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Add --data_parallel option to benchmark for concurrent example processing #210

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174 changes: 121 additions & 53 deletions scripts/benchmark.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import asyncio
import os
import re
from enum import Enum
Expand Down Expand Up @@ -241,6 +242,12 @@ def display_results(df: pd.DataFrame):
default=None,
help="use tokens_per_step instead of step_token for StepGeneration (easier for PF/BS algorithms)",
)
@click.option(
"--data_parallel",
type=int,
default=1,
help="number of dataset examples to process in parallel (increase for better GPU utilization with small models, e.g. 8-32)",
)
def main(
benchmark: BenchmarkDataset,
model_name: str,
Expand All @@ -263,6 +270,7 @@ def main(
eval_expected_pass_at_one: bool,
display_only: bool,
tokens_per_step: int,
data_parallel: int,
):
# print all arguments using click context
ctx = click.get_current_context()
Expand Down Expand Up @@ -362,14 +370,20 @@ def main(
os.makedirs(output_dir)

print(f"running inference-time scaling for {budgets=}...")
if data_parallel > 1:
print(f"using data parallelism with {data_parallel} concurrent examples")
rows = []
try:
for n in tqdm(budgets):

async def _run_async():
semaphore = asyncio.Semaphore(data_parallel)

for n in budgets:
cached_rows = []
examples_to_process = []

for x in dataset:
y_full = None
y = None
cached_row = None
if not force_run and len(df_existing) > 0:
# only skip if both the unique_id and budget matches
match = (df_existing["unique_id"] == x["unique_id"]) & (
df_existing["budget"] == n
)
Expand All @@ -378,64 +392,118 @@ def main(
f"expected exactly one match, got {match.sum()}"
)
if eval_expected_pass_at_one:
y_full = {
"responses": df_existing.loc[match, "responses"].values[
0
],
"log_probs": df_existing.loc[match, "log_probs"].values[
0
],
cached_row = {
"unique_id": x["unique_id"],
"budget": n,
"responses": df_existing.loc[
match, "responses"
].values[0],
"log_probs": df_existing.loc[
match, "log_probs"
].values[0],
"correct": None,
}
else:
y = df_existing.loc[match, "response"].values[0]
if y_full is None if eval_expected_pass_at_one else y is None:
cached_row = {
"unique_id": x["unique_id"],
"budget": n,
"response": df_existing.loc[
match, "response"
].values[0],
"correct": None,
}
if does_eval:
if eval_expected_pass_at_one:
c = [
math_verify.verify(
math_verify.parse(x["answer"]),
math_verify.parse(y),
)
for y in cached_row["responses"]
]
p = _softmax(cached_row["log_probs"])
cached_row["correct"] = np.dot(p, c)
else:
cached_row["correct"] = math_verify.verify(
math_verify.parse(x["answer"]),
math_verify.parse(cached_row["response"]),
)

if cached_row is not None:
cached_rows.append(cached_row)
else:
examples_to_process.append(x)

async def _process_example(x, budget):
async with semaphore:
try:
if eval_expected_pass_at_one:
y_full = scaling_alg.infer(
lm, x["problem"], n, return_response_only=False
result = await scaling_alg.ainfer(
lm,
x["problem"],
budget,
return_response_only=False,
)
y_full = {
"responses": y_full.responses_lst[-1],
"log_probs": y_full.log_weights_lst[-1],
"responses": result.responses_lst[-1],
"log_probs": result.log_weights_lst[-1],
}
row = {
"unique_id": x["unique_id"],
"budget": budget,
"responses": y_full["responses"],
"log_probs": y_full["log_probs"],
"correct": None,
}
else:
y = scaling_alg.infer(lm, x["problem"], n)
except KeyboardInterrupt:
raise
y = await scaling_alg.ainfer(
lm, x["problem"], budget
)
row = {
"unique_id": x["unique_id"],
"budget": budget,
"response": y,
"correct": None,
}

if does_eval:
if eval_expected_pass_at_one:
c = [
math_verify.verify(
math_verify.parse(x["answer"]),
math_verify.parse(y),
)
for y in row["responses"]
]
p = _softmax(row["log_probs"])
row["correct"] = np.dot(p, c)
else:
row["correct"] = math_verify.verify(
math_verify.parse(x["answer"]),
math_verify.parse(row["response"]),
)
return row
except Exception as e:
print(f"error scaling example {x['unique_id']}: {e}")
continue
if eval_expected_pass_at_one:
row = {
"unique_id": x["unique_id"],
"budget": n,
"responses": y_full["responses"],
"log_probs": y_full["log_probs"],
"correct": None,
}
else:
row = {
"unique_id": x["unique_id"],
"budget": n,
"response": y,
"correct": None,
}
if does_eval:
if eval_expected_pass_at_one:
c = [
math_verify.verify(
math_verify.parse(x["answer"]), math_verify.parse(y)
)
for y in row["responses"]
]
p = _softmax(row["log_probs"])
row["correct"] = np.dot(p, c)
else:
row["correct"] = math_verify.verify(
math_verify.parse(x["answer"]),
math_verify.parse(row["response"]),
)
rows.append(row)
return None

if examples_to_process:
tasks = [
_process_example(x, n) for x in examples_to_process
]
for future in tqdm(
asyncio.as_completed(tasks),
total=len(tasks),
desc=f"budget={n}",
):
result = await future
if result is not None:
rows.append(result)

rows.extend(cached_rows)

try:
asyncio.run(_run_async())
except KeyboardInterrupt:
print("\nkeyboard interrupt detected, saving partial results")

Expand Down
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