Merge branch 'model_parallel_generation' into 'master'

    return (1-boolean)*val1 + boolean*val2

def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, tokenizer, args, maxlen=None, type_ids=None):

    if isinstance(model, DDP):

        model = model.module

    if isinstance(model, FP16_Module):

        model = model.module

    original_output_parallel = model.parallel_output

    model.parallel_output = False

    model.eval()

    with torch.no_grad():

        context_length = context_lengths.min().item()

            if args.greedy:

                prev = torch.argmax(logits, dim=-1).view(-1)

            else:

                logits = logits.float()

                logits /= args.temperature

                logits = top_k_logits(logits, top_k=args.top_k, top_p=args.top_p)            

                log_probs = F.softmax(logits, dim=-1)

            yield tokens, lengths

            if done:

                break

    model.parallel_output = original_output_parallel

def prepare_tokenizer(args):