* remove dispatcher/interpreter python wrapper
* rename tensor_wrapper to array_method
GitOrigin-RevId: b8a402c2be
release-1.2
@@ -18,7 +18,7 @@ from ..core._imperative_rt.core2 import apply | |||
from ..core._wrap import device as as_device | |||
from ..core.ops import builtin | |||
from ..core.ops.special import Const | |||
from ..core.tensor.tensor_wrapper import _broadcast, _remove_axis | |||
from ..core.tensor.array_method import _broadcast, _remove_axis | |||
from ..core.tensor.utils import ( | |||
astensor1d, | |||
convert_inputs, | |||
@@ -18,7 +18,7 @@ import weakref | |||
import numpy as np | |||
from ..core._imperative_rt import GraphProfiler, common, put | |||
from ..core._imperative_rt import GraphProfiler, common | |||
from ..core._imperative_rt.core2 import Tensor as RawTensor | |||
from ..core._imperative_rt.core2 import TensorWeakRef | |||
from ..core._imperative_rt.core2 import __make_empty_tensor as make_empty_tensor | |||
@@ -18,7 +18,7 @@ from .core._imperative_rt.core2 import apply | |||
from .core._trace_option import use_symbolic_shape | |||
from .core._wrap import device as as_device | |||
from .core.ops.builtin import Copy, GetVarShape | |||
from .core.tensor.tensor_wrapper import ArrayMethodMixin | |||
from .core.tensor.array_method import ArrayMethodMixin | |||
from .device import _valid_device, get_default_device | |||
from .utils.deprecation import deprecated | |||
@@ -42,7 +42,6 @@ class Tensor(_Tensor, ArrayMethodMixin): | |||
else: | |||
cn = device._cn | |||
# import pdb; pdb.set_trace() | |||
if isinstance(data, _Tensor): | |||
obj = _Tensor.__new__(cls, data) | |||
else: | |||
@@ -14,7 +14,7 @@ from typing import Iterable, List, Optional | |||
from ..core._imperative_rt import OperatorNodeConfig, ProfileEntry | |||
from ..core._imperative_rt import ProfilerImpl as _Profiler | |||
from ..core._imperative_rt.imperative import sync | |||
from ..core._imperative_rt.core2 import sync | |||
from ..core._imperative_rt.ops import CollectiveComm | |||
@@ -1,5 +1,5 @@ | |||
from ..core._imperative_rt import TensorSanityCheckImpl | |||
from ..core._imperative_rt.imperative import sync | |||
from ..core._imperative_rt.core2 import sync | |||
class TensorSanityCheck: | |||
@@ -1,229 +0,0 @@ | |||
/** | |||
* \file imperative/python/src/dispatcher.cpp | |||
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License") | |||
* | |||
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved. | |||
* | |||
* Unless required by applicable law or agreed to in writing, | |||
* software distributed under the License is distributed on an | |||
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
*/ | |||
#include "./dispatcher.h" | |||
#include "./pyext17.h" | |||
#include "megbrain/exception.h" | |||
#include "megbrain/utils/hash.h" | |||
#include "megbrain/utils/small_vector.h" | |||
#include <unordered_map> | |||
#include <structmember.h> | |||
namespace py = pybind11; | |||
namespace pyx = pyext17; | |||
namespace { | |||
struct Handler { | |||
PyObject* func; // borrowed | |||
bool enabled; | |||
Handler() = default; | |||
Handler(PyObject* func_, bool enable = true) : func(func_), enabled(enable) {} | |||
}; | |||
using FastSig = mgb::SmallVector<void*, 8>; | |||
using MRO = std::vector<Handler*>; | |||
struct Frame { | |||
MRO* mro; | |||
size_t mro_offset; | |||
Frame() = default; | |||
Frame(MRO* mro_, size_t mro_offset_ = 0) : mro(mro_), mro_offset(mro_offset_) {} | |||
}; | |||
struct FastSigHash { | |||
size_t operator()(const FastSig& sig) const { | |||
auto* ptr = &sig.front(); | |||
return mgb::XXHash() | |||
.update(ptr, sig.size() * sizeof(FastSig::value_type)) | |||
.digest(); | |||
} | |||
}; | |||
struct ObjectIdHash : std::hash<void*> { | |||
size_t operator()(const py::handle& h) const { | |||
return std::hash<void*>::operator()(h.ptr()); | |||
} | |||
}; | |||
namespace { | |||
using Container = std::vector<Frame>; | |||
struct DispatcherStack: Container { | |||
constexpr static size_t MAX_RECURSIVE_DEPTH = 1024u; | |||
DispatcherStack() { reserve(MAX_RECURSIVE_DEPTH); } | |||
template<typename... Args> | |||
auto&& emplace_back_safely(Args&& ...args) { | |||
mgb_throw_if(size() >= MAX_RECURSIVE_DEPTH, mgb::MegBrainError, | |||
"recursion depth %zu is greater than the MAX_RECURSIVE_DEPTH(%zu)", | |||
size(), MAX_RECURSIVE_DEPTH); | |||
return emplace_back(std::forward<Args>(args)...); | |||
} | |||
}; | |||
} // anonymous namespace | |||
struct Dispatcher { | |||
std::unordered_map<FastSig, std::unique_ptr<MRO>, FastSigHash> cache; | |||
DispatcherStack stack; | |||
std::unordered_map<py::object, std::unique_ptr<Handler>, ObjectIdHash> registry; | |||
inline py::handle self() { | |||
return pyx::wrap<Dispatcher>::pycast(this); | |||
} | |||
bool prepare_call(PyObject*const* args, Py_ssize_t nargs) { | |||
FastSig sig(nargs); | |||
for (Py_ssize_t i = 0; i < nargs; ++i) { | |||
sig[i] = Py_TYPE(args[i]); | |||
} | |||
auto it = cache.find(sig); | |||
if (it == cache.end()) { | |||
if (auto mro = resolve(sig)) { | |||
it = cache.emplace(std::move(sig), std::move(mro)).first; | |||
} else { | |||
return false; | |||
} | |||
} | |||
stack.emplace_back_safely(it->second.get()); | |||
return true; | |||
} | |||
template<typename T> | |||
PyObject* do_call(T&& caller) { | |||
auto& frame = stack.back(); | |||
auto& mro = *frame.mro; | |||
auto& i = frame.mro_offset; | |||
if (!mro.size()) { | |||
PyErr_SetString(PyExc_NotImplementedError, "function not registered in dispatcher"); | |||
return nullptr; | |||
} | |||
for (; i < mro.size(); ++i) { | |||
if (mro[i]->enabled) { | |||
auto ret = caller(mro[i]->func); | |||
if (ret != Py_NotImplemented) { | |||
stack.pop_back(); | |||
return ret; | |||
} | |||
Py_DECREF(ret); | |||
} | |||
} | |||
PyErr_SetString(PyExc_NotImplementedError, "mro exhausted"); | |||
stack.pop_back(); | |||
return nullptr; | |||
} | |||
std::unique_ptr<MRO> resolve(const FastSig& sig) { | |||
try { | |||
py::tuple args(sig.size()); | |||
for (size_t i = 0; i < sig.size(); ++i) { | |||
args[i] = (PyObject*)sig[i]; | |||
} | |||
auto mro_iter = self().attr("dispatch_iter")(*args); | |||
auto ret = std::make_unique<MRO>(); | |||
for (auto i : mro_iter) { | |||
auto it = registry.find(py::reinterpret_borrow<py::object>(i)); | |||
if (it == registry.end()) { | |||
PyErr_SetString(PyExc_RuntimeError, "resolved to unregistered function"); | |||
return nullptr; | |||
} | |||
ret->push_back(it->second.get()); | |||
} | |||
return ret; | |||
} catch (py::error_already_set& e) { | |||
e.restore(); | |||
} catch (std::runtime_error& e) { | |||
PyErr_SetString(PyExc_RuntimeError, e.what()); | |||
} | |||
return nullptr; | |||
} | |||
public: | |||
static constexpr auto tp_name = "Dispatcher"; | |||
PyObject* tp_call(PyObject* args, PyObject* kwargs) { | |||
if (!prepare_call(&PyTuple_GET_ITEM(args, 0), PyTuple_GET_SIZE(args))) return nullptr; | |||
return do_call([=](PyObject* func){return PyObject_Call(func, args, kwargs);}); | |||
} | |||
#if PY_MINOR_VERSION >= 6 | |||
PyObject* tp_vectorcall(PyObject*const* args, Py_ssize_t nargs) { | |||
if (!prepare_call(args, nargs)) return nullptr; | |||
return do_call([=](PyObject* func){return _PyObject_FastCall(func, const_cast<PyObject**>(args), nargs);}); | |||
} | |||
#endif | |||
#if PY_MINOR_VERSION >= 6 | |||
PyObject* super(PyObject*const* args, Py_ssize_t nargs) { | |||
if (stack.empty()) { | |||
PyErr_SetString(PyExc_RuntimeError, "super called at top level"); | |||
return nullptr; | |||
} | |||
stack.emplace_back_safely(stack.back()).mro_offset++; | |||
return do_call([=](PyObject* func){return _PyObject_FastCall(func, const_cast<PyObject**>(args), nargs);}); | |||
} | |||
#else | |||
PyObject* super(PyObject* args, PyObject* kwargs) { | |||
if (stack.empty()) { | |||
PyErr_SetString(PyExc_RuntimeError, "super called at top level"); | |||
return nullptr; | |||
} | |||
stack.emplace_back_safely(stack.back()).mro_offset++; | |||
return do_call([=](PyObject* func){return PyObject_Call(func, args, kwargs);}); | |||
} | |||
#endif | |||
void enable(PyObject* func) { | |||
auto obj = py::reinterpret_borrow<py::object>(func); | |||
auto it = registry.find(obj); | |||
if (it != registry.end()) { | |||
it->second->enabled = true; | |||
} else { | |||
registry.emplace(std::move(obj), std::make_unique<Handler>(func)); | |||
} | |||
} | |||
PyObject* disable(PyObject* func) { | |||
auto obj = py::reinterpret_borrow<py::object>(func); | |||
auto it = registry.find(obj); | |||
if (it == registry.end()) { | |||
PyErr_SetString(PyExc_ValueError, "function not registered"); | |||
return nullptr; | |||
} else { | |||
it->second->enabled = false; | |||
} | |||
Py_RETURN_NONE; | |||
} | |||
void clear_cache() { | |||
cache.clear(); | |||
} | |||
}; | |||
} // namespace | |||
void init_dispatcher(py::module m) { | |||
auto* dispatcher_type = pyx::wrap<Dispatcher>::type() | |||
.def<&Dispatcher::enable>("enable") | |||
.def<&Dispatcher::disable>("disable") | |||
.def<&Dispatcher::clear_cache>("clear_cache") | |||
#if PY_MINOR_VERSION >= 6 | |||
.def<&Dispatcher::tp_vectorcall>("call") | |||
#else | |||
.def<&Dispatcher::tp_call>("call") | |||
#endif | |||
.def<&Dispatcher::super>("super") | |||
.finalize(); | |||
if (!dispatcher_type) throw py::error_already_set(); | |||
m.attr("Dispatcher") = dispatcher_type; | |||
} |
@@ -1,16 +0,0 @@ | |||
/** | |||
* \file imperative/python/src/dispatcher.h | |||
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License") | |||
* | |||
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved. | |||
* | |||
* Unless required by applicable law or agreed to in writing, | |||
* software distributed under the License is distributed on an | |||
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
*/ | |||
#pragma once | |||
#include <pybind11/pybind11.h> | |||
void init_dispatcher(pybind11::module); |
@@ -51,59 +51,5 @@ make_backward_graph( | |||
} // namespace | |||
void init_imperative_rt(py::module m) { | |||
py::class_<Interpreter::Channel>(m, "Interpreter") | |||
.def("put", [](Interpreter::Channel& self, py::array data, DType dtype, CompNode cn) { | |||
if (!cn.valid()) { | |||
cn = CompNode::load(get_default_device()); | |||
} | |||
constexpr int size_threshhold = TensorShape::MAX_NDIM; | |||
if (data.size() > size_threshhold) { | |||
return self.put(npy::np2tensor(data.ptr(), npy::Meth::borrow(cn), dtype)); | |||
} else { | |||
HostTensorND ret(cn); | |||
return self.put(npy::np2tensor(data.ptr(), npy::Meth::copy_into(&ret), dtype)); | |||
} | |||
}, py::arg(), py::arg("dtype") = py::none(), py::arg("device") = py::none()) | |||
.def("put", py::overload_cast<const DeviceTensorND&>(&Interpreter::Channel::put)) | |||
.def("delete", [](Interpreter::Channel& self, Interpreter::Handle handle) { | |||
return self.del(handle); | |||
}) | |||
.def("_swap_in", [](Interpreter::Channel& self, Interpreter::Handle handle) { | |||
self.swap_in(handle); | |||
}) | |||
.def("_swap_out", [](Interpreter::Channel& self, Interpreter::Handle handle) { | |||
self.swap_out(handle); | |||
}) | |||
.def("_drop", [](Interpreter::Channel& self, Interpreter::Handle handle) { | |||
self.drop(handle); | |||
}) | |||
.def("get_value", [](Interpreter::Channel& self, Interpreter::Handle handle) { | |||
PyObject* optr = npy::ndarray_from_tensor(self.get_value(handle), npy::ShareType::TRY_SHARE); | |||
return py::reinterpret_steal<py::object>(optr); | |||
}) | |||
.def("get_dtype", &Interpreter::Channel::get_dtype) | |||
.def("get_device", &Interpreter::Channel::get_device) | |||
.def("get_shape", &Interpreter::Channel::get_shape) | |||
.def("_get_dev_tensor", &Interpreter::Channel::get_dev_tensor) | |||
.def("_set_swap_flag", &Interpreter::Channel::set_swap_flag) | |||
.def("_set_drop_flag", &Interpreter::Channel::set_drop_flag) | |||
.def("apply_op", &Interpreter::Channel::apply_op) | |||
.def("config_async_level", &Interpreter::Channel::config_async_level) | |||
.def("get_async_level", &Interpreter::Channel::get_async_level) | |||
.def("sync", &Interpreter::Channel::sync, py::call_guard<py::gil_scoped_release>()); | |||
std::unique_ptr<Interpreter::Channel> ch = Interpreter::inst().create_channel(); | |||
m.attr("interpreter") = py::detail::make_caster<decltype(ch)>::cast( | |||
std::move(ch), py::return_value_policy::move, {}); | |||
for (auto name : {"put", "delete", "get_value", "get_dtype", "get_device", "get_shape", "_get_dev_tensor", "apply_op", "config_async_level", "get_async_level", "_drop", "_swap_in", "_swap_out", "_set_drop_flag", "_set_swap_flag"}) { | |||
m.attr(name) = m.attr("interpreter").attr(name); | |||
} | |||
m.def("sync", [m]() { | |||
m.attr("interpreter").attr("sync")(); | |||
py::gil_scoped_release _; | |||
py_task_q.wait_all_task_finish(); | |||
}); | |||
m.def("make_backward_graph", &make_backward_graph); | |||
} |
@@ -21,8 +21,6 @@ | |||
#include "./graph_rt.h" | |||
#include "./ops.h" | |||
#include "./dispatcher.h" | |||
#include "./tensor.h" | |||
namespace py = pybind11; | |||
@@ -70,7 +68,5 @@ PYBIND11_MODULE(MODULE_NAME, m) { | |||
)", | |||
py::getattr(m, "__dict__")); | |||
init_dispatcher(submodule(m, "dispatcher")); | |||
init_tensor(submodule(m, "core2")); | |||
} |
@@ -16,7 +16,7 @@ import pytest | |||
import megengine as mge | |||
import megengine.distributed as dist | |||
import megengine.functional as F | |||
from megengine.core._imperative_rt import CompNode, TensorAttr, core2, imperative | |||
from megengine.core._imperative_rt import CompNode, TensorAttr, imperative | |||
from megengine.core._imperative_rt.core2 import TensorWeakRef, apply, sync | |||
from megengine.core.autodiff.grad import Grad | |||
from megengine.core.ops.builtin import Elemwise | |||
@@ -54,10 +54,10 @@ def test_simple_arith(): | |||
def test_tensor_on_device(): | |||
device = megengine.core._imperative_rt.CompNode("cpu0:1") | |||
x = np.random.rand(10).astype("float32") | |||
xx = megengine.core._imperative_rt.put(x, device=device) | |||
assert str(megengine.core._imperative_rt.get_device(xx)) == "cpu0:1" | |||
np.testing.assert_equal(x, megengine.core._imperative_rt.get_value(xx)) | |||
megengine.core._imperative_rt.delete(xx) | |||
xx = megengine.tensor(x, device=device) | |||
assert str(xx.device) == "cpu0:1" | |||
np.testing.assert_equal(x, xx.numpy()) | |||
del xx | |||
def test_raw_tensor(): | |||