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from dataclasses import dataclass, field
from typing import Callable, Dict, List, Optional
import numpy as np
@dataclass
class BugTemplate:
bug_type: str
category: str
difficulty: str
primary_bug_file: str
related_files: List[str]
red_herring_file: Optional[str]
fix_strategy: str
line_range: List[int]
description: str
artifact_generator: Callable
repo_mutator: Callable
metadata: Dict = field(default_factory=dict)
BUG_CATEGORIES = {
"shape_mismatch": "model",
"missing_zero_grad": "optimization",
"wrong_loss_function": "optimization",
"learning_rate_too_high": "optimization",
"gradient_explosion": "optimization",
"memory_leak": "resource",
"data_leakage": "data",
"incorrect_normalization": "data",
"distributed_sync_error": "distributed",
"amp_overflow": "numerics",
}
# Realistic artifact generator
def dummy_artifact_generator(artifact_type: str, rng):
if artifact_type == "loss_curve":
t = np.arange(100)
base = 2.3 * np.exp(-0.01 * t) + 0.15
oscillation = 0.22 * np.sin(0.25 * t) * np.exp(-0.002 * t)
return [
{"step": int(i), "train_loss": float(base[i] + oscillation[i])}
for i in range(100)
]
if artifact_type == "gpu_profile":
t = np.arange(100)
allocated = 2048 + 2.4 * t
return [
{"step": int(i), "allocated_mb": float(allocated[i])}
for i in range(100)
]
if artifact_type == "training_log":
return "Epoch 1, Step 0: loss 2.45\nEpoch 1, Step 1: loss 2.43\n"
if artifact_type == "diagnostic_report":
return "No critical diagnostics found. Review optimizer and data pipeline."
return []
def artifact_generator_wrong_loss(artifact_type: str, rng):
if artifact_type == "loss_curve":
t = np.arange(100)
base = 1.8 + 0.05 * np.sin(0.15 * t)
return [{"step": int(i), "train_loss": float(base[i])} for i in range(100)]
if artifact_type == "gpu_profile":
t = np.arange(100)
allocated = 1900 + 1.8 * t
return [{"step": int(i), "allocated_mb": float(allocated[i])} for i in range(100)]
if artifact_type == "training_log":
return "Epoch 1: loss 1.82, acc 0.11\nEpoch 2: loss 1.80, acc 0.12\n"
if artifact_type == "diagnostic_report":
return "Loss plateaus early while accuracy stays near chance. Check loss function."
return []
def artifact_generator_lr_high(artifact_type: str, rng):
if artifact_type == "loss_curve":
t = np.arange(100)
base = 0.9 + 0.02 * (t ** 1.1)
return [{"step": int(i), "train_loss": float(base[i])} for i in range(100)]
if artifact_type == "gpu_profile":
t = np.arange(100)
allocated = 2100 + 2.0 * t
return [{"step": int(i), "allocated_mb": float(allocated[i])} for i in range(100)]
if artifact_type == "training_log":
return "Step 10: loss 3.20 (spike)\nStep 20: loss 5.10 (diverged)\n"
if artifact_type == "diagnostic_report":
return "Loss spikes suggest unstable updates. Consider lowering learning rate."
return []
def artifact_generator_amp_overflow(artifact_type: str, rng):
if artifact_type == "loss_curve":
t = np.arange(100)
base = 2.1 * np.exp(-0.008 * t) + 0.2
base[30:] = base[30:] + 0.6
return [{"step": int(i), "train_loss": float(base[i])} for i in range(100)]
if artifact_type == "gpu_profile":
t = np.arange(100)
allocated = 2300 + 3.2 * t
return [{"step": int(i), "allocated_mb": float(allocated[i])} for i in range(100)]
if artifact_type == "training_log":
return "AMP: overflow detected, skipping step\nAMP: scale reduced to 32768\n"
if artifact_type == "diagnostic_report":
return "AMP overflow warnings observed. Ensure GradScaler is used correctly."
return []
def mutate_missing_zero_grad(repo_files, rng):
repo_files["train.py"] = """import torch
from model.architecture import Net
model = Net()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
for epoch in range(10):
for x, y in dataloader:
# optimizer.zero_grad() # BUG: commented out
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
"""
return repo_files
def mutate_data_leakage(repo_files, rng):
repo_files["data/dataset.py"] = """from torch.utils.data import Dataset
class ImageDataset(Dataset):
def __init__(self, data, split="train"):
# BUG: We use the entire data instead of just the split
self.data = data
self.split = split
"""
return repo_files
def mutate_memory_leak(repo_files, rng):
repo_files["data/dataset.py"] = """from torch.utils.data import Dataset
class ImageDataset(Dataset):
def __init__(self):
# BUG: Storing huge tensors in a class-level variable leading to memory accumulation
self.cache = []
def load(self, x):
self.cache.append(x)
return x
"""
return repo_files
def mutate_wrong_loss_function(repo_files, rng):
repo_files["train.py"] = """import torch
from model.architecture import Net
model = Net()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.MSELoss() # BUG: wrong loss for classification
for epoch in range(10):
for x, y in dataloader:
optimizer.zero_grad()
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
"""
return repo_files
def mutate_learning_rate_too_high(repo_files, rng):
repo_files["config/training_config.yaml"] = """lr: 1.0
batch_size: 32
"""
return repo_files
def mutate_amp_overflow(repo_files, rng):
repo_files["train.py"] = """import torch
from model.architecture import Net
model = Net().cuda()
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for epoch in range(10):
for x, y in dataloader:
optimizer.zero_grad()
with torch.cuda.amp.autocast():
output = model(x.cuda())
loss = torch.nn.functional.cross_entropy(output, y.cuda())
# BUG: missing GradScaler handling can cause overflows
loss.backward()
optimizer.step()
"""
return repo_files
BUG_TEMPLATES = [
BugTemplate(
bug_type="missing_zero_grad",
category="optimization",
difficulty="easy",
primary_bug_file="train.py",
related_files=[],
red_herring_file="model/architecture.py",
fix_strategy="Call optimizer.zero_grad() before loss.backward()",
line_range=[9, 14],
description="Missing zero grad",
artifact_generator=dummy_artifact_generator,
repo_mutator=mutate_missing_zero_grad,
),
BugTemplate(
bug_type="wrong_loss_function",
category="optimization",
difficulty="easy",
primary_bug_file="train.py",
related_files=["config/training_config.yaml"],
red_herring_file="data/dataset.py",
fix_strategy="Use CrossEntropyLoss for classification logits",
line_range=[6, 12],
description="Wrong loss function",
artifact_generator=artifact_generator_wrong_loss,
repo_mutator=mutate_wrong_loss_function,
),
BugTemplate(
bug_type="data_leakage",
category="data",
difficulty="medium",
primary_bug_file="data/dataset.py",
related_files=["data/preprocessing.py"],
red_herring_file="train.py",
fix_strategy="Ensure validation split is strictly separate from training",
line_range=[4, 6],
description="Data leakage",
artifact_generator=dummy_artifact_generator,
repo_mutator=mutate_data_leakage,
),
BugTemplate(
bug_type="learning_rate_too_high",
category="optimization",
difficulty="medium",
primary_bug_file="config/training_config.yaml",
related_files=["train.py"],
red_herring_file="model/attention.py",
fix_strategy="Reduce learning rate or use a scheduler",
line_range=[1, 1],
description="Learning rate too high",
artifact_generator=artifact_generator_lr_high,
repo_mutator=mutate_learning_rate_too_high,
),
BugTemplate(
bug_type="memory_leak",
category="resource",
difficulty="hard",
primary_bug_file="data/dataset.py",
related_files=["train.py"],
red_herring_file="model/attention.py",
fix_strategy="Avoid holding reference to tensors in class cache",
line_range=[5, 9],
description="Memory leak",
artifact_generator=dummy_artifact_generator,
repo_mutator=mutate_memory_leak,
),
BugTemplate(
bug_type="amp_overflow",
category="numerics",
difficulty="hard",
primary_bug_file="train.py",
related_files=["config/training_config.yaml"],
red_herring_file="model/architecture.py",
fix_strategy="Use GradScaler and scale updates for AMP",
line_range=[7, 13],
description="AMP overflow",
artifact_generator=artifact_generator_amp_overflow,
repo_mutator=mutate_amp_overflow,
),
]
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