ikawrakow/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-01-26 17:20:01 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
eaa2510a28b60d43c2210c69cefdf750d5cc119f
ik_llama.cpp/github-data/issues/103 - Bug_ K cache without FA.md
Thomas eaa2510a28 Add GitHub data: filename sanitization (#640)
2025-07-23 13:31:53 +02:00

258 lines
17 KiB
Markdown
Raw Blame History

### 🐛 [#103](https://github.com/ikawrakow/ik_llama.cpp/issues/103) - Bug: K cache without FA
| **Author** | `Nexesenex` |
| :--- | :--- |
| **State** | ❌ **Closed** |
| **Created** | 2024-10-23 |
| **Updated** | 2024-10-24 |
---
#### Description
### What happened?
With the non-FA Quantum K cache, q6_0 works.
But q4_0, q4_1, q5_0, q5_1, q8_0 do not work anymore as K quant without FA, both on IK_L and mainline, and go NaN instead. As does iq4_nl K/no FA.
(I personally don't mind, K q6_0 is my new bff K cache quant).
Tested on Llama 3.1 8b Q5_K.
### Name and Version
b3962 on Mainline.
Pre granite merge on IK.
### What operating system are you seeing the problem on?
Windows
### Relevant log output
```shell
Q:\LLAMA_IK>llama-perplexity -m D:\text-generation-webui\models\Meta_Llama_3.1_8b_it-f16-iMat-Q5_K_S_q4_v6.gguf -f wiki.test.raw --parallel 1 -ngl 150 -b 1024 -ts 40,0 --no-mmap -ctk iq4_nl -c 512 --chunks 211
main: build = 3475 (ac156500)
main: built with MSVC 19.38.33141.0 for
main: seed = 1729657101
llama_model_loader: loaded meta data with 31 key-value pairs and 292 tensors from D:\text-generation-webui\models\Meta_Llama_3.1_8b_it-f16-iMat-Q5_K_S_q4_v6.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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta_Llama_3.1_8b_it
llama_model_loader: - kv 3: general.size_label str = 8.0B
llama_model_loader: - kv 4: general.license str = llama3.1
llama_model_loader: - kv 5: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 6: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 7: llama.block_count u32 = 32
llama_model_loader: - kv 8: llama.context_length u32 = 131072
llama_model_loader: - kv 9: llama.embedding_length u32 = 4096
llama_model_loader: - kv 10: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 11: llama.attention.head_count u32 = 32
llama_model_loader: - kv 12: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 13: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 14: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 15: general.file_type u32 = 16
llama_model_loader: - kv 16: llama.vocab_size u32 = 128256
llama_model_loader: - kv 17: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 18: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 19: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 20: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 21: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 22: tokenizer.ggml.merges arr[str,280147] = ["─á ─á", "─á ─á─á─á", "─á─á ─á─á", "...
llama_model_loader: - kv 23: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 24: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 25: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 26: general.quantization_version u32 = 2
llama_model_loader: - kv 27: quantize.imatrix.file str = Q:\iMatrix\Meta_Llama_3.1_8b_it-f16.i...
llama_model_loader: - kv 28: quantize.imatrix.dataset str = groups_merged-enhancedV3_FR_SRB_HR.txt
llama_model_loader: - kv 29: quantize.imatrix.entries_count i32 = 224
llama_model_loader: - kv 30: quantize.imatrix.chunks_count i32 = 145
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q4_K: 32 tensors
llama_model_loader: - type q5_K: 161 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
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 = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
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 = 8B
llm_load_print_meta: model ftype = Q5_K - Small
llm_load_print_meta: model params = 8.030 B
llm_load_print_meta: model size = 5.162 GiB (5.521 BPW)
llm_load_print_meta: repeating layers = 4.424 GiB (5.445 BPW, 6.980 B parameters)
llm_load_print_meta: general.name = Meta_Llama_3.1_8b_it
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 3 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 2: NVIDIA RTX A4000, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.27 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: CUDA_Host buffer size = 344.44 MiB
llm_load_tensors: CUDA0 buffer size = 4941.00 MiB
........................................................................................
llama_new_context_with_model: n_ctx = 1024
llama_new_context_with_model: n_batch = 1024
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_kv_cache_init: CUDA0 KV buffer size = 82.00 MiB
llama_new_context_with_model: KV self size = 82.00 MiB, K (iq4_nl): 18.00 MiB, V (f16): 64.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.98 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=1)
llama_new_context_with_model: CUDA0 compute buffer size = 266.50 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 10.01 MiB
llama_new_context_with_model: graph nodes = 933
llama_new_context_with_model: graph splits = 2
system_info: n_threads = 8 / 16 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
perplexity: tokenizing the input ..
perplexity: tokenization took 213.322 ms
perplexity: calculating perplexity over 211 chunks, n_ctx=512, batch_size=1024, n_seq=2
perplexity: 9.17 seconds per pass - ETA 16.12 minutes
[1]-nan,[2]-nan,[3]-nan,[4]-nan,[5]-nan,[6]-nan,[7]-nan,[8]-nan,[9]-nan,[10]-nan,[11]-nan,[12]-nan,[13]-nan,[14]-nan,[15]-nan,[16]-nan,[17]-nan,[18]-nan,[19]-nan,[20]-nan,[21]-nan,[22]-nan,[23]-nan,[24]-nan,[25]-nan,[26]-nan,[27]-nan,[28]-nan,[29]-nan,[30]-nan,[31]-nan,[32]-nan,[33]-nan,[34]-nan,[35]-nan,[36]-nan,[37]-nan,[38]-nan,[39]-nan,[40]-nan,[41]-nan,[42]-nan,[43]-nan,[44]-nan,[45]-nan,[46]-nan,[47]-nan,[48]-nan,[49]-nan,[50]-nan,[51]-nan,[52]-nan,[53]-nan,[54]-nan,[55]-nan,[56]-nan,[57]-nan,[58]-nan,[59]-nan,[60]-nan,[61]-nan,[62]-nan,[63]-nan,[64]-nan,[65]-nan,[66]-nan,[67]-nan,[68]-nan,[69]-nan,[70]-nan,[71]-nan,[72]-nan,[73]-nan,[74]-nan,[75]-nan,[76]-nan,[77]-nan,[78]-nan,[79]-nan,[80]-nan,[81]-nan,[82]-nan,[83]-nan,[84]-nan,[85]-nan,[86]-nan,[87]-nan,[88]-nan,[89]-nan,[90]-nan,[91]-nan,[92]-nan,[93]-nan,[94]-nan,[95]-nan,[96]-nan,[97]-nan,[98]-nan,[99]-nan,[100]-nan,[101]-nan,[102]-nan,[103]-nan,[104]-nan,[105]-nan,[106]-nan,[107]-nan,[108]-nan,[109]-nan,[110]-nan,[111]-nan,[112]-nan,[113]-nan,[114]-nan,[115]-nan,[116]-nan,[117]-nan,[118]-nan,[119]-nan,[120]-nan,[121]-nan,[122]-nan,[123]-nan,[124]-nan,[125]-nan,[126]-nan,[127]-nan,[128]-nan,[129]-nan,[130]-nan,[131]-nan,[132]-nan,[133]-nan,[134]-nan,[135]-nan,[136]-nan,[137]-nan,[138]-nan,[139]-nan,[140]-nan,[141]-nan,[142]-nan,[143]-nan,[144]-nan,[145]-nan,[146]-nan,[147]-nan,[148]-nan,[149]-nan,[150]-nan,[151]-nan,[152]-nan,[153]-nan,[154]-nan,[155]-nan,[156]-nan,[157]-nan,[158]-nan,[159]-nan,[160]-nan,[161]-nan,[162]-nan,[163]-nan,[164]-nan,[165]-nan,[166]-nan,[167]-nan,[168]-nan,[169]-nan,[170]-nan,[171]-nan,[172]-nan,[173]-nan,[174]-nan,[175]-nan,[176]-nan,[177]-nan,[178]-nan,[179]-nan,[180]-nan,[181]-nan,[182]-nan,[183]-nan,[184]-nan,[185]-nan,[186]-nan,[187]-nan,[188]-nan,[189]-nan,[190]-nan,[191]-nan,[192]-nan,[193]-nan,[194]-nan,[195]-nan,[196]-nan,[197]-nan,[198]-nan,[199]-nan,[200]-nan,[201]-nan,[202]-nan,[203]-nan,[204]-nan,[205]-nan,[206]-nan,[207]-nan,[208]-nan,[209]-nan,[210]-nan,[211]-nan,
Unexpected negative standard deviation of log(prob)
llama_print_timings: load time = 1581.30 ms
llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: prompt eval time = 47678.04 ms / 108032 tokens ( 0.44 ms per token, 2265.87 tokens per second)
llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: total time = 52725.87 ms / 108033 tokens
```
---
#### 💬 Conversation
👤 **ikawrakow** commented the **2024-10-23** at **06:27:44**:<br>
Thanks for the report. Happens for me too. I'll investigate.
---
👤 **ikawrakow** commented the **2024-10-23** at **07:09:28**:<br>
@Nexesenex
This is also broken on mainline `llama.cpp`, no?
With the latest `llama.cpp` (`873279b1592e433c4d9eb5065091cc98473c7bee`) without FA I get NaNs for any of the supported K-cache quantization types.
---
👤 **ikawrakow** commented the **2024-10-23** at **07:43:52**:<br>
CUDA on mainline `llama.cpp` without FA is broken with quantized K-cache for all models I tried (LLaMA-3.1-8B, LLaMA-3.2-3B, LLaMA-2-7B). So, I guess, this issue is inherited. Perhaps you should file a bug report there?
---
👤 **Nexesenex** commented the **2024-10-23** at **07:48:47**:<br>
Indeed, it's on mainline also.
I'll holler them. ^^
---
👤 **ikawrakow** commented the **2024-10-23** at **08:00:33**:<br>
It was puzzling to me why `Q6_0` works here but none of the other types, neither here nor on mainline. But I think I know what is the issue. I haven't implemented a MMQ kernel for `Q6_0`, so the `K*Q` matrix multiplication is done via dequantize `K` -> cuBLAS gemm. While all other types go via @JohannesGaessler MMQ kernels. There have been all these reports about `llama.cpp` producing gibberish for some models, the latest being the Granite models, and the typical fix is to set the `K*Q` matrix multiplication precision to `F32`. Well, if `F16` is not precise enough for `K*Q`, then quantized precision is definitely not precise enough either. So, basically, the issue has existed in mainline `llama.cpp` since @JohannesGaessler switched the default for matrix multiplications to MMQ. Strange that nobody has noticed for so long.
---
👤 **ikawrakow** commented the **2024-10-23** at **08:00:33**:<br>
It was puzzling to me why `Q6_0` works here but none of the other types, neither here nor on mainline. But I think I know what is the issue. I haven't implemented a MMQ kernel for `Q6_0`, so the `K*Q` matrix multiplication is done via dequantize `K` -> cuBLAS gemm. While all other types go via Johannes' MMQ kernels. There have been all these reports about `llama.cpp` producing gibberish for some models, the latest being the Granite models, and the typical fix is to set the `K*Q` matrix multiplication precision to `F32`. Well, if `F16` is not precise enough for `K*Q`, then quantized precision is definitely not precise enough either. So, basically, the issue has existed in mainline `llama.cpp` since Johannes switched the default for matrix multiplications to MMQ. Strange that nobody has noticed for so long.
---
👤 **ikawrakow** commented the **2024-10-23** at **11:16:41**:<br>
Thinking more about this, it is kind of strange. It does work on the CPU, where `Q` gets quantized to `Q8_K` when `K` is quantized, and `Q8_K` is less accurate than `Q8_0` (one float scale per 256 weights for `Q8_K` vs 1 float scale per 32 for `Q8_0`). So, precision/range loss does not seem to be the likely cause. Instead, more likely, there is some other bug in the MMQ kernel that manifests itself only under specific conditions.
---
👤 **ikawrakow** commented the **2024-10-24** at **07:43:55**:<br>
@Nexesenex Does [this PR](https://github.com/ggerganov/llama.cpp/pull/10021) fix it for you? It is approved and all, but I still get NaN's with a quantized model. It does appear to work with the `f16` model, so there is at least some progress.
---
👤 **ikawrakow** commented the **2024-10-24** at **07:43:55**:<br>
@Nexesenex Does [this PR](https://github.com/ggerganov/llama.cpp/pull/10021) fix it for you? It is approved and all, but I still get NaN's.
---
👤 **JohannesGaessler** commented the **2024-10-24** at **09:08:55**:<br>
I also get NaN with a q8_0 model when using `-ctk q8_0`, there are probably multiple bugs.
---
👤 **ikawrakow** commented the **2024-10-24** at **09:16:10**:<br>
> I also get NaN with a q8_0 model when using `-ctk q8_0`, there are probably multiple bugs.
It is not just `q8_0`. Any quantized model with any quantized k-cache without FA produces NaNs on `perplexity` runs. If it helps you, TG appears to work. PP also works if I use `-ub 8` to force the `K*Q` matrix multiplication to go via `MMVQ`.
---
👤 **Nexesenex** commented the **2024-10-24** at **12:39:43**:<br>
@ikawrakow I just confirmed that all K quantum cache no-FA modes present on mainline are now working : https://github.com/ggerganov/llama.cpp/issues/10011#issuecomment-2435180867
I also used https://github.com/ggerganov/llama.cpp/pull/10015 while I was at it.
---
👤 **Nexesenex** commented the **2024-10-24** at **12:39:43**:<br>
@ikawrakow I confirmed it works on master here : https://github.com/ggerganov/llama.cpp/issues/10011#issuecomment-2435180867
I also used https://github.com/ggerganov/llama.cpp/pull/10015 while I was at it.
Reference in New Issue View Git Blame Copy Permalink