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author | Sheri Zhang <sheri.zhang@arm.com> | 2020-04-21 13:10:24 +0100 |
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committer | Sheri Zhang <sheri.zhang@arm.com> | 2020-04-26 21:31:26 +0000 |
commit | 3a35398ed6cc5d9c0f45f33dabb2bfbb017bcf60 (patch) | |
tree | aaa4a8949e288157ab92404d1745214529c0c69b /src/runtime/CL | |
parent | 31b49caa2ca9308a5ba62a598afc9d1982b4af18 (diff) | |
download | ComputeLibrary-3a35398ed6cc5d9c0f45f33dabb2bfbb017bcf60.tar.gz |
COMPMID-3240: Add support for layer normalization to CLQLSTMLayer
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I45359a4ddb46c059097a2d77c008f802e8f4c143
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3065
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/runtime/CL')
-rw-r--r-- | src/runtime/CL/functions/CLQLSTMLayer.cpp | 120 |
1 files changed, 109 insertions, 11 deletions
diff --git a/src/runtime/CL/functions/CLQLSTMLayer.cpp b/src/runtime/CL/functions/CLQLSTMLayer.cpp index 88c5f77b9f..d9b5c7c64d 100644 --- a/src/runtime/CL/functions/CLQLSTMLayer.cpp +++ b/src/runtime/CL/functions/CLQLSTMLayer.cpp @@ -92,9 +92,6 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT 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); ARM_COMPUTE_ERROR_ON_NULLPTR(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); @@ -125,6 +122,21 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT _recurrent_to_output_weights = recurrent_to_output_weights; _projection_weights = lstm_params.projection_weights(); + // Layer normalization + _has_layer_norm = lstm_params.use_layer_norm(); + if(_has_layer_norm) + { + set_layer_norm_weight(lstm_params.forget_layer_norm_weights(), LayerNormGate::Forget); + set_layer_norm_weight(lstm_params.cell_layer_norm_weights(), LayerNormGate::Cell); + set_layer_norm_weight(lstm_params.input_layer_norm_weights(), LayerNormGate::Input); + set_layer_norm_weight(lstm_params.output_layer_norm_weights(), LayerNormGate::Output); + + set_layer_norm_bias(forget_gate_bias, LayerNormGate::Forget); + set_layer_norm_bias(cell_bias, LayerNormGate::Cell); + set_layer_norm_bias(lstm_params.input_gate_bias(), LayerNormGate::Input); + set_layer_norm_bias(output_gate_bias, LayerNormGate::Output); + } + _has_cifg = lstm_params.has_cifg_opt(); _has_projection = lstm_params.has_projection(); _has_peephole = lstm_params.has_peephole_opt(); @@ -218,14 +230,23 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT _cell_to_forget_outstage_res.allocator()->allocate(); } + CLTensor *forget_activation_input = &_recurrent_to_forget_outstage_res; + + if(_has_layer_norm) + { + configure_layer_norm(LayerNormGate::Forget, &_recurrent_to_forget_outstage_res); + _recurrent_to_forget_outstage_res.allocator()->allocate(); + forget_activation_input = &get_layer_norm_output(LayerNormGate::Forget); + } + // Output quantization info of Sigmoid and Tanh activations const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0); 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(compile_context, &_recurrent_to_forget_outstage_res, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); - _recurrent_to_forget_outstage_res.allocator()->allocate(); + _forget_gate_sigmoid.configure(compile_context, forget_activation_input, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + forget_activation_input->allocator()->allocate(); // Modulation gate. const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0)); @@ -245,11 +266,20 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT ConvertPolicy::SATURATE); _input_to_cell_outstage_res.allocator()->allocate(); + CLTensor *cell_activation_input = &_recurrent_to_cell_outstage_res; + + if(_has_layer_norm) + { + configure_layer_norm(LayerNormGate::Cell, &_recurrent_to_cell_outstage_res); + _recurrent_to_cell_outstage_res.allocator()->allocate(); + cell_activation_input = &get_layer_norm_output(LayerNormGate::Cell); + } + 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(compile_context, &_recurrent_to_cell_outstage_res, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); - _recurrent_to_cell_outstage_res.allocator()->allocate(); + _cell_gate_tanh.configure(compile_context, cell_activation_input, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); + cell_activation_input->allocator()->allocate(); // Input gate. const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); @@ -293,8 +323,17 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT _cell_to_input_outstage_res.allocator()->allocate(); } - _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(); + CLTensor *input_activation_input = &_recurrent_to_input_outstage_res; + + if(_has_layer_norm) + { + configure_layer_norm(LayerNormGate::Input, &_recurrent_to_input_outstage_res); + _recurrent_to_input_outstage_res.allocator()->allocate(); + input_activation_input = &get_layer_norm_output(LayerNormGate::Input); + } + + _input_gate_tanh.configure(compile_context, input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); + input_activation_input->allocator()->allocate(); } // Cell. // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel @@ -344,11 +383,20 @@ void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLT _mul_cell_to_output_res.allocator()->allocate(); } + CLTensor *output_activation_input = &_recurrent_to_output_outstage_res; + + if(_has_layer_norm) + { + configure_layer_norm(LayerNormGate::Output, &_recurrent_to_output_outstage_res); + _recurrent_to_output_outstage_res.allocator()->allocate(); + output_activation_input = &get_layer_norm_output(LayerNormGate::Output); + } + 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(compile_context, &_recurrent_to_output_outstage_res, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); - _recurrent_to_output_outstage_res.allocator()->allocate(); + _output_gate_sigmoid.configure(compile_context, output_activation_input, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + output_activation_input->allocator()->allocate(); // Hidden. _hidden_tanh.configure(compile_context, cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); @@ -525,6 +573,8 @@ Status CLQLSTMLayer::validate(const ITensorInfo *input, gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max(); gemmlowp_info.output_data_type = DataType::QSYMM16; + const bool has_layer_norm = lstm_params.use_layer_norm(); + // Forget gate. const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)); const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32); @@ -547,6 +597,13 @@ Status CLQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE)); } + if(has_layer_norm) + { + const ITensorInfo *w_info = lstm_params.forget_layer_norm_weights(); + const ITensorInfo *b_info = forget_gate_bias; + ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(forget_outstage_info, *w_info, *b_info)); + } + // Output quantization info of Sigmoid and Tanh activations const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0); @@ -563,6 +620,13 @@ Status CLQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE)); + if(has_layer_norm) + { + const ITensorInfo *w_info = lstm_params.cell_layer_norm_weights(); + const ITensorInfo *b_info = cell_bias; + ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info)); + } + const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_outstage_info, &cell_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); @@ -602,6 +666,13 @@ Status CLQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); } + if(has_layer_norm) + { + const ITensorInfo *w_info = lstm_params.input_layer_norm_weights(); + const ITensorInfo *b_info = lstm_params.input_gate_bias(); + ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info)); + } + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&input_outstage_info, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); } // Cell. @@ -634,6 +705,13 @@ Status CLQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE)); } + if(has_layer_norm) + { + const ITensorInfo *w_info = lstm_params.output_layer_norm_weights(); + const ITensorInfo *b_info = output_gate_bias; + ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(output_outstage_info, *w_info, *b_info)); + } + const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&output_outstage_info, &output_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); @@ -715,6 +793,11 @@ void CLQLSTMLayer::run() CLScheduler::get().enqueue(_accumulate_cell_forget); } + if(_has_layer_norm) + { + CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Forget)); + } + _forget_gate_sigmoid.run(); // Modulation gate. @@ -725,6 +808,11 @@ void CLQLSTMLayer::run() _recurrent_to_cell_outstage.run(); CLScheduler::get().enqueue(_accumulate_input_recurrent_modulation); + if(_has_layer_norm) + { + CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Cell)); + } + _cell_gate_tanh.run(); // Input gate @@ -747,6 +835,11 @@ void CLQLSTMLayer::run() CLScheduler::get().enqueue(_accumulate_cell_input); } + if(_has_layer_norm) + { + CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Input)); + } + _input_gate_tanh.run(); } @@ -771,6 +864,11 @@ void CLQLSTMLayer::run() CLScheduler::get().enqueue(_accumulate_cell_to_output); } + if(_has_layer_norm) + { + CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Output)); + } + _output_gate_sigmoid.run(); // Hidden. |