1.fix fp16 conv multi thread bug

2. fix bug of int8 conv per layer param
3. Add return error when set quant param failed

mindspore/lite/src/runtime/kernel/arm/base/convolution_base.cc

@@ -141,23 +141,23 @@ int ConvolutionBaseCPUKernel::SetIfAsymmetric() {
   uint8_t asymmetric = 0b0;
   auto filter_tensor = in_tensors_.at(kWeightIndex);
   auto filter_ele_num = filter_tensor->ElementsNum();
-  auto filter_data = reinterpret_cast<float *>(filter_tensor->Data());
-  float min_value = FLT_MAX;
-  float max_value = -FLT_MAX;
+  auto filter_data = reinterpret_cast<int8_t *>(filter_tensor->Data());
+  int min_value = INT8_MAX;
+  int max_value = INT8_MIN;
   for (int i = 0; i < filter_ele_num; ++i) {
     min_value = min_value < filter_data[i] ? min_value : filter_data[i];
     max_value = max_value > filter_data[i] ? max_value : filter_data[i];
   }
   if (conv_quant_arg_->filter_arg_num_ == kPerTensor) {
     auto filter_zp = conv_quant_arg_->filter_quant_args_[0].zp_;
-    if (filter_zp == 0 && min_value >= -127 && max_value <= 127) {
-      asymmetric = asymmetric & FILTER_ASYMMETRIC;
+    if (filter_zp != 0 && min_value >= -128 && max_value <= 127) {
+      asymmetric = asymmetric | FILTER_ASYMMETRIC;
     }
   } else {
     auto filter_arg = conv_quant_arg_->filter_quant_args_;
     for (int i = 0; i < conv_param_->output_channel_; ++i) {
-      if (filter_arg[i].zp_ == 0 && min_value >= -127 && max_value <= 127) {
-        asymmetric = asymmetric & FILTER_ASYMMETRIC;
+      if (filter_arg[i].zp_ != 0 && min_value >= -128 && max_value <= 127) {
+        asymmetric = asymmetric | FILTER_ASYMMETRIC;
       }
     }
   }

mindspore/lite/src/runtime/kernel/arm/fp16/convolution_3x3_fp16.cc

@@ -282,6 +282,39 @@ int Convolution3x3FP16CPUKernel::Run() {
     return RET_ERROR;
   }
 
+  // get real output
+  // todo
+  int out_w_block = UP_DIV(conv_param_->output_w_, C4NUM);
+  int out_h_block = UP_DIV(conv_param_->output_h_, C4NUM);
+  int oc8 = UP_DIV(conv_param_->output_channel_, C8NUM);
+  bool relu = conv_param_->is_relu_;
+  bool relu6 = conv_param_->is_relu6_;
+  for (int batch = 0; batch < conv_param_->output_batch_; batch++) {
+    int tmp_out_batch_offset =
+      batch * oc8 * C8NUM * out_w_block * out_h_block * conv_param_->output_unit_ * conv_param_->output_unit_;
+    int ro_batch_size = batch * conv_param_->output_channel_ * conv_param_->output_h_ * conv_param_->output_w_;
+    const float16_t *batch_tmp_out = tmp_out_ + tmp_out_batch_offset;
+    float16_t *batch_out = fp16_out_ + ro_batch_size;
+    for (int h = 0; h < conv_param_->output_h_; h++) {
+      for (int w = 0; w < conv_param_->output_w_; w++) {
+        for (int c = 0; c < conv_param_->output_channel_; c++) {
+          int oc8_block = c / C8NUM;
+          int oc8_res = c % C8NUM;
+          int src_offset = oc8_block * C8NUM * out_w_block * out_h_block * C4NUM * C4NUM +
+                           C8NUM * (h * out_w_block * conv_param_->output_unit_ + w) + oc8_res;
+          int dst_offset = (h * conv_param_->output_w_ + w) * conv_param_->output_channel_ + c;
+          (batch_out + dst_offset)[0] = (batch_tmp_out + src_offset)[0];
+          if (relu) {
+            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] < 0 ? 0 : (batch_out + dst_offset)[0];
+          } else if (relu6) {
+            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] < 0 ? 0 : (batch_out + dst_offset)[0];
+            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] > 6 ? 6 : (batch_out + dst_offset)[0];
+          }
+        }
+      }
+    }
+  }
+
   // cast fp16 out to fp32 data
   auto out_tensor = out_tensors_.at(kOutputIndex);
   auto out_ele_num = out_tensor->ElementsNum();

mindspore/lite/src/runtime/kernel/arm/fp16/convolution_sw_fp16.cc

@@ -263,6 +263,13 @@ int ConvolutionSWFP16CPUKernel::Run() {
   auto out_tensor = out_tensors_.at(kOutputIndex);
   auto out_ele_num = out_tensor->ElementsNum();
   auto output_addr = reinterpret_cast<float *>(out_tensor->Data());
+  // output nhwc4
+  int oc4_res = conv_param_->output_channel_ % C4NUM;
+  if (oc4_res != 0) {
+    PackNHWC4ToNHWCFp16(reinterpret_cast<const void *>(tmp_output_block_), reinterpret_cast<void *>(fp16_out_),
+                        conv_param_->output_batch_, conv_param_->output_h_ * conv_param_->output_w_,
+                        conv_param_->output_channel_);
+  }
   Float16ToFloat32(fp16_out_, output_addr, out_ele_num);
   return RET_OK;
 }

mindspore/lite/src/runtime/kernel/arm/fp32/convolution_slidewindow.cc

@@ -206,6 +206,14 @@ int ConvolutionSWCPUKernel::Run() {
     MS_LOG(ERROR) << "conv error error_code[" << error_code << "]";
     return RET_ERROR;
   }
+  // output nhwc4
+  auto out_tensor = out_tensors_.front();
+  auto out_data = reinterpret_cast<float *>(out_tensor->Data());
+  int oc4_res = conv_param_->output_channel_ % C4NUM;
+  if (oc4_res != 0) {
+    PackNHWC4ToNHWCFp32(tmp_output_block_, out_data, conv_param_->output_batch_,
+                        conv_param_->output_h_ * conv_param_->output_w_, conv_param_->output_channel_);
+  }
   return RET_OK;
 }
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/int8/convolution_3x3_int8.cc

@@ -170,7 +170,11 @@ int Convolution3x3Int8CPUKernel::Init() {
     MS_LOG(ERROR) << "ConvolutionBase init failed.";
     return RET_ERROR;
   }
-  SetQuantParam();
+  ret = SetQuantParam();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Set quant param failed.";
+    return ret;
+  }
   ret = InitWeightBias();
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init weight bias failed.";
@@ -249,6 +253,11 @@ int Convolution3x3Int8CPUKernel::Run() {
     MS_LOG(ERROR) << "conv3x3 int8 error error_code[" << error_code << "]";
     return RET_ERROR;
   }
+  // get real output
+  auto out_tensor = out_tensors_.front();
+  auto out_data = reinterpret_cast<int8_t *>(out_tensor->Data());
+  PackNC4HW4ToNHWCInt8(tmp_out_, out_data, conv_param_->output_batch_, conv_param_->output_h_ * conv_param_->output_w_,
+                       conv_param_->output_channel_);
   return RET_OK;
 }
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/int8/convolution_depthwise_int8.cc

@@ -111,10 +111,14 @@ int ConvolutionDepthwiseInt8CPUKernel::Init() {
   InitSlidingParamConvDw(sliding, conv_param_, C4NUM);
 
   // init quant param
-  ConvolutionBaseCPUKernel::SetQuantParam();
+  auto ret = ConvolutionBaseCPUKernel::SetQuantParam();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Set quant param failed.";
+    return ret;
+  }
 
   // init weight and bias
-  auto ret = InitWeightBias();
+  ret = InitWeightBias();
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Depthwise int8 InitWeightBias error!";
     return ret;

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

@@ -305,7 +305,11 @@ int ConvolutionInt8CPUKernel::Init() {
   // config input output
   ConfigInputOutput();
   CheckSupportOptimize();
-  SetQuantParam();
+  ret = SetQuantParam();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Set quant param failed.";
+    return ret;
+  }
   // init for opt
   if (support_optimize_) {
     ret = InitOpt();

mindspore/lite/src/runtime/kernel/arm/int8/deconvolution_depthwise_int8.cc

@@ -148,10 +148,14 @@ int DeconvolutionDepthwiseInt8CPUKernel::Init() {
   ConvolutionBaseCPUKernel::Init();
 
   // init quant param
-  ConvolutionBaseCPUKernel::SetQuantParam();
+  auto ret = ConvolutionBaseCPUKernel::SetQuantParam();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Set quant param failed.";
+    return ret;
+  }
 
   // init weight and bias
-  auto ret = InitWeightBias();
+  ret = InitWeightBias();
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Deconv Depthwise int8 InitWeightBias error!";
     return ret;

mindspore/lite/src/runtime/kernel/arm/nnacl/fp16/conv_fp16.c

@@ -307,11 +307,6 @@ void ConvSWFp16(const float16_t *input_data, const float16_t *packed_weight, con
     src += slidingWindow_param->in_step_;
     dst += slidingWindow_param->out_step_;
   }  // batch loop
-  // output nhwc4
-  if (oc4_res != 0) {
-    PackNHWC4ToNHWCFp16((const void *)tmp_out_block, (void *)output_data, conv_param->output_batch_,
-                        conv_param->output_h_ * conv_param->output_w_, conv_param->output_channel_);
-  }
 }
 
 // fp16 convolution common (im2col+gemm)
@@ -381,11 +376,6 @@ void Conv3x3Fp16(float16_t *input_data, float16_t *transed_weight, const float16
   int ic4 = UP_DIV(conv_param->input_channel_, C4NUM);
   int oc8 = UP_DIV(conv_param->output_channel_, C8NUM);
 
-  int output_batch = conv_param->output_batch_;
-  int output_channel = conv_param->output_channel_;
-  int output_w = conv_param->output_w_;
-  int output_h = conv_param->output_h_;
-
   int out_w_block = UP_DIV(conv_param->output_w_, C4NUM);
   int out_h_block = UP_DIV(conv_param->output_h_, C4NUM);
   int output_count = out_w_block * out_h_block;
@@ -414,33 +404,4 @@ void Conv3x3Fp16(float16_t *input_data, float16_t *transed_weight, const float16
                                  bias_data, start_index, real_cal_num, out_w_block, conv_param);
     }
   }
-
-  // get real output
-  // todo
-  bool relu = conv_param->is_relu_;
-  bool relu6 = conv_param->is_relu6_;
-  for (int batch = 0; batch < output_batch; batch++) {
-    int tmp_out_batch_offset = batch * oc8 * C8NUM * out_w_block * out_h_block * output_unit * output_unit;
-    int ro_batch_size = batch * output_channel * output_h * output_w;
-    const float16_t *batch_tmp_out = tmp_out + tmp_out_batch_offset;
-    float16_t *batch_out = output_data + ro_batch_size;
-    for (int h = 0; h < output_h; h++) {
-      for (int w = 0; w < output_w; w++) {
-        for (int c = 0; c < output_channel; c++) {
-          int oc8_block = c / C8NUM;
-          int oc8_res = c % C8NUM;
-          int src_offset = oc8_block * C8NUM * out_w_block * out_h_block * C4NUM * C4NUM +
-                           C8NUM * (h * out_w_block * output_unit + w) + oc8_res;
-          int dst_offset = (h * output_w + w) * output_channel + c;
-          (batch_out + dst_offset)[0] = (batch_tmp_out + src_offset)[0];
-          if (relu) {
-            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] < 0 ? 0 : (batch_out + dst_offset)[0];
-          } else if (relu6) {
-            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] < 0 ? 0 : (batch_out + dst_offset)[0];
-            (batch_out + dst_offset)[0] = (batch_out + dst_offset)[0] > 6 ? 6 : (batch_out + dst_offset)[0];
-          }
-        }
-      }
-    }
-  }
 }

mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/conv.c

@@ -182,11 +182,6 @@ void ConvSWFp32(const float *input_data, const float *packed_weight, const float
     src += slidingWindow_param->in_step_;
     dst += slidingWindow_param->out_step_;
   }  // batch loop
-  // output nhwc4
-  if (oc4_res != 0) {
-    PackNHWC4ToNHWCFp32(tmp_out_block, output_data, conv_param->output_batch_,
-                        conv_param->output_h_ * conv_param->output_w_, conv_param->output_channel_);
-  }
 }
 
 // fp32 conv common

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

@@ -264,7 +264,7 @@ 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;
 
   for (int b = 0; b < in_batch; b++) {
-    int in_batch_offset = b * in_channel * in_h * in_w;
+    int in_batch_offset = b * ic4 * C4NUM * in_h * in_w;
     int out_batch_offset = b * out_channel * out_h * out_w;
     int gemm_in_batch_offset = b * packed_input_size;
     for (int thread_id = task_id; thread_id < output_tile_count; thread_id += thread_count) {
@@ -319,7 +319,7 @@ 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;
 
   for (int b = 0; b < in_batch; b++) {
-    int in_batch_offset = b * in_channel * in_h * in_w;
+    int in_batch_offset = b * ic4 * C4NUM * in_h * in_w;
     int out_batch_offset = b * out_channel * out_h * out_w;
     int gemm_in_batch_offset = b * packed_input_size;
     for (int thread_id = task_id; thread_id < output_tile_count; thread_id += thread_count) {
@@ -358,10 +358,7 @@ void Conv3x3Int8(int16_t *input_data, int16_t *transed_weight, const int32_t *bi
                  int task_id, ConvParameter *conv_param) {
   int thread_count = conv_param->thread_num_;
   int ic8 = UP_DIV(conv_param->input_channel_, C8NUM);
-  int output_batch = conv_param->output_batch_;
   int output_channel = conv_param->output_channel_;
-  int output_w = conv_param->output_w_;
-  int output_h = conv_param->output_h_;
   int out_w_block = UP_DIV(conv_param->output_w_, OUPUT_UNIT);
   int out_h_block = UP_DIV(conv_param->output_h_, OUPUT_UNIT);
   int output_count = out_w_block * out_h_block;
@@ -373,22 +370,21 @@ void Conv3x3Int8(int16_t *input_data, int16_t *transed_weight, const int32_t *bi
 
   int input_batch = conv_param->input_batch_;
   for (int batch = 0; batch < input_batch; batch++) {
+    int in_batch_offset = batch * ic8 * C8NUM * conv_param->input_h_ * conv_param->input_w_;
+    int tmp_out_batch_offset = batch * oc4 * C4NUM * conv_param->output_w_ * conv_param->output_h_;
     for (int thread_id = task_id; thread_id < output_tile_count; thread_id += thread_count) {
       int start_index = thread_id * TILE_NUM;
       int real_cal_num = (output_count - start_index) < TILE_NUM ? (output_count - start_index) : TILE_NUM;
 
-      Conv3x3Uint8InputTransform(input_data, tile_buffer + task_id * tile_buffer_offset,
+      Conv3x3Uint8InputTransform(input_data + in_batch_offset, tile_buffer + task_id * tile_buffer_offset,
                                  block_unit_buffer + task_id * block_unit_buffer_offset, start_index, real_cal_num,
                                  out_w_block, conv_param);
 
       Conv3x3Uint8Gemm(tmp_dst_buffer + task_id * tmp_dst_buffer_offset, tile_buffer + task_id * tile_buffer_offset,
                        transed_weight, output_channel, ic8, real_cal_num);
 
-      Conv3x3Uint8OutputTransform(tmp_dst_buffer + task_id * tmp_dst_buffer_offset, tmp_out, bias_data, start_index,
-                                  real_cal_num, out_w_block, conv_param);
+      Conv3x3Uint8OutputTransform(tmp_dst_buffer + task_id * tmp_dst_buffer_offset, tmp_out + tmp_out_batch_offset,
+                                  bias_data, start_index, real_cal_num, out_w_block, conv_param);
     }
   }
-
-  // get real output
-  PackNC4HW4ToNHWCInt8(tmp_out, output_data, output_batch, output_h * output_w, output_channel);
 }

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

@@ -417,6 +417,12 @@ void PackWeightToC8Int8(const int8_t *origin_weight_data, int16_t *packed_weight
     int src_kernel_offset = k * input_channel;
     int dst_kernel_offset = k * C8NUM;
     for (int o = 0; o < output_channel; o++) {
+      int32_t zp;
+      if (conv_param->conv_quant_arg_.filter_arg_num_ == 1) {
+        zp = filter_zp[0].zp_;
+      } else {
+        zp = filter_zp[o].zp_;
+      }
       int src_oc_offset = src_kernel_offset + o * kernel_plane * input_channel;
       int dst_oc_offset = dst_kernel_offset + o * ic8 * kernel_plane * C8NUM;
       for (int i = 0; i < input_channel; i++) {
@@ -424,7 +430,7 @@ void PackWeightToC8Int8(const int8_t *origin_weight_data, int16_t *packed_weight
         int c8_block_rem = i % C8NUM;
         int src_ic_offset = src_oc_offset + i;
         int dst_ic_offset = dst_oc_offset + c8_block_num * kernel_plane * C8NUM + c8_block_rem;
-        (packed_weight_data + dst_ic_offset)[0] = (int16_t)((origin_weight_data + src_ic_offset)[0] - filter_zp[o].zp_);
+        (packed_weight_data + dst_ic_offset)[0] = (int16_t)((origin_weight_data + src_ic_offset)[0] - zp);
       }
     }
   }