Fix nightly failures in MatMulLowpNativeKernel when using bounded activation functions

- Added checks for supported activation functions in MatMulLowpKernel validate
- Replaced incorrect float activation macro with quantized implementation in mat_mul_quantized

Resolves: [COMPMID-6339]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I15661f14877f1d3305644e6473feb5482a67e773
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/532858
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Comments-Addressed: bsgcomp <bsgcomp@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9855
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 52 insertions, 52 deletions

diff --git a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
index 8cf857dd84..7029af2188 100644
--- a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
+++ b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
@@ -34,6 +34,7 @@
  * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
  * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
  * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
+ * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
  * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
  * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_NT_NT)
  * @note Only the following configurations of M0, N0 and K0 are currently supported:
@@ -196,12 +197,12 @@ __kernel void mat_mul_native_quantized_nt_nt(
     const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
     const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
 
-    T_ACTIVATION(int, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
-
     // Quantize the tile
     TILE(DATA_TYPE, M0, N0, accq);
     T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
 
+    T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
+
     TILE(int, M0, 1, indirect_buffer);
     LOOP_UNROLLING(int, _i, 0, 1, M0,
     {
@@ -221,6 +222,7 @@ __kernel void mat_mul_native_quantized_nt_nt(
  * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
  * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
  * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
+ * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
  * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
  * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_NT_T)
  * @note Only the following configurations of M0, N0 and K0 are currently supported:
@@ -375,12 +377,12 @@ __kernel void mat_mul_native_quantized_nt_t(
     const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
     const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
 
-    T_ACTIVATION(int, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
-
     // Quantize the tile
     TILE(DATA_TYPE, M0, N0, accq);
     T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
 
+    T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
+
     TILE(int, M0, 1, indirect_buffer);
     LOOP_UNROLLING(int, _i, 0, 1, M0,
     {
@@ -400,6 +402,7 @@ __kernel void mat_mul_native_quantized_nt_t(
  * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
  * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
  * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
+ * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
  * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
  * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_NT)
  * @note Only the following configurations of M0, N0 and K0 are currently supported:
@@ -556,12 +559,12 @@ __kernel void mat_mul_native_quantized_t_nt(
     const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
     const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
 
-    T_ACTIVATION(int, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
-
     // Quantize the tile
     TILE(DATA_TYPE, M0, N0, accq);
     T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
 
+    T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
+
     TILE(int, M0, 1, indirect_buffer);
     LOOP_UNROLLING(int, _i, 0, 1, M0,
     {
@@ -581,6 +584,7 @@ __kernel void mat_mul_native_quantized_t_nt(
  * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
  * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
  * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
+ * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
  * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
  * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_T)
  * @note Only the following configurations of M0, N0 and K0 are currently supported:
@@ -742,11 +746,11 @@ __kernel void mat_mul_native_quantized_t_t(
     const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
 
     // Quantize the tile
-    T_ACTIVATION(int, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
-
     TILE(DATA_TYPE, M0, N0, accq);
     T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
 
+    T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
+
     TILE(int, M0, 1, indirect_buffer);
     LOOP_UNROLLING(int, _i, 0, 1, M0,
     {
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h
index 85bd59afd4..8129606277 100644
--- a/src/core/CL/cl_kernels/tile_helpers.h
+++ b/src/core/CL/cl_kernels/tile_helpers.h
@@ -1144,19 +1144,21 @@
         })                                                                                     \
     })
 
+
+// NOTE : A_VAL and B_VAL should be quantized values (using same quantization info as x)
 // RELU Activation
-#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_VALUE, x))
+#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_POINT, x))
 // Bounded RELU Activation
-#define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_VALUE, x)))
+#define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_POINT, x)))
 // Lower Upper Bounded RELU Activation
-#define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL))
+#define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL))
 // Hard Swish Activation
-#define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f))
+#define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f))
 // Identity Activation
-#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x)
+#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (x)
 
-#define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x)
-#define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x)
+#define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x)
+#define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x)
 
 #define V_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL))
 #define V_SUB(A_VAL, B_VAL) ((A_VAL) - (B_VAL))
@@ -1171,17 +1173,17 @@
  * @param[in]  M0              Number of SRC/DST rows
  * @param[in]  N0              Number of SRC/DST columns
  * @param[in]  ACTIVATION_TYPE Activation type
- * @param[in]  ZERO_VALUE      The zero value to consider in the computation
- * @param[in]  A_VAL           A value used for the activation (e.g. tanh_op, brelu,..)
- * @param[in]  B_VAL           B value used for the activation (e.g. tanh_op, brelu,..)
+ * @param[in]  ZERO_POINT      The zero value to consider in the computation
+ * @param[in]  A_VAL           Quantized A value used for the activation (e.g. tanh_op, brelu,..)
+ * @param[in]  B_VAL           Quantized B value used for the activation (e.g. tanh_op, brelu,..)
  * @param[out] src             SRC tile
  * @param[out] dst             DST tile
  */
-#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_VALUE, A_VAL, B_VAL, src, dst)               \
+#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, src, dst)               \
     ({ \
         LOOP_UNROLLING(int, _m0, 0, 1, M0, \
         { \
-            dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_VALUE, A_VAL, B_VAL, src[_m0].v); \
+            dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_POINT, A_VAL, B_VAL, src[_m0].v); \
         })                                                                                          \
     })
 
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
index 38d78c618b..02c5754672 100644
--- a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
@@ -35,6 +35,8 @@
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/gpu/cl/ClCompileContext.h"
 
+#include "arm_compute/core/QuantizationInfo.h"
+
 #include "support/Cast.h"
 #include "support/StringSupport.h"
 
@@ -100,13 +102,16 @@ ClMatMulLowpNativeKernel::ClMatMulLowpNativeKernel()
 }
 Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info)
 {
-    ARM_COMPUTE_UNUSED(act_info);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
 
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.activation() != ActivationFunction::IDENTITY && act_info.activation() != ActivationFunction::RELU
+                                     && act_info.activation() != ActivationFunction::LU_BOUNDED_RELU && act_info.activation() != ActivationFunction::BOUNDED_RELU),
+                                    "Activation Function specified is unsupported.");
+
     if(dst->total_size() != 0)
     {
         const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
@@ -169,9 +174,16 @@ void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context
     build_opts.add_option("-DRHS_OFFSET=" + support::cpp11::to_string(rqinfo.offset));
     build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset)); // Passed as positive (unlike the above two)
 
-    build_opts.add_option(("-DA_VAL=" + float_to_string_with_full_precision(act_info.a())));
-    build_opts.add_option(("-DB_VAL=" + float_to_string_with_full_precision(act_info.b())));
+    // Floating point boundaries are quantized prior to being passed as arguments.
+    // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach
+    int a_val{};
+    int b_val{};
+    std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, dst->data_type(), dqinfo);
+
+    build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
+    build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
     build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
+    build_opts.add_option("-DZERO_POINT=" + support::cpp11::to_string(dqinfo.offset));
 
     std::string kernel_name("mat_mul_native_quantized");
     kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
diff --git a/tests/validation/CL/MatMul.cpp b/tests/validation/CL/MatMul.cpp
index 5a262a8e78..844597f3e9 100644
--- a/tests/validation/CL/MatMul.cpp
+++ b/tests/validation/CL/MatMul.cpp
@@ -69,30 +69,27 @@ using CLMatMulActivationAlphaBetaFixture = MatMulValidationWithActivationAlphaBe
 template <typename T>
 using CLQuantizedMatMulActivationFixture = QuantizedMatMulValidationWithActivationFixture<CLTensor, CLAccessor, CLMatMul, GpuMatMulSettings, T>;
 
-/* The main act functions matmul is expected to support */
+/* The main act functions matmul (float) is expected to support */
 const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
 {
     ActivationLayerInfo(),
     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)
 });
 
+/* (Float datatype only) Larger activation functions dataset, used during some nightly tests. */
+const auto AllActivationsDataset = combine(datasets::ActivationFunctions(), framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
+
+// Alpha beta values should be integer values
+// This is for testing purposes with quantized datatypes and is not a limitation of the kernel.
+// To properly remove this restriction, dst_qinfo should be auto-initialised with consideration for alpha beta values
+// The main act functions quantized matmul kernels are expected to support
 const auto ActivationFunctionsQuantizedDataset = concat(concat(concat(
                                                                    framework::dataset::make("ActivationInfo", ActivationLayerInfo()),
                                                                    framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
-                                                               framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f))),
-                                                        framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f)));
-
-/* Larger activation functions dataset, used during some nightly tests. */
-const auto AllActivationsDataset = combine(datasets::ActivationFunctions(), framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
-
-const auto AllQuantizedActivationsDataset = combine(concat(datasets::ActivationFunctionsQuantized(),
-                                                           framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::HARD_SWISH,
-                                                                                                            ActivationLayerInfo::ActivationFunction::LEAKY_RELU
-                                                                                                          })),
-                                                    framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
+                                                               framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 1.f))),
+                                                        framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 2.f, 1.f)));
 
 TEST_SUITE(CL)
 TEST_SUITE(MatMul)
@@ -218,22 +215,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLQuantizedMatMulFixture<int8_t>, framework::Da
                                                                                                                     framework::dataset::make("NumberOfExtraRuns", { 0, 1 })),
                                                                                                                     framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })),
                                                                                                                     framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })),
-                                                                                                            framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })))
-{
-    // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_quant);
-}
-
-FIXTURE_DATA_TEST_CASE(RunAllActivations, CLQuantizedMatMulActivationFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(
-                           datasets::LargeMatMulDataset(),
-                           framework::dataset::make("TransposeA", { false })),
-                       framework::dataset::make("TransposeB", { true })),
-                       framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
-                       AllQuantizedActivationsDataset),
-                       framework::dataset::make("NumberOfExtraRuns", { 0, 1 })),
-                       framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })),
-                       framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })),
-                       framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })))
+                                                                                                            framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 50) })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_quant);