import os import gradio as gr import pandas as pd import plotly.express as px from apscheduler.schedulers.background import BackgroundScheduler from src.assets.css_html_js import custom_css, custom_js from src.assets.text_content import ( TITLE, INTRODUCTION_TEXT, ABOUT_TEXT, EXAMPLE_CONFIG_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, ) from src.utils import ( change_tab, restart_space, load_dataset_repo, process_model_name, process_model_type, ) LLM_PERF_LEADERBOARD_REPO = "optimum/llm-perf-leaderboard" LLM_PERF_DATASET_REPO = "optimum/llm-perf-dataset" OPTIMUM_TOKEN = os.environ.get("OPTIMUM_TOKEN", None) TRUE_WEIGHT_CLASSES = { "6B": "7B", } ALL_COLUMNS_MAPPING = { "model_type": "Type 🤗", "weight_class": "Class 🏋️", # "backend.name": "Backend 🏭", "backend.torch_dtype": "Dtype 📥", "optimizations": "Optimizations 🛠️", # "generate.throughput(tokens/s)": "Throughput (tokens/s) ⬆️", # "forward.peak_memory(MB)": "Peak Memory (MB) ⬇️", # "best_scored_model": "Best Scored Model 🏆", "best_score": "Best Score (%) ⬆️", } ALL_COLUMNS_DATATYPES = [ "str", "str", # "str", "str", "str", # "number", # "number", # "markdown", "number", ] SORTING_COLUMN = ["tradeoff"] llm_perf_dataset_repo = load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN) def get_benchmark_df(benchmark="1xA100-80GB"): if llm_perf_dataset_repo: llm_perf_dataset_repo.git_pull() # load and merge bench_df = pd.read_csv(f"./llm-perf-dataset/reports/{benchmark}.csv") scores_df = pd.read_csv( "./llm-perf-dataset/reports/Weighted+Classed-Open-LLM-Leaderboard.csv" ) bench_df["merge_id"] = bench_df.experiment_name.str.split("_1_1000_").str[-1] scores_df["merge_id"] = scores_df.weight_class + "_" + scores_df.model_type merged_df = bench_df.merge(scores_df, on="merge_id") # fix some weight classes merged_df["weight_class"] = merged_df["weight_class"].apply( lambda x: TRUE_WEIGHT_CLASSES[x] if x in TRUE_WEIGHT_CLASSES else x ) # convert peak memory to int # merged_df["forward.peak_memory(MB)"] = merged_df["forward.peak_memory(MB)"].apply( # lambda x: int(x) # ) # add optimizations merged_df["optimizations"] = merged_df[ ["backend.bettertransformer", "backend.load_in_8bit", "backend.load_in_4bit"] ].apply( lambda x: ", ".join( filter( lambda x: x != "", [ "BetterTransformer" if x[0] == True else "", "LLM.int8" if x[1] == True else "", "LLM.fp4" if x[2] == True else "", ], ), ) if any([x[0] == True, x[1] == True, x[2] == True]) else "None", axis=1, ) merged_df["quantized"] = merged_df["optimizations"].str.contains("LLM.int8|LLM.fp4") # create composite score score_distance = 100 - merged_df["best_score"] # normalize latency between 0 and 100 latency_distance = merged_df["generate.latency(s)"] merged_df["tradeoff"] = (score_distance**2 + latency_distance**2) ** 0.5 merged_df["tradeoff"] = merged_df["tradeoff"].round(2) return merged_df def get_benchmark_table(bench_df): # add * to quantized models score copy_df = bench_df.copy() copy_df["best_score"] = copy_df.apply( lambda x: f"{x['best_score']}**" if x["quantized"] else x["best_score"], axis=1, ) # sort copy_df.sort_values(by=SORTING_COLUMN, ascending=True, inplace=True) # filter copy_df = copy_df[list(ALL_COLUMNS_MAPPING.keys())] # rename copy_df.rename(columns=ALL_COLUMNS_MAPPING, inplace=True) # transform copy_df["Type 🤗"] = copy_df["Type 🤗"].apply(process_model_type) copy_df["Best Scored Model 🏆"] = copy_df["Best Scored Model 🏆"].apply( process_model_name ) return copy_df def get_benchmark_plot(bench_df): fig = px.scatter( bench_df, x="generate.latency(s)", y="best_score", color="model_type", # size="forward.peak_memory(MB)", custom_data=[ "best_scored_model", "backend.name", "backend.torch_dtype", "optimizations", # "forward.peak_memory(MB)", "generate.throughput(tokens/s)", ], color_discrete_sequence=px.colors.qualitative.Light24, ) fig.update_layout( title={ "text": "Model Score vs. Latency", "y": 0.95, "x": 0.5, "xanchor": "center", "yanchor": "top", }, xaxis_title="Per 1000 Tokens Latency (s)", yaxis_title="Open LLM Score (%)", legend_title="Model Type", width=1200, height=600, ) fig.update_traces( hovertemplate="
".join( [ "Model: %{customdata[0]}", "Backend: %{customdata[1]}", "Load Datatype: %{customdata[2]}", "Optimizations: %{customdata[3]}", # "Peak Memory (MB): %{customdata[4]}", "Throughput (tokens/s): %{customdata[4]}", "Per 1000 Tokens Latency (s): %{x}", "Open LLM Score (%): %{y}", ] ) ) return fig def filter_query( text, backends, datatypes, optimizations, score, # memory, benchmark="1xA100-80GB", ): raw_df = get_benchmark_df(benchmark=benchmark) filtered_df = raw_df[ raw_df["best_scored_model"].str.lower().str.contains(text.lower()) & raw_df["backend.name"].isin(backends) & raw_df["backend.torch_dtype"].isin(datatypes) & ( pd.concat( [ raw_df["optimizations"].str.contains(optimization) for optimization in optimizations ], axis=1, ).any(axis="columns") if len(optimizations) > 0 else True ) & (raw_df["best_score"] >= score) # & (raw_df["forward.peak_memory(MB)"] <= memory) ] filtered_table = get_benchmark_table(filtered_df) filtered_plot = get_benchmark_plot(filtered_df) return filtered_table, filtered_plot # # Dataframes # A100_df = get_benchmark_df(benchmark="1xA100-80GB") # A100_table = get_benchmark_table(A100_df) # A100_plot = get_benchmark_plot(A100_df) # Demo interface demo = gr.Blocks(css=custom_css) with demo: # leaderboard title gr.HTML(TITLE) # introduction text gr.Markdown(INTRODUCTION_TEXT, elem_classes="descriptive-text") # maitnenance text gr.HTML( "🚧 This leaderboard is currently under maintenance. 🚧", elem_classes="descriptive-text", ) # # leaderboard tabs # with gr.Tabs(elem_classes="A100-tabs") as A100_tabs: # with gr.TabItem("🖥️ A100-80GB Benchmark 🏆", id=0): # gr.HTML( # "👉 Scroll to the right 👉 for more columns.", elem_id="descriptive-text" # ) # # Original leaderboard table # A100_leaderboard = gr.components.Dataframe( # value=A100_table, # datatype=ALL_COLUMNS_DATATYPES, # headers=list(ALL_COLUMNS_MAPPING.values()), # elem_id="1xA100-table", # ) # with gr.TabItem("🖥️ A100-80GB Plot 📊", id=1): # gr.HTML( # "👆 Hover over the points 👆 for additional information.", # elem_id="descriptive-text", # ) # # Original leaderboard plot # A100_plotly = gr.components.Plot( # value=A100_plot, # elem_id="1xA100-plot", # show_label=False, # ) # with gr.TabItem("Control Panel 🎛️", id=2): # gr.HTML( # "Use this control panel to filter the leaderboard's table and plot.", # elem_id="descriptive-text", # ) # # control panel interface # with gr.Row(): # with gr.Column(scale=1): # search_bar = gr.Textbox( # label="Model 🤗", # info="🔍 Search for a model name", # elem_id="search-bar", # ) # with gr.Column(scale=1): # with gr.Box(): # score_slider = gr.Slider( # label="Open LLM Score 📈", # info="🎚️ Slide to minimum Open LLM score", # value=0, # elem_id="threshold-slider", # ) # # with gr.Column(scale=1): # # with gr.Box(): # # memory_slider = gr.Slider( # # label="Peak Memory (MB) 📈", # # info="🎚️ Slide to maximum Peak Memory", # # minimum=0, # # maximum=80 * 1024, # # value=80 * 1024, # # elem_id="memory-slider", # # ) # with gr.Row(): # with gr.Column(scale=1): # backend_checkboxes = gr.CheckboxGroup( # label="Backends 🏭", # choices=["pytorch", "onnxruntime"], # value=["pytorch", "onnxruntime"], # info="☑️ Select the backends", # elem_id="backend-checkboxes", # ) # with gr.Column(scale=1): # datatype_checkboxes = gr.CheckboxGroup( # label="Dtypes 📥", # choices=["float32", "float16"], # value=["float32", "float16"], # info="☑️ Select the load dtypes", # elem_id="dtype-checkboxes", # ) # with gr.Column(scale=2): # optimizations_checkboxes = gr.CheckboxGroup( # label="Optimizations 🛠️", # choices=["None", "BetterTransformer", "LLM.int8", "LLM.fp4"], # value=["None", "BetterTransformer", "LLM.int8", "LLM.fp4"], # info="☑️ Select the optimizations", # elem_id="optimizations-checkboxes", # ) # with gr.Row(): # filter_button = gr.Button( # value="Filter 🚀", # elem_id="filter-button", # ) # with gr.TabItem("About 📖", id=3): # gr.HTML(ABOUT_TEXT, elem_classes="descriptive-text") # gr.Markdown(EXAMPLE_CONFIG_TEXT, elem_classes="descriptive-text") # demo.load( # change_tab, # A100_tabs, # _js=custom_js, # ) # filter_button.click( # filter_query, # [ # search_bar, # backend_checkboxes, # datatype_checkboxes, # optimizations_checkboxes, # score_slider, # # memory_slider, # ], # [A100_leaderboard, A100_plotly], # ) # with gr.Row(): # with gr.Accordion("📙 Citation", open=False): # citation_button = gr.Textbox( # value=CITATION_BUTTON_TEXT, # label=CITATION_BUTTON_LABEL, # elem_id="citation-button", # ).style(show_copy_button=True) # Restart space every hour scheduler = BackgroundScheduler() scheduler.add_job( restart_space, "interval", seconds=3600, args=[LLM_PERF_LEADERBOARD_REPO, OPTIMUM_TOKEN], ) scheduler.start() # Launch demo demo.queue(concurrency_count=40).launch()