34 @@ The list of (type, depth) pairs controls the type of specializations and the num /** Solves a system linear system AX = B * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.solve_ex */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensor_T solve_ex( + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @Cast("bool") boolean left, + @Cast("bool") boolean check_errors); + + +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer solve_ex_out( + @ByRef Tensor result, + @ByRef Tensor info, + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @Cast("bool") boolean left, + @Cast("bool") boolean check_errors); /** Computes a tensor {@code x} such that {@code matmul(input, x) = other}. * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.solve */ +@Namespace("torch::linalg") public static native @ByVal Tensor solve(@Const @ByRef Tensor input, @Const @ByRef Tensor other, @Cast("bool") boolean left); + + +/// +@Namespace("torch::linalg") public static native @ByRef Tensor solve_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @Cast("bool") boolean left); /** Computes a solution of a linear system AX = B for input = A and other = B * whenever A is square upper or lower triangular and does not have zeros in @@ -67549,14 +67633,55 @@ The list of (type, depth) pairs controls the type of specializations and the num * * See * https://pytorch.org/docs/master/linalg.html#torch.linalg.solve_triangular */ +@Namespace("torch::linalg") public static native @ByVal Tensor solve_triangular( + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @Cast("bool") boolean upper, + @Cast("bool") boolean left, + @Cast("bool") boolean unitriangular); + + +/// +@Namespace("torch::linalg") public static native @ByRef Tensor solve_triangular_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @Cast("bool") boolean upper, + @Cast("bool") boolean left, + @Cast("bool") boolean unitriangular); /** Computes the singular values and singular vectors * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.svd */ +@Namespace("torch::linalg") public static native @ByVal T_TensorTensorTensor_T svd( + @Const @ByRef Tensor input, + @Cast("bool") boolean full_matrices, + @ByVal StringViewOptional driver); + + +/// +@Namespace("torch::linalg") public static native @ByVal @Cast("std::tuple*") PointerPointer svd_out( + @ByRef Tensor U, + @ByRef Tensor S, + @ByRef Tensor Vh, + @Const @ByRef Tensor input, + @Cast("bool") boolean full_matrices, + @ByVal StringViewOptional driver); /** Computes the singular values * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.svdvals */ +@Namespace("torch::linalg") public static native @ByVal Tensor svdvals( + @Const @ByRef Tensor input, + @ByVal StringViewOptional driver); + + +/// +/// +@Namespace("torch::linalg") public static native @ByRef Tensor svdvals_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + @ByVal StringViewOptional driver); /** Computes the inverse of a tensor * @@ -67568,6 +67693,12 @@ The list of (type, depth) pairs controls the type of specializations and the num * int64_t ind = 2; * auto ainv = torch::linalg::tensorinv(a, ind); * } */ +@Namespace("torch::linalg") public static native @ByVal Tensor tensorinv(@Const @ByRef Tensor self, @Cast("int64_t") long ind); + + +/// +/// +@Namespace("torch::linalg") public static native @ByRef Tensor tensorinv_out(@ByRef Tensor result, @Const @ByRef Tensor self, @Cast("int64_t") long ind); /** Computes a tensor {@code x} such that {@code tensordot(input, x, dims=x.dim()) = other}. * @@ -67579,11 +67710,35 @@ The list of (type, depth) pairs controls the type of specializations and the num * auto b = torch::randn(2*3, 4); * auto x = torch::linalg::tensorsolve(a, b); * } */ +@Namespace("torch::linalg") public static native @ByVal Tensor tensorsolve( + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @ByVal LongArrayRefOptional dims); +@Namespace("torch::linalg") public static native @ByVal Tensor tensorsolve( + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long... dims); + + +/// +@Namespace("torch::linalg") public static native @ByRef Tensor tensorsolve_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @ByVal LongArrayRefOptional dims); +@Namespace("torch::linalg") public static native @ByRef Tensor tensorsolve_out( + @ByRef Tensor result, + @Const @ByRef Tensor input, + @Const @ByRef Tensor other, + @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector long... dims); /** Computes a tensor {@code inverse_input} such that {@code dot(input, inverse_input) = * eye(input.size(0))}. * * See https://pytorch.org/docs/master/linalg.html#torch.linalg.inv */ +@Namespace("torch::linalg") public static native @ByVal Tensor inv(@Const @ByRef Tensor input); + +@Namespace("torch::linalg") public static native @ByRef Tensor inv_out(@ByRef Tensor result, @Const @ByRef Tensor input); // namespace linalg // namespace torch @@ -67962,6 +68117,7 @@ The list of (type, depth) pairs controls the type of specializations and the num // #pragma once // #include +// #include // #include // namespace at @@ -68208,6 +68364,7 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #include +// #include // #include // #include @@ -68329,9 +68486,6 @@ scalar_t sf(scalar_t x, scalar_t y) // namespace impl // namespace profiler // namespace torch - // namespace profiler - // namespace autograd - // namespace torch // Parsed from torch/csrc/autograd/profiler_kineto.h @@ -68349,8 +68503,7 @@ scalar_t sf(scalar_t x, scalar_t y) // namespace kineto - // namespace impl - // namespace profiler + // namespace profiler::impl // Consolidating events returned directly from Kineto // with events manually created by us (e.g. start/stop marks, @@ -68418,14 +68571,12 @@ scalar_t sf(scalar_t x, scalar_t y) @Const @ByRef ProfilerConfig config, @Const @ByRef ActivityTypeSet activities); - // namespace profiler - // namespace autograd + // namespace autograd::profiler // Experimental. @Namespace("torch::profiler::impl") public static native void _reportVulkanEventToProfiler(@ByVal @Cast("torch::profiler::impl::vulkan_id_t*") Pointer id); - // namespace impl - // namespace profiler + // namespace profiler::impl // namespace torch @@ -68801,6 +68952,9 @@ scalar_t sf(scalar_t x, scalar_t y) // Targeting ../FractionalMaxPool3dImplCloneable.java +// Targeting ../LPPool3dImplCloneable.java + + // Targeting ../RNNImplCloneable.java @@ -69000,16 +69154,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor batch_norm( - @Const @ByRef Tensor input, - @Const @ByRef Tensor running_mean, - @Const @ByRef Tensor running_var, - @ByVal Tensor weight, - @ByVal Tensor bias, - @Cast("bool") boolean training, - @ByVal DoubleOptional momentum, - double eps); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69229,20 +69373,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @StdString BytePointer padding_unwrap(@ByVal kValid arg0); - -@Namespace("torch::nn::functional::detail") public static native @StdString BytePointer padding_unwrap(@ByVal kSame arg0); - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv1d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @Const @ByRef Conv1dPadding padding, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer dilation, - @Cast("int64_t") long groups); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69257,21 +69387,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::conv1d(x, weight, F::Conv1dFuncOptions().stride(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor conv1d( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @Const @ByRef(nullValue = "torch::nn::functional::Conv1dFuncOptions{}") Conv1dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv2d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @Const @ByRef Conv2dPadding padding, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer dilation, - @Cast("int64_t") long groups); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69286,21 +69410,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::conv2d(x, weight, F::Conv2dFuncOptions().stride(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor conv2d( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @Const @ByRef(nullValue = "torch::nn::functional::Conv2dFuncOptions{}") Conv2dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv3d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer stride, - @Const @ByRef Conv3dPadding padding, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer dilation, - @Cast("int64_t") long groups); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69315,6 +69433,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::conv3d(x, weight, F::Conv3dFuncOptions().stride(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor conv3d( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @@ -69323,25 +69444,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose1d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal LongArrayRef stride, - @ByVal LongArrayRef padding, - @ByVal LongArrayRef output_padding, - @Cast("int64_t") long groups, - @ByVal LongArrayRef dilation); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose1d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] stride, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] padding, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] output_padding, - @Cast("int64_t") long groups, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... dilation); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69357,31 +69459,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::conv_transpose1d(x, weight, F::ConvTranspose1dFuncOptions().stride(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor conv_transpose1d( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @Const @ByRef(nullValue = "torch::nn::functional::ConvTranspose1dFuncOptions{}") ConvTranspose1dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose2d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal LongArrayRef stride, - @ByVal LongArrayRef padding, - @ByVal LongArrayRef output_padding, - @Cast("int64_t") long groups, - @ByVal LongArrayRef dilation); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose2d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] stride, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] padding, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] output_padding, - @Cast("int64_t") long groups, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... dilation); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69397,31 +69483,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::conv_transpose2d(x, weight, F::ConvTranspose2dFuncOptions().stride(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor conv_transpose2d( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @Const @ByRef(nullValue = "torch::nn::functional::ConvTranspose2dFuncOptions{}") ConvTranspose2dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose3d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal LongArrayRef stride, - @ByVal LongArrayRef padding, - @ByVal LongArrayRef output_padding, - @Cast("int64_t") long groups, - @ByVal LongArrayRef dilation); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor conv_transpose3d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] stride, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] padding, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] output_padding, - @Cast("int64_t") long groups, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long... dilation); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69495,7 +69565,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69512,6 +69581,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::cosine_similarity(input1, input2, /** F::CosineSimilarityFuncOptions().dim(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor cosine_similarity( @Const @ByRef Tensor x1, @Const @ByRef Tensor x2, @@ -69520,7 +69592,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69640,10 +69711,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor dropout(@ByVal Tensor input, double p, @Cast("bool") boolean training, @Cast("bool") boolean inplace); - - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69658,16 +69725,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::dropout(input, F::DropoutFuncOptions().p(0.5)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor dropout(@ByVal Tensor input, @Const @ByRef(nullValue = "torch::nn::functional::DropoutFuncOptions{}") DropoutFuncOptions options); @Namespace("torch::nn::functional") public static native @ByVal Tensor dropout(@ByVal Tensor input); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor dropout2d(@ByVal Tensor input, double p, @Cast("bool") boolean training, @Cast("bool") boolean inplace); - - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69682,6 +69748,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::dropout2d(input, F::Dropout2dFuncOptions().p(0.5)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor dropout2d( @ByVal Tensor input, @Cast("const torch::nn::functional::Dropout2dFuncOptions*") @ByRef(nullValue = "torch::nn::functional::Dropout2dFuncOptions{}") DropoutFuncOptions options); @@ -69691,10 +69760,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor dropout3d(@ByVal Tensor input, double p, @Cast("bool") boolean training, @Cast("bool") boolean inplace); - - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69709,6 +69774,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::dropout3d(input, F::Dropout3dFuncOptions().p(0.5)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor dropout3d( @ByVal Tensor input, @Cast("const torch::nn::functional::Dropout3dFuncOptions*") @ByRef(nullValue = "torch::nn::functional::Dropout3dFuncOptions{}") DropoutFuncOptions options); @@ -69718,14 +69786,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor alpha_dropout( - @ByVal Tensor input, - double p, - @Cast("bool") boolean training, - @Cast("bool") boolean inplace); - - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69741,6 +69801,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::alpha_dropout(input, /** F::AlphaDropoutFuncOptions().p(0.5).training(false)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor alpha_dropout( @ByVal Tensor input, @Const @ByRef(nullValue = "torch::nn::functional::AlphaDropoutFuncOptions{}") AlphaDropoutFuncOptions options); @@ -69750,14 +69813,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor feature_alpha_dropout( - @ByVal Tensor input, - double p, - @Cast("bool") boolean training, - @Cast("bool") boolean inplace); - - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69836,21 +69891,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native void _no_grad_embedding_renorm_( - @ByVal Tensor weight, - @Const @ByRef Tensor input, - float max_norm, - float norm_type); - -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor embedding( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @ByVal LongOptional padding_idx, - @ByVal DoubleOptional max_norm, - double norm_type, - @Cast("bool") boolean scale_grad_by_freq, - @Cast("bool") boolean sparse); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69866,25 +69906,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::embedding(input, weight, /** F::EmbeddingFuncOptions().norm_type(2.5).scale_grad_by_freq(true).sparse(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor embedding( @Const @ByRef Tensor input, @Const @ByRef Tensor weight, @Const @ByRef(nullValue = "torch::nn::functional::EmbeddingFuncOptions{}") EmbeddingFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor embedding_bag( - @Const @ByRef Tensor input, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor offsets, - @ByVal DoubleOptional max_norm, - double norm_type, - @Cast("bool") boolean scale_grad_by_freq, - @ByVal EmbeddingBagMode mode, - @Cast("bool") boolean sparse, - @Const @ByRef Tensor per_sample_weights, - @Cast("bool") boolean include_last_offset, - @ByVal LongOptional padding_idx); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69961,14 +69991,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor fold( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer output_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer dilation, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -69983,18 +70005,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::fold(input, F::FoldFuncOptions({3, 2}, {2, 2})); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor fold(@Const @ByRef Tensor input, @Cast("const torch::nn::functional::FoldFuncOptions*") @ByRef FoldOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor unfold( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer dilation, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70072,16 +70090,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor instance_norm( - @Const @ByRef Tensor input, - @Const @ByRef Tensor running_mean, - @Const @ByRef Tensor running_var, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - @Cast("bool") boolean use_input_stats, - double momentum, - double eps); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70428,8 +70436,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor elu(@ByVal Tensor input, double alpha, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70444,13 +70450,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::elu(x, F::ELUFuncOptions().alpha(0.42).inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor elu(@ByVal Tensor input, @Cast("const torch::nn::functional::ELUFuncOptions*") @ByRef(nullValue = "torch::nn::functional::ELUFuncOptions{}") ELUOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor selu(@ByVal Tensor input, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70465,13 +70472,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::selu(input, F::SELUFuncOptions(false)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor selu(@ByVal Tensor input, @Cast("const torch::nn::functional::SELUFuncOptions*") @ByRef(nullValue = "torch::nn::functional::SELUFuncOptions{}") SELUOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor hardshrink(@Const @ByRef Tensor input, double lambda); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70486,6 +70494,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::hardshrink(x, F::HardshrinkFuncOptions().lambda(0.42)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor hardshrink( @Const @ByRef Tensor input, @Cast("const torch::nn::functional::HardshrinkFuncOptions*") @ByRef(nullValue = "torch::nn::functional::HardshrinkFuncOptions{}") HardshrinkOptions options); @@ -70493,12 +70504,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor hardtanh( - @ByVal Tensor input, - double min_val, - double max_val, - @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70514,13 +70519,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::hardtanh(x, /** F::HardtanhFuncOptions().min_val(-1.0).max_val(1.0).inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor hardtanh(@ByVal Tensor input, @Cast("const torch::nn::functional::HardtanhFuncOptions*") @ByRef(nullValue = "torch::nn::functional::HardtanhFuncOptions{}") HardtanhOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor leaky_relu(@ByVal Tensor input, double negative_slope, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70542,17 +70548,14 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor logsigmoid(@Const @ByRef Tensor input); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor gumbel_softmax( - @Const @ByRef Tensor logits, - double tau, - @Cast("bool") boolean hard, - int dim); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70567,6 +70570,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::gumbel_softmax(logits, F::GumbelSoftmaxFuncOptions().hard(true).dim(-1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor gumbel_softmax( @Const @ByRef Tensor logits, @Const @ByRef(nullValue = "torch::nn::functional::GumbelSoftmaxFuncOptions{}") GumbelSoftmaxFuncOptions options); @@ -70576,7 +70582,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70591,16 +70596,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::softmax(input, F::SoftmaxFuncOptions(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor softmax(@Const @ByRef Tensor input, @Const @ByRef SoftmaxFuncOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor softmin( - @Const @ByRef Tensor input, - @Cast("int64_t") long dim, - @ByVal ScalarTypeOptional dtype); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70615,12 +70618,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::softmin(input, F::SoftminFuncOptions(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor softmin(@Const @ByRef Tensor input, @Const @ByRef SoftminFuncOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70635,6 +70640,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::log_softmax(input, LogSoftmaxFuncOptions(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor log_softmax( @Const @ByRef Tensor input, @Const @ByRef LogSoftmaxFuncOptions options); @@ -70642,7 +70650,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70662,7 +70669,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ @Namespace("torch::nn::functional") public static native @ByVal Tensor gelu(@Const @ByRef Tensor input, @Cast("const torch::nn::functional::GELUFuncOptions*") @ByRef(nullValue = "torch::nn::functional::GELUFuncOptions{}") GELUOptions options); @@ -70676,8 +70682,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor relu(@ByVal Tensor input, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70692,13 +70696,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::relu(x, F::ReLUFuncOptions().inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor relu(@ByVal Tensor input, @Cast("const torch::nn::functional::ReLUFuncOptions*") @ByRef(nullValue = "torch::nn::functional::ReLUFuncOptions{}") ReLUOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor relu6(@ByVal Tensor input, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70713,18 +70718,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::relu6(x, F::ReLU6FuncOptions().inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor relu6(@ByVal Tensor input, @Cast("const torch::nn::functional::ReLU6FuncOptions*") @ByRef(nullValue = "torch::nn::functional::ReLU6FuncOptions{}") ReLU6Options options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor rrelu( - @ByVal Tensor input, - double lower, - double upper, - @Cast("bool") boolean training, - @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70739,13 +70740,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::rrelu(x, F::RReLUFuncOptions().lower(0.1).upper(0.4).inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor rrelu(@ByVal Tensor input, @Const @ByRef(nullValue = "torch::nn::functional::RReLUFuncOptions{}") RReLUFuncOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor celu(@ByVal Tensor input, double alpha, @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70760,13 +70762,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::celu(x, F::CELUFuncOptions().alpha(0.42).inplace(true)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor celu(@ByVal Tensor input, @Cast("const torch::nn::functional::CELUFuncOptions*") @ByRef(nullValue = "torch::nn::functional::CELUFuncOptions{}") CELUOptions options); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor softplus(@Const @ByRef Tensor input, double beta, double threshold); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70781,6 +70784,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::softplus(x, F::SoftplusFuncOptions().beta(0.5).threshold(3.0)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor softplus( @Const @ByRef Tensor input, @Cast("const torch::nn::functional::SoftplusFuncOptions*") @ByRef(nullValue = "torch::nn::functional::SoftplusFuncOptions{}") SoftplusOptions options); @@ -70788,8 +70794,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor softshrink(@Const @ByRef Tensor input, double lambda); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70814,17 +70818,14 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor tanhshrink(@Const @ByRef Tensor input); // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor threshold( - @ByVal Tensor input, - double threshold, - double value, - @Cast("bool") boolean inplace); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -70844,46 +70845,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T multi_head_attention_forward( - @Const @ByRef Tensor query, - @Const @ByRef Tensor key, - @Const @ByRef Tensor value, - @Cast("int64_t") long embed_dim_to_check, - @Cast("int64_t") long num_heads, - @Const @ByRef Tensor in_proj_weight, - @Const @ByRef Tensor in_proj_bias, - @Const @ByRef Tensor bias_k, - @Const @ByRef Tensor bias_v, - @Cast("bool") boolean add_zero_attn, - double dropout_p, - @Const @ByRef Tensor out_proj_weight, - @Const @ByRef Tensor out_proj_bias, - @Cast("bool") boolean training/*=true*/, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor key_padding_mask, - @Cast("bool") boolean need_weights/*=true*/, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor attn_mask, - @Cast("bool") boolean use_separate_proj_weight/*=false*/, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor q_proj_weight, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor k_proj_weight, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor v_proj_weight, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor static_k, - @Const @ByRef(nullValue = "torch::Tensor{}") Tensor static_v, - @Cast("bool") boolean average_attn_weights/*=true*/); -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T multi_head_attention_forward( - @Const @ByRef Tensor query, - @Const @ByRef Tensor key, - @Const @ByRef Tensor value, - @Cast("int64_t") long embed_dim_to_check, - @Cast("int64_t") long num_heads, - @Const @ByRef Tensor in_proj_weight, - @Const @ByRef Tensor in_proj_bias, - @Const @ByRef Tensor bias_k, - @Const @ByRef Tensor bias_v, - @Cast("bool") boolean add_zero_attn, - double dropout_p, - @Const @ByRef Tensor out_proj_weight, - @Const @ByRef Tensor out_proj_bias); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T multi_head_attention_forward( @@ -71215,11 +71176,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor l1_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71234,6 +71190,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::l1_loss(input, target, F::L1LossFuncOptions(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor l1_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71242,16 +71201,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor kl_div( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal KLDivLossReduction reduction, - @Cast("bool") boolean log_target/*=false*/); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor kl_div( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal KLDivLossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71267,6 +71216,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::kl_div(input, target, /** F::KLDivFuncOptions.reduction(torch::kNone).log_target(false)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor kl_div( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71275,11 +71227,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor mse_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71294,6 +71241,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::mse_loss(input, target, F::MSELossFuncOptions(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor mse_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71302,12 +71252,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor binary_cross_entropy( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Const @ByRef Tensor weight, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71324,6 +71268,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::binary_cross_entropy(input, target, /** F::BinaryCrossEntropyFuncOptions().weight(weight)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor binary_cross_entropy( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71332,12 +71279,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor hinge_embedding_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - double margin, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71354,6 +71295,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::hinge_embedding_loss(input, target, /** F::HingeEmbeddingLossFuncOptions().margin(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor hinge_embedding_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71362,14 +71306,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor multi_margin_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Cast("int64_t") long p, - double margin, - @Const @ByRef Tensor weight, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71386,6 +71322,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::multi_margin_loss(input, target, /** F::MultiMarginLossFuncOptions().margin(2).weight(weight)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor multi_margin_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71394,13 +71333,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor cosine_embedding_loss( - @Const @ByRef Tensor input1, - @Const @ByRef Tensor input2, - @Const @ByRef Tensor target, - double margin, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71425,6 +71357,9 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor _smooth_l1_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71434,16 +71369,6 @@ scalar_t sf(scalar_t x, scalar_t y) @Const @ByRef Tensor target); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor smooth_l1_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction, - @ByVal(nullValue = "c10::optional(c10::nullopt)") DoubleOptional beta_opt); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor smooth_l1_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71474,6 +71399,9 @@ scalar_t sf(scalar_t x, scalar_t y) * namespace F = torch::nn::functional; * F::smooth_l1_loss(input, target, /*options=* /torch::kNone, /*beta=* /0.5); * } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor smooth_l1_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71483,16 +71411,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor huber_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction, - double delta/*=1.*/); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor huber_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71508,6 +71426,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::huber_loss(input, target, /** F::HuberLossFuncOptions().reduction(torch::kNone).delta(0.5)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor huber_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71516,11 +71437,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor multilabel_margin_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71537,6 +71453,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::multilabel_margin_loss(input, target, /** F::MultilabelMarginLossFuncOptions(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor multilabel_margin_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71545,11 +71464,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor soft_margin_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71565,6 +71479,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::soft_margin_loss(input, target, /** F::SoftMarginLossFuncOptions(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor soft_margin_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71573,12 +71490,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor multilabel_soft_margin_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Const @ByRef Tensor weight, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71595,6 +71506,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::multilabel_soft_margin_loss(input, target, /** F::MultilabelSoftMarginLossFuncOptions().reduction(torch::kNone).weight(weight)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor multilabel_soft_margin_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71606,16 +71520,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor triplet_margin_loss( - @Const @ByRef Tensor anchor, - @Const @ByRef Tensor positive, - @Const @ByRef Tensor negative, - double margin, - double p, - double eps, - @Cast("bool") boolean swap, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71632,6 +71536,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::triplet_margin_loss(anchor, positive, negative, /** F::TripletMarginLossFuncOptions().margin(1.0)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor triplet_margin_loss( @Const @ByRef Tensor anchor, @Const @ByRef Tensor positive, @@ -71641,15 +71548,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor triplet_margin_with_distance_loss( - @Const @ByRef Tensor anchor, - @Const @ByRef Tensor positive, - @Const @ByRef Tensor negative, - @ByVal @Cast("c10::optional*") Pointer distance_function, - double margin, - @Cast("bool") boolean swap, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71666,6 +71564,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::triplet_margin_with_distance_loss(anchor, positive, negative, /** F::TripletMarginWithDistanceLossFuncOptions().margin(1.0)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor triplet_margin_with_distance_loss( @Const @ByRef Tensor anchor, @Const @ByRef Tensor positive, @@ -71679,15 +71580,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor ctc_loss( - @Const @ByRef Tensor log_probs, - @Const @ByRef Tensor targets, - @Const @ByRef Tensor input_lengths, - @Const @ByRef Tensor target_lengths, - @Cast("int64_t") long blank, - @ByVal LossReduction reduction, - @Cast("bool") boolean zero_infinity); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71703,6 +71595,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::ctc_loss(log_probs, targets, input_lengths, target_lengths, /** F::CTCLossFuncOptions().reduction(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor ctc_loss( @Const @ByRef Tensor log_probs, @Const @ByRef Tensor targets, @@ -71713,14 +71608,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor poisson_nll_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Cast("bool") boolean log_input, - @Cast("bool") boolean full, - double eps, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71736,6 +71623,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::poisson_nll_loss(input, target, /** F::PoissonNLLLossFuncOptions().reduction(torch::kNone)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor poisson_nll_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71747,13 +71637,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor margin_ranking_loss( - @Const @ByRef Tensor input1, - @Const @ByRef Tensor input2, - @Const @ByRef Tensor target, - double margin, - @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71770,6 +71653,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::margin_ranking_loss(input1, input2, target, /** F::MarginRankingLossFuncOptions().margin(0.5).reduction(torch::kSum)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor margin_ranking_loss( @Const @ByRef Tensor input1, @Const @ByRef Tensor input2, @@ -71779,13 +71665,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor nll_loss( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Const @ByRef Tensor weight, - @Cast("int64_t") long ignore_index, - @Const @ByVal LossReduction reduction); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71801,6 +71680,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::nll_loss(input, target, /** F::NLLLossFuncOptions().ignore_index(-100).reduction(torch::kMean)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor nll_loss( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71809,14 +71691,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor cross_entropy( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Const @ByRef Tensor weight, - @Cast("int64_t") long ignore_index, - @ByVal LossReduction reduction, - double label_smoothing); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -71832,6 +71706,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::cross_entropy(input, target, /** F::CrossEntropyFuncOptions().ignore_index(-100).reduction(torch::kMean)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor cross_entropy( @Const @ByRef Tensor input, @Const @ByRef Tensor target, @@ -71843,13 +71720,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor binary_cross_entropy_with_logits( - @Const @ByRef Tensor input, - @Const @ByRef Tensor target, - @Const @ByRef Tensor weight, - @ByVal LossReduction reduction, - @Const @ByRef Tensor pos_weight); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72066,17 +71936,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor pad( - @Const @ByRef Tensor input, - @ByVal LongArrayRef pad, - @ByVal PaddingMode mode, - double value); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor pad( - @Const @ByRef Tensor input, - @ByVal @Cast({"int64_t*", "c10::ArrayRef", "std::vector&"}) @StdVector("int64_t") long[] pad, - @ByVal PaddingMode mode, - double value); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72549,6 +72408,16 @@ scalar_t sf(scalar_t x, scalar_t y) * LPPool2d model(LPPool2dOptions(1, std::vector({3, 4})).stride({5, * 6}).ceil_mode(true)); * } */ + +/// + +/** {@code LPPoolOptions} specialized for the {@code LPPool3d} module. + * + * Example: + * 
{@code
+ *  LPPool3d model(LPPool3dOptions(1, std::vector({3, 4, 5})).stride(
+ *  {5, 6, 7}).ceil_mode(true));
+ *  }
*/ /** Options for {@code torch::nn::functional::lp_pool1d}. * * See the documentation for {@code torch::nn::LPPool1dOptions} class to learn what @@ -72571,6 +72440,17 @@ scalar_t sf(scalar_t x, scalar_t y) * F::lp_pool2d(x, F::LPPool2dFuncOptions(2, {2, 3}).stride(2)); * } */ // namespace functional +/** Options for {@code torch::nn::functional::lp_pool3d}. + * + * See the documentation for {@code torch::nn::LPPool3dOptions} class to learn what + * arguments are supported. + * + * Example: + * 
{@code
+ *  namespace F = torch::nn::functional;
+ *  F::lp_pool3d(x, F::LPPool3dFuncOptions(2, {2, 3, 4}).stride(2));
+ *  }
*/ + // namespace functional // namespace nn // namespace torch @@ -72586,14 +72466,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor avg_pool1d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer padding, - @Cast("bool") boolean ceil_mode, - @Cast("bool") boolean count_include_pad); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72608,20 +72480,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::avg_pool1d(x, F::AvgPool1dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor avg_pool1d( @Const @ByRef Tensor input, @Const @ByRef AvgPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor avg_pool2d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @Cast("bool") boolean ceil_mode, - @Cast("bool") boolean count_include_pad, - @ByVal LongOptional divisor_override); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72636,20 +72502,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::avg_pool2d(x, F::AvgPool2dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor avg_pool2d( @Const @ByRef Tensor input, @Const @ByRef AvgPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor avg_pool3d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer padding, - @Cast("bool") boolean ceil_mode, - @Cast("bool") boolean count_include_pad, - @ByVal LongOptional divisor_override); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72664,6 +72524,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::avg_pool3d(x, F::AvgPool3dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor avg_pool3d( @Const @ByRef Tensor input, @Const @ByRef AvgPool3dOptions options); @@ -72671,14 +72534,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_pool1d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72693,19 +72548,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool1d(x, F::MaxPool1dFuncOptions(3).stride(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_pool1d( @Const @ByRef Tensor input, @Const @ByRef MaxPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T max_pool1d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for {@code torch::nn::functional::MaxPool1dFuncOptions} @@ -72716,19 +72565,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool1d_with_indices(x, F::MaxPool1dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T max_pool1d_with_indices( @Const @ByRef Tensor input, @Const @ByRef MaxPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_pool2d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72743,19 +72587,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool2d(x, F::MaxPool2dFuncOptions(3).stride(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_pool2d( @Const @ByRef Tensor input, @Const @ByRef MaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T max_pool2d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for {@code torch::nn::functional::MaxPool2dFuncOptions} @@ -72766,19 +72604,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool2d_with_indices(x, F::MaxPool2dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T max_pool2d_with_indices( @Const @ByRef Tensor input, @Const @ByRef MaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_pool3d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72793,19 +72626,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool3d(x, F::MaxPool3dFuncOptions(3).stride(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_pool3d( @Const @ByRef Tensor input, @Const @ByRef MaxPool3dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T max_pool3d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer padding, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer dilation, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for {@code torch::nn::functional::MaxPool3dFuncOptions} @@ -72816,6 +72643,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_pool3d_with_indices(x, F::MaxPool3dFuncOptions(3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T max_pool3d_with_indices( @Const @ByRef Tensor input, @Const @ByRef MaxPool3dOptions options); @@ -72823,27 +72653,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T adaptive_max_pool1d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer output_size); - // namespace detail - -/** See the documentation for - * {@code torch::nn::functional::AdaptiveMaxPool1dFuncOptions} class to learn what - * optional arguments are supported for this functional. - * - * Example: - * 
{@code
- *  namespace F = torch::nn::functional;
- *  F::adaptive_max_pool1d_with_indices(x, F::AdaptiveMaxPool1dFuncOptions(3));
- *  }
*/ -@Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T adaptive_max_pool1d_with_indices( - @Const @ByRef Tensor input, - @Const @ByRef AdaptiveMaxPool1dOptions options); -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_max_pool1d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72859,15 +72668,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_max_pool1d(x, F::AdaptiveMaxPool1dFuncOptions(3)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor adaptive_max_pool1d( @Const @ByRef Tensor input, @Const @ByRef AdaptiveMaxPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T adaptive_max_pool2d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<2>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -72879,15 +72686,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_max_pool2d_with_indices(x, F::AdaptiveMaxPool2dFuncOptions(3)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T adaptive_max_pool2d_with_indices( @Const @ByRef Tensor input, @Const @ByRef AdaptiveMaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_max_pool2d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<2>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72903,15 +72709,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_max_pool2d(x, F::AdaptiveMaxPool2dFuncOptions(3)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor adaptive_max_pool2d( @Const @ByRef Tensor input, @Const @ByRef AdaptiveMaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T adaptive_max_pool3d_with_indices( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<3>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -72923,15 +72727,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_max_pool3d_with_indices(x, F::AdaptiveMaxPool3dFuncOptions(3)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T adaptive_max_pool3d_with_indices( @Const @ByRef Tensor input, @Const @ByRef AdaptiveMaxPool3dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_max_pool3d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<3>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72947,6 +72750,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_max_pool3d(x, F::AdaptiveMaxPool3dFuncOptions(3)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor adaptive_max_pool3d( @Const @ByRef Tensor input, @Const @ByRef AdaptiveMaxPool3dOptions options); @@ -72954,10 +72760,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_avg_pool1d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72973,15 +72775,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_avg_pool1d(x, F::AdaptiveAvgPool1dFuncOptions(3)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor adaptive_avg_pool1d( @Const @ByRef Tensor input, @Const @ByRef AdaptiveAvgPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_avg_pool2d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<2>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -72997,15 +72798,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::adaptive_avg_pool2d(x, F::AdaptiveAvgPool2dFuncOptions(3)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor adaptive_avg_pool2d( @Const @ByRef Tensor input, @Const @ByRef AdaptiveAvgPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor adaptive_avg_pool3d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArrayWithOptionalElem<3>*") LongOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73027,6 +72827,9 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal @Cast("std::vector*") LongVector _unpool_output_size( @Const @ByRef Tensor input, @Const @ByRef LongArrayRef kernel_size, @@ -73041,14 +72844,6 @@ scalar_t sf(scalar_t x, scalar_t y) @Const @ByRef LongVectorOptional output_size); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_unpool1d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor indices, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer padding, - @Const @ByRef LongVectorOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73064,20 +72859,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::max_unpool1d(x, indices, /** F::MaxUnpool1dFuncOptions(3).stride(2).padding(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_unpool1d( @Const @ByRef Tensor input, @Const @ByRef Tensor indices, @Const @ByRef MaxUnpool1dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_unpool2d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor indices, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer padding, - @Const @ByRef LongVectorOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73093,20 +72883,15 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::max_unpool2d(x, indices, /** F::MaxUnpool2dFuncOptions(3).stride(2).padding(1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_unpool2d( @Const @ByRef Tensor input, @Const @ByRef Tensor indices, @Const @ByRef MaxUnpool2dFuncOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor max_unpool3d( - @Const @ByRef Tensor input, - @Const @ByRef Tensor indices, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer stride, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer padding, - @Const @ByRef LongVectorOptional output_size); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73121,6 +72906,8 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::max_unpool3d(x, indices, F::MaxUnpool3dFuncOptions(3)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor max_unpool3d( @Const @ByRef Tensor input, @Const @ByRef Tensor indices, @@ -73129,13 +72916,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T fractional_max_pool2d_with_indices( - @Const @ByRef Tensor input, - @Cast("const torch::ExpandingArray<2>*") @ByRef LongPointer kernel_size, - @Cast("const c10::optional >*") @ByRef LongExpandingArrayOptional output_size, - @Cast("const c10::optional >*") @ByRef DoubleExpandingArrayOptional output_ratio, - @Const @ByRef Tensor _random_samples); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -73148,18 +72928,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::fractional_max_pool2d_with_indices(x, /** F::FractionalMaxPool2dFuncOptions(3).output_size(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T fractional_max_pool2d_with_indices( @Const @ByRef Tensor input, @Const @ByRef FractionalMaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor fractional_max_pool2d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("c10::optional >*") LongExpandingArrayOptional output_size, - @ByVal @Cast("c10::optional >*") DoubleExpandingArrayOptional output_ratio, - @Const @ByRef Tensor _random_samples); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -73172,18 +72947,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::fractional_max_pool2d(x, /** F::FractionalMaxPool2dFuncOptions(3).output_size(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor fractional_max_pool2d( @Const @ByRef Tensor input, @Const @ByRef FractionalMaxPool2dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal T_TensorTensor_T fractional_max_pool3d_with_indices( - @Const @ByRef Tensor input, - @Cast("const torch::ExpandingArray<3>*") @ByRef LongPointer kernel_size, - @Cast("const c10::optional >*") @ByRef LongExpandingArrayOptional output_size, - @Cast("const c10::optional >*") @ByRef DoubleExpandingArrayOptional output_ratio, - @Const @ByRef Tensor _random_samples); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -73196,18 +72966,13 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::fractional_max_pool3d_with_indices(x, /** F::FractionalMaxPool3dFuncOptions(3).output_size(2)); /** } */ + +/// @Namespace("torch::nn::functional") public static native @ByVal T_TensorTensor_T fractional_max_pool3d_with_indices( @Const @ByRef Tensor input, @Const @ByRef FractionalMaxPool3dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor fractional_max_pool3d( - @Const @ByRef Tensor input, - @ByVal @Cast("torch::ExpandingArray<3>*") LongPointer kernel_size, - @ByVal @Cast("c10::optional >*") LongExpandingArrayOptional output_size, - @ByVal @Cast("c10::optional >*") DoubleExpandingArrayOptional output_ratio, - @Const @ByRef Tensor _random_samples); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See the documentation for @@ -73220,6 +72985,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** F::fractional_max_pool3d(x, /** F::FractionalMaxPool3dFuncOptions(3).output_size(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor fractional_max_pool3d( @Const @ByRef Tensor input, @Const @ByRef FractionalMaxPool3dOptions options); @@ -73227,13 +72995,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor lp_pool1d( - @Const @ByRef Tensor input, - double norm_type, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<1>*") LongPointer stride, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73248,18 +73009,14 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::lp_pool1d(x, F::LPPool1dFuncOptions(2, 3).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor lp_pool1d( @Const @ByRef Tensor input, @Const @ByRef LPPool1dOptions options); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor lp_pool2d( - @Const @ByRef Tensor input, - double norm_type, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer kernel_size, - @ByVal @Cast("torch::ExpandingArray<2>*") LongPointer stride, - @Cast("bool") boolean ceil_mode); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73274,10 +73031,32 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::lp_pool2d(x, F::LPPool2dFuncOptions(2, {2, 3}).stride(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor lp_pool2d( @Const @ByRef Tensor input, @Const @ByRef LPPool2dOptions options); +// #ifndef DOXYGEN_SHOULD_SKIP_THIS +// #endif /* DOXYGEN_SHOULD_SKIP_THIS */ + +/** See +/** https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.lp_pool3d +/** about the exact behavior of this functional. +/** +/** See the documentation for {@code torch::nn::functional::LPPool3dFuncOptions} class +/** to learn what optional arguments are supported for this functional. +/** +/** Example: +/** 
{@code
+/** namespace F = torch::nn::functional;
+/** F::lp_pool3d(x, F::LPPool3dFuncOptions(3, {3, 3, 5}).stride(3));
+/** }
*/ +@Namespace("torch::nn::functional") public static native @ByVal Tensor lp_pool3d( + @Const @ByRef Tensor input, + @Const @ByRef LPPool3dOptions options); + // namespace functional // namespace nn // namespace torch @@ -73350,13 +73129,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor normalize( - @Const @ByRef Tensor input, - double p, - @Cast("int64_t") long dim, - double eps, - @ByVal TensorOptional out); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73371,6 +73143,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::normalize(input, F::NormalizeFuncOptions().p(1).dim(-1)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor normalize( @Const @ByRef Tensor input, @ByVal(nullValue = "torch::nn::functional::NormalizeFuncOptions{}") NormalizeFuncOptions options); @@ -73380,13 +73155,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor layer_norm( - @Const @ByRef Tensor input, - @Cast("const std::vector*") @ByRef LongVector normalized_shape, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - double eps); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73401,6 +73169,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::layer_norm(input, F::LayerNormFuncOptions({2, 2}).eps(2e-5)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor layer_norm( @Const @ByRef Tensor input, @Const @ByRef LayerNormFuncOptions options); @@ -73408,13 +73179,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor local_response_norm( - @Const @ByRef Tensor input, - @Cast("int64_t") long size, - double alpha, - double beta, - double k); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73430,6 +73194,9 @@ scalar_t sf(scalar_t x, scalar_t y) /** namespace F = torch::nn::functional; /** F::local_response_norm(x, F::LocalResponseNormFuncOptions(2)); /** } */ + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor local_response_norm( @Const @ByRef Tensor input, @Cast("const torch::nn::functional::LocalResponseNormFuncOptions*") @ByRef LocalResponseNormOptions options); @@ -73437,13 +73204,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor group_norm( - @Const @ByRef Tensor input, - @Cast("int64_t") long num_groups, - @Const @ByRef Tensor weight, - @Const @ByRef Tensor bias, - double eps); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73517,7 +73277,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73580,20 +73339,14 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #include + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal @Cast("std::vector*") LongVector _interp_output_size( @Cast("int64_t") long dim, @ByVal @Cast("std::tuple >,c10::optional >,c10::optional >*") Pointer closed_over_args); // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor interpolate( - @Const @ByRef Tensor input, - @Const @ByRef LongVectorOptional size, - @Const @ByRef DoubleVectorOptional scale_factor, - @ByVal InterpolateMode mode, - @ByVal BoolOptional align_corners, - @ByVal BoolOptional recompute_scale_factor, - @Cast("bool") boolean antialias); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -73644,6 +73397,9 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #include + +/// +/// @Namespace("torch::nn::functional") public static native @ByVal Tensor affine_grid( @Const @ByRef Tensor theta, @Const @ByRef LongArrayRef size, @@ -73662,13 +73418,6 @@ scalar_t sf(scalar_t x, scalar_t y) // ============================================================================ // #ifndef DOXYGEN_SHOULD_SKIP_THIS -@Namespace("torch::nn::functional::detail") public static native @ByVal Tensor grid_sample( - @Const @ByRef Tensor input, - @Const @ByRef Tensor grid, - @ByVal GridSampleMode mode, - @ByVal GridSamplePaddingMode padding_mode, - @ByVal BoolOptional align_corners); - // namespace detail // #endif /* DOXYGEN_SHOULD_SKIP_THIS */ /** See @@ -75607,6 +75356,9 @@ scalar_t sf(scalar_t x, scalar_t y) // Targeting ../LPPool2dImplBase.java +// Targeting ../LPPool3dImplBase.java + + // Targeting ../LPPool1dImpl.java @@ -75625,6 +75377,15 @@ scalar_t sf(scalar_t x, scalar_t y) * provides, and examples of how to use {@code LPPool2d} with * {@code torch::nn::LPPool2dOptions}. See the documentation for {@code ModuleHolder} to * learn about PyTorch's module storage semantics. */ +// Targeting ../LPPool3dImpl.java + + + +/** A {@code ModuleHolder} subclass for {@code LPPool3dImpl}. + * See the documentation for {@code LPPool3dImpl} class to learn what methods it + * provides, and examples of how to use {@code LPPool3d} with + * {@code torch::nn::LPPool3dOptions}. See the documentation for {@code ModuleHolder} to + * learn about PyTorch's module storage semantics. */ // namespace nn // namespace torch @@ -76335,12 +76096,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include // #include // #include - -// Forward declarations confuse Doxygen -// #ifndef DOXYGEN_SHOULD_SKIP_THIS - // namespace at - // namespace serialize - // Targeting ../OptimizerParamState.java @@ -77899,14 +77654,41 @@ scalar_t sf(scalar_t x, scalar_t y) public static final int TORCH_VERSION_MAJOR = 2; /** Indicates the minor version of LibTorch. */ -public static final int TORCH_VERSION_MINOR = 2; +public static final int TORCH_VERSION_MINOR = 3; /** Indicates the patch version of LibTorch. */ -public static final int TORCH_VERSION_PATCH = 2; +public static final int TORCH_VERSION_PATCH = 0; /** Indicates the version of LibTorch. */ public static final String TORCH_VERSION = - "2.2.2"; + "2.3.0"; + + +// Parsed from torch/csrc/api/include/torch/xpu.h + +// #pragma once + +// #include + +// #include +// #include + +/** Returns the number of XPU devices available. */ +@Namespace("torch::xpu") public static native @Cast("size_t") @Name("device_count") long xpu_device_count(); + +/** Returns true if at least one XPU device is available. */ +@Namespace("torch::xpu") public static native @Cast("bool") @Name("is_available") boolean xpu_is_available(); + +/** Sets the seed for the current GPU. */ +@Namespace("torch::xpu") public static native @Name("manual_seed") void xpu_manual_seed(@Cast("uint64_t") long seed); + +/** Sets the seed for all available GPUs. */ +@Namespace("torch::xpu") public static native @Name("manual_seed_all") void xpu_manual_seed_all(@Cast("uint64_t") long seed); + +/** Waits for all kernels in all streams on a XPU device to complete. */ +@Namespace("torch::xpu") public static native @Name("synchronize") void xpu_synchronize(@Cast("int64_t") long device_index); + + // namespace torch::xpu // Parsed from torch/csrc/autograd/InferenceMode.h @@ -77917,7 +77699,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #include - // namespace torch // Parsed from caffe2/serialize/read_adapter_interface.h @@ -78315,7 +78096,6 @@ scalar_t sf(scalar_t x, scalar_t y) // #pragma once // #include -// #include // #include // #include // #include @@ -79695,10 +79475,45 @@ scalar_t sf(scalar_t x, scalar_t y) @ByVal(nullValue = "c10::ArrayRef{}") TensorArrayRef tensor_table, TypeParser type_parser/*=torch::jit::Unpickler::defaultTypeParser*/); +// #ifndef C10_MOBILE +// #endif // namespace jit // namespace torch +// Parsed from torch/csrc/inductor/aoti_runner/model_container_runner.h + +// #if !defined(C10_MOBILE) && !defined(ANDROID) +// #pragma once + +// #include +// #include +// Targeting ../DynamicLibrary.java + + + +// Targeting ../AOTIModelContainerRunner.java + + + + // namespace torch::inductor +// #endif + + +// Parsed from torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h + +// #if !defined(C10_MOBILE) && !defined(ANDROID) +// #pragma once + +// #include +// Targeting ../AOTIModelContainerRunnerCpu.java + + + + // namespace torch::inductor +// #endif + + // Parsed from datasets.h /* diff --git a/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch_cuda.java b/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch_cuda.java index 82a41460be3..d2b49d9ed6c 100644 --- a/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch_cuda.java +++ b/pytorch/src/gen/java/org/bytedeco/pytorch/global/torch_cuda.java @@ -61,12 +61,18 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #pragma once +// #include // #include // #include // #include // #include +// #include +// #include +// #include // #include +// #include +// #include // #include // Targeting ../cuda/CUDAStreamArrayRef.java @@ -115,6 +121,12 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include // #include +// cublasLT was introduced in CUDA 10.1 but we enable only for 11.1 that also +// added bf16 support +// #if (!defined(USE_ROCM) && !defined(_MSC_VER)) || (defined(USE_ROCM) && ROCM_VERSION >= 50700) +// #include +// #endif + // #ifdef CUDART_VERSION // #include // #endif @@ -164,17 +176,20 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { @Namespace("at::cuda") public static native int warp_size(); -@Namespace("at::cuda") public static native Pointer getDeviceProperties(@Cast("int64_t") long device); +@Namespace("at::cuda") public static native Pointer getDeviceProperties(byte device); @Namespace("at::cuda") public static native @Cast("bool") boolean canDeviceAccessPeer( - @Cast("int64_t") long device, - @Cast("int64_t") long peer_device); + byte device, + byte peer_device); @Namespace("at::cuda") public static native Allocator getCUDADeviceAllocator(); /* Handles */ @Namespace("at::cuda") public static native @Cast("cusparseHandle_t") Pointer getCurrentCUDASparseHandle(); @Namespace("at::cuda") public static native @Cast("cublasHandle_t") Pointer getCurrentCUDABlasHandle(); +// #if (!defined(USE_ROCM) && !defined(_MSC_VER)) || (defined(USE_ROCM) && ROCM_VERSION >= 50700) + +// #endif @Namespace("at::cuda") public static native void clearCublasWorkspaces(); @@ -305,8 +320,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { @Namespace("c10::cuda") public static native @Cast("std::ostream*") @ByRef @Name("operator <<") Pointer shiftLeft(@Cast("std::ostream*") @ByRef Pointer stream, @Const @ByRef CUDAStream s); - // namespace cuda - // namespace c10 + // namespace c10::cuda // namespace std @@ -329,8 +343,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include - // namespace impl - // namespace c10 + // namespace c10::impl // Parsed from c10/cuda/CUDADeviceAssertionHost.h @@ -339,9 +352,11 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include +// #include // #include // #include // #include +// #include // #include // #ifdef USE_CUDA @@ -367,8 +382,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { - // namespace cuda - // namespace c10 + // namespace c10::cuda // Each kernel launched with TORCH_DSA_KERNEL_LAUNCH // requires the same input arguments. We introduce the following macro to @@ -418,9 +432,15 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #endif /** - * The maximum number of GPUs that we recognizes. - */ + * The maximum number of GPUs that we recognizes. Increasing this beyond the + * initial limit of 16 broke Caffe2 testing, hence the ifdef guards. + * This value cannot be more than 128 because our DeviceIndex is a uint8_t. +o */ +// #ifdef FBCODE_CAFFE2 +// fbcode depends on this value being 16 public static final int C10_COMPILE_TIME_MAX_GPUS = 16; +// #else +// #endif // Parsed from c10/cuda/impl/cuda_cmake_macros.h @@ -443,8 +463,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include @Namespace("c10::cuda") public static native @NoException(true) @Cast("const char*") BytePointer get_cuda_check_suffix(); - // namespace cuda - // namespace c10 + // namespace c10::cuda // Parsed from c10/cuda/CUDAException.h @@ -540,8 +559,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { int line_number, @Cast("const bool") boolean include_device_assertions); - // namespace cuda - // namespace c10 + // namespace c10::cuda // Parsed from c10/cuda/CUDAFunctions.h @@ -585,17 +603,17 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { @Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDeviceCount(IntBuffer dev_count); @Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDeviceCount(int[] dev_count); -@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(IntPointer device); -@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(IntBuffer device); -@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(int[] device); +@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(BytePointer device); +@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(ByteBuffer device); +@Namespace("c10::cuda") public static native @Cast("cudaError_t") int GetDevice(byte[] device); -@Namespace("c10::cuda") public static native @Cast("cudaError_t") int SetDevice(int device); +@Namespace("c10::cuda") public static native @Cast("cudaError_t") int SetDevice(byte device); -@Namespace("c10::cuda") public static native @Cast("cudaError_t") int MaybeSetDevice(int device); +@Namespace("c10::cuda") public static native @Cast("cudaError_t") int MaybeSetDevice(byte device); -@Namespace("c10::cuda") public static native int ExchangeDevice(int device); +@Namespace("c10::cuda") public static native byte ExchangeDevice(byte device); -@Namespace("c10::cuda") public static native int MaybeExchangeDevice(int device); +@Namespace("c10::cuda") public static native byte MaybeExchangeDevice(byte device); @Namespace("c10::cuda") public static native void SetTargetDevice(); @@ -626,8 +644,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { @Namespace("c10::cuda") public static native @Cast("bool") boolean hasPrimaryContext(byte device_index); @Namespace("c10::cuda") public static native @ByVal ByteOptional getDeviceIndexWithPrimaryContext(); - // namespace cuda - // namespace c10 + // namespace c10::cuda // Parsed from ATen/cuda/Exceptions.h @@ -651,6 +668,15 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // namespace c10 +// #define AT_CUDNN_FRONTEND_CHECK(EXPR, ...) +// do { +// auto error_object = EXPR; +// if (!error_object.is_good()) { +// TORCH_CHECK_WITH(CuDNNError, false, +// "cuDNN Frontend error: ", error_object.get_message()); +// } +// } while (0) + // #define AT_CUDNN_CHECK_WITH_SHAPES(EXPR, ...) AT_CUDNN_CHECK(EXPR, "\n", ##__VA_ARGS__) // See Note [CHECK macro] @@ -853,6 +879,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #pragma once // #include +// #include // #include // CUDA Graphs utils used by c10 and aten. @@ -877,8 +904,7 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // Use this version where you're sure a CUDA context exists already. @Namespace("c10::cuda") public static native @Cast("c10::cuda::CaptureStatus") int currentStreamCaptureStatusMayInitCtx(); - // namespace cuda - // namespace c10 + // namespace c10::cuda // Parsed from c10/util/ApproximateClock.h @@ -887,21 +913,15 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #pragma once +// #include +// #include // #include // #include // #include -// #include -// #include -// #include -// #include - -// #include -// #include -// #include - -// #ifndef _WIN32 // #include -// #endif +// #include +// #include + // #if defined(C10_IOS) && defined(C10_MOBILE) // #include // for gettimeofday() // #endif @@ -945,17 +965,22 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #pragma once // #include -// #include // #include // #include // #include // #include +// #include // #include // #include -// #include -// #include +// #include +// #include +// #include +// #include +// #include +// #include // #include +// #include // Caching allocator will execute every registered callback if it unable to find // block inside of already allocated area. @@ -963,7 +988,8 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #define REGISTER_FREE_MEMORY_CALLBACK(name, ...) // C10_REGISTER_CLASS(FreeCudaMemoryCallbacksRegistry, name, __VA_ARGS__); - + // namespace c10 + // // TODO: Turn this into an honest to goodness class. I briefly attempted to do // this, but it was a bit irritating to figure out how to also correctly // apply pimpl pattern so I didn't have to leak any internal implementation @@ -1012,6 +1038,9 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // Targeting ../cuda/TraceEntry.java +// Targeting ../cuda/AllocatorConfigInfo.java + + // Targeting ../cuda/SnapshotInfo.java @@ -1079,9 +1108,12 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // CUDAGraph interactions +@Namespace("c10::cuda::CUDACachingAllocator") public static native void beginAllocateToPool( + byte device, + @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id, + @ByVal StreamFilter filter); - - +@Namespace("c10::cuda::CUDACachingAllocator") public static native void endAllocateToPool(byte device, @ByVal @Cast("c10::cuda::MempoolId_t*") DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair mempool_id); @@ -1103,16 +1135,13 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { - // namespace CUDACachingAllocator - // namespace cuda - // namespace c10 + // namespace c10::cuda::CUDACachingAllocator // Parsed from c10/cuda/impl/CUDAGuardImpl.h // #pragma once -// #include // #include // #include // #include @@ -1123,11 +1152,15 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include // #include +// #include +// #include +// #include +// #include +// #include // #include +// #include - // namespace impl - // namespace cuda - // namespace c10 + // namespace c10::cuda::impl // Parsed from ATen/cudnn/Descriptors.h @@ -1152,6 +1185,10 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // #include // #endif +// #if defined(CUDNN_VERSION) && CUDNN_VERSION >= 8907 +// #define USE_CUDNN_RNN_V8_API +// #endif + // TODO: Add constructors for all of the descriptors @@ -1168,6 +1205,9 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { // that the stride for dim i is the product of the sizes of dims // i+1 to the end. This stride is indeed uniquely determined. This // function modifies 'stride' in place so this invariant holds. +// Targeting ../cuda/RNNDataDescriptor.java + + // Targeting ../cuda/TensorDescriptor.java @@ -1245,8 +1285,22 @@ public class torch_cuda extends org.bytedeco.pytorch.presets.torch_cuda { - // namespace cuda - // namespace c10 + // namespace c10::cuda + + +// Parsed from torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h + +// #if !defined(C10_MOBILE) && !defined(ANDROID) +// #pragma once + +// #include +// #include +// Targeting ../cuda/AOTIModelContainerRunnerCuda.java + + + + // namespace torch::inductor +// #endif } diff --git a/pytorch/src/main/java/org/bytedeco/pytorch/cuda/functions/StreamFilter.java b/pytorch/src/main/java/org/bytedeco/pytorch/cuda/functions/StreamFilter.java new file mode 100644 index 00000000000..92bbd21c28b --- /dev/null +++ b/pytorch/src/main/java/org/bytedeco/pytorch/cuda/functions/StreamFilter.java @@ -0,0 +1,29 @@ +package org.bytedeco.pytorch.cuda.functions; + +import org.bytedeco.javacpp.FunctionPointer; +import org.bytedeco.javacpp.Loader; +import org.bytedeco.javacpp.Pointer; +import org.bytedeco.javacpp.annotation.Properties; + +@Properties(inherit = org.bytedeco.pytorch.presets.torch_cuda.class) +public class StreamFilter extends FunctionPointer { + static { + Loader.load(); + } + + /** + * Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. + */ + public StreamFilter(Pointer p) { + super(p); + } + + protected StreamFilter() { + allocate(); + } + + private native void allocate(); + + // std::function + public native boolean call(Pointer stream); +} diff --git a/pytorch/src/main/java/org/bytedeco/pytorch/functions/StorageImplCreateHelper.java b/pytorch/src/main/java/org/bytedeco/pytorch/functions/StorageImplCreateHelper.java deleted file mode 100644 index f9f6f8d5f68..00000000000 --- a/pytorch/src/main/java/org/bytedeco/pytorch/functions/StorageImplCreateHelper.java +++ /dev/null @@ -1,34 +0,0 @@ -package org.bytedeco.pytorch.functions; - -import org.bytedeco.javacpp.FunctionPointer; -import org.bytedeco.javacpp.Loader; -import org.bytedeco.javacpp.Pointer; -import org.bytedeco.javacpp.annotation.*; -import org.bytedeco.pytorch.*; -import org.bytedeco.pytorch.Allocator; - -@Properties(inherit = org.bytedeco.pytorch.presets.torch.class) -public class StorageImplCreateHelper extends FunctionPointer { - static { - Loader.load(); - } - - /** - * Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. - */ - public StorageImplCreateHelper(Pointer p) { - super(p); - } - - protected StorageImplCreateHelper() { - allocate(); - } - - private native void allocate(); - - public native @ByVal StorageImplPtr call( - @ByVal StorageImpl.use_byte_size_t arg0, - @ByVal SymInt size_bytes, - Allocator allocator, - @Cast("bool") boolean resizable); -} diff --git a/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch.java b/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch.java index 184461d2261..be747c42a3a 100644 --- a/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch.java +++ b/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch.java @@ -42,9 +42,11 @@ import org.bytedeco.javacpp.annotation.Platform; import org.bytedeco.javacpp.annotation.Properties; +import org.bytedeco.javacpp.tools.BuildEnabled; import org.bytedeco.javacpp.tools.Info; import org.bytedeco.javacpp.tools.InfoMap; import org.bytedeco.javacpp.tools.InfoMapper; +import org.bytedeco.javacpp.tools.Logger; import org.bytedeco.openblas.presets.openblas; @@ -61,8 +63,7 @@ include = { "torch/torch.h", "torch/script.h", - // Doesn't compile on Windows. Waiting for 2.3. - //"torch/csrc/inductor/aoti_model_container_runner.h", + "torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h", // For inclusion in JNI only, not parsed (compiler needs some complete definitions) "torch/csrc/jit/runtime/instruction.h", @@ -100,7 +101,7 @@ target = "org.bytedeco.pytorch", global = "org.bytedeco.pytorch.global.torch" ) -public class torch implements LoadEnabled, InfoMapper { +public class torch implements LoadEnabled, InfoMapper, BuildEnabled { static { Loader.checkVersion("org.bytedeco", "pytorch"); } @@ -130,6 +131,8 @@ static void initIncludes(Class thisClass, ClassProperties properties) { } } + private boolean arm64; + @Override public void init(ClassProperties properties) { String platform = properties.getProperty("platform"); @@ -188,6 +191,11 @@ public void init(ClassProperties properties) { } } + @Override + public void init(Logger logger, java.util.Properties properties, String encoding) { + arm64 = properties.getProperty("platform").contains("arm64"); + } + public void mapModule(InfoMap infoMap, String name) { mapModule(infoMap, name, null, null, true); } @@ -246,7 +254,7 @@ public static void sharedMap(InfoMap infoMap) { //// Macros infoMap - .put(new Info("TORCH_API", "C10_API", "C10_EXPORT", "C10_HIDDEN", "C10_IMPORT", "C10_API_ENUM", "EXPORT_IF_NOT_GCC", + .put(new Info("TORCH_API", "C10_API", "TORCH_XPU_API", "C10_EXPORT", "C10_HIDDEN", "C10_IMPORT", "C10_API_ENUM", "EXPORT_IF_NOT_GCC", "TORCH_CUDA_CU_API", "TORCH_CUDA_CPP_API", "TORCH_HIP_API", "TORCH_PYTHON_API", "__ubsan_ignore_float_divide_by_zero__", "__ubsan_ignore_undefined__", "__ubsan_ignore_signed_int_overflow__", "__ubsan_ignore_function__", "C10_CLANG_DIAGNOSTIC_IGNORE", "C10_CLANG_DIAGNOSTIC_PUSH", "C10_CLANG_DIAGNOSTIC_POP", "C10_ATTR_VISIBILITY_HIDDEN", "C10_ERASE", @@ -270,6 +278,7 @@ public static void sharedMap(InfoMap infoMap) { "defined(NDEBUG)", "defined(__ANDROID__)", "defined(__APPLE__)", + "defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__)", "defined(__HIP_PLATFORM_HCC__)", "defined(_MSC_VER)", "_WIN32", "defined(USE_ROCM)", "USE_ROCM", "SYCL_LANGUAGE_VERSION", @@ -284,6 +293,8 @@ public static void sharedMap(InfoMap infoMap) { .put(new Info("CAFFE2_LOG_THRESHOLD").translate(false)) + .put(new Info("DOXYGEN_SHOULD_SKIP_THIS").define()) // Exclude what the devs decide to not be part of public API + .put(new Info("TORCH_CHECK").cppText("#define TORCH_CHECK(cond, ...)").define()) .put(new Info("DEFINE_SYMBOL").cppText("#define DEFINE_SYMBOL(ns, s) namespace ns { constexpr Symbol s; }").define()) .put(new Info("TORCH_ENUM_DECLARE").cppText("#define TORCH_ENUM_DECLARE(name) namespace torch { namespace enumtype { struct k##name { k##name() {} }; } }").define()) @@ -328,7 +339,6 @@ public void map(InfoMap infoMap) { .put(new Info("decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::Float8_e4m3fn>::t)").pointerTypes("Float8_e4m3fn")) .put(new Info("decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::Float8_e5m2fnuz>::t)").pointerTypes("Float8_e5m2fnuz")) .put(new Info("decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::Float8_e4m3fnuz>::t)").pointerTypes("Float8_e4m3fnuz")) - .put(new Info("c10::DataPtr", "at::DataPtr").valueTypes("@Cast({\"\", \"c10::DataPtr&&\"}) @StdMove DataPtr").pointerTypes("DataPtr")) // DataPtr::operator= deleted .put(new Info("c10::ClassType").purify().pointerTypes("ClassType")) // Issue #669 .put(new Info("c10::EnumType").purify().pointerTypes("EnumType")) // Issue #669 .put(new Info("c10::NamedType").purify().pointerTypes("NamedType")) // Issue #669 @@ -339,8 +349,26 @@ public void map(InfoMap infoMap) { .put(new Info("c10::prim::requires_grad").javaNames("requires_grad")) .put(new Info("c10::aten::clone").javaNames("_clone")) .put(new Info("at::TensorBase").base("AbstractTensor").pointerTypes("TensorBase")) + .put(new Info("torch::autograd::Variable").pointerTypes("Tensor")) ; + // c10::DataPtr + // DataPtr::operator= is deleted. + // So we must move all DataPtr passed by value. + // UniqueStorageShareExternalPointer has 2 overloads, 1 taking at::DataPtr&& and the other taking void *. StdMove adapter + // can be cast to both, so we need to disambiguate with a @Cast. + // No way to define a function pointer taking a moved DataPtr, so skipping GetStorageImplCreate and SetStorageImplCreate, + // and the automatic generation of the FunctionPointer. + infoMap + //.put(new Info("c10::DataPtr&&", "at::DataPtr&&").valueTypes("@Cast({\"\", \"c10::DataPtr&&\"}) @StdMove DataPtr").pointerTypes("DataPtr")) // DataPtr::operator= deleted + .put(new Info("c10::DataPtr", "at::DataPtr").valueTypes("@StdMove DataPtr").pointerTypes("DataPtr")) + .put(new Info("c10::StorageImpl::UniqueStorageShareExternalPointer(at::DataPtr&&, size_t)", + "c10::Storage::UniqueStorageShareExternalPointer(at::DataPtr&&, size_t)").javaText( + "public native void UniqueStorageShareExternalPointer(@Cast({\"\", \"c10::DataPtr&&\"}) @StdMove DataPtr data_ptr, @Cast(\"size_t\") long size_bytes);" + )) + .put(new Info("c10::GetStorageImplCreate", "c10::SetStorageImplCreate", + "c10::intrusive_ptr (*)(c10::StorageImpl::use_byte_size_t, c10::SymInt, c10::DataPtr, c10::Allocator*, bool)").skip()) + ; //// Enumerations infoMap .put(new Info("c10::ScalarType", "at::ScalarType", "torch::Dtype").enumerate().valueTypes("ScalarType").pointerTypes("@Cast(\"c10::ScalarType*\") BytePointer")) @@ -367,6 +395,7 @@ public void map(InfoMap infoMap) { .put(new Info("c10::optional >").pointerTypes("ShapeSymbolVectorOptional").define()) .put(new Info("c10::optional >").pointerTypes("TensorVectorOptional").define()) .put(new Info("c10::optional", "c10::optional", "c10::optional").pointerTypes("DeviceOptional").define()) + .put(new Info("c10::optional").pointerTypes("DeviceTypeOptional").define()) .put(new Info("c10::optional >", "c10::optional", "c10::optional", "c10::OptionalArrayRef", "c10::OptionalIntArrayRef", "at::OptionalIntArrayRef", "c10::remove_symint::type") // This second pointer type prevents optional.swap to work. I don't know exactly why. Skipping swap for now. @@ -580,6 +609,7 @@ public void map(InfoMap infoMap) { .put(new Info("std::array").cast().pointerTypes("FunctionalityOffsetAndMask")) .put(new Info("std::array").pointerTypes("IntPointer").cast()) .put(new Info("std::array >,at::COMPILE_TIME_MAX_DEVICE_TYPES>").pointerTypes("PointerPairOptional").cast()) + .put(new Info("std::array").pointerTypes("BytePointer").cast()) ; @@ -834,6 +864,7 @@ public void map(InfoMap infoMap) { .put(new Info("std::unordered_map").pointerTypes("ValueValueMap").define()) .put(new Info("std::unordered_map").pointerTypes("ArgumentSpecExecutionPlanMap").define()) .put(new Info("std::unordered_map").pointerTypes("TreeRefStringMap").define()) + .put(new Info("std::unordered_map").pointerTypes("StringIntMap").define()) ; @@ -842,6 +873,7 @@ public void map(InfoMap infoMap) { .put(new Info("std::atomic_bool", "std::atomic").cast().valueTypes("boolean").pointerTypes("BoolPointer")) .put(new Info("std::atomic_uint64_t", "std::atomic", "std::atomic", "std::atomic_size_t", "std::atomic").cast().valueTypes("long").pointerTypes("LongPointer")) .put(new Info("std::atomic").cast().pointerTypes("DeviceGuardImplInterface")) + .put(new Info("std::atomic").cast().valueTypes("int").pointerTypes("IntPointer")); ; @@ -1090,21 +1122,21 @@ public void map(InfoMap infoMap) { "c10::complex::operator -=(c10::Half)", "c10::complex::operator *=(c10::Half)", "c10::complex::operator /=(c10::Half)" - ).skip()) + ).skip()) .put(new Info("c10::complex::complex(const c10::Half&, const c10::Half&)").javaText( // Second argument not optional + add specific functions - "public HalfComplex(Half re, Half im) { super((Pointer)null); allocate(re, im); }\n" + - "private native void allocate(@Const @ByRef Half re, @Const @ByRef(nullValue = \"c10::Half()\") Half im);\n" + - "public HalfComplex(@Const @ByRef FloatComplex value) { super((Pointer)null); allocate(value); }\n" + - "private native void allocate(@Const @ByRef FloatComplex value);\n" + - "\n" + - "// Conversion operator\n" + - "public native @ByVal @Name(\"operator c10::complex\") FloatComplex asFloatComplex();\n" + - "\n" + - "public native @ByRef @Name(\"operator +=\") HalfComplex addPut(@Const @ByRef HalfComplex other);\n" + - "\n" + - "public native @ByRef @Name(\"operator -=\") HalfComplex subtractPut(@Const @ByRef HalfComplex other);\n" + - "\n" + - "public native @ByRef @Name(\"operator *=\") HalfComplex multiplyPut(@Const @ByRef HalfComplex other);" + "public HalfComplex(Half re, Half im) { super((Pointer)null); allocate(re, im); }\n" + + "private native void allocate(@Const @ByRef Half re, @Const @ByRef(nullValue = \"c10::Half()\") Half im);\n" + + "public HalfComplex(@Const @ByRef FloatComplex value) { super((Pointer)null); allocate(value); }\n" + + "private native void allocate(@Const @ByRef FloatComplex value);\n" + + "\n" + + "// Conversion operator\n" + + "public native @ByVal @Name(\"operator c10::complex\") FloatComplex asFloatComplex();\n" + + "\n" + + "public native @ByRef @Name(\"operator +=\") HalfComplex addPut(@Const @ByRef HalfComplex other);\n" + + "\n" + + "public native @ByRef @Name(\"operator -=\") HalfComplex subtractPut(@Const @ByRef HalfComplex other);\n" + + "\n" + + "public native @ByRef @Name(\"operator *=\") HalfComplex multiplyPut(@Const @ByRef HalfComplex other);" ) ) ; @@ -1179,7 +1211,7 @@ public void map(InfoMap infoMap) { .put(new Info("torch::jit::Wrap").pointerTypes("BlockWrap")) .put(new Info("torch::jit::Wrap").pointerTypes("JitNodeWrap")) .put(new Info("torch::jit::Wrap").pointerTypes("ValueWrap")) - ; + ; //// Data loader @@ -1629,7 +1661,7 @@ public void map(InfoMap infoMap) { if (i > 1) { mapModule(infoMap, "FractionalMaxPool" + i + "d", "torch::nn::FractionalMaxPoolImpl<" + i + ",torch::nn::FractionalMaxPool" + i + "dImpl>"); } - if (i < 3) { + if (i < 4) { mapModule(infoMap, "LPPool" + i + "d", "torch::nn::LPPoolImpl<" + i + ",torch::nn::LPPool" + i + "dImpl>"); } } @@ -1763,7 +1795,7 @@ public void map(InfoMap infoMap) { .put(new Info("torch::optim::" + opt + "Options", "torch::optim::" + opt + "ParamState")) // Help qualification .put(new Info("torch::optim::OptimizerCloneableOptions").pointerTypes("OptimizerCloneable" + opt + "Options")) .put(new Info("torch::optim::OptimizerCloneableParamState").pointerTypes("OptimizerCloneable" + opt + "ParamState")) - ; + ; new PointerInfo("torch::optim::" + opt + "Options").makeUnique(infoMap); new PointerInfo("torch::optim::OptimizerCloneableParamState").javaBaseName("OptimizerCloneable" + opt + "AdagradParamState").makeUnique(infoMap); new PointerInfo("torch::optim::OptimizerCloneableOptions").javaBaseName("OptimizerCloneable" + opt + "Options").makeUnique(infoMap); @@ -1963,6 +1995,11 @@ We need either to put an annotation info on each member, or javaName("@NoOffset .put(new Info("torch::cuda::manual_seed").javaNames("cuda_manual_seed")) .put(new Info("torch::cuda::manual_seed_all").javaNames("cuda_manual_seed_all")) .put(new Info("torch::cuda::synchronize").javaNames("cuda_synchronize")) + .put(new Info("torch::xpu::device_count").javaNames("xpu_device_count")) + .put(new Info("torch::xpu::is_available").javaNames("xpu_is_available")) + .put(new Info("torch::xpu::manual_seed").javaNames("xpu_manual_seed")) + .put(new Info("torch::xpu::manual_seed_all").javaNames("xpu_manual_seed_all")) + .put(new Info("torch::xpu::synchronize").javaNames("xpu_synchronize")) .put(new Info("torch::jit::Const").pointerTypes("ConstExpr")) .put(new Info("torch::jit::Node").pointerTypes("JitNode")) .put(new Info("torch::jit::Module").pointerTypes("JitModule")) @@ -2314,6 +2351,7 @@ We need either to put an annotation info on each member, or javaName("@NoOffset "torch::jit::Suspend", "torch::jit::TokenTrie", "torch::jit::TaggedRange", + "torch::jit::VectorReader", "torch::jit::WithCurrentScope", "torch::jit::WithInsertPoint", "torch::jit::variable_tensor_list", @@ -2374,7 +2412,7 @@ We need either to put an annotation info on each member, or javaName("@NoOffset "PyObject", "THPObjectPtr", "pyobj_list", "std::chrono::milliseconds", "std::exception_ptr", "std::type_info", "std::pair", "std::stack >", "torch::autograd::utils::DelayWarningHandler", "std::is_same,torch::detail::pack >", "at::cuda::NVRTC", "at::RecordFunctionCallback", "at::StepCallbacks", "THCState", "THHState", - "torch::autograd::ViewInfo", "torch::jit::InlinedCallStackPtr", "InlinedCallStackPtr", "torch::jit::ScopePtr", "torch::jit::BackendDebugInfoRecorder", + "torch::jit::InlinedCallStackPtr", "InlinedCallStackPtr", "torch::jit::ScopePtr", "torch::jit::BackendDebugInfoRecorder", "torch::detail::TensorDataContainer", "at::ArrayRef", "std::shared_ptr", "caffe2::serialize::PyTorchStreamWriter", "c10::detail::DictImpl::dict_map_type::iterator", @@ -2384,7 +2422,7 @@ We need either to put an annotation info on each member, or javaName("@NoOffset "c10::ArrayRef >", "torch::jit::DetachedBuffer::UniqueDetachedBuffer", "c10::optional", "c10::optional::ListOfOptionalElements>", "c10::optional::ListOfOptionalElements>", - "c10::optional", "c10::optional >", + "c10::optional >", "c10::optional", "c10::optional", "std::tuple >,c10::optional >,c10::optional >", @@ -2404,8 +2442,7 @@ We need either to put an annotation info on each member, or javaName("@NoOffset "std::enable_shared_from_this", "std::enable_shared_from_this", "std::enable_shared_from_this", "std::enable_shared_from_this" ).pointerTypes("Pointer").cast()) - .put(new Info("MTLCommandBuffer_t", "DispatchQueue_t").valueTypes("Pointer").pointerTypes("PointerPointer").skip()); - + .put(new Info("MTLCommandBuffer_t", "DispatchQueue_t").valueTypes("Pointer").pointerTypes("PointerPointer").skip()); ///// Special cases needing javaText @@ -2493,7 +2530,6 @@ We need either to put an annotation info on each member, or javaName("@NoOffset "c10::TypePtr (*)(const std::string&)", "c10::Type::SingletonOrSharedTypePtr (*)(const std::string&)" ).pointerTypes("TypeParser").skip()) - .put(new Info("c10::intrusive_ptr (*)(c10::StorageImpl::use_byte_size_t, c10::SymInt, c10::Allocator*, bool)").pointerTypes("StorageImplCreateHelper").skip()) .put(new Info("std::function(const c10::Type&)>").pointerTypes("TypePrinter")) .put(new Info("void (*)(void*, size_t)", "c10::PlacementDtor", "caffe2::TypeMeta::PlacementNew", "caffe2::TypeMeta::PlacementDelete").pointerTypes("PlacementConsumer").valueTypes("PlacementConsumer").skip()) .put(new Info("void (*)(const void*, void*, size_t)", "caffe2::TypeMeta::Copy").pointerTypes("PlacementCopier").valueTypes("PlacementCopier").skip()) @@ -2510,8 +2546,21 @@ We need either to put an annotation info on each member, or javaName("@NoOffset infoMap.put(new Info("caffe2::TypeMeta::deleteFn").javaText("public native @NoException(true) PointerConsumer deleteFn();")); // Parser picks up the wrong Delete - infoMap.put(new Info("c10::VaryingShape::merge").skip()); // https://github.com/pytorch/pytorch/issues/123248 + infoMap.put(new Info("c10::VaryingShape::merge").skip()); // https://github.com/pytorch/pytorch/issues/123248, waiting for the fix in 2.3.1 or 2.4 + //// Different C++ API between platforms + // This will produce different Java codes, but as long as the differences only concern + // JavaCPP annotations, we don't care. + if (arm64) { + infoMap + .put(new Info("c10::Half::Half(float)").javaText( + "public Half(float value) { super((Pointer)null); allocate(value); }\n" + + "private native void allocate(@Cast(\"float16_t\") float value);" + )) + .put(new Info("c10::Half::operator float()").javaText( + "public native @Name(\"operator float16_t\") @Cast(\"float\") float asFloat();" + )); + } } private static String template(String t, String... args) { @@ -2692,6 +2741,7 @@ void mapList(InfoMap infoMap) { .put(new Info(template("c10::List", t) + "::size_type").valueTypes("long")) .put(new Info(template("c10::impl::ListElementReference", t, "c10::detail::ListImpl::list_type::iterator") + "::" + template("swap", t, "c10::detail::ListImpl::list_type::iterator")) .javaNames("swap").friendly()) + .put(new Info(template("c10::List", t) + "::get(" + template("c10::List", t) + "::size_type)").javaText("public native " + elementValueType +" get(long pos);")) ; infoMap.put(new Info(template("c10::List", t) + "::operator []").skip()) // Returns an internal_reference_type by value, which is a ListElementReference, whose copy constructor is disabled. .put(new Info( diff --git a/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch_cuda.java b/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch_cuda.java index 90df6003626..c5e42eae804 100644 --- a/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch_cuda.java +++ b/pytorch/src/main/java/org/bytedeco/pytorch/presets/torch_cuda.java @@ -40,8 +40,7 @@ "ATen/cudnn/Descriptors.h", "ATen/cudnn/Types.h", "c10/cuda/CUDAGuard.h", - // Doesn't compile on Windows. Waiting for 2.3 - // "torch/csrc/inductor/aoti_model_container_runner_cuda.h", + "torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h", // For inclusion in JNI only, not parsed "ATen/cuda/CUDAGeneratorImpl.h", @@ -110,8 +109,9 @@ public void map(InfoMap infoMap) { .put(new Info("std::array", "c10::cuda::CUDACachingAllocator::StatArray").cast().pointerTypes("Stat")) //// Function pointers - .put(new Info("std::function").pointerTypes("AllocatorTraceTracker")) + .put(new Info("std::function").pointerTypes("OutOfMemoryObserver")) + .put(new Info("std::function").pointerTypes("StreamFilter")) // Function pointer returning shared_ptr don't compile on windows // "D:\a\javacpp-presets\javacpp-presets\pytorch\target\native\org\bytedeco\pytorch\windows-x86_64\jnitorch.cpp(98904): error C2526: 'JavaCPP_org_bytedeco_pytorch_functions_GatheredContextSupplier_allocate_callback': C linkage function cannot return C++ class 'std::shared_ptr'" @@ -145,7 +145,7 @@ public void map(InfoMap infoMap) { .put(new Info("c10::Allocator").pointerTypes("Allocator")) .put(new Info("CUDAContextLight.h").linePatterns("struct Allocator;").skip()) // Prevent regeneration of Allocator class in cuda package - .put(new Info("c10::DeviceIndex").valueTypes("byte")) + .put(new Info("c10::DeviceIndex").valueTypes("byte").pointerTypes("BytePointer", "ByteBuffer", "byte[]")) .put(new Info("c10::StreamId").valueTypes("long")) .put(new Info("c10::cuda::CaptureStatus").valueTypes("int").cast().skip()) // Enum doesn't parse .put(new Info("std::pair,std::vector >").pointerTypes("DeviceAssertionsDataVectorCUDAKernelLaunchInfoVectorPair").define()) @@ -167,6 +167,7 @@ public void map(InfoMap infoMap) { "at::native::Descriptor", "at::native::Descriptor", "at::native::Descriptor", + "at::native::Descriptor", "at::native::Descriptor", "at::native::Descriptor", "at::native::Descriptor", @@ -182,10 +183,11 @@ public void map(InfoMap infoMap) { "cudaDeviceProp" ).pointerTypes("Pointer")) .put(new Info( // Pointers to opaque structs - "cudaStream_t", "cusparseHandle_t", "cublasHandle_t", "cusolverDnHandle_t", "cudnnHandle_t", "cudaEvent_t" + "cudaStream_t", "cusparseHandle_t", "cublasHandle_t", "cusolverDnHandle_t", "cudnnHandle_t", "cudaEvent_t", + "cublasLtHandle_t" ).valueTypes("Pointer").cast()) .put(new Info( // Enums - "cudnnActivationMode_t", "cudnnLossNormalizationMode_t", "cudnnRNNInputMode_t", + "cudnnActivationMode_t", "cudnnLossNormalizationMode_t", "cudnnRNNInputMode_t", "cudnnRNNDataLayout_t", "cudnnDirectionMode_t", "cudnnRNNMode_t", "cudaStreamCaptureMode", "cudnnDataType_t", "cudnnNanPropagation_t", "cusparseStatus_t", "cusolverStatus_t", "cudnnRNNAlgo_t", "cudnnNanPropagation_t", "cublasStatus_t", "cudaError_t", "cudaMemcpyKind" @@ -205,5 +207,11 @@ public void map(InfoMap infoMap) { ).skip()) ; + infoMap.put(new Info("USE_CUDNN_RNN_V8_API").define()); // Using CuDNN 8.9.7 or more recent + + //// Different C++ API between platforms + infoMap + .put(new Info("at::cuda::getCurrentCUDABlasLtHandle").skip()) // No cublas lt with Microsoft compiler + ; } } diff --git a/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_cuda_include.h b/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_cuda_include.h index c1cb1af0ebb..7c766687c23 100644 --- a/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_cuda_include.h +++ b/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_cuda_include.h @@ -4,7 +4,7 @@ // ATen/cudnn/Descriptors.h // ATen/cudnn/Types.h // c10/cuda/CUDAGuard.h -// torch/csrc/inductor/aoti_model_container_runner_cuda.h +// torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h #include "ATen/cuda/CUDAContextLight.h" #include "c10/cuda/CUDAStream.h" #include "ATen/cuda/CUDAContext.h" @@ -27,5 +27,4 @@ #include "ATen/cudnn/Descriptors.h" #include "ATen/cudnn/Types.h" #include "c10/cuda/CUDAGuard.h" -// Doesn't compile on Windows. Waiting for 2.2.1. -// #include "torch/csrc/inductor/aoti_model_container_runner_cuda.h" \ No newline at end of file +#include "torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h" diff --git a/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_include.h b/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_include.h index 516c6889e7d..7b3ae1f98cc 100644 --- a/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_include.h +++ b/pytorch/src/main/resources/org/bytedeco/pytorch/presets/torch_include.h @@ -1,7 +1,7 @@ // All files included by // #include // #include -// #include +// #include // as listed by g++ -H torch/torch.h torch/script.h // Excluding: // - the ones that fill at::meta at::native and at::_ops namespaces @@ -43,6 +43,7 @@ #include "c10/util/floating_point_utils.h" #include "c10/util/Float8_e4m3fn-inl.h" #include "c10/util/Float8_e4m3fn.h" +#include "c10/util/Float8_fnuz_cvt.h" #include "c10/util/Float8_e4m3fnuz-inl.h" #include "c10/util/Float8_e4m3fnuz.h" #include "c10/util/complex_math.h" @@ -62,7 +63,6 @@ #include "c10/util/quint8.h" #include "c10/core/ScalarType.h" // #include "c10/util/Optional.h" // Incompatible with declaration of c10::optional as basic container -#include "c10/util/in_place.h" #include "c10/util/MaybeOwned.h" #include "c10/core/SymNodeImpl.h" #include "c10/core/SymFloat.h" @@ -80,6 +80,8 @@ #include "c10/util/ThreadLocalDebugInfo.h" #include "c10/util/UniqueVoidPtr.h" #include "c10/core/Allocator.h" +#include "c10/core/impl/COW.h" +//#include "c10/core/impl/COWDeleter.h" #include "c10/util/python_stub.h" #include "c10/core/StorageImpl.h" #include "c10/util/ExclusivelyOwned.h" @@ -188,7 +190,6 @@ #include "ATen/core/boxing/KernelFunction.h" #include "ATen/core/boxing/KernelFunction_impl.h" #include "c10/util/flat_hash_map.h" -#include "c10/util/either.h" #include "c10/core/PyHandleCache.h" #include "c10/core/SafePyObject.h" #include "c10/util/Bitset.h" @@ -228,6 +229,9 @@ #include "torch/csrc/autograd/graph_task.h" #include "ATen/core/MT19937RNGEngine.h" #include "ATen/CPUGeneratorImpl.h" +#include "ATen/detail/AcceleratorHooksInterface.h" +#include "ATen/detail/MTIAHooksInterface.h" +#include "ATen/DeviceAccelerator.h" #include "ATen/LinalgBackend.h" #include "ATen/core/ATenGeneral.h" #include "ATen/core/LegacyTypeDispatch.h" @@ -235,7 +239,6 @@ #include "ATen/detail/HIPHooksInterface.h" #include "ATen/detail/IPUHooksInterface.h" #include "ATen/detail/MPSHooksInterface.h" -#include "ATen/detail/MTIAHooksInterface.h" #include "ATen/detail/ORTHooksInterface.h" #include "ATen/detail/PrivateUse1HooksInterface.h" #include "ATen/detail/XPUHooksInterface.h" @@ -977,6 +980,7 @@ #include "ATen/ops/slice.h" #include "ATen/ops/slice_backward.h" #include "ATen/ops/slice_copy.h" +#include "ATen/ops/slice_inverse.h" #include "ATen/ops/slice_scatter.h" #include "ATen/ops/slogdet.h" #include "ATen/ops/slow_conv3d.h" @@ -1192,6 +1196,7 @@ #include "torch/csrc/autograd/saved_variable.h" #include "torch/csrc/utils/variadic.h" #include "torch/csrc/autograd/function.h" +#include "torch/csrc/autograd/variable_info.h" #include "torch/csrc/autograd/custom_function.h" #include "torch/csrc/api/include/torch/autograd.h" #include "torch/csrc/api/include/torch/cuda.h" @@ -1296,6 +1301,7 @@ #include "torch/csrc/api/include/torch/nn/module.h" #include "ATen/Config.h" // #include "ATen/ParallelOpenMP.h" // Internal only + #include "c10/util/ParallelGuard.h" #include "ATen/Parallel-inl.h" #include "ATen/Parallel.h" #include "torch/csrc/profiler/orchestration/observer.h" @@ -1402,6 +1408,7 @@ #include "torch/csrc/api/include/torch/sparse.h" #include "torch/csrc/api/include/torch/special.h" #include "torch/csrc/api/include/torch/version.h" +#include "torch/csrc/api/include/torch/xpu.h" #include "torch/csrc/autograd/InferenceMode.h" // #include "torch/csrc/jit/runtime/custom_operator.h" // Name conflict with torch::RegisterOperator + little chance to have any use #include "caffe2/serialize/read_adapter_interface.h" @@ -1425,8 +1432,9 @@ #include "torch/csrc/jit/frontend/tree_views.h" #include "torch/csrc/jit/serialization/pickle.h" // #include "torch/csrc/inductor/aoti_torch/c/shim.h" // model.so API, not part of libtorch API +// #include "torch/csrc/inductor/aoti_runtime/utils.h" // model.so API, not part of libtorch API // #include "torch/csrc/inductor/aoti_runtime/interface.h" // model.so API, not part of libtorch API -// Doesn't compile on Windows. Waiting for 2.2.1. -// #include "torch/csrc/inductor/aoti_model_container_runner.h" +#include "torch/csrc/inductor/aoti_runner/model_container_runner.h" +#include "torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h" -#include "datasets.h" \ No newline at end of file +#include "datasets.h"