export_onnx.py

#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import types
import torch
import torch.nn as nn
from funasr.register import tables


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)



    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,
    language: torch.Tensor,
    textnorm: torch.Tensor,
    **kwargs,
):
    speech = speech.to(device='cuda')
    speech_lengths = speech_lengths.to(device='cuda')
    
    language_query = self.embed(language.to(speech.device)).unsqueeze(1)
    
    textnorm_query = self.embed(textnorm.to(speech.device)).unsqueeze(1)
    print(textnorm_query.shape, speech.shape)
    speech = torch.cat((textnorm_query, speech), dim=1)
    speech_lengths += 1
    
    event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
        speech.size(0), 1, 1
    )
    input_query = torch.cat((language_query, event_emo_query), dim=1)
    speech = torch.cat((input_query, speech), dim=1)
    speech_lengths += 3
    
    # Encoder
    encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
    if isinstance(encoder_out, tuple):
        encoder_out = encoder_out[0]
    
    # c. Passed the encoder result and the beam search
    # ctc_logits = self.ctc.log_softmax(encoder_out)
    ctc_logits = self.ctc.ctc_lo(encoder_out)
    
    
    return ctc_logits, encoder_out_lens


def export_dummy_inputs(self):
    speech = torch.randn(2, 30, 560)
    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
    language = torch.tensor([0, 0], dtype=torch.int32)
    textnorm = torch.tensor([15, 15], dtype=torch.int32)
    return (speech, speech_lengths, language, textnorm)


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


def export_output_names(self):
    return ["ctc_logits", "encoder_out_lens"]


def export_dynamic_axes(self):
    return {
        "speech": {0: "batch_size", 1: "feats_length"},
        "speech_lengths": {
            0: "batch_size",
        },
        "language": {0: "batch_size"},
        "textnorm": {0: "batch_size"},
        "ctc_logits": {0: "batch_size", 1: "logits_length"},
    }


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



if __name__ == "__main__":
    from model import SenseVoiceSmall

    model_dir = "iic/SenseVoiceSmall"
    #model_dir = "./SenseVoiceSmall"
    model, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
    # model = model.to("cpu")
    model = export_rebuild_model(model, max_seq_len=512, device="cuda")
    # model.export()
    print("Export Done.")

    dummy_inputs = model.export_dummy_inputs()

    # Export the model
    torch.onnx.export(
        model,
        dummy_inputs,
        "model.onnx",
        input_names=model.export_input_names(),
        output_names=model.export_output_names(),
        dynamic_axes=model.export_dynamic_axes(),
        opset_version=18
    )
    # import os
    # import onnxmltools
    # from onnxmltools.utils.float16_converter import (
    #     convert_float_to_float16)
    # decoder_onnx_model = onnxmltools.utils.load_model("model.onnx")
    # decoder_onnx_model = convert_float_to_float16(decoder_onnx_model)
    # decoder_onnx_path = "model_fp16.onnx"
    # onnxmltools.utils.save_model(decoder_onnx_model, decoder_onnx_path)
    # print("Model has been successfully exported to model.onnx")