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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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@@ -486,9 +486,9 @@ void llama_grammar_sample_impl(const struct llama_grammar * grammar, const struc
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for (size_t i = 0; i < candidates->size; ++i) {
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const llama_token id = candidates->data[i].id;
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const std::string & piece = vocab->cache_token_to_piece.at(id);
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const std::string & piece = vocab->token_to_piece(id);
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if (llama_token_is_eog_impl(*vocab, id)) {
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if (vocab->is_eog(id)) {
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if (!allow_eog) {
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candidates->data[i].logit = -INFINITY;
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}
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@@ -511,7 +511,7 @@ void llama_grammar_sample_impl(const struct llama_grammar * grammar, const struc
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void llama_grammar_accept_token_impl(struct llama_grammar * grammar, const struct llama_vocab * vocab, const struct llama_sampling * smpl, llama_token token) {
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const int64_t t_start_sample_us = ggml_time_us();
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if (llama_token_is_eog_impl(*vocab, token)) {
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if (vocab->is_eog(token)) {
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for (const auto & stack : grammar->stacks) {
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if (stack.empty()) {
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return;
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@@ -520,7 +520,7 @@ void llama_grammar_accept_token_impl(struct llama_grammar * grammar, const struc
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GGML_ABORT("fatal error");
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}
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const std::string & piece = vocab->cache_token_to_piece.at(token);
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const std::string & piece = vocab->token_to_piece(token);
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// Note terminating 0 in decoded string
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const auto decoded = decode_utf8(piece, grammar->partial_utf8);
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