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47 Commits
| Author | SHA1 | Message | Date | |
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Georgi Gerganov
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d9c6ce46f7 |
kv-cache : support V-less cache (#19067)
* kv-cache : support V-less cache * cuda : better check for V_is_K_view * cuda : improve V_is_K_view check * graph : add comments * hparams : refactor |
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Georgi Gerganov
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557515be1e |
graph : utilize ggml_build_forward_select() to avoid reallocations (#18898)
* graph : avoid branches between embedding and token inputs * models : make deepstack graphs (e.g. Qwen3 VL) have constant topology * ci : enable -DGGML_SCHED_NO_REALLOC=ON for server CI * cont : pad token embeddings to n_embd_inp |
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Tarek Dakhran
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ad8d85bd94 |
memory : add llama_memory_hybrid_iswa (#18601)
* memory : add llama_memory_hybrid_iswa * Update src/llama-memory-hybrid-iswa.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Daniel Bevenius
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d3dce4e0a5 |
sampling : add support for backend sampling (#17004)
* sampling : add support for backend sampling This commit adds support for performing sampling operations on the backend (e.g. GPU) as part of the model computation graph. The motivation for this feature is to enable sampling to be performed directly on the backend as part of the computation graph being executed, allowing for some or all of the sampling to be done on the backend. For example, the backend sampler chain might select/sample a token directly in which case only the sampled token needs to be transferred from device memory to host memory. It is also possible for the backend samplers to perform filtering of the logits, or compute and filter the probability distribution, in which case only the filtered logits or probabilites need to be transferred back to system memory for further processing by CPU samplers. Currently the backend sampling works in a similar manner to how pooling works, it is a function that is called by build_graph and the sampler operations become part of the models computation graph. * llama-cli : add backend sampler configuration * server : add backend sampling options/configuration * webui : add backend sampling options * ggml : add initial cumsum implementation for CUDA * sampling : enable all backend sampler tests This commit enables all exisiting backend sampler tests in the test-backend-sampler. Previously, some tests were disabled because there were missing ggml operation implementations. * graph : do not include llama-model.h * sampling : always expose sampled_ids This commit precomputes and caches the full-vocab token id list in llama_context's constructor, so llama_get_backend_sampled_token_ids_ith always returns a valid pointer. The motivation for this is that this enables both common/sampling.cpp and src/llama-sampling.cpp can simplify their logic. Not all backends samplers that process logits need to set the sampled_tokens_id as they may not change the order of the logits, for example the temperature sampler only scales the logits but does not change their order. Simliar the logit bias sampler only adds bias to specific token ids but does not change the order of the logits. In these cases there will not be a device to host copy of the sampled token ids, and this is the use case where having this precomputed list is useful. * sampling : ensure at most one output token per seq This commit adds a check in the batch allocator to ensure that when backend sampling is enabled, at most one output token is specified per sequence. * CUDA: Optimize argsort for gpu-based token sampling Argsort is used for top-k currently. WE optimize argsort by 2 things: 1. Use `DeviceRadixSort` for single-row/sequence to parallelize it across our SMs 2. Use `DeviceSegmentedSort` for multi-row/sequence as this is the correct entrypoint (the function chooses different execution paths, it contains `DeviceSegmentedRadixSort` as one of the paths and will choose the best one according to heuristics. https://nvidia.github.io/cccl/cub/api/structcub_1_1DeviceSegmentedSort.html#overview Some perf numbers for a RTX PRO 6000: On the kernel level, tested with `GGML_CUDA_DISABLE_GRAPHS=1 ./test-backend-ops -o ARGSORT perf` Before: ``` ARGSORT(type=f32,ne=[65000,16,1,1],order=0): 4130 runs - 359.24 us/run ARGSORT(type=f32,ne=[200000,1,1,1],order=0): 8192 runs - 861.34 us/run ARGSORT(type=f32,ne=[200000,16,1,1],order=0): 1343 runs - 1020.01 us/run ``` After: ``` ARGSORT(type=f32,ne=[65000,16,1,1],order=0): 4130 runs - 312.41 us/run ARGSORT(type=f32,ne=[200000,1,1,1],order=0): 16384 runs - 63.48 us/run ARGSORT(type=f32,ne=[200000,16,1,1],order=0): 1343 runs - 874.36 us/run ``` --- On the model level, tested with `llama-cli -m gpt-oss-20b-mxfp4.gguf -n 200 -p "What is the Capital of Sweden?" -no-cnv -fa 1 --backend-sampling` Before: ``` llama_perf_sampler_print: sampling time = 0.25 ms / 207 runs ( 0.00 ms per token, 824701.20 tokens per second) llama_perf_context_print: load time = 18215.58 ms llama_perf_context_print: prompt eval time = 28.20 ms / 7 tokens ( 4.03 ms per token, 248.19 tokens per second) llama_perf_context_print: eval time = 714.79 ms / 199 runs ( 3.59 ms per token, 278.40 tokens per second) llama_perf_context_print: total time = 857.62 ms / 206 tokens ``` After ``` llama_perf_sampler_print: sampling time = 0.25 ms / 207 runs ( 0.00 ms per token, 828000.00 tokens per second) llama_perf_context_print: load time = 18366.92 ms llama_perf_context_print: prompt eval time = 35.92 ms / 7 tokens ( 5.13 ms per token, 194.87 tokens per second) llama_perf_context_print: eval time = 532.79 ms / 199 runs ( 2.68 ms per token, 373.50 tokens per second) llama_perf_context_print: total time = 683.65 ms / 206 tokens ``` * sampling : remove version from sampler chain This commit removes the version field from the sampler chain and instead used the sampler pointer itself for change detection. * sampling : always populate logits for sampled probs This commit updates common/sampler.cpp set_logits and src/llama-sampling.cpp llama_sampler_sample to always populate the logits field when backend sampled probabilities are available. The motivation for this is that this ensure that CPU sampler always have access to the logits values even when probabilites have been produced by backend samplers. * sampling : simplify backend sampling logic decode This commit tries to simplify the backend sampling logic in llama_context::decode. * squash! sampling : simplify backend sampling logic decode Fix condition to check if backend actually sampled tokens, not just that backend samplers are available. * common : fix regression caused by extra memory allocations during sampling * squash! sampling : simplify backend sampling logic decode The commit fixes a variable shadowing issue in the `llama_context::decode` function which was introduced in a previous refactoring. * squash! common : fix regression caused by extra memory allocations during sampling Apply the same changes to llama-sampling.cpp, llama_sampler_sample as were applied in commit |
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Georgi Gerganov
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c560316440 |
graph : reuse SSM graphs (#16490)
* graph : reuse hybrid graphs * graph : reuse recurrent graphs * graph : fix reuse check for recurrent inputs * memory : move the recurrent state into the memory context * Revert "memory : move the recurrent state into the memory context" This reverts commit 00f115fe810815d4a22a6dee0acc346131e970e1. * cont : fix build |
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Xuan-Son Nguyen
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0759b09c90 | graph: add f_attn_temp_offset (#18025) | ||
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Georgi Gerganov
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e38b7c6e9e |
graph : support cacheless embeddings with FA and iSWA (#16528)
* graph : support cacheless embeddings with FA and iSWA * cont : deduplicate mask creation * cont : fix name |
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Saba Fallah
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e08db42595 |
model: EmbeddingGemma Adding Support for SentenceTransformers Dense Modules (#16367)
* model: EmbeddingGemma sentence-transformers dense linear projections support * model: add support for EmbeddingGemma SentenceTransformers dense linear projections Adding support for the Dense modules used in EmbeddingGemma models. EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone. See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/ * model: add support for EmbeddingGemma SentenceTransformers dense linear projections - converting model with dense-layers is optional - introduced dense config params * Update convert_hf_to_gguf.py Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com> * fixed formatting issues * Update src/llama-graph.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * - removed pooling_type_opt, always allow overriding pooling_type - asserts checking dense features dims * fix python lint * fix ubuntu gcc build warning * - fixed thread-safety test - moved asserts to load_hparams * - tidying up code - simplifying graph-context expecting both dense weights * minor : add TODO --------- Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Douglas Hanley
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b5bd037832 | llama : add support for qwen3 reranker (#15824) | ||
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Daniel Bevenius
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fb15d649ed |
llama : add support for EmbeddingGemma 300m (#15798)
This commit add support for the EmbeddingGemma 300m. This model supports sliding window attention (SWA) and a new swq_type is introduced to support symmetric SWA masking. This commit also extracts the code from the function llama_is_masked_swa in llama-impl.h, so that the logic can be shared by both llm_graph_input_attn_no_cache::set_input and llama_kv_cache::set_input_kq_mask. With this commit the EmbeddingGemma 300m model can be converted to to GGUF and used with llama.cpp. Once the model has been uploaded to HuggingFace it can be used like this: ```console ./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-GGUF:Q8_0 ``` |
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Johannes Gäßler
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e81b8e4b7f |
llama: use FA + max. GPU layers by default (#15434)
* llama: use max. GPU layers by default, auto -fa * ggml-backend: abort instead of segfault |
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Georgi Gerganov
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3f196be84b |
graph : remove build_attn_with_sinks overload (#15469)
ggml-ci |
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Georgi Gerganov
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715a6db02c |
kv-cache : drop the "unified" prefix (#15467)
* kv-cache : drop the "unified" prefix ggml-ci * cont : fix comment [no ci] |
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Georgi Gerganov
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fd1234cb46 |
llama : add gpt-oss (#15091)
* oai moe * compat with new checkpoint * add attn sink impl * add rope scaling yarn * logits match with latest transformers code * wip chat template * rm trailing space * use ggml_scale_bias * rm redundant is_swa_all * convert interleaved gate_up * graph : fix activation function to match reference (#7) * vocab : handle o200k_harmony special tokens * ggml : add attention sinks support (#1) * llama : add attn sinks * ggml : add attn sinks * cuda : add attn sinks * vulkan : add support for sinks in softmax remove unnecessary return * ggml : add fused swiglu_oai op (#11) * ggml : add fused swiglu_oai op * Update ggml/src/ggml-cpu/ops.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update CUDA impl * cont : metal impl * add vulkan impl * test-backend-ops : more test cases, clean up * llama : remove unfused impl * remove extra lines --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com> * repack mxfp4 upon conversion * clean up a bit * enable thinking * add quick hack to render only some special tokens * fix bf16 conversion * remove vocab hack * webui ok * support chat parsing for gpt-oss * fix webui * direct mapping mxfp4, FINALLY * force using mxfp4 * properly use lazy tensor * ggml : add mxfp4 ggml : use e8m0 conversion instead of powf Co-authored-by: Diego Devesa <slarengh@gmail.com> change kvalues_mxfp4 table to match e2m1 (#6) metal : remove quantization for now (not used) cuda : fix disabled CUDA graphs due to ffn moe bias vulkan : add support for mxfp4 cont : add cm2 dequant * ggml : add ggml_add_id (#13) * ggml : add ggml_add_id * add cuda impl * llama : add weight support check for add_id * perf opt * add vulkan impl * rename cuda files * add metal impl * allow in-place ggml_add_id * llama : keep biases on CPU with --cpu-moe * llama : fix compile error ggml-ci * cuda : add fallback for __nv_cvt_e8m0_to_bf16raw ggml-ci * cleanup ggml-ci * sycl : fix supports_op for MXFP4 ggml-ci * fix Unknown reasoning format * ggml-cpu : fix AVX build ggml-ci * fix hip build ggml-ci * cuda : add mxfp4 dequantization support for cuBLAS ggml-ci * ggml-cpu : fix mxfp4 fallback definitions for some architectures ggml-ci * cuda : fix version required for __nv_cvt_e8m0_to_bf16raw --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: slaren <slarengh@gmail.com> |
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Georgi Gerganov
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ba42794c9e |
graph : fix equal_seq() check (#14986)
ggml-ci |
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Dongliang Wei
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c1dacaa99b | llama : merge build_moe_ffn_from_probs function into build_moe_ffn (#14968) | ||
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compilade
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66625a59a5 |
graph : reduce splits for recurrent and hybrid models (#14825)
* graph : avoid creating redundant s_copy views * graph : comment the s_copy views |
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Georgi Gerganov
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1e15bfd42c |
graph : fix stack-use-after-return (#14960)
ggml-ci |
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Dongliang Wei
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6c6e397aff |
model : add support for SmallThinker series (#14898)
* support smallthinker * support 20b softmax, 4b no sliding window * new build_moe_ffn_from_probs, and can run 4b * fix 4b rope bug * fix python type check * remove is_moe judge * remove set_dense_start_swa_pattern function and modify set_swa_pattern function * trim trailing whitespace * remove get_vocab_base of SmallThinkerModel in convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * better whitespace Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use GGML_ASSERT for expert count validation Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Improve null pointer check for probs Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use template parameter for SWA attention logic * better whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * move the creation of inp_out_ids before the layer loop * remove redundant judge for probs --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Georgi Gerganov
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8f974bc1e9 |
graph : refactor context to not pass gf explicitly (#14629)
ggml-ci |
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Georgi Gerganov
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01612b7409 |
llama : reuse compute graphs (#14482)
* llama : reuse compute graphs ggml-ci * llama-bench : add graph reuse parameter ggml-ci * cont : remove the parameter and the sched resets ggml-ci * graph : rename update() to can_reuse() ggml-ci * params : remove is_same() ggml-ci * graph : set res->params in llm_graph_context constructor ggml-ci * graph : avoid set_max_nodes in llm_graph_result ggml-ci * kv-cache : reuse llama_context's graph result instance ggml-ci * context : reset the previous graph result upon memory updates ggml-ci * batch : llama_ubatch now carries its data instead of pointing to balloc ggml-ci * merge : fix build ggml-ci * graph : fix can_reuse() checks when flash-attention is disabled * graph : move llm_graph_result impl in source file + debug env ggml-ci |
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Georgi Gerganov
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225e7a1438 |
llama : add high-throughput mode (#14363)
* kv-cache : prepare K/V buffers for separation ggml-ci * batched-bench : fix oob write ggml-ci * llama : add "virtual sequences" ggml-ci * llama : use "stream" vs "virtual sequence" ggml-ci * graph : fix stream splitting when KV cache is not used ggml-ci * kv-cache : add multi-stream save/load support ggml-ci * llama : add "--attn-streams" flag ggml-ci * kv-cache : fix handling when find_slot fails ggml-ci * kv-cache : restore find_slot impl ggml-ci * kv-cache : add comments * kv-cache : add bounds checks for sequence id ggml-ci * cont : add n_seq_max to batch allocr ggml-ci * kv-cache : perform stream copies lazily after llama_synchronize ggml-ci * kv-cache : avoid throwing exceptions across the C boundary ggml-ci * CUDA: 4D FlashAttention support (#14628) * CUDA: 4D FlashAttention support * CUDA: fix WMMA FA kernel * llama : rename attn_streams -> kv_unified ggml-ci * common : rename kv_split -> kv_unified ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> |
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Xuan-Son Nguyen
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cb9178f885 | llama : remove llm_graph_input_one (#14603) | ||
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compilade
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4a5686da22 |
llama : support Jamba hybrid Transformer-Mamba models (#7531)
* wip: llama : separate recurrent states from the KV cache This will be necessary to support Jamba (and other recurrent models mixed with Attention). Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states. * llama : use std::find for seq_nodes in llama_rs_cache * llama : state checkpoints for recurrent models * llama : correctly handle more edge cases for the rs cache * llama : rename many llama_kv_cache_* functions * llama : remove useless return value for some llama_cache_* functions * llama : rethink recurrent state cell counts * llama : begin work on support for variable GQA This will also be useful for Jamba if we consider the Mamba layers to have 0 KV heads. * llama : gracefully fail when not finding hybrid slot * llama : support Jamba * llama : fix BERT inference without KV cache * convert-hf : check for unprocessed Jamba experts * convert-hf : support Mini-Jamba conversion * llama : fix Jamba quantization sanity checks * llama : sequence-length-aware batch splitting * llama : use equal-sequence-length sub-batches for recurrent models * ggml : simplify SSM-related operators * llama : make recurrent state slot allocation contiguous * llama : adapt internal uses of batches to llama_ubatch * llama : fix batch split output count for embeddings * llama : minimize swaps when reordering logits This reduces overhead when running hellaswag on thousands of sequences with very small 100k params Mamba models. * llama : fix edge case finding batch seq_id of split recurrent cell This otherwise was a problem when running the HellaSwag benchmark with small batch sizes, making it crash. * llama : avoid copies for simple batch splits * ggml : make ggml_ssm_scan not modify its source tensors * llama : fix shared recurrent tail cell count for small ubatch sizes Otherwise it was impossible to run the 'parallel' example with '-ub 1' with a Mamba or Jamba model. * llama : fix .base() compilation error on Windows * llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL * ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors The implementation already supported it, and this makes Mamba's conv step slightly faster. * mamba : fix non-contiguous usage of ggml_silu * llama : session saving and reloading for hybrid models * convert_hf : fix Jamba conversion * llama : fix mixed signedness comparison * llama : use unused n_embd_k_gqa in k_shift This also slightly reduces the diff from the master branch * llama : begin renaming llama_past back to llama_kv_cache * llama : remove implicit recurrent state rollbacks * llama : partially apply clang-format style * convert : fix jamba conv1d shape squeezing * graph : add back hybrid memory graph input But this time it contains the sub-cache graph inputs. This *should* make it easier to handle updating the inputs when caching the graph (eventually). * model : add Jamba to Mamba-specific hparams printing * jamba : remove redundant nullptr initializations * model : remove unnecessary prefix for tensor loading constants Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : use ggml_swiglu_split for Mamba Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : make falcon-h1 use shared mamba2 layer builder * memory : avoid referring to KV in recurrent cache logs * gguf-py : avoid adding duplicate tensor mappings for Jamba Some of the tensor names are common with Llama4 --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Georgi Gerganov
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7b50f7c025 |
graph : prepare for 4D mask (#14515)
ggml-ci |
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Georgi Gerganov
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a70c8a0c4b |
kv-cache : use ggml_set_rows (#14285)
* kv-cache : use ggml_set_rows ggml-ci * graph : separate k and v indices ggml-ci * cont : remove redundant ifs ggml-ci * kv-cache : improve find_slot impl * kv-cache : bounds-check when accessing slot_info indices * kv-cache : add comments ggml-ci * ggml : add TODOs for adding GGML_OP_SET_ROWS support in the backends ggml-ci |
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compilade
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5d46babdc2 |
llama : initial Mamba-2 support (#9126)
* llama : initial Mamba-2 support * ggml : SIMD ggml_ssm_scan for Mamba-2 * ggml : improve ggml_mul speed when masking recurrent states * llama : support running Mamba-Codestral-7B-v0.1 * llama : fix Mamba-2 conv state saving * ggml : make the ggml_mul fast broadcast path more consistently formatted * llama : remove unused variable * llama : add missing break * convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires workarounds to work correctly. * llama : avoid redundant state copy for Mamba 1 and 2 * metal : attempt to adapt SSM_SCAN for Mamba-2 * metal : fix SSM_SCAN pipeline scope * metal : use log and exp instead of log1pf and expf in SSM_SCAN * metal : remove unused arguments for SSM_SCAN The max index is 31, so trimming the arguments is necessary. * metal : add back n_seqs to SSM_SCAN args Whoops, this is needed for the offset in the concatenated output. * metal : fix SSM_SCAN state head offset * metal : fix wrong number of tokens per sequence in SSM_SCAN * ggml : remove unused fast broadcast path in GGML_MUL This was initially added because states were masked with ggml_mul, but this is no longer done and so this "optimisation" is no longer necessary, or at least not worth the additional code complexity. * ggml : avoid multiply by D in GGML_OP_SSM_SCAN This makes the weight buft detection in src/llama.cpp simpler. * convert : transpose Mamba-2 A, D and reshape SSM_NORM This breaks existing conversions of Mamba-2 models to avoid some reshapes. Not sure if it's a good idea, but it makes the graph slightly cleaner. * llama : more appropriate SSM_SCAN and SSM_CONV buft support checks * convert : fix flake8 lint * metal : fix confusion between ; and , * metal : add missing args for nb references in ssm_scan_f32_group * metal : single-user mamba2 inference works * kv-cache : remove const_cast when setting inputs for s_copy And also fix multi-user inference for recurrent models by using cell_id instead of i as the kv cell index when populating s_copy. * convert : avoid AutoConfig for Mamba and Mamba2 hparams * kv-cache : allow context shift for recurrent models * graph : fix recurrent state copies when avoiding copies Works, but using lambda functions might not be that clean. * ggml : fix mamba2 ssm scan when compiled with SVE * ggml-cpu : reorder SVE FMA for consistency with other SIMD arches * cuda : implement ssm scan for Mamba2 There is still room for improvement, but it works! * cuda : adapt Mamba1 ssm scan to shape changes from Mamba2 * mamba : fix mismatched new and delete size for llm_build_mamba Subclasses of llm_graph_context cannot have extra fields, because the called destructor is not the one from the subclass. This otherwise would cause problems when runnning Mamba-(1|2) inference when compiled -DGGML_SANITIZE_ADDRESS=ON * cuda : graceful fallback for Mamba-1 models with weird embd size |
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Sigbjørn Skjæret
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a0535ffa0d |
ggml : implement REGLU/GEGLU/SWIGLU ops (#14158)
* implement unary REGLU/GEGLU/SWIGLU cpu ops * relax constraints * duplicate shape of source * fix ggml_vec_geglu_f16 * special case gated ops * implement unary REGLU/GEGLU/SWIGLU cuda ops * tighten constraints again * refactor into GGML_GLU_OP * metal : add glu kernels ggml-ci * add CUDA_GLU_BLOCK_SIZE [no ci] * more constraints and use 64bit ints ggml-ci * 64bit multiplication [no ci] * implement swapped variants (cpu/cuda) * update comment [no ci] ggml-ci * Vulkan: Add GLU ops and shaders * SYCL: Implement fused kernel GEGLU, SWIGLU and REGLU for single up+gate * ggml : implement GLU for split up/gate (#14181) * implement GLU for split up/gate * add tests for ggml_glu_split * Vulkan: Implement glu_split logic and shader support * add split to logging [no ci] * SYCL: refactor element_size ops and add split up and gate support to gated kernels * SYCL: switch GEGLU to use tanh approximation --------- Co-authored-by: 0cc4m <picard12@live.de> Co-authored-by: Akarshan <akarshan@menlo.ai> * GGML: increase OP count in assertion * Refactor: Optimize SYCL element-wise operations with unary function inlining This commit refactors the SYCL element-wise operations to improve performance by: - Inlining unary operations (sgn, abs, elu, gelu, silu, etc.) to reduce kernel launch overhead. - Introducing helper functions `op_xxx` for each unary operation to encapsulate the logic. - Replacing direct kernel calls with calls to these inlined functions. - Using `__dpct_inline__` to encourage compiler inlining. - Minor code cleanup and consistency improvements. The changes aim to reduce kernel launch overhead and improve the overall efficiency of element-wise operations on SYCL devices. * vulkan: Increase workgroup size for GLU, for performance (#14345) * vulkan: Increase workgroup size for GLU, for performance * vulkan: change GLU shaders to do one element per invocation rather than one row per workgroup * merge fix * metal : add support for split and swap ggml-ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: 0cc4m <picard12@live.de> Co-authored-by: Akarshan <akarshan@menlo.ai> Co-authored-by: Jeff Bolz <jbolz@nvidia.com> |
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Georgi Gerganov
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72babea5de |
graph : make llm_graph_context destructor virtual (#14410)
ggml-ci |
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Xuan-Son Nguyen
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8846aace49 |
model : gemma3n text-only (#14400)
* gemma3n * add llm_graph_input_one |
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Georgi Gerganov
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692e3cdd0a |
memory : rename interface to llama_memory_context_i (#14296)
* memory : rename interface to llama_memory_context_i ggml-ci * cont : fix comments * cont : use "mctx" for referencing a memory context ggml-ci |
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Georgi Gerganov
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4c9fdfbe15 |
ubatch : new splitting logic (#14217)
ggml-ci |
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Gabe Goodhart
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edc4a29eff |
memory : Hybrid recurrent cache (#13979)
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: https://github.com/ggml-org/llama.cpp/pull/13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140761069 Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140825128 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140748922 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2141728423 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Georgi Gerganov
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60c666347b |
batch : rework llama_batch_allocr (#14153)
* batch : rework llama_batch_allocr ggml-ci * cont : move validation inside class ggml-ci * cont : move output counting to class ggml-ci * cont : minor ggml-ci * batch : add TODOs ggml-ci |
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compilade
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dad5c44398 |
kv-cache : avoid modifying recurrent cells when setting inputs (#13834)
* kv-cache : avoid modifying recurrent cells when setting inputs * kv-cache : remove inp_s_mask It was replaced with equivalent and simpler functionality with rs_z (the first zeroed state) and the already-existing inp_s_copy. * kv-cache : fix non-consecutive token pos warning for recurrent models The problem was apparently caused by how the tail cells were swapped. * graph : simplify logic for recurrent state copies * kv-cache : use cell without src refs for rs_z in recurrent cache * llama-graph : fix recurrent state copy The `state_copy` shuffle assumes everything is moved at once, which is not true when `states_extra` is copied back to the cache before copying the range of states between `head` and `head + n_seqs`. This is only a problem if any of the cells in [`head`, `head + n_seqs`) have an `src` in [`head + n_seqs`, `head + n_kv`), which does happen when `n_ubatch > 1` in the `llama-parallel` example. Changing the order of the operations avoids the potential overwrite before use, although when copies are avoided (like with Mamba2), this will require further changes. * llama-graph : rename n_state to state_size in build_recurrent_state This naming should reduce confusion between the state size and the number of states. |
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Đinh Trọng Huy
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91a8ee6a6f |
add geglu activation function (#14074)
Co-authored-by: dinhhuy <huy.dinh@brains-tech.co.jp> |
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Georgi Gerganov
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7f37b6cf1e |
memory : migrate from llama_kv_cache to more generic llama_memory (#14006)
* memory : merge llama_kv_cache into llama_memory + new `llama_memory` API ggml-ci * context : fix casts ggml-ci |
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Georgi Gerganov
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12d0188c0d |
kv-cache : refactor + add llama_memory_state_i (#13746)
* kv-cache : simplify the "struct llama_kv_cache" interface ggml-ci * kv-cache : revert the (n_swa + n_ubatch) change (for next PR) ggml-ci * kv-cache : some comments ggml-ci * context : fix graph reserve for multiple sequences ggml-ci * kv-cache : fix typo [no ci] * kv-cache : fix find_slot() logic for free slots ggml-ci * llama : add TODO for deprecating the defrag API in the future * kv-cache : improve find_slot() using min/max seq pos info ggml-ci * llama : handle aborts and compute errors ggml-ci * memory : extract state into llama_memory_state ggml-ci * kv-cache : add comments ggml-ci * server : update batching logic to reset n_batch on successful decode * server : upon full re-processing, remove the sequence from the cache * kv-cache : add TODO for doing split_equal when split_simple fails ggml-ci |
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Georgi Gerganov
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e298d2fbd0 |
kv-cache : add SWA support (#13194)
* kv-cache : prepare for SWA ggml-ci * kv-cache : initial iSWA implementation ggml-ci * kv-cache : rework error recovery logic ggml-ci * models : fix Phi-3 SWA parameters ggml-ci * model : adjust Granite to rope factor changes ggml-ci * server : check if context can do shifts ggml-ci * iswa : for now, always enable shifts (experiment) ggml-ci * kv-cache : simplify SWA logic ggml-ci * kv-cache : apply defrag when we fail to find slots for the batch ggml-ci * llama : update docs about llama_decode ggml-ci * kv-cache : update warning logs when no space for the batch is available ggml-ci * llama : add llama_kv_self_seq_pos_min() * kv-cache : keep track of partial SWA computes and print warnings * server : disallow use cases involving partial SWA context ggml-ci * llama : add param to control SWA cache size ggml-ci * minor : clean-up ggml-ci |
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Johannes Gäßler
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10d2af0eaa |
llama/ggml: add LLM training support (#10544)
* llama/ggml: add LLM training support more compact progress bar llama_save_model_to_file llama_opt_param_filter ggml_graph_dup force_grads refactor ggml_opt, fix test-opt * remove logits_all * refactor CUDA implementation for ACC * reset graph at beginning of opt period |
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Georgi Gerganov
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c642bc014c |
kv-cache : separate recurrent vs non-recurrent impl (#12799)
* kv-cache : serparate recurrent vs non-recurrent impl (wip) ggml-ci * kv-cache : init -> contructor + add llama_memory_params ggml-ci * kv-cache : fix callback reference ggml-ci * context : llama_kv_cache -> llama_memory_i ggml-ci * context : move memory creation logic to model ggml-ci * llama : remove reference of memory during encode ggml-ci * kv-cache : hide padding details in the implementation ggml-ci * kv-cache : add ubatch_next() ggml-ci * context : simplify sbatch logic ggml-ci * kv-cache : hide defrag logic in the implementation ggml-ci * context : hide kv cache details in implementation ggml-ci * build : fix ggml-ci * cont : another fix ggml-ci * kv-cache : simplify interface (wip) ggml-ci * kv-cache : use separate KV cell structs for unified/recurrent ggml-ci * kv-cache : clean-up ggml-ci * model : better llama_model::create_model() signature ggml-ci * kv-cache : fix recurrent seq_rm() ggml-ci * kv-cache : replace `struct callbacks` with `llama_model &` ggml-ci * kv-cache : replace `struct graph_params` with `llama_context &` ggml-ci * kv-cache : fix offload check ggml-ci * context : avoid passing unique_ptr ggml-ci * kv-cache : avoid using the backends from the llama_context ref #13113 ggml-ci * kv-cache : more consistent debug logs [no ci] * kv-cache : do not pass the full llama_context for kv graphs ggml-ci * kv-cache : remove comment * kv-cache : ggml_rope_ext_inplace -> ggml_rope_ext ggml-ci * kv-cache : fix recurrent multi-user case ggml-ci * memory : remove comments [no ci] |
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Xuan-Son Nguyen
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d2b2031e5f |
llama : (mrope) allow using normal 1D position for text token (#13138)
* llama : (mrope) use normal position for text token * rm n_pos_per_embd from llm_graph_input_attn_temp |
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Juk Armstrong
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daa422881a |
llama : DeepSeek V2/V3 MLA implementation (#12801)
* Merged using squash to remove all noise commit messages * Force flash attention off for `LLM_ARCH_DEEPSEEK2` - embedding too large * Removed 3 conts (2x RoPE and 1x RMS-norm) * Changed to use `<cmath>` instead of `<math.h>` * Reverted removal of the 3 conts * Used `reshape` in `llm_graph_context::build_attn_mha()` * Use `k_pe = ggml_reshape` * Removed the 3 conts again * Removed the 3D views of `wk_b` and `wv_b`, and just save and 3D in GGUF * Removed MQA optimisation from `build_attn_mha()` as no gains now * Simplified `is_mla` branch in `llm_build_deepseek2()` * Removed `build_attn_mla` and added `nullptr` to all `build_atnn` calls * Fixed call to `build_attn` in `llm_build_t5_enc` |
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Xuan-Son Nguyen
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1466621e73 |
llama : Support llama 4 text-only (#12791)
* llama4 conversion * initial support, no chat template * clean up a bit * fix tokenizer conversion * correct hparams * try this * fix shexp * ffn_inp_normed * chat template * clean up model conversion * add_bos * add scale_before_ffn * fix order * weight_before_ffn * llm_graph_input_attn_temp * add chunk attn mask * build_inp_attn_scale() * add comment about ggml_repeat * clarify comments * fix build |
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Georgi Gerganov
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75422e8bc4 |
graph : normalize Q, K, V shapes + sync cross attention (#12449)
* graph : normalize Q, K, V shapes and add comments ggml-ci * context : synchronize before getting cross attention data * model : fix command-r attention norm check |
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Georgi Gerganov
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c522ce4143 |
graph : simplify attn input build for unified KV cache (#12381)
ggml-ci |
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Georgi Gerganov
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e0dbec0bc6 |
llama : refactor llama_context, llama_kv_cache, llm_build_context (#12181)
* llama : refactor llama_context, llama_kv_cache, llm_build_context ggml-ci * graph : don't mutate the KV cache during defrag ggml-ci * context : reduce virtuals + remove test function ggml-ci * context : move interface implementation to source file + factory ggml-ci * graph : move KV cache build functions to llama_context impl ggml-ci * graph : remove model reference from build_pooling ggml-ci * graph : remove llama_model reference ggml-ci * kv_cache : provide rope factors ggml-ci * graph : rework inputs to use only unique_ptr, remove attn input abstraction ggml-ci * context : remove llama_context_i abstraction ggml-ci * context : clean-up ggml-ci * graph : clean-up ggml-ci * llama : remove redundant keywords (struct, enum) ggml-ci * model : adapt gemma3 ggml-ci * graph : restore same attention ops as on master ggml-ci * llama : remove TODO + fix indent ggml-ci |