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https://github.com/hiyouga/LLaMA-Factory.git
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38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import os
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from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
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from llamafactory.hparams import get_infer_args
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from llamafactory.model import load_model, load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-LlamaForCausalLM")
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def test_attention():
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attention_available = ["off"]
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if is_torch_sdpa_available():
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attention_available.append("sdpa")
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if is_flash_attn_2_available():
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attention_available.append("fa2")
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llama_attention_classes = {
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"off": "LlamaAttention",
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"sdpa": "LlamaSdpaAttention",
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"fa2": "LlamaFlashAttention2",
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}
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for requested_attention in attention_available:
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model_args, _, finetuning_args, _ = get_infer_args(
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{
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"model_name_or_path": TINY_LLAMA,
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"template": "llama2",
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"flash_attn": requested_attention,
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}
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)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args)
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for module in model.modules():
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if "Attention" in module.__class__.__name__:
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assert module.__class__.__name__ == llama_attention_classes[requested_attention]
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