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from __future__ import annotations
import glob
import io
import os
import random
import struct
from contextlib import contextmanager
from html import escape
import msgpack
import streamlit as st
import torch
import tqdm
from huggingface_hub import HfFileSystem
from transformers import AutoTokenizer
st.set_page_config(layout="wide")
MODEL_NAME = os.environ.get("MODEL_NAME", "MonetLLM/monet-vd-1.4B-100BT-hf")
CONTEXT_WINDOW = int(os.environ.get("CONTEXT_WINDOW", "12"))
CANDIDATE_THRESHOLD = int(os.environ.get("CANDIDATE_THRESHOLD", "50"))
HORIZONTAL_STYLE = """<style class="hide-element">
/* Hides the style container and removes the extra spacing */
.element-container:has(.hide-element) {
display: none;
}
/*
The selector for >.element-container is necessary to avoid selecting the whole
body of the streamlit app, which is also a stVerticalBlock.
*/
div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) {
display: flex;
flex-direction: row !important;
flex-wrap: wrap;
gap: 0.5rem;
align-items: baseline;
}
/* Buttons and their parent container all have a width of 704px, which we need to override */
div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) div {
width: max-content !important;
}
/* Just an example of how you would style buttons, if desired */
/*
div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) button {
border-color: red;
}
*/
</style>"""
@st.cache_resource
def prepare_routing_resources():
fs = HfFileSystem()
for filename in fs.glob(f"datasets/{MODEL_NAME}-viewer-data/*"):
if not os.path.exists(os.path.basename(filename)):
print(f"[*] Download {filename}...")
fs.download(filename, ".")
input_tokens = torch.load("inputs.pt")
examples_tables = []
for i in tqdm.trange(len(glob.glob("examples-*.msgpack"))):
with open(f"examples-{i}.msgpack", "rb") as fp:
fp.seek(-4, io.SEEK_END)
table_size = struct.unpack(">I", fp.read(4))[0]
fp.seek(-(table_size + 4), io.SEEK_END)
examples_tables.append(msgpack.Unpacker(fp).unpack())
candidates = []
for i, table in enumerate(tqdm.tqdm(examples_tables)):
candidates.append([])
with open(f"examples-{i}.msgpack", "rb") as fp:
unpacker = msgpack.Unpacker(fp)
for j in range(len(table)):
if len(unpacker.unpack()) > CANDIDATE_THRESHOLD:
candidates[-1].append(j)
routing_tables = []
for i in tqdm.trange(len(examples_tables)):
with open(f"routings-{i}.msgpack", "rb") as fp:
fp.seek(-4, io.SEEK_END)
table_size = struct.unpack(">I", fp.read(4))[0]
fp.seek(-(table_size + 4), io.SEEK_END)
routing_tables.append(msgpack.Unpacker(fp).unpack())
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
return input_tokens, examples_tables, routing_tables, candidates, tokenizer
input_tokens, examples_tables, routing_tables, candidates, tokenizer = (
prepare_routing_resources()
)
def render_routing_examples_in_html(router_index: int, expert_id: int) -> str:
with open(f"examples-{router_index}.msgpack", "rb") as fp:
fp.seek(examples_tables[router_index][expert_id])
examples = msgpack.Unpacker(fp).unpack()
with open(f"routings-{router_index}.msgpack", "rb") as fp:
table = []
for i, j, _ in examples:
start = max(j - CONTEXT_WINDOW, 0)
end = min(j + CONTEXT_WINDOW, len(routing_tables[router_index][i]))
fp.seek(routing_tables[router_index][i][start])
unpacker = msgpack.Unpacker(fp, strict_map_key=False)
activated = [unpacker.unpack().get(expert_id, 0) for _ in range(start, end)]
full_text = tokenizer.decode(input_tokens[i])
encodings = tokenizer(full_text, add_special_tokens=False)
offset = len(encodings.input_ids) - input_tokens.size(1)
spans, lslice = [], None
for k in range(start, end):
if offset + k >= 0 and (sslice := encodings.token_to_chars(offset + k)):
span, score = full_text[slice(*sslice)], activated[k - start]
if lslice == sslice:
score = max(spans.pop(-1)[1], score)
spans.append((escape(span), score))
lslice = sslice
spans = [
f"<span style='background-color: rgba(144, 238, 144, {score}' title='Routing: {score*100:.2f}%'>{span}</span>"
for span, score in spans
]
table.append(
f"""
<tr>
<td align='right'>
<span style='font-weight: bold'>
{escape(tokenizer.decode(input_tokens[i, j]))} ({activated[j - start] * 100:.2f}%)
</span>
</td>
<td align='left'>
(...) {"".join(spans)} (...)
</td>
<td align='right'>
({i}, {j})
</td>
</tr>
"""
)
return f"""
<div style='background-color: white; color: black; padding: 1em 3em; font-size: 12pt'>
<h2 style='font-size: 18pt'> Activated Examples of Group {router_index} / Expert {expert_id} </h2>
<table>
{"".join(table)}
</table>
</div>
"""
@contextmanager
def st_horizontal():
st.markdown(HORIZONTAL_STYLE, unsafe_allow_html=True)
with st.container():
st.markdown(
'<span class="hide-element horizontal-marker"></span>',
unsafe_allow_html=True,
)
yield
col1, col2 = st.columns(2)
with col1:
router_groups = [f"Routing Group {i}" for i in range(len(examples_tables))]
router_index = st.selectbox("Expert Routing Group", router_groups, index=4)
with col2:
expert_id = st.number_input("Expert Index", 0, len(examples_tables[0]), 54136)
with st_horizontal():
show_btn = st.button("Show")
random_btn = st.button("Random")
if show_btn or random_btn:
router_index = router_groups.index(router_index)
if random_btn:
expert_id = random.choice(candidates[router_index])
st.html(render_routing_examples_in_html(router_index, expert_id))
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