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提交 a57317f4 编写于 作者: M Megvii Engine Team
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refactor(mgb): move profile cache out of mgb opr and update CACHE_KEY_VERSION 

GitOrigin-RevId: db433164d297c9d6438fc45203e57e72df3f9b0b
上级 278b2baa
隐藏空白更改
内联 并排
Showing with 46 addition and 67 deletion
src/opr/impl/dnn/convolution.cpp
浏览文件 @ a57317f4
......@@ -36,8 +36,6 @@ using namespace opr;
using namespace cg::static_infer;
using intl::WorkspaceLimitGetter;
#define CACHE_KEY_VERSION "v2"
/* ==================== misc impl ==================== */
mixin::Convolution::~Convolution() = default;
......@@ -103,26 +101,12 @@ void mixin::Convolution::init_output_static_infer_desc_for_bwd_data(
{SourceType::DEP, inp_deps, infer_wk});
}
#define IMPL_CONV(_cls, _prof_name) \
void _cls::init_profile_cache() { \
std::string name(_prof_name CACHE_KEY_VERSION); \
name.append(megdnn_opr()->get_algorithm_set_name()); \
m_profile_cache = std::make_unique<AlgoChooserProfileCache>( \
comp_node(), name.c_str()); \
} \
std::pair<const void*, size_t> _cls::param_blob() const { \
return {&param(), sizeof(Param)}; \
} \
#define IMPL_CONV(_cls) \
std::pair<const void*, size_t> _cls::param_blob() const { \
return {&param(), sizeof(Param)}; \
} \
MGB_DYN_TYPE_OBJ_FINAL_IMPL(_cls)
AlgoChooserProfileCache& mixin::Convolution::profile_cache() const {
if (!m_profile_cache) {
const_cast<Convolution*>(this)->init_profile_cache();
mgb_assert(m_profile_cache);
}
return *m_profile_cache;
}
class mixin::WeightPreprocessExecutor::PreprocessedFilterExecDep final
: public cg::GraphExecutable::ExecDependency {
std::unique_ptr<PreprocessedFilter> m_pf;
......@@ -209,7 +193,7 @@ bool mixin::WeightPreprocessExecutor::mixin_allow_weight_preprocess(
/* ==================== ConvolutionForward ==================== */
IMPL_CONV(ConvolutionForward, "conv_fwd");
IMPL_CONV(ConvolutionForward);
ConvolutionForward::ConvolutionForward(VarNode* src, VarNode* filter,
const Param& param,
......@@ -335,7 +319,7 @@ void ConvolutionForward::scn_do_execute_preprocess() {
}
/* ==================== ConvolutionBackwardData ==================== */
IMPL_CONV(ConvolutionBackwardData, "conv_bwd_data");
IMPL_CONV(ConvolutionBackwardData);
ConvolutionBackwardData::ConvolutionBackwardData(
VarNode* filter, VarNode* diff, VarNode* src_for_shp,
......@@ -426,7 +410,7 @@ MGB_IMPL_OPR_GRAD(ConvolutionBackwardData) {
#endif
/* ==================== ConvolutionBackwardFilter ==================== */
IMPL_CONV(ConvolutionBackwardFilter, "conv_bwd_filter");
IMPL_CONV(ConvolutionBackwardFilter);
ConvolutionBackwardFilter::ConvolutionBackwardFilter(
VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
......@@ -480,7 +464,7 @@ MGB_IMPL_OPR_GRAD(ConvolutionBackwardFilter) {
#endif
/* ==================== Convolution3DForward ==================== */
IMPL_CONV(Convolution3DForward, "conv3d_fwd");
IMPL_CONV(Convolution3DForward);
Convolution3DForward::Convolution3DForward(VarNode* src, VarNode* filter,
const Param& param,
......@@ -553,7 +537,7 @@ size_t Convolution3DForward::get_workspace_size_bytes(
}
/* ==================== Convolution3DBackwardData ==================== */
IMPL_CONV(Convolution3DBackwardData, "conv3d_bwd_data");
IMPL_CONV(Convolution3DBackwardData);
Convolution3DBackwardData::Convolution3DBackwardData(
VarNode* filter, VarNode* diff, VarNode* src_for_shp,
......@@ -631,7 +615,7 @@ MGB_IMPL_OPR_GRAD(Convolution3DBackwardData) {
#endif
/* ==================== Convolution3DBackwardFilter ==================== */
IMPL_CONV(Convolution3DBackwardFilter, "conv3d_bwd_filter");
IMPL_CONV(Convolution3DBackwardFilter);
Convolution3DBackwardFilter::Convolution3DBackwardFilter(
VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
......@@ -719,7 +703,7 @@ SymbolVar MaskPropagate::make(SymbolVar src, const Param& param,
}
/* ==================== ConvBiasForward ==================== */
IMPL_CONV(ConvBiasForward, "conv_bias_fwd");
IMPL_CONV(ConvBiasForward);
ConvBiasForward::ConvBiasForward(VarNode* src, VarNode* filter,
const Param& param,
......@@ -1005,7 +989,7 @@ void ConvBiasForward::scn_do_execute_preprocess() {
/* ===================== LocalShareForward ==================== */
IMPL_CONV(LocalShareForward, "local_share");
IMPL_CONV(LocalShareForward);
LocalShareForward::LocalShareForward(VarNode* src, VarNode* filter,
const Param& param,
......@@ -1073,7 +1057,7 @@ MGB_IMPL_OPR_GRAD(LocalShareForward) {
/* ===================== LocalShareBackwardData ==================== */
IMPL_CONV(LocalShareBackwardData, "local_share_bwd_data");
IMPL_CONV(LocalShareBackwardData);
LocalShareBackwardData::LocalShareBackwardData(VarNode* filter, VarNode* diff,
VarNode* src_for_shp,
......@@ -1153,7 +1137,7 @@ MGB_IMPL_OPR_GRAD(LocalShareBackwardData) {
/* ==================== LocalShareBackwardFilter ==================== */
IMPL_CONV(LocalShareBackwardFilter, "local_share_bwd_filter");
IMPL_CONV(LocalShareBackwardFilter);
LocalShareBackwardFilter::LocalShareBackwardFilter(
VarNode* src, VarNode* diff, VarNode* filter, const Param& param,
......@@ -1208,7 +1192,7 @@ MGB_IMPL_OPR_GRAD(LocalShareBackwardFilter) {
/* ===================== DeformableConvForward ==================== */
IMPL_CONV(DeformableConvForward, "deformable_conv");
IMPL_CONV(DeformableConvForward);
DeformableConvForward::DeformableConvForward(VarNode* src, VarNode* filter,
VarNode* offset, VarNode* mask,
......@@ -1293,7 +1277,7 @@ MGB_IMPL_OPR_GRAD(DeformableConvForward) {
/* ==================== DeformableConvBackwardData ==================== */
IMPL_CONV(DeformableConvBackwardData, "deformalbe_conv_backward_data");
IMPL_CONV(DeformableConvBackwardData);
DeformableConvBackwardData::DeformableConvBackwardData(
VarNode* src, VarNode* filter, VarNode* offset, VarNode* mask,
......@@ -1425,7 +1409,7 @@ void DeformableConvBackwardData::init_output_static_infer_desc() {
/* ==================== DeformableConvBackwardFilter ==================== */
IMPL_CONV(DeformableConvBackwardFilter, "deformalbe_conv_backward_filter");
IMPL_CONV(DeformableConvBackwardFilter);
DeformableConvBackwardFilter::DeformableConvBackwardFilter(
VarNode* src, VarNode* filter, VarNode* offset, VarNode* mask,
......@@ -1484,7 +1468,7 @@ size_t DeformableConvBackwardFilter::get_workspace_size_bytes(
}
/* ==================== BatchConvBiasForward ==================== */
IMPL_CONV(BatchConvBiasForward, "batch_conv_bias_fwd");
IMPL_CONV(BatchConvBiasForward);
BatchConvBiasForward::BatchConvBiasForward(VarNode* src, VarNode* filter,
const Param& param,
......
src/opr/impl/search_policy/algo_chooser.cpp
浏览文件 @ a57317f4
......@@ -36,15 +36,29 @@ using mgb::opr::intl::WorkspaceLimitGetter;
// timeout delta to be added with fastest known algorithm for new algos
constexpr double TIMEOUT_TOLERANCE = 2;
#define CACHE_KEY_VERSION "v3"
namespace {
template <typename Opr>
std::string profile_name(Opr* opr) {
std::string ret =
std::string(MegDNNOpr2MGBOpr<Opr>::MGBOpr::typeinfo()->name) +
CACHE_KEY_VERSION;
ret.append(opr->get_algorithm_set_name());
return ret;
}
}
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();
AlgoChooserProfileCache cache(ctx.mgb_opr()->comp_node(),
profile_name(ctx.megdnn_opr()).c_str());
ConvTensorLayouts origin_layouts = ctx.layouts();
TensorLayoutArray origin_layouts = ctx.layouts();
typename Opr::Param origin_param = ctx.mgb_opr()->param();
AlgoChooserProfileCache::Key cache_key{origin_layouts.data(),
origin_layouts.size(), &origin_param,
......@@ -131,12 +145,12 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
"profiling result but not in algo_map; please "
"report this "
"bug; opr: %s{%s}, shapes: %s %s %s",
i.algo.c_str(),
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());
ctx.layouts()[2].TensorShape::to_string().c_str());
return iter->second;
}
}
......@@ -153,7 +167,7 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::choose_by_profile(
}
template <typename Opr>
size_t AlgoChooser<Opr>::setup_algo(const ConvTensorLayouts& layouts,
size_t AlgoChooser<Opr>::setup_algo(const TensorLayoutArray& layouts,
Opr* megdnn_opr, const MGBOpr* mgb_opr,
bool allow_weight_preprocess) {
if (WorkspaceLimitGetter::is_prealloc_run(mgb_opr->owner_graph())) {
......@@ -220,7 +234,7 @@ typename AlgoChooser<Opr>::ImplAlgo AlgoChooser<Opr>::get_algo(
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 TensorLayoutArray& layouts, megdnn::Opr* megdnn_opr, \
const MGBOpr* mgb_opr, bool allow_weight_preprocess);
MGB_FOREACH_FASTRUN_OPR(INST)
......
src/opr/include/megbrain/opr/dnn/convolution.h
浏览文件 @ a57317f4
......@@ -74,12 +74,8 @@ class Convolution {
mutable bool m_policy_accessed = false;
ExecutionPolicy m_policy;
std::unique_ptr<AlgoChooserProfileCache> m_profile_cache;
AlgoChooserHook m_algo_chooser;
virtual void init_profile_cache() = 0;
//! init output desc for conv backward data oprs; it handles both grad
//! usage and deconv usage
template <class MgbOpr, class MegDNNOpr>
......@@ -159,7 +155,6 @@ class ConvolutionTestingPeer;
MGB_DEFINE_OPR_CLASS(ConvolutionForward,
intl::ConvolutionForwardBase, public mixin::Convolution) // {
void init_profile_cache() override;
void init_output_dtype() override;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
......@@ -245,7 +240,6 @@ public:
const ExecutionPolicy& policy = {},
const OperatorNodeConfig& config = {});
void init_profile_cache() override;
std::pair<const void*, size_t> param_blob() const override;
static void check_winograd_param_valid(
......@@ -268,7 +262,6 @@ MGB_DEFINE_OPR_CLASS(ConvolutionBackwardData,
void init_output_format() override;
void add_input_layout_constraint() override;
void init_profile_cache() override;
void scn_do_execute() override;
NodeProp *do_make_node_prop() const override;
......@@ -310,7 +303,6 @@ MGB_DEFINE_OPR_CLASS(ConvolutionBackwardFilter,
intl::MegDNNOprWrapperBwd<megdnn::ConvolutionBackwardFilter>,
public mixin::Convolution ) // {
void init_profile_cache() override final;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
......@@ -360,7 +352,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DForward,
intl::MegDNNOprWrapperFwd<megdnn::Convolution3DForward>,
public mixin::Convolution) // {
void init_profile_cache() override;
void init_output_dtype() override;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
......@@ -391,7 +382,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardData,
void init_output_static_infer_desc() override;
void add_input_layout_constraint() override;
void init_profile_cache() override;
void scn_do_execute() override;
NodeProp *do_make_node_prop() const override;
......@@ -433,8 +423,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardFilter,
intl::MegDNNOprWrapperBwd<megdnn::Convolution3DBackwardFilter>,
public mixin::Convolution) // {
void init_profile_cache() override final;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
const TensorShapeArray &output_shapes) const override final;
......@@ -455,7 +443,6 @@ MGB_DEFINE_OPR_CLASS(Convolution3DBackwardFilter,
MGB_DEFINE_OPR_CLASS(LocalShareForward,
intl::MegDNNOprWrapperFwd<megdnn::LocalShareForward>,
public mixin::Convolution) // {
void init_profile_cache() override final;
void init_output_dtype() override;
void init_output_format() override;
......@@ -483,7 +470,6 @@ MGB_DEFINE_OPR_CLASS(
void init_output_dtype() override;
void add_input_layout_constraint() override;
void init_profile_cache() override;
void scn_do_execute() override;
NodeProp* do_make_node_prop() const override;
......@@ -506,7 +492,6 @@ MGB_DEFINE_OPR_CLASS(
LocalShareBackwardFilter,
intl::MegDNNOprWrapperBwd<megdnn::LocalShareBackwardFilter>,
public mixin::Convolution) // {
void init_profile_cache() override final;
size_t get_workspace_size_bytes(
const TensorShapeArray& input_shapes,
......@@ -542,7 +527,6 @@ MGB_DEFINE_OPR_CLASS(DeformableConvForward,
std::pair<const void*, size_t> param_blob() const override;
private:
void init_profile_cache() override;
void init_output_dtype() override;
void init_output_format() override;
size_t get_workspace_size_bytes(
......@@ -589,7 +573,6 @@ private:
void add_input_layout_constraint() override {
mixin::megdnn_utils::add_input_layout_constraint_contig(*this);
}
void init_profile_cache() override;
};
MGB_DEFINE_OPR_CLASS(
......@@ -612,7 +595,6 @@ public:
std::pair<const void*, size_t> param_blob() const override;
private:
void init_profile_cache() override;
size_t get_workspace_size_bytes(const TensorShapeArray& input_shapes,
const TensorShapeArray& output_shapes)
const override final;
......@@ -668,7 +650,6 @@ public:
const ExecutionPolicy& policy = {},
const OperatorNodeConfig& config = {});
void init_profile_cache() override;
std::pair<const void*, size_t> param_blob() const override;
};
using BatchConvBias = BatchConvBiasForward;
......
src/opr/include/megbrain/opr/search_policy/algo_chooser.h
浏览文件 @ a57317f4
......@@ -48,16 +48,16 @@ class AlgoChooser {
using ImplAlgo = typename Opr::AlgorithmInfo;
using MGBOpr = typename MegDNNOpr2MGBOpr<Opr>::MGBOpr;
using ConvTensorLayouts = std::array<TensorLayout, arity>;
using TensorLayoutArray = std::array<TensorLayout, arity>;
class ExeContext {
const ConvTensorLayouts& m_layouts;
const TensorLayoutArray& m_layouts;
Opr* m_megdnn_opr;
const MGBOpr* m_mgb_opr;
bool m_allow_weight_preprocess;
public:
ExeContext(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
ExeContext(const TensorLayoutArray& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr, bool allow_weight_preprocess)
: m_layouts{layouts},
m_megdnn_opr{megdnn_opr},
......@@ -65,9 +65,9 @@ class AlgoChooser {
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,
std::tuple_size<TensorLayoutArray>::value == 3 ||
std::tuple_size<TensorLayoutArray>::value == 5 ||
std::tuple_size<TensorLayoutArray>::value == 8,
"Convolution AlgoChooser assumes arity = 3 , 5 or 8 (for "
"deformable conv)");
}
......@@ -80,7 +80,7 @@ class AlgoChooser {
return m_layouts[idx];
}
const ConvTensorLayouts& layouts() const { return m_layouts; }
const TensorLayoutArray& layouts() const { return m_layouts; }
ImplAlgo choose_by_heuristic(bool reproducible = false) const;
......@@ -125,7 +125,7 @@ public:
/*!
* \brief setup algorithm and return workspace size
*/
static size_t setup_algo(const ConvTensorLayouts& layouts, Opr* megdnn_opr,
static size_t setup_algo(const TensorLayoutArray& layouts, Opr* megdnn_opr,
const MGBOpr* mgb_opr,
bool allow_weight_preprocess = false);
};
......
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