Add quant8 signed generated tests

The tests are written semi-automatically by joining all of the 1.0-1.2
tests with TENSOR_QUANT8_ASYMM operands and converting them to
TENSOR_QUANT8_ASYMM_SIGNED.

Also:
* Fix implementation of CONCATENATION op for zero-sized tensors
* Add support for TENSOR_QUANT8_ASYMM_SIGNED in test generator

Bug: 136735770
Test: NNTest_static and VtsHalNeuralnetworksV1_3TargetTest
Change-Id: I250dbe85684aa594892494eb53e6312c1cacb6f3

1 files changed, 135 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/grouped_conv2d_quant8_signed.mod.py b/nn/runtime/test/specs/V1_3/grouped_conv2d_quant8_signed.mod.py
new file mode 100644
index 000000000..358613408
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/grouped_conv2d_quant8_signed.mod.py
@@ -0,0 +1,135 @@
+#
+# 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.
+#
+
+layout = BoolScalar("layout", False) # NHWC
+
+# TEST 1: GROUPED_CONV2D, pad = 0, stride = 1, numGroups = 2
+i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 2}") # input 0
+w1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 1}", [1, 2, 2, 1, 4, 3, 2, 1]) # weight
+b1 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [10, -33.5]) # bias
+act = Int32Scalar("act", 0) # act = none
+o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") # output 0
+Model().Operation("GROUPED_CONV_2D", i1, w1, b1, 0, 0, 0, 0, 1, 1, 2, act, layout).To(o1)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+    w1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+    b1: ("TENSOR_INT32", 0.0625, 0),
+    o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -48)
+})
+
+quant8_mult_gt_1_signed = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+    w1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+    b1: ("TENSOR_INT32", 0.0625, 0),
+    o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.05, -48)
+})
+
+# Per-channel quantization
+channelQuant8_signed = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+    w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
+    b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
+    o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -48)
+})
+
+channelQuant8_signed_mult_gt_1 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+    w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
+    b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
+    o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, -48)
+})
+
+example = Example({
+    i1: [1, 2, 3, 4, 5, 6,
+         6, 5, 4, 3, 2, 1,
+         2, 3, 3, 3, 3, 3],
+    o1: [33, -0.5,
+         33,  7.5,
+         31,  4.5,
+         27, -9.5]
+}).AddNchw(i1, o1, layout).AddAllActivations(o1, act).AddVariations(quant8_signed, quant8_mult_gt_1_signed, channelQuant8_signed, channelQuant8_signed_mult_gt_1, includeDefault=False)
+
+
+# TEST 2: GROUPED_CONV2D_LARGE, pad = same, stride = 1, numGroups = 2, act = none
+i2 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 2, 2}") # input 0
+w2 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 3, 1}", [100, 20, 1, 200, 10, 2, 200, 30, 1, 100, 20, 3]) # weight
+b2 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [500, -1000]) # bias
+o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 2, 2}") # output 0
+Model("large").Operation("GROUPED_CONV_2D", i2, w2, b2, 1, 1, 1, 2, 0, layout).To(o2)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+    i2: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+    w2: ("TENSOR_QUANT8_ASYMM_SIGNED", 1.0, -128),
+    b2: ("TENSOR_INT32", 0.25, 0),
+    o2: ("TENSOR_QUANT8_ASYMM_SIGNED", 10.0, -28)
+})
+
+# Per-channel quantization
+channelQuant8_signed = DataTypeConverter().Identify({
+    i2: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+    w2: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[2.0, 2.5])),
+    b2: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.5, 0.625], hide=True)),
+    o2: ("TENSOR_QUANT8_ASYMM_SIGNED", 10.0, -28)
+})
+
+example = Example({
+    i2: [1, 2, 3, 4,
+         4, 3, 2, 1,
+         2, 3, 3, 3],
+    o2: [567, -873,
+         1480, -160,
+         608, -840,
+         1370, -10,
+         543, -907,
+         760, -310]
+}).AddNchw(i2, o2, layout).AddVariations(quant8_signed, channelQuant8_signed, includeDefault=False)
+
+
+# TEST 3: GROUPED_CONV2D_CHANNEL, pad = same, stride = 1, numGroups = 3, act = none
+i3 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 9}") # input 0
+w3 = Parameter("op2", "TENSOR_FLOAT32", "{6, 1, 1, 3}", [1, 2, 3, 2, 1, 0, 2, 3, 3, 6, 6, 6, 9, 8, 5, 2, 1, 1]) # weight
+b3 = Parameter("op3", "TENSOR_FLOAT32", "{6}", [10, -20, 30, -40, 50, -60]) # bias
+o3 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 6}") # output 0
+Model("channel").Operation("GROUPED_CONV_2D", i3, w3, b3, 1, 1, 1, 3, 0, layout).To(o3)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+    i3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+    w3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -128),
+    b3: ("TENSOR_INT32", 0.125, 0),
+    o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.0, -68)
+})
+
+channelQuant8_signed = DataTypeConverter().Identify({
+    i3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+    w3: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.3] * 3)),
+    b3: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.125, 0.15] * 3, hide=True)),
+    o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.0, -68)
+})
+
+example = Example({
+    i3: [1, 2, 3, 4, 55, 4, 3, 2, 1,
+         5, 4, 3, 2, 11, 2, 3, 4, 5,
+         2, 3, 2, 3, 22, 3, 2, 3, 2,
+         1, 0, 2, 1, 33, 1, 2, 0, 1],
+    o3: [24, -16, 215, 338,  98, -51,
+         32,  -6,  73,  50, 134, -45,
+         24, -13, 111, 128, 102, -51,
+         17, -18, 134, 170,  73, -55]
+}).AddNchw(i3, o3, layout).AddVariations(quant8_signed, channelQuant8_signed, includeDefault=False)