#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import types
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import torch
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from funasr.register import tables
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def export_rebuild_model(model, **kwargs):
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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model.forward = types.MethodType(export_forward, model)
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model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
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model.export_input_names = types.MethodType(export_input_names, model)
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model.export_output_names = types.MethodType(export_output_names, model)
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model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
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model.export_name = types.MethodType(export_name, model)
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return model
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def export_forward(
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self,
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inputs: torch.Tensor,
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text_lengths: torch.Tensor,
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vad_indexes: torch.Tensor,
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sub_masks: torch.Tensor,
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):
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(inputs)
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# mask = self._target_mask(input)
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h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
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y = self.decoder(h)
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return y
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def export_dummy_inputs(self):
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length = 120
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text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
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text_lengths = torch.tensor([length], dtype=torch.int32)
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vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
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sub_masks = torch.ones(length, length, dtype=torch.float32)
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sub_masks = torch.tril(sub_masks).type(torch.float32)
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return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
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def export_input_names(self):
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return ["inputs", "text_lengths", "vad_masks", "sub_masks"]
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def export_output_names(self):
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return ["logits"]
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def export_dynamic_axes(self):
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return {
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"inputs": {1: "feats_length"},
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"vad_masks": {2: "feats_length1", 3: "feats_length2"},
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"sub_masks": {2: "feats_length1", 3: "feats_length2"},
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"logits": {1: "logits_length"},
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}
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def export_name(self):
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return "model.onnx"
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