mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
35b1c051f6
* update * update * update * update onnx * update with main (#1492) * contextual&seaco ONNX export (#1481) * contextual&seaco ONNX export * update ContextualEmbedderExport2 * update ContextualEmbedderExport2 * update code * onnx (#1482) * qwenaudio qwenaudiochat * qwenaudio qwenaudiochat * whisper * whisper * llm * llm * llm * llm * llm * llm * llm * llm * export onnx * export onnx * export onnx * dingding * dingding * llm * doc * onnx * onnx * onnx * onnx * onnx * onnx * v1.0.15 * qwenaudio * qwenaudio * issue doc * update * update * bugfix * onnx * update export calling * update codes * remove useless code * update code --------- Co-authored-by: zhifu gao <zhifu.gzf@alibaba-inc.com> * acknowledge --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> * update onnx * update onnx --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
import os
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import torch
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def export_onnx(model,
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data_in=None,
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quantize: bool = False,
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opset_version: int = 14,
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**kwargs):
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model_scripts = model.export(**kwargs)
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export_dir = kwargs.get("output_dir", os.path.dirname(kwargs.get("init_param")))
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os.makedirs(export_dir, exist_ok=True)
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if not isinstance(model_scripts, (list, tuple)):
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model_scripts = (model_scripts,)
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for m in model_scripts:
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m.eval()
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_onnx(m,
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data_in=data_in,
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quantize=quantize,
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opset_version=opset_version,
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export_dir=export_dir,
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**kwargs
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)
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print("output dir: {}".format(export_dir))
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return export_dir
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def _onnx(model,
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data_in=None,
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quantize: bool = False,
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opset_version: int = 14,
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export_dir:str = None,
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**kwargs):
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dummy_input = model.export_dummy_inputs()
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verbose = kwargs.get("verbose", False)
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export_name = model.export_name() if hasattr(model, "export_name") else "model.onnx"
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model_path = os.path.join(export_dir, export_name)
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torch.onnx.export(
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model,
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dummy_input,
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model_path,
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verbose=verbose,
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opset_version=opset_version,
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input_names=model.export_input_names(),
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output_names=model.export_output_names(),
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dynamic_axes=model.export_dynamic_axes()
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)
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if quantize:
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from onnxruntime.quantization import QuantType, quantize_dynamic
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import onnx
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quant_model_path = model_path.replace(".onnx", "_quant.onnx")
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if not os.path.exists(quant_model_path):
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onnx_model = onnx.load(model_path)
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nodes = [n.name for n in onnx_model.graph.node]
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nodes_to_exclude = [m for m in nodes if 'output' in m or 'bias_encoder' in m or 'bias_decoder' in m]
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quantize_dynamic(
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model_input=model_path,
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model_output=quant_model_path,
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op_types_to_quantize=['MatMul'],
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per_channel=True,
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reduce_range=False,
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weight_type=QuantType.QUInt8,
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nodes_to_exclude=nodes_to_exclude,
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) |