arm_compute v19.11
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index c5da649..a8167ce 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/Cast.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
@@ -32,13 +33,64 @@
#include <algorithm>
-using namespace arm_compute;
+namespace arm_compute
+{
using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::utils::cast;
namespace
{
-Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output)
+Status construct_gemmlowp_output_stage(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output,
+ GEMMLowpOutputStageInfo &gemmlowp_output_stage)
{
+ gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ gemmlowp_output_stage.gemmlowp_offset = 0;
+ gemmlowp_output_stage.gemmlowp_multiplier = 0;
+ gemmlowp_output_stage.gemmlowp_shift = 0;
+
+ // Configure output stage for quantized case
+ if(is_data_type_quantized_asymmetric(input.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();
+
+ const auto output_quant_info = (output.total_size() == 0) ? iq_info : oq_info;
+
+ const float multiplier = (iq_info.scale * wq_info.scale) / output_quant_info.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
+
+ // Set the GEMMLowp output stage info
+ gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
+ gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
+ gemmlowp_output_stage.gemmlowp_shift = output_shift;
+ gemmlowp_output_stage.gemmlowp_min_bound = 0;
+ gemmlowp_output_stage.gemmlowp_max_bound = 255;
+ gemmlowp_output_stage.gemmlowp_multipliers.push_back(output_multiplier);
+ gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift);
+ }
+
+ return Status{};
+}
+
+Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &output, const FullyConnectedLayerInfo &fc_info)
+{
+ GEMMLowpOutputStageInfo gemmlowp_output_stage;
+ ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(input, weights, output, gemmlowp_output_stage));
+
+ const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
+ false, // is_b_reshaped
+ true, // reshape_b_only_on_first_run
+ 0, // depth_output_gemm3d
+ false, // reinterpret_input_as_3d
+ fc_info.retain_internal_weights, // retain_internal_weights
+ gemmlowp_output_stage, // gemmlowp_output_stage
+ fc_info.fp_mixed_precision, // fp_mixed_precision
+ true, // broadcast_bias
+ ActivationLayerInfo()); // activation_info
+
if(is_data_type_quantized_asymmetric(input.data_type()))
{
const UniformQuantizationInfo iq_info = input.quantization_info().uniform();
@@ -52,12 +104,13 @@
// 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),
- nullptr,
- &output));
+ bias,
+ &output,
+ gemm_info));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input, &weights, nullptr, &output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input, &weights, bias, &output, 1.f, 1.f, gemm_info));
}
return Status{};
@@ -76,14 +129,28 @@
return CLTransposeKernel::validate(input, output);
}
-CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _convert_weights(), _flatten_layer(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
- _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true), _are_weights_reshaped(true),
- _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
+CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
+ : _memory_group(memory_manager), _weights_manager(weights_manager), _convert_weights(), _convert_weights_managed(), _reshape_weights_managed_function(), _flatten_layer(), _reshape_weights_function(),
+ _mm_gemm(memory_manager, weights_manager), _mm_gemmlowp(memory_manager), _flatten_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true),
+ _are_weights_reshaped(true), _is_fc_after_conv(true), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
{
}
-void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
+void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const FullyConnectedLayerInfo &fc_info)
{
+ GEMMLowpOutputStageInfo gemmlowp_output_stage;
+ construct_gemmlowp_output_stage(*input->info(), *weights->info(), *output->info(), gemmlowp_output_stage);
+
+ const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
+ false, // is_b_reshaped
+ true, // reshape_b_only_on_first_run
+ 0, // depth_output_gemm3d
+ false, // reinterpret_input_as_3d
+ fc_info.retain_internal_weights, // retain_internal_weights
+ gemmlowp_output_stage, // gemmlowp_output_stage
+ fc_info.fp_mixed_precision, // fp_mixed_precision
+ true, // broadcast_bias
+ ActivationLayerInfo()); // activation_info
+
if(_is_quantized)
{
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
@@ -95,7 +162,7 @@
weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
// Configure gemmlowp function
- _mm_gemmlowp.configure(input, weights, nullptr, output);
+ _mm_gemmlowp.configure(input, weights, bias, output, gemm_info);
// Revert back QuantizatioInfo as input and weights could be used in other fully connected layers
input->info()->set_quantization_info(input_quantization_info);
@@ -104,11 +171,11 @@
else
{
// Configure matrix multiply kernel
- _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */, 0, false, retain_internal_weights));
+ _mm_gemm.configure(input, weights, bias, output, 1.f, 1.f, gemm_info);
}
}
-void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
+void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const FullyConnectedLayerInfo &fc_info)
{
ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
@@ -123,18 +190,18 @@
_flatten_layer.configure(input, &_flatten_output);
// Configure matrix multiply kernel
- configure_mm(&_flatten_output, weights, output, retain_internal_weights);
+ configure_mm(&_flatten_output, weights, bias, output, fc_info);
// Allocate the output tensor for flatten once all the configure methods have been called
_flatten_output.allocator()->allocate();
}
-void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool retain_internal_weights)
+void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const FullyConnectedLayerInfo &fc_info)
{
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
// Configure matrix multiply kernel
- configure_mm(input, weights, output, retain_internal_weights);
+ configure_mm(input, weights, bias, output, fc_info);
}
void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
@@ -152,27 +219,13 @@
_are_weights_converted = true;
_are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
_is_fc_after_conv = true;
- _accumulate_biases = false;
_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
_is_prepared = fc_info.retain_internal_weights;
_original_weights = weights;
- // Configure gemmlowp output
- if(_is_quantized)
+ if(_weights_manager)
{
- _gemmlowp_output.allocator()->init(output->info()->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_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-
- _accumulate_biases = true;
-
- // Configure accumulate biases kernel
- _accumulate_biases_kernel.set_target(CLScheduler::get().target());
- _accumulate_biases_kernel.configure(output, biases);
+ _weights_manager->manage(weights);
}
const ICLTensor *weights_to_use = weights;
@@ -199,50 +252,51 @@
// Reshape weights if needed
if(!_are_weights_reshaped)
{
- // Reshape the weights
- _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
- weights_to_use = &_reshape_weights_output;
+ if(_weights_manager && _weights_manager->are_weights_managed(weights))
+ {
+ _reshape_weights_managed_function.configure(weights);
+ weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_reshape_weights_managed_function));
+ }
+ else
+ {
+ // Reshape the weights
+ _reshape_weights_function.configure(weights, &_reshape_weights_output);
+ weights_to_use = &_reshape_weights_output;
+ }
}
// Convert weights if needed
if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
{
- // Convert weights
- _convert_weights.configure(weights_to_use,
- &_converted_weights_output,
- input->info()->tensor_shape(),
- fc_info.weights_trained_layout);
+ if(_weights_manager && _weights_manager->are_weights_managed(weights_to_use))
+ {
+ _convert_weights_managed.configure(weights_to_use,
+ input->info()->tensor_shape(),
+ fc_info.weights_trained_layout);
+ weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_convert_weights_managed));
+ }
+ else
+ {
+ // Convert weights
+ _convert_weights.configure(weights_to_use,
+ &_converted_weights_output,
+ input->info()->tensor_shape(),
+ fc_info.weights_trained_layout);
- weights_to_use = &_converted_weights_output;
+ weights_to_use = &_converted_weights_output;
+ }
_are_weights_converted = false;
}
- // Configure fc core
- ICLTensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;
if(_is_fc_after_conv)
{
// Fully Connected layer after a Convolution Layer without batches
- configure_conv_fc(input, weights_to_use, tmp_output, fc_info.retain_internal_weights);
+ configure_conv_fc(input, weights_to_use, biases, output, fc_info);
}
else
{
// Fully Connected layer after a Fully Connected Layer without batches
- configure_fc_fc(input, weights_to_use, tmp_output, fc_info.retain_internal_weights);
- }
-
- // Configure output stage for asymmetric quantized types
- if(_is_quantized)
- {
- const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
-
- float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
- int output_multiplier;
- int output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, oq_info.offset);
- _gemmlowp_output.allocator()->allocate();
+ configure_fc_fc(input, weights_to_use, biases, output, fc_info);
}
}
@@ -254,22 +308,12 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
- bool weights_reshaped = fc_info.transpose_weights ? fc_info.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();
+ bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
+ bool is_fc_after_conv = true;
const ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)).set_data_layout(DataLayout::NCHW));
const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
- 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
@@ -279,7 +323,6 @@
const ITensorInfo *input_to_use = input;
const ITensorInfo *weights_to_use = weights;
- const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output;
// Check if we have a fully connected layer with batches
const bool is_batched_fc_layer = output->dimension(1) > 1;
@@ -325,21 +368,9 @@
// 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));
-
- // Validate output stage for asymmetric quantized types
- if(is_quantized)
- {
- const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
- const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
-
- ARM_COMPUTE_UNUSED(multiplier);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&gemmlowp_output, biases, output));
- }
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, biases, *output, fc_info));
return Status{};
}
@@ -365,26 +396,16 @@
{
_mm_gemm.run();
}
-
- // Accumulate biases if provided
- if(_is_quantized)
- {
- _gemmlowp_output_stage.run();
- }
- else
- {
- if(_accumulate_biases)
- {
- CLScheduler::get().enqueue(_accumulate_biases_kernel);
- }
- }
}
void CLFullyConnectedLayer::prepare()
{
if(!_is_prepared)
{
- ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+ if(!_weights_manager)
+ {
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+ }
auto release_unused = [](CLTensor * w)
{
@@ -401,22 +422,36 @@
// Reshape of the weights if needed (happens only once)
if(!_are_weights_reshaped)
{
- // Run reshape weights kernel and mark weights as unused
- _reshape_weights_output.allocator()->allocate();
- _reshape_weights_kernel.run();
+ if(_weights_manager && _weights_manager->are_weights_managed(_original_weights))
+ {
+ cur_weights = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->run(cur_weights, &_reshape_weights_managed_function));
+ }
+ else
+ {
+ // Run reshape weights kernel and mark weights as unused
+ _reshape_weights_output.allocator()->allocate();
+ _reshape_weights_function.run();
- cur_weights->mark_as_unused();
- cur_weights = &_reshape_weights_output;
+ cur_weights->mark_as_unused();
+ cur_weights = &_reshape_weights_output;
+ }
_are_weights_reshaped = true;
}
// Convert weights if needed (happens only once)
if(!_are_weights_converted)
{
- _converted_weights_output.allocator()->allocate();
- _convert_weights.run();
+ if(_weights_manager && _weights_manager->are_weights_managed(cur_weights))
+ {
+ _weights_manager->run(cur_weights, &_convert_weights_managed);
+ }
+ else
+ {
+ _converted_weights_output.allocator()->allocate();
+ _convert_weights.run();
+ cur_weights->mark_as_unused();
+ }
- cur_weights->mark_as_unused();
_are_weights_converted = true;
}
@@ -436,3 +471,4 @@
_is_prepared = true;
}
}
+} // namespace arm_compute