|
import gradio as gr |
|
|
|
from ecologits.tracers.utils import compute_llm_impacts, _avg |
|
from ecologits.impacts.llm import compute_llm_impacts as compute_llm_impacts_expert |
|
from ecologits.impacts.llm import IF_ELECTRICITY_MIX_GWP, IF_ELECTRICITY_MIX_ADPE, IF_ELECTRICITY_MIX_PE |
|
from ecologits.model_repository import models |
|
|
|
from src.assets import custom_css |
|
from src.electricity_mix import COUNTRY_CODES, find_electricity_mix |
|
from src.content import ( |
|
HERO_TEXT, |
|
ABOUT_TEXT, |
|
CITATION_LABEL, |
|
CITATION_TEXT, |
|
LICENCE_TEXT, METHODOLOGY_TEXT |
|
) |
|
from src.constants import ( |
|
PROVIDERS, |
|
OPENAI_MODELS, |
|
ANTHROPIC_MODELS, |
|
COHERE_MODELS, |
|
META_MODELS, |
|
MISTRALAI_MODELS, |
|
PROMPTS, |
|
CLOSED_SOURCE_MODELS, |
|
MODELS, |
|
) |
|
from src.utils import ( |
|
format_impacts, |
|
format_energy_eq_physical_activity, |
|
PhysicalActivity, |
|
format_energy_eq_electric_vehicle, |
|
format_gwp_eq_streaming, format_energy_eq_electricity_production, EnergyProduction, |
|
format_gwp_eq_airplane_paris_nyc, format_energy_eq_electricity_consumption_ireland |
|
) |
|
|
|
CUSTOM = "Custom" |
|
|
|
|
|
def model_list(provider: str) -> gr.Dropdown: |
|
if provider == "openai": |
|
return gr.Dropdown( |
|
OPENAI_MODELS, |
|
label="Model", |
|
value=OPENAI_MODELS[0][1], |
|
filterable=True, |
|
) |
|
elif provider == "anthropic": |
|
return gr.Dropdown( |
|
ANTHROPIC_MODELS, |
|
label="Model", |
|
value=ANTHROPIC_MODELS[0][1], |
|
filterable=True, |
|
) |
|
elif provider == "cohere": |
|
return gr.Dropdown( |
|
COHERE_MODELS, |
|
label="Model", |
|
value=COHERE_MODELS[0][1], |
|
filterable=True, |
|
) |
|
elif provider == "huggingface_hub/meta": |
|
return gr.Dropdown( |
|
META_MODELS, |
|
label="Model", |
|
value=META_MODELS[0][1], |
|
filterable=True, |
|
) |
|
elif provider == "mistralai": |
|
return gr.Dropdown( |
|
MISTRALAI_MODELS, |
|
label="Model", |
|
value=MISTRALAI_MODELS[0][1], |
|
filterable=True, |
|
) |
|
|
|
|
|
def custom(): |
|
return CUSTOM |
|
|
|
|
|
def model_active_params_fn(model_name: str, n_param: float): |
|
if model_name == CUSTOM: |
|
return n_param |
|
provider, model_name = model_name.split('/', 1) |
|
model = models.find_model(provider=provider, model_name=model_name) |
|
return model.active_parameters or _avg(model.active_parameters_range) |
|
|
|
|
|
def model_total_params_fn(model_name: str, n_param: float): |
|
if model_name == CUSTOM: |
|
return n_param |
|
provider, model_name = model_name.split('/', 1) |
|
model = models.find_model(provider=provider, model_name=model_name) |
|
return model.total_parameters or _avg(model.total_parameters_range) |
|
|
|
|
|
def mix_fn(country_code: str, mix_adpe: float, mix_pe: float, mix_gwp: float): |
|
if country_code == CUSTOM: |
|
return mix_adpe, mix_pe, mix_gwp |
|
return find_electricity_mix(country_code) |
|
|
|
|
|
with gr.Blocks(css=custom_css) as demo: |
|
gr.Markdown(HERO_TEXT) |
|
|
|
with gr.Tab("🧮 Calculator"): |
|
with gr.Row(): |
|
gr.Markdown("# Estimate the environmental impacts of LLM inference") |
|
with gr.Row(): |
|
input_provider = gr.Dropdown( |
|
PROVIDERS, |
|
label="Provider", |
|
value=PROVIDERS[0][1], |
|
filterable=True, |
|
) |
|
|
|
input_model = gr.Dropdown( |
|
OPENAI_MODELS, |
|
label="Model", |
|
value=OPENAI_MODELS[0][1], |
|
filterable=True, |
|
) |
|
input_provider.change(model_list, input_provider, input_model) |
|
|
|
input_prompt = gr.Dropdown( |
|
PROMPTS, |
|
label="Example prompt", |
|
value=400, |
|
) |
|
|
|
|
|
@gr.render(inputs=[input_provider, input_model, input_prompt]) |
|
def render_simple(provider, model, prompt): |
|
if provider.startswith("huggingface_hub"): |
|
provider = provider.split("/")[0] |
|
if models.find_model(provider, model) is not None: |
|
impacts = compute_llm_impacts( |
|
provider=provider, |
|
model_name=model, |
|
output_token_count=prompt, |
|
request_latency=100000 |
|
) |
|
impacts = format_impacts(impacts) |
|
|
|
|
|
with gr.Blocks(): |
|
if f"{provider}/{model}" in CLOSED_SOURCE_MODELS: |
|
with gr.Row(): |
|
gr.Markdown("""<p> ⚠️ You have selected a closed-source model. Please be aware that |
|
some providers do not fully disclose information about such models. Consequently, our |
|
estimates have a lower precision for closed-source models. For further details, refer to |
|
our FAQ in the About section. |
|
</p>""", elem_classes="warning-box") |
|
|
|
with gr.Row(): |
|
gr.Markdown(""" |
|
## Environmental impacts |
|
|
|
To understand how the environmental impacts are computed go to the 📖 Methodology tab. |
|
""") |
|
with gr.Row(): |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">⚡️ Energy</h2> |
|
$$ \Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units} $$ |
|
<p align="center"><i>Evaluates the electricity consumption<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">🌍️ GHG Emissions</h2> |
|
$$ \Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units} $$ |
|
<p align="center"><i>Evaluates the effect on global warming<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">🪨 Abiotic Resources</h2> |
|
$$ \Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units} $$ |
|
<p align="center"><i>Evaluates the use of metals and minerals<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">⛽️ Primary Energy</h2> |
|
$$ \Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units} $$ |
|
<p align="center"><i>Evaluates the use of energy resources<i></p><br> |
|
""") |
|
|
|
|
|
with gr.Blocks(): |
|
with gr.Row(): |
|
gr.Markdown(""" |
|
--- |
|
## That's equivalent to... |
|
|
|
Making this request to the LLM is equivalent to the following actions. |
|
""") |
|
with gr.Row(): |
|
physical_activity, distance = format_energy_eq_physical_activity(impacts.energy) |
|
if physical_activity == PhysicalActivity.WALKING: |
|
physical_activity = "🚶 " + physical_activity.capitalize() |
|
if physical_activity == PhysicalActivity.RUNNING: |
|
physical_activity = "🏃 " + physical_activity.capitalize() |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">{physical_activity} $$ \Large {distance.magnitude:.3g}\ {distance.units} $$ </h2> |
|
<p align="center"><i>Based on energy consumption<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
ev_eq = format_energy_eq_electric_vehicle(impacts.energy) |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">🔋 Electric Vehicle $$ \Large {ev_eq.magnitude:.3g}\ {ev_eq.units} $$ </h2> |
|
<p align="center"><i>Based on energy consumption<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
streaming_eq = format_gwp_eq_streaming(impacts.gwp) |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">⏯️ Streaming $$ \Large {streaming_eq.magnitude:.3g}\ {streaming_eq.units} $$ </h2> |
|
<p align="center"><i>Based on GHG emissions<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
|
|
with gr.Blocks(): |
|
with gr.Row(): |
|
gr.Markdown(""" |
|
## What if 1% of the planet does this request everyday for 1 year? |
|
|
|
If this use case is largely deployed around the world the equivalent impacts would be. (The |
|
impacts of this request x 1% of 8 billion people x 365 days in a year.) |
|
""") |
|
with gr.Row(): |
|
electricity_production, count = format_energy_eq_electricity_production(impacts.energy) |
|
if electricity_production == EnergyProduction.NUCLEAR: |
|
emoji = "☢️" |
|
name = "Nuclear power plants" |
|
if electricity_production == EnergyProduction.WIND: |
|
emoji = "💨️ " |
|
name = "Wind turbines" |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">{emoji} $$ \Large {count.magnitude:.0f} $$ {name} <span style="font-size: 12px">(yearly)</span></h2> |
|
<p align="center"><i>Based on electricity consumption<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
ireland_count = format_energy_eq_electricity_consumption_ireland(impacts.energy) |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">🇮🇪 $$ \Large {ireland_count.magnitude:.2g} $$ x Ireland <span style="font-size: 12px">(yearly ⚡️ cons.)</span></h2> |
|
<p align="center"><i>Based on electricity consumption<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
paris_nyc_airplane = format_gwp_eq_airplane_paris_nyc(impacts.gwp) |
|
with gr.Column(scale=1, min_width=300): |
|
gr.Markdown(f""" |
|
<h2 align="center">✈️ $$ \Large {paris_nyc_airplane.magnitude:,.0f} $$ Paris ↔ NYC </h2> |
|
<p align="center"><i>Based on GHG emissions<i></p><br> |
|
""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]) |
|
|
|
with gr.Tab("🤓 Expert Mode"): |
|
with gr.Row(): |
|
gr.Markdown("# 🤓 Expert mode") |
|
model = gr.Dropdown( |
|
MODELS + [CUSTOM], |
|
label="Model name", |
|
value="openai/gpt-3.5-turbo", |
|
filterable=True, |
|
interactive=True |
|
) |
|
input_model_active_params = gr.Number( |
|
label="Number of billions of active parameters", |
|
value=45.0, |
|
interactive=True |
|
) |
|
input_model_total_params = gr.Number( |
|
label="Number of billions of total parameters", |
|
value=45.0, |
|
interactive=True |
|
) |
|
|
|
model.change(fn=model_active_params_fn, |
|
inputs=[model, input_model_active_params], |
|
outputs=[input_model_active_params]) |
|
model.change(fn=model_total_params_fn, |
|
inputs=[model, input_model_total_params], |
|
outputs=[input_model_total_params]) |
|
input_model_active_params.input(fn=custom, outputs=[model]) |
|
input_model_total_params.input(fn=custom, outputs=[model]) |
|
|
|
input_tokens = gr.Number( |
|
label="Output tokens", |
|
value=100 |
|
) |
|
|
|
mix = gr.Dropdown( |
|
COUNTRY_CODES + [CUSTOM], |
|
label="Location", |
|
value="WOR", |
|
filterable=True, |
|
interactive=True |
|
) |
|
input_mix_gwp = gr.Number( |
|
label="Electricity mix - GHG emissions [kgCO2eq / kWh]", |
|
value=IF_ELECTRICITY_MIX_GWP, |
|
interactive=True |
|
) |
|
input_mix_adpe = gr.Number( |
|
label="Electricity mix - Abiotic resources [kgSbeq / kWh]", |
|
value=IF_ELECTRICITY_MIX_ADPE, |
|
interactive=True |
|
) |
|
input_mix_pe = gr.Number( |
|
label="Electricity mix - Primary energy [MJ / kWh]", |
|
value=IF_ELECTRICITY_MIX_PE, |
|
interactive=True |
|
) |
|
|
|
mix.change(fn=mix_fn, |
|
inputs=[mix, input_mix_adpe, input_mix_pe, input_mix_gwp], |
|
outputs=[input_mix_adpe, input_mix_pe, input_mix_gwp]) |
|
input_mix_gwp.input(fn=custom, outputs=mix) |
|
input_mix_adpe.input(fn=custom, outputs=mix) |
|
input_mix_pe.input(fn=custom, outputs=mix) |
|
|
|
|
|
@gr.render(inputs=[ |
|
input_model_active_params, |
|
input_model_total_params, |
|
input_tokens, |
|
input_mix_gwp, |
|
input_mix_adpe, |
|
input_mix_pe |
|
]) |
|
def render_expert( |
|
model_active_params, |
|
model_total_params, |
|
tokens, |
|
mix_gwp, |
|
mix_adpe, |
|
mix_pe |
|
): |
|
impacts = compute_llm_impacts_expert( |
|
model_active_parameter_count=model_active_params, |
|
model_total_parameter_count=model_total_params, |
|
output_token_count=tokens, |
|
request_latency=100000, |
|
if_electricity_mix_gwp=mix_gwp, |
|
if_electricity_mix_adpe=mix_adpe, |
|
if_electricity_mix_pe=mix_pe |
|
) |
|
impacts = format_impacts(impacts) |
|
|
|
with gr.Blocks(): |
|
with gr.Row(): |
|
gr.Markdown("## Environmental impacts") |
|
with gr.Row(): |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">⚡️ Energy</h2> |
|
$$ \Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units} $$ |
|
<p align="center"><i>Evaluates the electricity consumption<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">🌍️ GHG Emissions</h2> |
|
$$ \Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units} $$ |
|
<p align="center"><i>Evaluates the effect on global warming<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">🪨 Abiotic Resources</h2> |
|
$$ \Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units} $$ |
|
<p align="center"><i>Evaluates the use of metals and minerals<i></p><br> |
|
""") |
|
with gr.Column(scale=1, min_width=220): |
|
gr.Markdown(f""" |
|
<h2 align="center">⛽️ Primary Energy</h2> |
|
$$ \Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units} $$ |
|
<p align="center"><i>Evaluates the use of energy resources<i></p><br> |
|
""") |
|
|
|
with gr.Tab("📖 Methodology"): |
|
gr.Markdown(METHODOLOGY_TEXT, |
|
elem_classes="descriptive-text", |
|
latex_delimiters=[ |
|
{"left": "$$", "right": "$$", "display": True}, |
|
{"left": "$", "right": "$", "display": False} |
|
]) |
|
|
|
with gr.Tab("ℹ️ About"): |
|
gr.Markdown(ABOUT_TEXT, elem_classes="descriptive-text",) |
|
|
|
with gr.Accordion("📚 Citation", open=False): |
|
gr.Textbox( |
|
value=CITATION_TEXT, |
|
label=CITATION_LABEL, |
|
interactive=False, |
|
show_copy_button=True, |
|
lines=len(CITATION_TEXT.split('\n')), |
|
) |
|
|
|
|
|
gr.Markdown(LICENCE_TEXT) |
|
|
|
if __name__ == '__main__': |
|
demo.launch() |
|
|
