modify input sum array

mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc

@@ -235,10 +235,13 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector<lite::tensor::Ten
   conv_param->input_w_ = inputs.front()->Width();
   conv_param->output_h_ = outputs.front()->Height();
   conv_param->output_w_ = outputs.front()->Width();
+  bool prefer_flag = false;
+  if (conv_param->output_h_ * conv_param->output_w_ > 64) {
+    prefer_flag = true;
+  }
+
   kernel::LiteKernel *kernel = nullptr;
-  if (kernel_h == 3 && kernel_w == 3 && stride_h == 1 && stride_w == 1 && dilation_h == 1 && dilation_w == 1) {
-    kernel = new (std::nothrow) kernel::Convolution3x3FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
-  } else if (kernel_h == 1 && kernel_w == 1) {
+  if (kernel_h == 1 && kernel_w == 1) {
     kernel = new (std::nothrow) kernel::Convolution1x1FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
   } else {
     bool use_winograd = false;
@@ -249,6 +252,9 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector<lite::tensor::Ten
     if (use_winograd) {
       kernel = new (std::nothrow)
         kernel::ConvolutionWinogradFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive, out_unit);
+    } else if (prefer_flag && kernel_h == 3 && kernel_w == 3 && stride_h == 1 && stride_w == 1 && dilation_h == 1 &&
+               dilation_w == 1) {
+      kernel = new (std::nothrow) kernel::Convolution3x3FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
     }
     if (kernel_h != 1 && kernel_w != 1 && !use_winograd) {
       kernel = new (std::nothrow) kernel::ConvolutionFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);

mindspore/lite/src/runtime/kernel/arm/int8/convolution_int8.cc

@@ -115,7 +115,7 @@ int ConvolutionInt8CPUKernel::InitWeightBias() {
 
   size_t input_sum_size;
   if (conv_quant_arg_->per_channel_ & FILTER_PER_CHANNEL) {
-    input_sum_size = conv_param_->output_channel_ * tile_num_ * thread_count_ * sizeof(int32_t);
+    input_sum_size = oc4 * C4NUM * tile_num_ * thread_count_ * sizeof(int32_t);
   } else {
     input_sum_size = tile_num_ * thread_count_ * sizeof(int32_t);
   }
@@ -202,7 +202,7 @@ int ConvolutionInt8CPUKernel::InitWeightBiasOpt() {
 
   size_t input_sum_size;
   if (conv_quant_arg_->per_channel_ & FILTER_PER_CHANNEL) {
-    input_sum_size = conv_param_->output_channel_ * tile_num_ * thread_count_ * sizeof(int32_t);
+    input_sum_size = oc4 * C4NUM * tile_num_ * thread_count_ * sizeof(int32_t);
   } else {
     input_sum_size = tile_num_ * thread_count_ * sizeof(int32_t);
   }

mindspore/lite/src/runtime/kernel/arm/nnacl/int8/conv_depthwise_int8.c

@@ -169,9 +169,9 @@ void ConvDwInt8(int8_t *output_data, const int16_t *input_data, const int16_t *w
       const int32_t *bias = bias_data + oc * C4NUM;
 
       if (per_channel) {
-        out_multiplier = conv_param->conv_quant_arg_.quant_multiplier_ + oc;
-        left_shift = conv_param->conv_quant_arg_.left_shift_ + oc;
-        right_shift = conv_param->conv_quant_arg_.right_shift_ + oc;
+        out_multiplier = conv_param->conv_quant_arg_.quant_multiplier_ + oc * C4NUM;
+        left_shift = conv_param->conv_quant_arg_.left_shift_ + oc * C4NUM;
+        right_shift = conv_param->conv_quant_arg_.right_shift_ + oc * C4NUM;
       }
 
       DepthwiseBorderInt8(dst_data, src_data, weight, bias, 0, sliding->top_, 0, conv_param->output_w_, conv_param,

mindspore/lite/src/runtime/kernel/arm/nnacl/int8/conv_int8.c

@@ -28,6 +28,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
   int32_t out_zp = conv_param->conv_quant_arg_.output_quant_args_[0].zp_;
   int32_t act_min = conv_param->conv_quant_arg_.out_act_min_[0];
   int32_t act_max = conv_param->conv_quant_arg_.out_act_max_[0];
+  int oc4 = UP_DIV(output_channel, C4NUM);
 #ifdef __aarch64__
   IndirectGemmInt8_4x4(dst, src, weight, bias, UP_DIV(kernel_plane, C4NUM), ic4, output_channel,
                        output_channel * sizeof(int8_t), input_sum, act_min, act_max, out_zp, out_multiplier,
@@ -96,7 +97,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
         dst[dst_tile_offset] = (int8_t)result;
       } else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
                  (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
-        tmp_dst[dst_tile_offset] -= input_sum[n * output_channel + oc];
+        tmp_dst[dst_tile_offset] -= input_sum[n * oc4 * C4NUM + oc];
         int result = tmp_dst[dst_tile_offset] + bias[oc];
         result = RoundingDivideByPOT(
           SaturatingRoundingDoublingHighMul(result * (1 << (unsigned int)shift_before[oc]), out_multiplier[oc]),
@@ -120,6 +121,7 @@ void IndirectGemmInt8Opt(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const
   int32_t out_zp = conv_param->conv_quant_arg_.output_quant_args_[0].zp_;
   int32_t act_min = conv_param->conv_quant_arg_.out_act_min_[0];
   int32_t act_max = conv_param->conv_quant_arg_.out_act_max_[0];
+  int oc4 = UP_DIV(output_channel, C4NUM);
   if (gemm_func != NULL) {
 #ifdef __aarch64__
     gemm_func(dst, src, weight, bias, kernel_plane, ic4, output_channel, output_channel * sizeof(int8_t), input_sum,
@@ -181,7 +183,7 @@ void IndirectGemmInt8Opt(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const
           dst[dst_tile_offset] = (int8_t)result;
         } else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
                    (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
-          tmp_dst[dst_tile_offset] -= input_sum[n * output_channel + oc];
+          tmp_dst[dst_tile_offset] -= input_sum[n * oc4 * C4NUM + oc];
           int result = tmp_dst[dst_tile_offset] + bias[oc];
           result = RoundingDivideByPOT(
             SaturatingRoundingDoublingHighMul(result * (1 << (unsigned int)shift_before[oc]), out_multiplier[oc]),
@@ -252,6 +254,7 @@ void ConvInt8(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight, c
   int out_h = conv_param->output_h_;
   int out_w = conv_param->output_w_;
   int out_channel = conv_param->output_channel_;
+  int oc4 = UP_DIV(out_channel, C4NUM);
   int32_t input_zp = conv_param->conv_quant_arg_.input_quant_args_[0].zp_;
 
   int tile_n = conv_param->tile_num_;
@@ -264,7 +267,7 @@ void ConvInt8(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight, c
   int packed_input_size = output_tile_count * tile_n * unit_size;
   int input_sum_offset;
   if (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL) {
-    input_sum_offset = tile_n * out_channel;
+    input_sum_offset = tile_n * oc4 * C4NUM;
   } else {
     input_sum_offset = tile_n;
   }
@@ -314,6 +317,7 @@ void ConvInt8Opt(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight
   int out_h = conv_param->output_h_;
   int out_w = conv_param->output_w_;
   int out_channel = conv_param->output_channel_;
+  int oc4 = UP_DIV(out_channel, C4NUM);
   int32_t input_zp = conv_param->conv_quant_arg_.input_quant_args_[0].zp_;
   int tile_n = conv_param->tile_num_;
   int thread_count = conv_param->thread_num_;
@@ -325,7 +329,7 @@ void ConvInt8Opt(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight
   int packed_input_size = output_tile_count * tile_n * unit_size;
   int input_sum_offset;
   if (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL) {
-    input_sum_offset = tile_n * out_channel;
+    input_sum_offset = tile_n * oc4 * C4NUM;
   } else {
     input_sum_offset = tile_n;
   }

mindspore/lite/src/runtime/kernel/arm/nnacl/pack.c

@@ -255,6 +255,7 @@ void Im2ColPackUnitInt8(const int8_t *input_data, int8_t *packed_input, int real
   int in_h = conv_param->input_h_;
   int in_w = conv_param->input_w_;
   int ic4 = UP_DIV(in_channel, C4NUM);
+  int oc4 = UP_DIV(conv_param->output_channel_, C4NUM);
   int out_w = conv_param->output_w_;
 
   for (int i = 0; i < real_cal_num; i++) {
@@ -297,7 +298,7 @@ void Im2ColPackUnitInt8(const int8_t *input_data, int8_t *packed_input, int real
       continue;
     } else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
                (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
-      int cal_num_offset = i * conv_param->output_channel_;
+      int cal_num_offset = i * oc4 * C4NUM;
       for (int l = 0; l < conv_param->output_channel_; ++l) {
         input_sum[cal_num_offset + l] = input_accumulator * filter_arg[l].zp_;
       }
@@ -325,6 +326,7 @@ void Im2ColPackUnitInt8Opt(const int8_t *input_data, int8_t *packed_input, int r
   int in_h = conv_param->input_h_;
   int in_w = conv_param->input_w_;
   int ic4 = UP_DIV(in_channel, C4NUM);
+  int oc4 = UP_DIV(conv_param->output_channel_, C4NUM);
   int out_w = conv_param->output_w_;
   int block_size = kernel_h * kernel_w;
 
@@ -368,7 +370,7 @@ void Im2ColPackUnitInt8Opt(const int8_t *input_data, int8_t *packed_input, int r
       continue;
     } else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
                (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
-      int cal_num_offset = i * conv_param->output_channel_;
+      int cal_num_offset = i * oc4 * C4NUM;
       for (int l = 0; l < conv_param->output_channel_; ++l) {
         input_sum[cal_num_offset + l] = input_accumulator * filter_arg[l].zp_;
       }