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refactor(mgb/opr): move fastrun out of conv opr 

GitOrigin-RevId: d5ef5356f6
release-1.1
Megvii Engine Team 4 years ago
parent
2fc77caf44
commit
1e90c45793
7 changed files with 1031 additions and 810 deletions
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  1. +35
    -806
      src/opr/impl/dnn/convolution.cpp
  2. +401
    -0
      src/opr/impl/search_policy/algo_chooser.cpp
  3. +259
    -0
      src/opr/impl/search_policy/profiler.cpp
  4. +36
    -0
      src/opr/impl/search_policy/workspace_need_limit_getter.inl
  5. +140
    -0
      src/opr/include/megbrain/opr/search_policy/algo_chooser.h
  6. +160
    -0
      src/opr/include/megbrain/opr/search_policy/profiler.h
  7. +0
    -4
      src/opr/test/basic_arith/elemwise.cpp

+ 35
- 806
src/opr/impl/dnn/convolution.cpp View File

@@ -6,11 +6,14 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */


#include "megbrain/opr/dnn/convolution.h" #include "megbrain/opr/dnn/convolution.h"
#include "megbrain/opr/io.h" #include "megbrain/opr/io.h"
#include "megbrain/opr/search_policy/algo_chooser.h"
#include "megbrain/opr/search_policy/profiler.h"


#include "megbrain/graph/grad_impl.h" #include "megbrain/graph/grad_impl.h"
#include "megbrain/system.h" #include "megbrain/system.h"
@@ -19,28 +22,15 @@


#include "megdnn/oprs/utils.h" #include "megdnn/oprs/utils.h"


//! TODO: here has to be know some megdnn::opr when there is produced midout.h
//! fix it if there is another graceful way.
#include "megdnn/oprs.h"

#include "midout.h"

MIDOUT_DECL(megbrain_opr_convolution)
#define MIDOUT_B(...) \
MIDOUT_BEGIN(megbrain_opr_convolution, __VA_ARGS__) {
#define MIDOUT_E \
} \
MIDOUT_END();

#include "../internal/megdnn_opr_wrapper.inl"
#include "../internal/invoke.h" #include "../internal/invoke.h"
#include "../internal/megdnn_opr_wrapper.inl"
#include "../search_policy/workspace_need_limit_getter.inl"


#include <array> #include <array>
#include <chrono> #include <chrono>
#include <cstring> #include <cstring>
#include <thread> #include <thread>



using namespace mgb; using namespace mgb;
using namespace opr; using namespace opr;
using namespace cg::static_infer; using namespace cg::static_infer;
@@ -48,771 +38,6 @@ using intl::WorkspaceLimitGetter;


#define CACHE_KEY_VERSION "v2" #define CACHE_KEY_VERSION "v2"


#define MGB_FOREACH_FASTRUN_OPR(cb) \
cb(ConvolutionForward); \
cb(ConvBiasForward); \
cb(ConvolutionBackwardData); \
cb(ConvolutionBackwardFilter); \
cb(Convolution3DForward); \
cb(Convolution3DBackwardData); \
cb(Convolution3DBackwardFilter); \
cb(LocalShareForward); \
cb(LocalShareBackwardData); \
cb(LocalShareBackwardFilter); \
cb(DeformableConvForward); \
cb(DeformableConvBackwardFilter); \
cb(DeformableConvBackwardData); \
cb(BatchConvBiasForward);

namespace mgb {
namespace opr {
namespace intl {

#define cb(_Opr) \
template <> \
struct AutoAddWorkspaceNeedLimitGetter<megdnn::_Opr> { \
static constexpr bool val = true; \
};
MGB_FOREACH_FASTRUN_OPR(cb)

#undef cb

} // namespace intl
} // namespace opr
} // namespace mgb

namespace {

template <class MegDNNOpr>
struct MegDNNOpr2MGBOpr;

#define cb(_Opr) \
template <> \
struct MegDNNOpr2MGBOpr<megdnn::_Opr> { \
using MGBOpr = opr::_Opr; \
};

MGB_FOREACH_FASTRUN_OPR(cb)

#undef cb

template <typename Opr>
struct OprArityTrait;

template <typename Opr, int _arity_in, int _arity_out>
struct OprArityTraitTmpl {
static constexpr int arity_in = _arity_in;
static constexpr int arity_out = _arity_out;
static constexpr int arity = arity_in + arity_out;
};

#define INST_ARITY(_Opr, _in, _out) \
template <> \
struct OprArityTrait<_Opr> : public OprArityTraitTmpl<_Opr, _in, _out> {};

INST_ARITY(megdnn::ConvolutionBackwardData, 2, 1);
INST_ARITY(megdnn::ConvolutionBackwardFilter, 2, 1);
INST_ARITY(megdnn::Convolution3DForward, 2, 1);
INST_ARITY(megdnn::Convolution3DBackwardData, 2, 1);
INST_ARITY(megdnn::Convolution3DBackwardFilter, 2, 1);
INST_ARITY(megdnn::LocalShareForward, 2, 1);
INST_ARITY(megdnn::LocalShareBackwardData, 2, 1);
INST_ARITY(megdnn::LocalShareBackwardFilter, 2, 1);
INST_ARITY(megdnn::Convolution, 2, 1);
INST_ARITY(megdnn::DeformableConvForward, 4, 1);
INST_ARITY(megdnn::DeformableConvBackwardFilter, 4, 1);
INST_ARITY(megdnn::BatchConvBiasForward, 4, 1);
INST_ARITY(megdnn::ConvBias, 4, 1);
INST_ARITY(megdnn::DeformableConvBackwardData, 5, 3);

#undef INST_ARITY

template <typename Opr>
constexpr bool opr_supports_preprocess() {
return std::is_same<Opr, megdnn::ConvolutionForward>::value ||
std::is_same<Opr, megdnn::ConvBias>::value;
}

template <typename Opr, bool has_prep>
struct PreprocessFilterImpl {
using T = union {};
};

template <typename Opr>
struct PreprocessFilterImpl<Opr, true> {
using T = typename Opr::PreprocessedFilter;
};

template <typename Opr>
using PreprocessFilter =
typename PreprocessFilterImpl<Opr, opr_supports_preprocess<Opr>()>::T;

// timeout delta to be added with fastest known algorithm for new algos
constexpr double TIMEOUT_TOLERANCE = 2;

template <typename Opr>
struct AlgoChooserFuncId {};

#define DEF_FUNC_ID(func) \
template <> \
struct AlgoChooserFuncId<megdnn::func> { \
__attribute__( \
(unused)) static constexpr sys::TimedFuncInvoker::FuncId ID = \
static_cast<sys::TimedFuncInvoker::FuncId>( \
MGB_HASH_STR("megdnn::" #func)); \
};

MGB_FOREACH_FASTRUN_OPR(DEF_FUNC_ID)

#undef DEF_FUNC_ID

/* =================== TimedProfiler =================== */

/*!
* \brief profile a megdnn opr conv with given param
*
* This class only provides static methods, and the entry point is
* TimedProfiler::profile; it would run profiler in a timed environment by
* sys::TimedFuncInvoker
*
* \tparam Opr megdnn opr impl
*/
template <typename Opr>
class TimedProfiler {
static constexpr int arity_in = OprArityTrait<Opr>::arity_in;
static constexpr int arity_out = OprArityTrait<Opr>::arity_out;
static constexpr int arity = OprArityTrait<Opr>::arity;

using ConvTensorShapes = std::array<TensorShape, arity>;

public:
struct Param {
char algo_name[128];
size_t workspace;
DTypeEnum dtypes[arity];
CompNode::Locator comp_node_loc;
ConvTensorShapes shapes;
typename Opr::Param opr_param;
bool allow_weight_preprocess;

//! filled by profile()
mutable double actual_timeout;
};

struct Result {
double time;
};

static Maybe<Result> profile(const Param& param, double& timeout) {
mgb_assert(timeout >= 0);
if (!timeout) {
timeout = timeout_setting;
} else if (timeout_setting) {
timeout = std::min(timeout, timeout_setting);
}
param.actual_timeout =
timeout ? timeout : std::numeric_limits<double>::infinity();
auto res = sys::TimedFuncInvoker::ins().invoke(
AlgoChooserFuncId<Opr>::ID,
TParam::from_pod(const_cast<Param&>(param)), timeout);
if (res.valid())
return res.val().template as_single_pod<Result>();
return None;
}
private:
using TParam = sys::TimedFuncInvoker::Param;
using TResult = sys::TimedFuncInvoker::Result;

static const double timeout_setting;

static double init_timeout_setting();
static TResult prof_impl(const TParam& raw_param);
static void prof_init_device(const TParam& raw_param);
};
template <typename Opr>
const double TimedProfiler<Opr>::timeout_setting =
TimedProfiler<Opr>::init_timeout_setting();

template <typename Opr>
double TimedProfiler<Opr>::init_timeout_setting() {
#if MGB_ENABLE_FASTRUN
sys::TimedFuncInvoker::ins().register_func(
AlgoChooserFuncId<Opr>::ID, &TimedProfiler<Opr>::prof_impl,
&TimedProfiler<Opr>::prof_init_device);
auto to_set = MGB_GETENV("MGB_CONV_PROFILING_TIMEOUT");
if (to_set)
return std::stod(to_set);
#endif
return 0;
}

#define APPLY(statement, ...) \
mgb::apply([&](const auto&... args) { return statement; }, \
std::tuple_cat(__VA_ARGS__))

template <typename Opr>
typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
const TParam& raw_param) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("TimedProfiler::prof_impl")))
auto&& param = raw_param.as_single_pod<Param>();
CompNode cn = CompNode::load(param.comp_node_loc, param.comp_node_loc);
auto megdnn_opr = intl::create_megdnn_opr<Opr>(cn);
std::array<TensorLayout, arity> layouts;

auto from_enum = [&](DTypeEnum enumv) -> DType {
switch (enumv) {

#define cb(_dt) \
case DTypeTrait<_dt>::enumv: \
return _dt(1.0f, static_cast<uint8_t>(0))
cb(dtype::Quantized8Asymm);
#undef cb

#define cb(_dt) \
case DTypeTrait<_dt>::enumv: \
return _dt(1.0f)

cb(dtype::QuantizedS8);
cb(dtype::QuantizedS16);
cb(dtype::QuantizedS32);
default:
return DType::from_enum(enumv);
#undef cb
}
};
for (int i = 0; i < arity; ++i) {
layouts[i] = {param.shapes[i], from_enum(param.dtypes[i])};
}

megdnn_opr->param() = param.opr_param;
{
typename Opr::Algorithm* algo = nullptr;
for (auto i : APPLY(megdnn_opr->get_all_algorithms(args...), layouts)) {
if (!strcmp(i->name(), param.algo_name)) {
algo = i;
break;
}
}
mgb_assert(algo, "algorithm %s not found", param.algo_name);
megdnn_opr->execution_policy() = {algo};
}

// Allocate preprocessed weight buffers.
TensorLayoutArray preprocessed_layout;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (param.allow_weight_preprocess) {
preprocessed_layout = APPLY(
_(megdnn_opr)->deduce_preprocessed_filter_layout(args...),
layouts);
}
});

{
// first allocate a whole chunk to avoid memory fragmentation (here we
// rely on memory allocator to reuse memory)
auto align = cn.get_mem_addr_alignment();
size_t tot_size = align;
for (int i = 0; i < arity; ++i) {
tot_size += layouts[i].span().high_byte + align;
}
for (const auto& layout : preprocessed_layout) {
tot_size += layout.span().high_byte + align;
}
tot_size += param.workspace;
DeviceTensorStorage storage{cn};
storage.ensure_size(tot_size);
}

// allocate input and output memory
std::array<DeviceTensorND, arity_in> inp_val;
std::array<DeviceTensorND, arity_out> out_val;
DeviceTensorND workspace;
for (int i = 0; i < arity_in; ++i) {
inp_val[i]
.comp_node(cn)
.dtype(layouts[i].dtype)
.resize(layouts[i]);
}
for (int i = 0; i < arity_out; ++i) {
out_val[i]
.comp_node(cn)
.dtype(layouts[arity_in + i].dtype)
.resize(layouts[arity_in + i]);
}
megdnn::Workspace mdn_workspace;

// allocate workspace
if (param.workspace) {
workspace.comp_node(cn).dtype(dtype::Byte()).resize({param.workspace});
mdn_workspace.size = param.workspace;
mdn_workspace.raw_ptr = workspace.raw_ptr();
}

// allocate storage for preprocessed filter
SmallVector<DeviceTensorND> flt_val(preprocessed_layout.size());
for (size_t i = 0; i < preprocessed_layout.size(); i++) {
flt_val[i] = {cn, preprocessed_layout[i], preprocessed_layout[i].dtype,
preprocessed_layout[i].format};
}

for (int i = 0; i < arity_in; ++i) {
fill_zero_dev_tensor(inp_val[i]);
}

PreprocessFilter<Opr> prep_flt;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (!preprocessed_layout.empty()) {
auto&& pf = _(prep_flt);
pf.algorithm_id = nullptr;
pf.tensors.resize(flt_val.size());
for (size_t i = 0; i < flt_val.size(); i++) {
pf.tensors[i] = flt_val[i].as_megdnn();
}
APPLY(_(megdnn_opr)->exec_preprocess(args..., &pf, mdn_workspace),
std::forward_as_tuple(layouts[0], inp_val[1].as_megdnn()),
array_skip<2>(layouts));
}
});

RealTimer timer;
auto ev_start = cn.create_event(CompNode::Event::NEED_TIMER),
ev_end = cn.create_event(CompNode::Event::NEED_TIMER);
ev_start->record();
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
auto&& opr = _(megdnn_opr);
PreprocessFilter<Opr>* pf =
preprocessed_layout.empty() ? nullptr : &prep_flt;
APPLY(opr->exec(args.as_megdnn()..., pf, mdn_workspace), inp_val,
out_val);
}, /* else */ [&](auto _) {
APPLY(_(megdnn_opr)->exec(args.as_megdnn()..., mdn_workspace), inp_val,
out_val);
});
ev_end->record();

double next_report_time = 0.5;
while (!ev_end->finished()) {
if (timer.get_secs() >= next_report_time) {
mgb_log_warn(
"profiling conv algo %s already took %.3f/%.3f secs"
" (limit can be set by MGB_CONV_PROFILING_TIMEOUT) ",
param.algo_name, timer.get_secs(), param.actual_timeout);
next_report_time = timer.get_secs() + 1;
}
using namespace std::literals;
std::this_thread::sleep_for(1000us);
}

mgb_assert(ev_start->finished());
return TResult::from_pod(Result{ev_start->elapsed_time_until(*ev_end)});
MIDOUT_E
};

template <typename Opr>
void TimedProfiler<Opr>::prof_init_device(const TParam& raw_param) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("TimedProfiler::prof_init_device")))
auto&& param = raw_param.as_single_pod<Param>();
CompNode cn = CompNode::load(param.comp_node_loc, param.comp_node_loc);
// wait for cuda init, so its time does not get accounted in timeout
cn.sync();
MIDOUT_E
}

/* =================== AlgoChooser =================== */
/*!
* \brief choose algorithm according to ExecutionPolicy
*
* This class only provides static methods, and the entry point is
* AlgoChooser::setup_algo. When profiling is needed, it would first try to
* retrive profiling stats from cache, and run TimedProfiler when necessary
*
* \tparam Opr megdnn operator impl
*/
template <typename Opr>
class AlgoChooser {
static constexpr int arity_in = OprArityTrait<Opr>::arity_in;
static constexpr int arity_out = OprArityTrait<Opr>::arity_out;
static constexpr int arity = OprArityTrait<Opr>::arity;

using ImplAlgo = typename Opr::Algorithm*;
using MGBOpr = typename MegDNNOpr2MGBOpr<Opr>::MGBOpr;
using ConvTensorLayouts = std::array<TensorLayout, arity>;

class ExeContext {
const ConvTensorLayouts& m_layouts;
Opr* m_megdnn_opr;
const MGBOpr* m_mgb_opr;
bool m_allow_weight_preprocess;

public:
ExeContext(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr, bool allow_weight_preprocess)
: m_layouts{layouts},
m_megdnn_opr{megdnn_opr},
m_mgb_opr{mgb_opr},
m_allow_weight_preprocess{allow_weight_preprocess} {
mgb_assert(m_layouts.size() == layouts.size());
static_assert(
std::tuple_size<ConvTensorLayouts>::value == 3 ||
std::tuple_size<ConvTensorLayouts>::value == 5 ||
std::tuple_size<ConvTensorLayouts>::value == 8,
"Convolution AlgoChooser assumes arity = 3 , 5 or 8 (for "
"deformable conv)");
}

Opr* megdnn_opr() const { return m_megdnn_opr; }

const MGBOpr* mgb_opr() const { return m_mgb_opr; }

const TensorLayout& inp_layout(size_t idx) const {
return m_layouts[idx];
}

const ConvTensorLayouts& layouts() const { return m_layouts; }

ImplAlgo choose_by_heuristic(bool reproducible = false) const {
auto opr = m_mgb_opr;
auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit);
return APPLY(m_megdnn_opr->get_algorithm_heuristic(
args..., workspace_limit, reproducible),
m_layouts);
}

//! get all candidate algos, and the one choose_by_heuristic() is
//! put first
std::vector<ImplAlgo> get_all_candidates() const {
auto heu = choose_by_heuristic();
auto&& ret =
APPLY(m_megdnn_opr->get_all_algorithms(args...), m_layouts);
bool found = false;
for (size_t i = 0; i < ret.size(); ++i) {
if (ret[i] == heu) {
found = true;
std::swap(ret[i], ret[0]);
break;
}
}
mgb_assert(found,
"algo %s got by heuristic not found in "
"candidate list",
heu->name());
return std::move(ret);
}

//! get candidate algos with workspace limit.
std::vector<ImplAlgo> get_all_candidates_with_workspace_limit() const {
auto&& all_algos = get_all_candidates();
auto opr = m_mgb_opr;
auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit);
std::vector<ImplAlgo> ret;
for (auto&& algo : all_algos) {
if (get_workspace_size_bytes(algo) <= workspace_limit) {
ret.push_back(algo);
}
}
return ret;
}

//! get workspace size required for specific algo
size_t get_workspace_size_bytes(ImplAlgo algo) const {
m_megdnn_opr->execution_policy() = {algo};
size_t result;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
auto&& opr = _(m_megdnn_opr);
auto prep = this->construct_fake_preprocess_filter();
PreprocessFilter<Opr>* prep_ptr =
prep.valid() ? &prep.val() : nullptr;
result = std::max(
APPLY(opr->get_preprocess_workspace_in_bytes(args...),
m_layouts),
APPLY(opr->get_workspace_in_bytes(args..., prep_ptr),
m_layouts));
}, /* else */ [&](auto _) {
result = APPLY(_(m_megdnn_opr)->get_workspace_in_bytes(args...),
m_layouts);
});
return result;
}

/*!
* \brief profile a single algorithm
*
* This is actually a wrapper that constructs param and call
* TimedProfiler<Opr>::profile for the actual profiling
*
* \param[in,out] timeout set the timeout, and return the actual
* timeout used during profiling
*/
Maybe<AlgoChooserProfileCache::ResultEntry> profile_single_algo(
ImplAlgo algo, double& timeout) const;

private:
Maybe<PreprocessFilter<Opr>> construct_fake_preprocess_filter() const {
Maybe<PreprocessFilter<Opr>> result = None;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (!m_allow_weight_preprocess)
return;
auto opr = _(m_megdnn_opr);
auto layout =
APPLY(opr->deduce_preprocessed_filter_layout(args...),
m_layouts);
if (layout.empty())
return;
result = PreprocessFilter<Opr>{};
auto& res = result.val();
res.algorithm_id = nullptr;
res.tensors.resize(layout.size());
for (size_t i = 0; i < layout.size(); i++) {
res.tensors[i] = megdnn::TensorND(nullptr, layout[i]);
}
});
return result;
}
};

//! entrance for getting algorithm according to execution strategy
static ImplAlgo get_algo(ExeContext& ctx) {
using S = mixin::Convolution::ExecutionPolicy::Strategy;
MGB_MARK_USED_VAR(TIMEOUT_TOLERANCE);
switch (ctx.mgb_opr()->execution_policy().strategy) {
case S::HEURISTIC:
return ctx.choose_by_heuristic();
case S::HEURISTIC_REPRODUCIBLE:
return ctx.choose_by_heuristic(true);
case S::PROFILE_HEURISTIC: {
ImplAlgo algo = choose_by_profile(ctx, false, false);
if (algo == nullptr)
algo = ctx.choose_by_heuristic();
return algo;
}
#if MGB_ENABLE_FASTRUN
case S::PROFILE:
return choose_by_profile(ctx, false);
case S::PROFILE_REPRODUCIBLE:
return choose_by_profile(ctx, true);
#endif
default:
mgb_throw(GraphError,
"bad convolution ExecutionPolicy strategy");
}
}

static void get_origin_param_and_layouts(const ExeContext&,
ConvTensorLayouts&,
typename Opr::Param&) {}

//! get all profile result, either by retrieving cache or profiling
static AlgoChooserProfileCache::Result get_profile_result(
ExeContext& ctx, bool enable_update);

static ImplAlgo choose_by_profile(ExeContext& ctx,
bool require_reproducible,
bool enable_update = true);

public:
/*!
* \brief setup algorithm and return workspace size
*/
static size_t setup_algo(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr,
bool allow_weight_preprocess = false) {
if (WorkspaceLimitGetter::is_prealloc_run(mgb_opr->owner_graph())) {
return 0;
}

ExeContext ctx(layouts, megdnn_opr, mgb_opr, allow_weight_preprocess);

auto algo = get_algo(ctx);
size_t workspace = ctx.get_workspace_size_bytes(algo);
mgb_log_debug(
"%s:tensor layouts (%s %s, %s %s)->(%s %s) :algo=%s "
"workspace=%.2fMiB reproducible=%d",
mgb_opr->dyn_typeinfo()->name,
layouts[0].to_string().c_str(),
layouts[0].dtype.name(),
layouts[1].to_string().c_str(),
layouts[1].dtype.name(),
layouts[layouts.size() - 1].to_string().c_str(),
layouts[layouts.size() - 1].dtype.name(), algo->name(),
workspace / (1024 * 1024.0), algo->is_reproducible());
megdnn_opr->execution_policy() = {algo};
return workspace;
}
};

template <typename Opr>
AlgoChooserProfileCache::Result AlgoChooser<Opr>::get_profile_result(
ExeContext& ctx, bool enable_update) {
AlgoChooserProfileCache& cache = ctx.mgb_opr()->profile_cache();

ConvTensorLayouts origin_layouts = ctx.layouts();
typename Opr::Param origin_param = ctx.mgb_opr()->param();
get_origin_param_and_layouts(ctx, origin_layouts, origin_param);
AlgoChooserProfileCache::Key cache_key{origin_layouts.data(),
origin_layouts.size(), &origin_param,
sizeof(origin_param)};
{
auto&& rst = cache.get(cache_key);
if (rst.valid())
return rst.val();
}

AlgoChooserProfileCache::Result prof_rst;
if (!enable_update)
return prof_rst;

std::string str_on_inp_shape = ssprintf(
"on input layouts (%s, %s)", ctx.layouts()[0].to_string().c_str(),
ctx.layouts()[1].to_string().c_str());
double cur_timeout = 0;
RealTimer timer;
for (auto algo : ctx.get_all_candidates_with_workspace_limit()) {
Maybe<AlgoChooserProfileCache::ResultEntry> cur_rst;
std::string msg = ssprintf("profiling %s algorithm %s %s",
ctx.mgb_opr()->dyn_typeinfo()->name,
algo->name(), str_on_inp_shape.c_str());
timer.reset();
MGB_TRY { cur_rst = ctx.profile_single_algo(algo, cur_timeout); }
MGB_CATCH(std::exception & exc, {
mgb_log_warn("caught exception during %s: %s", msg.c_str(),
exc.what());
continue;
})
MGB_CATCH(..., {
mgb_log_warn("caught exception during %s", msg.c_str());
continue;
})
if (!cur_rst.valid()) {
mgb_log_warn("timeout when %s; timeout setting: %.3fsec",
msg.c_str(), cur_timeout);
continue;
}
if (!cur_timeout) {
cur_timeout = timer.get_secs() + TIMEOUT_TOLERANCE;
} else {
cur_timeout =
std::min(cur_timeout, timer.get_secs() + TIMEOUT_TOLERANCE);
}
auto&& rst = cur_rst.val();
mgb_log_debug("%s: workspace: %zu; time: %.3gsec", msg.c_str(),
rst.workspace, rst.time);
prof_rst.push_back(rst);
}
mgb_assert(!prof_rst.empty(), "no usable convolution algorithm %s",
str_on_inp_shape.c_str());

cache.put(cache_key, prof_rst);
return prof_rst;
}

template <>
void AlgoChooser<megdnn::ConvBias>::get_origin_param_and_layouts(
const ExeContext& ctx, ConvTensorLayouts& layouts,
megdnn::ConvBias::Param& param) {
auto format = static_cast<megdnn::param::ConvBias::Format>(
ctx.megdnn_opr()->param().format);
size_t output_block_size = ctx.megdnn_opr()->param().output_block_size;
megdnn::ConvBias::deduce_winograd_origin_layout_and_param(
format, output_block_size, ctx.layouts()[0], ctx.layouts()[1],
layouts[1], param);
}

template <typename Opr>
typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
ExeContext& ctx, bool require_reproducible, bool enable_update) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("AlgoChooser::choose_by_profile")))
auto opr = ctx.mgb_opr();
if (opr->owner_graph()->options().no_profiling_on_shape_change) {
auto algo = ctx.megdnn_opr()->execution_policy().algorithm;
if (algo)
return algo;
}

std::unordered_map<std::string, ImplAlgo> algo_map;
for (auto i : ctx.get_all_candidates()) {
auto ins = algo_map.emplace(i->name(), i);
mgb_assert(ins.second, "duplicated algo name: %s", i->name());
}

auto&& prof = get_profile_result(ctx, enable_update);
if (prof.empty())
return nullptr;
for (auto&& i : prof) {
if ((!require_reproducible || i.reproducible)) {
auto iter = algo_map.find(i.algo);
mgb_assert(
iter != algo_map.end(),
"algorithm %s exists in "
"profiling result but not in algo_map; please report this "
"bug; opr: %s{%s}, shapes: %s %s %s",
ctx.mgb_opr()->cname(), ctx.mgb_opr()->dyn_typeinfo()->name,
ctx.layouts()[0].TensorShape::to_string().c_str(),
ctx.layouts()[1].TensorShape::to_string().c_str(),
ctx.layouts()[2].TensorShape::to_string().c_str(),
i.algo.c_str());
return iter->second;
}
}

mgb_log_error(
"Workspace requirement (%zu) could not be satisfied. Abort now to "
"avoid further problems",
WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit));
mgb_trap();
MIDOUT_E
}

template <typename Opr>
Maybe<AlgoChooserProfileCache::ResultEntry>
AlgoChooser<Opr>::ExeContext::profile_single_algo(ImplAlgo algo,
double& timeout) const {
typename TimedProfiler<Opr>::Param param;
auto name = algo->name();
// force check copy size <= dest len-1 from gcc8 for safe
auto len = sizeof(param.algo_name);
strncpy(param.algo_name, name, len - 1);
param.algo_name[len - 1] = '\0';
mgb_assert(!param.algo_name[sizeof(param.algo_name) - 2],
"algo name too long: %s; len=%zu", name, strlen(name));
param.workspace = get_workspace_size_bytes(algo);
for (int i = 0; i < arity; ++i) {
auto&& src = m_layouts[i];
mgb_assert(src.format.is_default() &&
(src.dtype.category() == DTypeCategory::FLOAT ||
src.dtype.category() == DTypeCategory::INT ||
src.dtype.category() == DTypeCategory::QUANTIZED),
"unsupported layout in profiling: %s",
src.to_string().c_str());
param.dtypes[i] = src.dtype.enumv();
}
param.comp_node_loc = m_mgb_opr->output(0)->comp_node().locator();
mgb_assert(param.shapes.size() == m_layouts.size());
for (size_t i = 0; i < param.shapes.size(); ++i)
param.shapes[i] = m_layouts[i];
param.opr_param = m_megdnn_opr->param();
param.allow_weight_preprocess = m_allow_weight_preprocess;

auto rst = TimedProfiler<Opr>::profile(param, timeout);
// MIOpen conv profiles all available algos when a specfic shape is
// provided for the first time, which probably adds to the result time.
// Therefore, a second profile execution is needed.
if (strncmp(name, "MIOpen", 6) == 0)
rst = TimedProfiler<Opr>::profile(param, timeout);
if (!rst.valid())
return None;
return AlgoChooserProfileCache::ResultEntry{
algo->name(), algo->is_reproducible(), rst.val().time,
param.workspace};
}

} // anonymous namespace

/* ==================== misc impl ==================== */ /* ==================== misc impl ==================== */


mixin::Convolution::~Convolution() = default; mixin::Convolution::~Convolution() = default;
@@ -913,7 +138,8 @@ public:


void mixin::WeightPreprocessExecutor::mixin_update_preprocessed_filter( void mixin::WeightPreprocessExecutor::mixin_update_preprocessed_filter(
cg::OperatorNodeBase& opr) { cg::OperatorNodeBase& opr) {
if (!mixin_allow_weight_preprocess(opr)) return;
if (!mixin_allow_weight_preprocess(opr))
return;


auto new_layout = deduce_preprocessed_filter_layout(); auto new_layout = deduce_preprocessed_filter_layout();
if (new_layout.empty()) { if (new_layout.empty()) {
@@ -939,7 +165,8 @@ void mixin::WeightPreprocessExecutor::mixin_update_preprocessed_filter(
} }
} }
} }
if (!should_update) return;
if (!should_update)
return;


if (!m_preprocessed_filter) { if (!m_preprocessed_filter) {
m_preprocessed_filter.reset(new PreprocessedFilter{}); m_preprocessed_filter.reset(new PreprocessedFilter{});
@@ -1665,8 +892,7 @@ void ConvBiasForward::init_output_format() {
} }


void ConvBiasForward::check_winograd_param_valid( void ConvBiasForward::check_winograd_param_valid(
const megdnn::ConvBias::WinogradParam& param,
const DType& dtype) {
const megdnn::ConvBias::WinogradParam& param, const DType& dtype) {
if (dtype.enumv() == DTypeEnum::Float32) { if (dtype.enumv() == DTypeEnum::Float32) {
mgb_assert(param.channel_block_size == 1 || mgb_assert(param.channel_block_size == 1 ||
param.channel_block_size == 4 || param.channel_block_size == 4 ||
@@ -1784,20 +1010,20 @@ size_t LocalShareForward::get_workspace_size_bytes(
#if MGB_ENABLE_GRAD #if MGB_ENABLE_GRAD
MGB_IMPL_OPR_GRAD(LocalShareForward) { MGB_IMPL_OPR_GRAD(LocalShareForward) {
mgb_assert(opr.input(0)->dtype().category() == DTypeCategory::FLOAT, mgb_assert(opr.input(0)->dtype().category() == DTypeCategory::FLOAT,
"only float data type supported for grad");
"only float data type supported for grad");
mgb_assert(wrt_idx == 0 || wrt_idx == 1); mgb_assert(wrt_idx == 0 || wrt_idx == 1);
mgb_assert(out_grad.size() == 2); mgb_assert(out_grad.size() == 2);
if (wrt_idx == 0) { if (wrt_idx == 0) {
// data // data
SymbolVar grad = LocalShareBackwardData::make(
opr.input(1), out_grad[0], opr.input(0),
opr.param(), opr.execution_policy());
SymbolVar grad = LocalShareBackwardData::make(opr.input(1), out_grad[0],
opr.input(0), opr.param(),
opr.execution_policy());
return grad.node(); return grad.node();
} else { } else {
// filter // filter
SymbolVar grad = LocalShareBackwardFilter::make( SymbolVar grad = LocalShareBackwardFilter::make(
opr.input(0), out_grad[0], opr.input(1),
opr.param(), opr.execution_policy());
opr.input(0), out_grad[0], opr.input(1), opr.param(),
opr.execution_policy());
return grad.node(); return grad.node();
} }
} }
@@ -1812,7 +1038,10 @@ LocalShareBackwardData::LocalShareBackwardData(VarNode* filter, VarNode* diff,
const Param& param, const Param& param,
const ExecutionPolicy& policy, const ExecutionPolicy& policy,
const OperatorNodeConfig& config) const OperatorNodeConfig& config)
: Super{filter->owner_graph(), config, "local_share_bwd_data", {filter, diff}} {
: Super{filter->owner_graph(),
config,
"local_share_bwd_data",
{filter, diff}} {
init_megdnn_opr(*this, param); init_megdnn_opr(*this, param);
m_policy = policy; m_policy = policy;
add_input({filter, diff}); add_input({filter, diff});
@@ -1897,25 +1126,23 @@ LocalShareBackwardFilter::LocalShareBackwardFilter(
add_input({src, diff, filter}); add_input({src, diff, filter});
} }


SymbolVar LocalShareBackwardFilter::make(
SymbolVar src, SymbolVar diff, SymbolVar filter,
const Param &param,
const ExecutionPolicy &policy,
const OperatorNodeConfig &config) {

SymbolVar LocalShareBackwardFilter::make(SymbolVar src, SymbolVar diff,
SymbolVar filter, const Param& param,
const ExecutionPolicy& policy,
const OperatorNodeConfig& config) {
return src.insert_single_output_opr<LocalShareBackwardFilter>( return src.insert_single_output_opr<LocalShareBackwardFilter>(
src.node(), diff.node(), filter.node(), param, policy, config); src.node(), diff.node(), filter.node(), param, policy, config);
} }


size_t LocalShareBackwardFilter::get_workspace_size_bytes( size_t LocalShareBackwardFilter::get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
const TensorShapeArray &output_shapes) const {
const TensorShapeArray& input_shapes,
const TensorShapeArray& output_shapes) const {
mgb_assert(input_shapes.size() == 3 && output_shapes.size() == 1); mgb_assert(input_shapes.size() == 3 && output_shapes.size() == 1);
return AlgoChooser<megdnn::LocalShareBackwardFilter>::setup_algo( return AlgoChooser<megdnn::LocalShareBackwardFilter>::setup_algo(
{TensorLayout{input_shapes[0], input(0)->dtype(), {TensorLayout{input_shapes[0], input(0)->dtype(),
input(0)->format()}, input(0)->format()},
{input_shapes[1], input(1)->dtype(), input(1)->format()},
{output_shapes[0], output(0)->dtype(), output(0)->format()}},
{input_shapes[1], input(1)->dtype(), input(1)->format()},
{output_shapes[0], output(0)->dtype(), output(0)->format()}},
megdnn_opr(), this); megdnn_opr(), this);
} }


@@ -1924,12 +1151,14 @@ MGB_IMPL_OPR_GRAD(LocalShareBackwardFilter) {
mgb_assert(!out_grad[1]); mgb_assert(!out_grad[1]);
if (wrt_idx == 0) { if (wrt_idx == 0) {
return LocalShareBackwardData::make(out_grad[0], opr.input(1), return LocalShareBackwardData::make(out_grad[0], opr.input(1),
opr.input(0), opr.param(), opr.execution_policy()).node();
opr.input(0), opr.param(),
opr.execution_policy())
.node();
} }
if (wrt_idx == 1) { if (wrt_idx == 1) {
return LocalShare::make(
opr.input(0), out_grad[0], opr.param(), opr.execution_policy()).
node();
return LocalShare::make(opr.input(0), out_grad[0], opr.param(),
opr.execution_policy())
.node();
} }
return nullptr; return nullptr;
} }


+ 401
- 0
src/opr/impl/search_policy/algo_chooser.cpp View File

@@ -0,0 +1,401 @@
/**
* \file src/opr/impl/search_policy/algo_chooser.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 "megbrain/opr/search_policy/algo_chooser.h"
#include "megbrain/opr/search_policy/profiler.h"

#include "../internal/invoke.h"
#include "../internal/megdnn_opr_wrapper.inl"
#include "./workspace_need_limit_getter.inl"

//! TODO: here has to be know some megdnn::opr when there is produced midout.h
//! fix it if there is another graceful way.
#include "megdnn/oprs.h"
#include "midout.h"
MIDOUT_DECL(megbrain_opr_algo_chooser)
#define MIDOUT_B(...) MIDOUT_BEGIN(megbrain_opr_algo_chooser, __VA_ARGS__) {
#define MIDOUT_E \
} \
MIDOUT_END();

using mgb::opr::intl::WorkspaceLimitGetter;

#define APPLY(statement, ...) \
mgb::apply([&](const auto&... args) { return statement; }, \
std::tuple_cat(__VA_ARGS__))

// timeout delta to be added with fastest known algorithm for new algos
constexpr double TIMEOUT_TOLERANCE = 2;

namespace mgb {
namespace opr {

template <typename Opr>
AlgoChooserProfileCache::Result AlgoChooser<Opr>::get_profile_result(
ExeContext& ctx, bool enable_update) {
AlgoChooserProfileCache& cache = ctx.mgb_opr()->profile_cache();

ConvTensorLayouts origin_layouts = ctx.layouts();
typename Opr::Param origin_param = ctx.mgb_opr()->param();
get_origin_param_and_layouts(ctx, origin_layouts, origin_param);
AlgoChooserProfileCache::Key cache_key{origin_layouts.data(),
origin_layouts.size(), &origin_param,
sizeof(origin_param)};
{
auto&& rst = cache.get(cache_key);
if (rst.valid())
return rst.val();
}

AlgoChooserProfileCache::Result prof_rst;
if (!enable_update)
return prof_rst;

std::string str_on_inp_shape = ssprintf(
"on input layouts (%s, %s)", ctx.layouts()[0].to_string().c_str(),
ctx.layouts()[1].to_string().c_str());
double cur_timeout = 0;
RealTimer timer;
for (auto algo : ctx.get_all_candidates_with_workspace_limit()) {
Maybe<AlgoChooserProfileCache::ResultEntry> cur_rst;
std::string msg = ssprintf("profiling %s algorithm %s %s",
ctx.mgb_opr()->dyn_typeinfo()->name,
algo->name(), str_on_inp_shape.c_str());
timer.reset();
MGB_TRY { cur_rst = ctx.profile_single_algo(algo, cur_timeout); }
MGB_CATCH(std::exception & exc, {
mgb_log_warn("caught exception during %s: %s", msg.c_str(),
exc.what());
continue;
})
MGB_CATCH(..., {
mgb_log_warn("caught exception during %s", msg.c_str());
continue;
})
if (!cur_rst.valid()) {
mgb_log_warn("timeout when %s; timeout setting: %.3fsec",
msg.c_str(), cur_timeout);
continue;
}
if (!cur_timeout) {
cur_timeout = timer.get_secs() + TIMEOUT_TOLERANCE;
} else {
cur_timeout =
std::min(cur_timeout, timer.get_secs() + TIMEOUT_TOLERANCE);
}
auto&& rst = cur_rst.val();
mgb_log_debug("%s: workspace: %zu; time: %.3gsec", msg.c_str(),
rst.workspace, rst.time);
prof_rst.push_back(rst);
}
mgb_assert(!prof_rst.empty(), "no usable convolution algorithm %s",
str_on_inp_shape.c_str());

cache.put(cache_key, prof_rst);
return prof_rst;
}

template <>
void AlgoChooser<megdnn::ConvBias>::get_origin_param_and_layouts(
const ExeContext& ctx, ConvTensorLayouts& layouts,
megdnn::ConvBias::Param& param) {
auto format = static_cast<megdnn::param::ConvBias::Format>(
ctx.megdnn_opr()->param().format);
size_t output_block_size = ctx.megdnn_opr()->param().output_block_size;
megdnn::ConvBias::deduce_winograd_origin_layout_and_param(
format, output_block_size, ctx.layouts()[0], ctx.layouts()[1],
layouts[1], param);
}

template <typename Opr>
typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
ExeContext& ctx, bool require_reproducible, bool enable_update) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("AlgoChooser::choose_by_profile")))
auto opr = ctx.mgb_opr();
if (opr->owner_graph()->options().no_profiling_on_shape_change) {
auto algo = ctx.megdnn_opr()->execution_policy().algorithm;
if (algo)
return algo;
}

std::unordered_map<std::string, ImplAlgo> algo_map;
for (auto i : ctx.get_all_candidates()) {
auto ins = algo_map.emplace(i->name(), i);
mgb_assert(ins.second, "duplicated algo name: %s", i->name());
}

auto&& prof = get_profile_result(ctx, enable_update);
if (prof.empty())
return nullptr;
for (auto&& i : prof) {
if ((!require_reproducible || i.reproducible)) {
auto iter = algo_map.find(i.algo);
mgb_assert(iter != algo_map.end(),
"algorithm %s exists in "
"profiling result but not in algo_map; please "
"report this "
"bug; opr: %s{%s}, shapes: %s %s %s",
ctx.mgb_opr()->cname(),
ctx.mgb_opr()->dyn_typeinfo()->name,
ctx.layouts()[0].TensorShape::to_string().c_str(),
ctx.layouts()[1].TensorShape::to_string().c_str(),
ctx.layouts()[2].TensorShape::to_string().c_str(),
i.algo.c_str());
return iter->second;
}
}

mgb_log_error(
"Workspace requirement (%zu) could not be satisfied. Abort now "
"to "
"avoid further problems",
WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit));
mgb_trap();
MIDOUT_E
}

template <typename Opr>
size_t AlgoChooser<Opr>::setup_algo(const ConvTensorLayouts& layouts,
Opr* megdnn_opr, const MGBOpr* mgb_opr,
bool allow_weight_preprocess) {
if (WorkspaceLimitGetter::is_prealloc_run(mgb_opr->owner_graph())) {
return 0;
}

ExeContext ctx(layouts, megdnn_opr, mgb_opr, allow_weight_preprocess);

auto algo = get_algo(ctx);
size_t workspace = ctx.get_workspace_size_bytes(algo);
mgb_log_debug(
"%s: tensor layouts(%s %s, %s %s) -> (%s %s): algo=%s "
"workspace=%.2fMiB reproducible=%d",
mgb_opr->dyn_typeinfo()->name, layouts[0].to_string().c_str(),
layouts[0].dtype.name(), layouts[1].to_string().c_str(),
layouts[1].dtype.name(),
layouts[layouts.size() - 1].to_string().c_str(),
layouts[layouts.size() - 1].dtype.name(), algo->name(),
workspace / (1024 * 1024.0), algo->is_reproducible());
megdnn_opr->execution_policy() = {algo};
return workspace;
}

template <typename Opr>
typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::get_algo(
ExeContext& ctx) {
using S = mixin::Convolution::ExecutionPolicy::Strategy;
MGB_MARK_USED_VAR(TIMEOUT_TOLERANCE);
switch (ctx.mgb_opr()->execution_policy().strategy) {
case S::HEURISTIC:
return ctx.choose_by_heuristic();
case S::HEURISTIC_REPRODUCIBLE:
return ctx.choose_by_heuristic(true);
case S::PROFILE_HEURISTIC: {
ImplAlgo algo = choose_by_profile(ctx, false, false);
if (algo == nullptr)
algo = ctx.choose_by_heuristic();
return algo;
}
#if MGB_ENABLE_FASTRUN
case S::PROFILE:
return choose_by_profile(ctx, false);
case S::PROFILE_REPRODUCIBLE:
return choose_by_profile(ctx, true);
#endif
default:
mgb_throw(GraphError, "bad convolution ExecutionPolicy strategy");
}
}

#define INST(Opr) \
template AlgoChooser<megdnn::Opr>::ImplAlgo \
AlgoChooser<megdnn::Opr>::get_algo(ExeContext& ctx); \
template AlgoChooserProfileCache::Result \
AlgoChooser<megdnn::Opr>::get_profile_result(ExeContext& ctx, \
bool enable_update); \
template AlgoChooser<megdnn::Opr>::ImplAlgo \
AlgoChooser<megdnn::Opr>::choose_by_profile( \
ExeContext& ctx, bool require_reproducible, bool enable_update); \
template size_t AlgoChooser<megdnn::Opr>::setup_algo( \
const ConvTensorLayouts& layouts, megdnn::Opr* megdnn_opr, \
const MGBOpr* mgb_opr, bool allow_weight_preprocess);

MGB_FOREACH_FASTRUN_OPR(INST)

#undef INST

//////////////////////////////// ExeContext /////////////////////////////

template <typename Opr>
typename AlgoChooser<Opr>::ImplAlgo
AlgoChooser<Opr>::ExeContext::choose_by_heuristic(bool reproducible) const {
auto opr = m_mgb_opr;
auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit);
return APPLY(m_megdnn_opr->get_algorithm_heuristic(args..., workspace_limit,
reproducible),
m_layouts);
}

template <typename Opr>
std::vector<typename AlgoChooser<Opr>::ImplAlgo>
AlgoChooser<Opr>::ExeContext::get_all_candidates() const {
auto heu = choose_by_heuristic();
auto&& ret = APPLY(m_megdnn_opr->get_all_algorithms(args...), m_layouts);
bool found = false;
for (size_t i = 0; i < ret.size(); ++i) {
if (ret[i] == heu) {
found = true;
std::swap(ret[i], ret[0]);
break;
}
}
mgb_assert(found,
"algo %s got by heuristic not found in "
"candidate list", heu->name());
return std::move(ret);
}

template <typename Opr>
std::vector<typename AlgoChooser<Opr>::ImplAlgo>
AlgoChooser<Opr>::ExeContext::get_all_candidates_with_workspace_limit() const {
auto&& all_algos = get_all_candidates();
auto opr = m_mgb_opr;
auto workspace_limit = WorkspaceLimitGetter::get_workspace_limit(
opr->owner_graph(), opr->comp_node(),
opr->execution_policy().workspace_limit);
std::vector<ImplAlgo> ret;
for (auto&& algo : all_algos) {
if (get_workspace_size_bytes(algo) <= workspace_limit) {
ret.push_back(algo);
}
}
return ret;
}

template <typename Opr>
size_t AlgoChooser<Opr>::ExeContext::get_workspace_size_bytes(
ImplAlgo algo) const {
m_megdnn_opr->execution_policy() = {algo};
size_t result;
if_constexpr<opr_supports_preprocess<Opr>()>(
[&](auto _) {
auto&& opr = _(m_megdnn_opr);
auto prep = this->construct_fake_preprocess_filter();
PreprocessFilter<Opr>* prep_ptr =
prep.valid() ? &prep.val() : nullptr;
result = std::max(
APPLY(opr->get_preprocess_workspace_in_bytes(args...),
m_layouts),
APPLY(opr->get_workspace_in_bytes(args..., prep_ptr),
m_layouts));
},
/* else */
[&](auto _) {
result = APPLY(_(m_megdnn_opr)->get_workspace_in_bytes(args...),
m_layouts);
});
return result;
}

template <typename Opr>
Maybe<AlgoChooserProfileCache::ResultEntry>
AlgoChooser<Opr>::ExeContext::profile_single_algo(ImplAlgo algo,
double& timeout) const {
typename TimedProfiler<Opr>::Param param;
auto name = algo->name();
// force check copy size <= dest len-1 from gcc8 for safe
auto len = sizeof(param.algo_name);
strncpy(param.algo_name, name, len - 1);
param.algo_name[len - 1] = '\0';
mgb_assert(!param.algo_name[sizeof(param.algo_name) - 2],
"algo name too long: %s; len=%zu", name, strlen(name));
param.workspace = get_workspace_size_bytes(algo);
for (int i = 0; i < arity; ++i) {
auto&& src = m_layouts[i];
mgb_assert(src.format.is_default() &&
(src.dtype.category() == DTypeCategory::FLOAT ||
src.dtype.category() == DTypeCategory::INT ||
src.dtype.category() == DTypeCategory::QUANTIZED),
"unsupported layout in profiling: %s",
src.to_string().c_str());
param.dtypes[i] = src.dtype.enumv();
}
param.comp_node_loc = m_mgb_opr->output(0)->comp_node().locator();
mgb_assert(param.shapes.size() == m_layouts.size());
for (size_t i = 0; i < param.shapes.size(); ++i)
param.shapes[i] = m_layouts[i];
param.opr_param = m_megdnn_opr->param();
param.allow_weight_preprocess = m_allow_weight_preprocess;

auto rst = TimedProfiler<Opr>::profile(param, timeout);
// MIOpen conv profiles all available algos when a specfic shape is
// provided for the first time, which probably adds to the result time.
// Therefore, a second profile execution is needed.
if (strncmp(name, "MIOpen", 6) == 0)
rst = TimedProfiler<Opr>::profile(param, timeout);
if (!rst.valid())
return None;
return AlgoChooserProfileCache::ResultEntry{
algo->name(), algo->is_reproducible(), rst.val().time,
param.workspace};
}

template <typename Opr>
Maybe<PreprocessFilter<Opr>>
AlgoChooser<Opr>::ExeContext::construct_fake_preprocess_filter() const {
Maybe<PreprocessFilter<Opr>> result = None;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (!m_allow_weight_preprocess)
return;
auto opr = _(m_megdnn_opr);
auto layout = APPLY(opr->deduce_preprocessed_filter_layout(args...),
m_layouts);
if (layout.empty())
return;
result = PreprocessFilter<Opr>{};
auto& res = result.val();
res.algorithm_id = nullptr;
res.tensors.resize(layout.size());
for (size_t i = 0; i < layout.size(); i++) {
res.tensors[i] = megdnn::TensorND(nullptr, layout[i]);
}
});
return result;
}

#define INST(Opr) \
template typename AlgoChooser<megdnn::Opr>::ImplAlgo \
AlgoChooser<megdnn::Opr>::ExeContext::choose_by_heuristic( \
bool reproducible) const; \
template std::vector<typename AlgoChooser<megdnn::Opr>::ImplAlgo> \
AlgoChooser<megdnn::Opr>::ExeContext::get_all_candidates() const; \
template std::vector<typename AlgoChooser<megdnn::Opr>::ImplAlgo> \
AlgoChooser<megdnn::Opr>::ExeContext:: \
get_all_candidates_with_workspace_limit() const; \
template size_t \
AlgoChooser<megdnn::Opr>::ExeContext::get_workspace_size_bytes( \
typename AlgoChooser<megdnn::Opr>::ImplAlgo algo) const; \
template Maybe<AlgoChooserProfileCache::ResultEntry> \
AlgoChooser<megdnn::Opr>::ExeContext::profile_single_algo( \
typename AlgoChooser<megdnn::Opr>::ImplAlgo algo, double& timeout) \
const; \

MGB_FOREACH_FASTRUN_OPR(INST)

#undef INST
} // namespace opr
} // namespace mgb

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

+ 259
- 0
src/opr/impl/search_policy/profiler.cpp View File

@@ -0,0 +1,259 @@
/**
* \file src/opr/impl/search_policy/profile.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 "megbrain/opr/search_policy/profiler.h"

#include "../internal/invoke.h"


//! TODO: here has to be know some megdnn::opr when there is produced midout.h
//! fix it if there is another graceful way.
#include "megdnn/oprs.h"

#include "midout.h"

MIDOUT_DECL(megbrain_opr_profile)
#define MIDOUT_B(...) MIDOUT_BEGIN(megbrain_opr_profile, __VA_ARGS__) {
#define MIDOUT_E \
} \
MIDOUT_END();

namespace mgb {
namespace opr {
#define APPLY(statement, ...) \
mgb::apply([&](const auto&... args) { return statement; }, \
std::tuple_cat(__VA_ARGS__))

template <typename Opr>
const double TimedProfiler<Opr>::timeout_setting =
TimedProfiler<Opr>::init_timeout_setting();

template <typename Opr>
double TimedProfiler<Opr>::init_timeout_setting() {
#if MGB_ENABLE_FASTRUN
sys::TimedFuncInvoker::ins().register_func(
AlgoChooserFuncId<Opr>::ID, &TimedProfiler<Opr>::prof_impl,
&TimedProfiler<Opr>::prof_init_device);
auto to_set = MGB_GETENV("MGB_CONV_PROFILING_TIMEOUT");
if (to_set)
return std::stod(to_set);
#endif
return 0;
}

#define APPLY(statement, ...) \
mgb::apply([&](const auto&... args) { return statement; }, \
std::tuple_cat(__VA_ARGS__))

template <typename Opr>
typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
const TParam& raw_param) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("TimedProfiler::prof_impl")))
auto&& param = raw_param.as_single_pod<Param>();
CompNode cn = CompNode::load(param.comp_node_loc, param.comp_node_loc);
auto megdnn_opr = intl::create_megdnn_opr<Opr>(cn);
std::array<TensorLayout, arity> layouts;

auto from_enum = [&](DTypeEnum enumv) -> DType {
switch (enumv) {

#define cb(_dt) \
case DTypeTrait<_dt>::enumv: \
return _dt(1.0f, static_cast<uint8_t>(0))
cb(dtype::Quantized8Asymm);
#undef cb

#define cb(_dt) \
case DTypeTrait<_dt>::enumv: \
return _dt(1.0f)

cb(dtype::QuantizedS8);
cb(dtype::QuantizedS16);
cb(dtype::QuantizedS32);
default:
return DType::from_enum(enumv);
#undef cb
}
};
for (int i = 0; i < arity; ++i) {
layouts[i] = {param.shapes[i], from_enum(param.dtypes[i])};
}

megdnn_opr->param() = param.opr_param;
{
typename Opr::Algorithm* algo = nullptr;
for (auto i : APPLY(megdnn_opr->get_all_algorithms(args...), layouts)) {
if (!strcmp(i->name(), param.algo_name)) {
algo = i;
break;
}
}
mgb_assert(algo, "algorithm %s not found", param.algo_name);
megdnn_opr->execution_policy() = {algo};
}

// Allocate preprocessed weight buffers.
TensorLayoutArray preprocessed_layout;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (param.allow_weight_preprocess) {
preprocessed_layout = APPLY(
_(megdnn_opr)->deduce_preprocessed_filter_layout(args...),
layouts);
}
});

{
// first allocate a whole chunk to avoid memory fragmentation (here we
// rely on memory allocator to reuse memory)
auto align = cn.get_mem_addr_alignment();
size_t tot_size = align;
for (int i = 0; i < arity; ++i) {
tot_size += layouts[i].span().high_byte + align;
}
for (const auto& layout : preprocessed_layout) {
tot_size += layout.span().high_byte + align;
}
tot_size += param.workspace;
DeviceTensorStorage storage{cn};
storage.ensure_size(tot_size);
}

// allocate input and output memory
std::array<DeviceTensorND, arity_in> inp_val;
std::array<DeviceTensorND, arity_out> out_val;
DeviceTensorND workspace;
for (int i = 0; i < arity_in; ++i) {
inp_val[i].comp_node(cn).dtype(layouts[i].dtype).resize(layouts[i]);
}
for (int i = 0; i < arity_out; ++i) {
out_val[i]
.comp_node(cn)
.dtype(layouts[arity_in + i].dtype)
.resize(layouts[arity_in + i]);
}
megdnn::Workspace mdn_workspace;

// allocate workspace
if (param.workspace) {
workspace.comp_node(cn).dtype(dtype::Byte()).resize({param.workspace});
mdn_workspace.size = param.workspace;
mdn_workspace.raw_ptr = workspace.raw_ptr();
}

// allocate storage for preprocessed filter
SmallVector<DeviceTensorND> flt_val(preprocessed_layout.size());
for (size_t i = 0; i < preprocessed_layout.size(); i++) {
flt_val[i] = {cn, preprocessed_layout[i], preprocessed_layout[i].dtype,
preprocessed_layout[i].format};
}

for (int i = 0; i < arity_in; ++i) {
fill_zero_dev_tensor(inp_val[i]);
}

PreprocessFilter<Opr> prep_flt;
if_constexpr<opr_supports_preprocess<Opr>()>([&](auto _) {
if (!preprocessed_layout.empty()) {
auto&& pf = _(prep_flt);
pf.algorithm_id = nullptr;
pf.tensors.resize(flt_val.size());
for (size_t i = 0; i < flt_val.size(); i++) {
pf.tensors[i] = flt_val[i].as_megdnn();
}
APPLY(_(megdnn_opr)->exec_preprocess(args..., &pf, mdn_workspace),
std::forward_as_tuple(layouts[0], inp_val[1].as_megdnn()),
array_skip<2>(layouts));
}
});

RealTimer timer;
auto ev_start = cn.create_event(CompNode::Event::NEED_TIMER),
ev_end = cn.create_event(CompNode::Event::NEED_TIMER);
ev_start->record();
if_constexpr<opr_supports_preprocess<Opr>()>(
[&](auto _) {
auto&& opr = _(megdnn_opr);
PreprocessFilter<Opr>* pf =
preprocessed_layout.empty() ? nullptr : &prep_flt;
APPLY(opr->exec(args.as_megdnn()..., pf, mdn_workspace),
inp_val, out_val);
},
/* else */
[&](auto _) {
APPLY(_(megdnn_opr)->exec(args.as_megdnn()..., mdn_workspace),
inp_val, out_val);
});
ev_end->record();

double next_report_time = 0.5;
while (!ev_end->finished()) {
if (timer.get_secs() >= next_report_time) {
mgb_log_warn(
"profiling conv algo %s already took %.3f/%.3f secs"
" (limit can be set by MGB_CONV_PROFILING_TIMEOUT) ",
param.algo_name, timer.get_secs(), param.actual_timeout);
next_report_time = timer.get_secs() + 1;
}
using namespace std::literals;
std::this_thread::sleep_for(1000us);
}

mgb_assert(ev_start->finished());
return TResult::from_pod(Result{ev_start->elapsed_time_until(*ev_end)});
MIDOUT_E
};

template <typename Opr>
Maybe<typename TimedProfiler<Opr>::Result> TimedProfiler<Opr>::profile(
const Param& param, double& timeout) {
mgb_assert(timeout >= 0);
if (!timeout) {
timeout = timeout_setting;
} else if (timeout_setting) {
timeout = std::min(timeout, timeout_setting);
}
param.actual_timeout =
timeout ? timeout : std::numeric_limits<double>::infinity();
auto res = sys::TimedFuncInvoker::ins().invoke(
AlgoChooserFuncId<Opr>::ID,
TParam::from_pod(const_cast<Param&>(param)), timeout);
if (res.valid())
return res.val().template as_single_pod<Result>();
return None;
}

template <typename Opr>
void TimedProfiler<Opr>::prof_init_device(const TParam& raw_param) {
MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("TimedProfiler::prof_init_device")))
auto&& param = raw_param.as_single_pod<Param>();
CompNode cn = CompNode::load(param.comp_node_loc, param.comp_node_loc);
// wait for cuda init, so its time does not get accounted in timeout
cn.sync();
MIDOUT_E
}

#define INST(Opr) \
template const double TimedProfiler<megdnn::Opr>::timeout_setting; \
template double TimedProfiler<megdnn::Opr>::init_timeout_setting(); \
template typename TimedProfiler<megdnn::Opr>::TResult \
TimedProfiler<megdnn::Opr>::prof_impl(const TParam& raw_param); \
template Maybe<typename TimedProfiler<megdnn::Opr>::Result> \
TimedProfiler<megdnn::Opr>::profile(const Param& param, double& timeout); \
template void TimedProfiler<megdnn::Opr>::prof_init_device( \
const TParam& raw_param);

MGB_FOREACH_FASTRUN_OPR(INST)
#undef INST
} // namespace opr
} // namespace mgb

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

+ 36
- 0
src/opr/impl/search_policy/workspace_need_limit_getter.inl View File

@@ -0,0 +1,36 @@
/**
* \file src/opr/impl/search_policy/workspace_need_limit_getter.inl
* 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 "megbrain/opr/search_policy/profiler.h"

#include "../internal/megdnn_opr_wrapper.inl"

namespace mgb {
namespace opr {
namespace intl {

#define cb(_Opr) \
template <> \
struct AutoAddWorkspaceNeedLimitGetter<megdnn::_Opr> { \
static constexpr bool val = true; \
};
MGB_FOREACH_FASTRUN_OPR(cb)

#undef cb

} // namespace intl
} // namespace opr
} // namespace mgb

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

+ 140
- 0
src/opr/include/megbrain/opr/search_policy/algo_chooser.h View File

@@ -0,0 +1,140 @@
/**
* \file src/opr/include/megbrain/opr/search_policy/algo_chooser.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 "megbrain/opr/search_policy/profiler.h"

template <class MegDNNOpr>
struct MegDNNOpr2MGBOpr;

#define cb(_Opr) \
template <> \
struct MegDNNOpr2MGBOpr<megdnn::_Opr> { \
using MGBOpr = mgb::opr::_Opr; \
};

MGB_FOREACH_FASTRUN_OPR(cb)

#undef cb

namespace mgb {
namespace opr {

/* =================== AlgoChooser =================== */
/*!
* \brief choose algorithm according to ExecutionPolicy
*
* This class only provides static methods, and the entry point is
* AlgoChooser::setup_algo. When profiling is needed, it would first try to
* retrive profiling stats from cache, and run TimedProfiler when necessary
*
* \tparam Opr megdnn operator impl
*/
template <typename Opr>
class AlgoChooser {
static constexpr int arity_in = OprArityTrait<Opr>::arity_in;
static constexpr int arity_out = OprArityTrait<Opr>::arity_out;
static constexpr int arity = OprArityTrait<Opr>::arity;

using ImplAlgo = typename Opr::Algorithm*;
using MGBOpr = typename MegDNNOpr2MGBOpr<Opr>::MGBOpr;
using ConvTensorLayouts = std::array<TensorLayout, arity>;

class ExeContext {
const ConvTensorLayouts& m_layouts;
Opr* m_megdnn_opr;
const MGBOpr* m_mgb_opr;
bool m_allow_weight_preprocess;

public:
ExeContext(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr, bool allow_weight_preprocess)
: m_layouts{layouts},
m_megdnn_opr{megdnn_opr},
m_mgb_opr{mgb_opr},
m_allow_weight_preprocess{allow_weight_preprocess} {
mgb_assert(m_layouts.size() == layouts.size());
static_assert(
std::tuple_size<ConvTensorLayouts>::value == 3 ||
std::tuple_size<ConvTensorLayouts>::value == 5 ||
std::tuple_size<ConvTensorLayouts>::value == 8,
"Convolution AlgoChooser assumes arity = 3 , 5 or 8 (for "
"deformable conv)");
}

Opr* megdnn_opr() const { return m_megdnn_opr; }

const MGBOpr* mgb_opr() const { return m_mgb_opr; }

const TensorLayout& inp_layout(size_t idx) const {
return m_layouts[idx];
}

const ConvTensorLayouts& layouts() const { return m_layouts; }

ImplAlgo choose_by_heuristic(bool reproducible = false) const;

//! get all candidate algos, and the one choose_by_heuristic() is
//! put first
std::vector<ImplAlgo> get_all_candidates() const;

//! get candidate algos with workspace limit.
std::vector<ImplAlgo> get_all_candidates_with_workspace_limit() const;

//! get workspace size required for specific algo
size_t get_workspace_size_bytes(ImplAlgo algo) const;

/*!
* \brief profile a single algorithm
*
* This is actually a wrapper that constructs param and call
* TimedProfiler<Opr>::profile for the actual profiling
*
* \param[in,out] timeout set the timeout, and return the actual
* timeout used during profiling
*/
Maybe<AlgoChooserProfileCache::ResultEntry> profile_single_algo(
ImplAlgo algo, double& timeout) const;

private:
Maybe<PreprocessFilter<Opr>> construct_fake_preprocess_filter() const;
};

//! entrance for getting algorithm according to execution strategy
static ImplAlgo get_algo(ExeContext& ctx);

static void get_origin_param_and_layouts(const ExeContext&,
ConvTensorLayouts&,
typename Opr::Param&) {}

//! get all profile result, either by retrieving cache or profiling
static AlgoChooserProfileCache::Result get_profile_result(
ExeContext& ctx, bool enable_update);

static ImplAlgo choose_by_profile(ExeContext& ctx,
bool require_reproducible,
bool enable_update = true);

public:
/*!
* \brief setup algorithm and return workspace size
*/
static size_t setup_algo(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr,
bool allow_weight_preprocess = false);
};

} // namespace opr
} // namespace mgb

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

+ 160
- 0
src/opr/include/megbrain/opr/search_policy/profiler.h View File

@@ -0,0 +1,160 @@
/**
* \file src/opr/include/megbrain/opr/search_policy/profile.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 "megbrain/opr/dnn/convolution.h"
#include "megbrain/utils/hash_ct.h"
#include "megbrain/utils/timer.h"

#include "megdnn/basic_types.h"
#include "megdnn/oprs/nn.h"

namespace mgb {
namespace opr {

#define MGB_FOREACH_FASTRUN_OPR(cb) \
cb(ConvolutionForward); \
cb(ConvBiasForward); \
cb(ConvolutionBackwardData); \
cb(ConvolutionBackwardFilter); \
cb(Convolution3DForward); \
cb(Convolution3DBackwardData); \
cb(Convolution3DBackwardFilter); \
cb(LocalShareForward); \
cb(LocalShareBackwardData); \
cb(LocalShareBackwardFilter); \
cb(DeformableConvForward); \
cb(DeformableConvBackwardFilter); \
cb(DeformableConvBackwardData); \
cb(BatchConvBiasForward);

template <typename Opr>
struct OprArityTrait;

template <typename Opr, int _arity_in, int _arity_out>
struct OprArityTraitTmpl {
static constexpr int arity_in = _arity_in;
static constexpr int arity_out = _arity_out;
static constexpr int arity = arity_in + arity_out;
};

#define INST_ARITY(_Opr, _in, _out) \
template <> \
struct OprArityTrait<_Opr> : public OprArityTraitTmpl<_Opr, _in, _out> {};

INST_ARITY(megdnn::ConvolutionBackwardData, 2, 1);
INST_ARITY(megdnn::ConvolutionBackwardFilter, 2, 1);
INST_ARITY(megdnn::Convolution3DForward, 2, 1);
INST_ARITY(megdnn::Convolution3DBackwardData, 2, 1);
INST_ARITY(megdnn::Convolution3DBackwardFilter, 2, 1);
INST_ARITY(megdnn::LocalShareForward, 2, 1);
INST_ARITY(megdnn::LocalShareBackwardData, 2, 1);
INST_ARITY(megdnn::LocalShareBackwardFilter, 2, 1);
INST_ARITY(megdnn::Convolution, 2, 1);
INST_ARITY(megdnn::DeformableConvForward, 4, 1);
INST_ARITY(megdnn::DeformableConvBackwardFilter, 4, 1);
INST_ARITY(megdnn::BatchConvBiasForward, 4, 1);
INST_ARITY(megdnn::ConvBias, 4, 1);
INST_ARITY(megdnn::DeformableConvBackwardData, 5, 3);

#undef INST_ARITY

template <typename Opr>
constexpr bool opr_supports_preprocess() {
return std::is_same<Opr, megdnn::ConvolutionForward>::value ||
std::is_same<Opr, megdnn::ConvBias>::value;
}

template <typename Opr, bool has_prep>
struct PreprocessFilterImpl {
using T = union {};
};

template <typename Opr>
struct PreprocessFilterImpl<Opr, true> {
using T = typename Opr::PreprocessedFilter;
};

template <typename Opr>
using PreprocessFilter =
typename PreprocessFilterImpl<Opr, opr_supports_preprocess<Opr>()>::T;

template <typename Opr>
struct AlgoChooserFuncId {};

#define DEF_FUNC_ID(func) \
template <> \
struct AlgoChooserFuncId<megdnn::func> { \
__attribute__( \
(unused)) static constexpr sys::TimedFuncInvoker::FuncId ID = \
static_cast<sys::TimedFuncInvoker::FuncId>( \
MGB_HASH_STR("megdnn::" #func)); \
};

MGB_FOREACH_FASTRUN_OPR(DEF_FUNC_ID)

#undef DEF_FUNC_ID

/* =================== TimedProfiler =================== */

/*!
* \brief profile a megdnn opr conv with given param
*
* This class only provides static methods, and the entry point is
* TimedProfiler::profile; it would run profiler in a timed environment by
* sys::TimedFuncInvoker
*
* \tparam Opr megdnn opr impl
*/
template <typename Opr>
class TimedProfiler {
static constexpr int arity_in = OprArityTrait<Opr>::arity_in;
static constexpr int arity_out = OprArityTrait<Opr>::arity_out;
static constexpr int arity = OprArityTrait<Opr>::arity;

using ConvTensorShapes = std::array<TensorShape, arity>;

public:
struct Param {
char algo_name[128];
size_t workspace;
DTypeEnum dtypes[arity];
CompNode::Locator comp_node_loc;
ConvTensorShapes shapes;
typename Opr::Param opr_param;
bool allow_weight_preprocess;

//! filled by profile()
mutable double actual_timeout;
};

struct Result {
double time;
};

static Maybe<Result> profile(const Param& param, double& timeout);

private:
using TParam = sys::TimedFuncInvoker::Param;
using TResult = sys::TimedFuncInvoker::Result;

static const double timeout_setting;

static double init_timeout_setting();
static TResult prof_impl(const TParam& raw_param);
static void prof_init_device(const TParam& raw_param);
};
} // namespace opr
} // namespace mgb

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

+ 0
- 4
src/opr/test/basic_arith/elemwise.cpp View File

@@ -593,10 +593,6 @@ namespace {
struct enable_for_dtype_impl<dtype::Bool, Trait> { struct enable_for_dtype_impl<dtype::Bool, Trait> {
static constexpr bool value = Trait::ALLOW_BOOL; static constexpr bool value = Trait::ALLOW_BOOL;
}; };
template<>
struct enable_for_dtype_impl<dtype::Bool, void> {
static constexpr bool value = false;
};
} }


//! whether to enable test for specific dtype and Trait //! whether to enable test for specific dtype and Trait


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