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-rw-r--r--tests/validation/fixtures/GEMMFixture.h87
1 files changed, 51 insertions, 36 deletions
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index a225c71ab7..ac8ab2a949 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -33,6 +33,7 @@
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/GEMM.h"
#include <random>
@@ -158,7 +159,7 @@ class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture
public:
template <typename...>
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
- bool interleave_rhs, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ bool interleave_rhs, DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -181,8 +182,8 @@ public:
broadcast_bias ? 1 : m,
broadcast_bias ? 1 : batch_size);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
@@ -198,7 +199,7 @@ protected:
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- DataType data_type, float alpha, float beta, bool broadcast_bias)
+ DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -218,6 +219,7 @@ protected:
kernel_info.depth_output_gemm3d = 0;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = act_info;
// The output tensor will be auto-initialized within the function
@@ -261,7 +263,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
@@ -290,7 +293,7 @@ protected:
}
}
- return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
@@ -304,7 +307,7 @@ public:
template <typename...>
void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
bool interleave_lhs,
- bool interleave_rhs, DataType data_type, float alpha, float beta)
+ bool interleave_rhs, DataType data_type, float alpha, float beta, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -328,8 +331,8 @@ public:
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n, 1, 1);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
@@ -341,7 +344,7 @@ protected:
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- DataType data_type, float alpha, float beta, unsigned int m_h)
+ DataType data_type, float alpha, float beta, unsigned int m_h, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -361,6 +364,7 @@ protected:
kernel_info.depth_output_gemm3d = m_h;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = true;
+ kernel_info.activation_info = act_info;
// The output tensor will be auto-initialized within the function
@@ -404,7 +408,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
@@ -432,7 +437,7 @@ protected:
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
- return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
@@ -445,7 +450,7 @@ class GEMMMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fix
public:
template <typename...>
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0,
- bool interleave_rhs, bool transpose_rhs, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ bool interleave_rhs, bool transpose_rhs, DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -465,8 +470,8 @@ public:
broadcast_bias ? 1 : m,
broadcast_bias ? 1 : batch_size);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
@@ -482,7 +487,7 @@ protected:
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- DataType data_type, float alpha, float beta, bool broadcast_bias)
+ DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -501,6 +506,7 @@ protected:
kernel_info.depth_output_gemm3d = 0;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = act_info;
// The output tensor will be auto-initialized within the function
@@ -539,7 +545,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
@@ -568,7 +575,7 @@ protected:
}
}
- return (reference::gemm<T>(lhs, rhs, bias, alpha, beta));
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
@@ -581,7 +588,7 @@ class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::F
public:
template <typename...>
void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0,
- bool interleave_rhs, bool transpose_rhs, DataType data_type, float alpha, float beta)
+ bool interleave_rhs, bool transpose_rhs, DataType data_type, float alpha, float beta, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -602,8 +609,8 @@ public:
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n, 1, 1);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
@@ -616,7 +623,7 @@ protected:
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
DataType data_type, float alpha, float beta,
- unsigned int m_h)
+ unsigned int m_h, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -635,6 +642,7 @@ protected:
kernel_info.depth_output_gemm3d = m_h;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = true;
+ kernel_info.activation_info = act_info;
// The output tensor will be auto-initialized within the function
@@ -673,7 +681,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
@@ -701,7 +710,7 @@ protected:
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
- return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
@@ -713,7 +722,8 @@ class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias,
+ const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -730,8 +740,8 @@ public:
broadcast_bias ? 1 : m,
broadcast_bias ? 1 : batch_size);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
@@ -747,7 +757,7 @@ protected:
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- DataType data_type, float alpha, float beta, bool broadcast_bias)
+ DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -765,6 +775,7 @@ protected:
kernel_info.depth_output_gemm3d = 0;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = act_info;
// Create and configure function
GEMMFunctionType gemm;
@@ -796,7 +807,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
@@ -825,7 +837,7 @@ protected:
}
}
- return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
@@ -837,7 +849,8 @@ class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta)
+ void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta,
+ const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
@@ -855,8 +868,8 @@ public:
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n, 1, 1);
- _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
- _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h, act_info);
+ _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
@@ -868,7 +881,7 @@ protected:
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- DataType data_type, float alpha, float beta, unsigned int m_h)
+ DataType data_type, float alpha, float beta, unsigned int m_h, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
@@ -886,6 +899,7 @@ protected:
kernel_info.depth_output_gemm3d = m_h;
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = true;
+ kernel_info.activation_info = act_info;
// The output tensor will be auto-initialized within the function
@@ -919,7 +933,8 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
+ const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
@@ -947,7 +962,7 @@ protected:
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
- return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-15 08:45:38 +0000