@@ -392,6 +392,13 @@ struct PostProcess<ctype, dtype, megdnn::PostprocessMode::ADD_BIAS> { | |||
MIDOUT_END(); \ | |||
break; \ | |||
} \ | |||
case param::ConvBias::NonlineMode::H_SWISH: { \ | |||
MIDOUT_BEGIN(_midout_tag, _bias_id, 1) { \ | |||
cb(_bmode, HSwishOp<_src_type MEGDNN_COMMA _dst_type>, __VA_ARGS__); \ | |||
} \ | |||
MIDOUT_END(); \ | |||
break; \ | |||
} \ | |||
default: \ | |||
megdnn_assert(0); \ | |||
break; \ | |||
@@ -137,6 +137,12 @@ struct HSwishOp<dt_qint32, dt_qint8> : HSwishOpBase<dt_qint32, dt_qint8> { | |||
return QConverter::convert<int8x8_t, float32x4_t>(vitem0); | |||
} | |||
int8x8_t operator()(const float32x4_t& src) const { | |||
auto vitem0 = vmulq_f32(src, this->vscale_src); | |||
H_SWISH_KERN_N1(f32, vitem0); | |||
vitem0 = vmulq_f32(vitem0, this->vscale_dst); | |||
return QConverter::convert<int8x8_t, float32x4_t>(vitem0); | |||
} | |||
}; | |||
template <> | |||
@@ -421,7 +421,7 @@ std::vector<TestArg> get_int8_nchw44_args( | |||
using NLMode = param::ConvBias::NonlineMode; | |||
// clang-format off | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU}) { | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU, NLMode::H_SWISH}) { | |||
for (auto mode : {param::ConvBias::Mode::CROSS_CORRELATION}) { | |||
for (size_t b : {1,2}) { | |||
for (size_t ic : {8,16}) { | |||
@@ -542,7 +542,7 @@ std::vector<TestArg> get_int8_nchw4_args_small_batch(size_t kernel_size) { | |||
using NLMode = param::ConvBias::NonlineMode; | |||
// clang-format off | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU}) { | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU, NLMode::H_SWISH}) { | |||
for (auto mode : {param::ConvBias::Mode::CROSS_CORRELATION}) { | |||
for (size_t b : {12, 8, 4}) { | |||
for (size_t ic : {16, 32}) { | |||
@@ -577,7 +577,7 @@ std::vector<TestArg> get_int8_nchw4_small_channel_args(size_t kernel_size) { | |||
using NLMode = param::ConvBias::NonlineMode; | |||
// clang-format off | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU}) { | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU, NLMode::H_SWISH}) { | |||
for (auto mode : {param::ConvBias::Mode::CROSS_CORRELATION}) { | |||
for (size_t b : {64, 16}) { | |||
for (size_t ic : {4, 12}) { | |||
@@ -696,7 +696,7 @@ std::vector<TestArg> get_int8_nchw4_tensorcore_args(size_t kernel_size) { | |||
using NLMode = param::ConvBias::NonlineMode; | |||
// clang-format off | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU}) { | |||
for (auto nlmode : {NLMode::IDENTITY, NLMode::RELU, NLMode::H_SWISH}) { | |||
for (auto mode : {param::ConvBias::Mode::CROSS_CORRELATION}) { | |||
size_t b = 64, oc = 128; | |||
for (size_t ic : {32, 64}) { | |||
@@ -1291,7 +1291,8 @@ TEST_F(CUDA, CONV_BIAS_FORWARD_TENSORCORE_INT8) { | |||
param.format = ConvBias::Param::Format::NCHW32; | |||
using NonlineMode = ConvBias::Param::NonlineMode; | |||
for (NonlineMode mode : {NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
for (NonlineMode mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, NonlineMode::H_SWISH}) { | |||
for (size_t batch : {2}) { | |||
for (size_t ic : {64, 32}) { | |||
for (size_t oc : {32}) { | |||
@@ -1083,7 +1083,8 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32) { | |||
for (size_t p : {0, 2}) | |||
for (size_t size : {8, 24}) | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, | |||
NonlineMode::H_SWISH}) { | |||
run(oc, ic, size, size, kernel, p, nonline_mode); | |||
} | |||
@@ -1185,7 +1186,8 @@ TEST_F(X86, CONV_BIAS_IM2COLMATMUL_FP32_RECORD) { | |||
1, oc, (h + 2 * p - kernel) / param.stride_h + 1, | |||
(w + 2 * p - kernel) / param.stride_w + 1}); | |||
}; | |||
for (NonlineMode nonline_mode : {NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, NonlineMode::H_SWISH}) { | |||
run(1, 1, 24, 24, 2, 2, nonline_mode); | |||
} | |||
@@ -1230,7 +1232,8 @@ TEST_F(X86, CONV_BIAS_IM2COLMATMUL_FP32_NOPACK_PREPROCESS) { | |||
for (size_t p : {0, 2}) | |||
for (size_t size : {8, 24}) | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, | |||
NonlineMode::H_SWISH}) { | |||
run(oc, ic, size, size, kernel, p, nonline_mode); | |||
} | |||
@@ -1285,7 +1288,8 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32_6x16) { | |||
for (size_t p : {0, 2}) | |||
for (size_t size : {8, 24}) | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, | |||
NonlineMode::H_SWISH}) { | |||
run(oc, ic, size, size, kernel, p, nonline_mode); | |||
} | |||
@@ -1351,7 +1355,8 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32_PACKA) { | |||
for (size_t p : {0, 1}) | |||
for (size_t size : {8, 24}) | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, | |||
NonlineMode::H_SWISH}) { | |||
run(oc, ic, size, size, kernel, p, nonline_mode); | |||
} | |||
@@ -1418,7 +1423,8 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32_PACKA_FILTER_PREPROCESS) { | |||
for (size_t p : {0, 1}) | |||
for (size_t size : {8, 24}) | |||
for (NonlineMode nonline_mode : | |||
{NonlineMode::IDENTITY, NonlineMode::RELU}) { | |||
{NonlineMode::IDENTITY, NonlineMode::RELU, | |||
NonlineMode::H_SWISH}) { | |||
run(oc, ic, size, size, kernel, p, nonline_mode); | |||
} | |||
@@ -1827,6 +1827,10 @@ void FuseConvBiasNonlinPass::apply(OptState& state) const { | |||
elem->param().mode == Mode::FUSE_ADD_SIGMOID || | |||
elem->param().mode == Mode::SIGMOID) { | |||
return NonlineMode::SIGMOID; | |||
} else if ( | |||
elem->param().mode == Mode::FUSE_ADD_H_SWISH || | |||
elem->param().mode == Mode::H_SWISH) { | |||
return NonlineMode::H_SWISH; | |||
} else { | |||
return NonlineMode::IDENTITY; | |||
} | |||
@@ -1836,8 +1840,8 @@ void FuseConvBiasNonlinPass::apply(OptState& state) const { | |||
bool can_be_fused = true; | |||
can_be_fused &= (elem->input().size() == 2); | |||
can_be_fused &= (elem->param().mode == Mode::FUSE_ADD_RELU) || | |||
(elem->param().mode == Mode::FUSE_ADD_TANH) || | |||
(elem->param().mode == Mode::FUSE_ADD_SIGMOID); | |||
(elem->param().mode == Mode::FUSE_ADD_SIGMOID) || | |||
(elem->param().mode == Mode::FUSE_ADD_H_SWISH); | |||
return can_be_fused; | |||
}; | |||
@@ -1853,7 +1857,8 @@ void FuseConvBiasNonlinPass::apply(OptState& state) const { | |||
bool can_be_fused = true; | |||
can_be_fused &= (elem->input().size() == 1); | |||
can_be_fused &= (elem->param().mode == Mode::RELU) || | |||
(elem->param().mode == Mode::SIGMOID); | |||
(elem->param().mode == Mode::SIGMOID) || | |||
(elem->param().mode == Mode::H_SWISH); | |||
return can_be_fused; | |||
}; | |||
@@ -1937,6 +1937,52 @@ TEST(TestGoptInference, ConvBiasNonlinearityFusePass2) { | |||
MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-4); | |||
} | |||
TEST(TestGoptInference, ConvBiasNonlinearityFusePassHswish) { | |||
// hwcd4 is only supported in naive handle | |||
NaiveMegDNNHandleScope naive_megdnn_handle; | |||
auto cn = CompNode::load("cpu0"); | |||
HostTensorGenerator<> gen; | |||
auto graph = ComputingGraph::make(); | |||
graph->options().graph_opt_level = 0; | |||
auto mkvar = [&](const char* name, const TensorShape& shp) { | |||
return opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name); | |||
}; | |||
auto mkcvar = [&](const char* name, const TensorShape& shp) { | |||
return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name); | |||
}; | |||
opr::Convolution::Param param; | |||
auto x = mkvar("x", {5, 8, 16, 24}), w1 = mkcvar("w1", {4, 8, 1, 1}), | |||
w2 = mkcvar("w2", {4, 8, 1, 1}); | |||
auto b1 = mkcvar("b1", {1, 4, 1, 1}); | |||
auto y_cut = opr::Convolution::make(x, w1, param); | |||
auto y = opr::Elemwise::make({y_cut + b1}, opr::Elemwise::Param::Mode::H_SWISH); | |||
y = opr::Elemwise::make({y}, opr::Elemwise::Param::Mode::RELU); | |||
auto y_cut2 = opr::Convolution::make(x, w2, param); | |||
y_cut2 = opr::Elemwise::make({y_cut2}, opr::Elemwise::Param::Mode::H_SWISH); | |||
y_cut2 = opr::Elemwise::make({y_cut2}, opr::Elemwise::Param::Mode::RELU); | |||
y = y + y_cut2; | |||
SymbolVar y_opt; | |||
auto options = gopt::OptimizeForInferenceOptions{}; | |||
options.enable_nhwcd4().enable_fuse_conv_bias_nonlinearity(); | |||
unpack_vector(gopt::optimize_for_inference({y}, options), y_opt); | |||
ASSERT_EQ( | |||
opr::ConvBias::Param::NonlineMode::H_SWISH, | |||
find_opr<opr::ConvBias>(y_opt).param().nonlineMode); | |||
graph->compile({{y_opt, {}}}) | |||
->to_json() | |||
->writeto_fpath( | |||
output_file("TestGoptInference.FuseConvBiasNonlinPassHswish.json")); | |||
HostTensorND host_y, host_y_opt; | |||
auto func = graph->compile( | |||
{make_callback_copy(y, host_y), make_callback_copy(y_opt, host_y_opt)}); | |||
func->execute(); | |||
MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-4); | |||
} | |||
TEST(TestGoptInference, ConvBiasNonlinearityFusePass_FullBias) { | |||
NaiveMegDNNHandleScope naive_megdnn_handle; | |||