Update on "add option to run mmlu with 5 shots"

This PR does the following changes:
- add `--num_fewshot` option which is required for running MMLU task with 5 shots
- set the default value of `--limit` to none such that we can actually run all examples
- update `eval_llama` to call `simple_evaluate` which is a wrapper of `evaluate` and does some extra work for us like getting the task dict

Test Plan:
- Make sure UncycloText perplexity for llama 3.2 1B stays the same before and after the change.

Before, run eval_llama for llama 3.2 1B with limit set to None:
```
wikitext: {'word_perplexity,none': 12.78246428138387, 'word_perplexity_stderr,none': 'N/A', 'byte_perplexity,none': 1.610432252171856, 'byte_perplexity_stderr,none': 'N/A', 'bits_per_byte,none': 0.6874479705552373, 'bits_per_byte_stderr,none': 'N/A', 'alias': 'wikitext'}
```
After, run eval_llama for llama 3.2 1B:
```
wikitext: {'word_perplexity,none': 12.78246428138387, 'word_perplexity_stderr,none': 'N/A', 'byte_perplexity,none': 1.610432252171856, 'byte_perplexity_stderr,none': 'N/A', 'bits_per_byte,none': 0.6874479705552373, 'bits_per_byte_stderr,none': 'N/A', 'alias': 'wikitext'}
```
- Make sure that lm_eval(v0.4.2, which is used by eval_llama) and eval_llama reports similar number for llama 3.2 1B and 3B BF16 for MMLU task with 5 shots.

Example command for lm_eval:
```
lm_eval --model hf \
   --model_args pretrained=meta-llama/Llama-3.2-1B-Instruct \
   --tasks mmlu \
   --device cuda \
   -f 5 \
   --batch_size auto
```
Example command for eval_llama:
```
python -m examples.models.llama2.eval_llama \
	-c /home/lunwenh/models/1B_Instruct/consolidated.00.pth \
	-p /home/lunwenh/models/1B_Instruct/params.json \
	-t /home/lunwenh/models/1B_Instruct/tokenizer.model \
	-kv \
	-d bf16 \
	--tasks mmlu \
        -f 5 \
	--max_seq_length 2048
```

Differential Revision: [D64215268](https://our.internmc.facebook.com/intern/diff/D64215268)

[ghstack-poisoned]

Author: helunwencser

examples/models/llama2/eval_llama_lib.py

@@ -295,9 +295,9 @@ def eval_llama(
     with torch.no_grad():
         eval_results = simple_evaluate(
             model=eval_wrapper,
-            tasks=args.tasks,
-            num_fewshot=args.num_fewshot,
-            limit=args.limit,
+            tasks=args.tasks,  # pyre-ignore: Undefined attribute [16]: `argparse.ArgumentParser` has no attribute `tasks`
+            num_fewshot=args.num_fewshot,  # pyre-ignore: Undefined attribute [16]: `argparse.ArgumentParser` has no attribute `num_fewshot`
+            limit=args.limit,  # pyre-ignore: Undefined attribute [16]: `argparse.ArgumentParser` has no attribute `limit`
         )
 
     for task, res in eval_results["results"].items():