Set mem cache args on inference
Browse files- scripts/finetune.py +6 -0
scripts/finetune.py
CHANGED
@@ -77,6 +77,11 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
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importlib.import_module("axolotl.prompters"), prompter
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)
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while True:
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print("=" * 80)
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# support for multiline inputs
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@@ -90,6 +95,7 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
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else:
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prompt = instruction.strip()
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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print("=" * 40)
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model.eval()
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with torch.no_grad():
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importlib.import_module("axolotl.prompters"), prompter
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)
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+
if cfg.landmark_attention:
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+
model.set_mem_cache_args(
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max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
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)
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+
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while True:
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print("=" * 80)
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# support for multiline inputs
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else:
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prompt = instruction.strip()
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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+
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print("=" * 40)
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model.eval()
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with torch.no_grad():
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