transformers/pr_33915/en/executorch.md

ExecuTorch

ExecuTorch is a platform that enables PyTorch training and inference programs to be run on mobile and edge devices. It is powered by torch.compile and torch.export for performance and deployment.

You can use ExecuTorch with Transformers with torch.export. The convert_and_export_with_cache() method converts a PreTrainedModel into an exportable module. Under the hood, it uses torch.export to export the model, ensuring compatibility with ExecuTorch.

import torch
from transformers import LlamaForCausalLM, AutoTokenizer, GenerationConfig
from transformers.integrations.executorch import(
    TorchExportableModuleWithStaticCache,
    convert_and_export_with_cache
)

generation_config = GenerationConfig(
    use_cache=True,
    cache_implementation="static",
    cache_config={
        "batch_size": 1,
        "max_cache_len": 20,
    }
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B", pad_token="</s>", padding_side="right")
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", device_map="auto", dtype=torch.bfloat16, attn_implementation="sdpa", generation_config=generation_config)

exported_program = convert_and_export_with_cache(model)

The exported PyTorch model is now ready to be used with ExecuTorch. Wrap the model with TorchExportableModuleWithStaticCache to generate text.

prompts = ["Simply put, the theory of relativity states that "]
prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
prompt_token_ids = prompt_tokens["input_ids"]

generated_ids = TorchExportableModuleWithStaticCache.generate(
    exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=20,
)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text)
['Simply put, the theory of relativity states that 1) the speed of light is the']