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e2f8b29 206438f e2f8b29 206438f e2f8b29 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 | """PyTorch code snippet templates for Task 6 code-level debugging.
Each template is a real, syntactically valid Python/PyTorch training script
with one injected bug.
"""
from __future__ import annotations
import ast
import io
import tokenize
from typing import Optional
import torch # noqa: F401
# Bug variant templates: (buggy_code, correct_line_num, correct_replacement)
_TEMPLATES: dict[str, tuple[str, int, str]] = {
"eval_mode": (
"""\
import torch
import torch.nn as nn
model = SimpleCNN()
model.eval()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
for epoch in range(100):
for batch_x, batch_y in train_loader:
optimizer.zero_grad()
output = model(batch_x)
loss = criterion(output, batch_y)
loss.backward()
optimizer.step()""",
5,
"model.train()",
),
"detach_loss": (
"""\
import torch
import torch.nn as nn
model = SimpleCNN()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
for epoch in range(100):
for batch_x, batch_y in train_loader:
optimizer.zero_grad()
output = model(batch_x)
loss = criterion(output, batch_y).detach()
loss.backward()
optimizer.step()""",
14,
" loss = criterion(output, batch_y)",
),
"zero_grad_missing": (
"""\
import torch
import torch.nn as nn
model = SimpleCNN()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
for epoch in range(100):
for batch_x, batch_y in train_loader:
output = model(batch_x)
loss = criterion(output, batch_y)
loss.backward()
optimizer.step()""",
11,
" optimizer.zero_grad()",
),
"inplace_relu": (
"""\
import torch
import torch.nn as nn
import torch.nn.functional as F
model = SimpleCNN()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
for epoch in range(100):
for batch_x, batch_y in train_loader:
optimizer.zero_grad()
output = model(batch_x)
output = F.relu(output, inplace=True)
loss = criterion(output, batch_y)
loss.backward()
optimizer.step()""",
15,
" output = F.relu(output)",
),
}
# Semantic equivalence patterns per bug variant
_SEMANTIC_PATTERNS: dict[str, list[tuple[str, str]]] = {
"eval_mode": [
# (must_contain, must_not_contain)
("model.train()", "model.eval()"),
],
"detach_loss": [
("criterion(", ".detach()"),
],
"zero_grad_missing": [
("zero_grad()", ""), # just needs zero_grad present
],
"inplace_relu": [
("F.relu(", "inplace=True"),
],
}
def generate_code_snippet(bug_type: str, seed: int = 42) -> dict:
"""Generate a code snippet with the specified bug.
Returns dict with keys: code, filename, line_count, imports, hint.
"""
if bug_type not in _TEMPLATES:
raise ValueError(f"Unknown bug_type: {bug_type}")
code, _line, _replacement = _TEMPLATES[bug_type]
lines = code.strip().split("\n")
imports = [
line for line in lines if line.startswith("import ") or line.startswith("from ")
]
hint: Optional[str] = None
if bug_type == "eval_mode":
hint = "Check the model mode before the training loop."
elif bug_type == "detach_loss":
hint = "Examine how the loss is computed and used."
return {
"code": code,
"filename": "train.py",
"line_count": len(lines),
"imports": imports,
"hint": hint,
}
def _normalize_code(s: str) -> str:
"""Strip whitespace and inline comments for comparison."""
s = s.strip()
# Remove inline comments
result_lines: list[str] = []
for line in s.split("\n"):
# Remove trailing comment but preserve strings
stripped = line.rstrip()
result_lines.append(stripped)
return "\n".join(result_lines)
def _tokenize_compare(original: str, replacement: str) -> bool:
"""Compare token streams ignoring whitespace and comments."""
def get_tokens(code: str) -> list[tuple[int, str]]:
try:
tokens = list(tokenize.generate_tokens(io.StringIO(code).readline))
# Filter out COMMENT, NL, NEWLINE, INDENT, DEDENT, ENCODING, ENDMARKER
skip = {
tokenize.COMMENT,
tokenize.NL,
tokenize.NEWLINE,
tokenize.INDENT,
tokenize.DEDENT,
tokenize.ENCODING,
tokenize.ENDMARKER,
}
return [(t.type, t.string) for t in tokens if t.type not in skip]
except tokenize.TokenError:
return []
return get_tokens(original) == get_tokens(replacement)
def validate_fix(bug_type: str, line: int, replacement: str) -> bool:
"""Validate a code fix submission.
Multi-strategy pipeline per spec Section 22:
1. Normalize whitespace + strip comments
2. Token-stream comparison
3. Semantic equivalence patterns
4. AST fallback
"""
if bug_type not in _TEMPLATES:
return False
code, correct_line, correct_replacement = _TEMPLATES[bug_type]
lines = code.strip().split("\n")
# Check line number is valid
if line < 1 or line > len(lines):
return False
# For zero_grad_missing, the fix is inserting a line, not replacing
if bug_type == "zero_grad_missing":
# Accept if the replacement contains zero_grad
normalized = _normalize_code(replacement)
if "zero_grad" in normalized:
return True
return False
# Strategy 1: Normalize and compare
norm_replacement = _normalize_code(replacement)
norm_correct = _normalize_code(correct_replacement)
if norm_replacement == norm_correct:
return True
# Strategy 2: Token-stream comparison
if _tokenize_compare(correct_replacement, replacement):
return True
# Strategy 3: Semantic equivalence patterns
patterns = _SEMANTIC_PATTERNS.get(bug_type, [])
for must_contain, must_not_contain in patterns:
if must_contain and must_contain in norm_replacement:
if not must_not_contain or must_not_contain not in norm_replacement:
return True
# Strategy 4: AST fallback — verify buggy pattern absent
try:
# Replace the line in the full code and parse
new_lines = lines.copy()
new_lines[line - 1] = replacement.rstrip()
new_code = "\n".join(new_lines)
tree = ast.parse(new_code)
# Check that the buggy pattern is absent
ast.dump(tree) # Validates AST is well-formed
if bug_type == "eval_mode" and "eval" not in replacement.lower():
if "train" in replacement.lower():
return True
if bug_type == "detach_loss" and "detach" not in replacement.lower():
return True
if bug_type == "inplace_relu" and "inplace" not in replacement.lower():
if "relu" in replacement.lower():
return True
except SyntaxError:
pass
return False
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