Spaces:
Running
Running
add intel CPU to leaderboard
#32
by
baptistecolle
HF staff
- opened
- .gitignore +2 -0
- app.py +20 -16
- hardware.yml +46 -0
- src/hardware.py +26 -0
- src/llm_perf.py +7 -5
- src/panel.py +39 -39
.gitignore
CHANGED
@@ -4,5 +4,7 @@ __pycache__/
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*ipynb
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.vscode/
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dataset/
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.venv
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*ipynb
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.vscode/
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+
work-in-progress/
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+
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dataset/
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.venv
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app.py
CHANGED
@@ -4,6 +4,7 @@ from src.assets import custom_css
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# from src.attention import create_attn_plots
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from src.content import ABOUT, CITATION_BUTTON, CITATION_BUTTON_LABEL, LOGO, TITLE
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from src.leaderboard import create_leaderboard_table
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from src.llm_perf import get_llm_perf_df
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from src.map import create_lat_score_mem_plot
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@@ -13,14 +14,7 @@ from src.panel import (
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create_select_callback,
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)
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-
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-
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-
MACHINE_TO_HARDWARE = {
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"1xA10": "A10-24GB-150W 🖥️",
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"1xA100": "A100-80GB-275W 🖥️",
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"1xT4": "T4-16GB-70W 🖥️",
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# "1xH100": "H100-80GB-700W 🖥️",
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}
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demo = gr.Blocks(css=custom_css)
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@@ -29,12 +23,19 @@ with demo:
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gr.HTML(TITLE, elem_classes="title")
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####################### HARDWARE TABS #######################
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with gr.Tabs(elem_classes="tabs"):
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-
for id,
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with gr.TabItem(
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-
#######################
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(
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filter_button,
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machine_textbox,
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score_slider,
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memory_slider,
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backend_checkboxes,
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@@ -42,17 +43,18 @@ with demo:
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optimization_checkboxes,
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quantization_checkboxes,
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kernels_checkboxes,
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-
) = create_control_panel(machine=machine)
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####################### HARDWARE SUBTABS #######################
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with gr.Tabs(elem_classes="subtabs"):
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open_llm_perf_df = get_llm_perf_df(machine=machine)
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####################### LEADERBOARD TAB #######################
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with gr.TabItem("Leaderboard 🏅", id=0):
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search_bar, columns_checkboxes, leaderboard_table = (
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create_leaderboard_table(open_llm_perf_df)
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)
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-
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-
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###################### ATTENTIONS SPEEDUP TAB #######################
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# with gr.TabItem("Attention 📈", id=2):
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# attn_prefill_plot, attn_decode_plot = create_attn_plots(
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@@ -69,6 +71,7 @@ with demo:
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filter_button,
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# inputs
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machine_textbox,
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score_slider,
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memory_slider,
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backend_checkboxes,
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@@ -91,6 +94,7 @@ with demo:
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create_select_callback(
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# inputs
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machine_textbox,
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# interactive
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columns_checkboxes,
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search_bar,
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)
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####################### ABOUT TAB #######################
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with gr.TabItem("About 📖", id=
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gr.Markdown(ABOUT, elem_classes="descriptive-text")
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####################### CITATION
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with gr.Row():
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# from src.attention import create_attn_plots
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from src.content import ABOUT, CITATION_BUTTON, CITATION_BUTTON_LABEL, LOGO, TITLE
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+
from src.hardware import load_hardware_configs
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from src.leaderboard import create_leaderboard_table
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from src.llm_perf import get_llm_perf_df
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from src.map import create_lat_score_mem_plot
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create_select_callback,
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)
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configs = load_hardware_configs("hardware.yml")
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demo = gr.Blocks(css=custom_css)
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gr.HTML(TITLE, elem_classes="title")
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####################### HARDWARE TABS #######################
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with gr.Tabs(elem_classes="tabs"):
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for id, config in enumerate(configs):
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with gr.TabItem(config.description, id=id):
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####################### HARDWARE DETAILS #######################
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if config.detail:
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gr.Markdown(config.detail, elem_classes="descriptive-text")
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# ####################### CONTROL PANEL #######################
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(
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filter_button,
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machine_textbox,
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subsets_values,
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score_slider,
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memory_slider,
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backend_checkboxes,
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optimization_checkboxes,
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quantization_checkboxes,
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kernels_checkboxes,
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) = create_control_panel(machine=config.machine, subsets=config.subsets, hardware_provider=config.hardware_provider)
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####################### HARDWARE SUBTABS #######################
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with gr.Tabs(elem_classes="subtabs"):
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open_llm_perf_df = get_llm_perf_df(machine=config.machine, subsets=config.subsets)
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####################### LEADERBOARD TAB #######################
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with gr.TabItem("Leaderboard 🏅", id=0):
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search_bar, columns_checkboxes, leaderboard_table = (
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create_leaderboard_table(open_llm_perf_df)
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)
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if config.hardware_provider != "intel": # TODO intel CPU does not measure the memory requirements correctly, so disable the graph feature until we fix the underlying issue
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with gr.TabItem("Find Your Best Model 🧭", id=1):
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lat_score_mem_plot = create_lat_score_mem_plot(open_llm_perf_df)
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###################### ATTENTIONS SPEEDUP TAB #######################
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# with gr.TabItem("Attention 📈", id=2):
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# attn_prefill_plot, attn_decode_plot = create_attn_plots(
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filter_button,
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# inputs
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machine_textbox,
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+
subsets_values,
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score_slider,
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memory_slider,
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backend_checkboxes,
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create_select_callback(
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# inputs
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machine_textbox,
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subsets_values,
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# interactive
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columns_checkboxes,
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search_bar,
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)
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####################### ABOUT TAB #######################
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with gr.TabItem("About 📖", id=len(configs)):
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gr.Markdown(ABOUT, elem_classes="descriptive-text")
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####################### CITATION
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with gr.Row():
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hardware.yml
ADDED
@@ -0,0 +1,46 @@
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- machine: 1xA10
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description: A10-24GB-150W 🖥️
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hardware_provider: nvidia
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hardware_type: gpu
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subsets:
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- unquantized
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- awq
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- bnb
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- gptq
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backends:
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- pytorch
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- machine: 1xA100
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description: A100-80GB-275W 🖥️
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hardware_provider: nvidia
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hardware_type: gpu
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subsets:
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- unquantized
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- awq
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- bnb
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- gptq
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backends:
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- pytorch
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- machine: 1xT4
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description: T4-16GB-70W 🖥️
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hardware_provider: nvidia
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hardware_type: gpu
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subsets:
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- unquantized
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- awq
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- bnb
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- gptq
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backends:
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- pytorch
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- machine: 32vCPU-C7i
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description: Intel-Xeon-SPR-385W 🖥️
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detail: |
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We tested the [32vCPU AWS C7i](https://aws.amazon.com/ec2/instance-types/c7i/) instance for the benchmark.
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hardware_provider: intel
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hardware_type: cpu
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subsets:
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- unquantized
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backends:
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- pytorch
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src/hardware.py
ADDED
@@ -0,0 +1,26 @@
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from typing import Any, Dict, List
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import yaml
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class HardwareConfig:
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def __init__(self, data: Dict[str, Any]):
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self.machine = data["machine"]
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self.description = data["description"]
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self.hardware_provider = data["hardware_provider"]
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self.hardware_type = data["hardware_type"]
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self.subsets = data["subsets"]
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self.backends = data["backends"]
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self.detail = data.get("detail", None)
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def __repr__(self):
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return (
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f"HardwareConfig(machine='{self.machine}', description='{self.description}', "
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f"hardware_provider={self.hardware_provider}, hardware_type={self.hardware_type}, subsets={self.subsets}, backends={self.backends})"
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)
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def load_hardware_configs(file_path: str) -> List[HardwareConfig]:
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with open(file_path, "r") as file:
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data = yaml.safe_load(file)
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return [HardwareConfig(config) for config in data]
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src/llm_perf.py
CHANGED
@@ -1,7 +1,10 @@
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import os
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import pandas as pd
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from .utils import process_kernels, process_quantizations
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DATASET_DIRECTORY = "dataset"
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@@ -28,13 +31,12 @@ COLUMNS_MAPPING = {
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"#Params (B)": "Params (B)",
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}
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SORTING_COLUMNS = ["Open LLM Score (%)", "Decode (tokens/s)", "Prefill (s)"]
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-
SUBSETS = ["unquantized", "awq", "bnb", "gptq"]
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SORTING_ASCENDING = [False, True, False]
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-
def get_raw_llm_perf_df(machine: str
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dfs = []
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for subset in
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try:
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dfs.append(
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pd.read_csv(
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@@ -110,14 +112,14 @@ def processed_llm_perf_df(llm_perf_df):
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return llm_perf_df
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-
def get_llm_perf_df(machine: str
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if not os.path.exists(DATASET_DIRECTORY):
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os.makedirs(DATASET_DIRECTORY)
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if os.path.exists(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv"):
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llm_perf_df = pd.read_csv(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv")
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else:
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-
llm_perf_df = get_raw_llm_perf_df(machine)
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llm_perf_df = processed_llm_perf_df(llm_perf_df)
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llm_perf_df.to_csv(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv", index=False)
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import os
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from typing import List
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import pandas as pd
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from src.hardware import HardwareConfig
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from .utils import process_kernels, process_quantizations
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DATASET_DIRECTORY = "dataset"
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"#Params (B)": "Params (B)",
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}
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SORTING_COLUMNS = ["Open LLM Score (%)", "Decode (tokens/s)", "Prefill (s)"]
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SORTING_ASCENDING = [False, True, False]
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def get_raw_llm_perf_df(machine: str, subsets: List[str]):
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dfs = []
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for subset in subsets:
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try:
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dfs.append(
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pd.read_csv(
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return llm_perf_df
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def get_llm_perf_df(machine: str, subsets: List[str]):
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if not os.path.exists(DATASET_DIRECTORY):
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os.makedirs(DATASET_DIRECTORY)
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if os.path.exists(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv"):
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llm_perf_df = pd.read_csv(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv")
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else:
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llm_perf_df = get_raw_llm_perf_df(machine, subsets)
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llm_perf_df = processed_llm_perf_df(llm_perf_df)
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llm_perf_df.to_csv(f"{DATASET_DIRECTORY}/llm-perf-leaderboard-{machine}.csv", index=False)
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src/panel.py
CHANGED
@@ -1,3 +1,5 @@
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import gradio as gr
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from src.leaderboard import get_leaderboard_df
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from src.map import get_lat_score_mem_fig
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-
def create_control_panel(machine: str):
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# controls
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machine_textbox = gr.Textbox(value=machine, visible=False)
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with gr.Accordion("Control Panel 🎛️", open=False, elem_id="control-panel"):
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with gr.Row():
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with gr.Column(scale=2, variant="panel"):
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@@ -32,8 +51,8 @@ def create_control_panel(machine: str):
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with gr.Column(scale=1, variant="panel"):
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backend_checkboxes = gr.CheckboxGroup(
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label="Backends 🏭",
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-
choices=
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value=
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info="☑️ Select the backends",
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elem_id="backend-checkboxes",
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)
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@@ -49,8 +68,8 @@ def create_control_panel(machine: str):
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with gr.Column(scale=1, variant="panel"):
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optimization_checkboxes = gr.CheckboxGroup(
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label="Attentions 👁️",
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choices=
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value=
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info="☑️ Select the optimization",
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elem_id="optimization-checkboxes",
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)
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@@ -58,20 +77,8 @@ def create_control_panel(machine: str):
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with gr.Column(scale=1, variant="panel"):
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quantization_checkboxes = gr.CheckboxGroup(
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label="Quantizations 🗜️",
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choices=
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-
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"BnB.4bit",
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"BnB.8bit",
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"AWQ.4bit",
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"GPTQ.4bit",
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],
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value=[
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"Unquantized",
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"BnB.4bit",
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"BnB.8bit",
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"AWQ.4bit",
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"GPTQ.4bit",
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],
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info="☑️ Select the quantization schemes",
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elem_id="quantization-checkboxes",
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elem_classes="boxed-option",
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@@ -79,20 +86,8 @@ def create_control_panel(machine: str):
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with gr.Column(scale=1, variant="panel"):
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kernels_checkboxes = gr.CheckboxGroup(
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label="Kernels ⚛️",
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choices=
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"GPTQ.ExllamaV1",
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"GPTQ.ExllamaV2",
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"AWQ.GEMM",
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"AWQ.GEMV",
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],
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value=[
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"No Kernel",
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"GPTQ.ExllamaV1",
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"GPTQ.ExllamaV2",
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"AWQ.GEMM",
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"AWQ.GEMV",
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],
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info="☑️ Select the custom kernels",
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elem_id="kernel-checkboxes",
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elem_classes="boxed-option",
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@@ -107,6 +102,7 @@ def create_control_panel(machine: str):
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return (
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filter_button,
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machine_textbox,
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score_slider,
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memory_slider,
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backend_checkboxes,
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@@ -119,6 +115,7 @@ def create_control_panel(machine: str):
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|
120 |
def filter_rows_fn(
|
121 |
machine,
|
|
|
122 |
# inputs
|
123 |
score,
|
124 |
memory,
|
@@ -131,7 +128,7 @@ def filter_rows_fn(
|
|
131 |
columns,
|
132 |
search,
|
133 |
):
|
134 |
-
llm_perf_df = get_llm_perf_df(machine=machine)
|
135 |
# print(attentions)
|
136 |
# print(llm_perf_df["Attention 👁️"].unique())
|
137 |
filtered_llm_perf_df = llm_perf_df[
|
@@ -145,7 +142,7 @@ def filter_rows_fn(
|
|
145 |
& (llm_perf_df["Memory (MB)"] <= memory)
|
146 |
]
|
147 |
selected_filtered_llm_perf_df = select_columns_fn(
|
148 |
-
machine, columns, search, filtered_llm_perf_df
|
149 |
)
|
150 |
selected_filtered_lat_score_mem_fig = get_lat_score_mem_fig(filtered_llm_perf_df)
|
151 |
# filtered_bt_prefill_fig = get_bt_prefill_fig(filtered_df)
|
@@ -172,6 +169,7 @@ def create_control_callback(
|
|
172 |
filter_button,
|
173 |
# fixed
|
174 |
machine_textbox,
|
|
|
175 |
# inputs
|
176 |
score_slider,
|
177 |
memory_slider,
|
@@ -198,6 +196,7 @@ def create_control_callback(
|
|
198 |
inputs=[
|
199 |
# fixed
|
200 |
machine_textbox,
|
|
|
201 |
# inputs
|
202 |
score_slider,
|
203 |
memory_slider,
|
@@ -223,9 +222,9 @@ def create_control_callback(
|
|
223 |
)
|
224 |
|
225 |
|
226 |
-
def select_columns_fn(machine, columns, search, llm_perf_df=None):
|
227 |
if llm_perf_df is None:
|
228 |
-
llm_perf_df = get_llm_perf_df(machine=machine)
|
229 |
|
230 |
selected_leaderboard_df = get_leaderboard_df(llm_perf_df)
|
231 |
selected_leaderboard_df = selected_leaderboard_df[
|
@@ -239,6 +238,7 @@ def select_columns_fn(machine, columns, search, llm_perf_df=None):
|
|
239 |
def create_select_callback(
|
240 |
# fixed
|
241 |
machine_textbox,
|
|
|
242 |
# interactive
|
243 |
columns_checkboxes,
|
244 |
search_bar,
|
@@ -247,11 +247,11 @@ def create_select_callback(
|
|
247 |
):
|
248 |
columns_checkboxes.change(
|
249 |
fn=select_columns_fn,
|
250 |
-
inputs=[machine_textbox, columns_checkboxes, search_bar],
|
251 |
outputs=[leaderboard_table],
|
252 |
)
|
253 |
search_bar.change(
|
254 |
fn=select_columns_fn,
|
255 |
-
inputs=[machine_textbox, columns_checkboxes, search_bar],
|
256 |
outputs=[leaderboard_table],
|
257 |
)
|
|
|
1 |
+
from typing import List
|
2 |
+
|
3 |
import gradio as gr
|
4 |
|
5 |
from src.leaderboard import get_leaderboard_df
|
|
|
10 |
from src.map import get_lat_score_mem_fig
|
11 |
|
12 |
|
13 |
+
def create_control_panel(machine: str, subsets: List[str], hardware_provider: str):
|
14 |
# controls
|
15 |
machine_textbox = gr.Textbox(value=machine, visible=False)
|
16 |
+
subsets_values = gr.State(value=subsets)
|
17 |
+
|
18 |
+
|
19 |
+
if hardware_provider == "nvidia":
|
20 |
+
backends = ["pytorch"]
|
21 |
+
attention_implementations = ["Eager", "SDPA", "FAv2"]
|
22 |
+
quantizations = ["Unquantized", "BnB.4bit", "BnB.8bit", "AWQ.4bit", "GPTQ.4bit"]
|
23 |
+
kernels = ["No Kernel", "GPTQ.ExllamaV1", "GPTQ.ExllamaV2", "AWQ.GEMM", "AWQ.GEMV"]
|
24 |
+
elif hardware_provider == "intel":
|
25 |
+
backends = ["pytorch", "onnxruntime", "openvino"]
|
26 |
+
attention_implementations = ["Eager"]
|
27 |
+
quantizations = ["Unquantized"]
|
28 |
+
kernels = ["No Kernel"]
|
29 |
+
else:
|
30 |
+
raise ValueError(f"Unknown hardware provider: {hardware_provider}")
|
31 |
+
|
32 |
+
|
33 |
with gr.Accordion("Control Panel 🎛️", open=False, elem_id="control-panel"):
|
34 |
with gr.Row():
|
35 |
with gr.Column(scale=2, variant="panel"):
|
|
|
51 |
with gr.Column(scale=1, variant="panel"):
|
52 |
backend_checkboxes = gr.CheckboxGroup(
|
53 |
label="Backends 🏭",
|
54 |
+
choices=backends,
|
55 |
+
value=backends,
|
56 |
info="☑️ Select the backends",
|
57 |
elem_id="backend-checkboxes",
|
58 |
)
|
|
|
68 |
with gr.Column(scale=1, variant="panel"):
|
69 |
optimization_checkboxes = gr.CheckboxGroup(
|
70 |
label="Attentions 👁️",
|
71 |
+
choices=attention_implementations,
|
72 |
+
value=attention_implementations,
|
73 |
info="☑️ Select the optimization",
|
74 |
elem_id="optimization-checkboxes",
|
75 |
)
|
|
|
77 |
with gr.Column(scale=1, variant="panel"):
|
78 |
quantization_checkboxes = gr.CheckboxGroup(
|
79 |
label="Quantizations 🗜️",
|
80 |
+
choices=quantizations,
|
81 |
+
value=quantizations,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
82 |
info="☑️ Select the quantization schemes",
|
83 |
elem_id="quantization-checkboxes",
|
84 |
elem_classes="boxed-option",
|
|
|
86 |
with gr.Column(scale=1, variant="panel"):
|
87 |
kernels_checkboxes = gr.CheckboxGroup(
|
88 |
label="Kernels ⚛️",
|
89 |
+
choices=kernels,
|
90 |
+
value=kernels,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
91 |
info="☑️ Select the custom kernels",
|
92 |
elem_id="kernel-checkboxes",
|
93 |
elem_classes="boxed-option",
|
|
|
102 |
return (
|
103 |
filter_button,
|
104 |
machine_textbox,
|
105 |
+
subsets_values,
|
106 |
score_slider,
|
107 |
memory_slider,
|
108 |
backend_checkboxes,
|
|
|
115 |
|
116 |
def filter_rows_fn(
|
117 |
machine,
|
118 |
+
subsets,
|
119 |
# inputs
|
120 |
score,
|
121 |
memory,
|
|
|
128 |
columns,
|
129 |
search,
|
130 |
):
|
131 |
+
llm_perf_df = get_llm_perf_df(machine=machine, subsets=subsets)
|
132 |
# print(attentions)
|
133 |
# print(llm_perf_df["Attention 👁️"].unique())
|
134 |
filtered_llm_perf_df = llm_perf_df[
|
|
|
142 |
& (llm_perf_df["Memory (MB)"] <= memory)
|
143 |
]
|
144 |
selected_filtered_llm_perf_df = select_columns_fn(
|
145 |
+
machine, subsets, columns, search, filtered_llm_perf_df
|
146 |
)
|
147 |
selected_filtered_lat_score_mem_fig = get_lat_score_mem_fig(filtered_llm_perf_df)
|
148 |
# filtered_bt_prefill_fig = get_bt_prefill_fig(filtered_df)
|
|
|
169 |
filter_button,
|
170 |
# fixed
|
171 |
machine_textbox,
|
172 |
+
subsets_textbox,
|
173 |
# inputs
|
174 |
score_slider,
|
175 |
memory_slider,
|
|
|
196 |
inputs=[
|
197 |
# fixed
|
198 |
machine_textbox,
|
199 |
+
subsets_textbox,
|
200 |
# inputs
|
201 |
score_slider,
|
202 |
memory_slider,
|
|
|
222 |
)
|
223 |
|
224 |
|
225 |
+
def select_columns_fn(machine, subsets, columns, search, llm_perf_df=None):
|
226 |
if llm_perf_df is None:
|
227 |
+
llm_perf_df = get_llm_perf_df(machine=machine, subsets=subsets)
|
228 |
|
229 |
selected_leaderboard_df = get_leaderboard_df(llm_perf_df)
|
230 |
selected_leaderboard_df = selected_leaderboard_df[
|
|
|
238 |
def create_select_callback(
|
239 |
# fixed
|
240 |
machine_textbox,
|
241 |
+
subsets_values,
|
242 |
# interactive
|
243 |
columns_checkboxes,
|
244 |
search_bar,
|
|
|
247 |
):
|
248 |
columns_checkboxes.change(
|
249 |
fn=select_columns_fn,
|
250 |
+
inputs=[machine_textbox, subsets_values, columns_checkboxes, search_bar],
|
251 |
outputs=[leaderboard_table],
|
252 |
)
|
253 |
search_bar.change(
|
254 |
fn=select_columns_fn,
|
255 |
+
inputs=[machine_textbox, subsets_values, columns_checkboxes, search_bar],
|
256 |
outputs=[leaderboard_table],
|
257 |
)
|