Spaces:
Runtime error
Runtime error
Delete instruct_pipeline.py
Browse files- instruct_pipeline.py +0 -164
instruct_pipeline.py
DELETED
@@ -1,164 +0,0 @@
|
|
1 |
-
import logging
|
2 |
-
import re
|
3 |
-
|
4 |
-
import numpy as np
|
5 |
-
from transformers import Pipeline, PreTrainedTokenizer
|
6 |
-
|
7 |
-
logger = logging.getLogger(__name__)
|
8 |
-
|
9 |
-
INSTRUCTION_KEY = "### Instruction:"
|
10 |
-
RESPONSE_KEY = "### Response:"
|
11 |
-
END_KEY = "### End"
|
12 |
-
INTRO_BLURB = (
|
13 |
-
"Below is an instruction that describes a task. Write a response that appropriately completes the request."
|
14 |
-
)
|
15 |
-
|
16 |
-
# This is the prompt that is used for generating responses using an already trained model. It ends with the response
|
17 |
-
# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
|
18 |
-
PROMPT_FOR_GENERATION_FORMAT = """{intro}
|
19 |
-
|
20 |
-
{instruction_key}
|
21 |
-
{instruction}
|
22 |
-
|
23 |
-
{response_key}
|
24 |
-
""".format(
|
25 |
-
intro=INTRO_BLURB,
|
26 |
-
instruction_key=INSTRUCTION_KEY,
|
27 |
-
instruction="{instruction}",
|
28 |
-
response_key=RESPONSE_KEY,
|
29 |
-
)
|
30 |
-
|
31 |
-
|
32 |
-
def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
|
33 |
-
"""Gets the token ID for a given string that has been added to the tokenizer as a special token.
|
34 |
-
|
35 |
-
When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
|
36 |
-
treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.
|
37 |
-
|
38 |
-
Args:
|
39 |
-
tokenizer (PreTrainedTokenizer): the tokenizer
|
40 |
-
key (str): the key to convert to a single token
|
41 |
-
|
42 |
-
Raises:
|
43 |
-
RuntimeError: if more than one ID was generated
|
44 |
-
|
45 |
-
Returns:
|
46 |
-
int: the token ID for the given key
|
47 |
-
"""
|
48 |
-
token_ids = tokenizer.encode(key)
|
49 |
-
if len(token_ids) > 1:
|
50 |
-
raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
|
51 |
-
return token_ids[0]
|
52 |
-
|
53 |
-
|
54 |
-
class InstructionTextGenerationPipeline(Pipeline):
|
55 |
-
def __init__(
|
56 |
-
self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
|
57 |
-
):
|
58 |
-
super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)
|
59 |
-
|
60 |
-
def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
|
61 |
-
preprocess_params = {}
|
62 |
-
|
63 |
-
# newer versions of the tokenizer configure the response key as a special token. newer versions still may
|
64 |
-
# append a newline to yield a single token. find whatever token is configured for the response key.
|
65 |
-
tokenizer_response_key = next(
|
66 |
-
(token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
|
67 |
-
)
|
68 |
-
|
69 |
-
response_key_token_id = None
|
70 |
-
end_key_token_id = None
|
71 |
-
if tokenizer_response_key:
|
72 |
-
try:
|
73 |
-
response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
|
74 |
-
end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
|
75 |
-
|
76 |
-
# Ensure generation stops once it generates "### End"
|
77 |
-
generate_kwargs["eos_token_id"] = end_key_token_id
|
78 |
-
except ValueError:
|
79 |
-
pass
|
80 |
-
|
81 |
-
forward_params = generate_kwargs
|
82 |
-
postprocess_params = {
|
83 |
-
"response_key_token_id": response_key_token_id,
|
84 |
-
"end_key_token_id": end_key_token_id,
|
85 |
-
"return_instruction_text": return_instruction_text,
|
86 |
-
}
|
87 |
-
|
88 |
-
return preprocess_params, forward_params, postprocess_params
|
89 |
-
|
90 |
-
def preprocess(self, instruction_text, **generate_kwargs):
|
91 |
-
prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
|
92 |
-
inputs = self.tokenizer(
|
93 |
-
prompt_text,
|
94 |
-
return_tensors="pt",
|
95 |
-
)
|
96 |
-
inputs["prompt_text"] = prompt_text
|
97 |
-
inputs["instruction_text"] = instruction_text
|
98 |
-
return inputs
|
99 |
-
|
100 |
-
def _forward(self, model_inputs, **generate_kwargs):
|
101 |
-
input_ids = model_inputs["input_ids"]
|
102 |
-
attention_mask = model_inputs.get("attention_mask", None)
|
103 |
-
generated_sequence = self.model.generate(
|
104 |
-
input_ids=input_ids.to(self.model.device),
|
105 |
-
attention_mask=attention_mask,
|
106 |
-
pad_token_id=self.tokenizer.pad_token_id,
|
107 |
-
**generate_kwargs,
|
108 |
-
)[0].cpu()
|
109 |
-
instruction_text = model_inputs.pop("instruction_text")
|
110 |
-
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
|
111 |
-
|
112 |
-
def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text):
|
113 |
-
sequence = model_outputs["generated_sequence"]
|
114 |
-
instruction_text = model_outputs["instruction_text"]
|
115 |
-
|
116 |
-
# The response will be set to this variable if we can identify it.
|
117 |
-
decoded = None
|
118 |
-
|
119 |
-
# If we have token IDs for the response and end, then we can find the tokens and only decode between them.
|
120 |
-
if response_key_token_id and end_key_token_id:
|
121 |
-
# Find where "### Response:" is first found in the generated tokens. Considering this is part of the
|
122 |
-
# prompt, we should definitely find it. We will return the tokens found after this token.
|
123 |
-
response_pos = None
|
124 |
-
response_positions = np.where(sequence == response_key_token_id)[0]
|
125 |
-
if len(response_positions) == 0:
|
126 |
-
logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
|
127 |
-
else:
|
128 |
-
response_pos = response_positions[0]
|
129 |
-
|
130 |
-
if response_pos:
|
131 |
-
# Next find where "### End" is located. The model has been trained to end its responses with this
|
132 |
-
# sequence (or actually, the token ID it maps to, since it is a special token). We may not find
|
133 |
-
# this token, as the response could be truncated. If we don't find it then just return everything
|
134 |
-
# to the end. Note that even though we set eos_token_id, we still see the this token at the end.
|
135 |
-
end_pos = None
|
136 |
-
end_positions = np.where(sequence == end_key_token_id)[0]
|
137 |
-
if len(end_positions) > 0:
|
138 |
-
end_pos = end_positions[0]
|
139 |
-
|
140 |
-
decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
|
141 |
-
else:
|
142 |
-
# Otherwise we'll decode everything and use a regex to find the response and end.
|
143 |
-
|
144 |
-
fully_decoded = self.tokenizer.decode(sequence)
|
145 |
-
|
146 |
-
# The response appears after "### Response:". The model has been trained to append "### End" at the
|
147 |
-
# end.
|
148 |
-
m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
|
149 |
-
|
150 |
-
if m:
|
151 |
-
decoded = m.group(1).strip()
|
152 |
-
else:
|
153 |
-
# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
|
154 |
-
# return everything after "### Response:".
|
155 |
-
m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
|
156 |
-
if m:
|
157 |
-
decoded = m.group(1).strip()
|
158 |
-
else:
|
159 |
-
logger.warn(f"Failed to find response in:\n{fully_decoded}")
|
160 |
-
|
161 |
-
if return_instruction_text:
|
162 |
-
return {"instruction_text": instruction_text, "generated_text": decoded}
|
163 |
-
|
164 |
-
return decoded
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|