CUDA error with AMD GPU?

[mikeperalta1]

Hey all,

Trying to figure out what I'm doing wrong. Trying to run llama with an AMD GPU (6600XT) spits out a confusing error, as I don't have an NVIDIA GPU:

ggml_cuda_compute_forward: RMS_NORM failed
CUDA error: invalid device function
  current device: 0, in function ggml_cuda_compute_forward at ggml-cuda.cu:2320
  err
GGML_ASSERT: ggml-cuda.cu:100: !"CUDA error"
Could not attach to process.  If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user.  For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
/home/llm/bin/chat: line 43: 48298 Aborted                 (core dumped) HSA_OVERRIDE_GFX_VERSION=10.3.0 /home/llm/opt/llama.cpp/main --model "/home/llm/opt/llama.cpp/models/downloaded/llama-2-13b-chat.Q4_K_M.gguf" --interactive --main-gpu 0

It does detect my GPU:

ggml_cuda_init: found 1 ROCm devices:
  Device 0: AMD Radeon RX 6600 XT, compute capability 10.3, VMM: no

Currently building with make LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1032 -j8 but I've tried other variants to no avail. Here's my build script:

#!/bin/bash


DIR="$HOME/opt/llama.cpp"

cd "$DIR" || exit $?

make clean || exit $?

#make LLAMA_OPENBLAS=1 LLAMA_CLBLAST=1 LLAMA_HIPBLAS=1 -j4
#make LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1101 -j16

#make LLAMA_OPENBLAS=1 LLAMA_HIPBLAS=1 AMDGPU_TARGETS=gfx1032 -j8
#make LLAMA_HIPBLAS=1 AMDGPU_TARGETS=gfx1032 -j8
#make LLAMA_HIPBLAS=ON AMDGPU_TARGETS=gfx1032 -j8
make LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1032 -j8

exit $?

Clearly I'm just brute-forcing and don't know how this is supposed to work. Any advice?

[airpods69]

Same here, commenting to bump

I am trying to run it on an AMD RX7700XT

Logs below

 Log start
main: build = 3175 (5326bcce)
main: built with cc (GCC) 14.1.1 20240522 for x86_64-pc-linux-gnu
main: seed  = 1719059849
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from ../Utils/Models/mistral-7b-instruct-v0.2.Q5_K_S.gguf?download=true (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv   2:                       llama.context_length u32              = 32768
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   4:                          llama.block_count u32              = 32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  11:                          general.file_type u32              = 16
llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  19:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  20:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  21:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  22:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv  23:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q5_K:  225 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens cache size = 259
llm_load_vocab: token to piece cache size = 0.1637 MB
llm_load_print_meta: format           = GGUF V3 (latest)
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_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 4
llm_load_print_meta: n_embd_k_gqa     = 1024
llm_load_print_meta: n_embd_v_gqa     = 1024
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: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 14336
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 32768
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = Q5_K - Small
llm_load_print_meta: model params     = 7.24 B
llm_load_print_meta: model size       = 4.65 GiB (5.52 BPW) 
llm_load_print_meta: general.name     = mistralai_mistral-7b-instruct-v0.2
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: PAD token        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 2 ROCm devices:
  Device 0: AMD Radeon RX 7700 XT, compute capability 11.0, VMM: no
  Device 1: AMD Radeon Graphics, compute capability 10.3, VMM: no
llm_load_tensors: ggml ctx size =    0.44 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:      ROCm0 buffer size =  4577.00 MiB
llm_load_tensors:      ROCm1 buffer size =   102.55 MiB
llm_load_tensors:        CPU buffer size =    85.94 MiB
..................................................................................................
llama_new_context_with_model: n_ctx      = 32768
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      ROCm0 KV buffer size =  4096.00 MiB
llama_new_context_with_model: KV self size  = 4096.00 MiB, K (f16): 2048.00 MiB, V (f16): 2048.00 MiB
llama_new_context_with_model:  ROCm_Host  output buffer size =     0.12 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model:      ROCm0 compute buffer size =  2368.02 MiB
llama_new_context_with_model:      ROCm1 compute buffer size =   102.50 MiB
llama_new_context_with_model:  ROCm_Host compute buffer size =   264.02 MiB
llama_new_context_with_model: graph nodes  = 1030
llama_new_context_with_model: graph splits = 3
ggml_cuda_compute_forward: RMS_NORM failed
CUDA error: shared object initialization failed
  current device: 0, in function ggml_cuda_compute_forward at ggml-cuda.cu:2313
  err
GGML_ASSERT: ggml-cuda.cu:100: !"CUDA error"
[1]    10478 IOT instruction  sudo ./llama-cli -m  -ngl 33 -p "hey there, my name is Pablo Escobar"

[rudiservo]

Are you using docker?

[airpods69]

This yes I believe was in a docker (had distrobox running)

[rudiservo]

Well I tried metal compiled with ROCm 6.1.2 and it works. But I have been trying with Docker build for the past 7 days or so, I tried ROCm 5.6, 5.7.1 and 6.1.2 and always crashes.

the latest ones I get this error

llamacpp_1 | llama_new_context_with_model: ROCm_Host compute buffer size = 9.01 MiB llamacpp_1 | llama_new_context_with_model: graph nodes = 1030 llamacpp_1 | llama_new_context_with_model: graph splits = 2 llamacpp_1 | ggml_cuda_compute_forward: RMS_NORM failed llamacpp_1 | CUDA error: invalid device function llamacpp_1 | current device: 0, in function ggml_cuda_compute_forward at ggml/src/ggml-cuda.cu:2285 llamacpp_1 | err llamacpp_1 | GGML_ASSERT: ggml/src/ggml-cuda.cu:100: !"CUDA error"