COMPMID-2342: Add layer normalization support in CLLSTMLayer
Change-Id: I25d974aa94e69c5f79a0bd99d5869a351d6d954d
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1324
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
diff --git a/arm_compute/runtime/CL/functions/CLLSTMLayer.h b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
index 3add152..44b3baf 100644
--- a/arm_compute/runtime/CL/functions/CLLSTMLayer.h
+++ b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
@@ -39,6 +39,7 @@
#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+#include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/common/LSTMParams.h"
@@ -76,17 +77,22 @@
* @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.
+ * @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0f 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 0.0f then clipping is disabled.
*/
void configure(const ICLTensor *input,
const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
@@ -98,35 +104,40 @@
/** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
*
- * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
- * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
- * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
- * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
- * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
- * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
- * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
- * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
- * @param[in] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
- * @param[in] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
- * @param[in] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
- * @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.
+ * @param[in] input Source tensor info. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
+ * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] output_state_in 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
+ * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
+ * @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF.
+ * Data type supported: Same as @p input.
+ * @param[in] output_state_out 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
+ * @param[in] cell_state_out 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
+ * @param[in] output Destination tensor info. 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 info used in peephole optimization:
+ * input_to_input_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * cell_to_input_weights 1D weights tensor info with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
+ * cell_to_forget_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_to_output_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * input_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input
+ * projection_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * projection_bias 1D weights tensor info 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.
+ * @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0f 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 0.0f then clipping is disabled.
*
* @return a status
*/
@@ -145,23 +156,16 @@
private:
CLMemoryGroup _memory_group;
CLFullyConnectedLayer _fully_connected_input_gate;
- CLGEMM _gemm_input_gate;
- CLTransposeKernel _transpose_input_gate;
- CLSaturatedArithmeticOperationKernel _accum_input_gate1;
- CLArithmeticAddition _accum_input_gate2;
+ CLArithmeticAddition _accum_input_gate1;
CLSaturatedArithmeticOperationKernel _subtract_input_gate;
CLPixelWiseMultiplicationKernel _pixelwise_mul_input_gate;
CLActivationLayerKernel _activation_input_gate;
CLFullyConnectedLayer _fully_connected_forget_gate;
- CLGEMM _gemm_forget_gate;
- CLTransposeKernel _transpose_forget_gate;
- CLSaturatedArithmeticOperationKernel _accum_forget_gate1;
- CLArithmeticAddition _accum_forget_gate2;
+ CLArithmeticAddition _accum_forget_gate1;
CLPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate;
CLActivationLayerKernel _activation_forget_gate;
CLFullyConnectedLayer _fully_connected_cell_state;
CLGEMM _gemm_cell_state1;
- CLGEMM _gemm_cell_state2;
CLTransposeKernel _transpose_cell_state;
CLSaturatedArithmeticOperationKernel _accum_cell_state1;
CLSaturatedArithmeticOperationKernel _accum_cell_state2;
@@ -170,17 +174,12 @@
CLActivationLayerKernel _cell_clip;
CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_state2;
CLFullyConnectedLayer _fully_connected_output;
- CLGEMM _gemm_output;
CLPixelWiseMultiplicationKernel _pixelwise_mul_output_state1;
- CLTransposeKernel _transpose_output;
- CLSaturatedArithmeticOperationKernel _accum_output1;
- CLArithmeticAddition _accum_output2;
+ CLArithmeticAddition _accum_output1;
CLActivationLayerKernel _activation_output;
CLActivationLayerKernel _activation_output_state;
CLPixelWiseMultiplicationKernel _pixelwise_mul_output_state2;
CLFullyConnectedLayer _fully_connected_output_state;
- CLGEMM _gemm_output_state;
- CLSaturatedArithmeticOperationKernel _accum_output_state;
CLActivationLayerKernel _projection_clip;
CLCopyKernel _copy_cell_state;
CLCopyKernel _copy_output;
@@ -190,6 +189,18 @@
CLWidthConcatenate2TensorsKernel _concat_weights_input_gate;
CLWidthConcatenate2TensorsKernel _concat_weights_output;
CLMemsetKernel _ones_memset_kernel;
+ CLMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_input_gate_coeff;
+ CLSaturatedArithmeticOperationKernel _accum_input_gate_bias;
+ CLMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate_coeff;
+ CLSaturatedArithmeticOperationKernel _accum_forget_gate_bias;
+ CLMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_gate_coeff;
+ CLSaturatedArithmeticOperationKernel _accum_cell_gate_bias;
+ CLMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_output_gate_coeff;
+ CLSaturatedArithmeticOperationKernel _accum_output_gate_bias;
CLTensor _input_gate_out1;
CLTensor _input_gate_out2;
CLTensor _input_gate_out3;
@@ -212,12 +223,21 @@
CLTensor _cell_state_activation;
CLTensor _output_state1;
CLTensor _ones;
+ CLTensor _input_layer_norm_out1;
+ CLTensor _input_layer_norm_out2;
+ CLTensor _forget_layer_norm_out1;
+ CLTensor _forget_layer_norm_out2;
+ CLTensor _cell_layer_norm_out1;
+ CLTensor _cell_layer_norm_out2;
+ CLTensor _output_layer_norm_out1;
+ CLTensor _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_CLLSTMLAYER_H__ */