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db3685a35c
pytorch/torch/csrc/jit/python/init.cpp
Mikayla Gawarecki db3685a35c Add option to serialization config to reduce random reads from get_record_offset when loading with mmap=True (#143880) 
## Background

This PR adds `torch.utils.serialization.config.load.calculate_storage_offsets`. This option relies  on the previous PR in this stack, where storage order was changed to non lexicographical. A `.format_version` entry was added to the zipfile and `calculate_storage_offsets` will only work on checkpoints with `.format_version`.

When this is turned on, for `torch.load(mmap=True)`, offsets of each storage record (other than the 0th storage will be calculated instead of relying on `miniz` APIs to determine this).

The existing APIs will issue multiple random reads (reading the end of central directory record, then reading the zipfile header for the record) to determine the storage offset where the record starts. This can greatly degrade `torch.load(mmap=True)` performance for non-filesystem cases.

6aaae9d78f/caffe2/serialize/inline_container.cc (L589-L605)

## Testing strategy

The agreed upon testing strategy was as follows:
- Add debug code gated by an environment flag `TORCH_SERIALIZATION_DEBUG` that will run this offset calculation logic and verify it against getRecordOffset for each storage (when mmap=False)
- This flag is set throughout CI, which means that every time `torch.load` is called, the offset calculation logic is implicitly being tested.

Differential Revision: [D67673026](https://our.internmc.facebook.com/intern/diff/D67673026)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143880
Approved by: https://github.com/albanD
ghstack dependencies: #143879
2025-01-27 23:57:30 +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/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/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;
}
static bool opAllowsNumbersAsTensors(c10::Symbol symbol) {
return symbol.is_prims() || symbol.is_nvprims() ||
(symbol.is_aten() &&
torch::should_allow_numbers_as_tensors(symbol.toUnqualString()));
}
std::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 std::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");
// This is a static object, so we must leak the Python object
// "release()" is used here to preserve 1 refcount on the
// object, preventing it from ever being de-allocated by CPython.
static py::handle exc =
py::exception<JITException>(m, "JITException").release();
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(""));
// If we still had the py::exception<JITException> object, we could
// just call it. But we must get a handle to leak it and there is no
// way I can find to re-create it from the handle. So setting the
// exception manually
PyErr_SetString(exc.ptr(), 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) -> std::optional<std::shared_ptr<Graph>> {
if (!n->maybeSchema()) {
return std::nullopt;
}
return shapeComputeGraphForSchema(n->schema());
})
.def(
"_jit_decomposition_graph_for_node",
[](Node* n) -> std::optional<std::shared_ptr<Graph>> {
if (!n->maybeSchema()) {
return std::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,
std::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,
std::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, const 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, const 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",
[](const std::string& op_name, bool flip = true) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_set_nvfuser_skip_node_kind is deprecated and a no-op");
})
.def(
"_jit_set_nvfuser_enabled",
[](bool) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_set_nvfuser_enabled is deprecated and a no-op");
})
.def(
"_jit_nvfuser_can_be_enabled",
[]() {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_can_be_enabled is deprecated and a no-op");
})
.def(
"_jit_set_nvfuser_single_node_mode",
[](bool) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_set_nvfuser_single_node_mode is deprecated and a no-op");
})
.def(
"_jit_nvfuser_single_node_mode",
[]() {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_single_node_mode is deprecated and a no-op");
})
.def(
"_jit_set_nvfuser_horizontal_mode",
[](bool) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_set_nvfuser_horizontal_mode is deprecated and a no-op");
})
.def(
"_jit_nvfuser_horizontal_mode",
[]() {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_horizontal_mode is deprecated and a no-op");
})
.def(
"_jit_set_nvfuser_guard_mode",
[](bool) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_set_nvfuser_guard_mode is deprecated and a no-op");
})
.def(
"_jit_nvfuser_enabled",
[]() {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_enabled is deprecated and a no-op");
})
.def(
"_jit_nvfuser_set_comparison_callback",
[](bool, py::function) {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_set_comparison_callback is deprecated and a no-op");
})
.def(
"_jit_nvfuser_clear_comparison_callback",
[]() {
TORCH_WARN(
"nvfuser is no longer supported in torch script, use _jit_nvfuser_clear_comparison_callback is deprecated and a no-op");
})
.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",
[](const 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(std::move(loggingOption));
})
.def(
"_jit_set_logging_stream",
[](const 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" << '\n';
}
})
.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},
std::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, [](const c10::SymNode& a) { return a->n(); })
#define SYMNODE_BINARY(n) \
.def(#n, [](const c10::SymNode& a, const c10::SymNode& b) { return a->n(b); })
#define SYMNODE_SIZES_STRIDES(n) \
.def( \
#n, \
[](const 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(int_truediv)
SYMNODE_BINARY(float_truediv)
SYMNODE_BINARY(pow)
SYMNODE_BINARY(float_pow)
SYMNODE_BINARY(pow_by_natural)
SYMNODE_BINARY(floordiv)
SYMNODE_BINARY(int_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)
.def(
"guard_int",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->guard_int(file, line);
})
.def(
"guard_bool",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->guard_bool(file, line);
})
.def(
"guard_float",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->guard_float(file, line);
})
.def(
"expect_true",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->expect_true(file, line);
})
.def(
"expect_size",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->expect_size(file, line);
})
.def(
"guard_size_oblivious",
[](const c10::SymNode& a, const char* file, int64_t line) {
return a->guard_size_oblivious(file, line);
})
.def(
"has_hint",
[](const c10::SymNode& a) {
return a->has_hint();
})
.def(
"wrap_int",
[](const c10::SymNode& a, int64_t b) {
return a->wrap_int(b);
})
.def(
"wrap_float",
[](const c10::SymNode& a, double b) {
return a->wrap_float(b);
})
.def(
"wrap_bool",
[](const c10::SymNode& a, bool b) {
return a->wrap_bool(b);
})
.def(
"__str__",
[](const c10::SymNode& a) { return a->str(); })
.def(
"__repr__",
[](const c10::SymNode& a) { return a->str(); })
.def(
"_graph_repr",
[](const c10::SymNode& a) { return a->_graph_repr(); })
.def(
"is_constant",
[](const c10::SymNode& node){
return node->is_constant();
})
.def(
"is_nested_int",
[](const c10::SymNode& node) {
return node->is_nested_int();
})
.def(
"is_symbolic",
[](const c10::SymNode& node) {
return node->is_symbolic();
})
.def(
"nested_int",
[](const c10::SymNode& node) {
return node->nested_int();
})
.def(
"nested_int_coeff",
[](const c10::SymNode& node) {
return node->nested_int_coeff();
})
.def(
"__deepcopy__",
[](const c10::SymNode& node, py::handle memo) {
return node->clone();
});
// 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, bool>(),
py::arg("file_name"),
py::arg("compute_crc32") = true)
.def(
py::init([](const py::object& buffer, bool compute_crc32 = true) {
auto writer_func = [=](const void* data, size_t size) {
// Writing an empty file is a noop
if (size == 0) {
return size;
}
py::gil_scoped_acquire acquire;
if (!data) {
// See [Note: write_record_metadata]
buffer.attr("seek")(
size, py::module::import("os").attr("SEEK_CUR"));
} else {
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), compute_crc32);
}),
py::arg("buffer"),
py::arg("compute_crc32") = true)
.def(
py::init<const std::function<size_t(const void*, size_t)>&, bool>(),
py::arg("writer_func"),
py::arg("compute_crc32") = true)
// [Note: write_record_metadata]
// The write_record_metadata function is intended to write metadata (i.e.
// the zipfile header and end of central directory record) for a file
// while reserving nbytes of space for the file for the bytes of the
// actual file to be added in later. This functionality is achieved by
// defining `m_pWrite` to seek instead of write if the buffer passed is a
// nullptr. This has implications on CRC-32 which will not be written at
// write_record_metadata time, and will not be combined with the hash in
// combined_uncomp_crc32_. We define this in `m_pWrite` rather than
// extending the interface of miniz to have an `m_pSeek` since different
// versions of miniz are used in fbcode/oss.
.def(
"write_record_metadata",
[](PyTorchStreamWriter& self, const std::string& name, size_t size) {
return self.writeRecord(name, nullptr, size);
})
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
const char* data,
size_t size) {
// Since we don't know where the data come from, we cannot
// release the GIL in this overload
return self.writeRecord(name, data, size);
})
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
py::bytes data,
size_t size) {
// It is not clear from the doc but according to CPython own code,
// it is ok to use the result of PyBytes_AsString without the GIL
// being held
// https://github.com/python/cpython/blob/e2a3e4b7488aff6fdc704a0f258bc315e96c1d6e/Objects/stringlib/join.h#L67
const char* data_str = PyBytes_AsString(data.ptr());
py::gil_scoped_release release;
return self.writeRecord(name, data_str, size);
})
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
const c10::Storage& data,
size_t size) {
// Reading Tensor data is always ok without the GIL held
py::gil_scoped_release release;
return self.writeRecord(
name, reinterpret_cast<const char*>(data.data()), size);
})
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
uintptr_t data,
size_t size) {
TORCH_WARN_ONCE(
"write_record(): Passing Storage by data pointer is deprecated and will be an error in ",
"the future, please pass the Storage object instead.");
return self.writeRecord(
name, reinterpret_cast<const char*>(data), size);
})
.def("write_end_of_file", &PyTorchStreamWriter::writeEndOfFile)
.def("set_min_version", &PyTorchStreamWriter::setMinVersion)
.def("archive_name", &PyTorchStreamWriter::archiveName)
.def("serialization_id", &PyTorchStreamWriter::serializationId)
.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")(0, 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
// NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
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));
Py_DECREF(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) {
auto [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("serialization_id", &PyTorchStreamReader::serializationId)
.def(
"get_all_records",
[](PyTorchStreamReader& self) { return self.getAllRecords(); })
.def(
"get_record_offset",
[](PyTorchStreamReader& self, const std::string& key) {
return self.getRecordOffset(key);
})
.def(
"get_record_offset_no_read",
[](PyTorchStreamReader& self,
size_t zipfile_header_offset,
const std::string filename,
size_t size) {
return self.getRecordOffsetNoRead(
zipfile_header_offset, filename, size);
});
// 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) -> std::optional<py::tuple> {
try {
auto symbol = Symbol::fromQualString(op_name);
auto operations = getAllOperatorsFor(symbol);
bool allow_numbers_as_tensors = opAllowsNumbersAsTensors(symbol);
for (const auto& op : operations) {
if (op->schema().overload_name() == overload_name) {
auto func = py::cpp_function(
[op, symbol, allow_numbers_as_tensors](
const py::args& args, const 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_,
const py::args& args,
const py::kwargs& kwargs) {
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()));
}
}
return std::nullopt;
} 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(
"_check_schema_allow_fake_script_object",
[](const FunctionSchema& schema,
const py::args& args,
const py::kwargs& kwargs) {
// checkSchemaAllowFakeScriptObject will throw runtime error if there is
// a schema mismatch. Otherwise, it returns true.
return checkSchemaAllowFakeScriptObject(schema, args, kwargs);
});
m.def(
"_jit_resolve_packet",
[](const char* op_name, py::args args, const py::kwargs& kwargs) {
try {
auto symbol = Symbol::fromQualString(op_name);
bool allow_numbers_as_tensors = opAllowsNumbersAsTensors(symbol);
ToIValueAllowNumbersAsTensors g(allow_numbers_as_tensors);
const auto overloads = getAllSortedOperatorsFor(symbol);
auto opWithStack = getOpWithStack(overloads, args, kwargs);
std::shared_ptr<Operator> overload = std::get<0>(opWithStack);
auto result = overload->schema().overload_name();
if (result.empty()) {
result = "default";
}
return result;
} 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);
const auto sortedOps = getAllSortedOperatorsFor(symbol);
if (sortedOps.empty()) {
// No such operator
return py::make_tuple(py::none(), py::none());
}
std::ostringstream docstring;
docstring << "Automatically bound operator '" << op_name
<< "' with schema(s):\n";
for (const auto& op : sortedOps) {
docstring << " " << op->schema() << "\n";
}
py::list overload_names;
for (const auto& op : sortedOps) {
overload_names.append(py::str(op->schema().overload_name()));
}
bool allow_numbers_as_tensors = opAllowsNumbersAsTensors(symbol);
auto func = py::cpp_function(
[sortedOps, symbol, allow_numbers_as_tensors](
const py::args& args, const py::kwargs& kwargs) {
ToIValueAllowNumbersAsTensors g(allow_numbers_as_tensors);
return _get_operation_for_overload_or_packet(
sortedOps, 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(
"_maybe_call_torch_function_for_op_packet",
[](py::handle op_overload_packet,
const py::args& args,
const py::kwargs& kwargs) {
py::list ns_method =
op_overload_packet.attr("_qualified_op_name").attr("split")("::");
auto res = _maybe_handle_torch_function(
py::cast<std::string>(ns_method[0]),
py::cast<std::string>(ns_method[1]),
"",
false,
args,
kwargs);
if (res) {
return py::make_tuple(true, *res);
} else {
return py::make_tuple(false, py::none());
}
});
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,
py::arg("schema"),
py::arg("allow_typevars") = true);
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) {
std::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.");
std::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(
"__repr__",
[](FunctionSchema& self) {
std::stringstream ss;
ss << self;
return ss.str();
})
.def(py::pickle(
[](const FunctionSchema& self) { // __getstate__
std::stringstream ss;
ss << self;
return py::str(ss.str());
},
[](const py::str& schema) { // __setstate__, note: no `self` argument
return parseSchema(schema);
}))
.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_write",
[](Argument& self) {
if (self.alias_info() == nullptr) {
return false;
}
return self.alias_info()->isWrite();
})
.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) {
std::optional<IValue> self_value = toTypeInferredIValueOptional(self);
std::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) {
std::optional<IValue> self_value = toTypeInferredIValueOptional(self);
std::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.empty());
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>(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
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