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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

🚀 Feature

Add support for torch.max with:

• CUDA bfloat16
• CPU float16 and bfloat16

Motivation

Currently, torch.max has support for CUDA float16:

>>> torch.rand(10, dtype=torch.float16, device='cuda').max()
tensor(0.8530, device='cuda:0', dtype=torch.float16)

But all three other combinations of CPU/CUDA and float16/bfloat16 are not supported:

>>> torch.ra

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Add a new API for converting a model to external data. Today the conversion happens in 2 steps
external_data_helper.convert_model_to_external_data(<model>, <all_tensors_to_one_file>, <size_threshold>) save_model(model, output_path)
We want to add another api which combines the 2 steps
`
save_model_to_external_data(, <output_

I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))