1# 2# Copyright (C) 2018 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# LSTM Test, With Peephole, With Projection, No Clipping 18 19model = Model() 20 21n_batch = 2 22n_input = 5 23# n_cell and n_output have the same size when there is no projection. 24n_cell = 20 25n_output = 16 26 27input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) 28 29input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 30input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 31input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 32input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 33 34recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 35recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 36recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 37recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 38 39cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) 40cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell)) 41cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) 42 43input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 44forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 45cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 46output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 47 48projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell)) 49projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") 50 51output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 52cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) 53 54activation_param = Int32Scalar("activation_param", 4) # Tanh 55cell_clip_param = Float32Scalar("cell_clip_param", 0.) 56proj_clip_param = Float32Scalar("proj_clip_param", 0.) 57 58scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) 59output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 60cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) 61output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 62 63model = model.Operation("LSTM", 64 input, 65 66 input_to_input_weights, 67 input_to_forget_weights, 68 input_to_cell_weights, 69 input_to_output_weights, 70 71 recurrent_to_input_weights, 72 recurrent_to_forget_weights, 73 recurrent_to_cell_weights, 74 recurrent_to_output_weights, 75 76 cell_to_input_weights, 77 cell_to_forget_weights, 78 cell_to_output_weights, 79 80 input_gate_bias, 81 forget_gate_bias, 82 cell_gate_bias, 83 output_gate_bias, 84 85 projection_weights, 86 projection_bias, 87 88 output_state_in, 89 cell_state_in, 90 91 activation_param, 92 cell_clip_param, 93 proj_clip_param 94).To([scratch_buffer, output_state_out, cell_state_out, output]) 95model = model.RelaxedExecution(True) 96 97input0 = {input_to_input_weights: [ 98 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, 99 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, 100 -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, 101 -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, 102 -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, 103 -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, 104 -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, 105 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, 106 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, 107 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, 108 -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, 109 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, 110 -0.028545704, 0.024939531, 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-0.08261836, -0.51210177, 0.002913762, 0.17764764, 542 -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544, 543 -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817, 544 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733], 545 546 projection_weights: [ 547 -0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832, 548 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683, 549 -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931, 550 -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476, 551 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067, 552 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787, 553 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588, 554 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285, 555 -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949, 556 -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768, 557 -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929, 558 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504, 559 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946, 560 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117, 561 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253, 562 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456, 563 -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552, 564 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797, 565 -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272, 566 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165, 567 -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922, 568 -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548, 569 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786, 570 -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722, 571 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318, 572 -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776, 573 -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307, 574 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969, 575 -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593, 576 -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515, 577 -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288, 578 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723, 579 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097, 580 -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209, 581 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, 582 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, 583 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, 584 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, 585 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, 586 -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, 587 -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, 588 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, 589 -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, 590 -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, 591 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, 592 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, 593 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, 594 -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, 595 -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, 596 -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, 597 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, 598 -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, 599 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, 600 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, 601 -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, 602 -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, 603 -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, 604 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, 605 -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, 606 -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, 607 -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, 608 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, 609 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, 610 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656], 611 612 projection_bias: [], 613} 614 615# Batch0: 4 (input_sequence_size) * 5 (n_input) 616input0[input] = [0.787926, 0.151646, 0.071352, 0.118426, 0.458058] 617# Batch1: 4 (input_sequence_size) * 5 (n_input) 618input0[input].extend( 619 [0.295743, 0.544053, 0.690064, 0.858138, 0.497181], 620) 621input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ] 622input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ] 623output0 = { 624 scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], 625 cell_state_out: [ 626 -0.0531632, -0.0118138, 0.0870833, 0.0347929, 627 -0.076144, -0.0659219, -0.0463811, 0.0141307, 628 -0.0127706, -0.03782, -0.00402401, -0.00571876, 629 -0.187957, -0.0247127, 0.0711425, 0.008244, 630 0.0492649, 0.126972, 0.0933097, 0.29848, 631 -0.0966178, -0.114417, 0.0387229, 0.0453255, 632 -0.181286, -0.0651251, -0.0996879, -0.00276995, 633 0.0617558, -0.0100728, 0.056304, -0.077416, 634 -0.162858, -0.0541251, 0.0571202, -0.0525331, 635 0.0724297, 0.171029, 0.141738, 0.295483, 636 ], 637 output_state_out: [ 638 -0.00396806, 0.029352, -0.00279226, 0.0159977, 639 -0.00835577, -0.0211779, 0.0283512, -0.0114597, 640 0.00907307, -0.0244004, -0.0152191, -0.0259063, 641 0.00914318, 0.00415119, 0.017147, 0.0134203, 642 -0.013869, 0.0287268, -0.00334694, 0.00733397, 643 -0.0287926, -0.0186926, 0.0193662, -0.0115437, 644 0.00422612, -0.0345232, 0.00223253, -0.00957321, 645 0.0210624, 0.013331, 0.0150954, 0.0216801 646 ], 647} 648# Batch0: 4 (input_sequence_size) * 16 (n_output) 649output0[output] = [ 650 -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, 651 -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, 652 -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, 653 0.0134203] 654# Batch1: 4 (input_sequence_size) * 16 (n_output) 655output0[output].extend( 656 [-0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, 657 -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, 658 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, 659 0.02168], 660 ) 661 662Example((input0, output0)) 663