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2219798 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """Measured Small inference paths: fused CFG and reusable local CUDA graphs.
No weights, precision of the TTS model, codebooks, CFG equation, or sampling
distribution are changed. The legacy path remains available for comparison.
"""
from collections import OrderedDict
import time
import torch
import torch.nn.functional as F
def combine_branches(branches, pad_token, audio_pad):
length = max(ids.shape[1] for ids, _ in branches)
ids_list, mask_list = [], []
for ids, mask in branches:
amount = length - ids.shape[1]
if amount:
padding = torch.full((ids.shape[0], amount, ids.shape[-1]), audio_pad,
dtype=ids.dtype, device=ids.device)
padding[:, :, 0] = pad_token
ids = torch.cat((padding, ids), dim=1)
mask = F.pad(mask, (amount, 0), value=False)
ids_list.append(ids)
mask_list.append(mask)
return torch.cat(ids_list), torch.cat(mask_list).bool()
class FastGenerator:
def __init__(self, engine, graph_limit=4):
self.engine = engine
self.graphs = OrderedDict()
self.graph_limit = graph_limit
self.capture_seconds = 0.0
self.candidate_ids = engine.model._local_text_candidate_ids(torch.device(engine.device))
def clear_graphs(self):
self.graphs.clear()
def local_frame(self, local_input, n, nb, guidance, p, cache):
mdl = self.engine.model
for layer in cache:
layer["length"] = 0
hidden, past = mdl._decode_local_hidden_states_with_cache(local_input.unsqueeze(1), cache)
hidden = hidden[:, -1]
if not mdl._use_binary_local_text_head():
raise ValueError("Small fast path requires its released binary stop head")
stop_logits = mdl.local_text_lm_head(hidden[:n])
stop_index = mdl._sample_next_token(logits=stop_logits,
do_sample=p["text_temperature"] > 0, temperature=p["text_temperature"],
top_k=p["text_top_k"], top_p=p["text_top_p"])
token = self.candidate_ids[stop_index]
codes = []
for channel in range(mdl.config.n_vq):
logits = mdl.audio_lm_heads[channel](hidden).float()
if nb == 2:
conditional, neutral = logits[:n], logits[n:]
logits = neutral + guidance * (conditional - neutral)
code = mdl._sample_next_token(logits=logits, do_sample=True,
temperature=p["audio_temperature"], top_k=p["audio_top_k"],
top_p=p["audio_top_p"], previous_token_ids=None,
repetition_penalty=1.0)
codes.append(code)
if channel + 1 < mdl.config.n_vq:
shared_code = code.repeat(nb)
embed = mdl.audio_embeddings[channel](shared_code)
hidden, past = mdl._decode_local_hidden_states_with_cache(embed.unsqueeze(1), past)
hidden = hidden[:, -1]
return token, torch.stack(codes, dim=-1)
def frame(self, local_input, n, nb, guidance, p, graph=False):
mdl = self.engine.model
if not graph:
cache = mdl._new_static_local_past_key_values(n * nb, 13,
local_input.device, local_input.dtype)
return self.local_frame(local_input, n, nb, guidance, p, cache)
key = (n, nb, float(guidance), p["audio_temperature"], p["text_temperature"],
p["audio_top_k"], p["audio_top_p"])
if key not in self.graphs:
started = time.monotonic()
rng = torch.cuda.get_rng_state(local_input.device)
static = torch.empty_like(local_input)
static.copy_(local_input)
cache = mdl._new_static_local_past_key_values(n * nb, 13,
local_input.device, local_input.dtype)
# Warm on a side stream before capturing, as recommended by PyTorch.
stream = torch.cuda.Stream(device=local_input.device)
stream.wait_stream(torch.cuda.current_stream(local_input.device))
with torch.cuda.stream(stream):
for _ in range(3):
self.local_frame(static, n, nb, guidance, p, cache)
torch.cuda.current_stream(local_input.device).wait_stream(stream)
cuda_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(cuda_graph):
output = self.local_frame(static, n, nb, guidance, p, cache)
self.graphs[key] = (cuda_graph, static, output, cache)
torch.cuda.set_rng_state(rng, local_input.device)
self.capture_seconds += time.monotonic() - started
while len(self.graphs) > self.graph_limit:
self.graphs.popitem(last=False)
self.graphs.move_to_end(key)
cuda_graph, static, output, _ = self.graphs[key]
static.copy_(local_input)
cuda_graph.replay()
return output
@torch.inference_mode()
def stream_frames(self, branches, p, chunk_frames, guidance=1.5,
min_frames=0, graph=False):
mdl, cfg = self.engine.model, self.engine.model.config
if p["audio_repetition_penalty"] != 1.0:
raise ValueError("Fast path supports the demo's unchanged repetition penalty 1.0 only")
ids, initial_mask = combine_branches(branches, cfg.pad_token_id, cfg.audio_pad_token_id)
nb, n = len(branches), branches[0][0].shape[0]
budget, length = int(p["max_new_tokens"]), ids.shape[1]
mask_buffer = torch.zeros(n * nb, length + budget, dtype=torch.bool, device=ids.device)
mask_buffer[:, :length] = initial_mask
frames, past = [], None
finished = torch.zeros(n, dtype=torch.bool, device=ids.device)
last = 0
model_input = ids
for step in range(budget):
mask = mask_buffer[:, :length + step]
embeddings = mdl._build_inputs_embeds(model_input)
out = mdl.transformer(inputs_embeds=embeddings, input_ids=None,
attention_mask=mask, past_key_values=past, use_cache=True,
position_ids=None, output_attentions=False, output_hidden_states=False,
return_dict=True, cu_seqlens=None, num_sequences=None)
past = out.past_key_values
local_input = mdl._global_hidden_to_local(out.last_hidden_state[:, -1]).to(mdl.local_transformer.ln_f.weight.dtype)
token, sampled = self.frame(local_input, n, nb, guidance, p, graph)
continues = token.eq(cfg.audio_assistant_slot_token_id) & ~finished
finished = finished | token.eq(cfg.audio_end_token_id)
if step < min_frames:
continues = torch.ones_like(continues)
finished = torch.zeros_like(finished)
if not bool(continues.any().item()):
break
frame = sampled.masked_fill(~continues.unsqueeze(-1), cfg.audio_pad_token_id)
frames.append(frame)
row = mdl._build_generation_row(n, ids.device, frame)
row[:, 0, 0] = torch.where(continues, row[:, 0, 0], cfg.pad_token_id)
model_input = row.repeat(nb, 1, 1)
mask_buffer[:, length + step] = continues.repeat(nb)
if len(frames) - last >= chunk_frames:
last = len(frames)
yield frames
if len(frames) != last:
yield frames
class StreamingCodec:
"""True incremental PCM decoding: one codec KV cache per batch row.
Never re-decode a growing prefix. Uses the pinned codec's stateful frame
primitive under its streaming context, the same primitive as batch_decode.
"""
def __init__(self, codec, batch_size, audio_pad=-1):
self.codec = codec
self.audio_pad = audio_pad
self.context = codec.streaming(batch_size=batch_size)
def __enter__(self):
self.context.__enter__()
return self
def __exit__(self, *args):
return self.context.__exit__(*args)
def decode(self, codes):
# Input B,T,12; codec expects 12,B,T.
valid = codes[:, :, 0].ne(self.audio_pad)
lengths = valid.sum(-1)
codes = codes.masked_fill(~valid.unsqueeze(-1), 0)
self.codec._set_streaming_exec_mask(lengths > 0)
result = self.codec._decode_frame(codes.permute(2, 0, 1).contiguous(), lengths)
return result.audio.float().mean(1), result.audio_lengths
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