[OP] Accelerate GPU version of LayerNorm(axis=-1)

[sxjscience]

Description

Accelerate the speed of LayerNorm when (axis=-1).

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

• Accelerate the GPU version of LayerNorm when axis=-1

Comments

• Benchmark Results are listed in the comments

[sxjscience]

We tested the speed of LayerNorm(axis=-1) with different batch, channel, dtype combinations. The results are listed as follows. We use both nvprof to profile the speed of the kernels and time.time() to check the overall running time of the python script. Here, we also highlight the running times of the batch/channel combinations in Large BERT, i.e., (B = 4096, 8192, C = 768, 1024).

We run the speed test in a P3.2 machine (V100). All experiments are repeated for 3 times and the average running time is reported. codes: /~https://github.com/sxjscience/benchmark_ops/blob/master/gen_layernorm_benchmark.py

To reproduce, run the following code:

git clone /~https://github.com/sxjscience/benchmark_ops.git
cd benchmark_ops
python gen_layernorm_benchmark.py

PyTorch + Apex + FP32 --> time in microsecond (us), apex: /~https://github.com/NVIDIA/apex

Forward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	19	31	56	106
C=64	8	19	32	56	108
C=128	6	15	23	42	77
C=256	6	17	27	49	92
C=512	7	26	38	70	134
C=768	8	31	45	82	157
C=1024	9	38	58	106	204

Backward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	18	25	42	91
C=64	8	18	25	51	99
C=128	8	19	31	58	105
C=256	9	28	41	72	132
C=512	10	44	64	115	218
C=768	10	62	92	174	332
C=1024	11	82	122	230	450

Backward Data (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	2	8	11	20	38
C=64	3	8	11	21	41
C=128	3	8	12	23	45
C=256	3	11	17	32	64
C=512	4	19	31	63	126
C=768	4	30	49	103	203
C=1024	5	45	72	142	286

Backward Gamma & Beta (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	5	11	14	23	53
C=64	5	11	14	30	58
C=128	5	11	18	35	60
C=256	5	17	24	40	69
C=512	6	25	33	52	92
C=768	6	32	42	71	129
C=1024	6	37	50	89	164

Forward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	105	107	107	135	161
C=64	107	108	109	128	209
C=128	106	112	145	155	208
C=256	104	120	149	187	216
C=512	103	171	186	204	246
C=768	108	183	190	211	261
C=1024	111	191	195	229	294

Backward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	236	246	222	261	231
C=64	215	222	228	250	302
C=128	219	232	301	273	289
C=256	238	237	269	310	314
C=512	221	300	291	311	322
C=768	222	295	291	305	392
C=1024	248	311	329	321	467

[sxjscience]

MXNet (new kernel) + FP32

According to nvprof, the performance of the new kernel matches that of nvidia/apex. However, if we check the overall running time of the python script, MXNet is much slower than PyTorch. This is caused by some other overheads and is not related to the CUDA kernel.

Forward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	6	11	15	25	46
C=64	6	11	15	25	44
C=128	6	11	16	25	43
C=256	7	17	24	43	79
C=512	9	26	35	60	111
C=768	9	37	54	98	188
C=1024	10	44	65	116	221

Backward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	17	23	38	87
C=64	8	18	23	50	102
C=128	8	18	31	60	110
C=256	9	31	47	83	150
C=512	10	46	70	126	237
C=768	12	71	104	195	372
C=1024	13	85	127	237	465

Backward Data (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	3	7	10	17	30
C=64	3	8	10	17	31
C=128	3	8	11	19	35
C=256	4	12	18	34	67
C=512	5	19	32	66	132
C=768	5	31	48	96	189
C=1024	5	43	68	133	265

Backward Gamma & Beta (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	5	10	13	21	57
C=64	5	10	13	32	71
C=128	5	10	20	42	75
C=256	5	18	29	49	83
C=512	6	26	38	60	106
C=768	7	40	56	99	183
C=1024	8	42	59	104	201

Forward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	249	367	370	399	463
C=64	266	311	373	428	469
C=128	251	371	384	434	487
C=256	258	396	426	473	513
C=512	255	410	453	496	533
C=768	279	441	457	530	621
C=1024	253	451	478	550	661

Backward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	255	305	352	329	353
C=64	256	321	342	326	376
C=128	254	311	322	340	389
C=256	255	322	333	361	421
C=512	256	331	351	405	539
C=768	305	344	386	479	671
C=1024	262	371	398	509	741

[eric-haibin-lin]

Nice work! Can you retrigger CI?

[sxjscience]

PyTorch + Apex + FP16
Forward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	20	34	60	115
C=64	8	20	34	61	116
C=128	9	20	35	62	118
C=256	7	17	27	49	92
C=512	7	22	34	62	119
C=768	8	28	43	79	153
C=1024	8	36	53	98	190

Backward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	18	24	41	75
C=64	8	18	25	42	91
C=128	8	19	25	51	99
C=256	8	20	33	62	114
C=512	9	30	45	79	144
C=768	10	39	58	102	188
C=1024	11	48	71	122	232

Backward Data (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	2	8	11	20	37
C=64	2	8	11	20	39
C=128	3	8	12	22	43
C=256	3	9	14	27	52
C=512	4	13	20	37	72
C=768	4	18	28	53	103
C=1024	5	23	37	69	140

Backward Gamma & Beta (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	5	10	13	21	38
C=64	5	10	14	22	52
C=128	5	10	14	29	56
C=256	5	11	19	35	62
C=512	5	17	25	42	72
C=768	6	21	30	49	85
C=1024	6	25	34	53	92

Forward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	106	108	130	133	203
C=64	108	107	113	153	192
C=128	107	129	120	184	232
C=256	106	122	168	199	220
C=512	108	181	190	203	241
C=768	119	190	190	212	261
C=1024	117	193	195	225	286

Backward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	239	239	270	238	297
C=64	219	243	246	296	273
C=128	218	272	257	302	297
C=256	216	256	291	304	302
C=512	250	300	297	317	326
C=768	259	318	300	307	316
C=1024	236	312	306	320	330

[sxjscience]

MXNet (new kernel) + FP16

Forward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	6	10	14	22	39
C=64	5	10	13	21	37
C=128	6	11	15	26	46
C=256	7	17	24	43	81
C=512	9	23	33	59	112
C=768	9	34	52	99	193
C=1024	10	40	61	115	224

Backward (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	8	16	20	33	59
C=64	8	16	21	34	74
C=128	8	16	21	43	80
C=256	9	21	33	64	116
C=512	10	32	47	82	149
C=768	12	50	74	135	258
C=1024	13	57	86	152	286

Backward Data (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	3	7	10	16	30
C=64	3	8	10	17	30
C=128	3	8	10	17	31
C=256	4	11	16	30	57
C=512	4	15	22	40	77
C=768	5	24	37	70	137
C=1024	5	28	42	80	158

Backward Gamma & Beta (nvprof timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	5	9	11	17	29
C=64	5	9	11	17	44
C=128	5	9	11	26	50
C=256	5	10	17	34	59
C=512	6	17	26	42	73
C=768	7	26	37	65	120
C=1024	8	30	44	72	128

Forward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	271	362	371	390	476
C=64	259	390	333	406	463
C=128	225	370	415	419	475
C=256	261	413	403	455	512
C=512	252	410	428	472	546
C=768	256	441	452	509	626
C=1024	255	451	474	522	659

Backward (python timer)

	B=128	B=2560	B=4096	B=8192	B=16384
C=32	295	311	317	321	345
C=64	274	315	321	329	350
C=128	256	315	338	330	354
C=256	257	338	326	346	391
C=512	308	327	333	395	435
C=768	261	348	346	412	537
C=1024	264	347	359	423	564

[pinaraws]

@mxnet-label-bot add[Operator, pr-awaiting-review]