Open
Description
Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 ROCm devices:
Device 0: AMD Radeon 890M Graphics, gfx1102 (0x1102), VMM: no, Wave Size: 32
version: 5775 (bd9c981)
built with clang version 19.0.0git (/srcdest/rocm-llvm c87081df219c42dc27c5b6d86c0525bc7d01f727) for x86_64-pc-linux-gnu
Operating systems
Linux
GGML backends
HIP
Hardware
AMD Radeon 890M Graphics
Models
Gemma 3n
Problem description & steps to reproduce
Running all gemma-3n models works well when using cpu, using HIP result in same crash.
Quick debugging show that the array is all nan:
(gdb) p cur_p->data[0].p
$8 = nan(0x400000)
(gdb) p cur_p->data[1].p
$9 = nan(0x400000)
(gdb) p cur_p->data[2].p
$10 = nan(0x400000)
Relevant log output
$ llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF -co -c 0 -fa -ngl 1000
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 ROCm devices:
Device 0: AMD Radeon 890M Graphics, gfx1102 (0x1102), VMM: no, Wave Size: 32
curl_perform_with_retry: HEAD https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF/resolve/main/gemma-3n-E4B-it-Q4_K_M.gguf (attempt 1 of 1)...
common_download_file_single: using cached file: /home/user/.cache/llama.cpp/unsloth_gemma-3n-E4B-it-GGUF_gemma-3n-E4B-it-Q4_K_M.gguf
build: 5775 (bd9c981d7) with clang version 19.0.0git (/srcdest/rocm-llvm c87081df219c42dc27c5b6d86c0525bc7d01f727) for x86_64-pc-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device ROCm0 (AMD Radeon 890M Graphics) - 17672 MiB free
llama_model_loader: loaded meta data with 45 key-value pairs and 847 tensors from /home/user/.cache/llama.cpp/unsloth_gemma-3n-E4B-it-GGUF_gemma-3n-E4B-it-Q4_K_M.gguf (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 = gemma3n
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Gemma-3N-E4B-It
llama_model_loader: - kv 3: general.finetune str = 3n-E4B-it
llama_model_loader: - kv 4: general.basename str = Gemma-3N-E4B-It
llama_model_loader: - kv 5: general.quantized_by str = Unsloth
llama_model_loader: - kv 6: general.size_label str = 6.9B
llama_model_loader: - kv 7: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 8: gemma3n.context_length u32 = 32768
llama_model_loader: - kv 9: gemma3n.embedding_length u32 = 2048
llama_model_loader: - kv 10: gemma3n.block_count u32 = 35
llama_model_loader: - kv 11: gemma3n.feed_forward_length arr[i32,35] = [16384, 16384, 16384, 16384, 16384, 1...
llama_model_loader: - kv 12: gemma3n.attention.head_count u32 = 8
llama_model_loader: - kv 13: gemma3n.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 14: gemma3n.attention.key_length u32 = 256
llama_model_loader: - kv 15: gemma3n.attention.value_length u32 = 256
llama_model_loader: - kv 16: gemma3n.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 17: gemma3n.attention.sliding_window u32 = 512
llama_model_loader: - kv 18: gemma3n.attention.head_count_kv u32 = 2
llama_model_loader: - kv 19: gemma3n.altup.active_idx u32 = 0
llama_model_loader: - kv 20: gemma3n.altup.num_inputs u32 = 4
llama_model_loader: - kv 21: gemma3n.embedding_length_per_layer_input u32 = 256
llama_model_loader: - kv 22: gemma3n.attention.shared_kv_layers u32 = 15
llama_model_loader: - kv 23: gemma3n.activation_sparsity_scale arr[f32,35] = [1.644854, 1.644854, 1.644854, 1.6448...
llama_model_loader: - kv 24: gemma3n.attention.sliding_window_pattern arr[bool,35] = [true, true, true, true, false, true,...
llama_model_loader: - kv 25: tokenizer.chat_template str = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv 26: tokenizer.ggml.model str = llama
llama_model_loader: - kv 27: tokenizer.ggml.pre str = default
llama_model_loader: - kv 28: tokenizer.ggml.tokens arr[str,262144] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 29: tokenizer.ggml.scores arr[f32,262144] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 30: tokenizer.ggml.token_type arr[i32,262144] = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 32: tokenizer.ggml.eos_token_id u32 = 106
llama_model_loader: - kv 33: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 34: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 35: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 36: tokenizer.ggml.add_sep_token bool = false
llama_model_loader: - kv 37: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 38: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 39: general.quantization_version u32 = 2
llama_model_loader: - kv 40: general.file_type u32 = 15
llama_model_loader: - kv 41: quantize.imatrix.file str = gemma-3n-E4B-it-GGUF/imatrix_unsloth.dat
llama_model_loader: - kv 42: quantize.imatrix.dataset str = unsloth_calibration_gemma-3n-E4B-it.txt
llama_model_loader: - kv 43: quantize.imatrix.entries_count u32 = 459
llama_model_loader: - kv 44: quantize.imatrix.chunks_count u32 = 1326
llama_model_loader: - type f32: 422 tensors
llama_model_loader: - type f16: 108 tensors
llama_model_loader: - type q8_0: 1 tensors
llama_model_loader: - type q4_K: 282 tensors
llama_model_loader: - type q6_K: 34 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 4.90 GiB (6.14 BPW)
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch = gemma3n
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 2048
print_info: n_layer = 35
print_info: n_head = 8
print_info: n_head_kv = 2
print_info: n_rot = 256
print_info: n_swa = 512
print_info: is_swa_any = 1
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 1.0e+00
print_info: n_ff = 16384
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = E4B
print_info: model params = 6.87 B
print_info: general.name = Gemma-3N-E4B-It
print_info: vocab type = SPM
print_info: n_vocab = 262144
print_info: n_merges = 0
print_info: BOS token = 2 '<bos>'
print_info: EOS token = 106 '<end_of_turn>'
print_info: EOT token = 106 '<end_of_turn>'
print_info: UNK token = 3 '<unk>'
print_info: PAD token = 0 '<pad>'
print_info: LF token = 248 '<0x0A>'
print_info: EOG token = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 35 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 36/36 layers to GPU
load_tensors: ROCm0 model buffer size = 5022.53 MiB
load_tensors: CPU_Mapped model buffer size = 288.00 MiB
...............................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 32768
llama_context: n_ctx_per_seq = 32768
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 1
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: ROCm_Host output buffer size = 1.00 MiB
llama_kv_cache_unified_iswa: creating non-SWA KV cache, size = 32768 cells
llama_kv_cache_unified: ROCm0 KV buffer size = 256.00 MiB
llama_kv_cache_unified: size = 256.00 MiB ( 32768 cells, 4 layers, 1 seqs), K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_kv_cache_unified_iswa: creating SWA KV cache, size = 1024 cells
llama_kv_cache_unified: ROCm0 KV buffer size = 32.00 MiB
llama_kv_cache_unified: size = 32.00 MiB ( 1024 cells, 16 layers, 1 seqs), K (f16): 16.00 MiB, V (f16): 16.00 MiB
llama_context: ROCm0 compute buffer size = 520.00 MiB
llama_context: ROCm_Host compute buffer size = 102.01 MiB
llama_context: graph nodes = 3143
llama_context: graph splits = 22
common_init_from_params: KV cache shifting is not supported for this context, disabling KV cache shifting
common_init_from_params: setting dry_penalty_last_n to ctx_size = 32768
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 12
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
main: chat template example:
<start_of_turn>user
You are a helpful assistant
Hello<end_of_turn>
<start_of_turn>model
Hi there<end_of_turn>
<start_of_turn>user
How are you?<end_of_turn>
<start_of_turn>model
system_info: n_threads = 12 (n_threads_batch = 12) / 24 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : OPENMP = 1 | REPACK = 1 |
main: interactive mode on.
sampler seed: 1372900278
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 32768
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 32768, n_batch = 2048, n_predict = -1, n_keep = 1
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to the AI.
- To return control without starting a new line, end your input with '/'.
- If you want to submit another line, end your input with '\'.
- Not using system message. To change it, set a different value via -sys PROMPT
> hi
/usr/lib64/gcc/x86_64-pc-linux-gnu/15.1.1/../../../../include/c++/15.1.1/bits/random.tcc:2668: void std::discrete_distribution<>::param_type::_M_initialize() [_IntType = int]: Assertion '__sum > 0' failed.
zsh: IOT instruction (core dumped) llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF -co -c 0 -fa -ngl 1000