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[MSLITE] Add implementation for intReduceSum and fix bug of tanh

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pull/11979/head
wang_shaocong 4 years ago
parent 4ca4a148eb
commit cd07228ce9
15 changed files with 80 additions and 15 deletions
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6
mindspore/lite/nnacl/fp16/activation_fp16.c
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@ -99,6 +99,8 @@ int TanhFp16(const float16_t *src, float16_t *dst, int ele_num) {
{28.0f, 28.0f, 28.0f, 28.0f},
{3150.0f, 3150.0f, 3150.0f, 3150.0f},
{62370.0f, 62370.0f, 62370.0f, 62370.0f}};
float32x4_t neg_one = {-1.0f, -1.0f, -1.0f, -1.0f};
float32x4_t pos_one = {1.0f, 1.0f, 1.0f, 1.0f};
int count = (ele_num / C4NUM) * C4NUM;
for (; i < count; i += C4NUM) {
float32x4_t input = vcvt_f32_f16(vld1_f16(src + i));
@ -109,7 +111,7 @@ int TanhFp16(const float16_t *src, float16_t *dst, int ele_num) {
float32x4_t b = vaddq_f32(
vmulq_f32(vaddq_f32(vmulq_f32(vaddq_f32(vmulq_f32(paramv[3], square), paramv[4]), square), paramv[5]), square),
paramv[2]);
vst1_f16(dst + i, vcvt_f16_f32(vdivq_f32(a, b)));
vst1_f16(dst + i, vcvt_f16_f32(vminq_f32(vmaxq_f32(vdivq_f32(a, b), neg_one), pos_one)));
}
#endif
for (; i < ele_num; ++i) {
@ -118,6 +120,8 @@ int TanhFp16(const float16_t *src, float16_t *dst, int ele_num) {
float a = (((square + 378.0f) * square + 17325.0f) * square + 135135.0f) * input;
float b = ((28.0f * square + 3150.0f) * square + 62370.0f) * square + 135135.0f;
dst[i] = a / b;
dst[i] = MSMAX(dst[i], -1);
dst[i] = MSMIN(dst[i], 1);
}
return NNACL_OK;
}

6
mindspore/lite/nnacl/fp32/activation_fp32.c
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@ -109,6 +109,8 @@ int Tanh(const float *src, int length, float *dst) {
{28.0f, 28.0f, 28.0f, 28.0f},
{3150.0f, 3150.0f, 3150.0f, 3150.0f},
{62370.0f, 62370.0f, 62370.0f, 62370.0f}};
float32x4_t neg_one = {-1.0f, -1.0f, -1.0f, -1.0f};
float32x4_t pos_one = {1.0f, 1.0f, 1.0f, 1.0f};
int count = (length / C4NUM) * C4NUM;
for (; i < count; i += C4NUM) {
float32x4_t input = vld1q_f32(src + i);
@ -119,7 +121,7 @@ int Tanh(const float *src, int length, float *dst) {
float32x4_t b = vaddq_f32(
vmulq_f32(vaddq_f32(vmulq_f32(vaddq_f32(vmulq_f32(paramv[3], square), paramv[4]), square), paramv[5]), square),
paramv[2]);
vst1q_f32(dst + i, vdivq_f32(a, b));
vst1q_f32(dst + i, vminq_f32(vmaxq_f32(vdivq_f32(a, b), neg_one), pos_one));
}
#endif
for (; i < length; ++i) {
@ -128,6 +130,8 @@ int Tanh(const float *src, int length, float *dst) {
float a = (((square + 378.0f) * square + 17325.0f) * square + 135135.0f) * input;
float b = ((28.0f * square + 3150.0f) * square + 62370.0f) * square + 135135.0f;
dst[i] = a / b;
dst[i] = MSMAX(dst[i], -1);
dst[i] = MSMIN(dst[i], 1);
}
return NNACL_OK;
}

37
mindspore/lite/nnacl/fp32/reduce_fp32.c
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@ -81,6 +81,43 @@ int ReduceSum(int outer_size, int inner_size, int axis_size, const float *src_da
}
return NNACL_OK;
}
int IntReduceSum(int outer_size, int inner_size, int axis_size, const int *src_data, int *dst_data, int tid,
int thread_num) {
if (src_data == NULL || dst_data == NULL) {
return NNACL_NULL_PTR;
}
int i, j;
#ifdef ENABLE_NEON
int block_mod = inner_size % C4NUM;
int block_c4 = inner_size - block_mod;
#endif
for (j = tid; j < outer_size; j += thread_num) {
const int *outer_src = src_data + j * axis_size * inner_size;
int *outer_dst = dst_data + j * inner_size;
int k = 0;
#ifdef ENABLE_NEON
for (; k < block_c4; k += C4NUM) {
const int *inner_src = outer_src + k;
int *inner_dst = outer_dst + k;
int32x4_t tmp = {0, 0, 0, 0};
for (i = 0; i < axis_size; i++) {
tmp = vaddq_s32(tmp, vld1q_s32(inner_src + i * inner_size));
}
vst1q_s32(inner_dst, tmp);
}
#endif
for (; k < inner_size; k++) {
const int *inner_src = outer_src + k;
int *inner_dst = outer_dst + k;
int tmp = 0;
for (i = 0; i < axis_size; i++) {
tmp += inner_src[i * inner_size];
}
*inner_dst = tmp;
}
}
return NNACL_OK;
}
int ReduceMax(int outer_size, int inner_size, int axis_size, const float *src_data, float *dst_data, int tid,
int thread_num) {
if (src_data == NULL || dst_data == NULL) {

2
mindspore/lite/nnacl/fp32/reduce_fp32.h
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@ -26,6 +26,8 @@ int ReduceMean(int outer_size, int inner_size, int axis_size, const float *src_d
int thread_num);
int ReduceSum(int outer_size, int inner_size, int axis_size, const float *src_data, float *dst_data, int tid,
int thread_num);
int IntReduceSum(int outer_size, int inner_size, int axis_size, const int *src_data, int *dst_data, int tid,
int thread_num);
int ReduceMax(int outer_size, int inner_size, int axis_size, const float *src_data, float *dst_data, int tid,
int thread_num);
int IntReduceMax(int outer_size, int inner_size, int axis_size, const int *src_data, int *dst_data, int tid,

1
mindspore/lite/src/runtime/kernel/arm/fp32/reduce_fp32.cc
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@ -50,6 +50,7 @@ int ReduceCPUKernel::Init() {
switch (mode_) {
case static_cast<int>(ReduceMode_ReduceSum): {
reducer_ = ReduceSum;
int_reducer_ = IntReduceSum;
break;
}
case static_cast<int>(ReduceMode_ReduceMean): {

4
mindspore/lite/src/runtime/kernel/npu/convolution_npu.cc
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@ -23,10 +23,6 @@ using mindspore::schema::PrimitiveType_Conv2D;
namespace mindspore::kernel {
int ConvolutionNPUKernel::IsSupport(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter) {
if (conv_param_->group_ != 1) {
MS_LOG(WARNING) << "Only support group equals 1 for npu convolution op";
return RET_ERROR;
}
return RET_OK;
}

3
mindspore/lite/src/runtime/kernel/npu/pooling_npu.cc
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@ -56,10 +56,11 @@ int PoolingNPUKernel::SetPoolingParam() {
if (pooling_param_->round_mode_ == RoundMode_Floor) { // no use in cpu
pooling_->set_attr_ceil_mode(0);
pooling_->set_attr_data_mode(1);
} else {
pooling_->set_attr_ceil_mode(1);
pooling_->set_attr_data_mode(0);
}
// todo data mode
return RET_OK;
}

1
mindspore/lite/test/models_caffe.cfg
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@ -73,3 +73,4 @@ ml_video_edit_video_segment_gauss_adaptis_part1
ml_video_edit_Mnet
ml_video_edit_hairSeg_have_imageProcessLayer_interpTo145
ml_video_edit_person_divison_video
hdc_Face_Aesthetic_MTI_Aesthetic

9
mindspore/lite/test/models_npu.cfg
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@ -41,3 +41,12 @@ ml_video_edit_img_segment_adaptise.pb 0.5 2
ml_video_edit_video_segment_gauss_adaptis_part2.pb 3 2
ml_video_edit_person_divison_pic 8 2
ml_video_edit_person_divison_video 0.5
ml_video_edit_imitate_filter.onnx 230
ml_video_edit_judge.onnx 5
ml_video_edit_vignet.onnx 0.5
hdc_Face_Aesthetic_MTI_Aesthetic 0.5
hdc_Face_Emotion_MTI_Aesthetic.onnx 30
hdc_Face_Landmark5_MTI_Aesthetic.onnx 0.5
hdc_Image_Aesthetic_MTI_Aesthetic.onnx 0.5
hdc_mobilenet_1w_class.onnx 10
hdc_resnet_1w_class.onnx 5

6
mindspore/lite/test/models_onnx.cfg
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@ -45,3 +45,9 @@ ml_video_edit_style_transfer_starry.onnx
ml_video_edit_judge.onnx
ml_video_edit_vignet.onnx
ssd_mobilenet_v1_10.onnx;1,383,640,3
hdc_Face_Emotion_MTI_Aesthetic.onnx
hdc_Face_Landmark5_MTI_Aesthetic.onnx
hdc_Image_Aesthetic_MTI_Aesthetic.onnx
hdc_mobilenet_1w_class.onnx
hdc_resnet_1w_class.onnx
ml_video_edit_imitate_filter.onnx

2
mindspore/lite/test/models_onnx_fp16.cfg
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@ -26,7 +26,7 @@ crnn_lite_lstm_v2.onnx;32,32,32,1 0.3
psenet_lite_mbv2.onnx;1,32,32,3 0.6
super-resolution-10.onnx;1,224,224,1 4.5
tinyyolov2-8.onnx;1,416,416,3 5.5
ml_2012_ocr_cn.onnx 200
ml_2012_ocr_cn.onnx -1
#ml_2012_ocr_cn_noLSTM.onnx 1
candy-9.onnx 5
mosaic-9.onnx 4

1
mindspore/lite/test/models_with_multiple_inputs.cfg
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@ -10,3 +10,4 @@ ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2
decoder.onnx;2;1,7,512:1,7
fasterrcnn_crop.pb;1;420,630,3
ml_video_edit_person_divison_pic;2
hdc_tb_cn_neg.tflite;3

7
mindspore/lite/test/run_benchmark_nets.sh
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@ -1547,8 +1547,11 @@ function Run_arm64() {
echo 'cd /data/local/tmp/benchmark_test' > adb_run_cmd.txt
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/benchmark_test' >> adb_run_cmd.txt
echo './benchmark --modelFile='${model_name}'.fp16.ms --inDataFile=/data/local/tmp/input_output/input/'${model_name}'.ms.bin --benchmarkDataFile=/data/local/tmp/input_output/output/'${model_name}'.ms.out --enableFp16=true --accuracyThreshold='${accuracy_limit} ' --inputShapes='${input_shapes} >> adb_run_cmd.txt
if [[ $accuracy_limit == "-1" ]]; then
echo './benchmark --modelFile='${model_name}'.fp16.ms --inDataFile=/data/local/tmp/input_output/input/'${model_name}'.ms.bin --benchmarkDataFile=/data/local/tmp/input_output/output/'${model_name}'.ms.out --enableFp16=true --inputShapes='${input_shapes} >> adb_run_cmd.txt
else
echo './benchmark --modelFile='${model_name}'.fp16.ms --inDataFile=/data/local/tmp/input_output/input/'${model_name}'.ms.bin --benchmarkDataFile=/data/local/tmp/input_output/output/'${model_name}'.ms.out --enableFp16=true --accuracyThreshold='${accuracy_limit} ' --inputShapes='${input_shapes} >> adb_run_cmd.txt
fi
cat adb_run_cmd.txt >> "${run_arm64_log_file}"
adb -s ${device_id} shell < adb_run_cmd.txt >> "${run_arm64_log_file}"
if [ $? = 0 ]; then

8
mindspore/lite/tools/converter/parser/caffe/caffe_convolution_parser.cc
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@ -19,8 +19,8 @@
namespace mindspore {
namespace lite {
STATUS CaffeConvolutionParser::ParseGroupConvolution(schema::PrimitiveT *primitiveT, schema::Conv2DT *attr) {
if (attr->group == 1) {
STATUS CaffeConvolutionParser::ParseDepthwiseConvolution(schema::PrimitiveT *primitiveT, schema::Conv2DT *attr) {
if (attr->group == 1 || attr->group != attr->channelOut) {
return RET_OK;
}
std::unique_ptr<schema::DepthwiseConv2DT> depthwiseConv2DParam = std::make_unique<schema::DepthwiseConv2DT>();
@ -125,9 +125,9 @@ PrimitiveC *CaffeConvolutionParser::ParseLitePrimitive(const caffe::LayerParamet
primitive->value.type = schema::PrimitiveType_Conv2D;
primitive->value.value = attr.release();
status = ParseGroupConvolution(primitive.get(), static_cast<schema::Conv2DT *>(primitive->value.value));
status = ParseDepthwiseConvolution(primitive.get(), static_cast<schema::Conv2DT *>(primitive->value.value));
if (status != RET_OK) {
MS_LOG(ERROR) << "Parse group convolution failed";
MS_LOG(ERROR) << "Parse depthwise convolution failed";
return nullptr;
}

2
mindspore/lite/tools/converter/parser/caffe/caffe_convolution_parser.h
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@ -32,7 +32,7 @@ class CaffeConvolutionParser : public CaffeNodeParser {
PrimitiveC *ParseLitePrimitive(const caffe::LayerParameter &proto, const caffe::LayerParameter &weight) override;
private:
static STATUS ParseGroupConvolution(schema::PrimitiveT *primitiveT, schema::Conv2DT *attr);
static STATUS ParseDepthwiseConvolution(schema::PrimitiveT *primitiveT, schema::Conv2DT *attr);
};
} // namespace lite
} // namespace mindspore

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