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import os
import pandas as pd
from .utils import process_kernels, process_quantizations
COLUMNS_MAPPING = {
"config.name": "Experiment 🧪",
"config.backend.model": "Model 🤗",
# primary measurements
"report.prefill.latency.p50": "Prefill (s)",
"report.per_token.latency.p50": "Per Token (s)",
"report.decode.throughput.value": "Decode (tokens/s)",
"report.decode.efficiency.value": "Energy (tokens/kWh)",
"report.decode.memory.max_allocated": "Memory (MB)",
# deployment settings
"config.backend.name": "Backend 🏭",
"config.backend.torch_dtype": "Precision 📥",
"quantization": "Quantization 🗜️",
"attention": "Attention 👁️",
"kernel": "Kernel ⚛️",
# additional information
"architecture": "Architecture 🏛️",
"prefill+decode": "End-to-End (s)",
"Average ⬆️": "Open LLM Score (%)",
"#Params (B)": "Params (B)",
}
SORTING_COLUMNS = ["Open LLM Score (%)", "Decode (tokens/s)", "Prefill (s)"]
SUBSETS = ["unquantized", "awq", "bnb", "gptq"]
SORTING_ASCENDING = [False, True, False]
def get_raw_llm_perf_df(machine: str = "1xA10"):
dfs = []
for subset in SUBSETS:
try:
dfs.append(
pd.read_csv(
f"hf://datasets/optimum-benchmark/llm-perf-leaderboard/perf-df-{subset}-{machine}.csv"
)
)
except Exception:
print(f"Subset {subset} for machine {machine} not found")
perf_df = pd.concat(dfs)
llm_df = pd.read_csv(
"hf://datasets/optimum-benchmark/llm-perf-leaderboard/llm-df.csv"
)
llm_perf_df = pd.merge(
llm_df, perf_df, left_on="Model", right_on="config.backend.model"
)
return llm_perf_df
def processed_llm_perf_df(llm_perf_df):
# some assertions
assert llm_perf_df["config.scenario.input_shapes.batch_size"].nunique() == 1
assert llm_perf_df["config.scenario.input_shapes.sequence_length"].nunique() == 1
assert llm_perf_df["config.scenario.generate_kwargs.max_new_tokens"].nunique() == 1
assert llm_perf_df["config.scenario.generate_kwargs.min_new_tokens"].nunique() == 1
# fix couple stuff
llm_perf_df.dropna(subset=["report.decode.latency.p50"], inplace=True)
llm_perf_df["config.name"] = llm_perf_df["config.name"].str.replace(
"flash_attention_2", "fa2"
)
llm_perf_df["prefill+decode"] = (
llm_perf_df["report.prefill.latency.p50"]
+ (llm_perf_df["report.decode.latency.p50"])
)
# llm_perf_df["architecture"] = llm_perf_df["config.backend.model"].apply(
# process_architectures
# )
llm_perf_df["architecture"] = llm_perf_df["Architecture"]
llm_perf_df["attention"] = (
llm_perf_df["config.backend.attn_implementation"]
.str.replace("flash_attention_2", "FAv2")
.str.replace("eager", "Eager")
.str.replace("sdpa", "SDPA")
)
llm_perf_df["quantization"] = llm_perf_df.apply(process_quantizations, axis=1)
llm_perf_df["kernel"] = llm_perf_df.apply(process_kernels, axis=1)
# round numerical columns
llm_perf_df = llm_perf_df.round(
{
"report.prefill.latency.p50": 3,
"report.decode.latency.p50": 3,
"report.decode.throughput.value": 3,
"report.decode.efficiency.value": 3,
"report.decode.memory.max_allocated": 3,
"Average ⬆️": 3,
"prefill+decode": 3,
"#Params (B)": 3,
}
)
# filter columns
llm_perf_df = llm_perf_df[list(COLUMNS_MAPPING.keys())]
# rename columns
llm_perf_df.rename(columns=COLUMNS_MAPPING, inplace=True)
# sort by metric
llm_perf_df.sort_values(
by=SORTING_COLUMNS,
ascending=SORTING_ASCENDING,
inplace=True,
)
return llm_perf_df
def get_llm_perf_df(machine: str = "1xA10"):
if os.path.exists(f"llm-perf-leaderboard-{machine}.csv"):
llm_perf_df = pd.read_csv(f"llm-perf-leaderboard-{machine}.csv")
else:
llm_perf_df = get_raw_llm_perf_df(machine)
llm_perf_df = processed_llm_perf_df(llm_perf_df)
llm_perf_df.to_csv(f"llm-perf-leaderboard-{machine}.csv", index=False)
return llm_perf_df
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