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import torch
import functools
import warnings
warnings.filterwarnings("ignore")
def export(
model, data_in=None, quantize: bool = False, opset_version: int = 14, type="onnx", **kwargs
):
model_scripts = model.export(**kwargs)
export_dir = kwargs.get("output_dir", os.path.dirname(kwargs.get("init_param")))
os.makedirs(export_dir, exist_ok=True)
if not isinstance(model_scripts, (list, tuple)):
model_scripts = (model_scripts,)
for m in model_scripts:
m.eval()
if type == "onnx":
_onnx(
m,
data_in=data_in,
quantize=quantize,
opset_version=opset_version,
export_dir=export_dir,
**kwargs,
)
elif type == "torchscript":
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Exporting torchscripts on device {}".format(device))
_torchscripts(m, path=export_dir, device=device)
elif type == "bladedisc":
assert (
torch.cuda.is_available()
), "Currently bladedisc optimization for FunASR only supports GPU"
# bladedisc only optimizes encoder/decoder modules
if hasattr(m, "encoder") and hasattr(m, "decoder"):
_bladedisc_opt_for_encdec(m, path=export_dir, enable_fp16=True)
else:
print(f"export_dir: {export_dir}")
_torchscripts(m, path=export_dir, device="cuda")
elif type == "onnx_fp16":
assert (
torch.cuda.is_available()
), "Currently onnx_fp16 optimization for FunASR only supports GPU"
if hasattr(m, "encoder") and hasattr(m, "decoder"):
_onnx_opt_for_encdec(m, path=export_dir, enable_fp16=True)
return export_dir
def _onnx(
model,
data_in=None,
quantize: bool = False,
opset_version: int = 14,
export_dir: str = None,
**kwargs,
):
device = kwargs.get("device", "cpu")
dummy_input = model.export_dummy_inputs()
if isinstance(dummy_input, torch.Tensor):
dummy_input = dummy_input.to(device)
else:
dummy_input = tuple([input.to(device) for input in dummy_input])
verbose = kwargs.get("verbose", False)
if isinstance(model.export_name, str):
export_name = model.export_name + ".onnx"
else:
export_name = model.export_name()
model_path = os.path.join(export_dir, export_name)
torch.onnx.export(
model,
dummy_input,
model_path,
verbose=verbose,
do_constant_folding=True,
opset_version=opset_version,
input_names=model.export_input_names(),
output_names=model.export_output_names(),
dynamic_axes=model.export_dynamic_axes(),
)
if quantize:
try:
from onnxruntime.quantization import QuantType, quantize_dynamic
import onnx
except:
raise RuntimeError(
"You are quantizing the onnx model, please install onnxruntime first. via \n`pip install onnx`\n`pip install onnxruntime`."
)
quant_model_path = model_path.replace(".onnx", "_quant.onnx")
onnx_model = onnx.load(model_path)
nodes = [n.name for n in onnx_model.graph.node]
nodes_to_exclude = [
m for m in nodes if "output" in m or "bias_encoder" in m or "bias_decoder" in m
]
print("Quantizing model from {} to {}".format(model_path, quant_model_path))
quantize_dynamic(
model_input=model_path,
model_output=quant_model_path,
op_types_to_quantize=["MatMul"],
per_channel=True,
reduce_range=False,
weight_type=QuantType.QUInt8,
nodes_to_exclude=nodes_to_exclude,
)
def _torchscripts(model, path, device="cuda"):
dummy_input = model.export_dummy_inputs()
if device == "cuda":
model = model.cuda()
if isinstance(dummy_input, torch.Tensor):
dummy_input = dummy_input.cuda()
else:
dummy_input = tuple([i.cuda() for i in dummy_input])
model_script = torch.jit.trace(model, dummy_input)
if isinstance(model.export_name, str):
model_script.save(os.path.join(path, f"{model.export_name}".replace("onnx", "torchscript")))
else:
model_script.save(
os.path.join(path, f"{model.export_name()}".replace("onnx", "torchscript"))
)
def _bladedisc_opt(model, model_inputs, enable_fp16=True):
model = model.eval()
try:
import torch_blade
except Exception as e:
print(
f"Warning, if you are exporting bladedisc, please install it and try it again: pip install -U torch_blade\n"
)
torch_config = torch_blade.config.Config()
torch_config.enable_fp16 = enable_fp16
with torch.no_grad(), torch_config:
opt_model = torch_blade.optimize(
model,
allow_tracing=True,
model_inputs=model_inputs,
)
return opt_model
def _rescale_input_hook(m, x, scale):
if len(x) > 1:
return (x[0] / scale, *x[1:])
else:
return (x[0] / scale,)
def _rescale_output_hook(m, x, y, scale):
if isinstance(y, tuple):
return (y[0] / scale, *y[1:])
else:
return y / scale
def _rescale_encoder_model(model, input_data):
# Calculate absmax
absmax = torch.tensor(0).cuda()
def stat_input_hook(m, x, y):
val = x[0] if isinstance(x, tuple) else x
absmax.copy_(torch.max(absmax, val.detach().abs().max()))
encoders = model.encoder.model.encoders
hooks = [m.register_forward_hook(stat_input_hook) for m in encoders]
model = model.cuda()
model(*input_data)
for h in hooks:
h.remove()
# Rescale encoder modules
fp16_scale = int(2 * absmax // 65536)
print(f"rescale encoder modules with factor={fp16_scale}\n\n")
model.encoder.model.encoders0.register_forward_pre_hook(
functools.partial(_rescale_input_hook, scale=fp16_scale),
)
for name, m in model.encoder.model.named_modules():
if name.endswith("self_attn"):
m.register_forward_hook(functools.partial(_rescale_output_hook, scale=fp16_scale))
if name.endswith("feed_forward.w_2"):
state_dict = {k: v / fp16_scale for k, v in m.state_dict().items()}
m.load_state_dict(state_dict)
def _bladedisc_opt_for_encdec(model, path, enable_fp16):
# Get input data
# TODO: better to use real data
input_data = model.export_dummy_inputs()
if isinstance(input_data, torch.Tensor):
input_data = input_data.cuda()
else:
input_data = tuple([i.cuda() for i in input_data])
# Get input data for decoder module
decoder_inputs = list()
def get_input_hook(m, x):
decoder_inputs.extend(list(x))
hook = model.decoder.register_forward_pre_hook(get_input_hook)
model = model.cuda()
model(*input_data)
hook.remove()
# Prevent FP16 overflow
if enable_fp16:
_rescale_encoder_model(model, input_data)
# Export and optimize encoder/decoder modules
model.encoder = _bladedisc_opt(model.encoder, input_data[:2])
model.decoder = _bladedisc_opt(model.decoder, tuple(decoder_inputs))
model_script = torch.jit.trace(model, input_data)
model_script.save(os.path.join(path, f"{model.export_name}_blade.torchscript"))
def _onnx_opt_for_encdec(model, path, enable_fp16):
# Get input data
# TODO: better to use real data
input_data = model.export_dummy_inputs()
if isinstance(input_data, torch.Tensor):
input_data = input_data.cuda()
else:
input_data = tuple([i.cuda() for i in input_data])
# Get input data for decoder module
decoder_inputs = list()
def get_input_hook(m, x):
decoder_inputs.extend(list(x))
hook = model.decoder.register_forward_pre_hook(get_input_hook)
model = model.cuda()
model(*input_data)
hook.remove()
# Prevent FP16 overflow
if enable_fp16:
_rescale_encoder_model(model, input_data)
fp32_model_path = f"{path}/{model.export_name}_hook.onnx"
print("*" * 50)
print(f"[_onnx_opt_for_encdec(fp32)]: {fp32_model_path}\n\n")
if not os.path.exists(fp32_model_path):
torch.onnx.export(
model,
input_data,
fp32_model_path,
verbose=False,
do_constant_folding=True,
opset_version=13,
input_names=model.export_input_names(),
output_names=model.export_output_names(),
dynamic_axes=model.export_dynamic_axes(),
)
# fp32 to fp16
fp16_model_path = f"{path}/{model.export_name}_hook_fp16.onnx"
print("*" * 50)
print(f"[_onnx_opt_for_encdec(fp16)]: {fp16_model_path}\n\n")
if os.path.exists(fp32_model_path) and not os.path.exists(fp16_model_path):
try:
from onnxconverter_common import float16
except:
raise RuntimeError(
"You are converting the onnx model to fp16, please install onnxconverter-common first. via `pip install onnxconverter-common`."
)
fp32_onnx_model = onnx.load(fp32_model_path)
fp16_onnx_model = float16.convert_float_to_float16(fp32_onnx_model, keep_io_types=True)
onnx.save(fp16_onnx_model, fp16_model_path)
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