QLSTM add support for different qinfo in weights

Resolves COMPMID-5185

Change-Id: I61e1453e8851ab84c1cadc10587ebd23fd94799e
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7330
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 23 insertions, 10 deletions

diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
index fdfe95fb64..76bb8c01d2 100644
--- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2021 Arm Limited.
+ * Copyright (c) 2020-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -167,6 +167,13 @@ void NEQLSTMLayer::configure(const ITensor *input,
     LSTMParams<ITensorInfo> lstm_params_info{};
     build_lstm_params_tensor_info(lstm_params, &lstm_params_info);
 
+    _input_to_forget_weights_transposed.info()->set_quantization_info(input_to_forget_weights->info()->quantization_info());
+    _input_to_cell_weights_transposed.info()->set_quantization_info(input_to_cell_weights->info()->quantization_info());
+    _input_to_output_weights_transposed.info()->set_quantization_info(input_to_output_weights->info()->quantization_info());
+    _recurrent_to_forget_weights_transposed.info()->set_quantization_info(recurrent_to_forget_weights->info()->quantization_info());
+    _recurrent_to_cell_weights_transposed.info()->set_quantization_info(recurrent_to_cell_weights->info()->quantization_info());
+    _recurrent_to_output_weights_transposed.info()->set_quantization_info(recurrent_to_output_weights->info()->quantization_info());
+
     // Validate
     ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::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(),
@@ -689,20 +696,26 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
         }
     }
 
-    const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_forget_weights->quantization_info());
+    const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_cell_weights->quantization_info());
+    const TensorInfo input_to_output_weights_transposed(TensorShape(num_units, input_size), 1, input_to_output_weights->data_type(), input_to_output_weights->quantization_info());
+    const TensorInfo recurrent_to_forget_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
+    const TensorInfo recurrent_to_cell_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_cell_weights->quantization_info());
+    const TensorInfo recurrent_to_output_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_output_weights->quantization_info());
     const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
 
-    // Validate weights transpose
-    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_forget_weights, &input_weights_transposed));
     ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_cell_weights, &input_weights_transposed));
-    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_output_weights, &input_weights_transposed));
-    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_forget_weights, &recurrent_weights_transposed));
-    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_cell_weights, &recurrent_weights_transposed));
-    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_output_weights, &recurrent_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_output_weights, &input_to_output_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_forget_weights, &recurrent_to_forget_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_cell_weights, &recurrent_to_cell_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_output_weights, &recurrent_to_output_weights_transposed));
     if(!lstm_params.has_cifg_opt())
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.input_to_input_weights(), &input_weights_transposed));
-        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_weights_transposed));
+        const TensorInfo recurrent_to_input_weights_transposed(TensorShape(num_units, output_size), 1,
+                                                               recurrent_to_forget_weights->data_type(), lstm_params.recurrent_to_input_weights()->quantization_info());
+        const TensorInfo input_to_input_weights_transposed(TensorShape(num_units, input_size), 1,
+                                                           input_to_forget_weights->data_type(), lstm_params.input_to_input_weights()->quantization_info());
+        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.input_to_input_weights(), &input_to_input_weights_transposed));
+        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_to_input_weights_transposed));
     }
     if(lstm_params.has_projection())
     {