Add tools for auto filling traced models cache

[JingyaHuang]

What does this PR do?

• Add a tool to populate the cache of traced models.
• Use the same way as export to construct the configs to hash, to avoid hash key difference.

Tests

• Test with config file

  {
  "hf-internal-testing/tiny-stable-diffusion-torch": [
      {   "batch_size": 1, "height": 64, "width": 64, "num_images_per_prompt": 1, "auto_cast": "matmul", "auto_cast_type": "bf16" }
  ],
  "hf-internal-testing/tiny-random-gpt2": [
      {  "batch_size": 1, "sequence_length": 512, "num_cores": 1, "auto_cast_type": "fp16"  }
  ],
  "hf-internal-testing/tiny-random-BertModel": [
      {  "task": "text-classification", "batch_size": 1, "sequence_length": 16, "auto_cast": "matmul", "auto_cast_type": "fp16"  }
  ]
  }

  Command:

  python tools/auto_fill_inference_cache.py --config_file inference_cache_test_config.json

• Test to cache a single model

  • Encoder

    python tools/auto_fill_inference_cache.py --hf_model_id hf-internal-testing/tiny-random-BertModel --task text-classification --batch_size 1 --sequence_length 64 --auto_cast matmul --auto_cast_type bf16

  • Decoder

    python tools/auto_fill_inference_cache.py --hf_model_id hf-internal-testing/tiny-random-gpt2 --batch_size 1 --sequence_length 512 --num_cores 1 --auto_cast_type bf16

  • Stable Diffusion

    python tools/auto_fill_inference_cache.py --hf_model_id hf-internal-testing/tiny-stable-diffusion-torch --batch_size 1 --height 64 --width 64 --auto_cast matmul --auto_cast_type bf16

Next steps

• Will update existing configs in aws-neuron/optimum-neuron-cache
• Will upload configs for the encoder and stable diffusion models

[HuggingFaceDocBuilderDev]

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