diff options
Diffstat (limited to 'arm_compute/runtime/NEON/functions/NEQLSTMLayer.h')
| -rw-r--r-- | arm_compute/runtime/NEON/functions/NEQLSTMLayer.h | 92 |
1 files changed, 49 insertions, 43 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h b/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h index 9eb0654cfe..4dde85e895 100644 --- a/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h +++ b/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h @@ -26,6 +26,7 @@ #include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h" #include "arm_compute/core/NEON/kernels/NEArithmeticSubtractionKernel.h" +#include "arm_compute/core/NEON/kernels/NECopyKernel.h" #include "arm_compute/core/NEON/kernels/NEGEMMLowpReductionKernel.h" #include "arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h" #include "arm_compute/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h" @@ -49,6 +50,7 @@ class ITensor; * -# @ref NEActivationLayer Activation functions (tanh and logistic) * -# @ref NEArithmeticAdditionKernel Elementwise addition * -# @ref NEArithmeticSubtractionKernel Elementwise subtraction + * -# @ref NECopyKernel Copy kernel for copying output_state_out to output * -# @ref NEGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers * -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint Convert 32-bit integers into QSYMM16 * -# @ref NEGEMMLowpMatrixAReductionKernel For precomputing effective biases to use @@ -80,10 +82,11 @@ public: * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32. * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32. * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32. - * @param[in] cell_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. - * @param[in] output_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input. - * @param[out] cell_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. - * @param[out] output_state_out Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size].Data types supported: Same as @p input. + * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16. + * @param[in] output_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. + * @param[out] cell_state_out Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16. + * @param[out] output_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input. + * @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 Weights tensors used in peephole, CIFG and layer normalization optimizations: * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. @@ -113,48 +116,49 @@ public: 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, const ITensor *output_state_in, - ITensor *cell_state_out, ITensor *output_state_out, + ITensor *cell_state_out, ITensor *output_state_out, ITensor *output, const LSTMParams<ITensor> &lstm_params); /** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayer * - * @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED. - * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. - * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. - * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. - * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. - * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. - * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. - * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. - * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. - * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. - * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16. - * @param[in] output_state_in 2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input. - * @param[out] cell_state_out Destination tensor info. Output is a 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16. - * @param[out] output_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input. - * @param[in] lstm_params Weights tensors info used in peephole, CIFG and layer normalization optimizations: - * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. - * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. - * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. - * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. - * hidden_state_zero The zero point of the hidden state. - * hidden_state_scale The scale of the hidden state. - * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. - * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. - * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16. - * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32. - * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. - * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32. - * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. - * cell_threshold (Optional) 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. - * projection_threshold (Optional) 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] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED. + * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16. + * @param[in] output_state_in 2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input. + * @param[in] cell_state_out Destination tensor info. Output is a 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16. + * @param[in] output_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input. + * @param[in] output Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input. + * @param[in] lstm_params Weights tensors info used in peephole, CIFG and layer normalization optimizations: + * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. + * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. + * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. + * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. + * hidden_state_zero The zero point of the hidden state. + * hidden_state_scale The scale of the hidden state. + * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16. + * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32. + * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32. + * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_threshold (Optional) 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. + * projection_threshold (Optional) 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. * @return a status */ static Status validate(const ITensorInfo *input, @@ -162,7 +166,7 @@ public: 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 *cell_state_out, const ITensorInfo *output_state_out, const ITensorInfo *output, const LSTMParams<ITensorInfo> &lstm_params); // Inherited methods overridden: @@ -304,6 +308,8 @@ private: std::array<NEQLSTMLayerNormalizationKernel, _layer_norm_count> _layer_norms{ {} }; + NECopyKernel _copy_output{}; + // Tensor pointers const ITensor *_input_to_input_weights{ nullptr }; const ITensor *_recurrent_to_input_weights{ nullptr }; |