Add support for non-constant weights and biases in CpuFullyConnected

Changing the approach for specifying that weights and biases tensors are
non-constant by making it a member of TensorInfo rather than an option
of the functions.

Resolves: COMPMID-4222, COMPMID-4811

Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Change-Id: I9b0081ccbcf8271ce029ba6755563d64c59e1d32
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6313
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 107 insertions, 21 deletions

diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h
index 7d767642f3..3048c56f6b 100644
--- a/tests/validation/fixtures/FullyConnectedLayerFixture.h
+++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h
@@ -232,7 +232,7 @@ protected:
         fill(weights, 1);
         fill(bias, 2);
 
-        return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape), _activation_info, _quantization_info);
+        return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape, _quantization_info), _activation_info, _quantization_info);
     }
 
     TensorType          _target{};
@@ -273,7 +273,7 @@ public:
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class FullyConnectedWithDynamicWeightsFixture : public framework::Fixture
+class FullyConnectedWithDynamicTensorsFixture : public framework::Fixture
 {
 private:
     template <typename U>
@@ -289,6 +289,16 @@ private:
             std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
             library->fill(tensor, distribution, i);
         }
+        else if(_data_type == DataType::QASYMM8)
+        {
+            std::uniform_int_distribution<uint8_t> distribution(0, 30);
+            library->fill(tensor, distribution, i);
+        }
+        else if(_data_type == DataType::S32)
+        {
+            std::uniform_int_distribution<int32_t> distribution(-50, 50);
+            library->fill(tensor, distribution, i);
+        }
         else
         {
             library->fill_tensor_uniform(tensor, i);
@@ -324,6 +334,11 @@ private:
             constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f);
             validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32);
         }
+        else if(_data_type == DataType::QASYMM8)
+        {
+            constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);
+            validate(AccessorType(target), ref, tolerance_qasymm8);
+        }
         else
         {
             validate(AccessorType(target), ref);
@@ -331,32 +346,51 @@ private:
     }
 
 public:
+    using TDecay = typename std::decay<T>::type;
+    using TBias  = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type;
+
     template <typename...>
     void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape,
-               DataType data_type, ActivationLayerInfo activation_info)
+               DataType data_type, ActivationLayerInfo activation_info, bool constant_weights, bool constant_bias)
     {
         _data_type = data_type;
 
+        const bool is_quantized = is_data_type_quantized(data_type);
+
+        const DataType bias_data_type = (is_quantized) ? DataType::S32 : data_type;
+
+        const QuantizationInfo src_qinfo     = is_quantized ? QuantizationInfo(0.1f, 10) : QuantizationInfo();
+        const QuantizationInfo weights_qinfo = is_quantized ? QuantizationInfo(0.3f, 20) : QuantizationInfo();
+        const QuantizationInfo dst_qinfo     = is_quantized ? QuantizationInfo(0.2f, 5) : QuantizationInfo();
+
         // Setup tensor meta-data
-        TensorInfo src_info(src_shape, 1, data_type);
+        const TensorInfo src_info(src_shape, 1, data_type, src_qinfo);
         _src.allocator()->init(src_info);
 
-        TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] };
-        TensorInfo  wei_info(tr_weights_shape, 1, data_type);
+        TensorInfo wei_info(weights_shape, 1, data_type, weights_qinfo);
+        if(!constant_weights)
+        {
+            const TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] };
+            wei_info.set_tensor_shape(tr_weights_shape);
+        }
+        wei_info.set_are_values_constant(constant_weights);
         _weights.allocator()->init(wei_info);
 
-        TensorInfo bias_info(bias_shape, 1, data_type);
+        TensorInfo bias_info(bias_shape, 1, bias_data_type);
+        bias_info.set_are_values_constant(constant_bias);
         _bias.allocator()->init(bias_info);
 
-        TensorInfo dst_info(dst_shape, 1, data_type);
+        const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo);
         _dst.allocator()->init(dst_info);
 
         // Configure FC layer and mark the weights as non constant
         FullyConnectedLayerInfo fc_info;
-        fc_info.activation_info      = activation_info;
-        fc_info.are_weights_reshaped = true;
-        fc_info.transpose_weights    = false;
-        fc_info.constant_weights     = false;
+        fc_info.activation_info = activation_info;
+        if(!constant_weights)
+        {
+            fc_info.are_weights_reshaped = true;
+            fc_info.transpose_weights    = false;
+        }
         FunctionType fc;
         fc.configure(&_src, &_weights, &_bias, &_dst, fc_info);
 
@@ -369,29 +403,55 @@ public:
         // Run multiple iterations with different inputs
         constexpr int num_iterations    = 5;
         int           randomizer_offset = 0;
+
+        // Create reference tensors
+        SimpleTensor<T>     src{ src_shape, data_type, 1, src_qinfo };
+        SimpleTensor<T>     weights{ weights_shape, data_type, 1, weights_qinfo };
+        SimpleTensor<TBias> bias{ bias_shape, bias_data_type };
+
+        // Fill weights and/or bias if they remain constant
+        if(constant_weights)
+        {
+            fill(AccessorType(_weights), 1);
+            fill(weights, 1);
+        }
+        if(constant_bias)
+        {
+            fill(AccessorType(_bias), 2);
+            fill(bias, 2);
+        }
+
         for(int i = 0; i < num_iterations; ++i)
         {
             // Run target
             {
                 fill(AccessorType(_src), randomizer_offset);
-                fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1);
-                fill(AccessorType(_bias), randomizer_offset + 2);
+                if(!constant_weights)
+                {
+                    fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1);
+                }
+                if(!constant_bias)
+                {
+                    fill(AccessorType(_bias), randomizer_offset + 2);
+                }
 
                 fc.run();
             }
 
             // Run reference and compare
             {
-                SimpleTensor<T> src{ src_shape, data_type };
-                SimpleTensor<T> weights{ weights_shape, data_type };
-                SimpleTensor<T> bias{ bias_shape, data_type };
-
                 // Fill reference
                 fill(src, randomizer_offset);
-                fill(weights, randomizer_offset + 1);
-                fill(bias, randomizer_offset + 2);
+                if(!constant_weights)
+                {
+                    fill(weights, randomizer_offset + 1);
+                }
+                if(!constant_bias)
+                {
+                    fill(bias, randomizer_offset + 2);
+                }
 
-                auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape), activation_info);
+                auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape, dst_qinfo), activation_info, dst_qinfo);
 
                 // Validate
                 validate_with_tolerance(_dst, dst);
@@ -405,6 +465,32 @@ private:
     TensorType _src{}, _weights{}, _bias{}, _dst{};
     DataType   _data_type{ DataType::UNKNOWN };
 };
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedWithDynamicWeightsFixture : public FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape,
+               DataType data_type, ActivationLayerInfo activation_info)
+    {
+        FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape,
+                                                                                                  dst_shape, data_type, activation_info, false, true);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedWithDynamicBiasFixture : public FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape,
+               DataType data_type, ActivationLayerInfo activation_info)
+    {
+        FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape,
+                                                                                                  dst_shape, data_type, activation_info, true, false);
+    }
+};
 } // namespace validation
 } // namespace test
 } // namespace arm_compute