# Aten::mul.out receives tensors from mixed devices

**URL:** <https://dev-discuss.pytorch.org/t/aten-mul-out-receives-tensors-from-mixed-devices/376>\
**Category:** hardware-backends\
**Created:** [October 15, 2021, 9:31pm UTC](https://dev-discuss.pytorch.org/t/aten-mul-out-receives-tensors-from-mixed-devices/376 "2021-10-15T21:31:40Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![artyom-beilis](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/artyom-beilis/32/207_2.png) [@artyom-beilis](https://dev-discuss.pytorch.org/u/artyom-beilis)\
**Post date:** [October 15, 2021, 9:31pm UTC](https://dev-discuss.pytorch.org/t/aten-mul-out-receives-tensors-from-mixed-devices/376/1 "2021-10-15T21:31:40Z")

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I’m working on OpenCL backend for pytorch. When implementing operations needed for adam I got stuck with following case:

`aten::mul.out`

```
Tensor & mul_out(const Tensor & self, const Tensor & other, Tensor & out)
{
    std::cerr << "Self:" << self.device() << " " << self.numel() << std::endl;
    std::cerr << "Other:" << other.device() << " " << other.numel() << std::endl;
    std::cerr << "Out:" << out.device() << " " << out.numel() << std::endl;

```

I got correctly self and out as opencl devices but other was single element cpu device

```
Self:opencl:1 288
Other:cpu 1
Out:opencl:1 288

```

It was surprise for me since I expected that I will get tensors for my device/backend only. Why wasn’t it dispatched to mul\_.Scalar or other op?

Do I need to handle cpu tensors being another parameter to a GPU tensor as something normal? How do I handle such situation? (Basides manually check that `other` is cpu tensor of size 1)

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**Author:** ![tom](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/tom/32/26_2.png) [@tom](https://dev-discuss.pytorch.org/u/tom)\
**Post date:** [October 16, 2021, 9:52am UTC](https://dev-discuss.pytorch.org/t/aten-mul-out-receives-tensors-from-mixed-devices/376/2 "2021-10-16T09:52:06Z")

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> [@artyom-beilis](#):
>
> It was surprise for me since I expected that I will get tensors for my device/backend only. Why wasn’t it dispatched to mul\_.Scalar or other op?
> 
> Do I need to handle cpu tensors being another parameter to a GPU tensor as something normal? How do I handle such situation? (Basides manually check that `other` is cpu tensor of size 1)

- The dispatcher will typically use the first tensor argument to decide where to send to.  
You might find the discussion at [https://github.com/pytorch/pytorch/blob/6831d8e/aten/src/ATen/native/README.md#device\_guard](https://github.com/pytorch/pytorch/blob/6831d8e/aten/src/ATen/native/README.md#device_guard) useful.

- If you scroll down on the above link, there is a discussion that ATen usually generates checks that all your tensors are on the same device. ([https://github.com/pytorch/pytorch/blob/6831d8e379392da1340a28fdb3e7e1382176d1d4/aten/src/ATen/core/op\_registration/adaption.h#L48](https://github.com/pytorch/pytorch/blob/6831d8e379392da1340a28fdb3e7e1382176d1d4/aten/src/ATen/core/op_registration/adaption.h#L48) , calls inserted by the codegen in tools/) This would probably be the right thing to check in your custom dispatch targets, too.

- As you note, multiplication with scalars is handled by a different overload of mul (there is a difference between how shape-() tensors are handled vs. shape (1,)).

In general, I’d probably try to match what PyTorch does with cuda, but I guess you would already be doing that.

Best regards

Thomas

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<div class="post-metadata">

**Author:** ![artyom-beilis](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/artyom-beilis/32/207_2.png) [@artyom-beilis](https://dev-discuss.pytorch.org/u/artyom-beilis)\
**Post date:** [October 16, 2021, 9:08pm UTC](https://dev-discuss.pytorch.org/t/aten-mul-out-receives-tensors-from-mixed-devices/376/3 "2021-10-16T21:08:08Z")

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I see. It is strange since I expected dispatch to happen with scaler.

In any case I added handling of cpu-scalar tensor case:

> <https://github.com/artyom-beilis/pytorch_dlprim/blob/master/op.cpp#L654>

So for meanwhile I can continue.

At this point I have full working mnist training, and stock alexnet and vgg16 over opencl.

to be continued