onnx问题请教

问题描述：我参考infer/modules/onnx/export.py尝试将net_g的torch模型转onnx，遇到一些问题，望大佬帮忙看看：

1. onnx推理时不支持动态长度，export.py中默认hubert长度为200，转成onnx后也只能推理hubert长度为200的输入，其他长度的会报错；虽然问题通过简单的方式解决了，但是不知道会不会影响结果，我的解决方式是：根据转onnx的warning内容，把infer/lib/infer_pack/attentions.py中用到的int删去。
2. 即使在不修改代码，并且保持onnx推理时hubert长度为200的情况下，对比torch模型的输出和onnx模型的输出，发现输出结果差别挺大的，不知道是为什么呢？

如下是运行代码：

import torch
import sys
sys.path.append("/czc/Retrieval-based-Voice-Conversion-WebUI-main")
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
import time
def export_onnx(ModelPath, ExportedPath):
    cpt = torch.load(ModelPath, map_location="cpu")
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768

    test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
    test_pitchf = torch.rand(1, 200)  # nsf基频
    test_ds = torch.LongTensor([0])  # 说话人ID
    test_rnd = torch.rand(1, 192, 200)  # 噪声（加入随机因子）
    device = "cpu"  # 导出时设备（不影响使用模型）
    net_g = SynthesizerTrnMsNSFsidM(
        *cpt["config"], is_half=False, version=cpt.get("version", "v1")
    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
    net_g.load_state_dict(cpt["weight"], strict=False)
    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
    output_names = [
        "audio",
    ]
    # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
    torch.onnx.export(
        net_g,
        (
            test_phone.to(device),
            test_phone_lengths.to(device),
            test_pitch.to(device),
            test_pitchf.to(device),
            test_ds.to(device),
            test_rnd.to(device),
        ),
        ExportedPath,
        dynamic_axes={
            "phone": [1],
            "pitch": [1],
            "pitchf": [1],
            "rnd": [2],
        },
        do_constant_folding=False,
        opset_version=13,
        verbose=False,
        input_names=input_names,
        output_names=output_names,
    )
    return "Finished"
if __name__ == "__main__":
    # 有bug导致动态维度失效
    # infer/lib/infer_pack/attentions.py 将export中报错的地方的int去掉
    ModelPath = "/czc/Retrieval-based-Voice-Conversion-WebUI-main/assets/weights/nvzhubo.pth"
    model_path = "/czc/Retrieval-based-Voice-Conversion-WebUI-main/assets/weights/nvzhubo.onnx"
    # 转onnx
    export_onnx(ModelPath, model_path)
    cpt = torch.load(ModelPath, map_location="cpu")
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
    test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
    test_pitchf = torch.rand(1, 200)  # nsf基频
    test_ds = torch.LongTensor([0])  # 说话人ID
    test_rnd = torch.zeros(1, 192, 200)  # 噪声（加入随机因子）
    time1 = time.time()
    net_g = SynthesizerTrnMsNSFsidM(
        *cpt["config"], is_half=False, version=cpt.get("version", "v1")
    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
    net_g.eval()
    out1 = net_g(test_phone, test_phone_lengths, test_pitch, test_pitchf, test_ds, test_rnd)
    print(time.time()-time1)
    print(out1.shape)
    print(out1[0,0,:5])
    providers = ["CPUExecutionProvider"]
    # 保持输入一致
    test_phone = test_phone.numpy()  # hidden unit
    test_phone_lengths = test_phone_lengths.numpy()  # hidden unit 长度（貌似没啥用）
    test_pitch = test_pitch.numpy()  # 基频（单位赫兹）
    test_pitchf = test_pitchf.numpy()  # nsf基频
    test_ds = test_ds.numpy()  # 说话人ID
    test_rnd = test_rnd.numpy()  # 噪声（加入随机因子）
    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
    onnx_input = {
        "phone": test_phone,
        "phone_lengths": test_phone_lengths,
        "pitch": test_pitch,
        "pitchf": test_pitchf,
        "ds": test_ds,
        "rnd": test_rnd,
    }
    import onnxruntime
    import numpy as np
    onnx = onnxruntime.InferenceSession(model_path, providers=providers)
    time2 = time.time()
    out2 = torch.tensor(onnx.run(None, onnx_input)[0])
    # out2 = torch.tensor((onnx.run(None, onnx_input)[0] * 32767).astype(np.int16))
    print(time.time()-time2)
    print(out2.shape)
    print(out2[0,0,:5])

如下是两模型的输出结果：

torch.Size([1, 1, 80000])
tensor([0.0870, 0.0332, 0.1191, 0.2330, 0.2877], grad_fn=<SliceBackward0>)
torch.Size([1, 1, 80000])
tensor([0.0115, 0.0109, 0.0093, 0.0084, 0.0084])

如下是转onnx的输出日志：

/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:2033: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input phone
  warnings.warn(
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:2033: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input pitch
  warnings.warn(
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:2033: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input pitchf
  warnings.warn(
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:2033: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input rnd
  warnings.warn(
/czc/Retrieval-based-Voice-Conversion-WebUI-main/infer/lib/infer_pack/attentions.py:249: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  t_s == t_t
/czc/Retrieval-based-Voice-Conversion-WebUI-main/infer/lib/infer_pack/attentions.py:311: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  pad_length: int = max(length - (self.window_size + 1), 0)
/czc/Retrieval-based-Voice-Conversion-WebUI-main/infer/lib/infer_pack/attentions.py:312: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  slice_start_position = max((self.window_size + 1) - length, 0)
/czc/Retrieval-based-Voice-Conversion-WebUI-main/infer/lib/infer_pack/attentions.py:314: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if pad_length > 0:
/czc/Retrieval-based-Voice-Conversion-WebUI-main/infer/lib/infer_pack/attentions.py:366: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  x_flat = x.view([batch, heads, int(length**2) + int(length * (length - 1))])
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/_internal/jit_utils.py:306: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
  _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:689: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
  _C._jit_pass_onnx_graph_shape_type_inference(
/root/miniconda3/envs/valle/lib/python3.10/site-packages/torch/onnx/utils.py:1186: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
  _C._jit_pass_onnx_graph_shape_type_inference(
============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
****

Start with infer/modules/onnx/export.py and infer/lib/infer_pack/attentions.py, then reproduce the reported export and compare SynthesizerTrnMsNSFsidM outputs in PyTorch and ONNX Runtime using the issue's inputs. The work is done when dynamic Hubert lengths behave correctly and the output discrepancy is explained or resolved with a regression check.