8000 Update base for Update on "[Cutlass] Remove unused dtype conversion map" · pytorch/pytorch@41797f6 · GitHub
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Update base for Update on "[Cutlass] Remove unused dtype conversion map"
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov [ghstack-poisoned]
2 parents 5e1d014 + de20d76 commit 41797f6

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‎.ci/docker/common/install_inductor_benchmark_deps.sh

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@@ -14,13 +14,6 @@ function install_timm() {
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local commit
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commit=$(get_pinned_commit timm)
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# TODO (huydhn): There is no torchvision release on 3.13 when I write this, so
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# I'm using nightly here instead. We just need to package to be able to install
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# TIMM. Removing this once vision has a release on 3.13
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if [[ "${ANACONDA_PYTHON_VERSION}" == "3.13" ]]; then
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pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu124
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fi
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pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
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# Clean up
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conda_run pip uninstall -y cmake torch torchvision triton

‎.ci/magma/Makefile

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@@ -12,13 +12,12 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
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-e PACKAGE_NAME=${PACKAGE_NAME}${DESIRED_CUDA_SHORT} \
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-e DESIRED_CUDA=${DESIRED_CUDA} \
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-e CUDA_ARCH_LIST="${CUDA_ARCH_LIST}" \
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"pytorch/manylinux2_28-builder:cuda${DESIRED_CUDA}-main" \
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"pytorch/almalinux-builder:cuda${DESIRED_CUDA}-main" \
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magma/build_magma.sh
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.PHONY: all
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all: magma-cuda128
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all: magma-cuda126
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all: magma-cuda124
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all: magma-cuda118
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.PHONY:
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magma-cuda126:
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$(DOCKER_RUN)
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.PHONY: magma-cuda124
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magma-cuda124: DESIRED_CUDA := 12.4
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magma-cuda124:
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$(DOCKER_RUN)
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.PHONY: magma-cuda118
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magma-cuda118: DESIRED_CUDA := 11.8
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magma-cuda118: CUDA_ARCH_LIST += -gencode arch=compute_37,code=sm_37

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

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id-token: write
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strategy:
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matrix:
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cuda_version: ["128", "126", "124", "118"]
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cuda_version: ["128", "126", "118"]
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steps:
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- name: Checkout PyTorch
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uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

‎.github/workflows/llm_td_retrieval.yml

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ref: v0.0.2
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path: llm-target-determinator
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- name: Setup miniconda
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uses: pytorch/test-infra/.github/actions/setup-miniconda@main
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with:
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python-version: "3.9"
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- name: Install requirements
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shell: bash
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run: |
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set -euxo pipefail
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${CONDA_RUN} pip install -r llm-target-determinator/requirements.txt
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python3 -m pip install -r llm-target-determinator/requirements.txt
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cd "${GITHUB_WORKSPACE}/codellama"
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${CONDA_RUN} pip install -e .
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python3 -m pip install -e .
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- name: Fetch CodeLlama Checkpoint
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shell: bash
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shell: bash
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command: |
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set -euxo pipefail
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${CONDA_RUN} python -m pip install awscli==1.29.40
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python3 -m pip install awscli==1.29.40
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cd "${GITHUB_WORKSPACE}"/llm-target-determinator/assets
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aws s3 cp "s3://target-determinator-assets/indexes/latest" . --recursive
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@@ -94,7 +89,8 @@ jobs:
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run: |
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set -euxo pipefail
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cd "${GITHUB_WORKSPACE}"/llm-target-determinator
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${CONDA_RUN} torchrun \
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export PATH="$HOME/.local/bin:$PATH"
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torchrun \
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--standalone \
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--nnodes=1 \
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--nproc-per-node=1 \

‎.github/workflows/upload-test-stats-while-running.yml

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- name: Setup Linux
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uses: ./.github/actions/setup-linux
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- name: Setup miniconda
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uses: pytorch/test-infra/.github/actions/setup-miniconda@main
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with:
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python-version: "3.10"
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- name: Install requirements
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run: |
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${CONDA_RUN} pip install requests==2.32.2 boto3==1.35.42
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python3 -m pip install requests==2.32.2 boto3==1.35.42
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- name: Upload test stats
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: |
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${CONDA_RUN} python -m tools.stats.upload_test_stats_running_jobs
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python3 -m tools.stats.upload_test_stats_running_jobs

‎aten/src/ATen/core/List_test.cpp

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#include <gtest/gtest.h>
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using namespace c10;
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using std::string;
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// NOLINTBEGIN(performance-move-const-arg, bugprone-use-after-move, *analyzer*Move)
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TEST(ListTestIValueBasedList, givenEmptyList_whenCallingEmpty_thenReturnsTrue) {

‎aten/src/ATen/core/boxing/impl/kernel_function_legacy_test.cpp

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@@ -519,7 +519,7 @@ TEST(OperatorRegistrationTestLegacyFunctionBasedKernel, givenKernelWithDictInput
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EXPECT_EQ(2, captured_dict_size);
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}
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string kernelWithDictInputWithOutput(Dict<string, string> input1) {
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std::string kernelWithDictInputWithOutput(Dict<string, string> input1) {
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return input1.at("key2");
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}
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EXPECT_EQ(2, captured_dict_size);
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}
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string kernelWithUnorderedMapInputWithOutput(std::unordered_map<string, string> input1) {
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std::string kernelWithUnorderedMapInputWithOutput(std::unordered_map<string, string> input1) {
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return input1.at("key2");
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}
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‎aten/src/ATen/core/boxing/impl/kernel_function_test.cpp

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@@ -468,7 +468,7 @@ TEST(OperatorRegistrationTestFunctionBasedKernel, givenKernelWithDictInput_witho
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EXPECT_EQ(2, captured_dict_size);
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}
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string kernelWithDictInputWithOutput(Dict<string, string> input1) {
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std::string kernelWithDictInputWithOutput(Dict<string, string> input1) {
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return input1.at("key2");
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}
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‎aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor_test.cpp

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}
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struct KernelWithDictInputWithOutput final : OperatorKernel {
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string operator()(Dict<string, string> input1) {
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std::string operator()(Dict<string, std::string> input1) {
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return input1.at("key2");
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}
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};
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auto op = c10::Dispatcher::singleton().findSchema({"_test::dict_input", ""});
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ASSERT_TRUE(op.has_value());
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Dict<string, string> dict;
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Dict<string, std::string> dict;
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dict.insert("key1", "value1");
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dict.insert("key2", "value2");
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auto outputs = callOp(*op, dict);
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}
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struct KernelWithDictOutput final : OperatorKernel {
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Dict<string, string> operator()(Dict<string, string> input) {
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Dict<string, std::string> operator()(Dict<string, std::string> input) {
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return input;
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}
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};
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auto op = c10::Dispatcher::singleton().findSchema({"_test::dict_output", ""});
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ASSERT_TRUE(op.has_value());
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Dict<string, string> dict;
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Dict<string, std::string> dict;
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dict.insert("key1", "value1");
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dict.insert("key2", "value2");
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auto outputs = callOp(*op, dict);
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EXPECT_EQ(1, outputs.size());
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auto output = c10::impl::toTypedDict<string, string>(outputs[0].toGenericDict());
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auto output = c10::impl::toTypedDict<string, std::string>(outputs[0].toGenericDict());
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EXPECT_EQ(2, output.size());
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EXPECT_EQ("value1", output.at("key1"));
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};
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struct KernelWithTupleInput final : OperatorKernel {
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string operator()(std::tuple<string, int64_t, double> input1) {
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std::string operator()(std::tuple<string, int64_t, double> input1) {
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return std::get<0>(input1);
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}
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};

‎aten/src/ATen/native/MathBitsFallback.h

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// NOTE: To use this fallback, `clone` and `copy_` should fully understand and be able to correctly handle the semantic of your math bit.
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struct MathOpFallback {
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MathOpFallback(DispatchKey key_, string op_name_) : key(key_), op_name(std::move(op_name_)) {}
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MathOpFallback(DispatchKey key_, std::string op_name_) : key(key_), op_name(std::move(op_name_)) {}
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virtual bool is_bit_set(const Tensor&) = 0;
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void fallback_impl(const c10::OperatorHandle& op, DispatchKeySet dispatch_keys, torch::jit::Stack* stack) {
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/*
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virtual ~MathOpFallback() = default;
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DispatchKey key;
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string op_name;
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std::string op_name;
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};
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} // namespace at::native

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