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2023-07-30ggml : add graph tensor allocator (#2411)slaren
* ggml : add graph tensor allocator * ggml : don't calculate data pointer of unallocated tensors when creating a view with an offset * ggml : refactor ggml_view_Nd into ggml_view_tensor_offset
2023-07-26ggml : fix assert in ggml_set_unary_op (#2410)slaren
2023-07-26ggml : allocate graphs in a context (#2392)slaren
* ggml : graph allocation in contexts * allocate work buffer as a ggml_object in ggml_graph_compute_with_ctx * llama.cpp : allocate graph in the context * add GGML_PAD --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-25ggml : fix ggml_flash_attn to use op_params (#2387)slaren
* ggml : fix ggml_flash_attn to use op_params
2023-07-25ggml : relax contiguous constraints in activation function (#2371)Jiahao Li
2023-07-25ggml : improve graph build time via hash table lookup (#2329)slaren
* improve graph build time * ggml_tensor : use 1 bit per flag * use a hash table instead
2023-07-24make rms_norm_eps a parameter (#2374)slaren
* make rms_norm_eps a parameter * add rms_norm_eps to command line * fix baby llama, test-grad0 * use scientific notation for eps param in the help ggml-ci
2023-07-24ggml : sync (unary ops refactor, static-correctness) (#2370)Georgi Gerganov
* ggml : sync (unary ops, tests) ggml-ci * tests : remove unnecessary funcs
2023-07-23fix n_tasks (#2342)slaren
ggml-ci
2023-07-23ggml: move op parameters from tensors to ggml_tensor::op_params (#2333)slaren
* ggml: move op parameters from tensors to ggml_tensor::op_params * alibi: use memcpy for float params * remove `src[1] = NULL` in ops
2023-07-21ggml : fix the rope fix (513f8619535a64fa9ace808cdcbcf66211535f5c)Georgi Gerganov
2023-07-21ggml : fix rope args order + assert (#2054)Georgi Gerganov
2023-07-16ggml : fixed runtime bugs and compile errors related to GGML_PERF and ↵Qingyou Meng
GGML_DEBUG (#2219) * fixed runtime bugs and compile errors related to GGML_PERF and GGML_DEBUG * remove ifdef GGML_PERF; update fmt
2023-07-15llama : add custom RoPE (#2054)Xiao-Yong Jin
* Implement customizable RoPE The original RoPE has pre-defined parameters theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2] Our customizable RoPE, ggml_rope_custom_inplace, uses theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2] with the default matches the original scale = 1.0 base = 10000 The new command line arguments --rope-freq-base --rope-freq-scale set the two new RoPE parameter. Recent researches show changing these two parameters extends the context limit with minimal loss. 1. Extending Context to 8K kaiokendev https://kaiokendev.github.io/til#extending-context-to-8k 2. Extending Context Window of Large Language Models via Positional Interpolation Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian https://arxiv.org/abs/2306.15595 3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation. https://www.reddit.com/user/bloc97 https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ For the bold, try adding the following command line parameters to your favorite model: -c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5 * ggml-metal: fix custom rope * common: fix argument names in help * llama: increase MEM_REQ_EVAL for MODEL_3B It avoids crashing for quantized weights on CPU. Better ways to calculate the required buffer size would be better. * llama: make MEM_REQ_EVAL depend on n_ctx * server: use proper Content-Type in curl examples Without the header Content-Type: application/json, curl will POST with Content-Type: application/x-www-form-urlencoded Though our simple server doesn't care, the httplib.h used has a limit with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 With Content-Type: application/json, we can send large json data. * style : minor fixes, mostly indentations * ggml : fix asserts --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-14ggml : fix static_assert with older compilers #2024 (#2218)Evan Miller
2023-07-14ggml : sync (ggml_conv_2d, fix mul_mat bug, CUDA GLM rope)Georgi Gerganov
2023-07-12ggml : broadcast mul_mat + conv batch support (#2199)Georgi Gerganov
* ggml : broadcast mul_mat + conv batch support * ggml : apply mul_mat broadcast fix by @jploski
2023-07-12ggml : add ggml_pool_1d and ggml_pool_2dGeorgi Gerganov
2023-07-11ggml : sync (abort callback, mul / add broadcast, fix alibi) (#2183)Georgi Gerganov
2023-07-11ggml : remove src0 and src1 from ggml_tensor and rename opt to src (#2178)Spencer Sutton
* Add ggml changes * Update train-text-from-scratch for change * mpi : adapt to new ggml_tensor->src --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-09ggml : fix buidling with Intel MKL but ask for "cblas.h" issue (#2104) (#2115)clyang
* Fix buidling with Intel MKL but ask for "cblas.h" issue * Use angle brackets to indicate the system library
2023-07-07ggml : change ggml_graph_compute() API to not require context (#1999)Qingyou Meng
* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287 * rewrite: no longer consider backward compitability; plan and make_plan * minor: rename ctx as plan; const * remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward * add static ggml_graph_compute_sugar() * minor: update comments * reusable buffers * ggml : more consistent naming + metal fixes * ggml : fix docs * tests : disable grad / opt + minor naming changes * ggml : add ggml_graph_compute_with_ctx() - backwards compatible API - deduplicates a lot of copy-paste * ci : enable test-grad0 * examples : factor out plan allocation into a helper function * llama : factor out plan stuff into a helper function * ci : fix env * llama : fix duplicate symbols + refactor example benchmark * ggml : remove obsolete assert + refactor n_tasks section * ggml : fix indentation in switch * llama : avoid unnecessary bool * ggml : remove comments from source file and match order in header --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-07ggml : remove sched_yield() call in ggml_graph_compute_thread() (#2134)Georgi Gerganov
2023-07-05ggml : fix bug introduced in #1237Georgi Gerganov
2023-07-05ggml : generalize `quantize_fns` for simpler FP16 handling (#1237)Stephan Walter
* Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-04ggml : sync latest (new ops, macros, refactoring) (#2106)Georgi Gerganov
- add ggml_argmax() - add ggml_tanh() - add ggml_elu() - refactor ggml_conv_1d() and variants - refactor ggml_conv_2d() and variants - add helper macros to reduce code duplication in ggml.c
2023-07-02ggml : fix build with OpenBLAS (close #2066)Georgi Gerganov
2023-07-01ggml : disable GGML_TASK_INIT and GGML_TASK_FINALIZE by default (#1995)Qingyou Meng
Will not be scheduled unless explicitly enabled.
2023-06-27fix pthreads setaffinity usage on android (#2020)Erik Scholz
2023-06-27ggml : add support for ChatGLM RoPEGeorgi Gerganov
2023-06-26ggml : avoid conv 2d kernel round upGeorgi Gerganov
2023-06-26ggml : add NUMA support (#1556)zrm
* detect NUMA systems and pin work threads to nodes (linux) * disable mmap prefetch/readahead for NUMA systems * avoid sending finalize op to thread pool if it does nothing * silence robot * fix args * make --numa a param * recommendation that n_nodes evenly divide n_threads did not warrant such aggressive enforcement * lower synchronization overhead * statically allocate * move numa state to g_state * add description for --numa * ggml : minor style changes * ggml : minor style + try fix sanitizer build * llama : allow to initialize backend with NUMA support * llama : avoid ggml include in llama-util.h * ggml : style / formatting * ggml : fix handling of ops with n_threads > n_tasks > 1 * server : utilize numa parameter --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-25ggml : sync latest ggml (custom operators)Georgi Gerganov
2023-06-24#1869 Fix null reference errors when training from scratch with CUDA (#1907)Robyn
* #1869 Fix null reference errors when training from scratch with CUDA build Calling ggml_compute_forward when node->src0 was null was causing train-text-from-scratch.exe to terminate unexpectedly. * ggml : do not dereference src0 if NULL --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-24ggml : improve ggml_graph_dump_dot, add ggml_format_name (#1978)slaren
* Improve ggml_graph_dump_dot, add ggml_format_name * add more automatic names to view ops * fix name of copies
2023-06-19ggml : fix bug in LBFGS optimizer (found by ggml tests)Georgi Gerganov
2023-06-19ggml : sync latest ggml repo (#1924)Georgi Gerganov
* ggml : sync latest ggml repo * ggml : remove unused comments * ggml : asserts
2023-06-18ggml : fix bug in ggml_compute_forward_add_q_f32 (#1918)l3utterfly
2023-06-18metal : handle buffers larger than device's maxBufferLength (#1826)Georgi Gerganov
* metal : handle buffers larger than device's maxBufferLength * metal : print more verbose device info + handle errors * metal : fix prints for overlapping views * metal : minimize view overlap to try to utilize device memory better
2023-06-16build : fix and ignore MSVC warnings (#1889)Borislav Stanimirov
2023-06-14CUDA full GPU acceleration, KV cache in VRAM (#1827)Johannes Gäßler
* Fixed CUDA RoPE * ggml_cuda_mul_mat_vec_p021 * ggml_cuda_scale * ggml_cuda_diag_mask_inf * ggml_is_permuted * ggml_cuda_cpy * flatten rows for ggml_cuda_op * Added a --low-vram option * Fixed Windows performance * Fixed LLAMA_CUDA_DMMV_Y > 1 for WizardLM
2023-06-13train : improved training-from-scratch example (#1652)xaedes
* add python wrapper https://gist.github.com/abetlen/2b90e5f153f6efd00931d098de5c73ce * fix decoding error. adds errors=ignore parameter * add python bindings for functions to get and set the whole llama state (rng, logits, embedding and kv_cache) * update python bindings * add text generating baby-llama from scratch example * fix race condition bug in ggml_compute_forward_diag_mask_f32 * implement ggml_soft_max_back for more performant backward pass of soft_max avoids creating big intermediate matrices of size n_embd x n_embd for llama layers and n_vocab x n_vocab for cross entropy loss * improve softmax backward pass go from quadratic runtime to linear runtime by simplifying the formulas * fix race condition bug in non-inplace ggml_compute_forward_diag_mask_f32 memcpy needs to be synchronized across threads to avoid race conditions. => do it in INIT phase * fix bug in ggml_compute_forward_soft_max_back_f32 on DEBUG build * improve performance of mul_mat backward pass avoid transpose by using mul_mat with swapped arguments * avoid printing too much newlines in baby-llama-text * activate threading in baby-llama-text * add ggml_out_prod and use it for mul_mat backward pass for improved performance performance stats report improvement from 37 seconds to 16 seconds runtime during my training tests * better weight initialization improves training convergence at start * better weight initialization improves training convergence at start * improve ggml_out_prod performance - change iteration order (>15s -> 10s runtime) - parallelize over one more dimension: over dst matrix rows (10s -> <5s runtime) * add llama sampler, shuffle samples and constrain sampling to tokens occurring in train data * fix get_samples call, add model tensor names, increase model size, start training samples after newline * save train trained model to checkpoint and load model to be trained from checkpoint * use inplace functions where possible * initialize rng with srand * use different arguments for input and output checkpoint * ggml fixes to support backward pass on inplace operations * remove duplicate include * fix cross entropy loss - add target probabilities for each sample which is then used in cross entropy loss * print used memory before and after optimization * sample with non-greedy sampling parameters at the end of training * add cmake target for baby-llama-text * add ggml_add1_inplace to header * enable gradient propagation for inplace add1 and scale operations those functions backward passes don't need the original src0, so they also work when forward is inplace * implement AdamW in ggml_opt_adam by adding weight decay parameter (default 0.001f) also add a schedule parameter (default 1.0f) that can be used to scale alpha and decay according to learning schedule. setting the decay parameter to zero disables AdamW resulting in normal Adam optimizer. since the difference between Adam and AdamW is minimal it is not implemented as another optimizer, but integrated into the existing Adam optimizer. * use inplace operations in cross_entropy_loss * fix random weight initialization scale * add missing default parameters for adam optimizer * add ggml_opt_context, so that we can properly resume training otherwise the optimizer states, tracking statistics about the error function and its derivates, will reset to zero each time ggml_opt is called, hindering convergence on resumed training. now the optimizer context and all its memory is stored in a separate struct. * fix bug in llama_sample_token_mirostat_v2 when all candidates are filtered out through mu threshold, the following soft_max operation will fail. so keep at least one. * add forward function without using cache, for more performant training during training on whole samples no cache is required. removing the cache and simplifying the remaining code results in performance and memory usage improvement. * print suppressed newline tokens as string "\n" printing too much actual newlines is suppressed to avoid flooding the console. * store optimizer state in training checkpoint and add learning schedule persistent optimizer state allows to resume training without resetting the optimizer learning schedule consists of linear warmup ramp followed by cosine decay with restarts * remove unused functions * fix bug in get_samples which corrupted training targets * save checkpoint only when it was trained * simplify code * remove trailing whitespace * simplify backward pass for SQRT * replace inefficient repeat backward pass with dedicated repeat_back operation * add ggml_cross_entropy_loss with backward pass for faster training cross entropy loss can also be implemented using softmax and log, but as dedicated operation it is faster and especially avoids unnecessary memory overhead. * add tests for cross_entropy_loss backward pass finite differences regularly results in estimated gradient of zero, despite the backward pass giving non zero gradient. _probably_ the finite differences fails due to numerical issues * use ggml_cross_entropy_loss in text training example * remove trailing whitespace * slightly improve how cross entropy loss is compute btw: directly implemented cross entropy loss seems to have way lower magnitudes than when implemented with softmax and log. probably the input to log gets closer to zero due to float numerics. maybe the multiplication by (1.0-eps)/sum is more accurate.. * add llama_get_vocab to get the vocabulary as output parameters * set default model.type for unknown models with few layers * add export of training checkpoint to llama compatible model file * get vocabulary for exporting training checkpoint to llama compatible model file * implement backward pass of flash attention * bugfixes for backward pass of flash attention * test flash attention backward pass need to set loose error bounds to pass. the finitie differences are close to numeric limits and often return quite different values than the backward pass. reducing eps further lets the gradients vanish completely. likewise setting eps to big results in wronger values. the softmax in the middle of the function is probably the most responsible for the numeric issues using finite differences. * add option to train with flash attention and move options to the top of the main function training from scratch also works with flash attention training convergence and generation results after fix number of iterations are worse than when not using flash attention. maybe there still lingers a bug in the flash attention backward pass? but training works, just with slower convergence. flash attention is still worth to use, because it requires way less memory and is faster with high n_ctx * add train_params and command line option parser * remove unnecessary comments * add train params to specify memory size * remove python bindings * rename baby-llama-text to train-text-from-scratch * replace auto parameters in lambda function * add #include <climits> * add explicit cast to fix compile error "error: non-constant-expression cannot be narrowed from type 'int64_t' (aka 'long long') to 'uint32_t' (aka 'unsigned int') in initializer list [-Wc++11-narrowing]" * remove trailing whitespace * add ggml_opt_resume_g which accepts forward and backward cgraphs * fix formulas in comments * bug fix for ggml_compute_forward_get_rows_back_f32 the result should be set to zero, not to whatever data is in opt0 * improve training memory usage with scratch buffers instead of relying on the automatic backward pass, we manually create the graph for the backward pass. it turns out that all backward pass operations need only temporary memory which can be reused after each layer. will compute backward pass for ALL model parameters * add option to use scratch buffers in training or not make it configurable because currently training with scratch buffers implies flash attention and optimization over all parameters. * ci : disable temporary * store view offset and permute axes in opt[0] instead of storing it in padding use memcpy to store offset, because offset is of type size_t. when storing it as int32_t offset would have to be smaller than 2^31 which is not necessarily true. * minor : fix compile warnings + minor style changes * fix bug in threaded indices calculation of ggml_compute_forward_flash_attn_back_f32 * store view offset like in master branch * bug fix in forward_batch_wo_cache_flash_attn_train * scratch buffer bug fixes in forward_batch_wo_cache_flash_attn_train data of permute and reshape is the same as their input. if we want to preserve the output of permute/reshape, we also need to preserve their inputs. replace reshape(src0, src1) with reshape_nd calls so that we don't need src1. replace (temporary) t03 with ggml_repeat(ctx0, layer.attention_norm, t02). in the future we could also use the new broadcasting ggml_mul to avoid these repeat calls. for this we need backward pass of broadcasting ggml_mul. * remove unnecessary scratch buffer 0 buf 0 is persistent memory, so we can just disable scratch for this by using buf -1 * avoid creating unnecessary grad tensors previously we need to create grads for model parameters, so that expand(..) correctly populates cgraph->leafs & cgraph->grads this wasted memory, because unnecessary grad for each op were automatically created: the automatically generated grad was unnecessary because we later manually set the grad (e.g. t35->grad = expand(gb, ...) ). this discarded the automatically generated grad resulting in wasted memory. improved this by changing expand(..) to not use ggml_build_forward_expand. expand set cgraph->nodes but not the leafs. cgraph->leafs & cgraph->grads are set in another pass after the last expand call. * print used training seed * zero initialize gfbuf and gbbuf * ci : re-enable workflows + add README for training --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-13Allow "quantizing" to f16 and f32 (#1787)Kerfuffle
* Allow "quantizing" to f16 and f32 Fix an issue where quantizing didn't respect LLAMA_NO_K_QUANTS Add brief help to the list of quantization types in the quantize tool Ignore case for quantization type arguments in the quantize tool
2023-06-10ggml : force no_alloc == false when creating opt tensors (close #1699)Georgi Gerganov
This is needed to make operators like ggml_view() be able to store their parameters in the ggml context's memory and not get discarded when no_alloc is true
2023-06-10ggml : workaround for missing _mm256_setr_m128i in GCC < 8 (#1638)Xingchen Song(宋星辰)
2023-06-08ggml : fix fprintf warnings (#1720)Steven Roussey
2023-06-07k-quants : allow to optionally disable at compile time (#1734)Georgi Gerganov
* k-quants : put behind optional compile flag LLAMA_K_QUANTS * build : enable k-quants by default
2023-06-06llama : fix compile warningsGeorgi Gerganov
2023-06-06Multi GPU support, CUDA refactor, CUDA scratch buffer (#1703)Johannes Gäßler
* CUDA multi GPU + scratch ggml_cuda_compute_forward Tensor parallelism ggml_cuda_add ggml_cuda_rms_norm ggml_cuda_silu CUDA scratch buffer --main-gpu CLI option
2023-06-06ggml : fix builds, add ggml-quants-k.o (close #1712, close #1710)Georgi Gerganov