Fixed unreasonable type of parameter in broadcast (#505)

Change type of shape from int to uint & add ut for broadcast
From github issue #376
Define opversion to avoid incompatibility with upper level software

Type: Code Improvement

Signed-off-by: Feiyue Chen <Feiyue.Chen@verisilicon.com>

Author: chenfeiyue-cfy

include/tim/vx/ops/broadcast.h

@@ -24,7 +24,7 @@
 #ifndef OVXLIBXX_OPERATIONS_BROADCAST_H_
 #define OVXLIBXX_OPERATIONS_BROADCAST_H_
 #include "tim/vx/builtin_op.h"
-
+#define BROADCAST_OPVERSION 1
 namespace tim {
 namespace vx {
 namespace ops {
@@ -45,12 +45,12 @@ namespace ops {
 
 class Broadcast : public BuiltinOp {
   public:
-    Broadcast(Graph* graph, const std::vector<int32_t>& shape, const std::vector<int32_t>& dimensions = {});
+    Broadcast(Graph* graph, const std::vector<uint32_t>& shape, const std::vector<int32_t>& dimensions = {});
 
     std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
 
-   protected:
-    const std::vector<int32_t> shape_;
+  protected:
+    std::vector<uint32_t> shape_;
     std::vector<int32_t> dimensions_;
 };
 

src/tim/vx/ops/broadcast.cc

@@ -30,23 +30,24 @@
 namespace tim {
 namespace vx {
 namespace ops {
-Broadcast::Broadcast(Graph* graph, const std::vector<int32_t>& shape,
+Broadcast::Broadcast(Graph* graph, const std::vector <uint32_t>& shape,
                      const std::vector<int32_t>& dimensions)
     : BuiltinOp(graph, VSI_NN_OP_EXPAND_BROADCAST),
       shape_(shape),
       dimensions_(dimensions) {
   this->impl()->node()->nn_param.expand_broadcast.dim_num = shape_.size();
-  this->impl()->node()->nn_param.expand_broadcast.shape = (uint32_t*)shape_.data();
+  this->impl()->node()->nn_param.expand_broadcast.shape = shape_.data();
 #ifdef VSI_EXPAND_BROADCAST_ENABLE_DIMENSIONS
   this->impl()->node()->nn_param.expand_broadcast.dimensions_num = dimensions_.size();
   if (dimensions.size() > 0)
   {
-    int dim_num = shape.size();
-    for (uint32_t i = 0; i < dimensions.size(); ++i) {
-      dimensions_[i] += (dimensions[i] < 0 ? dim_num : 0U);
+    for (uint32_t i = 0; i < dimensions.size(); i++)
+    {
+      dimensions_[i] += (dimensions[i] < 0 ? shape_.size() : 0U );
     }
     this->impl()->node()->nn_param.expand_broadcast.dimensions = (uint32_t*)dimensions_.data();
-  } else {
+  } else
+  {
     this->impl()->node()->nn_param.expand_broadcast.dimensions = nullptr;
   }
 #endif

src/tim/vx/ops/broadcast_test.cc

@@ -62,7 +62,7 @@ TEST(Broadcast, ScalarTo2D_2x3) {
   std::vector<float> golden = {
       2.25f, 2.25f, 2.25f, 2.25f, 2.25f, 2.25f,
   };
-  std::vector<int32_t> shape = {3, 2};
+  std::vector<uint32_t> shape = {3, 2};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
                                              in_data.size() * sizeof(float)));
@@ -93,7 +93,7 @@ TEST(Broadcast, 1DTo2D) {
   std::vector<float> golden = {
       1.f, 2.f, 3.f, 1.f, 2.f, 3.f,
   };
-  std::vector<int32_t> shape = {3, 2};
+  std::vector<uint32_t> shape = {3, 2};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
                                              in_data.size() * sizeof(float)));
@@ -125,7 +125,7 @@ TEST(Broadcast, 1DTo2D_WithDims0) {
       1.f, 2.f,
       1.f, 2.f,
   };
-  std::vector<int32_t> shape = {2, 2};
+  std::vector<uint32_t> shape = {2, 2};
   std::vector<int32_t> dimensions = {0};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -158,7 +158,7 @@ TEST(Broadcast, 1DTo2D_WithDims1) {
       1.f, 1.f,
       2.f, 2.f,
   };
-  std::vector<int32_t> shape = {2, 2};
+  std::vector<uint32_t> shape = {2, 2};
   std::vector<int32_t> dimensions = {1};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -170,6 +170,44 @@ TEST(Broadcast, 1DTo2D_WithDims1) {
   CheckResult(graph, golden, output_tensor);
 }
 
+TEST(Broadcast, 1DTo2D_WithDimsMinus2) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3});
+  tim::vx::ShapeType output_shape({3, 2});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                  tim::vx::TensorAttribute::OUTPUT);
+
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto output_tensor = graph->CreateTensor(output_spec);
+
+  std::vector<float> in_data = {
+      1.f, 2.f, 3.f 
+  };
+  std::vector<float> golden = {
+      1.f, 2.f, 3.f,
+      1.f, 2.f, 3.f
+  };
+  std::vector<uint32_t> shape = {3, 2};
+  std::vector<int32_t> dimensions = {-2};
+  EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
+                                             in_data.size() * sizeof(float)));
+
+  auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
+  (*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
+
+ 
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<float> output(golden.size());
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
 TEST(Broadcast, 1DTo3D_WithDims0) {
   auto ctx = tim::vx::Context::Create();
   auto graph = ctx->CreateGraph();
@@ -190,7 +228,7 @@ TEST(Broadcast, 1DTo3D_WithDims0) {
   std::vector<float> golden = {
       1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f,
   };
-  std::vector<int32_t> shape = {2, 2, 2};
+  std::vector<uint32_t> shape = {2, 2, 2};
   std::vector<int32_t> dimensions = {0};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -222,7 +260,7 @@ TEST(Broadcast, 1DTo3D_WithDims1) {
   std::vector<float> golden = {
       1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f,
   };
-  std::vector<int32_t> shape = {2, 2, 2};
+  std::vector<uint32_t> shape = {2, 2, 2};
   std::vector<int32_t> dimensions = {1};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -254,7 +292,7 @@ TEST(Broadcast, 1DTo3D_WithDims2) {
   std::vector<float> golden = {
       1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f,
   };
-  std::vector<int32_t> shape = {2, 2, 2};
+  std::vector<uint32_t> shape = {2, 2, 2};
   std::vector<int32_t> dimensions = {2};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -286,7 +324,7 @@ TEST(Broadcast, 2DTo3D_WithDims02) {
   std::vector<float> golden = {
       1.f, 5.f, 1.f, 5.f, 2.f, 6.f, 2.f, 6.f,
   };
-  std::vector<int32_t> shape = {2, 2, 2};
+  std::vector<uint32_t> shape = {2, 2, 2};
   std::vector<int32_t> dimensions = {0, 2};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
@@ -318,7 +356,7 @@ TEST(Broadcast, 2DTo3D_WithDims12) {
   std::vector<float> golden = {
       1.f, 1.f, 5.f, 5.f, 2.f, 2.f, 6.f, 6.f,
   };
-  std::vector<int32_t> shape = {2, 2, 2};
+  std::vector<uint32_t> shape = {2, 2, 2};
   std::vector<int32_t> dimensions = {1, 2};
 
   EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),