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c235be42ddecc3c4e1cdf192ca2764455082c1c6
pytorch/torch/csrc/jit/python/init.cpp
Michael Suo c235be42dd [jit] kill script namespace (#34515) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515

Once upon a time we thought this was necessary. In reality it is not, so
removing it.

For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.

There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.

Test Plan: Imported from OSS

Differential Revision: D20353503

Pulled By: suo

fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
2020-03-11 23:32:48 -07:00

774 lines
29 KiB
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#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/csrc/jit/runtime/autodiff.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/codegen/fuser/kernel_cache.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/canonicalize_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/dead_code_elimination.h>
#include <torch/csrc/jit/passes/decompose_ops.h>
#include <torch/csrc/jit/passes/erase_number_types.h>
#include <torch/csrc/jit/passes/fuse_linear.h>
#include <torch/csrc/jit/passes/graph_fuser.h>
#include <torch/csrc/jit/passes/cuda_graph_fuser.h>
#include <torch/csrc/jit/passes/inline_fork_wait.h>
#include <torch/csrc/jit/passes/inliner.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/onnx.h>
#include <torch/csrc/jit/passes/onnx/cast_all_constant_to_floating.h>
#include <torch/csrc/jit/passes/onnx/constant_fold.h>
#include <torch/csrc/jit/passes/onnx/fixup_onnx_conditionals.h>
#include <torch/csrc/jit/passes/onnx/fixup_onnx_loop.h>
#include <torch/csrc/jit/passes/onnx/peephole.h>
#include <torch/csrc/jit/passes/onnx/prepare_division_for_onnx.h>
#include <torch/csrc/jit/passes/onnx/prepare_inplace_ops_for_onnx.h>
#include <torch/csrc/jit/passes/onnx/scalar_type_analysis.h>
#include <torch/csrc/jit/passes/onnx/unpack_quantized_weights.h>
#include <torch/csrc/jit/passes/peephole.h>
#include <torch/csrc/jit/passes/quantization.h>
#include <torch/csrc/jit/passes/remove_expands.h>
#include <torch/csrc/jit/passes/remove_inplace_ops.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/tensorexpr_fuser.h>
#include <torch/csrc/jit/passes/utils/check_alias_annotation.h>
#include <torch/csrc/jit/passes/freeze_module.h>
#include <torch/csrc/jit/runtime/print_handler.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/script_init.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/runtime/jit_exception.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/python/python_tree_views.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <c10/macros/Export.h>
#include <caffe2/serialize/inline_container.h>
#include <ATen/core/function_schema.h>
#include <pybind11/functional.h>
#include <pybind11/iostream.h>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
namespace torch {
namespace jit {
using ::c10::Argument;
using ::c10::FunctionSchema;
using caffe2::serialize::PyTorchStreamReader;
using caffe2::serialize::PyTorchStreamWriter;
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;
}
} // anonymous namespace
#if !defined(_WIN32) && !defined(__HIP_PLATFORM_HCC__)
TORCH_API void runJITCPPTests(bool runCuda);
#endif
void initJITBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
py::register_exception<JITException>(m, "JITException");
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_onnx_remove_print", RemovePrintOps)
.def("_jit_pass_onnx_preprocess_caffe2", PreprocessCaffe2Ops)
.def("_jit_pass_onnx", ToONNX)
.def("_jit_pass_lower_all_tuples", LowerAllTuples)
.def(
"_jit_pass_onnx_peephole",
[](std::shared_ptr<Graph>& graph,
int opset_version,
bool fixed_batch_size) {
return PeepholeOptimizeONNX(graph, opset_version, fixed_batch_size);
})
.def(
"_jit_pass_onnx_cast_all_constant_to_floating",
CastAllConstantToFloating)
.def(
"_jit_pass_onnx_constant_fold",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, at::Tensor>& paramsDict,
int opset_version) {
ConstantFoldONNX(
graph->block(),
paramsDict,
opset_version); // overload resolution
return paramsDict;
},
pybind11::return_value_policy::move)
.def("_jit_pass_onnx_scalar_type_analysis", ScalarTypeAnalysisForONNX)
.def(
"_jit_pass_onnx_prepare_inplace_ops_for_onnx",
PrepareInplaceOpsForONNX)
.def("_jit_pass_fuse", FuseGraph)
.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_insert_observers",
[](Module& module,
const std::string& method_name,
const py::dict& qconfig_dict,
bool inplace) {
auto dict = py::cast<std::unordered_map<
std::string,
std::tuple<Module, Module>>>(qconfig_dict);
return InsertObservers(module, method_name, dict, inplace);
},
py::arg("module"),
py::arg("method_name"),
py::arg("qconfig_dict"),
py::arg("inplace") = false)
.def(
"_jit_pass_insert_quant_dequant",
[](Module& module,
const std::string& method_name,
bool inplace) {
return InsertQuantDeQuant(module, method_name, inplace);
},
py::arg("module"),
py::arg("method_name"),
py::arg("inplace") = false)
.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", &FoldConvBatchNorm2d)
.def("_freeze_module",
[](Module& module) {
return freeze_module(module);
},
py::arg("module"))
.def("_jit_pass_fuse_linear", &FuseLinear)
.def(
"_jit_pass_fold_quantize",
[](Module& module, const std::string& method_name) {
FoldQuantizeCallIntoBuffer(module, method_name);
})
.def("_jit_pass_fold_prepack", &FoldPrepackedWeightIntoModule)
.def("_jit_pass_dedup_module_uses", &DedupModuleUses)
.def("_jit_pass_replicate_dequantize", &ReplicateDeQuant)
.def("_jit_pass_swap_dequantize", &SwapDeQuant)
.def("_jit_pass_swap_functional_linear",
[](std::shared_ptr<Graph>& graph) {
SwapFunctionalLinear(graph);
})
.def("_jit_pass_swap_functional_linear",
[](script::Module& module) {
SwapFunctionalLinear(module);
})
.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) {
SubgraphRewriter subgraph_rewriter;
subgraph_rewriter.RegisterRewritePattern(pattern, fused_node_name);
subgraph_rewriter.runOnGraph(g);
})
.def(
"_jit_pass_fold_quant_inputs",
[](std::shared_ptr<Graph>& g) {
return FoldQuantNodesIntoInputsOutputs(g);
})
.def(
"_jit_pass_remove_inplace_ops",
[](std::shared_ptr<Graph> g) { return RemoveInplaceOps(g); })
.def("_jit_pass_constant_pooling", ConstantPooling)
.def(
"_jit_pass_peephole",
[](const std::shared_ptr<Graph>& g, bool addmm_fusion_enabled) {
return PeepholeOptimize(g, addmm_fusion_enabled);
},
py::arg("graph"),
py::arg("addmm_fusion_enabled") = false)
.def(
"_jit_pass_canonicalize",
[](const std::shared_ptr<Graph>& g) { return Canonicalize(g); })
.def("_jit_pass_lint", LintGraph)
.def(
"_jit_pass_complete_shape_analysis",
[](std::shared_ptr<Graph> graph, 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 (size_t i = 0; i < inputs.size(); ++i) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
PropagateInputShapes(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_prepare_division_for_onnx", PrepareDivisionForONNX)
.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_propagation",
[](std::shared_ptr<Graph>& g) { return ConstantPropagation(g); })
.def("_jit_pass_erase_shape_information", EraseShapeInformation)
.def(
"_jit_pass_create_autodiff_subgraphs",
[](std::shared_ptr<Graph> graph) { CreateAutodiffSubgraphs(graph); })
#if defined(BUILDING_TESTS) && !defined(_WIN32) && !defined(__HIP_PLATFORM_HCC__)
.def(
"_jit_run_cpp_tests",
[](bool runCuda) {
// 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(runCuda);
},
py::arg("run_cuda"))
.def("_jit_has_cpp_tests", []() { return true; })
#else
.def("_jit_run_cpp_tests", []() { throw std::exception(); })
.def("_jit_has_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",
[](autograd::variable_list vars, python::IODescriptor& desc) {
return py::reinterpret_steal<py::object>(
python::unflatten(vars, desc));
})
.def("_jit_pass_onnx_block", BlockToONNX)
.def("_jit_pass_fixup_onnx_loops", FixupONNXLoops)
.def("_jit_pass_fixup_onnx_conditionals", FixupONNXConditionals)
.def("_jit_pass_canonicalize_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_register_tensorexpr_fuser", &registerTensorExprFuser)
.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",
[](std::shared_ptr<Graph> g,
py::tuple args,
const std::string& unqualified_op_name) {
auto stack = toTraceableStack(args);
checkAliasAnnotation(g, std::move(stack), unqualified_op_name);
})
.def(
"_jit_register_cuda_fuser", &registerCudaFuseGraph)
.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_set_bailout_depth",
[](size_t depth) {
size_t old_depth = getBailoutDepth();
getBailoutDepth() = depth;
return old_depth;
})
.def(
"_jit_set_inline_everything_mode",
[](bool enabled) { getInlineEverythingMode() = enabled; })
.def(
"_jit_get_inline_everything_mode",
[]() { return getInlineEverythingMode(); })
.def(
"_jit_try_infer_type",
[](py::object obj) -> TypePtr {
auto match = tryToInferType(obj);
if (match.success()) {
return match.type();
}
return nullptr;
})
.def(
"_jit_fuser_get_fused_kernel_code",
[](Graph& g, std::vector<at::Tensor> inps) {
return debugGetFusedKernelCode(g, inps);
})
.def(
"_jit_pass_onnx_unpack_quantized_weights",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, at::Tensor>& paramsDict) {
UnpackQuantizedWeights(graph, paramsDict);
return paramsDict;
},
pybind11::return_value_policy::move)
.def(
"_jit_pass_onnx_quantization_insert_permutes",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, at::Tensor>& paramsDict) {
insertPermutes(graph, paramsDict);
return paramsDict;
},
pybind11::return_value_policy::move);
// 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("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) {
auto bytes = py::bytes(reinterpret_cast<const char *>(data), size);
buffer.attr("write")(std::move(bytes));
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("write_record",
[](PyTorchStreamWriter &self, const std::string &name,
uintptr_t data, size_t size) {
return self.writeRecord(name, reinterpret_cast<const char *>(data),
size);
});
// 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 {
#if PY_MAJOR_VERSION >= 3
THPObjectPtr memview(PyMemoryView_FromMemory(
reinterpret_cast<char*>(buf), n, PyBUF_WRITE));
#else
THPObjectPtr memview(PyBuffer_FromReadWriteMemory(buf, n));
#endif
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) {
int i = PyInt_AsLong(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>(m, "PyTorchFileReader")
.def(py::init<std::string>())
.def(py::init([](const py::object& buffer) {
auto adapter = std::make_unique<BufferAdapter>(std::move(buffer));
return std::make_unique<PyTorchStreamReader>(std::move(adapter));
}))
.def("get_record", [](PyTorchStreamReader& self, const std::string& key) {
at::DataPtr data;
size_t size;
std::tie(data, size) = self.getRecord(key);
return py::bytes(reinterpret_cast<const char*>(data.get()), size);
})
.def("get_all_records", [](PyTorchStreamReader& self) {
return self.getAllRecords();
});
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);
TORCH_CHECK(
operations.size() == 1,
"Found ",
operations.size(),
" overloads for operator ",
op_name,
"! Overloads are not supported from Python.");
std::shared_ptr<Operator> op = operations[0];
AT_ASSERT(op != nullptr);
std::ostringstream docstring;
docstring << "Automatically bound operator '" << op_name
<< "' with schema: " << op->schema();
return py::cpp_function(
[op](py::args args, py::kwargs kwargs) {
return invokeOperatorFromPython(
*op, std::move(args), std::move(kwargs));
},
py::name(symbol.toUnqualString()),
py::doc(docstring.str().c_str()));
} catch (const c10::Error& error) {
throw std::runtime_error(error.what_without_backtrace());
}
},
py::arg("qualified_name"));
m.def("parse_ir", [](const std::string& input) {
auto graph = std::make_shared<Graph>();
parseIR(input, &*graph);
return graph;
});
m.def("parse_schema", parseSchema);
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("__eq__", [](const FunctionSchema& self,
const FunctionSchema& other) {
return self == other;
})
.def("__str__", [](FunctionSchema& self) {
std::stringstream ss;
ss << self;
return ss.str();
});
py::class_<Argument>(m, "Argument")
.def_property_readonly("name", [](Argument& self) { return self.name(); })
.def_property_readonly("type", [](Argument& self) { return self.type(); })
.def_property_readonly(
"N",
[](Argument& self) -> py::object {
return (self.N()) ? py::cast(*self.N()) : py::none();
})
.def_property_readonly("default_value", [](Argument& self) -> py::object {
if (!self.default_value())
return py::none();
IValue v = *self.default_value();
return toPyObject(std::move(v));
});
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_schemas_for_operator", [](const std::string& qualified_name) {
auto symbol = Symbol::fromQualString(qualified_name);
auto operations = getAllOperatorsFor(symbol);
return fmap(operations, [](const std::shared_ptr<Operator>& op) {
return op->schema();
});
});
struct PythonFutureWrapper {
explicit PythonFutureWrapper(c10::intrusive_ptr<c10::ivalue::Future> fut)
: fut(std::move(fut)) {}
c10::intrusive_ptr<c10::ivalue::Future> fut;
};
py::class_<PythonFutureWrapper>(m, "Future");
m.def("fork", [](py::args args) {
AT_ASSERT(args.size() >= 1);
py::function f = py::cast<py::function>(args[0]);
py::tuple args_tup(args.size() - 1);
for (size_t i = 1; i < args.size(); ++i) {
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;
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);
// 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 PythonFutureWrapper(retval);
} else {
auto result = toTypeInferredIValue(f(*args_tup));
auto retval = c10::make_intrusive<c10::ivalue::Future>(result.type());
retval->markCompleted(std::move(result));
return PythonFutureWrapper(retval);
}
});
m.def("wait", [](PythonFutureWrapper& fut) {
if (jit::tracer::isTracing()) {
auto graph = jit::tracer::getTracingState()->graph;
Value* fut_val = jit::tracer::getValueTrace(fut.fut);
auto output = graph->insert(aten::wait, {fut_val});
jit::tracer::setValueTrace(fut.fut->value(), output);
}
return fut.fut->value();
});
m.def("_jit_assert_is_instance", [](py::object obj, TypePtr type) {
toIValue(obj, type);
});
initPythonCustomClassBindings(module);
initPythonIRBindings(module);
tracer::initPythonTracerBindings(module);
initTreeViewBindings(module);
initJitScriptBindings(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());
}
});
}
} // namespace jit
} // namespace torch
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