run_bertology: AttributeError: 'NoneType' object has no attribute 'abs'
Author: ThomasSYT
Pull request: no
Labels: name: wontfix

name: Good First Issue

# 🐛 Bug

## Information
One more question did run_bertology also support albert model?
Model I am using (Bert, XLNet ...): Bert

Language I am using the model on (English, Chinese ...):English

The problem arises when using:
* [ ] the official example scripts: (give details below)
run_bertology.py
* [ ] my own modified scripts: (give details below)
python3.7/site-packages/transformers/data/processors/glue.py
# Because the dataset SciEntsBank-3way labeled as "correct", "incorrect", "contradictory".
-223 return ["contradiction", "entailment", "neutral"]
+223 return ["correct", "incorrect", "contradictory"] 
# Because the dataset SciEntsBank-3way structure. label at first position, text_a at second position and text_b at third position.
-232 text_a = line[8]
-233 text_b = line[9]
-234 label = line[-1]
+232 text_a = line[1]
+233 text_b = line[2]
+234 label = line[0]

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
mnli
* [ ] my own task or dataset: (give details below)
dataset: SciEntsBank-3way(https://www.cs.york.ac.uk/semeval-2013/task7.html)

## To reproduce

Steps to reproduce the behavior:

python ./run_bertology.py     --data_dir SciEntsBank-3way     
                              --model_name bert-base-uncased     
                              --task_name mnli    
                              --max_seq_length 128     
                              --output_dir ./tmp/$TASK_NAME/     
                              --try_masking 

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->
Iteration: 100%|██████████| 4561/4561 [02:15<00:00, 33.57it/s]
INFO:__main__:Attention entropies
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	3.03255	2.82196	1.77876	1.64802	3.27255	2.91101	3.34266	3.03600	2.73255	3.09043	1.35738	2.52412
INFO:__main__:layer 2:	2.73629	1.11241	2.86221	2.44852	0.95509	2.39331	0.45580	2.82749	2.93869	2.88269	2.19532	2.48865
INFO:__main__:layer 3:	0.05847	1.66529	1.91624	2.79214	2.31408	2.67645	2.18180	2.62745	2.48442	0.05168	2.52636	2.49648
INFO:__main__:layer 4:	1.54150	2.90387	2.40694	2.06858	2.77907	0.80181	2.69664	2.88957	2.70095	1.19583	2.33666	1.83265
INFO:__main__:layer 5:	2.34246	2.64519	2.03515	1.37404	2.88754	1.67422	2.14421	1.41457	2.03571	2.69347	1.98139	1.44582
INFO:__main__:layer 6:	1.71052	1.10676	2.28401	1.87228	2.55920	1.75916	1.22450	1.35704	1.92916	1.02535	1.67920	1.60766
INFO:__main__:layer 7:	1.63887	1.93625	1.83002	1.20811	1.58296	1.65662	1.55572	2.38742	2.09030	1.69326	1.42275	1.08153
INFO:__main__:layer 8:	1.95536	1.73146	1.59791	1.17307	1.12128	1.95980	1.11606	1.11680	1.97816	1.64787	1.53183	1.28007
INFO:__main__:layer 9:	1.54698	1.96436	1.45466	2.03807	1.60202	1.44075	1.36014	2.32559	2.59592	2.09076	1.75704	1.85274
INFO:__main__:layer 10:	2.00444	1.91784	2.12478	1.99289	1.58305	2.48627	2.08822	1.69971	2.70500	1.71860	2.03850	2.38604
INFO:__main__:layer 11:	2.76158	1.53031	1.99278	2.26007	1.97855	1.66471	1.90139	2.13217	2.45516	1.83803	1.99372	2.15438
INFO:__main__:layer 12:	1.73656	2.10304	2.72498	1.85723	2.04607	2.20456	2.16210	1.82173	2.18728	2.71702	1.84256	1.83663
INFO:__main__:Head importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	0.30328	0.05899	0.06971	0.03727	0.13938	1.00000	0.04436	0.03679	0.22807	0.07911	0.19918	0.05241
INFO:__main__:layer 2:	0.04867	0.02256	0.21194	0.04069	0.23058	0.15942	0.65188	0.38251	0.47535	0.40172	0.10869	0.34316
INFO:__main__:layer 3:	0.06349	0.08003	0.56604	0.41141	0.38410	0.16264	0.29070	0.37301	0.28161	0.18325	0.45048	0.02401
INFO:__main__:layer 4:	0.74869	0.15986	0.29754	0.02072	0.20961	0.06570	0.35717	0.44580	0.01144	0.11113	0.26962	0.28707
INFO:__main__:layer 5:	0.05413	0.58029	0.29859	0.64154	0.25539	0.11611	0.36774	0.05591	0.19390	0.34493	0.04906	0.02742
INFO:__main__:layer 6:	0.24067	0.06599	0.45376	0.22384	0.40461	0.53808	0.06806	0.21937	0.04209	0.13334	0.19226	0.57838
INFO:__main__:layer 7:	0.33972	0.12576	0.31489	0.10031	0.29630	0.19341	0.28052	0.29937	0.78337	0.09395	0.23640	0.05812
INFO:__main__:layer 8:	0.23342	0.27415	0.27682	0.22111	0.23234	0.79778	0.03235	0.09092	0.40418	0.01651	0.21795	0.22528
INFO:__main__:layer 9:	0.01306	0.88878	0.08858	0.45180	0.04019	0.08035	0.13417	0.15899	0.39753	0.01761	0.10785	0.01428
INFO:__main__:layer 10:	0.01597	0.01365	0.08691	0.04718	0.01268	0.32052	0.00453	0.05614	0.81534	0.00000	0.02659	0.66734
INFO:__main__:layer 11:	0.86446	0.00818	0.05306	0.12751	0.13587	0.00293	0.06480	0.22173	0.21643	0.04838	0.48050	0.32190
INFO:__main__:layer 12:	0.08048	0.32489	0.56753	0.28201	0.37204	0.09334	0.26549	0.07130	0.00372	0.53481	0.24909	0.36108
INFO:__main__:Head ranked by importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	41	108	102	123	81	0	119	124	62	100	72	114
INFO:__main__:layer 2:	116	129	70	121	61	79	8	28	17	25	89	35
INFO:__main__:layer 3:	107	99	13	22	27	77	46	29	49	76	20	128
INFO:__main__:layer 4:	6	78	44	130	71	105	33	21	138	88	53	47
INFO:__main__:layer 5:	112	10	43	9	55	87	31	111	73	34	115	126
INFO:__main__:layer 6:	57	104	18	64	23	14	103	67	120	84	75	11
INFO:__main__:layer 7:	36	86	40	91	45	74	50	42	5	92	58	109
INFO:__main__:layer 8:	59	52	51	66	60	4	125	94	24	132	68	63
INFO:__main__:layer 9:	136	1	95	19	122	98	83	80	26	131	90	134
INFO:__main__:layer 10:	133	135	96	118	137	39	140	110	3	143	127	7
INFO:__main__:layer 11:	2	139	113	85	82	142	106	65	69	117	16	38
INFO:__main__:layer 12:	97	37	12	48	30	93	54	101	141	15	56	32
Iteration: 100%|██████████| 4561/4561 [01:54<00:00, 39.80it/s]
INFO:__main__:Attention entropies
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 2:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 3:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 4:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 5:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 6:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 7:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 8:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 9:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 10:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 11:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 12:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:Head importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	0.30328	0.05899	0.06971	0.03727	0.13938	1.00000	0.04436	0.03679	0.22807	0.07911	0.19918	0.05241
INFO:__main__:layer 2:	0.04867	0.02256	0.21194	0.04069	0.23058	0.15942	0.65188	0.38251	0.47535	0.40172	0.10869	0.34316
INFO:__main__:layer 3:	0.06349	0.08003	0.56604	0.41141	0.38410	0.16264	0.29070	0.37301	0.28161	0.18325	0.45048	0.02401
INFO:__main__:layer 4:	0.74869	0.15986	0.29754	0.02072	0.20961	0.06570	0.35717	0.44580	0.01144	0.11113	0.26962	0.28707
INFO:__main__:layer 5:	0.05413	0.58029	0.29859	0.64154	0.25539	0.11611	0.36774	0.05591	0.19390	0.34493	0.04906	0.02742
INFO:__main__:layer 6:	0.24067	0.06599	0.45376	0.22384	0.40461	0.53808	0.06806	0.21937	0.04209	0.13334	0.19226	0.57838
INFO:__main__:layer 7:	0.33972	0.12576	0.31489	0.10031	0.29630	0.19341	0.28052	0.29937	0.78337	0.09395	0.23640	0.05812
INFO:__main__:layer 8:	0.23342	0.27415	0.27682	0.22111	0.23234	0.79778	0.03235	0.09092	0.40418	0.01651	0.21795	0.22528
INFO:__main__:layer 9:	0.01306	0.88878	0.08858	0.45180	0.04019	0.08035	0.13417	0.15899	0.39753	0.01761	0.10785	0.01428
INFO:__main__:layer 10:	0.01597	0.01365	0.08691	0.04718	0.01268	0.32052	0.00453	0.05614	0.81534	0.00000	0.02659	0.66734
INFO:__main__:layer 11:	0.86446	0.00818	0.05306	0.12751	0.13587	0.00293	0.06480	0.22173	0.21643	0.04838	0.48050	0.32190
INFO:__main__:layer 12:	0.08048	0.32489	0.56753	0.28201	0.37204	0.09334	0.26549	0.07130	0.00372	0.53481	0.24909	0.36108
INFO:__main__:Head ranked by importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	41	108	102	123	81	0	119	124	62	100	72	114
INFO:__main__:layer 2:	116	129	70	121	61	79	8	28	17	25	89	35
INFO:__main__:layer 3:	107	99	13	22	27	77	46	29	49	76	20	128
INFO:__main__:layer 4:	6	78	44	130	71	105	33	21	138	88	53	47
INFO:__main__:layer 5:	112	10	43	9	55	87	31	111	73	34	115	126
INFO:__main__:layer 6:	57	104	18	64	23	14	103	67	120	84	75	11
INFO:__main__:layer 7:	36	86	40	91	45	74	50	42	5	92	58	109
INFO:__main__:layer 8:	59	52	51	66	60	4	125	94	24	132	68	63
INFO:__main__:layer 9:	136	1	95	19	122	98	83	80	26	131	90	134
INFO:__main__:layer 10:	133	135	96	118	137	39	140	110	3	143	127	7
INFO:__main__:layer 11:	2	139	113	85	82	142	106	65	69	117	16	38
INFO:__main__:layer 12:	97	37	12	48	30	93	54	101	141	15	56	32
INFO:__main__:Pruning: original score: 0.091866, threshold: 0.082679
INFO:__main__:Heads to mask: [117, 125, 140, 114, 121, 44, 112, 96, 109, 107, 108, 93, 105, 39]
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 2:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 3:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 4:	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 5:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 6:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 7:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 8:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000
INFO:__main__:layer 9:	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	0.00000
INFO:__main__:layer 10:	0.00000	0.00000	1.00000	1.00000	0.00000	1.00000	0.00000	1.00000	1.00000	0.00000	1.00000	1.00000
INFO:__main__:layer 11:	1.00000	0.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 12:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000
Iteration: 100%|██████████| 4561/4561 [01:54<00:00, 39.68it/s]
INFO:__main__:Attention entropies
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 2:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 3:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 4:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 5:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 6:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 7:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 8:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 9:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 10:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 11:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 12:	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
INFO:__main__:Head importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	0.39574	0.02468	0.08140	0.12466	0.12766	1.00000	0.09364	0.03733	0.21125	0.05515	0.27669	0.01294
INFO:__main__:layer 2:	0.04871	0.01492	0.02147	0.00766	0.25008	0.16705	0.73248	0.48019	0.40388	0.39327	0.06609	0.38033
INFO:__main__:layer 3:	0.14803	0.02220	0.64758	0.29125	0.45867	0.02242	0.34411	0.33109	0.30959	0.29897	0.41782	0.01806
INFO:__main__:layer 4:	0.82395	0.20768	0.33463	0.05595	0.30457	0.01353	0.38665	0.33563	0.02096	0.12274	0.15990	0.30594
INFO:__main__:layer 5:	0.24467	0.77639	0.26634	0.43066	0.28580	0.15136	0.29888	0.09479	0.20161	0.38652	0.07106	0.09292
INFO:__main__:layer 6:	0.24192	0.04294	0.19242	0.18251	0.64465	0.64657	0.03439	0.17273	0.05866	0.20935	0.14715	0.51240
INFO:__main__:layer 7:	0.24221	0.11722	0.54783	0.09908	0.30887	0.33625	0.18271	0.09798	0.76243	0.19917	0.26639	0.02415
INFO:__main__:layer 8:	0.34639	0.10483	0.42852	0.23310	0.20756	0.85146	0.05960	0.06187	0.25805	0.12922	0.14193	0.28091
INFO:__main__:layer 9:	0.02696	0.97099	0.08023	0.36748	0.05116	0.06451	0.07015	0.23535	0.39404	0.14999	0.01570	0.01164
INFO:__main__:layer 10:	0.02307	0.01918	0.09727	0.05241	0.03105	0.32034	0.02875	0.08710	0.92011	0.00000	0.05011	0.59763
INFO:__main__:layer 11:	0.81794	0.00753	0.05141	0.14622	0.09715	0.00008	0.03071	0.09413	0.17420	0.05189	0.69652	0.31542
INFO:__main__:layer 12:	0.09312	0.36467	0.56134	0.25867	0.37935	0.06446	0.34758	0.09796	0.03789	0.56186	0.25654	0.37430
INFO:__main__:Head ranked by importance scores
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	24	126	100	85	84	0	96	120	64	111	52	138
INFO:__main__:layer 2:	117	136	131	140	58	75	8	18	23	26	104	29
INFO:__main__:layer 3:	79	130	10	49	19	129	36	40	43	47	22	134
INFO:__main__:layer 4:	4	66	39	110	46	137	27	38	132	86	76	45
INFO:__main__:layer 5:	59	6	54	20	50	77	48	94	68	28	102	98
INFO:__main__:layer 6:	61	118	70	72	12	11	121	74	109	65	80	17
INFO:__main__:layer 7:	60	87	16	89	44	37	71	90	7	69	53	127
INFO:__main__:layer 8:	35	88	21	63	67	3	108	107	56	83	82	51
INFO:__main__:layer 9:	125	1	101	32	115	105	103	62	25	78	135	139
INFO:__main__:layer 10:	128	133	92	112	122	41	124	99	2	143	116	13
INFO:__main__:layer 11:	5	141	114	81	93	142	123	95	73	113	9	42
INFO:__main__:layer 12:	97	33	15	55	30	106	34	91	119	14	57	31
INFO:__main__:Masking: current score: 0.092085, remaning heads 130 (90.3 percents)
INFO:__main__:Heads to mask: [15, 11, 41, 13, 106, 35, 14, 25, 29, 83, 1, 126, 66, 7]
INFO:__main__:lv, h >	1	2	3	4	5	6	7	8	9	10	11	12
INFO:__main__:layer 1:	1.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000	0.00000
INFO:__main__:layer 2:	1.00000	0.00000	0.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 3:	1.00000	0.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000
INFO:__main__:layer 4:	1.00000	1.00000	1.00000	0.00000	1.00000	0.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 5:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 6:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 7:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000
INFO:__main__:layer 8:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000
INFO:__main__:layer 9:	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	0.00000	0.00000
INFO:__main__:layer 10:	0.00000	0.00000	1.00000	1.00000	0.00000	1.00000	0.00000	1.00000	1.00000	0.00000	1.00000	1.00000
INFO:__main__:layer 11:	1.00000	0.00000	1.00000	1.00000	1.00000	0.00000	0.00000	1.00000	1.00000	1.00000	1.00000	1.00000
INFO:__main__:layer 12:	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.00000	1.00000	1.00000	1.00000
Iteration:   0%|          | 0/4561 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "run_bertology.py", line 426, in <module>
    main()
  File "run_bertology.py", line 421, in main
    head_mask = mask_heads(args, model, eval_dataloader)
  File "run_bertology.py", line 179, in mask_heads
    args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask
  File "run_bertology.py", line 104, in compute_heads_importance
    head_importance += head_mask.grad.abs().detach()
AttributeError: 'NoneType' object has no attribute 'abs'



## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version:2.8.0
- Platform:
- Python version:3.7
- PyTorch version (GPU?):
- Tensorflow version (GPU?):
- Using GPU in script?:yes
- Using distributed or parallel set-up in script?:


Comments:
Sorry, I think it is the same issue as #4103. 

Please don't open duplicate issues. If you have more info about this issue, post it here. Also, please use [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks).

> Please don't open duplicate issues. If you have more info about this issue, post it here. Also, please use [code blocks](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks).

Sorry for this.

Facing the same issue - 
Environment - 

- transformers version:2.2.2
- Platform: 
- Python version:3.7.3
- PyTorch version (GPU?): 1.3.1  (no GPU)
- Tensorflow version (GPU?): 1.14.0 (no GPU)
- Using GPU in script?:No
- Using distributed or parallel set-up in script?: No

While computing the initial importance score (When the head_mask is None) I am getting the following error. -
 
 File "/Users/user/Desktop/org//prune_attention_heads.py", line 226, in compute_heads_importance
    head_importance += head_mask.grad.abs().detach()
AttributeError: 'NoneType' object has no attribute 'abs'

On putting the line above in a try and except block and printing the head_mask I get the following - 

tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]], requires_grad=True)

Here, the head_mask has requires_grad=True and I am passing the head_mask into the model and calling loss.backward() as in the bertology script. 


I met the same problem, the head_mask has no grad at the second time.

Seems that during the second pruning, the head_mask tensor becomes a non-leaf node, when the grad is None, I printed the `head_mask.is_leaf` attribute and get the warning(PyTorch 1.5.0) as below:
> UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.
  warnings.warn("The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad "
head_mask is leaf: False

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.