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2023-04-26quantize : use `map` to assign quantization type from `string` (#1191)Pavol Rusnak
instead of `int` (while `int` option still being supported) This allows the following usage: `./quantize ggml-model-f16.bin ggml-model-q4_0.bin q4_0` instead of: `./quantize ggml-model-f16.bin ggml-model-q4_0.bin 2`
2023-04-25Update SHA256SUMS after quantization change (#1181)Stephan Walter
Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-04-25py : cast lora_alpha to int in convert-lora-to-ggml (#1170)ostix360
Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-04-25nix: use convert.py instead of legacy wrapper convert-pth-to-ggml.py (#981)Pavol Rusnak
2023-04-25ggml : add Q8_0 quantization format (rename the old one to Q8_1) (ARM NEON) ↵Georgi Gerganov
(#1179) * ggml : add Q8_0 quantization format (rename the old one to Q8_1) * tests : fix test-quantize-fns * ggml : finalize Q8_0 implementation * ggml : use q4_0_q8_0 and q4_2_q8_0 * ggml : fix Q8_0 dot product bug (ARM) * ggml : Q8_0 unroll x2 * ggml : fix bug - using wrong block type * ggml : extend quantize_fns_t with "vec_dot_type" * ggml : fix Q8_0 to use 255 values out of 256 * ggml : fix assert using wrong QK4_2 instead of QK4_3
2023-04-25ggml : use full range for Q4_0 and Q4_2 quantization (#729)unbounded
* Use full range for q4_0 quantization By keeping the sign of the highest magnitude, we can make sure the highest value maps to -8, which is currently unused. This is a bit of a freebie since it is fully backwards compatible with the current format. * Update quantize_row_q4_0 for AVX/AVX2 * Update quantize_row_q4_0 for WASM Untested * Update quantize_row_q4_0 for Arm NEON * Update quantize_row_q4_0 for PowerPC Untested * Use full range for q4_2 quantization
2023-04-24ggml : fix bug in ggml_compute_forward_sum_f32 (#1162)xaedes
The sum over all rows is now computed instead of just the last row
2023-04-24ggml : export symbols (#1155)Georgi Gerganov
2023-04-24examples : add save_load_state example (#1150)xaedes
* add save_load_state example * use <cstdio> instead of <iostream> and fprintf / printf instead of cout * renamed save-load-state example files replacing underscores by dashes
2023-04-24llama : increase scratch buffer size for 65B (ref #1152)Georgi Gerganov
Temporary solution
2023-04-24examples/main README improvements and some light refactoring (#1131)mgroeber9110
2023-04-24Fix build for gcc 8 and test in CI (#1154)Stephan Walter
2023-04-24Fix cuda compilation (#1128)slaren
* Fix: Issue with CUBLAS compilation error due to missing -fPIC flag --------- Co-authored-by: B1gM8c <89020353+B1gM8c@users.noreply.github.com>
2023-04-24llama : refactor get / set state + remove redundant kv cache API (#1143)Georgi Gerganov
2023-04-23Fix LoRA acronym (#1145)slaren
2023-04-23scripts : add helper scripts to synch ggml repoGeorgi Gerganov
2023-04-23Added README.md for main with examples and explanations (#1139)DannyDaemonic
2023-04-23ggml : do not print perf ops that have not been used at allGeorgi Gerganov
2023-04-23ggml : better PERF prints + support "LLAMA_PERF=1 make"Georgi Gerganov
2023-04-23Improve AVX2 for vec_dot_q4_3_q8_0 (#1138)Stephan Walter
2023-04-23readme : update gpt4all instructions (#980)Pavol Rusnak
2023-04-23A better `packNibbles` and `mul_sum_i8_pairs_float` implementation using ↵Yishuo Wang
AVX512 (#1119)
2023-04-22ggml : fix Q4_3 cuBLASGeorgi Gerganov
2023-04-22ci : trigger CI for drafts, but not most PR actions (#1125)Stephan Walter
2023-04-22Fix CI: ARM NEON, quantization unit tests, editorconfig (#1122)Stephan Walter
2023-04-22ggml : unit test for quantization functions (#953)unbounded
* 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
2023-04-22llama : print timings on ctrl+c exit (#1021)wbpxre150
* print timings on ctrl+c exit * remove redundant free memory call. * add global pointer to ctx.
2023-04-22llama : have n_batch default to 512 (#1091)eiery
* set default n_batch to 512 when using BLAS * spacing * alternate implementation of setting different n_batch for BLAS * set n_batch to 512 for all cases
2023-04-22cmake : fix build under Windows when enable BUILD_SHARED_LIBS (#1100)Howard Su
* Fix build under Windows when enable BUILD_SHARED_LIBS * Make AVX512 test on Windows to build the shared libs
2023-04-22ggml : fix AVX build + update to new Q8_0 formatGeorgi Gerganov
2023-04-22ggml : alternative Q4_3 implementation using modified Q8_0 (#1109)Georgi Gerganov
* ggml : prefer vzip to vuzp This way we always use the same type of instruction across all quantizations * ggml : alternative Q4_3 implementation using modified Q8_0 * ggml : fix Q4_3 scalar imlpementation * ggml : slight improvement of Q4_3 - no need for loop unrolling * ggml : fix AVX paths for Q8_0 quantization
2023-04-22ggml : AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring (#1099)Stephan Walter
* AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring * finish AVX vectorization of quantize_row_q8_0 * Rename hsum_int_8 to hsum_i32_8
2023-04-22examples : Improve Alpaca Default Repeat Penalty: Better Match Alpaca.cpp ↵Clint Herron
Experience (#1107) * Moving parameters to separate lines for readability. * Increasing repeate_penalty to 1.1 to make alpaca more usable by default. * Adding trailing newline.
2023-04-22llama : add api for getting/setting the complete state: rng, logits, ↵xaedes
embedding and kv_cache (#1105) * reserve correct size for logits * add functions to get and set the whole llama state: including rng, logits, embedding and kv_cache * remove unused variables * remove trailing whitespace * fix comment
2023-04-21Improve cuBLAS performance by using a memory pool (#1094)slaren
* Improve cuBLAS performance by using a memory pool * Move cuda specific definitions to ggml-cuda.h/cu * Add CXX flags to nvcc * Change memory pool synchronization mechanism to a spin lock General code cleanup
2023-04-21llama : fixed rlimit error message (#888)apaz
2023-04-21cmake : link threads publicly to ggml (#1042)源文雨
* fix: ld link test-tokenizer-0 error ``` cmake3 --build . --config Release [ 5%] Built target ggml [ 16%] Built target llama [ 22%] Linking CXX executable ../bin/test-tokenizer-0 ../libllama.a(ggml.c.o):在函数‘ggml_graph_compute’中: ggml.c:(.text+0xf2db):对‘pthread_create’未定义的引用 ggml.c:(.text+0xf9d4):对‘pthread_join’未定义的引用 collect2: error: ld returned 1 exit status gmake[2]: *** [bin/test-tokenizer-0] 错误 1 gmake[1]: *** [tests/CMakeFiles/test-tokenizer-0.dir/all] 错误 2 gmake: *** [all] 错误 2 ``` * Update CMakeLists.txt * Update CMakeLists.txt * Update CMakeLists.txt
2023-04-21main : evaluate tokens in batches after swapping context (#1014)Alex Klinkhamer
* examples : evaluate tokens in batches after swapping context * Update examples/main/main.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-21llama : remember and restore kv cache data pointers (#1104)xaedes
because their value is stored in buf and overwritten by memcpy
2023-04-21ggml : a faster version for Q4_1 x Q8_0 dot products (#1083)Kawrakow
* A faster version for Q4_1 x Q8_0 dot products The idea nehind being that Q8_0 quantized values get used many times in the matrix multiplications where they are involved. In the current implementations, when we are evaluating the dot products, we need to compute the sum of the quants in the Q8_0 vector, so the same operation is repeated many times. Here we pre-compute the sum during Q8_0 quantization, store it in the now modified block_q8_0 struct, and then reuse this result in the subsequent dot products. In a synthetic benchmark (just compute a bunch of dot products), this change speeds up the Q4_1 * Q8_0 dot product by 80%, making the performance identical to Q4_0 * Q8_0. In practical application, I see a ~15% gain in speed for token prediction on M2, and ~5% gain on Ryzen 7950X. The speed gain in the prompt evaluation is much bigger (around 50%). I have only done the change for the scalar version, ARM_NEON, and AVX2, so we still need an AVX implementation. * Cleaning up --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-04-21Show perplexity ETA in hours and minutes (#1096)slaren
2023-04-21llama : fix comment for "output.weight" tensorGeorgi Gerganov
2023-04-20Add ggml-model-*.bin checksums for 7B, 13B, 30B, 65B (#1088)Stephan Walter
* Add ggml-model-*.bin checksums for 7B, 13B, 30B * Add ggml-model-*.bin checksums for 65B --------- Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-04-20ggml : sync ggml (add GPT-NeoX RoPE implementation)Georgi Gerganov
2023-04-20ggml : fix bug in ggml_compute_forward_dup_f32()Georgi Gerganov
2023-04-20Add Q4_3 support to cuBLAS (#1086)slaren
2023-04-20ggml : do not break cuBLAS build (Q4_3 is not yet implemented)Georgi Gerganov
2023-04-20ggml : fix Q4_3 quantizationGeorgi Gerganov
Broke it during conflict resolution in last PR
2023-04-20llama : multi-threaded quantization (#1075)Kawrakow
* Multi-threading quantization. Not much gain for simple quantizations, bit it will be important for quantizations that require more CPU cycles. * Multi-threading for quantize-stats It now does the job in ~14 seconds on my Mac for Q4_0, Q4_1 and Q4_2. Single-threaded it was taking more than 2 minutes after adding the more elaborate version of Q4_2. * Reviewer comments * Avoiding compiler confusion After changing chunk_size to const int as suggested by @ggerganov, clang and GCC starting to warn me that I don't need to capture it in the lambda. So, I removed it from the capture list. But that makes the MSVC build fail. So, making it a constexpr to make every compiler happy. * Still fighting with lambda captures in MSVC --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-20ggml : add Q4_3 quantization (#1082)Georgi Gerganov