NEQLSTM: Add support for QASYMM8_SIGNED for input_to_forget_weights
* QLSTM only supports QSYMM8 for the argument input_to_forget_weights
* We add support for QASYMM8_SIGNED by dequantizing and requantizing to QSYMM8
* Resolves COMPMID-5184
Change-Id: I1cae18d81dafdb7ae722b520a1354cf4a56b9606
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7321
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
(cherry picked from commit 187a041dedf8e9db0c9e0652f13f8639dca880f3)
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
index 76bb8c0..c6e6a71 100644
--- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
@@ -111,17 +111,81 @@
NEQLSTMLayer::~NEQLSTMLayer() = default;
NEQLSTMLayer::NEQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(), _transpose_input_to_forget_weights(), _transpose_input_to_cell_weights(), _transpose_input_to_output_weights(), _transpose_input_to_input_weights(),
- _transpose_recurrent_to_forget_weights(), _transpose_recurrent_to_cell_weights(), _transpose_recurrent_to_output_weights(), _transpose_recurrent_to_input_weights(), _transpose_projection_weights(),
- _input_to_input_reduction(), _recurrent_to_input_reduction(), _input_to_forget_reduction(), _recurrent_to_forget_reduction(), _input_to_cell_reduction(), _recurrent_to_cell_reduction(),
- _input_to_output_reduction(), _recurrent_to_output_reduction(), _projection_reduction(), _projection_bias_add(), _mm_input_to_forget(), _mm_recurrent_to_forget(), _pixelwise_mul_cell_to_forget(),
- _input_to_forget_outstage(), _recurrent_to_forget_outstage(), _cell_to_forget_outstage(), _accumulate_input_recurrent_forget(), _accumulate_cell_forget(), _forget_gate_sigmoid(), _mm_input_to_cell(),
- _input_to_cell_outstage(), _mm_recurrent_to_cell(), _recurrent_to_cell_outstage(), _accumulate_input_recurrent_modulation(), _cell_gate_tanh(), _input_gate_sub(), _mm_input_to_input(),
- _input_to_input_outstage(), _mm_recurrent_to_input(), _recurrent_to_input_outstage(), _accumulate_input_recurrent_input(), _pixelwise_mul_cell_to_input(), _cell_to_input_outstage(),
- _accumulate_cell_input(), _input_gate_sigmoid(), _pixelwise_mul_forget_cell(), _pixelwise_mul_input_cell(), _add_forget_cell(), _cell_clip(), _mm_input_to_output(), _input_to_output_outstage(),
- _mm_recurrent_to_output(), _recurrent_to_output_outstage(), _accumulate_input_recurrent_output(), _pixelwise_mul_cell_to_output(), _cell_to_output_outstage(), _accumulate_cell_to_output(),
- _output_gate_sigmoid(), _hidden_tanh(), _pixelwise_mul_hidden(), _hidden_outstage(), _mm_projection(), _projection_outstage(), _accumulate_projection(), _projection_clip(), _projection_bias_copy(),
- _projection_output_to_accumulate_copy(), _projection_accumulate_to_output_copy(), _hidden_to_output_copy(), _layer_norms(), _copy_output(), _layer_norm_weights(), _layer_norm_bias(),
+ : _memory_group(),
+ _dequantize_input_to_forget_weights(),
+ _quantize_input_to_forget_weights(),
+ _transpose_input_to_forget_weights(),
+ _transpose_input_to_cell_weights(),
+ _transpose_input_to_output_weights(),
+ _transpose_input_to_input_weights(),
+ _transpose_recurrent_to_forget_weights(),
+ _transpose_recurrent_to_cell_weights(),
+ _transpose_recurrent_to_output_weights(),
+ _transpose_recurrent_to_input_weights(),
+ _transpose_projection_weights(),
+ _input_to_input_reduction(),
+ _recurrent_to_input_reduction(),
+ _input_to_forget_reduction(),
+ _recurrent_to_forget_reduction(),
+ _input_to_cell_reduction(),
+ _recurrent_to_cell_reduction(),
+ _input_to_output_reduction(),
+ _recurrent_to_output_reduction(),
+ _projection_reduction(),
+ _projection_bias_add(),
+ _mm_input_to_forget(),
+ _mm_recurrent_to_forget(),
+ _pixelwise_mul_cell_to_forget(),
+ _input_to_forget_outstage(),
+ _recurrent_to_forget_outstage(),
+ _cell_to_forget_outstage(),
+ _accumulate_input_recurrent_forget(),
+ _accumulate_cell_forget(),
+ _forget_gate_sigmoid(),
+ _mm_input_to_cell(),
+ _input_to_cell_outstage(),
+ _mm_recurrent_to_cell(),
+ _recurrent_to_cell_outstage(),
+ _accumulate_input_recurrent_modulation(),
+ _cell_gate_tanh(),
+ _input_gate_sub(),
+ _mm_input_to_input(),
+ _input_to_input_outstage(),
+ _mm_recurrent_to_input(),
+ _recurrent_to_input_outstage(),
+ _accumulate_input_recurrent_input(),
+ _pixelwise_mul_cell_to_input(),
+ _cell_to_input_outstage(),
+ _accumulate_cell_input(),
+ _input_gate_sigmoid(),
+ _pixelwise_mul_forget_cell(),
+ _pixelwise_mul_input_cell(),
+ _add_forget_cell(),
+ _cell_clip(),
+ _mm_input_to_output(),
+ _input_to_output_outstage(),
+ _mm_recurrent_to_output(),
+ _recurrent_to_output_outstage(),
+ _accumulate_input_recurrent_output(),
+ _pixelwise_mul_cell_to_output(),
+ _cell_to_output_outstage(),
+ _accumulate_cell_to_output(),
+ _output_gate_sigmoid(),
+ _hidden_tanh(),
+ _pixelwise_mul_hidden(),
+ _hidden_outstage(),
+ _mm_projection(),
+ _projection_outstage(),
+ _accumulate_projection(),
+ _projection_clip(),
+ _projection_bias_copy(),
+ _projection_output_to_accumulate_copy(),
+ _projection_accumulate_to_output_copy(),
+ _hidden_to_output_copy(),
+ _layer_norms(),
+ _copy_output(),
+ _layer_norm_weights(),
+ _layer_norm_bias(),
_layer_norm_output()
{
_memory_group = MemoryGroup(std::move(memory_manager));
@@ -174,12 +238,37 @@
_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());
- // Validate
- 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));
+ 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()));
+ // 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())));
+
+ _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);
+ _input_to_forget_weights_f32.allocator()->allocate();
+ _input_to_forget_weights_symm8.allocator()->allocate();
+
+ 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));
+ }
const int batch_size = input->info()->dimension(1);
const int num_units = input_to_output_weights->info()->dimension(1);
@@ -190,7 +279,7 @@
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;
+ _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;
@@ -611,10 +700,9 @@
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::QSYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_to_forget_weights, 1, DataType::QSYMM8,DataType::QASYMM8_SIGNED);
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);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, cell_bias, output_gate_bias);
@@ -967,6 +1055,12 @@
// Acquire all the temporaries
MemoryGroupResourceScope scope_mg(_memory_group);
+ if(_convert_input_to_forget_weights_to_qsymm8)
+ {
+ _dequantize_input_to_forget_weights.run();
+ _quantize_input_to_forget_weights.run();
+ }
+
// Forget gate.
_mm_input_to_forget.run();
_input_to_forget_outstage.run();