COMPMID-731 - Remove padding requirements for NEGEMMLowpOutputStage

Used a left-over for loop in:
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
in order to remove the padding requirements for AndroidNN

Change-Id: I8ef529fc3d1adecf15fbe42002d99bc0030f131f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/112266
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>

4 files changed, 295 insertions, 88 deletions

diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index 1e369ab03a..8b3f2383ab 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -66,7 +66,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con
 
 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
 {
-    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    // Note: This kernel performs 16 elements per iteration.
+    // However, since we use a left-over for loop, we cannot have any read or write out of memory
+    // For this reason num_elems_processed_per_iteration is set to 1
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
 
     // Configure kernel window
     Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
@@ -86,7 +89,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 
     if(bias != nullptr)
     {
-        AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]);
+        AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
         window_changed = window_changed || update_window_and_padding(win, bias_access);
     }
 
@@ -144,6 +147,37 @@ inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int result_fixedpoi
 
     return out_u8;
 }
+
+/* Function used by the left-over for loop to perform the quantization */
+template <bool is_bounded_relu>
+inline uint8_t finalize_quantization(int32x4_t in_s32, int result_fixedpoint_multiplier, int32_t result_shift, int32x4_t result_offset_after_shift_s32, uint8_t min_u8, uint8_t max_u8)
+{
+    const static int32x4_t zero_s32      = vdupq_n_s32(0);
+    const static int32x4_t sat_value_s32 = vdupq_n_s32(255);
+
+    // Fixed point multiplication with vector saturating rounding doubling multiply high with scalar
+    in_s32 = vqrdmulhq_n_s32(in_s32, result_fixedpoint_multiplier);
+
+    // Round to the nearest division by a power-of-two using result_shift_s32
+    in_s32 = rounding_divide_by_pow2(in_s32, result_shift);
+
+    // Add the offset terms
+    in_s32 = vaddq_s32(in_s32, result_offset_after_shift_s32);
+
+    // Saturate negative values
+    in_s32 = vmaxq_s32(in_s32, zero_s32);
+    in_s32 = vminq_s32(in_s32, sat_value_s32);
+
+    auto out_u8 = static_cast<uint8_t>(vgetq_lane_s32(in_s32, 0));
+
+    if(is_bounded_relu)
+    {
+        out_u8 = std::max(out_u8, min_u8);
+        out_u8 = std::min(out_u8, max_u8);
+    }
+
+    return out_u8;
+}
 } // namespace
 
 namespace arm_compute
@@ -161,63 +195,103 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);
 
-    Iterator in(_input, window);
-    Iterator out(_output, window);
+    const int  window_step_x  = 16;
+    const auto window_start_x = static_cast<int>(window.x().start());
+    const auto window_end_x   = static_cast<int>(window.x().end());
+
+    Window win(window);
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(_input, win);
+    Iterator out(_output, win);
 
     if(_bias != nullptr)
     {
         Window win_biases;
-        win_biases.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().step()));
+        win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
         win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
 
         Iterator bias(_bias, win_biases);
-        execute_window_loop(window, [&](const Coordinates & id)
+        execute_window_loop(win, [&](const Coordinates & id)
         {
-            int32x4x4_t in_s32 =
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
+                int32x4x4_t in_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12)
-                }
-            };
-
-            const int32x4x4_t bias_s32 =
-            {
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                const int32x4x4_t bias_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 12)
-                }
-            };
-
-            // Add the bias to GEMM's result
-            in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
-            in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
-            in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
-            in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
-
-            vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
+                    }
+                };
+
+                // Add the bias to GEMM's result
+                in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
+                in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
+                in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
+                in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
+
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x);
+                int32_t       in_value   = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+
+                // Add bias
+                in_value += bias_value;
+
+                // Finalize and store the result
+                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
+                                                                          static_cast<uint8_t>(_max));
+            }
         },
         in, bias, out);
     }
     else
     {
-        execute_window_loop(window, [&](const Coordinates & id)
+        execute_window_loop(win, [&](const Coordinates & id)
         {
-            int32x4x4_t in_s32 =
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
+                int32x4x4_t in_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12)
-                }
-            };
-
-            vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                const int32x4_t in_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+
+                // Finalize and store the result
+                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
+            }
         },
         in, out);
     }
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
index 7f351020b9..54513d8cdb 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
@@ -65,7 +65,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con
 
 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
 {
-    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    // Note: This kernel performs 16 elements per iteration.
+    // However, since we use a left-over for loop, we cannot have any read or write out of memory
+    // For this reason num_elems_processed_per_iteration is set to 1
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
 
     // Configure kernel window
     Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
@@ -85,7 +88,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 
     if(bias != nullptr)
     {
-        AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]);
+        AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
         window_changed = window_changed || update_window_and_padding(win, bias_access);
     }
 
@@ -163,69 +166,125 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);
 
-    Iterator in(_input, window);
-    Iterator out(_output, window);
+    const int  window_step_x  = 16;
+    const auto window_start_x = static_cast<int>(window.x().start());
+    const auto window_end_x   = static_cast<int>(window.x().end());
+
+    Window win(window);
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(_input, win);
+    Iterator out(_output, win);
 
     if(_bias != nullptr)
     {
         Window win_biases;
-        win_biases.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().step()));
+        win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
         win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
 
         Iterator bias(_bias, win_biases);
-        execute_window_loop(window, [&](const Coordinates & id)
+        execute_window_loop(win, [&](const Coordinates & id)
         {
-            int32x4x4_t in_s32 =
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
+                int32x4x4_t in_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12)
-                }
-            };
-
-            const int32x4x4_t bias_s32 =
-            {
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                const int32x4x4_t bias_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 12)
-                }
-            };
-
-            // Add the bias to GEMM's result
-            in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
-            in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
-            in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
-            in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
+                    }
+                };
+
+                // Add the bias to GEMM's result
+                in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
+                in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
+                in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
+                in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
+
+                // Add the offset terms to GEMM's result and multiply by result_mult_int
+                scale_input(in_s32, result_offset_s32, _result_mult_int);
+
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x);
+                int32_t       in_value   = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
 
-            // Add the offset terms to GEMM's result and multiply by result_mult_int
-            scale_input(in_s32, result_offset_s32, _result_mult_int);
+                // Quantize
+                in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;
 
-            vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+                // Finalize and store the result
+                if(is_bounded_relu)
+                {
+                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
+                }
+                else
+                {
+                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
+                }
+            }
         },
         in, bias, out);
     }
     else
     {
-        execute_window_loop(window, [&](const Coordinates & id)
+        execute_window_loop(win, [&](const Coordinates & id)
         {
-            int32x4x4_t in_s32 =
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
+                int32x4x4_t in_s32 =
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12)
-                }
-            };
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                // Add the offset terms to GEMM's result and multiply by result_mult_int
+                scale_input(in_s32, result_offset_s32, _result_mult_int);
+
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
 
-            // Add the offset terms to GEMM's result and multiply by result_mult_int
-            scale_input(in_s32, result_offset_s32, _result_mult_int);
+                // Quantize
+                in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;
 
-            vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+                // Finalize and store the result
+                if(is_bounded_relu)
+                {
+                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
+                }
+                else
+                {
+                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
+                }
+            }
         },
         in, out);
     }
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index 02a71aa7b5..c9e5510760 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -162,11 +162,11 @@ public:
         : ShapeDataset("Shape",
     {
         // Batch size 1
-        TensorShape{ 1920U, 1080U },
-                     TensorShape{ 640U, 480U, 2U, 3U },
-                     TensorShape{ 4160U, 3120U },
+        TensorShape{ 1921U, 1083U },
+                     TensorShape{ 641U, 485U, 2U, 3U },
+                     TensorShape{ 4159U, 3117U },
                      // Batch size 4
-                     TensorShape{ 800U, 600U, 1U, 4U },
+                     TensorShape{ 799U, 595U, 1U, 4U },
     })
     {
     }
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index 7616df9eaa..a901b442ab 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -187,6 +187,43 @@ const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::m
 
 using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>;
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+    framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
+                                             TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max
+                                             TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
+                                          }),
+    framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
+                                            TensorInfo(TensorShape(21U), 1, DataType::S32),
+                                            TensorInfo(TensorShape(20U), 1, DataType::S32),
+                                          })),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
+                                            TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
+                                            TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
+                                           })),
+    framework::dataset::make("Min",{        0,
+                                            8,
+                                            13,
+                                           })),
+    framework::dataset::make("Max",{        205,
+                                            300,
+                                            180,
+                                           })),
+    framework::dataset::make("Expected", { true, false, false })),
+    a_info, b_info, output_info, min, max, expected)
+{
+    // Lock tensors
+    Status status =  NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(&a_info.clone()->set_is_resizable(false),
+                                                                     &b_info.clone()->set_is_resizable(false),
+                                                                     &output_info.clone()->set_is_resizable(false),
+                                                                     min,
+                                                                     max);
+    ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), quantize_down_int32_to_uint8_scale_cases),
                shape, result_offset, result_mult_int, result_shift, min, max, add_bias)
 {
@@ -218,7 +255,7 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     }
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding(0);
     validate(in.info()->padding(), padding);
     validate(out.info()->padding(), padding);
 
@@ -269,6 +306,43 @@ const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases = framewo
 using NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture =
     GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint>;
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+    framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16
+                                             TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max
+                                             TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
+                                          }),
+    framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
+                                            TensorInfo(TensorShape(21U), 1, DataType::S32),
+                                            TensorInfo(TensorShape(20U), 1, DataType::S32),
+                                          })),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
+                                            TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8),
+                                            TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
+                                           })),
+    framework::dataset::make("Min",{        0,
+                                            8,
+                                            13,
+                                           })),
+    framework::dataset::make("Max",{        205,
+                                            300,
+                                            180,
+                                           })),
+    framework::dataset::make("Expected", { true, false, false })),
+    a_info, b_info, output_info, min, max, expected)
+{
+    // Lock tensors
+    Status status =  NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false),
+                                                                                 &b_info.clone()->set_is_resizable(false),
+                                                                                 &output_info.clone()->set_is_resizable(false),
+                                                                                 min,
+                                                                                 max);
+    ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
                                                                    quantize_down_int32_to_uint8_scale_by_fixedpoint_cases),
                shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias)
@@ -301,7 +375,7 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     }
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding(0);
     validate(in.info()->padding(), padding);
     validate(out.info()->padding(), padding);