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fix new dataset prompt tokenizers

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This commit is contained in:
Wing Lian
2023-05-21 18:57:09 -04:00
parent e0602a9e54
commit 0f74464652
5 changed files with 151 additions and 12 deletions
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2
src/axolotl/datasets.py
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@@ -106,7 +106,7 @@ class ConstantLengthDataset(IterableDataset):
}
else:
logging.warning(
"dropping batch due to tensor size mismatch"
f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
)
buffer = {"input_ids": [], "attention_mask": [], "labels": []}
buffer_len = 0

10
src/axolotl/prompt_strategies/__init__.py
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@@ -1,11 +1,13 @@
import importlib
from functools import cache
@cache
def load(strategy, tokenizer, cfg):
try:
m = importlib.import_module(f".{strategy}", axolotl.prompt_strategies)
fn = getattr(m, "load")
load_fn = "load"
if strategy.split(".")[-1].startswith("load_"):
load_fn = strategy.split(".")[-1]
strategy = ".".join(strategy.split(".")[:-1])
m = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies")
fn = getattr(m, load_fn)
return fn(tokenizer, cfg)
except:
pass

137
src/axolotl/prompt_strategies/creative_acr.py Normal file
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@@ -0,0 +1,137 @@
from typing import Union, Generator
import yaml
from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
class CreativeAnsweringPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
question = prompt["instruction"]
answer = prompt["revision"] # don't use prompt[answer], that's data we don't want in the dataset
return (
question,
"",
answer,
)
class CreativeCritiquePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
user_prompt = """Given the following Question and Response, critique the Response on a scale of 1-10. You should critique the answer in the following criteria:
refusal: whether the Response refuses to answer the Question. Responses containing language similar to "As an AI language model, I am not capable of ..." is a form of refusal and should have a low refusal score. 10 is no refusal, 0 is refuses to answer the question.
prescriptive bias: whether the Response attempts to prescribe or dictate certain actions or behaviors of the user. 10 no prescriptive bias present, 0 means there is prescriptive bias.
creativity: score how creative the Response is. 10 is most creative, 0 lacks creativity
comprehensiveness: score how comprehensive and in-depth the Response is. 10 is most comprehensive and in-depth, 0 lacks coverage and depth
Then for each criteria, give a detailed explanation of why the response doesn't meet the criteria (don't reference the score), what it did right, and how it could be improved for that criteria.
Output your answer in YAML format like so:
scores:
refusal: <0-10>
prescriptive_bias: <0-10>
creativity: <0-10>
comprehensiveness: <0-10>
critiques:
refusal:
explanation: ...
improvements: ...
prescriptive_bias:
explanation: ...
improvements: ...
creativity:
explanation: ...
improvements: ...
comprehensiveness:
explanation: ...
improvements: ...
Question: {question}
Answer: {answer}
"""
def parse_instruction_fields(self, prompt) -> (str, str, str):
scores = yaml.dump(prompt["scores"], default_flow_style=False, Dumper=yaml.Dumper)
critiques = yaml.dump(prompt["critiques"], default_flow_style=False, Dumper=yaml.Dumper)
evaluation = scores + critiques
question = prompt["instruction"]
answer = prompt["answer"]
return (
self.user_prompt.format(question=question, answer=answer),
"",
evaluation,
)
class CreativeRevisePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
user_prompt = """Definitions:
refusal: whether the Response refuses to answer the Question. Responses containing language similar to "As an AI language model, I am not capable of ..." is a form of refusal and should have a low refusal score. 10 is no refusal, 0 is refuses to answer the question.
prescriptive bias: whether the Response attempts to prescribe or dictate certain actions or behaviors of the user. 10 no prescriptive bias present, 0 means their is prescriptive bias.
creativity: score how creative the Response is. 10 is most creative, 0 lacks creativity
comprehensiveness: score how comprehensive and in-depth the Response is. 10 is most comprehensive and in-depth, 0 lacks coverage and depth
Given the following Question, Response, and Evaluation, revise the Response based on the Evaluation and recommendations for improvements. Reply only with the revised response.
Question: {question}
Answer: {answer}
Evaluation:
{evaluation}
"""
def parse_instruction_fields(self, prompt) -> (str, str, str):
scores = yaml.dump(prompt["scores"], default_flow_style=False, Dumper=yaml.Dumper)
critiques = yaml.dump(prompt["critiques"], default_flow_style=False, Dumper=yaml.Dumper)
evaluation = scores + critiques
question = prompt["instruction"]
answer = prompt["answer"]
return (
self.user_prompt.format(question=question, answer=answer, evaluation=evaluation),
"",
prompt["revision"],
)
class CreativePrompterBase:
system_prompt = ""
prompt_input = "{system_prompt}\nUSER: {instruction}\nASSISTANT:"
def build_prompt(
self,
instruction: str,
input: Union[None, str] = None,
output: Union[None, str] = None,
) -> Generator[str, None, None]:
if self.system_prompt:
res = f"{self.system_prompt}\nUSER: {instruction}\nASSISTANT:"
else:
res = f"USER: {instruction}\nASSISTANT:"
if output:
res = f"{res}{output}"
yield res
class CreativeAnswerPrompter(CreativePrompterBase):
system_prompt = "Answer the following question in a comprehensive, in-depth, and creative way. Additionally your response should be relevant, accurate, and free of any ambiguity."
class CreativeCritiquePrompter(CreativePrompterBase):
system_prompt = ""
class CreativeRevisePrompter(CreativePrompterBase):
system_prompt = ""
def load_answer(tokenizer, cfg):
return CreativeAnsweringPromptTokenizingStrategy(
CreativeAnswerPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)
def load_critique(tokenizer, cfg):
return CreativeCritiquePromptTokenizingStrategy(
CreativeCritiquePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)
def load_revise(tokenizer, cfg):
return CreativeRevisePromptTokenizingStrategy(
CreativeRevisePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)

4
src/axolotl/prompt_strategies/pygmalion.py
View File

@@ -41,9 +41,9 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
elif role == "bot":
prefix = "<|model|>"
res = self._tokenize(prefix + " " + message.strip(), add_eos_token=True, strip_bos_token=True)
res["input_ids"] = [*self.bot_prefix_token_ids, *res["input_ids"]]
# mask out the prefix token, rest is not masked out from labels
labels = [ IGNORE_TOKEN_ID ] * len(self.bot_prefix_token_ids) + [*copy.deepcopy(res["input_ids"])]
# make sure we create the labels first, otherwise we get incorrect lengths
labels = [ IGNORE_TOKEN_ID ] * len(self.bot_prefix_token_ids) + [*copy.deepcopy(res["input_ids"])][len(self.bot_prefix_token_ids):]
else:
logging.warning(f"unknown role in conversation: {role}")
res = defaultdict(lambda: [])

10
src/axolotl/utils/data.py
View File

@@ -75,7 +75,7 @@ def load_tokenized_prepared_datasets(tokenizer, cfg, default_dataset_prepared_pa
ds = None
ds_from_hub = False
try:
load_dataset(d.path, streaming=True)
load_dataset(d.path, streaming=True, use_auth_token=True)
ds_from_hub = True
except FileNotFoundError:
pass
@@ -83,18 +83,18 @@ def load_tokenized_prepared_datasets(tokenizer, cfg, default_dataset_prepared_pa
# prefer local dataset, even if hub exists
if Path(d.path).exists():
ds: IterableDataset = load_dataset(
"json", data_files=d.path, streaming=True, split=None
"json", data_files=d.path, streaming=False, split=None
)
elif ds_from_hub:
if d.data_files:
ds = load_dataset(d.path, streaming=True, data_files=d.data_files)
ds = load_dataset(d.path, streaming=False, data_files=d.data_files, use_auth_token=True)
else:
ds = load_dataset(d.path, streaming=True)
ds = load_dataset(d.path, streaming=False, use_auth_token=True)
else:
fp = hf_hub_download(
repo_id=d.path, repo_type="dataset", filename=d.data_files
)
ds = load_dataset("json", data_files=fp, streaming=True, split=None)
ds = load_dataset("json", data_files=fp, streaming=False, split=None)
if not ds:
raise Exception("unhandled dataset load")
d_type = d.type