more work (the wall is non-causal decoding......)
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@ -1,52 +1,130 @@
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# this is a VERY rudimentary script to test if a HF-ified model works (it sort of does)
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import torch
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from transformers import LlamaForCausalLM, LlamaTokenizer
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from torch.distributions import Categorical
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# tokenizer = LlamaTokenizer.from_pretrained("./training/llama-encodec-ar+nar-len/hf/")
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model = LlamaForCausalLM.from_pretrained("./training/llama-encodec-ar+nar-len/hf/")
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phns = [1,85,4,128,26,4,186,4,89,33,25,4,48,4,134,25,52,86,4,34,97,27,11,2]
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proms = [
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[780,835,835,835,339,395,798,537,537,537,537,222,76,989,548,65,705,375,261,375,297,503,529,571,707,346,464,862,148,496,574,115,115,438,934,339,865,876,63,40,779,461,602,794,10,220,398,869,639,705,869,917,705,893,215,705,869,938,439,175,139,506,375,529,297,705,651,238,962,461,195,441,377,581,473,795,644,626,459,981,767,670,696,73,779,257,408,1017,1019,133,133,1017,835,604,699,626,67,92,707,92,179,179,772,869,441,799,917,238,745,904,904,904,106,133,1019,1017,1017,395,883,87,519,594,1002,682,996,540,186,1019,430,202,347,889,61,92,542,297,67,669,571,707,346,67,359,571,707,669,604,25,1008,810,35,621,67,600,333,123,284,568,817,243,778,464,638,610,359,538,464,975,321,700,377,484,179,284,284,621,538,464,745,171,171,159,744,159,287,461,69,15,529,67,92,669,464,515,605,24,822,865,293,62,172,638,359,562,138,839,846,775,556,688,1006,917,297,312,148,331,496,646,67,314,15,705,131,855,662,287,172,85,538,519,762,450,391,609,643,778,80,287,794,794,115,785,794,461,699,519,932,522,652,262,508,902,932,932,391,769,18,507,90,442,762,610,610,669,605,310,855,56,989,863,195,464,604,257,904,632,786,951,461,239,195,878,771,146,481,146,481,434,643,917,280,67,464,115,744,744,115,115,115,819,709,63,368,359,519,996,616,464,996,616,519,762,917,841,772,568,954,600,422,893,592,464,626,86,143,615,171,744,744,196,115,821,415,521,799,654,839,644,473,592,953,523,855,738,855,876,876,1017,63,329]
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]
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sep = [17685]
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rvq_lvl = [17666]
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lang = [17686]
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len_seq = [17674]
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for i, t in enumerate( proms[0] ):
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proms[0][i] = t + 256 + 1024
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ids = torch.tensor(phns + sep + lang + sep + rvq_lvl + sep + proms[0] + sep + len_seq, device="cuda", dtype=torch.int32)
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pos_ids = torch.tensor( [*range(len(phns)+1)] + [*range(2)] + [*range(2)] + [*range(len(proms[0])+1)] + [0], device="cuda", dtype=torch.int32)
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start = 17674 # 8448
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end = start + 10 # 1025
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with torch.no_grad():
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original_lm_head = model.lm_head.weight
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model.lm_head = torch.nn.Linear(1024, end - start, bias=False)
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model.lm_head.weight.copy_(original_lm_head[start:end])
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model.to(device="cuda", dtype=torch.float16)
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model.to(device="cuda", dtype=torch.bfloat16)
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model.eval()
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n_decoded = 0
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while True:
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out = model(input_ids=ids.unsqueeze(0), position_ids=pos_ids.unsqueeze(0))
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mode = "nar"
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#logits = out.logits[0, -1:, start:end]
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logits = out.logits[0, -1:, :]
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phn = [1,22,111,100,4,37,115,169,11,2]
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prom = [
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[62,835,835,835,339,395,798,537,537,537,537,222,76,989,548,65,705,375,261,375,297,503,529,571,707,346,266,862,148,496,574,115,115,438,934,339,865,876,63,40,779,461,602,794,10,220,507,869,639,705,869,917,705,893,917,705,869,938,439,175,139,506,375,529,297,705,651,238,962,461,195,441,377,581,473,795,644,626,459,981,767,670,696,73,779,257,738,1017,1019,133,133,1017,835,604,699,626,67,92,707,92,179,179,772,869,441,799,630,238,745,904,904,904,106,133,133,1017,1017,395,883,87,519,594,1002,682,996,540,186,855,430,202,347,889,61,92,542,297,67,669,571,707,346,67,359,571,707,669,604,395,1008,810,35,621,67,600,333,123,284,568,817,243,778,464,638,610,359,538,464,975,321,700,377,484,179,284,284,621,538,464,745,171,171,159,744,744,287,461,69,15,529,67,92,669,464,515,605,24,822,865,293,865,172,638,359,562,138,839,846,775,556,775,1006,917,346,312,148,331,496,646,67,314,15,705,131,855,662,287,172,85,107,519,374,450,391,609,643,778,80,287,794,794,115,785,794,461,699,519,932,522,652,262,508,902,932,932,391,769,18,507,90,442,762,610,610,669,605,35,855,56,989,863,195,464,604,257,904,632,786,951,461,239,195,878,771,146,481,146,481,434,643,917,280,67,464,115,744,744,115,115,115,819,709,63,907,359,519,996,616,682,996,616,519,762,917,841,772,568,954,600,422,893,592,464,626,86,143,615,171,744,744,196,115,821,415,521,799,654,839,644,473,592,953,523,855,738,855,855,876,1017,63,329],
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[913,859,740,740,937,601,961,961,877,747,747,559,474,618,20,316,58,316,180,112,290,869,610,869,869,943,127,153,236,794,282,857,984,196,875,648,993,913,860,616,38,833,620,133,123,992,247,367,252,50,298,27,27,631,163,784,271,20,843,514,869,258,180,66,803,281,123,493,831,102,556,992,385,122,31,251,990,827,26,347,460,43,43,460,228,43,841,913,302,544,544,277,859,404,646,775,315,848,726,185,203,314,203,174,252,174,378,954,214,993,924,809,277,765,363,544,363,518,791,185,454,193,193,193,193,193,573,977,924,76,434,56,193,962,610,24,954,459,396,112,903,137,398,474,506,791,839,399,102,25,205,792,459,474,526,817,869,192,792,593,878,506,24,410,539,788,522,667,566,584,588,992,444,24,869,925,635,393,903,742,320,1023,833,136,216,924,220,24,563,630,968,96,708,24,708,127,399,364,67,740,381,981,203,248,607,744,252,996,474,582,248,527,423,25,387,94,229,775,122,474,792,367,650,371,413,448,448,784,506,795,848,298,27,526,96,905,70,693,956,1002,1002,37,747,857,993,124,193,193,193,193,732,732,732,992,447,792,929,291,289,524,451,27,27,524,202,693,374,1002,125,732,585,367,317,679,395,413,189,493,386,650,110,912,505,384,399,851,367,367,27,230,988,810,975,842,956,1002,4,551,729,956,1002,750,648,231,950,193,96,912,410,732,539,103,193,904,491,213,792,792,998,193,399,151,410,96,673,497,1002,241,833,956,630,43,399,775,732,792,792,792,792,917,750,185,812,812,700,859,841,363,833,630],
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[786,36,821,937,1000,705,1016,345,345,470,165,581,95,404,95,95,1006,477,95,95,691,254,997,657,176,124,95,673,489,326,218,437,907,590,752,541,1016,821,445,563,181,555,181,345,576,190,987,0,265,997,488,12,598,687,152,108,52,95,95,71,87,945,95,997,754,488,955,694,925,82,18,1020,1006,542,788,441,325,532,246,132,560,532,947,655,653,842,732,36,36,829,36,937,989,989,752,651,87,489,677,260,789,462,95,227,986,955,95,810,624,435,280,868,832,879,863,821,829,937,168,270,489,544,909,562,957,0,593,714,675,690,626,227,794,489,489,563,489,298,269,741,249,516,360,240,516,336,93,808,1022,682,555,737,147,405,476,895,323,694,412,689,963,72,193,298,181,521,741,193,93,153,773,677,689,495,30,564,719,1020,559,940,53,53,53,929,360,971,403,1012,997,919,957,433,919,787,401,401,355,276,370,414,690,697,330,629,552,930,720,259,579,221,62,945,135,1020,626,663,401,153,997,381,830,185,587,853,207,126,66,529,410,113,997,488,431,563,488,488,719,746,790,296,843,752,790,23,984,292,41,27,120,249,124,900,358,801,227,978,95,997,997,997,371,561,86,388,52,667,601,894,545,997,498,900,494,365,852,986,95,841,664,256,18,1020,963,901,447,498,262,388,691,997,646,651,757,468,114,601,437,940,212,655,541,970,870,521,237,957,563,794,563,564,620,489,351,489,489,257,733,629,489,227,622,962,7,598,374,470,114,159,211,298,363,843,818,153,59,452,529,258,419,605,689,526,39,982,829,982,752,678,1005,312],
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[673,673,919,866,762,961,52,674,528,528,675,526,12,753,297,967,661,845,482,303,338,1021,506,445,247,214,206,94,434,799,210,885,552,695,853,1022,916,762,764,721,445,434,529,999,771,708,767,498,282,736,227,150,299,12,536,767,321,561,12,530,147,530,262,325,196,990,874,997,944,875,426,12,282,571,571,282,365,534,365,424,89,388,563,222,31,1019,624,74,215,651,1018,74,956,1022,74,18,633,350,72,448,454,769,267,938,12,534,929,723,829,614,505,364,1018,1014,838,673,919,74,223,761,266,78,177,736,20,718,425,1001,366,58,874,58,153,627,312,197,801,530,767,674,196,633,327,425,376,413,1019,209,594,383,744,458,468,711,282,885,640,435,655,571,556,1020,310,116,273,116,504,633,15,736,633,448,662,612,487,345,19,612,665,556,198,778,705,403,706,31,196,197,536,805,427,339,161,241,116,504,58,945,853,734,670,424,807,19,397,175,144,419,19,221,697,68,321,800,210,824,972,712,911,362,427,694,182,651,972,863,684,887,548,806,27,627,639,432,193,103,198,436,837,366,212,125,1001,493,874,808,17,17,127,204,530,300,345,425,246,240,640,906,340,310,633,246,774,114,633,522,777,874,494,577,353,939,571,693,857,722,530,521,354,492,735,214,806,483,736,530,118,234,536,177,132,522,349,259,436,973,528,414,224,762,212,854,744,271,568,127,323,736,304,499,499,78,536,736,805,232,126,468,566,611,52,339,450,258,157,602,594,854,602,599,82,124,472,563,666,174,936,818,66,758,627,52,350,999,734,215,919,1018,874,885],
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[528,448,646,190,222,884,939,907,907,673,413,786,527,517,710,449,119,531,565,762,531,501,522,246,162,871,8,594,206,937,462,712,862,151,103,261,882,990,1007,314,683,864,693,812,319,786,107,531,31,342,632,460,269,429,531,531,717,417,321,671,1015,152,467,863,285,875,941,417,475,825,596,957,117,460,162,162,117,630,735,527,272,558,38,39,605,375,39,900,862,646,712,804,622,963,407,93,828,796,306,415,70,667,371,531,1000,411,710,162,812,381,673,498,691,884,928,712,528,48,630,24,593,901,973,579,722,75,139,909,919,328,764,393,777,753,512,577,175,577,512,922,834,863,30,69,94,68,616,691,835,335,486,345,306,374,732,938,580,311,715,495,527,1008,306,369,663,512,369,320,360,80,42,1021,1021,1021,175,568,526,362,320,317,488,613,937,548,966,545,596,177,306,480,522,577,512,512,638,1008,82,100,696,89,714,531,639,460,679,718,492,509,492,624,460,572,531,306,19,473,915,558,285,319,713,1018,381,877,667,425,905,43,437,632,634,324,306,207,324,303,48,69,467,39,902,599,3,617,465,78,918,459,1009,427,751,145,531,349,356,1021,157,507,780,624,165,507,144,270,94,414,899,379,947,994,853,107,586,652,877,92,19,91,188,544,624,470,503,513,13,192,563,145,531,618,743,470,62,701,499,436,679,505,198,959,3,766,839,437,491,395,1021,512,306,512,356,851,1021,1021,78,690,856,735,286,280,4,1008,369,359,309,651,864,561,170,692,952,877,520,959,306,37,1021,31,236,162,773,522,254,446,606,691,804,882,58,974],
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[1011,939,881,881,140,937,724,724,937,1011,381,229,965,251,745,69,305,206,566,813,503,116,940,127,353,621,57,779,595,744,755,530,701,862,760,443,293,768,156,281,960,504,327,979,55,790,545,953,830,759,667,485,861,63,485,55,898,581,520,49,99,651,940,945,685,621,728,487,650,530,934,378,522,522,522,996,534,522,739,534,378,543,94,602,390,948,692,692,41,41,768,412,982,692,692,774,176,791,526,497,57,940,542,685,694,916,813,890,357,193,430,863,929,412,412,903,140,763,465,707,569,925,859,985,24,411,835,298,293,791,837,460,182,296,137,474,809,111,376,1021,111,490,111,938,542,578,477,506,57,385,300,873,240,104,667,204,515,834,24,125,113,980,111,997,859,997,376,193,490,824,511,799,719,575,451,575,251,222,630,429,920,788,300,993,641,154,816,940,618,130,940,462,823,955,1001,569,508,632,2,903,399,333,709,489,726,932,725,777,970,843,717,940,211,534,274,161,392,103,31,462,813,985,638,213,352,219,236,381,287,111,87,818,953,112,336,980,1016,72,960,426,238,60,9,487,665,129,24,24,162,312,411,111,157,473,466,222,940,341,55,457,712,179,451,111,831,918,826,814,940,30,468,240,207,389,923,186,95,300,876,679,576,543,582,111,227,312,112,545,747,378,165,158,610,601,425,238,704,630,124,644,949,982,297,868,569,24,57,465,24,859,111,24,752,775,24,647,465,495,57,24,57,227,907,296,581,843,1013,514,555,319,937,347,478,186,684,15,241,534,369,381,846,578,314,711,814,435,41,986,673,991],
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[485,748,562,562,485,380,834,997,78,963,755,142,978,135,362,421,217,79,530,1012,972,946,127,587,838,818,456,548,424,479,944,650,694,447,391,616,938,908,206,259,998,292,818,128,353,273,566,796,333,146,110,986,571,451,166,229,421,300,911,689,329,145,287,273,542,808,301,491,0,278,825,442,0,100,818,826,66,904,642,566,135,305,999,993,905,485,755,782,365,977,485,1015,570,1002,755,169,967,36,721,1019,273,931,273,166,216,31,346,946,32,290,362,828,464,748,782,1002,1015,755,1014,100,315,777,549,177,882,110,603,975,531,608,67,1011,950,465,368,416,798,941,635,602,553,300,200,644,498,325,786,734,342,222,403,1,716,175,899,273,40,333,999,74,54,644,408,976,407,631,577,338,435,612,333,273,162,709,882,555,384,995,173,459,442,72,72,200,72,711,219,282,716,442,431,801,976,130,622,72,582,384,516,772,0,440,1001,249,1,953,65,945,438,249,511,561,205,507,821,998,427,746,290,544,426,693,999,190,214,167,219,534,166,325,975,414,326,326,268,679,991,418,868,445,632,160,380,890,346,315,806,258,806,486,326,797,471,18,790,33,66,63,66,224,38,599,599,110,801,761,18,936,230,253,171,393,774,887,887,403,466,495,524,261,666,256,687,759,263,713,185,454,242,988,185,161,911,430,86,550,439,327,527,671,782,383,916,590,315,806,583,465,785,321,315,421,856,66,352,0,634,540,362,948,185,16,224,372,694,259,648,87,733,659,603,67,269,901,66,566,173,705,746,566,911,10,743,860,78,782,1002,755,389,175],
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[948,948,975,975,948,322,672,639,902,55,916,439,498,389,407,682,451,401,386,440,499,348,736,891,603,762,783,407,886,76,543,699,137,458,639,253,63,475,55,436,502,888,542,131,524,167,738,131,907,29,378,545,227,382,478,399,218,872,917,202,330,2,371,264,667,355,1016,768,590,408,463,542,214,202,715,891,840,297,509,689,290,439,672,714,528,940,1019,534,975,475,1019,835,975,558,975,981,330,635,96,858,606,627,367,191,191,669,40,873,359,267,701,426,210,1012,899,975,475,1012,610,6,300,749,231,616,877,631,720,574,551,398,503,789,684,664,390,277,150,990,823,190,971,903,175,863,316,965,988,988,800,612,336,506,242,847,389,939,415,202,83,317,2,153,365,363,57,2,891,965,300,754,763,426,555,621,303,415,367,902,829,741,119,380,902,25,884,439,822,49,76,760,566,316,249,555,774,955,834,309,859,173,935,812,682,586,141,606,197,131,644,631,913,586,202,117,810,884,76,592,754,531,586,925,649,583,145,816,821,283,871,1017,316,377,646,339,201,76,780,76,976,217,38,598,977,617,825,833,49,231,749,749,633,205,231,271,50,249,684,555,982,526,895,288,22,57,722,996,260,1018,110,833,644,738,648,468,798,297,769,282,197,402,465,510,194,930,182,909,749,986,187,187,917,38,38,985,985,988,815,878,814,459,237,768,781,649,683,749,934,729,463,181,625,231,917,96,499,839,720,439,842,205,808,338,617,681,326,446,905,346,647,533,49,728,147,432,846,536,586,611,49,879,872,893,859,859,961,989,975,701,495,65],
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]
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resp = [
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[922,738,461,341,341,10,416,416,416,416,346,346,346,346,346,484,484,484,484,484,484,333,442,442,359,359,359,459,459,975,975,626,626,626,626,626,610,359,359,359,359,359,359,359,359,359,610,610,442,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,638,638,638,638,975,975,672,875,63,144],
|
||||
[993,700,384,213,794,10,305,778,58,225,118,260,768,768,260,474,903,732,70,992,447,70,1000,665,848,379,485,934,181,795,438,298,688,324,934,756,395,795,110,328,343,172,768,871,593,355,396,783,24,24,911,20,27,562,697,616,668,27,27,755,20,505,248,79,822,461,197,156,27,492,151,1013,669,669,562],
|
||||
[626,989,936,488,511,624,997,112,112,648,210,650,563,650,41,41,490,920,977,986,920,927,131,167,167,968,346,168,167,168,120,355,766,599,712,390,558,810,948,332,332,867,994,346,955,392,920,452,576,346,52,254,52,307,897,307,968,920,167,563,167,167,167,968,167,488,968,488,1001,938,563,741,432,566,758],
|
||||
[916,874,798,212,496,751,620,616,982,745,975,890,890,141,141,321,321,214,899,42,151,722,310,971,774,35,627,995,27,43,248,248,595,774,942,352,810,35,384,340,654,639,89,214,737,197,657,45,622,321,337,19,483,679,938,938,682,938,938,141,938,310,114,724,116,327,372,607,607,310,204,713,762,853,853],
|
||||
[528,222,992,727,536,191,202,483,306,568,533,577,398,533,202,24,753,753,739,739,643,513,4,324,369,66,447,201,66,802,66,957,665,526,602,749,483,447,193,853,531,201,201,71,888,202,66,66,650,228,533,102,639,513,533,531,533,471,344,566,201,639,471,639,732,594,464,308,116,533,116,174,959,621,539],
|
||||
[692,632,478,375,910,857,775,503,503,193,717,548,344,717,55,808,162,112,112,112,543,582,847,712,691,679,427,940,369,475,153,526,729,269,323,721,526,211,191,192,685,844,731,813,914,545,582,712,925,916,375,111,340,162,844,940,844,162,844,990,111,491,232,582,491,582,618,121,1020,664,670,254,315,438,723],
|
||||
[365,908,896,819,206,153,515,471,75,79,664,145,145,801,135,321,79,216,233,223,79,66,724,517,135,474,818,818,105,892,971,337,818,19,932,981,469,135,163,75,135,818,999,555,135,710,256,105,590,31,539,1003,517,130,445,40,549,130,859,385,1003,1003,549,33,286,932,329,774,321,664,686,16,834,703,290],
|
||||
[899,237,832,748,425,121,460,872,391,586,857,215,306,76,306,554,187,57,482,406,802,555,710,895,448,517,506,316,18,772,779,697,855,1005,792,96,402,96,517,775,506,938,114,986,986,503,749,984,524,527,506,749,463,490,188,374,506,49,537,188,494,900,526,524,524,500,500,345,630,338,982,761,700,598,749],
|
||||
]
|
||||
|
||||
sep = [291]
|
||||
rvq_lvl = [256]
|
||||
lang = [264]
|
||||
|
||||
for l, codes in enumerate( prom ):
|
||||
for i, t in enumerate( codes ):
|
||||
prom[l][i] += 292 + (1024 * l)
|
||||
|
||||
for l, codes in enumerate( resp ):
|
||||
for i, t in enumerate( codes ):
|
||||
resp[l][i] += 9509 + (1024 * l)
|
||||
|
||||
ids = torch.tensor([])
|
||||
pos_ids = torch.tensor([])
|
||||
|
||||
ids = torch.concat([ ids, torch.tensor(phn), torch.tensor(sep) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(phn) + 1 ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
ids = torch.concat([ ids, torch.tensor(lang), torch.tensor(sep) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(lang) + 1 ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
ids = torch.concat([ ids, torch.tensor(rvq_lvl), torch.tensor(sep) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(rvq_lvl) + 1 ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
ids = torch.concat([ ids, torch.tensor(prom[0]), torch.tensor(sep) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(prom[0]) + 1 ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
|
||||
start, end, stop = (None, None, None)
|
||||
if mode == "len":
|
||||
len_seq = [279]
|
||||
|
||||
ids = torch.concat([ ids, torch.tensor(len_seq) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(len_seq) ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
start, end, stop = (279, 279+11, 10)
|
||||
max_n = 10
|
||||
outputs = 1
|
||||
elif mode =="ar":
|
||||
start, end, stop = (8484, 8484+1025, 1024)
|
||||
max_n = 350
|
||||
outputs = 1
|
||||
elif mode =="nar":
|
||||
ids = torch.concat([ ids, torch.tensor(resp[0]) ])
|
||||
seq = torch.tensor([ _ for _ in range( len(resp[0]) ) ])
|
||||
pos_ids = torch.concat([ pos_ids, seq ])
|
||||
|
||||
start, end, stop = (9509, 9509+1024, None)
|
||||
max_n = 1
|
||||
outputs = len(resp[0])
|
||||
|
||||
ids = ids.to(device="cuda", dtype=torch.int32)
|
||||
pos_ids = pos_ids.to(device="cuda", dtype=torch.int32)
|
||||
attention_mask = torch.tensor([ True for _ in range( ids.shape[0] ) ], dtype=torch.bool)
|
||||
|
||||
n = 0
|
||||
with torch.no_grad():
|
||||
while n < max_n:
|
||||
"""
|
||||
if n == 0:
|
||||
embs = model.model.embed_tokens( ids )
|
||||
for i, emb in enumerate( embs ):
|
||||
print( i, ids[i].item(), sum(emb).item(), pos_ids[i].item() )
|
||||
"""
|
||||
|
||||
out = model(input_ids=ids.unsqueeze(0), position_ids=pos_ids.unsqueeze(0), attention_mask=attention_mask.unsqueeze(0))
|
||||
logits = out.logits[0, -outputs:, start:end]
|
||||
|
||||
if mode == "ar":
|
||||
tokens = Categorical(logits=logits).sample()
|
||||
else:
|
||||
tokens = logits.argmax(dim=-1)
|
||||
n_decoded += 1
|
||||
|
||||
print( n_decoded, tokens )
|
||||
n += 1
|
||||
|
||||
if end in tokens or n_decoded > 5:
|
||||
print( n, tokens )
|
||||
|
||||
if outputs == 1:
|
||||
if stop in tokens:
|
||||
break
|
||||
|
||||
ids = torch.concat( [ ids, tokens + start ] )
|
||||
pos_ids = torch.concat( [ pos_ids, torch.tensor([n_decoded]).to(pos_ids) ] )
|
||||
pos_ids = torch.concat( [ pos_ids, torch.tensor([n]).to(pos_ids) ] )
|
||||
attention_mask = torch.concat([ attention_mask, torch.tensor([True]).to(attention_mask) ])
|
||||
|
||||
print( out )
|
||||
print( ids )
|
||||
print( pos_ids )
|
||||
|
|
@ -12,20 +12,20 @@ Populate `./libs/` with the compiled libraries of `llama.cpp` and `encodec.cpp`.
|
|||
|
||||
Run `make`.
|
||||
|
||||
|
||||
### Required Modifications
|
||||
|
||||
`encodec.cpp` requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working.
|
||||
`llama.cpp` *might* not require any modifications, but:
|
||||
[`encodec.cpp`](https://github.com/e-c-k-e-r/encodec.cpp) requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working.
|
||||
[`llama.cpp`](https://github.com/e-c-k-e-r/llama.cpp) *might* not require any modifications, but:
|
||||
* `llm.build_vall_e` can mostly copy `llm.build_llama`, but with:
|
||||
* `KQ_mask = build_inp_KQ_mask( lctx.cparams.causal_attn )`
|
||||
* a unified output head (pain)
|
||||
* OR adjusting the `model.output` to the correct classifier head
|
||||
* OR adjusting the `model.output` to the correct classifier head (better option)
|
||||
* OR slicing that tensor with the right range (`ggml_view_2d` confuses me)
|
||||
* both require also require `*const_cast<uint32_t*>(&ctx->model.hparams.n_vocab) = output->ne[1];` because the logits are tied to `n_vocab`
|
||||
* commenting out `GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str());` because grabbing embeddings/classifiers require using `bid` to trick it thinking it's part of a layer
|
||||
* some helper functions to retrieve the embeddings tensor from the model
|
||||
* some helper functions to set the target classifier head
|
||||
* some fix for `GGML_ASSERT(mask->ne[0] == a->ne[0])` when using a non-causal attention mask (or I can test on the model that had a causal NAR......)
|
||||
|
||||
## To-Do
|
||||
|
||||
|
|
@ -42,10 +42,15 @@ Run `make`.
|
|||
* [x] load audio from disk
|
||||
* [x] encode audio
|
||||
* [x] sum embeddings for the `prom` and prior `resp`s
|
||||
* [ ] working `AR` output
|
||||
* [x] `AR` sampling
|
||||
* [ ] `NAR-len` demasking sampling
|
||||
* currently need a model that didn't regress with the `AR:0:0` output
|
||||
* [ ] working `NAR-len` output
|
||||
* [ ] `NAR-len` sampling
|
||||
* currently cannot inference with non-causal_attn
|
||||
* [ ] working `NAR` output
|
||||
* [x] `NAR` sampling
|
||||
* currently cannot inference with non-causal_attn
|
||||
* [x] decode audio to disk
|
||||
* [ ] a functional CLI
|
||||
* [ ] actually make it work
|
||||
* it seems naively stitching the model together isn't good enough since the output is wrong, it most likely needs training with a glued together classifier
|
||||
|
|
@ -14,8 +14,8 @@
|
|||
#include <unordered_map>
|
||||
#include <iostream>
|
||||
|
||||
#define LLAMA_CPP_EXTENDED 0 // whether the underlying llama.cpp has some extra functions
|
||||
#define LLAMA_CPP_USE_VALL_E_ARCH 0 // whether the underlying llama.cpp is to use the VALL_E arch
|
||||
#define LLAMA_CPP_EXTENDED 1 // whether the underlying llama.cpp has some extra functions
|
||||
#define LLAMA_CPP_USE_VALL_E_ARCH 1 // whether the underlying llama.cpp is to use the VALL_E arch (or using LLAMA arch)
|
||||
|
||||
#if !LLAMA_CPP_EXTENDED
|
||||
#include "_llama.h" // cringe hotfix but I have to do this until llama.cpp's API exposes the tok_embd
|
||||
|
|
@ -134,6 +134,10 @@ struct embeddings_map_t {
|
|||
return mapped_embeddings[name].embds.data();
|
||||
}
|
||||
|
||||
int32_t get_classifier_idx( const std::string& name ) {
|
||||
return mapped_embeddings[name].range.classifier_idx;
|
||||
}
|
||||
|
||||
void init( llama_model* model ) {
|
||||
this->n_embd = llama_n_embd( model );
|
||||
this->n_vocab = llama_n_vocab( model );
|
||||
|
|
@ -142,10 +146,10 @@ struct embeddings_map_t {
|
|||
#if LLAMA_CPP_USE_VALL_E_ARCH
|
||||
mapped_embeddings["text"] = { n_embd, 0, { "text", 0, 0, 9, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 0)) };
|
||||
mapped_embeddings["rvq_l"] = { n_embd, 0, { "rvq_l", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 1)) };
|
||||
mapped_embeddings["langs"] = { n_embd, 0, { "langs", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 2)) };
|
||||
mapped_embeddings["tasks"] = { n_embd, 0, { "tasks", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 3)) };
|
||||
mapped_embeddings["lang"] = { n_embd, 0, { "lang", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 2)) };
|
||||
mapped_embeddings["task"] = { n_embd, 0, { "task", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 3)) };
|
||||
mapped_embeddings["len"] = { n_embd, 0, { "len", 0, 0, 10, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 4)) };
|
||||
mapped_embeddings["tones"] = { n_embd, 0, { "tones", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 5)) };
|
||||
mapped_embeddings["tone"] = { n_embd, 0, { "tone", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 5)) };
|
||||
mapped_embeddings["sep"] = { n_embd, 0, { "sep", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 6)) };
|
||||
|
||||
mapped_embeddings["prom|0"] = { n_embd, 0, { "prom|0", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 0)) };
|
||||
|
|
@ -347,7 +351,7 @@ const int INFERENCE_MODE_NAR = 3;
|
|||
const int MODALITY_AR_NAR = 0;
|
||||
const int MODALITY_NAR_LEN = 1;
|
||||
|
||||
const int MAX_DURATION = 75; // * 12;
|
||||
const int MAX_DURATION = 75 * 12;
|
||||
const int CTX_SIZE = 2048;
|
||||
|
||||
// sums embeddings over a 2D "tensor"
|
||||
|
|
@ -486,11 +490,9 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
|
|||
auto& embeddings = embeddings_map.get_embeddings("resps|AR:0:0");
|
||||
range = embeddings.range;
|
||||
embds = embeddings.embds.data();
|
||||
stop_token = range.end - 1;
|
||||
stop_token = range.end - range.start - 1;
|
||||
|
||||
// llama_set_classifier_index( ctx, 0 );
|
||||
|
||||
printf("Generating in %s mode (%i:%i)\n", "AR", range.start, range.end);
|
||||
printf("Generating in %s (%i) mode (%i:%i) (%i)\n", "AR", range.classifier_idx, range.start, range.end, stop_token);
|
||||
} else if ( mode == INFERENCE_MODE_NAR ) {
|
||||
std::string k_embds[] = {
|
||||
"resps|NAR:0:0", // invalid
|
||||
|
|
@ -506,26 +508,27 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
|
|||
range = embeddings.range;
|
||||
embds = embeddings.embds.data();
|
||||
|
||||
// llama_set_classifier_index( ctx, rvq_l );
|
||||
printf("Generating in %s mode (%i:%i)\n", "NAR", range.start, range.end);
|
||||
printf("Generating in %s (%i) mode (%i:%i)\n", "NAR", range.classifier_idx, range.start, range.end);
|
||||
} else if ( mode == INFERENCE_MODE_LEN ) {
|
||||
auto& embeddings = embeddings_map.get_embeddings("len");
|
||||
range = embeddings.range;
|
||||
embds = embeddings.embds.data();
|
||||
stop_token = range.end - 1;
|
||||
stop_token = range.end - range.start - 1;
|
||||
|
||||
// llama_set_classifier_index( ctx, 10 );
|
||||
printf("Generating in %s mode (%i:%i)\n", "len", range.start, range.end);
|
||||
printf("Generating in %s (%i) mode (%i:%i) (%i)\n", "len", range.classifier_idx, range.start, range.end, stop_token);
|
||||
} else if ( mode == INFERENCE_MODE_NAR_DEMASK ) {
|
||||
auto& embeddings = embeddings_map.get_embeddings("NAR:0:0");
|
||||
auto& embeddings = embeddings_map.get_embeddings("resps|NAR:0:0");
|
||||
range = embeddings.range;
|
||||
embds = embeddings.embds.data();
|
||||
|
||||
// llama_set_classifier_index( ctx, 8 );
|
||||
printf("Generating in %s mode (%i:%i)\n", "NAR-len", range.start, range.end);
|
||||
printf("Generating in %s (%i) mode (%i:%i)\n", "NAR-len", range.classifier_idx, range.start, range.end);
|
||||
}
|
||||
|
||||
llama_set_causal_attn( ctx, n_logits == 1 );
|
||||
#if LLAMA_CPP_USE_VALL_E_ARCH
|
||||
llama_set_output_index( model, range.classifier_idx );
|
||||
#endif
|
||||
llama_set_causal_attn( ctx, true ) ; // n_logits == 1 );
|
||||
// to-do: fix GGML_ASSERT(mask->ne[0] == a->ne[0])
|
||||
|
||||
std::vector<llama_token> output_tokens;
|
||||
while ( output_tokens.size() < max_tokens ) {
|
||||
|
|
@ -533,7 +536,6 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
|
|||
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
|
||||
return output_tokens;
|
||||
}
|
||||
n_decode += 1;
|
||||
|
||||
// backwards iterate to start from beginning of sequence
|
||||
for ( auto i = n_logits; i > 0; --i ) {
|
||||
|
|
@ -555,7 +557,8 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
|
|||
t -= range.start;
|
||||
#endif
|
||||
|
||||
printf("%i: %i\n", n_decode, t);
|
||||
printf("%s: %i: %i: %i\n", __func__, i, n_decode, t);
|
||||
n_decode += 1;
|
||||
|
||||
// is stop token
|
||||
if ( t == stop_token ) {
|
||||
|
|
@ -596,9 +599,31 @@ int main(int argc, char ** argv) {
|
|||
embeddings_map_t embeddings_map{};
|
||||
|
||||
// input.phonemes = "hˈɛloː ʋˈɔrlt";
|
||||
input.phn = {1,85,4,128,26,4,186,4,89,33,25,4,48,4,134,25,52,86,4,34,97,27,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
|
||||
input.phn = {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
|
||||
input.prom = {
|
||||
{62,835,835,835,339,395,798,537,537,537,537,222,76,989,548,65,705,375,261,375,297,503,529,571,707,346,266,862,148,496,574,115,115,438,934,339,865,876,63,40,779,461,602,794,10,220,507,869,639,705,869,917,705,893,917,705,869,938,439,175,139,506,375,529,297,705,651,238,962,461,195,441,377,581,473,795,644,626,459,981,767,670,696,73,779,257,738,1017,1019,133,133,1017,835,604,699,626,67,92,707,92,179,179,772,869,441,799,630,238,745,904,904,904,106,133,133,1017,1017,395,883,87,519,594,1002,682,996,540,186,855,430,202,347,889,61,92,542,297,67,669,571,707,346,67,359,571,707,669,604,395,1008,810,35,621,67,600,333,123,284,568,817,243,778,464,638,610,359,538,464,975,321,700,377,484,179,284,284,621,538,464,745,171,171,159,744,744,287,461,69,15,529,67,92,669,464,515,605,24,822,865,293,865,172,638,359,562,138,839,846,775,556,775,1006,917,346,312,148,331,496,646,67,314,15,705,131,855,662,287,172,85,107,519,374,450,391,609,643,778,80,287,794,794,115,785,794,461,699,519,932,522,652,262,508,902,932,932,391,769,18,507,90,442,762,610,610,669,605,35,855,56,989,863,195,464,604,257,904,632,786,951,461,239,195,878,771,146,481,146,481,434,643,917,280,67,464,115,744,744,115,115,115,819,709,63,907,359,519,996,616,682,996,616,519,762,917,841,772,568,954,600,422,893,592,464,626,86,143,615,171,744,744,196,115,821,415,521,799,654,839,644,473,592,953,523,855,738,855,855,876,1017,63,329},
|
||||
{913,859,740,740,937,601,961,961,877,747,747,559,474,618,20,316,58,316,180,112,290,869,610,869,869,943,127,153,236,794,282,857,984,196,875,648,993,913,860,616,38,833,620,133,123,992,247,367,252,50,298,27,27,631,163,784,271,20,843,514,869,258,180,66,803,281,123,493,831,102,556,992,385,122,31,251,990,827,26,347,460,43,43,460,228,43,841,913,302,544,544,277,859,404,646,775,315,848,726,185,203,314,203,174,252,174,378,954,214,993,924,809,277,765,363,544,363,518,791,185,454,193,193,193,193,193,573,977,924,76,434,56,193,962,610,24,954,459,396,112,903,137,398,474,506,791,839,399,102,25,205,792,459,474,526,817,869,192,792,593,878,506,24,410,539,788,522,667,566,584,588,992,444,24,869,925,635,393,903,742,320,1023,833,136,216,924,220,24,563,630,968,96,708,24,708,127,399,364,67,740,381,981,203,248,607,744,252,996,474,582,248,527,423,25,387,94,229,775,122,474,792,367,650,371,413,448,448,784,506,795,848,298,27,526,96,905,70,693,956,1002,1002,37,747,857,993,124,193,193,193,193,732,732,732,992,447,792,929,291,289,524,451,27,27,524,202,693,374,1002,125,732,585,367,317,679,395,413,189,493,386,650,110,912,505,384,399,851,367,367,27,230,988,810,975,842,956,1002,4,551,729,956,1002,750,648,231,950,193,96,912,410,732,539,103,193,904,491,213,792,792,998,193,399,151,410,96,673,497,1002,241,833,956,630,43,399,775,732,792,792,792,792,917,750,185,812,812,700,859,841,363,833,630},
|
||||
{786,36,821,937,1000,705,1016,345,345,470,165,581,95,404,95,95,1006,477,95,95,691,254,997,657,176,124,95,673,489,326,218,437,907,590,752,541,1016,821,445,563,181,555,181,345,576,190,987,0,265,997,488,12,598,687,152,108,52,95,95,71,87,945,95,997,754,488,955,694,925,82,18,1020,1006,542,788,441,325,532,246,132,560,532,947,655,653,842,732,36,36,829,36,937,989,989,752,651,87,489,677,260,789,462,95,227,986,955,95,810,624,435,280,868,832,879,863,821,829,937,168,270,489,544,909,562,957,0,593,714,675,690,626,227,794,489,489,563,489,298,269,741,249,516,360,240,516,336,93,808,1022,682,555,737,147,405,476,895,323,694,412,689,963,72,193,298,181,521,741,193,93,153,773,677,689,495,30,564,719,1020,559,940,53,53,53,929,360,971,403,1012,997,919,957,433,919,787,401,401,355,276,370,414,690,697,330,629,552,930,720,259,579,221,62,945,135,1020,626,663,401,153,997,381,830,185,587,853,207,126,66,529,410,113,997,488,431,563,488,488,719,746,790,296,843,752,790,23,984,292,41,27,120,249,124,900,358,801,227,978,95,997,997,997,371,561,86,388,52,667,601,894,545,997,498,900,494,365,852,986,95,841,664,256,18,1020,963,901,447,498,262,388,691,997,646,651,757,468,114,601,437,940,212,655,541,970,870,521,237,957,563,794,563,564,620,489,351,489,489,257,733,629,489,227,622,962,7,598,374,470,114,159,211,298,363,843,818,153,59,452,529,258,419,605,689,526,39,982,829,982,752,678,1005,312},
|
||||
{673,673,919,866,762,961,52,674,528,528,675,526,12,753,297,967,661,845,482,303,338,1021,506,445,247,214,206,94,434,799,210,885,552,695,853,1022,916,762,764,721,445,434,529,999,771,708,767,498,282,736,227,150,299,12,536,767,321,561,12,530,147,530,262,325,196,990,874,997,944,875,426,12,282,571,571,282,365,534,365,424,89,388,563,222,31,1019,624,74,215,651,1018,74,956,1022,74,18,633,350,72,448,454,769,267,938,12,534,929,723,829,614,505,364,1018,1014,838,673,919,74,223,761,266,78,177,736,20,718,425,1001,366,58,874,58,153,627,312,197,801,530,767,674,196,633,327,425,376,413,1019,209,594,383,744,458,468,711,282,885,640,435,655,571,556,1020,310,116,273,116,504,633,15,736,633,448,662,612,487,345,19,612,665,556,198,778,705,403,706,31,196,197,536,805,427,339,161,241,116,504,58,945,853,734,670,424,807,19,397,175,144,419,19,221,697,68,321,800,210,824,972,712,911,362,427,694,182,651,972,863,684,887,548,806,27,627,639,432,193,103,198,436,837,366,212,125,1001,493,874,808,17,17,127,204,530,300,345,425,246,240,640,906,340,310,633,246,774,114,633,522,777,874,494,577,353,939,571,693,857,722,530,521,354,492,735,214,806,483,736,530,118,234,536,177,132,522,349,259,436,973,528,414,224,762,212,854,744,271,568,127,323,736,304,499,499,78,536,736,805,232,126,468,566,611,52,339,450,258,157,602,594,854,602,599,82,124,472,563,666,174,936,818,66,758,627,52,350,999,734,215,919,1018,874,885},
|
||||
{528,448,646,190,222,884,939,907,907,673,413,786,527,517,710,449,119,531,565,762,531,501,522,246,162,871,8,594,206,937,462,712,862,151,103,261,882,990,1007,314,683,864,693,812,319,786,107,531,31,342,632,460,269,429,531,531,717,417,321,671,1015,152,467,863,285,875,941,417,475,825,596,957,117,460,162,162,117,630,735,527,272,558,38,39,605,375,39,900,862,646,712,804,622,963,407,93,828,796,306,415,70,667,371,531,1000,411,710,162,812,381,673,498,691,884,928,712,528,48,630,24,593,901,973,579,722,75,139,909,919,328,764,393,777,753,512,577,175,577,512,922,834,863,30,69,94,68,616,691,835,335,486,345,306,374,732,938,580,311,715,495,527,1008,306,369,663,512,369,320,360,80,42,1021,1021,1021,175,568,526,362,320,317,488,613,937,548,966,545,596,177,306,480,522,577,512,512,638,1008,82,100,696,89,714,531,639,460,679,718,492,509,492,624,460,572,531,306,19,473,915,558,285,319,713,1018,381,877,667,425,905,43,437,632,634,324,306,207,324,303,48,69,467,39,902,599,3,617,465,78,918,459,1009,427,751,145,531,349,356,1021,157,507,780,624,165,507,144,270,94,414,899,379,947,994,853,107,586,652,877,92,19,91,188,544,624,470,503,513,13,192,563,145,531,618,743,470,62,701,499,436,679,505,198,959,3,766,839,437,491,395,1021,512,306,512,356,851,1021,1021,78,690,856,735,286,280,4,1008,369,359,309,651,864,561,170,692,952,877,520,959,306,37,1021,31,236,162,773,522,254,446,606,691,804,882,58,974},
|
||||
{1011,939,881,881,140,937,724,724,937,1011,381,229,965,251,745,69,305,206,566,813,503,116,940,127,353,621,57,779,595,744,755,530,701,862,760,443,293,768,156,281,960,504,327,979,55,790,545,953,830,759,667,485,861,63,485,55,898,581,520,49,99,651,940,945,685,621,728,487,650,530,934,378,522,522,522,996,534,522,739,534,378,543,94,602,390,948,692,692,41,41,768,412,982,692,692,774,176,791,526,497,57,940,542,685,694,916,813,890,357,193,430,863,929,412,412,903,140,763,465,707,569,925,859,985,24,411,835,298,293,791,837,460,182,296,137,474,809,111,376,1021,111,490,111,938,542,578,477,506,57,385,300,873,240,104,667,204,515,834,24,125,113,980,111,997,859,997,376,193,490,824,511,799,719,575,451,575,251,222,630,429,920,788,300,993,641,154,816,940,618,130,940,462,823,955,1001,569,508,632,2,903,399,333,709,489,726,932,725,777,970,843,717,940,211,534,274,161,392,103,31,462,813,985,638,213,352,219,236,381,287,111,87,818,953,112,336,980,1016,72,960,426,238,60,9,487,665,129,24,24,162,312,411,111,157,473,466,222,940,341,55,457,712,179,451,111,831,918,826,814,940,30,468,240,207,389,923,186,95,300,876,679,576,543,582,111,227,312,112,545,747,378,165,158,610,601,425,238,704,630,124,644,949,982,297,868,569,24,57,465,24,859,111,24,752,775,24,647,465,495,57,24,57,227,907,296,581,843,1013,514,555,319,937,347,478,186,684,15,241,534,369,381,846,578,314,711,814,435,41,986,673,991},
|
||||
{485,748,562,562,485,380,834,997,78,963,755,142,978,135,362,421,217,79,530,1012,972,946,127,587,838,818,456,548,424,479,944,650,694,447,391,616,938,908,206,259,998,292,818,128,353,273,566,796,333,146,110,986,571,451,166,229,421,300,911,689,329,145,287,273,542,808,301,491,0,278,825,442,0,100,818,826,66,904,642,566,135,305,999,993,905,485,755,782,365,977,485,1015,570,1002,755,169,967,36,721,1019,273,931,273,166,216,31,346,946,32,290,362,828,464,748,782,1002,1015,755,1014,100,315,777,549,177,882,110,603,975,531,608,67,1011,950,465,368,416,798,941,635,602,553,300,200,644,498,325,786,734,342,222,403,1,716,175,899,273,40,333,999,74,54,644,408,976,407,631,577,338,435,612,333,273,162,709,882,555,384,995,173,459,442,72,72,200,72,711,219,282,716,442,431,801,976,130,622,72,582,384,516,772,0,440,1001,249,1,953,65,945,438,249,511,561,205,507,821,998,427,746,290,544,426,693,999,190,214,167,219,534,166,325,975,414,326,326,268,679,991,418,868,445,632,160,380,890,346,315,806,258,806,486,326,797,471,18,790,33,66,63,66,224,38,599,599,110,801,761,18,936,230,253,171,393,774,887,887,403,466,495,524,261,666,256,687,759,263,713,185,454,242,988,185,161,911,430,86,550,439,327,527,671,782,383,916,590,315,806,583,465,785,321,315,421,856,66,352,0,634,540,362,948,185,16,224,372,694,259,648,87,733,659,603,67,269,901,66,566,173,705,746,566,911,10,743,860,78,782,1002,755,389,175},
|
||||
{948,948,975,975,948,322,672,639,902,55,916,439,498,389,407,682,451,401,386,440,499,348,736,891,603,762,783,407,886,76,543,699,137,458,639,253,63,475,55,436,502,888,542,131,524,167,738,131,907,29,378,545,227,382,478,399,218,872,917,202,330,2,371,264,667,355,1016,768,590,408,463,542,214,202,715,891,840,297,509,689,290,439,672,714,528,940,1019,534,975,475,1019,835,975,558,975,981,330,635,96,858,606,627,367,191,191,669,40,873,359,267,701,426,210,1012,899,975,475,1012,610,6,300,749,231,616,877,631,720,574,551,398,503,789,684,664,390,277,150,990,823,190,971,903,175,863,316,965,988,988,800,612,336,506,242,847,389,939,415,202,83,317,2,153,365,363,57,2,891,965,300,754,763,426,555,621,303,415,367,902,829,741,119,380,902,25,884,439,822,49,76,760,566,316,249,555,774,955,834,309,859,173,935,812,682,586,141,606,197,131,644,631,913,586,202,117,810,884,76,592,754,531,586,925,649,583,145,816,821,283,871,1017,316,377,646,339,201,76,780,76,976,217,38,598,977,617,825,833,49,231,749,749,633,205,231,271,50,249,684,555,982,526,895,288,22,57,722,996,260,1018,110,833,644,738,648,468,798,297,769,282,197,402,465,510,194,930,182,909,749,986,187,187,917,38,38,985,985,988,815,878,814,459,237,768,781,649,683,749,934,729,463,181,625,231,917,96,499,839,720,439,842,205,808,338,617,681,326,446,905,346,647,533,49,728,147,432,846,536,586,611,49,879,872,893,859,859,961,989,975,701,495,65},
|
||||
};
|
||||
input.resp = {
|
||||
/*
|
||||
[922,738,461,341,341,10,416,416,416,416,346,346,346,346,346,484,484,484,484,484,484,333,442,442,359,359,359,459,459,975,975,626,626,626,626,626,610,359,359,359,359,359,359,359,359,359,610,610,442,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,638,638,638,638,975,975,672,875,63,144],
|
||||
[993,700,384,213,794,10,305,778,58,225,118,260,768,768,260,474,903,732,70,992,447,70,1000,665,848,379,485,934,181,795,438,298,688,324,934,756,395,795,110,328,343,172,768,871,593,355,396,783,24,24,911,20,27,562,697,616,668,27,27,755,20,505,248,79,822,461,197,156,27,492,151,1013,669,669,562],
|
||||
[626,989,936,488,511,624,997,112,112,648,210,650,563,650,41,41,490,920,977,986,920,927,131,167,167,968,346,168,167,168,120,355,766,599,712,390,558,810,948,332,332,867,994,346,955,392,920,452,576,346,52,254,52,307,897,307,968,920,167,563,167,167,167,968,167,488,968,488,1001,938,563,741,432,566,758],
|
||||
[916,874,798,212,496,751,620,616,982,745,975,890,890,141,141,321,321,214,899,42,151,722,310,971,774,35,627,995,27,43,248,248,595,774,942,352,810,35,384,340,654,639,89,214,737,197,657,45,622,321,337,19,483,679,938,938,682,938,938,141,938,310,114,724,116,327,372,607,607,310,204,713,762,853,853],
|
||||
[528,222,992,727,536,191,202,483,306,568,533,577,398,533,202,24,753,753,739,739,643,513,4,324,369,66,447,201,66,802,66,957,665,526,602,749,483,447,193,853,531,201,201,71,888,202,66,66,650,228,533,102,639,513,533,531,533,471,344,566,201,639,471,639,732,594,464,308,116,533,116,174,959,621,539],
|
||||
[692,632,478,375,910,857,775,503,503,193,717,548,344,717,55,808,162,112,112,112,543,582,847,712,691,679,427,940,369,475,153,526,729,269,323,721,526,211,191,192,685,844,731,813,914,545,582,712,925,916,375,111,340,162,844,940,844,162,844,990,111,491,232,582,491,582,618,121,1020,664,670,254,315,438,723],
|
||||
[365,908,896,819,206,153,515,471,75,79,664,145,145,801,135,321,79,216,233,223,79,66,724,517,135,474,818,818,105,892,971,337,818,19,932,981,469,135,163,75,135,818,999,555,135,710,256,105,590,31,539,1003,517,130,445,40,549,130,859,385,1003,1003,549,33,286,932,329,774,321,664,686,16,834,703,290],
|
||||
[899,237,832,748,425,121,460,872,391,586,857,215,306,76,306,554,187,57,482,406,802,555,710,895,448,517,506,316,18,772,779,697,855,1005,792,96,402,96,517,775,506,938,114,986,986,503,749,984,524,527,506,749,463,490,188,374,506,49,537,188,494,900,526,524,524,500,500,345,630,338,982,761,700,598,749],
|
||||
*/
|
||||
};
|
||||
|
||||
std::string vall_e_model_path = "./data/vall_e-f16.gguf";
|
||||
std::string vall_e_model_path = "./data/vall_e.gguf";
|
||||
std::string encodec_model_path = "./data/encodec.bin";
|
||||
std::string input_prompt_path = "./data/prom.wav";
|
||||
std::string output_response_path = "./data/resp.wav";
|
||||
|
|
@ -637,11 +662,7 @@ int main(int argc, char ** argv) {
|
|||
llama_sampler * smpl_ar = llama_sampler_chain_init(sparams);
|
||||
llama_sampler * smpl_nar = llama_sampler_chain_init(sparams);
|
||||
|
||||
llama_sampler_chain_add(smpl_ar, llama_sampler_init_top_k(20));
|
||||
llama_sampler_chain_add(smpl_ar, llama_sampler_init_top_p(0.9, 20));
|
||||
llama_sampler_chain_add(smpl_ar, llama_sampler_init_temp (1.0));
|
||||
llama_sampler_chain_add(smpl_ar, llama_sampler_init_dist (1130));
|
||||
|
||||
llama_sampler_chain_add(smpl_nar, llama_sampler_init_greedy());
|
||||
|
||||
struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl);
|
||||
|
|
@ -654,7 +675,9 @@ int main(int argc, char ** argv) {
|
|||
encodec_set_sample_rate(ectx, 24000);
|
||||
|
||||
// load wavform
|
||||
if ( input.prom.empty() ) {
|
||||
input.prom = encode_audio_from_disk(ectx, input_prompt_path);
|
||||
}
|
||||
//input.resp = encode_audio_from_disk(ectx, output_response_path);
|
||||
|
||||
// prepare batch
|
||||
|
|
@ -684,9 +707,11 @@ int main(int argc, char ** argv) {
|
|||
// NAR-len demasking
|
||||
if ( modality == MODALITY_NAR_LEN ) {
|
||||
// inference len
|
||||
int len = 290; // 0;
|
||||
if ( !len ) {
|
||||
input.task = "len";
|
||||
output_tokens = generate( ctx, model, smpl_nar, input, embeddings_map, 5, INFERENCE_MODE_LEN );
|
||||
int len = 0; {
|
||||
{
|
||||
int digit = 1;
|
||||
for (int i = output_tokens.size() - 1; i >= 0; i--) {
|
||||
len += output_tokens[i] * digit;
|
||||
|
|
@ -695,6 +720,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
// cap for now
|
||||
if ( len <= 0 || len > MAX_DURATION ) len = MAX_DURATION;
|
||||
}
|
||||
|
||||
// fill with mask tokens
|
||||
input.resp.resize(1);
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -796,28 +796,10 @@ class Base(nn.Module):
|
|||
self.classifier = nn.Linear(d_model, n_vocab, bias=classifiers_bias)
|
||||
self.classifiers = None
|
||||
|
||||
self.accuracy_metric = MulticlassAccuracy(
|
||||
n_vocab,
|
||||
top_k=10,
|
||||
average="micro",
|
||||
multidim_average="global",
|
||||
ignore_index=self.ignore_index,
|
||||
)
|
||||
|
||||
self.precision_metric = MulticlassPrecision(
|
||||
n_vocab,
|
||||
top_k=10,
|
||||
average="micro",
|
||||
multidim_average="global",
|
||||
ignore_index=self.ignore_index,
|
||||
)
|
||||
|
||||
self.metrics = None
|
||||
else:
|
||||
self.classifier = None
|
||||
self.classifiers = Classifiers( classifier_l_tokens, d_model, l_names=classifier_l_names, bias=classifiers_bias )
|
||||
self.accuracy_metric = None
|
||||
self.precision_metric = None
|
||||
self.metrics = Metrics( classifier_l_tokens )
|
||||
|
||||
"""
|
||||
|
|
@ -1436,8 +1418,6 @@ class Base(nn.Module):
|
|||
continue
|
||||
|
||||
# offset to flattened vocab ranges
|
||||
if self.classifier is not None:
|
||||
compute_acc = False
|
||||
"""
|
||||
if self.classifier is not None:
|
||||
offsets = _get_offsets()
|
||||
|
|
@ -1505,7 +1485,15 @@ class Base(nn.Module):
|
|||
if self.metrics is not None:
|
||||
metrics = self.metrics.calc_accuracy( [ logit ], [ token ], self.classifiers.indices([ classifier_level ]) )
|
||||
else:
|
||||
metrics = self.accuracy_metric( logit, token )
|
||||
accuracy_metric = MulticlassAccuracy(
|
||||
logit.shape[-1],
|
||||
top_k = 10,
|
||||
average="micro",
|
||||
multidim_average="global",
|
||||
ignore_index = -100
|
||||
).to(logit.device)
|
||||
metrics = accuracy_metric( logit, token )
|
||||
|
||||
if f'{name}.acc' not in stats:
|
||||
stats[f'{name}.acc'] = []
|
||||
stats[f'{name}.acc'].append( metrics )
|
||||
|
|
@ -1534,7 +1522,14 @@ class Base(nn.Module):
|
|||
if self.metrics is not None:
|
||||
metrics = self.metrics.calc_accuracy( [ logit ], [ target ], self.classifiers.indices([ classifier_level ]) )
|
||||
else:
|
||||
metrics = self.accuracy_metric( logit, target )
|
||||
accuracy_metric = MulticlassAccuracy(
|
||||
logit.shape[-1],
|
||||
top_k = 10,
|
||||
average="micro",
|
||||
multidim_average="global",
|
||||
ignore_index = -100
|
||||
).to(logit.device)
|
||||
metrics = accuracy_metric( logit, target )
|
||||
|
||||
if 'acc' not in stats:
|
||||
stats['acc'] = []
|
||||
|
|
@ -1596,8 +1591,6 @@ class Base(nn.Module):
|
|||
if early:
|
||||
exited_layer = layer
|
||||
|
||||
#print( layer, early, metrics )
|
||||
|
||||
return early
|
||||
|
||||
# derive quant levels from inputs if not provided
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user