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1 files changed, 199 insertions, 0 deletions
diff --git a/tests/nnapi/specs/skip/V1_2/quantized_lstm.mod.py b/tests/nnapi/specs/skip/V1_2/quantized_lstm.mod.py
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index 000000000..5fd4c7a84
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+++ b/tests/nnapi/specs/skip/V1_2/quantized_lstm.mod.py
@@ -0,0 +1,199 @@
+#
+# Copyright (C) 2017 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. #
+
+# LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping.
+
+model = Model()
+
+n_batch = 2
+n_input = 2
+n_cell = 4
+n_output = n_cell
+
+input_ = Input("input", ("TENSOR_QUANT8_ASYMM", (n_batch, n_input), 1 / 128, 128))
+
+weights_scale = 0.00408021
+weights_zero_point = 100
+
+input_to_input_weights = Input("inputToInputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point))
+input_to_forget_weights = Input("inputToForgetWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point))
+input_to_cell_weights = Input("inputToCellWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point))
+input_to_output_weights = Input("inputToOutputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point))
+
+recurrent_to_input_weights = Input("recurrentToInputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point))
+recurrent_to_forget_weights = Input("recurrentToForgetWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point))
+recurrent_to_cell_weights = Input("recurrentToCellWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point))
+recurrent_to_output_weights = Input("recurrentToOutputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point))
+
+input_gate_bias = Input("inputGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0))
+forget_gate_bias = Input("forgetGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0))
+cell_gate_bias = Input("cellGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0))
+output_gate_bias = Input("outputGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0))
+
+prev_cell_state = Input("prevCellState", ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0))
+prev_output = Input("prevOutput", ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128))
+
+cell_state_out = Output("cellStateOut", ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0))
+output = Output("output", ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128))
+
+
+model = model.Operation("QUANTIZED_16BIT_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,
+ input_gate_bias,
+ forget_gate_bias,
+ cell_gate_bias,
+ output_gate_bias,
+ prev_cell_state,
+ prev_output
+).To([cell_state_out, output])
+
+input_dict = {
+ input_: [166, 179, 50, 150],
+ input_to_input_weights: [146, 250, 235, 171, 10, 218, 171, 108],
+ input_to_forget_weights: [24, 50, 132, 179, 158, 110, 3, 169],
+ input_to_cell_weights: [133, 34, 29, 49, 206, 109, 54, 183],
+ input_to_output_weights: [195, 187, 11, 99, 109, 10, 218, 48],
+ recurrent_to_input_weights: [254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26],
+ recurrent_to_forget_weights: [137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253],
+ recurrent_to_cell_weights: [172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216],
+ recurrent_to_output_weights: [106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98],
+ input_gate_bias: [-7876, 13488, -726, 32839],
+ forget_gate_bias: [9206, -46884, -11693, -38724],
+ cell_gate_bias: [39481, 48624, 48976, -21419],
+ output_gate_bias: [-58999, -17050, -41852, -40538],
+ prev_cell_state: [876, 1034, 955, -909, 761, 1029, 796, -1036],
+ prev_output: [136, 150, 140, 115, 135, 152, 138, 112],
+}
+
+output_dict = {
+ cell_state_out: [1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235],
+ output: [140, 151, 146, 112, 136, 156, 142, 112]
+}
+Example((input_dict, output_dict), model=model).AddVariations("relaxed")
+
+
+# TEST 2: same as the first one but only the first batch is tested and weights
+# are compile time constants
+model = Model()
+
+n_batch = 1
+n_input = 2
+n_cell = 4
+n_output = n_cell
+
+input_ = Input("input",
+ ("TENSOR_QUANT8_ASYMM", (n_batch, n_input), 1 / 128, 128))
+
+weights_scale = 0.00408021
+weights_zero_point = 100
+
+input_to_input_weights = Parameter(
+ "inputToInputWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_input), weights_scale, weights_zero_point),
+ [146, 250, 235, 171, 10, 218, 171, 108])
+input_to_forget_weights = Parameter(
+ "inputToForgetWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_input), weights_scale, weights_zero_point),
+ [24, 50, 132, 179, 158, 110, 3, 169])
+input_to_cell_weights = Parameter(
+ "inputToCellWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_input), weights_scale, weights_zero_point),
+ [133, 34, 29, 49, 206, 109, 54, 183])
+input_to_output_weights = Parameter(
+ "inputToOutputWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_input), weights_scale, weights_zero_point),
+ [195, 187, 11, 99, 109, 10, 218, 48])
+
+recurrent_to_input_weights = Parameter(
+ "recurrentToInputWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_output), weights_scale, weights_zero_point),
+ [254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26])
+recurrent_to_forget_weights = Parameter(
+ "recurrentToForgetWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_output), weights_scale, weights_zero_point),
+ [137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253])
+recurrent_to_cell_weights = Parameter(
+ "recurrentToCellWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_output), weights_scale, weights_zero_point),
+ [172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216])
+recurrent_to_output_weights = Parameter(
+ "recurrentToOutputWeights",
+ ("TENSOR_QUANT8_ASYMM",
+ (n_output, n_output), weights_scale, weights_zero_point),
+ [106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98])
+
+input_gate_bias = Parameter("inputGateBias",
+ ("TENSOR_INT32",
+ (n_output,), weights_scale / 128., 0),
+ [-7876, 13488, -726, 32839])
+forget_gate_bias = Parameter("forgetGateBias",
+ ("TENSOR_INT32",
+ (n_output,), weights_scale / 128., 0),
+ [9206, -46884, -11693, -38724])
+cell_gate_bias = Parameter("cellGateBias",
+ ("TENSOR_INT32",
+ (n_output,), weights_scale / 128., 0),
+ [39481, 48624, 48976, -21419])
+output_gate_bias = Parameter("outputGateBias",
+ ("TENSOR_INT32",
+ (n_output,), weights_scale / 128., 0),
+ [-58999, -17050, -41852, -40538])
+
+prev_cell_state = Input("prevCellState",
+ ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0))
+prev_output = Input("prevOutput",
+ ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128))
+
+cell_state_out = Output("cellStateOut",
+ ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0))
+output = Output("output",
+ ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128))
+
+model = model.Operation("QUANTIZED_16BIT_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, input_gate_bias,
+ forget_gate_bias, cell_gate_bias, output_gate_bias,
+ prev_cell_state,
+ prev_output).To([cell_state_out, output])
+
+input_dict = {
+ input_: [166, 179],
+ prev_cell_state: [876, 1034, 955, -909],
+ prev_output: [136, 150, 140, 115],
+}
+
+output_dict = {
+ cell_state_out: [1485, 1177, 1373, -1023],
+ output: [140, 151, 146, 112]
+}
+Example((input_dict, output_dict), model=model,
+ name="constant_weights").AddVariations("relaxed")
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