| /* |
| * 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 <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| CLLSTMLayer::CLLSTMLayer(std::shared_ptr<IMemoryManager> 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_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<ICLTensor> &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<ITensorInfo> 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()->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()); |
| _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())); |
| |
| // Configure block that calculates the forget gate |
| // forget_gate = Activation(input * input_to_forget_weights + output_state * recurrent_to_forget_weights + PixelWiseMul(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); |
| _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, &_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); |
| CLTensor *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, 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); |
| _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(); |
| |
| _input_gate_out1.allocator()->init(TensorInfo(cell_state_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 + PixelWiseMul(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_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, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f); |
| _input_gate_out2.allocator()->allocate(); |
| _memory_group.manage(&_input_gate_out4); |
| _pixelwise_mul_input_gate.configure(cell_state, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); |
| _memory_group.manage(&_input_gate_out5); |
| _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out5, ConvertPolicy::SATURATE); |
| _input_gate_out3.allocator()->allocate(); |
| _accum_input_gate2.configure(&_input_gate_out5, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE); |
| _input_gate_out4.allocator()->allocate(); |
| _input_gate_out5.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((PixelwiseMul(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); |
| _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, &_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()); |
| _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())); |
| |
| // Configure block that calculates the output |
| // output_state = Activation(input * input_to_output_weights + output_state * recurrent_to_output_weights + PixelWiseMul(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); |
| _memory_group.manage(&_output2); |
| _transpose_output.configure(recurrent_to_output_weights, &_output2); |
| _memory_group.manage(&_output3); |
| _gemm_output.configure(output_state, &_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(); |
| CLTensor *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_NEAREST_EVEN); |
| _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, 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_state2.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); |
| // 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<ICLTensor *> 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<ITensorInfo> &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_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 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)); |
| 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)); |
| 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)); |
| 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)); |
| 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)); |
| 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<TensorInfo> 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<ITensorInfo *> 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_gate); |
| _gemm_forget_gate.run(); |
| CLScheduler::get().enqueue(_accum_forget_gate1); |
| |
| if(_run_peephole_opt) |
| { |
| CLScheduler::get().enqueue(_pixelwise_mul_forget_gate); |
| _accum_forget_gate2.run(); |
| } |
| CLScheduler::get().enqueue(_activation_forget_gate); |
| |
| if(_run_cifg_opt) |
| { |
| _ones.map(true); |
| if(_ones.info()->data_type() == DataType::F16) |
| { |
| std::fill_n(reinterpret_cast<half *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); |
| } |
| else |
| { |
| std::fill_n(reinterpret_cast<float *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); |
| } |
| _ones.unmap(); |
| CLScheduler::get().enqueue(_subtract_input_gate); |
| } |
| else |
| { |
| _fully_connected_input_gate.run(); |
| CLScheduler::get().enqueue(_transpose_input_gate); |
| _gemm_input_gate.run(); |
| CLScheduler::get().enqueue(_pixelwise_mul_input_gate); |
| 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_state); |
| _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_output); |
| _gemm_output.run(); |
| CLScheduler::get().enqueue(_accum_output1); |
| |
| if(_run_peephole_opt) |
| { |
| CLScheduler::get().enqueue(_pixelwise_mul_output_state1); |
| _accum_output2.run(); |
| } |
| CLScheduler::get().enqueue(_activation_output); |
| |
| CLScheduler::get().enqueue(_activation_output_state); |
| CLScheduler::get().enqueue(_pixelwise_mul_output_state2); |
| |
| 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(); |
| } |