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988bd0173c6767b8cd4a8e8c008a348c62908d8f
pytorch/torch/csrc/jit/python/init.cpp
Edward Z. Yang 988bd0173c Add OpOverload.decompose API (#83075) 
This allows you to directly call into the CompositeImplicitAutograd
implementation of an operator, *without* changing any aspects of the
dispatcher state.  In particular, you can use this to recursively call
into a decomposition, dispatching back to your tensor subclass/mode
as desired.

Hypothetically, we should also make these available in the
decompositions dictionary, but I'm leaving this as future work as
enumerating these decompositions is annoying (as operators are lazily
registered.)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83075
Approved by: https://github.com/albanD
2022-08-09 18:53:19 +00:00

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#include <pybind11/pytypes.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <torch/csrc/utils/schema_info.h>
#include <ATen/core/operator_name.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/backends/backend_init.h>
#include <torch/csrc/jit/codegen/cuda/interface.h>
#include <torch/csrc/jit/codegen/cuda/python_frontend/python_bindings.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/codegen/fuser/kernel_cache.h>
#if (!defined(FBCODE_CAFFE2) && defined(BUILD_ONEDNN_GRAPH))
#include <torch/csrc/jit/codegen/onednn/interface.h>
#endif
#include <c10/core/SymIntNodeImpl.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/autocast.h>
#include <torch/csrc/jit/passes/batch_mm.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/canonicalize_graph_fuser_ops.h>
#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
#include <torch/csrc/jit/passes/constant_pooling.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/create_autodiff_subgraphs.h>
#include <torch/csrc/jit/passes/create_functional_graphs.h>
#include <torch/csrc/jit/passes/cuda_graph_fuser.h>
#include <torch/csrc/jit/passes/dbr_quantization/remove_redundant_aliases.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/decompose_ops.h>
#include <torch/csrc/jit/passes/device_type_analysis.h>
#include <torch/csrc/jit/passes/dtype_analysis.h>
#include <torch/csrc/jit/passes/erase_number_types.h>
#include <torch/csrc/jit/passes/fold_conv_bn.h>
#include <torch/csrc/jit/passes/freeze_module.h>
#include <torch/csrc/jit/passes/frozen_concat_linear.h>
#include <torch/csrc/jit/passes/frozen_conv_add_relu_fusion.h>
#include <torch/csrc/jit/passes/frozen_conv_folding.h>
#include <torch/csrc/jit/passes/frozen_graph_optimizations.h>
#include <torch/csrc/jit/passes/frozen_linear_transpose.h>
#include <torch/csrc/jit/passes/frozen_ops_to_mkldnn.h>
#include <torch/csrc/jit/passes/fuse_linear.h>
#include <torch/csrc/jit/passes/fuse_relu.h>
#include <torch/csrc/jit/passes/graph_fuser.h>
#include <torch/csrc/jit/passes/inline_fork_wait.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/passes/integer_value_refinement.h>
#include <torch/csrc/jit/passes/loop_unrolling.h>
#include <torch/csrc/jit/passes/lower_graph.h>
#include <torch/csrc/jit/passes/lower_tuples.h>
#include <torch/csrc/jit/passes/metal_rewrite.h>
#include <torch/csrc/jit/passes/normalize_ops.h>
#include <torch/csrc/jit/passes/peephole.h>
#include <torch/csrc/jit/passes/peephole_list_idioms.h>
#include <torch/csrc/jit/passes/quantization/dedup_module_uses.h>
#include <torch/csrc/jit/passes/quantization/finalize.h>
#include <torch/csrc/jit/passes/quantization/fusion_passes.h>
#include <torch/csrc/jit/passes/quantization/insert_observers.h>
#include <torch/csrc/jit/passes/quantization/insert_quant_dequant.h>
#include <torch/csrc/jit/passes/quantization/quantization_type.h>
#include <torch/csrc/jit/passes/refine_tuple_types.h>
#include <torch/csrc/jit/passes/remove_dropout.h>
#include <torch/csrc/jit/passes/remove_expands.h>
#include <torch/csrc/jit/passes/remove_inplace_ops.h>
#include <torch/csrc/jit/passes/remove_mutation.h>
#include <torch/csrc/jit/passes/replacement_of_old_operators.h>
#include <torch/csrc/jit/passes/restore_mutation.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/passes/specialize_autogradzero.h>
#include <torch/csrc/jit/passes/subgraph_rewrite.h>
#include <torch/csrc/jit/passes/symbolic_shape_analysis.h>
#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
#include <torch/csrc/jit/passes/utils/check_alias_annotation.h>
#include <torch/csrc/jit/passes/vulkan_rewrite.h>
#include <torch/csrc/jit/passes/xnnpack_rewrite.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_arg_flatten.h>
#include <torch/csrc/jit/python/python_custom_class.h>
#include <torch/csrc/jit/python/python_ir.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/python/python_tree_views.h>
#include <torch/csrc/jit/python/script_init.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/csrc/jit/runtime/autodiff.h>
#include <torch/csrc/jit/runtime/decomposition_registry.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/runtime/jit_exception.h>
#include <torch/csrc/jit/runtime/jit_trace.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/print_handler.h>
#include <torch/csrc/jit/runtime/static/init.h>
#include <torch/csrc/jit/runtime/symbolic_shape_registry.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/tensorexpr/kernel.h>
#include <torch/csrc/jit/tensorexpr/tensorexpr_init.h>
#include <torch/csrc/utils/cpp_stacktraces.h>
#include <c10/macros/Export.h>
#include <c10/util/irange.h>
#include <c10/util/signal_handler.h>
#include <caffe2/serialize/inline_container.h>
#include <pybind11/cast.h>
#include <pybind11/functional.h>
#include <pybind11/iostream.h>
#include <pybind11/operators.h>
#include <torch/csrc/jit/runtime/profiling_graph_executor_impl.h>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
namespace torch {
namespace jit {
using c10::AliasInfo;
using c10::Argument;
using c10::FunctionSchema;
using c10::SchemaArgType;
using c10::SchemaArgument;
using c10::SymIntNode;
using caffe2::serialize::PyTorchStreamReader;
using caffe2::serialize::PyTorchStreamWriter;
using torch::utils::SchemaInfo;
static c10::SymIntNode toSymIntNode(c10::SymIntNode a, py::object b) {
return torch::is_symint_node(b) ? b.cast<c10::SymIntNode>()
: a->wrap(b.cast<int64_t>());
}
class PythonSymIntNodeImpl : public c10::SymIntNodeImpl {
public:
PythonSymIntNodeImpl(py::object pyobj) : c10::SymIntNodeImpl() {
pyobj_ = std::make_shared<c10::SafePyObject>(
pyobj.release().ptr(), getPyInterpreter());
};
virtual SymIntNode wrap(int64_t num) override {
py::gil_scoped_acquire acquire;
auto r = getPyObj().attr("wrap")(num);
return c10::make_intrusive<PythonSymIntNodeImpl>(r);
}
virtual bool bool_() override {
py::gil_scoped_acquire acquire;
return getPyObj().attr("__bool__")().is(py::handle(Py_True));
}
virtual int64_t int_() override {
py::gil_scoped_acquire acquire;
return getPyObj().attr("__int__")().cast<int64_t>();
}
virtual std::string str() override {
py::gil_scoped_acquire acquire;
return getPyObj().attr("__str__")().cast<std::string>();
}
virtual SymIntNode dispatch_common_(
const char* fname,
const SymIntNode& other) {
auto pother = dynamic_cast<PythonSymIntNodeImpl*>(other.get());
TORCH_CHECK(pother);
py::gil_scoped_acquire acquire;
auto r = getPyObj().attr(fname)(pother->getPyObj());
return c10::make_intrusive<PythonSymIntNodeImpl>(r);
}
virtual SymIntNode add(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode sub(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode mul(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode truediv(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode floordiv(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode mod(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode eq(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode gt(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode lt(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode le(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
virtual SymIntNode ge(const SymIntNode& other) override {
return dispatch_common_(__FUNCTION__, other);
}
py::handle getPyObj() {
return py::handle(pyobj_.get()->ptr(getPyInterpreter()));
}
std::shared_ptr<c10::SafePyObject> pyobj_ = nullptr;
};
namespace {
using autograd::variable_list;
bool loadPythonClasses() {
// Leaving this code here, because it will likely be useful at some point
// PyObject *jit_module = PyImport_ImportModule("torch.jit");
// THPUtils_assert(jit_module, "class loader couldn't access "
//"torch.jit module");
// PyObject *jit_dict = PyModule_GetDict(jit_module);
return true;
}
bool isEmptyContainer(const py::handle self) {
bool is_empty_list =
PySequence_Check(self.ptr()) && !PySequence_Size(self.ptr());
return is_empty_list;
}
} // anonymous namespace
#if !defined(USE_ROCM)
TORCH_API void runJITCPPTests();
#endif
void initJITBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
auto jit = m.def_submodule("_jit");
static py::exception<JITException> exc(m, "JITException");
py::register_exception_translator([](std::exception_ptr p) {
try {
if (p) {
std::rethrow_exception(p);
}
} catch (const JITException& e) {
// special handling of JITException, to set its python class name and msg
py::gil_scoped_acquire acquire;
const auto& className = e.getPythonClassName();
const auto& originalMsg = e.getOriginalMsg();
JITException::setCaughtOriginalMsg(originalMsg.value_or(""));
JITException::setCaughtPythonClassName(className.value_or(""));
exc(e.what());
}
});
m.def(
"_get_caught_jit_exception_class_name",
JITException::getCaughtPythonClassName);
m.def(
"_get_caught_jit_exception_original_msg",
JITException::getCaughtOriginalMsg);
py::class_<python::IODescriptor> iodescriptor(
m,
"IODescriptor"); // NOLINT(bugprone-unused-raii)
m.def("_jit_init", loadPythonClasses)
.def(
"_jit_debug_fuser_num_cached_kernel_specs",
torch::jit::fuser::debugNumCachedKernelSpecs)
.def("_jit_pass_lower_all_tuples", LowerAllTuples)
.def(
"_new_symbolic_shape_symbol",
[]() { return c10::ShapeSymbol::newSymbol().value(); })
.def(
"_jit_shape_compute_graph_for_node",
[](Node* n) -> c10::optional<std::shared_ptr<Graph>> {
if (!n->maybeSchema()) {
return c10::nullopt;
}
return shapeComputeGraphForSchema(n->schema());
})
.def(
"_jit_decomposition_graph_for_node",
[](Node* n) -> c10::optional<std::shared_ptr<Graph>> {
if (!n->maybeSchema()) {
return c10::nullopt;
}
return GetDecomposition(n->schema());
})
.def("_jit_pass_run_decompositions", RunDecompositions)
// using Node* here instead of Schema because looking up the schema
// and passing it in from Python will have a different pointer than the
// schema that is globally used for caching
.def(
"_jit_register_shape_compute_graph_for_node",
[](Node* n, std::shared_ptr<Graph>& graph) {
if (n->maybeSchema()) {
const FunctionSchema& schema = n->schema();
RegisterShapeComputeGraphForSchema(schema, graph);
} else {
TORCH_INTERNAL_ASSERT(false, "Expected schema", n);
}
})
.def(
"_jit_register_decomposition_for_schema",
[](const FunctionSchema& s, std::shared_ptr<Graph>& graph) {
// because this is invoked by python, the function schema *
// becomes different, and we need to find and reuse the
// one that is used for caching
auto op =
findOperatorFor(c10::OperatorName(s.name(), s.overload_name()));
RegisterDecomposition(op->schema(), graph);
})
.def("_jit_pass_propagate_shapes_on_graph", PropagateShapesOnGraph)
.def(
"_jit_pass_propagate_shapes_on_graph_and_build_compute",
[](std::shared_ptr<Graph>& graph) {
return PropagateShapesAndBuildLargeShapeComputeGraph(
graph, *graph->nodes().begin(), *graph->nodes().end());
})
.def(
"_jit_pass_propagate_shapes_on_graph_and_build_compute",
[](std::shared_ptr<Graph>& graph, Node* beg) {
return PropagateShapesAndBuildLargeShapeComputeGraph(
graph, beg, *graph->nodes().end());
})
.def(
"_jit_pass_propagate_shapes_on_graph_and_build_compute",
PropagateShapesAndBuildLargeShapeComputeGraph)
.def("_jit_pass_integer_value_refinement", RefineIntegerValues)
.def(
"_jit_set_symbolic_shapes_test_mode",
&setSymbolicShapeAnalysisTestMode)
.def(
"_jit_symbolic_shapes_test_mode_enabled",
&symbolicShapeAnalysisTestModeEnabled)
.def("_jit_pass_autocast", Autocast)
.def("_jit_set_autocast_mode", &setAutocastMode)
.def("_jit_pass_fuse", FuseGraph)
.def(
"_jit_pass_replace_old_ops_with_upgraders",
[](std::shared_ptr<Graph>& g) {
return ReplaceOldOperatorsWithUpgraders(g);
})
.def(
"_jit_pass_dce",
[](std::shared_ptr<Graph>& g) {
return EliminateDeadCode(g->block()); // overload resolution
})
.def(
"_jit_pass_dce_allow_deleting_nodes_with_side_effects",
[](std::shared_ptr<Graph>& g) {
return EliminateDeadCode(
g->block(),
true,
DCESideEffectPolicy::
ALLOW_DELETING_NODES_WITH_SIDE_EFFECTS); // overload
// resolution
})
.def(
"_jit_pass_cse",
[](std::shared_ptr<Graph>& g) {
return EliminateCommonSubexpression(g); // overload resolution
})
.def(
"_jit_pass_fuse_quantized_add_relu",
[](std::shared_ptr<Graph>& g) {
return FuseQuantizedAddRelu(g); // overload resolution
})
.def(
"_jit_pass_insert_observers",
[](Module& module,
const std::string& method_name,
const py::dict& qconfig_dict,
bool inplace,
int quant_type_int) {
auto dict = py::cast<std::unordered_map<
std::string,
c10::optional<std::tuple<Module, Module>>>>(qconfig_dict);
auto quant_type = static_cast<QuantType>(quant_type_int);
return InsertObservers(
module, method_name, dict, inplace, quant_type);
},
py::arg("module"),
py::arg("method_name"),
py::arg("qconfig_dict"),
py::arg("inplace"),
py::arg("quant_type_int") = 1)
.def(
"_jit_pass_insert_quant_dequant",
[](Module& module,
const std::string& method_name,
bool inplace,
bool debug,
int quant_type_int) {
auto quant_type = static_cast<QuantType>(quant_type_int);
return InsertQuantDeQuant(
module, method_name, inplace, debug, quant_type);
},
py::arg("module"),
py::arg("method_name"),
py::arg("inplace"),
py::arg("debug"),
py::arg("quant_type_int") = 1)
.def(
"_jit_pass_insert_prepack_unpack",
[](std::shared_ptr<Graph>& g) { return InsertPrepackUnpack(g); })
.def(
"_jit_pass_insert_prepack_unpack",
[](Module& module) { return InsertPrepackUnpack(module); })
.def(
"_jit_pass_quant_fusion",
[](std::shared_ptr<Graph>& g) { return QuantFusion(g); })
.def(
"_jit_pass_fold_convbn",
[](Module& module) { return FoldConvBatchNorm(module); })
.def(
"_jit_pass_dbr_quant_remove_redundant_aliases",
[](Module& module) { return DBRQuantRemoveRedundantAliases(module); })
.def(
"_freeze_module",
[](Module& module,
std::vector<std::string>& preservedAttrs,
bool freezeInterfaces,
bool preserveParameters) {
return freeze_module(
module, preservedAttrs, freezeInterfaces, preserveParameters);
},
py::arg("module"),
py::arg("preservedAttrs") = std::vector<std::string>(),
py::arg("freezeInterfaces") = true,
py::arg("preserveParameters") = false)
.def("_jit_pass_concat_frozen_linear", &FrozenConcatLinear)
.def("_jit_pass_fold_frozen_conv_bn", &FoldFrozenConvBatchnorm)
.def("_jit_pass_fold_frozen_conv_add_or_sub", &FoldFrozenConvAddOrSub)
.def("_jit_pass_fold_frozen_conv_mul_or_div", &FoldFrozenConvMulOrDiv)
.def("_jit_pass_convert_frozen_ops_to_mkldnn", &ConvertFrozenOpsToMKLDNN)
.def("_jit_pass_fuse_frozen_conv_add_relu", &FuseFrozenConvAddRelu)
.def("_jit_pass_transpose_frozen_linear", &FrozenLinearTranspose)
.def("_jit_pass_optimize_frozen_graph", &OptimizeFrozenGraph)
.def(
"_jit_pass_optimize_for_inference",
[](Module& module, std::vector<std::string> other_methods) {
optimize_for_inference(module, other_methods);
},
py::arg("module"),
py::arg("other_methods") = std::vector<std::string>())
.def("_jit_pass_fuse_linear", &FuseLinear)
.def(
"_jit_pass_fuse_add_relu",
[](std::shared_ptr<Graph>& graph) { FuseAddRelu(graph); })
.def("_jit_pass_dedup_module_uses", &DedupModuleUses)
.def("_jit_pass_replicate_dequantize", &ReplicateDeQuant)
.def(
"_jit_pass_swap_functional_linear",
[](std::shared_ptr<Graph>& graph) { SwapFunctionalLinear(graph); })
.def(
"_jit_pass_swap_functional_linear",
[](Module& module) { SwapFunctionalLinear(module); })
.def(
"_jit_pass_quant_finalize",
[](Module& module,
int quant_type_int,
const std::vector<std::string>& preserved_attrs) {
auto quant_type = static_cast<QuantType>(quant_type_int);
return Finalize(module, quant_type, preserved_attrs);
},
py::arg("module"),
py::arg("quant_type_int") = 1,
py::arg("preserved_attrs") = std::vector<std::string>())
.def(
"_jit_pass_pattern_based_rewrite",
[](const Module& m) { return PatternBasedRewrite(m); })
.def(
"_jit_pass_custom_pattern_based_rewrite",
[](const std::string& pattern,
const std::string& fused_node_name,
const Module& m) {
SubgraphRewriter subgraph_rewriter;
subgraph_rewriter.RegisterRewritePattern(pattern, fused_node_name);
subgraph_rewriter.runOnModule(m);
})
.def(
"_jit_pass_custom_pattern_based_rewrite_graph",
[](const std::string& pattern,
const std::string& fused_node_name,
std::shared_ptr<Graph> g,
const std::vector<std::pair<std::string, std::string>>&
value_name_pairs) {
SubgraphRewriter subgraph_rewriter;
subgraph_rewriter.RegisterRewritePattern(
pattern, fused_node_name, value_name_pairs);
subgraph_rewriter.runOnGraph(g);
},
py::arg("pattern"),
py::arg("fused_node_name"),
py::arg("g"),
py::arg("value_name_pairs") =
std::vector<std::pair<std::string, std::string>>())
.def("_jit_pass_constant_pooling", ConstantPooling)
// RemoveInplaceOps is used by CoreML so it must be removed with care.
.def("_jit_pass_propagate_dtype", DtypePropagation)
.def("_jit_pass_propagate_device", DeviceTypePropagation)
.def(
"_jit_pass_remove_inplace_ops",
[](const std::shared_ptr<Graph>& g) { return RemoveInplaceOps(g); })
.def(
"_jit_pass_create_functional_graphs",
[](std::shared_ptr<Graph>& g) { return CreateFunctionalGraphs(g); })
.def(
"_jit_pass_remove_mutation",
[](std::shared_ptr<Graph>& g) {
RemoveListMutation(g);
return RemoveTensorMutation(g);
})
.def(
"_jit_pass_functional_to_inplace_activation",
[](std::shared_ptr<Graph>& g) {
return FunctionalToInplaceActivation(g);
})
.def(
"_jit_pass_inplace_to_functional_activation",
[](std::shared_ptr<Graph>& g) {
return InplaceToFunctionalActivation(g);
})
.def(
"_jit_pass_inline_functional_graphs",
[](std::shared_ptr<Graph>& g) { return InlineFunctionalGraphs(g); })
.def(
"_jit_pass_peephole",
[](const std::shared_ptr<Graph>& g, bool disable_shape_peepholes) {
return PeepholeOptimize(g, disable_shape_peepholes);
},
py::arg("graph"),
py::arg("disable_shape_peepholes") = false)
.def(
"_jit_pass_peephole_list_idioms",
[](const std::shared_ptr<Graph>& g, bool refine_list_len) {
return PeepholeOptimizeListIdioms(g, refine_list_len);
},
py::arg("graph"),
py::arg("refine_list_len") = false)
.def(
"_jit_pass_refine_integer_values",
[](std::shared_ptr<Graph>& g) { return RefineIntegerValues(g); })
.def(
"_jit_pass_fuse_addmm",
[](std::shared_ptr<Graph>& g) { return FuseAddMM(g); })
.def(
"_jit_pass_canonicalize",
[](const std::shared_ptr<Graph>& g, bool keep_unique_names = true) {
return Canonicalize(g, keep_unique_names);
},
py::arg("graph"),
py::arg("keep_unique_names") = true)
.def("_jit_pass_lint", LintGraph)
.def(
"_jit_pass_complete_shape_analysis",
[](const std::shared_ptr<Graph>& graph,
const py::tuple& inputs,
bool with_grad) {
ArgumentSpecCreator arg_spec_creator(*graph);
Stack stack;
stack.reserve(inputs.size()); // captures?
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
ArgumentSpec spec = arg_spec_creator.create(with_grad, stack);
arg_spec_creator.specializeTypes(*graph, spec);
// We only get partial specialization from the arg_spec_creator, but
// we want full shape specialization. The alternative would be to
// have a "complete type inference" function in ArguemntSpecCreator.
auto g_inputs = graph->inputs();
for (const auto i : c10::irange(inputs.size())) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
PropagateInputShapes(graph);
})
.def(
"_jit_interpret_graph",
[](std::shared_ptr<Graph>& graph, const py::tuple& inputs) {
Stack stack;
stack.reserve(inputs.size()); // captures?
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
auto g_inputs = graph->inputs();
for (const auto i : c10::irange(inputs.size())) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
Code code(graph, "<on-demand-func>");
InterpreterState(code).run(stack);
return createPyObjectForStack(std::move(stack));
},
py::doc(
"Interpret a JIT graph with given inputs without running any optimization passes on it"))
.def(
"_jit_trace_graph",
[](std::shared_ptr<Graph>& graph, const py::tuple& inputs) {
Stack stack;
stack.reserve(inputs.size()); // captures?
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
auto g_inputs = graph->inputs();
for (const auto i : c10::irange(inputs.size())) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
return TraceGraph(graph, stack);
})
.def(
"_jit_trace_module",
[](Module& model, const py::tuple& inputs) {
auto graph = model.get_method("forward").graph();
Stack stack;
stack.reserve(inputs.size() + 1); // captures?
push(stack, model._ivalue());
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
auto traced = TraceGraph(graph, stack);
GRAPH_DUMP("Traced Graph", traced);
// the easiest way to replace a graph in a module is
// to remove all the nodes in the original graph
// clone everything from the traced one
graph->block()->clear();
graph->block()->cloneFrom(traced->block(), nullptr);
GRAPH_DUMP("Copied Graph", graph);
})
.def("_jit_pass_remove_expands", RemoveExpands)
.def("_jit_pass_erase_number_types", EraseNumberTypes)
.def("_jit_pass_inline_fork_wait", InlineForkWait)
.def("_jit_pass_inline", Inline)
.def(
"_jit_pass_lower_graph",
[](std::shared_ptr<Graph>& graph, const Module& self) {
return LowerGraph(*graph, self._ivalue());
})
.def("_jit_pass_loop_unrolling", UnrollLoops)
.def("_jit_pass_constant_loop_unrolling", UnrollConstantLoops)
.def(
"_jit_pass_constant_propagation_immutable_types",
[](std::shared_ptr<Graph>& g) {
return ConstantPropagationImmutableTypes(g);
})
.def(
"_jit_pass_constant_propagation",
[](std::shared_ptr<Graph>& g) { return ConstantPropagation(g); },
py::arg("graph"))
.def("_jit_pass_erase_shape_information", EraseShapeInformation)
.def(
"_jit_object_is_non_holding",
[](Node& n) {
return toIValue(n.output())->toObject()->is_weak_compilation_ref();
})
.def(
"_jit_erase_non_input_shape_information",
[](std::shared_ptr<Graph>& g) {
std::vector<TypePtr> input_types;
for (Value* v : g->inputs()) {
if (auto tt = v->type()->cast<TensorType>()) {
input_types.push_back(tt);
} else {
input_types.push_back(nullptr);
}
}
EraseShapeInformation(g);
for (size_t i = 0; i < input_types.size(); ++i) {
if (input_types[i]) {
g->inputs().at(i)->setType(input_types[i]);
}
}
})
.def(
"_jit_pass_create_autodiff_subgraphs",
[](const std::shared_ptr<Graph>& graph, py::object threshold) {
if (threshold.is(py::none())) {
CreateAutodiffSubgraphs(graph);
} else {
CreateAutodiffSubgraphs(graph, py::cast<int>(threshold));
}
},
py::arg("graph"),
py::arg("threshold") = py::none())
#if defined(BUILDING_TESTS) && !defined(USE_ROCM)
.def(
"_jit_run_cpp_tests",
[]() {
// We have to release the GIL inside this method, because if we
// happen to initialize the autograd engine in these tests, the
// newly spawned worker threads will try to initialize their
// PyThreadState*, and they need the GIL for this.
pybind11::gil_scoped_release _no_gil;
return runJITCPPTests();
})
.def("_jit_has_cpp_tests", []() { return true; })
.def("_has_tensorexpr_cpp_tests", []() { return true; })
#else
.def("_jit_run_cpp_tests", []() { throw std::exception(); })
.def("_jit_has_cpp_tests", []() { return false; })
.def("_run_tensorexpr_cpp_tests", []() { throw std::exception(); })
.def("_has_tensorexpr_cpp_tests", []() { return false; })
#endif
.def(
"_jit_flatten",
[](py::handle& obj) {
auto res = python::flatten(obj);
return std::make_pair(res.vars, res.desc);
})
.def(
"_jit_unflatten",
[](const autograd::variable_list& vars, python::IODescriptor& desc) {
return py::reinterpret_steal<py::object>(
python::unflatten(vars, desc));
})
.def("_jit_pass_canonicalize_graph_fuser_ops", CanonicalizeOps)
.def("_jit_pass_decompose_ops", DecomposeOps)
.def("_jit_pass_specialize_autogradzero", specializeAutogradZero)
.def("_jit_override_can_fuse_on_cpu", &overrideCanFuseOnCPU)
.def("_jit_override_can_fuse_on_gpu", &overrideCanFuseOnGPU)
.def("_jit_can_fuse_on_cpu", &canFuseOnCPU)
.def("_jit_can_fuse_on_gpu", &canFuseOnGPU)
.def("_jit_can_fuse_on_cpu_legacy", &canFuseOnCPULegacy)
.def("_jit_override_can_fuse_on_cpu_legacy", &overrideCanFuseOnCPULegacy)
.def(
"_jit_differentiate",
[](Graph& g) {
// the python binding slightly differs in semantics
// it makes a copy of the input Graph, and works on that
// jit::differentiate mutates the input Graph
auto g_clone = g.copy();
return differentiate(g_clone);
})
.def(
"_jit_check_alias_annotation",
[](const std::shared_ptr<Graph>& g,
const py::tuple& args,
const std::string& unqualified_op_name) {
auto stack = toTraceableStack(args);
checkAliasAnnotation(g, std::move(stack), unqualified_op_name);
})
#if (!defined(FBCODE_CAFFE2) && defined(BUILD_ONEDNN_GRAPH))
.def("_jit_set_llga_enabled", &RegisterLlgaFuseGraph::setEnabled)
.def("_jit_llga_enabled", &RegisterLlgaFuseGraph::isEnabled)
#endif
.def(
"_jit_set_tracer_state_warn",
[](bool new_warn) {
jit::tracer::getTracerStateWarnMode() = new_warn;
})
.def(
"_jit_get_tracer_state_warn",
[]() {
bool current_tracer_warn = jit::tracer::getTracerStateWarnMode();
return current_tracer_warn;
})
.def(
"_jit_set_nvfuser_skip_node_kind",
// Args:
// `op_name`: Symbol of op;
// `flip`: flag indicating whether to flip the given op in the
// skip list.
// Returns:
// a bool flag indicating if `op_name` was already in the skip
// list.
[](const std::string& op_name, bool flip = true) {
return fuser::cuda::skipNode(op_name, flip);
})
.def("_jit_set_nvfuser_enabled", &fuser::cuda::setEnabled)
.def("_jit_nvfuser_can_be_enabled", &fuser::cuda::canBeEnabled)
.def(
"_jit_set_nvfuser_single_node_mode",
[](bool flag) { return fuser::cuda::setSingletonFusion(flag); })
.def(
"_jit_nvfuser_single_node_mode",
[]() { return fuser::cuda::getSingletonFusion(); })
.def(
"_jit_set_nvfuser_horizontal_mode",
[](bool flag) { return fuser::cuda::setHorizontalFusion(flag); })
.def(
"_jit_nvfuser_horizontal_mode",
[]() { return fuser::cuda::getHorizontalFusion(); })
.def(
"_jit_set_nvfuser_guard_mode",
[](bool profiling_flag) {
bool oldState = fuser::cuda::getCudaFusionGuardMode();
fuser::cuda::getCudaFusionGuardMode() = profiling_flag;
return oldState;
})
.def("_jit_nvfuser_enabled", &fuser::cuda::isEnabled)
.def(
"_jit_nvfuser_set_comparison_callback",
[](bool run_fallback, py::function fn) {
// If set, then the callback will be run after each nvfuser fusion
// group is executed. Can be used for testing accuracy.
// If run_fallback == True, then a fallback will be run and
// unfused_outputs will be nonempty, showing the result if the
// fusion didn't take place. Otherwise, unfused_outputs will
// be empty
auto fn_ptr = std::make_shared<py::function>(fn);
auto callback_lambda = [fn_ptr](
const Stack& fused_outputs,
const Stack& unfused_outputs,
const std::string& graph_ir) {
py::gil_scoped_acquire acquire{};
(*fn_ptr)(fused_outputs, unfused_outputs, graph_ir);
};
setCudaFuserComparisonCallback({run_fallback, callback_lambda});
})
.def(
"_jit_nvfuser_clear_comparison_callback",
[]() {
setCudaFuserComparisonCallback({false, nullptr});
})
.def(
"_jit_set_profiling_mode",
[](bool profiling_flag) {
bool oldState = getProfilingMode();
getProfilingMode() = profiling_flag;
return oldState;
})
.def(
"_jit_set_profiling_executor",
[](bool profiling_flag) {
bool oldState = getExecutorMode();
getExecutorMode() = profiling_flag;
return oldState;
})
.def(
"_jit_set_num_profiled_runs",
[](size_t num) {
size_t old_num = getNumProfiledRuns();
getNumProfiledRuns() = num;
return old_num;
})
.def(
"_jit_get_num_profiled_runs",
[] {
// pybind can't automatically bind to atomic size_t
size_t num_runs = getNumProfiledRuns();
return num_runs;
})
.def(
"_jit_set_bailout_depth",
[](size_t depth) {
TORCH_WARN(
"Use _jit_set_fusion_strategy, bailout depth is deprecated. Setting to (STATIC, ",
depth,
")");
size_t old_depth = getBailoutDepth();
FusionStrategy strat = {{FusionBehavior::STATIC, depth}};
setFusionStrategy(strat);
return old_depth;
})
.def(
"_jit_set_fusion_strategy",
[](std::vector<std::pair<std::string, size_t>> strategy) {
FusionStrategy vec_conv;
for (const auto& pair : strategy) {
if (pair.first == "STATIC") {
vec_conv.emplace_back(FusionBehavior::STATIC, pair.second);
} else if (pair.first == "DYNAMIC") {
vec_conv.emplace_back(FusionBehavior::DYNAMIC, pair.second);
} else {
TORCH_INTERNAL_ASSERT(
false,
"FusionBehavior only supported 'STATIC' or 'DYNAMIC', got: ",
pair.first);
}
}
auto old_strategy = getFusionStrategy();
auto strat =
fmap(old_strategy, [](std::pair<FusionBehavior, size_t> behav) {
return std::pair<std::string, size_t>(
behav.first == FusionBehavior::STATIC ? "STATIC"
: "DYNAMIC",
behav.second);
});
setFusionStrategy(vec_conv);
return strat;
})
.def(
"_jit_set_inline_everything_mode",
[](bool enabled) { getInlineEverythingMode() = enabled; })
.def(
"_jit_get_inline_everything_mode",
[]() { return getInlineEverythingMode(); })
.def(
"_jit_get_logging_option",
[]() { return ::torch::jit::get_jit_logging_levels(); })
.def(
"_jit_set_logging_option",
[](std::string loggingOption) -> void {
::torch::jit::set_jit_logging_levels(loggingOption);
})
.def(
"_jit_set_logging_stream",
[](std::string stream_name) -> void {
if (stream_name == "stdout") {
::torch::jit::set_jit_logging_output_stream(std::cout);
} else if (stream_name == "stderr") {
::torch::jit::set_jit_logging_output_stream(std::cerr);
} else {
std::cerr << "ERROR: only `stdout` and `stderr`"
<< "are supported as output options" << std::endl;
}
})
.def(
"_storage_id",
[](const at::Tensor& ten) -> int64_t {
return reinterpret_cast<int64_t>(
ten.storage().unsafeGetStorageImpl());
})
.def(
"_jit_try_infer_type",
[](py::object obj) -> InferredType {
return tryToInferType(std::move(obj));
})
.def(
"_jit_get_te_cuda_pointwise_loop_levels",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseLoopLevels();
})
.def(
"_jit_set_te_cuda_pointwise_loop_levels",
[](int level) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseLoopLevels() = level;
})
.def(
"_jit_get_te_cuda_pointwise_block_count",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockCount();
})
.def(
"_jit_set_te_cuda_pointwise_block_count",
[](int block_count) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockCount() = block_count;
})
.def(
"_jit_get_te_cuda_pointwise_block_size",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockSize();
})
.def(
"_jit_set_te_cuda_pointwise_block_size",
[](int block_size) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockSize() = block_size;
})
.def("_jit_set_texpr_fuser_enabled", &setTensorExprFuserEnabled)
.def("_jit_texpr_fuser_enabled", &tensorExprFuserEnabled)
.def("_jit_texpr_fallback_allowed", &tensorexpr::fallbackAllowed)
.def("_jit_texpr_set_fallback_allowed", &tensorexpr::setFallbackAllowed)
.def("_jit_set_texpr_reductions_enabled", &setTexprReductionsEnabled)
.def(
"_jit_set_texpr_dynamic_shape_enabled",
&setTensorExprDynamicShapeFusionEnabled)
.def(
"_jit_texpr_dynamic_shape_enabled",
&tensorExprDynamicShapeFusionEnabled)
.def("_jit_texpr_reductions_enabled", &texprReductionsEnabled)
.def(
"_jit_set_te_generate_block_code",
[](bool gen_block_code) {
using namespace torch::jit::tensorexpr;
return getTEGenerateBlockCode() = gen_block_code;
})
.def(
"_jit_get_te_generate_block_code",
[]() -> bool {
using namespace torch::jit::tensorexpr;
return getTEGenerateBlockCode();
})
.def(
"_jit_get_te_must_use_llvm_cpu",
[]() -> bool {
using namespace torch::jit::tensorexpr;
return getTEMustUseLLVMOnCPU();
})
.def(
"_jit_set_te_must_use_llvm_cpu",
[](bool use_llvm) {
using namespace torch::jit::tensorexpr;
getTEMustUseLLVMOnCPU() = use_llvm;
})
.def(
"_jit_cat_wo_conditionals",
[](bool optimize_cat) {
using namespace torch::jit::tensorexpr;
getCatWoConditionals() = optimize_cat;
})
.def(
"_jit_opt_conditionals",
[](bool opt_conds) {
using namespace torch::jit::tensorexpr;
getOptConditionals() = opt_conds;
})
.def(
"_llvm_enabled",
[]() {
#ifdef TORCH_ENABLE_LLVM
return true;
#else
return false;
#endif
})
.def(
"_jit_pass_fuse_tensorexprs",
[](std::shared_ptr<Graph>& g) {
FuseTensorExprs(g);
RemoveTensorTypeSpecializations(g);
})
.def(
"_jit_fuser_get_fused_kernel_code",
[](Graph& g, const std::vector<at::Tensor>& inps) {
return debugGetFusedKernelCode(g, inps);
})
.def(
"_jit_pass_remove_dropout",
[](script::Module& module) { return removeDropout(module); })
.def(
"_jit_pass_refine_tuple_types",
[](std::shared_ptr<Graph>& graph) { return RefineTupleTypes(graph); })
.def(
"_jit_pass_transform_conv1d_to_conv2d",
[](std::shared_ptr<Graph>& graph) {
return transformConv1dToConv2d(graph);
})
.def(
"_jit_pass_transform_conv1d_to_conv2d",
[](script::Module& module) {
return transformConv1dToConv2d(module);
})
.def(
"_jit_pass_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return insertPrePackedOps(graph);
})
.def(
"_jit_pass_insert_prepacked_ops",
[](script::Module& module) { return insertPrePackedOps(module); })
.def(
"_jit_pass_fuse_clamp_w_prepacked_linear_conv",
[](script::Module& module) {
return fusePrePackedLinearConvWithClamp(module);
})
.def(
"_jit_pass_fold_prepacking_ops",
[](script::Module& module) { return FoldPrePackingOps(module); })
.def(
"_jit_pass_optimize_for_mobile",
[](script::Module& module,
std::set<MobileOptimizerType>& optimization_blocklist,
std::vector<std::string>& preserved_methods) {
return optimizeForMobile(
module, optimization_blocklist, preserved_methods);
})
.def(
"_hack_do_not_use_clone_module_with_class",
[](script::Module& module,
std::vector<std::string>& ignored_methods,
std::vector<std::string>& ignored_attributes) {
const bool inplace = false;
const std::unordered_set<std::string> ignored_methods_set(
ignored_methods.begin(), ignored_methods.end());
const std::unordered_set<std::string> ignored_attributes_set(
ignored_attributes.begin(), ignored_attributes.end());
return module.clone(
inplace, ignored_methods_set, ignored_attributes_set);
})
.def(
"_jit_pass_vulkan_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return vulkanInsertPrePackedOps(graph);
})
.def(
"_jit_pass_vulkan_insert_prepacked_ops",
[](script::Module& module) {
return vulkanInsertPrePackedOps(module);
})
.def(
"_jit_pass_vulkan_fuse_clamp_w_prepacked_conv",
[](script::Module& module) {
return vulkanFusePrePackedConvWithClamp(module);
})
.def(
"_jit_pass_vulkan_fold_prepacking_ops",
[](script::Module& module) {
return vulkanFoldPrePackingOps(module);
})
.def(
"_jit_pass_vulkan_optimize_for_mobile",
[](script::Module& module,
std::vector<std::string>& preserved_methods) {
return vulkanOptimizeForMobile(module, preserved_methods);
})
.def(
"_jit_pass_metal_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return metalInsertPrePackedOps(graph);
})
.def(
"_jit_pass_metal_insert_prepacked_ops",
[](script::Module& module) {
return metalInsertPrePackedOps(module);
})
.def(
"_jit_pass_metal_fuse_clamp_w_prepacked_conv",
[](script::Module& module) {
return metalFusePrePackedConvWithClamp(module);
})
.def(
"_jit_pass_metal_fold_prepacking_ops",
[](script::Module& module) { return metalFoldPrePackingOps(module); })
.def(
"_jit_pass_metal_optimize_for_mobile",
[](script::Module& module,
std::vector<std::string>& preserved_methods) {
return metalOptimizeForMobile(module, preserved_methods);
})
.def(
"_jit_pass_filter_non_tensor_arguments",
[](std::map<std::string, IValue> params) {
std::map<std::string, at::Tensor> retval;
for (auto& kv : params) {
if (kv.second.isTensor()) {
retval[kv.first] = std::move(kv.second).toTensor();
}
}
return retval;
})
.def("_jit_pass_batch_mm", BatchMM)
.def("_jit_decay_packed_param_input_types", [](Graph& g) {
for (Value* i : g.inputs()) {
if (i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.Conv2dPackedParamsBase") ||
i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.Conv3dPackedParamsBase") ||
i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.LinearPackedParamsBase")) {
// Dummy CompleteTensorType to appease ONNX validator.
i->setType(TensorType::create(
at::kQInt8,
c10::kCPU,
std::vector<int64_t>{1},
std::vector<int64_t>{1},
c10::nullopt));
}
}
});
py::class_<c10::SymIntNodeImpl, c10::SymIntNode>(m, "SymIntNode")
.def_static(
"new_symint",
[](py::object obj) -> c10::SymIntNode {
return c10::make_intrusive<PythonSymIntNodeImpl>(obj);
})
.def(
"get_pyobj",
[](c10::SymIntNode a) -> py::object {
if (auto* psn = dynamic_cast<PythonSymIntNodeImpl*>(a.get())) {
return py::reinterpret_borrow<py::object>(psn->getPyObj());
}
return py::none();
})
.def(
"__add__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->add(snb);
})
.def(
"__radd__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->add(snb);
})
.def(
"__sub__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->sub(snb);
})
.def(
"__mul__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->mul(snb);
})
.def(
"__rmul__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->mul(snb);
})
.def(
"__truediv__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->truediv(snb);
})
.def(
"__rtruediv__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return snb->truediv(a);
})
.def(
"__floordiv__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->floordiv(snb);
})
.def(
"__rfloordiv__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return snb->floordiv(a);
})
.def(
"__mod__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->mod(snb);
})
.def(
"__eq__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->eq(snb);
})
.def(
"__gt__",
[](c10::SymIntNode a, py::object b) {
auto snb = toSymIntNode(a, b);
return a->gt(snb);
})
.def(
"__lt__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->lt(snb);
})
.def(
"__le__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->le(snb);
})
.def(
"__ge__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->ge(snb);
})
.def("__bool__", [](c10::SymIntNode a) { return a->bool_(); })
.def("__int__", [](c10::SymIntNode a) { return a->int_(); })
.def("__str__", [](c10::SymIntNode a) { return a->str(); });
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<CompleteArgumentSpec>(m, "CompleteArgumentSpec")
.def("__repr__", [](CompleteArgumentSpec& self) {
std::ostringstream s;
s << self;
return s.str();
});
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<ArgumentSpec>(m, "ArgumentSpec");
py::class_<Code>(m, "Code")
.def(
"grad_executor_states",
[](Code& c) {
std::vector<GraphExecutorState> states;
for (auto& e : c.grad_executors()) {
states.emplace_back(e->getDebugState());
}
return states;
})
.def(
"differentiable_op_executor_states",
[](Code& c) {
std::vector<GraphExecutorState> states;
for (auto& e : c.diff_graph_op_executors()) {
if (e->isOptimized()) {
states.emplace_back(e->getDebugState());
} else {
// we leave an empty entry for node that doesn't have an
// optimized plan
states.emplace_back();
}
}
return states;
})
.def("num_bailouts", [](Code& c) { return c.num_bailouts(); })
.def("request_bailout", [](Code& c, size_t index) {
c.request_bailout(index);
});
py::class_<ExecutionPlan>(m, "ExecutionPlan")
.def_property_readonly("graph", [](ExecutionPlan& s) { return s.graph; })
.def_property_readonly("code", [](ExecutionPlan& s) { return s.code; });
py::class_<Gradient>(m, "Gradient")
.def_property_readonly("f", [](Gradient& m) { return m.f; })
.def_property_readonly("df", [](Gradient& m) { return m.df; })
.def_property_readonly(
"f_real_outputs", [](Gradient& m) { return m.f_real_outputs; })
.def_property_readonly(
"df_input_vjps", [](Gradient& m) { return m.df_input_vjps; })
.def_property_readonly(
"df_input_captured_inputs",
[](Gradient& m) { return m.df_input_captured_inputs; })
.def_property_readonly(
"df_input_captured_outputs",
[](Gradient& m) { return m.df_input_captured_outputs; })
.def_property_readonly(
"df_output_vjps", [](Gradient& m) { return m.df_output_vjps; });
py::class_<GraphExecutorState>(m, "GraphExecutorState")
.def_property_readonly(
"graph", [](GraphExecutorState& s) { return s.graph; })
.def_property_readonly(
"execution_plans",
[](GraphExecutorState& s) { return s.execution_plans; })
.def_property_readonly(
"fallback", [](GraphExecutorState& s) { return s.fallback; });
py::class_<PyTorchStreamWriter>(m, "PyTorchFileWriter")
.def(py::init<std::string>())
.def(py::init([](const py::object& buffer) {
auto writer_func = [=](const void* data, size_t size) {
// Writting an empty file is a noop
if (size == 0) {
return size;
}
auto memory_view = py::memoryview::from_memory(
reinterpret_cast<const char*>(data), size);
buffer.attr("write")(std::move(memory_view));
return size;
};
return std::make_unique<PyTorchStreamWriter>(std::move(writer_func));
}))
.def(py::init<const std::function<size_t(const void*, size_t)>&>())
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
const char* data,
size_t size) { return self.writeRecord(name, data, size); })
.def("write_end_of_file", &PyTorchStreamWriter::writeEndOfFile)
.def("set_min_version", &PyTorchStreamWriter::setMinVersion)
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
uintptr_t data,
size_t size) {
return self.writeRecord(
name, reinterpret_cast<const char*>(data), size);
})
.def("archive_name", &PyTorchStreamWriter::archiveName)
.def(
"get_all_written_records",
&PyTorchStreamWriter::getAllWrittenRecords);
py::enum_<MobileOptimizerType>(m, "MobileOptimizerType")
.value("CONV_BN_FUSION", MobileOptimizerType::CONV_BN_FUSION)
.value(
"INSERT_FOLD_PREPACK_OPS",
MobileOptimizerType::INSERT_FOLD_PREPACK_OPS)
.value("REMOVE_DROPOUT", MobileOptimizerType::REMOVE_DROPOUT)
.value("FUSE_ADD_RELU", MobileOptimizerType::FUSE_ADD_RELU)
.value(
"HOIST_CONV_PACKED_PARAMS",
MobileOptimizerType::HOIST_CONV_PACKED_PARAMS)
.export_values();
// This allows PyTorchStreamReader to read from a Python buffer. It requires
// that the buffer implement `seek()`, `tell()`, and `read()`.
class BufferAdapter : public caffe2::serialize::ReadAdapterInterface {
public:
BufferAdapter(const py::object& buffer) : buffer_(buffer) {
// Jump to the end of the buffer to get its size
auto current = buffer.attr("tell")();
start_offset_ = py::cast<size_t>(current);
buffer.attr("seek")(current, py::module::import("os").attr("SEEK_END"));
size_ = py::cast<size_t>(buffer.attr("tell")()) - start_offset_;
buffer.attr("seek")(current);
// If we can read directly into a buffer, do that instead of an extra copy
use_readinto_ = py::hasattr(buffer, "readinto");
}
size_t size() const override {
return size_;
}
THPObjectPtr getMemview(void* buf, size_t n) const {
THPObjectPtr memview(PyMemoryView_FromMemory(
reinterpret_cast<char*>(buf), n, PyBUF_WRITE));
if (!memview) {
throw python_error();
}
return memview;
}
size_t read(uint64_t pos, void* buf, size_t n, const char* what)
const override {
// Seek to desired position (NB: this has to be a Py_ssize_t or Python
// throws a weird error)
Py_ssize_t absolute_pos = start_offset_ + pos;
buffer_.attr("seek")(absolute_pos);
if (use_readinto_) {
auto memview = getMemview(buf, n);
auto res =
PyObject_CallMethod(buffer_.ptr(), "readinto", "O", memview.get());
if (res) {
int64_t i = static_cast<int64_t>(PyLong_AsLongLong(res));
if (i > 0) {
return i;
}
}
}
// Read bytes into `buf` from the buffer
std::string bytes = py::cast<std::string>(buffer_.attr("read")(n));
std::copy(
bytes.data(),
bytes.data() + bytes.size(),
reinterpret_cast<char*>(buf));
return bytes.size();
}
py::object buffer_;
size_t size_;
size_t start_offset_;
bool use_readinto_;
};
py::class_<PyTorchStreamReader, std::shared_ptr<PyTorchStreamReader>>(
m, "PyTorchFileReader")
.def(py::init<std::string>())
.def(py::init([](const py::object& buffer) {
auto adapter = std::make_unique<BufferAdapter>(buffer);
return std::make_shared<PyTorchStreamReader>(std::move(adapter));
}))
.def(
"get_record",
[](PyTorchStreamReader& self, const std::string& key) {
at::DataPtr data;
size_t size = 0;
std::tie(data, size) = self.getRecord(key);
return py::bytes(reinterpret_cast<const char*>(data.get()), size);
})
.def(
"has_record",
[](PyTorchStreamReader& self, const std::string& key) {
return self.hasRecord(key);
})
.def(
"get_storage_from_record",
[](PyTorchStreamReader& self,
const std::string& key,
size_t numel,
py::object data_type_obj) {
at::DataPtr data(std::get<0>(self.getRecord(key)));
auto scalar_type =
reinterpret_cast<THPDtype*>(data_type_obj.ptr())->scalar_type;
c10::Storage storage(
c10::Storage::use_byte_size_t(),
numel * elementSize(scalar_type),
std::move(data),
/*allocator=*/nullptr,
/*resizable=*/false);
auto ptr =
c10::make_intrusive<at::TensorImpl, at::UndefinedTensorImpl>(
std::move(storage),
at::DispatchKeySet(),
at::CPU(scalar_type).typeMeta());
return at::Tensor(std::move(ptr));
})
.def("get_all_records", [](PyTorchStreamReader& self) {
return self.getAllRecords();
});
// Used by torch.Package to coordinate deserialization of storages across
// ScriptModules and eager modules
py::class_<
DeserializationStorageContext,
std::shared_ptr<DeserializationStorageContext>>(
m, "DeserializationStorageContext")
.def(py::init<>())
.def(
"get_storage",
[](DeserializationStorageContext& self,
const std::string& name,
py::object data_type_obj) {
c10::Storage storage = self.getStorage(name);
auto scalar_type =
reinterpret_cast<THPDtype*>(data_type_obj.ptr())->scalar_type;
auto ptr =
c10::make_intrusive<at::TensorImpl, at::UndefinedTensorImpl>(
std::move(storage),
at::DispatchKeySet(),
at::CPU(scalar_type).typeMeta());
return at::Tensor(std::move(ptr));
})
.def(
"add_storage",
[](DeserializationStorageContext& self,
const std::string& name,
const at::Tensor& tensor) {
return self.addStorage(name, tensor.storage());
})
.def("has_storage", &DeserializationStorageContext::hasStorage);
m.def(
"_get_schema",
[](const std::string& op_name, const std::string& overload_name) {
try {
auto symbol = Symbol::fromQualString(op_name);
auto operations = getAllOperatorsFor(symbol);
for (const auto& op : operations) {
if (op->schema().overload_name() == overload_name) {
return op->schema();
}
}
throw std::runtime_error("Found no matching schema");
} catch (const c10::Error& e) {
auto msg = torch::get_cpp_stacktraces_enabled()
? e.what()
: e.what_without_backtrace();
throw std::runtime_error(msg);
}
});
m.def(
"_get_operation_overload",
[](const std::string& op_name, const std::string& overload_name) {
try {
auto symbol = Symbol::fromQualString(op_name);
auto operations = getAllOperatorsFor(symbol);
bool allow_numbers_as_tensors = symbol.is_prims() ||
symbol.is_nvprims() ||
(symbol.is_aten() &&
torch::should_allow_numbers_as_tensors(symbol.toUnqualString()));
for (const auto& op : operations) {
if (op->schema().overload_name() == overload_name) {
auto func =
py::cpp_function([op, symbol, allow_numbers_as_tensors](
py::args args, py::kwargs kwargs) {
ToIValueAllowNumbersAsTensors g(allow_numbers_as_tensors);
return _get_operation_for_overload_or_packet(
{op}, symbol, args, kwargs, /*is_overload*/ true);
});
auto func_dk =
py::cpp_function([op, symbol, allow_numbers_as_tensors](
const std::string& str_dk,
py::args args,
py::kwargs kwargs) {
c10::optional<c10::DispatchKey> dk =
c10::make_optional(c10::parseDispatchKey(str_dk));
ToIValueAllowNumbersAsTensors g(allow_numbers_as_tensors);
return _get_operation_for_overload_or_packet(
{op}, symbol, args, kwargs, /*is_overload*/ true, dk);
});
return py::make_tuple(
func, func_dk, py::cast(op->getTags().vec()));
}
}
throw std::runtime_error("Found no matching operator overload");
} catch (const c10::Error& e) {
auto msg = torch::get_cpp_stacktraces_enabled()
? e.what()
: e.what_without_backtrace();
throw std::runtime_error(msg);
}
});
m.def(
"_jit_get_operation",
[](const std::string& op_name) {
try {
auto symbol = Symbol::fromQualString(op_name);
auto operations = getAllOperatorsFor(symbol);
TORCH_CHECK(!operations.empty(), "No such operator ", op_name);
std::ostringstream docstring;
docstring << "Automatically bound operator '" << op_name
<< "' with schema(s):\n";
for (const auto& op : operations) {
docstring << " " << op->schema() << "\n";
}
py::list overload_names;
for (const auto& op : operations) {
overload_names.append(py::str(op->schema().overload_name()));
}
bool allow_numbers_as_tensors = symbol.is_prims() ||
symbol.is_nvprims() ||
(symbol.is_aten() &&
torch::should_allow_numbers_as_tensors(symbol.toUnqualString()));
auto func = py::cpp_function(
[operations, symbol, allow_numbers_as_tensors](
py::args args, py::kwargs kwargs) {
ToIValueAllowNumbersAsTensors g(allow_numbers_as_tensors);
return _get_operation_for_overload_or_packet(
operations, symbol, args, kwargs, false);
},
py::name(symbol.toUnqualString()),
py::doc(docstring.str().c_str()));
return py::make_tuple(func, overload_names);
} catch (const c10::Error& e) {
auto msg = torch::get_cpp_stacktraces_enabled()
? e.what()
: e.what_without_backtrace();
throw std::runtime_error(msg);
}
},
py::arg("qualified_name"));
m.def(
"parse_ir",
[](const std::string& input, bool parse_tensor_constants) {
auto graph = std::make_shared<Graph>();
parseIR(input, &*graph, parse_tensor_constants);
return graph;
},
py::arg("input"),
py::arg("parse_tensor_constants") = false);
m.def("parse_schema", parseSchema);
m.def("unify_type_list", [](const std::vector<TypePtr>& types) {
std::ostringstream s;
auto type = unifyTypeList(types, s);
if (!type) {
throw std::runtime_error(s.str());
}
return type.value();
});
py::enum_<SchemaArgType>(m, "_SchemaArgType")
.value("input", SchemaArgType::input)
.value("output", SchemaArgType::output);
py::class_<SchemaArgument>(m, "_SchemaArgument")
.def(py::init<SchemaArgType, size_t>())
.def_readwrite("type", &SchemaArgument::type)
.def_readwrite("index", &SchemaArgument::index);
py::class_<SchemaInfo>(m, "_SchemaInfo")
.def(py::init<FunctionSchema>())
.def("is_mutable", [](SchemaInfo& self) { return self.is_mutable(); })
.def(
"is_mutable",
[](SchemaInfo& self, const SchemaArgument& argument) {
return self.is_mutable(argument);
})
.def(
"is_mutable",
[](SchemaInfo& self, const std::string& name) {
return self.is_mutable(name);
})
.def(
"may_alias",
[](SchemaInfo& self,
const SchemaArgument& lhs,
const SchemaArgument& rhs) { return self.may_alias(lhs, rhs); })
.def(
"may_contain_alias",
[](SchemaInfo& self,
const SchemaArgument& lhs,
const SchemaArgument& rhs) {
return self.may_contain_alias(lhs, rhs);
})
.def(
"add_argument_value",
[](SchemaInfo& self,
const std::string& name,
const py::object& value) {
if (isEmptyContainer(value)) {
return;
}
// For normalization purposes there is an inconsistency within
// torch.fx that turns all arguments named "self" into "input". Thus
// this check ensures that those arguments are checked correctly.
if (name == "input" && !self.hasInputArgumentNamed("input")) {
self.addArgumentValue("self", toTypeInferredIValue(value));
} else {
self.addArgumentValue(name, toTypeInferredIValue(value));
}
})
.def("add_argument_values", [](SchemaInfo& self, const py::dict& values) {
std::unordered_map<std::string, IValue> value_map;
for (const auto& key_pair : values) {
IValue key = toTypeInferredIValue(key_pair.first);
if (isEmptyContainer(key_pair.second)) {
continue;
}
IValue value = toTypeInferredIValue(key_pair.second);
TORCH_INTERNAL_ASSERT(
key.isString(),
"Add argument value keys types should be strings.");
// For normalization purposes there is an inconsistency within
// torch.fx that
// turns all arguments named "self" into "input". Thus this check
// ensures that those arguments are checked correctly.
if (key.toStringRef() == "input" &&
!self.hasInputArgumentNamed("input")) {
self.addArgumentValue("self", value);
} else {
value_map[key.toStringRef()] = value;
}
}
self.addArgumentValues(value_map);
});
py::class_<FunctionSchema>(m, "FunctionSchema")
.def_property_readonly(
"name", [](FunctionSchema& self) { return self.name(); })
.def_property_readonly(
"overload_name",
[](FunctionSchema& self) { return self.overload_name(); })
.def_property_readonly(
"arguments", [](FunctionSchema& self) { return self.arguments(); })
.def_property_readonly(
"returns", [](FunctionSchema& self) { return self.returns(); })
.def(
"is_backward_compatible_with",
[](const FunctionSchema& self, const FunctionSchema& old_schema) {
return self.isBackwardCompatibleWith(old_schema);
})
.def(
"check_forward_compatible_with",
[](const FunctionSchema& self, const FunctionSchema& old_schema) {
std::ostringstream out;
auto result = self.isForwardCompatibleWith(old_schema, out);
return std::make_pair(result, out.str());
})
.def(
"__eq__",
[](const FunctionSchema& self, const FunctionSchema& other) {
return self == other;
})
.def(
"__str__",
[](FunctionSchema& self) {
std::stringstream ss;
ss << self;
return ss.str();
})
.def_property_readonly(
"is_mutable", [](FunctionSchema& self) { return self.is_mutable(); });
py::class_<Argument>(m, "Argument")
.def_property_readonly("name", [](Argument& self) { return self.name(); })
.def_property_readonly("type", [](Argument& self) { return self.type(); })
.def_property_readonly(
"N",
[](Argument& self) -> py::object {
return (self.N()) ? py::cast(*self.N()) : py::none();
})
.def_property_readonly(
"default_value",
[](Argument& self) -> py::object {
if (!self.default_value()) {
return py::none();
}
IValue v = *self.default_value();
return toPyObject(std::move(v));
})
.def(
"has_default_value",
[](Argument& self) -> py::bool_ {
return self.default_value().has_value();
})
.def_property_readonly(
"alias_info", [](Argument& self) { return self.alias_info(); })
.def_property_readonly(
"is_out", [](Argument& self) { return self.is_out(); })
.def_property_readonly("kwarg_only", [](Argument& self) -> bool {
return self.kwarg_only();
});
py::class_<AliasInfo>(m, "_AliasInfo")
.def_property_readonly(
"is_write", [](AliasInfo& self) { return self.isWrite(); })
.def_property_readonly(
"before_set",
[](AliasInfo& self) {
std::set<py::str> before_set_python;
for (const auto& set : self.beforeSets()) {
before_set_python.insert(py::str(set.toUnqualString()));
}
return before_set_python;
})
.def_property_readonly("after_set", [](AliasInfo& self) {
std::set<py::str> after_set_python;
for (const auto& set : self.afterSets()) {
after_set_python.insert(py::str(set.toUnqualString()));
}
return after_set_python;
});
m.def("_jit_get_all_schemas", []() {
const std::vector<std::shared_ptr<Operator>>& operations =
getAllOperators();
return fmap(operations, [](const std::shared_ptr<Operator>& op) {
return op->schema();
});
});
m.def("_jit_get_custom_class_schemas", customClassSchemasForBCCheck);
m.def("_jit_get_schemas_for_operator", [](const std::string& qualified_name) {
auto symbol = Symbol::fromQualString(qualified_name);
const auto& operations = getAllOperatorsFor(symbol);
return fmap(operations, [](const std::shared_ptr<Operator>& op) {
return op->schema();
});
});
m.def("_is_tracing", []() { return jit::tracer::isTracing(); });
py::class_<PythonFutureWrapper, std::shared_ptr<PythonFutureWrapper>>(
m, "Future")
.def(py::init([](std::vector<c10::Device> devices = {}) {
return std::make_shared<PythonFutureWrapper>(
c10::make_intrusive<c10::ivalue::Future>(
PyObjectType::get(), std::move(devices)));
}))
.def(
"done",
// Intentionally not releasing GIL
&PythonFutureWrapper::done)
.def(
"value",
&PythonFutureWrapper::value,
py::call_guard<py::gil_scoped_release>())
.def(
"wait",
&PythonFutureWrapper::wait,
py::call_guard<py::gil_scoped_release>())
.def(
"then",
&PythonFutureWrapper::then,
py::call_guard<py::gil_scoped_release>())
.def(
"add_done_callback",
&PythonFutureWrapper::add_done_callback,
py::call_guard<py::gil_scoped_release>())
.def(
"set_result",
// Intentionally not releasing GIL
&PythonFutureWrapper::markCompleted)
.def(
"_set_unwrap_func",
// Intentionally not releasing GIL as this just does an assign
[](PythonFutureWrapper& self, py::function unwrapFunc) {
auto functionGuard =
std::make_shared<torch::jit::PythonFunctionGuard>(
std::move(unwrapFunc));
std::function<void(py::object)> pf =
[functionGuard(std::move(functionGuard))](
const py::object& inp) {
return functionGuard->func_(inp);
};
self.unwrap_func = std::move(pf);
})
.def(
py::pickle(
/* __getstate__ */
[](const PythonFutureWrapper& /* unused */) {
TORCH_CHECK(false, "Can not pickle torch.futures.Future");
// Note that this return has no meaning since we always
// throw, it's only here to satisfy Pybind API's
// requirement.
return py::make_tuple();
},
/* __setstate__ */
[](const py::tuple& /* unused */) { // NOLINT
TORCH_CHECK(false, "Can not unpickle torch.futures.Future");
// Note that this return has no meaning since we always
// throw, it's only here to satisfy PyBind's API
// requirement.
return nullptr;
}),
py::call_guard<py::gil_scoped_release>());
m.def("_is_alias_of", [](const py::object& self, const py::object& other) {
if (isEmptyContainer(self) || isEmptyContainer(other)) {
return false;
}
return toTypeInferredIValue(self).isAliasOf(toTypeInferredIValue(other));
});
m.def("_overlaps", [](const py::object& self, const py::object& other) {
if (isEmptyContainer(self) || isEmptyContainer(other)) {
return true;
}
return toTypeInferredIValue(self).overlaps(toTypeInferredIValue(other));
});
m.def("fork", [](const py::args& args, const py::kwargs& kwargs) {
AT_ASSERT(args.size() >= 1);
py::function f = py::cast<py::function>(args[0]);
py::tuple args_tup(args.size() - 1);
for (const auto i : c10::irange(1, args.size())) {
args_tup[i - 1] = args[i];
}
if (jit::tracer::isTracing()) {
auto graph = jit::tracer::getTracingState()->graph;
auto fork_node = graph->insertNode(graph->create(prim::TracedFork, 1));
auto body_block = fork_node->addBlock();
Value* node_output = nullptr;
py::object py_func_output;
// Insert new trace ops into the fork op's sub-block
WithInsertPoint guard(body_block);
IValue output_ivalue;
{
tracer::WithNestedTracingFrame env_guard;
// Run the user-supplied function
py_func_output = f(*args_tup, **kwargs);
// Convert the output of the user-supplied function to IValue. The type
// information of this IValue is used both to record the correct type in
// the trace.
output_ivalue = toTypeInferredIValue(py_func_output);
Value* out_val = jit::tracer::getValueTrace(output_ivalue);
body_block->registerOutput(out_val);
node_output =
fork_node->output()->setType(FutureType::create(out_val->type()));
}
auto retval =
c10::make_intrusive<c10::ivalue::Future>(output_ivalue.type());
// Record the ivalue in the tracer
jit::tracer::setValueTrace(retval, node_output);
// stuff the ivalue output in the Future
retval->markCompleted(output_ivalue);
return std::make_shared<PythonFutureWrapper>(retval);
} else {
auto result = toTypeInferredIValue(f(*args_tup, **kwargs));
auto retval = c10::make_intrusive<c10::ivalue::Future>(result.type());
retval->markCompleted(std::move(result));
return std::make_shared<PythonFutureWrapper>(retval);
}
});
m.def("wait", [](const std::shared_ptr<PythonFutureWrapper>& fut) {
return fut->wait();
});
m.def(
"_collect_all",
[](const std::vector<std::shared_ptr<jit::PythonFutureWrapper>>& futures)
-> std::shared_ptr<jit::PythonFutureWrapper> {
auto typePtr =
futures.empty() ? AnyType::get() : futures[0]->fut->elementType();
c10::List<c10::intrusive_ptr<c10::ivalue::Future>> asList(
c10::FutureType::create(typePtr));
asList.reserve(futures.size());
for (const auto& f : futures) {
asList.push_back(f->fut);
}
return std::make_shared<jit::PythonFutureWrapper>(
c10::collectAll(asList),
/* unwrap_func */ [futures](const py::object& /*unused*/) {
// Throw errors when calling wait() on the returned Future if
// any of the original futures would throw.
// NB: PythonFutureWrapper takes an unwrap_func which serves as a
// callback to evalute the value in the Future. RPC uses this
// unwrap_func to check whether the returned py::object is a
// RemoteException object, and re-throw the exception if it is.
// By extracting the c10::ivalue::Future from PythonFutureWrapper
// the unwrap_func on the original PythonFutureWrapper objects are
// discarded, and hence it will return the RemoteException as an
// object instead of re-throwing it.
for (auto& fut : futures) {
fut->wait();
}
});
},
py::call_guard<py::gil_scoped_release>());
m.def("_jit_assert_is_instance", [](py::object obj, const TypePtr& type) {
toIValue(std::move(obj), type);
});
#if defined(C10_SUPPORTS_FATAL_SIGNAL_HANDLERS)
m.def("_set_print_stack_traces_on_fatal_signal", [](bool print) {
c10::FatalSignalHandler::getInstance().setPrintStackTracesOnFatalSignal(
print);
});
#endif // defined(C10_SUPPORTS_SIGNAL_HANDLER)
initPythonCustomClassBindings(module);
initPythonIRBindings(module);
tracer::initPythonTracerBindings(module);
initTreeViewBindings(module);
initJitScriptBindings(module);
initJitBackendBindings(module);
initStaticModuleBindings(module);
initTensorExprBindings(module);
initNvFuserPythonBindings(module);
setPrintHandler([](const std::string& str) {
py::gil_scoped_acquire acquire;
try {
auto _stdout = py::module::import("sys").attr("stdout");
_stdout.attr("write")(str);
} catch (py::error_already_set& e) {
throw std::runtime_error(e.what());
}
});
// On exit we need to reset the print handler to default one,
// because otherwise prim::Print() instruction won't work for JIT modules.
auto atexit = py::module_::import("atexit");
atexit.attr("register")(
py::cpp_function([]() { setPrintHandler(getDefaultPrintHandler()); }));
}
} // namespace jit
} // namespace torch
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