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from io import BytesIO
import pickle
import time
import torch
from tqdm import tqdm
from collections import OrderedDict
def load_inputs(path, device, is_half=False):
parm = torch.load(path, map_location=torch.device("cpu"))
for key in parm.keys():
parm[key] = parm[key].to(device)
if is_half and parm[key].dtype == torch.float32:
parm[key] = parm[key].half()
elif not is_half and parm[key].dtype == torch.float16:
parm[key] = parm[key].float()
return parm
def benchmark(
model, inputs_path, device=torch.device("cpu"), epoch=1000, is_half=False
):
parm = load_inputs(inputs_path, device, is_half)
total_ts = 0.0
bar = tqdm(range(epoch))
for i in bar:
start_time = time.perf_counter()
o = model(**parm)
total_ts += time.perf_counter() - start_time
print(f"num_epoch: {epoch} | avg time(ms): {(total_ts*1000)/epoch}")
def jit_warm_up(model, inputs_path, device=torch.device("cpu"), epoch=5, is_half=False):
benchmark(model, inputs_path, device, epoch=epoch, is_half=is_half)
def to_jit_model(
model_path,
model_type: str,
mode: str = "trace",
inputs_path: str = None,
device=torch.device("cpu"),
is_half=False,
):
model = None
if model_type.lower() == "synthesizer":
from .get_synthesizer import get_synthesizer
model, _ = get_synthesizer(model_path, device)
model.forward = model.infer
elif model_type.lower() == "rmvpe":
from .get_rmvpe import get_rmvpe
model = get_rmvpe(model_path, device)
elif model_type.lower() == "hubert":
from .get_hubert import get_hubert_model
model = get_hubert_model(model_path, device)
model.forward = model.infer
else:
raise ValueError(f"No model type named {model_type}")
model = model.eval()
model = model.half() if is_half else model.float()
if mode == "trace":
assert not inputs_path
inputs = load_inputs(inputs_path, device, is_half)
model_jit = torch.jit.trace(model, example_kwarg_inputs=inputs)
elif mode == "script":
model_jit = torch.jit.script(model)
model_jit.to(device)
model_jit = model_jit.half() if is_half else model_jit.float()
# model = model.half() if is_half else model.float()
return (model, model_jit)
def export(
model: torch.nn.Module,
mode: str = "trace",
inputs: dict = None,
device=torch.device("cpu"),
is_half: bool = False,
) -> dict:
model = model.half() if is_half else model.float()
model.eval()
if mode == "trace":
assert inputs is not None
model_jit = torch.jit.trace(model, example_kwarg_inputs=inputs)
elif mode == "script":
model_jit = torch.jit.script(model)
model_jit.to(device)
model_jit = model_jit.half() if is_half else model_jit.float()
buffer = BytesIO()
# model_jit=model_jit.cpu()
torch.jit.save(model_jit, buffer)
del model_jit
cpt = OrderedDict()
cpt["model"] = buffer.getvalue()
cpt["is_half"] = is_half
return cpt
def load(path: str):
with open(path, "rb") as f:
return pickle.load(f)
def save(ckpt: dict, save_path: str):
with open(save_path, "wb") as f:
pickle.dump(ckpt, f)
def rmvpe_jit_export(
model_path: str,
mode: str = "script",
inputs_path: str = None,
save_path: str = None,
device=torch.device("cpu"),
is_half=False,
):
if not save_path:
save_path = model_path.rstrip(".pth")
save_path += ".half.jit" if is_half else ".jit"
if "cuda" in str(device) and ":" not in str(device):
device = torch.device("cuda:0")
from .get_rmvpe import get_rmvpe
model = get_rmvpe(model_path, device)
inputs = None
if mode == "trace":
inputs = load_inputs(inputs_path, device, is_half)
ckpt = export(model, mode, inputs, device, is_half)
ckpt["device"] = str(device)
save(ckpt, save_path)
return ckpt
def synthesizer_jit_export(
model_path: str,
mode: str = "script",
inputs_path: str = None,
save_path: str = None,
device=torch.device("cpu"),
is_half=False,
):
if not save_path:
save_path = model_path.rstrip(".pth")
save_path += ".half.jit" if is_half else ".jit"
if "cuda" in str(device) and ":" not in str(device):
device = torch.device("cuda:0")
from .get_synthesizer import get_synthesizer
model, cpt = get_synthesizer(model_path, device)
assert isinstance(cpt, dict)
model.forward = model.infer
inputs = None
if mode == "trace":
inputs = load_inputs(inputs_path, device, is_half)
ckpt = export(model, mode, inputs, device, is_half)
cpt.pop("weight")
cpt["model"] = ckpt["model"]
cpt["device"] = device
save(cpt, save_path)
return cpt
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