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63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import torch
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@torch.inference_mode()
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def generate_stream(
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model, tokenizer, params, device, context_len=42048, stream_interval=2
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):
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"""Fork from https://github.com/ShishirPatil/gorilla/blob/main/inference/serve/gorilla_cli.py"""
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prompt = params["prompt"]
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l_prompt = len(prompt)
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max_new_tokens = int(params.get("max_new_tokens", 1024))
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stop_str = params.get("stop", None)
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input_ids = tokenizer(prompt).input_ids
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output_ids = list(input_ids)
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input_echo_len = len(input_ids)
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max_src_len = context_len - max_new_tokens - 8
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input_ids = input_ids[-max_src_len:]
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past_key_values = out = None
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for i in range(max_new_tokens):
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if i == 0:
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out = model(torch.as_tensor([input_ids], device=device), use_cache=True)
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logits = out.logits
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past_key_values = out.past_key_values
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else:
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out = model(
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input_ids=torch.as_tensor([[token]], device=device),
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use_cache=True,
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past_key_values=past_key_values,
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)
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logits = out.logits
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past_key_values = out.past_key_values
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last_token_logits = logits[0][-1]
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probs = torch.softmax(last_token_logits, dim=-1)
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token = int(torch.multinomial(probs, num_samples=1))
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output_ids.append(token)
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if token == tokenizer.eos_token_id:
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stopped = True
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else:
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stopped = False
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if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
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tmp_output_ids = output_ids[input_echo_len:]
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output = tokenizer.decode(
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tmp_output_ids,
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skip_special_tokens=True,
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spaces_between_special_tokens=False,
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)
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pos = output.rfind(stop_str, l_prompt)
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if pos != -1:
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output = output[:pos]
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stopped = True
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yield output
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if stopped:
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break
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del past_key_values
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