namespace
{
-Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output)
+Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
{
- if(is_data_type_quantized_asymmetric(input.data_type()))
+ 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().uniform().scale, -input.quantization_info().uniform().offset);
- const QuantizationInfo weights_quantization_info(weights.quantization_info().uniform().scale, -weights.quantization_info().uniform().offset);
+ const QuantizationInfo input_quantization_info(input->quantization_info().uniform().scale, -input->quantization_info().uniform().offset);
+ const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset);
+
+ 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;
+ int32_t output_multiplier;
+ int32_t output_shift;
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+
+ GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
+ gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
+ gemmlowp_output_stage_info.gemmlowp_shift = output_shift;
+ gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;
+ gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ const auto min_max_bound = get_min_max(input->data_type());
+ gemmlowp_output_stage_info.gemmlowp_min_bound = (std::get<0>(min_max_bound)).get<int32_t>();
+ gemmlowp_output_stage_info.gemmlowp_max_bound = (std::get<1>(min_max_bound)).get<int32_t>();
+
+ GEMMInfo gemm_info;
+ gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);
// Validate gemmlowp function
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info),
- &weights.clone()->set_quantization_info(weights_quantization_info),
- nullptr,
- &output));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_quantization_info(input_quantization_info),
+ &weights->clone()->set_quantization_info(weights_quantization_info),
+ biases,
+ output,
+ gemm_info));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::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(NEGEMM::validate(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)));
}
return Status{};
NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
: _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(),
- _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(),
- _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _accumulate_biases(false),
- _is_quantized(false), _is_prepared(false)
+ _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(), _flatten_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr),
+ _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _is_quantized(false), _is_prepared(false)
{
}
-void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
+void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output)
{
if(_is_quantized)
{
input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
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);
+ // Configure gemmlowp function and output stage for asymmetric quantized types
+ 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;
+ int32_t output_multiplier;
+ int32_t output_shift;
+ quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+
+ GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
+ gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
+ gemmlowp_output_stage_info.gemmlowp_shift = output_shift;
+ gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;
+ gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ const auto min_max_bound = get_min_max(input->info()->data_type());
+ gemmlowp_output_stage_info.gemmlowp_min_bound = (std::get<0>(min_max_bound)).get<int32_t>();
+ gemmlowp_output_stage_info.gemmlowp_max_bound = (std::get<1>(min_max_bound)).get<int32_t>();
+ GEMMInfo gemm_info;
+ gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);
+ _mm_gemmlowp.configure(input, weights, biases, 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);
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 */));
+ _mm_gemm.configure(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
}
}
-void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, ITensor *output)
+void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output)
{
ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
_flatten_kernel.configure(input, &_flatten_output);
// Configure matrix multiply kernel
- configure_mm(&_flatten_output, weights, output);
+ configure_mm(&_flatten_output, weights, biases, output);
// Allocate the output tensor for flatten once all the configure methods have been called
_flatten_output.allocator()->allocate();
}
-void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output)
+void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output)
{
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
// Configure matrix multiply kernel
- configure_mm(input, weights, output);
+ configure_mm(input, weights, biases, output);
}
void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
_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());
_original_weights = weights;
_weights_manager->manage(weights);
}
- // Configure gemmlowp output
- if(_is_quantized)
- {
- _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)
- {
- _accumulate_biases = true;
-
- // Configure accumulate biases kernel
- _accumulate_biases_kernel.configure(output, biases);
- }
-
// 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
_are_weights_converted = false;
}
- ITensor *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);
+ configure_conv_fc(input, weights_to_use, biases, output);
}
else
{
// Fully Connected layer after a Fully Connected Layer without batches
- configure_fc_fc(input, weights_to_use, tmp_output);
- }
-
- // 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;
- int32_t output_multiplier;
- int32_t output_shift;
- quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
-
- GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
- gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
- gemmlowp_output_stage_info.gemmlowp_shift = output_shift;
- gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;
- gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
- _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, gemmlowp_output_stage_info);
- _gemmlowp_output.allocator()->allocate();
+ configure_fc_fc(input, weights_to_use, biases, output);
}
_are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
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 ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
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(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases));
- }
// With the Fully Connected layer we can have 4 different cases:
// 1) Convolution layer -> Fully Connected layer without batches
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;
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();
-
- float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale;
- int32_t output_multiplier;
- int32_t output_shift;
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
-
- GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
- gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
- gemmlowp_output_stage_info.gemmlowp_shift = output_shift;
- gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;
- gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&gemmlowp_output, biases, output, gemmlowp_output_stage_info));
- }
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(input_to_use, weights_to_use, biases, output));
return Status{};
}
{
_mm_gemm.run();
}
-
- // Accumulate biases if provided
- if(_is_quantized)
- {
- _gemmlowp_output_stage.run();
- }
- else
- {
- if(_accumulate_biases)
- {
- NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
- }
- }
}
void NEFullyConnectedLayer::prepare()
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