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提交 5bdc430e 编写于 作者: M Megvii Engine Team
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fix(mgb/fastrun): fix megbrain fastrun memory overflow bug 

GitOrigin-RevId: f56aa5a5059ef6c5ea9d51fe31d89a0d778aa702
上级 d7ddd43f
隐藏空白更改
内联 并排
Showing with 111 addition and 78 deletion
imperative/src/impl/algo_chooser.h
浏览文件 @ 5bdc430e
......@@ -11,7 +11,8 @@ MGE_WIN_DECLSPEC_FUC size_t setup_algo(
const typename mgb::rdnn::AlgoChooser<Opr>::FixedTensorLayouts& layouts,
Opr* megdnn_opr, uint32_t shared_batch_size, bool binary_equal_between_batch,
bool no_profiling_on_shape_change, CompNode comp_node,
megdnn::param::ExecutionPolicy execution_policy, bool allow_weight_preprocess) {
megdnn::param::ExecutionPolicy execution_policy, bool allow_weight_preprocess,
SmallVector<megdnn::TensorND>* inp_tensornds = nullptr) {
megdnn::AlgorithmCache::Key cache_key(
megdnn_opr->handle(), megdnn_opr->get_opr_type(), layouts.data(),
layouts.size(), &megdnn_opr->param(), sizeof(megdnn_opr->param()));
......@@ -39,7 +40,7 @@ MGE_WIN_DECLSPEC_FUC size_t setup_algo(
using AlgoChooserHelper = typename mgb::rdnn::AlgoChooser<Opr>::AlgoChooserHelper;
AlgoChooserHelper helper(
layouts, megdnn_opr, param_str, comp_node, execution_policy,
allow_weight_preprocess, desc);
allow_weight_preprocess, desc, inp_tensornds);
megdnn::ExecutionPolicy policy;
policy = mgb::rdnn::AlgoChooser<Opr>::get_policy(helper);
......
imperative/src/impl/ops/convolution.cpp
浏览文件 @ 5bdc430e
......@@ -141,11 +141,8 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
def, inputs[0]->layout().ndim, inputs[0]->layout(),
inputs[1]->layout());
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, out_layout);
using TensorND = megdnn::TensorND;
SmallVector<TensorND> inp_tensornds(inputs.size());
SmallVector<TensorND> inp_tensornds(inputs.size() + 2);
TensorLayoutArray inp_shapes(inputs.size()), oup_shapes(output_descs.size());
for (unsigned i = 0; i < inputs.size(); ++i) {
inp_tensornds[i] = inputs[i]->dnn_tensor();
......@@ -168,13 +165,20 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
TensorLayout empty_shp({0}, inputs[0]->dtype());
empty_shp.ndim = 0;
DeviceTensorND empty_bias =
BlobManager::inst()->alloc_workspace_with_defrag(cn, empty_shp);
inp_tensornds[2] = empty_bias.as_megdnn();
inp_tensornds[3] = empty_bias.as_megdnn();
size_t sz = setup_algo<megdnn::ConvBiasForward>(
{inp_shapes[0], inp_shapes[1], empty_shp, empty_shp, oup_shapes[0]},
dnn_opr.op.get(), 0, false, false, cn, conv.policy(), false);
dnn_opr.op.get(), 0, false, false, cn, conv.policy(), false,
&inp_tensornds);
// alloc memory
DeviceTensorND empty_bias =
BlobManager::inst()->alloc_workspace_with_defrag(cn, empty_shp);
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, out_layout);
TensorLayout w_layout({sz}, dtype::Byte());
auto dnn_wk = dnn_opr.create_workspace(w_layout);
......@@ -364,9 +368,6 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
def, inputs[1]->layout().ndim, inputs[0]->layout(), inputs[1]->layout(),
cn);
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, out_layout);
using TensorND = megdnn::TensorND;
SmallVector<TensorND> inp_tensornds(inputs.size());
TensorLayoutArray inp_shapes(inputs.size()), oup_shapes(output_descs.size());
......@@ -380,7 +381,10 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
size_t sz = setup_algo<megdnn::ConvolutionBackwardData>(
{inp_shapes[0], inp_shapes[1], oup_shapes[0]}, dnn_opr.op.get(), 0, false,
false, cn, convbwd.policy(), false);
false, cn, convbwd.policy(), false, &inp_tensornds);
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, out_layout);
auto wk = Blob::make(cn, sz);
auto ptr = wk->storage().get();
......@@ -542,7 +546,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
// shape infer
size_t sz = setup_algo<megdnn::Convolution3D>(
{inp_shapes[0], inp_shapes[1], oup_shapes[0]}, dnn_opr.op.get(), 0, false,
false, cn, conv.policy(), false);
false, cn, conv.policy(), false, &inp_tensornds);
// alloc memory
DeviceTensorND out =
......@@ -598,8 +602,9 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
SmallVector<LogicalTensorDesc>& output_descs, const bool& validated) {
auto&& op_def = def.cast_final_safe<Convolution3DBackwardData>();
auto cn = inputs[0]->comp_node();
megdnn::TensorND weight = inputs[0]->dnn_tensor();
megdnn::TensorND diff = inputs[1]->dnn_tensor();
auto&& wlayout = inputs[0]->layout();
auto&& dlayout = inputs[1]->layout();
DnnOprCaller<megdnn::Convolution3DBackwardData> caller(cn);
auto&& dnn_opr = caller.op;
......@@ -608,21 +613,24 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
TensorLayout& oup_layout = output_descs[0].layout;
if (!validated) {
megdnn::Convolution3DBackwardData::deduce_layout_impl(
weight.layout, diff.layout, op_def.param(), oup_layout);
wlayout, dlayout, op_def.param(), oup_layout);
}
DeviceTensorND oup =
BlobManager::inst()->alloc_workspace_with_defrag(cn, oup_layout);
SmallVector<megdnn::TensorND> inp_tensornds(inputs.size());
inp_tensornds[0] = inputs[0]->dnn_tensor();
inp_tensornds[1] = inputs[1]->dnn_tensor();
size_t wk_size = setup_algo<megdnn::Convolution3DBackwardData>(
{weight.layout, diff.layout, oup_layout}, dnn_opr.get(), 0, false, false,
cn, op_def.policy(), false);
{wlayout, dlayout, oup_layout}, dnn_opr.get(), 0, false, false, cn,
op_def.policy(), false, &inp_tensornds);
megdnn::Workspace dnn_wk;
if (wk_size != 0) {
TensorLayout w_layout({wk_size}, dtype::Byte());
dnn_wk = caller.create_workspace(w_layout);
}
dnn_opr->exec(weight, diff, oup.as_megdnn(), dnn_wk);
dnn_opr->exec(inp_tensornds[0], inp_tensornds[1], oup.as_megdnn(), dnn_wk);
return {Tensor::make(oup)};
}
......
imperative/src/impl/ops/matmul.cpp
浏览文件 @ 5bdc430e
......@@ -229,12 +229,11 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
inp_tensornds[0].layout = layout_a;
inp_tensornds[1].layout = layout_b;
}
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, dst_layout);
size_t sz = setup_algo<megdnn::MatrixMul>(
{layout_a, layout_b, dst_layout}, dnn_opr.op.get(), 0, false, false, cn,
matmul.policy(), false);
matmul.policy(), false, &inp_tensornds);
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, dst_layout);
TensorLayout w_layout({sz}, dtype::Byte());
auto dnn_wk = dnn_opr.create_workspace(w_layout);
......@@ -470,21 +469,22 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
return {Tensor::make(out)};
}
using TensorND = megdnn::TensorND;
TensorND inp_nd1 = inp1->dnn_tensor();
inp_nd1.layout = layout1;
TensorND inp_nd2 = inp2->dnn_tensor();
inp_nd2.layout = layout2;
SmallVector<megdnn::TensorND> inp_tensornds(2u);
inp_tensornds[0] = inp1->dnn_tensor();
inp_tensornds[0].layout = layout1;
inp_tensornds[1] = inp2->dnn_tensor();
inp_tensornds[1].layout = layout2;
size_t sz = setup_algo<megdnn::BatchedMatrixMul>(
{layout1, layout2, dst_layout}, dnn_opr.op.get(), 0, false, false, cn,
matmul.policy(), false, &inp_tensornds);
DeviceTensorND out =
BlobManager::inst()->alloc_workspace_with_defrag(cn, dst_layout);
size_t sz = setup_algo<megdnn::BatchedMatrixMul>(
{layout1, layout2, dst_layout}, dnn_opr.op.get(), 0, false, false, cn,
matmul.policy(), false);
TensorLayout w_layout({sz}, dtype::Byte());
auto dnn_wk = dnn_opr.create_workspace(w_layout);
dnn_opr.op->exec(inp_nd1, inp_nd2, out.as_megdnn(), dnn_wk);
dnn_opr.op->exec(inp_tensornds[0], inp_tensornds[1], out.as_megdnn(), dnn_wk);
shp1[shp1.ndim - 2] = dst_layout[dst_layout.ndim - 2];
shp1[shp1.ndim - 1] = dst_layout[dst_layout.ndim - 1];
......
imperative/src/impl/ops/pooling.cpp
浏览文件 @ 5bdc430e
......@@ -49,23 +49,25 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
auto&& op_def = def.cast_final_safe<Pooling>();
auto cn = inputs[0]->comp_node();
megdnn::TensorND inp_tensornd = inputs[0]->dnn_tensor();
DnnOprCaller<megdnn::Pooling> caller(cn);
auto&& dnn_opr = caller.op;
dnn_opr->param() = op_def.param();
SmallVector<megdnn::TensorND> inp_tensornds(inputs.size());
inp_tensornds[0] = inputs[0]->dnn_tensor();
TensorLayout& oup_layout = output_descs[0].layout;
if (!validated) {
megdnn::Pooling::deduce_layout_impl(
inp_tensornd.layout, op_def.param(), oup_layout);
inp_tensornds[0].layout, op_def.param(), oup_layout);
}
DeviceTensorND out_devtensor =
BlobManager::inst()->alloc_workspace_with_defrag(cn, oup_layout);
size_t wk_size = setup_algo<megdnn::Pooling>(
{inp_tensornd.layout, oup_layout}, dnn_opr.get(), 0, false, false, cn,
op_def.policy(), false);
{inp_tensornds[0].layout, oup_layout}, dnn_opr.get(), 0, false, false, cn,
op_def.policy(), false, &inp_tensornds);
DeviceTensorND out_devtensor =
BlobManager::inst()->alloc_workspace_with_defrag(cn, oup_layout);
megdnn::Workspace dnn_wk;
if (wk_size) {
......@@ -73,7 +75,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
dnn_wk = caller.create_workspace(w_layout);
}
dnn_opr->exec(inp_tensornd, out_devtensor.as_megdnn(), dnn_wk);
dnn_opr->exec(inp_tensornds[0], out_devtensor.as_megdnn(), dnn_wk);
return {Tensor::make(out_devtensor)};
}
......
src/rdnn/impl/algo_chooser.cpp
浏览文件 @ 5bdc430e
......@@ -265,7 +265,8 @@ std::vector<megdnn::Algorithm::SearchItem> flatten_search_space(
typename rdnn::AlgoChooser<_Opr>::AlgoChooserHelper sub_helper(
to_fixed_layouts<_Opr>(_item.layouts), megdnn_opr.get(),
_item.param, helper.comp_node(), helper.execution_policy(),
helper.allow_weight_preprocess(), helper.desc());
helper.allow_weight_preprocess(), helper.desc(),
helper.get_input());
auto space = flatten_search_space<_Opr>(sub_helper, checker);
ret.insert(ret.end(), space.begin(), space.end());
});
......@@ -488,7 +489,8 @@ AlgoChooser<Opr>::AlgoChooserHelper::AlgoChooserHelper(
const FixedTensorLayouts& layouts, Opr* megdnn_opr,
const std::string& param_str, const CompNode& cn,
const megdnn::param::ExecutionPolicy& execution_policy,
bool allow_weight_preprocess, const AlgoChooserDesc& desc)
bool allow_weight_preprocess, const AlgoChooserDesc& desc,
SmallVector<megdnn::TensorND>* inputs)
: m_fastrun_layouts{layouts},
m_incache_layouts{layouts},
m_dnn_opr{megdnn_opr},
......@@ -496,7 +498,8 @@ AlgoChooser<Opr>::AlgoChooserHelper::AlgoChooserHelper(
m_cn{cn},
m_execution_policy{execution_policy},
m_allow_weight_preprocess{allow_weight_preprocess},
m_desc{desc} {
m_desc{desc},
m_inputs{inputs} {
auto fastrun_batch_size = desc.shared_batch_size;
if (fastrun_batch_size) {
......@@ -604,7 +607,7 @@ typename AlgoChooser<Opr>::ImplExecutionPolicy AlgoChooser<Opr>::AlgoChooserHelp
typename AlgoChooser<_Opr>::AlgoChooserHelper sub_helper(
to_fixed_layouts<_Opr>(_item.layouts), megdnn_opr.get(),
_item.param, m_cn, m_execution_policy, m_allow_weight_preprocess,
m_desc);
m_desc, m_inputs);
sub_helper.profile(selected_strategy);
});
}
......@@ -868,6 +871,7 @@ Maybe<AlgoChooserProfileCache::ResultEntry> AlgoChooser<Opr>::AlgoChooserHelper:
param.shapes[i] = m_fastrun_layouts[i];
param.opr_param = m_dnn_opr->param();
param.allow_weight_preprocess = m_allow_weight_preprocess;
param.inp_tensornds = m_inputs;
Algorithm* palgo = m_dnn_opr->get_algorithm_from_desc(policy.algo);
mgb_assert(palgo, "can not find algo when profile single algo");
......@@ -964,7 +968,9 @@ void AlgoChooser<Opr>::AlgoChooserHelper::profile(
if (!policy.algo.valid())
continue;
size_t workspace_needed = get_workspace_size_bytes(policy);
if (data_size + workspace_needed >
if (m_inputs != nullptr)
workspace_needed += data_size;
if (workspace_needed >
m_desc.get_workspace_limit(m_cn, m_execution_policy.workspace_limit)) {
continue;
}
......@@ -1101,7 +1107,8 @@ std::pair<AlgoAttribute, AlgoAttribute> AlgoChooser<Opr>::AlgoChooserHelper::
const FixedTensorLayouts& layouts, megdnn::Opr* megdnn_opr, \
const std::string& param_str, const CompNode& cn, \
const megdnn::param::ExecutionPolicy& execution_policy, \
bool allow_weight_preprocess, const AlgoChooserDesc& desc); \
bool allow_weight_preprocess, const AlgoChooserDesc& desc, \
SmallVector<megdnn::TensorND>* inputs); \
template typename AlgoChooser<megdnn::Opr>::ImplExecutionPolicy \
AlgoChooser<megdnn::Opr>::AlgoChooserHelper::choose_by_heuristic( \
const ExecutionStrategy& select_strategy) const; \
......
src/rdnn/impl/profiler.cpp
浏览文件 @ 5bdc430e
......@@ -143,7 +143,7 @@ template <typename Opr>
void TimedProfiler<Opr>::preprocess(
const TensorLayoutArray&, const megdnn::SmallVector<DeviceTensorND>&,
UniqPtrWithCN<Opr>&, megdnn::Workspace&, std::array<TensorLayout, arity>&,
std::array<DeviceTensorND, arity_in>&, PreprocessFilter<Opr>&) {
std::array<megdnn::TensorND, arity_in>&, PreprocessFilter<Opr>&) {
// Opr is neither convbias nor convolution.This function do nothing.
}
......@@ -154,7 +154,7 @@ void TimedProfiler<megdnn::ConvBias>::preprocess(
const SmallVector<DeviceTensorND>& flt_val,
UniqPtrWithCN<megdnn::ConvBias>& megdnn_opr, megdnn::Workspace& mdn_workspace,
std::array<TensorLayout, arity>& layouts,
std::array<DeviceTensorND, arity_in>& inp_val,
std::array<megdnn::TensorND, arity_in>& inp_val,
PreprocessFilter<megdnn::ConvBias>& prep_flt) {
if (!preprocessed_layout.empty()) {
auto&& pf = prep_flt;
......@@ -164,8 +164,7 @@ void TimedProfiler<megdnn::ConvBias>::preprocess(
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(), inp_val[2].as_megdnn()),
std::forward_as_tuple(layouts[0], inp_val[1], inp_val[2]),
array_skip<arity_in - 1>(layouts));
}
}
......@@ -177,7 +176,7 @@ void TimedProfiler<megdnn::ConvolutionForward>::preprocess(
const megdnn::SmallVector<DeviceTensorND>& flt_val,
UniqPtrWithCN<megdnn::ConvolutionForward>& megdnn_opr,
megdnn::Workspace& mdn_workspace, std::array<TensorLayout, arity>& layouts,
std::array<DeviceTensorND, arity_in>& inp_val,
std::array<megdnn::TensorND, arity_in>& inp_val,
PreprocessFilter<megdnn::ConvolutionForward>& prep_flt) {
if (!preprocessed_layout.empty()) {
auto&& pf = prep_flt;
......@@ -187,8 +186,7 @@ void TimedProfiler<megdnn::ConvolutionForward>::preprocess(
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));
std::forward_as_tuple(layouts[0], inp_val[1]), array_skip<2>(layouts));
}
}
......@@ -259,8 +257,12 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
std::max(cn.get_free_mem(), cn.get_max_block_size_available());
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 (size_t i = 0; i < arity; ++i) {
// if input tensornds are given, only consider output tensornds
if (param.inp_tensornds != nullptr) {
if (i >= (*param.inp_tensornds).size())
tot_size += layouts[i].span().high_byte + align;
}
}
for (const auto& layout : preprocessed_layout) {
tot_size += layout.span().high_byte + align;
......@@ -275,20 +277,34 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
#endif
// allocate input and output memory
std::array<DeviceTensorND, arity_in> inp_val;
std::array<DeviceTensorND, arity_out> out_val;
std::array<DeviceTensorND, arity_in> inp_dev;
std::array<DeviceTensorND, arity_out> out_dev;
std::array<megdnn::TensorND, arity_in> inp_val;
std::array<megdnn::TensorND, 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]);
if (param.inp_tensornds != nullptr) {
// if inp_tensornds exists, then reusing it
for (int i = 0; i < arity_in; ++i) {
inp_val[i] = (*param.inp_tensornds)[i];
}
} else {
// inp_tensornds does not exist, create zero tensor with the same layout
for (int i = 0; i < arity_in; ++i) {
inp_dev[i].comp_node(cn).dtype(layouts[i].dtype).resize(layouts[i]);
fill_zero_dev_tensor(inp_dev[i]);
inp_val[i] = inp_dev[i].as_megdnn();
}
}
for (int i = 0; i < arity_out; ++i) {
out_val[i]
out_dev[i]
.comp_node(cn)
.dtype(layouts[arity_in + i].dtype)
.resize(layouts[arity_in + i]);
out_val[i] = out_dev[i].as_megdnn();
}
megdnn::Workspace mdn_workspace;
megdnn::Workspace mdn_workspace;
// allocate workspace
if (param.workspace) {
workspace.comp_node(cn).dtype(dtype::Byte()).resize({param.workspace});
......@@ -304,10 +320,6 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
preprocessed_layout[i].format};
}
for (int i = 0; i < arity_in; ++i) {
fill_zero_dev_tensor(inp_val[i]);
}
PreprocessFilter<Opr> prep_flt;
preprocess(
preprocessed_layout, flt_val, megdnn_opr, mdn_workspace, layouts, inp_val,
......@@ -322,13 +334,12 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
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);
APPLY(opr->exec(args..., pf, mdn_workspace), inp_val, out_val);
},
/* else */
[&](auto _) {
APPLY(_(megdnn_opr)->exec(args.as_megdnn()..., mdn_workspace),
inp_val, out_val);
APPLY(_(megdnn_opr)->exec(args..., mdn_workspace), inp_val,
out_val);
});
}
ev_start->record();
......@@ -337,13 +348,11 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
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);
APPLY(opr->exec(args..., pf, mdn_workspace), inp_val, out_val);
},
/* else */
[&](auto _) {
APPLY(_(megdnn_opr)->exec(args.as_megdnn()..., mdn_workspace), inp_val,
out_val);
APPLY(_(megdnn_opr)->exec(args..., mdn_workspace), inp_val, out_val);
});
ev_end->record();
......@@ -370,10 +379,10 @@ typename TimedProfiler<Opr>::TResult TimedProfiler<Opr>::prof_impl(
DeviceTensorStorage storage;
for (int i = 0; i < arity_in; ++i) {
inp_val[i].reset(storage, TensorLayout{});
inp_dev[i].reset(storage, TensorLayout{});
}
for (int i = 0; i < arity_out; ++i) {
out_val[i].reset(storage, TensorLayout{});
out_dev[i].reset(storage, TensorLayout{});
}
for (size_t i = 0; i < preprocessed_layout.size(); i++) {
flt_val[i].reset(storage, TensorLayout{});
......
src/rdnn/include/megbrain/rdnn/algo_chooser.h
浏览文件 @ 5bdc430e
......@@ -60,13 +60,15 @@ public:
megdnn::param::ExecutionPolicy m_execution_policy;
bool m_allow_weight_preprocess;
const AlgoChooserDesc& m_desc;
SmallVector<megdnn::TensorND>* m_inputs;
public:
MGE_WIN_DECLSPEC_FUC AlgoChooserHelper(
const FixedTensorLayouts& layouts, Opr* megdnn_opr,
const std::string& param_str, const CompNode& cn,
const megdnn::param::ExecutionPolicy& execution_policy,
bool allow_weight_preprocess, const AlgoChooserDesc& desc);
bool allow_weight_preprocess, const AlgoChooserDesc& desc,
SmallVector<megdnn::TensorND>* inputs = nullptr);
Opr* megdnn_opr() const { return m_dnn_opr; }
......@@ -93,6 +95,8 @@ public:
const AlgoChooserDesc& desc() const { return m_desc; }
SmallVector<megdnn::TensorND>* get_input() const { return m_inputs; }
//! construct algo chain by heuristic
ImplExecutionPolicy choose_by_heuristic(
const ExecutionStrategy& selected_strategy) const;
......
src/rdnn/include/megbrain/rdnn/profiler.h
浏览文件 @ 5bdc430e
......@@ -122,6 +122,8 @@ public:
//! filled by profile()
mutable double actual_timeout;
// input
SmallVector<megdnn::TensorND>* inp_tensornds;
};
struct Result {
......@@ -141,7 +143,7 @@ private:
const megdnn::TensorLayoutArray& preprocessed_layout,
const SmallVector<DeviceTensorND>& flt_val, UniqPtrWithCN<Opr>& megdnn_opr,
megdnn::Workspace& mdn_workspace, std::array<TensorLayout, arity>& layouts,
std::array<DeviceTensorND, arity_in>& inp_val,
std::array<megdnn::TensorND, arity_in>& inp_val,
PreprocessFilter<Opr>& prep_flt);
static TResult prof_impl(const TParam& raw_param);
static void prof_init_device(const TParam& raw_param);
......
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