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-rw-r--r--tests/validation/fixtures/MatMulFixture.h383
1 files changed, 326 insertions, 57 deletions
diff --git a/tests/validation/fixtures/MatMulFixture.h b/tests/validation/fixtures/MatMulFixture.h
index 2e79612a37..ffd12e56d0 100644
--- a/tests/validation/fixtures/MatMulFixture.h
+++ b/tests/validation/fixtures/MatMulFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -27,15 +27,17 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
#include "src/core/utils/quantization/AsymmHelpers.h"
#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT
#include "tests/framework/Fixture.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/GEMM.h"
#include "tests/validation/reference/GEMMLowp.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"
+#include "tests/validation/Validation.h"
+
#include <limits>
#include <random>
#include <type_traits>
@@ -50,32 +52,50 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ
class MatMulGenericValidationFixture : public framework::Fixture
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs,
- Settings settings, QuantizationInfo a_qinfo = QuantizationInfo(), QuantizationInfo b_qinfo = QuantizationInfo(), QuantizationInfo o_qinfo = QuantizationInfo())
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs,
+ Settings settings,
+ QuantizationInfo a_qinfo = QuantizationInfo(),
+ QuantizationInfo b_qinfo = QuantizationInfo(),
+ QuantizationInfo o_qinfo = QuantizationInfo())
{
// For brevity, the input shapes are assumed to be not-transposed for both a and b matrices.
- if(transpose_a)
+ if (transpose_a)
{
permute(shape_a, PermutationVector(1U, 0U));
}
- if(transpose_b)
+ if (transpose_b)
{
permute(shape_b, PermutationVector(1U, 0U));
}
- _target = compute_target(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, settings, a_qinfo, b_qinfo, o_qinfo);
- _reference = compute_reference(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, a_qinfo, b_qinfo, o_qinfo);
+ _target = compute_target(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info,
+ num_extra_runs, settings, a_qinfo, b_qinfo, o_qinfo);
+ _reference = compute_reference(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info,
+ a_qinfo, b_qinfo, o_qinfo);
}
protected:
template <typename U>
void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
{
- switch(tensor.data_type())
+ switch (tensor.data_type())
{
+ case DataType::BFLOAT16:
+ {
+ arm_compute::utils::uniform_real_distribution_16bit<bfloat16> distribution{float(lo), float(hi)};
+ library->fill(tensor, distribution, i);
+ break;
+ }
case DataType::F16:
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{float(lo), float(hi)};
library->fill(tensor, distribution, i);
break;
}
@@ -98,8 +118,18 @@ protected:
}
}
- TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool transpose_a, bool transpose_b, DataType data_type,
- ActivationLayerInfo act_info, int num_extra_runs, const Settings &settings, QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo)
+ virtual TensorType compute_target(const TensorShape &shape_a,
+ const TensorShape &shape_b,
+ const TensorShape &output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs,
+ const Settings &settings,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo)
{
// 1. Create Classes and configure function
// ----------------------------------------------------
@@ -137,7 +167,7 @@ protected:
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
// For multiple runs.
- for(int i = 0; i < num_extra_runs; i++)
+ for (int i = 0; i < num_extra_runs; i++)
{
// Stress dynamic tensors by running multiple times.
// --------------------------------------------------------
@@ -164,7 +194,12 @@ protected:
template <typename TT>
typename std::enable_if < !std::is_integral<TT>::value, SimpleTensor<TT >>::type
- compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo)
+ compute_reference_gemm(const SimpleTensor<TT> &a,
+ const SimpleTensor<TT> &b,
+ const SimpleTensor<TT> &c,
+ float alpha,
+ float beta,
+ const QuantizationInfo &o_qinfo)
{
ARM_COMPUTE_UNUSED(o_qinfo);
@@ -173,7 +208,12 @@ protected:
template <typename TT>
typename std::enable_if<std::is_integral<TT>::value, SimpleTensor<TT>>::type
- compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo)
+ compute_reference_gemm(const SimpleTensor<TT> &a,
+ const SimpleTensor<TT> &b,
+ const SimpleTensor<TT> &c,
+ float alpha,
+ float beta,
+ const QuantizationInfo &o_qinfo)
{
ARM_COMPUTE_UNUSED(alpha, beta);
@@ -186,23 +226,30 @@ protected:
int32_t output_multiplier = 0;
int32_t output_shift = 0;
quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
- std::vector<int32_t> output_multipliers{ output_multiplier };
- std::vector<int32_t> output_shifts{ output_shift };
+ std::vector<int32_t> output_multipliers{output_multiplier};
+ std::vector<int32_t> output_shifts{output_shift};
//The lhs and rhs offsets are negated here to keep the reference aligned with the function implementation where the lhs and rhs offsets are also negated.
- const auto tmp = reference::gemmlowp_matrix_multiply_core<int32_t>(
- a, b, c.shape(), -aq.offset, -bq.offset);
+ const auto tmp = reference::gemmlowp_matrix_multiply_core<int32_t>(a, b, c.shape(), -aq.offset, -bq.offset);
auto output = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TT>(
- tmp, output_multipliers, output_shifts, oq.offset,
- std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max());
+ tmp, output_multipliers, output_shifts, oq.offset, std::numeric_limits<int32_t>::lowest(),
+ std::numeric_limits<int32_t>::max());
output.quantization_info(o_qinfo);
return output;
}
- SimpleTensor<T> compute_reference(const TensorShape &a_shape, const TensorShape &b_shape, const TensorShape &output_shape, bool transpose_a, bool transpose_b, DataType data_type,
- ActivationLayerInfo act_info, QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo)
+ SimpleTensor<T> compute_reference(const TensorShape &a_shape,
+ const TensorShape &b_shape,
+ const TensorShape &output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo)
{
// We collapse dimensions > 2 onto dimension 2, i.e. 4D+ tensors will look like 3D
// This is necessary unless we choose to extend gemm reference for 4D+ tensors
@@ -211,9 +258,9 @@ protected:
TensorShape b_shape_collapsed = b_shape.collapsed_from(Window::DimZ);
// Create reference
- SimpleTensor<T> a{ a_shape_collapsed, data_type, 1, a_qinfo };
- SimpleTensor<T> b{ b_shape_collapsed, data_type, 1, b_qinfo };
- SimpleTensor<T> c{ output_shape_collapsed, data_type, 1 };
+ SimpleTensor<T> a{a_shape_collapsed, data_type, 1, a_qinfo};
+ SimpleTensor<T> b{b_shape_collapsed, data_type, 1, b_qinfo};
+ SimpleTensor<T> c{output_shape_collapsed, data_type, 1};
// Fill reference
fill(a, 2);
@@ -234,16 +281,16 @@ protected:
b_transposed_shape.set(1, b.shape().x());
// Define transposed tensors
- SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
- SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };
+ SimpleTensor<T> a_transposed{a_transposed_shape, data_type};
+ SimpleTensor<T> b_transposed{b_transposed_shape, data_type};
// pretranspose a if necessary
- if(transpose_a)
+ if (transpose_a)
{
a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
}
// pretranspose b if necessary
- if(transpose_b)
+ if (transpose_b)
{
b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
}
@@ -251,12 +298,13 @@ protected:
// Setting beta to 0 will effectively disable C for the
// computation of the reference: alpha * A * B + 0 * C
// Use transposed tensors if boolean enabled else use original tensors
- auto result = compute_reference_gemm<T>((transpose_a) ? a_transposed : a, (transpose_b) ? b_transposed : b, c, 1.0f, 0.f, o_qinfo);
+ auto result = compute_reference_gemm<T>((transpose_a) ? a_transposed : a, (transpose_b) ? b_transposed : b, c,
+ 1.0f, 0.f, o_qinfo);
result = reference::activation_layer<T>(result, act_info, o_qinfo);
// We reshape the gemm output back if the tensor is high dimensional
- if(output_shape_collapsed != output_shape)
+ if (output_shape_collapsed != output_shape)
{
result = reference::reshape_layer(result, output_shape);
}
@@ -268,72 +316,293 @@ protected:
SimpleTensor<T> _reference{};
};
+/// TODO: (ONCPUML-1451) The current state of this fixture is interim and a longer-term testing method will be implemented later.
+/// @note: Currently we support only a 2x2 test due to the lack of reorder ref. implementation.
+template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
+class MatMulFixedFormatFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+{
+public:
+ TensorType compute_target(const TensorShape &shape_a,
+ const TensorShape &shape_b,
+ const TensorShape &output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs,
+ const Settings &settings,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo) override
+ {
+ // 1. Create Classes and configure function
+ // ----------------------------------------------------
+ // Create tensors
+ // Configure relevant classes and matmul function
+ TensorType a = create_tensor<TensorType>(shape_a, data_type, 1, a_qinfo);
+ TensorType b = create_tensor<TensorType>(shape_b, data_type, 1, b_qinfo);
+ TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, o_qinfo);
+
+ const auto weight_tensor_info = TensorInfo(*b.info());
+ const TensorInfo new_tensor_info = prepare_weights(weight_tensor_info);
+ TensorType weights_transformed = create_tensor<TensorType>(new_tensor_info);
+
+ // Configure MatMulInfo class
+ MatMulInfo mm_info;
+ mm_info.adj_lhs(transpose_a).adj_rhs(transpose_b);
+
+ // Ensure values are dynamic
+ a.info()->set_are_values_constant(false);
+ b.info()->set_are_values_constant(false);
+ weights_transformed.info()->set_are_values_constant(false);
+
+ FunctionType matmul;
+
+ // Configure operator
+ matmul.configure(&a, &weights_transformed, &dst, mm_info, settings, act_info);
+
+ // Assertions
+ ARM_COMPUTE_ASSERT(a.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(b.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(weights_transformed.info()->is_resizable());
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+ dst.allocator()->allocate();
+ weights_transformed.allocator()->allocate();
+
+ ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!weights_transformed.info()->is_resizable());
+
+ // For multiple runs.
+ for (int i = 0; i < num_extra_runs; i++)
+ {
+ // Stress dynamic tensors by running multiple times.
+ // --------------------------------------------------------
+ // Fill tensors with new seed
+ // Run function
+ const int seed_offset = num_extra_runs * 100;
+ this->fill(AccessorType(a), seed_offset);
+ this->fill(AccessorType(b), seed_offset + 1);
+
+ matmul.run();
+ }
+
+ // 2. Final Run for reference comparison
+ // --------------------------------------------------------
+ // Re-fill tensors same seed as reference run
+ // Compute MatMul operation
+ this->fill(AccessorType(a), 2);
+ this->fill(AccessorType(b), 3);
+
+ rearrange_data(AccessorType(b), AccessorType(weights_transformed));
+
+ matmul.run();
+
+ return dst;
+ }
+
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs,
+ Settings settings,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo)
+ {
+ if (CPUInfo::get().has_bf16())
+ {
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, settings,
+ a_qinfo, b_qinfo, o_qinfo);
+ }
+ }
+
+private:
+ TensorInfo prepare_weights(const TensorInfo tensor_info)
+ {
+ const DataLayout data_layout = tensor_info.data_layout();
+ ARM_COMPUTE_EXPECT(data_layout == DataLayout::NCHW, framework::LogLevel::ERRORS);
+ const DataType data_type = tensor_info.data_type();
+ const TensorShape tensor_shape = tensor_info.tensor_shape();
+ const int H = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)];
+ const int W = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)];
+ ARM_COMPUTE_EXPECT(H <= 2 && W <= 2, framework::LogLevel::ERRORS);
+
+ arm_compute::Strides strides_in_bytes = tensor_info.strides_in_bytes();
+ strides_in_bytes.set(1, 32);
+ strides_in_bytes.set(2, 32);
+
+ const size_t offset_first_element_in_bytes = tensor_info.offset_first_element_in_bytes();
+ const size_t total_size_in_bytes = 32;
+
+ const TensorShape TS(H, W);
+
+ TensorInfo new_tensor_info = tensor_info;
+ new_tensor_info.init(TS, tensor_info.num_channels(), data_type, strides_in_bytes, offset_first_element_in_bytes,
+ total_size_in_bytes);
+
+ return new_tensor_info;
+ }
+
+ void rearrange_data(const AccessorType src, AccessorType dst)
+ {
+ const TensorShape src_tensor_shape = src.shape();
+ const DataLayout data_layout = src.data_layout();
+ ARM_COMPUTE_EXPECT(data_layout == DataLayout::NCHW, framework::LogLevel::ERRORS);
+ const unsigned int O =
+ src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES)]; // N=O
+ const unsigned int H =
+ src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)];
+ const unsigned int W =
+ src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)];
+ const unsigned int I =
+ src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)]; // C=I
+ ARM_COMPUTE_EXPECT(H <= 2 && W <= 2, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(I == 1 && O == 1, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(src.num_elements() <= dst.num_elements(), framework::LogLevel::ERRORS);
+
+ const T *src_ptr = reinterpret_cast<const T *>(src.data());
+ T *dst_ptr = reinterpret_cast<T *>(dst.data());
+
+ // rearrange indexes for 2x2 input and weight
+ int dst_idx[] = {0, 4, 1, 5};
+ for (int i = 0; i < 4; i++)
+ {
+ dst_ptr[dst_idx[i]] = src_ptr[i];
+ }
+ }
+};
+
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class MatMulValidationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class MatMulValidationFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type)
{
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, ActivationLayerInfo(), 0,
- Settings());
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, ActivationLayerInfo(), 0, Settings());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class MatMulValidationWithDynamicTensorsFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class MatMulValidationWithDynamicTensorsFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs)
{
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings());
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class QuantizedMatMulValidationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class QuantizedMatMulValidationFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs,
- QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info,
+ int num_extra_runs,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo)
{
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(),
- a_qinfo, b_qinfo, o_qinfo);
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(),
+ a_qinfo, b_qinfo, o_qinfo);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class MatMulValidationWithActivationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class MatMulValidationWithActivationFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo act_info)
{
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings());
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class MatMulValidationWithActivationAlphaBetaFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class MatMulValidationWithActivationAlphaBetaFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo::ActivationFunction function,
- float alpha_beta)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo::ActivationFunction function,
+ float alpha_beta)
{
ActivationLayerInfo act_info(function, alpha_beta, alpha_beta);
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings());
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T>
-class QuantizedMatMulValidationWithActivationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
+class QuantizedMatMulValidationWithActivationFixture
+ : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>
{
public:
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo::ActivationFunction function,
- float alpha_beta, int num_extra_runs,
- QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo)
+ void setup(TensorShape shape_a,
+ TensorShape shape_b,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ DataType data_type,
+ ActivationLayerInfo::ActivationFunction function,
+ float alpha_beta,
+ int num_extra_runs,
+ QuantizationInfo a_qinfo,
+ QuantizationInfo b_qinfo,
+ QuantizationInfo o_qinfo)
{
ActivationLayerInfo act_info(function, alpha_beta, alpha_beta);
- MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(),
- a_qinfo, b_qinfo, o_qinfo);
+ MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(
+ shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(),
+ a_qinfo, b_qinfo, o_qinfo);
}
};