Vulkan embeddings give incorrect outputs
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backend tester
module: vulkan
- Dominant language
- Python
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Description
🐛 Describe the bug
The following repro gives incorrect outputs on Vulkan. Outputs match eager when not delegated.
import torch
from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
from executorch.exir import to_edge_transform_and_lower, EdgeCompileConfig, to_edge
from executorch.extension.pybindings.portable_lib import _load_for_executorch_from_buffer
from typing import Callable, List, Optional, Tuple, Union
class Model(torch.nn.Module):
def __init__(
self,
):
super().__init__()
self.embedding = torch.nn.Embedding(
num_embeddings=10,
embedding_dim=5,
)
def forward(self, x):
return self.embedding(x)
model = Model()
inputs = (
torch.randint(0, 10, (1, 4), dtype=torch.long),
)
eager_outputs = model(*inputs)
ep = torch.export.export(model.eval(), inputs)
print(ep)
lowered = to_edge_transform_and_lower(
ep,
partitioner=[VulkanPartitioner()],
compile_config=EdgeCompileConfig(_check_ir_validity=False)
).to_executorch()
print(lowered.exported_program())
et_model = _load_for_executorch_from_buffer(lowered.buffer)
et_outputs = et_model([*inputs])[0]
print(f"Inputs: {inputs}")
print(f"Eager: {eager_outputs}")
print(f"ET: {et_outputs}")
Outputs:
Inputs: (tensor([[0, 0, 5, 9]]),)
Eager: tensor([[[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057],
[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057],
[-0.3060, 0.7337, 2.3316, -1.8212, 0.7317],
[-1.2270, 1.6362, 0.8507, -1.4619, 0.6319]]],
grad_fn=<EmbeddingBackward0>)
ET: tensor([[[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057],
[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057],
[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057],
[ 0.2909, -1.0046, -0.4795, -2.5402, 0.1057]]])
Versions
Run on Meta internal master, Jul 3, fbcode/SwiftShader
cc @SS-JIA @manuelcandales @cbilgin
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the provided embedding reproduction and comparing eager outputs with the Executorch outputs. Inspect executorch.backends.vulkan.partitioner.vulkan_partitioner.VulkanPartitioner and the to_edge_transform_and_lower path to trace how the embedding is delegated. Done means the Vulkan-delegated embedding returns the same values as eager for the repro inputs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- backend, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 42/100