Here is a screenshot created by the same script with different precision. On the left is the results of a dense layer given FP32 and the right is the results of a dense layer given FP16, with --dp-inference enabled. The qkv calculated from the ds_qkv_gemm are incorrectly masked as...
We have examples of how to use these two different forms of model parallelism the example scripts ending indistributed_with_mp.sh, note that pipeline parallelism is not currently supported in the T5 model: Other than these minor changes, the distributed training is identical to the training on ...
We have examples of how to use these two different forms of model parallelism the example scripts ending indistributed_with_mp.sh, note that pipeline parallelism is not currently supported in the T5 model: Other than these minor changes, the distributed training is identical to the training on ...
## Model parallelism, 1 is no MP mp_size=1 ## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true. ## Note that currently both curriculum learning and random-LTD are NOT ## compatible with pipeline parallelism. pp_size=8 no_pp="false" ## ZeRO-based data par...
self.decay_tokens is not None: self.warmup_tokens = self.num_tokens return self.max_lr * float(self.num_steps) / \ float(self.warmup_steps) # If the learning rate is constant, just return the initial value. if self.decay_style == 'constant': return self.max_lr # For any steps...