8000 Update on "[dynamo] handle 3.13.0 __dict__ watcher bug" · pytorch/pytorch@797aa18 · GitHub
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Update on "[dynamo] handle 3.13.0 __dict__ watcher bug"
 python/cpython#116115 introduced a bug (python/cpython#125608) where changing the attributes of an object may not fire the dict watchers registered to the object's `__dict__`. It has been fixed by python/cpython#125611 but will only be in 3.13.1+. This PR disables the dict watcher guard shortcut for `__dict__`s on 3.13.0 and warns the user to try using 3.13.1+ instead. We also added a simple test to check for this functionality in the future. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames rec [ghstack-poisoned]
2 parents 7a7c74f + 679f84b commit 797aa18

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‎.circleci/scripts/binary_populate_env.sh

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@@ -114,6 +114,12 @@ if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_B
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fi
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fi
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USE_GLOO_WITH_OPENSSL="ON"
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if [[ "$GPU_ARCH_TYPE" =~ .*aarch64.* ]]; then
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USE_GLOO_WITH_OPENSSL="OFF"
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USE_GOLD_LINKER="OFF"
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fi
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cat >"$envfile" <<EOL
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# =================== The following code will be executed inside Docker container ===================
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export TZ=UTC
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export USE_GOLD_LINKER="${USE_GOLD_LINKER}"
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export USE_GLOO_WITH_OPENSSL="ON"
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export USE_GLOO_WITH_OPENSSL="${USE_GLOO_WITH_OPENSSL}"
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# =================== The above code will be executed inside Docker container ===================
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EOL
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‎.github/lf-scale-config.yml

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‎.github/workflows/_binary-build-linux.yml

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@@ -271,7 +271,7 @@ jobs:
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)
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docker exec -t -w "${PYTORCH_ROOT}" "${container_name}" bash -c "bash .circleci/scripts/binary_populate_env.sh"
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if [[ ${BUILD_ENVIRONMENT} == *"aarch64"* ]]; then
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docker exec -t "${container_name}" bash -c "bash /builder/aarch64_linux/aarch64_ci_build.sh"
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docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /builder/aarch64_linux/aarch64_ci_build.sh"
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elif [[ ${{ inputs.PACKAGE_TYPE }} == "manywheel" || ${{ inputs.PACKAGE_TYPE }} == "libtorch" ]]; then
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docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /pytorch/.ci/${{ inputs.PACKAGE_TYPE }}/build.sh"
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else

‎aten/src/ATen/DLConvertor.h

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@@ -13,12 +13,10 @@ namespace at {
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TORCH_API ScalarType toScalarType(const DLDataType& dtype);
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TORCH_API DLManagedTensor* toDLPack(const Tensor& src);
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TORCH_API Tensor fromDLPack(DLManagedTensor* src);
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[[deprecated("Please migrate to a non-const variant")]] inline Tensor fromDLPack(
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const DLManagedTensor* src) {
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C10_DEPRECATED_MESSAGE("Please migrate to a non-const variant")
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inline Tensor fromDLPack(const DLManagedTensor* src) {
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return fromDLPack(const_cast<DLManagedTensor*>(src));
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}
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TORCH_API Tensor
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fromDLPack(DLManagedTensor* src, std::function<void(void*)> deleter);
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TORCH_API DLDataType getDLDataType(const Tensor& t);

‎aten/src/ATen/Dispatch.h

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@@ -104,23 +104,23 @@ inline at::ScalarType scalar_type(at::ScalarType s) {
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return s;
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}
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[[deprecated(
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C10_DEPRECATED_MESSAGE(
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"passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, "
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"pass an at::ScalarType instead")]] inline at::ScalarType
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scalar_type(const at::DeprecatedTypeProperties& t) {
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"pass an at::ScalarType instead")
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inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) {
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return t.scalarType();
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}
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[[deprecated(
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C10_DEPRECATED_MESSAGE(
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"AT_DISPATCH_ALL_TYPES_AND_HALF is deprecated, "
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"use AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, ...) instead")]] inline void
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deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF() {}
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"use AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, ...) instead")
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inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF() {}
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[[deprecated(
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C10_DEPRECATED_MESSAGE(
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"AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX is deprecated, "
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"use AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(at::ScalarType::Half, ...) "
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"instead")]] inline void
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deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX() {}
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"instead")
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inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX() {}
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} // namespace detail
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‎aten/src/ATen/MemoryOverlap.cpp

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@@ -61,7 +61,7 @@ MemOverlapStatus get_overlap_status(const TensorImpl* a, const TensorImpl* b) {
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// same pointer across multiple storages there are many
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// similar situations (e.g., storage().data() == storage().data()+1)
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// which we will miss.
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auto a_storage = a->unsafe_storage();
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const auto& a_storage = a->unsafe_storage();
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if (a_storage && a_storage.is_alias_of(b->unsafe_storage())) {
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const auto a_begin = static_cast<const char*>(a->data());
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const auto a_end = a_begin + a->numel() * a->itemsize();

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