SYCL bug: DeepSeek-V2-Lite-Chat-Q4_K_M does not work as expected #12390
Comments
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@aubreyli In non-interactive mode (-no-cnv) you have to include a proper prompt template in your prompt. For DeepSeek-V2 Lite it will be something like this: |
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@fairydreaming same issue here: root@alc-ai:/home/aubrey/work/llama-gpu# ./build/bin/llama-cli -m /srv/models/DeepSeek-V2-Lite-Chat-Q4_K_M/DeepSeek-V2-Lite-64x1.5B-Chat-Q4_K_M.gguf -ngl 99 -sm none -mg 0 -p "User: what is your name?\n\nAssistant:" -n 30 -no-cnv --snip-- sampler seed: 457823384 User: what is your name? Assistant: This is my first time using this program. I have a question about how to use it. Sure, I'd be happy to llama_perf_sampler_print: sampling time = 1.37 ms / 42 runs ( 0.03 ms per token, 30746.71 tokens per second) |
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@aubreyli Hmm, that's weird. Where can I download this model file? |
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@fairydreaming you can download it from here: |
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@fairydreaming I have the same model files working properly by CUDA. $ ./build/bin/llama-cli -m /srv/models/DeepSeek-V2-Lite-Chat-Q4_K_M/DeepSeek-V2-Lite-64x1.5B-Chat-Q4_K_M.gguf -ngl 99 -sm none -mg 0 -p "User: what is your name?\n\nAssistant:" -n 30 -no-cnv ----snip---- sampler seed: 563822659 User: what is your name? Assistant: I am DeepSeek Chat, an intelligent assistant developed by DeepSeek company. [end of text] llama_perf_sampler_print: sampling time = 1.47 ms / 28 runs ( 0.05 ms per token, 19099.59 tokens per second) |
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@aubreyli I confirm the problem, when using DeepSeek V2 Lite running on a GPU in a SYCL build (I used my RTX 4090 card) the model generates nonsense answers. Model answers look somewhat coherent, but they seem to ignore the user prompt. |
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Please run it with |
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Interesting. Does test-backend-ops passes without fail? |
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@qnixsynapse Yeah: In attached zip there are txt files with printed tensor values from CPU and SYCL backends. They seem to diverge more and more as the inference progresses. |
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I narrowed the problem to GGML_OP_MUL_MAT/GGML_OP_MUL_MAT_ID. When I remove them from the list of operations supported by SYCL everything starts working correctly. |
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@qnixsynapse I narrowed it further to multiplication of these tensors (I filtered them by size): Remaining multiplications can be offloaded to SYCL without any negative consequences. |
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I found the cause and it has nothing to do with matrix multiplication. The problem is in addition of tensor views in |
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This patch works on my side as well, Thanks @fairydreaming! |
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@fairydreaming Excellent work! Thank you! I think adding some nc tests for binary ops in tests-backend-ops will be useful too. |
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@fairydreaming I tested #12399 works on my Arc770 for DeepSeek-V2-Lite-Chat model. Thanks for your great work! |
Name and Version
root@alc-ai:/home/aubrey/work/llama-gpu# ./build/bin/llama-cli --version
version: 4887 (8fcb563)
built with Intel(R) oneAPI DPC++/C++ Compiler 2025.0.4 (2025.0.4.20241205) for x86_64-unknown-linux-gnu
Operating systems
No response
Which llama.cpp modules do you know to be affected?
No response
Command line
./build/bin/llama-cli -m /srv/models/DeepSeek-V2-Lite-Chat-Q4_K_M/DeepSeek-V2-Lite-64x1.5B-Chat-Q4_K_M.gguf -ngl 99 -sm none -mg 0 -p "what is your name?" -n 30 -no-cnvProblem description & steps to reproduce
root@alc-ai:/home/aubrey/work/llama-gpu# ./build/bin/llama-cli -m /srv/models/DeepSeek-V2-Lite-Chat-Q4_K_M/DeepSeek-V2-Lite-64x1.5B-Chat-Q4_K_M.gguf -ngl 99 -sm none -mg 0 -p "what is your name?" -n 30 -no-cnv
build: 4887 (8fcb563) with Intel(R) oneAPI DPC++/C++ Compiler 2025.0.4 (2025.0.4.20241205) for x86_64-unknown-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device SYCL0 (Intel(R) Arc(TM) A770 Graphics) - 15473 MiB free
llama_model_loader: loaded meta data with 47 key-value pairs and 377 tensors from /srv/models/DeepSeek-V2-Lite-Chat-Q4_K_M/DeepSeek-V2-Lite-64x1.5B-Chat-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 = deepseek2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = DeepSeek V2 Lite Chat
llama_model_loader: - kv 3: general.finetune str = Chat
llama_model_loader: - kv 4: general.basename str = DeepSeek-V2-Lite
llama_model_loader: - kv 5: general.size_label str = 64x1.5B
llama_model_loader: - kv 6: general.license str = other
llama_model_loader: - kv 7: general.license.name str = deepseek
llama_model_loader: - kv 8: general.license.link str = https://github.com/deepseek-ai/DeepSe...
llama_model_loader: - kv 9: deepseek2.block_count u32 = 27
llama_model_loader: - kv 10: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 11: deepseek2.embedding_length u32 = 2048
llama_model_loader: - kv 12: deepseek2.feed_forward_length u32 = 10944
llama_model_loader: - kv 13: deepseek2.attention.head_count u32 = 16
llama_model_loader: - kv 14: deepseek2.attention.head_count_kv u32 = 16
llama_model_loader: - kv 15: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 16: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 17: deepseek2.expert_used_count u32 = 6
llama_model_loader: - kv 18: deepseek2.leading_dense_block_count u32 = 1
llama_model_loader: - kv 19: deepseek2.vocab_size u32 = 102400
llama_model_loader: - kv 20: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 21: deepseek2.attention.key_length u32 = 192
llama_model_loader: - kv 22: deepseek2.attention.value_length u32 = 128
llama_model_loader: - kv 23: deepseek2.expert_feed_forward_length u32 = 1408
llama_model_loader: - kv 24: deepseek2.expert_count u32 = 64
llama_model_loader: - kv 25: deepseek2.expert_shared_count u32 = 2
llama_model_loader: - kv 26: deepseek2.expert_weights_scale f32 = 1.000000
llama_model_loader: - kv 27: deepseek2.expert_weights_norm bool = false
llama_model_loader: - kv 28: deepseek2.expert_gating_func u32 = 1
llama_model_loader: - kv 29: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 30: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 31: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 32: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 33: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.070700
llama_model_loader: - kv 34: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 35: tokenizer.ggml.pre str = deepseek-llm
llama_model_loader: - kv 36: tokenizer.ggml.tokens arr[str,102400] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 37: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 38: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv 39: tokenizer.ggml.bos_token_id u32 = 100000
llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 100001
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 100001
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 44: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 45: general.quantization_version u32 = 2
llama_model_loader: - kv 46: general.file_type u32 = 15
llama_model_loader: - type f32: 108 tensors
llama_model_loader: - type q5_0: 14 tensors
llama_model_loader: - type q8_0: 13 tensors
llama_model_loader: - type q4_K: 229 tensors
llama_model_loader: - type q6_K: 13 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 9.65 GiB (5.28 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 2
load: token to piece cache size = 0.6408 MB
print_info: arch = deepseek2
print_info: vocab_only = 0
print_info: n_ctx_train = 163840
print_info: n_embd = 2048
print_info: n_layer = 27
print_info: n_head = 16
print_info: n_head_kv = 16
print_info: n_rot = 64
print_info: n_swa = 0
print_info: n_swa_pattern = 1
print_info: n_embd_head_k = 192
print_info: n_embd_head_v = 128
print_info: n_gqa = 1
print_info: n_embd_k_gqa = 3072
print_info: n_embd_v_gqa = 2048
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 = 0.0e+00
print_info: n_ff = 10944
print_info: n_expert = 64
print_info: n_expert_used = 6
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = yarn
print_info: freq_base_train = 10000.0
print_info: freq_scale_train = 0.025
print_info: n_ctx_orig_yarn = 4096
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 = 16B
print_info: model params = 15.71 B
print_info: general.name = DeepSeek V2 Lite Chat
print_info: n_layer_dense_lead = 1
print_info: n_lora_q = 0
print_info: n_lora_kv = 512
print_info: n_ff_exp = 1408
print_info: n_expert_shared = 2
print_info: expert_weights_scale = 1.0
print_info: expert_weights_norm = 0
print_info: expert_gating_func = softmax
print_info: rope_yarn_log_mul = 0.0707
print_info: vocab type = BPE
print_info: n_vocab = 102400
print_info: n_merges = 99757
print_info: BOS token = 100000 '<｜begin▁of▁sentence｜>'
print_info: EOS token = 100001 '<｜end▁of▁sentence｜>'
print_info: EOT token = 100001 '<｜end▁of▁sentence｜>'
print_info: PAD token = 100001 '<｜end▁of▁sentence｜>'
print_info: LF token = 185 'Ċ'
print_info: EOG token = 100001 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 27 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 28/28 layers to GPU
load_tensors: CPU_Mapped model buffer size = 112.50 MiB
load_tensors: SYCL0 model buffer size = 9767.98 MiB
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llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 0
llama_context: freq_base = 10000.0
llama_context: freq_scale = 0.025
llama_context: n_ctx_per_seq (4096) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
Running with Environment Variables:
GGML_SYCL_DEBUG: 0
GGML_SYCL_DISABLE_OPT: 0
Build with Macros:
GGML_SYCL_FORCE_MMQ: no
GGML_SYCL_F16: no
Found 2 SYCL devices:
| | | | |Max | |Max |Global | |
| | | | |compute|Max work|sub |mem | |
system_info: n_threads = 8 (n_threads_batch = 8) / 32 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
sampler seed: 2656463
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 = 4096
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-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = 30, n_keep = 1
what is your name? is the difference between a man and a boy?
2 Answers | Add Yours
A man is an adult human male, while a boy is a
llama_perf_sampler_print: sampling time = 1.10 ms / 36 runs ( 0.03 ms per token, 32786.89 tokens per second)
llama_perf_context_print: load time = 3147.22 ms
llama_perf_context_print: prompt eval time = 288.22 ms / 6 tokens ( 48.04 ms per token, 20.82 tokens per second)
llama_perf_context_print: eval time = 1660.91 ms / 29 runs ( 57.27 ms per token, 17.46 tokens per second)
llama_perf_context_print: total time = 1952.91 ms / 35 tokens
First Bad Commit
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Relevant log output
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