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fix(mgb): fix spell error

GitOrigin-RevId: acae00e0a5
release-1.3
Megvii Engine Team 4 years ago
parent
commit
36fbd5a65a
8 changed files with 14 additions and 13 deletions
  1. +1
    -1
      dnn/src/x86/conv_bias/opr_impl.cpp
  2. +1
    -1
      imperative/python/megengine/functional/debug_param.py
  3. +1
    -1
      imperative/python/src/graph_rt.cpp
  4. +1
    -1
      sdk/load-and-run/src/mgblar.cpp
  5. +2
    -2
      src/gopt/test/inference.cpp
  6. +1
    -1
      src/opr/impl/search_policy/algo_chooser.cpp
  7. +5
    -5
      src/opr/test/dnn/convolution.cpp
  8. +2
    -1
      tools/param_defs/mgb_opr_param_defs.py

+ 1
- 1
dnn/src/x86/conv_bias/opr_impl.cpp View File

@@ -185,7 +185,7 @@ SmallVector<AlgoCategory> ConvBiasImpl::suggest_algo_category_order(
}
//! conv1x1
im2col_prefer |= (FH == 1 && FW == 1);
//! x86 8x8x16 not optmized, so it will use fallback im2col+matmul
//! x86 8x8x16 not optimized, so it will use fallback im2col+matmul
if (param.deduce_algo_data_type() == AlgoDataType::INT8X8X16) {
im2col_prefer = true;
}


+ 1
- 1
imperative/python/megengine/functional/debug_param.py View File

@@ -40,7 +40,7 @@ def set_execution_strategy(option):
* HEURISTIC uses heuristic to choose the fastest algorithm.
* PROFILE runs possible algorithms on real device to find the best one.
* REPRODUCIBLE uses the algorithms that is reproducible.
* OPTMIZED uses the algorithms that is optimized.
* OPTIMIZED uses the algorithms that is optimized.

The default strategy is HEURISTIC, this options can be combined to
form a combination option, e.g. PROFILE | REPRODUCIBLE


+ 1
- 1
imperative/python/src/graph_rt.cpp View File

@@ -266,7 +266,7 @@ void init_graph_rt(py::module m) {
{"HEURISTIC", [&]() { stg = _AlgoStrategy::HEURISTIC; }},
{"PROFILE", [&]() { stg = _AlgoStrategy::PROFILE; }},
{"REPRODUCIBLE", [&]() { stg = _AlgoStrategy::REPRODUCIBLE; }},
{"OPTMIZED", [&]() { stg = _AlgoStrategy::OPTMIZED; }},
{"OPTIMIZED", [&]() { stg = _AlgoStrategy::OPTIMIZED; }},
};
auto it = m.find(strategy);
mgb_assert(it != m.end(), "Invalid strategy string!");


+ 1
- 1
sdk/load-and-run/src/mgblar.cpp View File

@@ -709,7 +709,7 @@ void run_test_st(Args &env) {
strategy = S::PROFILE;
}
} else if (env.use_fast_run) {
strategy = S::PROFILE | S::OPTMIZED;
strategy = S::PROFILE | S::OPTIMIZED;
} else if (env.reproducible) {
strategy = S::HEURISTIC | S::REPRODUCIBLE;
}


+ 2
- 2
src/gopt/test/inference.cpp View File

@@ -1756,8 +1756,8 @@ TEST(TestGoptInference, FastProfileCache) {
using S = opr::Convolution::ExecutionPolicy::Strategy;
ASSERT_EQ(S::HEURISTIC, conv.execution_policy_transient().strategy);
gopt::modify_opr_algo_strategy_inplace({z + 2.3f},
S::PROFILE | S::OPTMIZED);
ASSERT_EQ(S::PROFILE | S::OPTMIZED, conv.execution_policy().strategy);
S::PROFILE | S::OPTIMIZED);
ASSERT_EQ(S::PROFILE | S::OPTIMIZED, conv.execution_policy().strategy);
}

TEST(TestGoptInference, AlgoWorkspaceLimit) {


+ 1
- 1
src/opr/impl/search_policy/algo_chooser.cpp View File

@@ -283,7 +283,7 @@ std::vector<megdnn::Algorithm::SearchItem> flatten_search_space(
static bool algo_attribute_match_strategy(AlgoAttribute attribute,
ExecutionStrategy selected_strategy) {
bool ret = true;
if (selected_strategy & ExecutionStrategy::OPTMIZED) {
if (selected_strategy & ExecutionStrategy::OPTIMIZED) {
ret &= (!static_cast<bool>(AlgoAttribute::NAIVE & attribute));
} else if (selected_strategy & ExecutionStrategy::REPRODUCIBLE) {
ret &= static_cast<bool>(AlgoAttribute::REPRODUCIBLE & attribute);


+ 5
- 5
src/opr/test/dnn/convolution.cpp View File

@@ -357,7 +357,7 @@ TEST(TestOprDNN, ConvBiasExePolicy) {
#if MGB_ENABLE_FASTRUN
for (auto strategy :
SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
#else
for (auto strategy :
SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -444,7 +444,7 @@ TEST(TestOprDNN, ConvolutionExePolicy) {
#if MGB_ENABLE_FASTRUN
for (auto strategy :
SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
#else
for (auto strategy :
SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1717,7 +1717,7 @@ TEST(TestOprDNN, LocalShareForwardExecPolicy) {
#if MGB_ENABLE_FASTRUN
for (auto strategy :
SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
#else
for (auto strategy :
SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1828,7 +1828,7 @@ TEST(TestOprDNN, DeformableConvForward) {
#if MGB_ENABLE_FASTRUN
for (auto strategy :
SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
#else
for (auto strategy :
SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1997,7 +1997,7 @@ TEST(TestOprDNN, BatchConvBiasForward) {
#if MGB_ENABLE_FASTRUN
for (auto strategy :
SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
#else
for (auto strategy :
SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {


+ 2
- 1
tools/param_defs/mgb_opr_param_defs.py View File

@@ -41,12 +41,13 @@ pdef('PersistentOutputStorage').add_fields(
Doc('REPRODUCIBLE',
'when profile or heuristic algo selection it require the algos'
'must be reproducible'),
Doc('OPTMIZED',
Doc('OPTIMIZED',
'profile require algos are optmized to achieve fast-profile'),
default=('HEURISTIC',),
member_alias=[(('HEURISTIC', 'REPRODUCIBLE'), 'HEURISTIC_REPRODUCIBLE'),
(('PROFILE', 'REPRODUCIBLE'), 'PROFILE_REPRODUCIBLE'),
(('PROFILE', 'HEURISTIC'), 'PROFILE_HEURISTIC'),
(('OPTIMIZED',), 'OPTMIZED'),
]).
add_fields('uint64',
Doc('workspace_limit', 'workspace limit in bytes'),


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