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support gorilla
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@ -35,6 +35,7 @@ LLM_MODEL_CONFIG = {
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"chatglm-6b": os.path.join(MODEL_PATH, "chatglm-6b"),
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"text2vec-base": os.path.join(MODEL_PATH, "text2vec-base-chinese"),
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"guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
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"gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
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"proxyllm": "proxyllm",
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}
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@ -109,6 +109,20 @@ class GuanacoAdapter(BaseLLMAdaper):
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model_path, load_in_4bit=True, device_map={"": 0}, **from_pretrained_kwargs
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)
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return model, tokenizer
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class GorillaAdapter(BaseLLMAdaper):
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"""TODO Support guanaco"""
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def match(self, model_path: str):
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return "gorilla" in model_path
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def loader(self, model_path: str, from_pretrained_kwargs: dict):
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(
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model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
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)
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return model, tokenizer
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class CodeGenAdapter(BaseLLMAdaper):
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@ -166,6 +180,7 @@ class ProxyllmAdapter(BaseLLMAdaper):
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register_llm_model_adapters(VicunaLLMAdapater)
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register_llm_model_adapters(ChatGLMAdapater)
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register_llm_model_adapters(GuanacoAdapter)
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register_llm_model_adapters(GorillaAdapter)
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# TODO Default support vicuna, other model need to tests and Evaluate
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# just for test, remove this later
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58
pilot/model/llm_out/gorilla_llm.py
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58
pilot/model/llm_out/gorilla_llm.py
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@ -0,0 +1,58 @@
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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(tmp_output_ids, skip_special_tokens=True, spaces_between_special_tokens=False,)
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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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@ -95,6 +95,16 @@ class GuanacoChatAdapter(BaseChatAdpter):
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return guanaco_generate_stream
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class GorillaChatAdapter(BaseChatAdpter):
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"""Model chat adapter for Guanaco"""
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def match(self, model_path: str):
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return "gorilla" in model_path
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def get_generate_stream_func(self):
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from pilot.model.llm_out.gorilla_llm import generate_stream
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return generate_stream
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class ProxyllmChatAdapter(BaseChatAdpter):
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def match(self, model_path: str):
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@ -109,7 +119,7 @@ class ProxyllmChatAdapter(BaseChatAdpter):
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register_llm_model_chat_adapter(VicunaChatAdapter)
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register_llm_model_chat_adapter(ChatGLMChatAdapter)
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register_llm_model_chat_adapter(GuanacoChatAdapter)
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register_llm_model_chat_adapter(GorillaChatAdapter)
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# Proxy model for test and develop, it's cheap for us now.
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register_llm_model_chat_adapter(ProxyllmChatAdapter)
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