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python_ir.cpp
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#include <torch/csrc/jit/python/python_ir.h>
#include <aten/src/ATen/core/jit_type.h>
#include <pybind11/pybind11.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/Dtype.h>
#include <torch/csrc/api/include/torch/python.h>
#include <torch/csrc/jit/ir/alias_analysis.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/onnx/helper.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/passes/symbolic_shape_analysis.h>
#include <torch/csrc/jit/python/pybind.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/serialization/python_print.h>
#include <torch/csrc/python_headers.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_strings.h>
#include <iostream>
#include <sstream>
#include <utility>
namespace torch {
namespace jit {
// Controls whether graph source ranges are printed by default
bool global_print_source_ranges = true;
Symbol ConcretePythonOp::Kind = prim::PythonOp;
using c10::Type;
std::string getPythonName(const PyObject* obj_) {
pybind11::gil_scoped_acquire gil;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
PyObject* obj = const_cast<PyObject*>(obj_);
auto v = py::getattr(obj, "__name__", py::str("<python_value>"));
// if this was a autograd.Function recover the name of the class
return py::str(v);
}
std::ostream& printPyObject(std::ostream& out, const THPObjectPtr& obj) {
pybind11::gil_scoped_acquire gil;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
auto pyobj = py::handle(const_cast<PyObject*>(obj.get()));
if (py::isinstance<py::tuple>(pyobj)) {
// This special-case for printing tuples handles a problem where
// str((2L, 3L)) outputs "(2L, 3L)" in Python 2 but "(2, 3)"
// in Python 3. In order to suppress the L-suffix, we must
// manually print the string ourselves, calling str() on the
// sub-elements.
//
// This is a fairly fragile fix (What if you have nested tuples
// in tuples? What if you have dictionaries?) but it seems to hit
// the cases that are triggered in practice in onnx-pytorch. Revisit
// this code if this is not the case.
//
// By the way, one non-solution for this problem is to monkeypatch
// tuple.__str__; this doesn't work because Python doesn't allow
// monkeypatching methods of built-in types.
auto pytuple = pyobj.cast<py::tuple>();
out << "(";
size_t i = 0;
for (const auto& o : pytuple) {
if (i > 0) {
out << ", ";
}
THPObjectPtr str(py::str(o).release().ptr());
out << THPUtils_unpackString(str.get());
i++;
}
if (i == 1) {
out << ",";
}
out << ")";
return out;
} else {
return out << THPUtils_unpackString(py::str(pyobj).ptr());
}
}
Node* findNode(
c10::ArrayRef<torch::jit::Block*> blocks,
Symbol kind,
bool recurse = true) {
for (Block* block : blocks) {
for (Node* n : block->nodes()) {
if (n->kind() == kind) {
return n;
}
if (recurse) {
auto node = findNode(n->blocks(), kind, recurse);
if (node != nullptr) {
return node;
}
}
}
}
return nullptr;
}
Node* findNode(Block* block, Symbol kind, bool recurse = true) {
std::vector<Block*> blocks = {block};
return findNode(blocks, kind, recurse);
}
std::string ConcretePythonOp::name() const {
pybind11::gil_scoped_acquire gil;
if (auto autograd = autogradFunction()) {
return getPythonName(autograd->get());
} else {
return getPythonName(pyobj.get());
}
}
void ConcretePythonOp::cloneFrom(Node* other_) {
// NOLINTNEXTLINE(bugprone-parent-virtual-call)
Node::cloneFrom(other_);
auto other = other_->cast<ConcretePythonOp>();
this->cconv = other->cconv;
Py_INCREF(other->pyobj.get());
this->pyobj = THPObjectPtr(other->pyobj.get());
for (auto& sa : other->scalar_args) {
Py_INCREF(sa.get());
this->scalar_args.emplace_back(sa.get());
}
}
// recover the autograd.Function instance, if this PythonOp's function
// was originally SomeFunction.apply
// used in ONNX for discovering symbolics
c10::optional<THPObjectPtr> ConcretePythonOp::autogradFunction() const {
pybind11::gil_scoped_acquire gil;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
py::handle obj = const_cast<PyObject*>(pyobj.get());
auto r = py::getattr(obj, "__self__", py::none());
if (r.is_none())
return c10::nullopt;
auto apply = py::getattr(r, "apply", py::none());
if (apply.is_none())
return c10::nullopt;
auto c = PyObject_RichCompareBool(apply.ptr(), obj.ptr(), Py_NE);
if (PyErr_Occurred())
throw py::error_already_set();
if (c)
return c10::nullopt;
return THPObjectPtr(r.release().ptr());
}
void ConcretePythonOp::writeScalars(std::ostream& out) const {
out << "(";
int i = 0;
for (auto& scalar : scalar_args) {
if (i++ > 0)
out << ", ";
printPyObject(out, scalar);
}
out << ")";
}
void ConcretePythonOp::lint_python() const {
size_t n_scalars = 0, n_tensors = 0;
for (auto c : cconv) {
if (c == 'c') {
n_scalars++;
} else if (c == 'd') {
n_tensors++;
} else {
AT_ASSERT(0);
}
AT_ASSERT(static_cast<bool>(pyobj));
}
AT_ASSERT(n_scalars == scalar_args.size());
AT_ASSERT(n_tensors == inputs().size());
}
Node* Graph::createPythonOp(
THPObjectPtr&& pyobj,
const std::string& cconv,
pyobj_list&& scalar_args) {
ConcretePythonOp* op = new ConcretePythonOp(this);
return op->init(std::move(pyobj), cconv, std::move(scalar_args));
}
void initPythonIRBindings(PyObject* module_) {
auto m = py::handle(module_).cast<py::module>();
py::class_<AliasDb, std::shared_ptr<AliasDb>>(m, "AliasDb")
.def("dump", &AliasDb::dump)
.def("to_graphviz_str", &AliasDb::toGraphviz)
.def(
"may_contain_alias",
[&](AliasDb& db, Value* v1, Value* v2) {
return db.mayContainAlias(v1, v2);
})
.def(
"has_writers",
[&](AliasDb& db, Value* v1) { return db.hasWriters(v1); })
.def("__str__", &AliasDb::toString);
#define GS(name) def(#name, &Graph ::name)
py::class_<Graph, std::shared_ptr<Graph>>(m, "Graph")
.def(py::init<>())
.def(
"__repr__",
[&](Graph& g) { return g.toString(global_print_source_ranges); })
.def("str", &Graph::toString, py::arg("print_source_ranges") = true)
.def_readonly_static(
"global_print_source_ranges", &global_print_source_ranges)
.def_static(
"set_global_print_source_ranges",
[&](const bool enabled) { global_print_source_ranges = enabled; },
py::arg("enabled") = true)
.def(
"alias_db",
[](std::shared_ptr<Graph> g,
bool isFrozen = false,
bool descend_function_calls = false) {
return std::make_shared<AliasDb>(
std::move(g), isFrozen, descend_function_calls);
},
py::arg("isFrozen") = false,
py::arg("descend_function_calls") = false)
.def(
"dump_alias_db",
[](std::shared_ptr<Graph> g) {
AliasDb db(std::move(g));
db.dump();
})
.def(
"_export_onnx",
[](const std::shared_ptr<Graph>& g,
const std::map<std::string, at::Tensor>& initializers,
int64_t onnx_opset_version,
const std::unordered_map<
std::string,
std::unordered_map<int64_t, std::string>>& dynamic_axes,
bool defer_weight_export,
::torch::onnx::OperatorExportTypes operator_export_type,
bool strip_doc_string,
bool keep_initializers_as_inputs,
const std::map<std::string, int>& custom_opsets,
bool add_node_names,
const std::string& onnx_file_path,
const NodeAttrNameMap& node_attr_to_name) {
std::string graph;
std::shared_ptr<::ONNX_NAMESPACE::ModelProto> model_proto;
RawDataExportMap export_map;
SymbolDimMap symbol_map;
bool val_use_external_data_format = false;
NodeNameMap onnx_node_names;
std::tie(
model_proto,
export_map,
symbol_map,
val_use_external_data_format,
onnx_node_names) =
export_onnx(
g,
initializers,
onnx_opset_version,
dynamic_axes,
defer_weight_export,
operator_export_type,
strip_doc_string,
keep_initializers_as_inputs,
custom_opsets,
add_node_names,
val_use_external_data_format,
onnx_file_path,
node_attr_to_name);
std::unordered_map<std::string, py::bytes>
python_serialized_export_map;
for (auto& kv : export_map) {
auto t = kv.second;
size_t copy_bytes = t.element_size() * t.numel();
// TODO: this is an unnecessary copy. In theory we can directly
// return the map from identifier to Tensor, but we need some API
// in Python to get raw `bytes` containing the raw tensor data.
python_serialized_export_map[kv.first] =
py::bytes(static_cast<const char*>(t.data_ptr()), copy_bytes);
}
graph = serialize_model_proto_to_string(model_proto);
return std::make_tuple(
py::bytes(graph),
python_serialized_export_map,
val_use_external_data_format,
onnx_node_names);
},
py::arg("initializers"),
py::arg("onnx_opset_version") = 0,
py::arg("dynamic_axes"),
py::arg("defer_weight_export") = false,
py::arg("operator_export_type") =
::torch::onnx::OperatorExportTypes::ONNX,
py::arg("strip_doc_string") = true,
py::arg("keep_initializers_as_inputs") = true,
py::arg("custom_opsets"),
py::arg("add_node_names") = true,
py::arg("onnx_file_path") = std::string(),
py::arg("node_attr_to_name") = NodeAttrNameMap())
.def(
"_pretty_print_onnx",
[](const std::shared_ptr<Graph>& g,
const std::map<std::string, at::Tensor>& initializers,
int64_t onnx_opset_version,
bool defer_weight_export,
::torch::onnx::OperatorExportTypes operator_export_type,
bool google_printer,
bool keep_initializers_as_inputs,
const std::map<std::string, int>& custom_opsets,
bool add_node_names) {
return pretty_print_onnx(
g,
initializers,
onnx_opset_version,
defer_weight_export,
operator_export_type,
google_printer,
keep_initializers_as_inputs,
custom_opsets,
add_node_names);
},
py::arg("initializers"),
py::arg("onnx_opset_version") = 0,
py::arg("defer_weight_export") = false,
py::arg("operator_export_type") =
::torch::onnx::OperatorExportTypes::ONNX,
py::arg("google_printer") = false,
py::arg("keep_initializers_as_inputs") = true,
py::arg("custom_opsets"),
py::arg("add_node_names") = true)
.def(
"inputs",
[](Graph& g) {
return py::make_iterator(g.inputs().begin(), g.inputs().end());
},
py::keep_alive<0, 1>())
.def(
"outputs",
[](Graph& g) {
return py::make_iterator(g.outputs().begin(), g.outputs().end());
},
py::keep_alive<0, 1>())
// We keep the graph alive while the iterator lives. Destroying
// nodes might still be hazardous.
.def(
"nodes",
[](Graph& g) {
return py::make_iterator(g.nodes().begin(), g.nodes().end());
},
py::keep_alive<0, 1>())
.def(
"findNode",
[](Graph& g, const std::string& kind, bool recurse) {
return findNode(g.block(), Symbol::fromQualString(kind), recurse);
},
"Find Node",
py::arg("kind"),
py::arg("recurse") = true)
.def(
"findAllNodes",
[](Graph& g, const std::string& kind, bool recurse) {
return findAllNodes(g, Symbol::fromQualString(kind), recurse);
},
"Find all nodes",
py::arg("kind"),
py::arg("recurse") = true)
.def("addInput", [](Graph& g) { return g.addInput(); })
.def("copy", [](Graph& g) { return g.copy(); })
.GS(eraseInput)
.GS(eraseOutput)
.GS(registerOutput)
.def(
"create",
[](Graph& g, const char* str) {
return g.create(Symbol::fromQualString(str));
})
.def(
"create",
[](Graph& g, const char* str, size_t noutputs) {
return g.create(Symbol::fromQualString(str), noutputs);
})
.def(
"create",
[](Graph& g, const char* str, const std::vector<Value*>& inputs) {
TORCH_CHECK_VALUE(
std::all_of(
inputs.begin(),
inputs.end(),
[](Value* v) { return (v != nullptr); }),
"cannot pass None in inputs");
return g.create(Symbol::fromQualString(str), inputs);
})
.def(
"create",
[](Graph& g,
const char* str,
const std::vector<Value*>& inputs,
size_t noutputs) {
TORCH_CHECK_VALUE(
std::all_of(
inputs.begin(),
inputs.end(),
[](Value* v) { return (v != nullptr); }),
"cannot pass None in inputs");
return g.create(Symbol::fromQualString(str), inputs, noutputs);
})
.def("param_node", [](Graph& g) { return g.block()->param_node(); })
.def("return_node", [](Graph& g) { return g.block()->return_node(); })
.def(
"createFusionGroup",
[](Graph& g) { return g.createWithSubgraph(prim::FusionGroup); })
.def(
"createCudaFusionGroup",
[](Graph& g) { return g.createWithSubgraph(prim::CudaFusionGroup); })
.def(
"createClone",
[](Graph& g, Node* n, py::object fn) {
return g.createClone(
n, [&](Value* e) { return fn(e).cast<Value*>(); });
})
.GS(appendNode)
.GS(prependNode)
// NB: insert_point_guard defined over direct modification of insert point
.def(
"insert_point_guard",
[](Graph& g, Node* n) {
return py::module::import("torch.jit._ir_utils")
.attr("insert_point_guard")(g, n);
})
.def(
"insert_point_guard",
[](Graph& g, Block* b) {
return py::module::import("torch.jit._ir_utils")
.attr("insert_point_guard")(g, b);
})
.GS(insertPoint)
.def("setInsertPoint", [](Graph& g, Node* n) { g.setInsertPoint(n); })
.def("setInsertPoint", [](Graph& g, Block* n) { g.setInsertPoint(n); })
.def(
"insertGraph",
[](Graph& g, Graph& callee, std::vector<Value*> inputs) {
return insertGraph(g, callee, inputs);
})
.def(
"insertGraph",
[](Graph& g,
Graph& callee,
std::vector<Value*> inputs,
std::unordered_map<Value*, Value*> value_map) {
return insertGraph(g, callee, inputs, value_map);
})
.def(
"insert",
[](Graph& g, Symbol opname, std::vector<Value*> args) {
std::vector<NamedValue> args_named;
args_named.reserve(args.size());
for (Value* v : args) {
args_named.emplace_back(v);
}
return g.insert(opname, args_named);
})
.def(
"makeMultiOutputIntoTuple",
[](Graph& g) {
auto tup = g.createTuple(g.outputs());
tup->insertBefore(g.return_node());
for (int64_t i = g.outputs().size() - 1; i >= 0; i--) {
g.eraseOutput(0);
}
g.registerOutput(tup->output());
})
.def(
"insertConstant",
[](Graph& g, const IValue& ival) { return g.insertConstant(ival); })
.GS(lint)
.def("block", [](Graph& g) { return g.block(); })
.GS(insertNode);
#undef GS
#define VS(name) def(#name, &Value ::name)
py::class_<Value, unwrapping_shared_ptr<Value>>(m, "Value")
.def(
"__repr__",
[](Value& n) {
std::stringstream ss;
ss << n.debugName() << " defined in (" << *n.node() << ")";
return ss.str();
})
.VS(type)
.VS(setType)
.def(
"inferTypeFrom",
py::overload_cast<const at::Tensor&>(&Value::inferTypeFrom))
.def(
"inferTypeFrom",
py::overload_cast<const c10::intrusive_ptr<c10::ivalue::Object>&>(
&Value::inferTypeFrom))
// skip owningGraph because it returns a raw pointer to a otherwise
// std::shared_ptr stored graph object, and would cause a double free
.VS(unique)
.VS(debugName)
.VS(setDebugName)
.VS(offset)
.VS(uses)
.VS(replaceAllUsesWith)
.VS(replaceAllUsesAfterNodeWith)
.def("node", [](Value& v) { return v.node(); })
.def(
"setTypeAs",
[](Value* node, Value* other) {
node->setType(other->type());
return node;
})
.VS(copyMetadata)
.VS(isCompleteTensor)
.VS(requires_grad)
.def(
"requiresGrad",
[](Value& n) {
return n.type()->expectRef<TensorType>().requiresGrad();
})
.def("toIValue", [](Value& n) { return toIValue(&n); })
.def("type", [](Value& v) { return v.type(); });
#undef VS
py::class_<Block, unwrapping_shared_ptr<Block>>(m, "Block")
.def(
"nodes",
[](Block& b) {
return py::make_iterator(b.nodes().begin(), b.nodes().end());
})
.def(
"findNode",
[](Block& b, const std::string& kind, bool recurse) {
return findNode(&b, Symbol::fromQualString(kind), recurse);
},
"Find Node",
py::arg("kind"),
py::arg("recurse") = true)
.def(
"findAllNodes",
[](Block& b, const std::string& kind, bool recurse) {
return findAllNodes(b, Symbol::fromQualString(kind), recurse);
},
"Find all nodes",
py::arg("kind"),
py::arg("recurse") = true)
.def(
"inputs",
[](Block& b) {
return py::make_iterator(b.inputs().begin(), b.inputs().end());
})
.def(
"outputs",
[](Block& b) {
return py::make_iterator(b.outputs().begin(), b.outputs().end());
})
.def("returnNode", [](Block& b) { return b.return_node(); })
.def("paramNode", [](Block& b) { return b.param_node(); })
.def("owningNode", [](Block& b) { return b.owningNode(); })
.def(
"addNode",
[](Block& b, const char* str, const std::vector<Value*>& inputs) {
return addNodeToBlock(&b, Symbol::fromQualString(str), inputs);
})
.def("addInputToBlock", [](Block& b) { return addInputToBlock(&b); })
.def("registerOutput", [](Block& b, Value* value) {
return b.registerOutput(value);
});
#define NS(name) def(#name, &Node ::name)
py::class_<Node, unwrapping_shared_ptr<Node>>(m, "Node")
.def(
"__repr__",
[](Node& n) {
std::stringstream ss;
ss << n;
return ss.str();
})
.def("sourceRange", [](Node& n) { return n.sourceRange().str(); })
.def("hasMultipleOutputs", [](Node& n) { return n.outputs().size() > 1; })
.def("inputsSize", [](Node& n) { return n.inputs().size(); })
.def("outputsSize", [](Node& n) { return n.outputs().size(); })
.NS(kind)
.def("prev", [](Node& n) { return n.prev(); })
.def("matches", [](Node& n, const char* s) { return n.matches(s); })
.def("owningBlock", [](Node& n) { return n.owningBlock(); })
.def("inputsAt", [](Node& n, size_t i) { return n.inputs().at(i); })
.def(
"inputs",
[](Node& n) {
return py::make_iterator(n.inputs().begin(), n.inputs().end());
})
.def(
"schema",
[](Node& n) {
std::stringstream ss;
if (auto sch = n.maybeSchema()) {
ss << n.schema();
} else {
ss << "(no schema)";
}
return ss.str();
})
.def(
"outputs",
[](Node& n) {
return py::make_iterator(n.outputs().begin(), n.outputs().end());
})
.def("outputsAt", [](Node& n, size_t i) { return n.outputs().at(i); })
.def(
"findNode",
[](Node& n, const std::string& kind, bool recurse) {
return findNode(n.blocks(), Symbol::fromQualString(kind), recurse);
},
"Find Node",
py::arg("kind"),
py::arg("recurse") = true)
.def(
"findAllNodes",
[](Node& n, const std::string& kind, bool recurse) {
return findAllNodes(
n.blocks(), Symbol::fromQualString(kind), recurse);
},
"Find all nodes",
py::arg("kind"),
py::arg("recurse") = true)
.def("input", [](Node& n) { return n.input(); })
.def("output", [](Node& n) { return n.output(); })
.def(
"getModuleHierarchy",
[](Node& n) { return torch::jit::utils::getNodesModuleHierarchy(n); })
.NS(addInput)
.NS(copyMetadata)
.NS(replaceInput)
.NS(replaceInputWith)
.NS(replaceAllUsesWith)
.NS(insertBefore)
.NS(insertAfter)
.NS(isBefore)
.NS(isAfter)
.NS(moveAfter)
.NS(moveBefore)
.NS(removeInput)
.NS(removeAllInputs)
.NS(destroy)
.NS(hasUses)
.NS(eraseOutput)
.NS(addOutput)
.NS(scopeName)
.NS(isNondeterministic)
.def(
"blocks",
[](Node& n) {
return py::make_iterator(n.blocks().begin(), n.blocks().end());
})
.NS(addBlock)
.NS(mustBeNone)
#define AS(name) def(#name, &Node::name)
// methods from Attributes
.AS(copyAttributes)
.AS(hasAttributes)
#undef AS
#define AS(name) def(#name, &Node::name##S)
// The default method names take Symbol, but the string conversion for
// Symbol you to qualify with attr::. This is not very user friendly
// for attributes, so expose the string variants instead.
.AS(hasAttribute)
.AS(kindOf)
.AS(removeAttribute)
.AS(attributeNames)
#undef AS
#define CREATE_ACCESSOR(Kind, method) \
def(#method "_", [](Node& n, const char* name, Kind##Attr::ValueType v) { \
return n.method##_(Symbol::attr(name), std::move(v)); \
}).def(#method, [](Node& n, const char* name) { \
return n.method(Symbol::attr(name)); \
})
.CREATE_ACCESSOR(Float, f)
.CREATE_ACCESSOR(Floats, fs)
.CREATE_ACCESSOR(Complex, c)
.CREATE_ACCESSOR(String, s)
.CREATE_ACCESSOR(Strings, ss)
.CREATE_ACCESSOR(Int, i)
.CREATE_ACCESSOR(Ints, is)
.CREATE_ACCESSOR(Graph, g)
.CREATE_ACCESSOR(Graphs, gs)
.CREATE_ACCESSOR(IValue, ival)
#undef CREATE_ACCESSOR
// Tensor (t_) -- manually written to unwrap the variable into a tensor.
.def(
"t_",
[](Node& n, const char* name, const torch::autograd::Variable& v) {
AT_ASSERT(!v.requires_grad());
return n.t_(Symbol::attr(name), v);
})
.def(
"t",
[](Node& n, const char* name) { return n.t(Symbol::attr(name)); })
// Tensors (ts_) -- manually written to unwrap variables into tensors.
.def(
"ts_",
[](Node& n,
const char* name,
const std::vector<torch::autograd::Variable>& vs) {
std::vector<at::Tensor> tensors;
tensors.reserve(vs.size());
for (auto& variable : vs) {
AT_ASSERT(!variable.requires_grad());
tensors.push_back(variable);
}
return n.ts_(Symbol::attr(name), std::move(tensors));
})
.def(
"ts",
[](Node& n, const char* name) {
auto tensors = n.ts(Symbol::attr(name));
std::vector<torch::autograd::Variable> variables;
variables.reserve(tensors.size());
for (auto& tensor : tensors) {
variables.emplace_back(std::move(tensor));
}
return variables;
})
.def(
"z_",
[](Node& n, const char* name, const at::Tensor& v) {
return n.t_(
Symbol::attr(name),
autograd::Variable(v.view(std::vector<int64_t>{}))
.set_requires_grad(false));
})
.def(
"z",
[](Node& n, const char* name) { return n.t(Symbol::attr(name)); })
.def(
"zs_",
[](Node& n, const char* name, TensorsAttr::ValueType v) {
for (auto& i : v) {
i = autograd::Variable(i.view(std::vector<int64_t>{}))
.set_requires_grad(false);
}
return n.ts_(Symbol::attr(name), std::move(v));
})
.def(
"zs",
[](Node& n, const char* name) { return n.ts(Symbol::attr(name)); })
.def(
"pyobj",
[](Node& n) {
return py::handle(n.expect<ConcretePythonOp>()->pyobj.get())
.cast<py::object>();
})
.def("cconv", [](Node& n) { return n.expect<ConcretePythonOp>()->cconv; })
.def(
"pyname",
[](Node& n) { return n.expect<ConcretePythonOp>()->name(); })
.def("scalar_args", [](Node& n) {
auto op = n.expect<ConcretePythonOp>();
auto scalars = py::list();
auto append = scalars.attr("append");
for (auto& arg : op->scalar_args) {
append(py::handle(arg.get()));
}
return scalars;
});
using ::c10::Type;
py::class_<Type, TypePtr>(m, "Type")
.def("__repr__", [](Type& t) { return t.annotation_str(); })
.def(
"str",
[](Type& t) {
std::ostringstream s;
s << t;
return s.str();
})
.def("kind", [](const Type& t) { return typeKindToString(t.kind()); })
.def(
"dim",
[](Type& t) {
auto vshape = t.expectRef<TensorType>().sizes();
return vshape.size() ? py::cast(*vshape.size())
: py::cast<py::none>(Py_None);
})
.def(
"undefined",
[](Type& t) {
auto undef = t.expectRef<TensorType>().undefined();
return undef.has_value() ? py::cast(*undef)
: py::cast<py::none>(Py_None);
})
.def(
"sizes",
[](Type& t) -> py::object {
if (auto ptt = t.expect<TensorType>()) {
if (auto cs = ptt->sizes().concrete_sizes()) {
return py::cast(*cs);
}
}
return py::none();
})
.def(
"symbolic_sizes",
[](Type& t) -> py::object {
if (auto ptt = t.expect<TensorType>()) {
auto ss = ptt->symbolic_sizes();
if (!ss.rank().has_value()) {
return py::none();
}
std::vector<int64_t> ss_vals;
for (size_t i = 0; i < *ss.rank(); ++i) {
ss_vals.push_back(ss.at(i).value());
}
return py::cast(ss_vals);
}
return py::none();
})
.def(
"with_sizes",
[](Type& t, c10::optional<std::vector<c10::optional<int64_t>>> sizes)
-> py::object {
auto ptt = t.expect<TensorType>();
if (!ptt) {
return py::none();
}
if (!sizes) {
return py::cast(ptt->withSymbolicShapes(c10::SymbolicShape()));
}
return py::cast(ptt->withSymbolicShapes(*sizes));
})
.def(
"varyingSizes",
[](Type& t) -> py::object {
if (auto ptt = t.expect<TensorType>()) {
if (auto s = ptt->sizes().sizes()) {
return py::cast(s.value());
}
}
return py::none();
})
.def(
"strides",
[](Type& t) -> py::object {
if (auto ptt = t.expect<TensorType>()) {
if (auto cs = ptt->strides().concrete_sizes()) {
return py::cast(*cs);
}
}
return py::none();
})
.def(
"contiguous",
[](Type& t) {
return std::static_pointer_cast<Type>(
t.expectRef<TensorType>().contiguous());
})
.def(
"scalarType",
[](Type& t) {
auto scalar_type = t.expectRef<TensorType>().scalarType();
return (scalar_type) ? toString(*scalar_type) : nullptr;
})
.def(
"device",
[](Type& t) -> py::object {
auto device = t.expectRef<TensorType>().device();
if (!device) {
return py::none();
}
PyObject* thp_device = THPDevice_New(device.value());
return py::reinterpret_borrow<py::object>(thp_device);
// return toPyObject(device.value());
})
.def(
"with_device",
[](Type& t, py::object device) -> py::object {
at::Device c_device = python::detail::py_object_to_device(device);
if (auto ptt = t.expect<TensorType>()) {
return py::cast(ptt->withDevice(c_device));
}
return py::none();
})
.def(
"dtype",
[](Type& t) -> py::object {
auto scalar_type = t.expectRef<TensorType>().scalarType();
if (!scalar_type) {
return py::none();
}
THPDtype* thp_dtype = torch::getTHPDtype(*scalar_type);
py::object dtype =
py::reinterpret_borrow<py::object>((PyObject*)thp_dtype);
return dtype;
})
.def(
"with_dtype",
[](Type& t, py::object dtype) -> py::object {
at::ScalarType scalar_type =
python::detail::py_object_to_dtype(dtype);
if (auto ptt = t.expect<TensorType>()) {
// auto scalar_type = dtype->scalar_type;
return py::cast(ptt->withScalarType(scalar_type));
}
return py::none();
})
.def(
"__eq__",
[](const TypePtr& self, const TypePtr& other) {
if (!other) {
return false;
}
return *self == *other;
})
.def(
"isSubtypeOf",
[](const TypePtr& self, const TypePtr& other) {
if (!other) {
return false;
}
return self->isSubtypeOf(other);
})
.def(
"is_interface_type",
[](const TypePtr& self) {
return self->castRaw<InterfaceType>() != nullptr;
})
.def(
"requires_grad",
[](const TypePtr& self) -> bool { return self->requires_grad(); })
.def_property_readonly(
"annotation_str", [](const std::shared_ptr<Type>& self) {
return self->annotation_str();
});
py::class_<AnyType, Type, AnyTypePtr>(m, "AnyType")
.def_static("get", &AnyType::get);
py::class_<NumberType, Type, NumberTypePtr>(m, "NumberType")
.def_static("get", &NumberType::get);
py::class_<IntType, Type, IntTypePtr>(m, "IntType")
.def_static("get", &IntType::get);
py::class_<FloatType, Type, FloatTypePtr>(m, "FloatType")
.def_static("get", &FloatType::get);
py::class_<ComplexType, Type, ComplexTypePtr>(m, "ComplexType")
.def_static("get", &ComplexType::get);
py::class_<TensorType, Type, TensorTypePtr>(m, "TensorType")
.def_static("get", &TensorType::get)
.def_static("getInferred", &TensorType::getInferred)
.def_static("create_from_tensor", [](const at::Tensor& t) {
return TensorType::create(t);
});
py::class_<BoolType, Type, BoolTypePtr>(m, "BoolType")
.def_static("get", &BoolType::get);
py::class_<StringType, Type, StringTypePtr>(m, "StringType")
.def_static("get", &StringType::get);
py::class_<DeviceObjType, Type, DeviceObjTypePtr>(m, "DeviceObjType")
.def_static("get", &DeviceObjType::get);
py::class_<StreamObjType, Type, StreamObjTypePtr>(m, "StreamObjType")
.def_static("get", &StreamObjType::get);
py::class_<PyObjectType, Type, PyObjectTypePtr>(m, "PyObjectType")
.def_static("get", &PyObjectType::get);
py::class_<NoneType, Type, NoneTypePtr>(m, "NoneType")
.def_static("get", &NoneType::get);
py::class_<TupleType, Type, TupleTypePtr>(m, "TupleType")
.def(py::init([](std::vector<TypePtr> a) {
return TupleType::create(std::move(a));
}))
.def("elements", [](TupleType& self) {
std::vector<TypePtr> types;
for (const auto& type : self.elements()) {
types.push_back(type);
}
return types;
});
py::class_<UnionType, Type, UnionTypePtr>(m, "UnionType")
.def(py::init(
[](const std::vector<TypePtr>& a) { return UnionType::create(a); }))
.def("containedTypes", [](UnionType& self) {
return self.containedTypes().vec();
});
py::class_<ListType, Type, ListTypePtr>(m, "ListType")
.def(py::init([](TypePtr a) { return ListType::create(a); }))
.def_static("ofInts", &ListType::ofInts)
.def_static("ofTensors", &ListType::ofTensors)
.def_static("ofFloats", &ListType::ofFloats)
.def_static("ofComplexDoubles", &ListType::ofComplexDoubles)
.def_static("ofBools", &ListType::ofBools)