Implement PReLU backward

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• Added PReLU backward operation and kernels.
• Added driver test and gtest for PReLU backward operation.
• New API is guarded by MIOPEN_BETA_API macro.
• Compared to ROCm pytorch:

float16

op_name	dtype	size	num_param	model	direction	ROCm pytorch	MIOpen HIP	Improvement
PReLU	float16	[512 64 112 112]	1	arcface	bwd	99312671	12277100	8.11
PReLU	float16	[512 64 56 56]	1	arcface	bwd	26715752	3073780	8.79
PReLU	float16	[512 128 56 56]	1	arcface	bwd	52103300	6140360	8.53
PReLU	float16	[512 128 28 28]	1	arcface	bwd	13279513	1545730	8.73
PReLU	float16	[512 256 28 28]	1	arcface	bwd	26771242	3072230	8.79
PReLU	float16	[512 256 14 14]	1	arcface	bwd	6605507	786651	8.68
PReLU	float16	[512 512 14 14]	1	arcface	bwd	13283229	1550010	8.71
PReLU	float16	[512 512 7 7]	1	arcface	bwd	3289272	403211	8.72
PReLU	float16	[512 64 112 112]	64	arcface	bwd	107275183	46587900	2.31
PReLU	float16	[512 64 56 56]	64	arcface	bwd	30255041	15273200	2.00
PReLU	float16	[512 128 56 56]	128	arcface	bwd	56547437	13889100	4.09
PReLU	float16	[512 128 28 28]	128	arcface	bwd	15626705	3868870	4.10
PReLU	float16	[512 256 28 28]	256	arcface	bwd	30232264	5028100	6.07
PReLU	float16	[512 256 14 14]	256	arcface	bwd	7826620	1531590	5.26
PReLU	float16	[512 512 14 14]	512	arcface	bwd	15628058	2114630	7.50
PReLU	float16	[512 512 7 7]	512	arcface	bwd	3877272	682737	6.01

float32

op_name	dtype	size	num_param	model	direction	ROCm pytorch	MIOpen HIP	Improvement
PReLU	float32	[512 64 112 112]	1	arcface	bwd	103155863	12389000	8.35
PReLU	float32	[512 64 56 56]	1	arcface	bwd	27476400	3102070	8.95
PReLU	float32	[512 128 56 56]	1	arcface	bwd	52891035	6194870	8.58
PReLU	float32	[512 128 28 28]	1	arcface	bwd	14441185	1560250	9.43
PReLU	float32	[512 256 28 28]	1	arcface	bwd	27334300	3102970	8.90
PReLU	float32	[512 256 14 14]	1	arcface	bwd	7261653	791718	9.51
PReLU	float32	[512 512 14 14]	1	arcface	bwd	14419330	1559770	9.42
PReLU	float32	[512 512 7 7]	1	arcface	bwd	3608816	406560	9.54
PReLU	float32	[512 64 112 112]	64	arcface	bwd	109970712	46718600	2.36
PReLU	float32	[512 64 56 56]	64	arcface	bwd	30409798	13333300	2.30
PReLU	float32	[512 128 56 56]	128	arcface	bwd	57683256	13940500	4.16
PReLU	float32	[512 128 28 28]	128	arcface	bwd	16256649	3878210	4.27
PReLU	float32	[512 256 28 28]	256	arcface	bwd	30886236	5124050	6.09
PReLU	float32	[512 256 14 14]	256	arcface	bwd	8320915	1522140	5.66
PReLU	float32	[512 512 14 14]	512	arcface	bwd	16164181	2137560	7.70
PReLU	float32	[512 512 7 7]	512	arcface	bwd	4095063	685281	6.41

bfloat16

op_name	dtype	size	num_param	model	direction	ROCm pytorch	MIOpen HIP	Improvement
PReLU	bfloat16	[512 64 112 112]	1	arcface	bwd	99413514	12540100	7.95
PReLU	bfloat16	[512 64 56 56]	1	arcface	bwd	26839289	3139650	8.63
PReLU	bfloat16	[512 128 56 56]	1	arcface	bwd	52355515	6275980	8.38
PReLU	bfloat16	[512 128 28 28]	1	arcface	bwd	13353346	1579090	8.60
PReLU	bfloat16	[512 256 28 28]	1	arcface	bwd	26856779	3140230	8.64
PReLU	bfloat16	[512 256 14 14]	1	arcface	bwd	6645672	801566	8.56
PReLU	bfloat16	[512 512 14 14]	1	arcface	bwd	13434983	1577450	8.66
PReLU	bfloat16	[512 512 7 7]	1	arcface	bwd	3310021	410969	8.57
PReLU	bfloat16	[512 64 112 112]	64	arcface	bwd	106599289	46867700	2.28
PReLU	bfloat16	[512 64 56 56]	64	arcface	bwd	30100583	11790800	2.57
PReLU	bfloat16	[512 128 56 56]	128	arcface	bwd	55829383	14050900	3.99
PReLU	bfloat16	[512 128 28 28]	128	arcface	bwd	15548941	3913070	4.03
PReLU	bfloat16	[512 256 28 28]	256	arcface	bwd	29955610	5126710	5.89
PReLU	bfloat16	[512 256 14 14]	256	arcface	bwd	7793750	1546550	5.17
PReLU	bfloat16	[512 512 14 14]	512	arcface	bwd	15556630	2171380	7.27
PReLU	bfloat16	[512 512 7 7]	512	arcface	bwd	3862986	692056	5.91

• Average over all cases:

type	average
float16	7.13
float32	7.83
bfloat16	7.32

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