From bcedf513938fca9e33331bdef975f0488288bad4 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Thu, 22 Mar 2018 14:55:08 +0000 Subject: COMPMID-993 Implement CL LSTM function Change-Id: Iee4ad387c41dd8ccfe31b3044d797f2d7448e552 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126655 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- src/runtime/CL/functions/CLLSTMLayer.cpp | 508 +++++++++++++++++++++++++++++++ 1 file changed, 508 insertions(+) create mode 100644 src/runtime/CL/functions/CLLSTMLayer.cpp (limited to 'src/runtime/CL/functions/CLLSTMLayer.cpp') diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp new file mode 100644 index 0000000000..930d311d1d --- /dev/null +++ b/src/runtime/CL/functions/CLLSTMLayer.cpp @@ -0,0 +1,508 @@ +/* + * 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/CL/functions/CLLSTMLayer.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/CL/CLScheduler.h" + +#include +#include +#include + +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_gate1(), _gemm_input_gate2(), _transpose_input_gate1(), _transpose_input_gate2(), _accum_input_gate1(), + _accum_input_gate2(), _subtract_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate1(), _gemm_forget_gate2(), _transpose_forget_gate1(), + _transpose_forget_gate2(), _accum_forget_gate1(), _accum_forget_gate2(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _transpose_cell_state1(), + _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), _gemm_output1(), + _gemm_output2(), _transpose_output1(), _transpose_output2(), _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state(), + _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(), _input_gate_out6(), _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(), _output5(), _output6(), + _cell_state_activation(), _output_projection1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), + _perform_projection_clipping(false) +{ +} + +void CLLSTMLayer::configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, + const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, + const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, + ICLTensor *output_state, ICLTensor *cell_state, ICLTensor *scratch_buffer, ICLTensor *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, cell_state); + LSTMParams lstm_params_info; + if(lstm_params.has_peephole_opt()) + { + lstm_params_info.set_peephole_params(lstm_params.cell_to_input_weights()->info(), 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()->info()); + } + if(!lstm_params.has_cifg_opt()) + { + lstm_params_info.set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(), + lstm_params.cell_to_input_weights()->info(), lstm_params.input_gate_bias()->info()); + } + ARM_COMPUTE_ERROR_THROW_ON(CLLSTMLayer::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->info(), cell_state->info(), scratch_buffer->info(), output->info(), lstm_params_info, + activation_info, cell_threshold, projection_threshold)); + + const TensorShape cell_state_shape = cell_state->info()->tensor_shape(); + + TensorShape forget_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + TensorShape forget_gate2_shape = compute_transposed_shape(*forget_gate_bias->info()); + TensorShape forget_gate3_shape{ 1, output_state->info()->dimension(1) }; + _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_out6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + // Configure block that calculates the forget gate + // forget_gate = Activation(input * input_to_forget_weights + output_state * recurrent_to_forget_weights + cell_state * cell_to_forget_weights + forget_gate_bias) + _memory_group.manage(&_forget_gate_out1); + _fully_connected_forget_gate.configure(input, input_to_forget_weights, forget_gate_bias, &_forget_gate_out1, true, false); + _memory_group.manage(&_forget_gate_out2); + _transpose_forget_gate1.configure(recurrent_to_forget_weights, &_forget_gate_out2); + _memory_group.manage(&_forget_gate_out3); + _gemm_forget_gate1.configure(output_state, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f); + _forget_gate_out2.allocator()->allocate(); + _memory_group.manage(&_forget_gate_out6); + _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out6, ConvertPolicy::SATURATE); + CLTensor *forget_gate_out = &_forget_gate_out6; + + if(lstm_params.has_peephole_opt()) + { + _forget_gate_out4.allocator()->init(TensorInfo(forget_gate2_shape, 1, input->info()->data_type())); + _forget_gate_out5.allocator()->init(TensorInfo(forget_gate3_shape, 1, input->info()->data_type())); + + _run_peephole_opt = true; + _memory_group.manage(&_forget_gate_out4); + _transpose_forget_gate2.configure(lstm_params.cell_to_forget_weights(), &_forget_gate_out4); + _memory_group.manage(&_forget_gate_out5); + _gemm_forget_gate2.configure(cell_state, &_forget_gate_out4, nullptr, &_forget_gate_out5, 1.f, 0.f); + _forget_gate_out4.allocator()->allocate(); + _accum_forget_gate2.configure(&_forget_gate_out6, &_forget_gate_out5, &_forget_gate_out3, ConvertPolicy::SATURATE); + _forget_gate_out5.allocator()->allocate(); + _forget_gate_out6.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(); + + TensorShape input_gate3_shape{ 1, output_state->info()->dimension(1) }; + _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _input_gate_out5.allocator()->init(TensorInfo(input_gate3_shape, 1, input->info()->data_type())); + + // Configure block that calculates the input gate + // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + cell_state * cell_to_input_weights + input_gate_bias), without CIFG + // input_gate = 1 - forget_gate, with CIFG + 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_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + TensorShape input_gate2_shape = compute_transposed_shape(*lstm_params.cell_to_input_weights()->info()); + + _input_gate_out2.allocator()->init(TensorInfo(input_gate1_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(input_gate2_shape, 1, input->info()->data_type())); + _input_gate_out6.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, true, false); + _memory_group.manage(&_input_gate_out2); + _transpose_input_gate1.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2); + _memory_group.manage(&_input_gate_out3); + _gemm_input_gate1.configure(output_state, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f); + _input_gate_out2.allocator()->allocate(); + _memory_group.manage(&_input_gate_out4); + _transpose_input_gate2.configure(lstm_params.cell_to_input_weights(), &_input_gate_out4); + _memory_group.manage(&_input_gate_out5); + _gemm_input_gate2.configure(cell_state, &_input_gate_out4, nullptr, &_input_gate_out5, 1.f, 0.f); + _input_gate_out4.allocator()->allocate(); + _memory_group.manage(&_input_gate_out6); + _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out6, ConvertPolicy::SATURATE); + _input_gate_out3.allocator()->allocate(); + _accum_input_gate2.configure(&_input_gate_out6, &_input_gate_out5, &_input_gate_out1, ConvertPolicy::SATURATE); + _input_gate_out5.allocator()->allocate(); + _input_gate_out6.allocator()->allocate(); + _activation_input_gate.configure(&_input_gate_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + } + + 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())); + + // Configure block that calculates the cell state + // cell_state = Clip((RixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold) + _memory_group.manage(&_cell_state_out1); + _fully_connected_cell_state.configure(input, input_to_cell_weights, cell_bias, &_cell_state_out1, true, false); + _memory_group.manage(&_cell_state_out2); + _transpose_cell_state1.configure(recurrent_to_cell_weights, &_cell_state_out2); + _memory_group.manage(&_cell_state_out3); + _gemm_cell_state1.configure(output_state, &_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_NEAREST_EVEN); + _input_gate_out1.allocator()->allocate(); + _cell_state_out4.allocator()->allocate(); + _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _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)); + } + + TensorShape output1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); + TensorShape output2_shape = compute_transposed_shape(*cell_bias->info()); + TensorShape output3_shape{ 1, output_state->info()->dimension(1) }; + _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())); + _output6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + // Configure block that calculates the output + // output_gate = Activation(input * input_to_output_weights + output_state * recurrent_to_output_weights + cell_state * cell_to_output_weights + output_gate_bias) + _memory_group.manage(&_output1); + _fully_connected_output.configure(input, input_to_output_weights, output_gate_bias, &_output1, true, false); + _memory_group.manage(&_output2); + _transpose_output1.configure(recurrent_to_output_weights, &_output2); + _memory_group.manage(&_output3); + _gemm_output1.configure(output_state, &_output2, nullptr, &_output3, 1.f, 0.f); + _output2.allocator()->allocate(); + _memory_group.manage(&_output6); + _accum_output1.configure(&_output1, &_output3, &_output6, ConvertPolicy::SATURATE); + _output3.allocator()->allocate(); + CLTensor *output_gate_out = &_output6; + if(lstm_params.has_peephole_opt()) + { + _output4.allocator()->init(TensorInfo(output2_shape, 1, input->info()->data_type())); + _output5.allocator()->init(TensorInfo(output3_shape, 1, input->info()->data_type())); + + _memory_group.manage(&_output4); + _transpose_output2.configure(lstm_params.cell_to_output_weights(), &_output4); + _memory_group.manage(&_output5); + _gemm_output2.configure(&_cell_state_out1, &_output4, nullptr, &_output5, 1.f, 0.f); + _accum_output2.configure(&_output6, &_output5, &_output1, ConvertPolicy::SATURATE); + _output6.allocator()->allocate(); + output_gate_out = &_output1; + + // Allocate intermediate buffers + _output4.allocator()->allocate(); + _output5.allocator()->allocate(); + } + else + { + _output1.allocator()->allocate(); + } + _activation_output.configure(output_gate_out, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + output_gate_out->allocator()->allocate(); + + _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + + // 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 + */ + _memory_group.manage(&_cell_state_activation); + _activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info); + _pixelwise_mul_output_state.configure(&_cell_state_activation, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _cell_state_activation.allocator()->allocate(); + + if(lstm_params.has_projection()) + { + _has_projection_weights = true; + _output_projection1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); + _memory_group.manage(&_output_projection1); + _fully_connected_output_state.configure(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), &_output_projection1, true, false); + // Perform clipping + if(projection_threshold != 0.f) + { + _perform_projection_clipping = true; + _projection_clip.configure(&_output_projection1, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)); + } + + // Allocate intermediate buffer + _output_projection1.allocator()->allocate(); + } + + // Copy cell state and output + _copy_cell_state.configure(&_cell_state_out1, cell_state); + _cell_state_out1.allocator()->allocate(); + _copy_output.configure(output_state, 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 CLLSTMLayer::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, const ITensorInfo *cell_state, const ITensorInfo *scratch_buffer, 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, cell_state); + 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, cell_state); + 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->num_dimensions() != 2); + ARM_COMPUTE_RETURN_ERROR_ON(cell_state->num_dimensions() != 2); + ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->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)); + + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights(), lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights()); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() != 1); + 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 gemmv_shape{ 1, output_state->dimension(1) }; + 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 gemmv_shape_info = TensorInfo(gemmv_shape, 1, input->data_type()); + const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type()); + + // Validate forget gate + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, cell_state, true, false)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state, &units_out_transposed_info, nullptr, cell_state, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE)); + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE)); + } + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, 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.cell_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.cell_to_input_weights()->num_dimensions() != 1); + ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() != 1); + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), cell_state, true, false)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo())); + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE)); + } + + // Validate cell state + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, cell_state, true, false)); + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, activation_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, cell_state, cell_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); + + if(cell_threshold != 0.f) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold))); + } + + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, cell_state, true, false)); + if(lstm_params.has_peephole_opt()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE)); + } + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + + // Validate output state + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, activation_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); + if(lstm_params.has_projection()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), cell_state, true, false)); + if(projection_threshold != 0.f) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, + projection_threshold))); + } + } + + std::vector inputs_vector_info; + if(lstm_params.has_cifg_opt()) + { + inputs_vector_info.emplace_back(*cell_state); + } + inputs_vector_info.emplace_back(*cell_state); + inputs_vector_info.emplace_back(*cell_state); + inputs_vector_info.emplace_back(*cell_state); + + std::vector inputs_vector_info_raw; + for(auto &input : inputs_vector_info) + { + inputs_vector_info_raw.emplace_back(&input); + } + + ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer)); + return Status{}; +} + +void CLLSTMLayer::run() +{ + _memory_group.acquire(); + + _fully_connected_forget_gate.run(); + CLScheduler::get().enqueue(_transpose_forget_gate1); + _gemm_forget_gate1.run(); + CLScheduler::get().enqueue(_accum_forget_gate1); + + if(_run_peephole_opt) + { + CLScheduler::get().enqueue(_transpose_forget_gate2); + _gemm_forget_gate2.run(); + _accum_forget_gate2.run(); + } + CLScheduler::get().enqueue(_activation_forget_gate); + + if(_run_cifg_opt) + { + _ones.map(true); + std::fill_n(_ones.buffer(), _ones.info()->total_size(), 1); + _ones.unmap(); + CLScheduler::get().enqueue(_subtract_input_gate); + } + else + { + _fully_connected_input_gate.run(); + CLScheduler::get().enqueue(_transpose_input_gate1); + _gemm_input_gate1.run(); + CLScheduler::get().enqueue(_transpose_input_gate2); + _gemm_input_gate2.run(); + CLScheduler::get().enqueue(_accum_input_gate1); + _accum_input_gate2.run(); + CLScheduler::get().enqueue(_activation_input_gate); + } + + _fully_connected_cell_state.run(); + CLScheduler::get().enqueue(_transpose_cell_state1); + _gemm_cell_state1.run(); + CLScheduler::get().enqueue(_accum_cell_state1); + CLScheduler::get().enqueue(_activation_cell_state); + CLScheduler::get().enqueue(_pixelwise_mul_cell_state1); + CLScheduler::get().enqueue(_pixelwise_mul_cell_state2); + CLScheduler::get().enqueue(_accum_cell_state2); + + if(_perform_cell_clipping) + { + CLScheduler::get().enqueue(_cell_clip); + } + + _fully_connected_output.run(); + CLScheduler::get().enqueue(_transpose_output1); + _gemm_output1.run(); + CLScheduler::get().enqueue(_accum_output1); + CLScheduler::get().enqueue(_pixelwise_mul_output_state); + + if(_run_peephole_opt) + { + CLScheduler::get().enqueue(_transpose_output2); + _gemm_output2.run(); + _accum_output2.run(); + } + CLScheduler::get().enqueue(_activation_output); + + CLScheduler::get().enqueue(_activation_output_state); + CLScheduler::get().enqueue(_pixelwise_mul_output_state); + + if(_has_projection_weights) + { + _fully_connected_output_state.run(); + if(_perform_projection_clipping) + { + CLScheduler::get().enqueue(_projection_clip); + } + } + + CLScheduler::get().enqueue(_copy_cell_state); + CLScheduler::get().enqueue(_copy_output); + + _concat_scratch_buffer.run(); + + _memory_group.release(); +} \ No newline at end of file -- cgit v1.2.1