diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEQLSTMLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEQLSTMLayer.cpp | 208 |
1 files changed, 179 insertions, 29 deletions
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp index a279bba2ab..9c78ea8b75 100644 --- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp +++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp @@ -46,6 +46,36 @@ Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm } } // namespace +Status NEQLSTMLayer::TensorCopyKernel::validate(const ITensorInfo &src, const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON(src.tensor_shape().num_dimensions() > max_dimension_supported); + ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().num_dimensions() > max_dimension_supported); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst); + ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().y() != src.tensor_shape().y()); + return Status{}; +} + +void NEQLSTMLayer::TensorCopyKernel::configure(ITensor &src, ITensor &dst) +{ + ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::TensorCopyKernel::validate(*src.info(), *dst.info())); + _src = &src; + _dst = &dst; + _row_size = std::min(_src->info()->tensor_shape().x(), _dst->info()->tensor_shape().x()); + _window = calculate_max_window(*_src->info(), Steps()); +} + +void NEQLSTMLayer::TensorCopyKernel::run() +{ + Iterator input_iter{ _src, _window }; + Iterator output_iter{ _dst, _window }; + + execute_window_loop(_window, [&](const Coordinates &) + { + memcpy(output_iter.ptr(), input_iter.ptr(), _row_size); + }, + input_iter, output_iter); +} + NEQLSTMLayer::NEQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager) { _memory_group = MemoryGroup(std::move(memory_manager)); @@ -93,8 +123,9 @@ void NEQLSTMLayer::configure(const ITensor *input, 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(), lstm_params_info)); - const int batch_size = input->info()->dimension(1); - const int num_units = input_to_output_weights->info()->dimension(1); + const int batch_size = input->info()->dimension(1); + const int num_units = input_to_output_weights->info()->dimension(1); + const int output_size = output_state_out->info()->dimension(_out_state_output_size_dimension_idx); const UniformQuantizationInfo qinput = input->info()->quantization_info().uniform(); const UniformQuantizationInfo qcell_state_in = cell_state_in->info()->quantization_info().uniform(); @@ -154,10 +185,9 @@ void NEQLSTMLayer::configure(const ITensor *input, _recurrent_to_cell_reduction.configure(recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); _input_to_output_reduction.configure(input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)); _recurrent_to_output_reduction.configure(recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); - if(_projection_bias != nullptr) + if(_has_projection) { - _projection_reduction.configure(_projection_weights, &_projection_reduction_res, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), true)); - _projection_bias_add.configure(_projection_bias, &_projection_reduction_res, &_projection_eff_bias, ConvertPolicy::SATURATE); + _projection_reduction.configure(_projection_weights, &_projection_eff_bias, GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true)); } // Pre-transpose weights to be used in GEMM. @@ -203,6 +233,7 @@ void NEQLSTMLayer::configure(const ITensor *input, if(_has_peephole) { + _mul_cell_to_forget_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32)); _memory_group.manage(&_mul_cell_to_forget_res); _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); _cell_to_forget_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0))); @@ -287,7 +318,7 @@ void NEQLSTMLayer::configure(const ITensor *input, const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale(); configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info, - input, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias, + output_state_in, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias, &_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale, mm_out_info, input_outstage_info); _accumulate_input_recurrent_input.configure(&_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE); @@ -295,6 +326,7 @@ void NEQLSTMLayer::configure(const ITensor *input, if(_has_peephole) { + _mul_cell_to_input_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32)); _memory_group.manage(&_mul_cell_to_input_res); _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale(); @@ -316,7 +348,7 @@ void NEQLSTMLayer::configure(const ITensor *input, input_activation_input = &get_layer_norm_output(LayerNormGate::Input); } - _input_gate_tanh.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); + _input_gate_sigmoid.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); input_activation_input->allocator()->allocate(); } // Cell. @@ -357,12 +389,19 @@ void NEQLSTMLayer::configure(const ITensor *input, { // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplicationKernel // Here we are not using the output stage because all operations are done in float - // const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale(); - // quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); + _mul_cell_to_output_res.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::S32)); _memory_group.manage(&_mul_cell_to_output_res); _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); - _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_mul_cell_to_output_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE); + + const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale(); + quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); + _cell_to_output_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_output_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0))); + _memory_group.manage(&_cell_to_output_outstage_res); + _cell_to_output_outstage.configure(&_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res, gemmlowp_info); _mul_cell_to_output_res.allocator()->allocate(); + + _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE); + _cell_to_output_outstage_res.allocator()->allocate(); } Tensor *output_activation_input = &_recurrent_to_output_outstage_res; @@ -393,13 +432,24 @@ void NEQLSTMLayer::configure(const ITensor *input, quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true); gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero(); gemmlowp_info.output_data_type = output_state_in->info()->data_type(); - _hidden_outstage.configure(&_hidden_mul_res, nullptr, output_state_out, gemmlowp_info); + + _projection_tensor_copy_required = (num_units != output_size); + ITensor *hidden_gate_result = output_state_out; + + if(_projection_tensor_copy_required) + { + _hidden_gate.allocator()->init(*output_state_out->info()); + _hidden_gate.info()->set_tensor_shape(_hidden_mul_res.info()->tensor_shape()); + hidden_gate_result = &_hidden_gate; + } + + _hidden_outstage.configure(&_hidden_mul_res, nullptr, hidden_gate_result, gemmlowp_info); _hidden_mul_res.allocator()->allocate(); // Projection. if(_has_projection) { - const TensorInfo projection_outstage_info(*output_state_out->info()); + const TensorInfo projection_outstage_info(*hidden_gate_result->info()); const UniformQuantizationInfo qprojection = _projection_weights->info()->quantization_info().uniform(); const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale; gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset; @@ -407,14 +457,44 @@ void NEQLSTMLayer::configure(const ITensor *input, gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; + _memory_group.manage(&_projection_eff_bias_adjusted); + ITensor *bias_to_use = &_projection_eff_bias; + + if(_projection_tensor_copy_required) + { + _projection_eff_bias_adjusted.allocator()->init(*_projection_eff_bias.info()); + _projection_eff_bias_adjusted.info()->set_tensor_shape(TensorShape(num_units)); + _projection_bias_copy.configure(_projection_eff_bias, _projection_eff_bias_adjusted); + bias_to_use = &_projection_eff_bias_adjusted; + } + configure_mm(_mm_projection, _projection_outstage, gemmlowp_info, - output_state_out, &_projection_weights_transposed, &_projection_eff_bias, + hidden_gate_result, &_projection_weights_transposed, bias_to_use, &_mm_projection_res, &_projection_outstage_res, projection_scale, mm_out_info, projection_outstage_info); - _accumulate_projection.configure(&_projection_outstage_res, output_state_out, output_state_out, ConvertPolicy::SATURATE); + _projection_eff_bias_adjusted.allocator()->allocate(); + + ITensor *accumulate_destination = output_state_out; + + if(_projection_tensor_copy_required) + { + _hidden_gate.allocator()->allocate(); + _projection_accumulate_res.allocator()->init(*output_state_out->info()); + _projection_accumulate_res.info()->set_tensor_shape(_projection_outstage_res.info()->tensor_shape()); + _projection_output_to_accumulate_copy.configure(*output_state_out, _projection_accumulate_res); + accumulate_destination = &_projection_accumulate_res; + } + + _accumulate_projection.configure(&_projection_outstage_res, accumulate_destination, accumulate_destination, ConvertPolicy::SATURATE); _projection_outstage_res.allocator()->allocate(); + if(_projection_tensor_copy_required) + { + _projection_accumulate_to_output_copy.configure(_projection_accumulate_res, *output_state_out); + _projection_accumulate_res.allocator()->allocate(); + } + int8_t quantized_projection_clip{ 0 }; if(lstm_params.projection_clip() > 0.0f) { @@ -427,6 +507,14 @@ void NEQLSTMLayer::configure(const ITensor *input, _has_projection_clipping = true; } } + else + { + if(_projection_tensor_copy_required) + { + _hidden_to_output_copy.configure(_hidden_gate, *output_state_out); + _hidden_gate.allocator()->allocate(); + } + } } Status NEQLSTMLayer::validate(const ITensorInfo *input, @@ -446,7 +534,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, const unsigned int input_size = input->dimension(0); const unsigned int batch_size = input->dimension(1); const unsigned int num_units = input_to_output_weights->dimension(1); - const unsigned int output_size = recurrent_to_output_weights->dimension(0); + const unsigned int output_size = output_state_out->dimension(_out_state_output_size_dimension_idx); ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2); ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size); @@ -509,6 +597,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, // Precompute effective bias for optimizing the matmul computations. const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32); + const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32); if(!lstm_params.has_cifg_opt()) { ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true))); @@ -521,11 +610,11 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true))); ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true))); ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true))); - if(lstm_params.projection_bias() != nullptr) + if(lstm_params.has_projection()) { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false, + lstm_params.hidden_state_zero(), true))); - ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(lstm_params.projection_bias(), &eff_bias_info, &eff_bias_info, ConvertPolicy::SATURATE)); } const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_forget_weights->quantization_info()); @@ -545,7 +634,8 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, } if(lstm_params.has_projection()) { - ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.projection_weights(), &recurrent_weights_transposed)); + const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info()); + ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed)); } GEMMLowpOutputStageInfo gemmlowp_info; @@ -627,23 +717,22 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.input_gate_bias()); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias()); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(input, lstm_params.input_to_input_weights(), nullptr, &mm_out_info)); const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0)); const float input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale(); - validate_mm(gemmlowp_info, input, lstm_params.input_to_input_weights(), &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info); + validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info); const float recurrent_to_input_scale = lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale(); - validate_mm(gemmlowp_info, input, lstm_params.recurrent_to_input_weights(), &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info); + validate_mm(gemmlowp_info, input, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); if(lstm_params.has_peephole_opt()) { - ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_outstage_info, 1.f, ConvertPolicy::SATURATE, + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale(); ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&input_outstage_info, &eff_bias_info, &input_outstage_info, gemmlowp_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info)); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); } @@ -654,7 +743,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(input_outstage_info, *w_info, *b_info)); } - ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); } // Cell. ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); @@ -699,11 +788,14 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, // Hidden. ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32); + const TensorInfo hidden_out_info(TensorShape(num_units, batch_size), 1, DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15); ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true)); gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero(); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, output_state_out, gemmlowp_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info)); + + const bool projection_tensor_copy_required = num_units != output_size; // Projection. if(lstm_params.has_projection()) @@ -719,11 +811,30 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; - const TensorInfo projection_outstage_info(*output_state_out); - validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info); + TensorInfo projection_outstage_info(hidden_out_info); + + if(projection_tensor_copy_required) + { + TensorInfo projection_eff_bias_adjusted_info{ projection_eff_bias_info }; + projection_eff_bias_adjusted_info.set_tensor_shape(TensorShape(num_units)); + + ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_eff_bias_info, projection_eff_bias_adjusted_info)); + } + + validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &projection_eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info); + + if(projection_tensor_copy_required) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(*output_state_out, projection_outstage_info)); + } ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE)); + if(projection_tensor_copy_required) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out)); + } + int8_t quantized_projection_clip{ 0 }; if(lstm_params.projection_clip() > 0.0f) { @@ -736,6 +847,13 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, quantized_projection_clip))); } } + else + { + if(projection_tensor_copy_required) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(hidden_out_info, *output_state_out)); + } + } if(cell_state_out->total_size() > 0) { @@ -821,7 +939,7 @@ void NEQLSTMLayer::run() NEScheduler::get().schedule(&get_layer_norm(LayerNormGate::Input), Window::DimY); } - _input_gate_tanh.run(); + _input_gate_sigmoid.run(); } // Cell. @@ -842,6 +960,7 @@ void NEQLSTMLayer::run() if(_has_peephole) { NEScheduler::get().schedule(&_pixelwise_mul_cell_to_output, Window::DimY); + _cell_to_output_outstage.run(); NEScheduler::get().schedule(&_accumulate_cell_to_output, Window::DimY); } @@ -860,14 +979,38 @@ void NEQLSTMLayer::run() // Projection. if(_has_projection) { + if(_projection_tensor_copy_required) + { + _projection_bias_copy.run(); + } + _mm_projection.run(); _projection_outstage.run(); + + if(_projection_tensor_copy_required) + { + _projection_output_to_accumulate_copy.run(); + } + NEScheduler::get().schedule(&_accumulate_projection, Window::DimY); + + if(_projection_tensor_copy_required) + { + _projection_accumulate_to_output_copy.run(); + } + if(_has_projection_clipping) { _projection_clip.run(); } } + else + { + if(_projection_tensor_copy_required) + { + _hidden_to_output_copy.run(); + } + } } void NEQLSTMLayer::prepare() @@ -932,6 +1075,13 @@ void NEQLSTMLayer::prepare() _projection_weights_transposed.allocator()->allocate(); _transpose_projection_weights.run(); _projection_weights->mark_as_unused(); + + if(!_projection_tensor_copy_required) + { + _hidden_gate.mark_as_unused(); + _projection_eff_bias_adjusted.mark_as_unused(); + _projection_accumulate_res.mark_as_unused(); + } } // Mark weights as unused |