COMPMID-2343: Add layer normalization support in NELSTMLayer
Change-Id: I1f620d70c6eaadfb9e3a1b345de350ac0253b65c
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1366
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/runtime/NEON/functions/NELSTMLayer.h b/arm_compute/runtime/NEON/functions/NELSTMLayer.h
index cf0f06c..183745c 100644
--- a/arm_compute/runtime/NEON/functions/NELSTMLayer.h
+++ b/arm_compute/runtime/NEON/functions/NELSTMLayer.h
@@ -35,6 +35,7 @@
#include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
+#include "arm_compute/runtime/NEON/functions/NEMeanStdDevNormalizationLayer.h"
#include "arm_compute/runtime/common/LSTMParams.h"
namespace arm_compute
@@ -68,14 +69,18 @@
* @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
* Data types supported: Same as @p input.
* @param[in] lstm_params (Optional) Weights tensors used in peephole optimization:
- * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
- * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
- * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
- * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
+ * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
+ * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * input_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * forget_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * output_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
* @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled.
* @param[in] projection_threshold The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
@@ -108,14 +113,18 @@
* @param[in] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
* Data types supported: Same as @p input.
* @param[in] lstm_params (Optional) Weights tensors used in peephole optimization:
- * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
- * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
- * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
- * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
+ * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
+ * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * input_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * forget_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * output_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
* @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled.
* @param[in] projection_threshold The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
@@ -137,23 +146,16 @@
private:
MemoryGroup _memory_group;
NEFullyConnectedLayer _fully_connected_input_gate;
- NEGEMM _gemm_input_gate;
- NETransposeKernel _transpose_input_gate;
- NEArithmeticAdditionKernel _accum_input_gate1;
- NEArithmeticAddition _accum_input_gate2;
+ NEArithmeticAddition _accum_input_gate1;
NEArithmeticSubtractionKernel _subtract_input_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_input_gate;
NEActivationLayerKernel _activation_input_gate;
NEFullyConnectedLayer _fully_connected_forget_gate;
- NEGEMM _gemm_forget_gate;
- NETransposeKernel _transpose_forget_gate;
- NEArithmeticAdditionKernel _accum_forget_gate1;
- NEArithmeticAddition _accum_forget_gate2;
+ NEArithmeticAddition _accum_forget_gate1;
NEPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate;
NEActivationLayerKernel _activation_forget_gate;
NEFullyConnectedLayer _fully_connected_cell_state;
NEGEMM _gemm_cell_state1;
- NEGEMM _gemm_cell_state2;
NETransposeKernel _transpose_cell_state;
NEArithmeticAdditionKernel _accum_cell_state1;
NEArithmeticAdditionKernel _accum_cell_state2;
@@ -162,17 +164,12 @@
NEActivationLayerKernel _cell_clip;
NEPixelWiseMultiplicationKernel _pixelwise_mul_cell_state2;
NEFullyConnectedLayer _fully_connected_output;
- NEGEMM _gemm_output;
NEPixelWiseMultiplicationKernel _pixelwise_mul_output_state1;
- NETransposeKernel _transpose_output;
- NEArithmeticAdditionKernel _accum_output1;
- NEArithmeticAddition _accum_output2;
+ NEArithmeticAddition _accum_output1;
NEActivationLayerKernel _activation_output;
NEActivationLayerKernel _activation_output_state;
NEPixelWiseMultiplicationKernel _pixelwise_mul_output_state2;
NEFullyConnectedLayer _fully_connected_output_state;
- NEGEMM _gemm_output_state;
- NEArithmeticAdditionKernel _accum_output_state;
NEActivationLayerKernel _projection_clip;
NECopyKernel _copy_cell_state;
NECopyKernel _copy_output;
@@ -181,6 +178,18 @@
NEConcatenateLayer _concat_weights_forget_gate;
NEConcatenateLayer _concat_weights_input_gate;
NEConcatenateLayer _concat_weights_output;
+ NEMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
+ NEPixelWiseMultiplicationKernel _pixelwise_mul_input_gate_coeff;
+ NEArithmeticAdditionKernel _accum_input_gate_bias;
+ NEMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
+ NEPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate_coeff;
+ NEArithmeticAdditionKernel _accum_forget_gate_bias;
+ NEMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
+ NEPixelWiseMultiplicationKernel _pixelwise_mul_cell_gate_coeff;
+ NEArithmeticAdditionKernel _accum_cell_gate_bias;
+ NEMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
+ NEPixelWiseMultiplicationKernel _pixelwise_mul_output_gate_coeff;
+ NEArithmeticAdditionKernel _accum_output_gate_bias;
Tensor _input_gate_out1;
Tensor _input_gate_out2;
Tensor _input_gate_out3;
@@ -203,12 +212,21 @@
Tensor _cell_state_activation;
Tensor _output_state1;
Tensor _ones;
+ Tensor _input_layer_norm_out1;
+ Tensor _input_layer_norm_out2;
+ Tensor _forget_layer_norm_out1;
+ Tensor _forget_layer_norm_out2;
+ Tensor _cell_layer_norm_out1;
+ Tensor _cell_layer_norm_out2;
+ Tensor _output_layer_norm_out1;
+ Tensor _output_layer_norm_out2;
bool _run_peephole_opt;
bool _run_cifg_opt;
bool _perform_cell_clipping;
bool _has_projection_weights;
bool _perform_projection_clipping;
bool _is_prepared;
+ bool _is_layer_norm_lstm;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NELSTMLAYER_H__ */
diff --git a/src/runtime/NEON/functions/NELSTMLayer.cpp b/src/runtime/NEON/functions/NELSTMLayer.cpp
index 42b8057..ee2b2f4 100644
--- a/src/runtime/NEON/functions/NELSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NELSTMLayer.cpp
@@ -38,15 +38,18 @@
using namespace arm_compute::misc::shape_calculator;
NELSTMLayer::NELSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _gemm_input_gate(), _transpose_input_gate(), _accum_input_gate1(), _accum_input_gate2(), _subtract_input_gate(),
- _pixelwise_mul_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate(), _transpose_forget_gate(), _accum_forget_gate1(), _accum_forget_gate2(),
- _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _transpose_cell_state(), _accum_cell_state1(), _accum_cell_state2(),
- _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), _gemm_output(), _pixelwise_mul_output_state1(), _transpose_output(),
- _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _gemm_output_state(), _accum_output_state(),
- _projection_clip(), _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(),
- _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(),
- _forget_gate_out6(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _cell_state_activation(),
- _output_state1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false), _is_prepared(false)
+ : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _accum_input_gate1(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(),
+ _fully_connected_forget_gate(), _accum_forget_gate1(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _transpose_cell_state(),
+ _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(),
+ _pixelwise_mul_output_state1(), _accum_output1(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _projection_clip(),
+ _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(),
+ _mean_std_norm_input_gate(), _pixelwise_mul_input_gate_coeff(), _accum_input_gate_bias(), _mean_std_norm_forget_gate(), _pixelwise_mul_forget_gate_coeff(), _accum_forget_gate_bias(),
+ _mean_std_norm_cell_gate(), _pixelwise_mul_cell_gate_coeff(), _accum_cell_gate_bias(), _mean_std_norm_output_gate(), _pixelwise_mul_output_gate_coeff(), _accum_output_gate_bias(), _input_gate_out1(),
+ _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(), _forget_gate_out6(),
+ _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _cell_state_activation(), _output_state1(), _ones(),
+ _input_layer_norm_out1(), _input_layer_norm_out2(), _forget_layer_norm_out1(), _forget_layer_norm_out2(), _cell_layer_norm_out1(), _cell_layer_norm_out2(), _output_layer_norm_out1(),
+ _output_layer_norm_out2(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false), _is_prepared(false),
+ _is_layer_norm_lstm(false)
{
}
@@ -65,6 +68,8 @@
output_state_in, cell_state_in,
scratch_buffer, output_state_out, cell_state_out, output);
+ _is_layer_norm_lstm = lstm_params.use_layer_norm();
+
// Set lstm parameters
LSTMParams<ITensorInfo> lstm_params_info;
if(lstm_params.has_peephole_opt())
@@ -117,7 +122,7 @@
_concat_weights_forget_gate.configure(weights_vector, &_forget_gate_out6, Window::DimX);
_memory_group.manage(&_forget_gate_out5);
- _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, forget_gate_bias, &_forget_gate_out5);
+ _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5);
_memory_group.manage(&_forget_gate_out1);
_memory_group.manage(&_forget_gate_out3);
_forget_gate_out6.allocator()->allocate();
@@ -130,7 +135,7 @@
_run_peephole_opt = true;
_memory_group.manage(&_forget_gate_out4);
_pixelwise_mul_forget_gate.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- _accum_forget_gate2.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE);
+ _accum_forget_gate1.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE);
_forget_gate_out4.allocator()->allocate();
_forget_gate_out5.allocator()->allocate();
forget_gate_out = &_forget_gate_out3;
@@ -139,6 +144,20 @@
{
_forget_gate_out3.allocator()->allocate();
}
+ if(_is_layer_norm_lstm)
+ {
+ _forget_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _forget_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_forget_layer_norm_out1);
+ _memory_group.manage(&_forget_layer_norm_out2);
+ _mean_std_norm_forget_gate.configure(forget_gate_out);
+ _pixelwise_mul_forget_gate_coeff.configure(forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ // forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ forget_gate_out->allocator()->allocate();
+ _accum_forget_gate_bias.configure(&_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE);
+ _forget_layer_norm_out1.allocator()->allocate();
+ forget_gate_out = &_forget_layer_norm_out2;
+ }
_activation_forget_gate.configure(forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
// Configure block that calculates the input gate
@@ -170,7 +189,7 @@
_memory_group.manage(&_input_gate_out1);
_memory_group.manage(&_input_gate_out4);
- _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, lstm_params.input_gate_bias(), &_input_gate_out3);
+ _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3);
_input_gate_out2.allocator()->allocate();
input_gate_out = &_input_gate_out3;
@@ -178,7 +197,7 @@
{
_memory_group.manage(&_input_gate_out4);
_pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- _accum_input_gate2.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _accum_input_gate1.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
_input_gate_out3.allocator()->allocate();
_input_gate_out4.allocator()->allocate();
input_gate_out = &_input_gate_out1;
@@ -187,6 +206,21 @@
{
_input_gate_out1.allocator()->allocate();
}
+
+ if(_is_layer_norm_lstm)
+ {
+ _input_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_input_layer_norm_out1);
+ _memory_group.manage(&_input_layer_norm_out2);
+ _mean_std_norm_input_gate.configure(input_gate_out);
+ _pixelwise_mul_input_gate_coeff.configure(input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ // input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ input_gate_out->allocator()->allocate();
+ _accum_input_gate_bias.configure(&_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE);
+ _input_layer_norm_out1.allocator()->allocate();
+ input_gate_out = &_input_layer_norm_out2;
+ }
_activation_input_gate.configure(input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
}
@@ -200,7 +234,7 @@
_cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_memory_group.manage(&_cell_state_out1);
- _fully_connected_cell_state.configure(input, input_to_cell_weights, cell_bias, &_cell_state_out1);
+ _fully_connected_cell_state.configure(input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1);
_memory_group.manage(&_cell_state_out2);
_transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2);
_memory_group.manage(&_cell_state_out3);
@@ -208,10 +242,25 @@
_cell_state_out2.allocator()->allocate();
_memory_group.manage(&_cell_state_out4);
_accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
- _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info);
+ Tensor *cell_state_out_ptr = &_cell_state_out4;
+ if(_is_layer_norm_lstm)
+ {
+ _cell_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _cell_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_cell_layer_norm_out1);
+ _memory_group.manage(&_cell_layer_norm_out2);
+ _mean_std_norm_cell_gate.configure(cell_state_out_ptr);
+ _pixelwise_mul_cell_gate_coeff.configure(cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ // cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before
+ cell_state_out_ptr->allocator()->allocate();
+ _accum_cell_gate_bias.configure(&_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE);
+ _cell_layer_norm_out1.allocator()->allocate();
+ cell_state_out_ptr = &_cell_layer_norm_out2;
+ }
+ _activation_cell_state.configure(cell_state_out_ptr, nullptr, activation_info);
_memory_group.manage(&_cell_state_out5);
- _pixelwise_mul_cell_state1.configure(&_cell_state_out4, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- _cell_state_out4.allocator()->allocate();
+ _pixelwise_mul_cell_state1.configure(cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ cell_state_out_ptr->allocator()->allocate();
_pixelwise_mul_cell_state2.configure(forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
_accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
_cell_state_out3.allocator()->allocate();
@@ -238,7 +287,7 @@
_memory_group.manage(&_output1);
_memory_group.manage(&_output4);
- _fully_connected_output.configure(&_forget_gate_out2, &_output2, output_gate_bias, &_output4);
+ _fully_connected_output.configure(&_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4);
_output2.allocator()->allocate();
_forget_gate_out2.allocator()->allocate();
@@ -250,7 +299,7 @@
_memory_group.manage(&_output3);
_pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
- _accum_output2.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE);
+ _accum_output1.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE);
_output4.allocator()->allocate();
output_gate_out = &_output1;
@@ -261,6 +310,20 @@
{
_output1.allocator()->allocate();
}
+ if(_is_layer_norm_lstm)
+ {
+ _output_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_output_layer_norm_out1);
+ _memory_group.manage(&_output_layer_norm_out2);
+ _mean_std_norm_output_gate.configure(output_gate_out);
+ _pixelwise_mul_output_gate_coeff.configure(output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+ // output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ output_gate_out->allocator()->allocate();
+ _accum_output_gate_bias.configure(&_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE);
+ _output_layer_norm_out1.allocator()->allocate();
+ output_gate_out = &_output_layer_norm_out2;
+ }
_activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
// Configure block that calculates the output state
@@ -362,6 +425,31 @@
const unsigned int num_batches = input->dimension(1);
const unsigned int num_cells = input_to_output_weights->dimension(1);
+ if(lstm_params.use_layer_norm())
+ {
+ // If CIFG is used, input layer normalization weights tensor is omitted
+ if(lstm_params.has_cifg_opt())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights() != nullptr);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights());
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_batches);
+ }
+
// Check peephole optimization
if(lstm_params.has_peephole_opt())
{
@@ -388,13 +476,20 @@
ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(inputs_vector, &forget_gate_concat, Window::DimX));
// Validate forget gate
- ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_forget_weights, (lstm_params.use_layer_norm()) ? nullptr : forget_gate_bias, &forget_gate));
if(lstm_params.has_peephole_opt())
{
ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
}
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&forget_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&forget_gate, lstm_params.forget_layer_norm_weights(), &forget_gate, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate input gate
@@ -413,7 +508,7 @@
TensorShape lstm_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(lstm_weights, 0);
TensorInfo lstm_gate_concat = TensorInfo(lstm_weights_concat_shape, 1, input->data_type());
ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(lstm_weights, &lstm_gate_concat, Window::DimX));
- ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &input_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), (lstm_params.use_layer_norm()) ? nullptr : lstm_params.input_gate_bias(), &input_gate));
if(lstm_params.has_peephole_opt())
{
@@ -422,6 +517,13 @@
ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE));
}
+
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&input_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&input_gate, lstm_params.input_layer_norm_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_gate, lstm_params.input_gate_bias(), &input_gate, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
}
else
@@ -430,9 +532,16 @@
}
// Validate cell state
- ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, &cell_state_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_cell_weights, (lstm_params.use_layer_norm()) ? nullptr : cell_bias, &cell_state_tmp));
ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &cell_state_tmp, 1.f, 0.f, GEMMInfo()));
ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE));
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&cell_state_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_layer_norm_weights(), &cell_state_tmp, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, cell_bias, &cell_state_tmp, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, nullptr, activation_info));
ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
@@ -451,7 +560,7 @@
TensorInfo in_out_gate_concat = TensorInfo(in_out_weights_concat_shape, 1, input->data_type());
ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(in_out_weights, &in_out_gate_concat, Window::DimX));
- ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, &output_gate_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_output_weights, (lstm_params.use_layer_norm()) ? nullptr : output_gate_bias, &output_gate_tmp));
if(lstm_params.has_peephole_opt())
{
@@ -459,6 +568,13 @@
RoundingPolicy::TO_ZERO));
ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE));
}
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&output_gate_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&output_gate_tmp, lstm_params.output_layer_norm_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_ZERO));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate output state
@@ -504,7 +620,13 @@
if(_run_peephole_opt)
{
NEScheduler::get().schedule(&_pixelwise_mul_forget_gate, Window::DimY);
- _accum_forget_gate2.run();
+ _accum_forget_gate1.run();
+ }
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_forget_gate.run();
+ NEScheduler::get().schedule(&_pixelwise_mul_forget_gate_coeff, Window::DimY);
+ NEScheduler::get().schedule(&_accum_forget_gate_bias, Window::DimY);
}
NEScheduler::get().schedule(&_activation_forget_gate, Window::DimY);
@@ -527,7 +649,14 @@
if(_run_peephole_opt)
{
NEScheduler::get().schedule(&_pixelwise_mul_input_gate, Window::DimY);
- _accum_input_gate2.run();
+ _accum_input_gate1.run();
+ }
+
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_input_gate.run();
+ NEScheduler::get().schedule(&_pixelwise_mul_input_gate_coeff, Window::DimY);
+ NEScheduler::get().schedule(&_accum_input_gate_bias, Window::DimY);
}
NEScheduler::get().schedule(&_activation_input_gate, Window::DimY);
}
@@ -536,6 +665,12 @@
NEScheduler::get().schedule(&_transpose_cell_state, Window::DimY);
_gemm_cell_state1.run();
NEScheduler::get().schedule(&_accum_cell_state1, Window::DimY);
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_cell_gate.run();
+ NEScheduler::get().schedule(&_pixelwise_mul_cell_gate_coeff, Window::DimY);
+ NEScheduler::get().schedule(&_accum_cell_gate_bias, Window::DimY);
+ }
NEScheduler::get().schedule(&_activation_cell_state, Window::DimY);
NEScheduler::get().schedule(&_pixelwise_mul_cell_state1, Window::DimY);
NEScheduler::get().schedule(&_pixelwise_mul_cell_state2, Window::DimY);
@@ -550,7 +685,13 @@
if(_run_peephole_opt)
{
NEScheduler::get().schedule(&_pixelwise_mul_output_state1, Window::DimY);
- _accum_output2.run();
+ _accum_output1.run();
+ }
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_output_gate.run();
+ NEScheduler::get().schedule(&_pixelwise_mul_output_gate_coeff, Window::DimY);
+ NEScheduler::get().schedule(&_accum_output_gate_bias, Window::DimY);
}
NEScheduler::get().schedule(&_activation_output, Window::DimY);
diff --git a/tests/validation/NEON/LSTMLayer.cpp b/tests/validation/NEON/LSTMLayer.cpp
index c503972..45beb36 100644
--- a/tests/validation/NEON/LSTMLayer.cpp
+++ b/tests/validation/NEON/LSTMLayer.cpp
@@ -157,7 +157,7 @@
DataType::F32)),
framework::dataset::make("ProjectionOpt", { true, false })),
framework::dataset::make("PeepholeOpt", { true, false })),
- framework::dataset::make("UseLayerNorm", { false })))
+ framework::dataset::make("UseLayerNorm", { true, false })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
@@ -171,7 +171,7 @@
DataType::F16)),
framework::dataset::make("ProjectionOpt", { true, false })),
framework::dataset::make("PeepholeOpt", { true, false })),
- framework::dataset::make("UseLayerNorm", { false })))
+ framework::dataset::make("UseLayerNorm", { true, false })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);