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review.mlplatform.org / ml / ComputeLibrary / f3622becf1f0d6bf5147ebb7d6d0f14d5252860a / . / tests / validation / fixtures / GEMMFixture.h
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/*
* Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_TEST_GEMM_FIXTURE
#define ARM_COMPUTE_TEST_GEMM_FIXTURE
#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#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>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool disable_c = false, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
class GEMMValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape output_shape, float alpha, float beta, bool pretranspose, DataType data_type)
{
_target = compute_target(shape_a, shape_b, shape_c, output_shape, alpha, beta, pretranspose, data_type);
_reference = compute_reference(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type);
}
protected:
template <typename U>
void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
{
switch(tensor.data_type())
{
case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(lo, hi);
library->fill(tensor, distribution, i);
break;
}
default:
library->fill_tensor_uniform(tensor, i);
}
}
TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta,
bool pretranspose, DataType data_type)
{
// Create tensors
TensorType a = create_tensor<TensorType>(shape_a, data_type, 1);
TensorType b = create_tensor<TensorType>(shape_b, data_type, 1);
TensorType c = create_tensor<TensorType>(shape_c, data_type, 1);
TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);
// Create and configure function
FunctionType gemm;
// The GEMMinfo includes the values of the depth in case of reinterpreted 3d output.
// If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 0),
// in the other case we have to use the reinterpreted version of GEMM (depth_output_reinterpreted_as_3D = depth of the 3D output).
gemm.configure(&a,
&b,
(disable_c) ? nullptr : &c,
&dst,
alpha, beta,
GEMMInfo(false, false, false, (reinterpret_output_as_3d ? output_shape[2] : 0), reinterpret_input_as_3d, false, GEMMLowpOutputStageInfo(), false, (reinterpret_input_as_3d
|| reinterpret_output_as_3d)));
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);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
a.allocator()->allocate();
b.allocator()->allocate();
c.allocator()->allocate();
dst.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);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(a), 0);
fill(AccessorType(b), 1);
if(!disable_c)
{
fill(AccessorType(c), 2);
}
// Compute GEMM function
gemm.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta,
DataType data_type)
{
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);
}
// Create reference
SimpleTensor<T> a{ shape_a_to_use, data_type, 1 };
SimpleTensor<T> b{ shape_b, data_type, 1 };
SimpleTensor<T> c{ output_shape, data_type, 1 };
// Fill reference
fill(a, 0);
fill(b, 1);
fill(c, 2);
if(reinterpret_input_as_3d || reinterpret_output_as_3d)
{
const int n = shape_b[0];
const int m = reinterpret_output_as_3d ? output_shape[1] * output_shape[2] : output_shape[1];
const int batch_size = reinterpret_output_as_3d ? output_shape[3] : output_shape[2];
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(c.data() + i * n, c.data(), n * sizeof(T));
}
}
// Setting beta to 0 will effectively disable C for the
// computation of the reference: alpha * A * B + 0 * C
return reference::gemm<T>(a, b, c, alpha, disable_c ? 0.f : beta);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
class GEMMMatrixMultiplyValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision, const ActivationLayerInfo &act_info,
DataType data_type, GPUTarget gpu_arch)
{
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n,
broadcast_bias ? 1 : m,
broadcast_bias ? 1 : batch_size);
_target = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, fp16_mixed_precision, act_info, gpu_arch);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
// Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
library->fill_borders_with_garbage(tensor, distribution_inf, i);
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType dst;
const unsigned int m = lhs_shape[1];
const unsigned int n = rhs_shape[0];
const unsigned int k = lhs_shape[0];
GEMMReshapeInfo reshape_info(m, n, k, 1, 1, 0, false, broadcast_bias);
// The output tensor will be auto-initialized within the function
// Create and configure function
GEMMFunctionType gemm;
gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
if(broadcast_bias)
{
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
class GEMMMatrixMultiply3DValidationFixture : 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, float alpha, float beta, bool broadcast_bias, bool fp16_mixed_precision,
const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
{
// In case of GEMM3D, m is the product between m_w and m_h
const unsigned int m = m_w * m_h;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n, 1, 1);
_target = compute_target(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, fp16_mixed_precision, act_info, gpu_arch);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
}
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h,
bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType dst;
const unsigned int m = lhs_shape[1];
const unsigned int n = rhs_shape[0];
const unsigned int k = lhs_shape[0];
GEMMReshapeInfo reshape_info(m, n, k, 1, 1, m_h, false, true);
// The output tensor will be auto-initialized within the function
// Create and configure function
GEMMFunctionType gemm;
gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
dst_shape.set(1, lhs_shape[1] / m_h);
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyInterleavedTransposedValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias, bool fp16_mixed_precision,
const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = 4;
lhs_info.k0 = 4;
lhs_info.v0 = v0;
lhs_info.interleave = true;
lhs_info.transpose = true;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = 16 / sizeof(T);
rhs_info.k0 = 1;
rhs_info.h0 = h0;
rhs_info.interleave = false;
rhs_info.transpose = false;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n,
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, fp16_mixed_precision, act_info, gpu_arch);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
// Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
library->fill_borders_with_garbage(tensor, distribution_inf, i);
}
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, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
const unsigned int m = lhs_shape[1];
const unsigned int n = rhs_shape[0];
const unsigned int k = lhs_shape[0];
GEMMReshapeInfo reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, 0, false, broadcast_bias);
// The output tensor will be auto-initialized within the function
// Create and configure function
ReshapeLHSFunctionType reshape_lhs;
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
lhs_reshaped.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_lhs.run();
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
if(broadcast_bias)
{
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture : 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, float alpha, float beta, unsigned int v0, unsigned int h0, bool broadcast_bias,
bool fp16_mixed_precision, const ActivationLayerInfo &act_info, DataType data_type, GPUTarget gpu_arch)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = 4;
lhs_info.k0 = 4;
lhs_info.v0 = v0;
lhs_info.interleave = true;
lhs_info.transpose = true;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = 16 / sizeof(T);
rhs_info.k0 = 1;
rhs_info.h0 = h0;
rhs_info.interleave = false;
rhs_info.transpose = false;
// In case of GEMM3D, m is the product between m_w and m_h
const unsigned int m = m_w * m_h;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
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, fp16_mixed_precision, act_info, gpu_arch);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
}
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, bool fp16_mixed_precision, const ActivationLayerInfo &act_info, GPUTarget gpu_arch)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
const unsigned int m = lhs_shape[1];
const unsigned int n = rhs_shape[0];
const unsigned int k = lhs_shape[0];
GEMMReshapeInfo reshape_info(m, n, k, rhs_info.h0, lhs_info.v0, m_h, false, true);
// The output tensor will be auto-initialized within the function
// Create and configure function
ReshapeLHSFunctionType reshape_lhs;
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
lhs_reshaped.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_lhs.run();
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
dst_shape.set(1, lhs_shape[1] / m_h);
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
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, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
lhs_info.k0 = k0;
lhs_info.v0 = v0;
lhs_info.interleave = interleave_lhs;
lhs_info.transpose = false;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
rhs_info.h0 = h0;
rhs_info.interleave = interleave_rhs;
rhs_info.transpose = true;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n,
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
// Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
library->fill_borders_with_garbage(tensor, distribution_inf, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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
// Create and configure function
ReshapeLHSFunctionType reshape_lhs;
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
lhs_reshaped.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_lhs.run();
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
if(broadcast_bias)
{
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyReshaped3DValidationFixture : 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, unsigned int v0, unsigned int h0,
bool interleave_lhs,
bool interleave_rhs, DataType data_type, float alpha, float beta, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
lhs_info.k0 = k0;
lhs_info.v0 = v0;
lhs_info.interleave = interleave_lhs;
lhs_info.transpose = false;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
rhs_info.h0 = h0;
rhs_info.interleave = interleave_rhs;
rhs_info.transpose = true;
// In case of GEMM3D, m is the product between m_w and m_h
const unsigned int m = m_w * m_h;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType lhs_reshaped;
TensorType rhs_reshaped;
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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
// Create and configure function
ReshapeLHSFunctionType reshape_lhs;
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
lhs_reshaped.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_lhs.run();
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
dst_shape.set(1, lhs_shape[1] / m_h);
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyReshapedOnlyRHSValidationFixture : 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 h0,
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;
lhs_info.k0 = k0;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
rhs_info.h0 = h0;
rhs_info.interleave = interleave_rhs;
rhs_info.transpose = transpose_rhs;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n,
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
// Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
library->fill_borders_with_garbage(tensor, distribution_inf, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType rhs_reshaped;
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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
// Create and configure function
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
if(broadcast_bias)
{
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : 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, unsigned int h0,
bool interleave_rhs, bool transpose_rhs, DataType data_type, float alpha, float beta, const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
lhs_info.k0 = k0;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
rhs_info.h0 = h0;
rhs_info.interleave = interleave_rhs;
rhs_info.transpose = transpose_rhs;
// In case of GEMM3D, m is the product between m_w and m_h
const unsigned int m = m_w * m_h;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType rhs_reshaped;
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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
// Create and configure function
ReshapeRHSFunctionType reshape_rhs;
GEMMFunctionType gemm;
reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
rhs_reshaped.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
reshape_rhs.run();
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
dst_shape.set(1, lhs_shape[1] / m_h);
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
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,
const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
lhs_info.k0 = k0;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
const TensorShape rhs_shape(n, k, batch_size);
const TensorShape bias_shape(n,
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
// Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
library->fill_borders_with_garbage(tensor, distribution_inf, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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;
gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape[0] = rhs_shape[0];
dst_shape[1] = lhs_shape[1];
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
if(broadcast_bias)
{
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
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,
const ActivationLayerInfo &act_info)
{
GEMMLHSMatrixInfo lhs_info;
lhs_info.m0 = m0;
lhs_info.k0 = k0;
GEMMRHSMatrixInfo rhs_info;
rhs_info.n0 = n0;
rhs_info.k0 = k0;
// In case of GEMM3D, m is the product between m_w and m_h
const unsigned int m = m_w * m_h;
// Set the tensor shapes for LHS and RHS matrices
const TensorShape lhs_shape(k, m, batch_size);
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, act_info);
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h, act_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
}
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, const ActivationLayerInfo &act_info)
{
// Create tensors
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
TensorType dst;
const unsigned int M = lhs_shape[1];
const unsigned int N = rhs_shape[0];
const unsigned int K = lhs_shape[0];
GEMMKernelInfo kernel_info;
kernel_info.m = M;
kernel_info.n = N;
kernel_info.k = K;
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
// Create and configure function
GEMMFunctionType gemm;
gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
lhs.allocator()->allocate();
rhs.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(lhs), 0);
fill(AccessorType(rhs), 1);
fill(AccessorType(bias), 2);
// Compute GEMM
gemm.run();
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,
const ActivationLayerInfo &act_info)
{
TensorShape dst_shape = lhs_shape;
dst_shape.set(0, rhs_shape[0]);
dst_shape.set(1, lhs_shape[1] / m_h);
dst_shape.set(2, m_h);
dst_shape.set(3, lhs_shape[2]);
// Create reference
SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
SimpleTensor<T> bias{ dst_shape, data_type, 1 };
const int n = rhs_shape[0];
const int m = lhs_shape[1];
const int batch_size = lhs_shape[2];
// Fill reference
fill(lhs, 0);
fill(rhs, 1);
fill(bias, 2);
// In case of broadcast, we need simply copy the first into the following "M" ones
for(int i = 1; i < m * batch_size; i++)
{
memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
}
return reference::activation_layer(reference::gemm<T>(lhs, rhs, bias, alpha, beta), act_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */