Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
index 2caaea0..dd78d10 100644
--- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
@@ -27,13 +27,14 @@
#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/QuantizationInfo.h"
#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/InfoHelpers.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
+
#include "src/common/utils/Log.h"
-#include "src/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h"
#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h"
#include "src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h"
namespace arm_compute
@@ -41,12 +42,19 @@
using namespace arm_compute::utils::info_helpers;
namespace
{
-Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm_input, const ITensorInfo *mm_weights, const ITensorInfo *bias,
- float gemmlowp_scale, const TensorInfo *mm_res_info, const TensorInfo *outstage_tensor_info)
+Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info,
+ const ITensorInfo *mm_input,
+ const ITensorInfo *mm_weights,
+ const ITensorInfo *bias,
+ float gemmlowp_scale,
+ const TensorInfo *mm_res_info,
+ const TensorInfo *outstage_tensor_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(mm_input, mm_weights, nullptr, mm_res_info));
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(mm_res_info, bias, outstage_tensor_info, gemmlowp_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(
+ gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEGEMMLowpOutputStage::validate(mm_res_info, bias, outstage_tensor_info, gemmlowp_info));
return Status{};
}
} // namespace
@@ -55,10 +63,7 @@
{
// Output quantization scale will be different, but ignored here
// since it will be configured at configure() stage.
- const TensorInfo out
- {
- in
- };
+ const TensorInfo out{in};
return NEQLSTMLayerNormalizationKernel::validate(&in, &out, &weight, &bias);
}
@@ -98,14 +103,12 @@
void NEQLSTMLayer::TensorCopyKernel::run()
{
- Iterator input_iter{ _src, _window };
- Iterator output_iter{ _dst, _window };
+ Iterator input_iter{_src, _window};
+ Iterator output_iter{_dst, _window};
- execute_window_loop(_window, [&](const Coordinates &)
- {
- memcpy(output_iter.ptr(), input_iter.ptr(), _row_size);
- },
- input_iter, output_iter);
+ execute_window_loop(
+ _window, [&](const Coordinates &) { memcpy(output_iter.ptr(), input_iter.ptr(), _row_size); }, input_iter,
+ output_iter);
}
NEQLSTMLayer::~NEQLSTMLayer() = default;
@@ -191,10 +194,17 @@
_memory_group = MemoryGroup(std::move(memory_manager));
}
-void NEQLSTMLayer::configure_mm(NEGEMMLowpMatrixMultiplyCore &mm, NEGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
- const ITensor *mm_input, const ITensor *mm_weights, const ITensor *bias,
- Tensor *mm_res, Tensor *outstage_res, float gemmlowp_scale,
- const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info)
+void NEQLSTMLayer::configure_mm(NEGEMMLowpMatrixMultiplyCore &mm,
+ NEGEMMLowpOutputStage &outstage,
+ GEMMLowpOutputStageInfo &gemmlowp_info,
+ const ITensor *mm_input,
+ const ITensor *mm_weights,
+ const ITensor *bias,
+ Tensor *mm_res,
+ Tensor *outstage_res,
+ float gemmlowp_scale,
+ const TensorInfo &mm_res_info,
+ const TensorInfo &outstage_tensor_info)
{
_memory_group.manage(mm_res);
_memory_group.manage(outstage_res);
@@ -206,66 +216,87 @@
mm.configure(mm_input, mm_weights, nullptr, mm_res);
// Configure output stage
- quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+ quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift);
outstage.configure(mm_res, bias, outstage_res, gemmlowp_info);
mm_res->allocator()->allocate();
}
-void NEQLSTMLayer::configure(const ITensor *input,
- const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights,
- const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights,
- const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias,
- const ITensor *cell_state_in, ITensor *output_state_in,
- ITensor *cell_state_out, ITensor *output_state_out, ITensor *output,
+void NEQLSTMLayer::configure(const ITensor *input,
+ const ITensor *input_to_forget_weights,
+ const ITensor *input_to_cell_weights,
+ const ITensor *input_to_output_weights,
+ const ITensor *recurrent_to_forget_weights,
+ const ITensor *recurrent_to_cell_weights,
+ const ITensor *recurrent_to_output_weights,
+ const ITensor *forget_gate_bias,
+ const ITensor *cell_bias,
+ const ITensor *output_gate_bias,
+ const ITensor *cell_state_in,
+ ITensor *output_state_in,
+ ITensor *cell_state_out,
+ ITensor *output_state_out,
+ ITensor *output,
const LSTMParams<ITensor> &lstm_params)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
- forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
+ forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
+ cell_state_out, output_state_out);
ARM_COMPUTE_LOG_PARAMS(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
- forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
+ forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
+ cell_state_out, output_state_out);
// Set lstm parameters
LSTMParams<ITensorInfo> lstm_params_info{};
build_lstm_params_tensor_info(lstm_params, &lstm_params_info);
- _input_to_forget_weights_transposed.info()->set_quantization_info(input_to_forget_weights->info()->quantization_info());
+ _input_to_forget_weights_transposed.info()->set_quantization_info(
+ input_to_forget_weights->info()->quantization_info());
_input_to_cell_weights_transposed.info()->set_quantization_info(input_to_cell_weights->info()->quantization_info());
- _input_to_output_weights_transposed.info()->set_quantization_info(input_to_output_weights->info()->quantization_info());
- _recurrent_to_forget_weights_transposed.info()->set_quantization_info(recurrent_to_forget_weights->info()->quantization_info());
- _recurrent_to_cell_weights_transposed.info()->set_quantization_info(recurrent_to_cell_weights->info()->quantization_info());
- _recurrent_to_output_weights_transposed.info()->set_quantization_info(recurrent_to_output_weights->info()->quantization_info());
+ _input_to_output_weights_transposed.info()->set_quantization_info(
+ input_to_output_weights->info()->quantization_info());
+ _recurrent_to_forget_weights_transposed.info()->set_quantization_info(
+ recurrent_to_forget_weights->info()->quantization_info());
+ _recurrent_to_cell_weights_transposed.info()->set_quantization_info(
+ recurrent_to_cell_weights->info()->quantization_info());
+ _recurrent_to_output_weights_transposed.info()->set_quantization_info(
+ recurrent_to_output_weights->info()->quantization_info());
- if(input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED)
+ if (input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED)
{
_convert_input_to_forget_weights_to_qsymm8 = true;
// Setup dequantize output tensor to go from QASYMM8_SIGNED -> F32
- _input_to_forget_weights_f32.allocator()->init(TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::F32)
- .set_data_layout(input_to_forget_weights->info()->data_layout()));
+ _input_to_forget_weights_f32.allocator()->init(
+ TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::F32)
+ .set_data_layout(input_to_forget_weights->info()->data_layout()));
// Setup the quantize output tensor to go from F32 -> QSYMM8
- _input_to_forget_weights_symm8.allocator()->init((TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::QSYMM8)
- .set_data_layout(input_to_forget_weights->info()->data_layout())
- .set_quantization_info(input_to_forget_weights->info()->quantization_info())));
+ _input_to_forget_weights_symm8.allocator()->init(
+ (TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::QSYMM8)
+ .set_data_layout(input_to_forget_weights->info()->data_layout())
+ .set_quantization_info(input_to_forget_weights->info()->quantization_info())));
_dequantize_input_to_forget_weights.configure(input_to_forget_weights, &_input_to_forget_weights_f32);
_quantize_input_to_forget_weights.configure(&_input_to_forget_weights_f32, &_input_to_forget_weights_symm8);
- ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(input->info(), _input_to_forget_weights_symm8.info(), input_to_cell_weights->info(), input_to_output_weights->info(),
- recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
- forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
- cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(), output->info(),
- lstm_params_info));
+ ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(
+ input->info(), _input_to_forget_weights_symm8.info(), input_to_cell_weights->info(),
+ input_to_output_weights->info(), recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(),
+ recurrent_to_output_weights->info(), forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
+ cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(),
+ output->info(), lstm_params_info));
}
else
{
- ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(input->info(), input_to_forget_weights->info(), input_to_cell_weights->info(), input_to_output_weights->info(),
- recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
- forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
- cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(), output->info(),
- lstm_params_info));
+ ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(
+ input->info(), input_to_forget_weights->info(), input_to_cell_weights->info(),
+ input_to_output_weights->info(), recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(),
+ recurrent_to_output_weights->info(), forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
+ cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(),
+ output->info(), lstm_params_info));
}
const int batch_size = input->info()->dimension(1);
@@ -277,7 +308,9 @@
const UniformQuantizationInfo qoutput_state_in = output_state_in->info()->quantization_info().uniform();
_projection_bias = lstm_params.projection_bias();
- _input_to_forget_weights = (input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED) ? &_input_to_forget_weights_symm8 : input_to_forget_weights;
+ _input_to_forget_weights = (input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED)
+ ? &_input_to_forget_weights_symm8
+ : input_to_forget_weights;
_input_to_cell_weights = input_to_cell_weights;
_input_to_output_weights = input_to_output_weights;
_recurrent_to_forget_weights = recurrent_to_forget_weights;
@@ -287,7 +320,7 @@
// Layer normalization
_has_layer_norm = lstm_params.use_layer_norm();
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
set_layer_norm_weight(lstm_params.forget_layer_norm_weights(), LayerNormGate::Forget);
set_layer_norm_weight(lstm_params.cell_layer_norm_weights(), LayerNormGate::Cell);
@@ -309,22 +342,25 @@
// Calculate quantized parameters for clipping.
int16_t quantized_cell_clip = 0;
- if(lstm_params.cell_clip() > 0.0f)
+ if (lstm_params.cell_clip() > 0.0f)
{
quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
}
_has_cell_clipping = quantized_cell_clip > 0;
// Precompute effective bias for optimizing the matmul computations.
- if(!_has_cifg)
+ if (!_has_cifg)
{
_input_to_input_weights = lstm_params.input_to_input_weights();
_recurrent_to_input_weights = lstm_params.recurrent_to_input_weights();
_input_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
_recurrent_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
- _input_to_input_reduction->configure(_input_to_input_weights->info(), _input_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_input_reduction->configure(_recurrent_to_input_weights->info(), _recurrent_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_input_reduction->configure(_input_to_input_weights->info(), _input_to_input_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_input_reduction->configure(
+ _recurrent_to_input_weights->info(), _recurrent_to_input_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
}
_input_to_forget_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
@@ -334,19 +370,31 @@
_input_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
_recurrent_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
- _input_to_forget_reduction->configure(input_to_forget_weights->info(), _input_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_forget_reduction->configure(recurrent_to_forget_weights->info(), _recurrent_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_cell_reduction->configure(input_to_cell_weights->info(), _input_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_cell_reduction->configure(recurrent_to_cell_weights->info(), _recurrent_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_output_reduction->configure(input_to_output_weights->info(), _input_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_output_reduction->configure(recurrent_to_output_weights->info(), _recurrent_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- if(_has_projection)
+ _input_to_forget_reduction->configure(input_to_forget_weights->info(), _input_to_forget_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_forget_reduction->configure(
+ recurrent_to_forget_weights->info(), _recurrent_to_forget_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_cell_reduction->configure(input_to_cell_weights->info(), _input_to_cell_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_cell_reduction->configure(
+ recurrent_to_cell_weights->info(), _recurrent_to_cell_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_output_reduction->configure(input_to_output_weights->info(), _input_to_output_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_output_reduction->configure(
+ recurrent_to_output_weights->info(), _recurrent_to_output_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ if (_has_projection)
{
_projection_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
- _projection_reduction->configure(_projection_weights->info(), _projection_eff_bias.info(), GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
- if(_projection_bias != nullptr)
+ _projection_reduction->configure(
+ _projection_weights->info(), _projection_eff_bias.info(),
+ GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
+ if (_projection_bias != nullptr)
{
- _projection_bias_add.configure(_projection_bias, &_projection_eff_bias, &_projection_eff_bias, ConvertPolicy::SATURATE);
+ _projection_bias_add.configure(_projection_bias, &_projection_eff_bias, &_projection_eff_bias,
+ ConvertPolicy::SATURATE);
}
}
@@ -354,15 +402,19 @@
_transpose_input_to_forget_weights.configure(input_to_forget_weights, &_input_to_forget_weights_transposed);
_transpose_input_to_cell_weights.configure(input_to_cell_weights, &_input_to_cell_weights_transposed);
_transpose_input_to_output_weights.configure(input_to_output_weights, &_input_to_output_weights_transposed);
- _transpose_recurrent_to_forget_weights.configure(recurrent_to_forget_weights, &_recurrent_to_forget_weights_transposed);
+ _transpose_recurrent_to_forget_weights.configure(recurrent_to_forget_weights,
+ &_recurrent_to_forget_weights_transposed);
_transpose_recurrent_to_cell_weights.configure(recurrent_to_cell_weights, &_recurrent_to_cell_weights_transposed);
- _transpose_recurrent_to_output_weights.configure(recurrent_to_output_weights, &_recurrent_to_output_weights_transposed);
- if(!_has_cifg)
+ _transpose_recurrent_to_output_weights.configure(recurrent_to_output_weights,
+ &_recurrent_to_output_weights_transposed);
+ if (!_has_cifg)
{
- _transpose_input_to_input_weights.configure(lstm_params.input_to_input_weights(), &_input_to_input_weights_transposed);
- _transpose_recurrent_to_input_weights.configure(lstm_params.recurrent_to_input_weights(), &_recurrent_to_input_weights_transposed);
+ _transpose_input_to_input_weights.configure(lstm_params.input_to_input_weights(),
+ &_input_to_input_weights_transposed);
+ _transpose_recurrent_to_input_weights.configure(lstm_params.recurrent_to_input_weights(),
+ &_recurrent_to_input_weights_transposed);
}
- if(_has_projection)
+ if (_has_projection)
{
_transpose_projection_weights.configure(_projection_weights, &_projection_weights_transposed);
}
@@ -375,40 +427,52 @@
const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
// Forget gate.
- const TensorInfo forget_gate_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
- const float input_to_forget_scale = input_to_forget_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
- configure_mm(_mm_input_to_forget, _input_to_forget_outstage, gemmlowp_info,
- input, &_input_to_forget_weights_transposed, &_input_to_forget_eff_bias,
- &_mm_input_to_forget_res, &_input_to_forget_outstage_res, input_to_forget_scale,
- mm_out_info, forget_gate_outstage_info);
+ const TensorInfo forget_gate_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
+ const float input_to_forget_scale = input_to_forget_weights->info()->quantization_info().uniform().scale *
+ qinput.scale / lstm_params.forget_intermediate_scale();
+ configure_mm(_mm_input_to_forget, _input_to_forget_outstage, gemmlowp_info, input,
+ &_input_to_forget_weights_transposed, &_input_to_forget_eff_bias, &_mm_input_to_forget_res,
+ &_input_to_forget_outstage_res, input_to_forget_scale, mm_out_info, forget_gate_outstage_info);
- const float recurrent_to_forget_scale = recurrent_to_forget_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
- configure_mm(_mm_recurrent_to_forget, _recurrent_to_forget_outstage, gemmlowp_info,
- output_state_in, &_recurrent_to_forget_weights_transposed, &_recurrent_to_forget_eff_bias,
- &_mm_recurrent_to_forget_res, &_recurrent_to_forget_outstage_res, recurrent_to_forget_scale,
- mm_out_info, forget_gate_outstage_info);
+ const float recurrent_to_forget_scale = recurrent_to_forget_weights->info()->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
+ configure_mm(_mm_recurrent_to_forget, _recurrent_to_forget_outstage, gemmlowp_info, output_state_in,
+ &_recurrent_to_forget_weights_transposed, &_recurrent_to_forget_eff_bias, &_mm_recurrent_to_forget_res,
+ &_recurrent_to_forget_outstage_res, recurrent_to_forget_scale, mm_out_info, forget_gate_outstage_info);
- _accumulate_input_recurrent_forget.configure(&_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_forget.configure(&_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res,
+ &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
_input_to_forget_outstage_res.allocator()->allocate();
- if(_has_peephole)
+ if (_has_peephole)
{
_mul_cell_to_forget_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
_memory_group.manage(&_mul_cell_to_forget_res);
- _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- _cell_to_forget_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)));
+ _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(),
+ &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
+ _cell_to_forget_outstage_res.allocator()->init(
+ TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)));
_memory_group.manage(&_cell_to_forget_outstage_res);
- const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->info()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
- quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
- _cell_to_forget_outstage.configure(&_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res, gemmlowp_info);
+ const float cell_to_forget_scale =
+ std::pow(2, cell_shift) *
+ lstm_params.cell_to_forget_weights()->info()->quantization_info().uniform().scale /
+ lstm_params.forget_intermediate_scale();
+ quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift);
+ _cell_to_forget_outstage.configure(&_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res,
+ gemmlowp_info);
_mul_cell_to_forget_res.allocator()->allocate();
- _accumulate_cell_forget.configure(&_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_cell_forget.configure(&_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res,
+ &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
_cell_to_forget_outstage_res.allocator()->allocate();
}
Tensor *forget_activation_input = &_recurrent_to_forget_outstage_res;
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
configure_layer_norm(LayerNormGate::Forget, forget_activation_input);
forget_activation_input->allocator()->allocate();
@@ -417,33 +481,36 @@
// Output quantization info of Sigmoid and Tanh activations
const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
- const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+ const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
_memory_group.manage(&_forget_gate);
_forget_gate.allocator()->init(forget_gate_info);
- _forget_gate_sigmoid.configure(forget_activation_input, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _forget_gate_sigmoid.configure(forget_activation_input, &_forget_gate,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
forget_activation_input->allocator()->allocate();
// Modulation gate.
- const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
- const float input_to_cell_scale = input_to_cell_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
- configure_mm(_mm_input_to_cell, _input_to_cell_outstage, gemmlowp_info,
- input, &_input_to_cell_weights_transposed, &_input_to_cell_eff_bias,
- &_mm_input_to_cell_res, &_input_to_cell_outstage_res, input_to_cell_scale,
+ const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
+ const float input_to_cell_scale = input_to_cell_weights->info()->quantization_info().uniform().scale *
+ qinput.scale / lstm_params.cell_intermediate_scale();
+ configure_mm(_mm_input_to_cell, _input_to_cell_outstage, gemmlowp_info, input, &_input_to_cell_weights_transposed,
+ &_input_to_cell_eff_bias, &_mm_input_to_cell_res, &_input_to_cell_outstage_res, input_to_cell_scale,
mm_out_info, cell_outstage_info);
- const float recurrent_to_cell_scale = recurrent_to_cell_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
- configure_mm(_mm_recurrent_to_cell, _recurrent_to_cell_outstage, gemmlowp_info,
- output_state_in, &_recurrent_to_cell_weights_transposed, &_recurrent_to_cell_eff_bias,
- &_mm_recurrent_to_cell_res, &_recurrent_to_cell_outstage_res, recurrent_to_cell_scale,
- mm_out_info, cell_outstage_info);
+ const float recurrent_to_cell_scale = recurrent_to_cell_weights->info()->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
+ configure_mm(_mm_recurrent_to_cell, _recurrent_to_cell_outstage, gemmlowp_info, output_state_in,
+ &_recurrent_to_cell_weights_transposed, &_recurrent_to_cell_eff_bias, &_mm_recurrent_to_cell_res,
+ &_recurrent_to_cell_outstage_res, recurrent_to_cell_scale, mm_out_info, cell_outstage_info);
- _accumulate_input_recurrent_modulation.configure(&_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_modulation.configure(&_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res,
+ &_recurrent_to_cell_outstage_res, ConvertPolicy::SATURATE);
_input_to_cell_outstage_res.allocator()->allocate();
Tensor *cell_activation_input = &_recurrent_to_cell_outstage_res;
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
configure_layer_norm(LayerNormGate::Cell, cell_activation_input);
cell_activation_input->allocator()->allocate();
@@ -454,14 +521,15 @@
_memory_group.manage(&_cell_gate);
_cell_gate.allocator()->init(cell_gate_info);
- _cell_gate_tanh.configure(cell_activation_input, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+ _cell_gate_tanh.configure(cell_activation_input, &_cell_gate,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
cell_activation_input->allocator()->allocate();
// Input gate.
const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
_input_gate.allocator()->init(input_gate_info);
_memory_group.manage(&_input_gate);
- if(_has_cifg)
+ if (_has_cifg)
{
_ones.allocator()->init(*_forget_gate.info());
_input_gate_sub.configure(&_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE);
@@ -469,104 +537,137 @@
}
else
{
- const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
- const float input_to_input_scale = _input_to_input_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
- configure_mm(_mm_input_to_input, _input_to_input_outstage, gemmlowp_info,
- input, &_input_to_input_weights_transposed, &_input_to_input_eff_bias,
- &_mm_input_to_input_res, &_input_to_input_outstage_res, input_to_input_scale,
- mm_out_info, input_outstage_info);
+ const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
+ const float input_to_input_scale = _input_to_input_weights->info()->quantization_info().uniform().scale *
+ qinput.scale / lstm_params.input_intermediate_scale();
+ configure_mm(_mm_input_to_input, _input_to_input_outstage, gemmlowp_info, input,
+ &_input_to_input_weights_transposed, &_input_to_input_eff_bias, &_mm_input_to_input_res,
+ &_input_to_input_outstage_res, input_to_input_scale, mm_out_info, input_outstage_info);
- const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
- configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info,
- output_state_in, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
+ const float recurrent_to_input_scale =
+ _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale /
+ lstm_params.input_intermediate_scale();
+ configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info, output_state_in,
+ &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
&_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale,
mm_out_info, input_outstage_info);
- _accumulate_input_recurrent_input.configure(&_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_input.configure(&_input_to_input_outstage_res, &_recurrent_to_input_outstage_res,
+ &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
_input_to_input_outstage_res.allocator()->allocate();
- if(_has_peephole)
+ if (_has_peephole)
{
- _mul_cell_to_input_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
+ _mul_cell_to_input_res.allocator()->init(
+ TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
_memory_group.manage(&_mul_cell_to_input_res);
- _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
- quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
- _cell_to_input_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_input_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0)));
+ _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(),
+ &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
+ const float cell_to_input_scale =
+ std::pow(2, cell_shift) *
+ lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale /
+ lstm_params.input_intermediate_scale();
+ quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift);
+ _cell_to_input_outstage_res.allocator()->init(
+ TensorInfo(_mul_cell_to_input_res.info()->tensor_shape(), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.input_intermediate_scale(), 0)));
_memory_group.manage(&_cell_to_input_outstage_res);
- _cell_to_input_outstage.configure(&_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res, gemmlowp_info);
+ _cell_to_input_outstage.configure(&_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res,
+ gemmlowp_info);
_mul_cell_to_input_res.allocator()->allocate();
- _accumulate_cell_input.configure(&_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_cell_input.configure(&_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res,
+ &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
_cell_to_input_outstage_res.allocator()->allocate();
}
Tensor *input_activation_input = &_recurrent_to_input_outstage_res;
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
configure_layer_norm(LayerNormGate::Input, input_activation_input);
input_activation_input->allocator()->allocate();
input_activation_input = &get_layer_norm_output(LayerNormGate::Input);
}
- _input_gate_sigmoid.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _input_gate_sigmoid.configure(input_activation_input, &_input_gate,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
input_activation_input->allocator()->allocate();
}
// Cell.
// TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication
- _pixelwise_mul_forget_cell.configure(&_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _pixelwise_mul_forget_cell.configure(&_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
const float cell_gate_scale = _cell_gate.info()->quantization_info().uniform().scale;
const float mul_input_cell_scale = cell_gate_scale * std::pow(2, 15 + cell_shift);
- const TensorInfo mul_input_cell_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(mul_input_cell_scale, 0));
+ const TensorInfo mul_input_cell_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(mul_input_cell_scale, 0));
_memory_group.manage(&_mul_input_cell_res);
_mul_input_cell_res.allocator()->init(mul_input_cell_info);
- _pixelwise_mul_input_cell.configure(&_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _pixelwise_mul_input_cell.configure(&_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
_cell_gate.allocator()->allocate();
_add_forget_cell.configure(&_forget_gate, &_mul_input_cell_res, cell_state_out, ConvertPolicy::SATURATE);
_mul_input_cell_res.allocator()->allocate();
_forget_gate.allocator()->allocate();
- if(_has_cell_clipping)
+ if (_has_cell_clipping)
{
- _cell_clip.configure(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, quantized_cell_clip));
+ _cell_clip.configure(cell_state_out, nullptr,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
+ -quantized_cell_clip, quantized_cell_clip));
}
// Output gate.
- const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
- const float input_to_output_scale = input_to_output_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
- configure_mm(_mm_input_to_output, _input_to_output_outstage, gemmlowp_info,
- input, &_input_to_output_weights_transposed, &_input_to_output_eff_bias,
- &_mm_input_to_output_res, &_input_to_output_outstage_res, input_to_output_scale,
- mm_out_info, output_outstage_info);
+ const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
+ const float input_to_output_scale = input_to_output_weights->info()->quantization_info().uniform().scale *
+ qinput.scale / lstm_params.output_intermediate_scale();
+ configure_mm(_mm_input_to_output, _input_to_output_outstage, gemmlowp_info, input,
+ &_input_to_output_weights_transposed, &_input_to_output_eff_bias, &_mm_input_to_output_res,
+ &_input_to_output_outstage_res, input_to_output_scale, mm_out_info, output_outstage_info);
- const float recurrent_to_output_scale = recurrent_to_output_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
- configure_mm(_mm_recurrent_to_output, _recurrent_to_output_outstage, gemmlowp_info,
- output_state_in, &_recurrent_to_output_weights_transposed, &_recurrent_to_output_eff_bias,
- &_mm_recurrent_to_output_res, &_recurrent_to_output_outstage_res, recurrent_to_output_scale,
- mm_out_info, output_outstage_info);
+ const float recurrent_to_output_scale = recurrent_to_output_weights->info()->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.output_intermediate_scale();
+ configure_mm(_mm_recurrent_to_output, _recurrent_to_output_outstage, gemmlowp_info, output_state_in,
+ &_recurrent_to_output_weights_transposed, &_recurrent_to_output_eff_bias, &_mm_recurrent_to_output_res,
+ &_recurrent_to_output_outstage_res, recurrent_to_output_scale, mm_out_info, output_outstage_info);
- _accumulate_input_recurrent_output.configure(&_recurrent_to_output_outstage_res, &_input_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_output.configure(&_recurrent_to_output_outstage_res, &_input_to_output_outstage_res,
+ &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
_input_to_output_outstage_res.allocator()->allocate();
- if(_has_peephole)
+ if (_has_peephole)
{
// TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication
// Here we are not using the output stage because all operations are done in float
_mul_cell_to_output_res.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::S32));
_memory_group.manage(&_mul_cell_to_output_res);
- _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(),
+ &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
- const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
- quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
- _cell_to_output_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_output_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0)));
+ const float cell_to_output_scale =
+ std::pow(2, cell_shift) *
+ lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale /
+ lstm_params.output_intermediate_scale();
+ quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift);
+ _cell_to_output_outstage_res.allocator()->init(
+ TensorInfo(_mul_cell_to_output_res.info()->tensor_shape(), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.output_intermediate_scale(), 0)));
_memory_group.manage(&_cell_to_output_outstage_res);
- _cell_to_output_outstage.configure(&_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res, gemmlowp_info);
+ _cell_to_output_outstage.configure(&_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res,
+ gemmlowp_info);
_mul_cell_to_output_res.allocator()->allocate();
- _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res,
+ &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
_cell_to_output_outstage_res.allocator()->allocate();
}
Tensor *output_activation_input = &_recurrent_to_output_outstage_res;
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
configure_layer_norm(LayerNormGate::Output, output_activation_input);
output_activation_input->allocator()->allocate();
@@ -576,20 +677,24 @@
_memory_group.manage(&_output_gate);
_output_gate.allocator()->init(output_gate_info);
- _output_gate_sigmoid.configure(output_activation_input, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _output_gate_sigmoid.configure(output_activation_input, &_output_gate,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
output_activation_input->allocator()->allocate();
// Hidden.
- _hidden_tanh.configure(cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+ _hidden_tanh.configure(cell_state_out, &_input_gate,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
// TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication
_memory_group.manage(&_hidden_mul_res);
const TensorInfo hidden_mul_res(_input_gate.info()->tensor_shape(), 1, DataType::S32);
_hidden_mul_res.allocator()->init(hidden_mul_res);
- _pixelwise_mul_hidden.configure(&_output_gate, &_input_gate, &_hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _pixelwise_mul_hidden.configure(&_output_gate, &_input_gate, &_hidden_mul_res, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO);
_output_gate.allocator()->allocate();
_input_gate.allocator()->allocate();
const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
- quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true);
+ quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true);
gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
gemmlowp_info.output_data_type = output_state_in->info()->data_type();
@@ -598,7 +703,7 @@
_memory_group.manage(&_hidden_gate);
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_hidden_gate.allocator()->init(*output_state_out->info());
_hidden_gate.info()->set_tensor_shape(_hidden_mul_res.info()->tensor_shape());
@@ -609,27 +714,26 @@
_hidden_mul_res.allocator()->allocate();
// Projection.
- if(_has_projection)
+ if (_has_projection)
{
const TensorInfo projection_outstage_info(*output_state_out->info());
- const UniformQuantizationInfo qprojection = _projection_weights->info()->quantization_info().uniform();
- const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
- gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset;
- gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest();
- gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
- gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED;
+ const UniformQuantizationInfo qprojection = _projection_weights->info()->quantization_info().uniform();
+ const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
+ gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset;
+ gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest();
+ gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
+ gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED;
- TensorInfo projection_mm_out_info{ mm_out_info };
+ TensorInfo projection_mm_out_info{mm_out_info};
projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size));
- configure_mm(_mm_projection, _projection_outstage, gemmlowp_info,
- hidden_gate_result, &_projection_weights_transposed, &_projection_eff_bias,
- &_mm_projection_res, &_projection_outstage_res, projection_scale,
- projection_mm_out_info, projection_outstage_info);
+ configure_mm(_mm_projection, _projection_outstage, gemmlowp_info, hidden_gate_result,
+ &_projection_weights_transposed, &_projection_eff_bias, &_mm_projection_res,
+ &_projection_outstage_res, projection_scale, projection_mm_out_info, projection_outstage_info);
ITensor *accumulate_destination = output_state_out;
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_hidden_gate.allocator()->allocate();
_projection_accumulate_res.allocator()->init(*output_state_in->info());
@@ -638,30 +742,34 @@
accumulate_destination = &_projection_accumulate_res;
}
- _accumulate_projection.configure(&_projection_outstage_res, accumulate_destination, accumulate_destination, ConvertPolicy::SATURATE);
+ _accumulate_projection.configure(&_projection_outstage_res, accumulate_destination, accumulate_destination,
+ ConvertPolicy::SATURATE);
_projection_outstage_res.allocator()->allocate();
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_projection_accumulate_to_output_copy.configure(_projection_accumulate_res, *output_state_out);
_projection_accumulate_res.allocator()->allocate();
}
- int8_t quantized_projection_clip{ 0 };
- if(lstm_params.projection_clip() > 0.0f)
+ int8_t quantized_projection_clip{0};
+ if (lstm_params.projection_clip() > 0.0f)
{
- quantized_projection_clip = utility::clamp<int8_t>(lstm_params.projection_clip() / qprojection.scale, -128, 127);
+ quantized_projection_clip =
+ utility::clamp<int8_t>(lstm_params.projection_clip() / qprojection.scale, -128, 127);
}
- if(quantized_projection_clip > 0)
+ if (quantized_projection_clip > 0)
{
- _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip, quantized_projection_clip));
+ _projection_clip.configure(output_state_out, nullptr,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
+ -quantized_projection_clip, quantized_projection_clip));
_has_projection_clipping = true;
}
}
else
{
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_hidden_to_output_copy.configure(_hidden_gate, *output_state_out);
_hidden_gate.allocator()->allocate();
@@ -672,17 +780,27 @@
_copy_output.configure(output_state_out, output);
}
-Status NEQLSTMLayer::validate(const ITensorInfo *input,
- const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
- const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
- const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
- const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
- const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out, const ITensorInfo *output,
+Status NEQLSTMLayer::validate(const ITensorInfo *input,
+ const ITensorInfo *input_to_forget_weights,
+ const ITensorInfo *input_to_cell_weights,
+ const ITensorInfo *input_to_output_weights,
+ const ITensorInfo *recurrent_to_forget_weights,
+ const ITensorInfo *recurrent_to_cell_weights,
+ const ITensorInfo *recurrent_to_output_weights,
+ const ITensorInfo *forget_gate_bias,
+ const ITensorInfo *cell_bias,
+ const ITensorInfo *output_gate_bias,
+ const ITensorInfo *cell_state_in,
+ const ITensorInfo *output_state_in,
+ const ITensorInfo *cell_state_out,
+ const ITensorInfo *output_state_out,
+ const ITensorInfo *output,
const LSTMParams<ITensorInfo> &lstm_params)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
- recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
- cell_state_out, output_state_out, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+ recurrent_to_forget_weights, recurrent_to_cell_weights,
+ recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias,
+ cell_state_in, output_state_in, cell_state_out, output_state_out, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != 2, "Input must have exactly 2 dimensions");
@@ -694,22 +812,27 @@
ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2);
ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_output_weights, input_to_forget_weights, input_to_cell_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_output_weights, input_to_forget_weights,
+ input_to_cell_weights);
ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() != 2);
ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->dimension(1) != num_units);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_to_forget_weights, 1, DataType::QASYMM8_SIGNED, DataType::QSYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_output_weights, recurrent_to_forget_weights,
+ recurrent_to_cell_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_to_forget_weights, 1, DataType::QASYMM8_SIGNED,
+ DataType::QSYMM8);
// If the input_to_forget_weights data type is DataType::QSYMM8 then it can never match the other weights as they are all DataType::QASYMM8_SIGNED
if (input_to_forget_weights->data_type() == DataType::QSYMM8)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_cell_weights, input_to_output_weights,
- recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
+ recurrent_to_forget_weights, recurrent_to_cell_weights,
+ recurrent_to_output_weights);
}
else
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
- recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, input_to_cell_weights,
+ input_to_output_weights, recurrent_to_forget_weights,
+ recurrent_to_cell_weights, recurrent_to_output_weights);
}
ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() != 1);
ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->dimension(0) != num_units);
@@ -728,20 +851,25 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_in);
// Check whether peephole weights are all there or none
- if(lstm_params.has_peephole_opt())
+ if (lstm_params.has_peephole_opt())
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1,
+ DataType::QSYMM16);
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() != 1);
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->dimension(0) != num_units);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(),
+ lstm_params.cell_to_output_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(),
+ lstm_params.cell_to_output_weights());
- if(!lstm_params.has_cifg_opt())
+ if (!lstm_params.has_cifg_opt())
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(),
+ lstm_params.cell_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(),
+ lstm_params.cell_to_input_weights());
}
}
@@ -755,7 +883,7 @@
// Calculate quantized parameters for clipping.
int16_t quantized_cell_clip = 0;
- if(lstm_params.cell_clip() > 0.0f)
+ if (lstm_params.cell_clip() > 0.0f)
{
quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
}
@@ -763,60 +891,90 @@
// Precompute effective bias for optimizing the matmul computations.
const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32);
const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32);
- if(!lstm_params.has_cifg_opt())
+ if (!lstm_params.has_cifg_opt())
{
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
- -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.recurrent_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
- -qoutput_state_in.offset,
- true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ lstm_params.input_to_input_weights(), &eff_bias_info,
+ GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ lstm_params.recurrent_to_input_weights(), &eff_bias_info,
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
}
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
- -qoutput_state_in.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset,
- true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
- -qoutput_state_in.offset, true)));
- if(lstm_params.has_projection())
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ recurrent_to_forget_weights, &eff_bias_info,
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ recurrent_to_cell_weights, &eff_bias_info,
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ recurrent_to_output_weights, &eff_bias_info,
+ GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+ if (lstm_params.has_projection())
{
- ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false,
- lstm_params.hidden_state_zero(),
- true)));
- if(lstm_params.projection_bias() != nullptr)
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(
+ lstm_params.projection_weights(), &projection_eff_bias_info,
+ GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true)));
+ if (lstm_params.projection_bias() != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.projection_bias(), 1, DataType::S32);
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(lstm_params.projection_bias(), &projection_eff_bias_info, &projection_eff_bias_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEArithmeticAddition::validate(lstm_params.projection_bias(), &projection_eff_bias_info,
+ &projection_eff_bias_info, ConvertPolicy::SATURATE));
}
}
- const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_cell_weights->data_type(), input_to_cell_weights->quantization_info());
- const TensorInfo input_to_output_weights_transposed(TensorShape(num_units, input_size), 1, input_to_output_weights->data_type(), input_to_output_weights->quantization_info());
- const TensorInfo recurrent_to_forget_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
- const TensorInfo recurrent_to_cell_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_cell_weights->data_type(), recurrent_to_cell_weights->quantization_info());
- const TensorInfo recurrent_to_output_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_output_weights->data_type(), recurrent_to_output_weights->quantization_info());
- const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
+ const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_cell_weights->data_type(),
+ input_to_cell_weights->quantization_info());
+ const TensorInfo input_to_output_weights_transposed(TensorShape(num_units, input_size), 1,
+ input_to_output_weights->data_type(),
+ input_to_output_weights->quantization_info());
+ const TensorInfo recurrent_to_forget_weights_transposed(TensorShape(num_units, output_size), 1,
+ recurrent_to_forget_weights->data_type(),
+ recurrent_to_forget_weights->quantization_info());
+ const TensorInfo recurrent_to_cell_weights_transposed(TensorShape(num_units, output_size), 1,
+ recurrent_to_cell_weights->data_type(),
+ recurrent_to_cell_weights->quantization_info());
+ const TensorInfo recurrent_to_output_weights_transposed(TensorShape(num_units, output_size), 1,
+ recurrent_to_output_weights->data_type(),
+ recurrent_to_output_weights->quantization_info());
+ const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1,
+ recurrent_to_forget_weights->data_type(),
+ recurrent_to_forget_weights->quantization_info());
ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_cell_weights, &input_weights_transposed));
ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_output_weights, &input_to_output_weights_transposed));
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_forget_weights, &recurrent_to_forget_weights_transposed));
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_cell_weights, &recurrent_to_cell_weights_transposed));
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_output_weights, &recurrent_to_output_weights_transposed));
- if(!lstm_params.has_cifg_opt())
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(recurrent_to_forget_weights, &recurrent_to_forget_weights_transposed));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(recurrent_to_cell_weights, &recurrent_to_cell_weights_transposed));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(recurrent_to_output_weights, &recurrent_to_output_weights_transposed));
+ if (!lstm_params.has_cifg_opt())
{
- const TensorInfo recurrent_to_input_weights_transposed(TensorShape(num_units, output_size), 1,
- recurrent_to_forget_weights->data_type(), lstm_params.recurrent_to_input_weights()->quantization_info());
+ const TensorInfo recurrent_to_input_weights_transposed(
+ TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(),
+ lstm_params.recurrent_to_input_weights()->quantization_info());
const TensorInfo input_to_input_weights_transposed(TensorShape(num_units, input_size), 1,
- lstm_params.input_to_input_weights()->data_type(), lstm_params.input_to_input_weights()->quantization_info());
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.input_to_input_weights(), &input_to_input_weights_transposed));
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_to_input_weights_transposed));
+ lstm_params.input_to_input_weights()->data_type(),
+ lstm_params.input_to_input_weights()->quantization_info());
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(lstm_params.input_to_input_weights(), &input_to_input_weights_transposed));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_to_input_weights_transposed));
}
- if(lstm_params.has_projection())
+ if (lstm_params.has_projection())
{
- const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info());
- ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed));
+ const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1,
+ lstm_params.projection_weights()->data_type(),
+ lstm_params.projection_weights()->quantization_info());
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NETranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed));
}
GEMMLowpOutputStageInfo gemmlowp_info;
@@ -829,28 +987,42 @@
// Forget gate.
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_intermediate_scale() == 0);
- const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
+ const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
- const float input_to_forget_scale = input_to_forget_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_forget_scale, &mm_out_info, &forget_outstage_info));
+ const float input_to_forget_scale = input_to_forget_weights->quantization_info().uniform().scale * qinput.scale /
+ lstm_params.forget_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info,
+ input_to_forget_scale, &mm_out_info, &forget_outstage_info));
- const float recurrent_to_forget_scale = recurrent_to_forget_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_forget_scale, &mm_out_info, &forget_outstage_info));
+ const float recurrent_to_forget_scale = recurrent_to_forget_weights->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed,
+ &eff_bias_info, recurrent_to_forget_scale, &mm_out_info,
+ &forget_outstage_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info,
+ &forget_outstage_info, ConvertPolicy::SATURATE));
- if(lstm_params.has_peephole_opt())
+ if (lstm_params.has_peephole_opt())
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16);
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE,
- RoundingPolicy::TO_ZERO));
- const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, nullptr, &forget_outstage_info, gemmlowp_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1,
+ DataType::QSYMM16);
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &mm_out_info, 1.f,
+ ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ const float cell_to_forget_scale = std::pow(2, cell_shift) *
+ lstm_params.cell_to_forget_weights()->quantization_info().uniform().scale /
+ lstm_params.forget_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(
+ cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEGEMMLowpOutputStage::validate(&mm_out_info, nullptr, &forget_outstage_info, gemmlowp_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info,
+ &forget_outstage_info, ConvertPolicy::SATURATE));
}
- if(has_layer_norm)
+ if (has_layer_norm)
{
const ITensorInfo *w_info = lstm_params.forget_layer_norm_weights();
const ITensorInfo *b_info = forget_gate_bias;
@@ -859,22 +1031,31 @@
// Output quantization info of Sigmoid and Tanh activations
const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
- const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+ const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&forget_outstage_info, &forget_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(&forget_outstage_info, &forget_gate_info,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Modulation gate.
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_intermediate_scale() == 0);
- const TensorInfo cell_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
- const float input_to_cell_scale = input_to_cell_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_cell_scale, &mm_out_info, &cell_outstage_info));
+ const TensorInfo cell_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
+ const float input_to_cell_scale = input_to_cell_weights->quantization_info().uniform().scale * qinput.scale /
+ lstm_params.cell_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info,
+ input_to_cell_scale, &mm_out_info, &cell_outstage_info));
- const float recurrent_to_cell_scale = recurrent_to_cell_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_cell_scale, &mm_out_info, &cell_outstage_info));
+ const float recurrent_to_cell_scale = recurrent_to_cell_weights->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed,
+ &eff_bias_info, recurrent_to_cell_scale, &mm_out_info,
+ &cell_outstage_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_outstage_info, &cell_outstage_info,
+ &cell_outstage_info, ConvertPolicy::SATURATE));
- if(has_layer_norm)
+ if (has_layer_norm)
{
const ITensorInfo *w_info = lstm_params.cell_layer_norm_weights();
const ITensorInfo *b_info = cell_bias;
@@ -882,94 +1063,134 @@
}
const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&cell_outstage_info, &cell_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(&cell_outstage_info, &cell_gate_info,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
// Input gate.
const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
- if(lstm_params.has_cifg_opt())
+ if (lstm_params.has_cifg_opt())
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(lstm_params.input_gate_bias() != nullptr, "Input gate bias must not be present when CIFG is used");
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtraction::validate(&input_gate_info, &forget_gate_info, &forget_gate_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lstm_params.input_gate_bias() != nullptr,
+ "Input gate bias must not be present when CIFG is used");
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtraction::validate(&input_gate_info, &forget_gate_info,
+ &forget_gate_info, ConvertPolicy::SATURATE));
}
else
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.input_gate_bias());
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(),
+ lstm_params.recurrent_to_input_weights(), lstm_params.input_gate_bias());
// If the input_to_forget_weights data type is DataType::QSYMM8 then it can never match the other weights as they are all DataType::QASYMM8_SIGNED
if (input_to_forget_weights->data_type() == DataType::QSYMM8)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.input_to_input_weights(),
+ lstm_params.recurrent_to_input_weights());
}
else
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights,
+ lstm_params.input_to_input_weights(),
+ lstm_params.recurrent_to_input_weights());
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_forget_weights, lstm_params.input_to_input_weights());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_forget_weights, lstm_params.recurrent_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_forget_weights,
+ lstm_params.recurrent_to_input_weights());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.input_gate_bias());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias());
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_intermediate_scale() == 0);
- const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
- const float input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info));
+ const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
+ const float input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale *
+ qinput.scale / lstm_params.input_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info,
+ input_to_input_scale, &mm_out_info, &input_outstage_info));
- const float recurrent_to_input_scale = lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info));
+ const float recurrent_to_input_scale =
+ lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale /
+ lstm_params.input_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed,
+ &eff_bias_info, recurrent_to_input_scale, &mm_out_info,
+ &input_outstage_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info,
+ &input_outstage_info, ConvertPolicy::SATURATE));
- if(lstm_params.has_peephole_opt())
+ if (lstm_params.has_peephole_opt())
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_input_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE,
- RoundingPolicy::TO_ZERO));
- const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_input_weights(), &mm_out_info,
+ 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ const float cell_to_input_scale = std::pow(2, cell_shift) *
+ lstm_params.cell_to_input_weights()->quantization_info().uniform().scale /
+ lstm_params.input_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(
+ cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info,
+ &input_outstage_info, ConvertPolicy::SATURATE));
}
- if(has_layer_norm)
+ if (has_layer_norm)
{
const ITensorInfo *w_info = lstm_params.input_layer_norm_weights();
const ITensorInfo *b_info = lstm_params.input_gate_bias();
ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(input_outstage_info, *w_info, *b_info));
}
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(&input_outstage_info, &input_gate_info,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
}
// Cell.
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&input_gate_info, cell_state_in, &cell_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate_info, &cell_gate_info, cell_state_out, ConvertPolicy::SATURATE));
- if(quantized_cell_clip > 0)
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(
+ &forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(
+ &input_gate_info, cell_state_in, &cell_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEArithmeticAddition::validate(&forget_gate_info, &cell_gate_info, cell_state_out, ConvertPolicy::SATURATE));
+ if (quantized_cell_clip > 0)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip,
- quantized_cell_clip)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(cell_state_out, nullptr,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
+ -quantized_cell_clip, quantized_cell_clip)));
}
// Output gate.
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_intermediate_scale() == 0);
- const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
- const float input_to_output_scale = input_to_output_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &output_outstage_info));
+ const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16,
+ QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
+ const float input_to_output_scale = input_to_output_weights->quantization_info().uniform().scale * qinput.scale /
+ lstm_params.output_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info,
+ input_to_output_scale, &mm_out_info, &output_outstage_info));
- const float recurrent_to_output_scale = recurrent_to_output_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_output_scale, &mm_out_info, &output_outstage_info));
+ const float recurrent_to_output_scale = recurrent_to_output_weights->quantization_info().uniform().scale *
+ qoutput_state_in.scale / lstm_params.output_intermediate_scale();
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed,
+ &eff_bias_info, recurrent_to_output_scale, &mm_out_info,
+ &output_outstage_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
- if(lstm_params.has_peephole_opt())
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info,
+ &output_outstage_info, ConvertPolicy::SATURATE));
+ if (lstm_params.has_peephole_opt())
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_output_weights(), 1, DataType::QSYMM16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_output_weights(), 1,
+ DataType::QSYMM16);
// TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication
// Here we are not using the output stage because all operations are done in float
// const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
// ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_out, lstm_params.cell_to_output_weights(), &output_outstage_info, 1.f, ConvertPolicy::SATURATE,
- RoundingPolicy::TO_ZERO));
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(
+ cell_state_out, lstm_params.cell_to_output_weights(), &output_outstage_info, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info,
+ &output_outstage_info, ConvertPolicy::SATURATE));
}
- if(has_layer_norm)
+ if (has_layer_norm)
{
const ITensorInfo *w_info = lstm_params.output_layer_norm_weights();
const ITensorInfo *b_info = output_gate_bias;
@@ -977,85 +1198,103 @@
}
const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&output_outstage_info, &output_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(&output_outstage_info, &output_gate_info,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Hidden.
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEActivationLayer::validate(cell_state_out, &input_gate_info,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32);
const TensorInfo hidden_out_info(TensorShape(num_units, batch_size), 1, DataType::QASYMM8_SIGNED);
- ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(
+ &output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.hidden_state_scale() == 0);
const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier,
+ &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true));
gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
gemmlowp_info.output_data_type = hidden_out_info.data_type();
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info));
const bool projection_tensor_copy_required = num_units != output_size;
// Projection.
- if(lstm_params.has_projection())
+ if (lstm_params.has_projection())
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_forget_weights, lstm_params.projection_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_forget_weights,
+ lstm_params.projection_weights());
ARM_COMPUTE_RETURN_ERROR_ON(qoutput_state_in.scale == 0);
- const UniformQuantizationInfo qprojection = lstm_params.projection_weights()->quantization_info().uniform();
- const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(projection_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+ const UniformQuantizationInfo qprojection = lstm_params.projection_weights()->quantization_info().uniform();
+ const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(
+ projection_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset;
gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest();
gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED;
const TensorInfo projection_outstage_info(*output_state_out);
- const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info());
+ const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1,
+ lstm_params.projection_weights()->data_type(),
+ lstm_params.projection_weights()->quantization_info());
- TensorInfo projection_mm_out_info{ mm_out_info };
+ TensorInfo projection_mm_out_info{mm_out_info};
projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, &hidden_out_info, &projection_weights_transposed, &projection_eff_bias_info, projection_scale, &projection_mm_out_info,
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, &hidden_out_info, &projection_weights_transposed,
+ &projection_eff_bias_info, projection_scale, &projection_mm_out_info,
&projection_outstage_info));
- if(projection_tensor_copy_required)
+ if (projection_tensor_copy_required)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(*output_state_in, projection_outstage_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEQLSTMLayer::TensorCopyKernel::validate(*output_state_in, projection_outstage_info));
}
- ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(output_state_out, output_state_out, output_state_out,
+ ConvertPolicy::SATURATE));
- if(projection_tensor_copy_required)
+ if (projection_tensor_copy_required)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ NEQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out));
}
- int8_t quantized_projection_clip{ 0 };
- if(lstm_params.projection_clip() > 0.0f)
+ int8_t quantized_projection_clip{0};
+ if (lstm_params.projection_clip() > 0.0f)
{
quantized_projection_clip = quantize_qasymm8_signed(lstm_params.projection_clip(), qprojection);
}
- if(quantized_projection_clip > 0)
+ if (quantized_projection_clip > 0)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip,
- quantized_projection_clip)));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(
+ output_state_out, nullptr,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
+ -quantized_projection_clip, quantized_projection_clip)));
}
}
else
{
- if(projection_tensor_copy_required)
+ if (projection_tensor_copy_required)
{
ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(hidden_out_info, *output_state_out));
}
}
- if(cell_state_out->total_size() > 0)
+ if (cell_state_out->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(cell_state_in, cell_state_out);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(cell_state_in, cell_state_out);
}
- if(output_state_out->total_size() > 0)
+ if (output_state_out->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_out);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output_state_in, output_state_out);
@@ -1080,14 +1319,14 @@
_recurrent_to_forget_outstage.run();
_accumulate_input_recurrent_forget.run();
- if(_has_peephole)
+ if (_has_peephole)
{
_pixelwise_mul_cell_to_forget.run();
_cell_to_forget_outstage.run();
_accumulate_cell_forget.run();
}
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Forget).get(), Window::DimY);
}
@@ -1102,7 +1341,7 @@
_recurrent_to_cell_outstage.run();
_accumulate_input_recurrent_modulation.run();
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Cell).get(), Window::DimY);
}
@@ -1110,7 +1349,7 @@
_cell_gate_tanh.run();
// Input gate
- if(_has_cifg)
+ if (_has_cifg)
{
_input_gate_sub.run();
}
@@ -1122,14 +1361,14 @@
_recurrent_to_input_outstage.run();
_accumulate_input_recurrent_input.run();
- if(_has_peephole)
+ if (_has_peephole)
{
_pixelwise_mul_cell_to_input.run();
_cell_to_input_outstage.run();
_accumulate_cell_input.run();
}
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Input).get(), Window::DimY);
}
@@ -1142,7 +1381,7 @@
_pixelwise_mul_input_cell.run();
_add_forget_cell.run();
- if(_has_cell_clipping)
+ if (_has_cell_clipping)
{
_cell_clip.run();
}
@@ -1153,14 +1392,14 @@
_mm_recurrent_to_output.run();
_recurrent_to_output_outstage.run();
_accumulate_input_recurrent_output.run();
- if(_has_peephole)
+ if (_has_peephole)
{
_pixelwise_mul_cell_to_output.run();
_cell_to_output_outstage.run();
_accumulate_cell_to_output.run();
}
- if(_has_layer_norm)
+ if (_has_layer_norm)
{
NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Output).get(), Window::DimY);
}
@@ -1173,31 +1412,31 @@
_hidden_outstage.run();
// Projection.
- if(_has_projection)
+ if (_has_projection)
{
_mm_projection.run();
_projection_outstage.run();
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_projection_output_to_accumulate_copy.run();
}
_accumulate_projection.run();
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_projection_accumulate_to_output_copy.run();
}
- if(_has_projection_clipping)
+ if (_has_projection_clipping)
{
_projection_clip.run();
}
}
else
{
- if(_projection_tensor_copy_required)
+ if (_projection_tensor_copy_required)
{
_hidden_to_output_copy.run();
}
@@ -1209,9 +1448,9 @@
void NEQLSTMLayer::prepare()
{
- if(!_is_prepared)
+ if (!_is_prepared)
{
- if(_convert_input_to_forget_weights_to_qsymm8)
+ if (_convert_input_to_forget_weights_to_qsymm8)
{
_input_to_forget_weights_f32.allocator()->allocate();
_input_to_forget_weights_symm8.allocator()->allocate();
@@ -1234,28 +1473,25 @@
_transpose_recurrent_to_output_weights.run();
// Precompute effective biases
- if(_has_cifg)
+ if (_has_cifg)
{
- std::fill_n(reinterpret_cast<int16_t *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 32767);
+ std::fill_n(reinterpret_cast<int16_t *>(_ones.buffer()),
+ _ones.info()->total_size() / _ones.info()->element_size(), 32767);
}
else
{
_input_to_input_eff_bias.allocator()->allocate();
_recurrent_to_input_eff_bias.allocator()->allocate();
- ITensorPack packII =
- {
- { TensorType::ACL_SRC, _input_to_input_weights },
- { TensorType::ACL_DST, &_input_to_input_eff_bias }
- };
- NEScheduler::get().schedule_op(_input_to_input_reduction.get(), Window::DimY, _input_to_input_reduction->window(), packII);
+ ITensorPack packII = {{TensorType::ACL_SRC, _input_to_input_weights},
+ {TensorType::ACL_DST, &_input_to_input_eff_bias}};
+ NEScheduler::get().schedule_op(_input_to_input_reduction.get(), Window::DimY,
+ _input_to_input_reduction->window(), packII);
- ITensorPack packRI =
- {
- { TensorType::ACL_SRC, _recurrent_to_input_weights },
- { TensorType::ACL_DST, &_recurrent_to_input_eff_bias }
- };
- NEScheduler::get().schedule_op(_recurrent_to_input_reduction.get(), Window::DimY, _recurrent_to_input_reduction->window(), packRI);
+ ITensorPack packRI = {{TensorType::ACL_SRC, _recurrent_to_input_weights},
+ {TensorType::ACL_DST, &_recurrent_to_input_eff_bias}};
+ NEScheduler::get().schedule_op(_recurrent_to_input_reduction.get(), Window::DimY,
+ _recurrent_to_input_reduction->window(), packRI);
_input_to_input_weights_transposed.allocator()->allocate();
_recurrent_to_input_weights_transposed.allocator()->allocate();
@@ -1271,58 +1507,44 @@
_input_to_output_eff_bias.allocator()->allocate();
_recurrent_to_output_eff_bias.allocator()->allocate();
- ITensorPack packIF =
- {
- { TensorType::ACL_SRC, _input_to_forget_weights },
- { TensorType::ACL_DST, &_input_to_forget_eff_bias }
- };
- NEScheduler::get().schedule_op(_input_to_forget_reduction.get(), Window::DimY, _input_to_forget_reduction->window(), packIF);
+ ITensorPack packIF = {{TensorType::ACL_SRC, _input_to_forget_weights},
+ {TensorType::ACL_DST, &_input_to_forget_eff_bias}};
+ NEScheduler::get().schedule_op(_input_to_forget_reduction.get(), Window::DimY,
+ _input_to_forget_reduction->window(), packIF);
- ITensorPack packRF =
- {
- { TensorType::ACL_SRC, _recurrent_to_forget_weights },
- { TensorType::ACL_DST, &_recurrent_to_forget_eff_bias }
- };
- NEScheduler::get().schedule_op(_recurrent_to_forget_reduction.get(), Window::DimY, _recurrent_to_forget_reduction->window(), packRF);
+ ITensorPack packRF = {{TensorType::ACL_SRC, _recurrent_to_forget_weights},
+ {TensorType::ACL_DST, &_recurrent_to_forget_eff_bias}};
+ NEScheduler::get().schedule_op(_recurrent_to_forget_reduction.get(), Window::DimY,
+ _recurrent_to_forget_reduction->window(), packRF);
- ITensorPack packIC =
- {
- { TensorType::ACL_SRC, _input_to_cell_weights },
- { TensorType::ACL_DST, &_input_to_cell_eff_bias }
- };
- NEScheduler::get().schedule_op(_input_to_cell_reduction.get(), Window::DimY, _input_to_cell_reduction->window(), packIC);
+ ITensorPack packIC = {{TensorType::ACL_SRC, _input_to_cell_weights},
+ {TensorType::ACL_DST, &_input_to_cell_eff_bias}};
+ NEScheduler::get().schedule_op(_input_to_cell_reduction.get(), Window::DimY, _input_to_cell_reduction->window(),
+ packIC);
- ITensorPack packRC =
- {
- { TensorType::ACL_SRC, _recurrent_to_cell_weights },
- { TensorType::ACL_DST, &_recurrent_to_cell_eff_bias }
- };
- NEScheduler::get().schedule_op(_recurrent_to_cell_reduction.get(), Window::DimY, _recurrent_to_cell_reduction->window(), packRC);
+ ITensorPack packRC = {{TensorType::ACL_SRC, _recurrent_to_cell_weights},
+ {TensorType::ACL_DST, &_recurrent_to_cell_eff_bias}};
+ NEScheduler::get().schedule_op(_recurrent_to_cell_reduction.get(), Window::DimY,
+ _recurrent_to_cell_reduction->window(), packRC);
- ITensorPack packIO =
- {
- { TensorType::ACL_SRC, _input_to_output_weights },
- { TensorType::ACL_DST, &_input_to_output_eff_bias }
- };
- NEScheduler::get().schedule_op(_input_to_output_reduction.get(), Window::DimY, _input_to_output_reduction->window(), packIO);
+ ITensorPack packIO = {{TensorType::ACL_SRC, _input_to_output_weights},
+ {TensorType::ACL_DST, &_input_to_output_eff_bias}};
+ NEScheduler::get().schedule_op(_input_to_output_reduction.get(), Window::DimY,
+ _input_to_output_reduction->window(), packIO);
- ITensorPack packRO =
- {
- { TensorType::ACL_SRC, _recurrent_to_output_weights },
- { TensorType::ACL_DST, &_recurrent_to_output_eff_bias }
- };
- NEScheduler::get().schedule_op(_recurrent_to_output_reduction.get(), Window::DimY, _recurrent_to_output_reduction->window(), packRO);
+ ITensorPack packRO = {{TensorType::ACL_SRC, _recurrent_to_output_weights},
+ {TensorType::ACL_DST, &_recurrent_to_output_eff_bias}};
+ NEScheduler::get().schedule_op(_recurrent_to_output_reduction.get(), Window::DimY,
+ _recurrent_to_output_reduction->window(), packRO);
- if(_has_projection)
+ if (_has_projection)
{
_projection_eff_bias.allocator()->allocate();
- ITensorPack pack =
- {
- { TensorType::ACL_SRC, _projection_weights },
- { TensorType::ACL_DST, &_projection_eff_bias }
- };
- NEScheduler::get().schedule_op(_projection_reduction.get(), Window::DimY, _projection_reduction->window(), pack);
- if(_projection_bias != nullptr)
+ ITensorPack pack = {{TensorType::ACL_SRC, _projection_weights},
+ {TensorType::ACL_DST, &_projection_eff_bias}};
+ NEScheduler::get().schedule_op(_projection_reduction.get(), Window::DimY, _projection_reduction->window(),
+ pack);
+ if (_projection_bias != nullptr)
{
_projection_bias_add.run();
_projection_bias->mark_as_unused();
@@ -1332,7 +1554,7 @@
_transpose_projection_weights.run();
_projection_weights->mark_as_unused();
- if(!_projection_tensor_copy_required)
+ if (!_projection_tensor_copy_required)
{
_hidden_gate.mark_as_unused();
_projection_accumulate_res.mark_as_unused();