Support BF16 in Rust Numpy

[guoqingbao]

Can we support numpy bfloat16 in Rust? Since we have half::bf16 in Rust-numpy but cannot pass the numpy array with "bfloat16" to Rust using this crate. I think the support of bf16 is similar to that of b16 you have done before. Thanks!

[adamreichold]

What NumPy dtype does half::bf16 correspond to? Does NumPy actually support BFloat16 and if so, which version is required?

[guoqingbao]

Thanks for the reply. I used numpy 1.19.5 and I can create a numpy array with:

arr = np.ones((16,16), "bfloat16")

You may observe the dtype info of the array created above in vs code.

[adamreichold]

I suspect that you have some additional package installed/imported which provides the dtype. On plain NumPy installation, I get

> python3
Python 3.11.3 (main, Apr 27 2023, 22:08:21) [GCC] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.version.full_version
'1.24.2'
>>> arr = np.ones((16,16), "bfloat16")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib64/python3.11/site-packages/numpy/core/numeric.py", line 205, in ones
    a = empty(shape, dtype, order)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: data type 'bfloat16' not understood

[guoqingbao]

I suspect that you have some additional package installed/imported which provides the dtype. On plain NumPy installation, I get

> python3
Python 3.11.3 (main, Apr 27 2023, 22:08:21) [GCC] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.version.full_version
'1.24.2'
>>> arr = np.ones((16,16), "bfloat16")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib64/python3.11/site-packages/numpy/core/numeric.py", line 205, in ones
    a = empty(shape, dtype, order)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: data type 'bfloat16' not understood

I think there is a package to extend numpy for bfloat16 (pip install bfloat16), but I didn't install it. Perhaps Tensorflow registered the bfloat16 type for numpy. You may use:

    import tensorflow as tf
    bfloat16 = tf.bfloat16.as_numpy_dtype
    arr = np.ones((16,16), bfloat16)

Since we have all the dtype info and half::bf16 for type conversion, can we still implement bfloat16 in Rust-numpy?

[adamreichold]

Since we have all the dtype info and half::bf16 for type conversion, can we still implement bfloat16 in Rust-numpy?

In principle, we can but it does complicate things: Testing becomes more complex because we would need additional dependencies. Getting at the actual type descriptor is more complex because we need to use the Python API (instead of NumPy's native API).

It is possible, but I am not sure yet whether I want to commit to including it under these conditions. For example, I would not be happy about our CI depending on Tensorflow. Maybe on a best effort basis, i.e. just the code but without tests. But that would mean we get more bug reports. 🤔

[guoqingbao]

Since we have all the dtype info and half::bf16 for type conversion, can we still implement bfloat16 in Rust-numpy?

In principle, we can but it does complicate things: Testing becomes more complex because we would need additional dependencies. Getting at the actual type descriptor is more complex because we need to use the Python API (instead of NumPy's native API).

It is possible, but I am not sure yet whether I want to commit to including it under these conditions. For example, I would not be happy about our CI depending on Tensorflow. Maybe on a best effort basis, i.e. just the code but without tests. But that would mean we get more bug reports. 🤔

I think the implementation for bfloat16 may not depend on Tensorflow or other external python packages because the rust half::bf16 have such type conversion and what we are going to do is to make it compatible with python bfloat16, e.g., Tensorflow -> numpy bf16 array. There is a pure C++ implementation for supporting numpy bfloat16 https://github.com/GreenWaves-Technologies/bfloat16/blob/main/bfloat16.cc Is that still feasible?

[adamreichold]

I think the implementation for bfloat16 may not depend on Tensorflow or other external python packages because the rust half::bf16 have such type conversion and what we are going to do is to make it compatible with python bfloat16, e.g., Tensorflow -> numpy bf16 array. There is a pure C++ implementation for supporting numpy bfloat16 https://github.com/GreenWaves-Technologies/bfloat16/blob/main/bfloat16.cc Is that still feasible?

My idea was rather to not rely on the specific package which provides the dtype implementation at all by creating the array descriptor through the Python API and caching it.

Could you give #381 a try whether it would work for you?

[guoqingbao]

I think the implementation for bfloat16 may not depend on Tensorflow or other external python packages because the rust half::bf16 have such type conversion and what we are going to do is to make it compatible with python bfloat16, e.g., Tensorflow -> numpy bf16 array. There is a pure C++ implementation for supporting numpy bfloat16 https://github.com/GreenWaves-Technologies/bfloat16/blob/main/bfloat16.cc Is that still feasible?

My idea was rather to not rely on the specific package which provides the dtype implementation at all by creating the array descriptor through the Python API and caching it.

Could you give #381 a try whether it would work for you?

That's great! I will try and report the results.

[guoqingbao]

I think the implementation for bfloat16 may not depend on Tensorflow or other external python packages because the rust half::bf16 have such type conversion and what we are going to do is to make it compatible with python bfloat16, e.g., Tensorflow -> numpy bf16 array. There is a pure C++ implementation for supporting numpy bfloat16 https://github.com/GreenWaves-Technologies/bfloat16/blob/main/bfloat16.cc Is that still feasible?

My idea was rather to not rely on the specific package which provides the dtype implementation at all by creating the array descriptor through the Python API and caching it.

Could you give #381 a try whether it would work for you?

I can confirm that it works! Thanks for the superfast support.