Port NEGEMM to memory injecting interface (Part 3)

- Complete porting of NEGEMM to the new API

Resolves: COMPMID-4402

Change-Id: I14904102b25332dbb4fc048d45dca068a15b6eca
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5890
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 39 insertions, 379 deletions

diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index a52ca79504..168d93022f 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -23,79 +23,32 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEGEMM.h"
 
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/ITensorPack.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "arm_compute/runtime/MemoryGroup.h"
 #include "arm_compute/runtime/Tensor.h"
-#include "arm_compute/runtime/TensorAllocator.h"
 #include "src/core/CPP/Validate.h"
-#include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h"
-#include "src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h"
-#include "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
-#include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h"
-#include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/MemoryHelpers.h"
-#include "src/runtime/cpu/operators/CpuActivation.h"
-#include "src/runtime/cpu/operators/CpuAdd.h"
-#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
+#include "src/runtime/cpu/operators/CpuGemm.h"
 
 using namespace arm_compute::experimental;
-using namespace arm_compute::misc::shape_calculator;
 
 namespace arm_compute
 {
-namespace
-{
-cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info)
-{
-    cpu::AsmGemmInfo asm_info;
-    asm_info.method                  = cpu::AsmConvMethod::Im2Col;
-    asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d();
-    asm_info.depth_output_gemm3d     = info.depth_output_gemm3d();
-    asm_info.activation_info         = info.activation_info();
-
-    return asm_info;
-}
-} // namespace
-
 struct NEGEMM::Impl
 {
     MemoryGroup      memory_group{};
     IWeightsManager *weights_manager{ nullptr };
 
-    std::unique_ptr<cpu::kernels::CpuGemmInterleave4x4Kernel>  interleave_kernel{ nullptr };
-    std::unique_ptr<cpu::kernels::CpuGemmTranspose1xWKernel>   transpose_kernel{ nullptr };
-    std::unique_ptr<cpu::kernels::CpuGemmMatrixMultiplyKernel> mm_kernel{ nullptr };
-    std::unique_ptr<cpu::CpuGemmAssemblyDispatch>              asm_glue{ nullptr };
-    std::unique_ptr<cpu::kernels::CpuGemmMatrixAdditionKernel> ma_kernel{ nullptr };
-    std::unique_ptr<cpu::CpuActivation>                        alpha_scale_func{ nullptr };
-    std::unique_ptr<cpu::CpuAdd>                               add_bias{ nullptr };
-    std::unique_ptr<cpu::CpuActivation>                        activation_func{ nullptr };
+    std::unique_ptr<cpu::CpuGemm> op{ nullptr };
 
-    const ITensor *a{ nullptr };
-    const ITensor *c{ nullptr };
-    ITensor       *d{ nullptr };
-    ITensor       *gemm_output_to_use{ nullptr };
-    Tensor         tmp_a{};
-    Tensor         tmp_b{};
-    Tensor         tmp_d{};
     const ITensor *original_b{ nullptr };
-    bool           run_vector_matrix_multiplication{ false };
-    bool           run_alpha_scale{ false };
-    bool           run_addition{ false };
-    bool           run_bias_addition{ false };
-    bool           run_activation{ false };
-    bool           reshape_b_only_on_first_run{ false };
     bool           is_prepared{ false };
 
-    ITensorPack                      asm_glue_run_pack{};
-    ITensorPack                      asm_glue_prep_pack{};
-    WorkspaceData<Tensor>            asm_glue_workspace{};
+    ITensorPack                      run_pack{};
+    ITensorPack                      prep_pack{};
+    WorkspaceData<Tensor>            workspace{};
     experimental::MemoryRequirements aux_mem_req{};
 };
 
@@ -111,259 +64,24 @@ NEGEMM::~NEGEMM() = default;
 void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, d);
-    ARM_COMPUTE_ERROR_THROW_ON(NEGEMM::validate(a->info(), b->info(), (c != nullptr) ? c->info() : nullptr, d->info(), alpha, beta, gemm_info));
-
-    const cpu::AsmGemmInfo asm_info      = init_assembly_metadata(gemm_info);
-    const bool             is_c_bias     = gemm_info.reshape_b_only_on_first_run();
-    bool                   run_optimised = bool(cpu::CpuGemmAssemblyDispatch::validate(a->info(), b->info(), (is_c_bias && c != nullptr) ? c->info() : nullptr, d->info(), asm_info));
+    ARM_COMPUTE_ERROR_THROW_ON(cpu::CpuGemm::validate(a->info(), b->info(), (c != nullptr) ? c->info() : nullptr, d->info(), alpha, beta, gemm_info));
 
-    _impl->a                  = a;
-    _impl->c                  = c;
-    _impl->d                  = d;
-    _impl->gemm_output_to_use = d;
     // Check if we need to reshape the matrix B only on the first run
-    _impl->is_prepared                      = false;
-    _impl->reshape_b_only_on_first_run      = gemm_info.reshape_b_only_on_first_run();
-    _impl->run_vector_matrix_multiplication = a->info()->dimension(1) < 2;
-    _impl->original_b                       = b;
-    _impl->run_alpha_scale                  = alpha != 1.f;
-    _impl->run_bias_addition                = c != nullptr && gemm_info.reshape_b_only_on_first_run();
-    _impl->run_addition                     = beta != 0 && c != nullptr && !gemm_info.reshape_b_only_on_first_run();
-    _impl->run_activation                   = gemm_info.activation_info().enabled() && (!run_optimised || (run_optimised
-                                                                                                           && !cpu::CpuGemmAssemblyDispatch::is_activation_supported(gemm_info.activation_info())));
-
-    if(run_optimised)
-    {
-        const ITensor     *c_to_use      = is_c_bias ? c : nullptr;
-        const ITensorInfo *c_info_to_use = c_to_use != nullptr ? c_to_use->info() : nullptr;
-        _impl->asm_glue                  = std::make_unique<cpu::CpuGemmAssemblyDispatch>();
-        _impl->asm_glue->configure(a->info(), b->info(), c_info_to_use, d->info(), asm_info);
-        ARM_COMPUTE_ERROR_ON(!_impl->asm_glue->is_configured());
-
-        _impl->aux_mem_req = _impl->asm_glue->workspace();
-        _impl->asm_glue_run_pack =
-        {
-            { ACL_SRC_0, a },
-            { ACL_SRC_1, b },
-            { ACL_SRC_2, c_to_use },
-            { ACL_DST, d },
-        };
-        _impl->asm_glue_prep_pack = { { ACL_SRC_1, b }, { ACL_SRC_2, c_to_use } };
-        _impl->asm_glue_workspace = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->asm_glue_run_pack, _impl->asm_glue_prep_pack);
-
-        // Scale product by alpha
-        if(_impl->run_alpha_scale)
-        {
-            _impl->alpha_scale_func = std::make_unique<cpu::CpuActivation>();
-            _impl->alpha_scale_func->configure(d->info(), nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, alpha, 0.f));
-        }
-    }
-    else
-    {
-        // Pick output tensor in case bias addition should be performed
-        if(_impl->run_bias_addition)
-        {
-            _impl->gemm_output_to_use = &_impl->tmp_d;
-            _impl->memory_group.manage(&_impl->tmp_d);
-        }
-
-        _impl->mm_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixMultiplyKernel>();
-
-        // Select between GEMV and GEMM
-        if(_impl->run_vector_matrix_multiplication)
-        {
-            // Configure the matrix multiply kernel
-            _impl->mm_kernel->configure(a->info(), b->info(), _impl->gemm_output_to_use->info(), alpha, false);
-        }
-        else
-        {
-            TensorShape shape_tmp_a = a->info()->tensor_shape();
-            TensorShape shape_tmp_b = b->info()->tensor_shape();
-
-            shape_tmp_a.set(0, a->info()->dimension(0) * 4);
-            shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
-
-            const unsigned int transpose_w = 16 / data_size_from_type(b->info()->data_type());
-            shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
-            shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
-
-            TensorInfo info_a = a->info()->clone()->set_tensor_shape(shape_tmp_a).set_is_resizable(true);
-            TensorInfo info_b = b->info()->clone()->set_tensor_shape(shape_tmp_b).set_is_resizable(true);
-
-            _impl->tmp_a.allocator()->init(info_a);
-            _impl->tmp_b.allocator()->init(info_b);
-
-            // Manage intermediate buffers
-            _impl->memory_group.manage(&_impl->tmp_a);
-            if(!_impl->reshape_b_only_on_first_run)
-            {
-                _impl->memory_group.manage(&_impl->tmp_b);
-            }
-
-            int m = a->info()->dimension(1);
-            int n = b->info()->dimension(0);
-            int k = a->info()->dimension(0);
+    _impl->is_prepared = false;
+    _impl->original_b  = b;
+    _impl->op          = std::make_unique<cpu::CpuGemm>();
 
-            // Configure interleave kernel
-            _impl->interleave_kernel = std::make_unique<cpu::kernels::CpuGemmInterleave4x4Kernel>();
-            _impl->interleave_kernel->configure(a->info(), &info_a);
+    _impl->op->configure(a->info(), b->info(), (c != nullptr) ? c->info() : nullptr, d->info(), alpha, beta, gemm_info);
 
-            // Configure transpose kernel
-            _impl->transpose_kernel = std::make_unique<cpu::kernels::CpuGemmTranspose1xWKernel>();
-            _impl->transpose_kernel->configure(b->info(), _impl->tmp_b.info());
-
-            // Configure matrix multiplication kernel
-            _impl->mm_kernel->configure(_impl->tmp_a.info(), _impl->tmp_b.info(), _impl->gemm_output_to_use->info(), alpha, true, GEMMReshapeInfo(m, n, k));
-
-            // Allocate once the all configure methods have been called
-            _impl->tmp_a.allocator()->allocate();
-            if(!_impl->reshape_b_only_on_first_run)
-            {
-                _impl->tmp_b.allocator()->allocate();
-            }
-        }
-
-        if(_impl->run_bias_addition)
-        {
-            _impl->add_bias = std::make_unique<cpu::CpuAdd>();
-            _impl->add_bias->configure(_impl->gemm_output_to_use->info(), c->info(), d->info(), ConvertPolicy::SATURATE);
-            _impl->tmp_d.allocator()->allocate();
-        }
-    }
-
-    // Configure matrix addition kernel
-    if(_impl->run_addition)
-    {
-        _impl->ma_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixAdditionKernel>();
-        _impl->ma_kernel->configure(c->info(), d->info(), beta);
-    }
-
-    // Configure activation
-    const ActivationLayerInfo &activation = gemm_info.activation_info();
-    if(_impl->run_activation)
-    {
-        _impl->activation_func = std::make_unique<cpu::CpuActivation>();
-        _impl->activation_func->configure(d->info(), nullptr, activation);
-    }
+    _impl->aux_mem_req = _impl->op->workspace();
+    _impl->run_pack    = { { ACL_SRC_0, a }, { ACL_SRC_1, b }, { ACL_SRC_2, c }, { ACL_DST, d } };
+    _impl->prep_pack   = { { ACL_SRC_1, b }, { ACL_SRC_2, c } };
+    _impl->workspace   = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
 }
 
 Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_UNUSED(alpha);
-    const bool is_c_bias = gemm_info.reshape_b_only_on_first_run();
-
-    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a);
-    ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(a);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::BFLOAT16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
-    if(a->data_type() != DataType::BFLOAT16)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, output);
-    }
-
-    if(c != nullptr && !is_c_bias)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.depth_output_gemm3d() != 0);
-        ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.reinterpret_input_as_3d());
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(c, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->dimension(1), "The C matrix must have the same number of rows as the matrix A");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != c->dimension(0), "The C matrix must have the same number of columns as the matrix B");
-    }
-
-    if(output->total_size() != 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
-        if(gemm_info.depth_output_gemm3d() != 0)
-        {
-            if(gemm_info.reinterpret_input_as_3d())
-            {
-                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
-                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2));
-            }
-            else
-            {
-                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2));
-            }
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
-        }
-    }
-
-    // Check if we need to run the optimized assembly kernel
-    cpu::AsmGemmInfo asm_info      = init_assembly_metadata(gemm_info);
-    const bool       run_optimised = bool(cpu::CpuGemmAssemblyDispatch::validate(a, b, is_c_bias ? c : nullptr, output, asm_info));
-
-    if(!run_optimised)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D");
-
-        // Check if the first input tensor is a vector.
-        const bool run_vector_matrix_multiplication = a->dimension(1) < 2;
-        // Check if we need to reshape the matrix A and matrix B
-        const bool run_interleave_transpose = !run_vector_matrix_multiplication && !(gemm_info.reshape_b_only_on_first_run());
-
-        // Arguments used by GEMMReshapeInfo
-        // If we pass the matrix A and matrix B reshaped to NEGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GEMMReshapeInfo
-        // in order to know how the matrices have been reshaped
-        const int m                         = a->dimension(1);
-        const int n                         = b->dimension(0);
-        const int k                         = a->dimension(0);
-        int       mult_transpose1xW_width   = 1;
-        int       mult_interleave4x4_height = 1;
-
-        const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, gemm_info.depth_output_gemm3d());
-
-        const ITensorInfo *matrix_a_info = a;
-        const ITensorInfo *matrix_b_info = b;
-
-        TensorInfo tmp_a_info{};
-        TensorInfo tmp_b_info{};
-        TensorInfo tmp_output_info = *output->clone();
-
-        if(run_interleave_transpose)
-        {
-            matrix_a_info = &tmp_a_info;
-            matrix_b_info = &tmp_b_info;
-
-            // Validate interleave kernel
-            auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_interleaved_shape(*a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d())));
-            ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmInterleave4x4Kernel::validate(a, &tmp_a_info));
-
-            // Validate transpose kernel
-            auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width)));
-            ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmTranspose1xWKernel::validate(b, &tmp_b_info));
-        }
-
-        // Validate matrix multiply
-        auto_init_if_empty(tmp_output_info, matrix_a_info->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, run_interleave_transpose, reshape_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &tmp_output_info, alpha, run_interleave_transpose, reshape_info));
-
-        if(c != nullptr && gemm_info.reshape_b_only_on_first_run())
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuAdd::validate(&tmp_output_info, c, output, ConvertPolicy::SATURATE));
-        }
-    }
-
-    // Validate matrix addition kernel
-    if(beta != 0 && c != nullptr && !is_c_bias)
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixAdditionKernel::validate(c, output, beta));
-    }
-
-    // Validate activation
-    const ActivationLayerInfo &activation = gemm_info.activation_info();
-    if(activation.enabled())
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuActivation::validate(output, nullptr, activation));
-    }
-
-    return Status{};
+    return cpu::CpuGemm::validate(a, b, c, output, alpha, beta, gemm_info);
 }
 
 void NEGEMM::run()
@@ -371,100 +89,42 @@ void NEGEMM::run()
     prepare();
 
     MemoryGroupResourceScope scope_mg(_impl->memory_group);
-
-    if(_impl->asm_glue->is_configured())
-    {
-        _impl->asm_glue->run(_impl->asm_glue_run_pack);
-        if(_impl->run_alpha_scale)
-        {
-            ITensorPack pack{ { ACL_SRC, _impl->d }, { ACL_DST, _impl->d } };
-            _impl->alpha_scale_func->run(pack);
-        }
-    }
-    else
-    {
-        ITensorPack mm_pack{ { ACL_SRC_0, _impl->a }, { ACL_SRC_1, _impl->original_b }, { ACL_DST, _impl->gemm_output_to_use } };
-        if(!_impl->run_vector_matrix_multiplication)
-        {
-            // Run interleave kernel
-            ITensorPack interleave_pack{ { ACL_SRC, _impl->a }, { ACL_DST, &_impl->tmp_a } };
-            NEScheduler::get().schedule_op(_impl->interleave_kernel.get(), Window::DimY, _impl->interleave_kernel->window(), interleave_pack);
-
-            if(!_impl->reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                ITensorPack transpose_pack{ { ACL_SRC, _impl->original_b }, { ACL_DST, &_impl->tmp_b } };
-                NEScheduler::get().schedule_op(_impl->transpose_kernel.get(), Window::DimY, _impl->transpose_kernel->window(), transpose_pack);
-            }
-
-            // Use reshaped matrices
-            mm_pack.add_const_tensor(ACL_SRC_0, &_impl->tmp_a);
-            mm_pack.add_const_tensor(ACL_SRC_1, &_impl->tmp_b);
-        }
-
-        NEScheduler::get().schedule_op(_impl->mm_kernel.get(), _impl->run_vector_matrix_multiplication ? Window::DimX : Window::DimY, _impl->mm_kernel->window(), mm_pack);
-
-        // Run bias addition kernel
-        if(_impl->run_bias_addition)
-        {
-            ITensorPack pack{ { ACL_SRC_0, _impl->gemm_output_to_use }, { ACL_SRC_1, _impl->c }, { ACL_DST, _impl->d } };
-            _impl->add_bias->run(pack);
-        }
-    }
-
-    // Run matrix addition kernel
-    if(_impl->run_addition)
-    {
-        ITensorPack c_add_pack{ { ACL_SRC, _impl->c }, { ACL_DST, _impl->d } };
-        NEScheduler::get().schedule_op(_impl->ma_kernel.get(), Window::DimY, _impl->ma_kernel->window(), c_add_pack);
-    }
-
-    // Run activation function
-    if(_impl->run_activation)
-    {
-        ITensorPack pack{ { ACL_SRC, _impl->d }, { ACL_DST, _impl->d } };
-        _impl->activation_func->run(pack);
-    }
+    _impl->op->run(_impl->run_pack);
 }
 
 void NEGEMM::prepare()
 {
     if(!_impl->is_prepared)
     {
-        const bool original_b_managed_by_weights_manager = _impl->weights_manager && _impl->weights_manager->are_weights_managed(_impl->original_b);
-        if(_impl->asm_glue->is_configured())
-        {
-            _impl->asm_glue->prepare(_impl->asm_glue_prep_pack);
+        _impl->op->prepare(_impl->prep_pack);
 
-            auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
-                                            _impl->aux_mem_req.end(),
-                                            [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+        auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
+                                        _impl->aux_mem_req.end(),
+                                        [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
 
-            if(has_reshape != std::end(_impl->aux_mem_req))
-            {
-                _impl->original_b->mark_as_unused();
-            }
-            else
-            {
-                _impl->asm_glue_run_pack.add_const_tensor(ACL_SRC_1, _impl->original_b);
-            }
+        if(has_reshape != std::end(_impl->aux_mem_req))
+        {
+            _impl->original_b->mark_as_unused();
         }
-        else if(_impl->reshape_b_only_on_first_run && !_impl->run_vector_matrix_multiplication && !_impl->asm_glue->is_configured())
+        else
         {
-            if(!original_b_managed_by_weights_manager)
-            {
-                ARM_COMPUTE_ERROR_ON(!_impl->original_b->is_used());
-            }
+            _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->original_b);
+        }
 
-            _impl->tmp_b.allocator()->allocate();
-            ITensorPack transpose_pack{ { ACL_SRC, _impl->original_b }, { ACL_DST, &_impl->tmp_b } };
-            NEScheduler::get().schedule_op(_impl->transpose_kernel.get(), Window::DimY, _impl->transpose_kernel->window(), transpose_pack);
-            if(!original_b_managed_by_weights_manager)
+        // Release temporary tensors that are only used in prepare stage
+        for(auto &ws : _impl->workspace)
+        {
+            const int slot = ws.first;
+            for(auto &m : _impl->aux_mem_req)
             {
-                _impl->original_b->mark_as_unused();
+                if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare)
+                {
+                    auto tensor = ws.second.get();
+                    tensor->allocator()->free();
+                    break;
+                }
             }
         }
-
         _impl->is_prepared = true;
     }
 }