pytorch / pytorch/executorch

Failed to load data for backend XnnpackBackend

Open
#3,848 6 comments 0 reactions 2 assignees View on GitHub

@davidlin54 is already working on this.

Since Jun 14, 2024.

module: examples triaged
Dominant language
Python
Stars
5k
Forks
1.2k
Avg merge
2d 10h
Merged PRs (30d)
581

Description

I'm trying to build llava_encoder using XNNPack & Android ToolChain.
Whenever i attempt to pass inputs to my model, it failed to delegate data to XNNBackend.

I've already checked .pte model working using xnn_executor_runner in linux (below command)

./cmake-out/backends/xnnpack/xnn_executor_runner --model_path=./xnn_llava_encoder.pte

and the input tensor shape was same with android input tensor. (1,3,336,336).

If anyone can help, I would greatly appreciate it.

type=1327 audit(1717601983.365:799): proctitle="com.example.executorchdemo"
File /data/user/0/com.example.executorchdemo/files/xnn_llava_encoder.pte: offset 5320704 + size 4198960 > file_size_ 8912896
Failed to load data for backend XnnpackBackend
Process: com.example.executorchdemo, PID: 886
   java.lang.Exception: Execution of method forward failed with status 0x12
                             	at org.pytorch.executorch.NativePeer.forward(Native Method)
                                at org.pytorch.executorch.Module.forward(Module.java:56)
                                at com.example.executorchdemo.MainActivity.run(MainActivity.java:199)
                                at java.lang.Thread.run(Thread.java:1012)

<MainActivity.java>

mBitmap = Bitmap.createScaledBitmap(mBitmap, 336, 336, true);
final Tensor inputTensor =
        TensorImageUtils.bitmapToFloat32Tensor(
            mBitmap,
            TensorImageUtils.TORCHVISION_NORM_MEAN_RGB,
                TensorImageUtils.TORCHVISION_NORM_STD_RGB);
Tensor outputTensor = mModule.forward(EValue.from(inputTensor))[0].toTensor();

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Assessment

This issue has not been assessed yet.

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.