Implements Quantized LSTM op for R.

Also adds support for TENSOR_QUANT8_ASYMM_SIGNED in Test Generator.

Bug: 144841609
Bug: 145916330

Test: NeuralNetworksTest_static

Change-Id: I14b0d284b1945833d532cbaa33c66e4d77afd8b7

1 files changed, 178 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/qlstm.mod.py b/nn/runtime/test/specs/V1_3/qlstm.mod.py
new file mode 100644
index 000000000..c00c61400
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/qlstm.mod.py
@@ -0,0 +1,178 @@
+#
+# Copyright (C) 2019 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.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Test for QUANTIZED_LSTM op.
+import copy
+
+model = Model()
+
+batch_size = 2
+input_size = 5
+num_units = 4
+output_size = 3
+
+input = Input("input",
+              ("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, input_size), 0.0078125, 0))
+
+input_to_input_weights = Input("input_to_input_weights",
+                               ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
+input_to_forget_weights = Input("input_to_forget_weights",
+                                ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
+input_to_cell_weights = Input("input_to_cell_weights",
+                              ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
+input_to_output_weights = Input("input_to_output_weights",
+                                ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights",
+                                   ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
+                                    0.00784314, 0))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights",
+                                    ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
+                                     0.00784314, 0))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights",
+                                  ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
+                                   0.00784314, 0))
+recurrent_to_output_weights = Input("recurrent_to_output_weights",
+                                    ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
+                                     0.00784314, 0))
+
+cell_to_input_weights = Input("cell_to_input_weights",
+                              ("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
+cell_to_forget_weights = Input("cell_to_forget_weights",
+                               ("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
+cell_to_output_weights = Input("cell_to_output_weights",
+                               ("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
+
+input_gate_bias = Input("input_gate_bias",
+                        ("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
+forget_gate_bias = Input("forget_gate_bias",
+                         ("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
+cell_gate_bias = Input("cell_gate_bias",
+                       ("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
+output_gate_bias = Input("output_gate_bias",
+                         ("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
+
+projection_weights = Input("projection_weights",
+                           ("TENSOR_QUANT8_SYMM", "{%d,%d}" % (output_size, num_units), 0.00392157, 0))
+projection_bias = Input("projection_bias", "TENSOR_INT32", "{%d}" % (output_size))
+
+output_state_in = Input("output_state_in",
+                        ("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
+                         3.05176e-05, 0))
+cell_state_in = Input("cell_state_in",
+                      ("TENSOR_QUANT16_SYMM", "{%d, %d}" % (batch_size, num_units), 3.05176e-05, 0))
+
+input_layer_norm_weights = Input("input_layer_norm_weights",
+                                 ("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
+forget_layer_norm_weights = Input("forget_layer_norm_weights",
+                                  ("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
+cell_layer_norm_weights = Input("cell_layer_norm_weights",
+                                ("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
+output_layer_norm_weights = Input("output_layer_norm_weights",
+                                  ("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
+
+cell_clip = Float32Scalar("cell_clip", 0.)
+projection_clip = Float32Scalar("projection_clip", 0.)
+
+input_intermediate_scale = Float32Scalar("input_intermediate_scale", 0.007059)
+forget_intermediate_scale = Float32Scalar("forget_intermediate_scale", 0.007812)
+cell_intermediate_scale = Float32Scalar("cell_intermediate_scale", 0.007059)
+output_intermediate_scale = Float32Scalar("output_intermediate_scale", 0.007812)
+hidden_state_zero_point = Int32Scalar("hidden_state_zero_point", 0)
+hidden_state_scale = Float32Scalar("hidden_state_scale", 0.007)
+
+output_state_out = Output("output_state_out",
+                          ("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
+                           3.05176e-05, 0))
+cell_state_out = Output("cell_state_out",
+                        ("TENSOR_QUANT16_SYMM", "{%d, %d}" % (batch_size, num_units), 3.05176e-05, 0))
+output = Output("output",
+                ("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
+                           3.05176e-05, 0))
+
+model = model.Operation(
+    "QUANTIZED_LSTM", 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, cell_to_input_weights, cell_to_forget_weights,
+    cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias,
+    output_gate_bias, projection_weights, projection_bias, output_state_in,
+    cell_state_in, input_layer_norm_weights, forget_layer_norm_weights,
+    cell_layer_norm_weights, output_layer_norm_weights, cell_clip, projection_clip,
+    input_intermediate_scale, forget_intermediate_scale, cell_intermediate_scale,
+    output_intermediate_scale, hidden_state_zero_point, hidden_state_scale).To([output_state_out,
+    cell_state_out, output])
+
+# Example 1. Input in operand 0,
+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
+    ],
+    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: [644245, 3221226, 4724464, 8160438],
+    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: [
+        -25, -38, 51, 13, -64, 115, -25, -38, -89, 6, -25, -77
+    ],
+    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: [3277, 6553, 9830, 16384],
+    forget_layer_norm_weights: [6553, 6553, 13107, 9830],
+    cell_layer_norm_weights: [22937, 6553, 9830, 26214],
+    output_layer_norm_weights: [19660, 6553, 6553, 16384],
+}
+
+test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64]
+
+golden_output = [
+    127, 127, -108, -67, 127, 127
+]
+
+output0 = {
+    output_state_out: golden_output,
+    cell_state_out: [-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939],
+    output: golden_output,
+}
+
+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))