Use MatMul in fully connected layer with dynamic weights when supported

 - Use MatMul kernels in FC layer when using dynamic weights without broadcasting or bias.
 - Fix minor typo in IClMatMulNativeKernelConfig.h

Partially Resolves : [COMPMID-6193]

Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: Id494062b5b4f4e75ff9714c202dde941955afa52
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9797
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 43 insertions, 20 deletions

diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h
index 75bef144ad..e13c01d1e2 100644
--- a/tests/validation/fixtures/FullyConnectedLayerFixture.h
+++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h
@@ -335,9 +335,9 @@ private:
 
     void validate_with_tolerance(TensorType &target, SimpleTensor<half_float::half> &ref)
     {
-        constexpr AbsoluteTolerance<float> abs_tolerance_f16(0.3f);
+        constexpr AbsoluteTolerance<float>        abs_tolerance_f16(0.3f);
         const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f));
-        constexpr float tolerance_num_f16 = 0.07f;
+        constexpr float                           tolerance_num_f16 = 0.07f;
 
         validate(AccessorType(target), ref, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16);
     }
@@ -360,36 +360,36 @@ public:
 
     template <typename...>
     void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape,
-               DataType data_type, ActivationLayerInfo activation_info, bool constant_weights, bool constant_bias, bool weights_reshaped)
+               DataType data_type, ActivationLayerInfo activation_info, bool constant_weights, bool constant_bias, bool weights_reshaped, bool remove_bias = false)
     {
         _data_type = data_type;
 
-        const bool is_quantized = is_data_type_quantized(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
+        // Configure TensorInfo Objects
         const TensorInfo src_info(src_shape, 1, data_type, src_qinfo);
-        _src.allocator()->init(src_info);
+        const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo);
+        TensorInfo       bias_info(bias_shape, 1, bias_data_type);
+        TensorInfo       wei_info(weights_shape, 1, data_type, weights_qinfo);
 
-        TensorInfo wei_info(weights_shape, 1, data_type, weights_qinfo);
         if(!constant_weights && weights_reshaped)
         {
             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, bias_data_type);
         bias_info.set_are_values_constant(constant_bias);
-        _bias.allocator()->init(bias_info);
 
-        const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo);
+        // Initialise Tensors
+        _src.allocator()->init(src_info);
+        _weights.allocator()->init(wei_info);
+        if(!remove_bias)
+            _bias.allocator()->init(bias_info);
         _dst.allocator()->init(dst_info);
 
         // Configure FC layer and mark the weights as non constant
@@ -401,12 +401,13 @@ public:
             fc_info.transpose_weights    = !weights_reshaped;
         }
         FunctionType fc;
-        fc.configure(&_src, &_weights, &_bias, &_dst, fc_info);
+        fc.configure(&_src, &_weights, (remove_bias) ? nullptr : &_bias, &_dst, fc_info);
 
         // Allocate all the tensors
         _src.allocator()->allocate();
         _weights.allocator()->allocate();
-        _bias.allocator()->allocate();
+        if(!remove_bias)
+            _bias.allocator()->allocate();
         _dst.allocator()->allocate();
 
         // Run multiple iterations with different inputs
@@ -424,11 +425,20 @@ public:
             fill(AccessorType(_weights), 1);
             fill(weights, 1);
         }
-        if(constant_bias)
+        if(constant_bias && !remove_bias)
         {
             fill(AccessorType(_bias), 2);
             fill(bias, 2);
         }
+        // To remove bias, fill with 0
+        if(remove_bias && is_quantized)
+        {
+            library->fill_tensor_value(bias, 0);
+        }
+        else if(remove_bias)
+        {
+            library->fill_tensor_value(bias, (float)0.0);
+        }
 
         for(int i = 0; i < num_iterations; ++i)
         {
@@ -446,7 +456,7 @@ public:
                         fill(AccessorType(_weights), randomizer_offset + 1);
                     }
                 }
-                if(!constant_bias)
+                if(!constant_bias && !remove_bias)
                 {
                     fill(AccessorType(_bias), randomizer_offset + 2);
                 }
@@ -462,7 +472,7 @@ public:
                 {
                     fill(weights, randomizer_offset + 1);
                 }
-                if(!constant_bias)
+                if(!constant_bias && !remove_bias)
                 {
                     fill(bias, randomizer_offset + 2);
                 }
@@ -491,7 +501,20 @@ public:
                DataType data_type, ActivationLayerInfo activation_info, bool weights_reshaped)
     {
         FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape,
-                                                                                                  dst_shape, data_type, activation_info, false, true, weights_reshaped);
+                                                                                                  dst_shape, data_type, activation_info, false, true, weights_reshaped, false);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedDynamicNoBiasFixture : 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, bool weights_reshaped)
+    {
+        FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape,
+                                                                                                  dst_shape, data_type, activation_info, false, true, weights_reshaped, true);
     }
 };
 
@@ -504,7 +527,7 @@ public:
                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, false /* weights_reshaped (not used) */);
+                                                                                                  dst_shape, data_type, activation_info, true, false, false, false /* weights_reshaped (not used) */);
     }
 };
 } // namespace validation