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2024-05-21tests : test-tokenizer-0.sh print more info (#7402)Georgi Gerganov
2024-05-21Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425)jaime-m-p
* Update brute force test: add_special * Update brute force test: default values for add_bos_token and add_eos_token * Enable rtrim when pre-inserting BOS Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Revert "server : fix test regexes"
2024-05-20Tokenizer SPM fixes for phi-3 and llama-spm (#7375)jaime-m-p
* Update brute force test: special tokens * Fix added tokens - Try to read 'added_tokens.json'. - Try to read 'tokenizer_config.json'. - Try to read 'tokenizer.json'. * Fix special tokens rtrim Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server : fix test regexes
2024-05-18ggml : fix quants nans when all the group weights are very close to zero (#7313)slaren
2024-05-18Unicode codepoint flags for custom regexs (#7245)jaime-m-p
* Replace CODEPOINT_TYPE_* with codepoint_flags * Update and bugfix brute force random test * Deterministic brute force random test * Unicode normalization NFD * Get rid of BOM
2024-05-15ggml : add `ggml_upscale_ext` (ggml/814)John Balis
* initial commit with CPU implementation of upscale to shape and test, cuda implementation next * experimental commit to see if dst shape is correct * test version * test * removed unnecessary params * refactor * fixed tests * ggml : metal impl + cleanup + sycl dev warnings * patched ggml_upscale cuda op to handle non-contiguous tensors, added test for non-contiguous behavior * metal : fix upsacle op to support nb00 + style --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-14metal : support FA without mask + add asserts (#7278)Georgi Gerganov
* ggml : fa without mask + add asserts ggml-ci * metal : support non-contiguous KV ggml-ci
2024-05-14Add left recursion check: quit early instead of going into an infinite loop ↵Haggai Nuchi
(#7083) * Add left recursion check: quit early instead of going into an infinite loop * Remove custom enum, rename left recursion check and move to "grammar internal" section, add handling for edge case where a leftmost nonterminal may be empty * Remove unnecessary declaration
2024-05-12CUDA: add FP32 FlashAttention vector kernel (#7188)Johannes Gäßler
* CUDA: add FP32 FlashAttention vector kernel * fixup! CUDA: add FP32 FlashAttention vector kernel * fixup! fixup! CUDA: add FP32 FlashAttention vector kernel * fixup! fixup! fixup! CUDA: add FP32 FlashAttention vector kernel
2024-05-11llama : lookup word in vocab before doing BPE merges (#7193)Haoxiang Fei
* fix: llama-3 ignore_merges * test: add test for llama-3 bpe ignore_merges * fix: set ignore_merges only for llama-3 * fix: test-tokenizer-1-bpe --ingore-merges detection * fix: copy to fix fallthrough * fix: change ignore_merges to bool * fix: add ignore merges tests to cmake * llama : alternative merge ignore logic --------- Co-authored-by: Haoxiang Fei <feihaoxiang@idea.edu.cn> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11ggml : full ALiBi support (#7192)Georgi Gerganov
* ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * ggml : fix assert message * vulkan : add dev notes * ggml : require mask when using ALiBi ggml-ci * convert : fix convert for refact models
2024-05-09llama3 custom regex split (#6965)jaime-m-p
* merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * llama3 custom regex split * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * Using char32_t for codepoints * lint : fix * already exists unicode_tolower() * Typing * Restore BOM * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * Fix merge * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci * Move unused variable value * GPT2 custom regex split * Add alternative regex for custom aplit llama3 Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Style * Add bruteforce random tests for token encoding * wip: fixing unicode codepoint ranges * Fix merge * Unicode tables: separator, lowercase, uppercase and whitespace * llama3 custom regex split: fix \s * Restore BOM * Style * wip: generate NDF table * Ignore special tokens for testing * Clean gen-unicode-data.py * Refactor random tokenizer test * lint : fix * tests : add fail test for llama-bpe --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: jaime-m-p <>
2024-05-09CUDA: generalize FP16 fattn vec kernel (#7061)Johannes Gäßler
* CUDA: generalize FP16 fattn vec kernel * disable unsupported head sizes for AMD in test * try AMD fix * fix batch size 2-8 * partially revert changes
2024-05-08JSON: [key] -> .at(key), assert() -> GGML_ASSERT (#7143)Johannes Gäßler
2024-05-08llama : add BPE pre-tokenization for Qwen2 (#7114)Ren Xuancheng
* Add BPE pre-tokenization for Qwen2. * minor : fixes --------- Co-authored-by: Ren Xuancheng <17811943+jklj077@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-08ggml : introduce bfloat16 support (#6412)Justine Tunney
* Introduce bfloat16 support Many models on Hugging Face (e.g. Mistral, TinyLLaMA) use bfloat16 as their canonical floating point format. ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌──┴───┐┌─┴───┐ 0b0000000000000000 brain16 This encoding has the same number of exponent bits as float32. That makes conversion relatively straightforward, even in the absence of hardware support. For example, converting brain16 to binary32 means simply shifting 16 bits to the left. ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌──┴───┐┌─┴───────────────────┐ 0b00000000000000000000000000000000 IEEE binary32 The issue is that converting bf16 to fp16 can result in information loss. Only 13% of bf16 numbers can be precisely represented in fp16 which in practice ends up being 99.71% of Mistral 7b v0.2's weights however there is currently no way other than fp32 to get the others ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌─┴─┐┌─┴──────┐ 0b0000000000000000 IEEE binary16 This change fixes that, by adding a bf16 data type to GGML. Support for CPU inference has been implemented along with optimizations for the AVX2, AVX512, and AVX512BF16 ISAs. Perplexity on Mistral 7b 0.2 improves somewhere around -0.0024 to -0.0046 compared to using fp16 * Remove GGML code that's not needed * Minimize the GGML API surface area for BF16 * Remove bf16 luts * Make the GGML header look nicer * Fix documentation * Apply ggerganov's fixes for test-backend-ops * Add BF16 code for new ggml_validate_row_data() function
2024-05-05command-r : add BPE pre-tokenization (#7063)DAN™
* Add BPE pre-tokenization for Command-R/R+. * Bump transformers convert requirement. * command-r : add individual digits regex --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-05py : logging and flake8 suppression refactoring (#7081)Brian
Set one as executable and add basicConfig() to another. Also added noqa tag to test scripts.
2024-05-04tests : add test-tokenizer-0.sh + fix some tokenizers (#7036)Georgi Gerganov
* tests : add test-tokenizer-0.sh * unicode : add all unicode number ranges * starcoder : fix pre-tokenizer * tests : add test that fails with DeepSeek tokenizers * falcon : fix regex * unicode : regenerate unicode tables * refact : add tokenizer model * lint : fix * tests : disable failing tests ggml-ci * refact : add tests files ggml-ci * convert : print -> logging ggml-ci * lint : fix * unicode : digit -> number * phi-3 : update
2024-05-03convert.py : add python logging instead of print() (#6511)Brian
* convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-04-30ggml : add Flash Attention (#5021)Georgi Gerganov
* ggml : add ggml_flash_attn_ext API * ggml : fix GQA support in ggml_flash_attn_ext * ggml : online attention (CPU) * metal : initial implementation * metal : f16 precision * metal : reduce branches * metal : specialize for head size * wip : 8 rows per simd group * wip : 4 rows per simd group * wip : template for rows per warp * metal : parallelize across KV size * metal : parallel reduce across heads * metal : efficient flash_attn_f16 implementation * metal : avoid redundant loads of the attention * metal : scale and mask in matrix form * metal : fix comment * llama : avoid ggml_cast, use F32 query * metal : add parallel reduce version (disabled) * metal : move output into local memory + optimize - the result from each simdgroup now stays in the registers - significantly reduced SRAM usage - more efficient skipping of -INF blocks - avoid simdgroup barrier in hot loop - add comments * metal : add tests, fix scaling, support C > 32 * metal : improve precision * ggml : fix f16 mad * metal : minor * metal : support Q > 8 * tests : add ATTN tests * metal : disable buffer allocation logs * tests : more * metal : faster inner loop for C == 32 * metal : fix array initialization * tests : ifdef * ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext * ggml : fix ggml_soft_max mask requirement * cuda : fix soft_max to use correct mask size * cuda : add flash_attn kernel (wip) * metal : optimize softmax for C > 32 * metal : optimize softmax * tests : minor fix * cuda : avoid zeroing fragments * tests : update dims * cuda : fix __hisinf() result check * cuda : avoid warp_reduce for smax * cuda : use int instead of int64_t Noticeably improves performance (thanks to Johannes) * cuda : make loops use the same loop values Thanks Johannes again for the tip * cuda : unroll some of the loops * cuda : avoid __hisinf branches * cuda : use half2 in softmax * cuda : switch to 1 warp for bs > 16 * cuda : speed-up reduce part of the kernel * cuda : unroll Q*K^T loop * cuda : fix -INF block check * cuda : simplify softmax * cuda : fix matrix names * cuda : minor * llama : adapt to F16 KQ_pos * llama : adapt new models to F16 KQ_mask * ggml : fix F16 store (ARM NEON) * llama : fix type of KQ_mask and KQ_pos * ggml : fix CPU soft_max * tests : add hs=256 * cuda : fix build * metal : improve perf via smaller int registers * cuda : adapt soft_max to F16 mask and pos * CUDA: faster FlashAttention, kernel for bs == 1 * 16 cols for Phi-2 * no vec for hs, no hs==256 ncols==32 for Volta * adjust kernel selection logic * 4 warps, 256 stride for all D * no ncols == 64 * Multiple parallel blocks for batch size 1 * fix compile warnings * fix excessive KQ_b loads * fix cmake build * fix KV cache padding, NaN from INFINITY (#6438) * llama : flash_attn cparam + fix defrag * server: support flash_attn param * server: bench: enable flash_attn param * CUDA: refactor host code, dyn. par. blocks * fix flash_attn_vec_f16 race condition * flush softmax exp below threshold to 0 * store temp KQ in registers * Calculate KQ as FP32 if KQV has GGML_PREC_F32 * Add __hgt2_mask implementation for CUDA 11 * fix KQ FP32 precision fpr parallel_blocks > 1 * llama-bench : add -fa,--flash-attn arg * metal : add BS=1 kernel for flash attention (#6508) * metal : add BS=1 kernel for flash attention (wip) * metal : support more than 1 warps * metal : opts * metal : opt * metal : switch to parallel reduce * metal : reduce registers * metal : simplify * metal : initial FA vec kernel * metal : use F32 attention accumulators * batched-bench : add fattn arg * llama : simplify llama_build_kv_store ggml-ci * llama : adapt build_olmo to changes * ggml : fix arm fp16 store on windows * metal : clean-up * metal : clean-up kernel code * metal : minor * tests : remove benchmarks ggml-ci * ggml : fix avx512 const correctness ggml-ci * ggml : fix soft_max with bias on CPU ggml-ci * common : print --flash-attn in help * ggml : fix num dimensions in ggml_flash_attn_ext * llama : force disable flash attention for incompatible models * ggml : ggml_soft_max support F16/F32 mask/pos ggml-ci * cuda : uint -> uint32_t * cuda : "constexpr dim3" -> "const dim3" ggml-ci * cuda : try to fix __hgt2_mask ggml-ci * ggml : add TODO's for F16/F32 mask/pos support in other backends * llama : replace bool need_kq_pos with use_alibi * llama : prep ALiBi support for BERT models ggml-ci * llama : fix n_batch requirements ggml-ci * cont * server : add help for --flash-attn arg * llama : disable FA for AMD * tests : remove TMP_ATTN_BENCH ggml-ci * llama : support save/load state with FA enabled ggml-ci * ci : add CUDA save-load-state tests ggml-ci * llama : llama_kv_cache_clear zeroes data + fix save-load seq ggml-ci * llama : fix copy-paste errors, add TODO * llama : disallow incompatible states * llama : update llama_state_get_size after v_trans field * metal : remove tmp log * llama : add static reminder for llama_state_get_size * metal : fix max nsg ggml-ci * ci : fix arg order ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
2024-04-29Extending grammar integration tests (#6644)Clint Herron
* Cleaning up integration tests to share code between tests and make it simpler to add new tests. * Add tests around quantifiers to ensure both matching and non-matching compliance. * Add slightly more complex grammar with quantifiers to test references with quantifiers. * Fixing build when C++17 is not present. * Separating test calls to give more helpful stack traces on failure. Adding verbose messages to give visibility for what is being tested. * Adding quotes around strings to explicitly show whitespace * Removing trailing whitespace. * Implementing suggestions from @ochafik -- grammars and test strings now print and flush before tests to aid in debugging segfaults and whatnot. * Cleaning up forgotten symbols. Modifying simple test to use test harness. Added comments for more verbose descriptions of what each test is accomplishing. * Unicode symbol modifications to hopefully make log easier to parse visually.
2024-04-29llama : fix BPE pre-tokenization (#6920)Georgi Gerganov
* merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-24llama : add phi 3 chat template (#6857)Tristan Druyen
* Add phi 3 chat template & tests * test : fix chat template result --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21llama : add llama-3 chat template (#6751)Wouter
* Added llama-3 chat template * Update llama.cpp Co-authored-by: Samuel Tallet <36248671+SamuelTallet@users.noreply.github.com> * Update llama.cpp Co-authored-by: Samuel Tallet <36248671+SamuelTallet@users.noreply.github.com> * Update tests/test-chat-template.cpp Co-authored-by: Samuel Tallet <36248671+SamuelTallet@users.noreply.github.com> * Added EOS stop sequence according to https://github.com/ggerganov/llama.cpp/pull/6751#issuecomment-2065602862 * Removed adding of BOS token before first message * Removed bos token from expected output from llama-3 * Update tests/test-chat-template.cpp Co-authored-by: Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> * Update tests/test-chat-template.cpp Co-authored-by: Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> * Added <|end_of_text|> as another stop token * Reverted last change of adding the end_of_text stop word for llama 3 --------- Co-authored-by: Wouter Tichelaar <tichelaarw@spar.net> Co-authored-by: Samuel Tallet <36248671+SamuelTallet@users.noreply.github.com> Co-authored-by: Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-18ggml : group all experts in a single ggml_mul_mat_id (#6505)slaren
* ggml : group all experts in a single ggml_mul_mat_id cuda : improve mmid row copy * cuda : fix bin bcast with non-cont src0 * test-backend-ops : only run all mul mat tests for base types * llama : disable moe offloading with SYCL --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16llama : add qwen2moe (#6074)Shijie
* support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-15`main`: add --json-schema / -j flag (#6659)Olivier Chafik
* main: add --json-schema / -j * json: move json-schema-to-grammar to common lib * json: fix zig build
2024-04-14Add Command R chat template (#6650)Chao Jiang
* Add chat template for command-r model series * Fix indentation * Add chat template test for command-r models and update the implementation to trim whitespaces * Remove debug print
2024-04-12JSON schema conversion: ⚡️ faster repetitions, min/maxLength for ↵Olivier Chafik
strings, cap number length (#6555) * json: rename python schema converter to make import easier * server: skip null json_schema / grammar fields * json: deps management for primitive rules (+ allow null values) * json: optimize repetitions for minItems/maxItems and regexps: `a{,3}` goes from `"a"? "a"? "a"?` (explosive combos) to `(a (a (a)?)?)?` * grammars: add troubleshooting section to readme * json: cap length of numbers to 15 digits before/after decimal point (avoids infinite gen, e.g. "one third" -> `0.333333333333...`) * json: unify all repetition code (w/ or w/o sep) * json: support string minLength/maxLength * server+json: update server/README w/ result_format * nits * json: fix type error w/ python 3.8 * json: fix server/README (json_schema in /completion vs. result_format in /v1/chat/completions) * json: simplify DOT `{"type": "string", "pattern": "^.$"}` * json: remove recursion in opt_repetitions (avoids Python stack overflow) * json: rm dead code * json: rm useless assert & ggml.h import
2024-04-12metal : unify mul_mv_id kernels (#6556)slaren
2024-04-11grammars: 1.5x faster inference w/ complex grammars (vector reserves / ↵Olivier Chafik
reuses) (#6609) * grammars: reserve rejects & next candidates * grammars: reuse new_stacks * grammars: fix missing sig change in llama.h * grammars: fix test (api changed) * grammars: update gbnf-validator.cpp * grammars: simpler syntax (no swap)
2024-04-06Tests: Added integration tests for GBNF parser (#6472)Clint Herron
* Added integration tests for GBNF parser to validate correctness of parsing, as well as correctness of string matching. Intended for use to pin behavior while working on performance improvements. * Fixing whitespace errors and cleaning error message alert to be clearer. * Removing hacky include to llama.cpp from grammar integration test now that needed functions are available via internal API. * Comment cleanup. * Reorganizing tests for readability. * Cleaning up debug message to make a bit more sense.
2024-04-03Add OpenChat, Alpaca, Vicuna chat templates (#6397)kaizau
* Add openchat chat template * Add chat template test for openchat * Add chat template for vicuna * Add chat template for orca-vicuna * Add EOS for vicuna templates * Combine vicuna chat templates * Add tests for openchat and vicuna chat templates * Add chat template for alpaca * Add separate template name for vicuna-orca * Remove alpaca, match deepseek with jinja output * Regenerate chat template test with add_generation_prompt * Separate deepseek bos from system message * Match openchat template with jinja output * Remove BOS token from templates, unprefix openchat
2024-04-03ggml : mul_mat_id use the same tensor for all the experts (#6387)slaren
* ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-26IQ1_M: 1.75 bpw quantization (#6302)Kawrakow
* iq1_m: basics * iq1_m: basics-2 * iq1_m: CUDA dequantize works Very 1st shot I get PPL = 9.76 for LLaMA-v2-7B. * iq1_m: separate shifts for each group of 8 in a block We get PPL(LLaMA-v2-7B ) = 9.2810 PPL(LLaMA-v2-13B) = 6.8105 Not bad, but slightly higher than sqrt(PPL(IQ1_S) * PPL(IQ2_XXS)) which is the expected outcome given that IQ1_M is halfway between IQ1_S and IQ2_XXS in terms of bpw. From this, we would expect PPL = 9.14 for LLaMA-v2-7B PPL = 6.63 for LLaMA-v2-13B * iq1_m: go to 3-bit scales There is slight increase in PPL, but the 0.0625 bpw reduction in size is totally worth it. We now have PPL(LLaMA-v2-7B ) = 9.4469 at 1.96 bpw PPL(LLaMA-v2-13B) = 6.8717 at 1.93 bpw PPL(LLaMA-v2-70B) = 4.8568 at 1.85 bpw * iq1_m: scalar dot product * iq1_m: AVX2 dot product * iq1_m: very slightly faster AVX2 dot product * iq1_m: ARM_NEON dot product Works, but very slow (10.5 t/s) * iq1_m: Metal - dequantize works, dot product does not * iq1_m: Metal now works About the same performance as iq1_s. * iq1_m: minor * iq1_m: checking pure iq1_m quantization It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight with Q4_K. * iiq1_m: slightly faster ARM_NEON dot product 10.5 t/s -> 11.65 t/s * iq1_m: faster ARM_NEON dot product 11.65 t/s -> 14.9 t/s * iq1_m: another minor ARM_NEON dot product improvement 14.9 -> 15.0 t/s * iq1_m: small PPL improvement via super-block scale adjustment After quantizing block scales redo the super-block scale fit. PPL(LLaMA-v2-7B ) = 9.3346 PPL(LLaMA-v2-13B) = 6.8419 PPL(LLaMA-v2-70B) = 4.8294 PPL(Mistral-7B ) = 8.1624 * iq1_m: adapt to CUDA refactoring * iq1_m: remove unused variable We have progressed to warnings being errors. * iq1_m: add to backend-ops tests * iq1_m: fix Windows ARM * iq1_m: use common definition of iq1m_scale_t * cuda: assert -> NO_DEVICE_CODE * iq1_M: PR comments --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-25tests : include IQ2_XXS and IQ2_XS in test-quantize-fns (#6303)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-22tests : conditional python & node json schema tests (#6207)Olivier Chafik
* json: only attempt python & node schema conversion tests if their bins are present Tests introduced in https://github.com/ggerganov/llama.cpp/pull/5978 disabled in https://github.com/ggerganov/llama.cpp/pull/6198 * json: orange warnings when tests skipped * json: ensure py/js schema conv tested on ubuntu-focal-make * json: print env vars in test
2024-03-22json-schema-to-grammar : fix order of props + non-str const/enum (#6232)Olivier Chafik
* json: ordered json in server/schema converter to respect orig order * json: ws nits * json: support non-string const / enums
2024-03-22metal : pad n_ctx by 32 (#6177)Georgi Gerganov
* metal : require ne00 >= 128 for mat-mat kernels ggml-ci * llama : pad n_ctx by 32 ggml-ci
2024-03-21tests : disable system() calls (#6198)Georgi Gerganov
ggml-ci
2024-03-21json-schema-to-grammar improvements (+ added to server) (#5978)Olivier Chafik
* json: fix arrays (disallow `[,1]`) * json: support tuple types (`[number, string]`) * json: support additionalProperties (`{[k: string]: [string,number][]}`) * json: support required / optional properties * json: add support for pattern * json: resolve $ref (and support https schema urls) * json: fix $ref resolution * join: support union types (mostly for nullable types I think) * json: support allOf + nested anyOf * json: support any (`{}` or `{type: object}`) * json: fix merge * json: temp fix for escapes * json: spaces in output and unrestricted output spaces * json: add typings * json:fix typo * Create ts-type-to-grammar.sh * json: fix _format_literal (json.dumps already escapes quotes) * json: merge lit sequences and handle negatives {"type": "string", "pattern": "^({\"question\": \"[^\"]+\", \"response\": \"[^\"]+\"}\\n)+$"} * json: handle pattern repetitions * Update json-schema-to-grammar.mjs * Create regex-to-grammar.py * json: extract repeated regexp patterns to subrule * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * json: handle schema from pydantic Optional fields * Update json-schema-to-grammar.py * Update json-schema-to-grammar.py * Update ts-type-to-grammar.sh * Update ts-type-to-grammar.sh * json: simplify nullable fields handling * json: accept duplicate identical rules * json: revert space to 1 at most * json: reuse regexp pattern subrules * json: handle uuid string format * json: fix literal escapes * json: add --allow-fetch * json: simplify range escapes * json: support negative ranges in patterns * Delete commit.txt * json: custom regex parser, adds dot support & JS-portable * json: rm trailing spaces * Update json-schema-to-grammar.mjs * json: updated server & chat `( cd examples/server && ./deps.sh )` * json: port fixes from mjs to python * Update ts-type-to-grammar.sh * json: support prefixItems alongside array items * json: add date format + fix uuid * json: add date, time, date-time formats * json: preserve order of props from TS defs * json: port schema converter to C++, wire in ./server * json: nits * Update json-schema-to-grammar.cpp * Update json-schema-to-grammar.cpp * Update json-schema-to-grammar.cpp * json: fix mjs implementation + align outputs * Update json-schema-to-grammar.mjs.hpp * json: test C++, JS & Python versions * json: nits + regen deps * json: cleanup test * json: revert from c++17 to 11 * json: nit fixes * json: dirty include for test * json: fix zig build * json: pass static command to std::system in tests (fixed temp files) * json: fix top-level $refs * json: don't use c++20 designated initializers * nit * json: basic support for reserved names `{number:{number:{root:number}}}` * Revamp test cmake to allow args (WORKING_DIRECTORY needed for JSON test) * json: re-ran server deps.sh * json: simplify test * json: support mix of additional props & required/optional * json: add tests for some expected failures * json: fix type=const in c++, add failure expectations for non-str const&enum * json: test (& simplify output of) empty schema * json: check parsing in test + fix value & string refs * json: add server tests for OAI JSON response_format * json: test/fix top-level anyOf * json: improve grammar parsing failures * json: test/fix additional props corner cases * json: fix string patterns (was missing quotes) * json: ws nit * json: fix json handling in server when there's no response_format * json: catch schema conversion errors in server * json: don't complain about unknown format type in server if unset * json: cleaner build of test * json: create examples/json-schema-pydantic-example.py * json: fix date pattern * json: move json.hpp & json-schema-to-grammar.{cpp,h} to common * json: indent 4 spaces * json: fix naming of top-level c++ function (+ drop unused one) * json: avoid using namespace std * json: fix zig build * Update server.feature * json: iostream -> fprintf * json: space before & refs for consistency * json: nits
2024-03-15llama : add Orion chat template (#6066)Xuan Son Nguyen
2024-03-13test-backend-ops : skip CPU backend by default (#6028)slaren
2024-03-11llama : refactor unicode stuff (#5992)Georgi Gerganov
* llama : refactor unicode stuff ggml-ci * unicode : names * make : fix c++ compiler * unicode : names * unicode : straighten tables * zig : fix build * unicode : put nfd normalization behind API ggml-ci * swift : fix build * unicode : add BOM * unicode : add <cstdint> ggml-ci * unicode : pass as cpts as const ref
2024-03-09ggml : remove old quantization functions (#5942)Georgi Gerganov
* ggml : remove old quantization functions ggml-ci * ggml : simplify ggml_quantize_chunk ggml-ci * ggml : restrict correctness ggml-ci * ggml : remove hist data from the quantization API ggml-ci * tests : remove hist usage in test-backend-ops ggml-ci * vulkan : remove hist and fix typo
2024-03-09tests : gitignore ggml-common.hGeorgi Gerganov
2024-03-04add some new ops, fix some operators and add batch operations to certain ↵leejet
operators. (ggml/747) * cuda: fix group_norm * cuda: add batch inference support for ggml_pad/ggml_upscale * add ggml_arrange * add ggml_timestep_embedding * update ggml_arange/ggml_timestep_embedding tests * cuda: fix im2col * add ggml_arange/ggml_timestep_embbeding support for metal backend * fix some bugs * fix some bugs * Update ggml.h Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml-cuda.cu Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml-metal.metal Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * modify according to the review comments * ggml : fix compile warnings + code style * ggml : normalize compute_forward calls + fix seg fault in debug * minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-02-27IQ4_XS: a 4.25 bpw quantization (#5747)Kawrakow
* Try IQ4_NL with blocks of 64 - does not look good * iq4_xs: go to super-blocks of 256 and 6-bit scales for blocks of 32 * iq4_xs: CUDA works - 133.2 t/s * iq4_xs: AVX2 dot product * iq4_xs: ARM_NEON dot product * iq4_nl: Metal implementation As usual, Metal / Apple Silicon don't like my quants. * iq3_xs: minor fix * iq4_xs: shrink by using IQ3_S for attn_k and attn_q * iq4_xs: revert using IQ3_S for attn_k and attn_v PPL vs size is good, but CPU performance suffers: on M2 Max TG-128 drops to 21.7 t/s from 28.8, and on a Ryzen-7950X to 14.5 t/s from 15.8 t/s. On CUDA we have 135 t/s when using IQ3_S vs 133 t/s with pure IQ4_XS. * Fix CI * iq4_xs: Added forgotten check for 256 divisibility --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-26Adding IQ2_S and IQ2_M to complete coverage of the 2-3 bit quantization ↵Kawrakow
range (#5721) * Adding IQ2_S and IQ2_M as a single cumulative commit * Update examples/quantize/quantize.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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