COMPMID-665 - NEON: Add QASYMM8 in place Activation layer
- Added min and max arguments for QuantizeDownInt32ToUint8Scale in order
to apply bounded relu
- Added support for int32_t biases
- Extended tests
Change-Id: I015dae17faa7284766b5435ca33bcf593c1b2b69
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/96512
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/tests/validation/CPP/GEMMLowp.cpp b/tests/validation/CPP/GEMMLowp.cpp
index 8670a22..bf002cf 100644
--- a/tests/validation/CPP/GEMMLowp.cpp
+++ b/tests/validation/CPP/GEMMLowp.cpp
@@ -33,6 +33,36 @@
{
namespace reference
{
+namespace
+{
+template <typename T>
+void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,
+ int32_t min, int32_t max)
+{
+ const int cols_in = in->shape().x();
+
+ for(int i = 0; i < in->num_elements(); ++i)
+ {
+ int32_t result = ((*in)[i] + result_offset) * result_mult_int;
+
+ if(bias != nullptr)
+ {
+ result += (*bias)[i % cols_in];
+ }
+
+ result >>= result_shift;
+
+ // Bounded ReLu
+ if(min != max)
+ {
+ result = std::max(min, std::min(max, result));
+ }
+
+ (*dst)[i] = static_cast<uint8_t>(std::max(0, std::min(255, result)));
+ }
+}
+} // namespace
+
template <typename T>
SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<T> &a, const SimpleTensor<T> &b, int32_t a_offset, int32_t b_offset)
{
@@ -80,21 +110,31 @@
}
template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max)
{
SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8);
- for(int i = 0; i < in.num_elements(); ++i)
- {
- const int32_t result = ((in[i] + result_offset) * result_mult_int) >> result_shift;
- dst[i] = static_cast<uint8_t>(std::max(0, std::min(255, result)));
- }
+ quantize_down_int32_to_uint8_scale<T>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max);
+
+ return dst;
+}
+
+template <typename T>
+SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,
+ int32_t min, int32_t max)
+{
+ SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8);
+
+ quantize_down_int32_to_uint8_scale<T>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max);
return dst;
}
template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, int32_t a_offset, int32_t b_offset);
-template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift);
+template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min,
+ int32_t max);
+template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, int32_t result_mult_int,
+ int32_t result_shift, int32_t min, int32_t max);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/CPP/GEMMLowp.h b/tests/validation/CPP/GEMMLowp.h
index cbed2206..ee33d8e 100644
--- a/tests/validation/CPP/GEMMLowp.h
+++ b/tests/validation/CPP/GEMMLowp.h
@@ -35,14 +35,17 @@
{
namespace reference
{
+SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b);
+
template <typename T>
SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<T> &a, const SimpleTensor<T> &b, int32_t a_offset, int32_t b_offset);
template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift);
+SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min = 0, int32_t max = 0);
-SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b);
-
+template <typename T>
+SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,
+ int32_t min = 0, int32_t max = 0);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index ba91ced..078096a 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -131,34 +131,55 @@
TEST_SUITE(QuantizeDownInt32ToUint8Scale)
+const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 2) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2,
+ 3)
+ * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true });
+
+const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::make("result_offset", -2, 2) * framework::dataset::make("result_mult_int", 1,
+ 2)
+ * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true });
+
using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>;
-const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -4, 4) * framework::dataset::make("result_mult_int", 1, 3) * framework::dataset::make("result_shift", 2,
- 4);
-
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)
+ shape, result_offset, result_mult_int, result_shift, min, max, add_bias)
{
+ TensorShape shape_bias(shape[0]);
+
// Create tensors
- Tensor in = create_tensor<Tensor>(shape, DataType::S32);
- Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8);
+ Tensor in = create_tensor<Tensor>(shape, DataType::S32);
+ Tensor bias = create_tensor<Tensor>(shape_bias, DataType::S32);
+ Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8);
ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS);
// Create and configure function
NEGEMMLowpQuantizeDownInt32ToUint8Scale output_stage;
- output_stage.configure(&in, &out, result_offset, result_mult_int, result_shift);
+ output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_offset, result_mult_int, result_shift, min, max);
- // Validate valid region
+ // Validate valid region input and output
const ValidRegion valid_region = shape_to_valid_region(shape);
validate(in.info()->valid_region(), valid_region);
validate(out.info()->valid_region(), valid_region);
+ // Validate valid region bias
+ if(add_bias)
+ {
+ const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias);
+ validate(bias.info()->valid_region(), valid_region_bias);
+ }
+
// Validate padding
const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
validate(in.info()->padding(), padding);
validate(out.info()->padding(), padding);
+
+ if(add_bias)
+ {
+ validate(bias.info()->padding(), padding);
+ }
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases))
@@ -173,8 +194,35 @@
validate(Accessor(_target), _reference);
}
-TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale
+TEST_SUITE(BoundedReLu)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+TEST_SUITE_END() // BoundedReLu
+
+TEST_SUITE(AddBias)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+TEST_SUITE_END() // AddBias
+
+TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale
TEST_SUITE_END() // OutputStage
TEST_SUITE_END() // GEMMLowp
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index f9b0dbd..a99e932 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -122,10 +122,10 @@
{
public:
template <typename...>
- void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+ void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
{
- _target = compute_target(shape, result_offset, result_mult_int, result_shift);
- _reference = compute_reference(shape, result_offset, result_mult_int, result_shift);
+ _target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
+ _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
}
protected:
@@ -136,43 +136,72 @@
library->fill(tensor, distribution, i);
}
- TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+ TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_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, DataType::QASYMM8, 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, &b, result_offset, result_mult_int, result_shift);
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max);
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);
// Allocate tensors
a.allocator()->allocate();
- b.allocator()->allocate();
+ c.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(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
- // Fill tensors
+ // 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 b;
+ return c;
}
- SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift)
+ SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_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);
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift);
+ if(add_bias)
+ {
+ // Fill bias
+ fill(b, 1);
+
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int, result_shift, min, max);
+ }
+ else
+ {
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift, min, max);
+ }
}
TensorType _target{};