Bug: CodeShell inference not working correctly #8250
Description
What happened?
The latest llama.cpp produces bad outputs for CodeShell, which previously performed well when merged into llama.cpp.
After updating convert-hf-to-gguf.py and convert-hf-to-gguf-update.py, I have converted the CodeShell-7b, a ckpt working well with an old version(5d55b0c) to gguf. But running inference with it on the latest version produces poor outputs.
Tested command:
./llama-simple -m codeshell-7b.gguf -p "def merge_sort(array, start, end):" -n 100
Name and Version
version: 3281 (023b880)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
What operating system are you seeing the problem on?
Linux
Relevant log output
# ./llama-simple -m cd7b.gguf -p "def merge_sort(array, start, end):" -n 100
llama_model_loader: loaded meta data with 23 key-value pairs and 508 tensors from cd7b.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 = codeshell llama_model_loader: - kv 1: general.name str = CodeShell llama_model_loader: - kv 2: codeshell.context_length u32 = 8192 llama_model_loader: - kv 3: codeshell.embedding_length u32 = 4096 llama_model_loader: - kv 4: codeshell.feed_forward_length u32 = 16384 llama_model_loader: - kv 5: codeshell.block_count u32 = 42 llama_model_loader: - kv 6: codeshell.attention.head_count u32 = 32 llama_model_loader: - kv 7: codeshell.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: codeshell.attention.layer_norm_epsilon f32 = 0.000010 llama_model_loader: - kv 9: general.file_type u32 = 1 llama_model_loader: - kv 10: codeshell.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 11: codeshell.rope.scaling.type str = linear llama_model_loader: - kv 12: codeshell.rope.scaling.factor f32 = 1.000000 llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.pre str = codeshell llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,70144] = ["æ½»", "æ¶ģ", "ïĴĻ", "amily... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,70144] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,72075] = ["Ġ Ġ", "ĠĠ ĠĠ", "ĠĠĠĠ ĠĠ... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 70000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 70000 llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 70000 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 70000 llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - type f32: 338 tensors
llama_model_loader: - type f16: 170 tensors
llm_load_vocab: special tokens cache size = 144
llm_load_vocab: token to piece cache size = 0.2985 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = codeshell
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 70144
llm_load_print_meta: n_merges = 72075
llm_load_print_meta: n_ctx_train = 8192
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 = 42
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 = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 16384
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 = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 8192
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 = 0.1B
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 7.98 B
llm_load_print_meta: model size = 14.86 GiB (16.00 BPW)
llm_load_print_meta: general.name = CodeShell
llm_load_print_meta: BOS token = 70000 '<|endoftext|>'
llm_load_print_meta: EOS token = 70000 '<|endoftext|>'
llm_load_print_meta: UNK token = 70000 '<|endoftext|>'
llm_load_print_meta: PAD token = 70000 '<|endoftext|>'
llm_load_print_meta: LF token = 28544 'ÄĬ'
llm_load_print_meta: EOT token = 70000 '<|endoftext|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.22 MiB
llm_load_tensors: CPU buffer size = 15215.58 MiB
...............................................................................................
llama_new_context_with_model: n_ctx = 8192
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 = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 1344.00 MiB
llama_new_context_with_model: KV self size = 1344.00 MiB, K (f16): 672.00 MiB, V (f16): 672.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.27 MiB
llama_new_context_with_model: CPU compute buffer size = 564.01 MiB
llama_new_context_with_model: graph nodes = 1687
llama_new_context_with_model: graph splits = 1
main: n_predict = 100, n_ctx = 8192, n_kv_req = 100
def merge_sort(array, start, end):
if start < end:
mid = (start + end) // 2
merge_sort(array, start, mid)
merge_sort(array, mid + 1, end)
merge(array, start, mid, end)
def merge(array, start, mid, end):
left = array[start:mid + 1]
right = mid + 1
if array[
main: decoded 89 tokens in 14.22 s, speed: 6.26 t/s
llama_print_timings: load time = 1487.96 ms
llama_print_timings: sample time = 9.78 ms / 90 runs ( 0.11 ms per token, 9206.22 tokens per second)
llama_print_timings: prompt eval time = 260.92 ms / 11 tokens ( 23.72 ms per token, 42.16 tokens per second)
llama_print_timings: eval time = 14146.74 ms / 89 runs ( 158.95 ms per token, 6.29 tokens per second)
llama_print_timings: total time = 15708.60 ms / 100 tokens