Eval bug: llama-server: illegal memory access was encountered

[eamonnmag]

Name and Version

Using ghcr.io/ggerganov/llama.cpp@sha256:cb0f16e6eae440da844b3a80b8c15e82ac4b2b8f6637f674b10b263452e649aa

Operating systems

Linux

GGML backends

CUDA

Hardware

Nvidia H100
Cuda 12.2

Models

Qwen2.5-32B-Instruct-GGUF

URL https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-GGUF/resolve/main/qwen2.5-32b-instruct-q6_k-00001-of-00007.gguf
...

Problem description & steps to reproduce

When I run llamacpp server (using the docker image server-cuda) I get this error after the first token is emitted

/app/ggml/src/ggml-cuda/ggml-cuda.cu:70: CUDA error
jade1        | CUDA error: an illegal memory access was encountered
jade1        |   current device: 0, in function ggml_backend_cuda_synchronize at /app/ggml/src/ggml-cuda/ggml-cuda.cu:2273
jade1        |   cudaStreamSynchronize(cuda_ctx->stream())

First Bad Commit

Not sure.

Relevant log output

jade1        | .................................................................................................
jade1        | llama_new_context_with_model: n_seq_max     = 4
jade1        | llama_new_context_with_model: n_ctx         = 100000
jade1        | llama_new_context_with_model: n_ctx_per_seq = 25000
jade1        | llama_new_context_with_model: n_batch       = 2048
jade1        | llama_new_context_with_model: n_ubatch      = 512
jade1        | llama_new_context_with_model: flash_attn    = 0
jade1        | llama_new_context_with_model: freq_base     = 1000000.0
jade1        | llama_new_context_with_model: freq_scale    = 1
jade1        | llama_new_context_with_model: n_ctx_per_seq (25000) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
jade1        | llama_kv_cache_init:      CUDA0 KV buffer size = 25000.00 MiB
jade1        | llama_new_context_with_model: KV self size  = 25000.00 MiB, K (f16): 12500.00 MiB, V (f16): 12500.00 MiB
jade1        | llama_new_context_with_model:  CUDA_Host  output buffer size =     2.32 MiB
jade1        | llama_new_context_with_model:      CUDA0 compute buffer size =  8047.82 MiB
jade1        | llama_new_context_with_model:  CUDA_Host compute buffer size =   205.32 MiB
jade1        | llama_new_context_with_model: graph nodes  = 2246
jade1        | llama_new_context_with_model: graph splits = 2
jade1        | common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
jade1        | request: GET /health 172.18.0.3 503
jade1        | srv          init: initializing slots, n_slots = 4
jade1        | slot         init: id  0 | task -1 | new slot n_ctx_slot = 25000
jade1        | slot         init: id  1 | task -1 | new slot n_ctx_slot = 25000
jade1        | slot         init: id  2 | task -1 | new slot n_ctx_slot = 25000
jade1        | slot         init: id  3 | task -1 | new slot n_ctx_slot = 25000
jade1        | main: model loaded
jade1        | main: chat template, built_in: 1, chat_example: '<|im_start|>system
jade1        | You are a helpful assistant<|im_end|>
jade1        | <|im_start|>user
jade1        | Hello<|im_end|>
jade1        | <|im_start|>assistant
jade1        | Hi there<|im_end|>
jade1        | <|im_start|>user
jade1        | How are you?<|im_end|>
jade1        | <|im_start|>assistant
jade1        | '
jade1        | main: server is listening on http://0.0.0.0:15029 - starting the main loop
jade1        | srv  update_slots: all slots are idle
ai-worker-4  | 2024-12-09T12:42:45.057140Z  INFO main{worker_id="2FOIM8HI"}:worker_loop{state="ready" job_id="58f316b9-83a4-4a74-8f3a-2df8bd943960"}: Starting completion opts=LlamaCompletionTask { target: Title, system: Some("Write a short subject that summarizes what the user says or asks for. Write only the subject and nothing else. Be concise."), turns: None, llama: LlamaOptions { temperature: Some(0.2), dynatemp_range: None, dynatemp_exponent: None, top_k: None, top_p: None, min_p: None, n_predict: Some(1024), n_keep: None, stop: ["<|", "\n\n"], tfs_z: None, typical_p: None, repeat_penalty: None, repeat_last_n: None, penalize_nl: None, presence_penalty: None, frequency_penalty: None, penalty_prompt: None, mirostat: None, mirostat_tau: None, mirostat_eta: None, grammar: None, json_schema: None, seed: None, ignore_eos: None, logit_bias: [], n_probs: None, min_keep: None, image_data: [], id_slot: None, system_prompt: None, samplers: [] } }
jade1        | slot launch_slot_: id  0 | task 0 | processing task
jade1        | slot update_slots: id  0 | task 0 | new prompt, n_ctx_slot = 25000, n_keep = 0, n_prompt_tokens = 60
jade1        | slot update_slots: id  0 | task 0 | kv cache rm [0, end)
jade1        | slot update_slots: id  0 | task 0 | prompt processing progress, n_past = 60, n_tokens = 60, progress = 1.000000
jade1        | slot update_slots: id  0 | task 0 | prompt done, n_past = 60, n_tokens = 60
jade1        | request: GET /health 172.18.0.3 200
jade1        | /app/ggml/src/ggml-cuda/ggml-cuda.cu:70: CUDA error
jade1        | CUDA error: an illegal memory access was encountered
jade1        |   current device: 0, in function ggml_backend_cuda_synchronize at /app/ggml/src/ggml-cuda/ggml-cuda.cu:2273
jade1        |   cudaStreamSynchronize(cuda_ctx->stream())

[eamonnmag]

FYI on A100s it works, only H100s where it's failing.

[JohannesGaessler]

Does the FP16 version of that model work correctly on an H100?
What happens when you run the q6_K version of the model with -ub 1?

[ggerganov]

I am incidentally doing some tests on H100 now and also observe the same failure with a llama 3 8B F16 model:

./bin/llama-cli -m ../models/llama-f16.gguf -p "Hello" -ngl 99

ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes
build: 4296 (07a61394) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_load_model_from_file: using device CUDA0 (NVIDIA H100 80GB HBM3) - 80469 MiB free

llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:        CUDA0 model buffer size = 14393.14 MiB
llm_load_tensors:   CPU_Mapped model buffer size =  1080.12 MiB
.........................................................................................
llama_new_context_with_model: n_seq_max     = 1
llama_new_context_with_model: n_ctx         = 4096
llama_new_context_with_model: n_ctx_per_seq = 4096
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     = 500000.0
llama_new_context_with_model: freq_scale    = 1
llama_new_context_with_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init:      CUDA0 KV buffer size =   512.00 MiB
llama_new_context_with_model: KV self size  =  512.00 MiB, K (f16):  256.00 MiB, V (f16):  256.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.53 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   296.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =    16.01 MiB
llama_new_context_with_model: graph nodes  = 1030
llama_new_context_with_model: graph splits = 2
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
/ggml/src/ggml-cuda/ggml-cuda.cu:70: CUDA error
CUDA error: an illegal memory access was encountered
  current device: 0, in function ggml_backend_cuda_buffer_clear at /ggml/src/ggml-cuda/ggml-cuda.cu:507
  cudaDeviceSynchronize()
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.
Aborted

Adding -ub 1 also fails.

The Q8_0 version of the same model works correctly.

I will try to bisect now, but if you get any other ideas to try something, let me know.

[JohannesGaessler]

Compile with -lineinfo for NVCC, then use compute-sanitizer to determine the line which causes the bad access. Our CMake config currently does not seem to have support for -lineinfo, you have to either edit it in or use make with LLAMA_DEBUG.

If q6_k fails but q8_0 works the issue could be related to the number of values consumed in one iteration, can be checked by increasing MATRIX_ROW_PADDING in common.cuh.

[ggerganov]

Ok, I will try that in a bit. Currently narrowed down the regression between 100 commits and want to finish the bisect.

[ggerganov]

This is the commit that introduces the regression: c3ea58a

[ggerganov]

This fixes it:

diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu
index cfe91f42..93e9ffe1 100644
--- a/ggml/src/ggml-cuda/mmv.cu
+++ b/ggml/src/ggml-cuda/mmv.cu
@@ -92,7 +92,7 @@ static void launch_mul_mat_vec_cuda(
         }
     }
 
-    const int smem = WARP_SIZE*sizeof(float);
+    const int smem = 2*WARP_SIZE*sizeof(float);
     const dim3 block_nums(nrows, 1, nchannels_y);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {

@JohannesGaessler Do you think it makes sense?

[JohannesGaessler]

It seems the condition for memory access is wrong, please confirm whether #10740 works as a fix.

[ggerganov]

Yes, it works with #10740