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datasets.py
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datasets.py
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# %%
import random
import collections
import typing
import torch
from typing import Dict, Sequence
from torch import Tensor
# %%
Question = collections.namedtuple("Question", ["x", "y"])
RationaleStep = collections.namedtuple("RationaleStep", ["x", "y", "acc", "carry", "step", "n_steps"])
Rationale = typing.List[RationaleStep]
Correction = collections.namedtuple("Correction", ["line", "actual_rs", "expected_rs"])
CorruptedExample = collections.namedtuple("CorruptedExample", [
"question",
"incorrect_rational",
"incorrect_answer",
"correction",
"correction_rationale",
"correction_answer",
])
# %%
def make_question(n_digits: int) -> Question:
assert n_digits > 0, "need at least one digit to do arithmetic"
min_digit = int("1" + "0" * (n_digits-1)) if n_digits > 1 else 0
max_digit = int("9" * n_digits)
x = random.randint(min_digit, max_digit)
y = random.randint(min_digit, max_digit)
return Question(x, y)
# %%
def make_rationale_step_corrupt(rs: RationaleStep) -> RationaleStep:
corrupt_x, corrupt_y, corrupt_acc, corrupt_carry, corrupt_step, corrupt_n_steps = rs
sample = random.uniform(0, 1)
if 0 < sample <= 0.1:
corrupt_x = corrupt_x + random.randint(1, 9) # corrupt x
elif 0.1 < sample <= 0.2:
corrupt_y = corrupt_y + random.randint(1, 9) # corrupt y
elif 0.2 < sample <= 0.5:
if corrupt_acc == "":
corrupt_acc = "0" # since acc might be empty string
corrupt_acc = str(int(corrupt_acc) + random.randint(1, 9)) # corrupt acc
else:
corrupt_carry = 1 if corrupt_carry == 0 else 0 # corrupt carry
return RationaleStep(
x=corrupt_x,
y=corrupt_y,
acc=corrupt_acc,
carry=corrupt_carry,
step=corrupt_step,
n_steps=corrupt_n_steps,
)
# %%
def next_rational_step(rs: RationaleStep) -> RationaleStep:
"""
7 2 + 5 , C: 0 step = 0, n_steps = 4
7 + 0 , 7 C: 0 step = 1, n_steps = 4
, 7 7 C: 1 step = 2, n_steps = 4
1 7 7 step = 3, n_steps = 4
"""
if rs.step == rs.n_steps - 1:
return rs
unit_x = rs.x % 10
unit_y = rs.y % 10
unit_sum = unit_x + unit_y + rs.carry
new_x = rs.x // 10
new_y = rs.y // 10
new_acc = f"{unit_sum % 10}{rs.acc}"
new_carry = unit_sum // 10
new_step = rs.step + 1
new_n_steps = rs.n_steps
return RationaleStep(
x=new_x,
y=new_y,
acc=new_acc,
carry=new_carry,
step=new_step,
n_steps=new_n_steps,
)
# %%
def make_rationale(q: Question, is_corrupted=False) -> Rationale:
n_steps = max(len(str(q.x)), len(str(q.y))) + 2
corrupt_step = random.randint(0, n_steps-3) if is_corrupted else -1
rationale = []
rationale_step = RationaleStep(
x=q.x,
y=q.y,
acc="",
carry=0,
step=0,
n_steps=n_steps,
)
for i in range(n_steps):
if corrupt_step == i:
rationale_step = make_rationale_step_corrupt(rationale_step) # accumulate mistakes
rationale.append(rationale_step)
rationale_step = next_rational_step(rationale_step)
return rationale
# %%
def correct_rationale(q: Question, actual_r: Rationale) -> typing.Union[Correction, None]:
"""
Loops through rationale steps, finding first that doesn't match expected.
Returns None if no mistake is found.
"""
if len(actual_r) == 0:
return None
expected_r = make_rationale(q, is_corrupted=False)
for i, (actual_rs, expected_rs) in enumerate(zip(actual_r, expected_r)):
if actual_rs == expected_rs:
continue # nothing to see here
return Correction(
line=i,
actual_rs=actual_rs,
expected_rs=expected_rs,
)
return None
# %%
def complete_rationale(rs: RationaleStep, is_corrupted=False) -> Rationale:
n_remaining_steps = (rs.n_steps - 1) - rs.step
assert n_remaining_steps > 0, "Rationale step is already complete."
if is_corrupted:
assert n_remaining_steps > 1, "Accumulator wont be changed if carry is corrupted in the final rationale step, so at least two remaining steps are needed to gaurantee corruption."
rationale = [rs]
corrupted_step = random.randint(0, n_remaining_steps-2) if is_corrupted else -1
for i in range(n_remaining_steps):
rs = next_rational_step(rs)
if corrupted_step == i:
rs = make_rationale_step_corrupt(rs)
rationale.append(rs)
return rationale
# %%
def make_corrupted_example(q: typing.Union[Question, None] = None, default_n_digits=3, is_correction_r_corrupted=False) -> CorruptedExample:
if q is None:
q = make_question(n_digits=default_n_digits)
incorrect_r = make_rationale(q, is_corrupted=True)
incorrect_a = int(incorrect_r[-1].acc)
assert incorrect_a != q.x + q.y, f"Incorrect answer should not be correct! Got {incorrect_a} but expected {q.x + q.y}."
assert correct_rationale(q, incorrect_r) is not None, f"Mistake must be present in corrupted example.\n{incorrect_r}."
correction = correct_rationale(q, incorrect_r)
assert correction is not None, f"Mistake must be present in corrupted example.\n{q}\n{incorrect_r}"
correction_r = complete_rationale(correction.expected_rs, is_corrupted=is_correction_r_corrupted)
correction_a = int(correction_r[-1].acc)
if not is_correction_r_corrupted:
assert correction_a == q.x + q.y, f"Correction answer should be correct! Got {correction_a} but expected {q.x + q.y}."
if is_correction_r_corrupted:
assert correction_a != q.x + q.y, f"Correction answer should not be correct! Got {correction_a} but expected something different from {q.x + q.y}."
return CorruptedExample(
question=q,
incorrect_rational=incorrect_r,
incorrect_answer=incorrect_a,
correction=correction,
correction_rationale=correction_r,
correction_answer=correction_a,
)
# %%
def number_to_str(x: int) -> str:
return " ".join(list(str(x)))
# %%
def rationale_step_to_str(rs: RationaleStep) -> str:
if rs.step == rs.n_steps - 1:
return number_to_str(rs.acc)
elif rs.step == rs.n_steps - 2:
return f", {number_to_str(rs.acc)} C: {rs.carry}"
else:
return f"{number_to_str(rs.x)} + {number_to_str(rs.y)} , {number_to_str(rs.acc)} C: {rs.carry}"
# %%
def rationale_to_str(r: Rationale) -> str:
return "\n".join(rationale_step_to_str(step) for step in r)
# %%
def correction_to_str_concise(c: Correction) -> str:
if c.actual_rs.x != c.expected_rs.x:
return f"line {c.line} : \"{c.actual_rs.x}\" should be \"{c.expected_rs.x}\""
elif c.actual_rs.y != c.expected_rs.y:
return f"line {c.line} : \"{c.actual_rs.y}\" should be \"{c.expected_rs.y}\""
elif c.actual_rs.acc != c.expected_rs.acc:
return f"line {c.line} : \"{c.actual_rs.acc}\" should be \"{c.expected_rs.acc}\""
elif c.actual_rs.carry != c.expected_rs.carry:
return f"line {c.line} : \"C: {c.actual_rs.carry}\" should be \"C: {c.expected_rs.carry}\""
else:
raise ValueError(f"Expected rational step is the same as actual! Got {c.actual_rs} for both actual and expected.")
# %%
def correction_to_str_verbose(c: Correction) -> str:
if c.actual_rs == c.expected_rs:
raise ValueError(f"Expected rational step is the same as actual! Got {c.actual_rs} for both actual and expected.")
actual_rs_str = rationale_step_to_str(c.actual_rs)
expected_rs_str = rationale_step_to_str(c.expected_rs)
return f"\"{actual_rs_str}\" should be \"{expected_rs_str}\""
# %%
def corrupted_example_to_str(ce: CorruptedExample, include_rationale_in_critique=True) -> str:
question, incorrect_r, incorrect_a, correction, correction_r, correction_a = ce
x_str = number_to_str(question.x)
y_str = number_to_str(question.y)
incorrect_r_str = rationale_to_str(incorrect_r)
incorrect_a_str = number_to_str(incorrect_a)
correction_str = correction_to_str_verbose(correction)
correction_r_str = rationale_to_str(correction_r)
correction_a_str = number_to_str(correction_a)
if not include_rationale_in_critique:
return f"""Input:
{x_str} + {y_str}
Target:
<scratch>
{incorrect_r_str}
</scratch>
{incorrect_a_str}
Correction:
{correction_str}"""
return f"""Input:
{x_str} + {y_str}
Target:
<scratch>
{incorrect_r_str}
</scratch>
{incorrect_a_str}
Correction:
{correction_str}
<scratch>
{correction_r_str}
</scratch>
{correction_a_str}"""
# %%
def corrupted_example_to_prompt_critique(ce: CorruptedExample, include_rationale_in_critique=True) -> typing.Tuple[str, str]:
question, incorrect_r, incorrect_a, correction, correction_r, correction_a = ce
x_str = number_to_str(question.x)
y_str = number_to_str(question.y)
incorrect_r_str = rationale_to_str(incorrect_r)
incorrect_a_str = number_to_str(incorrect_a)
correction_str = correction_to_str_verbose(correction)
correction_r_str = rationale_to_str(correction_r)
correction_a_str = number_to_str(correction_a)
if not include_rationale_in_critique:
correction_str = correction_to_str_verbose(correction)
return (
f"""Input:
{x_str} + {y_str}
Target:
<scratch>
{incorrect_r_str}
</scratch>
{incorrect_a_str}
Correction:
""",
f"""{correction_str}""")
return (
f"""Input:
{x_str} + {y_str}
Target:
<scratch>
{incorrect_r_str}
</scratch>
{incorrect_a_str}
Correction:
""",
f"""{correction_str}
<scratch>
{correction_r_str}
</scratch>
{correction_a_str}""")
# %%
def generate_few_shot_examples(min_n_digits=1, max_n_digits=5, n_examples=4, include_rationale_in_critique=False, random_seed=42):
random.seed(random_seed) # make dataset same each time
few_shot_examples = ""
for _ in range(n_examples):
n_digits = random.randint(min_n_digits, max_n_digits)
q = make_question(n_digits=n_digits)
ce = make_corrupted_example(q)
ce_str = corrupted_example_to_str(ce, include_rationale_in_critique=include_rationale_in_critique)
few_shot_examples += ce_str + "\n\n"
return few_shot_examples
# %%
def generate_labelled_few_shot_examples(min_n_digits=1, max_n_digits=5, n_examples=4, include_rationale_in_critique=False, random_seed=42):
random.seed(random_seed) # make dataset same each time
few_shot_examples = ""
for _ in range(n_examples):
is_correction_corrupted = random.random() < 0.5
label = "[FAIL]" if is_correction_corrupted else "[PASS]"
n_digits = random.randint(min_n_digits, max_n_digits)
q = make_question(n_digits=n_digits)
ce = make_corrupted_example(q, is_correction_r_corrupted=is_correction_corrupted)
ce_str = corrupted_example_to_str(ce, include_rationale_in_critique=include_rationale_in_critique)
few_shot_examples += ce_str + f" {label}" + "\n\n"
return few_shot_examples
# %%
class ArithmeticDataset(torch.utils.data.Dataset):
def __init__(self, min_n_digits=1, max_n_digits=8, n_examples=10_000, random_seed=42, include_rationale_in_critique=True, mask=None):
random.seed(random_seed) # make dataset same each time
self.n_examples = n_examples
self.questions = [make_question(random.randint(min_n_digits, max_n_digits)) for _ in range(n_examples)]
self.examples = [corrupted_example_to_prompt_critique(make_corrupted_example(q), include_rationale_in_critique=include_rationale_in_critique) for q in self.questions]
if mask is not None:
self.examples = [e for i, e in enumerate(self.examples) if mask[i]]
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
# %%
class LabelledArithmeticDataset(torch.utils.data.Dataset):
def __init__(self, min_n_digits=1, max_n_digits=1, n_examples=10_000, random_seed=42):
random.seed(random_seed) # make dataset same each time
self.n_examples = n_examples
self.questions = [make_question(random.randint(min_n_digits, max_n_digits)) for _ in range(n_examples)]
self.corrupted_examples = [(make_corrupted_example(q, is_correction_r_corrupted=i%2==0), i%2!=0) for i, q in enumerate(self.questions)]
self.prompt_and_completions = [(corrupted_example_to_prompt_critique(ce), label) for ce, label in self.corrupted_examples]
self.examples = [(prompt + completion, label) for ((prompt, completion), label) in self.prompt_and_completions]
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
# %%
class FewshotDiscriminatorArithmeticDataset(torch.utils.data.Dataset):
def __init__(self, min_n_digits=1, max_n_digits=1, n_few_shot_examples=5, n_examples=10_000, random_seed=42):
random.seed(random_seed) # make dataset same each time
self.n_examples = n_examples
few_shot_examples = generate_labelled_few_shot_examples(
min_n_digits=min_n_digits,
max_n_digits=max_n_digits,
n_examples=n_few_shot_examples,
include_rationale_in_critique=True,
random_seed=random_seed,
)
self.qs = [make_question(random.randint(min_n_digits, max_n_digits)) for _ in range(n_examples)]
self.corrupted_examples = [(make_corrupted_example(q, is_correction_r_corrupted=i%2==0), i%2!=0) for i, q in enumerate(self.qs)]
self.prompt_and_completions = [(corrupted_example_to_str(ce), label) for ce, label in self.corrupted_examples]
self.examples = [(few_shot_examples + ce_str + " [", label) for (ce_str, label) in self.prompt_and_completions]
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
# %%
class DictDataset(torch.utils.data.Dataset):
"""Makes a dataset from a dictionary of tensors."""
def __init__(self, inputs: Dict[str, Tensor]):
assert len(inputs) > 0, "inputs must be non-empty"
keys = list(inputs.keys())
key = keys[0]
self._length = inputs[key].shape[0]
for v in inputs.values():
assert v.shape[0] == self._length, "all tensors must have same shape in first dimension"
self._inputs = inputs
def __len__(self) -> int:
return self._length
def __getitem__(self, idx) -> Dict[str, Tensor]:
return {k: v[idx] for k, v in self._inputs.items()}
# %%
def dictdataset_collate_fn(batch: Sequence[Dict[str, Tensor]]) -> Dict[str, Tensor]:
"""Collate function for DictDataset."""
assert len(batch) > 0, "batch must be non-empty"
keys = list(batch[0].keys())
return {k: torch.vstack([example[k] for example in batch]) for k in keys}
# %%