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import gradio as gr
import pandas as pd
from hub_utils import check_for_discussion, report_results
from model_utils import calculate_memory, get_model
from huggingface_hub.utils import HfHubHTTPError
# We need to store them as globals because gradio doesn't have a way for us to pass them in to the button
MODEL = None
def get_results(model_name: str, library: str, precision: list, training: list, access_token: str, zero_stage: int, num_nodes: int, num_gpus: int, offloading: list, zero_init: list, additional_buffer_factor: float):
global MODEL
MODEL = get_model(model_name, library, access_token)
try:
has_discussion = check_for_discussion(model_name)
except HfHubHTTPError:
has_discussion = True
options = {
"precision": precision,
"zero_stage": zero_stage,
"cpu_offload": True if "Optimizer" in offloading else False,
"cpu_offload_params": True if "Parameters" in offloading else False,
"zero_init": True if "zero.Init" in zero_init else False,
"num_nodes": num_nodes,
"num_gpus_per_node": num_gpus,
"training_regime": training,
"additional_buffer_factor": additional_buffer_factor
}
data = calculate_memory(MODEL, options)
title = f"## Memory usage for '{model_name}'"
return [title, gr.update(visible=True, value=pd.DataFrame(data)), gr.update(visible=not has_discussion)]
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown(
"""<img src="https://huggingface.co/spaces/andstor/deepspeed-model-memory-usage/resolve/main/measure_model_size_deepspeed.svg" style="float: left;" width="250" height="250"><h1>🤗 DeepSpeed Model Memory Calculator</h1>
This tool will help you calculate how much memory is required for the various Zero Redundancy Optimizer (ZeRO), given a model hosted on the 🤗 Hugging Face Hub and a hardware setup. The optimizer states assume that Adam is used.
To use this tool pass in the URL or model name of the model you want to calculate the memory usage for,
select which framework it originates from ("auto" will try and detect it from the model metadata), and
what precisions you want to use. Then select the select the desired ZeRO configuration."""
)
out_text = gr.Markdown()
out = gr.DataFrame(
headers=["dtype", "Largest Layer", "Total Size", "Training using Adam"],
interactive=False,
visible=False,
)
with gr.Row():
inp = gr.Textbox(label="Model Name or URL", value="bert-base-cased")
with gr.Row():
library = gr.Radio(["auto", "transformers", "timm"], label="Library", value="auto")
precision = gr.CheckboxGroup(
["float32", "float16/bfloat16"],
value="float32",
label="Model Precision",
)
training = gr.Radio(
["Mixed precision", "Single precision"],
value="Mixed precision",
label="Training Paradigm",
)
access_token = gr.Textbox(label="API Token", placeholder="Optional (for gated models)")
num_gpus = gr.Number(label="GPUs per node", value=4, minimum=1, step=1)
num_nodes = gr.Number(label="Nodes", value=1, minimum=1, step=1)
with gr.Column(variant="panel"):
with gr.Row(equal_height=True):
zero_stage = gr.Radio(["Stage 0", "Stage 1", "Stage 2", "Stage 3"], label="ZeRO Stage", value="Stage 3", type="index")
zero_description = gr.CheckboxGroup(["Optimizer state", "Gradients", "Parameters"], label="Partitioning", value=["Optimizer state", "Gradients", "Parameters"], interactive=False)
with gr.Row(equal_height=True):
#with gr.Column():
offloading = gr.CheckboxGroup(["Optimizer", "Parameters"], label="ZeRO-Offload", info="Offloading data and compute to CPU", value=["Optimizer", "Parameters"])
zero_init = gr.CheckboxGroup(["zero.Init"], value=["zero.Init"], label="Initialization")
#with gr.Column():
additional_buffer_factor = gr.Number(label="Additional Buffer Factor", value=1.5, minimum=1, step=0.1)
with gr.Row():
btn = gr.Button("Calculate Memory Usage")
post_to_hub = gr.Button(
value="Report results in this model repo's discussions!\n(Will open in a new tab)", visible=False
)
def change_zero_settings(evt: gr.SelectData): # SelectData is a subclass of EventData
if evt.index == 0:
return [gr.update(visible = False), gr.update(visible = False)]
if evt.index == 1 or evt.index == 2:
return [gr.update(choices=["Optimizer"], visible=True), gr.update(visible = False)]
if evt.index == 3:
return [gr.update(choices=["Optimizer", "Parameters"], visible=True), gr.update(visible = True)]
def change_zero_description(evt: gr.SelectData): # SelectData is a subclass of EventData
if evt.index == 0:
return gr.update(value=None)
if evt.index == 1:
return gr.update(value=["Optimizer state"])
if evt.index == 2:
return gr.update(value=["Optimizer state", "Gradients"])
if evt.index == 3:
return gr.update(value=["Optimizer state", "Gradients", "Parameters"])
def change_offloading(evt: gr.SelectData, zero_stage): # SelectData is a subclass of EventData
if evt.value == "Optimizer" and evt.selected == False:
return gr.CheckboxGroup.update(choices=["Optimizer"], value=[])
if evt.value == "Optimizer" and evt.selected == True:
if zero_stage in [1, 2]:
return gr.CheckboxGroup.update(choices=["Optimizer"], value=["Optimizer"])
elif zero_stage == 3:
return gr.CheckboxGroup.update(choices=["Optimizer", "Parameters"], value=["Optimizer"])
if evt.value == "Parameters" and evt.selected == False:
return gr.CheckboxGroup.update(value=["Optimizer"])
if evt.value == "Parameters" and evt.selected == True:
return gr.CheckboxGroup.update(value=["Optimizer", "Parameters"])
zero_stage.select(change_zero_settings, None, [offloading, zero_init])
zero_stage.select(change_zero_description, None, zero_description)
offloading.select(change_offloading, zero_stage, offloading)
btn.click(
get_results,
inputs=[inp, library, precision, training, access_token, zero_stage, num_nodes, num_gpus, offloading, zero_init, additional_buffer_factor],
outputs=[out_text, out, post_to_hub],
)
post_to_hub.click(lambda: gr.Button.update(visible=False), outputs=post_to_hub).then(
report_results, inputs=[inp, library, access_token]
)
demo.launch()
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