From ba27e4467dfc04e23ce9483330be062e9aaebdc5 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Tue, 28 May 2019 10:04:57 +0100 Subject: COMPMID-2236: QUANTIZED_16BIT_LSTM operator for NEON Change-Id: I554023508e09b790ecc1bbdada529697d6c7b616 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/1551 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michalis Spyrou --- .../NEON/kernels/NEDequantizationLayerKernel.cpp | 71 +++- src/core/NEON/kernels/NEStridedSliceKernel.cpp | 2 +- .../NEON/kernels/NEWidthConcatenateLayerKernel.cpp | 2 +- .../NEON/functions/NELSTMLayerQuantized.cpp | 376 +++++++++++++++++++++ 4 files changed, 447 insertions(+), 4 deletions(-) create mode 100644 src/runtime/NEON/functions/NELSTMLayerQuantized.cpp (limited to 'src') diff --git a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp index bf0a2ca7bf..d11f04a82f 100644 --- a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp @@ -28,6 +28,7 @@ #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/NEON/NEAsymm.h" +#include "arm_compute/core/NEON/NESymm.h" #include "arm_compute/core/NEON/wrapper/wrapper.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" @@ -42,7 +43,7 @@ namespace Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QSYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QSYMM8, DataType::QSYMM16); if(output->tensor_shape().total_size() > 0) { @@ -94,6 +95,27 @@ inline void store_result(float16_t *ptr, const float32x4x4_t &v) } #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +template +inline void store_result(T *ptr, const float32x4x2_t &v) +{ + ARM_COMPUTE_UNUSED(ptr, v); +} + +template <> +inline void store_result(float *ptr, const float32x4x2_t &v) +{ + wrapper::vstore(ptr, v.val[0]); + wrapper::vstore(ptr + 4, v.val[1]); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template <> +inline void store_result(float16_t *ptr, const float32x4x2_t &v) +{ + wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1]))); +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + template void run_dequantization_qasymm8(const ITensor *input, ITensor *output, const Window &window) { @@ -179,6 +201,48 @@ void run_dequantization_qsymm8(const ITensor *input, ITensor *output, const Wind in, out); } +template +void run_dequantization_qsymm16(const ITensor *input, ITensor *output, const Window &window) +{ + const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform(); + const float scale = qinfo.scale; + + const int window_step_x = 8; + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + + // Collapse window and reset first dimension to handle tail calculations manually + Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); + win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); + + // Create iterators + Iterator in(input, win_collapsed); + Iterator out(output, win_collapsed); + + execute_window_loop(win_collapsed, [&](const Coordinates &) + { + const auto in_ptr = reinterpret_cast(in.ptr()); + const auto out_ptr = reinterpret_cast(out.ptr()); + + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) + { + const auto vin = wrapper::vloadq(in_ptr + x); + const auto vdeq = vdequantize_int16(vin, scale); + + store_result(reinterpret_cast(out_ptr + x), vdeq); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + int16_t val = *(in_ptr + x); + *(out_ptr + x) = static_cast(dequantize_qsymm16(val, scale)); + } + }, + in, out); +} + template void run_dequantization_core(const ITensor *input, ITensor *output, const Window &window) { @@ -190,6 +254,9 @@ void run_dequantization_core(const ITensor *input, ITensor *output, const Window case DataType::QSYMM8: run_dequantization_qsymm8(input, output, window); break; + case DataType::QSYMM16: + run_dequantization_qsymm16(input, output, window); + break; default: ARM_COMPUTE_ERROR("Unsupported data type."); } @@ -244,4 +311,4 @@ void NEDequantizationLayerKernel::run(const Window &window, const ThreadInfo &in ARM_COMPUTE_ERROR("Unsupported data type."); } } -} // namespace arm_compute \ No newline at end of file +} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEStridedSliceKernel.cpp b/src/core/NEON/kernels/NEStridedSliceKernel.cpp index ece291e0a3..c33e699999 100644 --- a/src/core/NEON/kernels/NEStridedSliceKernel.cpp +++ b/src/core/NEON/kernels/NEStridedSliceKernel.cpp @@ -45,7 +45,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, - DataType::U16, DataType::S16, + DataType::U16, DataType::S16, DataType::QSYMM16, DataType::U32, DataType::S32, DataType::F16, DataType::F32); diff --git a/src/core/NEON/kernels/NEWidthConcatenateLayerKernel.cpp b/src/core/NEON/kernels/NEWidthConcatenateLayerKernel.cpp index 28f655c529..7b1ad9c2e8 100644 --- a/src/core/NEON/kernels/NEWidthConcatenateLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWidthConcatenateLayerKernel.cpp @@ -61,7 +61,7 @@ Status validate_arguments(const ITensorInfo *input, unsigned int width_offset, c ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::F16, - DataType::U32, DataType::F32); + DataType::U32, DataType::S32, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) + width_offset > output->dimension(0)); diff --git a/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp new file mode 100644 index 0000000000..05e05a5e57 --- /dev/null +++ b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp @@ -0,0 +1,376 @@ +/* + * Copyright (c) 2019 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/NELSTMLayerQuantized.h" + +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +#include +#include +#include + +namespace arm_compute +{ +namespace +{ +// Quantization info structures used in the LSTMQuantize layer +const QuantizationInfo qasymm(1.f / 128.f, 128); +const QuantizationInfo qsymm_3(8.f / 32768.f, 0); // qsymm16 with 3 integer bit +const QuantizationInfo qsymm_4(16.f / 32768.f, 0); // qsymm16 with 4 integer bit +const QuantizationInfo qsymm_0(1.f / 32768.f, 0); // qsymm16 with 0 integer bit +} // namespace + +NELSTMLayerQuantized::NELSTMLayerQuantized(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _gemmlowp(), _output_stage(), _transpose_weights(), _concat_input_weights(), _concat_recurrent_weights(), _concat_weights(), _concat_inputs(), + _concat_bias(), _sigmoid_forget_gate(), _sigmoid_input_gate(), _sigmoid_output_gate(), _tanh_modulation_gate(), _tanh_output_state(), _add1(), _add2(), _mul1(), _mul2(), _mul3(), + _slice_input_tensor(), _slice_forget_tensor(), _slice_cell_tensor(), _slice_output_tensor(), _dequantize(), _quantize(), _input_to_input_weights(nullptr), _input_to_forget_weights(nullptr), + _input_to_cell_weights(nullptr), _input_to_output_weights(nullptr), _recurrent_to_input_weights(nullptr), _recurrent_to_forget_weights(nullptr), _recurrent_to_cell_weights(nullptr), + _recurrent_to_output_weights(nullptr), _input_gate_bias(nullptr), _forget_gate_bias(nullptr), _cell_bias(nullptr), _output_gate_bias(nullptr), _recurrent_weights(), _input_weights(), _weights(), + _input(), _weights_transposed(), _output_highp(), _output_lowp(), _bias(), _forget_gate_input(), _input_gate_input(), _output_gate_input(), _input_modulation_gate_input(), _forget_gate_output(), + _input_gate_output(), _output_gate_output(), _input_modulation_gate_output(), _cell_state1(), _cell_state2(), _output_state_tmp(), _output_state_out_symm(), _output_state_out_f32(), + _is_prepared(false) +{ +} + +void NELSTMLayerQuantized::configure(const ITensor *input, + const ITensor *input_to_input_weights, const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights, + const ITensor *recurrent_to_input_weights, const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights, + const ITensor *input_gate_bias, const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias, + ITensor *cell_state_in, const ITensor *output_state_in, + ITensor *cell_state_out, ITensor *output_state_out) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, + input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out); + + ARM_COMPUTE_ERROR_THROW_ON(NELSTMLayerQuantized::validate(input->info(), input_to_input_weights->info(), input_to_forget_weights->info(), input_to_cell_weights->info(), + input_to_output_weights->info(), + recurrent_to_input_weights->info(), recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(), + input_gate_bias->info(), forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(), cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info())); + + const int input_size = input->info()->dimension(0); + const int batch_size = input->info()->dimension(1); + const int output_size = input_to_input_weights->info()->dimension(1); + + const QuantizationInfo qweights = input_to_input_weights->info()->quantization_info(); // Weights quantization + + auto_init_if_empty(*cell_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QSYMM16, qsymm_4)); + auto_init_if_empty(*output_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QASYMM8, qasymm)); + + _input_to_input_weights = input_to_input_weights; + _input_to_forget_weights = input_to_forget_weights; + _input_to_cell_weights = input_to_cell_weights; + _input_to_output_weights = input_to_output_weights; + _recurrent_to_input_weights = recurrent_to_input_weights; + _recurrent_to_forget_weights = recurrent_to_forget_weights; + _recurrent_to_cell_weights = recurrent_to_cell_weights; + _recurrent_to_output_weights = recurrent_to_output_weights; + _input_gate_bias = input_gate_bias; + _forget_gate_bias = forget_gate_bias; + _cell_bias = cell_bias; + _output_gate_bias = output_gate_bias; + + // Weights concatenation + std::vector inputs_weights_vector{ input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights }; + std::vector recurrent_weights_vector{ recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights }; + + _input_weights.allocator()->init(TensorInfo(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights)); + _concat_input_weights.configure(inputs_weights_vector, &_input_weights, Window::DimY); + + _recurrent_weights.allocator()->init(TensorInfo(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights)); + _concat_recurrent_weights.configure(recurrent_weights_vector, &_recurrent_weights, Window::DimY); + + std::vector weights_vector{ &_recurrent_weights, &_input_weights }; + _weights.allocator()->init(TensorInfo(TensorShape(output_size + input_size, 4 * output_size), 1, DataType::QASYMM8, qweights)); + _concat_weights.configure(weights_vector, &_weights, Window::DimX); + _transpose_weights.configure(&_weights, &_weights_transposed); + + // Input concatenation + std::vector input_vector{ input, output_state_in }; + _memory_group.manage(&_input); + _input.allocator()->init(TensorInfo(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm)); + _concat_inputs.configure(input_vector, &_input, Window::DimX); + + // Bias concatenation + std::vector bias_vector{ input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias }; + _bias.allocator()->init(TensorInfo(TensorShape(4 * output_size), 1, DataType::S32)); + _concat_bias.configure(bias_vector, &_bias, Window::DimX); + + // Invert the offset for gemmlowp + _input.info()->set_quantization_info(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset)); + _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset)); + + // Run gemmlowp + _memory_group.manage(&_output_highp); + _output_highp.allocator()->init(TensorInfo(TensorShape(4 * output_size, batch_size), 1, DataType::S32)); + _gemmlowp.configure(&_input, &_weights_transposed, nullptr, &_output_highp); + _input.allocator()->allocate(); + + // Set the offset back + _input.info()->set_quantization_info(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset)); + _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset)); + + // multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12)) + _output_lowp.allocator()->init(TensorInfo(_output_highp.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_3)); + + const float multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale; + int output_multiplier = 0; + int output_shift = 0; + + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + _memory_group.manage(&_output_lowp); + _output_stage.configure(&_output_highp, &_bias, &_output_lowp, output_multiplier, output_shift); + _output_highp.allocator()->allocate(); + _bias.allocator()->allocate(); + + // Get the gate tensors + _memory_group.manage(&_input_gate_input); + _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size }); + _memory_group.manage(&_forget_gate_input); + _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }); + _memory_group.manage(&_input_modulation_gate_input); + _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }); + _memory_group.manage(&_output_gate_input); + _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }); + _output_lowp.allocator()->allocate(); + + // Forget gate + _memory_group.manage(&_forget_gate_output); + _forget_gate_output.allocator()->init(TensorInfo(_forget_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _sigmoid_forget_gate.configure(&_forget_gate_input, &_forget_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _forget_gate_input.allocator()->allocate(); + + // Input gate + _memory_group.manage(&_input_gate_output); + _input_gate_output.allocator()->init(TensorInfo(_input_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _sigmoid_input_gate.configure(&_input_gate_input, &_input_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _input_gate_input.allocator()->allocate(); + + // Input modulation gate equation + _memory_group.manage(&_input_modulation_gate_output); + _input_modulation_gate_output.allocator()->init(TensorInfo(_input_modulation_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _tanh_modulation_gate.configure(&_input_modulation_gate_input, &_input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f)); + _input_modulation_gate_input.allocator()->allocate(); + + // Output gate + _memory_group.manage(&_output_gate_output); + _output_gate_output.allocator()->init(TensorInfo(_output_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _sigmoid_output_gate.configure(&_output_gate_input, &_output_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _output_gate_input.allocator()->allocate(); + + // Long term memory + _memory_group.manage(&_cell_state1); + _cell_state1.allocator()->init(TensorInfo(_forget_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4)); + _mul1.configure(&_forget_gate_output, cell_state_in, &_cell_state1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _forget_gate_output.allocator()->allocate(); + + _memory_group.manage(&_cell_state2); + _cell_state2.allocator()->init(TensorInfo(_input_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4)); + _mul2.configure(&_input_gate_output, &_input_modulation_gate_output, &_cell_state2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _input_modulation_gate_output.allocator()->allocate(); + _input_gate_output.allocator()->allocate(); + + _add1.configure(&_cell_state1, &_cell_state2, cell_state_out, ConvertPolicy::SATURATE); + _cell_state1.allocator()->allocate(); + _cell_state2.allocator()->allocate(); + + // Short term memory + _memory_group.manage(&_output_state_tmp); + _output_state_tmp.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _tanh_output_state.configure(cell_state_out, &_output_state_tmp, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f)); + + _memory_group.manage(&_output_state_out_symm); + _output_state_out_symm.allocator()->init(TensorInfo(_output_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0)); + _mul3.configure(&_output_state_tmp, &_output_gate_output, &_output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); + _output_gate_output.allocator()->allocate(); + _output_state_tmp.allocator()->allocate(); + + // Requantize the output state from QSYMM16 to QASYMM8 + _memory_group.manage(&_output_state_out_f32); + _output_state_out_f32.allocator()->init(TensorInfo(_output_state_out_symm.info()->tensor_shape(), 1, DataType::F32)); + _dequantize.configure(&_output_state_out_symm, &_output_state_out_f32); + _output_state_out_symm.allocator()->allocate(); + + _quantize.configure(&_output_state_out_f32, output_state_out); + _output_state_out_f32.allocator()->allocate(); +} + +Status NELSTMLayerQuantized::validate(const ITensorInfo *input, + const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, + const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, + const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, + const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in, + const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights, + recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, + output_state_in, cell_state_out, output_state_out); + + const int input_size = input->dimension(0); + const int batch_size = input->dimension(1); + const int output_size = input_to_input_weights->dimension(1); + + // Dimensionality checks + ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(input_to_input_weights->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(input_gate_bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2); + + TensorInfo input_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(input_size, output_size)).set_data_type(DataType::QASYMM8)); + TensorInfo recurrent_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(output_size, output_size)).set_data_type(DataType::QASYMM8).set_quantization_info(qasymm)); + TensorInfo bias_info(input_gate_bias->clone()->set_tensor_shape(TensorShape(output_size)).set_data_type(DataType::S32)); + TensorInfo output_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QASYMM8).set_quantization_info(qasymm)); + TensorInfo cell_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QSYMM16).set_quantization_info(qsymm_4)); + + // Shape checks + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_in); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_in); + + // Data type checks + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_in); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_in); + + // Quantization checks + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_in); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_in); + + if(cell_state_out->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_out); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_out); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_out); + } + + if(output_state_out->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_out); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_out); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_out); + } + + return Status{}; +} + +void NELSTMLayerQuantized::run() +{ + prepare(); + + // Acquire all the temporaries + MemoryGroupResourceScope scope_mg(_memory_group); + + // Concat and transpose the input + _concat_inputs.run(); + + // Run gemmlowp + _gemmlowp.run(); + _output_stage.run(); + + // Slice the results + _slice_input_tensor.run(); + _slice_forget_tensor.run(); + _slice_cell_tensor.run(); + _slice_output_tensor.run(); + + // Gates + // Forget gate + _sigmoid_forget_gate.run(); + + // Input gate + _sigmoid_input_gate.run(); + + // Input modulation gate + _tanh_modulation_gate.run(); + + // Output gate + _sigmoid_output_gate.run(); + + // Cell state (long term memory) + _mul1.run(); + _mul2.run(); + _add1.run(); + + // Output state (short term memory) + _tanh_output_state.run(); + _mul3.run(); + + // Requantize output state from QSYMM16 to QASYMM16 + _dequantize.run(); + _quantize.run(); +} + +void NELSTMLayerQuantized::prepare() +{ + if(!_is_prepared) + { + _input_weights.allocator()->allocate(); + _concat_input_weights.run(); + + _input_to_input_weights->mark_as_unused(); + _input_to_forget_weights->mark_as_unused(); + _input_to_cell_weights->mark_as_unused(); + _input_to_output_weights->mark_as_unused(); + + _recurrent_weights.allocator()->allocate(); + _concat_recurrent_weights.run(); + _recurrent_to_input_weights->mark_as_unused(); + _recurrent_to_forget_weights->mark_as_unused(); + _recurrent_to_cell_weights->mark_as_unused(); + _recurrent_to_output_weights->mark_as_unused(); + + _weights.allocator()->allocate(); + _concat_weights.run(); + + _input_weights.mark_as_unused(); + _input_weights.allocator()->free(); + _recurrent_weights.mark_as_unused(); + _recurrent_weights.allocator()->free(); + + _weights_transposed.allocator()->allocate(); + _transpose_weights.run(); + + _weights.mark_as_unused(); + _weights.allocator()->free(); + + _bias.allocator()->allocate(); + _concat_bias.run(); + _input_gate_bias->mark_as_unused(); + _forget_gate_bias->mark_as_unused(); + _cell_bias->mark_as_unused(); + _output_gate_bias->mark_as_unused(); + + _is_prepared = true; + } +} + +} // namespace arm_compute -- cgit v1.2.1