Skip to content

pyright mt-bench: Fix type issues #80

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tomtseng merged 1 commit into from
Feb 7, 2026
Merged

pyright mt-bench: Fix type issues #80

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
43 changes: 22 additions & 21 deletions src/tamperbench/whitebox/evals/mt_bench/mt_bench.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,9 @@

from dataclasses import dataclass
from functools import partial
from typing import Any
from typing import Any, cast

import pandera.polars as pa
import polars as pl
import torch
from openai import OpenAI
Expand Down Expand Up @@ -66,23 +67,23 @@ class MTBenchEvaluationConfig(WhiteBoxEvaluationConfig):
class MTBenchInferenceSchema(InferenceSchema):
"""Schema for MT-Bench multi-turn inferences."""

question_id: int
category: str
turn_1_question: str
turn_1_response: str
turn_2_question: str
turn_2_response: str
question_id: int = cast("int", pa.Field(nullable=False))
category: str = cast("str", pa.Field(nullable=False))
turn_1_question: str = cast("str", pa.Field(nullable=False))
turn_1_response: str = cast("str", pa.Field(nullable=False))
turn_2_question: str = cast("str", pa.Field(nullable=False))
turn_2_response: str = cast("str", pa.Field(nullable=False))


class MTBenchScoreSchema(ScoreSchema):
"""Schema for MT-Bench scores with per-turn details."""

question_id: int
category: str
turn_1_score: float
turn_2_score: float
judge_response_1: str
judge_response_2: str
question_id: int = cast("int", pa.Field(nullable=False))
category: str = cast("str", pa.Field(nullable=False))
turn_1_score: float = cast("float", pa.Field(nullable=False, coerce=True))
turn_2_score: float = cast("float", pa.Field(nullable=False, coerce=True))
judge_response_1: str = cast("str", pa.Field(nullable=False))
judge_response_2: str = cast("str", pa.Field(nullable=False))


class MTBenchEvaluation(WhiteBoxEvaluation[MTBenchEvaluationConfig]):
Expand Down Expand Up @@ -151,11 +152,11 @@ def _generate_response(
max_length=2048,
)

input_ids: Tensor = encoding["input_ids"].to(model.device)
attention_mask: Tensor = encoding["attention_mask"].to(model.device)
input_ids: Tensor = cast(Tensor, encoding["input_ids"]).to(model.device)
attention_mask: Tensor = cast(Tensor, encoding["attention_mask"]).to(model.device)

with torch.no_grad():
outputs: GenerateOutput = model.generate(
outputs: GenerateOutput = model.generate( # pyright: ignore[reportCallIssue]
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=max_new_tokens,
Expand Down Expand Up @@ -261,7 +262,7 @@ def _score_row(self, row: dict[str, Any], question_map: dict[int, MTBenchQuestio
return {
InferenceSchema.prompt: row[InferenceSchema.prompt],
InferenceSchema.response: row[InferenceSchema.response],
ScoreSchema.score: (score_1 + score_2) / 2,
ScoreSchema.score: (score_1 + score_2) / 2, # pyright: ignore[reportReturnType]
"question_id": row["question_id"],
"category": row["category"],
"turn_1_score": score_1,
Expand Down Expand Up @@ -305,11 +306,11 @@ def compute_results(self, scores: DataFrame[ScoreSchema]) -> DataFrame[Evaluatio
scores_df = ScoreSchema.validate(scores)

# Overall average score
mean_score: float = float(scores_df[ScoreSchema.score].mean())
mean_score: float = float(scores_df[ScoreSchema.score].mean()) # pyright: ignore[reportArgumentType,reportCallIssue]

# Per-turn averages
mean_turn_1: float = float(scores_df["turn_1_score"].mean())
mean_turn_2: float = float(scores_df["turn_2_score"].mean())
mean_turn_1: float = float(scores_df["turn_1_score"].mean()) # pyright: ignore[reportArgumentType]
mean_turn_2: float = float(scores_df["turn_2_score"].mean()) # pyright: ignore[reportArgumentType]

_metrics_df: pl.DataFrame = pl.from_dict(
data={
Expand All @@ -318,7 +319,7 @@ def compute_results(self, scores: DataFrame[ScoreSchema]) -> DataFrame[Evaluatio
str(MetricName.MT_BENCH_TURN_1_SCORE),
str(MetricName.MT_BENCH_TURN_2_SCORE),
],
EvaluationSchema.metric_value: [mean_score, mean_turn_1, mean_turn_2],
EvaluationSchema.metric_value: [mean_score, mean_turn_1, mean_turn_2], # pyright: ignore[reportArgumentType]
}
)
return EvaluationSchema.validate(_metrics_df)