Spaces:
Runtime error
Runtime error
Add application file
Browse files
app.py
ADDED
@@ -0,0 +1,192 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import sys
|
3 |
+
|
4 |
+
import fire
|
5 |
+
import gradio as gr
|
6 |
+
import torch
|
7 |
+
import transformers
|
8 |
+
from peft import PeftModel
|
9 |
+
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
|
10 |
+
|
11 |
+
from utils.callbacks import Iteratorize, Stream
|
12 |
+
from utils.prompter import Prompter
|
13 |
+
|
14 |
+
if torch.cuda.is_available():
|
15 |
+
device = "cuda"
|
16 |
+
else:
|
17 |
+
device = "cpu"
|
18 |
+
|
19 |
+
try:
|
20 |
+
if torch.backends.mps.is_available():
|
21 |
+
device = "mps"
|
22 |
+
except:
|
23 |
+
pass
|
24 |
+
|
25 |
+
|
26 |
+
def main(
|
27 |
+
load_8bit: bool = False,
|
28 |
+
base_model: str = "",
|
29 |
+
lora_weights: str = "tiedong/goat-lora-7b",
|
30 |
+
prompt_template: str = "goat",
|
31 |
+
server_name: str = "0.0.0.0",
|
32 |
+
share_gradio: bool = True,
|
33 |
+
):
|
34 |
+
base_model = base_model or os.environ.get("BASE_MODEL", "")
|
35 |
+
assert (
|
36 |
+
base_model
|
37 |
+
), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
|
38 |
+
|
39 |
+
prompter = Prompter(prompt_template)
|
40 |
+
tokenizer = LlamaTokenizer.from_pretrained('hf-internal-testing/llama-tokenizer')
|
41 |
+
if device == "cuda":
|
42 |
+
model = LlamaForCausalLM.from_pretrained(
|
43 |
+
base_model,
|
44 |
+
load_in_8bit=load_8bit,
|
45 |
+
torch_dtype=torch.float16,
|
46 |
+
device_map="auto",
|
47 |
+
)
|
48 |
+
model = PeftModel.from_pretrained(
|
49 |
+
model,
|
50 |
+
lora_weights,
|
51 |
+
torch_dtype=torch.float16,
|
52 |
+
)
|
53 |
+
elif device == "mps":
|
54 |
+
model = LlamaForCausalLM.from_pretrained(
|
55 |
+
base_model,
|
56 |
+
device_map={"": device},
|
57 |
+
torch_dtype=torch.float16,
|
58 |
+
)
|
59 |
+
model = PeftModel.from_pretrained(
|
60 |
+
model,
|
61 |
+
lora_weights,
|
62 |
+
device_map={"": device},
|
63 |
+
torch_dtype=torch.float16,
|
64 |
+
)
|
65 |
+
else:
|
66 |
+
model = LlamaForCausalLM.from_pretrained(
|
67 |
+
base_model, device_map={"": device}, low_cpu_mem_usage=True
|
68 |
+
)
|
69 |
+
model = PeftModel.from_pretrained(
|
70 |
+
model,
|
71 |
+
lora_weights,
|
72 |
+
device_map={"": device},
|
73 |
+
)
|
74 |
+
|
75 |
+
if not load_8bit:
|
76 |
+
model.half()
|
77 |
+
|
78 |
+
model.eval()
|
79 |
+
if torch.__version__ >= "2" and sys.platform != "win32":
|
80 |
+
model = torch.compile(model)
|
81 |
+
|
82 |
+
def evaluate(
|
83 |
+
instruction,
|
84 |
+
temperature=0.1,
|
85 |
+
top_p=0.75,
|
86 |
+
top_k=40,
|
87 |
+
num_beams=4,
|
88 |
+
max_new_tokens=512,
|
89 |
+
stream_output=True,
|
90 |
+
**kwargs,
|
91 |
+
):
|
92 |
+
prompt = prompter.generate_prompt_inference(instruction)
|
93 |
+
inputs = tokenizer(prompt, return_tensors="pt")
|
94 |
+
input_ids = inputs["input_ids"].to(device)
|
95 |
+
generation_config = GenerationConfig(
|
96 |
+
temperature=temperature,
|
97 |
+
top_p=top_p,
|
98 |
+
top_k=top_k,
|
99 |
+
num_beams=num_beams,
|
100 |
+
**kwargs,
|
101 |
+
)
|
102 |
+
|
103 |
+
generate_params = {
|
104 |
+
"input_ids": input_ids,
|
105 |
+
"generation_config": generation_config,
|
106 |
+
"return_dict_in_generate": True,
|
107 |
+
"output_scores": True,
|
108 |
+
"max_new_tokens": max_new_tokens,
|
109 |
+
}
|
110 |
+
|
111 |
+
if stream_output:
|
112 |
+
# Stream the reply 1 token at a time.
|
113 |
+
# This is based on the trick of using 'stopping_criteria' to create an iterator,
|
114 |
+
# from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
|
115 |
+
|
116 |
+
def generate_with_callback(callback=None, **kwargs):
|
117 |
+
kwargs.setdefault(
|
118 |
+
"stopping_criteria", transformers.StoppingCriteriaList()
|
119 |
+
)
|
120 |
+
kwargs["stopping_criteria"].append(
|
121 |
+
Stream(callback_func=callback)
|
122 |
+
)
|
123 |
+
with torch.no_grad():
|
124 |
+
model.generate(**kwargs)
|
125 |
+
|
126 |
+
def generate_with_streaming(**kwargs):
|
127 |
+
return Iteratorize(
|
128 |
+
generate_with_callback, kwargs, callback=None
|
129 |
+
)
|
130 |
+
|
131 |
+
with generate_with_streaming(**generate_params) as generator:
|
132 |
+
for output in generator:
|
133 |
+
# new_tokens = len(output) - len(input_ids[0])
|
134 |
+
decoded_output = tokenizer.decode(output)
|
135 |
+
|
136 |
+
if output[-1] in [tokenizer.eos_token_id]:
|
137 |
+
break
|
138 |
+
|
139 |
+
yield prompter.get_response(decoded_output)
|
140 |
+
return # early return for stream_output
|
141 |
+
|
142 |
+
# Without streaming
|
143 |
+
with torch.no_grad():
|
144 |
+
generation_output = model.generate(
|
145 |
+
input_ids=input_ids,
|
146 |
+
generation_config=generation_config,
|
147 |
+
return_dict_in_generate=True,
|
148 |
+
output_scores=True,
|
149 |
+
max_new_tokens=max_new_tokens,
|
150 |
+
)
|
151 |
+
s = generation_output.sequences[0]
|
152 |
+
output = tokenizer.decode(s, skip_special_tokens=True).strip()
|
153 |
+
yield prompter.get_response(output)
|
154 |
+
|
155 |
+
gr.Interface(
|
156 |
+
fn=evaluate,
|
157 |
+
inputs=[
|
158 |
+
gr.components.Textbox(
|
159 |
+
lines=2,
|
160 |
+
label="Arithmetic",
|
161 |
+
placeholder="What is 63303235 + 20239503",
|
162 |
+
),
|
163 |
+
gr.components.Slider(
|
164 |
+
minimum=0, maximum=1, value=0.1, label="Temperature"
|
165 |
+
),
|
166 |
+
gr.components.Slider(
|
167 |
+
minimum=0, maximum=1, value=0.75, label="Top p"
|
168 |
+
),
|
169 |
+
gr.components.Slider(
|
170 |
+
minimum=0, maximum=100, step=1, value=40, label="Top k"
|
171 |
+
),
|
172 |
+
gr.components.Slider(
|
173 |
+
minimum=1, maximum=4, step=1, value=4, label="Beams"
|
174 |
+
),
|
175 |
+
gr.components.Slider(
|
176 |
+
minimum=1, maximum=1024, step=1, value=512, label="Max tokens"
|
177 |
+
),
|
178 |
+
gr.components.Checkbox(label="Stream output"),
|
179 |
+
],
|
180 |
+
outputs=[
|
181 |
+
gr.inputs.Textbox(
|
182 |
+
lines=5,
|
183 |
+
label="Output",
|
184 |
+
)
|
185 |
+
],
|
186 |
+
title="Goat-loRA-7b",
|
187 |
+
description="Goat-LoRA-7b is a 7B-parameter LLaMA finetuned to perform arithmetic tasks, including addition, subtraction, multiplication, and division of integers. It is trained on a synthetic dataset (https://github.com/liutiedong/goat) and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/liutiedong/goat).", # noqa: E501
|
188 |
+
).queue().launch(server_name="0.0.0.0", share=share_gradio)
|
189 |
+
|
190 |
+
|
191 |
+
if __name__ == "__main__":
|
192 |
+
fire.Fire(main)
|