import os from pathlib import Path import pandas as pd from src.model_list import MODEL_MAPPING, MODEL_SHORT_TO_LONG, get_all_model_list from src.utils import process_kernels, process_quantizations COLUMNS_MAPPING = { "config.name": "Experiment ๐Ÿงช", "config.backend.model": "Model ๐Ÿค—", # 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 ๐Ÿญ", "config.backend.torch_dtype": "Precision ๐Ÿ“ฅ", "quantization": "Quantization ๐Ÿ—œ๏ธ", "attention": "Attention ๐Ÿ‘๏ธ", "kernel": "Kernel โš›๏ธ", # additional information "architecture": "Architecture ๐Ÿ›๏ธ", "prefill+decode": "End-to-End (s)", "Average โฌ†๏ธ": "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] BGB_SORTING_COLUMNS = ["Average"] # Use the above capabilities to create the columns BGB_COLUMNS_MAPPING = { "model_name_or_path": "Model ๐Ÿค—", "model_params": "Model Params (B)", "model_type": "Model Type", "average": "Average", "grounding": "Grounding โšก๏ธ", "instruction_following": "Instruction Following ๐Ÿ“", "planning": "Planning ๐Ÿ“…", "reasoning": "Reasoning ๐Ÿ’ก", "refinement": "Refinement ๐Ÿ”ฉ", "safety": "Safety โš ๏ธ", "theory_of_mind": "Theory of Mind ๐Ÿค”", "tool_usage": "Tool Usage ๐Ÿ› ๏ธ", "multilingual": "Multilingual ๐Ÿ‡ฌ๐Ÿ‡ซ", } 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 โฌ†๏ธ": 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 def get_eval_df(eval_model_name: str): assert eval_model_name in ["gpt-4-turbo-2024-04-09", "prometheus-bgb-8x7b-v2.0"] base_dir = Path(__file__).parent.parent / "data" filepath = base_dir / f"bgb-leaderboard-{eval_model_name}.pkl" # For debugging csv_filepath = base_dir / f"bgb-leaderboard-{eval_model_name}.csv" def change_model_name(model_name: str): # TODO: Hard code models with different names model_name_or_path = MODEL_SHORT_TO_LONG.get(model_name, model_name) if model_name == "qwen/qwen-110b-chat": model_name_or_path = "Qwen/Qwen1.5-110B-Chat" if model_name_or_path.endswith("-hjpark"): model_name_or_path = model_name_or_path.replace("-hjpark", "") return model_name_or_path if os.path.exists(filepath) and False: eval_df = pd.read_pickle(filepath) else: # Process the df raw_filepath = base_dir / f"eval_by_{eval_model_name}.csv" eval_df = pd.read_csv(raw_filepath) eval_df["model_name_or_path"] = eval_df["model_name"].apply(lambda x: change_model_name(x)) eval_df.drop(columns=["model_name"], inplace=True) eval_df["model_params"] = eval_df["model_name_or_path"].apply( lambda x: MODEL_MAPPING.get(x, [None, "Unknown"])[0] ) eval_df["model_type"] = eval_df["model_name_or_path"].apply( lambda x: MODEL_MAPPING.get(x, [None, "Unknown"])[1] ) capabilities = [ "grounding", "instruction_following", "planning", "reasoning", "refinement", "safety", "theory_of_mind", "tool_usage", "multilingual", ] # Make the average of the capabilities eval_df["average"] = eval_df[capabilities[:-1]].mean(axis=1) # Round to 3 decimal places for capabilities and average eval_df = eval_df.round( { "average": 3, "grounding": 3, "instruction_following": 3, "planning": 3, "reasoning": 3, "refinement": 3, "safety": 3, "theory_of_mind": 3, "tool_usage": 3, "multilingual": 3, } ) # print(eval_df[eval_df['model_params'] == 'Unknown']) eval_df.rename(columns=BGB_COLUMNS_MAPPING, inplace=True) eval_df.sort_values( by=BGB_SORTING_COLUMNS, ascending=False, inplace=True, ) eval_df.to_pickle(str(filepath)) eval_df.to_csv(str(csv_filepath), index=False) # import pdb; pdb.set_trace() return eval_df if __name__ == "__main__": get_eval_df("gpt-4-turbo-2024-04-09") get_eval_df("prometheus-bgb-8x7b-v2.0")