Native API returns: -996 invalid_kernel("uses-fp64-math")

[JustSomeRandomUsername]

I am trying to train a yolov7 model on my a770 on Ubuntu 22.04 I have onaApi, intel extension for pytorch installed and have trained a different model successfully.

But with this model iam getting a
"RuntimeError: Native API failed. Native API returns: -996 (Function exists but address is not available)
invalid_kernel("uses-fp64-math")
-996 (Function exists but address is not available)"

I am running the code from
https://github.com/WongKinYiu/yolov7
with slight modifications for running on xpu.
import intel_extension_for_pytorch
changed device to "xpu"
model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.float32)

I am still a bit new to work on neuralnets am i missing something obvious or is this a intel specific driver problem ?

[jingxu10]

thanks for reporting the issue. We will try to reproduce this issue.

[fredlarochelle]

Here is a quick way to reproduce the problem, it works fine on cpu, not on xpu:

import torch
import intel_extension_for_pytorch as ipex

device = torch.device('xpu' if torch.xpu.is_available() else 'cpu')
print(f'Using device: {device}, named {torch.xpu.get_device_name(0)}.')

# float16, bfloat16 and float32 get the same error...
x = torch.randn([100], dtype=torch.float16, device=device)

print(x * 0.5)

Btw, it would be nice if torch.xpu.get_device_name(0) would return the name of the GPU instead of the IDs!

[jingxu10]

@fredlarochelle Thanks for sharing the reproducer.
By the way, could you share the output of that device name print? Also, what is your GPU? Arc? Do you use 1.13.10+xpu or 1.10?
The script run on my environment shows Using device: xpu, named Intel(R) Data Center GPU Flex Series 170 [0x56c0].

[fredlarochelle]

I am using an A770 and when I call the function get_device_name(), it returns Intel(R) Graphics [0x56a0]. Running my script, it returns Using device: xpu, named Intel(R) Graphics [0x56a0]..

I am using 1.13.10+xpu, on Python 3.10.6 (the rest is the exact setup in the latest installation guide).

[jingxu10]

the reason of this error triggered by print is that double data type is invoked internally in print, which is not supported by the GPU hardware. Please move the tensor to CPU first then print.

import torch
import intel_extension_for_pytorch as ipex

device = torch.device('xpu' if torch.xpu.is_available() else 'cpu')
print(f'Using device: {device}, named {torch.xpu.get_device_name(0)}.')

# float16, bfloat16 and float32 get the same error...
x = torch.randn([100], dtype=torch.float16, device=device)
y=x*0.5
y=y.to('cpu')
print(y)

Regarding to the device name, it is fully retrieved from driver. May I know your driver info? like version and/or how you installed it.

[fredlarochelle]

Doesn't seem to solve the problem, running your code example, I still get the same error. Here is the full output:

But, you are right that it seems related to the print function, the conversion to string actually. Trying to print(x), gets the same error and replacing print() by str() also gets the same error. But, digging around a bit, I found a weird case:

import torch
import intel_extension_for_pytorch as ipex

# works fine
x = torch.ones([10], dtype=torch.float16, device='cpu')
x.to('xpu')
x.to('cpu')
print(x)

# doesn't work
x = torch.ones([10], dtype=torch.float16, device='xpu')
x.to('cpu')
print(x)

# but casting to integer works?
x = torch.ones([10], dtype=torch.float16, device='xpu')
x.to('cpu')
print(x.int())

Here is another weird example:

import torch
import intel_extension_for_pytorch as ipex

# works fine
torch.arange(10, device='cpu')

# works fine
torch.arange(10, device='xpu')

# works fine
torch.arange(10, dtype=torch.float32, device='cpu')

# doesn't works
torch.arange(10, dtype=torch.float32, device='xpu')

About the device name, just checked with XPU Manager and it too can't retrieve the full name. Seems like it is a problem upstream with the driver and not with ipex. For the driver install, I followed the exact instructions here and my driver version with clinfo is 22.49.25018.21.

[jingxu10]

Oh, sorry. I'll correct in the code snippet as well.

...
y=y.to('cpu')
print(y)

I'll check the arange one.

[jingxu10]

The arange one works on my side.
@ashokei @sanchitintel would you double confirm on your side?

[jingxu10]

Hi @JustSomeRandomUsername , since your workload is a training task. Double could be used in this case. Arc GPU doesn't support double precision in hardware level. Currently we don't recommend to run training on Arc and ATS-M GPUs.

[fredlarochelle]

Oh yeah, my bad, I didn't catch it either! I can confirm that it works on my side too.

And just to make sure, I double checked and I get the -996 error for the arange one.

A note should be added somewhere in the documentation, all of this is a different behavior than "standard" PyTorch with CUDA. You don't need to transfer the tensor back to the CPU to print it normally.

[jingxu10]

Exactly. Thanks for the advice. We will put this as a known issue.

[JustSomeRandomUsername]

@jingxu10 Seems likely that the code uses doubles somewhere, ill check if removing the use of doubles solves my problem. I have never heard that training is not recommended on arc, is this temporary ? Do you expect training on Arc to get simpler with software updates ?

[jingxu10]

To clarify this statement, comparing to inference, training invokes double precision data type more probably. Since the hardware itself doesn't support double, if the training workload doesn't depend on usage of double, it is OK. If not, there will be accuracy issues.