Implement dynamic quantization for GEMMLowp tests

This patch calculates the output quantization info based on the inputs'
quantization information.
The previous approach was using the same quantization information for
input, weights and output.

Remove QSYMM8_PER_CHANNEL path from the fixture as there are no
related tests

Remove repeated shapes from the dataset now that we get rid of the
quantization info from the dataset.

Combine signed and unsigned SmallGEMMLowpFusedBatchedMatMulDataset
into one as they become identical

Resolves COMPMID-6481, COMPMID-6634

Change-Id: I9f5a20f4bb45c3e5adab388564135ae8a5c0a9ea
Signed-off-by: SiCong Li <sicong.li@arm.com>
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10680
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 23 insertions, 18 deletions

diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index 46058bd148..9c4d1741eb 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2023 Arm Limited.
+ * Copyright (c) 2017-2024 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -50,9 +50,12 @@ namespace validation
 TEST_SUITE(NEON)
 TEST_SUITE(GEMMLowp)
 TEST_SUITE(MatrixMultiplyCore)
+
 using NEGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
 using NEGEMMLowpBatchedMatMulFixture      = GEMMLowpMatrixMultiplyCoreValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore, false, false, true>;
 
+using framework::dataset::make;
+
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()),
                shape_a, shape_b, shape_c, a_offset, b_offset)
 {
@@ -80,26 +83,26 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::c
 
 // *INDENT-OFF*
 // clang-format off
-DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
-    framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(
+    make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4
                                              TensorInfo(TensorShape(21U, 13U), 1, DataType::S32),                                 // Mismatching data type
                                              TensorInfo(TensorShape(20U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
                                              TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
                                              TensorInfo(TensorShape(16U, 32U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)),
                                           }),
-    framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
+    make("InputBInfo",{ TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
                                             TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
                                             TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
                                             TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
                                             TensorInfo(TensorShape(64U, 16U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)),
-                                          })),
-    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
+                                          }),
+    make("OutputInfo",{ TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
                                             TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
                                             TensorInfo(TensorShape(33U, 13U), 1, DataType::S32),
                                             TensorInfo(TensorShape(8U, 11U), 1, DataType::S32),
                                             TensorInfo(TensorShape(64U, 32U), 1, DataType::S32),
-                                           })),
-    framework::dataset::make("Expected", { true, false, false, false, true })),
+                                           }),
+    make("Expected", { true, false, false, false, true })),
     a_info, b_info, output_info, expected)
 {
     // Lock tensors
@@ -231,9 +234,9 @@ using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixtureBatchedUnsigned =
 TEST_SUITE(BatchedMatMul)
 TEST_SUITE(QASYMM8)
 FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixtureBatchedUnsigned, framework::DatasetMode::ALL,
-                       combine(combine(datasets::SmallGEMMLowpFusedBatchedMatMulDatasetUnsigned(),
-                                       framework::dataset::make("DataType", { DataType::QASYMM8 })),
-                               framework::dataset::make("bool", { false })))
+    combine(datasets::SmallGEMMLowpFusedBatchedMatMulDataset(),
+        make("DataType", { DataType::QASYMM8 }),
+        make("reshape_b_only_on_first_run", { false })))
 {
     validate(Accessor(_target), _reference, tolerance_batched);
 }
@@ -243,9 +246,9 @@ using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixtureBatchedSigned =
     GEMMLowpMatrixMultiplyCoreFusedOffsetOutputGenericValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore, false, false, int8_t, int8_t, true>;
 TEST_SUITE(QASYMM8_SIGNED)
 FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixtureBatchedSigned, framework::DatasetMode::ALL,
-                       combine(combine(datasets::SmallGEMMLowpFusedBatchedMatMulDatasetSigned(),
-                                       framework::dataset::make("DataType", { DataType::QASYMM8_SIGNED })),
-                               framework::dataset::make("bool", { false })))
+    combine(datasets::SmallGEMMLowpFusedBatchedMatMulDataset(),
+        make("DataType", { DataType::QASYMM8_SIGNED }),
+        make("reshape_b_only_on_first_run", { false })))
 {
     validate(Accessor(_target), _reference, tolerance_batched);
 }
@@ -256,15 +259,17 @@ using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMulti
 constexpr AbsoluteTolerance<float> tolerance_quant(1);
 
 TEST_SUITE(FusedOffsetOutput)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, combine(datasets::SmallGEMMLowpFusedOffsetOutputUint8Dataset(),
-                       framework::dataset::make("DataType", { DataType::QASYMM8 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL,
+    combine(datasets::SmallGEMMLowpFusedOffsetOutputUint8Dataset(),
+        make("DataType", { DataType::QASYMM8 })))
 {
     // Validate output
     validate(Accessor(_target), _reference, tolerance_quant);
 }
 
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeGEMMLowpFusedOffsetOutputUint8Dataset(),
-                       framework::dataset::make("DataType", { DataType::QASYMM8 })))
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY,
+    combine(datasets::LargeGEMMLowpFusedOffsetOutputUint8Dataset(),
+        make("DataType", { DataType::QASYMM8 })))
 {
     // Validate output
     validate(Accessor(_target), _reference, tolerance_quant);