working vall_e.cpp
This commit is contained in:
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@ -1,16 +1,78 @@
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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 transformers import LlamaForCausalLM, LlamaModel, LlamaConfig, LlamaTokenizer
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from torch.distributions import Categorical
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from vall_e.emb.qnt import decode_to_file
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from vall_e.utils.io import torch_load
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# hack in a non-causal mask
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def _update_noncausal_mask(
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attention_mask,
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inputs_embeds,
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cache_positions,
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past_key_values_length,
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output_attentions,
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):
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# create noncausal mask
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# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
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bsz, seq_len, _ = inputs_embeds.size()
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# generate default mask based on input
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if attention_mask is None:
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attention_mask = torch.ones( (bsz, seq_len), dtype=torch.bool, device=inputs_embeds.device )
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# make square
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expanded_mask = attention_mask[:, None, None, :].expand( bsz, 1, seq_len, seq_len ).to( dtype=inputs_embeds.dtype )
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# invert from 1.0 = attend, 0.0 = masked to 0.0 = valid, -inf = masked
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill( inverted_mask.to(dtype=torch.bool), torch.finfo(inputs_embeds.dtype).min )
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device = "cuda"
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dtype = torch.bfloat16
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is_from_pretrained = True
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if is_from_pretrained:
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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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model.to(device="cuda", dtype=torch.bfloat16)
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hf_model = LlamaForCausalLM.from_pretrained("./training/llama-encodec-ar+nar-len/hf/")
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hf_model.to(device=device, dtype=dtype)
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hf_model.eval()
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model = hf_model.model
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else:
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model = LlamaModel(LlamaConfig(
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vocab_size=1024,
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hidden_size=1024,
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max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
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intermediate_size=1024*4,
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num_hidden_layers=12,
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num_attention_heads=16,
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attention_dropout=0.0,
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num_key_value_heads=16,
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sliding_window=75 * 12, # 12 second context window
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hidden_act="gelu",
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is_encoder_decoder=False,
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is_decoder=True,
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))
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state_dict = torch_load("./training/llama-encodec-ar+nar-len/ckpt/ar+nar-len-llama-8/fp32.sft")['module']
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state_dict_model = {}
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for k, v in state_dict.items():
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if not k.startswith('model.'):
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continue
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state_dict_model[k.replace("model.", "")] = v
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model.load_state_dict( state_dict_model, strict=False )
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model.to(device=device, dtype=dtype)
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model.eval()
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mode = "nar"
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model._original_update_causal_mask = model._update_causal_mask
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model._update_noncausal_mask = _update_noncausal_mask
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phn = [1,22,111,100,4,37,115,169,11,2]
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@ -24,6 +86,8 @@ prom = [
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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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"""
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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],
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[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],
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@ -34,97 +98,196 @@ resp = [
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[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],
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[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],
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]
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"""
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sep = [291]
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rvq_lvl = [256]
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lang = [264]
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# name, (start, end), classifier, src_name
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io_map = {
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'text': [(0, 256), 9, "text_emb.weight"],
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'rvq_l': [(256, 264), -1, "rvq_l_emb.weight"],
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'lang': [(264, 270), -1, "langs_emb.weight"],
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'task': [(270, 279), -1, "tasks_emb.weight"],
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'len': [(279, 290), 10, "len_emb.weight"],
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'tone': [(290, 291), -1, "tones_emb.weight"],
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'sep': [(291, 292), -1, "sep"],
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'prom|0': [(292, 1316), -1, "proms_emb.embeddings.0.weight"],
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'prom|1': [(1316, 2340), -1, "proms_emb.embeddings.1.weight"],
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'prom|2': [(2340, 3364), -1, "proms_emb.embeddings.2.weight"],
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'prom|3': [(3364, 4388), -1, "proms_emb.embeddings.3.weight"],
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'prom|4': [(4388, 5412), -1, "proms_emb.embeddings.4.weight"],
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'prom|5': [(5412, 6436), -1, "proms_emb.embeddings.5.weight"],
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'prom|6': [(6436, 7460), -1, "proms_emb.embeddings.6.weight"],
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'prom|7': [(7460, 8484), -1, "proms_emb.embeddings.7.weight"],
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'resp|AR:0:0': [(8484, 9509), 0, "resps_emb.embeddings.0.weight"],
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'resp|NAR:0:1': [(9509, 10533), 1, "resps_emb.embeddings.1.weight"],
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'resp|NAR:1:2': [(10533, 11557), 2, "resps_emb.embeddings.2.weight"],
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'resp|NAR:2:3': [(11557, 12581), 3, "resps_emb.embeddings.3.weight"],
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'resp|NAR:3:4': [(12581, 13605), 4, "resps_emb.embeddings.4.weight"],
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'resp|NAR:4:5': [(13605, 14629), 5, "resps_emb.embeddings.5.weight"],
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'resp|NAR:5:6': [(14629, 15653), 6, "resps_emb.embeddings.6.weight"],
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'resp|NAR:6:7': [(15653, 16677), 7, "resps_emb.embeddings.7.weight"],
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'resp|NAR:0:0': [(16677, 17702), 8, "resps_emb.embeddings.8.weight"],
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}
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for l, codes in enumerate( prom ):
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for i, t in enumerate( codes ):
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prom[l][i] += 292 + (1024 * l)
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mode_lvl_map = {
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'AR:0:0': 0,
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'NAR:0:1': 1,
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'NAR:1:2': 2,
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'NAR:2:3': 3,
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'NAR:3:4': 4,
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'NAR:4:5': 5,
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'NAR:5:6': 6,
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'NAR:6:7': 7,
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'NAR:0:0': 0,
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'len': 0,
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}
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for l, codes in enumerate( resp ):
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for i, t in enumerate( codes ):
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resp[l][i] += 9509 + (1024 * l)
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embds = {}
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heads = {}
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n_embd = 1024
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ids = torch.tensor([])
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pos_ids = torch.tensor([])
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ids = torch.concat([ ids, torch.tensor(phn), torch.tensor(sep) ])
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seq = torch.tensor([ _ for _ in range( len(phn) + 1 ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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ids = torch.concat([ ids, torch.tensor(lang), torch.tensor(sep) ])
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seq = torch.tensor([ _ for _ in range( len(lang) + 1 ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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ids = torch.concat([ ids, torch.tensor(rvq_lvl), torch.tensor(sep) ])
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seq = torch.tensor([ _ for _ in range( len(rvq_lvl) + 1 ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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ids = torch.concat([ ids, torch.tensor(prom[0]), torch.tensor(sep) ])
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seq = torch.tensor([ _ for _ in range( len(prom[0]) + 1 ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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start, end, stop = (None, None, None)
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if mode == "len":
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len_seq = [279]
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ids = torch.concat([ ids, torch.tensor(len_seq) ])
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seq = torch.tensor([ _ for _ in range( len(len_seq) ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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start, end, stop = (279, 279+11, 10)
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max_n = 10
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outputs = 1
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elif mode =="ar":
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start, end, stop = (8484, 8484+1025, 1024)
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max_n = 350
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outputs = 1
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elif mode =="nar":
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ids = torch.concat([ ids, torch.tensor(resp[0]) ])
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seq = torch.tensor([ _ for _ in range( len(resp[0]) ) ])
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pos_ids = torch.concat([ pos_ids, seq ])
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start, end, stop = (9509, 9509+1024, None)
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max_n = 1
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outputs = len(resp[0])
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ids = ids.to(device="cuda", dtype=torch.int32)
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pos_ids = pos_ids.to(device="cuda", dtype=torch.int32)
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attention_mask = torch.tensor([ True for _ in range( ids.shape[0] ) ], dtype=torch.bool)
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n = 0
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with torch.no_grad():
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while n < max_n:
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"""
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if n == 0:
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embs = model.model.embed_tokens( ids )
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for i, emb in enumerate( embs ):
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print( i, ids[i].item(), sum(emb).item(), pos_ids[i].item() )
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"""
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for k, v in io_map.items():
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start, end = v[0]
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classifier_idx = v[1]
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embd_name = v[2]
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out = model(input_ids=ids.unsqueeze(0), position_ids=pos_ids.unsqueeze(0), attention_mask=attention_mask.unsqueeze(0))
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logits = out.logits[0, -outputs:, start:end]
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if is_from_pretrained:
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n_vocab = end - start
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if mode == "ar":
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tokens = Categorical(logits=logits).sample()
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embds[k] = torch.nn.Embedding( n_vocab, n_embd ).to(model.embed_tokens.weight)
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embds[k].weight[:] = model.embed_tokens.weight[start:end, :]
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if classifier_idx >= 0:
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# NAR:0:0 does not have a masked token output
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if k == "resp|NAR:0:0":
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end -= 1
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n_vocab -= 1
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heads[k] = torch.nn.Linear( n_embd, n_vocab, bias=False ).to(hf_model.lm_head.weight)
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heads[k].weight[:] = hf_model.lm_head.weight[start:end, :]
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else:
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tokens = logits.argmax(dim=-1)
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embd_weight = state_dict[embd_name].unsqueeze(0) if state_dict[embd_name].dim() == 1 else state_dict[embd_name]
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embds[k] = torch.nn.Embedding( embd_weight.shape[0], embd_weight.shape[1] ).to(device=device, dtype=dtype)
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embds[k].load_state_dict({ "weight": embd_weight })
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n += 1
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if classifier_idx >= 0:
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head_weight = state_dict[f'classifiers.proj.{classifier_idx}.weight']
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print( n, tokens )
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heads[k] = torch.nn.Linear( head_weight.shape[1], head_weight.shape[0], bias=False ).to(device=device, dtype=dtype)
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heads[k].load_state_dict({ "weight": head_weight })
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if outputs == 1:
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if stop in tokens:
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def create_inputs( phn, prom, lang=0, seq=None, mode="AR:0:0" ):
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rvq_l = mode_lvl_map[mode]
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inputs = torch.tensor([])
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pos_ids = torch.tensor([])
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attn_mask = torch.tensor([])
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seqs = []
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phn = torch.tensor(phn, device=device,dtype=torch.int32)
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prom = torch.tensor(prom, device=device,dtype=torch.int32)
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lang = torch.tensor([lang], device=device,dtype=torch.int32)
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rvq_l = torch.tensor([rvq_l], device=device,dtype=torch.int32)
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zero = torch.tensor([0], device=device,dtype=torch.int32)
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if mode == "len":
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seq = zero if not seq else torch.concat([zero, torch.tensor(seq, device=device, dtype=torch.int32)])
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elif seq:
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seq = torch.tensor(seq, device=device,dtype=torch.int32)
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seq = seq[:rvq_l, :] if rvq_l > 0 else seq
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sep_embd = embds["sep"](zero)
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phn_embd = embds["text"](phn)
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rvq_l_embd = embds["rvq_l"](rvq_l)
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lang_embd = embds["lang"](lang)
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prom_embd = torch.zeros(prom.shape[-1], n_embd, device=device, dtype=dtype)
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seq_embd = None
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for i, p in enumerate(prom):
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if i > rvq_l:
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break
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prom_embd += embds[f"prom|{i}"](p)
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ids = torch.concat( [ ids, tokens + start ] )
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pos_ids = torch.concat( [ pos_ids, torch.tensor([n]).to(pos_ids) ] )
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attention_mask = torch.concat([ attention_mask, torch.tensor([True]).to(attention_mask) ])
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if seq is not None:
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if mode == "len":
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seq_embd = embds["len"](seq)
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elif mode == "AR:0:0":
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seq_embd = embds["resp|AR:0:0"](seq)
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else:
|
||||
seq_embd = torch.zeros(seq.shape[-1], n_embd, device=device, dtype=dtype)
|
||||
for i, r in enumerate(seq):
|
||||
seq_embd += embds[f"resp|NAR:{i}:{i+1}"](r)
|
||||
|
||||
print( out )
|
||||
print( ids )
|
||||
print( pos_ids )
|
||||
seqs.append(torch.concat([phn_embd, sep_embd]))
|
||||
seqs.append(torch.concat([lang_embd, sep_embd]))
|
||||
seqs.append(torch.concat([rvq_l_embd, sep_embd]))
|
||||
seqs.append(torch.concat([prom_embd, sep_embd]))
|
||||
|
||||
if seq_embd is not None:
|
||||
seqs.append(seq_embd)
|
||||
|
||||
inputs = torch.concat(seqs)
|
||||
pos_ids = torch.tensor([ i for seq in seqs for i, _ in enumerate(seq) ], device=device, dtype=torch.int32)
|
||||
attn_mask = torch.tensor([ True for seq in seqs for i, _ in enumerate(seq) ], device=device, dtype=torch.bool)
|
||||
|
||||
return inputs, pos_ids, attn_mask
|
||||
|
||||
def generate( phn, prom, sequence=[], mode="resp|AR:0:0", max_tokens = 75 * 4, temperature = 1.0 ):
|
||||
lm_head = heads[mode]
|
||||
model._update_causal_mask = model._original_update_causal_mask
|
||||
|
||||
n_outputs = 1
|
||||
stop_token = 1024
|
||||
if mode == "len":
|
||||
temperature = 0.0
|
||||
max_tokens = 5
|
||||
stop_token = 10
|
||||
elif mode != "resp|AR:0:0":
|
||||
temperature = 0.0
|
||||
max_tokens = len(sequence)+1
|
||||
n_outputs = len(sequence[0])
|
||||
|
||||
model._update_causal_mask = model._update_noncausal_mask
|
||||
|
||||
while len(sequence) < max_tokens:
|
||||
inputs, pos_ids, attn_mask = create_inputs( phn, prom, seq=sequence, mode=mode.split("|")[-1] )
|
||||
out = model(inputs_embeds=inputs.unsqueeze(0), position_ids=pos_ids.unsqueeze(0), attention_mask=attn_mask.unsqueeze(0))
|
||||
logits = lm_head(out[0]).float()
|
||||
|
||||
logits = logits[0, -n_outputs:, :]
|
||||
t = Categorical(logits=logits / temperature).sample() if temperature > 0 else logits.argmax(dim=-1)
|
||||
if n_outputs > 1:
|
||||
sequence.append([ _.item() for _ in t ])
|
||||
break
|
||||
else:
|
||||
t = t[0]
|
||||
if stop_token in t:
|
||||
break
|
||||
sequence.append(t.item())
|
||||
return sequence
|
||||
|
||||
# check embds
|
||||
if False:
|
||||
inputs, pos_ids, attn_mask = create_inputs( phn, prom, mode="len" )
|
||||
flattened = [ sum(embd).item() for embd in inputs ]
|
||||
|
||||
for i, embd in enumerate( flattened ):
|
||||
print(f'{i}: ', pos_ids[i].item(), "\t", embd )
|
||||
|
||||
|
||||
# test len inferencing
|
||||
print( "len:", generate( phn, prom, mode="len" ) )
|
||||
|
||||
# test ar ouptut
|
||||
if resp:
|
||||
resp = [ resp[0] ]
|
||||
else:
|
||||
resp = [ generate( phn, prom ) ]
|
||||
print( "AR:", resp )
|
||||
|
||||
# test nar ouptut
|
||||
for i in range(1, 8):
|
||||
resp = generate( phn, prom, sequence=resp, mode=f"resp|NAR:{i-1}:{i}" )
|
||||
print( f"NAR:{i-1}:{i}: ", resp[-1] )
|
||||
|
||||
decode_to_file( torch.tensor(resp, dtype=torch.int16, device=device).t(), "./data/test.wav" )
|
||||
|
|
@ -4,7 +4,7 @@ INCS += -I./include
|
|||
LIBS += -L./libs
|
||||
|
||||
LINKS += -lggml -lggml-base -lllama -lencodec
|
||||
FLAGS += -g
|
||||
FLAGS += -march=native -O3
|
||||
|
||||
SRCS := $(shell find ./ -name "*.cpp")
|
||||
OBJS += $(patsubst %.cpp,%.o,$(SRCS))
|
||||
|
|
|
|||
|
|
@ -187,8 +187,8 @@ void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const
|
|||
// insert raw embedding instead
|
||||
if ( embds ) {
|
||||
// signals to not map the embedding from the array
|
||||
if ( id < 0 ) for ( auto i = 0; i < n_embd; ++i ) batch.embd[batch.n_tokens + i] = embds[i];
|
||||
else for ( auto i = 0; i < n_embd; ++i ) batch.embd[batch.n_tokens + i] = embds[id * n_embd + i];
|
||||
if ( id < 0 ) for ( auto i = 0; i < n_embd; ++i ) batch.embd[batch.n_tokens * n_embd + i] = embds[i];
|
||||
else for ( auto i = 0; i < n_embd; ++i ) batch.embd[batch.n_tokens * n_embd + i] = embds[id * n_embd + i];
|
||||
// insert token (never gets used here)
|
||||
} else {
|
||||
batch.token[batch.n_tokens] = id;
|
||||
|
|
@ -267,33 +267,29 @@ std::vector<std::vector<int32_t>> VALL_E_API encode_audio_from_disk( struct enco
|
|||
int n_codebooks = 8;
|
||||
int n_frames = n_codes / n_codebooks;
|
||||
|
||||
std::vector<int32_t> flattened_codes(codes_data, codes_data + n_codes);
|
||||
std::vector<std::vector<int32_t>> codes_2ds(8);
|
||||
std::vector<std::vector<int32_t>> res(n_codebooks);
|
||||
|
||||
for ( auto l = 0; l < n_codebooks; ++l ) {
|
||||
codes_2ds[l].resize( n_frames );
|
||||
for ( auto i = 0; i < n_frames; ++i ) {
|
||||
codes_2ds[l][i] = flattened_codes[i + l * n_codebooks];
|
||||
}
|
||||
res[l].insert( res[l].end(), codes_data + (l * n_frames), codes_data + ((l+1) * n_frames) );
|
||||
}
|
||||
|
||||
return codes_2ds;
|
||||
return res;
|
||||
}
|
||||
// decodes a 2D codebook into a waveform
|
||||
std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const std::vector<std::vector<int32_t>>& codes_2d ) {
|
||||
int n_codebooks = codes_2d.size();
|
||||
int n_frames = codes_2d[0].size();
|
||||
std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const std::vector<std::vector<int32_t>>& codes ) {
|
||||
int n_codebooks = codes.size();
|
||||
int n_frames = codes[0].size();
|
||||
|
||||
std::vector<int32_t> codes( n_frames * n_codebooks );
|
||||
|
||||
std::vector<int32_t> res;
|
||||
res.reserve(n_frames * n_codebooks);
|
||||
for ( auto l = 0; l < n_codebooks; ++l ) {
|
||||
for ( auto i = 0; i < n_frames; ++i ) {
|
||||
codes[i + l * n_codebooks] = codes_2d[l][i];
|
||||
}
|
||||
print_tokens( codes[l] );
|
||||
res.insert( res.end(), codes[l].begin(), codes[l].end() );
|
||||
}
|
||||
|
||||
// decompress audio
|
||||
if (!encodec_decompress_audio(ectx, codes.data(), codes.size(), 1)) {
|
||||
if (!encodec_decompress_audio(ectx, res.data(), res.size(), N_THREADS)) {
|
||||
fprintf(stderr, "%s: error during decompression\n", __func__);
|
||||
return {};
|
||||
}
|
||||
|
|
@ -306,9 +302,11 @@ std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const
|
|||
|
||||
// sums embeddings over a 2D "tensor"
|
||||
std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode ) {
|
||||
std::vector<std::vector<float>> res( input.size() );
|
||||
res.resize( input[0].size() );
|
||||
for ( auto& e : res ) e.resize( n_embd );
|
||||
auto n_tokens = input[0].size();
|
||||
//auto n_embd = input[0].size();
|
||||
|
||||
std::vector<std::vector<float>> res( n_tokens, std::vector<float>( n_embd, 0.0 ) );
|
||||
|
||||
// iterate through rvq levels (only up to inclusive the target rvq level)
|
||||
for ( auto l = 0; l < input.size() && l <= rvq_l; ++l ) {
|
||||
int offset = 0;
|
||||
|
|
@ -318,16 +316,13 @@ std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std
|
|||
} else if ( mode == EMBEDDING_MODE_RESP_NAR_LEN ) {
|
||||
offset = input.size() == 1 ? 8 : 1;
|
||||
}
|
||||
// get tokens
|
||||
auto& tokens = input[l];
|
||||
// get output buffer
|
||||
auto& summed = res[l];
|
||||
// embed the current level's tokens
|
||||
auto embedded = map_embeddings( input[l], n_embd, embds[l + offset] );
|
||||
// iterate through embedded tokens
|
||||
for ( auto i = 0; i < tokens.size(); ++i ) {
|
||||
// sum with buffer
|
||||
for ( auto j = 0; j < n_embd; ++j ) summed[j] += embedded[i][j];
|
||||
|
||||
for ( auto idx = 0; idx < n_tokens; ++idx ) {
|
||||
for ( auto embd_idx = 0; embd_idx < n_embd; ++embd_idx ) {
|
||||
res[idx][embd_idx] += embedded[idx][embd_idx];
|
||||
}
|
||||
}
|
||||
}
|
||||
return res;
|
||||
|
|
@ -414,7 +409,7 @@ void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& io_map
|
|||
// insert prom tokens
|
||||
auto summed_proms_embds = sum_embeddings( input.prom, n_embd, input.rvq_l, prom_embds );
|
||||
for ( auto i = 0; i < summed_proms_embds.size(); ++i ) {
|
||||
batch_add( batch, -1, n_embd, &summed_proms_embds[i][0], pos++, false );
|
||||
batch_add( batch, -1, n_embd, summed_proms_embds[i].data(), pos++, false );
|
||||
}
|
||||
batch_add( batch, 0, n_embd, sep_embds, pos++, mode == INFERENCE_MODE_AR ); // set as the last logit if AR
|
||||
pos = 0;
|
||||
|
|
@ -436,7 +431,7 @@ void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& io_map
|
|||
}
|
||||
|
||||
// generation code, should handle all modalities easily
|
||||
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, llama_sampler* smpl, input_t& input, io_map_t& io_map, int max_tokens, int mode, bool verbose ) {
|
||||
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, input_t& input, io_map_t& io_map, int max_tokens, int mode, bool verbose ) {
|
||||
bool causal = true; // sample autoregressively or not
|
||||
int n_outputs = 0; // number of output tokens to expect
|
||||
|
||||
|
|
@ -504,6 +499,15 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
|
||||
// if INFERENCE_MODE_AR || INFERENCE_MODE_LEN
|
||||
if ( causal ) {
|
||||
auto sparams = llama_sampler_chain_default_params();
|
||||
sparams.no_perf = false;
|
||||
llama_sampler * smpl = llama_sampler_chain_init(sparams);
|
||||
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
|
||||
output_tokens.reserve(max_tokens);
|
||||
while ( output_tokens.size() < max_tokens ) {
|
||||
if ( llama_decode(ctx, batch) ) {
|
||||
|
|
@ -527,6 +531,8 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
|
||||
if ( verbose ) print_tokens( output_tokens );
|
||||
}
|
||||
|
||||
llama_sampler_free(smpl);
|
||||
} else if ( mode == INFERENCE_MODE_NAR_DEMASK ) {
|
||||
// to-do: assert n_outputs == input.resp[rvq_l-1].size()
|
||||
const llama_token MASK_TOKEN = 1024; // token value for masking
|
||||
|
|
@ -577,6 +583,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
std::vector<score_t> sorted_scores( n_outputs );
|
||||
for ( auto i = 0; i < n_outputs; ++i ) sorted_scores[i] = { i, scores[i] };
|
||||
std::sort(sorted_scores.begin(), sorted_scores.end());
|
||||
std::reverse(sorted_scores.begin(), sorted_scores.end());
|
||||
|
||||
// and top-k pick the worst scores
|
||||
for ( auto i = 0; i < n_masked_tokens; ++i ) {
|
||||
|
|
@ -619,10 +626,10 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
sparams.no_perf = false;
|
||||
llama_sampler * smpl = llama_sampler_chain_init(sparams);
|
||||
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(20));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (sampling_temperature));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (1130));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
|
||||
auto* logits = llama_get_logits( ctx );
|
||||
|
||||
|
|
@ -636,7 +643,6 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
for ( auto idx = 0; idx < n_outputs; ++idx ) {
|
||||
// skip if not masked
|
||||
if ( !is_masked[idx] ) {
|
||||
scores[idx] = 1.0f;
|
||||
continue;
|
||||
}
|
||||
|
||||
|
|
@ -655,7 +661,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
// store token if it was masked
|
||||
output_tokens[idx] = t;
|
||||
// update score if it was masked
|
||||
scores[idx] = softmaxed[t]; // invert so we pick the worst tokens later
|
||||
scores[idx] = 1.0f - softmaxed[t]; // invert so we pick the worst tokens later
|
||||
}
|
||||
|
||||
llama_sampler_free(smpl);
|
||||
|
|
@ -677,10 +683,10 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
sparams.no_perf = false;
|
||||
llama_sampler * smpl = llama_sampler_chain_init(sparams);
|
||||
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(20));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (1130));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
|
||||
for ( auto idx = 0; idx < n_outputs; ++idx ) {
|
||||
// sample ith token
|
||||
|
|
@ -702,7 +708,6 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
__func__, output_tokens.size(), (t_main_end - t_main_start) / 1000000.0f, output_tokens.size() / ((t_main_end - t_main_start) / 1000000.0f));
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
llama_perf_sampler_print(smpl);
|
||||
llama_perf_context_print(ctx);
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
|
@ -721,7 +726,16 @@ int main( int argc, char** argv ) {
|
|||
|
||||
// input.phonemes = "hˈɛloː ʋˈɔrlt";
|
||||
input.phn = {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
|
||||
input.prom = {};
|
||||
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 = {};
|
||||
|
||||
std::string vall_e_model_path = "./data/vall_e.gguf";
|
||||
|
|
@ -747,6 +761,8 @@ int main( int argc, char** argv ) {
|
|||
ctx_params.n_ctx = CTX_SIZE;
|
||||
ctx_params.n_batch = CTX_SIZE;
|
||||
ctx_params.n_ubatch = CTX_SIZE;
|
||||
ctx_params.n_threads = N_THREADS;
|
||||
ctx_params.n_threads_batch = N_THREADS;
|
||||
ctx_params.no_perf = false;
|
||||
ctx_params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL;
|
||||
|
||||
|
|
@ -757,6 +773,7 @@ int main( int argc, char** argv ) {
|
|||
}
|
||||
|
||||
// initialize the sampler
|
||||
/*
|
||||
auto sparams = llama_sampler_chain_default_params();
|
||||
sparams.no_perf = false;
|
||||
llama_sampler * smpl = llama_sampler_chain_init(sparams);
|
||||
|
|
@ -764,7 +781,9 @@ int main( int argc, char** argv ) {
|
|||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (1130));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
*/
|
||||
|
||||
|
||||
struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl);
|
||||
if (!ectx) {
|
||||
|
|
@ -781,9 +800,6 @@ int main( int argc, char** argv ) {
|
|||
}
|
||||
//input.resp = encode_audio_from_disk(ectx, output_response_path);
|
||||
|
||||
// prepare batch
|
||||
auto n_embd = llama_n_embd( model );
|
||||
auto n_vocab = llama_n_vocab( model );
|
||||
|
||||
// grab input embeddings
|
||||
vall_e_inputs_map_init( io_map, model );
|
||||
|
|
@ -803,6 +819,26 @@ int main( int argc, char** argv ) {
|
|||
printf("\n");
|
||||
}
|
||||
|
||||
// check for embds
|
||||
/*
|
||||
{
|
||||
input.task = "len";
|
||||
printf("batch init\n");
|
||||
llama_batch batch = llama_batch_init( CTX_SIZE, io_map.n_embd, CTX_SIZE );
|
||||
printf("fill init\n");
|
||||
fill_batch( batch, input, io_map, INFERENCE_MODE_LEN );
|
||||
printf("filled init\n");
|
||||
|
||||
for ( auto i = 0; i < batch.n_tokens; ++i ) {
|
||||
float summed = 0;
|
||||
for ( auto j = 0; j < 1024; ++j ) {
|
||||
summed += batch.embd[i * 1024 + j];
|
||||
}
|
||||
printf("%i: \t%i \t%f\n", i, batch.pos[i], summed);
|
||||
}
|
||||
}
|
||||
*/
|
||||
|
||||
// inference
|
||||
std::vector<llama_token> output_tokens;
|
||||
// NAR-len demasking
|
||||
|
|
@ -811,29 +847,36 @@ int main( int argc, char** argv ) {
|
|||
int len = 0;
|
||||
if ( !len ) {
|
||||
input.task = "len";
|
||||
output_tokens = generate( ctx, model, smpl, input, io_map, 5, INFERENCE_MODE_LEN );
|
||||
output_tokens = generate( ctx, model, input, io_map, 5, INFERENCE_MODE_LEN );
|
||||
{
|
||||
int digit = 1;
|
||||
for (int i = output_tokens.size() - 1; i >= 0; i--) {
|
||||
len += output_tokens[i] * digit;
|
||||
for (auto it = output_tokens.rbegin(); it < output_tokens.rend(); ++it) {
|
||||
len += (*it) * digit;
|
||||
digit *= 10;
|
||||
}
|
||||
}
|
||||
// cap for now
|
||||
if ( len <= 0 || len > MAX_DURATION ) len = MAX_DURATION;
|
||||
}
|
||||
|
||||
// fill with mask tokens
|
||||
input.resp.resize(1);
|
||||
for ( auto i = 0; i < len; ++i ) {
|
||||
input.resp[0].emplace_back( 1024 ); // fill with masked tokens
|
||||
}
|
||||
/*
|
||||
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},
|
||||
};
|
||||
*/
|
||||
|
||||
// inference NAR-len 0
|
||||
input.task = "tts";
|
||||
for ( auto l = 0; l < 8; ++l ) {
|
||||
input.rvq_l = l;
|
||||
output_tokens = generate( ctx, model, smpl, input, io_map, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR );
|
||||
output_tokens = generate( ctx, model, input, io_map, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR );
|
||||
if ( l == 0 ) input.resp.clear();
|
||||
input.resp.emplace_back( output_tokens );
|
||||
}
|
||||
|
|
@ -842,7 +885,7 @@ int main( int argc, char** argv ) {
|
|||
input.task = "tts";
|
||||
for ( auto l = 0; l < 8; ++l ) {
|
||||
input.rvq_l = l;
|
||||
output_tokens = generate( ctx, model, smpl, input, io_map, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR );
|
||||
output_tokens = generate( ctx, model, input, io_map, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR );
|
||||
input.resp.emplace_back( output_tokens );
|
||||
}
|
||||
}
|
||||
|
|
@ -854,8 +897,6 @@ int main( int argc, char** argv ) {
|
|||
// cleanup
|
||||
encodec_free(ectx);
|
||||
|
||||
llama_sampler_free(smpl);
|
||||
|
||||
llama_free(ctx);
|
||||
|
||||
llama_free_model(model);
|
||||
|
|
|
|||
|
|
@ -34,6 +34,7 @@ const int MODALITY_NAR_LEN = 1;
|
|||
|
||||
const int MAX_DURATION = 75 * 12;
|
||||
const int CTX_SIZE = 2048;
|
||||
const int N_THREADS = 8;
|
||||
|
||||
// stores the raw inputs to be fed
|
||||
struct input_t {
|
||||
|
|
@ -121,7 +122,7 @@ std::vector<float> VALL_E_API soft_max( int n_logits, const float* logits );
|
|||
// batch and inferencing
|
||||
void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} );
|
||||
void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& inputs_map, int mode );
|
||||
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, llama_sampler* smpl, input_t& input, io_map_t& inputs_map, int max_tokens, int mode, bool verbose = true );
|
||||
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, input_t& input, io_map_t& inputs_map, int max_tokens, int mode, bool verbose = true );
|
||||
|
||||
// encodec helpers
|
||||
bool VALL_E_API read_wav_from_disk( std::string in_path, std::vector<float>& audio_arr );
|
||||
|
|
|
|||
|
|
@ -678,7 +678,7 @@ class Base(nn.Module):
|
|||
LlamaClass = LlamaModel_Adapted # if (self.layerskip or "len" in self.capabilities) else LlamaModel
|
||||
|
||||
if n_experts <= 1:
|
||||
self.model = LlamaClass(LlamaConfig(
|
||||
config = LlamaConfig(
|
||||
vocab_size=n_vocab,
|
||||
hidden_size=d_model,
|
||||
max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
|
||||
|
|
@ -693,7 +693,9 @@ class Base(nn.Module):
|
|||
is_decoder=True,
|
||||
attn_implementation=hf_attention,
|
||||
#gradient_checkpointing=self.gradient_checkpointing,
|
||||
))
|
||||
)
|
||||
print( config )
|
||||
self.model = LlamaClass(config)
|
||||
|
||||
# replace with desired attention
|
||||
if attention_backend not in HF_ATTENTIONS:
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user