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Merge branch 'main' into feat/allow-set-torch-dtype

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ywfang 2024-02-02 17:00:22 +08:00 committed by GitHub
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2
README.md
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@ -109,7 +109,7 @@ python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/promp
``` ```
#### Huggingface 模型 #### Huggingface 模型
（注：我们发现当前Huggingface的推理代码推理效果差于Vllm的推理代码，我们正在对齐中，请耐心等待） （注：我们发现当前Huggingface的推理代码推理效果差于Vllm的推理代码，我们正在对齐中，目前已定为到PageAttention和普通attention的区别，请耐心等待）
##### MiniCPM-2B ##### MiniCPM-2B
* 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码 * 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码
```python ```python

9
demo/hf_based_demo.py
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@ -14,7 +14,9 @@ from transformers import (
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="") parser.add_argument("--model_path", type=str, default="")
parser.add_argument("--torch_dtype", type=str, default="bfloat16") parser.add_argument("--torch_dtype", type=str, default="bfloat16", choices=["float32", "bfloat16"]))
parser.add_argument("--server_name", type=str, default="127.0.0.1")
parser.add_argument("--server_port", type=int, default=7860)
args = parser.parse_args() args = parser.parse_args()
# init model torch dtype # init model torch dtype
@ -31,6 +33,9 @@ path = args.model_path
tokenizer = AutoTokenizer.from_pretrained(path) tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch_dtype, device_map="auto", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch_dtype, device_map="auto", trust_remote_code=True)
# init gradio demo host and port
server_name=args.server_name
server_port=args.server_port
def hf_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int): def hf_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int):
"""generate model output with huggingface api """generate model output with huggingface api
@ -161,4 +166,4 @@ with gr.Blocks(theme="soft") as demo:
reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot]) reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot])
demo.queue() demo.queue()
demo.launch(server_name="127.0.0.1", show_error=True) demo.launch(server_name=server_name, server_port=server_port, show_error=True)

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demo/vllm_based_demo.py
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@ -9,7 +9,9 @@ from vllm import LLM, SamplingParams
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="") parser.add_argument("--model_path", type=str, default="")
parser.add_argument("--torch_dtype", type=str, default="bfloat16") parser.add_argument("--torch_dtype", type=str, default="bfloat16", choices=["float32", "bfloat16"]))
parser.add_argument("--server_name", type=str, default="127.0.0.1")
parser.add_argument("--server_port", type=int, default=7860)
args = parser.parse_args() args = parser.parse_args()
@ -26,6 +28,9 @@ else:
path = args.model_path path = args.model_path
llm = LLM(model=path, tensor_parallel_size=1, dtype=torch_dtype) llm = LLM(model=path, tensor_parallel_size=1, dtype=torch_dtype)
# init gradio demo host and port
server_name=args.server_name
server_port=args.server_port
def vllm_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int): def vllm_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int):
"""generate model output with huggingface api """generate model output with huggingface api
@ -169,4 +174,4 @@ with gr.Blocks(theme="soft") as demo:
reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot]) reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot])
demo.queue() demo.queue()
demo.launch(server_name="127.0.0.1", show_error=True) demo.launch(server_name=server_name, server_port=server_port, show_error=True)