COMPMID-3280: Make all ML primitives for CL use the new interface - Part 2
- CLFunctions have been updated
Change-Id: Ie3256a6c775bc12f3126482bd8e8a46da54b267c
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3053
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLQLSTMLayer.cpp b/src/runtime/CL/functions/CLQLSTMLayer.cpp
index 4b994d4..88c5f77 100644
--- a/src/runtime/CL/functions/CLQLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLQLSTMLayer.cpp
@@ -51,7 +51,7 @@
_memory_group = MemoryGroup(std::move(memory_manager));
}
-void CLQLSTMLayer::configure_mm(CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
+void CLQLSTMLayer::configure_mm(const CLCompileContext &compile_context, CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias,
CLTensor *mm_res, CLTensor *outstage_res, float gemmlowp_scale,
const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info)
@@ -63,11 +63,11 @@
outstage_res->allocator()->init(outstage_tensor_info);
// Configure matrix-multiplication
- mm.configure(mm_input, mm_weights, nullptr, mm_res);
+ mm.configure(compile_context, mm_input, mm_weights, nullptr, mm_res);
// Configure output stage
quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
- outstage.configure(mm_res, bias, outstage_res, gemmlowp_info);
+ outstage.configure(compile_context, mm_res, bias, outstage_res, gemmlowp_info);
mm_res->allocator()->allocate();
}
@@ -79,6 +79,19 @@
ICLTensor *cell_state_out, ICLTensor *output_state_out,
const LSTMParams<ICLTensor> &lstm_params)
{
+ configure(CLKernelLibrary::get().get_compile_context(), 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, lstm_params);
+}
+
+void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input,
+ const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+ const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
+ const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
+ const ICLTensor *cell_state_in, const ICLTensor *output_state_in,
+ ICLTensor *cell_state_out, ICLTensor *output_state_out,
+ const LSTMParams<ICLTensor> &lstm_params)
+{
ARM_COMPUTE_UNUSED(forget_gate_bias);
ARM_COMPUTE_UNUSED(cell_bias);
ARM_COMPUTE_UNUSED(output_gate_bias);
@@ -133,36 +146,36 @@
_input_to_input_weights = lstm_params.input_to_input_weights();
_recurrent_to_input_weights = lstm_params.recurrent_to_input_weights();
- _input_to_input_reduction.configure(_input_to_input_weights, &_input_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_input_reduction.configure(_recurrent_to_input_weights, &_recurrent_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_input_reduction.configure(compile_context, _input_to_input_weights, &_input_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_input_reduction.configure(compile_context, _recurrent_to_input_weights, &_recurrent_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
}
- _input_to_forget_reduction.configure(input_to_forget_weights, &_input_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_forget_reduction.configure(recurrent_to_forget_weights, &_recurrent_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_cell_reduction.configure(input_to_cell_weights, &_input_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_cell_reduction.configure(recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_output_reduction.configure(input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_output_reduction.configure(recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_forget_reduction.configure(compile_context, input_to_forget_weights, &_input_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_forget_reduction.configure(compile_context, recurrent_to_forget_weights, &_recurrent_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_cell_reduction.configure(compile_context, input_to_cell_weights, &_input_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_cell_reduction.configure(compile_context, recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_output_reduction.configure(compile_context, input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_output_reduction.configure(compile_context, recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
if(_projection_bias != nullptr)
{
- _projection_reduction.configure(_projection_weights, &_projection_reduction_res, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), true));
- _projection_bias_add.configure(ArithmeticOperation::ADD, _projection_bias, &_projection_reduction_res, &_projection_eff_bias, ConvertPolicy::SATURATE);
+ _projection_reduction.configure(compile_context, _projection_weights, &_projection_reduction_res, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), true));
+ _projection_bias_add.configure(compile_context, ArithmeticOperation::ADD, _projection_bias, &_projection_reduction_res, &_projection_eff_bias, ConvertPolicy::SATURATE);
}
// Pre-transpose weights to be used in GEMM.
- _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_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);
+ _transpose_input_to_forget_weights.configure(compile_context, input_to_forget_weights, &_input_to_forget_weights_transposed);
+ _transpose_input_to_cell_weights.configure(compile_context, input_to_cell_weights, &_input_to_cell_weights_transposed);
+ _transpose_input_to_output_weights.configure(compile_context, input_to_output_weights, &_input_to_output_weights_transposed);
+ _transpose_recurrent_to_forget_weights.configure(compile_context, recurrent_to_forget_weights, &_recurrent_to_forget_weights_transposed);
+ _transpose_recurrent_to_cell_weights.configure(compile_context, recurrent_to_cell_weights, &_recurrent_to_cell_weights_transposed);
+ _transpose_recurrent_to_output_weights.configure(compile_context, 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(compile_context, lstm_params.input_to_input_weights(), &_input_to_input_weights_transposed);
+ _transpose_recurrent_to_input_weights.configure(compile_context, lstm_params.recurrent_to_input_weights(), &_recurrent_to_input_weights_transposed);
}
if(_has_projection)
{
- _transpose_projection_weights.configure(_projection_weights, &_projection_weights_transposed);
+ _transpose_projection_weights.configure(compile_context, _projection_weights, &_projection_weights_transposed);
}
GEMMLowpOutputStageInfo gemmlowp_info;
@@ -175,31 +188,33 @@
// 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,
+ configure_mm(compile_context, _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,
+ configure_mm(compile_context, _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(ArithmeticOperation::ADD, &_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_forget.configure(compile_context, ArithmeticOperation::ADD, &_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)
{
_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);
+ _pixelwise_mul_cell_to_forget.configure(compile_context, 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);
+ _cell_to_forget_outstage.configure(compile_context, &_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res, gemmlowp_info);
_mul_cell_to_forget_res.allocator()->allocate();
- _accumulate_cell_forget.configure(ArithmeticOperation::ADD, &_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_cell_forget.configure(compile_context, ArithmeticOperation::ADD, &_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res,
+ ConvertPolicy::SATURATE);
_cell_to_forget_outstage_res.allocator()->allocate();
}
@@ -209,30 +224,31 @@
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(&_recurrent_to_forget_outstage_res, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _forget_gate_sigmoid.configure(compile_context, &_recurrent_to_forget_outstage_res, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
_recurrent_to_forget_outstage_res.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,
+ configure_mm(compile_context, _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,
+ configure_mm(compile_context, _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(ArithmeticOperation::ADD, &_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_modulation.configure(compile_context, ArithmeticOperation::ADD, &_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res,
+ ConvertPolicy::SATURATE);
_input_to_cell_outstage_res.allocator()->allocate();
const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
_memory_group.manage(&_cell_gate);
_cell_gate.allocator()->init(cell_gate_info);
- _cell_gate_tanh.configure(&_recurrent_to_cell_outstage_res, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+ _cell_gate_tanh.configure(compile_context, &_recurrent_to_cell_outstage_res, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
_recurrent_to_cell_outstage_res.allocator()->allocate();
// Input gate.
@@ -242,75 +258,77 @@
if(_has_cifg)
{
_ones.allocator()->init(*_forget_gate.info());
- _input_gate_sub.configure(ArithmeticOperation::SUB, &_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE);
+ _input_gate_sub.configure(compile_context, ArithmeticOperation::SUB, &_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE);
_ones.allocator()->allocate();
}
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,
+ configure_mm(compile_context, _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,
+ configure_mm(compile_context, _mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info,
input, &_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(ArithmeticOperation::ADD, &_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_input.configure(compile_context, ArithmeticOperation::ADD, &_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)
{
_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);
+ _pixelwise_mul_cell_to_input.configure(compile_context, 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(compile_context, &_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res, gemmlowp_info);
_mul_cell_to_input_res.allocator()->allocate();
_accumulate_cell_input.configure(ArithmeticOperation::ADD, &_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
_cell_to_input_outstage_res.allocator()->allocate();
}
- _input_gate_tanh.configure(&_recurrent_to_input_outstage_res, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+ _input_gate_tanh.configure(compile_context, &_recurrent_to_input_outstage_res, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
_recurrent_to_input_outstage_res.allocator()->allocate();
}
// Cell.
// TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
- _pixelwise_mul_forget_cell.configure(&_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _pixelwise_mul_forget_cell.configure(compile_context, &_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));
_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(compile_context, &_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
_cell_gate.allocator()->allocate();
- _add_forget_cell.configure(ArithmeticOperation::ADD, &_forget_gate, &_mul_input_cell_res, cell_state_out, ConvertPolicy::SATURATE);
+ _add_forget_cell.configure(compile_context, ArithmeticOperation::ADD, &_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)
{
- _cell_clip.configure(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, quantized_cell_clip));
+ _cell_clip.configure(compile_context, 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,
+ configure_mm(compile_context, _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,
+ configure_mm(compile_context, _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(ArithmeticOperation::ADD, &_recurrent_to_output_outstage_res, &_input_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+ _accumulate_input_recurrent_output.configure(compile_context, ArithmeticOperation::ADD, &_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)
@@ -320,31 +338,32 @@
// 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);
_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);
- _accumulate_cell_to_output.configure(ArithmeticOperation::ADD, &_recurrent_to_output_outstage_res, &_mul_cell_to_output_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+ _pixelwise_mul_cell_to_output.configure(compile_context, cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ _accumulate_cell_to_output.configure(compile_context, ArithmeticOperation::ADD, &_recurrent_to_output_outstage_res, &_mul_cell_to_output_res, &_recurrent_to_output_outstage_res,
+ ConvertPolicy::SATURATE);
_mul_cell_to_output_res.allocator()->allocate();
}
const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
_memory_group.manage(&_output_gate);
_output_gate.allocator()->init(output_gate_info);
- _output_gate_sigmoid.configure(&_recurrent_to_output_outstage_res, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _output_gate_sigmoid.configure(compile_context, &_recurrent_to_output_outstage_res, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
_recurrent_to_output_outstage_res.allocator()->allocate();
// Hidden.
- _hidden_tanh.configure(cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+ _hidden_tanh.configure(compile_context, cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
// TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
_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(compile_context, &_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);
gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
gemmlowp_info.output_data_type = output_state_in->info()->data_type();
- _hidden_outstage.configure(&_hidden_mul_res, nullptr, output_state_out, gemmlowp_info);
+ _hidden_outstage.configure(compile_context, &_hidden_mul_res, nullptr, output_state_out, gemmlowp_info);
_hidden_mul_res.allocator()->allocate();
// Projection.
@@ -358,12 +377,12 @@
gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED;
- configure_mm(_mm_projection, _projection_outstage, gemmlowp_info,
+ configure_mm(compile_context, _mm_projection, _projection_outstage, gemmlowp_info,
output_state_out, &_projection_weights_transposed, &_projection_eff_bias,
&_mm_projection_res, &_projection_outstage_res, projection_scale,
mm_out_info, projection_outstage_info);
- _accumulate_projection.configure(ArithmeticOperation::ADD, &_projection_outstage_res, output_state_out, output_state_out, ConvertPolicy::SATURATE);
+ _accumulate_projection.configure(compile_context, ArithmeticOperation::ADD, &_projection_outstage_res, output_state_out, output_state_out, ConvertPolicy::SATURATE);
_projection_outstage_res.allocator()->allocate();
int8_t quantized_projection_clip{ 0 };
@@ -374,7 +393,8 @@
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(compile_context, output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip,
+ quantized_projection_clip));
_has_projection_clipping = true;
}
}