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14 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Georgi Gerganov
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ef3f333d37
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ggml : sync latest (SAM + SD operators, CUDA alibi) (#2709)
* ggml : sync latest (SAM + SD operators, CUDA alibi) ggml-ci * ggml : fix tabs |
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Georgi Gerganov
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6381d4e110
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gguf : new file format with flexible meta data (beta) (#2398)
* gguf : first API pass
* gguf : read header + meta data
* gguf : read tensor info
* gguf : initial model loading - not tested
* gguf : add gguf_get_tensor_name()
* gguf : do not support passing existing ggml_context to gguf_init
* gguf : simplify gguf_get_val
* gguf : gguf.c is now part of ggml.c
* gguf : read / write sample models
* gguf : add comments
* refactor : reduce code duplication and better API (#2415)
* gguf : expose the gguf_type enum through the API for now
* gguf : add array support
* gguf.py : some code style changes
* convert.py : start a new simplified implementation by removing old stuff
* convert.py : remove GGML vocab + other obsolete stuff
* GGUF : write tensor (#2426)
* WIP: Write tensor
* GGUF : Support writing tensors in Python
* refactor : rm unused import and upd todos
* fix : fix errors upd writing example
* rm example.gguf
* gitignore *.gguf
* undo formatting
* gguf : add gguf_find_key (#2438)
* gguf.cpp : find key example
* ggml.h : add gguf_find_key
* ggml.c : add gguf_find_key
* gguf : fix writing tensors
* gguf : do not hardcode tensor names to read
* gguf : write sample tensors to read
* gguf : add tokenization constants
* quick and dirty conversion example
* gguf : fix writing gguf arrays
* gguf : write tensors one by one and code reuse
* gguf : fix writing gguf arrays
* gguf : write tensors one by one
* gguf : write tensors one by one
* gguf : write tokenizer data
* gguf : upd gguf conversion script
* Update convert-llama-h5-to-gguf.py
* gguf : handle already encoded string
* ggml.h : get array str and f32
* ggml.c : get arr str and f32
* gguf.py : support any type
* Update convert-llama-h5-to-gguf.py
* gguf : fix set is not subscriptable
* gguf : update convert-llama-h5-to-gguf.py
* constants.py : add layer norm eps
* gguf.py : add layer norm eps and merges
* ggml.h : increase GGML_MAX_NAME to 64
* ggml.c : add gguf_get_arr_n
* Update convert-llama-h5-to-gguf.py
* add gptneox gguf example
* Makefile : add gptneox gguf example
* Update convert-llama-h5-to-gguf.py
* add gptneox gguf example
* Update convert-llama-h5-to-gguf.py
* Update convert-gptneox-h5-to-gguf.py
* Update convert-gptneox-h5-to-gguf.py
* Update convert-llama-h5-to-gguf.py
* gguf : support custom alignment value
* gguf : fix typo in function call
* gguf : mmap tensor data example
* fix : update convert-llama-h5-to-gguf.py
* Update convert-llama-h5-to-gguf.py
* convert-gptneox-h5-to-gguf.py : Special tokens
* gptneox-main.cpp : special tokens
* Update gptneox-main.cpp
* constants.py : special tokens
* gguf.py : accumulate kv and tensor info data + special tokens
* convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens
* gguf : gguf counterpart of llama-util.h
* gguf-util.h : update note
* convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens
* convert-llama-h5-to-gguf.py : special tokens
* Delete gptneox-common.cpp
* Delete gptneox-common.h
* convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer
* gptneox-main.cpp : gpt2 bpe tokenizer
* gpt2 bpe tokenizer (handles merges and unicode)
* Makefile : remove gptneox-common
* gguf.py : bytesarray for gpt2bpe tokenizer
* cmpnct_gpt2bpe.hpp : comments
* gguf.py : use custom alignment if present
* gguf : minor stuff
* Update gptneox-main.cpp
* map tensor names
* convert-gptneox-h5-to-gguf.py : map tensor names
* convert-llama-h5-to-gguf.py : map tensor names
* gptneox-main.cpp : map tensor names
* gguf : start implementing libllama in GGUF (WIP)
* gguf : start implementing libllama in GGUF (WIP)
* rm binary commited by mistake
* upd .gitignore
* gguf : calculate n_mult
* gguf : inference with 7B model working (WIP)
* gguf : rm deprecated function
* gguf : start implementing gguf_file_saver (WIP)
* gguf : start implementing gguf_file_saver (WIP)
* gguf : start implementing gguf_file_saver (WIP)
* gguf : add gguf_get_kv_type
* gguf : add gguf_get_kv_type
* gguf : write metadata in gguf_file_saver (WIP)
* gguf : write metadata in gguf_file_saver (WIP)
* gguf : write metadata in gguf_file_saver
* gguf : rm references to old file formats
* gguf : shorter name for member variable
* gguf : rm redundant method
* gguf : get rid of n_mult, read n_ff from file
* Update gguf_tensor_map.py
* Update gptneox-main.cpp
* gguf : rm references to old file magics
* gguf : start implementing quantization (WIP)
* gguf : start implementing quantization (WIP)
* gguf : start implementing quantization (WIP)
* gguf : start implementing quantization (WIP)
* gguf : start implementing quantization (WIP)
* gguf : start implementing quantization (WIP)
* gguf : quantization is working
* gguf : roper closing of file
* gguf.py : no need to convert tensors twice
* convert-gptneox-h5-to-gguf.py : no need to convert tensors twice
* convert-llama-h5-to-gguf.py : no need to convert tensors twice
* convert-gptneox-h5-to-gguf.py : simplify nbytes
* convert-llama-h5-to-gguf.py : simplify nbytes
* gptneox-main.cpp : n_layer --> n_block
* constants.py : n_layer --> n_block
* gguf.py : n_layer --> n_block
* convert-gptneox-h5-to-gguf.py : n_layer --> n_block
* convert-llama-h5-to-gguf.py : n_layer --> n_block
* gptneox-main.cpp : n_layer --> n_block
* Update gguf_tensor_map.py
* convert-gptneox-h5-to-gguf.py : load model in parts to save memory
* convert-llama-h5-to-gguf.py : load model in parts to save memory
* convert : write more metadata for LLaMA
* convert : rm quantization version
* convert-gptneox-h5-to-gguf.py : add file_type key
* gptneox-main.cpp : add file_type key
* fix conflicts
* gguf : add todos and comments
* convert-gptneox-h5-to-gguf.py : tensor name map changes
* Create gguf_namemap.py : tensor name map changes
* Delete gguf_tensor_map.py
* gptneox-main.cpp : tensor name map changes
* convert-llama-h5-to-gguf.py : fixes
* gguf.py : dont add empty strings
* simple : minor style changes
* gguf : use UNIX line ending
* Create convert-llama-7b-pth-to-gguf.py
* llama : sync gguf-llama.cpp with latest llama.cpp (#2608)
* llama : sync gguf-llama.cpp with latest llama.cpp
* minor : indentation + assert
* llama : refactor gguf_buffer and gguf_ctx_buffer
* llama : minor
* gitignore : add gptneox-main
* llama : tokenizer fixes (#2549)
* Merge tokenizer fixes into the gguf branch.
* Add test vocabularies
* convert : update convert-new.py with tokenizer fixes (#2614)
* Merge tokenizer fixes into the gguf branch.
* Add test vocabularies
* Adapt convert-new.py (and fix a clang-cl compiler error on windows)
* llama : sync gguf-llama with llama (#2613)
* llama : sync gguf-llama with llama
* tests : fix build + warnings (test-tokenizer-1 still fails)
* tests : fix wstring_convert
* convert : fix layer names
* llama : sync gguf-llama.cpp
* convert : update HF converter to new tokenizer voodoo magics
* llama : update tokenizer style
* convert-llama-h5-to-gguf.py : add token types
* constants.py : add token types
* gguf.py : add token types
* convert-llama-7b-pth-to-gguf.py : add token types
* gguf-llama.cpp : fix n_head_kv
* convert-llama-h5-to-gguf.py : add 70b gqa support
* gguf.py : add tensor data layout
* convert-llama-h5-to-gguf.py : add tensor data layout
* convert-llama-7b-pth-to-gguf.py : add tensor data layout
* gptneox-main.cpp : add tensor data layout
* convert-llama-h5-to-gguf.py : clarify the reverse permute
* llama : refactor model loading code (#2620)
* llama : style formatting + remove helper methods
* llama : fix quantization using gguf tool
* llama : simplify gguf_file_saver
* llama : fix method names
* llama : simplify write_header()
* llama : no need to pass full file loader to the file saver
just gguf_ctx
* llama : gguf_file_saver write I32
* llama : refactor tensor names (#2622)
* gguf: update tensor names searched in quantization
* gguf : define tensor names as constants
* gguf : initial write API (not tested yet)
* gguf : write to file API (not tested)
* gguf : initial write API ready + example
* gguf : fix header write
* gguf : fixes + simplify example + add ggml_nbytes_pad()
* gguf : minor
* llama : replace gguf_file_saver with new gguf write API
* gguf : streaming support when writing files
* gguf : remove oboslete write methods
* gguf : remove obosolete gguf_get_arr_xxx API
* llama : simplify gguf_file_loader
* llama : move hparams and vocab from gguf_file_loader to llama_model_loader
* llama : merge gguf-util.h in llama.cpp
* llama : reorder definitions in .cpp to match .h
* llama : minor simplifications
* llama : refactor llama_model_loader (WIP)
wip : remove ggml_ctx from llama_model_loader
wip : merge gguf_file_loader in llama_model_loader
* llama : fix shape prints
* llama : fix Windows build + fix norm_rms_eps key
* llama : throw error on missing KV paris in model meta data
* llama : improve printing + log meta data
* llama : switch print order of meta data
---------
Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
* gguf : deduplicate (#2629)
* gguf : better type names
* dedup : CPU + Metal is working
* ggml : fix warnings about unused results
* llama.cpp : fix line feed and compiler warning
* llama : fix strncpy warning + note token_to_str does not write null
* llama : restore the original load/save session implementation
Will migrate this to GGUF in the future
* convert-llama-h5-to-gguf.py : support alt ctx param name
* ggml : assert when using ggml_mul with non-F32 src1
* examples : dedup simple
---------
Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
* gguf.py : merge all files in gguf.py
* convert-new.py : pick #2427 for HF 70B support
* examples/gguf : no need to keep q option for quantization any more
* llama.cpp : print actual model size
* llama.cpp : use ggml_elements()
* convert-new.py : output gguf (#2635)
* convert-new.py : output gguf (WIP)
* convert-new.py : add gguf key-value pairs
* llama : add hparams.ctx_train + no longer print ftype
* convert-new.py : minor fixes
* convert-new.py : vocab-only option should work now
* llama : fix tokenizer to use llama_char_to_byte
* tests : add new ggml-vocab-llama.gguf
* convert-new.py : tensor name mapping
* convert-new.py : add map for skipping tensor serialization
* convert-new.py : convert script now works
* gguf.py : pick some of the refactoring from #2644
* convert-new.py : minor fixes
* convert.py : update to support GGUF output
* Revert "ci : disable CI temporary to not waste energy"
This reverts commit
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Kawrakow
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eb542d3932
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Add LLAMA_DEFAULT_RMS_EPS so we can change the default (#2384)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> |
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slaren
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41c674161f
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make rms_norm_eps a parameter (#2374)
* 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 |
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Georgi Gerganov
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513f861953
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ggml : fix rope args order + assert (#2054) | ||
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Spencer Sutton
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5bf2a27718
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ggml : remove src0 and src1 from ggml_tensor and rename opt to src (#2178)
* 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> |
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Qingyou Meng
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1d656d6360
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ggml : change ggml_graph_compute() API to not require context (#1999)
* 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> |
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Georgi Gerganov
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04606a1599
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train : fix compile warning | ||
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Howard Su
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b8c8dda75f
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Use unsigned for random seed (#2006)
* Use unsigned for random seed. Keep -1 as the value to use a time based seed. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Georgi Gerganov
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181e8d9755
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llama : fix rope usage after ChatGLM change | ||
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David Yang
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eaa6ca5a61
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ggml : increase max tensor name + clean up compiler warnings in train-text (#1988)
* Clean up compiler warnings in train-text Some brackets to disambiguate order of operations * Increase GGML_MAX_NAME Avoiding strncpy danger in train-text-from-scratch and reducing potential future name length issues |
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Didzis Gosko
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527b6fba1d
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llama : make model stateless and context stateful (llama_state) (#1797)
* llama : make model stateless and context stateful * llama : minor cleanup * llama : update internal API declaration * Apply suggestions from code review fix style Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Missing model memory release * Fix style * Add deprecated warning for public API function llama_init_from_file * Update public API use cases: move away from deprecated llama_init_from_file * Deprecate public API function llama_apply_lora_from_file --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Borislav Stanimirov
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9cbf50c041
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build : fix and ignore MSVC warnings (#1889) | ||
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xaedes
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e32089b2c2
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train : improved training-from-scratch example (#1652)
* 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> |