File size: 10,279 Bytes
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import gc
import os
import sys
import time
import traceback
from importlib import import_module
import torch
from rich.progress import BarColumn, Progress, TextColumn
from ..logger import SpeedColumnToken, console, logger
from .structs import T2SEngineProtocol, T2SRequest, T2SResult, T2SSession
from .t2s_model_abc import (
CUDAGraphCacheABC,
T2SDecoderABC,
TorchProfiler,
)
class T2SEngine(T2SEngineProtocol):
def __init__(
self,
decoder_model: T2SDecoderABC,
device: torch.device = torch.device("cpu"),
dtype: torch.dtype = torch.float32,
) -> None:
assert device.type in {"cpu", "cuda", "mps", "xpu", "mtia"}
assert dtype in {torch.float16, torch.bfloat16, torch.float32}
self.device = device
self.dtype = dtype
self.decoder_model: T2SDecoderABC = decoder_model.to(self.device, self.dtype)
self.graphcache: CUDAGraphCacheABC = self.init_cache()
def _handle_request(self, request: T2SRequest):
with self.device:
decoder = self.decoder_model
session = T2SSession(decoder, request, device=self.device, dtype=self.dtype)
batch_idx = torch.arange(session.bsz)
t1 = 0.0
infer_speed = 0.0
infer_time = 0.0
torch_profiler = TorchProfiler(request.debug)
with (
torch_profiler.profiler(),
Progress(
TextColumn("[cyan]{task.description}"),
BarColumn(),
TextColumn("{task.completed}/{task.total} tokens"),
SpeedColumnToken(show_speed=True),
console=console,
transient=True,
) as progress,
):
max_token = int(min(2000 - session.input_pos.max(), 1500))
task = progress.add_task("T2S Decoding", total=max_token)
for idx in range(max_token):
progress.update(task, advance=1)
if idx == 0:
session.kv_cache = decoder.init_cache(session.bsz)
xy_dec = decoder.h.prefill(session.xy_pos, session.kv_cache, session.attn_mask)
xy_dec = xy_dec[None, batch_idx, session.input_pos - 1]
else:
if (
request.use_cuda_graph
and session.graph is None
and self.graphcache.is_applicable
and torch.cuda.is_available()
):
self.graphcache.assign_graph(session)
with torch_profiler.record("AR"):
if session.graph:
assert session.stream
session.stream.wait_stream(torch.cuda.default_stream())
with torch.cuda.stream(session.stream):
session.xy_pos_.copy_(session.xy_pos)
session.graph.replay()
xy_dec = session.xy_dec_.clone()
else:
args, kwds = decoder.pre_forward(session)
xy_dec = decoder.h(
session.input_pos,
session.xy_pos,
session.kv_cache,
*args,
**kwds,
)
with torch.cuda.stream(session.stream) if session.stream is not None else contextlib.nullcontext():
decoder.post_forward(idx, session)
logits = decoder.ar_predict_layer(xy_dec[:, -1])
if idx == 0:
logits[:, -1] = float("-inf")
with torch_profiler.record("Sampling"):
samples = session.sample(
logits=logits,
previous_tokens=session.y[:, : session.y_len + idx],
top_k=request.top_k,
top_p=request.top_p,
repetition_penalty=request.repetition_penalty,
temperature=request.temperature,
)
session.y[batch_idx, session.y_len + idx] = samples
session.input_pos.add_(1)
with torch_profiler.record("EOS"):
argmax_token = torch.argmax(logits, dim=-1)
sample_token = samples.squeeze(1)
EOS_mask = (argmax_token == decoder.EOS) | (sample_token == decoder.EOS)
newly_done_mask = EOS_mask & (~session.completed)
newly_done_indices = newly_done_mask.nonzero()
if newly_done_indices.numel() > 0:
for i in newly_done_indices:
session.y_results[i] = session.y[i, session.y_len : session.y_len + idx]
session.completed[newly_done_indices] = True
if torch.all(session.completed).item():
if session.y[:, session.y_len :].sum() == 0:
session.y_results = [torch.tensor(0) for _ in range(session.bsz)]
logger.error("Bad Zero Prediction")
else:
logger.info(
f"T2S Decoding EOS {session.prefill_len.tolist().__str__().strip('[]')} -> {[i.size(-1) for i in session.y_results].__str__().strip('[]')}"
)
logger.info(f"Infer Speed: {(idx - 1) / (time.perf_counter() - t1):.2f} token/s")
infer_time = time.perf_counter() - t1
infer_speed = (idx - 1) / infer_time
break
if (request.early_stop_num != -1 and idx >= request.early_stop_num) or idx == max_token - 1:
for i in range(session.bsz):
if not session.completed[i].item():
session.y_results[i] = session.y[i, session.y_len : session.y_len + 1499]
session.completed[i] = True
logger.error("Bad Full Prediction")
break
with torch_profiler.record("NextPos"):
y_emb = decoder.ar_audio_embedding(samples)
session.xy_pos = decoder.ar_audio_position(session.input_pos - session.x_lens, y_emb)
if idx == 1:
torch_profiler.start()
t1 = time.perf_counter()
if idx == 51:
torch_profiler.end()
if idx % 100 == 0:
match session.device.type:
case "cuda":
torch.cuda.empty_cache()
case "mps":
torch.mps.empty_cache()
case "xpu":
torch.xpu.empty_cache()
case "mtia":
torch.mtia.empty_cache()
match session.device.type:
case "cuda":
if session.stream is not None:
torch.cuda.current_stream().wait_stream(session.stream)
torch.cuda.empty_cache()
case "mps":
torch.mps.empty_cache()
case "xpu":
torch.xpu.empty_cache()
case "mtia":
torch.mtia.empty_cache()
case "cpu":
gc.collect()
torch_profiler.end()
if request.use_cuda_graph and torch.cuda.is_available():
self.graphcache.release_graph(session)
return session.y_results[: request.valid_length], infer_speed, infer_time
def generate(self, request: T2SRequest):
try:
result, infer_speed, infer_time = self._handle_request(request)
t2s_result = T2SResult(result=result, infer_speed=(infer_speed, infer_time), status="Success")
except Exception as e:
t2s_result = T2SResult(status="Error", exception=e, traceback=traceback.format_exc())
return t2s_result
@staticmethod
def load_decoder(weights_path: os.PathLike, max_batch_size: int = 1, backend: str = "Flash-Attn-Varlen-CUDAGraph"):
logger.info(f"Loading Text2Semantic Weights from {weights_path} with {backend} Backend")
module_path = f".backends.{backend.lower().replace('-', '_').replace('cudagraph', 'cuda_graph')}"
decoder_cls_name = "T2SDecoder"
decoder_mod = import_module(module_path, package=__package__)
decoder_cls: type[T2SDecoderABC] = getattr(decoder_mod, decoder_cls_name)
dict_s1 = torch.load(weights_path, map_location="cpu", weights_only=False, mmap=True)
config = dict_s1["config"]
decoder: T2SDecoderABC = decoder_cls(config, max_batch_size=max_batch_size)
state_dict = dict_s1["weight"]
decoder.load_state_dict(state_dict)
return decoder.eval()
def init_cache(self):
assert self.decoder_model
module_name = self.decoder_model.__class__.__module__
module = sys.modules.get(module_name)
assert module
target_class: type[CUDAGraphCacheABC] = getattr(module, "CUDAGraphCache")
return target_class(self.decoder_model)
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