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import json
import numpy as np
from typing import Optional
def make_id(config: dict, keys_to_ignore: list[str]) -> str:
keys = sorted(set(config.keys()))
return "_".join(str(config[k]) for k in keys if k not in keys_to_ignore)
class ModelBenchmarkData:
def __init__(self, json_path: str) -> None:
with open(json_path, "r") as f:
self.data = json.load(f)
def compute_e2e_latency(self, measures: dict) -> tuple[float, Optional[float]]:
return measures["e2e_latency"]
def compute_ttft(self, measures: dict) -> float:
return measures["t_tokens"][0] - measures["wall_time_start"]
def compute_itl(self, measures: dict) -> Optional[float]:
if len(measures["t_tokens"]) < 2:
return None
delta_t = measures["t_tokens"][-1] - measures["t_tokens"][0]
num_tokens = len(measures["t_tokens"]) - 1
return delta_t / num_tokens
def get_main_batch_size(self) -> int:
batch_sizes = {}
for cfg_name, data in self.data.items():
for measure in data["measures"]:
bs = measure["batch_size"]
if bs not in batch_sizes:
batch_sizes[bs] = 0
batch_sizes[bs] += 1
return max(batch_sizes, key=batch_sizes.get)
def get_bar_plot_data(self, collapse_on_cache: bool = True, collapse_on_compile_mode: bool = True) -> dict:
# Gather data for each scenario
per_scenario_data = {}
for i, (cfg_name, data) in enumerate(self.data.items()):
per_scenario_data[cfg_name] = {
"ttft": [self.compute_ttft(d) for d in data["measures"]],
"itl": [self.compute_itl(d) for d in data["measures"]],
"e2e": [self.compute_e2e_latency(d) for d in data["measures"]],
"config": data["metadata"]["config"],
}
# Eventually collapse on cache
if collapse_on_cache:
collapsed_keys = {}
for cfg_name, data in per_scenario_data.items():
keys_to_ignore = ["name"]
keys_to_ignore += (["use_cache"] if collapse_on_cache else [])
keys_to_ignore += (["compile_mode"] if collapse_on_compile_mode else [])
cfg_id = make_id(data["config"], keys_to_ignore)
cfg_e2e = np.mean(data["e2e"])
other_name, other_e2e = collapsed_keys.get(cfg_id, (None, 1e16))
if cfg_e2e < other_e2e:
collapsed_keys[cfg_id] = (cfg_name, cfg_e2e)
per_scenario_data = {k: per_scenario_data[k] for k, _ in collapsed_keys.values()}
return per_scenario_data
def load_data(keep_common_scenarios_only: bool = False) -> dict[str, ModelBenchmarkData]:
data = {
"MI325": ModelBenchmarkData("mi325_data.json"),
"H100": ModelBenchmarkData("h100_data.json"),
}
if keep_common_scenarios_only:
common_scenarios = set(data["MI325"].data.keys()) & set(data["H100"].data.keys())
for device_name, device_data in data.items():
device_data.data = {k: v for k, v in device_data.data.items() if k in common_scenarios}
return data
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