1#
2# Copyright (C) 2020 The Android Open Source Project
3#
4# Licensed under the Apache License, Version 2.0 (the "License");
5# you may not use this file except in compliance with the License.
6# You may obtain a copy of the License at
7#
8#      http://www.apache.org/licenses/LICENSE-2.0
9#
10# Unless required by applicable law or agreed to in writing, software
11# distributed under the License is distributed on an "AS IS" BASIS,
12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13# See the License for the specific language governing permissions and
14# limitations under the License.
15#
16
17# Test for QUANTIZED_LSTM op.
18import copy
19
20model = Model()
21
22batch_size = 2
23input_size = 5
24num_units = 4
25output_size = 4
26
27InputType = ("TENSOR_QUANT8_ASYMM_SIGNED", [batch_size, input_size], 0.0078125, 0)
28input = Input("input", InputType)
29
30InputWeightsType = ("TENSOR_QUANT8_SYMM", [num_units, input_size], 0.00784314, 0)
31input_to_input_weights = Input("input_to_input_weights", InputWeightsType)
32input_to_forget_weights = Input("input_to_forget_weights", InputWeightsType)
33input_to_cell_weights = Input("input_to_cell_weights", InputWeightsType)
34input_to_output_weights = Input("input_to_output_weights", InputWeightsType)
35
36RecurrentWeightsType = ("TENSOR_QUANT8_SYMM", [num_units, output_size], 0.00784314, 0)
37recurrent_to_input_weights = Input("recurrent_to_input_weights", RecurrentWeightsType)
38recurrent_to_forget_weights = Input("recurrent_to_forget_weights", RecurrentWeightsType)
39recurrent_to_cell_weights = Input("recurrent_to_cell_weights", RecurrentWeightsType)
40recurrent_to_output_weights = Input("recurrent_to_output_weights", RecurrentWeightsType)
41
42CellWeightsType = ("TENSOR_QUANT16_SYMM", [num_units], 1.0, 0)
43cell_to_input_weights = Input("cell_to_input_weights", CellWeightsType)
44cell_to_forget_weights = Input("cell_to_forget_weights", CellWeightsType)
45cell_to_output_weights = Input("cell_to_output_weights", CellWeightsType)
46
47# The bias scale value here is not used.
48BiasType = ("TENSOR_INT32", [num_units], 0.0, 0)
49input_gate_bias = Input("input_gate_bias", BiasType)
50forget_gate_bias = Input("forget_gate_bias", BiasType)
51cell_gate_bias = Input("cell_gate_bias", BiasType)
52output_gate_bias = Input("output_gate_bias", BiasType)
53
54projection_weights = Input("projection_weights",
55                           ("TENSOR_QUANT8_SYMM", [output_size, num_units], 0.00392157, 0))
56projection_bias = Input("projection_bias", ("TENSOR_INT32", [output_size]))
57
58OutputStateType = ("TENSOR_QUANT8_ASYMM_SIGNED", [batch_size, output_size], 3.05176e-05, 0)
59CellStateType = ("TENSOR_QUANT16_SYMM", [batch_size, num_units], 3.05176e-05, 0)
60output_state_in = Input("output_state_in", OutputStateType)
61cell_state_in = Input("cell_state_in", CellStateType)
62
63LayerNormType = ("TENSOR_QUANT16_SYMM", [num_units], 3.05182e-05, 0)
64input_layer_norm_weights = Input("input_layer_norm_weights", LayerNormType)
65forget_layer_norm_weights = Input("forget_layer_norm_weights", LayerNormType)
66cell_layer_norm_weights = Input("cell_layer_norm_weights", LayerNormType)
67output_layer_norm_weights = Input("output_layer_norm_weights", LayerNormType)
68
69cell_clip = Float32Scalar("cell_clip", 0.)
70projection_clip = Float32Scalar("projection_clip", 0.)
71
72input_intermediate_scale = Float32Scalar("input_intermediate_scale", 0.007059)
73forget_intermediate_scale = Float32Scalar("forget_intermediate_scale", 0.007812)
74cell_intermediate_scale = Float32Scalar("cell_intermediate_scale", 0.007059)
75output_intermediate_scale = Float32Scalar("output_intermediate_scale", 0.007812)
76hidden_state_zero_point = Int32Scalar("hidden_state_zero_point", 0)
77hidden_state_scale = Float32Scalar("hidden_state_scale", 0.007)
78
79output_state_out = Output("output_state_out", OutputStateType)
80cell_state_out = Output("cell_state_out", CellStateType)
81output = Output("output", OutputStateType)
82
83model = model.Operation(
84    "QUANTIZED_LSTM", input, input_to_input_weights, input_to_forget_weights,
85    input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
86    recurrent_to_forget_weights, recurrent_to_cell_weights,
87    recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights,
88    cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias,
89    output_gate_bias, projection_weights, projection_bias, output_state_in,
90    cell_state_in, input_layer_norm_weights, forget_layer_norm_weights,
91    cell_layer_norm_weights, output_layer_norm_weights, cell_clip, projection_clip,
92    input_intermediate_scale, forget_intermediate_scale, cell_intermediate_scale,
93    output_intermediate_scale, hidden_state_zero_point, hidden_state_scale).To([output_state_out,
94    cell_state_out, output])
95
96# Example 1. CIFG, Layer Norm.
97input0 = {
98    input_to_input_weights: [],
99    input_to_forget_weights: [
100        -77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64
101    ],
102    input_to_cell_weights: [
103        -51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77
104    ],
105    input_to_output_weights: [
106        -102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51
107    ],
108    input_gate_bias: [],
109    forget_gate_bias: [2147484, -6442451, -4294968, 2147484],
110    cell_gate_bias: [-1073742, 15461883, 5368709, 1717987],
111    output_gate_bias: [1073742, -214748, 4294968, 2147484],
112    recurrent_to_input_weights: [],
113    recurrent_to_forget_weights: [
114        -64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51
115    ],
116    recurrent_to_cell_weights: [
117        -38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64
118    ],
119    recurrent_to_output_weights: [
120        38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38
121    ],
122    projection_weights: [],
123    projection_bias: [],
124    input_layer_norm_weights: [],
125    forget_layer_norm_weights: [6553, 6553, 13107, 9830],
126    cell_layer_norm_weights: [22937, 6553, 9830, 26214],
127    output_layer_norm_weights: [19660, 6553, 6553, 16384],
128    output_state_in: [ 0 for _ in range(batch_size * output_size) ],
129    cell_state_in: [ 0 for _ in range(batch_size * num_units) ],
130    cell_to_input_weights: [],
131    cell_to_forget_weights: [],
132    cell_to_output_weights: [],
133}
134
135test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64]
136
137golden_output = [
138    -15, 21, 14, 20, -15, 15, 5, 27
139]
140
141output0 = {
142    output_state_out: golden_output,
143    cell_state_out: [-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149],
144    output: golden_output,
145}
146
147input0[input] = test_input
148
149Example((input0, output0))
150