From 25f45a4a0158a709d28a5207a867a9b5ce390621 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 8 Aug 2018 12:53:05 +0100 Subject: COMPMID-1060 LSTM FP32 NEON Change-Id: I0bdf874e61917903c26f713ec41a7ffc29e07233 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/143892 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- src/runtime/NEON/functions/NELSTMLayer.cpp | 545 +++++++++++++++++++++++++++++ 1 file changed, 545 insertions(+) create mode 100644 src/runtime/NEON/functions/NELSTMLayer.cpp (limited to 'src/runtime/NEON/functions/NELSTMLayer.cpp') diff --git a/src/runtime/NEON/functions/NELSTMLayer.cpp b/src/runtime/NEON/functions/NELSTMLayer.cpp new file mode 100644 index 0000000000..934761a8ef --- /dev/null +++ b/src/runtime/NEON/functions/NELSTMLayer.cpp @@ -0,0 +1,545 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/NEON/functions/NELSTMLayer.h" + +#include "arm_compute/core/PixelValue.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/common/LSTMParams.h" + +#include +#include +#include + +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + +NELSTMLayer::NELSTMLayer(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(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _input_gate_out5(), + _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), + _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _output5(), _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) +{ +} + +void NELSTMLayer::configure(const ITensor *input, + const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights, + 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 *output_state_in, const ITensor *cell_state_in, + ITensor *scratch_buffer, ITensor *output_state_out, ITensor *cell_state_out, ITensor *output, + const LSTMParams &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold) +{ + 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, + output_state_in, cell_state_in, + scratch_buffer, output_state_out, cell_state_out, output); + + // Set lstm parameters + LSTMParams lstm_params_info; + if(lstm_params.has_peephole_opt()) + { + lstm_params_info.set_peephole_params(lstm_params.cell_to_forget_weights()->info(), lstm_params.cell_to_output_weights()->info()); + } + if(lstm_params.has_projection()) + { + lstm_params_info.set_projection_params(lstm_params.projection_weights()->info(), + lstm_params.projection_bias() != nullptr ? lstm_params.projection_bias()->info() : nullptr); + } + if(!lstm_params.has_cifg_opt()) + { + const ITensorInfo *cell_to_input_weights_info = (lstm_params.has_peephole_opt()) ? lstm_params.cell_to_input_weights()->info() : nullptr; + lstm_params_info.set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(), + cell_to_input_weights_info, lstm_params.input_gate_bias()->info()); + } + + // Validate + ARM_COMPUTE_ERROR_THROW_ON(NELSTMLayer::validate(input->info(), input_to_forget_weights->info(), + input_to_cell_weights->info(), input_to_output_weights->info(), + recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(), + forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(), + output_state_in->info(), cell_state_in->info(), + scratch_buffer->info(), output_state_out->info(), cell_state_out->info(), output->info(), + lstm_params_info, activation_info, cell_threshold, projection_threshold)); + + const TensorShape cell_state_shape = cell_state_in->info()->tensor_shape(); + + // Configure block that calculates the forget gate + // forget_gate = Activation(input * input_to_forget_weights + output_state_in * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias) + TensorShape forget_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + _forget_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _forget_gate_out2.allocator()->init(TensorInfo(forget_gate1_shape, 1, input->info()->data_type())); + _forget_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _forget_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + _memory_group.manage(&_forget_gate_out1); + _fully_connected_forget_gate.configure(input, input_to_forget_weights, forget_gate_bias, &_forget_gate_out1); + _memory_group.manage(&_forget_gate_out2); + _transpose_forget_gate.configure(recurrent_to_forget_weights, &_forget_gate_out2); + _memory_group.manage(&_forget_gate_out3); + _gemm_forget_gate.configure(output_state_in, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f); + _forget_gate_out2.allocator()->allocate(); + _memory_group.manage(&_forget_gate_out5); + _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE); + Tensor *forget_gate_out = &_forget_gate_out5; + + if(lstm_params.has_peephole_opt()) + { + _forget_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + _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); + _forget_gate_out4.allocator()->allocate(); + _forget_gate_out5.allocator()->allocate(); + forget_gate_out = &_forget_gate_out3; + } + else + { + _forget_gate_out3.allocator()->allocate(); + } + _activation_forget_gate.configure(forget_gate_out, &_forget_gate_out1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + forget_gate_out->allocator()->allocate(); + + // Configure block that calculates the input gate + // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG + // input_gate = 1 - forget_gate, with CIFG + _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + if(lstm_params.has_cifg_opt()) + { + _memory_group.manage(&_input_gate_out1); + _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _subtract_input_gate.configure(&_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE); + _ones.allocator()->allocate(); + _run_cifg_opt = true; + } + else + { + TensorShape input_gate_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + + _input_gate_out2.allocator()->init(TensorInfo(input_gate_shape, 1, input->info()->data_type())); + _input_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _input_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _input_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + _memory_group.manage(&_input_gate_out1); + _fully_connected_input_gate.configure(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &_input_gate_out1); + _memory_group.manage(&_input_gate_out2); + _transpose_input_gate.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2); + _memory_group.manage(&_input_gate_out3); + _gemm_input_gate.configure(output_state_in, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f); + _input_gate_out2.allocator()->allocate(); + _memory_group.manage(&_input_gate_out4); + _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE); + if(_run_peephole_opt) + { + _memory_group.manage(&_input_gate_out5); + _pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _accum_input_gate2.configure(&_input_gate_out4, &_input_gate_out5, &_input_gate_out1, ConvertPolicy::SATURATE); + _input_gate_out5.allocator()->allocate(); + } + _input_gate_out3.allocator()->allocate(); + _input_gate_out4.allocator()->allocate(); + _activation_input_gate.configure(&_input_gate_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + } + + // Configure block that calculates the cell state + // cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state_in * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold) + TensorShape cell_state1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + _cell_state_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _cell_state_out2.allocator()->init(TensorInfo(cell_state1_shape, 1, input->info()->data_type())); + _cell_state_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _cell_state_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _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); + _memory_group.manage(&_cell_state_out2); + _transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2); + _memory_group.manage(&_cell_state_out3); + _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f); + _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); + _memory_group.manage(&_cell_state_out5); + _pixelwise_mul_cell_state1.configure(&_cell_state_out4, &_input_gate_out1, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _input_gate_out1.allocator()->allocate(); + _cell_state_out4.allocator()->allocate(); + _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _forget_gate_out1.allocator()->allocate(); + _accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); + _cell_state_out3.allocator()->allocate(); + _cell_state_out5.allocator()->allocate(); + // Perform clipping + if(cell_threshold != 0.f) + { + _perform_cell_clipping = true; + _cell_clip.configure(&_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)); + } + + // Configure block that calculates the output + // output_state_out = Activation(input * input_to_output_weights + output_state_in * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias) + TensorShape output1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + _output1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _output2.allocator()->init(TensorInfo(output1_shape, 1, input->info()->data_type())); + _output3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _output5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + _memory_group.manage(&_output1); + _fully_connected_output.configure(input, input_to_output_weights, output_gate_bias, &_output1); + _memory_group.manage(&_output2); + _transpose_output.configure(recurrent_to_output_weights, &_output2); + _memory_group.manage(&_output3); + _gemm_output.configure(output_state_in, &_output2, nullptr, &_output3, 1.f, 0.f); + _output2.allocator()->allocate(); + _memory_group.manage(&_output5); + _accum_output1.configure(&_output1, &_output3, &_output5, ConvertPolicy::SATURATE); + _output3.allocator()->allocate(); + Tensor *output_gate_out = &_output5; + if(lstm_params.has_peephole_opt()) + { + _output4.allocator()->init(TensorInfo(_cell_state_out1.info()->tensor_shape(), 1, input->info()->data_type())); + + _memory_group.manage(&_output4); + _pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _accum_output2.configure(&_output5, &_output4, &_output1, ConvertPolicy::SATURATE); + _output5.allocator()->allocate(); + output_gate_out = &_output1; + + // Allocate intermediate buffers + _output4.allocator()->allocate(); + } + else + { + _output1.allocator()->allocate(); + } + _activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + output_gate_out->allocator()->allocate(); + + // Configure block that calculates the output state + /** lstm_res = PixelwiseMul(output, Activation(cell_state)) + * + * -- Clip(lstm_res * projection_weights + projection_bias, projection_threshold) , if there is a projection + * / + * output_state = -- + * \ + * -- lstm_res , otherwise + */ + ITensor *output_state_out_tmp = lstm_params.has_projection() ? &_output_state1 : output_state_out; + _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _output_state1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + _memory_group.manage(&_cell_state_activation); + _activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info); + _pixelwise_mul_output_state2.configure(&_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _cell_state_activation.allocator()->allocate(); + + if(lstm_params.has_projection()) + { + _has_projection_weights = true; + _fully_connected_output_state.configure(output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out); + _output_state1.allocator()->allocate(); + // Perform clipping + if(projection_threshold != 0.f) + { + _perform_projection_clipping = true; + _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)); + } + } + + // Copy cell state and output + _copy_cell_state.configure(&_cell_state_out1, cell_state_out); + _cell_state_out1.allocator()->allocate(); + _copy_output.configure(output_state_out, output); + + // Vector for holding the tensors to store in scratch buffer + std::vector scratch_inputs; + if(lstm_params.has_cifg_opt()) + { + scratch_inputs.emplace_back(&_input_gate_out1); + } + scratch_inputs.emplace_back(&_cell_state_out1); + scratch_inputs.emplace_back(forget_gate_out); + scratch_inputs.emplace_back(output_gate_out); + _concat_scratch_buffer.configure(scratch_inputs, scratch_buffer); +} + +Status NELSTMLayer::validate(const ITensorInfo *input, + const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, + 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 *output_state_in, const ITensorInfo *cell_state_in, + const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output, + const LSTMParams &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold) +{ + ARM_COMPUTE_RETURN_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, + output_state_in, cell_state_in, + scratch_buffer, output_state_out, cell_state_out, output); + + // Check data types + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(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, + output_state_in, cell_state_in, + scratch_buffer, output_state_out, cell_state_out, output); + + // Check dimensions + ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(input_to_forget_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(input_to_cell_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_forget_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_cell_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(output_gate_bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(output_state_out->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(cell_state_out->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0) + && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0)); + + const unsigned int num_batches = input->dimension(1); + const unsigned int num_cells = input_to_output_weights->dimension(1); + + // Check peephole optimization + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights()); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_output_weights()->num_dimensions() > 1); + } + + TensorShape units_out_transposed_shape = compute_transposed_shape(*recurrent_to_output_weights); + TensorShape num_units_transposed_shape = compute_transposed_shape(*forget_gate_bias); + const TensorInfo units_out_transposed_info = TensorInfo(units_out_transposed_shape, 1, input->data_type()); + const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type()); + + TensorInfo input_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); + TensorInfo forget_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); + TensorInfo output_gate_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); + TensorInfo cell_state_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); + + // 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(NEGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &forget_gate, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); + 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)); + } + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + + // Validate input gate + if(!lstm_params.has_cifg_opt()) + { + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), + lstm_params.recurrent_to_input_weights(), + lstm_params.input_gate_bias()); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_to_input_weights()->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.recurrent_to_input_weights()->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() > 1); + + 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(NEGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &input_gate, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE)); + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights()); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1); + 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)); + } + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtractionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); + } + + // 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(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)); + 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)); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE)); + if(cell_threshold != 0.f) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, + cell_threshold))); + } + + // Validate output gate tmp + ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, &output_gate_tmp)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &output_gate_tmp, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE)); + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_to_output_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_ZERO)); + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE)); + } + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + + // Validate output state + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, &cell_state_tmp, activation_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &output_gate_tmp, &output_gate_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); + if(lstm_params.has_projection()) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(&output_gate_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out)); + if(projection_threshold != 0.f) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(output_state_out, output_state_out, + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold))); + } + } + + // Validate copy kernel + ARM_COMPUTE_RETURN_ON_ERROR(NECopyKernel::validate(&cell_state_tmp, cell_state_out)); + ARM_COMPUTE_RETURN_ON_ERROR(NECopyKernel::validate(output_state_out, output)); + + // Validate scratch concatenation + std::vector inputs_vector_info_raw; + if(lstm_params.has_cifg_opt()) + { + inputs_vector_info_raw.push_back(&input_gate); + } + inputs_vector_info_raw.push_back(&cell_state_tmp); + inputs_vector_info_raw.push_back(&forget_gate); + inputs_vector_info_raw.push_back(&output_gate_tmp); + + ARM_COMPUTE_RETURN_ON_ERROR(NEWidthConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer)); + return Status{}; +} + +void NELSTMLayer::run() +{ + _memory_group.acquire(); + + _fully_connected_forget_gate.run(); + NEScheduler::get().schedule(&_transpose_forget_gate, Window::DimY); + _gemm_forget_gate.run(); + NEScheduler::get().schedule(&_accum_forget_gate1, Window::DimY); + + if(_run_peephole_opt) + { + NEScheduler::get().schedule(&_pixelwise_mul_forget_gate, Window::DimY); + _accum_forget_gate2.run(); + } + NEScheduler::get().schedule(&_activation_forget_gate, Window::DimY); + + if(_run_cifg_opt) + { + if(_ones.info()->data_type() == DataType::F16) + { + std::fill_n(reinterpret_cast(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); + } + else + { + std::fill_n(reinterpret_cast(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); + } + NEScheduler::get().schedule(&_subtract_input_gate, Window::DimY); + } + else + { + _fully_connected_input_gate.run(); + NEScheduler::get().schedule(&_transpose_input_gate, Window::DimY); + _gemm_input_gate.run(); + NEScheduler::get().schedule(&_accum_input_gate1, Window::DimY); + if(_run_peephole_opt) + { + NEScheduler::get().schedule(&_pixelwise_mul_input_gate, Window::DimY); + _accum_input_gate2.run(); + } + NEScheduler::get().schedule(&_activation_input_gate, Window::DimY); + } + + _fully_connected_cell_state.run(); + NEScheduler::get().schedule(&_transpose_cell_state, Window::DimY); + _gemm_cell_state1.run(); + NEScheduler::get().schedule(&_accum_cell_state1, 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); + NEScheduler::get().schedule(&_accum_cell_state2, Window::DimY); + + if(_perform_cell_clipping) + { + NEScheduler::get().schedule(&_cell_clip, Window::DimY); + } + + _fully_connected_output.run(); + NEScheduler::get().schedule(&_transpose_output, Window::DimY); + _gemm_output.run(); + NEScheduler::get().schedule(&_accum_output1, Window::DimY); + + if(_run_peephole_opt) + { + NEScheduler::get().schedule(&_pixelwise_mul_output_state1, Window::DimY); + _accum_output2.run(); + } + NEScheduler::get().schedule(&_activation_output, Window::DimY); + + NEScheduler::get().schedule(&_activation_output_state, Window::DimY); + NEScheduler::get().schedule(&_pixelwise_mul_output_state2, Window::DimY); + + if(_has_projection_weights) + { + _fully_connected_output_state.run(); + if(_perform_projection_clipping) + { + NEScheduler::get().schedule(&_projection_clip, Window::DimY); + } + } + + NEScheduler::get().schedule(&_copy_cell_state, Window::DimY); + NEScheduler::get().schedule(&_copy_output, Window::DimY); + + _concat_scratch_buffer.run(); + + _memory_group.release(); +} \ No newline at end of file -- cgit v1.2.1