From 30abf8814d962cc9389ec2a25873aa1ee4526f5f Mon Sep 17 00:00:00 2001 From: mqy Date: Mon, 29 May 2023 07:54:21 +0800 Subject: [PATCH] mulmat-tune-tool: add --n_pass; document; fix tailing spaces --- examples/mulmat-tune/README.md | 400 +++++++++++++----- .../mulmat-tune/analyze/4096x4096_q4_0.png | Bin 0 -> 689578 bytes examples/mulmat-tune/analyze/accelerate.xlsx | Bin 0 -> 326179 bytes examples/mulmat-tune/bench-out/13b.q4_0.txt | 1 - examples/mulmat-tune/bench-out/13b.q5_0.txt | 1 - examples/mulmat-tune/bench-out/7b.f16.txt | 1 - examples/mulmat-tune/bench-out/7b.f32.txt | 1 - .../bench-out/7b.q4_0.openblas.txt | 1 - examples/mulmat-tune/bench-out/7b.q4_0.txt | 1 - examples/mulmat-tune/bench-out/7b.q4_1.txt | 1 - examples/mulmat-tune/bench-out/7b.q5_1.txt | 1 - examples/mulmat-tune/bench-out/7b.q8_0.txt | 1 - examples/mulmat-tune/mulmat-tune-tool.c | 37 +- examples/mulmat-tune/mulmat-tune.h | 2 +- ggml.c | 4 +- tests/test-mulmat-tune | Bin 0 -> 371088 bytes tests/test-mulmat-tune.c | 6 +- 17 files changed, 324 insertions(+), 134 deletions(-) create mode 100644 examples/mulmat-tune/analyze/4096x4096_q4_0.png create mode 100644 examples/mulmat-tune/analyze/accelerate.xlsx create mode 100755 tests/test-mulmat-tune diff --git a/examples/mulmat-tune/README.md b/examples/mulmat-tune/README.md index 20bc99a5a2d203..9ab558484784f7 100644 --- a/examples/mulmat-tune/README.md +++ b/examples/mulmat-tune/README.md @@ -1,24 +1,41 @@ # Fine Tune MUL_MAT with Bench +To readers: this is a length introduction to this big PR. Recommend you review in +the following steps: + +1. read this file first and browse the files in dirs bench-out and analyze. +2. pull and checkout codes, build and evaluate `mulmat-tune`. +3. optionally run `tests/test-mulmat-tune`. +4. place a `mulmat-tune.tx` into the parent dir of `main`, evaluate main. +5. dive deeply into code details. + ## Background GGML defines three task types(stages): INIT, COMPUTE, FINALIZE. All nodes has COMPUTE stage, some has INIT stage, the FINALIZE is never used. -General speaking, codes run in GPU(BLAS) MAY not suitable to run with multi OS -threads -- sometimes very slow, but CPU could and scales well. So to speedup -large prompt and avoid spinning, the master code force 1-thread when (M >=32). +GGML supports pure CPU, CL, CUDA and optional CBLAS (Accelerate/OpenBLAS/BLIS). +In recent days, the compute framework keep evolving, introduced a lot of new +things: backend, vendor. In this CL, I follow the `backend` definition, and +defined new backend `CBLAS` for the Accelerate etc. -So, problems to solve: +General speaking, as of my tests, it's very slow to run `cblas_sgemm` with multi +OS threads. From master codes I saw that the `ggml_graph_compute` sets `node->n_tasks = 1` +to when compute `mul_mat` in `GPU` or `CBLAS`. -1. the `xxx_mul_mat_can_use_blas` rule is not accurate. We need bench. -2. with multiple threads, when run those heavy BLAS stages, we have to avoid - busy spinning. +To speedup large prompt and avoid spinning, the master set `n_threads` to 1 when +token size >= 32. `ggml_compute_forward_mul_mat_use_blas` checks: +- data of `src0` and `src1` are continuous +- M >=32 && >= 32 && K >= 32. +It's obvious that this simple rule is not accurate at all. There are several TODOs +that mentioned this problem. I have been focused on the `threading` problem(s) since Apr this year. I dropped two simple pull requests because they are not solutions but noises. In the second pull request, @gerganov hinted me the `1-thread blas` problem, so I followed this -direction since then. +direction since then. My benchmarks on 7B/13B and all Qx_x shows that: for all +N/K (e.g, 4096x4096), Accelerate always run faster than pure GPU at `M = 32`, +the difference may vary up to `30%` as of 1 thread. I will show you image for this. When I observed that the de-quantization takes about half of total time, I think it's a good chance to start from here. At first I implemented the new threading @@ -26,11 +43,32 @@ framework that supports `wait/notify`, subtle and fragile to dead lock. I'm happ that it works. I had tried to bench online by comparing CPU/GPU time, finally I replaced that with offline bench. To explicitly control details (how to parallel, when to wait, how to select the best executing plan), I had to define task config, -task profiles. Finally I got the demo solution. +task profiles. Finally after over a month busy coding I got the demo solution. + +New CUDA and GPU codes are keep changing in recent days, new rules for when and +how to run `mul_mat` were introduced. I often had trouble in merging/rebasing +with master, let alone understand the details. For example: + +- To test ClBlast on my device, I managed to solve the OOM problem with smaller + matrices in multi-passes. Finally unable to catchup with recent change. +- I periodically refactor codes to keep up with `enum ggml_backend`. Also, I just + noticed [multi GPU support in #1670](https://github.com/ggerganov/llama.cpp/pull/1607), looks like this PR will change + `enum ggml_backend` again. Although I have'nt read all codes in this PR and + must not able to understand all even if I did, but I like one of the changes + that moving `enum ggml_task_type` and `struct ggml_task_params`. -Data files in in bench result dir were generated in MacBook Pro 2018 with 32 GB -2400 MHz DDR4 memory, 2.6 GHz 6-Core Intel Core i7-8850H @2.60GHz, Intel UHD -Graphics 630 1536 MB. +In the following sections, all bench data will will be mentioned are generated +on MacBook Pro 2018 with 32 GB 2400 MHz DDR4 memory, 2.6 GHz 6-Core Intel Core +i7-8850H @2.60GHz, Intel UHD Graphics 630 1536 MB. + +When you read the following sections, please note that current incoming codes are +based on this commit: + +``` +commit 66874d4fbcc7866377246efbcee938e8cc9c7d76 (origin/master, origin/HEAD) +Author: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> +Date: Thu May 25 20:18:01 2023 -0600 +``` ## Solution and Result @@ -46,35 +84,35 @@ I observed the following "facts" on Accelerate/OpenBLAS. - The mul_mat time in GPU is heavy (tens to hundreds ms), goes up very slow when M doubles. - In the large M range, the de-quantization time accounts for a large proportion - of the total calculation time. For example, for 7B, Q4_0, NxK=4096x4096, when - M is less than 100, the proportion of de-quantization time exceeds `50%`. Other - NxK combinations have similar situation, except that the range of M is not as - large as NxK=4096x4096. + of the total calculation time. For example, for 7B, Q4_0, NxK=4096x4096, the + proportion of de-quantization time exceeds or near `50%` for large M range (up + to 128). Other NxK combinations have similar situation. You may look at dirs + [bench-out](./bench-out/) and [analyze](./analyze/) for more examples. In theory, if we split COMPUTE stage as INIT + COMPUTE, we MAY speedup prompt -eval time a lot: up to 50% for large M range (e.g. 32 - 128). +eval time a lot: up to 50% for large M range. -The eval time of long prompt decreases a lot. For example, `examples/chat.sh` with -4 threads, the prompt eval time of 99 tokens decreases up to **-40%** in my device. -Tests for broad prompt size show speed up of `10% - 40%`. +The actual tests show that, eval time of long prompt decreases a lot. For example, +`examples/chat.sh` with 4 threads, the prompt eval time of 99 tokens decreases up +to **-40%** on my device. The key factor for speeding up is parallelism, followed by more accurate profile -selection. The latter, although secondary, is necessary in the case of multithreading. +selecting. The latter, although secondary, is necessary for time estimation. In conclusion, let me list the key point how does it work. -1. Update mul_mat BLAS codes: allow de-quantizing in CPU or GPU (if possible). -2. Explicitly task config and profiles: +1. Explicitly task config and profiles: * define conf profiles (for example, init in CPU, compute in GPU); * define for any stage: compute in parallel or not, idle wait or not. * non-existing compute stages are not called. -3. New threading framework: combine `spin` + `wait/notify`. Without wait, workers +2. New threading framework: combine `spin` + `wait/notify`. Without wait, workers busy spinning may causes overheat and slow down the overall speed. The mul_mat compute time is long enough (often tens of ms), so the wait/notify overhead (at most tens of us) is OK. +3. Update mul_mat BLAS codes to support the new task profile. 4. A tune tool for benching. With bench data, given N/K and n_threads, we could - estimate total computing time for any M (if within range), thus could select - the fastest profile. + estimate total computing time for any M (even if out of bench range), thus + could select the fastest profile. 5. On llama start, it loads the bench data from file (if exists). Before computing node, we select the fastest profile. When compute, the `dst->task_conf` along with `params` controls which part of the codes to run. @@ -87,7 +125,8 @@ With explicit task config profiles and bench data, we are able to run any task stage in any backend. For example: for q4_0, we could run INIT in CUDA and COMPUTE in Accelerate -- if that makes sense. -## Task profile and task stage +Too much changes to explain. Not enough time to write them in details at present +when codes are still unstable, so just list changes in ggml.h here. ```c // ggml.h @@ -99,23 +138,19 @@ enum ggml_backend { GGML_BACKEND_CBLAS = 3, // has API `cblas_sgemm` }; -// new -struct ggml_task_stage { - int backend; // enum ggml_backend - bool parallel; - bool wait; -}; - -struct ggml_task_profile { - // index 0: INIT, 1: COMPUTE, 2: FINALIZE - struct ggml_task_stage stages[3]; -}; - struct ggml_tensor { //... struct ggml_task_profile task_profile; // new //... } + +// Experimental for mul_mat tune, do not use! +void ggml_internal_compute_forward_mul_mat( + const struct ggml_task_profile *profile, int stage, + size_t wsize, void * wdata, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst); ``` ## Misc Assets @@ -126,80 +161,133 @@ length. Run `./examples/mulmat-tune/prompt.sh -h` for help. I had run it with The [bench-out dir](./bench-out) contains various bench result files generated on my device. -BTW, `.*.cl.txt` were generated by [early version](https://github.com/ggerganov/llama.cpp/compare/master...mqy:blas-n_threads-fix-10#diff-e40acc281787b19c5975346837f154e7e75351733a9f9575317c64d2dbe38799). -I solved the OOM problem, but unfortunately, I'm unable to catch up with latest updates. -The [images dir](./images) contains images drawn from [bench results](./bench-out) -of N/K combinations from 7B and 13B. I compared two profiles for Q4_0. +The [analyze dir](./analyze/) contains various analysis files generated with +`./mulmat-tune analyze `. I strongly recommend you have a look at them. + +Let me introduce them with [the image](./analyze/4096x4096_q4_0.png) which contains bench analysis for 7B/Q4_0/4096x4096. + +**Firstly**, I defined three task profiles: - #0: pure cpu. INIT in CPU with 1 thread, COMPUTE in GPU parallel. -- #2: use Blas. INIT in CPU parallel, COMPUTE in with Accelerate with 1 thread. +- #1: the `q_f32` CBLAS implementation in master (when defined either + `GGML_USE_ACCELERATE` or `GGML_USE_OPENBLAS`) +- #2: splt `#1` into `INIT` and `COMPUTE`. Where INIT in CPU parallel, COMPUTE + in with Accelerate with 1 thread. The `#0_0` is read as "profile #0, stage 0 (INIT)", the `#0_1` is read as -"profile #0 stage 1(COMPUTE)". `#2` is profile 2. +"profile #0 stage 1(COMPUTE)". `#2` is profile 2. `nth=x` is read as `run with `x` +thread(s)`. With 1 thread, the overall time of `profile #1` is almost equal to +the that of `profile #2`, so I did not draw `profile #1`. + +**Secondly**, I defined several `shape`s for attention, feed-forward and RoPE. + +```c +// examples/mulmat-tune/mulmat-tune.h + +struct ggml_task_stage { + /*enum ggml_backend*/ int backend; + bool parallel; + bool wait; +}; + +struct ggml_task_profile { + // index 0: INIT, 1: COMPUTE, 2: FINALIZE + struct ggml_task_stage stages[3]; +}; + +struct ggml_mulmat_tune_shape { + // For RoPE, one of N / K is 0. + int N; + int K; -The data data were generated like this: + int src0_type; + int src1_type; + + int n_profiles; + struct ggml_task_profile *profiles; + + int m_num; + int *arr_m; + + struct ggml_mulmat_tune_m *items; +}; +``` + +The `mulmat-tune` tool runs benches for every shape and create bench results. +The data were generated like this: Bench: ``` -./mulmat-tune-tool bench 7B --file mulmat-tune.7b.q4_0.txt +./mulmat-tune bench --model 7B --file 7b.q4_0.txt +# or omit the --file and let it print to stdout. ``` Analyze: ``` -./mulmat-tune-tool analyze mulmat-tune.7b.q4_0.txt +# print to stdout +./mulmat-tune analyze 7b.q4_0.txt ``` -The m512 bench data were created by run bench for 3 times, manually combine the -results into one file. +Let's come back to the [the image](./analyze/4096x4096_q4_0.png). There is a table +and five pictures in the image: + +- The table contains analysis data block that was copied from output of `./mulmat-tune analyze`. +- The top right picture shows: with 1 thread, cpu INIT, and BLAS compute time and + total time. From this, given n_threads, we can estimate total time. +- The top second picture is used to compare the overall time between profile #0 + (`pure CPU`) with profile #2. The pure CPU INIT is very fast (so can be totally omitted), + but the COMPUTE is heavy and scales almost linear with M. The BLAS compute grows + slowly when M doubles. From this picture, we can see the location of intersection + point between both lines: the M is less than 32 -- this is true for all shapes + (with N/K >= 4096) on my device. +- The last three pictures at bottom are estimated time for nth=2/4/8. We could + see that the intersection point (M) grows with `n_threads`. Suppose given M/N/K + src0_type and src1_type, we could find corresponding shape. With this shape, + we could estimate overall time for every profile and choose the fastest profile. ## Limitations -- Only tested models 7B and 13B. +- I'm new to machine learning this year and have little knowledge of AI and + corresponding technologies (BLAS, CUDA, ...). +- Only tested models 7B and 13B. Focused on 7B/Q4_0 more than others. - My OSs/devices can not use CUDA, so did not run benches for CUDA. -- I had managed to solve the OOM problem for ClBlast but failed in recent rebase: - the opencl codes changed a lot, I did not have time to patch them again. +- Only focused on llama model. - It's incomplete to validate bench data. -- and more ... - -The mul_mat related codes keep changing, It's a bit hard for me to follow up and -merge/rebase again and again. I think the codes and data can speak for themselves, -so it's time to initiate a discussion or pull request. +- Lack APIs for freeing memory. +- Lack documents. +- And many more ... -I'm new to machine learning this year and have little knowledge of AI. There must -be a lot of problems with this pull request, please do not hesitate to advise. +There must be a lot of problems, please do not hesitate to advise. ## How to Evaluate **Build** ``` +make clean && make + ./mulmat-tune -h ``` **Bench** ``` -usage: ./mulmat-tune [bench ...] | [analyze FILE] | test | [-h | --help] - bench [-m MODEL] [-t TYPE] [-f FILE] [-y] --model MODEL 7B | 13B | 30B | 65B default 7B --type TYPE Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | F32 | F16 default Q4_0 ---m_start M_START start value of M - requires: even number - default 16 ---m_step M_STEP delta between adjacent M - requires: even number, in range[2, 32] - default 16 ---m_num M_NUM number of M, the max M = M_STEP * M_NUM - requires: in range [2, 128] - default 8 +--m_num M_NUM number of M, the max M = 2^(M_NUM-1) + requires: in range [8, 12] + default 10 --backend BACKEND blas backend: CUDA | CL | CBLAS default: auto detect +--n_pass number of passes to run + default 3 + requires: in range [1, 5] --file FILE data file to write default stdout -y always answer "yes" to all prompts @@ -215,20 +303,32 @@ BLIS: make clean; LLAMA_NO_ACCELERATE=1 LLAMA_BLIS=1 make NOTE: to disable ACCELERATE, use LLAMA_NO_ACCELERATE=1 ``` -Examples: +**Examples** ``` # run with default params (7B, Q4_0, ...) ./mulmat-tune -# run 13B and Q4_1 with alway-yes -./mulmat-tune bench -model 13B --type Q4_1 -y +# set model +./mulmat-tune bench --model 13B + +# set type (src0 type) +./mulmat-tune bench --type Q5_0 + +# customized m_num +./mulmat-tune bench --m_num 8 -# customized m_start, m_step, m_num -./mulmat-tune bench -model 7B --m_start 8 --m_step 8 -m_num 8 +# customized n_pass: run 1 pass only instead of the default 3. +./mulmat-tune bench --n_pass 1 + +# customized backend: +./mulmat-tune bench --backend CBLAS # save to file -./mulmat-tune bench -model 7B --file mulmat-tune.txt +./mulmat-tune bench --file mulmat-tune.txt + +# save to file, always override if exists (CAUTION!) +./mulmat-tune bench --file mulmat-tune.txt -y # analyze ./mulmat-tune analyze mulmat-tune.txt @@ -243,32 +343,106 @@ Firstly, prepare the bench file for your model. SHOULD run in idle and cool stat Place `mulmat-tune.txt` into the llama.cpp dir. Then run your favorite program: main, perplexity, etc. -The program will print debug log when or when not found the file. +The program will: + +- print a INFO log when the file was loaded and passed validation. +- or print a ERROR log when failed to open/read/validate the file. ## Bench Data Format **Example** ``` -$ ./mulmat-tune bench --m_num 2 -[BENCH] model: 7B, type: Q4_0, backend: CBLAS, BLAS vendor: ACCELERATE. - -1 7B 2 Q4_0 3 ACCELERATE 4 16 3 - 0 0 0 0 1 0 -1 0 0 --1 0 0 3 0 1 -1 0 0 - 0 1 0 3 0 1 -1 0 0 -4096 4096 - 16 26 18636 0 0 0 0 15280 6139 0 - 32 52 38288 0 0 0 0 15038 7565 0 -4096 11008 - 16 73 45606 0 0 0 0 30241 9104 0 - 32 140 84720 0 0 0 0 35621 13743 0 -11008 4096 - 16 29 52994 0 0 0 0 47186 20651 0 - 32 61 72840 0 0 0 0 36833 25601 0 -32000 4096 - 16 26 113438 0 0 0 0 100848 66797 0 - 32 37 207217 0 0 0 0 87275 74103 0 +$ ./mulmat-tune bench +1 7B 2 Q4_0 3 ACCELERATE 6 + +4096 4096 2 0 3 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 0 1 0 3 0 1 -1 0 0 + 1 2 1268 0 0 17509 0 12349 2646 0 + 2 3 2172 0 0 18774 0 13309 5259 0 + 4 4 3371 0 0 15617 0 11130 4252 0 + 8 9 6502 0 0 16059 0 10999 4542 0 + 16 18 13068 0 0 17877 0 11231 5857 0 + 32 36 25508 0 0 16456 0 10987 6642 0 + 64 72 52853 0 0 18331 0 10742 7239 0 + 128 151 107543 0 0 21935 0 11003 11009 0 + 256 344 213692 0 0 34317 0 11106 23582 0 + 512 719 427260 0 0 63208 0 10851 52081 0 + +4096 11008 2 0 3 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 0 1 0 3 0 1 -1 0 0 + 1 2 2634 0 0 36973 0 29153 6780 0 + 2 6 4693 0 0 41844 0 29069 8387 0 + 4 14 9451 0 0 38839 0 29524 8197 0 + 8 24 16986 0 0 38396 0 29668 8781 0 + 16 49 33504 0 0 39546 0 29265 9504 0 + 32 97 73112 0 0 40188 0 28914 12318 0 + 64 231 142693 0 0 46231 0 28886 14946 0 + 128 458 278432 0 0 53696 0 28944 24663 0 + 256 1034 559654 0 0 80642 0 28867 50285 0 + 512 2048 1223493 0 0 163302 0 28911 124995 0 + +11008 4096 2 0 3 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 0 1 0 3 0 1 -1 0 0 + 1 1 2772 0 0 36419 0 29893 7002 0 + 2 2 4933 0 0 53566 0 29255 18753 0 + 4 4 9464 0 0 46532 0 29464 17370 0 + 8 10 17501 0 0 48742 0 29076 20653 0 + 16 18 34575 0 0 53407 0 29197 20391 0 + 32 65 69640 0 0 53865 0 29231 21660 0 + 64 116 143035 0 0 53907 0 29347 25843 0 + 128 187 292926 0 0 66320 0 29155 35409 0 + 256 307 573328 0 0 95725 0 29202 62151 0 + 512 684 1147818 0 0 154412 0 29346 119402 0 + +32000 4096 2 0 3 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 0 1 0 3 0 1 -1 0 0 + 1 1 8193 0 0 106846 0 85343 20258 0 + 2 2 14363 0 0 143913 0 85267 62360 0 + 4 4 26854 0 0 141903 0 85637 60764 0 + 8 10 51134 0 0 146443 0 85571 64745 0 + 16 17 101408 0 0 146146 0 85311 64629 0 + 32 35 197469 0 0 149357 0 85225 68780 0 + 64 71 410188 0 0 160478 0 85638 78818 0 + 128 145 833197 0 0 192508 0 85354 108469 0 + 256 327 1667077 0 0 256686 0 85200 174326 0 + 512 745 3326948 0 0 409654 0 85777 330655 0 + +128 0 1 0 2 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 1 0 1 0 0 0 0 + 2 0 2 0 0 0 0 + 4 0 4 0 0 1 0 + 8 0 9 0 0 1 0 + 16 0 31 0 0 2 0 + 32 1 23 0 0 6 0 + 64 2 55 0 0 19 0 + 128 9 167 0 0 70 0 + 256 39 614 0 0 144 0 + 512 150 2516 0 0 341 0 + +0 128 1 0 2 10 + 0 0 0 0 1 0 -1 0 0 +-1 0 0 3 0 1 -1 0 0 + 1 0 0 0 0 0 0 + 2 0 0 0 0 0 0 + 4 0 0 0 0 0 0 + 8 0 1 0 0 1 0 + 16 1 3 0 0 3 0 + 32 2 11 0 0 6 0 + 64 5 48 0 0 22 0 + 128 10 203 0 0 111 0 + 256 19 705 0 0 165 0 + 512 33 2832 0 0 313 0 ``` See example files in dir [bench-out](bench-out) for details. @@ -279,10 +453,8 @@ See example files in dir [bench-out](bench-out) for details. head groups+ -head := version model type type_name backend blas_vendor n_shapes m_num n_profiles -task_conf_profile+ -shape -bench_item+ +head := version model type type_name backend blas_vendor n_shapes +shape+ # head version: 1 @@ -291,9 +463,11 @@ type: 2 | 3 | 8 | 9 | 7 | 0 | 1 type_name: "Q4_0" | "Q4_1" | "Q5_0" | "Q5_1" | "Q8_0" | "F32" | "F16" backend: 1 (CUDA) | 2 (CL)| 3 (CBLAS) blas_vendor: "CUDA" | "CLBLAST" | "ACCELERATE" | "OPENBLAS" | "BLIS" -n_shapes: 4 -m_step: 8 -m_num: 16 +n_shapes: number of shapes + +shape := N K m_num n_profiles +task_conf_profile+ +bench_item+ task_conf_profile: stage_conf(init) stage_conf(compute) stage_conf(finalize) stage_conf: backend parallel wait @@ -301,8 +475,6 @@ backend: -1 (UNKNOWN) | 0 (CPU) | 1 (CUDA) | 2 (CL) | 3 (CBLAS) parallel: 0 | 1 wait: 0 | 1 -shape := N K - bench_item: M profile_time+ profile_time := stage_time[3] stage_time[3]: init_time, compute_time, finalize_time @@ -340,14 +512,18 @@ two `M`s whenever is in bench range or not. See `ggml_mulmat_tune_estimate_time()` in file [mulmat-tune.c](./mulmat-tune.c) for how to estimate time. -## Configure task profile for nodes +The linear interpolate (t = aM + b) should works well for N/K that are both > 0. +But for RoPE, either N or K is not specified (thus set as M), the actual line +shape is not linear at all (looks like t = aM^2 + bM + c). Anyway, it's Ok because +the execution time is far small than that of other big N/K. + +## Configure Task Profile for Nodes Configuring task profile is executed at the very beginning of `ggml_graph_compute()`. For non mul_mat nodes, the original `n_tasks` logic are replaced with task profile. When bench exists, we choose the fastest profile for every mul_mat node. Otherwise -fall back to original algorithm: when M >= 32, change n_threads to 1 and use the -original profile that run with 1 thread. +fall back to original (>=32) rule (without change n_threads). See `ggml_graph_compute_mul_mat_set_task_profile` from `ggml.c` for how to configure for mul_mat nodes. @@ -382,13 +558,13 @@ wait-notify overhead is acceptable. I assume we agree that: 1. Discuss and evaluate, determine whether this pull request make sense. -2. If it useful and being accepted, then split in to pull requests and merge - step by step. +2. Fix and enhance, and rebase onto latest master. +3. If it useful and being accepted, then split in to smaller pull requests. Here is the possible merge steps I think: 1. Apply the task config/profile for incoming changes. Including update blas - codes to support task config. + codes to support task config/profile. 2. Add the experimental mulmat tune tool, so we are able to use and maintain it. 3. Merge the new threading codes (as v2?). May have to keep original threading. 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of file diff --git a/examples/mulmat-tune/bench-out/13b.q5_0.txt b/examples/mulmat-tune/bench-out/13b.q5_0.txt index f80013396ddfde..bf740874875a35 100644 --- a/examples/mulmat-tune/bench-out/13b.q5_0.txt +++ b/examples/mulmat-tune/bench-out/13b.q5_0.txt @@ -87,4 +87,3 @@ 128 10 167 0 0 60 0 256 20 733 0 0 129 0 512 43 3462 0 0 262 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.f16.txt b/examples/mulmat-tune/bench-out/7b.f16.txt index 80b443af1ad1cc..57d5a6dcee7e39 100644 --- a/examples/mulmat-tune/bench-out/7b.f16.txt +++ b/examples/mulmat-tune/bench-out/7b.f16.txt @@ -83,4 +83,3 @@ 128 10 167 0 0 79 0 256 19 835 0 0 136 0 512 39 2856 0 0 283 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.f32.txt b/examples/mulmat-tune/bench-out/7b.f32.txt index 8d6e0db8444478..08f1310ef8fc00 100644 --- a/examples/mulmat-tune/bench-out/7b.f32.txt +++ b/examples/mulmat-tune/bench-out/7b.f32.txt @@ -83,4 +83,3 @@ 128 9 198 0 0 64 0 256 20 766 0 0 166 0 512 40 3464 0 0 276 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.q4_0.openblas.txt b/examples/mulmat-tune/bench-out/7b.q4_0.openblas.txt index b0cb4ffe2ff8fb..3bbfd069d24f5c 100644 --- a/examples/mulmat-tune/bench-out/7b.q4_0.openblas.txt +++ b/examples/mulmat-tune/bench-out/7b.q4_0.openblas.txt @@ -87,4 +87,3 @@ 128 9 201 0 0 128 0 256 19 886 0 0 172 0 512 39 3227 0 0 405 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.q4_0.txt b/examples/mulmat-tune/bench-out/7b.q4_0.txt index 8e28ca2779e6b1..b51f01477ff94e 100644 --- a/examples/mulmat-tune/bench-out/7b.q4_0.txt +++ b/examples/mulmat-tune/bench-out/7b.q4_0.txt @@ -87,4 +87,3 @@ 128 10 203 0 0 111 0 256 19 705 0 0 165 0 512 33 2832 0 0 313 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.q4_1.txt b/examples/mulmat-tune/bench-out/7b.q4_1.txt index e26251581b4650..72a7fbd8e85575 100644 --- a/examples/mulmat-tune/bench-out/7b.q4_1.txt +++ b/examples/mulmat-tune/bench-out/7b.q4_1.txt @@ -87,4 +87,3 @@ 128 10 210 0 0 70 0 256 20 856 0 0 128 0 512 40 2949 0 0 334 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.q5_1.txt b/examples/mulmat-tune/bench-out/7b.q5_1.txt index cb8dbabfccd7c6..89860b1809ecb3 100644 --- a/examples/mulmat-tune/bench-out/7b.q5_1.txt +++ b/examples/mulmat-tune/bench-out/7b.q5_1.txt @@ -87,4 +87,3 @@ 128 10 202 0 0 76 0 256 20 850 0 0 123 0 512 39 2944 0 0 301 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/bench-out/7b.q8_0.txt b/examples/mulmat-tune/bench-out/7b.q8_0.txt index 2f1cc8105c2fd0..280abd7786e740 100644 --- a/examples/mulmat-tune/bench-out/7b.q8_0.txt +++ b/examples/mulmat-tune/bench-out/7b.q8_0.txt @@ -87,4 +87,3 @@ 128 9 206 0 0 79 0 256 19 784 0 0 132 0 512 38 2780 0 0 310 0 - \ No newline at end of file diff --git a/examples/mulmat-tune/mulmat-tune-tool.c b/examples/mulmat-tune/mulmat-tune-tool.c index 7e24eb44e78c50..fa2254a206b13c 100644 --- a/examples/mulmat-tune/mulmat-tune-tool.c +++ b/examples/mulmat-tune/mulmat-tune-tool.c @@ -24,7 +24,7 @@ static void print_blas_build_tips(void); static void progress(int i, int max); static bool prompt_yes_no(const char *prompt); -static void cmd_tune(struct ggml_mulmat_tune *b, bool verbose); +static void cmd_tune(struct ggml_mulmat_tune *b, int n_pass, bool verbose); static void cmd_analyze(struct ggml_mulmat_tune *b); static void usage(char *prog) { @@ -41,6 +41,9 @@ static void usage(char *prog) { " default 10\n", "--backend BACKEND blas backend: CUDA | CL | CBLAS\n", " default: auto detect\n", + "--n_pass number of passes to run\n", + " default 3\n", + " requires: in range [1, 5]\n", "--file FILE data file to write\n", " default stdout\n", "-y always answer \"yes\" to all prompts\n", @@ -94,6 +97,7 @@ int main(int argc, char **argv) { const char *arg_model = NULL; const char *arg_type = NULL; const char *arg_m_num = NULL; + const char *arg_n_pass = NULL; const char *arg_backend = NULL; const char *arg_file = NULL; bool always_yes = false; @@ -114,6 +118,11 @@ int main(int argc, char **argv) { arg_m_num = argv[i + 1]; ++i; } + } else if (strcmp(argv[i], "--n_pass") == 0) { + if (i + 1 < argc) { + arg_n_pass = argv[i + 1]; + ++i; + } } else if (strcmp(argv[i], "--backend") == 0) { if (i + 1 < argc) { arg_backend = argv[i + 1]; @@ -203,6 +212,20 @@ int main(int argc, char **argv) { } } + int n_pass = 3; + { + if (arg_n_pass != NULL) { + int v = atoi(arg_n_pass); + n_pass = v; + } + if (n_pass < 1 || n_pass > MAX_NUM_PASS) { + fprintf(stderr, "invalid n_pass: %d, expect in range [1, 5]\n", + n_pass); + usage(argv[0]); + exit(1); + } + } + { enum ggml_backend backend = GGML_BACKEND_UNKNOWN; if (arg_backend == NULL) { @@ -265,7 +288,7 @@ int main(int argc, char **argv) { tune.model, tune.type_name, ggml_get_backend_name(tune.backend), tune.blas_vendor); - cmd_tune(&tune, true /* verbose */); + cmd_tune(&tune, n_pass, true /* verbose */); FILE *fp = NULL; if (arg_file != NULL) { @@ -334,7 +357,7 @@ int main(int argc, char **argv) { return 0; } -void cmd_tune(struct ggml_mulmat_tune *tune, bool verbose) { +void cmd_tune(struct ggml_mulmat_tune *tune, int n_pass, bool verbose) { size_t wsize = 0; void *q_buf = NULL; void *wdata = NULL; @@ -477,8 +500,8 @@ void cmd_tune(struct ggml_mulmat_tune *tune, bool verbose) { // without memset, the first run may be significant slow. memset(wdata, 0, wsize); - int stage_time[NUM_BENCH]; - for (int i_bench = 0; i_bench < NUM_BENCH; i_bench++) { + int stage_time[MAX_NUM_PASS]; + for (int i_bench = 0; i_bench < n_pass; i_bench++) { int t0 = (int)ggml_time_us(); ggml_internal_compute_forward_mul_mat( @@ -486,12 +509,12 @@ void cmd_tune(struct ggml_mulmat_tune *tune, bool verbose) { stage_time[i_bench] = (int)ggml_time_us() - t0; if (verbose) { - progress(i_bench, NUM_BENCH); + progress(i_bench, n_pass); } } item->stages_time[stage] = - tune_time_min(stage_time, NUM_BENCH); + tune_time_min(stage_time, n_pass); if (verbose) { line_len++; diff --git a/examples/mulmat-tune/mulmat-tune.h b/examples/mulmat-tune/mulmat-tune.h index e04087776fe3bc..ac971585629ce0 100644 --- a/examples/mulmat-tune/mulmat-tune.h +++ b/examples/mulmat-tune/mulmat-tune.h @@ -8,7 +8,7 @@ extern "C" { #endif -#define NUM_BENCH 4 +#define MAX_NUM_PASS 5 #define GGML_MULMAT_N_SHAPES 6 #define GGML_MULMAT_MAX_PROFILES 8 diff --git a/ggml.c b/ggml.c index e2e19ec70396a6..ae7c1d01a0c7d6 100644 --- a/ggml.c +++ b/ggml.c @@ -14396,7 +14396,7 @@ void ggml_graph_compute_mul_mat_set_task_profile(struct ggml_cgraph *cgraph) { if (shape != NULL) { memset(profile_time, 0, sizeof(profile_time)); ggml_mulmat_tune_shape_estimate_time(shape, M, cgraph->n_threads, profile_time); - + int min = INT32_MAX; for (int j = 0; j < shape->n_profiles; j++) { int total = profile_time[j].total_time; @@ -14435,7 +14435,7 @@ void 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ggml_mulmat_tune_setup_model(&tune, "7B", m_num); struct ggml_mulmat_tune_shape *shape = NULL; for (int i = 0; i < tune.n_shapes; i++) { @@ -149,7 +149,7 @@ void test_ggml_mulmat_tune_estimate_time_non_zero_NK(void) { ggml_mulmat_tune_get_shape(&tune, shape->N, shape->K, shape->src0_type, shape->src1_type); GGML_ASSERT(matched_shape); - + ggml_mulmat_tune_shape_estimate_time(matched_shape, e->M, e->nth, profile_time); @@ -193,7 +193,7 @@ void test_ggml_mulmat_tune_estimate_time_zero_NK(void) { const int m_start = m_step; const int m_num = 2; - ggml_mulmat_tune_setup_model(&tune, "7B", m_start, m_step, m_num); + ggml_mulmat_tune_setup_model(&tune, "7B", m_num); struct ggml_mulmat_tune_shape *shape = NULL; for (int i = 0; i < tune.n_shapes; i++) {

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