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Utility functions for benchmark scripts.
"""
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional
import numpy as np
import sglang as sgl
@dataclass
class BenchmarkMetrics:
"""Container for benchmark performance metrics."""
latency: float
output_throughput: float
accept_length: float
accuracy: Optional[float] = None
num_questions: int = 0
num_valid_predictions: int = 0
categorical_performance: Optional[Dict[str, "BenchmarkMetrics"]] = None
def compute_metrics(
states: List[Any],
latency: float,
answer_key: str = "answer",
additional_answer_keys: Optional[List[str]] = None,
) -> BenchmarkMetrics:
"""
Compute performance metrics from SGLang states.
Args:
states: List of SGLang state objects from run_batch
latency: Total latency in seconds
answer_key: Primary key for answer in state meta info
additional_answer_keys: Additional keys to include in token count (e.g., ["answer_1", "answer_2"])
Returns:
BenchmarkMetrics object with computed metrics
"""
# Compute output tokens
num_output_tokens = 0
if additional_answer_keys:
for key in [answer_key] + additional_answer_keys:
num_output_tokens += sum(
s.get_meta_info(key)["completion_tokens"] for s in states
)
else:
num_output_tokens = sum(
s.get_meta_info(answer_key)["completion_tokens"] for s in states
)
output_throughput = num_output_tokens / latency if latency > 0 else 0.0
# Compute accept length (speculative decoding metric)
has_verify = "spec_verify_ct" in states[0].get_meta_info(answer_key)
if has_verify:
num_verify_tokens = 0
if additional_answer_keys:
for key in [answer_key] + additional_answer_keys:
num_verify_tokens += sum(
s.get_meta_info(key).get("spec_verify_ct", 0) for s in states
)
else:
num_verify_tokens = sum(
s.get_meta_info(answer_key).get("spec_verify_ct", 0) for s in states
)
if num_verify_tokens == 0:
accept_length = 1.0
else:
accept_length = num_output_tokens / num_verify_tokens
else:
accept_length = 1.0
return BenchmarkMetrics(
latency=latency,
output_throughput=output_throughput,
accept_length=accept_length,
num_questions=len(states),
)
def print_results(
metrics_list: List[BenchmarkMetrics],
benchmark_name: str,
show_accuracy: bool = False,
):
"""
Print benchmark results in a formatted way.
Args:
metrics_list: List of BenchmarkMetrics from multiple runs
benchmark_name: Name of the benchmark
show_accuracy: Whether to show accuracy metrics
"""
avg_latency = np.mean([m.latency for m in metrics_list])
avg_throughput = np.mean([m.output_throughput for m in metrics_list])
avg_accept_length = np.mean([m.accept_length for m in metrics_list])
print(f"\n{'='*50}")
print(f"{benchmark_name} Evaluation Results")
print(f"{'='*50}")
print(f"Number of questions: {metrics_list[0].num_questions}")
if show_accuracy:
if metrics_list[0].accuracy is not None:
avg_accuracy = np.mean(
[m.accuracy for m in metrics_list if m.accuracy is not None]
)
print(f"Average Accuracy: {avg_accuracy:.4f} ({avg_accuracy*100:.2f}%)")
else:
print(f"Average Accuracy: None")
print(f"Average Latency: {avg_latency:.3f} s")
print(f"Average Output throughput: {avg_throughput:.3f} token/s")
print(f"Average Accept length: {avg_accept_length:.3f}")
print(f"{'='*50}\n")
def create_simple_sgl_function(
function_name: str = "get_answer",
answer_key: str = "answer",
system_prompt: Optional[str] = None,
max_tokens: int = 2048,
stop: Optional[List[str]] = None,
user_prefix: Optional[str] = None,
) -> Callable:
"""
Create a simple SGL function for single-turn Q&A.
Args:
function_name: Name of the function
answer_key: Key for storing the answer
system_prompt: Optional system prompt
max_tokens: Maximum tokens to generate
stop: Optional stop sequences
user_prefix: Optional suffix to append to user message (appended after question)
Returns:
SGL function decorated with @sgl.function
"""
@sgl.function
def sgl_func(s, question):
if system_prompt:
s += sgl.system(system_prompt)
user_content = question
if user_prefix:
user_content = question + user_prefix
s += sgl.user(user_content)
gen_kwargs = {"max_tokens": max_tokens}
if stop:
gen_kwargs["stop"] = stop
s += sgl.assistant(sgl.gen(answer_key, **gen_kwargs))
sgl_func.__name__ = function_name
return sgl_func
def create_few_shot_sgl_function(
few_shot_examples: str,
function_name: str = "few_shot_answer",
answer_key: str = "answer",
max_tokens: int = 512,
stop: Optional[List[str]] = None,
) -> Callable:
"""
Create an SGL function for few-shot learning.
Args:
few_shot_examples: String containing few-shot examples
function_name: Name of the function
answer_key: Key for storing the answer
max_tokens: Maximum tokens to generate
stop: Optional stop sequences
Returns:
SGL function decorated with @sgl.function
"""
@sgl.function
def sgl_func(s, question):
s += few_shot_examples + question
gen_kwargs = {"max_tokens": max_tokens}
if stop:
gen_kwargs["stop"] = stop
s += sgl.gen(answer_key, **gen_kwargs)
sgl_func.__name__ = function_name
return sgl_func
def create_multi_turn_sgl_function(
function_name: str = "multi_turn_answer",
system_prompt: Optional[str] = None,
num_turns: int = 2,
max_tokens: int = 2048,
) -> Callable:
"""
Create an SGL function for multi-turn conversations (e.g., MT-Bench with 2 turns).
Args:
function_name: Name of the function
system_prompt: Optional system prompt
num_turns: Number of conversation turns (default: 2)
max_tokens: Maximum tokens to generate per turn
Returns:
SGL function decorated with @sgl.function
"""
if num_turns == 2:
# Most common case: 2-turn conversation
@sgl.function
def sgl_func(s, question_1, question_2):
if system_prompt:
s += sgl.system(system_prompt)
s += sgl.user(question_1)
s += sgl.assistant(sgl.gen("answer_1", max_tokens=max_tokens))
s += sgl.user(question_2)
s += sgl.assistant(sgl.gen("answer_2", max_tokens=max_tokens))
else:
# Generic case: create function with dynamic number of turns
# Note: This requires the caller to pass arguments as a dict
@sgl.function
def sgl_func(s, **kwargs):
if system_prompt:
s += sgl.system(system_prompt)
for i in range(num_turns):
question_key = f"question_{i+1}"
answer_key = f"answer_{i+1}"
if question_key in kwargs:
s += sgl.user(kwargs[question_key])
s += sgl.assistant(sgl.gen(answer_key, max_tokens=max_tokens))
sgl_func.__name__ = function_name
return sgl_func
def create_image_sgl_function(
function_name: str = "get_image_answer",
answer_key: str = "answer",
max_tokens: int = 2048,
) -> Callable:
"""
Create an SGL function for image-based Q&A.
Args:
function_name: Name of the function
answer_key: Key for storing the answer
max_tokens: Maximum tokens to generate
Returns:
SGL function decorated with @sgl.function
"""
@sgl.function
def sgl_func(s, image_path, question, **kwargs):
"""
The body of the SGL function: constructs a multimodal conversation flow.
- First, it inputs an image + text question as 'user'.
- Then, it generates an answer as 'assistant', binding the response to the specified `answer_key`.
Note: sgl.image() automatically encodes the image into a format supported by the model for multimodal input.
"""
# User input: Image + Text question
s += sgl.user(sgl.image(image_path) + question)
s += sgl.assistant(sgl.gen(answer_key, max_tokens=max_tokens))
sgl_func.__name__ = function_name
return sgl_func
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