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pipeline fill_mask.py - needs to convert input_ids to cpu before calling numpy

See original GitHub issue

https://github.com/huggingface/transformers/blob/b7d264be0d70d5309dbf24a6c7ebba1e073bdda5/src/transformers/pipelines/fill_mask.py#L127

Should be

tokens = input_ids.cpu().numpy()


from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM, pipeline
#tokenizer = AutoTokenizer.from_pretrained("google/fnet-base")
#model = AutoModelForMaskedLM.from_pretrained("google/fnet-base").cuda()
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased").cuda().half()
unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer, device=0)
unmasker("Hello I'm a [MASK] model.")

[
    {"sequence": "hello i'm a new model.", "score": 0.12073223292827606, "token": 351, "token_str": "new"},
    {"sequence": "hello i'm a first model.", "score": 0.08501081168651581, "token": 478, "token_str": "first"},
    {"sequence": "hello i'm a next model.", "score": 0.060546260327100754, "token": 1037, "token_str": "next"},
    {"sequence": "hello i'm a last model.", "score": 0.038265593349933624, "token": 813, "token_str": "last"},
    {"sequence": "hello i'm a sister model.", "score": 0.033868927508592606, "token": 6232, "token_str": "sister"},
]

Will produce the right result with the change. But will raise an error without the change:

TypeError: can’t convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.