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2023-05-14benchmark-matmul: fix clang-tidy issues, report results in GFLOPS (#1458)slaren
* benchmark-matmul: fix command line parsing, replace macros with functions, report results in GFLOPS
2023-05-13ggml : GPU-accelerated token generation (#1412)Johannes Gäßler
* CUDA kernel for q4_0 dequant. + mat. vec. mult. * Added q4_1 via template * Added missing __syncthreads(); * --gpu_layers -> --gpu-layers * Shorter dequantize_mul_mat_vec line * q5_0 dequantize_mul_mat kernel * More readable dequantize_mul_mat_vec logic * dequantize_mul_mat_vec kernels for q5_1, q8_0, f16 * llama : offload "output" tensor to GPU too + coding style fixes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-13ggml : implement backward pass for llama + small training-llama-from-scratch ↵xaedes
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>
2023-05-13embedding : remove unused code (#1426)Rinne
2023-05-12llama : fix --mtest option (close #1414)Georgi Gerganov
2023-05-12CLI args use - instead of _, backwards compatible (#1416)Johannes Gäßler
2023-05-12ggml : remove bit shuffling (#1405)Georgi Gerganov
* ggml : remove Q4_0 bit shufling (ARM NEON) * ggml : remove Q4_1 bit shuffling (ARM NEON + reference) * ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON) * ggml : remove Q4_2 bit shuffling (WIP, BROKEN) * ggml : remove Q5_0 bit shuffling (ARM NEON) * ggml : 2x faster scalar implementations * ggml : remove Q5_1 bit shuffling (ARM NEON + scalar) * ggml : simplify scalar dot * ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit * ggml : fix Q4_1 quantization * ggml : update cuBLAS + normalize variable names * ggml : remove Q4_2 mode * ggml : minor formatting * ggml : fix Q5_0 quantization * scripts : add script for measuring the time per token * AVX implementations (#1370) * ggml : uniform 5th bit extraction * llama : produce error upon loading old model files * llama : fix model magic/version write * ggml : speed-up Q5_0 + Q5_1 at 4 threads * ggml : preserve old Q4 and Q5 formats * ggml : simplify Q8_1 - no need for low / high sums anymore * ggml : fix Q8_0 and Q8_1 rounding * Revert "AVX implementations (#1370)" This reverts commit 948d124837f9d287d8490f41338e0e4cceb0814f. * ggml : fix AVX2 implementation * sha : update hashes for 7B and 13B * readme : update timings + remove warning banner * llama : update v2 PR number to 1405 * ggml : fix WASM comments * ggml : back to original bit order * readme : add note that Q4 and Q5 have been changed * llama : fix return for unknown version --------- Co-authored-by: Stephan Walter <stephan@walter.name>
2023-05-10main : add option to save full output to session (#1338)Evan Jones
* main : add option to save full output to session * split behavior into --session and --prompt-cache * restore original implementation with new names * PR comments * move the check for incompatible parameters to gpt_params_parse * Fix whitespace Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com> --------- Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com>
2023-05-09Locale fix for Windows (#1379)DannyDaemonic
2023-05-08Interface improvements and `--multiline-input` (previously `--author-mode`) ↵DannyDaemonic
(#1040) * Interface improvements * Multiline input * Track character width * Works with all characters and control codes + Windows console fixes
2023-05-08llama : require first token to be BOS (#1303)Georgi Gerganov
* llama : require first token to be BOS * scripts : add ppl-run-all.sh * perplexity : add BOS for each chunk * readme : update perplexity values after BOS fix * perplexity : add clarifying comments
2023-05-08Documented CUDA reproducibility, added warning (#1346)Johannes Gäßler
2023-05-06Remove default arguments from sampling functions (#1343)Jed Fox
2023-05-05quantize: make output filename optional, default to ggml-model-<ftype>.bin ↵slaren
(#1301)
2023-05-04main : add --in-suffix option (#1318)44670
* adding --in-suffix option * print input suffix before generation
2023-05-04Only escape prompts when used with `-e` (#1311)DannyDaemonic
2023-05-04Update main's README.md with new features (#1296)DannyDaemonic
2023-05-04fix #1224 reverse prompt and multi line (#1297)Tomas
* fix reverse prompt and multi line * Code Formatting Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-03examples : read chat prompts from a template file (#1196)khimaros
2023-05-03examples : various prompt and example fixes (#1298)CRD716
* fix dan.txt * miku prompt improvements * use common characters
2023-05-02Process escape sequences given in prompts (#1173)DannyDaemonic
2023-05-02Handle signals properly on Windows (#1123)DannyDaemonic
2023-05-03fix missing parameters in `llama_init_from_gpt_params` (#1293)slaren
2023-05-02examples : add llama_init_from_gpt_params() common function (#1290)Ron Evans
Signed-off-by: deadprogram <ron@hybridgroup.com>
2023-05-02llama : fix compile warningsGeorgi Gerganov
2023-05-02examples : improve vertical alignment of a few variables (#1286)Ron Evans
Signed-off-by: deadprogram <ron@hybridgroup.com>
2023-05-02llama : allow 0 as a seed number. (#1275)Robert Brisita
2023-05-02main : switch input_noecho to input_echo to remove negation (#979)Ron Evans
Signed-off-by: deadprogram <ron@hybridgroup.com>
2023-05-01Add git-based build information for better issue tracking (#1232)DannyDaemonic
* Add git-based build information for better issue tracking * macOS fix * "build (hash)" and "CMAKE_SOURCE_DIR" changes * Redo "CMAKE_CURRENT_SOURCE_DIR" and clearer build messages * Fix conditional dependency on missing target * Broke out build-info.cmake, added find_package fallback, and added build into to all examples, added dependencies to Makefile * 4 space indenting for cmake, attempt to clean up my mess in Makefile * Short hash, less fancy Makefile, and don't modify build-info.h if it wouldn't change it
2023-05-01llama : fix session load / save (#1263)Georgi Gerganov
2023-04-30common : better default number of threads (#934)jon-chuang
* commit * fix * try-catch * apply code review * improve * improve * add macos headers * done * remove color * fix windows * minor * fix * Apply suggestions from code review Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com> * remove * minor * minor --------- Co-authored-by: jon-chuang <jon-chuang@users.noreply.github.com> Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com>
2023-04-30Various fixes to mat_mul benchmark (#1253)Stephan Walter
2023-04-29build : fix reference to old llama_util.hGeorgi Gerganov
2023-04-29examples : fix save-load-state + rename llama-util.hGeorgi Gerganov
2023-04-29common : change default parameters to pre-#1126 (#1223)Georgi Gerganov
2023-04-29llama : new sampling algorithms (#1126)Ivan Stepanov
* 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
2023-04-28Remove Q4_3 which is no better than Q5 (#1218)Stephan Walter
2023-04-28examples : add Jeopardy example (#1168)CRD716
* Basic Setup * Prevent Results.txt from coming up * Prefixes, Line separators, etc * editorcheck * introduction to give more consistent results * Basic graph thing * Grading, ready for testing! * Y'all ready to get funky? * fix column removal stuff * missed a few
2023-04-28llama : add session file format and saved sessions in main (#1169)Evan Jones
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>
2023-04-26quantize : 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`
2023-04-25ggml : 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
2023-04-24examples : 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
2023-04-24examples/main README improvements and some light refactoring (#1131)mgroeber9110
2023-04-23Fix LoRA acronym (#1145)slaren
2023-04-23Added README.md for main with examples and explanations (#1139)DannyDaemonic
2023-04-22Fix CI: ARM NEON, quantization unit tests, editorconfig (#1122)Stephan Walter
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
2023-04-22examples : 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.