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2023-05-02ggml : fix 32-bit ARMGeorgi Gerganov
2023-05-02ggml : fix ppc64le build error and make cmake detect Power processors (#1284)Marvin Gießing
* Fix ppc64le build issue * Added support to detect ppc64* processors
2023-05-02ggml: add names to tensors (#1268)slaren
* ggml: add names to tensors * minor improvements to dot file formatting
2023-05-01cuBLAS: refactor and optimize f16 mat mul performance (#1259)slaren
* cuBLAS: refactor, convert fp16 to fp32 on device * cuBLAS: use multiple streams, choose smartly between mul_mat_q and mul_mat_f16 * fix build * cuBLAS: update block_q5_1
2023-05-01ggml : fix ggml_used_mem() (#1264)Kerfuffle
2023-04-30ggml : fix UB (int << 31)Georgi Gerganov
2023-04-30ggml : add Q5 WASM SIMD + GGML_FTYPEGeorgi Gerganov
2023-04-30ggml : fix labels for GGML_OP_ALIBIGeorgi Gerganov
2023-04-29ggml : fix 32-bit ARM NEONGeorgi Gerganov
2023-04-29ggml : use vzip instead of vuzp for consistencyGeorgi Gerganov
2023-04-29ggml : fix visibility and unused warningsGeorgi Gerganov
2023-04-29ggml : fix #if for f32_f32 mul_mat (CLBlast) (#1229)Georgi Gerganov
2023-04-29ggml : adjust mul_mat_f16 work memory (#1226)Georgi Gerganov
* llama : minor - remove explicity int64_t cast * ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS * ggml : add asserts to guard for incorrect wsize
2023-04-29cuBLAS: use host pinned memory and dequantize while copying (#1207)slaren
* cuBLAS: dequantize simultaneously while copying memory * cuBLAS: use host pinned memory * cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory * cuBLAS: also pin kv cache * fix rebase
2023-04-29cuBLAS: non-contiguous tensor support (#1215)Henri Vasserman
* Cuda: non-contiguous tensor support * remove extra stuff * rename * fix error * more fixes, now OpenBLAS and CLBlast build too * now then?
2023-04-28Remove Q4_3 which is no better than Q5 (#1218)Stephan Walter
2023-04-28ggml : sync ggml (ggml_alibi)Georgi Gerganov
2023-04-28ggml : add helper debug printf in soft_maxGeorgi Gerganov
2023-04-28ggml : add CLBlast support (#1164)0cc4m
* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing * Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers * Finish merge of ClBlast support * Move CLBlast implementation to separate file Add buffer reuse code (adapted from slaren's cuda implementation) * Add q4_2 and q4_3 CLBlast support, improve code * Double CLBlast speed by disabling OpenBLAS thread workaround Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren <2141330+slaren@users.noreply.github.com> * Fix device selection env variable names * Fix cast in opencl kernels * Add CLBlast to CMakeLists.txt * Replace buffer pool with static buffers a, b, qb, c Fix compile warnings * Fix typos, use GGML_TYPE defines, improve code * Improve btype dequant kernel selection code, add error if type is unsupported * Improve code quality * Move internal stuff out of header * Use internal enums instead of CLBlast enums * Remove leftover C++ includes and defines * Make event use easier to read Co-authored-by: Henri Vasserman <henv@hot.ee> * Use c compiler for opencl files * Simplify code, fix include * First check error, then release event * Make globals static, fix indentation * Rename dequant kernels file to conform with other file names * Fix import cl file name --------- Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Henri Vasserman <henv@hot.ee> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-28add avx2 for dot_q8_0_q8_0, 2x faster than scalar (#1211)Yann Follet
2023-04-26ggml : slightly faster AVX2 implementation for Q5 (#1197)Stephan Walter
2023-04-26ggml : add Q5_0 and Q5_1 quantization (#1187)Georgi Gerganov
* ggml : add Q5_0 quantization (cuBLAS only) * ggml : fix Q5_0 qh -> uint32_t * ggml : fix q5_0 histogram stats * ggml : q5_0 scalar dot product * ggml : q5_0 ARM NEON dot * ggml : q5_0 more efficient ARM NEON using uint64_t masks * ggml : rename Q5_0 -> Q5_1 * ggml : adding Q5_0 mode * quantize : add Q5_0 and Q5_1 to map * ggml : AVX2 optimizations for Q5_0, Q5_1 (#1195) --------- Co-authored-by: Stephan Walter <stephan@walter.name>
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
2023-04-24Fix build for gcc 8 and test in CI (#1154)Stephan Walter
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
2023-04-23A better `packNibbles` and `mul_sum_i8_pairs_float` implementation using ↵Yishuo Wang
AVX512 (#1119)
2023-04-22ggml : fix Q4_3 cuBLASGeorgi Gerganov
2023-04-22Fix CI: ARM NEON, quantization unit tests, editorconfig (#1122)Stephan Walter
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
2023-04-21Improve 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
2023-04-21ggml : 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>
2023-04-20ggml : sync ggml (add GPT-NeoX RoPE implementation)Georgi Gerganov
2023-04-20ggml : fix bug in ggml_compute_forward_dup_f32()Georgi Gerganov
2023-04-20ggml : 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
2023-04-20AVX2 optimization for vec_dot_q4_2_q8_0 (#1068)Stephan Walter
2023-04-20Improve cuBLAS performance by dequantizing on the GPU (#1065)slaren
2023-04-19Q4_2 quantization with rmse-optimized scale and quants (#1062)Kawrakow
* Q4_2 quantization with rmse-optimized scale and quants For quantize-stats we get q4_2: rmse 0.00159301, maxerr 0.17480469, 95pct<0.0030, median<0.0012 For 7B perplexity with BLAS enabled we get 6.2038 after 655 chunks. Quantization is slow (~90 seconds on my Mac for 7B) as not multi-threaded as in PR #896. * ggml : satisfy the sanitizer builds Not sure why this makes them fail * Better follow ggml conventions for function names * Fixed type as per reviewer comment --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-19ggml : use 8-bit precision for Q4_1 intermediate results (#1047)Georgi Gerganov
* ggml : use 8-bit precision for Q4_1 intermediate results (ARM) * ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32 56 ms/token with Q4_1 ! * ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051) * gitignore : ignore ppl-*.txt files --------- Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19ggml : Q4 cleanup - remove 4-bit dot product code (#1061)Stephan Walter
* Q4 cleanup * Remove unused AVX512 Q4_0 code
2023-04-19Add NVIDIA cuBLAS support (#1044)slaren
2023-04-19Multi-threaded ggml_cpy (#1035)slaren
* Multi-threaded ggml_cpy * Update ggml.c Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Also fix wdata offset in ggml_compute_forward_add_q_f32 --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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