1 # 2 # Copyright (C) 2017 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 # LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping. 17 18 model = Model() 19 20 n_batch = 2 21 n_input = 2 22 n_cell = 4 23 n_output = n_cell 24 25 input_ = Input("input", ("TENSOR_QUANT8_ASYMM", (n_batch, n_input), 1 / 128, 128)) 26 27 weights_scale = 0.00408021 28 weights_zero_point = 100 29 30 input_to_input_weights = Input("inputToInputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point)) 31 input_to_forget_weights = Input("inputToForgetWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point)) 32 input_to_cell_weights = Input("inputToCellWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point)) 33 input_to_output_weights = Input("inputToOutputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_input), weights_scale, weights_zero_point)) 34 35 recurrent_to_input_weights = Input("recurrentToInputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point)) 36 recurrent_to_forget_weights = Input("recurrentToForgetWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point)) 37 recurrent_to_cell_weights = Input("recurrentToCellWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point)) 38 recurrent_to_output_weights = Input("recurrentToOutputWeights", ("TENSOR_QUANT8_ASYMM", (n_output, n_output), weights_scale, weights_zero_point)) 39 40 input_gate_bias = Input("inputGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0)) 41 forget_gate_bias = Input("forgetGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0)) 42 cell_gate_bias = Input("cellGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0)) 43 output_gate_bias = Input("outputGateBias", ("TENSOR_INT32", (n_output,), weights_scale / 128., 0)) 44 45 prev_cell_state = Input("prevCellState", ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0)) 46 prev_output = Input("prevOutput", ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)) 47 48 cell_state_out = Output("cellStateOut", ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0)) 49 output = Output("output", ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)) 50 51 52 model = model.Operation("QUANTIZED_16BIT_LSTM", 53 input_, 54 input_to_input_weights, 55 input_to_forget_weights, 56 input_to_cell_weights, 57 input_to_output_weights, 58 recurrent_to_input_weights, 59 recurrent_to_forget_weights, 60 recurrent_to_cell_weights, 61 recurrent_to_output_weights, 62 input_gate_bias, 63 forget_gate_bias, 64 cell_gate_bias, 65 output_gate_bias, 66 prev_cell_state, 67 prev_output 68 ).To([cell_state_out, output]) 69 70 input_dict = { 71 input_: [166, 179, 50, 150], 72 input_to_input_weights: [146, 250, 235, 171, 10, 218, 171, 108], 73 input_to_forget_weights: [24, 50, 132, 179, 158, 110, 3, 169], 74 input_to_cell_weights: [133, 34, 29, 49, 206, 109, 54, 183], 75 input_to_output_weights: [195, 187, 11, 99, 109, 10, 218, 48], 76 recurrent_to_input_weights: [254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26], 77 recurrent_to_forget_weights: [137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253], 78 recurrent_to_cell_weights: [172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216], 79 recurrent_to_output_weights: [106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98], 80 input_gate_bias: [-7876, 13488, -726, 32839], 81 forget_gate_bias: [9206, -46884, -11693, -38724], 82 cell_gate_bias: [39481, 48624, 48976, -21419], 83 output_gate_bias: [-58999, -17050, -41852, -40538], 84 prev_cell_state: [876, 1034, 955, -909, 761, 1029, 796, -1036], 85 prev_output: [136, 150, 140, 115, 135, 152, 138, 112], 86 } 87 88 output_dict = { 89 cell_state_out: [1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235], 90 output: [140, 151, 146, 112, 136, 156, 142, 112] 91 } 92 Example((input_dict, output_dict), model=model).AddVariations("relaxed") 93 94 95 # TEST 2: same as the first one but only the first batch is tested and weights 96 # are compile time constants 97 model = Model() 98 99 n_batch = 1 100 n_input = 2 101 n_cell = 4 102 n_output = n_cell 103 104 input_ = Input("input", 105 ("TENSOR_QUANT8_ASYMM", (n_batch, n_input), 1 / 128, 128)) 106 107 weights_scale = 0.00408021 108 weights_zero_point = 100 109 110 input_to_input_weights = Parameter( 111 "inputToInputWeights", 112 ("TENSOR_QUANT8_ASYMM", 113 (n_output, n_input), weights_scale, weights_zero_point), 114 [146, 250, 235, 171, 10, 218, 171, 108]) 115 input_to_forget_weights = Parameter( 116 "inputToForgetWeights", 117 ("TENSOR_QUANT8_ASYMM", 118 (n_output, n_input), weights_scale, weights_zero_point), 119 [24, 50, 132, 179, 158, 110, 3, 169]) 120 input_to_cell_weights = Parameter( 121 "inputToCellWeights", 122 ("TENSOR_QUANT8_ASYMM", 123 (n_output, n_input), weights_scale, weights_zero_point), 124 [133, 34, 29, 49, 206, 109, 54, 183]) 125 input_to_output_weights = Parameter( 126 "inputToOutputWeights", 127 ("TENSOR_QUANT8_ASYMM", 128 (n_output, n_input), weights_scale, weights_zero_point), 129 [195, 187, 11, 99, 109, 10, 218, 48]) 130 131 recurrent_to_input_weights = Parameter( 132 "recurrentToInputWeights", 133 ("TENSOR_QUANT8_ASYMM", 134 (n_output, n_output), weights_scale, weights_zero_point), 135 [254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26]) 136 recurrent_to_forget_weights = Parameter( 137 "recurrentToForgetWeights", 138 ("TENSOR_QUANT8_ASYMM", 139 (n_output, n_output), weights_scale, weights_zero_point), 140 [137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253]) 141 recurrent_to_cell_weights = Parameter( 142 "recurrentToCellWeights", 143 ("TENSOR_QUANT8_ASYMM", 144 (n_output, n_output), weights_scale, weights_zero_point), 145 [172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216]) 146 recurrent_to_output_weights = Parameter( 147 "recurrentToOutputWeights", 148 ("TENSOR_QUANT8_ASYMM", 149 (n_output, n_output), weights_scale, weights_zero_point), 150 [106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98]) 151 152 input_gate_bias = Parameter("inputGateBias", 153 ("TENSOR_INT32", 154 (n_output,), weights_scale / 128., 0), 155 [-7876, 13488, -726, 32839]) 156 forget_gate_bias = Parameter("forgetGateBias", 157 ("TENSOR_INT32", 158 (n_output,), weights_scale / 128., 0), 159 [9206, -46884, -11693, -38724]) 160 cell_gate_bias = Parameter("cellGateBias", 161 ("TENSOR_INT32", 162 (n_output,), weights_scale / 128., 0), 163 [39481, 48624, 48976, -21419]) 164 output_gate_bias = Parameter("outputGateBias", 165 ("TENSOR_INT32", 166 (n_output,), weights_scale / 128., 0), 167 [-58999, -17050, -41852, -40538]) 168 169 prev_cell_state = Input("prevCellState", 170 ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0)) 171 prev_output = Input("prevOutput", 172 ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)) 173 174 cell_state_out = Output("cellStateOut", 175 ("TENSOR_QUANT16_SYMM", (n_batch, n_cell), 1 / 2048, 0)) 176 output = Output("output", 177 ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)) 178 179 model = model.Operation("QUANTIZED_16BIT_LSTM", input_, input_to_input_weights, 180 input_to_forget_weights, input_to_cell_weights, 181 input_to_output_weights, recurrent_to_input_weights, 182 recurrent_to_forget_weights, recurrent_to_cell_weights, 183 recurrent_to_output_weights, input_gate_bias, 184 forget_gate_bias, cell_gate_bias, output_gate_bias, 185 prev_cell_state, 186 prev_output).To([cell_state_out, output]) 187 188 input_dict = { 189 input_: [166, 179], 190 prev_cell_state: [876, 1034, 955, -909], 191 prev_output: [136, 150, 140, 115], 192 } 193 194 output_dict = { 195 cell_state_out: [1485, 1177, 1373, -1023], 196 output: [140, 151, 146, 112] 197 } 198 Example((input_dict, output_dict), model=model, 199 name="constant_weights").AddVariations("relaxed") 200
