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Enable CUDA graphs for MoE models + GPT-OSS support (#689)
* gmp-oss: common * gpt-oss: attnetion sinks, swiglu_oai * gpt-oss: WIP llama Model loads and runs (CPU only), but PPL is much to high (~1500 for 1st batch vs ~200 in mainline). Is it because of SWA, because of vocab, or did I introduce a bug somewhere? * gpt-oss: CPU seems to be working It was the SWA thta was missing in the previous commit. There are issues with EOG tokens, so this still needs to be added. * CUDA: ADD_ID Just a copy from mainline * gpt-oss: Seems to be working on CUDA * gpt-oss: add sinks to the attn-vec kernels * CUDA: add head size of 64 to new mma Haven't turned it on yet, but observe slightly better PP and slightly worse TG performance with that. * gpt-oss: add ability to use -fmoe (only CUDA for now) * Move row sums to the write place * Add sinks to iqk flash attention * gpt_oss: Implement -fmoe on the CPU * Simdify swiglu_oai Turning it off for now as performance becomes more variable, so perhaps I'm running into thermal trottling imore often because of making the CPU work too hard. * llama: factor out model loader * Builds successfully * It runs, but mmap does not work * Fix llama_mmap so mmap works * Minor * Fix CUDA after latest changes * Attempt to use CUDA graphs with MoE models - not working * CUDA graphs WIP - still not working * CUDA graphs - seems to be working Likely not all MLA variants are working. I no longer remember why I added the q8_0 cpy that transposes the tensor, but if really needed, this is now missing. Also missing is q6_0. * Make q8_0 cache work for DeepSeek models with CUDA graphs * cuda: cpy for q6_0 * Fix llama_mmap on non-Linux platforms * Adding forgotten file * Iterating on Windows build failures * cuda: re-add q8_0 -> q8_0 transpose so mla = 2 can be used with CUDA graphs and q8_0 cache. * Disable graphs without -fmoe * Minor * Turn graphs on by default --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@@ -734,7 +734,7 @@ llama_token llama_sample_token_impl(struct llama_sampling * smpl, llama_token_da
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// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am)
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static void get_overlapping_token_sequences(const llama_vocab& vocab, const std::string& str, std::unordered_multimap<llama_token, std::vector<llama_token>>& token_sequences, int max_tail_len = -1) {
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for (llama_token token_id = 0; token_id < (llama_token)vocab.n_tokens(); token_id++) {
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std::string word = llama_detokenize(vocab, { token_id }, true);
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auto word = vocab.detokenize( { token_id }, true);
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if (word.find(str) != std::string::npos) {
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token_sequences.emplace(token_id, std::vector<llama_token>());
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}
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@@ -751,7 +751,8 @@ static void get_overlapping_token_sequences(const llama_vocab& vocab, const std:
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}
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}
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if (match) {
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std::vector<llama_token> tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false);
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auto tokenization = vocab.tokenize(str.substr(i), false, false);
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//std::vector<llama_token> tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false);
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if (max_tail_len >= 0 && tokenization.size() > (size_t)max_tail_len) {
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tokenization.resize(max_tail_len);
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}
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