llama.cpp: Segmentation fault after model load on ROCm multi-gpu, multi-gfx
Prerequisites
Please answer the following questions for yourself before submitting an issue.
- I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- I carefully followed the README.md.
- I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
- I reviewed the Discussions, and have a new bug or useful enhancement to share.
Expected Behavior
NA
Current Behavior
Segmentation fault after model load for ROCm multi-gpu, multi-gfx. Best I can remember it worked a couple months ago, but has now been broken at least 2 weeks.
Environment and Context
- Physical (or virtual) hardware you are using, e.g. for Linux:
rocminfo
ROCk module is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.1
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD Ryzen 9 7950X 16-Core Processor
Uuid: CPU-XX
Marketing Name: AMD Ryzen 9 7950X 16-Core Processor
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32768(0x8000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 6021
BDFID: 0
Internal Node ID: 0
Compute Unit: 32
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 65539100(0x3e80c1c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 65539100(0x3e80c1c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 65539100(0x3e80c1c) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx1100
Uuid: GPU-28b5961221d81024
Marketing Name: AMD Radeon RX 7900 XTX
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2526
BDFID: 768
Internal Node ID: 1
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 528
SDMA engine uCode:: 19
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS:
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*******
Agent 3
*******
Name: gfx1030
Uuid: GPU-8de346d621abe448
Marketing Name: AMD Radeon RX 6900 XT
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 2
Device Type: GPU
Cache Info:
L1: 16(0x10) KB
L2: 4096(0x1000) KB
L3: 131072(0x20000) KB
Chip ID: 29615(0x73af)
ASIC Revision: 1(0x1)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2720
BDFID: 1792
Internal Node ID: 2
Compute Unit: 80
SIMDs per CU: 2
Shader Engines: 4
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 115
SDMA engine uCode:: 83
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 16760832(0xffc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS:
Size: 16760832(0xffc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1030
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
lscpu
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 9 7950X 16-Core Processor
CPU family: 25
Model: 97
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 2
CPU(s) scaling MHz: 52%
CPU max MHz: 6021.0000
CPU min MHz: 400.0000
BogoMIPS: 9000.59
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf ra
pl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perf
ctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt cl
wb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clea
n flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov s
uccor smca fsrm flush_l1d
Virtualization features:
Virtualization: AMD-V
Caches (sum of all):
L1d: 512 KiB (16 instances)
L1i: 512 KiB (16 instances)
L2: 16 MiB (16 instances)
L3: 64 MiB (2 instances)
NUMA:
NUMA node(s): 1
NUMA node0 CPU(s): 0-31
Vulnerabilities:
Gather data sampling: Not affected
Itlb multihit: Not affected
L1tf: Not affected
Mds: Not affected
Meltdown: Not affected
Mmio stale data: Not affected
Retbleed: Not affected
Spec rstack overflow: Mitigation; safe RET, no microcode
Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Spectre v2: Mitigation; Enhanced / Automatic IBRS, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Srbds: Not affected
Tsx async abort: Not affected
- Operating System, e.g. for Linux:
uname -a
Linux dc1a626b91a2 6.5.9-301.fsync.fc39.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Oct 28 16:08:46 UTC 2023 x86_64 GNU/Linux
- SDK version, e.g. for Linux:
ROCm 5.7.1
llamacpp https://github.com/ggerganov/llama.cpp/commit/4a4fd3eefad5bd17ab6bcd8e2181b4f62eae76cf
python3 --version
Python 3.11.5
make --version
GNU Make 4.4.1
Built for x86_64-pc-linux-gnu
Copyright (C) 1988-2023 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <https://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
g++ --version
g++ (GCC) 13.2.1 20230801
Copyright (C) 2023 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Failure Information (for bugs)
Provided below.
Steps to Reproduce
make LLAMA_HIPBLAS=1
I llama.cpp build info:
I UNAME_S: Linux
I UNAME_P: unknown
I UNAME_M: x86_64
I CFLAGS: -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS -std=c11 -fPIC -O3 -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int -Werror=implicit-function-declaration -Wdouble-promotion -pthread -march=native -mtune=native
I CXXFLAGS: -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS -std=c++11 -fPIC -O3 -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wmissing-declarations -Wmissing-noreturn -pthread -Wno-array-bounds -Wno-format-truncation -Wextra-semi -march=native -mtune=native
I NVCCFLAGS: -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS -std=c++11 -fPIC -O3 -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wmissing-declarations -Wmissing-noreturn -pthread -Wno-pedantic -Xcompiler "-Wno-array-bounds -Wno-format-truncation -Wextra-semi -march=native -mtune=native "
I LDFLAGS: -L/opt/rocm/lib -Wl,-rpath=/opt/rocm/lib -lhipblas -lamdhip64 -lrocblas
I CC: cc (GCC) 13.2.1 20230801
I CXX: g++ (GCC) 13.2.1 20230801
(Removed build log, no errors)
./main -ngl 99 -m ../koboldcpp/models/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/mistral-7b-instruct-v0.1.Q5_K_M.gguf -mg 0 -p "Write a function in TypeScript that sums numbers"
Log start
main: build = 1503 (4a4fd3e)
main: built with cc (GCC) 13.2.1 20230801 for x86_64-pc-linux-gnu
main: seed = 1699662201
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 2 ROCm devices:
Device 0: AMD Radeon RX 7900 XTX, compute capability 11.0
Device 1: AMD Radeon RX 6900 XT, compute capability 10.3
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from ../koboldcpp/models/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/mistral-7b-instruct-v0.1.Q5_K_M.gguf (version GGUF V2)
llama_model_loader: - tensor 0: token_embd.weight q5_K [ 4096, 32000, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 19: blk.2.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 20: blk.2.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 21: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 22: blk.2.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 24: blk.2.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 25: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.3.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 29: blk.3.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 30: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 31: blk.3.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 33: blk.3.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 34: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 37: blk.4.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 38: blk.4.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 39: blk.4.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 40: blk.4.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 42: blk.4.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 43: blk.4.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 46: blk.5.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 47: blk.5.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 48: blk.5.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 49: blk.5.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 51: blk.5.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 52: blk.5.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 55: blk.6.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 56: blk.6.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 57: blk.6.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 58: blk.6.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 60: blk.6.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 61: blk.6.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 64: blk.7.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 65: blk.7.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 66: blk.7.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 67: blk.7.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 69: blk.7.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 70: blk.7.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 73: blk.8.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 74: blk.8.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 75: blk.8.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 76: blk.8.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 78: blk.8.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 79: blk.8.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 82: blk.9.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 83: blk.9.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 84: blk.9.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 85: blk.9.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 87: blk.9.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 88: blk.9.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 91: blk.10.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 92: blk.10.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 93: blk.10.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 94: blk.10.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 96: blk.10.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 97: blk.10.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 100: blk.11.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 101: blk.11.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 102: blk.11.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 103: blk.11.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 105: blk.11.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 106: blk.11.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 109: blk.12.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 110: blk.12.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 111: blk.12.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 112: blk.12.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 114: blk.12.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 115: blk.12.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 118: blk.13.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 119: blk.13.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 120: blk.13.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 121: blk.13.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 123: blk.13.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 124: blk.13.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 127: blk.14.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 128: blk.14.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 129: blk.14.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 130: blk.14.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 132: blk.14.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 133: blk.14.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 136: blk.15.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 137: blk.15.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 138: blk.15.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 139: blk.15.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 141: blk.15.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 142: blk.15.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 145: blk.16.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 146: blk.16.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 147: blk.16.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 148: blk.16.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 150: blk.16.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 151: blk.16.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 154: blk.17.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 155: blk.17.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 156: blk.17.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 157: blk.17.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 159: blk.17.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 160: blk.17.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 163: blk.18.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 164: blk.18.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 165: blk.18.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 166: blk.18.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 168: blk.18.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 169: blk.18.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 172: blk.19.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 173: blk.19.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 174: blk.19.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 175: blk.19.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 177: blk.19.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 178: blk.19.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 181: blk.20.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 182: blk.20.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 183: blk.20.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 184: blk.20.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 186: blk.20.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 187: blk.20.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 190: blk.21.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 191: blk.21.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 192: blk.21.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 193: blk.21.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 195: blk.21.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 196: blk.21.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 199: blk.22.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 200: blk.22.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 201: blk.22.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 202: blk.22.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 203: blk.22.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 204: blk.22.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 205: blk.22.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 206: blk.22.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 208: blk.23.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 209: blk.23.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 210: blk.23.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 211: blk.23.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 212: blk.23.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 213: blk.23.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 214: blk.23.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 217: blk.24.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 218: blk.24.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 219: blk.24.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 220: blk.24.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 221: blk.24.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 222: blk.24.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 223: blk.24.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 226: blk.25.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 227: blk.25.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 228: blk.25.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 229: blk.25.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 231: blk.25.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 232: blk.25.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 235: blk.26.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 236: blk.26.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 237: blk.26.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 238: blk.26.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 240: blk.26.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 241: blk.26.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 244: blk.27.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 245: blk.27.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 246: blk.27.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 247: blk.27.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 249: blk.27.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 250: blk.27.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 253: blk.28.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 254: blk.28.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 255: blk.28.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 256: blk.28.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 258: blk.28.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 259: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 262: blk.29.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 263: blk.29.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 264: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 265: blk.29.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 267: blk.29.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 268: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 271: blk.30.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 272: blk.30.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 273: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 274: blk.30.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 276: blk.30.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 277: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 280: blk.31.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 281: blk.31.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 282: blk.31.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 283: blk.31.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 285: blk.31.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 286: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 290: output.weight q6_K [ 4096, 32000, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: llama.rope.freq_base f32
llama_model_loader: - kv 11: general.file_type u32
llama_model_loader: - kv 12: tokenizer.ggml.model str
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr
llama_model_loader: - kv 14: tokenizer.ggml.scores arr
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 19: general.quantization_version u32
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q5_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly Q5_K - Medium
llm_load_print_meta: model params = 7.24 B
llm_load_print_meta: model size = 4.78 GiB (5.67 BPW)
llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.1
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MB
llm_load_tensors: using ROCm for GPU acceleration
ggml_cuda_set_main_device: using device 0 (AMD Radeon RX 7900 XTX) as main device
llm_load_tensors: mem required = 86.04 MB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 35/35 layers to GPU
llm_load_tensors: VRAM used: 4807.05 MB
..................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: offloading v cache to GPU
llama_kv_cache_init: offloading k cache to GPU
llama_kv_cache_init: VRAM kv self = 64.00 MB
llama_new_context_with_model: kv self size = 64.00 MB
llama_build_graph: non-view tensors processed: 740/740
llama_new_context_with_model: compute buffer total size = 79.63 MB
llama_new_context_with_model: VRAM scratch buffer: 73.00 MB
llama_new_context_with_model: total VRAM used: 4944.06 MB (model: 4807.05 MB, context: 137.00 MB)
fish: Job 1, './main -ngl 99 -m ../koboldcpp/…' terminated by signal SIGSEGV (Address boundary error)
Failure Logs
Provided above.
About this issue
- Original URL
- State: closed
- Created 8 months ago
- Reactions: 2
- Comments: 23 (1 by maintainers)
As per https://dlcdnets.asus.com/pub/ASUS/mb/Socket AM5/ProArt X670E-CREATOR WIFI/E21293_ProArt_X670E-CREATOR_WIFI_UM_V2_WEB.pdf?model=ProArt X670E-CREATOR WIFI (page vii) my motherboard’s top two slots, the ones I use for GPUs, are in 8x 8x bifurcation mode which uses lanes directly from the cpu.
I don’t at the moment know what commit llamacpp last worked with–but I did remember a few days ago when talking to some koboldcpp folk that it ONLY ever worked for me with the
lowvramoption, which was removed I believe somewhat recently. I’ve heard this corroborated by a few other users a while ago, in the koboldai discord, and on this repo as far back as here: https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1647478583 . If I have time to start somewhere, I’d definitely look for a commit where that option was still available. (the linked MR’s merge date as a lower cap and the removal of lowvram as a high cap, to where things might have gone wrong)I would be suspicious of any AMD support claims both in the negative and positive direction. Don’t let it get your hopes down (but maybe don’t expect AMD to directly help either…). I’d guess that page has more to do with enterprise support commitment rather than if it should actually function or not. I haven’t gotten a single gfx1100 pytorch error since I purchased that card, almost a year before AMD claimed any support at all for it.
I just ran HEAD again and it works again on two AMD GPUs with ROCm 6. Whatever you changed in #4766 (tag b1843) fixed it!
On Wed, Dec 20, 2023 at 3:29 PM slaren @.***> wrote: