hzhwcmhf commited on
Commit
976cf27
1 Parent(s): 0daa47a
Files changed (7) hide show
  1. README.md +2 -1
  2. app.py +545 -0
  3. global.js +446 -0
  4. graph.html +49 -0
  5. packages.txt +10 -0
  6. requirements.txt +2 -0
  7. vis-network.min.js +0 -0
README.md CHANGED
@@ -1,5 +1,5 @@
1
  ---
2
- title: DA Transformer
3
  emoji: 😻
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  colorFrom: red
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  colorTo: gray
@@ -7,6 +7,7 @@ sdk: gradio
7
  sdk_version: 3.28.3
8
  app_file: app.py
9
  pinned: false
 
10
  ---
11
 
12
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: DA-Transformer
3
  emoji: 😻
4
  colorFrom: red
5
  colorTo: gray
 
7
  sdk_version: 3.28.3
8
  app_file: app.py
9
  pinned: false
10
+ license: apache-2.0
11
  ---
12
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
@@ -0,0 +1,545 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ from collections import defaultdict
3
+ import datetime
4
+ import json
5
+ import os, sys
6
+ import time
7
+ import concurrent
8
+
9
+ import math
10
+ import gradio as gr
11
+ import requests
12
+ import logging
13
+ import numpy as np
14
+ import matplotlib.pyplot as plt
15
+ import fairseq
16
+
17
+
18
+ fairseq_path = os.path.dirname(os.path.dirname(fairseq.__file__))
19
+
20
+ sys.path.insert(1, f"{fairseq_path}")
21
+ from fs_plugins.models.glat_decomposed_with_link import GlatDecomposedLink
22
+
23
+ sys.path.insert(1, f"{fairseq_path}/examples")
24
+ from mass.s2s_model import TransformerMASSModel
25
+ from transformer.hub_interface import TransformerHubInterface
26
+
27
+ logger = logging.getLogger(__name__)
28
+
29
+ notice_markdown = ("""
30
+ # Directed Acyclic Transformer: A Non-Autoregressive Sequence-to-Sequence Model designed for Parallel Text Generation.
31
+ - **Fast Generation**: DA-Transformer offers faster inference compared to autoregressive Transformers (with fairseq implementation), with a reduction in latency by 7~14x and an increase in throughput by ~20x.
32
+ - **High Quality**: DA-Transformer performs competitively with autoregressive Transformers, even with pre-trained models like BART, in a variety of text generation tasks.
33
+ - **Easy Training**: DA-Transformer can be trained end-to-end without requiring knowledge distillation, making it simple and straightforward to train.
34
+
35
+ ## Resources
36
+
37
+ - [[Github]](https://github.com/thu-coai/DA-Transformer)
38
+ - Papers: [[Machine Translation]](https://proceedings.mlr.press/v162/huang22m/huang22m.pdf) [[Pre-training]](https://arxiv.org/pdf/2304.11791.pdf)
39
+
40
+ ## Terms of use
41
+ By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It does not gaurantee the correctness of the output text. The service may collect user data for future research.
42
+
43
+ ## This demo contains models
44
+ - [En-De Translation]()
45
+ - [Zh-En Translation]()
46
+ - [Question Generation]()
47
+ """)
48
+
49
+ learn_more_markdown = ("""
50
+ """)
51
+
52
+
53
+ css = """
54
+ pre {
55
+ white-space: pre-wrap; /* Since CSS 2.1 */
56
+ white-space: -moz-pre-wrap; /* Mozilla, since 1999 */
57
+ white-space: -pre-wrap; /* Opera 4-6 */
58
+ white-space: -o-pre-wrap; /* Opera 7 */
59
+ word-wrap: break-word; /* Internet Explorer 5.5+ */
60
+ }
61
+ """
62
+
63
+ available_models = {
64
+ "dat_base_translation_ende": {
65
+ "class": GlatDecomposedLink,
66
+ "args":{
67
+ "model_name_or_path": "hfhub://thu-coai/dat_base_translation_ende",
68
+ "decode_strategy": "beamsearch",
69
+ "decode_max_workers": 1,
70
+ "decode_threads_per_worker": 4,
71
+ "decode_dedup": True,
72
+ "decode_alpha": 1.1,
73
+ "decode_gamma": 0,
74
+ "decode_beam_size": 200,
75
+ "decode_batch_size": 1,
76
+ "decode_top_cand": 5,
77
+ "decode_max_beam_per_length": 10,
78
+ "max_decoder_batch_tokens": 2048
79
+ },
80
+ "examples": ["I am a fast translation model."],
81
+ "expected_load_time": 17
82
+ },
83
+ "dat_base_translation_zhen": {
84
+ "class": GlatDecomposedLink,
85
+ "args":{
86
+ "model_name_or_path": "hfhub://thu-coai/dat_base_translation_zhen",
87
+ "decode_strategy": "beamsearch",
88
+ "decode_max_workers": 1,
89
+ "decode_threads_per_worker": 4,
90
+ "decode_dedup": True,
91
+ "decode_alpha": 1.1,
92
+ "decode_gamma": 0,
93
+ "decode_beam_size": 200,
94
+ "decode_batch_size": 1,
95
+ "decode_top_cand": 5,
96
+ "decode_max_beam_per_length": 10,
97
+ "max_decoder_batch_tokens": 2048
98
+ },
99
+ "examples": ["我是一个高速的机器翻译模型。"],
100
+ "expected_load_time": 17
101
+ },
102
+ "dat_uncased_squad": {
103
+ "class": GlatDecomposedLink,
104
+ "args":{
105
+ "model_name_or_path": "hfhub://thu-coai/dat_uncased_squad",
106
+ "decode_strategy": "beamsearch",
107
+ "decode_max_workers": 1,
108
+ "decode_threads_per_worker": 4,
109
+ "decode_gamma": 0,
110
+ "decode_beam_size": 200,
111
+ "decode_batch_size": 1,
112
+ "decode_top_cand": 5,
113
+ "decode_no_consecutive_repeated_tokens": 3,
114
+ "decode_no_repeated_tokens": 2,
115
+ "decode_max_beam_per_length": 10,
116
+ "max_decoder_batch_tokens": 2048
117
+ },
118
+ "examples": ["Two [SEP] Two additional teams of 40 attendants each will accompany the flame on its mainland China route."],
119
+ "expected_load_time": 20
120
+ },
121
+ "mass_uncased_squad": {
122
+ "class": TransformerMASSModel,
123
+ "args":{
124
+ "model_name_or_path": "hfhub://thu-coai/mass_uncased_squad"
125
+ },
126
+ "examples": ["Two [SEP] Two additional teams of 40 attendants each will accompany the flame on its mainland China route."],
127
+ "expected_load_time": 10
128
+ },
129
+ "transformer_base_translation_ende": {
130
+ "class": TransformerHubInterface,
131
+ "args":{
132
+ "model_name_or_path": "hfhub://thu-coai/transformer_base_translation_ende"
133
+ },
134
+ "examples": ["I am a fast translation model."],
135
+ "expected_load_time": 10
136
+ },
137
+ "transformer_base_translation_zhen": {
138
+ "class": TransformerHubInterface,
139
+ "args":{
140
+ "model_name_or_path": "hfhub://thu-coai/transformer_base_translation_zhen"
141
+ },
142
+ "examples": ["我是一个高速的机器翻译模型。"],
143
+ "expected_load_time": 10
144
+ }
145
+ }
146
+
147
+ compare_available_types = {
148
+ "Translation Zh-En: DA-Transformer v.s. Autoregressive Transformer": {
149
+ "models": ['dat_base_translation_zhen', 'transformer_base_translation_zhen'],
150
+ "examples": ["我是一个高速的机器翻译模型。", "非自回归模型可以用来加速自然语言生成。",
151
+ "使用本服务前,用户必须同意以下条款:该服务是仅供非商业用途的研究预览。它不保证输出文本的正确性。本服务可能会收集用户数据以供将来研究。"],
152
+ "placeholder": "请输入一个中文句子。 (The model will translate the input into English.)"
153
+ },
154
+ "Question Generation: DA-Transformer v.s. MASS": {
155
+ "models": ['dat_uncased_squad', "mass_uncased_squad"],
156
+ "examples": ["Two [SEP] Two additional teams of 40 attendants each will accompany the flame on its mainland China route.", "DA-Transformer [SEP] Directed Acyclic Transformer (DA-Transformer) is a non-autoregressive sequence-to-sequence model designed for parallel text generation."],
157
+ "placeholder": "Answer [SEP] Your Passage Here (the answer should be appearred in the passage)."
158
+ },
159
+ "Translation En-De: DA-Transformer v.s. Autoregressive Transformer": {
160
+ "models": ['dat_base_translation_ende', 'transformer_base_translation_ende'],
161
+ "examples": ["I am a fast translation model.", "Non-autoregressive models are designed for fast natural language generation.",
162
+ "By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only."],
163
+ "placeholder": "Any English sentence here. (The model will translate the input into German.)"
164
+ },
165
+ }
166
+
167
+ detail_available_types = {
168
+ "Translation Zh-En": {
169
+ "model": 'dat_base_translation_zhen',
170
+ "examples": compare_available_types['Translation Zh-En: DA-Transformer v.s. Autoregressive Transformer']["examples"],
171
+ "placeholder": compare_available_types['Translation Zh-En: DA-Transformer v.s. Autoregressive Transformer']["placeholder"]
172
+ },
173
+ "Question Generation": {
174
+ "model": 'dat_uncased_squad',
175
+ "examples": compare_available_types['Question Generation: DA-Transformer v.s. MASS']["examples"],
176
+ "placeholder": compare_available_types['Question Generation: DA-Transformer v.s. MASS']["placeholder"]
177
+ },
178
+ "Translation En-De": {
179
+ "model": 'dat_base_translation_ende',
180
+ "examples": compare_available_types['Translation En-De: DA-Transformer v.s. Autoregressive Transformer']["examples"],
181
+ "placeholder": compare_available_types['Translation En-De: DA-Transformer v.s. Autoregressive Transformer']["placeholder"],
182
+ },
183
+ }
184
+
185
+ models = {}
186
+ workers = None
187
+
188
+ def softplus(x, beta=1):
189
+ return math.log1p(math.exp(-abs(x * beta))) / beta + max(x, 0)
190
+
191
+ def get_fake_progress(min_progress, max_progress, used_time, expected_time):
192
+ percentage = max(1 - softplus(expected_time - used_time) / expected_time, 0)
193
+ return min_progress + (max_progress - min_progress) * percentage
194
+
195
+ def generate(model, model_input):
196
+ return {"output": model.translate(model_input)}
197
+
198
+ def generate_detail(model, model_input):
199
+ output, graph_info = model.generate_graph(model_input)
200
+ return {"output": output, "graph_info": graph_info}
201
+
202
+ def load_model(model_name):
203
+ assert model_name in available_models
204
+ model = available_models[model_name]['class'].from_pretrained(**available_models[model_name]['args'])
205
+ return model
206
+
207
+ def warmup_model(model, model_name):
208
+ model.translate(available_models[model_name]['examples'][0])
209
+
210
+ def submit(model_name, model_input, generate_fn, request: gr.Request, progress=gr.Progress()):
211
+ assert workers is not None, "No workers"
212
+ current_progress = 0
213
+
214
+ progress(0, desc="Downloading Checkpoints and Loading Models")
215
+ if model_name not in models:
216
+ load_start = time.time()
217
+ future = workers.submit(load_model, model_name)
218
+ while True:
219
+ try:
220
+ model = future.result(timeout=1)
221
+ break
222
+ except concurrent.futures._base.TimeoutError as _:
223
+ progress(get_fake_progress(min_progress=current_progress, max_progress=0.8, used_time=time.time() - load_start, expected_time=available_models[model_name]['expected_load_time']),
224
+ desc="Downloading Checkpoints and Loading Models")
225
+ logger.info(f"Model Loaded: {model_name} Load Time: {time.time() - load_start}")
226
+ current_progress = 0.8
227
+ models[model_name] = model
228
+ else:
229
+ model = models[model_name]
230
+
231
+ # warmup for better inference time
232
+ progress(current_progress, desc="Downloading Checkpoints and Loading Models")
233
+ if current_progress == 0.8:
234
+ target_progress = 0.9
235
+ else:
236
+ target_progress = 0.5
237
+ warmup_start = time.time()
238
+ future = workers.submit(warmup_model, model, model_name)
239
+ while True:
240
+ try:
241
+ result = future.result(timeout=1)
242
+ break
243
+ except concurrent.futures._base.TimeoutError as _:
244
+ progress(get_fake_progress(min_progress=current_progress, max_progress=target_progress, used_time=time.time() - warmup_start, expected_time=1),
245
+ desc="Downloading Checkpoints and Loading Models")
246
+ current_progress = target_progress
247
+
248
+ # running
249
+ progress(current_progress, desc="Running")
250
+ try:
251
+ generate_start = time.time()
252
+ future = workers.submit(generate_fn, model, model_input)
253
+ while True:
254
+ try:
255
+ result = future.result(timeout=1)
256
+ break
257
+ except concurrent.futures._base.TimeoutError as _:
258
+ progress(get_fake_progress(min_progress=current_progress, max_progress=1, used_time=time.time() - generate_start, expected_time=1),
259
+ desc="Running")
260
+ inference_time = time.time() - generate_start
261
+
262
+ result_abbrev = {}
263
+ for key, value in result.items():
264
+ log_str = str(value)
265
+ if len(log_str) > 1024:
266
+ log_str = log_str[:1024] + "..."
267
+ result_abbrev[key] = log_str
268
+ logger.info(f"Input: [{model_input}] Output: [{result_abbrev}] Inference Time: {inference_time}")
269
+ return result, inference_time
270
+ except RuntimeError as err:
271
+ return f"Runtime Error: {str(err)}", 0
272
+
273
+
274
+ def compare_init_state(model_selector):
275
+ model1 = compare_available_types[model_selector]['models'][0]
276
+ model2 = compare_available_types[model_selector]['models'][1]
277
+ state = [{"model_name": model1}, {"model_name": model2}]
278
+ return state
279
+
280
+ def compare_refresh(model_selector, samples):
281
+ model1 = compare_available_types[model_selector]['models'][0]
282
+ model2 = compare_available_types[model_selector]['models'][1]
283
+ model_output1 = gr.Textbox.update(visible=True, label=model1)
284
+ model_output2 = gr.Textbox.update(visible=True, label=model2)
285
+ model_input = gr.Textbox.update(value="", placeholder=compare_available_types[model_selector]['placeholder'])
286
+ samples.clear()
287
+ samples += [[x]for x in compare_available_types[model_selector]['examples']]
288
+ examples = gr.Dataset.update(samples=samples)
289
+ model_speed = gr.Plot.update(visible=False)
290
+ return model_input, model_output1, model_output2, examples, samples, model_speed
291
+
292
+ def compare_submit(model_input, idx, state, request: gr.Request, progress=gr.Progress()):
293
+ model_name = state[idx]['model_name']
294
+ model_output, inference_time = submit(model_name, model_input, generate, request, progress)
295
+ state[idx]['inference_time'] = inference_time
296
+ return model_output['output'], state
297
+
298
+ def compare_dataset_click(examples, samples):
299
+ return samples[examples][0]
300
+
301
+ def compare_show_plot(state):
302
+ x = [state[0]['model_name'], state[1]['model_name']]
303
+ y = [state[0]['inference_time'], state[1]['inference_time']]
304
+
305
+ fig = plt.figure(figsize=(12, 2.5))
306
+ ax = plt.subplot(111)
307
+ bars = ax.barh(x, y, 0.75)
308
+ ax.bar_label(bars, fmt="%.2f")
309
+ ax.set_yticks(np.arange(len(x)), labels=x)
310
+ ax.set_xlabel('Inference Time on CPU (s)')
311
+ plt.tight_layout()
312
+ # plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0, hspace=0)
313
+
314
+ return gr.Row.update(visible=True), gr.Plot.update(value=fig, visible=True)
315
+
316
+ def compare_clear():
317
+ return "", "", "", gr.Row.update(visible=False)
318
+
319
+ example_list = []
320
+
321
+ def build_tab_compare():
322
+ state = gr.State()
323
+ samples = gr.State(example_list)
324
+
325
+ available_type_names = list(compare_available_types.keys())
326
+ with gr.Row(elem_id="compare_model_selector_row"):
327
+ model_selector = gr.Dropdown(
328
+ choices=available_type_names,
329
+ value=available_type_names[0] if len(available_type_names) > 0 else "",
330
+ interactive=True,
331
+ show_label=False).style(container=False)
332
+
333
+ with gr.Row(elem_id="compare_model_input"):
334
+ model_input = gr.Textbox(lines=5, label="input")
335
+ # examples = gr.Dataset(examples=[], inputs=[model_input], elem_id="compare_examples")
336
+ examples = gr.Dataset(components=[model_input],
337
+ label="Examples",
338
+ type='index',
339
+ samples=example_list,
340
+ visible=True
341
+ )
342
+
343
+ # with gr.Row(elem_id="compare_examples"):
344
+
345
+ with gr.Row():
346
+ clear_btn = gr.Button(value="Clear")
347
+ submit_btn = gr.Button(value="Submit", variant="primary")
348
+
349
+ # with gr.Accordion("Parameters", open=False, visible=False) as parameter_row:
350
+ # temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Temperature",)
351
+ # max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
352
+
353
+ with gr.Row(elem_id="compare_model_output"):
354
+ model_output1 = gr.Textbox(lines=5, label="output", visible=False)
355
+ model_output2 = gr.Textbox(lines=5, label="output", visible=False)
356
+
357
+ with gr.Row(elem_id="compare_model_speed", visible=False) as row:
358
+ with gr.Column():
359
+ model_speed = gr.Plot(value=None, label="Speed")
360
+ compare_hints = gr.Markdown("**Note the above time is measured on a free cloud server, which does not use GPU and is thus different from the setting in the papers.**")
361
+
362
+ model_selector.change(compare_refresh, [model_selector, samples], [model_input, model_output1, model_output2, examples, samples, model_speed])
363
+
364
+ clear_btn.click(compare_clear, None, [model_input, model_output1, model_output2, row])
365
+
366
+ submit_btn.click(compare_init_state, [model_selector], [state]).\
367
+ then(compare_submit, [model_input, gr.Number(value=0, visible=False, precision=0), state], [model_output1, state]).\
368
+ then(compare_submit, [model_input, gr.Number(value=1, visible=False, precision=0), state], [model_output2, state]).\
369
+ then(compare_show_plot, [state], [row, model_speed])
370
+ # submit_btn.click(compare_show_plot, [state], [model_speed])
371
+
372
+ examples.click(compare_dataset_click, [examples, samples], [model_input])
373
+
374
+ def load(fn):
375
+ fn(compare_refresh, [model_selector, samples], [model_input, model_output1, model_output2, examples, samples])
376
+
377
+ return load
378
+
379
+ def detail_init_state(model_selector):
380
+ model = detail_available_types[model_selector]['model']
381
+ state = {"model_name": model, "cnt": 0}
382
+ return state
383
+
384
+ def detail_refresh(model_selector, samples):
385
+ model = detail_available_types[model_selector]['model']
386
+ model_output = gr.Textbox.update(visible=True, label=model)
387
+ model_input = gr.Textbox.update(value="", placeholder=detail_available_types[model_selector]['placeholder'])
388
+ samples.clear()
389
+ samples += [[x]for x in detail_available_types[model_selector]['examples']]
390
+ examples = gr.Dataset.update(samples=samples)
391
+ model_speed = gr.Plot.update(visible=False)
392
+ return model_input, model_output, examples, samples, model_speed
393
+
394
+ def detail_submit(model_input, state, request: gr.Request, progress=gr.Progress()):
395
+ model_name = state['model_name']
396
+ model_output, inference_time = submit(model_name, model_input, generate_detail, request, progress)
397
+ state['inference_time'] = inference_time
398
+ state["graph_info"] = model_output['graph_info']
399
+ # html_code = open("graph.html").read()
400
+
401
+ # state["cnt"] += 1
402
+ # if state["cnt"] > 2:
403
+ # html_code += r"""<script type="text/javascript">addNode();</script>\n"""
404
+ # print(html_code)
405
+
406
+ return model_output['output'], state, gr.Row.update(visible=True), json.dumps(state)
407
+
408
+ def detail_dataset_click(examples, samples):
409
+ return samples[examples][0]
410
+
411
+ def detail_clear():
412
+ return "", "", gr.Row.update(visible=False)
413
+
414
+ def build_tab_detail():
415
+
416
+ state = gr.State()
417
+ samples = gr.State(example_list)
418
+
419
+ available_type_names = list(detail_available_types.keys())
420
+ with gr.Row(elem_id="detail_model_selector_row"):
421
+ model_selector = gr.Dropdown(
422
+ choices=available_type_names,
423
+ value=available_type_names[0] if len(available_type_names) > 0 else "",
424
+ interactive=True,
425
+ show_label=False).style(container=False)
426
+
427
+ with gr.Row(elem_id="detail_model_input"):
428
+ model_input = gr.Textbox(lines=5, label="input")
429
+ # examples = gr.Dataset(examples=[], inputs=[model_input], elem_id="compare_examples")
430
+ examples = gr.Dataset(components=[model_input],
431
+ label="Examples",
432
+ type='index',
433
+ samples=example_list,
434
+ visible=True
435
+ )
436
+
437
+ # with gr.Row(elem_id="compare_examples"):
438
+
439
+ with gr.Row():
440
+ clear_btn = gr.Button(value="Clear")
441
+ submit_btn = gr.Button(value="Submit", variant="primary")
442
+
443
+ # with gr.Accordion("Parameters", open=False, visible=False) as parameter_row:
444
+ # temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Temperature",)
445
+ # max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
446
+
447
+ with gr.Row(elem_id="detail_model_output"):
448
+ model_output = gr.Textbox(lines=5, label="output", visible=False)
449
+
450
+ with gr.Row(visible=False) as dag_graph:
451
+ with gr.Column(scale=1.8):
452
+ html = gr.HTML(open("graph.html").read())
453
+ with gr.Column(scale=1):
454
+ minimum_node_pass_prob = gr.Slider(0, 1, value=0.2, label="Show nodes with passing probability greater than", info="Nodes that predict the output sequence are always visible")
455
+ minimum_edge_prob = gr.Slider(0, 1, value=0.1, label="Show edges with transition probability greater than")
456
+ max_out_edge_num = gr.Slider(1, 10, value=5, step=1, label="Show top-k outgoing edges with k")
457
+ max_out_edge_prob = gr.Slider(0, 1, value=0.9, label="Show top-p outgoing edges with p")
458
+ force_in_edge = gr.Checkbox(True, label="Show at least one incoming edge for each node")
459
+ show_node_detail = gr.Checkbox(False, label="Show verbose node information")
460
+ show_edge_label = gr.Checkbox(False, label="Show transition probability")
461
+ network_refresh = gr.Button(value="Reinitialize DAG Visualization")
462
+ graph_parameters = [minimum_node_pass_prob, minimum_edge_prob, max_out_edge_num, max_out_edge_prob, force_in_edge, show_node_detail, show_edge_label]
463
+
464
+ js_state = gr.Textbox(visible=False)
465
+
466
+ model_selector.change(detail_refresh, [model_selector, samples], [model_input, model_output, examples, samples])
467
+
468
+ clear_btn.click(detail_clear, None, [model_input, model_output, dag_graph])
469
+
470
+ graph_create_js = """(state_str, minimum_node_pass_prob, minimum_edge_prob, max_out_edge_num, max_out_edge_prob, force_in_edge, show_node_detail, show_edge_label) => {
471
+ var state = JSON.parse(state_str);
472
+ var options = {
473
+ minimum_node_pass_prob: minimum_node_pass_prob,
474
+ minimum_edge_prob: minimum_edge_prob,
475
+ max_out_edge_num: max_out_edge_num,
476
+ max_out_edge_prob: max_out_edge_prob,
477
+ force_in_edge: force_in_edge,
478
+ show_node_detail: show_node_detail,
479
+ show_edge_label: show_edge_label,
480
+ }
481
+ startNetwork(state.graph_info, options);
482
+ }"""
483
+ graph_update_js = """(minimum_node_pass_prob, minimum_edge_prob, max_out_edge_num, max_out_edge_prob, force_in_edge, show_node_detail, show_edge_label) => {
484
+ var options = {
485
+ minimum_node_pass_prob: minimum_node_pass_prob,
486
+ minimum_edge_prob: minimum_edge_prob,
487
+ max_out_edge_num: max_out_edge_num,
488
+ max_out_edge_prob: max_out_edge_prob,
489
+ force_in_edge: force_in_edge,
490
+ show_node_detail: show_node_detail,
491
+ show_edge_label: show_edge_label,
492
+ }
493
+ updateNetwork(options);
494
+ }"""
495
+ submit_btn.click(detail_init_state, [model_selector], [state]).\
496
+ then(detail_submit, [model_input, state], [model_output, state, dag_graph, js_state]).\
497
+ then(None, [js_state] + graph_parameters, None, _js=graph_create_js)
498
+ network_refresh.click(None, [js_state] + graph_parameters, None, _js=graph_create_js)
499
+ minimum_node_pass_prob.change(None, graph_parameters, None, _js=graph_update_js)
500
+ minimum_edge_prob.change(None, graph_parameters, None, _js=graph_update_js)
501
+ max_out_edge_num.change(None, graph_parameters, None, _js=graph_update_js)
502
+ max_out_edge_prob.change(None, graph_parameters, None, _js=graph_update_js)
503
+ force_in_edge.select(None, graph_parameters, None, _js=graph_update_js)
504
+ show_node_detail.select(None, graph_parameters, None, _js=graph_update_js)
505
+ show_edge_label.select(None, graph_parameters, None, _js=graph_update_js)
506
+
507
+ examples.click(detail_dataset_click, [examples, samples], [model_input])
508
+
509
+ def load(fn):
510
+ fn(detail_refresh, [model_selector, samples], [model_input, model_output, examples, samples])
511
+
512
+ return load
513
+
514
+ def build_demo():
515
+ with gr.Blocks(title="DA-Transformer Demo", theme=gr.themes.Base(), css=css) as demo:
516
+ gr.Markdown(notice_markdown)
517
+
518
+ with gr.Tab("Speed Comparison") as compare_tab:
519
+ compare_load = build_tab_compare()
520
+ compare_load(compare_tab.select)
521
+ with gr.Tab("DA-Transformer Inspection") as detail_tab:
522
+ detail_load = build_tab_detail()
523
+ detail_load(detail_tab.select)
524
+
525
+ gr.Markdown(learn_more_markdown)
526
+
527
+ compare_load(demo.load)
528
+
529
+ demo.load(None,None,None,_js=open("global.js").read())
530
+ return demo
531
+
532
+ if __name__ == "__main__":
533
+ parser = argparse.ArgumentParser()
534
+ parser.add_argument("--host", type=str, default="0.0.0.0")
535
+ parser.add_argument("--port", type=int)
536
+ parser.add_argument("--concurrency-count", type=int, default=1)
537
+ parser.add_argument("--share", action="store_true")
538
+ args = parser.parse_args()
539
+ logger.info(f"args: {args}")
540
+
541
+ workers = concurrent.futures.ThreadPoolExecutor(max_workers=1)
542
+ demo = build_demo()
543
+ demo.queue(concurrency_count=args.concurrency_count, status_update_rate=10,
544
+ api_open=False).launch(server_name=args.host, server_port=args.port,
545
+ share=args.share, max_threads=5)
global.js ADDED
@@ -0,0 +1,446 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ () => {
2
+ var script = document.createElement("script");
3
+ script.src = "https://visjs.github.io/vis-network/standalone/umd/vis-network.min.js";
4
+ document.head.appendChild(script);
5
+
6
+ // var current_options;
7
+ // var node_pass_prob, prelen, max_path, node_tokens, node_probs, links, tarlen;
8
+ // var visible_nodes, map_max_path;
9
+ // var visible_edges, sorted_links_out, sorted_links_in;
10
+ // var nodes, edges, network;
11
+ // var disable_edge_select;
12
+ showing_node = undefined;
13
+ showing_edge = undefined;
14
+
15
+ function obtain_node_object(node_id) {
16
+ var title = "Node " + node_id.toString() + " (Pass Prob = " + node_pass_prob[node_id].toFixed(2) + ")\n";
17
+ title += "==========\n"
18
+ for(var j=0;j<5;j++){
19
+ if(node_probs[node_id][j]> 0.01){
20
+ title += node_tokens[node_id][j] + "\t" + node_probs[node_id][j].toFixed(2) + "\n";
21
+ }
22
+ }
23
+
24
+ var currentNode = {"id": node_id, "label": node_tokens[node_id][0], "value":node_pass_prob[node_id], "title": title};
25
+ if (current_options.show_node_detail){
26
+ currentNode.label = title;
27
+ }
28
+ if(map_max_path[node_id] !== undefined){
29
+ currentNode.x = map_max_path[node_id].x;
30
+ currentNode.y = map_max_path[node_id].y;
31
+ currentNode.fixed = true;
32
+ currentNode.mass = 5;
33
+ currentNode.color = {border: "red", background: "orange", highlight: {border: "red", background: "#ffcc66"}};
34
+ }
35
+ return currentNode;
36
+ }
37
+
38
+ function set_node_visibility(node_id, flag){
39
+ if(visible_nodes[node_id] == flag) return;
40
+ visible_nodes[node_id] = flag;
41
+ if(flag){
42
+ nodes.add(obtain_node_object(node_id));
43
+ }else{
44
+ nodes.remove(node_id);
45
+ }
46
+ }
47
+
48
+ function update_visible_nodes(clear_state=false){
49
+ if(typeof visible_nodes === "undefined" || clear_state){
50
+ visible_nodes = [...Array(prelen)].map((_, __) => false); // size: [prelen]
51
+ map_max_path = {}; // {node_id -> position on max_path}
52
+ var accumulated_x = 0;
53
+ for (var i=0;i<tarlen;i++){
54
+ accumulated_x += node_tokens[max_path[i]][0].length * 5
55
+ var y = Math.floor(Math.random() * 3) * 100 - 100;
56
+ map_max_path[max_path[i]] = {position: i, x: accumulated_x, y:y};
57
+ accumulated_x += node_tokens[max_path[i]][0].length * 5 + 100;
58
+ }
59
+ nodes = new vis.DataSet();
60
+ }
61
+
62
+ for (var i=0;i<prelen;i++){
63
+ if(node_pass_prob[i] >= current_options.minimum_node_pass_prob || map_max_path[i] !== undefined){
64
+ set_node_visibility(i, true);
65
+ }else{
66
+ set_node_visibility(i, false);
67
+ }
68
+ }
69
+ }
70
+
71
+
72
+ function update_node_details(){
73
+ for(var i=0;i<prelen;i++) if (visible_nodes[i]){
74
+ currentNode = obtain_node_object(i);
75
+ nodes.updateOnly(currentNode);
76
+ }
77
+ }
78
+
79
+ function obtain_edge_object(i, j){
80
+ var edge_id = i.toString() + "-" + j.toString();
81
+ var label = links[i][j].toFixed(2);
82
+ var pass_label = (node_pass_prob[i] * links[i][j]).toFixed(2);
83
+ var title = "From Node " + i.toString() + " to Node " + j.toString() + "\n" + "Transition Probability:" + label + "\nPassing Probability:" + pass_label;
84
+ var currentEdge = {id: edge_id,
85
+ from: i, to: j, value: links[i][j] * node_pass_prob[i], title: title};
86
+ if (map_max_path[i] !== undefined && map_max_path[j] !== undefined && map_max_path[i].position + 1 == map_max_path[j].position){
87
+ currentEdge.color = "red";
88
+ }
89
+ if(current_options.show_edge_label){
90
+ currentEdge.label = label;
91
+ }else{
92
+ currentEdge.label = " ";
93
+ }
94
+
95
+ return currentEdge;
96
+ }
97
+
98
+ function set_edge_visibility(i, j, flag){
99
+ if(visible_edges[i][j] == flag) return;
100
+ visible_edges[i][j] = flag;
101
+ if(flag){
102
+ edges.add(obtain_edge_object(i, j));
103
+ }else{
104
+ var edge_id = i.toString() + "-" + j.toString();
105
+ edges.remove(edge_id);
106
+ }
107
+ }
108
+
109
+ function update_visible_edges(clear_state=false){
110
+ if(typeof visible_edges === "undefined" || clear_state){
111
+ visible_edges = [...Array(prelen)].map((_, __) => {return [...Array(prelen)].map((_, __) => false);}); // size: [prelen, prelen]
112
+
113
+ sorted_links_out = []; // size: [prelen, prelen]
114
+ for (var i=0;i<prelen;i++){
115
+ // sort the out edge according to their prob
116
+ sorted_links_out.push(links[i].map((val, idx) => {return {'idx': idx, 'val': val};}).
117
+ sort((v1, v2) => v2.val - v1.val)
118
+ );
119
+ }
120
+
121
+ sorted_links_in = []; // size: [prelen, prelen], element {idx, val}, from big to small
122
+ for (var i=0;i<prelen;i++){
123
+ links_in = []
124
+ for(var j=0;j<prelen;j++) links_in.push({idx: j, val: links[j][i] * node_pass_prob[j]})
125
+ sorted_links_in.push(links_in.sort((v1, v2) => v2.val - v1.val));
126
+ }
127
+ edges = new vis.DataSet();
128
+ }
129
+
130
+ var next_visible_edges = [...Array(prelen)].map((_, __) => {return [...Array(prelen)].map((_, __) => false);}); // size: [prelen, prelen]
131
+ var links_in_num = [...Array(prelen)].map((_, __) => 0);
132
+
133
+ for (var i=0;i<prelen - 1;i++){
134
+ if(!visible_nodes[i]) continue;
135
+
136
+ // select visible out edge of node i
137
+ var left_visible_edge_num = current_options.max_out_edge_num;
138
+ var left_visible_edge_prob = current_options.max_out_edge_prob;
139
+ for(var j=0; j<prelen;j++){
140
+ var idx = sorted_links_out[i][j]['idx'];
141
+ if (sorted_links_out[i][j]['val'] < current_options.minimum_edge_prob) break;
142
+ if (visible_nodes[idx]){
143
+ links_in_num[idx]++;
144
+ next_visible_edges[i][idx] = true;
145
+ left_visible_edge_num--;
146
+ left_visible_edge_prob -= sorted_links_out[i][j]['val'];
147
+ if (left_visible_edge_num==0 || left_visible_edge_prob < 0){
148
+ break;
149
+ }
150
+ }
151
+ }
152
+ }
153
+
154
+ if(current_options.force_in_edge){
155
+ // add at least one in edge per node
156
+ for (var i=0;i<prelen;i++){
157
+ if(i == 0 || !visible_nodes[i] || links_in_num[i] > 0) continue;
158
+ for(var j=0; j<prelen;j++){
159
+ var idx = sorted_links_in[i][j]['idx'];
160
+ if (visible_nodes[idx]){
161
+ next_visible_edges[idx][i] = true;
162
+ break;
163
+ }
164
+ }
165
+ }
166
+ }
167
+
168
+ for(var i=0;i<prelen;i++){
169
+ for(var j=i+1;j<prelen;j++){
170
+ set_edge_visibility(i, j, next_visible_edges[i][j]);
171
+ }
172
+ }
173
+ }
174
+
175
+ function update_edge_label(){
176
+ for(var i=0;i<prelen;i++){
177
+ for(var j=i+1;j<prelen;j++) if (visible_edges[i][j]){
178
+ currentEdge = obtain_edge_object(i, j);
179
+ edges.updateOnly(currentEdge);
180
+ }
181
+ }
182
+ }
183
+
184
+ function customScalingFunction(min,max,total,value) {
185
+ min = 0;
186
+ var scale = 1 / (max - min);
187
+ return Math.max(0,(value - min)*scale);
188
+ }
189
+
190
+ function escapeHtml(unsafe)
191
+ {
192
+ return unsafe
193
+ .replace(/&/g, "&amp;")
194
+ .replace(/</g, "&lt;")
195
+ .replace(/>/g, "&gt;")
196
+ .replace(/"/g, "&quot;")
197
+ .replace(/'/g, "&#039;");
198
+ }
199
+ function get_jumpable_node(idx){
200
+ if(visible_nodes[idx]){
201
+ return "<a href=\"javascript:network.selectNodes(["+ idx.toString() +"]);show_hint_node(" + idx.toString() + ");\">" + idx.toString() + "</a>";
202
+ }else{
203
+ return "<a class=\"invisible\" href=\"javascript:show_hint_node(["+ idx.toString() +"]);network.unselectAll();\">" + idx.toString() + "</a>";
204
+ }
205
+ }
206
+ function get_jumpable_edge(i, j, label){
207
+ var edge_id = i.toString() + "-" + j.toString();
208
+ if(visible_edges[i][j]){
209
+ return "<a href=\"javascript:network.selectEdges(['"+ edge_id +"']);show_hint_edge('" + edge_id + "');\">" + label + "</a>";
210
+ }else{
211
+ return "<a class=\"invisible\" href=\"javascript:show_hint_edge('" + edge_id + "');network.unselectAll();\">" + label + "</a>";
212
+ }
213
+ }
214
+
215
+ show_hint_node = function(node_id){
216
+ showing_node = node_id;
217
+ showing_edge = undefined;
218
+ var title = "<p>You selected <b>Node " + node_id.toString() + "</b> ";
219
+ if (visible_nodes[node_id]){
220
+ title += "(<a href=\"javascript:network.fit({nodes:[" + node_id.toString() + "],animation:true});\">Find Me!</a>). "
221
+ }else{
222
+ title += "(Not shown). "
223
+ }
224
+ title += "Passing Probability: <b>" + node_pass_prob[node_id].toFixed(2) + "</b>. You can click the links below to jump to other edges or nodes.</p>";
225
+ document.getElementById("hintsupper").innerHTML = title;
226
+
227
+ title = "<table><thead><tr><th>Rank</th><th>Candidate</th><th>Probability</th></tr></thead><tbody>";
228
+ for (var j=0;j<5;j++){
229
+ title += "<tr><td>#" + (j+1).toString() + "</td><td>" + escapeHtml(node_tokens[node_id][j]) + "</td><td>" + node_probs[node_id][j].toFixed(2) + "</td></tr>";
230
+ }
231
+ title += "</tbody>"
232
+ title += "<p>Top-5 Token Candidates: </p>";
233
+ document.getElementById("hintsleft").innerHTML = title;
234
+
235
+ title = "<table><thead><tr><th>Rank</th><th>To</th><th>Transition Prob.</th><th>Passing Prob.</th></tr></thead><tbody>";
236
+ for (var j=0;j<prelen;j++){
237
+ var idx = sorted_links_out[node_id][j].idx;
238
+ if(j < 5 || visible_edges[node_id][idx]){
239
+ title += "<tr><td>" + get_jumpable_edge(node_id, idx, "#" + (j+1).toString()) + "</td><td>" + get_jumpable_node(idx) + "</td><td>" + links[node_id][idx].toFixed(2) + "</td><td>" +
240
+ (node_pass_prob[node_id] * links[node_id][idx]).toFixed(2) + "</td></tr>";
241
+ }
242
+ }
243
+ title += "</tbody>"
244
+ title += "<p>Top Outgoing Edges: </p>"
245
+ document.getElementById("hintscenter").innerHTML = title;
246
+
247
+ title = "<table><thead><tr><th>Rank</th><th>From</th><th>Transition Prob.</th><th>Passing Prob.</th></tr></thead><tbody>";
248
+ for (var j=0;j<prelen;j++){
249
+ var idx = sorted_links_in[node_id][j].idx;
250
+ if(j < 5 || visible_edges[idx][node_id]){
251
+ title += "<tr><td>" + get_jumpable_edge(idx, node_id, "#" + (j+1).toString()) + "</td><td>" + get_jumpable_node(idx) + "</td><td>" + links[idx][node_id].toFixed(2) + "</td><td>" +
252
+ (node_pass_prob[idx] * links[idx][node_id]).toFixed(2) + "</td></tr>";
253
+ }
254
+ }
255
+ title += "</tbody>"
256
+ title += "<p>Top Incoming Edges: </p>"
257
+ document.getElementById("hintsright").innerHTML = title;
258
+
259
+ document.getElementById("hintsbottom").innerHTML =
260
+ "<br>"+
261
+ "Passing probability of a node V represents how likely the node will be choosen in a random path, i.e., P(V \\in A). <br>" +
262
+ "Passing probability of an edge from U to V represents how likely the node V follows the node U in a random path, i.e., P(a_i = U && a_{i+1} = V). <br>" +
263
+ "Token probability represents how likely a token is predicted on the given node, i.e., P(y_i| v_{a_{i}}). <br>" +
264
+ "Transition probability represents how likely a specific node is following the given node, i.e. P(a_{i+1} | a_{i}).<br>"
265
+ }
266
+
267
+ show_hint_edge = function(edge_id){
268
+ showing_edge = edge_id;
269
+ showing_node = undefined;
270
+ var i = parseInt(edge_id.split("-")[0]);
271
+ var j = parseInt(edge_id.split("-")[1]);
272
+ var label = links[i][j].toFixed(2);
273
+ var passing_label = (links[i][j] * node_pass_prob[i]).toFixed(2);
274
+ var title = "You selected an edge from <b>Node " + get_jumpable_node(i) + " to Node " + get_jumpable_node(j) + "</b>."
275
+ if (visible_edges[i][j]){
276
+ title += "(<a href=\"javascript:network.fit({nodes:[" + i.toString() + "," + j.toString() + "],animation:true});\">Find Me!</a>). "
277
+ }else{
278
+ title += "(Not shown). "
279
+ }
280
+ title += "<br> You can click the links above to jump to the nodes. <br><br>"
281
+ title += "Transition Probability:<b>" + label + "</b><br>";
282
+ title += "Passing Probability:<b>" + passing_label + "</b><br>";
283
+ title += "<br>" +
284
+ "Transition probability represents how likely a specific node is following the given node, i.e. P(a_{i+1} | a_{i}).<br>" +
285
+ "Passing probability of an edge from U to V represents how likely the node V follows the node U in a random path, i.e., P(a_i = U && a_{i+1} = V). <br>"
286
+ document.getElementById("hintsupper").innerHTML = title;
287
+ document.getElementById("hintsleft").innerHTML = "";
288
+ document.getElementById("hintsright").innerHTML = "";
289
+ document.getElementById("hintscenter").innerHTML = "";
290
+ document.getElementById("hintsbottom").innerHTML = "";
291
+ }
292
+
293
+ function clear_hint(){
294
+ showing_node = undefined;
295
+ showing_edge = undefined;
296
+ document.getElementById("hintsupper").innerHTML = "Use scroll to zoom in or out. Select or Hover over nodes and edges for more information ... (Try dragging nodes to replace them.)";
297
+ document.getElementById("hintsleft").innerHTML = "";
298
+ document.getElementById("hintsright").innerHTML = "";
299
+ document.getElementById("hintscenter").innerHTML = "";
300
+ document.getElementById("hintsbottom").innerHTML = "";
301
+ }
302
+
303
+ startNetwork = function(graph_info, options) {
304
+ current_options = options;
305
+
306
+ global_graph_info = graph_info;
307
+ node_pass_prob = graph_info['node_pass_prob'][0] // size: [prelen]
308
+ prelen = node_pass_prob.length
309
+ max_path = graph_info['max_paths'][0] // size: [tarlen]
310
+ tarlen = max_path.length
311
+ node_tokens = graph_info['node_tokens'][0] // size: [prelen, 5]
312
+ node_probs = graph_info['node_probs'][0] // size: [prelen, 5]
313
+ links = graph_info['links'][0] // size: [prelen, prelen]
314
+
315
+ update_visible_nodes(true);
316
+ update_visible_edges(true);
317
+
318
+ // create a network
319
+ var container = document.getElementById("daggraph");
320
+ var data = {
321
+ nodes: nodes,
322
+ edges: edges,
323
+ };
324
+ network_options = {
325
+ nodes: {
326
+ shape: "ellipse",
327
+ scaling: {
328
+ label: {
329
+ min: 8,
330
+ max: 20,
331
+ },
332
+ customScalingFunction: customScalingFunction,
333
+ },
334
+ },
335
+ edges: {
336
+ arrowStrikethrough: false,
337
+ arrows: "to",
338
+ smooth: {
339
+ type: "continuous"
340
+ },
341
+ color: "#2B7CE9",
342
+ font: { align: "bottom" },
343
+ length: 120,
344
+ scaling: {
345
+ min: 0.5,
346
+ max: 3,
347
+ label: {
348
+ min: 8,
349
+ max: 15,
350
+ },
351
+ customScalingFunction: customScalingFunction,
352
+ }
353
+ }
354
+ };
355
+ network = new vis.Network(container, data, network_options);
356
+
357
+ network.off("dragStart");
358
+ network.on("dragStart", function (params) {
359
+ var idx = this.getNodeAt(params.pointer.DOM);
360
+ if (idx !== undefined) {
361
+ // console.log("dragstart " + idx.toString());
362
+ if (map_max_path[idx] !== undefined){
363
+ data.nodes.update({id: idx, fixed: false});
364
+ }
365
+ }
366
+ });
367
+ network.off("dragEnd");
368
+ network.on("dragEnd", function (params) {
369
+ var idx = this.getNodeAt(params.pointer.DOM);
370
+ if (idx !== undefined){
371
+ // console.log("dragend " + idx.toString());
372
+ if (map_max_path[idx] !== undefined){
373
+ data.nodes.update({id: idx, fixed: true});
374
+ map_max_path[idx].x = params.pointer.canvas.x;
375
+ map_max_path[idx].y = params.pointer.canvas.y;
376
+ }
377
+ }
378
+ });
379
+
380
+ disable_edge_select = false;
381
+ network.off("selectNode");
382
+ network.on("selectNode", function (params) {
383
+ var node_id = params.nodes[0];
384
+ show_hint_node(node_id);
385
+ disable_edge_select = true;
386
+ setTimeout(() => {disable_edge_select=false;}, 200);
387
+ });
388
+
389
+ network.off("selectEdge");
390
+ network.on("selectEdge", function (params) {
391
+ if(disable_edge_select) return;
392
+ var edge_id = params.edges[0];
393
+ show_hint_edge(edge_id);
394
+ });
395
+
396
+ network.off("deselectNode");
397
+ network.on("deselectNode", function (params) {
398
+ clear_hint();
399
+ showing_node = undefined;
400
+ showing_edge = undefined;
401
+ });
402
+ network.off("deselectEdge");
403
+ network.on("deselectEdge", function (params) {
404
+ clear_hint();
405
+ });
406
+ }
407
+
408
+ updateNetwork = function(options) {
409
+ if(typeof node_pass_prob === "undefined") return;
410
+ old_options = current_options;
411
+ current_options = options;
412
+ if(options.minimum_node_pass_prob != old_options.minimum_node_pass_prob){
413
+ update_visible_nodes();
414
+ }
415
+ if(options.minimum_node_pass_prob != old_options.minimum_node_pass_prob ||
416
+ options.minimum_edge_prob != old_options.minimum_edge_prob ||
417
+ options.max_out_edge_num != old_options.max_out_edge_num ||
418
+ options.max_out_edge_prob != old_options.max_out_edge_prob ||
419
+ options.force_in_edge != old_options.force_in_edge){
420
+ update_visible_edges();
421
+ }
422
+ if(options.show_node_detail != old_options.show_node_detail){
423
+ if(options.show_node_detail) {
424
+ network_options.nodes.shape = "dot";
425
+ network_options.nodes.scaling.label.min=10;
426
+ network_options.nodes.scaling.label.max=10;
427
+ }else{
428
+ network_options.nodes.shape = "ellipse";
429
+ network_options.nodes.scaling.label.min=8;
430
+ network_options.nodes.scaling.label.max=20;
431
+ }
432
+ network.setOptions(network_options);
433
+ update_node_details();
434
+ }
435
+ if(options.show_edge_label != old_options.show_edge_label){
436
+ update_edge_label();
437
+ }
438
+
439
+ if(showing_node != undefined){
440
+ show_hint_node(showing_node);
441
+ }
442
+ if(showing_edge != undefined){
443
+ show_hint_edge(showing_edge);
444
+ }
445
+ }
446
+ }
graph.html ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <style type="text/css">
2
+ #contents {
3
+ height: 400px;
4
+ margin: 0;
5
+ padding: 0;
6
+ box-sizing: border-box;
7
+ position: relative;
8
+ }
9
+ #hints {
10
+ height: 400px;
11
+ margin: 0;
12
+ padding: 0;
13
+ box-sizing: border-box;
14
+ position: relative;
15
+ }
16
+
17
+ #daggraph {
18
+ width: 100%;
19
+ height: 100%;
20
+ border: 1px solid #d3d3d3;
21
+ box-sizing: border-box;
22
+ }
23
+
24
+ #hints .invisible:link, #hints .invisible:visited {
25
+ color: gray;
26
+ }
27
+
28
+ .container {right: 0; text-align: center;}
29
+ .container .left, .container .center, .container .right { display: inline-block; padding: 5px 10px; text-align: left;}
30
+ .container .left { float: left; }
31
+ .container .center { margin: 0 auto; }
32
+ .container .right { float: right; }
33
+ .clear { clear: both; }
34
+ #hintsleft td, #hintsleft th, #hintscenter td, #hintscenter th, #hintsright td, #hintsright th {padding: 3px 8px; }
35
+ </style>
36
+
37
+ <div id="contents">
38
+ <div id="daggraph"></div>
39
+ </div>
40
+ <div id="hints">
41
+ <div id="hintsupper">Use scroll to zoom in or out. Select or Hover over nodes and edges for more information ... (Try dragging nodes to replace them.)</div>
42
+ <div class="container">
43
+ <div class="left" id="hintsleft"></div>
44
+ <div class="center" id="hintscenter"></div>
45
+ <div class="right" id="hintsright"></div>
46
+ <div class="clear"></div>
47
+ </div>
48
+ <div id="hintsbottom"></div>
49
+ </div>
packages.txt ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ build-essential
2
+ cmake
3
+ libboost-system-dev
4
+ libboost-thread-dev
5
+ libboost-program-options-dev
6
+ libboost-test-dev
7
+ libeigen3-dev
8
+ zlib1g-dev
9
+ libbz2-dev
10
+ liblzma-dev
requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ git+https://github.com/thu-coai/DA-Transformer.git[demo]
2
+ git+https://github.com/thu-coai/DAG-Search.git
vis-network.min.js ADDED
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