aboutsummaryrefslogtreecommitdiff
path: root/tests/validation/fixtures/GEMMLowpFixture.h
diff options
context:
space:
mode:
Diffstat (limited to 'tests/validation/fixtures/GEMMLowpFixture.h')
-rw-r--r--tests/validation/fixtures/GEMMLowpFixture.h194
1 files changed, 136 insertions, 58 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 836f8eddfe..90a4b5cf40 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -42,86 +42,164 @@ namespace test
{
namespace validation
{
-template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
-class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
+namespace
{
-public:
- template <typename...>
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
+template <typename U>
+void fill(U &&tensor, int i)
+{
+ // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
+ std::uniform_int_distribution<> distribution(1, 254);
+ library->fill(tensor, distribution, i);
+}
+
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false>
+TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo())
+{
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
+ TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
+ TensorType output = create_tensor<TensorType>(shape_output, output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : DataType::QASYMM8, 1);
+
+ a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
+ b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
+
+ TensorType bias;
+ if(is_fused)
{
- _target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset);
- _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset);
+ TensorShape bias_shape(shape_b[0]);
+ bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1);
}
-protected:
- template <typename U>
- void fill(U &&tensor, int i)
+ // Create and configure function
+ // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
+ FunctionType gemmlowp;
+ // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
+ gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false, output_stage));
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+ output.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(a), 0);
+ fill(AccessorType(b), 1);
+
+ if(is_fused)
{
- // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
- std::uniform_int_distribution<> distribution(1, 254);
- library->fill(tensor, distribution, i);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ bias.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(bias), 2);
}
- TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
+ // Compute GEMM function
+ gemmlowp.run();
+ return output;
+}
+
+template <bool reinterpret_input_as_3d>
+SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
+{
+ TensorShape shape_a_to_use = shape_a;
+ if(reinterpret_input_as_3d)
{
- // Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
- TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
- TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1);
+ // Collapse the second and third dimension if the input is 3D
+ shape_a_to_use.collapse(2U, 1U);
+ }
- a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
- b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
+ // Create reference
+ SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
+ SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
- // Create and configure function
- // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
- FunctionType gemmlowp;
- // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
- gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), reinterpret_input_as_3d));
+ // Fill reference
+ fill(a, 0);
+ fill(b, 1);
- ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
+ return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_output, a_offset, b_offset);
+}
+}
- // Allocate tensors
- a.allocator()->allocate();
- b.allocator()->allocate();
- c.allocator()->allocate();
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
+class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset)
+ {
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset);
+ }
- ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
+protected:
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
+ {
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t>(shape_a, shape_b, shape_output, a_offset, b_offset);
+ }
- // Fill tensors
- fill(AccessorType(a), 0);
- fill(AccessorType(b), 1);
+ SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
+ {
+ return compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
+ }
- // Compute GEMM function
- gemmlowp.run();
- return c;
+ TensorType _target{};
+ SimpleTensor<int32_t> _reference{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
+class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS);
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
}
- SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
+protected:
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
{
- TensorShape shape_a_to_use = shape_a;
- if(reinterpret_input_as_3d)
- {
- // Collapse the second and third dimension if the input is 3D
- shape_a_to_use.collapse(2U, 1U);
- }
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true>(shape_a, shape_b, shape_output, a_offset, b_offset,
+ output_stage);
+ }
- // Create reference
- SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
- SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
+ SimpleTensor<qasymm8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage)
+ {
+ SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
- // Fill reference
- fill(a, 0);
- fill(b, 1);
+ TensorShape bias_shape(shape_b[0]);
+ SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
+ fill(bias, 2);
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
+ switch(output_stage.type)
+ {
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(output, bias,
+ output_stage.gemmlowp_offset, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(output, bias,
+ output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not Supported!");
+ }
}
- TensorType _target{};
- SimpleTensor<int32_t> _reference{};
+ TensorType _target{};
+ SimpleTensor<qasymm8_t> _reference{};
};
template <typename TensorType, typename AccessorType, typename FunctionType>
@@ -536,4 +614,4 @@ protected:
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */ \ No newline at end of file
+#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-13 21:55:09 +0000