Merge branch 'master' into l2_grad_clip_fix

* [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m): megatron\_bert\_345m:v0.0

* [GPT-2-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m): megatron\_lm\_345m:v0.0 

The models require vocabulary files to run. The BERT uncased WordPiece vocab file can be extracted from Google's [pretrained BERT models](https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip). The GPT-2 [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.

The models require vocabulary files to run. The BERT uncased WordPiece vocab file can be extracted from Google's [pretrained BERT models](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt). The GPT-2 [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.

Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1)

        self.module = module

        self.data_parallel_group = mpu.get_data_parallel_group()

        src_rank = mpu.get_model_parallel_rank()

        for p in self.module.parameters():

            if torch.is_tensor(p):

                dist.broadcast(p, src_rank, group=self.data_parallel_group)

        def allreduce_params(reduce_after=True, no_scale=False, fp32_allreduce=False):

            if(self.needs_reduction):