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14 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
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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drbh
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7cf54e1f74
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tests : adds simple llama grammar tests (#2618)
* adds simple llama grammar tests * fix lint and add Makefile * 0 terminate code_points * avoid dangling pointers in candidate cleanup * cleanup grammar at end of test |
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drbh
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ee77efea2a
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test : add simple grammar parsing tests (#2594)
* adds simple grammar parsing tests * adds cassert header |
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Eve
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81844fbcfd
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tests : Fix compilation warnings (Linux/GCC) (#2451)
* fix hellaswag print format, cast away warning in test-double-float * c++11 cannot use designated initializers * add static to test-grad0.c internal functions * use memcpy in test-double-float.c * port c tests to c++ * use initializer list for ggml_init_params |
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wzy
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b1f4290953
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cmake : install targets (#2256)
fix #2252 |
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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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xaedes
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f954edda93
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ggml : implement backward pass for llama + small training-llama-from-scratch example (#1360)
* implement 8 of 14 missing backward pass operations used by llama - GGML_OP_ADD_AT - GGML_OP_CPY - GGML_OP_MUL_MAT (src0.grad) - GGML_OP_PERMUTE - GGML_OP_RESHAPE - GGML_OP_SCALE - GGML_OP_TRANSPOSE - GGML_OP_VIEW implement additional ggml operation GGML_OP_ADD_AT, which is necessary for backward pass of GGML_OP_VIEW. this operation adds src1 to src0 with data offset, i.e. to view(src0, ..., offset). the values are return in a tensor size of src0. values outside of [data+offset:data+offset+nbytes(src1)] are just the original values from src0. still missing backward passes for llama: - GGML_OP_DIAG_MASK_INF - GGML_OP_GET_ROWS - GGML_OP_RMS_NORM - GGML_OP_ROPE - GGML_OP_SILU - GGML_OP_SOFT_MAX * implement 5 of 6 missing backward pass operations used by llama - GGML_OP_DIAG_MASK_INF - GGML_OP_GET_ROWS - GGML_OP_RMS_NORM - GGML_OP_SILU - GGML_OP_SOFT_MAX add necessary ggml operations GGML_OP_ADD1, GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK, GGML_OP_DIAG_MASK_ZERO, and GGML_OP_ROPE_BACK GGML_OP_ADD1 is necessary to add a scalar value in the backward pass of GGML_OP_SOFT_MAX GGML_OP_ADD1 could also be replaced by using GGML_OP_ADD and GGML_OP_REPEAT, but the performance would be worse. additionally GGML_OP_REPEAT will return unexpected value when the the input to GGML_OP_SOFT_MAX contains only a single scalar. in this case GGML_OP_REPEAT will not return the value that should be repeated (src1) but the value which shape the result should take (src0). So in this case it can not replace GGML_OP_ADD1. GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK and GGML_OP_ROPE_BACK are necessary for backward pass of GGML_OP_SILU, GGML_OP_RMS_NORM and GGML_OP_ROPE. The backward pass for these functions cannot be easily composed of existing operations. Since the backward pass builds a computation graph we need operations forward pass implementations of the the required backward passes. Sounds a bit confusing at first, I know... GGML_OP_DIAG_MASK_ZERO is necessary for backward pass of GGML_OP_DIAG_MASK_INF. Some operations where previously inplace-only. for backward pass there needs to be non-inplace variants. staying consistent with other operations that have non-inplace and inplace variants, the operations are changed to non-inplace and functions with "_inplace" are added which are inplace. in llama we need to call the inplace variants so that it is implemented as before. for llama backward pass we need to use the non-inplace variants. still not completely implemented backward passes for llama: - GGML_OP_ROPE: needs forward pass for GGML_OP_ROPE_BACK - GGML_OP_GET_ROWS: only necessary for tokenizer * norm & rms_norm can not be threaded: after investigation rms norm for quite some time I come to the conclusion that neither norm, nor rms_norm can be threaded, because we need mean over all items, not just of the slices each thread sees. * remove already resolved TODO * implement backward pass of ggml_rope and ggml_rope_back * implement backward pass for ggml_get_rows and for new operation ggml_get_rows_back * add test-grad0.c * use GGML_PRINT_DEBUG for debug messages which will otherwise flood the console * test both gradients of mul_mat * disable graph dot export as it floods console * bug fixes for silu_back * successfully test silu backward * bug fix for scale backward pass use sum instead of mean for gradient of scalar scale parameter * successfully test scale backward * improve performance of sum backward pass use add1(x,y) instead of add(x,repeat(y,x)) * improve performance of sqr backward pass use scale(x,y) instead of mul(x,repeat(y,x)) * successfully test rope backward * bug fix for cpy backward pass * successfully test cpy backward * bug fix for reshape backward pass * successfully test reshape backward * add test-opt.c this uses ggml_opt to train a,b for minimal e=sum(sqr(c - a*b)) for random initial a,b,c * correctly implement softmax backward pass using new operation ggml_diag ggml_diag constructs diagonal matrices with entries. ggml_diag(shape[a,1,c,d]) -> shape[a,a,c,d] * successfully test soft_max backward * align shape annotations * add shape annotations for llama * de-duplicate ggml_forward_dup code taking care of contiguous tensors of same type. with this we can duplicate tensor of any typ as long as they are contiguous. * fix ggml_compute_forward_dup_same_cont for when nelements < nthreads when more threads are used than elements exist ie1 was less than ie0, resulting in invalid negative byte count argument in memcpy * bug fix for add_at forward required for view backward pass src0 values must be copied to dst, because during addition we don't touch all dst elements in contrast to the normal add function. * successfully test view backward * minor code format improvement * fix ggml_forward_add functions to work correctly with transposed tensors uses the same logic as in ggml_compute_forward_add_q_f32, but make it consistent across all ggml_compute_forward_add_... functions. this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add_q_f32. * fix ggml_forward_add1 functions to work correctly with transposed tensors uses the same logic as in ggml_compute_forward_add1_q_f32, but make it consistent across all ggml_compute_forward_add1_... functions. this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add1_q_f32. * test-grad0.c : add print_elements to help with debugging * successfully test permute backward * some minor test-grad0 fixes * fix sub, mul and div functions to work correctly with transposed tensors uses the same logic as in add * implement ggml_cont backward pass * successfully test transpose backward and permute for all permutations also test sub, mul and div up to max n_dims * test-grad0.c add TODO for view_2d and view_3d add_at (required for view backward pass) is a bit tricky for n_dims > 1. * fix comments * successfully test diag_mask_inf and diag_mask_zero backward * test-grad0 : fix test for div nargs and ndims was swapped, corrupting the stack * fix diag_mask to work with non-inplace input * move dup call into the actual add_at functions * fix get rows backward pass * successfully test get_rows backward * fix view backward pass add nb parameters to add_at like in view. together with offset they define how to view dst and src0 during the add_at operation. * successfully test backward pass of view_1d, view_2d and view_3d * fix backward pass for rms_norm I would have used formulas from other frameworks, but they differed so I could not decide which is correct. Instead it was derived here in comment using manual forward-backward automatic differention of rms_norm and simplification. * successfully test backward pass of rms_norm some tests may fail when gradients are large. could not find a satisfying configuration to check for abs error and relative error that passes all tests while still actually testing the results with tight enough error bounds. when looking at the values the "failed" tests look actually ok. for example: rms_norm: ndims=2, i=0, k=2, x0=0.000153, xm=0.000053, xp=0.000253, f0=0.278594, f1=0.086213, g0=961.905457, g1=966.064941, eps=0.000100, error_abs=4.159485, error_rel=0.004324 it is due to the test logic in check_gradients that they fail. * add todos for llama backward pass - implementation for ADD1 backward pass should probably use sum instead of mean (but this backward pass is not required) - repeat is not yet tested and looks like it only works for single element src0 inputs. * add operation ggml_sum_rows ggml_sum_rows(shape[a,b,c,d]) -> shape[1,b,c,d] * add missing GGML_OP_SUM_ROWS * fix backward pass for repeat requires ggml_sum_rows * successfully test backward pass of repeat * update quantization types in switch-case of add_at and add1 * add baby-llama example training a very small llama model from scratch to output a sinusoidal wave. had to increase maximum number of optimization parameters to train from scratch. * fix softmax in baby-llama example * switching from training with adam to lbfgs produces much better results in the baby-llama example * train with two examples, creating new tensors each time.. * fix bug when using ggml_opt to optimize params in one context and use a renewable context for eval and opt when not keeping gradients of model parameters they are overwritten by tensors created by opt, which may be invalid after opt context is renewed. so we need to keep the original gradients and make dups for opt * train on multiple examples, generate & print tokens with trained model afterwards ctx0 for evaluation and optimization is renewed for each sample * add ggml_reshape_1d, ggml_reshape_4d and ggml_view_4d * fix soft_max backward pass for input->ne[1] != 1 * add ggml_log operation necessary for cross entropy loss * add test for ggml_log gradients * implement backward pass for ggml_sum_rows, necessary for cross entropy loss * implement ggml_repeat support for rank > 2 tensors * add test for ggml_sum_rows gradients * fix training get_example_targets predict the next token, not the current token! * add square_error_loss and cross_entropy_loss functions * optimize loss over multiple samples this increases computation graph, need parallel batched forward for more efficiency. * fix backward pass for add_at and change arguments to have same order as in view * add ggml_set(ctx, a, b) to set b in view of a and return modified a necessary to set values into kv_self cache and properly propagate the gradients * fix kv_self gradients for training use ggml_set instead of ggml_cpy to set kv_self cache with properly propagating gradients * replace inplace operations for training with copying operations to allow gradient propagation * add GGML_ASSERT to catch ggml_rope and back value errors * add trainable lora-only model with all big matrices C split into A,B with A*B=C this is not a lora-finetune, but the whole model changed to have only low-rank "lora" matrices. training this instead of the normal model resulted in much worse results though... * vastly improve training results instead of logit targets 0 and 1 use -1 and +1. * shorten code using a variable * change name of GGML_OP_ADD_AT to GGML_OP_ACC * smaller default values for baby llama model parameters * update static assert of GGML_OP_COUNT * remove shape annotations in llama_eval_internal * revert disabling of threading for rms_norm and norm * rename print functions in baby-llama example * fix call to ggml_set_name * add missing include for strcmp, etc * remove trailing whitespace * reduce number of test-grad0 iterations avoid exceeding timeout of automated tests * remove busy loop that was used as sleep for slower sinus wave generation * disable slow tests grad0 and opt to avoid exceeding timeouts * c++ in baby-llama example use c++ includes instead of c includes use std::min, std::max instead of MIN, MAX macros * c++ in baby-llama example use c++ includes instead of c includes use std::min, std::max instead of MIN, MAX macros * ggml : fix compiler warnings + cosmetic changes * ggml : fix nullptr derefs in GGML_OP_CONT and GGML_OP_RESHAPE back * swap arguments to vDSP_vdiv call documentation for vDSP_vdiv states: "Note that B comes before A!" * swap arguments to vDSP_vdiv call documentation for vDSP_vdiv states: "Note that B comes before A!" * ggml : swap vDSP_vsub args as per documentation * add parallel batched forward function for baby-llama training * cleanup code for batched training * remove trailing whitespace * minor : fix compiler warnings + indentation style * ggml : fix null ptr deref in backward pass * ggml : remove Q4_2 remnants * ggml : fix clang-tidy warnings * baby-llama : couple of clang-tidy warnings --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Ivan Stepanov
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dd7eff57d8
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llama : new sampling algorithms (#1126)
* Sample interface, new samplers. New samplers: - locally typical sampling - tail free sampling - frequency and presence penalty - mirostat Ignore EOS fix: -inf should be used. * mirostat * Added --logit-bias and --no-penalize-nl, removed std::span * Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k) Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k) * Save and load example adjust * Tests * Windows build fix * Windows test fix |
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unbounded
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5f939498d5
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ggml : unit test for quantization functions (#953)
* Unit test for quantization functions Use the ggml_internal_get_quantize_fn function to loop through all quantization formats and run a sanity check on the result. Also add a microbenchmark that times these functions directly without running the rest of the GGML graph. * test-quantize-fns: CI fixes Fix issues uncovered in CI - need to use sizes divisible by 32*8 for loop unrolling - use intrinsic header that should work on Mac * test-quantize: remove Per PR comment, subsumed by test-quantize-fns * test-quantize: fix for q8_0 intermediates |
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Stephan Walter
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436e561931
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all : be more strict about converting float to double (#458)
* Be more strict about converting float to double * Test equivalence of round, SILU implementations Test module is commented out in CMakeLists.txt because the tests may take a long time, depending on how much the compiler optimizes. * Fix softmax in perplexity.cpp * all : prefer float over double where appropriate * perplexity : add <cmath> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Georgi Gerganov
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a316a425d0
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Overhaul the examples structure
- main -> examples - utils -> examples (renamed to "common") - quantize -> examples - separate tools for "perplexity" and "embedding" Hope I didn't break something ! |
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Stephan Walter
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69c92298a9
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Deduplicate q4 quantization functions (#383)
* Deduplicate q4 quantization functions * Use const; add basic test * Re-enable quantization test * Disable AVX2 flags in CI --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Georgi Gerganov
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f5a77a629b
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Introduce C-style API (#370)
* Major refactoring - introduce C-style API * Clean up * Add <cassert> * Add <iterator> * Add <algorithm> .... * Fix timing reporting and accumulation * Measure eval time only for single-token calls * Change llama_tokenize return meaning |
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Georgi Gerganov
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eb34620aec
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Add tokenizer test + revert to C++11 (#355)
* Add test-tokenizer-0 to do a few tokenizations - feel free to expand * Added option to convert-pth-to-ggml.py script to dump just the vocabulary * Added ./models/ggml-vocab.bin containing just LLaMA vocab data (used for tests) * Added utility to load vocabulary file from previous point (temporary implementation) * Avoid using std::string_view and drop back to C++11 (hope I didn't break something) * Rename gpt_vocab -> llama_vocab * All CMake binaries go into ./bin/ now |