Add a test for quantized LSTM op for CIFG, Layer Norm, Projection.

Bug: 148938903

Test: NeuralNetworksTest_static

Change-Id: Ia267a7f90bc49b79ef47121bed202ef81d982664

1 files changed, 64 insertions, 7 deletions

diff --git a/nn/runtime/test/specs/V1_3/qlstm.mod.py b/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py
index c00c61400..8895962cc 100644
--- a/nn/runtime/test/specs/V1_3/qlstm.mod.py
+++ b/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py
@@ -1,5 +1,5 @@
 #
-# Copyright (C) 2019 The Android Open Source Project
+# Copyright (C) 2020 The Android Open Source Project
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -116,7 +116,7 @@ model = model.Operation(
     output_intermediate_scale, hidden_state_zero_point, hidden_state_scale).To([output_state_out,
     cell_state_out, output])
 
-# Example 1. Input in operand 0,
+# Example 1. Layer Norm, Projection.
 input0 = {
     input_to_input_weights: [
         64, 77, 89, -102, -115, 13, 25, 38, -51, 64, -102, 89, -77, 64, -51, -64, -51, -38, -25, -13
@@ -154,6 +154,11 @@ input0 = {
     forget_layer_norm_weights: [6553, 6553, 13107, 9830],
     cell_layer_norm_weights: [22937, 6553, 9830, 26214],
     output_layer_norm_weights: [19660, 6553, 6553, 16384],
+    output_state_in: [ 0 for _ in range(batch_size * output_size) ],
+    cell_state_in: [ 0 for _ in range(batch_size * num_units) ],
+    cell_to_input_weights: [],
+    cell_to_forget_weights: [],
+    cell_to_output_weights: [],
 }
 
 test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64]
@@ -169,10 +174,62 @@ output0 = {
 }
 
 input0[input] = test_input
-input0[output_state_in] = [ 0 for _ in range(batch_size * output_size) ]
-input0[cell_state_in] = [ 0 for _ in range(batch_size * num_units) ]
-input0[cell_to_input_weights] = [0 for _ in range(num_units) ]
-input0[cell_to_forget_weights] = [0 for _ in range(num_units) ]
-input0[cell_to_output_weights] = [0 for _ in range(num_units) ]
+
+Example((input0, output0))
+
+# Example 2. CIFG, Layer Norm, Projection.
+input0 = {
+    input_to_input_weights: [],
+    input_to_forget_weights: [
+        -77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64
+    ],
+    input_to_cell_weights: [
+        -51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77
+    ],
+    input_to_output_weights: [
+        -102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51
+    ],
+    input_gate_bias: [],
+    forget_gate_bias: [2147484, -6442451, -4294968, 2147484],
+    cell_gate_bias: [-1073742, 15461883, 5368709, 1717987],
+    output_gate_bias: [1073742, -214748, 4294968, 2147484],
+    recurrent_to_input_weights: [],
+    recurrent_to_forget_weights: [
+        -64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25
+    ],
+    recurrent_to_cell_weights: [
+        -38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25
+    ],
+    recurrent_to_output_weights: [
+        38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25
+    ],
+    projection_weights: [
+        -25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51
+    ],
+    projection_bias: [ 0 for _ in range(output_size) ],
+    input_layer_norm_weights: [],
+    forget_layer_norm_weights: [6553, 6553, 13107, 9830],
+    cell_layer_norm_weights: [22937, 6553, 9830, 26214],
+    output_layer_norm_weights: [19660, 6553, 6553, 16384],
+    output_state_in: [ 0 for _ in range(batch_size * output_size) ],
+    cell_state_in: [ 0 for _ in range(batch_size * num_units) ],
+    cell_to_input_weights: [],
+    cell_to_forget_weights: [],
+    cell_to_output_weights: [],
+}
+
+test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64]
+
+golden_output = [
+    127, 127, 127, -128, 127, 127
+]
+
+output0 = {
+    output_state_out: golden_output,
+    cell_state_out: [-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149],
+    output: golden_output,
+}
+
+input0[input] = test_input
 
 Example((input0, output0))