Create CpuGemmDirectConv2d

As the first phase of making NEGEMMConv2d stateless,
CpuGemmDirectConv2d operator is created. Kernels and
operators used by the operator use TensorInfo pointers
instead of Tensor pointers.

The CpuGemmDirectConv2d isn't completely stateless
because it manages one intermediate tensor internally.
This will be resolved by implementing memory injection
mechanism with the following patches.

Also, weight manager of CpuGemmAssemblyDispatch is disabled
to enable this work.

Implements: COMPMID-4506

Change-Id: Iec3ca6de29d98bef7ea95e8f4473d6dc0024a140
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5672
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 18 insertions, 121 deletions

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