File size: 7,557 Bytes
7e60a5e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
import time
import pytest
from tests.utils import wrap_test_forked
from src.enums import source_prefix, source_postfix
from src.prompter import generate_prompt
example_data_point0 = dict(instruction="Summarize",
input="Ducks eat seeds by the lake, then swim in the lake where fish eat small animals.",
output="Ducks eat and swim at the lake.")
example_data_point1 = dict(instruction="Who is smarter, Einstein or Newton?",
output="Einstein.")
example_data_point2 = dict(input="Who is smarter, Einstein or Newton?",
output="Einstein.")
example_data_points = [example_data_point0, example_data_point1, example_data_point2]
@wrap_test_forked
def test_train_prompt(prompt_type='instruct', data_point=0):
example_data_point = example_data_points[data_point]
return generate_prompt(example_data_point, prompt_type, '', False, False, False)
@wrap_test_forked
def test_test_prompt(prompt_type='instruct', data_point=0):
example_data_point = example_data_points[data_point]
example_data_point.pop('output', None)
return generate_prompt(example_data_point, prompt_type, '', False, False, False)
@wrap_test_forked
def test_test_prompt2(prompt_type='human_bot', data_point=0):
example_data_point = example_data_points[data_point]
example_data_point.pop('output', None)
res = generate_prompt(example_data_point, prompt_type, '', False, False, False)
print(res, flush=True)
return res
prompt_fastchat = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hi!</s>USER: How are you? ASSISTANT: I'm good</s>USER: Go to the market? ASSISTANT:"""
prompt_humanbot = """<human>: Hello!\n<bot>: Hi!\n<human>: How are you?\n<bot>: I'm good\n<human>: Go to the market?\n<bot>:"""
prompt_prompt_answer = "<|prompt|>Hello!<|endoftext|><|answer|>Hi!<|endoftext|><|prompt|>How are you?<|endoftext|><|answer|>I'm good<|endoftext|><|prompt|>Go to the market?<|endoftext|><|answer|>"
prompt_prompt_answer_openllama = "<|prompt|>Hello!</s><|answer|>Hi!</s><|prompt|>How are you?</s><|answer|>I'm good</s><|prompt|>Go to the market?</s><|answer|>"
prompt_mpt_instruct = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction
Hello!
### Response
Hi!
### Instruction
How are you?
### Response
I'm good
### Instruction
Go to the market?
### Response
"""
prompt_mpt_chat = """<|im_start|>system
A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.
<|im_end|><|im_start|>user
Hello!<|im_end|><|im_start|>assistant
Hi!<|im_end|><|im_start|>user
How are you?<|im_end|><|im_start|>assistant
I'm good<|im_end|><|im_start|>user
Go to the market?<|im_end|><|im_start|>assistant
"""
prompt_falcon = """User: Hello!
Assistant: Hi!
User: How are you?
Assistant: I'm good
User: Go to the market?
Assistant:"""
@wrap_test_forked
@pytest.mark.parametrize("prompt_type,expected",
[
('vicuna11', prompt_fastchat),
('human_bot', prompt_humanbot),
('prompt_answer', prompt_prompt_answer),
('prompt_answer_openllama', prompt_prompt_answer_openllama),
('mptinstruct', prompt_mpt_instruct),
('mptchat', prompt_mpt_chat),
('falcon', prompt_falcon),
]
)
def test_prompt_with_context(prompt_type, expected):
prompt_dict = None # not used unless prompt_type='custom'
langchain_mode = 'Disabled'
chat = True
model_max_length = 2048
memory_restriction_level = 0
keep_sources_in_context1 = False
iinput = ''
stream_output = False
debug = False
from src.prompter import Prompter
from src.gen import history_to_context
t0 = time.time()
history = [["Hello!", "Hi!"],
["How are you?", "I'm good"],
["Go to the market?", None]
]
print("duration1: %s %s" % (prompt_type, time.time() - t0), flush=True)
t0 = time.time()
context = history_to_context(history, langchain_mode, prompt_type, prompt_dict, chat,
model_max_length, memory_restriction_level,
keep_sources_in_context1)
print("duration2: %s %s" % (prompt_type, time.time() - t0), flush=True)
t0 = time.time()
instruction = history[-1][0]
# get prompt
prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output)
print("duration3: %s %s" % (prompt_type, time.time() - t0), flush=True)
t0 = time.time()
data_point = dict(context=context, instruction=instruction, input=iinput)
prompt = prompter.generate_prompt(data_point)
print(prompt)
print("duration4: %s %s" % (prompt_type, time.time() - t0), flush=True)
assert prompt == expected
assert prompt.find(source_prefix) == -1
prompt_fastchat1 = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Go to the market? ASSISTANT:"""
prompt_humanbot1 = """<human>: Go to the market?\n<bot>:"""
prompt_prompt_answer1 = "<|prompt|>Go to the market?<|endoftext|><|answer|>"
prompt_prompt_answer_openllama1 = "<|prompt|>Go to the market?</s><|answer|>"
prompt_mpt_instruct1 = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction
Go to the market?
### Response
"""
prompt_mpt_chat1 = """<|im_start|>system
A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.
<|im_end|><|im_start|>user
Go to the market?<|im_end|><|im_start|>assistant
"""
prompt_falcon1 = """User: Go to the market?
Assistant:"""
@pytest.mark.parametrize("prompt_type,expected",
[
('vicuna11', prompt_fastchat1),
('human_bot', prompt_humanbot1),
('prompt_answer', prompt_prompt_answer1),
('prompt_answer_openllama', prompt_prompt_answer_openllama1),
('mptinstruct', prompt_mpt_instruct1),
('mptchat', prompt_mpt_chat1),
('falcon', prompt_falcon1),
]
)
@wrap_test_forked
def test_prompt_with_no_context(prompt_type, expected):
prompt_dict = None # not used unless prompt_type='custom'
chat = True
iinput = ''
stream_output = False
debug = False
from src.prompter import Prompter
context = ''
instruction = "Go to the market?"
# get prompt
prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output)
data_point = dict(context=context, instruction=instruction, input=iinput)
prompt = prompter.generate_prompt(data_point)
print(prompt)
assert prompt == expected
assert prompt.find(source_prefix) == -1
@wrap_test_forked
def test_source():
prompt = "Who are you?%s\nFOO\n%s" % (source_prefix, source_postfix)
assert prompt.find(source_prefix) >= 0
|