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author | Gian Marco <gianmarco.iodice@arm.com> | 2017-11-28 09:10:03 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:41:58 +0000 |
commit | 58c5794b917dae10ff115dd85ec69e2ca41136c1 (patch) | |
tree | f2cea2d94e6566be720256dc6105056798723699 /tests/validation/fixtures | |
parent | 754e9526a7caf50876c2db9563dc72f096093b34 (diff) | |
download | ComputeLibrary-58c5794b917dae10ff115dd85ec69e2ca41136c1.tar.gz |
COMPMID-706 - Add GEMMLowp output stage for scaling by a fixed point number
DoD:
- Implement NEON kernel for quantizing down the gemmlowp result. The
result should be scaled by a fixedpoint number
- Implement OpenCL kernel for quantizing down the gemmlowp result. The
result should be scaled by a fixedpoint number
- Add test for validating the result
Required for:
- Integration of GEMMLowp in Android NN
- Convolution quantized
- Fully connected quantized
Change-Id: Ia963d25d695471e963961fb49a5600e78374ac4f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110981
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'tests/validation/fixtures')
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 92 |
1 files changed, 92 insertions, 0 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 60b89bc653..d3e2aacbe1 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -207,6 +207,98 @@ protected: TensorType _target{}; SimpleTensor<uint8_t> _reference{}; }; + +template <typename TensorType, typename AccessorType, typename FunctionType> +class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias) + { + _target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias); + _reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + std::uniform_int_distribution<> distribution(-6000, 6000); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias) + { + TensorShape shape_bias(shape[0]); + + // Create tensors + TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1); + TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1); + TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1); + + // Create and configure function + FunctionType output_stage; + output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + a.allocator()->allocate(); + c.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensor + fill(AccessorType(a), 0); + + if(add_bias) + { + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate bias tensor + b.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensor + fill(AccessorType(b), 1); + } + + // Compute GEMM function + output_stage.run(); + return c; + } + + SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, + bool add_bias) + { + // Create reference + TensorShape shape_bias(shape[0]); + + SimpleTensor<int32_t> a{ shape, DataType::S32, 1 }; + SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 }; + + // Fill reference + fill(a, 0); + + if(add_bias) + { + // Fill bias + fill(b, 1); + + return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max); + } + else + { + return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max); + } + } + + TensorType _target{}; + SimpleTensor<uint8_t> _reference{}; +}; } // namespace validation } // namespace test } // namespace arm_compute |