Spaces:
Running
Running
File size: 4,607 Bytes
46ef3d8 590064e 46ef3d8 fd7914e 1f1be44 fd7914e 35e3254 fd7914e 64721de 590064e 17ca086 590064e 17ca086 590064e 17ca086 590064e 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de 35e3254 64721de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import gradio as gr
from backend import get_message_single, get_message_spam, send_single, send_spam
from defaults import (
ADDRESS_BETTERTRANSFORMER,
ADDRESS_VANILLA,
defaults_bt_single,
defaults_bt_spam,
defaults_vanilla_single,
defaults_vanilla_spam,
)
TTILE_IMAGE = """
<div
style="
display: block;
margin-left: auto;
margin-right: auto;
width: 50%;
"
>
<img src="https://huggingface.co/spaces/fxmarty/bettertransformer-demo/resolve/main/header.webp"/>
</div>
"""
TITLE = """
<div
style="
display: inline-flex;
align-items: center;
text-align: center;
max-width: 1400px;
gap: 0.8rem;
font-size: 2.2rem;
"
>
<h1 style="font-weight: 700; margin-bottom: 10px; margin-top: 10px;">
Speed up your inference and support more workload with PyTorch's BetterTransformer 🤗
</h1>
</div>
"""
with gr.Blocks() as demo:
gr.HTML(TTILE_IMAGE)
gr.HTML(TITLE)
gr.Markdown(
"""
Let's try out TorchServe + BetterTransformer!
BetterTransformer is a stable feature made available with [PyTorch 1.13](https://pytorch.org/blog/PyTorch-1.13-release/) allowing to use a fastpath execution for encoder attention blocks.
As a one-liner, you can convert your 🤗 Transformers models to use BetterTransformer thanks to the [🤗 Optimum](https://huggingface.co/docs/optimum/main/en/index) library:
```
from optimum.bettertransformer import BetterTransformer
better_model = BetterTransformer.transform(model)
```
This Space is a demo of an **end-to-end** deployement of PyTorch eager-mode models, both with and without BetterTransformer. The goal is to see what are the benefits server-side and client-side of using BetterTransformer.
## Inference using...
"""
)
with gr.Row():
with gr.Column(scale=50):
gr.Markdown("### Vanilla Transformers + TorchServe")
address_input_vanilla = gr.Textbox(
max_lines=1, label="ip vanilla", value=ADDRESS_VANILLA, visible=False
)
input_model_vanilla = gr.Textbox(
max_lines=1,
label="Text",
value="Expectations were low, enjoyment was high",
)
btn_single_vanilla = gr.Button("Send single text request")
output_single_vanilla = gr.Markdown(
label="Output single vanilla",
value=get_message_single(**defaults_vanilla_single),
)
btn_spam_vanilla = gr.Button(
"Spam text requests (from sst2 validation set)"
)
output_spam_vanilla = gr.Markdown(
label="Output spam vanilla",
value=get_message_spam(**defaults_vanilla_spam),
)
btn_single_vanilla.click(
fn=send_single,
inputs=[input_model_vanilla, address_input_vanilla],
outputs=output_single_vanilla,
)
btn_spam_vanilla.click(
fn=send_spam,
inputs=[address_input_vanilla],
outputs=output_spam_vanilla,
)
with gr.Column(scale=50):
gr.Markdown("### BetterTransformer + TorchServe")
address_input_bettertransformer = gr.Textbox(
max_lines=1,
label="ip bettertransformer",
value=ADDRESS_BETTERTRANSFORMER,
visible=False,
)
input_model_bettertransformer = gr.Textbox(
max_lines=1,
label="Text",
value="Expectations were low, enjoyment was high",
)
btn_single_bt = gr.Button("Send single text request")
output_single_bt = gr.Markdown(
label="Output single bt", value=get_message_single(**defaults_bt_single)
)
btn_spam_bt = gr.Button("Spam text requests (from sst2 validation set)")
output_spam_bt = gr.Markdown(
label="Output spam bt", value=get_message_spam(**defaults_bt_spam)
)
btn_single_bt.click(
fn=send_single,
inputs=[input_model_bettertransformer, address_input_bettertransformer],
outputs=output_single_bt,
)
btn_spam_bt.click(
fn=send_spam,
inputs=[address_input_bettertransformer],
outputs=output_spam_bt,
)
demo.queue(concurrency_count=1)
demo.launch()
|