1 files changed, 134 insertions, 0 deletions
diff --git a/nn/runtime/test/specs/V1_3/while_fib_dynamic.mod.py b/nn/runtime/test/specs/V1_3/while_fib_dynamic.mod.py
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+++ b/nn/runtime/test/specs/V1_3/while_fib_dynamic.mod.py
@@ -0,0 +1,134 @@
+#
+# 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.
+# 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.
+#
+
+# Model: given n >= 2, produces [fib(1), ..., fib(n)].
+#
+# i = 2
+# fib = [1, 1]                         # shape=[1, i + 1]
+# while i < n:
+#     last2 = fib[0:1, (i - 2):i]      # shape=[1, 2]
+#     new_fib = matmul(last2, [1; 1])  # shape=[1, 1]
+#     fib = append(fib, new_fib)       # shape=[1, i + 2]
+#     i = i + 1
+
+FibType = ["TENSOR_FLOAT32", []]  # Unknown rank.
+CounterType = ["TENSOR_INT32", [1]]
+BoolType = ["TENSOR_BOOL8", [1]]
+
+Quant8Params = ["TENSOR_QUANT8_ASYMM", 1.0, 0]
+Quant8SignedParams = ["TENSOR_QUANT8_ASYMM_SIGNED", 1.0, 0]
+
+def MakeConditionModel():
+  fib = Input("fib", FibType)
+  i = Input("i", CounterType)
+  n = Input("n", CounterType)
+  out = Output("out", BoolType)
+  model = Model()
+  model.IdentifyInputs(fib, i, n)
+  model.IdentifyOutputs(out)
+  model.Operation("LESS", i, n).To(out)
+
+  quant8 = DataTypeConverter().Identify({
+      fib: Quant8Params,
+  })
+  quant8_signed = DataTypeConverter().Identify({
+      fib: Quant8SignedParams,
+  })
+
+  return SubgraphReference("cond", model), quant8, quant8_signed
+
+def MakeBodyModel():
+  fib = Input("fib", FibType)
+  i = Input("i", CounterType)
+  n = Input("n", CounterType)
+  fib_out = Output("fib_out", FibType)
+  i_out = Output("i_out", CounterType)
+  matrix = Parameter("matrix", ["TENSOR_FLOAT32", [1, 2]], [1, 1])
+  zero_bias = Parameter("zero_bias", ["TENSOR_FLOAT32", [1]], [0])
+  i_minus_2 = Internal("i_minus_2", ["TENSOR_INT32", [1]])
+  slice_start = Internal("slice_start", ["TENSOR_INT32", [2]])
+  slice_size = Parameter("slice_size", ["TENSOR_INT32", [2]], [1, 2])
+  last2 = Internal("last2", ["TENSOR_FLOAT32", [1, 2]])
+  new_fib = Internal("new_fib", ["TENSOR_FLOAT32", [1, 1]])
+  model = Model()
+  model.IdentifyInputs(fib, i, n)
+  model.IdentifyOutputs(fib_out, i_out)
+  model.Operation("ADD", i, [-2], 0).To(i_minus_2)
+  model.Operation("MUL", i_minus_2, [0, 1], 0).To(slice_start)
+  model.Operation("SLICE", fib, slice_start, slice_size).To(last2)
+  model.Operation("FULLY_CONNECTED", last2, matrix, zero_bias, 0).To(new_fib)
+  model.Operation("CONCATENATION", fib, new_fib, 1).To(fib_out)
+  model.Operation("ADD", i, [1], 0).To(i_out)
+
+  quant8 = DataTypeConverter().Identify({
+      fib_out: Quant8Params,
+      fib: Quant8Params,
+      last2: Quant8Params,
+      matrix: Quant8Params,
+      new_fib: Quant8Params,
+      zero_bias: ["TENSOR_INT32", 1.0, 0],
+  })
+  quant8_signed = DataTypeConverter().Identify({
+      fib_out: Quant8SignedParams,
+      fib: Quant8SignedParams,
+      last2: Quant8SignedParams,
+      matrix: Quant8SignedParams,
+      new_fib: Quant8SignedParams,
+      zero_bias: ["TENSOR_INT32", 1.0, 0],
+  })
+
+  return SubgraphReference("body", model), quant8, quant8_signed
+
+def Test(n_data, fib_data, add_unused_output=False):
+  n = Input("n", CounterType)
+  fib_out = Output("fib_out", ["TENSOR_FLOAT32", [1, len(fib_data)]])  # Known dimensions.
+  cond, cond_quant8, cond_quant8_signed = MakeConditionModel()
+  body, body_quant8, body_quant8_signed = MakeBodyModel()
+  fib_init = Parameter("fib_init", ["TENSOR_FLOAT32", [1, 2]], [1, 1])
+  i_init = [2]
+  outputs = [fib_out]
+  if add_unused_output:
+    i_out = Internal("i_out", CounterType)  # Unused.
+    outputs.append(i_out)
+  model = Model().Operation("WHILE", cond, body, fib_init, i_init, n).To(outputs)
+
+  quant8 = DataTypeConverter().Identify({
+      fib_init: Quant8Params,
+      fib_out: Quant8Params,
+      cond: cond_quant8,
+      body: body_quant8,
+  })
+  quant8_signed = DataTypeConverter().Identify({
+      fib_init: Quant8SignedParams,
+      fib_out: Quant8SignedParams,
+      cond: cond_quant8_signed,
+      body: body_quant8_signed,
+  })
+
+  name = "n_{}".format(n_data)
+  if add_unused_output:
+    name += "_unused_output"
+  example = Example({n: [n_data], fib_out: fib_data}, name=name)
+  example.AddVariations("relaxed", "float16", quant8, quant8_signed)
+  example.AddVariations(AllOutputsAsInternalCoverter())
+
+for use_shm_for_weights in [False, True]:
+  Configuration.use_shm_for_weights = use_shm_for_weights
+  Test(n_data=2, fib_data=[1, 1], add_unused_output=True)
+  Test(n_data=3, fib_data=[1, 1, 2], add_unused_output=True)
+  Test(n_data=4, fib_data=[1, 1, 2, 3], add_unused_output=True)
+  Test(n_data=5, fib_data=[1, 1, 2, 3, 5])
+  Test(n_data=6, fib_data=[1, 1, 2, 3, 5, 8])