From 2b84be544e4a27f7e8e80827e9c85c8f0d58b4ce Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Wed, 8 Apr 2020 10:15:51 +0100 Subject: COMPMID-3280: Make all ML primitives for CL use the new interface - Part 2 - CLFunctions have been updated Change-Id: Ie3256a6c775bc12f3126482bd8e8a46da54b267c Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3053 Reviewed-by: Georgios Pinitas Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins --- src/runtime/CL/functions/CLLSTMLayer.cpp | 109 ++++++++++++++++++------------- 1 file changed, 63 insertions(+), 46 deletions(-) (limited to 'src/runtime/CL/functions/CLLSTMLayer.cpp') diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp index 3a3917784b..32ff813f43 100644 --- a/src/runtime/CL/functions/CLLSTMLayer.cpp +++ b/src/runtime/CL/functions/CLLSTMLayer.cpp @@ -58,6 +58,19 @@ void CLLSTMLayer::configure(const ICLTensor *input, const ICLTensor *output_state_in, const ICLTensor *cell_state_in, ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output, const LSTMParams &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold) +{ + configure(CLKernelLibrary::get().get_compile_context(), input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, + recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, output_state_in, cell_state_in, scratch_buffer, output_state_out, cell_state_out, output, lstm_params, activation_info, + cell_threshold, projection_threshold); +} + +void CLLSTMLayer::configure(const CLCompileContext &compile_context, 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, + const ICLTensor *output_state_in, const ICLTensor *cell_state_in, + ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, 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, @@ -97,7 +110,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _forget_gate_out2.allocator()->init(TensorInfo(concat_shape, 1, input->info()->data_type())); _memory_group.manage(&_forget_gate_out2); - _concat_inputs_forget_gate.configure(input, output_state_in, &_forget_gate_out2); + _concat_inputs_forget_gate.configure(compile_context, input, output_state_in, &_forget_gate_out2); std::vector weights_vector; @@ -106,10 +119,10 @@ void CLLSTMLayer::configure(const ICLTensor *input, const TensorShape weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(weights_vector, 0); _forget_gate_out6.allocator()->init(TensorInfo(weights_concat_shape, 1, input->info()->data_type())); - _concat_weights_forget_gate.configure(input_to_forget_weights, recurrent_to_forget_weights, &_forget_gate_out6); + _concat_weights_forget_gate.configure(compile_context, input_to_forget_weights, recurrent_to_forget_weights, &_forget_gate_out6); _memory_group.manage(&_forget_gate_out5); - _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5); + _fully_connected_forget_gate.configure(compile_context, &_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5); _memory_group.manage(&_forget_gate_out1); _memory_group.manage(&_forget_gate_out3); _forget_gate_out6.allocator()->allocate(); @@ -121,8 +134,8 @@ void CLLSTMLayer::configure(const ICLTensor *input, _run_peephole_opt = true; _memory_group.manage(&_forget_gate_out4); - _pixelwise_mul_forget_gate.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - _accum_forget_gate1.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE); + _pixelwise_mul_forget_gate.configure(compile_context, cell_state_in, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _accum_forget_gate1.configure(compile_context, &_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; @@ -137,15 +150,16 @@ void CLLSTMLayer::configure(const ICLTensor *input, _forget_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_forget_layer_norm_out1); _memory_group.manage(&_forget_layer_norm_out2); - _mean_std_norm_forget_gate.configure(forget_gate_out); - _pixelwise_mul_forget_gate_coeff.configure(forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _mean_std_norm_forget_gate.configure(compile_context, forget_gate_out); + _pixelwise_mul_forget_gate_coeff.configure(compile_context, forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN); // forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before forget_gate_out->allocator()->allocate(); - _accum_forget_gate_bias.configure(ArithmeticOperation::ADD, &_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE); + _accum_forget_gate_bias.configure(compile_context, ArithmeticOperation::ADD, &_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE); _forget_layer_norm_out1.allocator()->allocate(); forget_gate_out = &_forget_layer_norm_out2; } - _activation_forget_gate.configure(forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _activation_forget_gate.configure(compile_context, forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); // 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 @@ -158,8 +172,8 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _memory_group.manage(&_input_gate_out1); _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); - _ones_memset_kernel.configure(&_ones, PixelValue(1, _ones.info()->data_type())); - _subtract_input_gate.configure(ArithmeticOperation::SUB, &_ones, forget_gate_out, &_input_gate_out1, ConvertPolicy::SATURATE); + _ones_memset_kernel.configure(compile_context, &_ones, PixelValue(1, _ones.info()->data_type())); + _subtract_input_gate.configure(compile_context, ArithmeticOperation::SUB, &_ones, forget_gate_out, &_input_gate_out1, ConvertPolicy::SATURATE); _ones.allocator()->allocate(); _run_cifg_opt = true; } @@ -174,20 +188,20 @@ void CLLSTMLayer::configure(const ICLTensor *input, TensorShape lstm_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(lstm_weights, 0); _input_gate_out2.allocator()->init(TensorInfo(lstm_weights_concat_shape, 1, input->info()->data_type())); - _concat_weights_input_gate.configure(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), &_input_gate_out2); + _concat_weights_input_gate.configure(compile_context, lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), &_input_gate_out2); _memory_group.manage(&_input_gate_out1); _memory_group.manage(&_input_gate_out3); - _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3); + _fully_connected_input_gate.configure(compile_context, &_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3); _input_gate_out2.allocator()->allocate(); input_gate_out = &_input_gate_out3; if(_run_peephole_opt) { _memory_group.manage(&_input_gate_out4); - _pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - _accum_input_gate1.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE); + _pixelwise_mul_input_gate.configure(compile_context, cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _accum_input_gate1.configure(compile_context, &_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE); _input_gate_out3.allocator()->allocate(); _input_gate_out4.allocator()->allocate(); input_gate_out = &_input_gate_out1; @@ -203,15 +217,16 @@ void CLLSTMLayer::configure(const ICLTensor *input, _input_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_input_layer_norm_out1); _memory_group.manage(&_input_layer_norm_out2); - _mean_std_norm_input_gate.configure(input_gate_out); - _pixelwise_mul_input_gate_coeff.configure(input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _mean_std_norm_input_gate.configure(compile_context, input_gate_out); + _pixelwise_mul_input_gate_coeff.configure(compile_context, input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN); // input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before input_gate_out->allocator()->allocate(); - _accum_input_gate_bias.configure(ArithmeticOperation::ADD, &_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE); + _accum_input_gate_bias.configure(compile_context, ArithmeticOperation::ADD, &_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE); _input_layer_norm_out1.allocator()->allocate(); input_gate_out = &_input_layer_norm_out2; } - _activation_input_gate.configure(input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _activation_input_gate.configure(compile_context, input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); } // Configure block that calculates the cell state @@ -224,14 +239,14 @@ void CLLSTMLayer::configure(const ICLTensor *input, _cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_cell_state_out1); - _fully_connected_cell_state.configure(input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1); + _fully_connected_cell_state.configure(compile_context, input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1); _memory_group.manage(&_cell_state_out2); - _transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2); + _transpose_cell_state.configure(compile_context, recurrent_to_cell_weights, &_cell_state_out2); _memory_group.manage(&_cell_state_out3); - _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f); + _gemm_cell_state1.configure(compile_context, output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f); _cell_state_out2.allocator()->allocate(); _memory_group.manage(&_cell_state_out4); - _accum_cell_state1.configure(ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); + _accum_cell_state1.configure(compile_context, ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); CLTensor *cell_state_out_ptr = &_cell_state_out4; if(_is_layer_norm_lstm) { @@ -239,27 +254,28 @@ void CLLSTMLayer::configure(const ICLTensor *input, _cell_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_cell_layer_norm_out1); _memory_group.manage(&_cell_layer_norm_out2); - _mean_std_norm_cell_gate.configure(cell_state_out_ptr); - _pixelwise_mul_cell_gate_coeff.configure(cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _mean_std_norm_cell_gate.configure(compile_context, cell_state_out_ptr); + _pixelwise_mul_cell_gate_coeff.configure(compile_context, cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN); // cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before cell_state_out_ptr->allocator()->allocate(); - _accum_cell_gate_bias.configure(ArithmeticOperation::ADD, &_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE); + _accum_cell_gate_bias.configure(compile_context, ArithmeticOperation::ADD, &_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE); _cell_layer_norm_out1.allocator()->allocate(); cell_state_out_ptr = &_cell_layer_norm_out2; } - _activation_cell_state.configure(cell_state_out_ptr, nullptr, activation_info); + _activation_cell_state.configure(compile_context, cell_state_out_ptr, nullptr, activation_info); _memory_group.manage(&_cell_state_out5); - _pixelwise_mul_cell_state1.configure(cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _pixelwise_mul_cell_state1.configure(compile_context, cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); cell_state_out_ptr->allocator()->allocate(); - _pixelwise_mul_cell_state2.configure(forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - _accum_cell_state2.configure(ArithmeticOperation::ADD, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); + _pixelwise_mul_cell_state2.configure(compile_context, forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _accum_cell_state2.configure(compile_context, ArithmeticOperation::ADD, &_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)); + _cell_clip.configure(compile_context, &_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)); } // Configure block that calculates the output @@ -274,12 +290,12 @@ void CLLSTMLayer::configure(const ICLTensor *input, TensorShape in_out_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(in_out_weights, 0); _output2.allocator()->init(TensorInfo(in_out_weights_concat_shape, 1, input->info()->data_type())); - _concat_weights_output.configure(input_to_output_weights, recurrent_to_output_weights, &_output2); + _concat_weights_output.configure(compile_context, input_to_output_weights, recurrent_to_output_weights, &_output2); _memory_group.manage(&_output1); _memory_group.manage(&_output4); - _fully_connected_output.configure(&_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4); + _fully_connected_output.configure(compile_context, &_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4); _output2.allocator()->allocate(); _forget_gate_out2.allocator()->allocate(); @@ -290,8 +306,8 @@ void CLLSTMLayer::configure(const ICLTensor *input, _output3.allocator()->init(TensorInfo(_cell_state_out1.info()->tensor_shape(), 1, input->info()->data_type())); _memory_group.manage(&_output3); - _pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - _accum_output1.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE); + _pixelwise_mul_output_state1.configure(compile_context, &_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _accum_output1.configure(compile_context, &_output4, &_output3, &_output1, ConvertPolicy::SATURATE); _output4.allocator()->allocate(); output_gate_out = &_output1; @@ -308,15 +324,16 @@ void CLLSTMLayer::configure(const ICLTensor *input, _output_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_output_layer_norm_out1); _memory_group.manage(&_output_layer_norm_out2); - _mean_std_norm_output_gate.configure(output_gate_out); - _pixelwise_mul_output_gate_coeff.configure(output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _mean_std_norm_output_gate.configure(compile_context, output_gate_out); + _pixelwise_mul_output_gate_coeff.configure(compile_context, output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE, + RoundingPolicy::TO_NEAREST_EVEN); // output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before output_gate_out->allocator()->allocate(); - _accum_output_gate_bias.configure(ArithmeticOperation::ADD, &_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE); + _accum_output_gate_bias.configure(compile_context, ArithmeticOperation::ADD, &_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE); _output_layer_norm_out1.allocator()->allocate(); output_gate_out = &_output_layer_norm_out2; } - _activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + _activation_output.configure(compile_context, output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); // Configure block that calculates the output state /** lstm_res = PixelwiseMul(output, Activation(cell_state)) @@ -332,26 +349,26 @@ void CLLSTMLayer::configure(const ICLTensor *input, _output_state1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); _memory_group.manage(&_cell_state_activation); - _activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info); - _pixelwise_mul_output_state2.configure(&_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + _activation_output_state.configure(compile_context, &_cell_state_out1, &_cell_state_activation, activation_info); + _pixelwise_mul_output_state2.configure(compile_context, &_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); _cell_state_activation.allocator()->allocate(); if(lstm_params.has_projection()) { _has_projection_weights = true; - _fully_connected_output_state.configure(output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out); + _fully_connected_output_state.configure(compile_context, output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out); _output_state1.allocator()->allocate(); // Perform clipping if(projection_threshold != 0.f) { _perform_projection_clipping = true; - _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)); + _projection_clip.configure(compile_context, output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)); } } // Copy cell state and output - _copy_cell_state.configure(&_cell_state_out1, cell_state_out); - _copy_output.configure(output_state_out, output); + _copy_cell_state.configure(compile_context, &_cell_state_out1, cell_state_out); + _copy_output.configure(compile_context, output_state_out, output); // Vector for holding the tensors to store in scratch buffer std::vector scratch_inputs; @@ -362,7 +379,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, 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, Window::DimX); + _concat_scratch_buffer.configure(compile_context, scratch_inputs, scratch_buffer, Window::DimX); input_gate_out->allocator()->allocate(); _cell_state_out1.allocator()->allocate(); forget_gate_out->allocator()->allocate(); -- cgit v1.2.1