blob: 76572dc097b2ec6183185f17e9f2106a01a7cfdf [file] [log] [blame]
#include "torch/csrc/utils/pybind.h"
#include "torch/csrc/jit/python_tracer.h"
#include "torch/csrc/jit/tracer.h"
#include "torch/csrc/jit/python_ir.h"
#include "torch/csrc/jit/python_arg_flatten.h"
#include "torch/csrc/jit/export.h"
#include "torch/csrc/jit/argument_spec.h"
#include "torch/csrc/jit/passes/remove_expands.h"
#include "torch/csrc/jit/passes/graph_fuser.h"
#include "torch/csrc/jit/passes/onnx.h"
#include "torch/csrc/jit/passes/dead_code_elimination.h"
#include "torch/csrc/jit/passes/erase_number_types.h"
#include "torch/csrc/jit/passes/common_subexpression_elimination.h"
#include "torch/csrc/jit/passes/peephole.h"
#include "torch/csrc/jit/passes/canonicalize.h"
#include "torch/csrc/jit/passes/onnx/peephole.h"
#include "torch/csrc/jit/passes/onnx/fixup_onnx_loop.h"
#include "torch/csrc/jit/passes/shape_analysis.h"
#include "torch/csrc/jit/passes/decompose_addmm.h"
#include "torch/csrc/jit/passes/constant_propagation.h"
#include "torch/csrc/jit/passes/loop_unrolling.h"
#include "torch/csrc/jit/passes/to_batch.h"
#include "torch/csrc/jit/passes/specialize_undef.h"
#include "torch/csrc/jit/graph_executor.h"
#include "torch/csrc/jit/script/init.h"
#include "torch/csrc/jit/script/python_tree_views.h"
#include "torch/csrc/jit/batched/BatchTensor.h"
#include "torch/csrc/jit/pybind_utils.h"
#include "torch/csrc/jit/function_schema.h"
#include "torch/csrc/jit/serialization.h"
#include "torch/csrc/jit/operator.h"
#include <pybind11/functional.h>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
namespace torch { namespace jit {
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
extern std::string runJITCPPTests();
void initJITBindings(PyObject *module) {
auto m = py::handle(module).cast<py::module>();
py::class_<python::IODescriptor>(m, "IODescriptor");
m.def("_jit_init", loadPythonClasses)
.def("_jit_pass_onnx", ToONNX)
.def("_jit_pass_onnx_peephole", PeepholeOptimizeONNX)
.def("_jit_pass_fuse", FuseGraph)
.def("_jit_pass_dce", [](std::shared_ptr<Graph>& g) {
return EliminateDeadCode(g); // overload resolution
})
.def("_jit_pass_cse", [](std::shared_ptr<Graph>& g) {
return EliminateCommonSubexpression(g); // overload resolution
})
.def("_jit_pass_peephole", PeepholeOptimize)
.def("_jit_pass_canonicalize", [](const std::shared_ptr<Graph>& g) {
return Canonicalize(g);
})
.def("_jit_pass_lint", LintGraph)
.def("_jit_pass_shape_analysis", [](Graph& graph, py::tuple inputs, bool with_grad) {
PropagateInputShapes(graph, ArgumentSpec(with_grad, evilDeprecatedBadCreateStackDoNotUse(inputs, graph.inputs())));
})
.def("_jit_pass_complete_shape_analysis", [](Graph& graph, py::tuple inputs, bool with_grad) {
PropagateInputShapes(graph, CompleteArgumentSpec(with_grad, evilDeprecatedBadCreateStackDoNotUse(inputs, graph.inputs())));
})
.def("_jit_pass_remove_expands", RemoveExpands)
.def("_jit_pass_erase_number_types", EraseNumberTypes)
.def("_jit_pass_loop_unrolling", UnrollLoops)
.def("_jit_pass_constant_propagation", [](std::shared_ptr<Graph>& g) {
return ConstantPropagation(g);
})
.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.
AutoNoGIL _no_gil;
return runJITCPPTests();
})
.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_decompose_addmm", DecomposeAddmm)
.def("_jit_pass_specialize_undef", specializeUndef)
.def("_jit_differentiate", [](Graph &g, const std::vector<bool>& requires_grad) {
// 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, requires_grad);
});
py::class_<CompleteArgumentSpec>(m, "CompleteArgumentSpec")
.def("__repr__", [](CompleteArgumentSpec& self) {
std::ostringstream s;
s << self;
return s.str();
});
py::class_<ArgumentSpec>(m, "ArgumentSpec");
py::class_<Code>(m, "Code")
.def("executors", [](Code& c) {
return py::make_iterator(c.executors().begin(), c.executors().end());
});
py::class_<ExecutionPlanState>(m, "ExecutionPlanState")
.def_property_readonly("graph", [](ExecutionPlanState& s) {
return s.graph;
})
.def_property_readonly("code", [](ExecutionPlanState& s) {
return s.f;
})
.def_property_readonly("grad_executor", [](ExecutionPlanState& s) {
return s.grad_executor.get();
});
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("autograd_fallback", [](GraphExecutorState& s) {
return s.autograd_fallback;
})
.def_property_readonly("autograd_fallback_graph", [](GraphExecutorState& s) {
return s.autograd_fallback_graph;
});
py::class_<GraphExecutor>(m, "GraphExecutor", py::dynamic_attr())
.def(
py::init([](py::function func,
py::tuple inputs,
bool optimize) {
auto graph = tracer::createGraphByTracing(func, toStack(inputs));
return GraphExecutor(graph, optimize);
}),
py::arg("func"),
py::arg("inputs"),
py::arg("optimize") = true)
.def(
py::init([](std::shared_ptr<Graph> graph, bool optimize) {
return GraphExecutor(std::move(graph), optimize);
}),
py::arg("graph"),
py::arg("optimize") = true)
.def("graph_for", [](GraphExecutor& ge, py::args args) {
return ge.graphFor(evilDeprecatedBadCreateStackDoNotUse(args, ge.graph()->inputs()));
})
.def_property_readonly("graph", [](GraphExecutor& ge) {
return ge.graph();
})
.def("get_debug_state", [](GraphExecutor& ge) {
return ge.getDebugState();
})
.def("__call__", [](GraphExecutor& ge, py::args args) -> py::object {
const auto & graph = ge.graph();
auto stack = evilDeprecatedBadCreateStackDoNotUse(args, graph->inputs());
ge.run(stack);
return createPyObjectForStack(std::move(stack));
});
py::class_<PyTorchFileWriter>(m, "PyTorchFileWriter")
.def(py::init<std::string>())
.def("write_record", &PyTorchFileWriter::writeRecord)
.def("write_end_of_file", &PyTorchFileWriter::writeEndOfFile);
py::class_<PyTorchFileReader>(m, "PyTorchFileReader")
.def(py::init<std::string>())
.def("get_record_with_key", [](PyTorchFileReader &self, uint64_t key) {
std::shared_ptr<void> data;
size_t size;
std::tie(data, size) = self.getRecordWithKey(key);
return py::bytes(reinterpret_cast<const char*>(data.get()), size);
})
.def("get_last_record", [](PyTorchFileReader &self){
std::shared_ptr<void> data;
size_t size;
std::tie(data, size) = self.getLastRecord();
return py::bytes(reinterpret_cast<const char*>(data.get()), size);
});
m.def("_jit_get_operation", [](const std::string& qualified_name) {
try {
auto symbol = Symbol::fromQualString(qualified_name);
auto operations = getAllOperatorsFor(std::move(symbol));
AT_CHECK(!operations.empty(), "No such operator ", qualified_name);
AT_CHECK(
operations.size() == 1,
"Found ", operations.size(), " overloads for operator ",
qualified_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 '" << qualified_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(qualified_name.c_str()), py::doc(docstring.str().c_str()));
} catch (const at::Error& error) {
throw std::runtime_error(error.what_without_backtrace());
}
}, py::arg("qualified_name"));
initPythonIRBindings(module);
tracer::initPythonTracerBindings(module);
script::initTreeViewBindings(module);
script::initJitScriptBindings(module);
initBatchTensorBindings(module);
initRegisterBatchOpsBindings(module);
}
}}