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android / platform / external / pytorch / db8abde9b6c4735d18d4681a1f70a55ff0b09f5b / . / torch / csrc / jit / python / init.cpp
blob: 15c3e8e3ad387bf7d9bde383970350a31d30ca0c [file] [log] [blame]
#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/SymNodeImpl.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_folding.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/mobile_optimizer_type.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/python/utf8_decoding_ignore.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::jit {
using c10::AliasInfo;
using c10::Argument;
using c10::FunctionSchema;
using c10::SchemaArgType;
using c10::SchemaArgument;
using c10::SymNode;
using caffe2::serialize::PyTorchStreamReader;
using caffe2::serialize::PyTorchStreamWriter;
using torch::utils::SchemaInfo;
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;
}
c10::optional<IValue> toTypeInferredIValueOptional(py::handle input) {
// Errors need to be caught here because toTypeInferredIValue errors out
// on various object types, but we want it to work with all types.
try {
return toTypeInferredIValue(input);
} catch (const c10::Error& e) {
return c10::nullopt;
}
}
} // 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_observer_method_for_ondevice_ptq",
[](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 InsertObserversForOnDevicePTQ(
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_quant_dequant_for_ondevice_ptq",
[](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 InsertQuantDeQuantOnDevicePTQ(
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_fold_frozen_linear_bn", &FoldFrozenLinearBatchnorm)
.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_quant_finalize_for_ondevice_ptq",
[](Module& module,
int quant_type_int,
const std::string& method_name) {
auto quant_type = static_cast<QuantType>(quant_type_int);
return FinalizeOnDevicePTQ(module, quant_type, method_name);
},
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.emplace_back(tt);
} else {
input_types.emplace_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_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)
#else
.def("_jit_set_llga_enabled", [](bool flag) { return false; })
.def("_jit_llga_enabled", []() { return false; })
#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::set<MobileOptimizerType>& optimization_blocklist,
std::vector<std::string>& preserved_methods) {
return vulkanOptimizeForMobile(
module, optimization_blocklist, 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));
}
}
})
.def("_jit_set_utf8_decoding_ignore", &setUTF8DecodingIgnore);
// NB: This isn't actually used for regular PyTorch symbolic tracing;
// XLA is what needs this
#define SYMNODE_UNARY(n) .def(#n, [](c10::SymNode a) { return a->n(); })
#define SYMNODE_BINARY(n) \
.def(#n, [](c10::SymNode a, c10::SymNode b) { return a->n(b); })
#define SYMNODE_SIZES_STRIDES(n) \
.def( \
#n, \
[](c10::SymNode a, \
c10::ArrayRef<c10::SymNode> sizes, \
c10::ArrayRef<c10::SymNode> strides) { \
return a->n(sizes, strides); \
})
auto symnode_class =
py::class_<c10::SymNodeImpl, c10::SymNode>(m, "_SymNode")
// clang-format off
// These DO NOT install magic methods; the SymInt/SymFloat wrapper in
// Python is responsible for this
SYMNODE_UNARY(clone)
SYMNODE_UNARY(is_int)
SYMNODE_UNARY(is_float)
SYMNODE_UNARY(is_bool)
SYMNODE_UNARY(bool_)
SYMNODE_UNARY(int_)
SYMNODE_UNARY(sym_float)
SYMNODE_BINARY(add)
SYMNODE_BINARY(sub)
SYMNODE_BINARY(mul)
SYMNODE_BINARY(truediv)
SYMNODE_BINARY(pow)
SYMNODE_BINARY(floordiv)
SYMNODE_BINARY(mod)
SYMNODE_BINARY(eq)
SYMNODE_BINARY(ne)
SYMNODE_BINARY(gt)
SYMNODE_BINARY(lt)
SYMNODE_BINARY(le)
SYMNODE_BINARY(ge)
SYMNODE_BINARY(sym_min)
SYMNODE_BINARY(sym_max)
SYMNODE_BINARY(sym_and)
SYMNODE_BINARY(sym_or)
SYMNODE_UNARY(sym_not)
SYMNODE_UNARY(ceil)
SYMNODE_UNARY(floor)
SYMNODE_UNARY(neg)
SYMNODE_SIZES_STRIDES(is_contiguous)
SYMNODE_SIZES_STRIDES(is_channels_last_contiguous_2d)
SYMNODE_SIZES_STRIDES(is_channels_last_contiguous_3d)
SYMNODE_SIZES_STRIDES(is_channels_last_strides_2d)
SYMNODE_SIZES_STRIDES(is_channels_last_strides_3d)
SYMNODE_SIZES_STRIDES(is_non_overlapping_and_dense)
// Intentionally don't set file line, as the
// Python backtrace matters more here
.def(
"guard_int",
[](c10::SymNode a) {
return a->guard_int(nullptr, 0);
})
.def(
"guard_bool",
[](c10::SymNode a) {
return a->guard_bool(nullptr, 0);
})
.def(
"guard_float",
[](c10::SymNode a) {
return a->guard_float(nullptr, 0);
})
.def(
"has_hint",
[](c10::SymNode a) {
return a->has_hint();
})
.def(
"wrap_int",
[](c10::SymNode a, int64_t b) {
return a->wrap_int(b);
})
.def(
"wrap_float",
[](c10::SymNode a, double b) {
return a->wrap_float(b);
})
.def(
"wrap_bool",
[](c10::SymNode a, bool b) {
return a->wrap_bool(b);
})
.def(
"__str__",
[](c10::SymNode a) { return a->str(); })
.def("__repr__", [](c10::SymNode a) {
return a->str();
});
// clang-format on
// 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) {
// Writing 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)
.value(
"VULKAN_AUTOMATIC_GPU_TRANSFER",
MobileOptimizerType::VULKAN_AUTOMATIC_GPU_TRANSFER);
// 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](
c10::DispatchKey dk_, py::args args, py::kwargs kwargs) {
c10::optional<c10::DispatchKey> dk =
c10::make_optional(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(
"has_argument",
[](SchemaInfo& self, const std::string& name) {
return self.has_argument(name);
})
.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) {
c10::optional<IValue> i_value = toTypeInferredIValueOptional(value);
if (i_value) {
// 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", *i_value);
} else {
self.addArgumentValue(name, *i_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);
TORCH_INTERNAL_ASSERT(
key.isString(),
"Add argument value keys types should be strings.");
c10::optional<IValue> value =
toTypeInferredIValueOptional(key_pair.second);
if (value) {
// 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(
"__hash__",
[](const FunctionSchema& self) {
return std::hash<FunctionSchema>{}(self);
})
.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(
"real_type", [](Argument& self) { return self.real_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>());
py::class_<PythonAwaitWrapper, std::shared_ptr<PythonAwaitWrapper>>(
m, "_Await")
.def(
"wait",
&PythonAwaitWrapper::wait,
py::call_guard<py::gil_scoped_release>())
.def("fn", &PythonAwaitWrapper::fn)
.def("args", &PythonAwaitWrapper::args)
.def("type", &PythonAwaitWrapper::type)
.def("is_nowait", &PythonAwaitWrapper::is_nowait)
.def(
"__getattr__",
[](PythonAwaitWrapper& self, const std::string& name) -> py::object {
// In eager mode allow Await[W] to be used as W, redirecting getattr
// to the result of delayed function.
return py::getattr(self.wait(), name.c_str(), py::none());
})
.def(
py::pickle(
/* __getstate__ */
[](const PythonAwaitWrapper& /* unused */) {
TORCH_CHECK(false, "Can not pickle torch.jit._Await");
// 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.jit._Await");
// 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) {
c10::optional<IValue> self_value = toTypeInferredIValueOptional(self);
c10::optional<IValue> other_value = toTypeInferredIValueOptional(other);
// Only return true if we are certain that self and other are aliasing.
if (!self_value || !other_value) {
return false;
}
return self_value->isAliasOf(*other_value);
});
m.def("_overlaps", [](const py::object& self, const py::object& other) {
c10::optional<IValue> self_value = toTypeInferredIValueOptional(self);
c10::optional<IValue> other_value = toTypeInferredIValueOptional(other);
// Only return true if we are certain that self and other are overlapping.
if (!self_value || !other_value) {
return false;
}
return self_value->overlaps(*other_value);
});
m.def("_awaitable", [](const py::args& args, const py::kwargs& kwargs) {
AT_ASSERT(args.size() >= 1);
py::tuple args_tup(args.size() - 1);
for (const auto i : c10::irange(1, args.size())) {
args_tup[i - 1] = args[i];
}
return std::make_shared<PythonAwaitWrapper>(
py::cast<py::function>(args[0]), std::move(args_tup));
});
m.def("_awaitable_nowait", [](py::handle input) {
return std::make_shared<PythonAwaitWrapper>(std::move(input));
});
m.def(
"_awaitable_wait", [](const std::shared_ptr<PythonAwaitWrapper>& py_aw) {
TORCH_CHECK(py_aw, "Await can't be None");
return py_aw->wait();
});
m.def("fork", [](const py::args& args, const py::kwargs& kwargs) {
AT_ASSERT(!args.empty());
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) {
TORCH_CHECK(fut, "Future can't be None");
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() || futures[0] == nullptr
? 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) {
TORCH_CHECK(f, "Future can't be None");
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 torch::jit