Remove early stopping from LLaMA end-to-end benchmarking (microsoft#2…

…0033)

### Description
This PR removes early stopping from the end-to-end LLaMA-2 benchmark
script.

### Motivation and Context
This allows models to always generate the requested number of new
tokens.

onnxruntime/python/tools/transformers/models/llama/benchmark_e2e.py

@@ -400,11 +400,7 @@ def main():
                 sampling_times.append(sampling_end_time - sampling_start_time)
 
                 all_token_ids = torch.cat([all_token_ids, tokens_to_add], dim=-1)
-
-                # Return early if all batch entries have reached EOS token id
                 current_length += 1
-                if torch.all(has_eos) or current_length > max_length:
-                    break
 
                 # Update inputs for next inference run
                 inputs["input_ids"] = tokens_to_add