!5050 [MS][LITE][Develop]Fp16 conv1x1 inputptr memeset bug

Merge pull request !5050 from ling/bug

mindspore/lite/src/ops/deconv2d.cc

@@ -124,6 +124,20 @@ int DeConv2D::InferShape(std::vector<lite::tensor::Tensor *> inputs_, std::vecto
   }
   std::vector<int> out_shape = {output_n, output_h, output_w, output_c};
   output->set_shape(out_shape);
+
+  if (pad_mode == schema::PadMode_SAME) {
+    pad_h_ = ((input_h - 1) * stride_h + (kernel_h - 1) * dilate_h + 1 - output_h) / 2;
+    pad_w_ = ((input_w - 1) * stride_w + (kernel_w - 1) * dilate_w + 1 - output_w) / 2;
+  } else if (pad_mode == schema::PadMode_VALID) {
+    pad_h_ = 0;
+    pad_w_ = 0;
+  } else if (pad_mode == schema::PadMode_CAFFE) {
+    pad_h_ = pad_u_;
+    pad_w_ = pad_l_;
+  } else {
+    MS_LOG(ERROR) << "unsupported pad mode for deconv";
+  }
+
   return 0;
 }
 }  // namespace lite

mindspore/lite/src/ops/deconv2d.h

@@ -74,12 +74,16 @@ class DeConv2D : public PrimitiveC {
   int PadDown() const { return this->pad_d_; }
   int PadLeft() const { return this->pad_l_; }
   int PadRight() const { return this->pad_r_; }
+  int PadH() const { return this->pad_h_; }
+  int PadW() const { return this->pad_w_; }
 
  protected:
   int pad_u_ = 0;
   int pad_d_ = 0;
   int pad_l_ = 0;
   int pad_r_ = 0;
+  int pad_h_ = 0;
+  int pad_w_ = 0;
 };
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/populate_parameter.cc

@@ -506,6 +506,8 @@ OpParameter *PopulateDeconvParameter(const mindspore::lite::PrimitiveC *primitiv
   conv_param->pad_d_ = deconv_lite_primitive->PadDown();
   conv_param->pad_l_ = deconv_lite_primitive->PadLeft();
   conv_param->pad_r_ = deconv_lite_primitive->PadRight();
+  conv_param->pad_h_ = deconv_lite_primitive->PadH();
+  conv_param->pad_w_ = deconv_lite_primitive->PadW();
   conv_param->dilation_h_ = conv_primitive->GetDilateH();
   conv_param->dilation_w_ = conv_primitive->GetDilateW();
   auto act_type = conv_primitive->GetActivationType();
@@ -523,26 +525,6 @@ OpParameter *PopulateDeconvParameter(const mindspore::lite::PrimitiveC *primitiv
       conv_param->is_relu6_ = false;
       break;
   }
-
-  auto pad_mode = conv_primitive->GetPadMode();
-  switch (pad_mode) {
-    case schema::PadMode_SAME:
-      conv_param->pad_h_ = (conv_param->kernel_h_ - 1) / 2;
-      conv_param->pad_w_ = (conv_param->kernel_w_ - 1) / 2;
-      break;
-    case schema::PadMode_VALID:
-      conv_param->pad_h_ = 0;
-      conv_param->pad_w_ = 0;
-      break;
-    case schema::PadMode_CAFFE:
-      conv_param->pad_h_ = conv_param->pad_u_;
-      conv_param->pad_w_ = conv_param->pad_l_;
-      break;
-    default:
-      MS_LOG(ERROR) << "invalid pad mode!";
-      return nullptr;
-  }
-
   return reinterpret_cast<OpParameter *>(conv_param);
 }
 

mindspore/lite/src/runtime/kernel/arm/fp16/convolution_1x1_fp16.cc

@@ -70,6 +70,15 @@ int Convolution1x1FP16CPUKernel::InitConv1x1Param() {
     return RET_MEMORY_FAILED;
   }
   memset(pack_input_, 0, matmul_param_->row_16_ * matmul_param_->deep_ * sizeof(float16_t));
+
+  if (pre_trans_input_) {
+    input_ptr_ = reinterpret_cast<float16_t *>(malloc(matmul_param_->row_ * matmul_param_->deep_ * sizeof(float16_t)));
+    if (input_ptr_ == nullptr) {
+      MS_LOG(ERROR) << "Conv1x1 Malloc input_ptr_ error!";
+      return RET_MEMORY_FAILED;
+    }
+    memset(input_ptr_, 0, matmul_param_->row_ * matmul_param_->deep_ * sizeof(float16_t));
+  }
   return RET_OK;
 }
 
@@ -131,6 +140,10 @@ void Convolution1x1FP16CPUKernel::FreeTmpBuffer() {
     free(pack_input_);
     pack_input_ = nullptr;
   }
+  if (pre_trans_input_ && input_ptr_ != nullptr) {
+    free(input_ptr_);
+    input_ptr_ = nullptr;
+  }
   return;
 }
 
@@ -205,15 +218,6 @@ int Convolution1x1FP16CPUKernel::Run() {
     return ret;
   }
 
-  if (pre_trans_input_) {
-    input_ptr_ = reinterpret_cast<float16_t *>(
-      ctx_->allocator->Malloc(matmul_param_->row_ * matmul_param_->deep_ * sizeof(float16_t)));
-    if (input_ptr_ == nullptr) {
-      MS_LOG(ERROR) << "Conv1x1 Malloc input_ptr_ error!";
-      return RET_MEMORY_FAILED;
-    }
-  }
-
   for (int batch_index = 0; batch_index < conv_param_->input_batch_; batch_index++) {
     Pre1x1Trans(
       execute_input_ + batch_index * conv_param_->input_h_ * conv_param_->input_w_ * conv_param_->input_channel_,
@@ -229,10 +233,6 @@ int Convolution1x1FP16CPUKernel::Run() {
   ConvolutionBaseFP16CPUKernel::IfCastOutput();
   ConvolutionBaseFP16CPUKernel::FreeTmpBuffer();
 
-  if (pre_trans_input_ && input_ptr_ != nullptr) {
-    ctx_->allocator->Free(input_ptr_);
-    input_ptr_ = nullptr;
-  }
   return RET_OK;
 }
 }  // namespace mindspore::kernel

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

@@ -29,15 +29,15 @@ MatmulCPUKernel::~MatmulCPUKernel() { FreeTmpBuffer(); }
 
 void MatmulCPUKernel::FreeTmpBuffer() {
   if (a_c12_ptr_ != nullptr) {
-    ctx_->allocator->Free(a_c12_ptr_);
+    free(a_c12_ptr_);
     a_c12_ptr_ = nullptr;
   }
   if (b_r8_ptr_ != nullptr) {
-    ctx_->allocator->Free(b_r8_ptr_);
+    free(b_r8_ptr_);
     b_r8_ptr_ = nullptr;
   }
   if (bias_ptr_ != nullptr) {
-    ctx_->allocator->Free(bias_ptr_);
+    free(bias_ptr_);
     bias_ptr_ = nullptr;
   }
 }
@@ -67,23 +67,28 @@ int MatmulCPUKernel::ReSize() {
   thread_count_ = MSMIN(thread_count_, UP_DIV(params_->col_8_, 8));
   thread_stride_ = UP_DIV(UP_DIV(params_->col_8_, 8), thread_count_);
 
-  a_c12_ptr_ = reinterpret_cast<float *>(ctx_->allocator->Malloc(params_->row_12_ * params_->deep_ * sizeof(float)));
+  a_c12_ptr_ = reinterpret_cast<float *>(malloc(params_->batch * params_->row_12_ * params_->deep_ * sizeof(float)));
   if (a_c12_ptr_ == nullptr) {
     FreeTmpBuffer();
     return RET_MEMORY_FAILED;
   }
   memset(a_c12_ptr_, 0, params_->row_12_ * params_->deep_ * sizeof(float));
-  b_r8_ptr_ = reinterpret_cast<float *>(ctx_->allocator->Malloc(params_->col_8_ * params_->deep_ * sizeof(float)));
+
+  b_r8_ptr_ = reinterpret_cast<float *>(malloc(params_->batch * params_->col_8_ * params_->deep_ * sizeof(float)));
   if (b_r8_ptr_ == nullptr) {
     FreeTmpBuffer();
     return RET_MEMORY_FAILED;
   }
   memset(b_r8_ptr_, 0, params_->col_8_ * params_->deep_ * sizeof(float));
 
-  params_->a_const_ = false;
-  params_->b_const_ = false;
-  InitMatrixA(reinterpret_cast<float *>(in_tensors_[0]->Data()), a_c12_ptr_);
-  InitMatrixB(reinterpret_cast<float *>(in_tensors_[1]->Data()), b_r8_ptr_);
+  params_->a_const_ = (in_tensors_[0]->Data() != nullptr);
+  params_->b_const_ = (in_tensors_[1]->Data() != nullptr);
+  if (params_->a_const_ == true) {
+    InitMatrixA(reinterpret_cast<float *>(in_tensors_[0]->Data()), a_c12_ptr_);
+  }
+  if (params_->b_const_ == true) {
+    InitMatrixB(reinterpret_cast<float *>(in_tensors_[1]->Data()), b_r8_ptr_);
+  }
 
   bias_ptr_ = reinterpret_cast<float *>(malloc(params_->col_8_ * sizeof(float)));
   if (bias_ptr_ == nullptr) {
@@ -99,35 +104,27 @@ int MatmulCPUKernel::ReSize() {
 }
 
 void MatmulCPUKernel::InitMatrixA(float *src_ptr, float *dst_ptr) {
-  if (params_->a_const_ == true) {
-    return;
-  }
-  if (src_ptr == nullptr) {
-    return;
-  }
-  params_->a_const_ = true;
-
-  if (params_->a_transpose_) {
-    RowMajor2Row12Major(src_ptr, dst_ptr, params_->deep_, params_->row_);
-  } else {
-    RowMajor2Col12Major(src_ptr, dst_ptr, params_->row_, params_->deep_);
+  for (int i = 0; i < params_->batch; i++) {
+    float *src = src_ptr + i * params_->deep_ * params_->row_;
+    float *dst = dst_ptr + i * params_->deep_ * params_->row_12_;
+    if (params_->a_transpose_) {
+      RowMajor2Row12Major(src, dst, params_->deep_, params_->row_);
+    } else {
+      RowMajor2Col12Major(src, dst, params_->row_, params_->deep_);
+    }
   }
   return;
 }
 
 void MatmulCPUKernel::InitMatrixB(float *src_ptr, float *dst_ptr) {
-  if (params_->b_const_ == true) {
-    return;
-  }
-  if (src_ptr == nullptr) {
-    return;
-  }
-  params_->b_const_ = true;
-
-  if (params_->b_transpose_) {
-    RowMajor2Col8Major(src_ptr, dst_ptr, params_->col_, params_->deep_);
-  } else {
-    RowMajor2Row8Major(src_ptr, dst_ptr, params_->deep_, params_->col_);
+  for (int i = 0; i < params_->batch; i++) {
+    float *src = src_ptr + i * params_->deep_ * params_->col_;
+    float *dst = dst_ptr + i * params_->deep_ * params_->col_8_;
+    if (params_->b_transpose_) {
+      RowMajor2Col8Major(src, dst, params_->col_, params_->deep_);
+    } else {
+      RowMajor2Row8Major(src, dst, params_->deep_, params_->col_);
+    }
   }
   return;
 }
@@ -144,8 +141,8 @@ int MatmulCPUKernel::RunImpl(int task_id) {
   if (cur_oc <= 0) {
     return RET_OK;
   }
-  MatMulOpt(a_c12_ptr_, b_r8_ptr_ + task_id * thread_stride_ * C8NUM * params_->deep_,
-            c_r_ptr_ + task_id * thread_stride_ * C8NUM, bias_ptr_ + task_id * thread_stride_ * C8NUM, ActType_No,
+  MatMulOpt(a_ptr_, b_ptr_ + task_id * thread_stride_ * C8NUM * params_->deep_,
+            c_ptr_ + task_id * thread_stride_ * C8NUM, bias_ptr_ + task_id * thread_stride_ * C8NUM, ActType_No,
             params_->deep_, params_->row_, cur_oc, params_->col_, OutType_Nhwc);
   return RET_OK;
 }
@@ -166,20 +163,21 @@ int MatmulCPUKernel::Run() {
     MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
     return prepare_ret;
   }
-  auto a_ptr = reinterpret_cast<float *>(in_tensors_[0]->Data());
-  auto b_ptr = reinterpret_cast<float *>(in_tensors_[1]->Data());
-  auto c_ptr = reinterpret_cast<float *>(out_tensors_[0]->Data());
-  auto a_stride = params_->row_ * params_->deep_;
-  auto b_stride = params_->deep_ * params_->col_;
-  auto c_stride = params_->row_ * params_->col_;
+  auto a_src = reinterpret_cast<float *>(in_tensors_[0]->Data());
+  auto b_src = reinterpret_cast<float *>(in_tensors_[1]->Data());
+  auto c_src = reinterpret_cast<float *>(out_tensors_[0]->Data());
+
+  if (params_->a_const_ == false) {
+    InitMatrixA(a_src, a_c12_ptr_);
+  }
+  if (params_->b_const_ == false) {
+    InitMatrixB(b_src, b_r8_ptr_);
+  }
+
   for (int i = 0; i < params_->batch; ++i) {
-    auto cur_a_ptr = a_ptr + i * a_stride;
-    auto cur_b_ptr = b_ptr + i * b_stride;
-    c_r_ptr_ = c_ptr + i * c_stride;
-
-    InitMatrixA(cur_a_ptr, a_c12_ptr_);
-    InitMatrixB(cur_b_ptr, b_r8_ptr_);
-
+    a_ptr_ = a_c12_ptr_ + i * params_->row_12_ * params_->deep_;
+    b_ptr_ = b_r8_ptr_ + i * params_->deep_ * params_->col_8_;
+    c_ptr_ = c_src + i * params_->row_ * params_->col_;
     LiteBackendParallelLaunch(MatmulFloatRun, this, thread_count_);
   }
   return RET_OK;

mindspore/lite/src/runtime/kernel/arm/fp32/matmul.h

@@ -43,8 +43,10 @@ class MatmulCPUKernel : public MatmulBaseCPUKernel {
  private:
   float *a_c12_ptr_ = nullptr;
   float *b_r8_ptr_ = nullptr;
-  float *c_r_ptr_ = nullptr;
   float *bias_ptr_ = nullptr;
+  float *a_ptr_ = nullptr;
+  float *b_ptr_ = nullptr;
+  float *c_ptr_ = nullptr;
 };
 }  // namespace mindspore::kernel