Closed Jipok closed 5 months ago
I can't reproduce the issue on my Rx 6800. Which GPU are you using (I don't think this information was in rocminfo
)?
AMD Ryzen 7 6800H have integrated graphics Radeon 680M
Should the output be exactly the same for different -ngl X
?
I also have problem with docker version.
podman run --rm -it --init --device /dev/dri --device /dev/kfd -e HSA_OVERRIDE_GFX_VERSION=10.3.0 -v ~/Downloads:/models llama.cpp:rocm main -s 0 -ngl 1 -m /models/codellama-13b-instruct.Q6_K.gguf -p "[INST] 1+1= [/INST]"
Same works good with -ngl 0
Should the output be exactly the same for different -ngl X?
No, due to differences in rounding error you cannot expect bit-for-bit identical results if you vary the number of GPU layers.
Is this a graphics card support issue in the rocm code?
I don't know. The hardware you're using is to my knowledge not supported for ROCm but at the same time I did not implement the CUDA code with integrated graphics in mind.
I've done exactly the same thing as above. Built from latest commit cf9b08485c4c2d4d945c6e74fe20f273a38b6104 . Used same docker setup as above. GPU RX6600
docker run --rm -it --init --device /dev/dri --device /dev/kfd -e HSA_OVERRIDE_GFX_VERSION=10.3.0 \
-v $PWD:/models llama-cpp:rocm -m /models/openbuddy-llama2-13b-v11.1.Q4_K_M.gguf -s 0 -mg 0 \
--interactive-first -ngl 30
with -ngl 40 i would run out of vram 30 seemed stable
however output
specifically this line concerns me the most
CUDA error 98 at ggml-cuda.cu:6063: invalid device function
This is clearly a different problem altogether. Please make a separate issue.
This is clearly a different problem altogether. Please make a separate issue.
Fair point i will do so , when i am back home.
This issue was closed because it has been inactive for 14 days since being marked as stale.
OS: Void Linux Kernel: 6.3.13 ROCm: 5.6.0
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): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 6800H with Radeon Graphics CPU family: 25 Model: 68 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 1 Frequency boost: enabled CPU(s) scaling MHz: 37% CPU max MHz: 4784.3750 CPU min MHz: 1600.0000 BogoMIPS: 6387.93 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 m mxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pcl mulqdq 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 perfctr_nb bp ext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_ll c cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_s cale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm Virtualization features: Virtualization: AMD-V Caches (sum of all): L1d: 256 KiB (8 instances) L1i: 256 KiB (8 instances) L2: 4 MiB (8 instances) L3: 16 MiB (1 instance) NUMA: NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerabilities: Itlb multihit: Not affected L1tf: Not affected Mds: Not affected Meltdown: Not affected Mmio stale data: Not affected Retbleed: Not affected Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Srbds: Not affected Tsx async abort: Not affected ```/opt/rocm/bin/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 ========== HSA Agents ========== ******* Agent 1 ******* Name: AMD Ryzen 7 6800H with Radeon Graphics Uuid: CPU-XX Marketing Name: AMD Ryzen 7 6800H with Radeon Graphics 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): 3200 BDFID: 0 Internal Node ID: 0 Compute Unit: 16 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: 30545284(0x1d21584) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 2 Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED Size: 30545284(0x1d21584) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 3 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 30545284(0x1d21584) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE ISA Info: ******* Agent 2 ******* Name: gfx1035 Uuid: GPU-XX Marketing Name: AMD Radeon Graphics 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: 16(0x10) KB L2: 2048(0x800) KB Chip ID: 5761(0x1681) ASIC Revision: 2(0x2) Cacheline Size: 64(0x40) Max Clock Freq. (MHz): 2200 BDFID: 29696 Internal Node ID: 1 Compute Unit: 12 SIMDs per CU: 2 Shader Engines: 1 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 Pool Info: Pool 1 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 2097152(0x200000) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: FALSE Pool 2 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--gfx1035 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 *** ```Build:
Run:
Output
``` Log start main: build = 1152 (8b56b4f) main: seed = 0 ggml_init_cublas: found 1 ROCm devices: Device 0: AMD Radeon Graphics, compute capability 10.3 llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from /home/kiv/Downloads/puddlejumper-13b.q8_0.gguf (version GGUF V1 (support until nov 2023)) llama_model_loader: - tensor 0: token_embd.weight q8_0 [ 5120, 32002, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 2: blk.0.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 3: blk.0.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 4: blk.0.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 6: blk.0.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 7: blk.0.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 11: blk.1.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 12: blk.1.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 13: blk.1.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 15: blk.1.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 16: blk.1.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 19: blk.2.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 20: blk.2.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 21: blk.2.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 22: blk.2.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 24: blk.2.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 25: blk.2.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 28: blk.3.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 29: blk.3.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 30: blk.3.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 31: blk.3.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 33: blk.3.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 34: blk.3.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 37: blk.4.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 38: blk.4.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 39: blk.4.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 40: blk.4.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 42: blk.4.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 43: blk.4.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 46: blk.5.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 47: blk.5.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 48: blk.5.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 49: blk.5.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 51: blk.5.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 52: blk.5.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 55: blk.6.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 56: blk.6.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 57: blk.6.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 58: blk.6.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 60: blk.6.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 61: blk.6.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 64: blk.7.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 65: blk.7.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 66: blk.7.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 67: blk.7.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 69: blk.7.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 70: blk.7.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 73: blk.8.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 74: blk.8.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 75: blk.8.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 76: blk.8.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 78: blk.8.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 79: blk.8.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 82: blk.9.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 83: blk.9.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 84: blk.9.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 85: blk.9.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 87: blk.9.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 88: blk.9.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 91: blk.10.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 92: blk.10.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 93: blk.10.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 94: blk.10.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 96: blk.10.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 97: blk.10.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 100: blk.11.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 101: blk.11.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 102: blk.11.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 103: blk.11.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 105: blk.11.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 106: blk.11.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 109: blk.12.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 110: blk.12.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 111: blk.12.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 112: blk.12.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 114: blk.12.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 115: blk.12.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 118: blk.13.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 119: blk.13.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 120: blk.13.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 121: blk.13.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 123: blk.13.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 124: blk.13.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 127: blk.14.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 128: blk.14.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 129: blk.14.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 130: blk.14.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 132: blk.14.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 133: blk.14.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 136: blk.15.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 137: blk.15.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 138: blk.15.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 139: blk.15.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 141: blk.15.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 142: blk.15.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 145: blk.16.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 146: blk.16.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 147: blk.16.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 148: blk.16.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 150: blk.16.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 151: blk.16.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - 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tensor 321: blk.35.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 322: blk.35.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 323: blk.35.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 324: blk.35.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 325: blk.36.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 326: blk.36.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 327: blk.36.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 328: blk.36.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 329: blk.36.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 330: blk.36.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 331: blk.36.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 332: blk.36.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 333: blk.36.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 334: blk.37.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 335: blk.37.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 336: blk.37.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 337: blk.37.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 338: blk.37.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 339: blk.37.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 340: blk.37.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 341: blk.37.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 342: blk.37.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 343: blk.38.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 344: blk.38.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 345: blk.38.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 346: blk.38.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 347: blk.38.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 348: blk.38.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 349: blk.38.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 350: blk.38.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 351: blk.38.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 352: blk.39.attn_q.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_k.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 354: blk.39.attn_v.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.attn_output.weight q8_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_gate.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_up.weight q8_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 358: blk.39.ffn_down.weight q8_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 360: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 361: output_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 362: output.weight q8_0 [ 5120, 32002, 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: general.file_type u32 llama_model_loader: - kv 11: tokenizer.ggml.model str llama_model_loader: - kv 12: tokenizer.ggml.tokens arr llama_model_loader: - kv 13: tokenizer.ggml.scores arr llama_model_loader: - kv 14: tokenizer.ggml.token_type arr llama_model_loader: - kv 15: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q8_0: 282 tensors llm_load_print_meta: format = GGUF V1 (support until nov 2023) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32002 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_ctx = 512 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 1.0e-05 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base = 10000.0 llm_load_print_meta: freq_scale = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q8_0 llm_load_print_meta: model size = 13.02 B llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 'I tried other models, the result is the same. With
-ngl 0
the output is great. Withngl 2
, some models (like codelamma) show better results:./main -s 0 --temp 0 -e -m ~/Downloads/codellama-13b-instruct.Q6_K.gguf -ngl 2 -p "[INST] Make a python function to load PDF files. Use the nltk library to split it into paragraphs. [/INST]"
Output
``` Log start main: build = 1152 (8b56b4f) main: seed = 0 ggml_init_cublas: found 1 ROCm devices: Device 0: AMD Radeon Graphics, compute capability 10.3 llama_model_loader: loaded meta data with 17 key-value pairs and 363 tensors from /home/kiv/Downloads/codellama-13b-instruct.Q6_K.gguf (version GGUF V1 (support until nov 2023)) llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 5120, 32016, 1, 1 ] ... llama_model_loader: - tensor 362: blk.39.ffn_norm.weight f32 [ 5120, 1, 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: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type f16: 1 tensors llama_model_loader: - type q4_0: 1 tensors llama_model_loader: - type q6_K: 280 tensors llm_load_print_meta: format = GGUF V1 (support until nov 2023) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32016 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 16384 llm_load_print_meta: n_ctx = 512 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 1.0e-05 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base = 1000000.0 llm_load_print_meta: freq_scale = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q6_K llm_load_print_meta: model size = 13.02 B llm_load_print_meta: general.name = LLaMA llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 'With
-nommq
I get complete garbage even with-ngl 4
. Large values cannot be checked because for some reason I getggml-cuda.cu:5048: out of memory
. Although I have 32GB of RAM. And I thought that the memory for the vram is dynamically allocated, but llama.cpp always shows 2GB