From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/utils/export_utils.py | 103 +++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 94 insertions(+), 9 deletions(-)
diff --git a/funasr/utils/export_utils.py b/funasr/utils/export_utils.py
index af9f37b..b03b052 100644
--- a/funasr/utils/export_utils.py
+++ b/funasr/utils/export_utils.py
@@ -2,6 +2,10 @@
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
@@ -24,19 +28,27 @@
**kwargs,
)
elif type == "torchscript":
- device = "cuda" if torch.cuda.is_available() else "cpu"
+ device = "cuda" if torch.cuda.is_available() else "xpu" if torch.xpu.is_available() else "mps" if torch.backends.mps.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()
+ torch.cuda.is_available() or torch.xpu.is_available() or torch.backends.mps.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")
- print("output dir: {}".format(export_dir))
+
+ elif type == "onnx_fp16":
+ assert (
+ torch.cuda.is_available() or torch.xpu.is_available() or torch.backends.mps.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
@@ -50,7 +62,13 @@
**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)
@@ -64,6 +82,7 @@
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(),
@@ -71,8 +90,13 @@
)
if quantize:
- from onnxruntime.quantization import QuantType, quantize_dynamic
- import onnx
+ 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)
@@ -94,19 +118,21 @@
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")))
+ model_script.save(
+ os.path.join(path, f"{model.export_name()}".replace("onnx", "torchscript"))
+ )
def _bladedisc_opt(model, model_inputs, enable_fp16=True):
@@ -159,7 +185,7 @@
# Rescale encoder modules
fp16_scale = int(2 * absmax // 65536)
- print(f"rescale encoder modules with factor={fp16_scale}")
+ 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),
)
@@ -200,3 +226,62 @@
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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