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import gradio as gr
from src.llm_perf import get_llm_perf_df
from src.leaderboard import get_leaderboard_df
from src.latency_score_memory import get_lat_score_mem_fig
from src.bettertransformer import get_bt_prefill_fig, get_bt_decode_fig
from src.flashattentionv2 import get_fa2_prefill_fig, get_fa2_decode_fig
def create_control_panel(machine: str = "hf-dgx-01"):
# descriptive text
gr.HTML("Use this control panel to filter this leaderboard.", elem_id="text")
# controls
machine_textbox = gr.Textbox(value=machine, visible=False)
with gr.Row():
with gr.Column():
search_bar = gr.Textbox(
label="Model 🤗",
info="🔍 Search for a model name",
elem_id="search-bar",
)
with gr.Row():
with gr.Column(scale=1):
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):
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.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.Row():
with gr.Column(scale=1):
datatype_checkboxes = gr.CheckboxGroup(
label="DTypes 📥",
choices=["float32", "float16"],
value=["float32", "float16"],
info="☑️ Select the load data types",
elem_id="dtype-checkboxes",
)
with gr.Column(scale=1):
optimization_checkboxes = gr.CheckboxGroup(
label="Optimizations 🛠️",
choices=["None", "BetterTransformer", "FlashAttentionV2"],
value=["None", "BetterTransformer", "FlashAttentionV2"],
info="☑️ Select the optimization",
elem_id="optimization-checkboxes",
)
with gr.Column(scale=1):
quantization_checkboxes = gr.CheckboxGroup(
label="Quantizations 🗜️",
choices=["None", "BnB.4bit", "GPTQ.4bit+ExllamaV1", "GPTQ.4bit+ExllamaV2"],
value=["None", "BnB.4bit", "GPTQ.4bit", "GPTQ.4bit+ExllamaV1", "GPTQ.4bit+ExllamaV2"],
info="☑️ Select the quantization schemes",
elem_id="quantization-checkboxes",
)
with gr.Row():
filter_button = gr.Button(
value="Filter 🚀",
elem_id="filter-button",
)
return (
filter_button,
machine_textbox,
search_bar,
score_slider,
memory_slider,
backend_checkboxes,
datatype_checkboxes,
optimization_checkboxes,
quantization_checkboxes,
)
def filter_fn(
machine,
model,
backends,
datatypes,
optimizations,
quantizations,
score,
memory,
):
raw_df = get_llm_perf_df(machine=machine)
filtered_df = raw_df[
raw_df["Model 🤗"].str.contains(model, case=False)
& raw_df["Backend 🏭"].isin(backends)
& raw_df["DType 📥"].isin(datatypes)
& raw_df["Optimization 🛠️"].isin(optimizations)
& raw_df["Quantization 🗜️"].isin(quantizations)
& (raw_df["Open LLM Score (%)"] >= score)
& (raw_df["Allocated Memory (MB)"] <= memory)
]
filtered_leaderboard_df = get_leaderboard_df(filtered_df)
filtered_lat_score_mem_fig = get_lat_score_mem_fig(filtered_df)
filtered_bt_prefill_fig = get_bt_prefill_fig(filtered_df)
filtered_bt_decode_fig = get_bt_decode_fig(filtered_df)
filtered_fa2_prefill_fig = get_fa2_prefill_fig(filtered_df)
filtered_fa2_decode_fig = get_fa2_decode_fig(filtered_df)
return [
filtered_leaderboard_df,
filtered_lat_score_mem_fig,
filtered_bt_prefill_fig,
filtered_bt_decode_fig,
filtered_fa2_prefill_fig,
filtered_fa2_decode_fig,
]
def create_control_callback(
# button
filter_button,
# inputs
machine_textbox,
search_bar,
score_slider,
memory_slider,
backend_checkboxes,
datatype_checkboxes,
optimization_checkboxes,
quantization_checkboxes,
# outputs
leaderboard_table,
lat_score_mem_plot,
bt_prefill_plot,
bt_decode_plot,
fa2_prefill_plot,
fa2_decode_plot,
):
filter_button.click(
fn=filter_fn,
inputs=[
machine_textbox,
search_bar,
backend_checkboxes,
datatype_checkboxes,
optimization_checkboxes,
quantization_checkboxes,
score_slider,
memory_slider,
],
outputs=[
leaderboard_table,
lat_score_mem_plot,
bt_prefill_plot,
bt_decode_plot,
fa2_prefill_plot,
fa2_decode_plot,
],
)
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