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grammars: cache decoded token codepoints for faster sampling #6811

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98f33ba
grammars: early exit when no next_candidates to reject
Apr 21, 2024
f608415
grammars: cache decoded tokens
Apr 21, 2024
cbc7580
grammars: faster llama_grammar_copy
Apr 21, 2024
1fb300c
Merge remote-tracking branch 'origin/master' into grammar-fast
Apr 21, 2024
24769f9
grammars: fix bad merge
Apr 21, 2024
05efa34
grammars: keep llama_grammar_copy non-quadratic optim for later
Apr 24, 2024
9f13623
Merge remote-tracking branch 'origin/master' into grammar-fast
Apr 27, 2024
d41f314
grammars: move token caches to llama_context
Apr 28, 2024
49f0faa
grammars: cache codepoints in llama_new_context_with_model
Apr 28, 2024
d3425f5
grammar: nit (layout)
Apr 28, 2024
c70037f
grammars: nits (revert const grammar sig, fix comment)
Apr 28, 2024
0148661
Merge remote-tracking branch 'origin/master' into grammar-fast
Apr 30, 2024
80736c5
Update llama.cpp
ochafik May 1, 2024
939e143
grammars: mutex-guarded lazy caching of token pieces in llama_sample_…
May 18, 2024
60745ac
grammars: remove early exit --> https://github.com/ggerganov/llama.cp…
May 18, 2024
40655e8
Merge remote-tracking branch 'origin/master' into grammar-fast
May 18, 2024
2477d84
Merge remote-tracking branch 'origin/master' into grammar-fast
May 21, 2024
c208932
Merge remote-tracking branch 'origin/master' into grammar-fast
Jun 4, 2024
6a9b626
Merge remote-tracking branch 'origin/master' into grammar-fast
Jun 10, 2024
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38 changes: 33 additions & 5 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2328,6 +2328,11 @@ struct llama_context {
// control vectors
struct llama_control_vector cvec;

// caching token pieces & their decoded codepoints.
std::vector<std::string> token_pieces;
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>>
token_codepoints_without_partial_utf8_prefix;

#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
Expand Down Expand Up @@ -13037,6 +13042,10 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
}
}

if (next_candidates.empty()) {
return rejects;
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This is such a small and isolated change, I almost wonder if it shouldn't be pulled out into its own PR so that we can evaluate this performance improvement separate from the other one. As it is, it's difficult to know how much to attribute to each change...?

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It happens to be the first commit on the branch so you can git checkout before / after it and compare performance as follows:

( export COMMON_ARGS=(
    -mu https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf
    -m models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf
    --prompt-cache issue4218.bin
    --grammar-file issue4218.gbnf
    -f issue4218.txt
    -c 3400
  ) && \
  hyperfine --warmup 1 --runs 5 \
    -L branch 98f33bae767dd19e213ef663b22ad99979ca71d7^,98f33bae767dd19e213ef663b22ad99979ca71d7 \
    --setup "\
      git checkout {branch} && \
      make clean && make -j LLAMA_CURL=1 main && \
      rm -f issue4218.bin && \
      ./main ${COMMON_ARGS[*]} -n 1" \
    "BRANCH={branch} \
      ./main ${COMMON_ARGS[*]} -n 128 --prompt-cache-ro --seed 12345 --no-display-prompt" )
show output 
Benchmark 1: BRANCH=98f33bae767dd19e213ef663b22ad99979ca71d7^       ./main -mu https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf -m models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf --prompt-cache issue4218.bin --grammar-file issue4218.gbnf -f issue4218.txt -c 3400 -n 128 --prompt-cache-ro --seed 12345 --no-display-prompt
  Time (mean ± σ):      7.970 s ±  0.060 s    [User: 3.829 s, System: 0.292 s]
  Range (min … max):    7.877 s …  8.025 s    5 runs
 
Benchmark 2: BRANCH=98f33bae767dd19e213ef663b22ad99979ca71d7       ./main -mu https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf -m models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf --prompt-cache issue4218.bin --grammar-file issue4218.gbnf -f issue4218.txt -c 3400 -n 128 --prompt-cache-ro --seed 12345 --no-display-prompt
  Time (mean ± σ):      5.814 s ±  0.037 s    [User: 1.674 s, System: 0.277 s]
  Range (min … max):    5.758 s …  5.857 s    5 runs
 
Summary
  'BRANCH=98f33bae767dd19e213ef663b22ad99979ca71d7       ./main -mu https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf -m models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf --prompt-cache issue4218.bin --grammar-file issue4218.gbnf -f issue4218.txt -c 3400 -n 128 --prompt-cache-ro --seed 12345 --no-display-prompt' ran
    1.37 ± 0.01 times faster than 'BRANCH=98f33bae767dd19e213ef663b22ad99979ca71d7^       ./main -mu https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf -m models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf --prompt-cache issue4218.bin --grammar-file issue4218.gbnf -f issue4218.txt -c 3400 -n 128 --prompt-cache-ro --seed 12345 --no-display-prompt'

It doesn't help w/ all grammars, though.

}

const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;

// update top of stack to next element, if any
Expand Down Expand Up @@ -13615,21 +13624,26 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
}
}

// Store decoded codepoints when they are not cached (happens when there's a partial utf8 string prefix).
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
candidates_decoded.reserve(candidates->size);
if (grammar->partial_utf8.n_remain > 0) {
candidates_decoded.reserve(candidates->size);
}
std::vector<llama_grammar_candidate> candidates_grammar;
candidates_grammar.reserve(candidates->size);

for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_piece(ctx, id, false);

const auto & piece = ctx->token_pieces[id];
if (llama_token_is_eog(&ctx->model, id)) {
if (!allow_eog) {
candidates->data[i].logit = -INFINITY;
}
} else if (piece.empty() || piece[0] == 0) {
candidates->data[i].logit = -INFINITY;
} else if (grammar->partial_utf8.n_remain == 0){
const auto & decoded = ctx->token_codepoints_without_partial_utf8_prefix.at(id);
candidates_grammar.push_back({ i, decoded.first.data(), decoded.second });
} else {
candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
Expand Down Expand Up @@ -13826,10 +13840,12 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}

const std::string piece = llama_token_to_piece(ctx, token, false);
const auto & piece = ctx->token_pieces.at(token);

// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
const auto decoded = grammar->partial_utf8.n_remain == 0
? ctx->token_codepoints_without_partial_utf8_prefix[token]
: decode_utf8(piece, grammar->partial_utf8);
const auto & code_points = decoded.first;
std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
Expand Down Expand Up @@ -15714,6 +15730,18 @@ struct llama_context * llama_new_context_with_model(
}
}

// cache tokens & their decoded codepoints (for common case where there's no partial utf8 prefix bytes) for grammar-constrained sampling.
{
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Only reservation that I have is that we're caching these data structures whether there is a grammar or not. These data structures are only used in llama_sample_grammar and llama_grammar_accept_token, so if there is no grammar, then this is wasted time (and perhaps more importantly, memory).

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Fair point! A “grammars_enabled” context param might make sense, with a flag to turn it off in server (+ explode when grammar requested), and turned on in main only when grammar or schema flag set. Would save a couple of MBs and a tiny bit of preprocessing. Will add tonight 👌

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Could also perhaps restructure this cache building that happens as a memorization step that happens inside of the first calls to decode_utf8 / llama_token_to_piece, rather than when the context is built.

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That’s what I initially did (and was storing it in the grammar itself, which was pointed out as a not ideal by @ejones ). Lazy init comes with big concurrency concerns (say two slots start working with grammars at the same time in the server: can’t let them both populate the data lazily if it’s in the context)

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@ochafik ochafik May 1, 2024 •
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(Oh but then, no sure anymore whether context is slot-specific? I’ll look again tonight 😅)

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(Oh but then, no sure anymore whether context is slot-specific? I’ll look again tonight 😅)

Yeah context is shared. And as @ejones pointed out grammar itself technically could be shared in other contexts.

I played with the idea of allowing to disable the preprocessing (here) but I haven't found a good way to articulate the flag wrt/ all the ways the API can be used. I think it'd be simpler to leave it as is and keep it as an area where to potentially squeeze a couple of MB when times are scarce. wdyt?

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Apologies in advance for this comment -- this is very long, and very stream-of-consciousness, so please take everything with a grain of salt. However, I want to keep moving forward on this PR, so I figure at least some reply is better than nothing -- even if it's a bit rambly. :)

I played with the idea of allowing to disable the preprocessing (here) but I haven't found a good way to articulate the flag wrt/ all the ways the API can be used.

Yeah, that's a tricky question. I wonder if instead of a command-line flag, if the precache step at the end of llama_new_context_with_model should maybe be broken out into a separate call -- something like llama_context_precache_grammar_token_pieces? Then, whoever is initializing the context can also add precaching to it...? calling the API can enable this pre-caching feature if they want to or not, but it takes an extra call...?

So I'm back to wondering if there is a way where we can still break this precache functionality into a separate function. Can we check for the need of it in llama_sample_grammar / llama_grammar_accept_token and call the precaching there as-needed?

if (ctx->token_pieces.empty()) {
    llama_context_precache_grammar_token_pieces(ctx);
}
const auto & piece = ctx->token_pieces.at(token);
...

It's annoying to run that check on every token, but the performance improvements when using a grammar should still win out. And we also remove the extra memory usage in the case of no grammar being used.

But as I'm thinking through this now, I think what you said earlier is the problem of ctx being shared across multiple threads / processes, so at the very least, we would need to add a blocking mutex here. Is that feasible / reasonable? We do a number of other little mutexes scattered throughout the code in other places, so it feels like it wouldn't be awful here...?

So yeah, I'm still on the fence about making it as a separate call that needs to be called manually if one wants the performance boost (maybe with an automatic fallback mechanism in the sampler), or else transparent caching on first call at runtime with a mutex (provided we can implement this without it being too annoying to run this check inside such a tight loop).

I think it'd be simpler to leave it as is and keep it as an area where to potentially squeeze a couple of MB when times are scarce. wdyt?

This also sounds not unreasonable, but I don't know how to weigh such things. I know I really like grammar-constrained sampling, but I don't know how popular the feature is overall, and is it worth negatively impacting hyper-resource-constrained usages (such as Raspberry Pis or whatnot) vs. grammars? That's what I'm unable to weigh -- I feel like that's a strategic decision that's a bit above my level.

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@gabriel-peracio gabriel-peracio May 3, 2024 •
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I don't know how popular the feature is overall

User here: I've been finding the grammar feature extremely useful and can't live without it. There are also no substitutes anywhere, the competitor's solutions are worse than llama.cpp's implementation IMO

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@HanClinto thanks++ for your reply & sorry it took me so long to get back to it, the Spring has been full of distractions 😅

I had somehow written concurrent synchronization off but as you mention it, seems worth exploring, looking now!

@gabriel-peracio same vibe here, realized I couldn't live without it and then that I couldn't stand how slow it was when pushed to its limits haha! It's nearly there :-)

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@HanClinto I've moved the caching back to llama_sample_grammar in lazy & mutex-protected form (thanks for the suggestion!). And sent the early exit change separately -> #7370

auto n_vocab = llama_n_vocab(llama_get_model(ctx));
ctx->token_codepoints_without_partial_utf8_prefix.resize(n_vocab);
ctx->token_pieces.resize(n_vocab);
for (llama_token id = 0; id < n_vocab; ++id) {
const std::string piece = llama_token_to_piece(ctx, id, false);
ctx->token_pieces[id] = piece;
ctx->token_codepoints_without_partial_utf8_prefix[id] = decode_utf8(piece, {0, 0});
}
}

#ifdef GGML_USE_MPI
ctx->ctx_mpi = ggml_mpi_init();

Expand Down