3 files changed, 42 insertions, 129 deletions

diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index b84128e6c0..7318c3e492 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -89,10 +89,19 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe
 
     if(run_optimised)
     {
-        const ITensor *c_to_use = is_c_bias ? c : nullptr;
-        _asm_glue->configure(a, b, c_to_use, d, asm_info);
+        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;
+        _asm_glue->configure(a->info(), b->info(), c_info_to_use, d->info(), asm_info);
         ARM_COMPUTE_ERROR_ON(!_asm_glue->is_configured());
 
+        _asm_glue_tensors =
+        {
+            { ACL_SRC_0, a },
+            { ACL_SRC_1, b },
+            { ACL_SRC_2, c_to_use },
+            { ACL_DST, d },
+        };
+
         // Scale product by alpha
         if(_run_alpha_scale)
         {
@@ -314,7 +323,7 @@ void NEGEMM::run()
 
     if(_asm_glue->is_configured())
     {
-        _asm_glue->run();
+        _asm_glue->run(_asm_glue_tensors);
         if(_run_alpha_scale)
         {
             _alpha_scale_func.run();
@@ -368,7 +377,7 @@ void NEGEMM::prepare()
                 ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
             }
 
-            _asm_glue->prepare();
+            _asm_glue->prepare(_asm_glue_tensors);
             if(!original_b_managed_by_weights_manager)
             {
                 _original_b->mark_as_unused();
diff --git a/src/runtime/NEON/functions/NEGEMMConv2d.cpp b/src/runtime/NEON/functions/NEGEMMConv2d.cpp
index ddeacc85f5..94ceb6d27c 100644
--- a/src/runtime/NEON/functions/NEGEMMConv2d.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConv2d.cpp
@@ -26,151 +26,48 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
+#include "src/runtime/cpu/operators/CpuGemmDirectConv2d.h"
 
 #include <set>
 
 namespace arm_compute
 {
-namespace
-{
-GEMMLowpOutputStageInfo calculate_output_stage_metadata(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act)
-{
-    // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
-    // Extract and negate input and weights offset
-    const QuantizationInfo        iqinfo    = input->quantization_info();
-    const QuantizationInfo        wqinfo    = weights->quantization_info();
-    const QuantizationInfo        oqinfo    = (output->total_size() == 0) ? iqinfo : output->quantization_info();
-    const UniformQuantizationInfo uoqinfo   = oqinfo.uniform();
-    const DataType                data_type = input->data_type();
-    // Merge activation with output stage
-    const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
-                                                                               ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
-                                                                               ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
-                                                                             };
-    PixelValue type_min{};
-    PixelValue type_max{};
-    std::tie(type_min, type_max) = get_min_max(data_type);
-    int32_t min_activation = type_min.get<int32_t>();
-    int32_t max_activation = type_max.get<int32_t>();
-    if(supported_acts.count(act.activation()) != 0)
-    {
-        std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act, data_type, uoqinfo);
-    }
-    GEMMLowpOutputStageInfo os_info;
-    os_info.type                     = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
-    os_info.gemmlowp_offset          = uoqinfo.offset;
-    os_info.gemmlowp_min_bound       = min_activation;
-    os_info.gemmlowp_max_bound       = max_activation;
-    os_info.is_quantized_per_channel = (weights->data_type() == DataType::QSYMM8_PER_CHANNEL);
-    quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, os_info);
-    return os_info;
-}
-cpu::AsmGemmInfo init_assembly_metadata(const Conv2dInfo &info, bool is_indirect)
+using OperatorType = cpu::CpuGemmDirectConv2d;
+
+struct NEGEMMConv2d::Impl
 {
-    cpu::AsmGemmInfo asm_info;
-    asm_info.method                  = is_indirect ? cpu::AsmConvMethod::Indirect : cpu::AsmConvMethod::Conv;
-    asm_info.ps_info                 = info.conv_info;
-    asm_info.activation_info         = info.act_info;
-    asm_info.depth_output_gemm3d     = true;
-    asm_info.reinterpret_input_as_3d = true;
-    asm_info.padding_top             = info.conv_info.pad_top();
-    asm_info.padding_left            = info.conv_info.pad_left();
-    asm_info.padding_value           = 0.f;
-    asm_info.negated_offsets         = false;
-    return asm_info;
-}
-} // namespace
+    ITensorPack                   tensors{};
+    std::unique_ptr<OperatorType> op{ nullptr };
+};
 
 NEGEMMConv2d::NEGEMMConv2d(const std::shared_ptr<IMemoryManager> &memory_manager)
-    : _gemm_asm_func(std::make_unique<cpu::CpuGemmAssemblyDispatch>(memory_manager)), _activation_func(), _weights_permute_func(), _original_weights(nullptr), _permuted_weights(), _is_prepared(false),
-      _run_activation(false)
+    : _impl(std::make_unique<Impl>())
 {
+    _impl->op = std::make_unique<OperatorType>(memory_manager);
 }
 
 NEGEMMConv2d::~NEGEMMConv2d() = default;
 
 void NEGEMMConv2d::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const Conv2dInfo &info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_ERROR_THROW_ON(NEGEMMConv2d::validate(input->info(),
-                                                      weights->info(),
-                                                      biases != nullptr ? biases->info() : nullptr,
-                                                      output->info(),
-                                                      info));
-    _original_weights = weights;
-    _weights_permute_func.configure(weights, &_permuted_weights, PermutationVector{ 3, 0, 1, 2 });
+    _impl->tensors.add_const_tensor(TensorType::ACL_SRC_0, input);
+    _impl->tensors.add_const_tensor(TensorType::ACL_SRC_1, weights);
+    _impl->tensors.add_const_tensor(TensorType::ACL_SRC_2, biases);
+    _impl->tensors.add_tensor(TensorType::ACL_DST, output);
 
-    // Configure assembly dispatch
-    cpu::AsmGemmInfo asm_info = init_assembly_metadata(info, false);
-    if(is_data_type_quantized(input->info()->data_type()))
-    {
-        asm_info.output_stage = calculate_output_stage_metadata(input->info(), weights->info(), output->info(), info.act_info);
-    }
-    _gemm_asm_func->configure(input, &_permuted_weights, biases, output, asm_info);
-
-    // Configure activation
-    if(info.act_info.enabled() && !_gemm_asm_func->is_activation_supported(info.act_info))
-    {
-        _activation_func.configure(output, nullptr, info.act_info);
-        _run_activation = true;
-    }
+    _impl->op->configure(input->info(), weights->info(), biases->info(), output->info(), info);
 }
+
 Status NEGEMMConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const Conv2dInfo &info)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.num_groups > 1, "Grouping (num_groups != 1) is not supported on Neon");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() != DataLayout::NHWC, "Data layout supported is NHWC");
-    const DataType    data_type = input->data_type();
-    const TensorShape i_shape   = input->tensor_shape();
-    const TensorShape w_shape   = weights->tensor_shape();
-    ARM_COMPUTE_RETURN_ERROR_ON(w_shape[0] != i_shape[0]);
-    ARM_COMPUTE_RETURN_ERROR_ON(info.dilation != Size2D(1U, 1U));
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-    // Validate biases
-    if(biases != nullptr)
-    {
-        if(is_data_type_quantized_asymmetric(data_type))
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
-        }
-        else if(data_type == DataType::BFLOAT16)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        }
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
-    }
-
-    cpu::AsmGemmInfo asm_info = init_assembly_metadata(info, false);
-    ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuGemmAssemblyDispatch::validate(input, weights, biases, output, asm_info));
-    return Status{};
+    return OperatorType::validate(input, weights, biases, output, info);
 }
 void NEGEMMConv2d::run()
 {
-    prepare();
-
-    _gemm_asm_func->run();
-    if(_run_activation)
-    {
-        _activation_func.run();
-    }
+    _impl->op->run(_impl->tensors);
 }
 void NEGEMMConv2d::prepare()
 {
-    if(!_is_prepared)
-    {
-        _permuted_weights.allocator()->allocate();
-        _weights_permute_func.run();
-        _original_weights->mark_as_unused();
-        _is_prepared = true;
-    }
+    _impl->op->prepare(_impl->tensors);
 }
 } // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 53dd39e549..cc0f20e695 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -146,14 +146,21 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b,
         {
             if(is_data_type_quantized_asymmetric(a_to_use->info()->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
             {
-                _asm_glue->configure(a_to_use, b, c, output, asm_info);
+                auto c_info_to_use = c == nullptr ? nullptr : c->info();
+                _asm_glue->configure(a_to_use->info(), b->info(), c_info_to_use, output->info(), asm_info);
                 _fused_assembly_path = _asm_glue->is_configured();
+                _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_2, c);
+                _asm_glue_tensors.add_tensor(TensorType::ACL_DST, output);
             }
             else
             {
-                _asm_glue->configure(a_to_use, b, nullptr, _fuse_output_stage ? &_mm_result_s32 : output, asm_info);
+                auto output_to_use = (_fuse_output_stage ? &_mm_result_s32 : output);
+                _asm_glue->configure(a_to_use->info(), b->info(), nullptr, output_to_use->info(), asm_info);
+                _asm_glue_tensors.add_tensor(TensorType::ACL_DST, output_to_use);
             }
             _assembly_path = _asm_glue->is_configured();
+            _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use);
+            _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b);
             break;
         }
         default:
@@ -513,7 +520,7 @@ void NEGEMMLowpMatrixMultiplyCore::run()
     // Run GEMM
     if(_asm_glue->is_configured())
     {
-        _asm_glue->run();
+        _asm_glue->run(_asm_glue_tensors);
     }
     else
     {
@@ -583,7 +590,7 @@ void NEGEMMLowpMatrixMultiplyCore::prepare()
                 ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
             }
 
-            _asm_glue->prepare();
+            _asm_glue->prepare(_asm_glue_tensors);
             if(!original_b_managed_by_weights_manager)
             {
                 _original_b->mark_as_unused();