arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 7fd81cd..68c6576 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 @@
_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 @@
// 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(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 @@
}
}
+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)