File size: 18,073 Bytes
360f81c
 
 
 
7b26aba
360f81c
 
 
 
 
 
 
 
9899c96
663521e
360f81c
 
 
 
 
663521e
 
 
 
 
 
 
 
 
 
 
 
 
 
360f81c
 
 
 
55aeee4
360f81c
 
 
 
 
 
 
 
 
 
 
 
55aeee4
e9693d3
360f81c
 
 
67dda77
 
 
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8b30258
55aeee4
 
 
 
 
e9693d3
55aeee4
 
 
 
 
 
8b30258
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e9693d3
9899c96
e9693d3
 
 
9899c96
e9693d3
360f81c
 
 
9899c96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
360f81c
 
 
 
 
8b30258
360f81c
e9693d3
 
1138cdd
360f81c
 
 
 
8b30258
 
 
07a2d08
8b30258
360f81c
64e7ccb
360f81c
8b30258
 
67dda77
8b30258
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64e7ccb
 
663521e
7b26aba
663521e
7b26aba
 
 
 
 
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fe0d167
360f81c
 
fe0d167
360f81c
 
 
1138cdd
360f81c
fe0d167
360f81c
 
 
 
 
 
 
 
fe0d167
360f81c
 
 
e9693d3
 
360f81c
 
 
 
 
 
 
e9693d3
 
 
 
 
 
 
 
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fe0d167
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e9693d3
360f81c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fe0d167
360f81c
64e7ccb
360f81c
 
 
 
 
 
 
67dda77
360f81c
 
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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
from __future__ import annotations

import json
import yaml
import requests
import itertools
import contextlib

import numpy as np
import gradio as gr
import pandas as pd
import plotly.io as pio
import plotly.express as px
from pandas.api.types import is_numeric_dtype, is_float_dtype

pio.templates.default = "plotly_white"


class TableManager:
    def __init__(self, data_dir: str) -> None:
        """Load leaderboard data from CSV files in data_dir.

        Inside `data_dir`, there should be:
        - `models.json`: a JSON file containing information about each model.
        - `schema.yaml`: a YAML file containing the schema of the benchmark.
        - `score.csv`: a CSV file containing the NLP evaluation metrics of each model.
        - `*_benchmark.csv`: CSV files containing the system benchmark results.

        Especially, the `*_benchmark.csv` files should be named after the
        parameters used in the benchmark. For example, for the CSV file that
        contains benchmarking results for A100 and the chat-concise task
        (see `schema.yaml`) for possible choices, the file should be named
        `A100_chat-concise_benchmark.csv`.
        """
        # Load and merge CSV files.
        df = self._read_tables(data_dir)

        # Add the #params column.
        models = json.load(open(f"{data_dir}/models.json"))
        df["parameters"] = df["model"].apply(lambda x: models[x]["params"])

        # Make the first column (model) an HTML anchor to the model's website.
        def format_model_link(model_name: str) -> str:
            url = models[model_name]["url"]
            nickname = models[model_name]["nickname"]
            return (
                f'<a style="text-decoration: underline; text-decoration-style: dotted" '
                f'target="_blank" href="{url}">{nickname}</a>'
            )
        df["model"] = df["model"].apply(format_model_link)

        # Sort by our 'energy efficiency' score.
        df = df.sort_values(by="energy", ascending=True)

        # The full table where all the data are.
        self.full_df = df

        # Default view of the table is to only show the first options.
        self.set_filter_get_df()

    def _read_tables(self, data_dir: str) -> pd.DataFrame:
        """Read tables."""
        df_score = pd.read_csv(f"{data_dir}/score.csv")

        with open(f"{data_dir}/schema.yaml") as file:
            self.schema: dict[str, list] = yaml.safe_load(file)

        res_df = pd.DataFrame()

        # Do a cartesian product of all the choices in the schema
        # and try to read the corresponding CSV files.
        for choice in itertools.product(*self.schema.values()):
            filepath = f"{data_dir}/{'_'.join(choice)}_benchmark.csv"
            with contextlib.suppress(FileNotFoundError):
                df = pd.read_csv(filepath)
                for key, val in zip(self.schema.keys(), choice):
                    df.insert(1, key, val)
                res_df = pd.concat([res_df, df])

        if res_df.empty:
            raise ValueError(f"No benchmark CSV files were read from {data_dir=}.")

        df = pd.merge(res_df, df_score, on=["model"]).round(2)

        # Order columns.
        columns = df.columns.to_list()
        cols_to_order = ["model"]
        cols_to_order.extend(self.schema.keys())
        cols_to_order.append("energy")
        columns = cols_to_order + [col for col in columns if col not in cols_to_order]
        df = df[columns]

        # Delete rows with *any* NaN values.
        df = df.dropna()

        return df

    def _format_msg(self, text: str) -> str:
        """Formats into HTML that prints in Monospace font."""
        return f"<pre style='font-family: monospace'>{text}</pre>"

    def add_column(self, column_name: str, formula: str):
        """Create and add a new column with the given formula."""
        # If the user did not provide the name of the new column,
        # generate a unique name for them.
        if not column_name:
            counter = 1
            while (column_name := f"custom{counter}") in self.full_df.columns:
                counter += 1

        # If the user did not provide a formula, return an error message.
        if not formula:
            return self.cur_df, self._format_msg("Please enter a formula.")

        # If there is an equal sign in the formula, `df.eval` will
        # return an entire DataFrame with the new column, instead of
        # just the new column. This is not what we want, so we check
        # for this case and return an error message.
        if "=" in formula:
            return self.cur_df, self._format_msg("Invalid formula: expr cannot contain '='.")

        # The user may want to update an existing column.
        verb = "Updated" if column_name in self.full_df.columns else "Added"

        # Evaluate the formula and catch any error.
        try:
            # Give the users some helper functions that can be used in the formula
            # like "@sum(response_length)". Also wipe out some global variables.
            col = self.full_df.eval(
                formula,
                local_dict={"sum": sum, "len": len, "max": max, "min": min},
                global_dict={"global_tbm": None},
            )
        except Exception as exc:
            return self.cur_df, self._format_msg(f"Invalid formula: {exc}")

        # If the result is a numeric scalar, make it a Series.
        # We may have deleted some models (rows) form the full dataframe when we
        # called dropna, so we need to query the maximum index instead of taking len.
        if isinstance(col, (int, float)):
            col = pd.Series([col] * (self.full_df.index.max() + 1))
        # We only accept numeric columns.
        if not is_numeric_dtype(col):
            return self.cur_df, self._format_msg("Invalid formula: result must be numeric.")
        # Round if it's floating point.
        if is_float_dtype(col):
            col = col.round(2)

        # If the column already exists, update it.
        if column_name in self.full_df.columns:
            self.full_df[column_name] = col
        else:
            self.full_df.insert(len(self.schema) + 1, column_name, col)

        # If adding a column succeeded, `self.cur_df` should also be updated.
        self.cur_df = self.full_df.loc[self.cur_index]
        return self.cur_df, self._format_msg(f"{verb} column '{column_name}'.")

    def get_dropdown(self):
        columns = self.full_df.columns.tolist()[1:]
        return [
            gr.Dropdown(choices=columns, label="X"),
            gr.Dropdown(choices=columns, label="Y"),
            gr.Dropdown(choices=["None", *columns], label="Z (optional)"),
        ]

    def update_dropdown(self):
        columns = self.full_df.columns.tolist()[1:]
        return [
            gr.Dropdown.update(choices=columns),
            gr.Dropdown.update(choices=columns),
            gr.Dropdown.update(choices=["None", *columns]),
        ]

    def set_filter_get_df(self, *filters) -> pd.DataFrame:
        """Set the current set of filters and return the filtered DataFrame."""
        # If the filter is empty, we default to the first choice for each key.
        if not filters:
            filters = [choices[:1] for choices in self.schema.values()]

        index = np.full(len(self.full_df), True)
        for setup, choice in zip(self.schema, filters):
            index = index & self.full_df[setup].isin(choice)
        self.cur_df = self.full_df.loc[index]
        self.cur_index = index
        return self.cur_df

    def plot_scatter(self, width, height, x, y, z):
        # The user did not select either x or y.
        if not x or not y:
            return None, width, height, self._format_msg("Please select both X and Y.")

        # Width and height may be an empty string. Then we set them to 600.
        if not width and not height:
            width, height = "600", "600"
        elif not width:
            width = height
        elif not height:
            height = width
        try:
            width, height = int(width), int(height)
        except ValueError:
            return None, width, height, self._format_msg("Width and height should be positive integers.")

        # Strip the <a> tag from model names.
        text = self.cur_df["model"].apply(lambda x: x.split(">")[1].split("<")[0])
        if z is None or z == "None" or z == "":
            fig = px.scatter(self.cur_df, x=x, y=y, text=text)
        else:
            fig = px.scatter_3d(self.cur_df, x=x, y=y, z=z, text=text)
        fig.update_traces(textposition="top center")
        fig.update_layout(width=width, height=height)

        return fig, width, height, ""


# The global instance of the TableManager should only be used when
# initializing components in the Gradio interface. If the global instance
# is mutated while handling user sessions, the change will be reflected
# in every user session. Instead, the instance provided by gr.State should
# be used.
global_tbm = TableManager("data")

# Fetch the latest update date of the leaderboard repository.
resp = requests.get("https://api.github.com/repos/ml-energy/leaderboard/commits/master")
if resp.status_code != 200:
    current_date = "[Failed to fetch]"
    print("Failed to fetch the latest release date of the leaderboard repository.")
    print(resp.json())
else:
    current_date = resp.json()["commit"]["author"]["date"][:10]

# Custom JS.
# XXX: This is a hack to make the model names clickable.
#      Ideally, we should set `datatype` in the constructor of `gr.DataFrame` to
#      `["markdown"] + ["number"] * (len(df.columns) - 1)` and format models names
#      as an HTML <a> tag. However, because we also want to dynamically add new
#      columns to the table and Gradio < 4.0 does not support updating `datatype` with
#      `gr.DataFrame.update` yet, we need to manually walk into the DOM and replace
#      the innerHTML of the model name cells with dynamically interpreted HTML.
#      Desired feature tracked at https://github.com/gradio-app/gradio/issues/3732
dataframe_update_js = f"""
function format_model_link() {{
    // Iterate over the cells of the first column of the leaderboard table.
    for (let index = 1; index <= {len(global_tbm.full_df)}; index++) {{
        // Get the cell.
        var cell = document.querySelector(
            `#tab-leaderboard > div > div > div > table > tbody > tr:nth-child(${{index}}) > td:nth-child(1) > div > span`
        );

        // If nothing was found, it likely means that now the visible table has less rows
        // than the full table. This happens when the user filters the table. In this case,
        // we should just return.
        if (cell == null) break;

        // This check exists to make this function idempotent.
        // Multiple changes to the Dataframe component may invoke this function,
        // multiple times to the same HTML table (e.g., adding and sorting cols).
        // Thus, we check whether we already formatted the model names by seeing
        // whether the child of the cell is a text node. If it is not,
        // it means we already parsed it into HTML, so we should just return.
        if (cell.firstChild.nodeType != 3) break;

        // Decode and interpret the innerHTML of the cell as HTML.
        var decoded_string = new DOMParser().parseFromString(cell.innerHTML, "text/html").documentElement.textContent;
        var temp = document.createElement("template");
        temp.innerHTML = decoded_string;
        var model_anchor = temp.content.firstChild;

        // Replace the innerHTML of the cell with the interpreted HTML.
        cell.replaceChildren(model_anchor);
    }}

    // Return all arguments as is.
    return arguments
}}
"""

# Custom CSS.
css = """
/* Make ML.ENERGY look like a clickable logo. */
.text-logo {
    color: #27cb63 !important;
    text-decoration: none !important;
}

/* Make the submit button the same color as the logo. */
.btn-submit {
    background: #27cb63 !important;
    color: white !important;
    border: 0 !important;
}

/* Center the plotly plot inside its container. */
.plotly > div {
    margin: auto !important;
}

/* Limit the width of the first column to 300 px. */
table td:first-child,
table th:first-child {
    max-width: 300px;
    overflow: auto;
    white-space: nowrap;
}
"""

block = gr.Blocks(css=css)
with block:
    tbm = gr.State(global_tbm)  # type: ignore
    gr.HTML("<h1><a href='https://ml.energy' class='text-logo'>ML.ENERGY</a> Leaderboard</h1>")

    with gr.Tabs():
        # Tab 1: Leaderboard.
        with gr.TabItem("Leaderboard"):
            # Block 1: Checkboxes to select benchmarking parameters.
            with gr.Row():
                with gr.Box():
                    gr.Markdown("### Benchmark results to show")
                    checkboxes = []
                    for key, choices in global_tbm.schema.items():
                        # Specifying `value` makes everything checked by default.
                        checkboxes.append(gr.CheckboxGroup(choices=choices, value=choices[:1], label=key))

            # Block 2: Leaderboard table.
            with gr.Row():
                dataframe = gr.Dataframe(type="pandas", elem_id="tab-leaderboard")
            # Make sure the models have clickable links.
            dataframe.change(None, None, None, _js=dataframe_update_js)
            # Table automatically updates when users check or uncheck any checkbox.
            for checkbox in checkboxes:
                checkbox.change(TableManager.set_filter_get_df, inputs=[tbm, *checkboxes], outputs=dataframe)

            # Block 3: Allow users to add new columns.
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Row():
                        colname_input = gr.Textbox(lines=1, label="Custom column name")
                        formula_input = gr.Textbox(lines=1, label="Formula (@sum, @len, @max, and @min are supported)")
                with gr.Column(scale=1):
                    with gr.Row():
                        add_col_btn = gr.Button("Add to table (⏎)", elem_classes=["btn-submit"])
                    with gr.Row():
                        clear_input_btn = gr.Button("Clear")
            with gr.Row():
                add_col_message = gr.HTML("")
            gr.Examples(
                examples=[
                    ["power", "energy / latency"],
                    ["token_per_joule", "response_length / energy"],
                    ["verbose", "response_length > @sum(response_length) / @len(response_length)"],
                ],
                inputs=[colname_input, formula_input],
            )
            colname_input.submit(
                TableManager.add_column,
                inputs=[tbm, colname_input, formula_input],
                outputs=[dataframe, add_col_message],
            )
            formula_input.submit(
                TableManager.add_column,
                inputs=[tbm, colname_input, formula_input],
                outputs=[dataframe, add_col_message],
            )
            add_col_btn.click(
                TableManager.add_column,
                inputs=[tbm, colname_input, formula_input],
                outputs=[dataframe, add_col_message],
            )
            clear_input_btn.click(
                lambda: (None, None, None),
                inputs=None,
                outputs=[colname_input, formula_input, add_col_message],
            )

            # Block 4: Allow users to plot 2D and 3D scatter plots.
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Row():
                        # Initialize the dropdown choices with the global TableManager with just the original columns.
                        axis_dropdowns = global_tbm.get_dropdown()
                with gr.Column(scale=1):
                    with gr.Row():
                        plot_btn = gr.Button("Plot", elem_classes=["btn-submit"])
                    with gr.Row():
                        clear_plot_btn = gr.Button("Clear")
            with gr.Accordion("Plot size (600 x 600 by default)", open=False):
                with gr.Row():
                    plot_width_input = gr.Textbox("600", lines=1, label="Width (px)")
                    plot_height_input = gr.Textbox("600", lines=1, label="Height (px)")
            with gr.Row():
                plot = gr.Plot()
            with gr.Row():
                plot_message = gr.HTML("")
            add_col_btn.click(TableManager.update_dropdown, inputs=tbm, outputs=axis_dropdowns)  # type: ignore
            plot_width_input.submit(
                TableManager.plot_scatter,
                inputs=[tbm, plot_width_input, plot_height_input, *axis_dropdowns],
                outputs=[plot, plot_width_input, plot_height_input, plot_message],
            )
            plot_height_input.submit(
                TableManager.plot_scatter,
                inputs=[tbm, plot_width_input, plot_height_input, *axis_dropdowns],
                outputs=[plot, plot_width_input, plot_height_input, plot_message],
            )
            plot_btn.click(
                TableManager.plot_scatter,
                inputs=[tbm, plot_width_input, plot_height_input, *axis_dropdowns],
                outputs=[plot, plot_width_input, plot_height_input, plot_message],
            )
            clear_plot_btn.click(
                lambda: (None,) * 7,
                None,
                outputs=[*axis_dropdowns, plot, plot_width_input, plot_height_input, plot_message],
            )

            # Block 5: Leaderboard date.
            with gr.Row():
                gr.HTML(f"<h3 style='color: gray'>Date: {current_date}</h3>")

        # Tab 2: About page.
        with gr.TabItem("About"):
            # Read in LEADERBOARD.md
            gr.Markdown(open("LEADERBOARD.md").read())

    # Load the table on page load.
    block.load(lambda: global_tbm.set_filter_get_df(), outputs=dataframe)

block.launch()