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Always build USE_DISTRIBUTED. (#160449)
Signed-off-by: Edward Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/160449 Approved by: https://github.com/wconstab, https://github.com/albanD, https://github.com/dcci
This commit is contained in:
Edward Yang
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
13b65196db
commit
b7034e9c92
@ -35,11 +35,10 @@ fi
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print_cmake_info
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if [[ ${BUILD_ENVIRONMENT} == *"distributed"* ]]; then
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# Needed for inductor benchmarks, as lots of HF networks make `torch.distribtued` calls
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USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
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USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
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else
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# Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
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# that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
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# NB: we always build with distributed; USE_DISTRIBUTED turns off all
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# backends (specifically the gloo backend), so test that this case works too
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USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel --plat-name macosx_11_0_arm64
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fi
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if which sccache > /dev/null; then
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@ -16,6 +16,8 @@ popd
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# enable debug asserts in serialization
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export TORCH_SERIALIZATION_DEBUG=1
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python -mpip install --no-input -r requirements.txt
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setup_test_python() {
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# The CircleCI worker hostname doesn't resolve to an address.
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# This environment variable makes ProcessGroupGloo default to
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@ -213,7 +213,8 @@ pip install requests ninja typing-extensions
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retry pip install -r "${pytorch_rootdir}/requirements.txt" || true
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retry brew install libomp
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# For USE_DISTRIBUTED=1 on macOS, need libuv, which is build as part of tensorpipe submodule
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# For USE_DISTRIBUTED=1 on macOS, this enables gloo, which needs libuv, which
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# is build as part of tensorpipe submodule
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export USE_DISTRIBUTED=1
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export USE_MKLDNN=OFF
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@ -22,7 +22,6 @@ COMMON_COPTS = [
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"-DHAVE_SHM_UNLINK=1",
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"-D_FILE_OFFSET_BITS=64",
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"-DUSE_FBGEMM",
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"-DUSE_DISTRIBUTED",
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"-DAT_PER_OPERATOR_HEADERS",
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"-DATEN_THREADING=NATIVE",
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"-DNO_CUDNN_DESTROY_HANDLE",
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@ -181,8 +181,9 @@ elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(ppc64le)")
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set(CPU_POWER ON)
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endif()
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# For non-supported platforms, turn USE_DISTRIBUTED off by default. It is not
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# tested and likely won't work without additional changes.
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# For non-supported platforms, turn USE_DISTRIBUTED off by default.
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# NB: USE_DISTRIBUTED simply disables the backend; distributed code
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# still gets built
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if(NOT LINUX AND NOT WIN32)
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set(USE_DISTRIBUTED
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OFF
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@ -261,11 +262,11 @@ option(USE_PYTORCH_METAL "Use Metal for PyTorch iOS build" OFF)
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option(USE_PYTORCH_METAL_EXPORT "Export Metal models on MacOSX desktop" OFF)
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option(USE_NATIVE_ARCH "Use -march=native" OFF)
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cmake_dependent_option(USE_MPS "Use MPS for macOS build" ON "MPS_FOUND" OFF)
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option(USE_DISTRIBUTED "Use distributed" ON)
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option(USE_DISTRIBUTED "Enable default distributed backends" ON)
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cmake_dependent_option(USE_NCCL "Use NCCL" ON
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"USE_DISTRIBUTED;USE_CUDA OR USE_ROCM;UNIX;NOT APPLE" OFF)
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cmake_dependent_option(USE_XCCL "Use XCCL" ON
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"USE_XPU;UNIX;NOT APPLE" OFF)
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"USE_DISTRIBUTED;USE_XPU;UNIX;NOT APPLE" OFF)
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cmake_dependent_option(USE_RCCL "Use RCCL" ON USE_NCCL OFF)
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cmake_dependent_option(USE_RCCL "Use RCCL" ON "USE_NCCL;NOT WIN32" OFF)
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cmake_dependent_option(USE_STATIC_NCCL "Use static NCCL" OFF "USE_NCCL" OFF)
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@ -430,11 +431,10 @@ if(WIN32)
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PATH_SUFFIXES lib
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NO_DEFAULT_PATH)
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if(NOT libuv_tmp_LIBRARY)
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set(USE_DISTRIBUTED OFF)
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set(USE_GLOO OFF)
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message(
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WARNING
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"Libuv is not installed in current conda env. Set USE_DISTRIBUTED to OFF. "
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"Libuv is not installed in current conda env. Set USE_GLOO to OFF. "
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"Please run command 'conda install -c conda-forge libuv=1.39' to install libuv."
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)
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else()
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@ -540,12 +540,10 @@ if(NOT INTERN_BUILD_MOBILE AND NOT BUILD_LITE_INTERPRETER)
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${TORCH_SRC_DIR}/csrc/utils/byte_order.cpp
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)
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if(USE_DISTRIBUTED)
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append_filelist("libtorch_distributed_base_sources" TORCH_SRCS)
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if(NOT WIN32)
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append_filelist("libtorch_distributed_extra_sources" TORCH_SRCS)
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endif()
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endif()
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endif()
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if(USE_CUDA OR USE_ROCM)
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@ -568,7 +566,6 @@ if(USE_CUDA)
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list(APPEND Caffe2_GPU_SRCS
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${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
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endif()
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if(USE_DISTRIBUTED)
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append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_GPU_SRCS)
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if(NOT WIN32)
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append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_GPU_SRCS)
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@ -594,7 +591,6 @@ if(USE_CUDA)
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if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.0 AND CUDA_NVCC_FLAGS MATCHES ".*compute_90.*")
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set_source_files_properties(${ASYNC_MM_FILE} PROPERTIES COMPILE_FLAGS "-gencode arch=compute_90a,code=sm_90a")
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endif()
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endif()
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set_source_files_properties(
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${TORCH_ROOT}/aten/src/ATen/cuda/detail/LazyNVRTC.cpp
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PROPERTIES COMPILE_DEFINITIONS "NVRTC_SHORTHASH=${CUDA_NVRTC_SHORTHASH}"
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@ -626,12 +622,10 @@ if(USE_ROCM)
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list(APPEND Caffe2_HIP_SRCS
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${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
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endif()
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if(USE_DISTRIBUTED)
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append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_HIP_SRCS)
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if(NOT WIN32)
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append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_HIP_SRCS)
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endif()
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endif()
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# caffe2_nvrtc's stubs to driver APIs are useful for HIP.
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# See NOTE [ ATen NVRTC Stub and HIP ]
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add_library(caffe2_nvrtc SHARED ${ATen_NVRTC_STUB_SRCS})
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@ -1351,13 +1345,11 @@ if(BUILD_TEST)
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add_subdirectory(${TORCH_ROOT}/test/cpp/jit ${CMAKE_BINARY_DIR}/test_jit)
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add_subdirectory(${TORCH_ROOT}/test/cpp/nativert ${CMAKE_BINARY_DIR}/test_nativert)
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add_subdirectory(${TORCH_ROOT}/test/inductor ${CMAKE_BINARY_DIR}/test_inductor)
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if(USE_DISTRIBUTED)
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add_subdirectory(${TORCH_ROOT}/test/cpp/c10d ${CMAKE_BINARY_DIR}/test_cpp_c10d)
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if(NOT WIN32)
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add_subdirectory(${TORCH_ROOT}/test/cpp/dist_autograd ${CMAKE_BINARY_DIR}/dist_autograd)
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add_subdirectory(${TORCH_ROOT}/test/cpp/rpc ${CMAKE_BINARY_DIR}/test_cpp_rpc)
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endif()
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endif()
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if(NOT NO_API)
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add_subdirectory(${TORCH_ROOT}/test/cpp/api ${CMAKE_BINARY_DIR}/test_api)
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endif()
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@ -1461,46 +1453,40 @@ if(BUILD_LITE_INTERPRETER)
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endif()
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endif()
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# Pass USE_DISTRIBUTED to torch_cpu, as some codes in jit/pickler.cpp and
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# jit/unpickler.cpp need to be compiled only when USE_DISTRIBUTED is set
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if(USE_DISTRIBUTED)
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target_compile_definitions(torch_cpu PUBLIC USE_DISTRIBUTED)
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if(USE_GLOO AND USE_C10D_GLOO)
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if(USE_GLOO AND USE_C10D_GLOO)
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target_compile_definitions(torch_cpu PUBLIC USE_C10D_GLOO)
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endif()
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if(USE_UCC AND USE_C10D_UCC)
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endif()
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if(USE_UCC AND USE_C10D_UCC)
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target_compile_definitions(torch_cpu PUBLIC USE_C10D_UCC)
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if(USE_CUDA)
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target_compile_definitions(torch_cuda PUBLIC USE_C10D_UCC)
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endif()
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endif()
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if(USE_NCCL AND USE_C10D_NCCL)
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endif()
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if(USE_NCCL AND USE_C10D_NCCL)
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if(USE_ROCM)
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target_compile_definitions(torch_hip PUBLIC USE_C10D_NCCL)
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else()
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target_compile_definitions(torch_cuda PUBLIC USE_C10D_NCCL)
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endif()
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endif()
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if(USE_MPI AND USE_C10D_MPI)
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endif()
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if(USE_MPI AND USE_C10D_MPI)
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if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
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set_source_files_properties(
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"${TORCH_SRC_DIR}/csrc/distributed/c10d/ProcessGroupMPI.cpp"
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PROPERTIES COMPILE_FLAGS -Wno-deprecated-declarations)
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endif()
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target_compile_definitions(torch_cpu PUBLIC USE_C10D_MPI)
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endif()
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# Pass USE_RPC in order to reduce use of
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# #if defined(USE_DISTRIBUTED) && !defined(_WIN32)
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# need to be removed when RPC is supported
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if(NOT WIN32)
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endif()
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# Pass USE_RPC in order to reduce use of
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# #if defined(USE_DISTRIBUTED) && !defined(_WIN32)
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# need to be removed when RPC is supported
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if(NOT WIN32)
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target_compile_definitions(torch_cpu PUBLIC USE_RPC)
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endif()
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# Pass USE_TENSORPIPE to torch_cpu as some parts of rpc/utils.cpp
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# can only be compiled with USE_TENSORPIPE is set.
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if(USE_TENSORPIPE)
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endif()
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# Pass USE_TENSORPIPE to torch_cpu as some parts of rpc/utils.cpp
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# can only be compiled with USE_TENSORPIPE is set.
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if(USE_TENSORPIPE)
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target_compile_definitions(torch_cpu PUBLIC USE_TENSORPIPE)
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endif()
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endif()
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if(NOT INTERN_BUILD_MOBILE)
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@ -1126,7 +1126,7 @@ if(USE_CUDA AND CUDA_VERSION VERSION_LESS 13.0)
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include_directories(SYSTEM ${CUB_INCLUDE_DIRS})
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endif()
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if(USE_DISTRIBUTED AND USE_TENSORPIPE)
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if(USE_TENSORPIPE)
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if(MSVC)
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message(WARNING "Tensorpipe cannot be used on Windows.")
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else()
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@ -191,13 +191,11 @@ function(caffe2_print_configuration_summary)
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message(STATUS " USE_PYTORCH_QNNPACK : ${USE_PYTORCH_QNNPACK}")
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message(STATUS " USE_XNNPACK : ${USE_XNNPACK}")
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message(STATUS " USE_DISTRIBUTED : ${USE_DISTRIBUTED}")
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if(${USE_DISTRIBUTED})
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message(STATUS " USE_MPI : ${USE_MPI}")
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message(STATUS " USE_GLOO : ${USE_GLOO}")
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message(STATUS " USE_GLOO_WITH_OPENSSL : ${USE_GLOO_WITH_OPENSSL}")
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message(STATUS " USE_GLOO_IBVERBS : ${USE_GLOO_IBVERBS}")
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message(STATUS " USE_TENSORPIPE : ${USE_TENSORPIPE}")
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endif()
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if(NOT "${SELECTED_OP_LIST}" STREQUAL "")
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message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}")
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endif()
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@ -3331,13 +3331,6 @@ def coverage_post_process(app, exception):
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if not isinstance(app.builder, CoverageBuilder):
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return
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if not torch.distributed.is_available():
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raise RuntimeError(
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"The coverage tool cannot run with a version "
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"of PyTorch that was built with USE_DISTRIBUTED=0 "
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"as this module's API changes."
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)
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# These are all the modules that have "automodule" in an rst file
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# These modules are the ones for which coverage is checked
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# Here, we make sure that no module is missing from that list
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@ -1,4 +1,4 @@
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if(USE_DISTRIBUTED AND NOT WIN32)
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if(NOT WIN32)
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set(DIST_AUTOGRAD_TEST_DIR "${TORCH_ROOT}/test/cpp/dist_autograd")
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set(DIST_AUTOGRAD_TEST_SOURCES
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${TORCH_ROOT}/test/cpp/common/main.cpp
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@ -63,10 +63,7 @@ from torch.export.passes import move_to_device_pass
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from torch.fx.experimental.proxy_tensor import make_fx
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from torch.fx.experimental.symbolic_shapes import ShapeEnv
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from torch.testing import FileCheck
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from torch.testing._internal.common_cuda import (
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PLATFORM_SUPPORTS_FLASH_ATTENTION,
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xfailIfDistributedNotSupported,
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)
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from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
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from torch.testing._internal.common_utils import (
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find_library_location,
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IS_FBCODE,
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@ -15360,7 +15357,6 @@ class GraphModule(torch.nn.Module):
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finally:
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torch.distributed.destroy_process_group()
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@xfailIfDistributedNotSupported
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def test_distributed_all_reduce(self):
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class Foo(torch.nn.Module):
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def __init__(self):
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@ -15378,7 +15374,6 @@ class GraphModule(torch.nn.Module):
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inp = (torch.randn(4, 4),)
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self.assertTrue(torch.allclose(ep.module()(*inp), m(*inp)))
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@xfailIfDistributedNotSupported
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def test_distributed_all_gather(self):
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class Foo(torch.nn.Module):
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def forward(self, x):
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@ -15394,7 +15389,6 @@ class GraphModule(torch.nn.Module):
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torch.allclose(a, b) for a, b in zip(ep.module()(*inp), m(*inp))
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)
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@xfailIfDistributedNotSupported
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def test_distributed_all_gather_into_tensor(self):
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class Foo(torch.nn.Module):
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def forward(self, x):
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@ -15408,7 +15402,6 @@ class GraphModule(torch.nn.Module):
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inp = (torch.randn(2),)
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self.assertTrue(torch.allclose(ep.module()(*inp), m(*inp)))
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@xfailIfDistributedNotSupported
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@testing.expectedFailureCppRuntime
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def test_distributed_all_to_all_single(self):
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class Foo(torch.nn.Module):
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@ -15426,7 +15419,6 @@ class GraphModule(torch.nn.Module):
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)
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self.assertEqual(len(nodes), 1)
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@xfailIfDistributedNotSupported
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@testing.expectedFailureCppRuntime
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def test_distributed_reduce_scatter_tensor(self):
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class Foo(torch.nn.Module):
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@ -88,8 +88,7 @@ def build_pytorch(
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) -> None:
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my_env = _create_build_env()
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if (
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not check_negative_env_flag("USE_DISTRIBUTED")
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and not check_negative_env_flag("USE_CUDA")
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not check_negative_env_flag("USE_CUDA")
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and not check_negative_env_flag("USE_NCCL")
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and not check_env_flag("USE_SYSTEM_NCCL")
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):
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@ -273,32 +273,30 @@ add_custom_command(
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WORKING_DIRECTORY
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"${TORCH_ROOT}"
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)
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if(USE_DISTRIBUTED)
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if(WIN32)
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if(WIN32)
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append_filelist("libtorch_python_distributed_core_sources" TORCH_PYTHON_SRCS)
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else()
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else()
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append_filelist("libtorch_python_distributed_sources" TORCH_PYTHON_SRCS)
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endif()
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# Disable certain warnings for GCC-9.X
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if(CMAKE_COMPILER_IS_GNUCXX)
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endif()
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# Disable certain warnings for GCC-9.X
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if(CMAKE_COMPILER_IS_GNUCXX)
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set_source_files_properties(${TORCH_SRC_DIR}/csrc/distributed/autograd/init.cpp PROPERTIES COMPILE_FLAGS "-Wno-cast-function-type")
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set_source_files_properties(${TORCH_SRC_DIR}/csrc/distributed/rpc/testing/init.cpp PROPERTIES COMPILE_FLAGS "-Wno-cast-function-type")
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set_source_files_properties(${TORCH_SRC_DIR}/csrc/distributed/c10d/init.cpp PROPERTIES COMPILE_FLAGS "-Wno-cast-function-type")
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endif()
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# NCCL is a private dependency of libtorch, but libtorch_python includes
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# some private headers of libtorch, which in turn include NCCL. As a hacky
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# alternative to making NCCL a public dependency of libtorch, we make it
|
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# a private dependency of libtorch_python as well.
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if(USE_NCCL)
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list(APPEND TORCH_PYTHON_LINK_LIBRARIES __caffe2_nccl)
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endif()
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# Same for MPI.
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if(USE_MPI)
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list(APPEND TORCH_PYTHON_LINK_LIBRARIES MPI::MPI_CXX)
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endif()
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list(APPEND TORCH_PYTHON_COMPILE_DEFINITIONS USE_C10D)
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endif()
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# NCCL is a private dependency of libtorch, but libtorch_python includes
|
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# some private headers of libtorch, which in turn include NCCL. As a hacky
|
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# alternative to making NCCL a public dependency of libtorch, we make it
|
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# a private dependency of libtorch_python as well.
|
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if(USE_NCCL)
|
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list(APPEND TORCH_PYTHON_LINK_LIBRARIES __caffe2_nccl)
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endif()
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# Same for MPI.
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if(USE_MPI)
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list(APPEND TORCH_PYTHON_LINK_LIBRARIES MPI::MPI_CXX)
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endif()
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list(APPEND TORCH_PYTHON_COMPILE_DEFINITIONS USE_C10D)
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|
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if(USE_NCCL AND NOT WIN32)
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list(APPEND TORCH_PYTHON_SRCS
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@ -366,10 +364,6 @@ if(BUILD_LIBTORCHLESS)
|
||||
target_compile_definitions(torch_python PRIVATE USE_C10D_NCCL)
|
||||
endif()
|
||||
|
||||
if(USE_DISTRIBUTED)
|
||||
target_compile_definitions(torch_python PRIVATE USE_DISTRIBUTED)
|
||||
endif()
|
||||
|
||||
if(USE_MPI AND USE_C10D_MPI)
|
||||
target_compile_definitions(torch_python PRIVATE USE_C10D_MPI)
|
||||
endif()
|
||||
|
||||
@ -15,9 +15,7 @@
|
||||
#include <torch/csrc/utils/cpp_stacktraces.h>
|
||||
#include <torch/csrc/utils/pybind.h>
|
||||
|
||||
#if defined(USE_DISTRIBUTED)
|
||||
#include <torch/csrc/distributed/c10d/exception.h>
|
||||
#endif
|
||||
|
||||
inline void PyErr_SetString(PyObject* type, const std::string& message) {
|
||||
PyErr_SetString(type, message.c_str());
|
||||
|
||||
@ -120,14 +120,12 @@
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#ifdef USE_C10D
|
||||
#include <torch/csrc/distributed/autograd/python_autograd.h>
|
||||
#include <torch/csrc/distributed/c10d/c10d.h>
|
||||
#include <torch/csrc/distributed/rpc/rpc.h>
|
||||
#include <torch/csrc/distributed/rpc/testing/testing.h>
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#if defined(USE_VALGRIND)
|
||||
#include <callgrind.h>
|
||||
@ -552,11 +550,7 @@ static PyObject* THPModule_getBackcompatKeepdimWarn(
|
||||
}
|
||||
|
||||
static PyObject* THPModule_hasDistributed(PyObject* _unused, PyObject* noargs) {
|
||||
#ifdef USE_DISTRIBUTED
|
||||
Py_RETURN_TRUE;
|
||||
#else
|
||||
Py_RETURN_FALSE;
|
||||
#endif
|
||||
}
|
||||
|
||||
static PyObject* THPModule_showConfig(PyObject* module, PyObject* noargs) {
|
||||
@ -1993,7 +1987,7 @@ PyObject* initModule() {
|
||||
#ifdef USE_XPU
|
||||
THPUtils_addPyMethodDefs(methods, THXPModule_methods());
|
||||
#endif
|
||||
#if defined(USE_DISTRIBUTED) && defined(USE_C10D)
|
||||
#ifdef USE_C10D
|
||||
THPUtils_addPyMethodDefs(
|
||||
methods, torch::distributed::c10d::python_functions());
|
||||
#ifndef _WIN32
|
||||
|
||||
@ -8,9 +8,7 @@
|
||||
#include <torch/csrc/autograd/python_autograd.h>
|
||||
#include <torch/csrc/autograd/python_cpp_function.h>
|
||||
#include <torch/csrc/autograd/python_variable.h>
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#include <torch/csrc/distributed/autograd/functions/sendrpc_backward.h>
|
||||
#endif
|
||||
#include <torch/csrc/jit/python/python_tracer.h>
|
||||
#include <torch/csrc/utils/pybind.h>
|
||||
#include <torch/csrc/utils/python_numbers.h>
|
||||
@ -150,11 +148,9 @@ void THPAutograd_initFunctions() {
|
||||
static PyTypeObject CopyBackwardsClass;
|
||||
addClass<CopyBackwards, NoCtor>(module, CopyBackwardsClass, "CopyBackwards");
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
static PyTypeObject SendRpcBackwardClass;
|
||||
addClass<torch::distributed::autograd::SendRpcBackward, NoCtor>(
|
||||
module, SendRpcBackwardClass, "SendRpcBackward");
|
||||
#endif
|
||||
|
||||
static PyTypeObject CopySlicesClass;
|
||||
addClass<CopySlices, NoCtor>(module, CopySlicesClass, "CopySlices");
|
||||
|
||||
@ -1,7 +1,5 @@
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#include <torch/csrc/distributed/c10d/Functional.hpp>
|
||||
#endif
|
||||
#include <torch/csrc/inductor/aoti_torch/c/shim_cpu.h>
|
||||
#include <torch/csrc/inductor/aoti_torch/utils.h>
|
||||
|
||||
@ -533,7 +531,6 @@ AOTITorchError aoti_torch_cpu__weight_int4pack_mm_cpu_tensor(
|
||||
});
|
||||
}
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
AOTITorchError aoti_torch_cpu__c10d_functional_all_reduce_(
|
||||
AtenTensorHandle inp,
|
||||
const char* reduce_op,
|
||||
@ -566,4 +563,3 @@ AOTITorchError aoti_torch_cpu__c10d_functional_wait_tensor(
|
||||
*ret0 = new_tensor_handle(std::move(tmp_result));
|
||||
});
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -13,6 +13,8 @@
|
||||
#include <torch/csrc/Layout.h>
|
||||
#include <torch/csrc/QScheme.h>
|
||||
#include <torch/csrc/Stream.h>
|
||||
#include <torch/csrc/distributed/rpc/py_rref.h>
|
||||
#include <torch/csrc/distributed/rpc/rref_impl.h>
|
||||
#include <torch/csrc/jit/api/module.h>
|
||||
#include <torch/csrc/jit/frontend/schema_matching.h>
|
||||
#include <torch/csrc/jit/frontend/tracer.h>
|
||||
@ -24,10 +26,6 @@
|
||||
#include <torch/csrc/utils/pybind.h>
|
||||
#include <torch/csrc/utils/python_arg_parser.h>
|
||||
#include <torch/csrc/utils/six.h>
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#include <torch/csrc/distributed/rpc/py_rref.h>
|
||||
#include <torch/csrc/distributed/rpc/rref_impl.h>
|
||||
#endif
|
||||
|
||||
#include <ATen/core/function_schema.h>
|
||||
#include <c10/core/Stream.h>
|
||||
|
||||
@ -1225,7 +1225,7 @@ std::shared_ptr<SugaredValue> toSugaredValue(
|
||||
} else if (obj.ptr() == py::module::import("torch").attr("_check").ptr()) {
|
||||
return std::make_shared<TorchCheckValue>();
|
||||
#ifdef USE_RPC
|
||||
// RPC module is only available when build flag "USE_DISTRIBUTED" is on.
|
||||
// This is not defined on WINDOWS
|
||||
} else if (
|
||||
isRpcAvailable &&
|
||||
obj.ptr() ==
|
||||
@ -1238,7 +1238,6 @@ std::shared_ptr<SugaredValue> toSugaredValue(
|
||||
return SpecialFormValue::create(prim::rpc_sync);
|
||||
} else if (
|
||||
isRpcAvailable &&
|
||||
// RPC module is only available when build flag "USE_DISTRIBUTED" is on.
|
||||
obj.ptr() ==
|
||||
py::module::import("torch.distributed.rpc").attr("remote").ptr()) {
|
||||
return SpecialFormValue::create(prim::rpc_remote);
|
||||
|
||||
@ -128,13 +128,8 @@ struct InterpreterContinuation {
|
||||
std::optional<at::ThreadLocalState> tls_state = std::nullopt)
|
||||
: state(std::move(state_)),
|
||||
stack(std::move(stack_)),
|
||||
tls_state_(std::move(tls_state))
|
||||
#ifdef USE_DISTRIBUTED
|
||||
,
|
||||
dist_autograd_context_id_(dist_autograd_context_id)
|
||||
#endif
|
||||
{
|
||||
}
|
||||
tls_state_(std::move(tls_state)),
|
||||
dist_autograd_context_id_(dist_autograd_context_id) {}
|
||||
|
||||
void operator()();
|
||||
|
||||
@ -142,9 +137,10 @@ struct InterpreterContinuation {
|
||||
InterpreterState state;
|
||||
Stack stack;
|
||||
std::optional<at::ThreadLocalState> tls_state_ = std::nullopt;
|
||||
#ifdef USE_DISTRIBUTED
|
||||
int64_t dist_autograd_context_id_;
|
||||
#ifndef USE_RPC
|
||||
[[maybe_unused]]
|
||||
#endif
|
||||
int64_t dist_autograd_context_id_;
|
||||
};
|
||||
|
||||
// what is the tensors type, including state from the current execution context
|
||||
|
||||
@ -79,9 +79,7 @@ class TORCH_API Pickler {
|
||||
void pushTuple(const IValue& ivalue);
|
||||
void pushString(const std::string& string);
|
||||
void pushDevice(const IValue& ivalue);
|
||||
#ifdef USE_DISTRIBUTED
|
||||
void pushRRef(const IValue& ivalue);
|
||||
#endif
|
||||
// unmemoized version
|
||||
void pushStringImpl(const std::string& string);
|
||||
void pushStorageOfTensor(const at::Tensor& tensor);
|
||||
|
||||
@ -140,9 +140,7 @@ class TORCH_API Unpickler {
|
||||
void rebuildParameter();
|
||||
void rebuildTensorFromTypeV2();
|
||||
void rebuildSparseTensor();
|
||||
#ifdef USE_DISTRIBUTED
|
||||
void rebuildRRef();
|
||||
#endif
|
||||
PickleOpCode readInstruction();
|
||||
PickleOpCode readOpCode() {
|
||||
return static_cast<PickleOpCode>(read<uint8_t>());
|
||||
|
||||
@ -30,15 +30,12 @@
|
||||
#include <torch/csrc/profiler/standalone/execution_trace_observer.h>
|
||||
#include <torch/csrc/profiler/util.h>
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#include <torch/csrc/distributed/c10d/ParamCommsUtils.hpp>
|
||||
#endif // USE_DISTRIBUTED
|
||||
|
||||
using namespace at;
|
||||
|
||||
// Collective property attributes
|
||||
// https://github.com/pytorch/pytorch/issues/124674
|
||||
#ifdef USE_DISTRIBUTED
|
||||
constexpr auto kETCommsName = "collective_name";
|
||||
constexpr auto kETInMsgNelems = "in_msg_nelems";
|
||||
constexpr auto kETOutMsgNelems = "out_msg_nelems";
|
||||
@ -49,7 +46,6 @@ constexpr auto kETGlobalRankStride = "global_rank_stride";
|
||||
constexpr auto kETGroupSize = "pg_size";
|
||||
constexpr auto kETProcessGroupName = "pg_name";
|
||||
constexpr auto kETProcessGroupDesc = "pg_desc";
|
||||
#endif // USE_DISTRIBUTED
|
||||
|
||||
namespace torch::profiler::impl {
|
||||
|
||||
@ -269,7 +265,6 @@ static std::ofstream openOutputFile(const std::string& name) {
|
||||
return stream;
|
||||
}
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
static std::string getAttrJson(
|
||||
const std::string& name,
|
||||
const std::string& type,
|
||||
@ -282,7 +277,6 @@ static std::string getAttrJson(
|
||||
type,
|
||||
value);
|
||||
}
|
||||
#endif
|
||||
|
||||
static void writeJsonNode(
|
||||
std::ofstream& out,
|
||||
@ -660,7 +654,6 @@ static void handleKernelBackendInfo(
|
||||
inline std::string getCommsNodeAttrs(const RecordFunction& fn) { // NOLINT
|
||||
std::vector<std::string> attrs;
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
// We rely on paramcommsdebug object that is available in thread local info
|
||||
auto debugInfo = dynamic_cast<ParamCommsDebugInfo*>(
|
||||
c10::ThreadLocalDebugInfo::get(c10::DebugInfoKind::PARAM_COMMS_INFO));
|
||||
@ -704,8 +697,6 @@ inline std::string getCommsNodeAttrs(const RecordFunction& fn) { // NOLINT
|
||||
|
||||
addAttr(kGroupSize, kETGroupSize, "uint64");
|
||||
|
||||
#endif // USE_DISTRIBUTED
|
||||
|
||||
// XXX consider using as string stream?
|
||||
return attrs.empty() ? "" : fmt::format(", {}", fmt::join(attrs, ", "));
|
||||
}
|
||||
|
||||
@ -11,9 +11,7 @@
|
||||
#ifdef USE_KINETO
|
||||
#include <libkineto.h>
|
||||
#endif
|
||||
#ifdef USE_DISTRIBUTED
|
||||
#include <torch/csrc/distributed/c10d/ParamCommsUtils.hpp>
|
||||
#endif // USE_DISTRIBUTED
|
||||
|
||||
namespace torch::profiler::impl {
|
||||
|
||||
@ -455,7 +453,6 @@ std::unordered_map<std::string, std::string> saveNcclMeta(
|
||||
// @lint-ignore CLANGTIDY
|
||||
const SaveNcclMetaConfig& config) {
|
||||
std::unordered_map<std::string, std::string> map;
|
||||
#ifdef USE_DISTRIBUTED
|
||||
auto debugInfo = dynamic_cast<ParamCommsDebugInfo*>(
|
||||
c10::ThreadLocalDebugInfo::get(c10::DebugInfoKind::PARAM_COMMS_INFO));
|
||||
|
||||
@ -565,7 +562,6 @@ std::unordered_map<std::string, std::string> saveNcclMeta(
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif // USE_DISTRIBUTED
|
||||
return map;
|
||||
}
|
||||
|
||||
|
||||
@ -185,7 +185,6 @@ struct HashCombine {
|
||||
}
|
||||
};
|
||||
|
||||
#ifdef USE_DISTRIBUTED
|
||||
constexpr auto kCommsName = "Collective name";
|
||||
constexpr auto kDtype = "dtype";
|
||||
constexpr auto kInMsgNelems = "In msg nelems";
|
||||
@ -203,6 +202,5 @@ constexpr auto kP2pSrc = "Src Rank";
|
||||
constexpr auto kP2pDst = "Dst Rank";
|
||||
constexpr auto kInTensorsStart = "Input Tensors start";
|
||||
constexpr auto kOutTensorsStart = "Output Tensors start";
|
||||
#endif // USE_DISTRIBUTED
|
||||
|
||||
} // namespace torch::profiler::impl
|
||||
|
||||
@ -14,16 +14,10 @@ log = logging.getLogger(__name__)
|
||||
|
||||
def is_available() -> bool:
|
||||
"""
|
||||
Return ``True`` if the distributed package is available.
|
||||
|
||||
Otherwise,
|
||||
``torch.distributed`` does not expose any other APIs. Currently,
|
||||
``torch.distributed`` is available on Linux, MacOS and Windows. Set
|
||||
``USE_DISTRIBUTED=1`` to enable it when building PyTorch from source.
|
||||
Currently, the default value is ``USE_DISTRIBUTED=1`` for Linux and Windows,
|
||||
``USE_DISTRIBUTED=0`` for MacOS.
|
||||
Always returns ``True``. Note that even if distributed is available,
|
||||
there may not necessarily be any usable backends.
|
||||
"""
|
||||
return hasattr(torch._C, "_c10d_init")
|
||||
return True
|
||||
|
||||
|
||||
if is_available() and not torch._C._c10d_init():
|
||||
|
||||
@ -5,10 +5,6 @@ from typing import Union
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
# The two imports below are not always available depending on the
|
||||
# USE_DISTRIBUTED compile flag. Make sure they raise import error
|
||||
# if we're trying to use them.
|
||||
from torch.distributed import group, ProcessGroup
|
||||
|
||||
|
||||
|
||||
@ -2,10 +2,6 @@
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from torch.autograd import Function
|
||||
|
||||
# The two imports below are not always available depending on the
|
||||
# USE_DISTRIBUTED compile flag. Make sure they raise import error
|
||||
# if we're trying to use them.
|
||||
from torch.distributed import group, ReduceOp
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user