support gorilla

This commit is contained in:
zhanghy-sketchzh 2023-06-07 12:05:33 +08:00
parent b24d2fe0c0
commit c2bfab11e0
4 changed files with 85 additions and 1 deletions

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@ -35,6 +35,7 @@ LLM_MODEL_CONFIG = {
"chatglm-6b": os.path.join(MODEL_PATH, "chatglm-6b"),
"text2vec-base": os.path.join(MODEL_PATH, "text2vec-base-chinese"),
"guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
"gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
"proxyllm": "proxyllm",
}

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@ -109,6 +109,20 @@ class GuanacoAdapter(BaseLLMAdaper):
model_path, load_in_4bit=True, device_map={"": 0}, **from_pretrained_kwargs
)
return model, tokenizer
class GorillaAdapter(BaseLLMAdaper):
"""TODO Support guanaco"""
def match(self, model_path: str):
return "gorilla" in model_path
def loader(self, model_path: str, from_pretrained_kwargs: dict):
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
)
return model, tokenizer
class CodeGenAdapter(BaseLLMAdaper):
@ -166,6 +180,7 @@ class ProxyllmAdapter(BaseLLMAdaper):
register_llm_model_adapters(VicunaLLMAdapater)
register_llm_model_adapters(ChatGLMAdapater)
register_llm_model_adapters(GuanacoAdapter)
register_llm_model_adapters(GorillaAdapter)
# TODO Default support vicuna, other model need to tests and Evaluate
# just for test, remove this later

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@ -0,0 +1,58 @@
import torch
@torch.inference_mode()
def generate_stream(
model, tokenizer, params, device, context_len=42048, stream_interval=2
):
"""Fork from https://github.com/ShishirPatil/gorilla/blob/main/inference/serve/gorilla_cli.py"""
prompt = params["prompt"]
l_prompt = len(prompt)
max_new_tokens = int(params.get("max_new_tokens", 1024))
stop_str = params.get("stop", None)
input_ids = tokenizer(prompt).input_ids
output_ids = list(input_ids)
input_echo_len = len(input_ids)
max_src_len = context_len - max_new_tokens - 8
input_ids = input_ids[-max_src_len:]
past_key_values = out = None
for i in range(max_new_tokens):
if i == 0:
out = model(torch.as_tensor([input_ids], device=device), use_cache=True)
logits = out.logits
past_key_values = out.past_key_values
else:
out = model(
input_ids=torch.as_tensor([[token]], device=device),
use_cache=True,
past_key_values=past_key_values,
)
logits = out.logits
past_key_values = out.past_key_values
last_token_logits = logits[0][-1]
probs = torch.softmax(last_token_logits, dim=-1)
token = int(torch.multinomial(probs, num_samples=1))
output_ids.append(token)
if token == tokenizer.eos_token_id:
stopped = True
else:
stopped = False
if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
tmp_output_ids = output_ids[input_echo_len:]
output = tokenizer.decode(tmp_output_ids, skip_special_tokens=True, spaces_between_special_tokens=False,)
pos = output.rfind(stop_str, l_prompt)
if pos != -1:
output = output[:pos]
stopped = True
yield output
if stopped:
break
del past_key_values

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@ -95,6 +95,16 @@ class GuanacoChatAdapter(BaseChatAdpter):
return guanaco_generate_stream
class GorillaChatAdapter(BaseChatAdpter):
"""Model chat adapter for Guanaco"""
def match(self, model_path: str):
return "gorilla" in model_path
def get_generate_stream_func(self):
from pilot.model.llm_out.gorilla_llm import generate_stream
return generate_stream
class ProxyllmChatAdapter(BaseChatAdpter):
def match(self, model_path: str):
@ -109,7 +119,7 @@ class ProxyllmChatAdapter(BaseChatAdpter):
register_llm_model_chat_adapter(VicunaChatAdapter)
register_llm_model_chat_adapter(ChatGLMChatAdapter)
register_llm_model_chat_adapter(GuanacoChatAdapter)
register_llm_model_chat_adapter(GorillaChatAdapter)
# Proxy model for test and develop, it's cheap for us now.
register_llm_model_chat_adapter(ProxyllmChatAdapter)