Support for bfloat16

[philippwitte]

Description

Are there plans to support the bfloat16 data type in the near future? This data type is becoming increasingly popular in LLM training. It looks like currently it's not supported. I.e., calling y = cp.asarray(x), where x is a torch tensor of type torch.bfloat16, returns "TypeError: Got unsupported ScalarType BFloat16". Are there any recommended workarounds in the meantime?

Additional Information

No response

[leofang]

Curious where/how would you use bf16 if CuPy were to support it? Any pointer or reference? Thanks! 🙂

[jglaser]

It would be good if numpy data type extensions à la https://github.com/jax-ml/ml_dtypes/tree/main were supported, which includes bfloat16, fp8 etc.

[guberti]

Seconding this! bfloat16 and fp8 support are important for my use case. I'd love to see these.

[wuxibin89]

Any progress on this? We really need it for LLM training and inference.

[borisfom]

bfloat16 support is sorely missed in cupy. Would really appreciate it getting fixed!
We are currently forced to work around it like this (thankfully we have torch.view):

x = torch.arange(10, dtype=torch.bfloat16, device="cuda")
print(x)
# tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], device='cuda:0',
#        dtype=torch.bfloat16)

# view as uint8
y = x.view(dtype=torch.uint8)

array_size_in_bytes = y.nelement()*y.element_size()
mem = cupy.cuda.UnownedMemory(y.data_ptr(), array_size_in_bytes, owner=None)
memptr = cupy.cuda.MemoryPointer(mem, offset=0)
arr = cupy.ndarray(y.size(), dtype=cupy.uint8, memptr=memptr)
out = torch.as_tensor(arr, device=x.device, dtype=torch.uint8)
print(out)
# tensor([  0,   0, 128,  63,   0,  64,  64,  64, 128,  64, 160,  64, 192,  64,
#         224,  64,   0,  65,  16,  65], device='cuda:0', dtype=torch.uint8)

# view as bfloat16 again
out = out.view(x.dtype)
print(out)
# tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], device='cuda:0',
#        dtype=torch.bfloat16)

[yuanlin2004]

I see (in #8269) that the bfloat16 feature is planned for v14 release. @asi1024 , is there a WIP branch that others can play with or help if needed?

[dakofler]

Would also love this!

[CloudyDory]

We are using a spiking neural network training library that actually implements custom CuPy functions for forward and backward propagation. The fact that CuPy lacks bfloat16 support is real pain for us. I would highly appreciate any progress on this issue.