wangwei
/
MegEngine
Not watched
1
0
Fork 0
Code
Releases 31 Wiki Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain
Browse Source

fix(mge/trace): fix sublinear in trace 

GitOrigin-RevId: 356dcd9523
tags/v1.1.0
Megvii Engine Team 5 years ago
parent
b9918c329d
commit
cc85047bf0
7 changed files with 224 additions and 49 deletions
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +24
    -3
      imperative/python/megengine/core/tensor/megbrain_graph.py
  2. +135
    -12
      imperative/python/megengine/jit/tracing.py
  3. +17
    -13
      imperative/python/src/graph_rt.cpp
  4. +3
    -1
      imperative/python/test/integration/test_optimizer.py
  5. +2
    -0
      imperative/python/test/unit/test_tracing.py
  6. +38
    -18
      imperative/src/impl/opr_utility.cpp
  7. +5
    -2
      imperative/src/include/megbrain/imperative/opr_utility.h

+ 24
- 3
imperative/python/megengine/core/tensor/megbrain_graph.py View File

@@ -74,6 +74,11 @@ class Graph(_imperative_rt.ComputingGraph):
self.execute(*args)
return self.wait()

def _make_const_for_backward(self, data):
device = as_device(data.comp_node).to_c()
data = data.numpy()
return self._wrap(_imperative_rt.make_const(self, data, device, data.dtype))

def make_const(self, data, dtype=None, device=None):
if isinstance(data, _imperative_rt.DeviceTensorND):
assert dtype is None and device is None
@@ -437,7 +442,9 @@ def _(op: OpDef, *args: VarNode):
def _(op: BackwardGraph, *args: VarNode):
assert args
graph = args[0].graph
return op.interpret(lambda op, args: apply(op, *args), graph.make_const, args)
return op.interpret(
lambda op, args: apply(op, *args), graph._make_const_for_backward, args
)


def input_callback(callback, *args, device=None, dtype=None, shape=None, graph=None):
@@ -449,12 +456,26 @@ def input_callback(callback, *args, device=None, dtype=None, shape=None, graph=N


class InputNode(OpNode):
def __init__(self, *args: VarNode, device=None, dtype=None, shape=None, graph=None):
def __init__(
self,
*args: VarNode,
device=None,
dtype=None,
shape=None,
graph=None,
use_static_shape=False
):
r = _imperative_rt.DeviceTensorNDRendezvous()
if device is not None:
device = as_device(device).to_c()
outputs = _imperative_rt.input_callback(
r, device, dtype, shape, _unwrap(args), graph=graph
r,
device,
dtype,
shape,
_unwrap(args),
graph=graph,
use_static_shape=use_static_shape,
)
super().__init__(outputs[0].owner)
self._rendezvous = r


+ 135
- 12
imperative/python/megengine/jit/tracing.py View File

@@ -11,6 +11,7 @@ import contextlib
import functools
import itertools
import json
import os
import typing
import warnings
import weakref
@@ -35,6 +36,10 @@ from ..core.tensor.tensor import Tensor
from .sublinear_memory_config import SublinearMemoryConfig


def _input_node_use_static_shape():
return os.environ.get("MEGENGINE_INPUT_NODE_USE_STATIC_SHAPE") is not None


class TraceMismatchError(RuntimeError):
pass

@@ -76,6 +81,7 @@ class TensorInfo:
"device",
"dtype",
"shape",
"is_const",
"bound_data",
# resources for execution
"varnode",
@@ -242,6 +248,28 @@ class trace:
self._active_tensors.update(outputs)
return outputs

def _apply_const(self, op, args):
assert not self._untraced
# check against trace
if self._pc >= len(self._seq):
raise TraceMismatchError("trace should end here, but more op observed")
record = self._seq[self._pc]
op_, ihandles, ohandles = record
assert isinstance(op_, Const)

eq = op_.value == op.value
if not isinstance(eq, bool):
eq = all(eq)
if not eq:
raise TraceMismatchError(
"const tensor violated: got a different tensor this time"
)

self._pc += 1
(h,) = ohandles
outputs = tuple([self._tinfo[h].bound_data])
return outputs

def _record_op(self, op, inputs, outputs):
if skip_tracing:
for x in inputs:
@@ -275,7 +303,24 @@ class trace:
self._active_tensors.update(outputs)

def _record_const(self, op, outputs):
pass
if skip_tracing:
(x,) = outputs
h = getattr(x, "_TraceMixin__handle", None)
if h is not None:
self._tinfo[h].data_read = True
return

(x,) = outputs
h, info = self._new_handle()
ohandles = [h]
info.external = True
info.device = x.device
info.dtype = x.dtype
info.shape = x.shape
info.bound_data = x
info.is_const = True
TraceMixin._TraceMixin__inject(x, h)
self._seq.append((op, tuple(), tuple(ohandles)))

def _set_active(self, active: bool):
global active_trace
@@ -308,6 +353,11 @@ class trace:
for x in lazy_eval_tensors
]
self._apply_graph_options(lazy_eval_graph)
# FIXME
if self._graph_opt_level is not None:
lazy_eval_graph.options.graph_opt_level = self._graph_opt_level
else:
lazy_eval_graph.options.graph_opt_level = 2
lazy_eval_graph.compile(*lazy_eval_links, *readers)
lazy_eval_graph()
for r, x in zip(readers, lazy_eval_tensors):
@@ -323,6 +373,7 @@ class trace:
self._init_trace(self._symbolic)
else:
apply.enable(apply_compiled_mode)
apply.enable(apply_const_compiled_mode)
if self._graph is None:
self._compile()
self._graph.execute()
@@ -370,6 +421,7 @@ class trace:
apply.disable(apply_symbolic_mode)
apply.disable(apply_const_symbolic_mode)
apply.disable(apply_compiled_mode)
apply.disable(apply_const_compiled_mode)
self._set_active(False)

def do_exit():
@@ -409,8 +461,10 @@ class trace:
graph.options.no_force_inplace = True
graph.options.seq_opt.enable_seq_comp_node_opt = False
# graph opt level
if self._graph_opt_level is not None:
graph.options.graph_opt_level = self._graph_opt_level
# if self._graph_opt_level is not None:
# graph.options.graph_opt_level = self._graph_opt_level
# FIXME
graph.options.graph_opt_level = 0
# sublinear
if self._sublinear_memory_config is not None:
graph.options.enable_sublinear_memory_opt = True
@@ -442,22 +496,49 @@ class trace:
for h in itertools.chain(self._arg_bindings, self._kwarg_bindings.values()):
info = self._tinfo[h]
opnode = info.data_setter = G.InputNode(
device=info.device, dtype=info.dtype, shape=info.shape, graph=graph
device=info.device,
dtype=info.dtype,
shape=info.shape,
graph=graph,
use_static_shape=_input_node_use_static_shape(),
)
need_reset_nodes.append(opnode)
info.varnode = opnode.outputs[0]
links += opnode.outputs[1:]

for op, ihandles, ohandles in self._seq:
require_links = type(op) in _io_op_types
if isinstance(op, Const):
assert len(ihandles) == 0
(h,) = ohandles
info = self._tinfo[h]
if not hasattr(info, "varnode"):
assert info.external
assert info.bound_data
info.varnode = graph.make_const(
info.bound_data.numpy(),
info.bound_data.dtype,
info.bound_data.device,
)
continue

require_links = type(op) in _io_op_types
ivars = []
for i, h in enumerate(ihandles):
info = self._tinfo[h]
if not hasattr(info, "varnode"):
assert info.external
if info.bound_data:
info.varnode = graph.make_const(info.bound_data._dev_tensor())
if hasattr(info, "is_const") and info.is_const:
info.varnode = graph.make_const(
info.bound_data.numpy(),
info.bound_data.dtype,
info.bound_data.device,
)
else:
info.varnode = graph.make_const(
info.bound_data._dev_tensor()
# info.bound_data.numpy()
)
else:
opnode = info.data_setter = G.InputNode(
*links,
@@ -465,6 +546,7 @@ class trace:
dtype=info.dtype,
shape=info.shape,
graph=graph,
use_static_shape=_input_node_use_static_shape(),
)
need_reset_nodes.append(opnode)
info.varnode, *links = opnode.outputs
@@ -500,7 +582,11 @@ class trace:
if info.shape_read:
opnode = info.shape_reader = G.AttrOutputNode(v, *links)
add_reader(opnode)

# FIXME
if self._graph_opt_level is not None:
graph.options.graph_opt_level = self._graph_opt_level
else:
graph.options.graph_opt_level = 2
graph.compile(*readers)

def _reset_exec_env(self):
@@ -643,6 +729,17 @@ class trace:
)

for op, ihandles, ohandles in self._seq:
if isinstance(op, Const):
assert len(ihandles) == 0
(h,) = ohandles
info = self._tinfo[h]
if h not in h2v:
assert info.external
assert info.bound_data
h2v[h] = graph.make_const(
info.bound_data.numpy(), dtype=info.dtype, device=info.device,
)
continue
ivars = []
for h in ihandles:
info = self._tinfo[h]
@@ -874,6 +971,7 @@ class CompiledTensorProxy(RawTensor):

class LazyEvalTensor(RawTensor):
def __init__(self, varnode):
super(LazyEvalTensor, self).__init__()
self.__varnode = varnode

@property
@@ -953,11 +1051,22 @@ def assign_raw_tensor(lhs, rhs):
@apply.register()
def apply_symbolic_mode(op: OpDef, *args: RawTensor):
graph = active_trace._lazy_eval_graph
ivars = [
getattr(x, "_LazyEvalTensor__varnode", None)
or graph.make_const(x._dev_tensor())
for x in args
]
ivars = []
for x in args:
var = getattr(x, "_LazyEvalTensor__varnode", None)
if var:
ivars.append(var)
else:
data_setter = G.InputNode(
device=x.device,
dtype=x.dtype,
shape=x.shape,
graph=graph,
use_static_shape=True,
)
var = data_setter.outputs[0]
ivars.append(var)
data_setter.set_value(x._dev_tensor())

require_links = type(op) in _io_op_types

@@ -1004,6 +1113,20 @@ def apply_compiled_mode(op: OpDef, *args: RawTensor):
apply.disable(apply_compiled_mode)


@apply.register()
def apply_const_compiled_mode(op: Const, *args: RawTensor):
if skip_tracing:
args = [
as_raw_tensor(x._dev_tensor()) if x.__class__ is CompiledTensorProxy else x
for x in args
]
return apply.super(op, *args)
return active_trace._apply_const(op, args)


apply.disable(apply_const_compiled_mode)


# this hook injects TraceMixin
@apply.register()
def apply_with_tracing(op: OpDef, *args: RawTensor):


+ 17
- 13
imperative/python/src/graph_rt.cpp View File

@@ -145,11 +145,6 @@ void init_graph_rt(py::module m) {
.def_property_readonly("comp_node", [](cg::VarNode* v) {return v->comp_node();})
.def_property_readonly("shape", [](cg::VarNode* v) -> const TensorShape* {
auto&& mgr = v->owner_graph()->static_infer_manager();
auto&& type = mgr.get_infer_type(v);
using InferType = cg::static_infer::InferType;
if (!(type.shape & (InferType::CONST | InferType::RT_STATIC))) {
return nullptr;
}
return mgr.infer_shape_fallible(v);
})
.def_property_readonly("value", [](cg::VarNode* v) -> py::object {
@@ -437,7 +432,8 @@ void init_graph_rt(py::module m) {
const DType& dtype,
const TensorShape& shape,
const std::vector<cg::VarNode*>& inputs,
cg::ComputingGraph* graph) {
cg::ComputingGraph* graph,
bool use_static_shape) {
if (!graph) {
graph = inputs[0]->owner_graph();
}
@@ -446,7 +442,9 @@ void init_graph_rt(py::module m) {
sinputs.emplace_back(i);
}
static_assert(!std::is_reference<decltype(callback)>::value);
auto soutputs = opr::InputCallback::make(*graph, std::move(callback), comp_node, dtype, shape, sinputs);
auto soutputs = opr::InputCallback::make(*graph, std::move(callback),
comp_node, dtype, shape,
sinputs, use_static_shape);
std::vector<VarNode*> outputs;
outputs.reserve(soutputs.size());
for (auto i : soutputs) {
@@ -490,23 +488,29 @@ void init_graph_rt(py::module m) {
const DType& dtype,
const TensorShape& shape,
const std::vector<cg::VarNode*>& inputs,
cg::ComputingGraph* graph) {
return input_callback([f=std::move(callback)](){py::gil_scoped_acquire _; return f();}, comp_node, dtype, shape, inputs, graph);
cg::ComputingGraph* graph,
bool use_static_shape) {
return input_callback(
[f=std::move(callback)](){py::gil_scoped_acquire _; return f();},
comp_node, dtype, shape, inputs, graph, use_static_shape);
},
py::arg(), py::arg(), py::arg(), py::arg() = py::none(), py::arg() = py::tuple(), py::arg("graph") = py::none());
py::arg(), py::arg(), py::arg(), py::arg() = py::none(), py::arg() = py::tuple(),
py::arg("graph") = py::none(), py::arg("use_static_shape") = false);

m.def("input_callback", [input_callback](std::shared_ptr<Rendezvous<DeviceTensorND>> p,
const CompNode& comp_node,
const DType& dtype,
const TensorShape& shape,
const std::vector<cg::VarNode*>& inputs,
cg::ComputingGraph* graph) {
cg::ComputingGraph* graph,
bool use_static_shape) {
auto f = [p]() -> DeviceTensorND {
return p->get();
};
return input_callback(std::move(f), comp_node, dtype, shape, inputs, graph);
return input_callback(std::move(f), comp_node, dtype, shape, inputs, graph, use_static_shape);
},
py::arg(), py::arg(), py::arg(), py::arg() = py::none(), py::arg() = py::tuple(), py::arg("graph") = py::none());
py::arg(), py::arg(), py::arg(), py::arg() = py::none(), py::arg() = py::tuple(),
py::arg("graph") = py::none(), py::arg("use_static_shape") = false);

auto output_callback = [](auto callback, const std::vector<cg::VarNode*>& inputs,
std::shared_ptr<RendezvousBase> r = {}, bool borrow = false, bool prefer_host_value = false) {


+ 3
- 1
imperative/python/test/integration/test_optimizer.py View File

@@ -97,7 +97,9 @@ def _test_optimizer(opt_str, test_case, check_class, update_lr=False):
for param in net.parameters():
ori_params[param] = np.copy(param.numpy())

train_func(np.random.random(data_shape).astype(np.float32), opt=opt, gm=gm)
train_func(
tensor(np.random.random(data_shape).astype(np.float32)), opt=opt, gm=gm
)
step += 1
check_func(ori_params, net.parameters(), step)



+ 2
- 0
imperative/python/test/unit/test_tracing.py View File

@@ -176,6 +176,7 @@ def test_trace_profiler():
assert out.get("profiler")


@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
@@ -194,6 +195,7 @@ def test_goptions():
np.testing.assert_equal(g(d).numpy().item(), 1.0)


@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):


+ 38
- 18
imperative/src/impl/opr_utility.cpp View File

@@ -33,14 +33,18 @@ MGB_DYN_TYPE_OBJ_FINAL_IMPL(InputCallback);
InputCallback::InputCallback(cg::ComputingGraph& graph, callback_t callback,
const VarNodeArray& inputs,
const TensorShape& output_shape,
const OperatorNodeConfig& config)
const OperatorNodeConfig& config,
bool use_static_shape)
: Super(&graph, config, "input_callback", inputs),
m_output_shape(output_shape), m_callback(callback) {
m_output_shape(output_shape), m_callback(callback), m_use_static_shape(use_static_shape) {
for (VarNode* i : inputs) {
add_input({i});
}
DType dt = config.output_dtype();
mgb_assert(dt.valid());
if(m_use_static_shape){
mgb_assert(m_output_shape.ndim);
}
add_output(None)->add_flag(VarNode::Flag::NO_SYS_MEM_ALLOC).dtype(dt);
add_output(None)
->add_flag(VarNode::Flag::ALLOW_EMPTY_SHAPE)
@@ -52,7 +56,8 @@ InputCallback::InputCallback(cg::ComputingGraph& graph, callback_t callback,
SymbolVarArray InputCallback::make(cg::ComputingGraph& graph,
callback_t callback, CompNode comp_node,
DType dtype, const TensorShape& shape,
const SymbolVarArray& inputs) {
const SymbolVarArray& inputs,
bool use_static_shape) {
mgb_assert(comp_node.valid());
mgb_assert(dtype.valid());
OperatorNodeConfig config;
@@ -60,24 +65,33 @@ SymbolVarArray InputCallback::make(cg::ComputingGraph& graph,
config.output_dtype(dtype);
auto vinputs = to_var_node_array(inputs);
auto opr = graph.insert_opr(
std::make_unique<InputCallback>(graph, callback, vinputs, shape, config));
std::make_unique<InputCallback>(graph, callback, vinputs, shape, config, use_static_shape));
return to_symbol_var_array(opr->output());
}

void InputCallback::init_output_static_infer_desc() {
if (m_output_shape.ndim) {
// Write this shape to static infer manager. The effect is
// that infer_shape_fallible() will return a non-empty shape
// while get_infer_type() remains NO_DESC. Most places check
// infer type before relying on inferred shape so things
// won't break. Memory optimizer however, deliberately omits
// infer type check so it will be able to use this shape for hint.
using namespace cg::static_infer;
auto* var = output(0);
var->shape(m_output_shape);
auto&& mgr = cg::ComputingGraphImpl::downcast(owner_graph())->static_infer_manager_impl();
auto* handle = mgr.get_tag_handler_for_shape(var);
handle->sync_from_var();
using namespace cg::static_infer;
if(m_use_static_shape) {
auto &&mgr = owner_graph()->static_infer_manager();
auto infer_shape = [this](TensorShape &dest, const InpVal &) {
dest = m_output_shape;
return true;
};
mgr.register_shape_infer(output(0), {SourceType::CONSTANT, {}, infer_shape});
} else {
if (m_output_shape.ndim) {
// Write this shape to static infer manager. The effect is
// that infer_shape_fallible() will return a non-empty shape
// while get_infer_type() remains NO_DESC. Most places check
// infer type before relying on inferred shape so things
// won't break. Memory optimizer however, deliberately omits
// infer type check so it will be able to use this shape for hint.
auto* var = output(0);
var->shape(m_output_shape);
auto&& mgr = cg::ComputingGraphImpl::downcast(owner_graph())->static_infer_manager_impl();
auto* handle = mgr.get_tag_handler_for_shape(var);
handle->sync_from_var();
}
}
}

@@ -92,6 +106,9 @@ cg::OperatorNodeBase::NodeProp* InputCallback::do_make_node_prop() const {

void InputCallback::scn_do_execute() {
auto dev_tensor = m_callback();
if (m_use_static_shape) {
mgb_assert(dev_tensor.shape().eq_shape(m_output_shape));
}
output(0)->reset_dev_tensor_from_tensor(dev_tensor);
}

@@ -101,7 +118,10 @@ cg::OperatorNodeBase* InputCallback::shallow_copy(
const OperatorNodeConfig &config) {
auto &&opr = opr_.cast_final_safe<InputCallback>();
auto* graph = ctx.owner_graph(opr, inputs);
return graph->insert_opr(std::make_unique<InputCallback>(*graph, opr.m_callback, inputs, opr.m_output_shape, config));
return graph->insert_opr(
std::make_unique<InputCallback>(*graph, opr.m_callback,
inputs, opr.m_output_shape,
config, opr.m_use_static_shape));
}

MGB_REG_OPR_SHALLOW_COPY(InputCallback, InputCallback::shallow_copy);


+ 5
- 2
imperative/src/include/megbrain/imperative/opr_utility.h View File

@@ -35,13 +35,15 @@ public:
callback_t callback,
const VarNodeArray& inputs,
const TensorShape& output_shape,
const OperatorNodeConfig &config);
const OperatorNodeConfig &config,
bool use_static_shape);
static SymbolVarArray make(cg::ComputingGraph& graph,
callback_t callback,
CompNode comp_node,
DType dtype,
const TensorShape& shape,
const SymbolVarArray& inputs = {});
const SymbolVarArray& inputs = {},
bool use_static_shape = false);
static cg::OperatorNodeBase* shallow_copy(
const serialization::OprShallowCopyContext &ctx,
const cg::OperatorNodeBase &opr_, const VarNodeArray &inputs,
@@ -53,6 +55,7 @@ protected:
private:
TensorShape m_output_shape;
callback_t m_callback;
bool m_use_static_shape;
};

MGB_DEFINE_OPR_CLASS(OutputCallback, cg::SingleCNOperatorNodeBase) // {


Write Preview
Loading…
Cancel
Save