This is 100% reproducible in my set up. However, I feel like this issue may or may not be easily reproducible on other systems.
Anyway, although it may be tempting to dismiss this as a bug in the model, this looks like a llama.cpp bug to me for the following reasons:
Note that llama_print_timings in main.log suggests that the prompt is not even evaluated by the model since it thinks eval time for prompt is 0.00ms, and the total running time of the program is 10.93 ms. Clearly not enough to evaluate the prompt of 2k+ tokens.
embd_inp.size(): 2057, which is suspiciously close to 2048, which is also the n_batch size. Upon inspection, it seems like the <|eot_id|><|start_header_id|> tokens are actually the 2048-th token (+/-1 I guess). I wonder whether there's some issues with these special tokens being around the batch size mark. Or maybe some other 2048-related mark. FWIW, changing the batch size slightly -b 2047 or -b 2049 seems to workaround the issue.
Increasing the temperature doesn't seem to help.
I hit similar issues with some other prompts, but they seem to not reproduce as reliably (as in, if you run llama.cpp again with the same args, the text generation works normally) -- this suggests the bug might be subtle and might not be easily reproducible on other machines
Making small changes to the prompt file sometimes fixes the issue, this may or may not be because the changes shifted the token count.
Note that for some reason this issue does not repro on all models. Curiously using Llama3-70B also seems to exhibit this bug. FWIW the Llama3-8B model has shasum c3a1b6a18f22a057ca5eb4733132b99a6fb853d6 .
Name and Version
./llama-cli --version
version: 3198 (c5a8d4b7)
built with Apple clang version 14.0.3 (clang-1403.0.22.14.1) for arm64-apple-darwin23.4.0
% sw_vers
ProductName: macOS
ProductVersion: 14.4.1
BuildVersion: 23E224
Apple M2 Max , 96 GB RAM.
What operating system are you seeing the problem on?
Mac
Relevant log output
[1719242809] Log start
[1719242809] Cmd: /Users/sidney_fong/projects/llama.gguf/llama-cli --temp 0.1 -ngl 99 -c 6144 --n-predict -2 -m /Users/sidney_fong/Downloads/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf -f silent-end-repro.txt
[1719242809] main: build = 3198 (c5a8d4b7)
[1719242809] main: built with Apple clang version 14.0.3 (clang-1403.0.22.14.1) for arm64-apple-darwin23.4.0
[1719242809] main: seed = 1719242809
[1719242809] main: llama backend init
[1719242809] main: load the model and apply lora adapter, if any
[1719242809] llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /Users/sidney_fong/Downloads/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf (version GGUF V3 (latest))
[1719242809] llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
[1719242809] llama_model_loader: - kv 0: general.architecture str = llama
[1719242809] llama_model_loader: - kv 1: general.name str = .
[1719242809] llama_model_loader: - kv 2: llama.vocab_size u32 = 128256
[1719242809] llama_model_loader: - kv 3: llama.context_length u32 = 8192
[1719242809] llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
[1719242809] llama_model_loader: - kv 5: llama.block_count u32 = 32
[1719242809] llama_model_loader: - kv 6: llama.feed_forward_length u32 = 14336
[1719242809] llama_model_loader: - kv 7: llama.rope.dimension_count u32 = 128
[1719242809] llama_model_loader: - kv 8: llama.attention.head_count u32 = 32
[1719242809] llama_model_loader: - kv 9: llama.attention.head_count_kv u32 = 8
[1719242809] llama_model_loader: - kv 10: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
[1719242809] llama_model_loader: - kv 11: llama.rope.freq_base f32 = 500000.000000
[1719242809] llama_model_loader: - kv 12: general.file_type u32 = 17
[1719242809] llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
[1719242809] llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
[1719242809] llama_model_loader: - kv 15: tokenizer.ggml.scores arr[f32,128256] = [0.000000, 0.000000, 0.000000, 0.0000...
[1719242809] llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
[1719242809] llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
[1719242809] llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
[1719242809] llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128001
[1719242809] llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
[1719242809] llama_model_loader: - kv 21: general.quantization_version u32 = 2
[1719242809] llama_model_loader: - type f32: 65 tensors
[1719242809] llama_model_loader: - type q5_K: 193 tensors
[1719242809] llama_model_loader: - type q6_K: 33 tensors
[1719242809] llm_load_vocab: missing pre-tokenizer type, using: 'default'
[1719242809] llm_load_vocab:
[1719242809] llm_load_vocab: ************************************
[1719242809] llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!
[1719242809] llm_load_vocab: CONSIDER REGENERATING THE MODEL
[1719242809] llm_load_vocab: ************************************
[1719242809] llm_load_vocab:
[1719242809] llm_load_vocab: special tokens cache size = 256
[1719242809] llm_load_vocab: token to piece cache size = 0.8000 MB
[1719242809] llm_load_print_meta: format = GGUF V3 (latest)
[1719242809] llm_load_print_meta: arch = llama
[1719242809] llm_load_print_meta: vocab type = BPE
[1719242809] llm_load_print_meta: n_vocab = 128256
[1719242809] llm_load_print_meta: n_merges = 280147
[1719242809] llm_load_print_meta: n_ctx_train = 8192
[1719242809] llm_load_print_meta: n_embd = 4096
[1719242809] llm_load_print_meta: n_head = 32
[1719242809] llm_load_print_meta: n_head_kv = 8
[1719242809] llm_load_print_meta: n_layer = 32
[1719242809] llm_load_print_meta: n_rot = 128
[1719242809] llm_load_print_meta: n_embd_head_k = 128
[1719242809] llm_load_print_meta: n_embd_head_v = 128
[1719242809] llm_load_print_meta: n_gqa = 4
[1719242809] llm_load_print_meta: n_embd_k_gqa = 1024
[1719242809] llm_load_print_meta: n_embd_v_gqa = 1024
[1719242809] llm_load_print_meta: f_norm_eps = 0.0e+00
[1719242809] llm_load_print_meta: f_norm_rms_eps = 1.0e-05
[1719242809] llm_load_print_meta: f_clamp_kqv = 0.0e+00
[1719242809] llm_load_print_meta: f_max_alibi_bias = 0.0e+00
[1719242809] llm_load_print_meta: f_logit_scale = 0.0e+00
[1719242809] llm_load_print_meta: n_ff = 14336
[1719242809] llm_load_print_meta: n_expert = 0
[1719242809] llm_load_print_meta: n_expert_used = 0
[1719242809] llm_load_print_meta: causal attn = 1
[1719242809] llm_load_print_meta: pooling type = 0
[1719242809] llm_load_print_meta: rope type = 0
[1719242809] llm_load_print_meta: rope scaling = linear
[1719242809] llm_load_print_meta: freq_base_train = 500000.0
[1719242809] llm_load_print_meta: freq_scale_train = 1
[1719242809] llm_load_print_meta: n_ctx_orig_yarn = 8192
[1719242809] llm_load_print_meta: rope_finetuned = unknown
[1719242809] llm_load_print_meta: ssm_d_conv = 0
[1719242809] llm_load_print_meta: ssm_d_inner = 0
[1719242809] llm_load_print_meta: ssm_d_state = 0
[1719242809] llm_load_print_meta: ssm_dt_rank = 0
[1719242809] llm_load_print_meta: model type = 8B
[1719242809] llm_load_print_meta: model ftype = Q5_K - Medium
[1719242809] llm_load_print_meta: model params = 8.03 B
[1719242809] llm_load_print_meta: model size = 5.33 GiB (5.70 BPW)
[1719242809] llm_load_print_meta: general.name = .
[1719242809] llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
[1719242809] llm_load_print_meta: EOS token = 128001 '<|end_of_text|>'
[1719242809] llm_load_print_meta: LF token = 128 'Ä'
[1719242809] llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
[1719242809] llm_load_print_meta: max token length = 256
[1719242809] llm_load_tensors: ggml ctx size = 0.30 MiB
[1719242809] ggml_backend_metal_log_allocated_size: allocated buffer, size = 5115.52 MiB, ( 5115.58 / 73728.00)[1719242809]
[1719242809] llm_load_tensors: offloading 32 repeating layers to GPU
[1719242809] llm_load_tensors: offloading non-repeating layers to GPU
[1719242809] llm_load_tensors: offloaded 33/33 layers to GPU
[1719242809] llm_load_tensors: Metal buffer size = 5115.50 MiB
[1719242809] llm_load_tensors: CPU buffer size = 344.44 MiB
[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809] .[1719242809]
[1719242809] llama_new_context_with_model: n_ctx = 6144
[1719242809] llama_new_context_with_model: n_batch = 2048
[1719242809] llama_new_context_with_model: n_ubatch = 512
[1719242809] llama_new_context_with_model: flash_attn = 0
[1719242809] llama_new_context_with_model: freq_base = 500000.0
[1719242809] llama_new_context_with_model: freq_scale = 1
[1719242809] ggml_metal_init: allocating
[1719242809] ggml_metal_init: found device: Apple M2 Max
[1719242809] ggml_metal_init: picking default device: Apple M2 Max
[1719242809] ggml_metal_init: default.metallib not found, loading from source
[1719242809] ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
[1719242809] ggml_metal_init: loading '/Users/sidney_fong/projects/llama.gguf/ggml-metal.metal'
[1719242809] ggml_metal_init: GPU name: Apple M2 Max
[1719242809] ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
[1719242809] ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
[1719242809] ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
[1719242809] ggml_metal_init: simdgroup reduction support = true
[1719242809] ggml_metal_init: simdgroup matrix mul. support = true
[1719242809] ggml_metal_init: hasUnifiedMemory = true
[1719242809] ggml_metal_init: recommendedMaxWorkingSetSize = 77309.41 MB
[1719242809] llama_kv_cache_init: Metal KV buffer size = 768.00 MiB
[1719242809] llama_new_context_with_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB
[1719242809] llama_new_context_with_model: CPU output buffer size = 0.49 MiB
[1719242809] llama_new_context_with_model: Metal compute buffer size = 428.00 MiB
[1719242809] llama_new_context_with_model: CPU compute buffer size = 20.01 MiB
[1719242809] llama_new_context_with_model: graph nodes = 1030
[1719242809] llama_new_context_with_model: graph splits = 2
[1719242809] warming up the model with an empty run
[1719242810] n_ctx: 6144
[1719242810]
[1719242810] system_info: n_threads = 8 / 12 | AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
[1719242810] add_bos: 0
[1719242810] tokenize the prompt
[1719242810] prompt: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful, thoughtful and creative AI assistant. Give concise answers unless the answer would be better with more detail.<|eot_id|><|start_header_id|>user<|end_header_id|>
Rewrite the following text in modern English. Make it short, modern and concise. Make it engaging to a young and impatient reader. You may take some liberties as to the rewrite as long as the original meaning is preserved.
Prefix your rewrite with "-BEGIN REWRITE-". When finished, write "-END REWRITE-".
The wool of Scotland fell very considerably in its price in consequence of
the union with England, by which it was excluded from the great market of
Europe, and confined to the narrow one of Great Britain. The value of the
greater part of the lands in the southern counties of Scotland, which are
chiefly a sheep country, would have been very deeply affected by this
event, had not the rise in the price of butcher’s meat fully compensated
the fall in the price of wool.
As the efficacy of human industry, in increasing the quantity either of
wool or of raw hides, is limited, so far as it depends upon the produce of
the country where it is exerted; so it is uncertain so far as it depends
upon the produce of other countries. It so far depends not so much upon
the quantity which they produce, as upon that which they do not
manufacture; and upon the restraints which they may or may not think
proper to impose upon the exportation of this sort of rude produce. These
circumstances, as they are altogether independent of domestic industry, so
they necessarily render the efficacy of its efforts more or less
uncertain. In multiplying this sort of rude produce, therefore, the
efficacy of human industry is not only limited, but uncertain.
In multiplying another very important sort of rude produce, the quantity
of fish that is brought to market, it is likewise both limited and
uncertain. It is limited by the local situation of the country, by the
proximity or distance of its different provinces from the sea, by the
number of its lakes and rivers, and by what may be called the fertility or
barrenness of those seas, lakes, and rivers, as to this sort of rude
produce. As population increases, as the annual produce of the land and
labour of the country grows greater and greater, there come to be more
buyers of fish; and those buyers, too, have a greater quantity and variety
of other goods, or, what is the same thing, the price of a greater
quantity and variety of other goods, to buy with. But it will generally be
impossible to supply the great and extended market, without employing a
quantity of labour greater than in proportion to what had been requisite
for supplying the narrow and confined one. A market which, from requiring
only one thousand, comes to require annually ten thousand ton of fish, can
seldom be supplied, without employing more than ten times the quantity of
labour which had before been sufficient to supply it. The fish must
generally be sought for at a greater distance, larger vessels must be
employed, and more expensive machinery of every kind made use of. The real
price of this commodity, therefore, naturally rises in the progress of
improvement. It has accordingly done so, I believe, more or less in every
country.
Though the success of a particular day’s fishing may be a very uncertain
matter, yet the local situation of the country being supposed, the general
efficacy of industry in bringing a certain quantity of fish to market,
taking the course of a year, or of several years together, it may,
perhaps, be thought is certain enough; and it, no doubt, is so. As it
depends more, however, upon the local situation of the country, than upon
the state of its wealth and industry; as upon this account it may in
different countries be the same in very different periods of improvement,
and very different in the same period; its connection with the state of
improvement is uncertain; and it is of this sort of uncertainty that I am
here speaking.
In increasing the quantity of the different minerals and metals which are
drawn from the bowels of the earth, that of the more precious ones
particularly, the efficacy of human industry seems not to be limited, but
to be altogether uncertain.
The quantity of the precious metals which is to be found in any country,
is not limited by any thing in its local situation, such as the fertility
or barrenness of its own mines. Those metals frequently abound in
countries which possess no mines. Their quantity, in every particular
country, seems to depend upon two different circumstances; first, upon its
power of purchasing, upon the state of its industry, upon the annual
produce of its land and labour, in consequence of which it can afford to
employ a greater or a smaller quantity of labour and subsistence, in
bringing or purchasing such superfluities as gold and silver, either from
its own mines, or from those of other countries; and, secondly, upon the
fertility or barrenness of the mines which may happen at any particular
time to supply the commercial world with those metals. The quantity of
those metals in the countries most remote from the mines, must be more or
less affected by this fertility or barrenness, on account of the easy and
cheap transportation of those metals, of their small bulk and great value.
Their quantity in China and Indostan must have been more or less affected
by the abundance of the mines of America.
So far as their quantity in any particular country depends upon the former
of those two circumstances (the power of purchasing), their real price,
like that of all other luxuries and superfluities, is likely to rise with
the wealth and improvement of the country, and to fall with its poverty
and depression. Countries which have a great quantity of labour and
subsistence to spare, can afford to purchase any particular quantity of
those metals at the expense of a greater quantity of labour and
subsistence, than countries which have less to spare.
So far as their quantity in any particular country depends upon the latter
of those two circumstances (the fertility or barrenness of the mines which
happen to supply the commercial world), their real price, the real
quantity of labour and subsistence which they will purchase or exchange
for, will, no doubt, sink more or less in proportion to the fertility, and
rise in proportion to the barrenness of those mines.
The fertility or barrenness of the mines, however, which may happen at any
particular time to supply the commercial world, is a circumstance which,
it is evident, may have no sort of connection with the state of industry
in a particular country. It seems even to have no very necessary
connection with that of the world in general. As arts and commerce,
indeed, gradually spread themselves over a greater and a greater part of
the earth, the search for new mines, being extended over a wider surface,
may have somewhat a better chance for being successful than when confined
within narrower bounds. The discovery of new mines, however, as the old
ones come to be gradually exhausted, is a matter of the greatest
uncertainty, and such as no human skill or industry can insure. All
indications, it is acknowledged, are doubtful; and the actual discovery
and successful working of a new mine can alone ascertain the reality of
its value, or even of its existence. In this search there seem to be no
certain limits, either to the possible success, or to the possible
disappointment of human industry. In the course of a century or two, it is
possible that new mines may be discovered, more fertile than any that have
ever yet been known; and it is just equally possible, that the most
fertile mine then known may be more barren than any that was wrought
before the discovery of the mines of America. Whether the one or the other
of those two events may happen to take place, is of very little importance
to the real wealth and prosperity of the world, to the real value of the
annual produce of the land and labour of mankind. Its nominal value, the
quantity of gold and silver by which this annual produce could be
expressed or represented, would, no doubt, be very different; but its real
value, the real quantity of labour which it could purchase or command,
would be precisely the same. A shilling might, in the one case, represent
no more labour than a penny does at present; and a penny, in the other,
might represent as much as a shilling does now. But in the one case, he
who had a shilling in his pocket would be no richer than he who has a
penny at present; and in the other, he who had a penny would be just as
rich as he who has a shilling now. The cheapness and abundance of gold and
silver plate would be the sole advantage which the world could derive from
the one event; and the dearness and scarcity of those trifling
superfluities, the only inconveniency it could suffer from the other.
[1719242810] embd_inp.size(): 2057, n_consumed: 0
[1719242810] [end of text]
[1719242810]
[1719242810] llama_print_timings: load time = 401.42 ms
[1719242810] llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
[1719242810] llama_print_timings: prompt eval time = 0.00 ms / 0 tokens ( nan ms per token, nan tokens per second)
[1719242810] llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
[1719242810] llama_print_timings: total time = 10.93 ms / 1 tokens
[1719242810] ggml_metal_free: deallocating
[1719242810] Log end
What happened?
llama-cli exits after generating a '[end of text]' token, without even processing the whole prompt.
Reproduction is simple:
llama-cli --temp 0.1 -ngl 99 -c 6144 --n-predict -2 -m ~/Downloads/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf -f silent-end-repro.txtThe reproduction file is attached together with logs.
main.log silent-end-repro.txt
This is 100% reproducible in my set up. However, I feel like this issue may or may not be easily reproducible on other systems.
Anyway, although it may be tempting to dismiss this as a bug in the model, this looks like a llama.cpp bug to me for the following reasons:
llama_print_timingsin main.log suggests that the prompt is not even evaluated by the model since it thinks eval time for prompt is 0.00ms, and the total running time of the program is 10.93 ms. Clearly not enough to evaluate the prompt of 2k+ tokens.<|eot_id|><|start_header_id|>tokens are actually the 2048-th token (+/-1 I guess). I wonder whether there's some issues with these special tokens being around the batch size mark. Or maybe some other 2048-related mark. FWIW, changing the batch size slightly-b 2047or-b 2049seems to workaround the issue.Name and Version
Apple M2 Max , 96 GB RAM.
What operating system are you seeing the problem on?
Mac
Relevant log output
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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could':1436, ' suffer':7831, ' from':505, ' the':279, ' other':1023, '.':13, ' ':7361, '':198, '':74694, '':271, '<|eot_id|>':128009, '<|start_header_id|>':128006, 'assistant':78191, '<|end_header_id|>':128007, '':271, '-':12, 'BEGIN':38688, ' RE':3680, 'WRITE':33881, '-':12 ] [1719242810] recalculate the cached logits (check): embd_inp.empty() false, n_matching_session_tokens 0, embd_inp.size() 2057, session_tokens.size() 0, embd_inp.size() 2057 [1719242810] sampling: repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.100 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 [1719242810] sampling order: CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature [1719242810] generate: n_ctx = 6144, n_batch = 2048, n_predict = -2, n_keep = 0 [1719242810][1719242810] embd_inp.size(): 2057, n_consumed: 0 [1719242810] [end of text] [1719242810] [1719242810] llama_print_timings: load time = 401.42 ms [1719242810] llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) [1719242810] llama_print_timings: prompt eval time = 0.00 ms / 0 tokens ( nan ms per token, nan tokens per second) [1719242810] llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) [1719242810] llama_print_timings: total time = 10.93 ms / 1 tokens [1719242810] ggml_metal_free: deallocating [1719242810] Log end