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taylanbil
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Nov 3, 2020
initial changes to support training on tpus changed tpu configuration to use training.device replace parallelLoader with mpLoader to solved loader exhaust issue. removed debug message. updated the comment added comments for drop_last change. removed pdb lines removed redundant device config added comments for pending changes default init not applicable for xla device type moved wrapping of dataloader to build added line-debug function metsumm removed some .item calls from reporting xla equivalents in the distributed module, earlier eval was failing at the metrics all reduce step implemented broadcast in terms of all_to_all changes for checkpoint saving change to make execution even across cores corrected the is_master logic one more fix for is_master clean up of debug messages
taylanbil
reviewed
Nov 23, 2020
| v = v.mean() | ||
| v = v.item() | ||
| assert isinstance(v, (float, int)) | ||
| #v = v.item() |
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instead of commenting out here, let's have a util function like
def item(self, v):
if torch.is_tensor(v) and v.device.type == 'xla':
return v
return v.item()and use v = self.item(v) and then assert on assert isinstance(v, (float, int)) or v.device.type == 'xla'
| # since other device types such as xla can be passed | ||
| # falling back to cpu should only happen when device_type | ||
| # is set to cude but cuda is not available. | ||
| if not torch.cuda.is_available() and device == "cuda": |
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ordering as device == 'cude' and torch.cuda.is_available() will save you the cuda available check.
| # Since, this is iterator, we need to return total length == number of batches | ||
| batch_size = get_batch_size() | ||
| # Changed the length to accomadate drop_last == True | ||
| # drop_last is required if the batch is split intor multiple cores |
| # Changed the length to accomadate drop_last == True | ||
| # drop_last is required if the batch is split intor multiple cores | ||
| # some of the cores may not have enough examples. | ||
| if is_xla(): |
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can you use thee bool drop_last here instead of is_xla?
| self.device = xm.xla_device() | ||
| self.distributed = True | ||
| self.local_rank = xm.get_local_ordinal() | ||
| self.tpu = True |
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I think using self.xla to denote xla usage is better than using self.tpu
| bool -- Tells whether early stopping occurred or not | ||
| """ | ||
| if not is_master(): | ||
| if not is_master() and not is_xla(): |
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I don't understand this. why always False if not the master ordinal?
| if init_distributed: | ||
| distributed_init(config) | ||
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| if config.distributed.init_method is None: | ||
| infer_init_method(config) | ||
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| ) | ||
| if is_xla(): | ||
| import torch_xla.distributed.xla_multiprocessing as xmp | ||
| torch.multiprocessing.set_sharing_strategy("file_system") |
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For Internal review.