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@@ -115,7 +115,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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print(f"\n\n{question}\n--------------------\n")
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print(f"\n\n{question}\n--------------------\n")
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input_ids = encode(question, max_new_tokens)
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input_ids = encode(question, max_new_tokens)
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- original_input_ids = output = input_ids
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+ original_input_ids = input_ids
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+ output = input_ids[0]
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cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
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cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
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n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
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n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
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if stopping_string is not None:
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if stopping_string is not None:
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@@ -186,7 +187,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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if 'stopping_criteria' not in kwargs:
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if 'stopping_criteria' not in kwargs:
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kwargs['stopping_criteria'] = []
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kwargs['stopping_criteria'] = []
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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- shared.model.generate(**kwargs)[0]
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+ clear_torch_cache()
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+ shared.model.generate(**kwargs)
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def generate_with_streaming(**kwargs):
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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@@ -208,7 +210,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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else:
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else:
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for i in range(max_new_tokens//8+1):
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for i in range(max_new_tokens//8+1):
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clear_torch_cache()
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clear_torch_cache()
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-
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with torch.no_grad():
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
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output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
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if shared.soft_prompt:
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if shared.soft_prompt:
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oobabooga