From 39438b427b293c6d2e7066c68d3c3d3cb6d98a15 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Tue, 4 Jun 2019 12:41:45 +0100 Subject: COMPMID-2342: Add layer normalization support in CLLSTMLayer Change-Id: I25d974aa94e69c5f79a0bd99d5869a351d6d954d Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/1324 Reviewed-by: Manuel Bottini Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michalis Spyrou --- src/runtime/CL/functions/CLLSTMLayer.cpp | 192 ++++++++++++++++++++++++++----- 1 file changed, 166 insertions(+), 26 deletions(-) (limited to 'src/runtime/CL/functions/CLLSTMLayer.cpp') diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp index 85a81a8cd4..793d5ca1a9 100644 --- a/src/runtime/CL/functions/CLLSTMLayer.cpp +++ b/src/runtime/CL/functions/CLLSTMLayer.cpp @@ -38,16 +38,18 @@ using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; CLLSTMLayer::CLLSTMLayer(std::shared_ptr 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(), - _ones_memset_kernel(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), + : _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(), + _ones_memset_kernel(), _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(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false), - _is_prepared(false) + _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) { } @@ -66,6 +68,8 @@ void CLLSTMLayer::configure(const ICLTensor *input, 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 lstm_params_info; if(lstm_params.has_peephole_opt()) @@ -121,7 +125,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _concat_weights_forget_gate.configure(input_to_forget_weights, recurrent_to_forget_weights, &_forget_gate_out6); _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(); @@ -134,7 +138,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _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_NEAREST_EVEN); - _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; @@ -143,6 +147,20 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _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_NEAREST_EVEN); + // 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(ArithmeticOperation::ADD, &_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 @@ -177,7 +195,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _memory_group.manage(&_input_gate_out1); _memory_group.manage(&_input_gate_out3); - _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; @@ -185,7 +203,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _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_NEAREST_EVEN); - _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; @@ -194,6 +212,21 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _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_NEAREST_EVEN); + // 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(ArithmeticOperation::ADD, &_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)); } @@ -207,7 +240,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _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); @@ -215,10 +248,25 @@ void CLLSTMLayer::configure(const ICLTensor *input, _cell_state_out2.allocator()->allocate(); _memory_group.manage(&_cell_state_out4); _accum_cell_state1.configure(ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); - _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info); + CLTensor *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_NEAREST_EVEN); + // 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(ArithmeticOperation::ADD, &_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_NEAREST_EVEN); - _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_NEAREST_EVEN); + 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_NEAREST_EVEN); _accum_cell_state2.configure(ArithmeticOperation::ADD, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); _cell_state_out3.allocator()->allocate(); @@ -247,7 +295,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _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(); @@ -259,7 +307,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _memory_group.manage(&_output3); _pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - _accum_output2.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE); + _accum_output1.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE); _output4.allocator()->allocate(); output_gate_out = &_output1; @@ -270,6 +318,20 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _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_NEAREST_EVEN); + // 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(ArithmeticOperation::ADD, &_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 @@ -370,6 +432,31 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, 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()) { @@ -389,7 +476,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, TensorInfo cell_state_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); // Validate forget gate - ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate)); + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, (lstm_params.use_layer_norm()) ? nullptr : forget_gate_bias, &forget_gate)); std::vector inputs_vector; inputs_vector.emplace_back(input); @@ -404,6 +491,13 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); } + if(lstm_params.use_layer_norm()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&forget_gate)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&forget_gate, lstm_params.forget_layer_norm_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN)); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE)); + } ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); // Validate input gate @@ -423,7 +517,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, TensorInfo lstm_gate_concat = TensorInfo(lstm_weights_concat_shape, 1, input->data_type()); ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate2TensorsKernel::validate(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), &lstm_gate_concat)); - ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &input_gate)); + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::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()) { @@ -432,6 +526,13 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE)); } + + if(lstm_params.use_layer_norm()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&input_gate)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&input_gate, lstm_params.input_layer_norm_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, lstm_params.input_gate_bias(), &input_gate, ConvertPolicy::SATURATE)); + } ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); } else @@ -440,9 +541,16 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, } // Validate cell state - ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, &cell_state_tmp)); + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, (lstm_params.use_layer_norm()) ? nullptr : cell_bias, &cell_state_tmp)); ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &cell_state_tmp, 1.f, 0.f, GEMMInfo())); ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE)); + if(lstm_params.use_layer_norm()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&cell_state_tmp)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_layer_norm_weights(), &cell_state_tmp, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN)); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, cell_bias, &cell_state_tmp, ConvertPolicy::SATURATE)); + } ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&cell_state_tmp, nullptr, activation_info)); ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); @@ -460,7 +568,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, TensorInfo in_out_gate_concat = TensorInfo(in_out_weights_concat_shape, 1, input->data_type()); ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate2TensorsKernel::validate(input_to_output_weights, recurrent_to_output_weights, &in_out_gate_concat)); // Validate output gate tmp - ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, &output_gate_tmp)); + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, (lstm_params.use_layer_norm()) ? nullptr : output_gate_bias, &output_gate_tmp)); if(lstm_params.has_peephole_opt()) { @@ -468,6 +576,13 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, RoundingPolicy::TO_NEAREST_EVEN)); ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE)); } + if(lstm_params.use_layer_norm()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&output_gate_tmp)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&output_gate_tmp, lstm_params.output_layer_norm_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN)); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE)); + } ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); // Validate output state @@ -514,7 +629,13 @@ void CLLSTMLayer::run() if(_run_peephole_opt) { CLScheduler::get().enqueue(_pixelwise_mul_forget_gate); - _accum_forget_gate2.run(); + _accum_forget_gate1.run(); + } + if(_is_layer_norm_lstm) + { + _mean_std_norm_forget_gate.run(); + CLScheduler::get().enqueue(_pixelwise_mul_forget_gate_coeff); + CLScheduler::get().enqueue(_accum_forget_gate_bias); } CLScheduler::get().enqueue(_activation_forget_gate); @@ -530,7 +651,14 @@ void CLLSTMLayer::run() if(_run_peephole_opt) { CLScheduler::get().enqueue(_pixelwise_mul_input_gate); - _accum_input_gate2.run(); + _accum_input_gate1.run(); + } + + if(_is_layer_norm_lstm) + { + _mean_std_norm_input_gate.run(); + CLScheduler::get().enqueue(_pixelwise_mul_input_gate_coeff); + CLScheduler::get().enqueue(_accum_input_gate_bias); } CLScheduler::get().enqueue(_activation_input_gate); } @@ -539,6 +667,12 @@ void CLLSTMLayer::run() CLScheduler::get().enqueue(_transpose_cell_state); _gemm_cell_state1.run(); CLScheduler::get().enqueue(_accum_cell_state1); + if(_is_layer_norm_lstm) + { + _mean_std_norm_cell_gate.run(); + CLScheduler::get().enqueue(_pixelwise_mul_cell_gate_coeff); + CLScheduler::get().enqueue(_accum_cell_gate_bias); + } CLScheduler::get().enqueue(_activation_cell_state); CLScheduler::get().enqueue(_pixelwise_mul_cell_state1); CLScheduler::get().enqueue(_pixelwise_mul_cell_state2); @@ -554,7 +688,13 @@ void CLLSTMLayer::run() if(_run_peephole_opt) { CLScheduler::get().enqueue(_pixelwise_mul_output_state1); - _accum_output2.run(); + _accum_output1.run(); + } + if(_is_layer_norm_lstm) + { + _mean_std_norm_output_gate.run(); + CLScheduler::get().enqueue(_pixelwise_mul_output_gate_coeff); + CLScheduler::get().enqueue(_accum_output_gate_bias); } CLScheduler::get().enqueue(_activation_output); -- cgit v1.2.1