COMPMID-3280: Make all ML primitives for CL use the new interface - Part 2

- CLFunctions have been updated

Change-Id: Ie3256a6c775bc12f3126482bd8e8a46da54b267c
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3053
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 37 insertions, 33 deletions

diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
index 3298858215..1dcb341fe7 100644
--- a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
@@ -28,7 +28,6 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
-#include "utils/TypePrinter.h"
 
 #include <memory>
 #include <tuple>
@@ -64,29 +63,29 @@ std::pair<Coordinates, Coordinates> compute_start_end_slice_coordinates(const IT
 }
 Status construct_gemmlowp_output_stage(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, GEMMLowpOutputStageInfo &output_stage_info)
 {
-        const auto data_type = input->data_type();
+    const auto data_type = input->data_type();
 
-        if(is_data_type_quantized_asymmetric(data_type))
-        {
-            const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
-            const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
-            const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
-
-            float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
-            int   output_multiplier(0);
-            int   output_shift(0);
-            ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
-
-            output_stage_info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
-            output_stage_info.gemmlowp_multiplier = output_multiplier;
-            output_stage_info.gemmlowp_shift      = output_shift;
-            output_stage_info.gemmlowp_offset     = oq_info.offset;
-            const auto min_max_bound              = get_min_max(data_type);
-            output_stage_info.gemmlowp_min_bound  = (std::get<0>(min_max_bound)).get<int32_t>();
-            output_stage_info.gemmlowp_max_bound  = (std::get<1>(min_max_bound)).get<int32_t>();
-            output_stage_info.output_data_type    = data_type;
-        }
-        return Status{};
+    if(is_data_type_quantized_asymmetric(data_type))
+    {
+        const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+        const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+        const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
+
+        float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
+        int   output_multiplier(0);
+        int   output_shift(0);
+        ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+
+        output_stage_info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+        output_stage_info.gemmlowp_multiplier = output_multiplier;
+        output_stage_info.gemmlowp_shift      = output_shift;
+        output_stage_info.gemmlowp_offset     = oq_info.offset;
+        const auto min_max_bound              = get_min_max(data_type);
+        output_stage_info.gemmlowp_min_bound  = (std::get<0>(min_max_bound)).get<int32_t>();
+        output_stage_info.gemmlowp_max_bound  = (std::get<1>(min_max_bound)).get<int32_t>();
+        output_stage_info.output_data_type    = data_type;
+    }
+    return Status{};
 }
 
 } // namespace
@@ -175,7 +174,6 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, &gemm_output_info.set_data_type(DataType::S32),
                                                                            gemm_info));
         ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(input, weights, output, output_stage_info));
-
     }
     else
     {
@@ -215,6 +213,12 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso
 
 void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info)
 {
+    configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, deconv_info);
+}
+
+void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output,
+                                         const PadStrideInfo &deconv_info)
+{
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_ERROR_THROW_ON(CLGEMMDeconvolutionLayer::validate(input->info(),
                                                                   weights->info(),
@@ -237,9 +241,9 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor
     if(_is_nchw)
     {
         _memory_group.manage(&_permuted_input);
-        _permute_input_to_nhwc.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
+        _permute_input_to_nhwc.configure(compile_context, input, &_permuted_input, PermutationVector(2U, 0U, 1U));
 
-        _permute_weights_to_nhwc.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
+        _permute_weights_to_nhwc.configure(compile_context, weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
 
         input_to_use   = &_permuted_input;
         weights_to_use = &_permuted_weights;
@@ -251,8 +255,8 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor
                                                    1,
                                                    input->info()->data_type(), weights->info()->quantization_info()));
 
-    _reshape_weights.configure(weights_to_use, &_reshaped_weights);
-    _transpose_weights.configure(&_reshaped_weights, &_reshaped_weights_t);
+    _reshape_weights.configure(compile_context, weights_to_use, &_reshaped_weights);
+    _transpose_weights.configure(compile_context, &_reshaped_weights, &_reshaped_weights_t);
 
     const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
     GEMMInfo     gemm_info(false, false, true, input->info()->dimension(idx_h), true);
@@ -268,14 +272,14 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor
         input_to_use->info()->set_quantization_info(QuantizationInfo(iq_info.uniform().scale, -iq_info.uniform().offset));
         _reshaped_weights_t.info()->set_quantization_info(QuantizationInfo(wq_info.uniform().scale, -wq_info.uniform().offset));
 
-        _mm_gemmlowp.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, gemm_info);
+        _mm_gemmlowp.configure(compile_context, input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, gemm_info);
 
         input_to_use->info()->set_quantization_info(iq_info);
         _reshaped_weights_t.info()->set_quantization_info(wq_info);
     }
     else
     {
-        _mm_gemm.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, gemm_info);
+        _mm_gemm.configure(compile_context, input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, gemm_info);
     }
 
     if(_is_nchw)
@@ -313,14 +317,14 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor
     }
 
     // Configure a Col2Im call to reshape the output of GEMM
-    _deconv_reshape.configure(&_gemm_output, bias, deconv_reshape_output, input->info(), weights->info(), deconv_info);
+    _deconv_reshape.configure(compile_context, &_gemm_output, bias, deconv_reshape_output, input->info(), weights->info(), deconv_info);
     _gemm_output.allocator()->allocate();
 
     if(_is_quantized)
     {
         GEMMLowpOutputStageInfo output_stage_info;
         construct_gemmlowp_output_stage(input->info(), weights->info(), output->info(), output_stage_info);
-        _gemmlowp_output_stage.configure(&_gemmlowp_final, nullptr, output_stage_output, output_stage_info);
+        _gemmlowp_output_stage.configure(compile_context, &_gemmlowp_final, nullptr, output_stage_output, output_stage_info);
         _gemmlowp_final.allocator()->allocate();
     }
 
@@ -328,7 +332,7 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor
     if(_padded_input)
     {
         const auto start_end = compute_start_end_slice_coordinates(*deconv_reshape_output->info(), deconv_info, _is_nchw);
-        _slice_gemm.configure(&_slice_gemm_input, slice_output, start_end.first, start_end.second);
+        _slice_gemm.configure(compile_context, &_slice_gemm_input, slice_output, start_end.first, start_end.second);
         _slice_gemm_input.allocator()->allocate();
     }
 }