ggml-zdnn: rm user mapped buffers

[taronaeo]

cont #15839 (comment)

This PR removes the user mapped buffers to allow weight tensors to go through .set_tensor, allowing zDNN to create zTensor objects inside tensor->extra during setup instead of doing it on-the-fly, where we would lose performance.

Also, it fixes a missed buffer free for extra buffers used for things like bias.

Performance

model	size	params	backend	threads	test	t/s master	t/s PR	speedup
granite 3B all F32	9.44 GiB	2.53 B	zDNN,BLAS	8	pp512	215.82	214.32	0.99
granite 3B all F32	9.44 GiB	2.53 B	zDNN,BLAS	8	tg128	4.95	4.95	1.00
granite 3B F16	4.72 GiB	2.53 B	zDNN,BLAS	8	pp512	213.83	218.44	1.02
granite 3B F16	4.72 GiB	2.53 B	zDNN,BLAS	8	tg128	4.90	4.81	0.98
granite 3B BF16	4.72 GiB	2.53 B	zDNN,BLAS	8	pp512	213.85	216.12	1.01
granite 3B BF16	4.72 GiB	2.53 B	zDNN,BLAS	8	tg128	4.79	4.66	0.97

Note

Tests were conducted on an IBM z17 Mainframe with 40 IFLs (cores) and 128 GB Memory on a shared R&D LPAR.

test-backend-ops

build/bin/test-backend-ops -b zDNN | grep -v "not supported"                                                                
ggml_zdnn_init: allocating                                                                                                                                        
ggml_zdnn_init: found 1 device                                                                                                                                    
ggml_zdnn_init: picking default device: zDNN                                                                                                                      
ggml_zdnn_init: NNPA name: zDNN                                                                                                                                   
ggml_zdnn_init: NNPA_PARMBLKFORMAT_0 = true                                                                                                                       
ggml_zdnn_init: NNPA_PARMBLKFORMAT_1 = true                                                                                                                       
Testing 3 devices                                                                                                                                                 
                                                                                                                                                                  
Backend 1/3: zDNN                                                                                                                                                 
  Device description: IBM Z Neural Network Processing Assist (NNPA)                                                                                               
  Device memory: 0 MB (0 MB free)                                                                                                                                 
                                                                                                                                                                  
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK                                                                       
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
  MUL_MAT(type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
ggml_zdnn_free: deallocating
  14471/14471 tests passed
  Backend zDNN: OK
Backend 2/3: BLAS
  Skipping
Backend 3/3: CPU
  Skipping
3/3 backends passed
OK

[taronaeo]

The xcode and macos CIs failing do not seem to be related to this PR. I'll merge in the morning if all is good