fix "can not find model issue when running libtorch runtime" (#2504)

Yu Cao

2025-10-01 c4ac64fd5d24bb3fc8ccc441d36a07c83c8b9015

 funasr/models/paraformer/export_meta.py

@@ -9,36 +9,37 @@


def export_rebuild_model(model, **kwargs):
        model.device = kwargs.get("device")
        is_onnx = kwargs.get("type", "onnx") == "onnx"
        encoder_class = tables.encoder_classes.get(kwargs["encoder"]+"Export")
        model.encoder = encoder_class(model.encoder, onnx=is_onnx)
        
        predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export")
        model.predictor = predictor_class(model.predictor, onnx=is_onnx)
    model.device = kwargs.get("device")
    is_onnx = kwargs.get("type", "onnx") == "onnx"
    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
    model.encoder = encoder_class(model.encoder, onnx=is_onnx)

    predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
    model.predictor = predictor_class(model.predictor, onnx=is_onnx)

        decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
        model.decoder = decoder_class(model.decoder, onnx=is_onnx)
        
        from funasr.utils.torch_function import sequence_mask
        model.make_pad_mask = sequence_mask(kwargs['max_seq_len'], flip=False)
        
        model.forward = types.MethodType(export_forward, model)
        model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
        model.export_input_names = types.MethodType(export_input_names, model)
        model.export_output_names = types.MethodType(export_output_names, model)
        model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
        model.export_name = types.MethodType(export_name, model)
        
        return model
    decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
    model.decoder = decoder_class(model.decoder, onnx=is_onnx)

    from funasr.utils.torch_function import sequence_mask

    model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)

    model.forward = types.MethodType(export_forward, model)
    model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
    model.export_input_names = types.MethodType(export_input_names, model)
    model.export_output_names = types.MethodType(export_output_names, model)
    model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
    model.export_name = types.MethodType(export_name, model)

    model.export_name = "model"
    return model


def export_forward(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
    ):
    self,
    speech: torch.Tensor,
    speech_lengths: torch.Tensor,
):
    # a. To device
    batch = {"speech": speech, "speech_lengths": speech_lengths}
    # batch = to_device(batch, device=self.device)
@@ -54,6 +55,7 @@

    return decoder_out, pre_token_length


def export_dummy_inputs(self):
    speech = torch.randn(2, 30, 560)
    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
@@ -61,25 +63,25 @@


def export_input_names(self):
    return ['speech', 'speech_lengths']
    return ["speech", "speech_lengths"]


def export_output_names(self):
    return ['logits', 'token_num']
    return ["logits", "token_num"]


def export_dynamic_axes(self):
    return {
        'speech': {
            0: 'batch_size',
            1: 'feats_length'
        "speech": {0: "batch_size", 1: "feats_length"},
        "speech_lengths": {
            0: "batch_size",
        },
        'speech_lengths': {
            0: 'batch_size',
        },
        'logits': {
            0: 'batch_size',
            1: 'logits_length'
        },
        "logits": {0: "batch_size", 1: "logits_length"},
        "token_num": {0: "batch_size"}
    }

def export_name(self, ):
    return "model.onnx"

def export_name(
    self,
):
    return "model.onnx"