COMPMID-3928: Fix output conversion in gemmlowp_mm_native

This patch solves the following issues that arose from nightly tests:

 - The accumulated result of gemmlowp_mm_native can be either uint or
int and in order to be stored in memory we need to convert it to int.

- The RHS matrix still needs padding on the X dimension. Hence, revert
few changes to add the necessary padding elements.

- Replace zero padding validation tests with assertion in the configure
method of the kernel.

Change-Id: Ib6614a91bd0e98f2b850f52eef14d4fbf55517c8
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4259
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 71 deletions

diff --git a/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp
index 9e717dfac9..ce000bd8e1 100644
--- a/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMLowpMatrixMultiplyNative.cpp
@@ -88,81 +88,10 @@ const auto n0_values_nightly = framework::dataset::make("N0", { 1, 2, 3, 4, 8 })
 
 /** K0 values to test - Nightly */
 const auto k0_values_nightly = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 });
-
-/** Zero padding test */
-bool validate_zero_padding(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info)
-{
-    const unsigned int M = m_value;
-    const unsigned int N = n_value;
-    const unsigned int K = k_value;
-
-    GEMMLHSMatrixInfo lhs_info;
-    lhs_info.m0         = m0_value;
-    lhs_info.k0         = k0_value;
-
-    GEMMRHSMatrixInfo rhs_info;
-    rhs_info.n0         = n0_value;
-    rhs_info.k0         = k0_value;
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m               = M;
-    kernel_info.n               = N;
-    kernel_info.k               = K;
-    kernel_info.broadcast_bias  = broadcast_bias;
-    kernel_info.activation_info = act_info;
-
-    const TensorShape lhs_shape(K, M, b_value);
-    const TensorShape rhs_shape(N, K, b_value);
-    const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type),
-                                                   TensorInfo(rhs_shape, 1, data_type),
-                                                   kernel_info);
-
-    // Create tensors
-    CLTensor lhs  = create_tensor<CLTensor>(lhs_shape, data_type);
-    CLTensor rhs  = create_tensor<CLTensor>(rhs_shape, data_type);
-    CLTensor dst  = create_tensor<CLTensor>(dst_shape, DataType::S32);
-
-    ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
-    ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
-    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-    // Create and configure function
-    CLGEMMLowpMatrixMultiplyNative gemm;
-    gemm.configure(&lhs, &rhs,  &dst, lhs_info, rhs_info, GEMMReshapeInfo(m_value, n_value, k_value));
-
-    // Padding can be added along rhs and bias's X dimension
-    return dst.info()->padding().empty() && lhs.info()->padding().empty() && rhs.info()->padding().empty();
-}
 } // namespace
 
 TEST_SUITE(CL)
 TEST_SUITE(GEMMLowpMatrixMultiplyNative)
-
-/** Validate zero padding tests
- *
- * A series of validation tests to check that no padding is added as part of configuration for 4 different scenarios.
- *
- * Checks performed in order:
- *     - No partial blocks in both x and y dimensions
- *     - Partial blocks in x dimension
- *     - Partial blocks in y dimension
- *     - Partial blocks in both x and y dimensions
- *     - No blocks in both x and y dimensions, scalar store (N0==1)
- *     - Special case: partial_n0 == 5 (vstore1 should be invoked instead of vstore_partial_1)
- */
-DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip(
-framework::dataset::make("M",                   { 24, 63,   1, 51, 255, }),
-framework::dataset::make("N",                   { 47, 29, 122, 20,  21, })),
-framework::dataset::make("M0",                  { 4,   8,   2,  1,   8, })),
-framework::dataset::make("N0",                  { 4,   4,   3,  1,   8, })),
-m_value, n_value, m0_value, n0_value)
-{
-    bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, false, DataType::QASYMM8, ActivationLayerInfo());
-    ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS);
-}
-
-
-
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyNativeFixture, framework::DatasetMode::ALL,
                 combine(combine(combine(combine(combine(combine(m_values,
                                                                 n_values),