contextual&seaco ONNX export (#1481)

Shi Xian

2024-03-13 e04489ce4c0fd0095d0c79ef8f504f425e0435a8

 funasr/models/paraformer/model.py

@@ -13,15 +13,16 @@
from funasr.models.ctc.ctc import CTC
from funasr.utils import postprocess_utils
from funasr.metrics.compute_acc import th_accuracy
from funasr.train_utils.device_funcs import to_device
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
from funasr.models.paraformer.cif_predictor import mae_loss
from funasr.train_utils.device_funcs import force_gatherable
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.train_utils.device_funcs import to_device


@tables.register("model_classes", "Paraformer")
class Paraformer(torch.nn.Module):
@@ -549,78 +550,10 @@
                
        return results, meta_data

    def export(
        self,
        max_seq_len=512,
        **kwargs,
    ):
        self.device = kwargs.get("device")
        is_onnx = kwargs.get("type", "onnx") == "onnx"
        encoder_class = tables.encoder_classes.get(kwargs["encoder"]+"Export")
        self.encoder = encoder_class(self.encoder, onnx=is_onnx)
        
        predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export")
        self.predictor = predictor_class(self.predictor, onnx=is_onnx)
    def export(self, **kwargs):
        from .export_meta import export_rebuild_model
        if 'max_seq_len' not in kwargs:
            kwargs['max_seq_len'] = 512
        models = export_rebuild_model(model=self, **kwargs)
        return models


        decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
        self.decoder = decoder_class(self.decoder, onnx=is_onnx)
        
        from funasr.utils.torch_function import sequence_mask


        self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
        
        self.forward = self.export_forward
        
        return self

    def export_forward(
        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)
    
        enc, enc_len = self.encoder(**batch)
        mask = self.make_pad_mask(enc_len)[:, None, :]
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
        pre_token_length = pre_token_length.floor().type(torch.int32)
    
        decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out = torch.log_softmax(decoder_out, dim=-1)
        # sample_ids = decoder_out.argmax(dim=-1)
    
        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)
        return (speech, speech_lengths)


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

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

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

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