COMPMID-617: Adds CLFullyConnectionLayer validation support

Change-Id: I4d2eb9872a3165fdcaa7784596e441cbe563dbc2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/112577
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Ioan-Cristian Szabo <ioan-cristian.szabo@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 124 insertions, 5 deletions

diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 7fd81cdb94..68c6576a79 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -25,6 +25,7 @@
 
 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "support/ToolchainSupport.h"
@@ -32,6 +33,34 @@
 #include <algorithm>
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output, bool is_interleaved_transposed)
+{
+    const GPUTarget gpu_target = CLScheduler::get().target();
+
+    if(is_data_type_quantized_asymmetric(input.data_type()))
+    {
+        // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
+        // Extract and negate input and weights offset
+        const QuantizationInfo input_quantization_info(input.quantization_info().scale, -input.quantization_info().offset);
+        const QuantizationInfo weights_quantization_info(weights.quantization_info().scale, -weights.quantization_info().offset);
+
+        // Validate gemmlowp function
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info),
+                                                                           &weights.clone()->set_quantization_info(weights_quantization_info),
+                                                                           &output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(&input, &weights, &output, 1.f, is_interleaved_transposed, gpu_target));
+    }
+
+    return Status{};
+}
+} // namespace
 
 void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output)
 {
@@ -40,6 +69,11 @@ void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLT
     _kernel = std::move(k);
 }
 
+Status CLFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    return CLTransposeKernel::validate(input, output);
+}
+
 CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _im2col_output(),
       _gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false)
@@ -80,8 +114,7 @@ void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLT
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
     // Initialize output tensor for im2col
-    TensorShape shape_im2col = input->info()->tensor_shape();
-    shape_im2col.collapse(3);
+    TensorShape shape_im2col = compute_im2col_shape(*input->info());
     _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
 
     // Configure im2col kernel
@@ -105,9 +138,15 @@ void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTen
 
 void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights, bool are_weights_reshaped)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    // Perform validate step
+    ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayer::validate(input->info(),
+                                                               weights->info(),
+                                                               biases != nullptr ? biases->info() : nullptr,
+                                                               output->info(),
+                                                               transpose_weights,
+                                                               are_weights_reshaped));
 
     _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
     _is_fc_after_conv     = true;
@@ -192,6 +231,86 @@ void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *w
     }
 }
 
+Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
+
+    bool            weights_reshaped = transpose_weights ? are_weights_reshaped : true;
+    bool            is_fc_after_conv = true;
+    bool            is_quantized     = is_data_type_quantized_asymmetric(input->data_type());
+    const GPUTarget gpu_target       = CLScheduler::get().target();
+
+    const ITensorInfo &im2col_input     = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(*input)));
+    const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
+    const ITensorInfo &gemmlowp_output  = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+
+    // Configure accumulate biases kernel for non quantized asymmetric types
+    if(biases != nullptr && !is_quantized)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAccumulateBiasesKernel::validate(output, biases, gpu_target));
+    }
+
+    // With the Fully Connected layer we can have 4 different cases:
+    //  1) Convolution layer -> Fully Connected layer without batches
+    //  2) Fully Connected layer -> Fully Connected layer without batches
+    //  3) Convolution layer -> Fully Connected layer with batches
+    //  4) Fully Connected layer -> Fully Connected layer with batches
+
+    const ITensorInfo *input_to_use   = input;
+    const ITensorInfo *weights_to_use = weights;
+    const ITensorInfo *tmp_output     = (is_quantized) ? &gemmlowp_output : output;
+
+    if(!weights_reshaped)
+    {
+        // Validate reshape weights kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
+        weights_to_use = &reshaped_weights;
+    }
+
+    // Check if we have a fully connected layer with batches
+    const bool is_batched_fc_layer = output->dimension(1) > 1;
+
+    if(is_batched_fc_layer)
+    {
+        is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3,
+                                                                                 input->tensor_shape().cend(),
+                                                                                 output->tensor_shape().cbegin() + 1));
+    }
+    else
+    {
+        is_fc_after_conv = input->num_dimensions() > 1;
+    }
+
+    if(is_fc_after_conv)
+    {
+        // Fully Connected layer after a Convolution Layer without batches
+        ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));
+
+        // Validate im2col kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false));
+        input_to_use = &im2col_input;
+    }
+    else
+    {
+        // Fully Connected layer after a Fully Connected Layer without batches
+        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
+    }
+    // Validate matrix multiply kernel
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, *tmp_output, false));
+
+    // Validate output stage for asymmetric quantized types
+    if(is_quantized)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&gemmlowp_output, biases, output));
+    }
+
+    return Status{};
+}
+
 void CLFullyConnectedLayer::run()
 {
     // Reshape of the weights (happens only once)