Merge branch 'master' into HEAD
This commit is contained in:
Georgi Gerganov
commit
278f3e99c2
30 changed files with 1419 additions and 981 deletions
177
.github/ISSUE_TEMPLATE/bug.md
vendored
177
.github/ISSUE_TEMPLATE/bug.md
vendored
|
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@ -6,179 +6,4 @@ assignees: ''
|
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|
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---
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|
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# Prerequisites
|
||||
|
||||
Please answer the following questions for yourself before submitting an issue.
|
||||
|
||||
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
|
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- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
|
||||
- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
|
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- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
|
||||
|
||||
# Expected Behavior
|
||||
|
||||
Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do.
|
||||
|
||||
# Current Behavior
|
||||
|
||||
Please provide a detailed written description of what `llama.cpp` did, instead.
|
||||
|
||||
# Environment and Context
|
||||
|
||||
Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
|
||||
|
||||
* Physical (or virtual) hardware you are using, e.g. for Linux:
|
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|
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`$ lscpu`
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|
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* Operating System, e.g. for Linux:
|
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|
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`$ uname -a`
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* SDK version, e.g. for Linux:
|
||||
|
||||
```
|
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$ python3 --version
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$ make --version
|
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$ g++ --version
|
||||
```
|
||||
|
||||
# Failure Information (for bugs)
|
||||
|
||||
Please help provide information about the failure / bug.
|
||||
|
||||
# Steps to Reproduce
|
||||
|
||||
Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.
|
||||
|
||||
1. step 1
|
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2. step 2
|
||||
3. step 3
|
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4. etc.
|
||||
|
||||
# Failure Logs
|
||||
|
||||
Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
|
||||
|
||||
Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.
|
||||
|
||||
Example environment info:
|
||||
```
|
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llama.cpp$ git log | head -1
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commit 2af23d30434a677c6416812eea52ccc0af65119c
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|
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llama.cpp$ lscpu | egrep "AMD|Flags"
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Vendor ID: AuthenticAMD
|
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Model name: AMD Ryzen Threadripper 1950X 16-Core Processor
|
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Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
|
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Virtualization: AMD-V
|
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|
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llama.cpp$ python3 --version
|
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Python 3.10.9
|
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|
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llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
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numpy 1.24.2
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numpydoc 1.5.0
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sentencepiece 0.1.97
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torch 1.13.1
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torchvision 0.14.1
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|
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llama.cpp$ make --version | head -1
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GNU Make 4.3
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|
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$ md5sum ./models/65B/ggml-model-q4_0.bin
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dbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin
|
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```
|
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|
||||
Example run with the Linux command [perf](https://www.brendangregg.com/perf.html)
|
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```
|
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llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
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main: seed = 1679149377
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llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
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llama_model_load: n_vocab = 32000
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llama_model_load: n_ctx = 512
|
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llama_model_load: n_embd = 8192
|
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llama_model_load: n_mult = 256
|
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llama_model_load: n_head = 64
|
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llama_model_load: n_layer = 80
|
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llama_model_load: n_rot = 128
|
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llama_model_load: f16 = 2
|
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llama_model_load: n_ff = 22016
|
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llama_model_load: n_parts = 8
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llama_model_load: ggml ctx size = 41477.73 MB
|
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llama_model_load: memory_size = 2560.00 MB, n_mem = 40960
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llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
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llama_model_load: .......................................................................................... done
|
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llama_model_load: model size = 4869.09 MB / num tensors = 723
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llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
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llama_model_load: .......................................................................................... done
|
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llama_model_load: model size = 4869.09 MB / num tensors = 723
|
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llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
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llama_model_load: .......................................................................................... done
|
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llama_model_load: model size = 4869.09 MB / num tensors = 723
|
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llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
|
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llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
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llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
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llama_model_load: .......................................................................................... done
|
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llama_model_load: model size = 4869.09 MB / num tensors = 723
|
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llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
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llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
|
||||
llama_model_load: .......................................................................................... done
|
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llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
|
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llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
|
||||
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
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|
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main: prompt: 'Please close your issue when it has been answered.'
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main: number of tokens in prompt = 11
|
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1 -> ''
|
||||
12148 -> 'Please'
|
||||
3802 -> ' close'
|
||||
596 -> ' your'
|
||||
2228 -> ' issue'
|
||||
746 -> ' when'
|
||||
372 -> ' it'
|
||||
756 -> ' has'
|
||||
1063 -> ' been'
|
||||
7699 -> ' answered'
|
||||
29889 -> '.'
|
||||
|
||||
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
|
||||
|
||||
|
||||
Please close your issue when it has been answered.
|
||||
@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
|
||||
I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
|
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@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
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|
||||
|
||||
main: mem per token = 71159620 bytes
|
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main: load time = 19309.95 ms
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main: sample time = 168.62 ms
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main: predict time = 223895.61 ms / 888.47 ms per token
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main: total time = 246406.42 ms
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|
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Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
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|
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3636882.89 msec task-clock # 14.677 CPUs utilized
|
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13509 context-switches # 3.714 /sec
|
||||
2436 cpu-migrations # 0.670 /sec
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||||
10476679 page-faults # 2.881 K/sec
|
||||
13133115082869 cycles # 3.611 GHz (16.77%)
|
||||
29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%)
|
||||
10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%)
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||||
23479217109614 instructions # 1.79 insn per cycle
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||||
# 0.44 stalled cycles per insn (16.76%)
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||||
2353072268027 branches # 647.002 M/sec (16.77%)
|
||||
1998682780 branch-misses # 0.08% of all branches (16.76%)
|
||||
|
||||
247.802177522 seconds time elapsed
|
||||
|
||||
3618.573072000 seconds user
|
||||
18.491698000 seconds sys
|
||||
```
|
||||
Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
|
||||
|
|
|
|||
2
.github/workflows/docker.yml
vendored
2
.github/workflows/docker.yml
vendored
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@ -98,5 +98,5 @@ jobs:
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|||
context: .
|
||||
push: ${{ github.event_name == 'push' }}
|
||||
platforms: ${{ matrix.config.platforms }}
|
||||
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}" , "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
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||||
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
|
||||
file: ${{ matrix.config.dockerfile }}
|
||||
|
|
|
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1
.gitignore
vendored
1
.gitignore
vendored
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@ -48,6 +48,7 @@ models-mnt
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/llama-bench
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/llava-cli
|
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/lookahead
|
||||
/lookup
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||||
/main
|
||||
/metal
|
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/perplexity
|
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|
|
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@ -302,6 +302,8 @@ if (LLAMA_CUBLAS)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
|
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endif()
|
||||
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver)
|
||||
|
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if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
|
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# 52 == lowest CUDA 12 standard
|
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# 60 == f16 CUDA intrinsics
|
||||
|
|
|
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31
Makefile
31
Makefile
|
|
@ -2,7 +2,7 @@
|
|||
BUILD_TARGETS = \
|
||||
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
|
||||
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
|
||||
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
|
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speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o
|
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|
||||
# Binaries only useful for tests
|
||||
TEST_TARGETS = \
|
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@ -282,8 +282,17 @@ endif
|
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ifneq ($(filter aarch64%,$(UNAME_M)),)
|
||||
# Apple M1, M2, etc.
|
||||
# Raspberry Pi 3, 4, Zero 2 (64-bit)
|
||||
# Nvidia Jetson
|
||||
MK_CFLAGS += -mcpu=native
|
||||
MK_CXXFLAGS += -mcpu=native
|
||||
JETSON_RELEASE_INFO = $(shell jetson_release)
|
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ifdef JETSON_RELEASE_INFO
|
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ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),)
|
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JETSON_EOL_MODULE_DETECT = 1
|
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CC = aarch64-unknown-linux-gnu-gcc
|
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cxx = aarch64-unknown-linux-gnu-g++
|
||||
endif
|
||||
endif
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv6%,$(UNAME_M)),)
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@ -357,15 +366,16 @@ ifdef LLAMA_BLIS
|
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endif # LLAMA_BLIS
|
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|
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ifdef LLAMA_CUBLAS
|
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MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
|
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MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
|
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MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
|
||||
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib
|
||||
OBJS += ggml-cuda.o
|
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MK_NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
|
||||
|
||||
MK_NVCCFLAGS = -use_fast_math
|
||||
ifndef JETSON_EOL_MODULE_DETECT
|
||||
MK_NVCCFLAGS += --forward-unknown-to-host-compiler
|
||||
endif # JETSON_EOL_MODULE_DETECT
|
||||
ifdef LLAMA_DEBUG
|
||||
MK_NVCCFLAGS += -lineinfo
|
||||
endif
|
||||
|
||||
endif # LLAMA_DEBUG
|
||||
ifdef LLAMA_CUDA_NVCC
|
||||
NVCC = $(LLAMA_CUDA_NVCC)
|
||||
else
|
||||
|
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@ -417,7 +427,11 @@ ifdef LLAMA_CUDA_CCBIN
|
|||
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
|
||||
endif
|
||||
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
|
||||
ifdef JETSON_EOL_MODULE_DETECT
|
||||
$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
|
||||
else
|
||||
$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
|
||||
endif # JETSON_EOL_MODULE_DETECT
|
||||
endif # LLAMA_CUBLAS
|
||||
|
||||
ifdef LLAMA_CLBLAST
|
||||
|
|
@ -648,6 +662,9 @@ parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
|||
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
ifdef LLAMA_METAL
|
||||
metal: examples/metal/metal.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
|
||||
|
|
|
|||
|
|
@ -102,6 +102,7 @@ as the main playground for developing new features for the [ggml](https://github
|
|||
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
|
||||
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
|
||||
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
|
||||
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
|
||||
|
||||
**Multimodal models:**
|
||||
|
||||
|
|
@ -132,6 +133,7 @@ as the main playground for developing new features for the [ggml](https://github
|
|||
- [withcatai/catai](https://github.com/withcatai/catai)
|
||||
- [semperai/amica](https://github.com/semperai/amica)
|
||||
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
|
||||
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -396,6 +398,9 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
- #### cuBLAS
|
||||
|
||||
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
|
||||
|
||||
For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
|
||||
|
||||
- Using `make`:
|
||||
```bash
|
||||
make LLAMA_CUBLAS=1
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ struct gpt_params {
|
|||
int32_t n_ctx = 512; // context size
|
||||
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
|
||||
int32_t n_draft = 8; // number of tokens to draft during speculative decoding
|
||||
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
|
||||
int32_t n_parallel = 1; // number of parallel sequences to decode
|
||||
int32_t n_sequences = 1; // number of sequences to decode
|
||||
|
|
@ -240,3 +240,4 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
|
|||
|
||||
// Dump the KV cache view showing individual sequences in each cell (long output).
|
||||
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
|
||||
|
||||
|
|
|
|||
|
|
@ -149,11 +149,12 @@ static void sampler_queue(
|
|||
}
|
||||
}
|
||||
|
||||
llama_token llama_sampling_sample(
|
||||
static llama_token llama_sampling_sample_impl(
|
||||
struct llama_sampling_context * ctx_sampling,
|
||||
struct llama_context * ctx_main,
|
||||
struct llama_context * ctx_cfg,
|
||||
const int idx) {
|
||||
const int idx,
|
||||
bool is_resampling) { // Add a parameter to indicate if we are resampling
|
||||
const llama_sampling_params & params = ctx_sampling->params;
|
||||
|
||||
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
|
||||
|
|
@ -173,8 +174,17 @@ llama_token llama_sampling_sample(
|
|||
|
||||
llama_token id = 0;
|
||||
|
||||
// Get a pointer to the logits
|
||||
float * logits = llama_get_logits_ith(ctx_main, idx);
|
||||
|
||||
// Declare original_logits at the beginning of the function scope
|
||||
std::vector<float> original_logits;
|
||||
|
||||
if (!is_resampling) {
|
||||
// Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this.
|
||||
original_logits = std::vector<float>(logits, logits + llama_n_vocab(llama_get_model(ctx_main)));
|
||||
}
|
||||
|
||||
// apply params.logit_bias map
|
||||
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
|
||||
logits[it->first] += it->second;
|
||||
|
|
@ -193,12 +203,14 @@ llama_token llama_sampling_sample(
|
|||
}
|
||||
|
||||
// apply penalties
|
||||
if (!prev.empty()) {
|
||||
const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
|
||||
const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
|
||||
if (penalty_tokens_used_size) {
|
||||
const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
|
||||
|
||||
llama_sample_repetition_penalties(ctx_main, &cur_p,
|
||||
prev.data() + prev.size() - penalty_last_n,
|
||||
penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
|
||||
penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
|
||||
penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
|
||||
|
||||
if (!penalize_nl) {
|
||||
for (size_t idx = 0; idx < cur_p.size; idx++) {
|
||||
|
|
@ -210,7 +222,8 @@ llama_token llama_sampling_sample(
|
|||
}
|
||||
}
|
||||
|
||||
if (ctx_sampling->grammar != NULL) {
|
||||
// If we are in the resampling phase, apply grammar checks before sampling logic
|
||||
if (is_resampling && ctx_sampling->grammar != NULL) {
|
||||
llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
|
||||
}
|
||||
|
||||
|
|
@ -252,9 +265,40 @@ llama_token llama_sampling_sample(
|
|||
}
|
||||
}
|
||||
|
||||
if (ctx_sampling->grammar != NULL && !is_resampling) {
|
||||
// Create an array with a single token data element for the sampled id
|
||||
llama_token_data single_token_data = {id, logits[id], 0.0f};
|
||||
llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
|
||||
|
||||
// Apply grammar constraints to the single token
|
||||
llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
|
||||
|
||||
// Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
|
||||
bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
|
||||
|
||||
// If the token is not valid according to the grammar, perform resampling
|
||||
if (!is_valid) {
|
||||
LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
|
||||
|
||||
// Restore logits from the copy
|
||||
std::copy(original_logits.begin(), original_logits.end(), logits);
|
||||
|
||||
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling
|
||||
}
|
||||
}
|
||||
|
||||
return id;
|
||||
}
|
||||
|
||||
llama_token llama_sampling_sample(
|
||||
struct llama_sampling_context * ctx_sampling,
|
||||
struct llama_context * ctx_main,
|
||||
struct llama_context * ctx_cfg,
|
||||
const int idx) {
|
||||
// Call the implementation function with is_resampling set to false by default
|
||||
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
|
||||
}
|
||||
|
||||
void llama_sampling_accept(
|
||||
struct llama_sampling_context * ctx_sampling,
|
||||
struct llama_context * ctx_main,
|
||||
|
|
|
|||
|
|
@ -36,6 +36,9 @@ typedef struct llama_sampling_params {
|
|||
float cfg_scale = 1.f; // how strong is guidance
|
||||
|
||||
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
|
||||
|
||||
std::vector<llama_token> penalty_prompt_tokens;
|
||||
bool use_penalty_prompt_tokens = false;
|
||||
} llama_sampling_params;
|
||||
|
||||
// general sampler context
|
||||
|
|
|
|||
|
|
@ -184,6 +184,8 @@ class Model:
|
|||
return MixtralModel
|
||||
if model_architecture == "PhiForCausalLM":
|
||||
return Phi2Model
|
||||
if model_architecture == "PlamoForCausalLM":
|
||||
return PlamoModel
|
||||
return Model
|
||||
|
||||
def _is_model_safetensors(self) -> bool:
|
||||
|
|
@ -225,6 +227,8 @@ class Model:
|
|||
return gguf.MODEL_ARCH.LLAMA
|
||||
if arch == "PhiForCausalLM":
|
||||
return gguf.MODEL_ARCH.PHI2
|
||||
if arch == "PlamoForCausalLM":
|
||||
return gguf.MODEL_ARCH.PLAMO
|
||||
|
||||
raise NotImplementedError(f'Architecture "{arch}" not supported!')
|
||||
|
||||
|
|
@ -1006,11 +1010,91 @@ class Phi2Model(Model):
|
|||
self.gguf_writer.add_add_bos_token(False)
|
||||
|
||||
|
||||
class PlamoModel(Model):
|
||||
def set_vocab(self):
|
||||
self._set_vocab_sentencepiece()
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
hparams = self.hparams
|
||||
block_count = hparams["num_hidden_layers"]
|
||||
|
||||
self.gguf_writer.add_name("PLaMo")
|
||||
self.gguf_writer.add_context_length(4096) # not in config.json
|
||||
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
|
||||
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
||||
self.gguf_writer.add_block_count(block_count)
|
||||
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
|
||||
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
|
||||
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
|
||||
|
||||
def shuffle_attn_q_weight(self, data_torch):
|
||||
assert data_torch.size() == (5120, 5120)
|
||||
data_torch = data_torch.reshape(8, 5, 128, 5120)
|
||||
data_torch = torch.permute(data_torch, (1, 0, 2, 3))
|
||||
data_torch = torch.reshape(data_torch, (5120, 5120))
|
||||
return data_torch
|
||||
|
||||
def shuffle_attn_output_weight(self, data_torch):
|
||||
assert data_torch.size() == (5120, 5120)
|
||||
data_torch = data_torch.reshape(5120, 8, 5, 128)
|
||||
data_torch = torch.permute(data_torch, (0, 2, 1, 3))
|
||||
data_torch = torch.reshape(data_torch, (5120, 5120))
|
||||
return data_torch
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
|
||||
for name, data_torch in self.get_tensors():
|
||||
if "self_attn.rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
# shuffle for broadcasting of gqa in ggml_mul_mat
|
||||
if new_name.endswith("attn_q.weight"):
|
||||
data_torch = self.shuffle_attn_q_weight(data_torch)
|
||||
elif new_name.endswith("attn_output.weight"):
|
||||
data_torch = self.shuffle_attn_output_weight(data_torch)
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.ftype == 0 and data_dtype == np.float16:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
###### CONVERSION LOGIC ######
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Convert a huggingface model to a GGML compatible file")
|
||||
parser.add_argument(
|
||||
"--vocab-only", action="store_true",
|
||||
help="extract only the vocab",
|
||||
|
|
|
|||
10
convert.py
10
convert.py
|
|
@ -357,6 +357,7 @@ class VocabLoader:
|
|||
for tok in self.tokenizer.all_special_tokens
|
||||
}
|
||||
self.special_ids: set[int] = set(self.tokenizer.all_special_ids)
|
||||
self.reverse_vocab = {id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()}
|
||||
self.vocab_size_base: int = self.tokenizer.vocab_size
|
||||
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict)
|
||||
self.fname_tokenizer: Path = fname_tokenizer
|
||||
|
|
@ -370,15 +371,13 @@ class VocabLoader:
|
|||
self.spm = None
|
||||
|
||||
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
tokenizer = self.tokenizer
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.get_vocab().items()}
|
||||
added_tokens_ids = set(self.added_tokens_dict.values())
|
||||
|
||||
for i in range(self.vocab_size_base):
|
||||
if i in added_tokens_ids:
|
||||
continue
|
||||
|
||||
text = reverse_vocab[i].encode("utf-8")
|
||||
text = self.reverse_vocab[i].encode("utf-8")
|
||||
yield text, self.get_token_score(i), self.get_token_type(i)
|
||||
|
||||
def get_token_type(self, token_id: int) -> gguf.TokenType:
|
||||
|
|
@ -394,10 +393,13 @@ class VocabLoader:
|
|||
if self.spm.is_byte(token_id):
|
||||
toktype = gguf.TokenType.BYTE
|
||||
else:
|
||||
token = self.reverse_vocab[token_id]
|
||||
if token_id == self.unk_token_id:
|
||||
toktype = gguf.TokenType.UNKNOWN
|
||||
if token_id in self.special_ids:
|
||||
elif token_id in self.special_ids:
|
||||
toktype = gguf.TokenType.CONTROL
|
||||
elif len(token) == 6 and token.startswith("<0x") and token.endswith(">"):
|
||||
toktype = gguf.TokenType.BYTE
|
||||
|
||||
return toktype
|
||||
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ else()
|
|||
add_subdirectory(simple)
|
||||
add_subdirectory(speculative)
|
||||
add_subdirectory(lookahead)
|
||||
add_subdirectory(lookup)
|
||||
add_subdirectory(train-text-from-scratch)
|
||||
if (LLAMA_METAL)
|
||||
add_subdirectory(metal)
|
||||
|
|
|
|||
|
|
@ -196,13 +196,13 @@ static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down";
|
|||
static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up";
|
||||
|
||||
static void print_params(struct my_llama_hparams * params) {
|
||||
printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
|
||||
printf("%s: n_ctx: %u\n", __func__, params->n_ctx);
|
||||
printf("%s: n_embd: %u\n", __func__, params->n_embd);
|
||||
printf("%s: n_ff: %u\n", __func__, params->n_ff);
|
||||
printf("%s: n_head: %u\n", __func__, params->n_head);
|
||||
printf("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
|
||||
printf("%s: n_layer: %u\n", __func__, params->n_layer);
|
||||
printf("%s: n_vocab : %u\n", __func__, params->n_vocab);
|
||||
printf("%s: n_ctx : %u\n", __func__, params->n_ctx);
|
||||
printf("%s: n_embd : %u\n", __func__, params->n_embd);
|
||||
printf("%s: n_ff : %u\n", __func__, params->n_ff);
|
||||
printf("%s: n_head : %u\n", __func__, params->n_head);
|
||||
printf("%s: n_head_kv : %u\n", __func__, params->n_head_kv);
|
||||
printf("%s: n_layer : %u\n", __func__, params->n_layer);
|
||||
printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps);
|
||||
printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base);
|
||||
printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale);
|
||||
|
|
|
|||
5
examples/lookup/CMakeLists.txt
Normal file
5
examples/lookup/CMakeLists.txt
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
set(TARGET lookup)
|
||||
add_executable(${TARGET} lookup.cpp)
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
13
examples/lookup/README.md
Normal file
13
examples/lookup/README.md
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
# llama.cpp/examples/lookup
|
||||
|
||||
Demonstration of Prompt Lookup Decoding
|
||||
|
||||
https://github.com/apoorvumang/prompt-lookup-decoding
|
||||
|
||||
The key parameters for lookup decoding are `ngram_min`, `ngram_max` and `n_draft`. The first two determine the size of the ngrams to search for in the prompt for a match. The latter specifies how many subsequent tokens to draft if a match is found.
|
||||
|
||||
More info:
|
||||
|
||||
https://github.com/ggerganov/llama.cpp/pull/4484
|
||||
https://github.com/ggerganov/llama.cpp/issues/4226
|
||||
|
||||
230
examples/lookup/lookup.cpp
Normal file
230
examples/lookup/lookup.cpp
Normal file
|
|
@ -0,0 +1,230 @@
|
|||
#include "common.h"
|
||||
#include "llama.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
int main(int argc, char ** argv){
|
||||
gpt_params params;
|
||||
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
// max/min n-grams size to search for in prompt
|
||||
const int ngram_max = 4;
|
||||
const int ngram_min = 1;
|
||||
|
||||
// length of the candidate / draft sequence, if match is found
|
||||
const int n_draft = params.n_draft;
|
||||
|
||||
const bool dump_kv_cache = params.dump_kv_cache;
|
||||
|
||||
#ifndef LOG_DISABLE_LOGS
|
||||
log_set_target(log_filename_generator("lookup", "log"));
|
||||
LOG_TEE("Log start\n");
|
||||
log_dump_cmdline(argc, argv);
|
||||
#endif // LOG_DISABLE_LOGS
|
||||
|
||||
// init llama.cpp
|
||||
llama_backend_init(params.numa);
|
||||
|
||||
llama_model * model = NULL;
|
||||
llama_context * ctx = NULL;
|
||||
|
||||
// load the model
|
||||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
|
||||
// tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
LOG("add_bos tgt: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
|
||||
const int max_context_size = llama_n_ctx(ctx);
|
||||
const int max_tokens_list_size = max_context_size - 4;
|
||||
|
||||
if ((int) inp.size() > max_tokens_list_size) {
|
||||
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
|
||||
return 1;
|
||||
}
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
for (auto id : inp) {
|
||||
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
|
||||
}
|
||||
|
||||
fflush(stderr);
|
||||
|
||||
const int n_input = inp.size();
|
||||
|
||||
const auto t_enc_start = ggml_time_us();
|
||||
|
||||
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
|
||||
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
|
||||
|
||||
const auto t_enc_end = ggml_time_us();
|
||||
|
||||
int n_predict = 0;
|
||||
int n_drafted = 0;
|
||||
int n_accept = 0;
|
||||
|
||||
int n_past = inp.size();
|
||||
|
||||
bool has_eos = false;
|
||||
|
||||
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
|
||||
|
||||
std::vector<llama_token> draft;
|
||||
|
||||
llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
|
||||
|
||||
// debug
|
||||
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
|
||||
|
||||
const auto t_dec_start = ggml_time_us();
|
||||
|
||||
while (true) {
|
||||
// debug
|
||||
if (dump_kv_cache) {
|
||||
llama_kv_cache_view_update(ctx, &kvc_view);
|
||||
dump_kv_cache_view_seqs(kvc_view, 40);
|
||||
}
|
||||
|
||||
// print current draft sequence
|
||||
LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
|
||||
|
||||
int i_dft = 0;
|
||||
while (true) {
|
||||
// sample from the target model
|
||||
llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft);
|
||||
|
||||
llama_sampling_accept(ctx_sampling, ctx, id, true);
|
||||
|
||||
const std::string token_str = llama_token_to_piece(ctx, id);
|
||||
|
||||
if (!params.use_color) {
|
||||
printf("%s", token_str.c_str());
|
||||
}
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
++n_predict;
|
||||
|
||||
// check if the target token matches the draft
|
||||
if (i_dft < (int) draft.size() && id == draft[i_dft]) {
|
||||
LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
|
||||
++n_accept;
|
||||
++n_past;
|
||||
++i_dft;
|
||||
inp.push_back(id);
|
||||
|
||||
if (params.use_color) {
|
||||
// color accepted draft token
|
||||
printf("\033[34m%s\033[0m", token_str.c_str());
|
||||
fflush(stdout);
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (params.use_color) {
|
||||
printf("%s", token_str.c_str());
|
||||
}
|
||||
fflush(stdout);
|
||||
|
||||
|
||||
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
|
||||
|
||||
draft.clear();
|
||||
draft.push_back(id);
|
||||
inp.push_back(id);
|
||||
break;
|
||||
}
|
||||
|
||||
if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) {
|
||||
break;
|
||||
}
|
||||
|
||||
// KV cache management
|
||||
// clean the cache of draft tokens that weren't accepted
|
||||
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
|
||||
|
||||
llama_batch_clear(batch_tgt);
|
||||
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
|
||||
|
||||
// generate n_pred tokens through prompt lookup
|
||||
auto prompt_lookup = [&]() -> void {
|
||||
int inp_size = inp.size();
|
||||
for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
|
||||
const llama_token * ngram = &inp[inp_size - ngram_size];
|
||||
|
||||
for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
|
||||
bool match = true;
|
||||
for (int j = 0; j < ngram_size; ++j) {
|
||||
if (inp[i + j] != ngram[j]) {
|
||||
match = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (match) {
|
||||
const int startIdx = i + ngram_size;
|
||||
const int endIdx = startIdx + n_draft;
|
||||
if (endIdx < inp_size) {
|
||||
for (int j = startIdx; j < endIdx; ++j) {
|
||||
LOG(" - draft candidate %d: %d\n", j, inp[j]);
|
||||
draft.push_back(inp[j]);
|
||||
llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
|
||||
++n_drafted;
|
||||
}
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return;
|
||||
};
|
||||
|
||||
prompt_lookup();
|
||||
|
||||
llama_decode(ctx, batch_tgt);
|
||||
++n_past;
|
||||
|
||||
draft.erase(draft.begin());
|
||||
}
|
||||
|
||||
auto t_dec_end = ggml_time_us();
|
||||
|
||||
LOG_TEE("\n\n");
|
||||
|
||||
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
|
||||
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
|
||||
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("n_draft = %d\n", n_draft);
|
||||
LOG_TEE("n_predict = %d\n", n_predict);
|
||||
LOG_TEE("n_drafted = %d\n", n_drafted);
|
||||
LOG_TEE("n_accept = %d\n", n_accept);
|
||||
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
|
||||
|
||||
LOG_TEE("\ntarget:\n");
|
||||
llama_print_timings(ctx);
|
||||
|
||||
llama_sampling_free(ctx_sampling);
|
||||
llama_batch_free(batch_tgt);
|
||||
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
||||
llama_backend_free();
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -148,6 +148,8 @@ node index.js
|
|||
|
||||
`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
|
||||
|
||||
`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
|
||||
|
||||
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
|
||||
|
||||
`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
|
||||
|
|
|
|||
|
|
@ -761,6 +761,42 @@ struct llama_server_context
|
|||
slot->prompt = "";
|
||||
}
|
||||
|
||||
slot->sparams.penalty_prompt_tokens.clear();
|
||||
slot->sparams.use_penalty_prompt_tokens = false;
|
||||
const auto &penalty_prompt = data.find("penalty_prompt");
|
||||
if (penalty_prompt != data.end())
|
||||
{
|
||||
if (penalty_prompt->is_string())
|
||||
{
|
||||
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
|
||||
auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
|
||||
slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
|
||||
if (slot->params.n_predict > 0)
|
||||
{
|
||||
slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
|
||||
}
|
||||
slot->sparams.use_penalty_prompt_tokens = true;
|
||||
}
|
||||
else if (penalty_prompt->is_array())
|
||||
{
|
||||
const auto n_tokens = penalty_prompt->size();
|
||||
slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
|
||||
const int n_vocab = llama_n_vocab(model);
|
||||
for (const auto &penalty_token : *penalty_prompt)
|
||||
{
|
||||
if (penalty_token.is_number_integer())
|
||||
{
|
||||
const auto tok = penalty_token.get<llama_token>();
|
||||
if (tok >= 0 && tok < n_vocab)
|
||||
{
|
||||
slot->sparams.penalty_prompt_tokens.push_back(tok);
|
||||
}
|
||||
}
|
||||
}
|
||||
slot->sparams.use_penalty_prompt_tokens = true;
|
||||
}
|
||||
}
|
||||
|
||||
slot->sparams.logit_bias.clear();
|
||||
|
||||
if (json_value(data, "ignore_eos", false))
|
||||
|
|
@ -992,6 +1028,12 @@ struct llama_server_context
|
|||
slot.generated_text += token_str;
|
||||
slot.has_next_token = true;
|
||||
|
||||
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
|
||||
{
|
||||
// we can change penalty_prompt_tokens because it is always created from scratch each request
|
||||
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
|
||||
}
|
||||
|
||||
// check if there is incomplete UTF-8 character at the end
|
||||
bool incomplete = false;
|
||||
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
|
||||
|
|
@ -1183,6 +1225,8 @@ struct llama_server_context
|
|||
{"repeat_penalty", slot.sparams.penalty_repeat},
|
||||
{"presence_penalty", slot.sparams.penalty_present},
|
||||
{"frequency_penalty", slot.sparams.penalty_freq},
|
||||
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
|
||||
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
|
||||
{"mirostat", slot.sparams.mirostat},
|
||||
{"mirostat_tau", slot.sparams.mirostat_tau},
|
||||
{"mirostat_eta", slot.sparams.mirostat_eta},
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * t
|
|||
|
||||
// check if a tensor is allocated by this buffer
|
||||
static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) {
|
||||
return tensor->buffer == alloc->buffer;
|
||||
return tensor->buffer == alloc->buffer && (!tensor->view_src || tensor->view_src->buffer == alloc->buffer);
|
||||
}
|
||||
|
||||
static bool ggml_is_view(struct ggml_tensor * t) {
|
||||
|
|
|
|||
|
|
@ -297,7 +297,7 @@ static void ggml_backend_registry_init(void) {
|
|||
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
|
||||
GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
|
||||
|
||||
int id = ggml_backend_registry_count;
|
||||
size_t id = ggml_backend_registry_count;
|
||||
|
||||
ggml_backend_registry[id] = (struct ggml_backend_reg) {
|
||||
/* .name = */ {0},
|
||||
|
|
@ -330,6 +330,8 @@ size_t ggml_backend_reg_find_by_name(const char * name) {
|
|||
return i;
|
||||
}
|
||||
}
|
||||
|
||||
// not found
|
||||
return SIZE_MAX;
|
||||
}
|
||||
|
||||
|
|
@ -340,15 +342,15 @@ ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str)
|
|||
const char * params = strchr(backend_str, ':');
|
||||
char backend_name[128];
|
||||
if (params == NULL) {
|
||||
strcpy(backend_name, backend_str);
|
||||
snprintf(backend_name, sizeof(backend_name), "%s", backend_str);
|
||||
params = "";
|
||||
} else {
|
||||
strncpy(backend_name, backend_str, params - backend_str);
|
||||
backend_name[params - backend_str] = '\0';
|
||||
snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str);
|
||||
params++;
|
||||
}
|
||||
|
||||
size_t backend_i = ggml_backend_reg_find_by_name(backend_name);
|
||||
|
||||
if (backend_i == SIZE_MAX) {
|
||||
fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name);
|
||||
return NULL;
|
||||
|
|
@ -396,18 +398,12 @@ static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
|
||||
memcpy((char *)tensor->data + offset, data, size);
|
||||
|
||||
GGML_UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
|
||||
memcpy(data, (const char *)tensor->data + offset, size);
|
||||
|
||||
GGML_UNUSED(buffer);
|
||||
|
|
|
|||
882
ggml-cuda.cu
882
ggml-cuda.cu
File diff suppressed because it is too large
Load diff
367
ggml-quants.c
367
ggml-quants.c
|
|
@ -407,6 +407,18 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
|
|||
#define ggml_vld1q_s8_x4 vld1q_s8_x4
|
||||
|
||||
#endif
|
||||
|
||||
#if !defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
inline static int32x4_t vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
|
||||
const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
|
||||
const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
|
||||
|
||||
return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
||||
#if defined(__ARM_NEON) || defined(__wasm_simd128__)
|
||||
|
|
@ -2468,32 +2480,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
|
|||
const int8x16_t v1_1l = vld1q_s8(y1->qs);
|
||||
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
// dot product into int32x4_t
|
||||
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
|
||||
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
#else
|
||||
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
|
||||
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
|
||||
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h));
|
||||
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
|
||||
|
||||
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l));
|
||||
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
|
||||
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h));
|
||||
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
|
||||
|
||||
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
|
||||
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
|
||||
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
|
||||
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
|
||||
|
|
@ -2776,32 +2768,12 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
|
|||
const int8x16_t v1_1l = vld1q_s8(y1->qs);
|
||||
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
// dot product into int32x4_t
|
||||
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
|
||||
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
|
||||
#else
|
||||
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
|
||||
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
|
||||
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h));
|
||||
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));
|
||||
|
||||
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l));
|
||||
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
|
||||
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h));
|
||||
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h));
|
||||
|
||||
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
|
||||
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
|
||||
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
|
||||
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
|
||||
|
|
@ -2963,32 +2935,12 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
|
|||
const int8x16_t v1_1l = vld1q_s8(y1->qs);
|
||||
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
|
||||
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
|
||||
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
#else
|
||||
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
|
||||
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
|
||||
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
|
||||
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
|
||||
|
||||
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
|
||||
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
|
||||
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
|
||||
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
|
||||
|
||||
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
|
||||
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
|
||||
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
|
||||
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
|
||||
|
|
@ -3275,32 +3227,12 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
|
|||
const int8x16_t v1_1l = vld1q_s8(y1->qs);
|
||||
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
|
||||
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
|
||||
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
|
||||
#else
|
||||
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
|
||||
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
|
||||
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
|
||||
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
|
||||
|
||||
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
|
||||
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
|
||||
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
|
||||
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
|
||||
|
||||
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
|
||||
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
|
||||
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
|
||||
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
|
||||
|
|
@ -3550,7 +3482,6 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
|
|||
const int8x16_t y1_0 = vld1q_s8(y1->qs);
|
||||
const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
|
||||
vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
|
|
@ -3558,26 +3489,6 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
|
|||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
|
||||
vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
|
||||
vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
|
||||
#else
|
||||
const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
|
||||
const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
|
||||
const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1));
|
||||
const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
|
||||
|
||||
const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0));
|
||||
const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
|
||||
const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1));
|
||||
const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1));
|
||||
|
||||
const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
|
||||
const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
|
||||
const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
|
||||
const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
|
||||
|
||||
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
|
||||
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
|
||||
|
|
@ -3650,12 +3561,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m3 = vdupq_n_u8(0x3);
|
||||
const uint8x16_t m4 = vdupq_n_u8(0xF);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
|
||||
ggml_int8x16x2_t q2bytes;
|
||||
uint8_t aux[16];
|
||||
|
|
@ -3663,7 +3572,6 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
float sum = 0;
|
||||
|
||||
for (int i = 0; i < nb; ++i) {
|
||||
|
||||
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
|
||||
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
|
||||
|
||||
|
|
@ -3677,7 +3585,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4);
|
||||
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
|
||||
const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))};
|
||||
const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}};
|
||||
const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])),
|
||||
vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0])));
|
||||
const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])),
|
||||
|
|
@ -3689,20 +3597,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
// We use this macro instead of a function call because for some reason
|
||||
// the code runs 2-3% slower, even if the function is declared inline
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
#define MULTIPLY_ACCUM_WITH_SCALE(index)\
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)];
|
||||
#else
|
||||
#define MULTIPLY_ACCUM_WITH_SCALE(index)\
|
||||
{\
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])),\
|
||||
vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0])));\
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])),\
|
||||
vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1])));\
|
||||
isum += vaddvq_s16(p1) * aux[is+(index)] + vaddvq_s16(p2) * aux[is+1+(index)];\
|
||||
}
|
||||
#endif
|
||||
|
||||
#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\
|
||||
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\
|
||||
|
|
@ -3710,26 +3607,23 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\
|
||||
MULTIPLY_ACCUM_WITH_SCALE((index));
|
||||
|
||||
|
||||
for (int j = 0; j < QK_K/128; ++j) {
|
||||
|
||||
const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32;
|
||||
|
||||
ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
|
||||
q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3));
|
||||
q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3));
|
||||
|
||||
MULTIPLY_ACCUM_WITH_SCALE(0);
|
||||
|
||||
SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2);
|
||||
|
||||
SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4);
|
||||
|
||||
SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6);
|
||||
|
||||
is += 8;
|
||||
}
|
||||
sum += d * isum;
|
||||
|
||||
sum += d * isum;
|
||||
}
|
||||
|
||||
*s = sum;
|
||||
|
|
@ -4043,11 +3937,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m3 = vdupq_n_u8(0x3);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
|
||||
ggml_int8x16x4_t q2bytes;
|
||||
|
||||
|
|
@ -4081,28 +3973,12 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3));
|
||||
q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0];
|
||||
isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1];
|
||||
isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2];
|
||||
isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3];
|
||||
#else
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
isum1 += vaddvq_s16(p1) * scales[0];
|
||||
isum2 += vaddvq_s16(p2) * scales[1];
|
||||
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q2bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
const int16x8_t p4 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q2bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
isum1 += vaddvq_s16(p3) * scales[2];
|
||||
isum2 += vaddvq_s16(p4) * scales[3];
|
||||
#endif
|
||||
sum += d * (isum1 + isum2);
|
||||
|
||||
}
|
||||
|
||||
*s = sum;
|
||||
|
|
@ -4328,9 +4204,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
uint32_t utmp[4];
|
||||
|
||||
const uint8x16_t m3b = vdupq_n_u8(0x3);
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
const uint8x16_t m0 = vdupq_n_u8(1);
|
||||
const uint8x16_t m1 = vshlq_n_u8(m0, 1);
|
||||
|
|
@ -4382,22 +4256,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
|
||||
q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
|
||||
#else
|
||||
int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_1.val[0])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_1.val[0])));
|
||||
int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_1.val[1])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_1.val[1])));
|
||||
int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_1.val[2])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_1.val[2])));
|
||||
int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_1.val[3])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_1.val[3])));
|
||||
isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
|
||||
#endif
|
||||
|
||||
scale += 4;
|
||||
|
||||
q3h.val[0] = vbicq_u8(m2, qhbits.val[0]);
|
||||
|
|
@ -4410,22 +4273,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
|
||||
q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
|
||||
#else
|
||||
p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_2.val[0])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_2.val[0])));
|
||||
p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_2.val[1])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_2.val[1])));
|
||||
p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_2.val[2])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_2.val[2])));
|
||||
p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_2.val[3])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_2.val[3])));
|
||||
isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
|
||||
#endif
|
||||
|
||||
scale += 4;
|
||||
|
||||
if (j == 0) {
|
||||
|
|
@ -4864,10 +4716,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
|
||||
const uint8x16_t m3b = vdupq_n_u8(0x3);
|
||||
const uint8x16_t mh = vdupq_n_u8(4);
|
||||
|
|
@ -4908,22 +4757,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2]));
|
||||
q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1];
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3];
|
||||
#else
|
||||
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
isum += vaddvq_s16(p0) * scales[0] + vaddvq_s16(p1) * scales[2] + vaddvq_s16(p2) * scales[1] + vaddvq_s16(p3) * scales[3];
|
||||
#endif
|
||||
|
||||
sum += d * isum;
|
||||
|
||||
|
|
@ -5228,11 +5065,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
uint32_t utmp[4];
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xf);
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
const int32x4_t mzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
ggml_int8x16x2_t q4bytes;
|
||||
ggml_int8x16x2_t q8bytes;
|
||||
|
|
@ -5269,10 +5103,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
int32_t sumi2 = 0;
|
||||
|
||||
for (int j = 0; j < QK_K/64; ++j) {
|
||||
|
||||
const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32;
|
||||
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
|
||||
|
|
@ -5287,26 +5119,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
|
||||
|
||||
sumi2 += vaddvq_s32(p2) * scales[2*j+1];
|
||||
#else
|
||||
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
|
||||
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0];
|
||||
|
||||
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
sumi2 += vaddvq_s16(vaddq_s16(p2, p3)) * scales[2*j+1];
|
||||
|
||||
#endif
|
||||
}
|
||||
|
||||
sumf += d * (sumi1 + sumi2);
|
||||
|
|
@ -5603,12 +5415,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xf);
|
||||
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
const int32x4_t mzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
float sumf = 0;
|
||||
|
||||
|
|
@ -5636,7 +5445,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
|
||||
|
||||
#ifdef __ARM_FEATURE_DOTPROD
|
||||
q8bytes = ggml_vld1q_s8_x4(q8);
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
|
||||
|
|
@ -5650,27 +5458,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]);
|
||||
const int32_t sumi2 = vaddvq_s32(p2) * scales[1];
|
||||
|
||||
#else
|
||||
q8bytes = ggml_vld1q_s8_x4(q8);
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
|
||||
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
int32_t sumi1 = vaddvq_s16(vaddq_s16(p0, p1)) * scales[0];
|
||||
|
||||
q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
|
||||
q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[2])));
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[3])));
|
||||
int32_t sumi2 = vaddvq_s16(vaddq_s16(p2, p3)) * scales[1];
|
||||
|
||||
#endif
|
||||
sumf += d * (sumi1 + sumi2);
|
||||
|
||||
}
|
||||
|
||||
*s = sumf - sum_mins;
|
||||
|
|
@ -5875,15 +5663,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
uint32_t utmp[4];
|
||||
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xf);
|
||||
const uint8x16_t mone = vdupq_n_u8(1);
|
||||
const uint8x16_t mtwo = vdupq_n_u8(2);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t mzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
ggml_int8x16x4_t q5bytes;
|
||||
|
||||
|
|
@ -5938,28 +5722,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2]));
|
||||
q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++;
|
||||
sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++;
|
||||
#else
|
||||
|
||||
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
sumi += vaddvq_s16(vaddq_s16(p0, p1)) * *scales++;
|
||||
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
sumi += vaddvq_s16(vaddq_s16(p2, p3)) * *scales++;
|
||||
#endif
|
||||
}
|
||||
|
||||
sumf += d * sumi - dmin * sumi_mins;
|
||||
|
||||
}
|
||||
|
||||
*s = sumf;
|
||||
|
|
@ -6311,12 +6078,9 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xf);
|
||||
const uint8x16_t mh = vdupq_n_u8(16);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t mzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
ggml_int8x16x4_t q5bytes;
|
||||
ggml_uint8x16x4_t q5h;
|
||||
|
|
@ -6348,32 +6112,12 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2]));
|
||||
q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]));
|
||||
int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1]));
|
||||
int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]));
|
||||
int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3]));
|
||||
|
||||
sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
|
||||
|
||||
#else
|
||||
|
||||
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
int32_t sumi = sc[0] * vaddvq_s16(p0) + sc[1] * vaddvq_s16(p1);
|
||||
|
||||
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
sumi += sc[2] * vaddvq_s16(p2) + sc[3] * vaddvq_s16(p3);
|
||||
|
||||
sumf += d*sumi;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
*s = sumf;
|
||||
|
|
@ -6600,13 +6344,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
float sum = 0;
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xF);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
//const int8x16_t m32s = vdupq_n_s8(32);
|
||||
|
||||
const uint8x16_t mone = vdupq_n_u8(3);
|
||||
|
|
@ -6626,7 +6367,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
|
||||
const int8x16_t scales = vld1q_s8(scale);
|
||||
const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))};
|
||||
const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}};
|
||||
|
||||
const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])),
|
||||
vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))),
|
||||
|
|
@ -6658,31 +6399,13 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2]));
|
||||
q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
|
||||
|
||||
scale += 4;
|
||||
|
||||
#else
|
||||
|
||||
int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
|
||||
scale += 2;
|
||||
|
||||
int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1];
|
||||
scale += 2;
|
||||
#endif
|
||||
|
||||
q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64;
|
||||
|
||||
shifted = vshrq_n_u8(qhbits.val[0], 4);
|
||||
|
|
@ -6703,34 +6426,11 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2]));
|
||||
q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3]));
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
|
||||
scale += 4;
|
||||
|
||||
//for (int l = 0; l < 4; ++l) {
|
||||
// const int32x4_t p = vdotq_s32(vzero, q6bytes.val[l], q8bytes.val[l]);
|
||||
// isum += vaddvq_s32(p) * *scale++;
|
||||
//}
|
||||
#else
|
||||
p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
|
||||
scale += 2;
|
||||
|
||||
p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1];
|
||||
scale += 2;
|
||||
#endif
|
||||
|
||||
}
|
||||
//sum += isum * d_all * y[i].d;
|
||||
sum += d_all * y[i].d * (isum - 32 * isum_mins);
|
||||
|
|
@ -7076,14 +6776,11 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const int nb = n / QK_K;
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
|
||||
float sum = 0;
|
||||
|
||||
const uint8x16_t m4b = vdupq_n_u8(0xF);
|
||||
const int8x16_t m32s = vdupq_n_s8(32);
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
const int32x4_t vzero = vdupq_n_s32(0);
|
||||
#endif
|
||||
|
||||
const uint8x16_t mone = vdupq_n_u8(3);
|
||||
|
||||
|
|
@ -7119,26 +6816,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s);
|
||||
q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s);
|
||||
|
||||
#if defined(__ARM_FEATURE_DOTPROD)
|
||||
|
||||
isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
|
||||
vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
|
||||
#else
|
||||
|
||||
int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
|
||||
int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
|
||||
isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
|
||||
|
||||
int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
|
||||
int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
|
||||
vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
|
||||
isum += vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
|
||||
#endif
|
||||
|
||||
sum += isum * d_all * y[i].d;
|
||||
|
||||
|
|
|
|||
15
ggml.c
15
ggml.c
|
|
@ -4041,7 +4041,6 @@ static struct ggml_tensor * ggml_group_norm_impl(
|
|||
result->op = GGML_OP_GROUP_NORM;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = NULL; // TODO: maybe store epsilon here?
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -5541,7 +5540,6 @@ static struct ggml_tensor * ggml_upscale_impl(
|
|||
result->op_params[0] = scale_factor;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = NULL;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -5846,7 +5844,6 @@ struct ggml_tensor * ggml_get_rel_pos(
|
|||
result->op = GGML_OP_GET_REL_POS;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = NULL;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -10335,7 +10332,8 @@ static void ggml_compute_forward_scale_f32(
|
|||
}
|
||||
|
||||
// scale factor
|
||||
const float v = *(float *) dst->op_params;
|
||||
float v;
|
||||
memcpy(&v, dst->op_params, sizeof(float));
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
|
@ -15152,7 +15150,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
{
|
||||
// necessary for llama
|
||||
if (src0->grad) {
|
||||
const float s = ((float *) tensor->op_params)[0];
|
||||
float s;
|
||||
memcpy(&s, tensor->op_params, sizeof(float));
|
||||
|
||||
src0->grad =
|
||||
ggml_add_or_set(ctx,
|
||||
|
|
@ -17454,9 +17453,9 @@ static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g
|
|||
}
|
||||
|
||||
//
|
||||
// ADAM
|
||||
// Using AdamW - ref: https://arxiv.org/pdf/1711.05101v3.pdf
|
||||
//
|
||||
// ref: https://arxiv.org/pdf/1412.6980.pdf
|
||||
// (Original Adam - ref: https://arxiv.org/pdf/1412.6980.pdf)
|
||||
//
|
||||
|
||||
static enum ggml_opt_result ggml_opt_adam(
|
||||
|
|
@ -19349,7 +19348,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
|
|||
data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
|
||||
}
|
||||
gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
|
||||
free(data);
|
||||
free((void *)data);
|
||||
} else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
|
||||
GGML_ASSERT(false && "nested arrays not supported");
|
||||
} else {
|
||||
|
|
|
|||
2
ggml.h
2
ggml.h
|
|
@ -255,6 +255,8 @@
|
|||
#define GGML_UNREACHABLE() GGML_ASSERT(!"statement should not be reached")
|
||||
#elif defined(__GNUC__)
|
||||
#define GGML_UNREACHABLE() __builtin_unreachable()
|
||||
#elif defined(_MSC_VER)
|
||||
#define GGML_UNREACHABLE() __assume(0)
|
||||
#else
|
||||
#define GGML_UNREACHABLE() ((void) 0)
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -96,6 +96,7 @@ class MODEL_ARCH(IntEnum):
|
|||
STABLELM = auto()
|
||||
QWEN = auto()
|
||||
PHI2 = auto()
|
||||
PLAMO = auto()
|
||||
|
||||
|
||||
class MODEL_TENSOR(IntEnum):
|
||||
|
|
@ -143,6 +144,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.STABLELM: "stablelm",
|
||||
MODEL_ARCH.QWEN: "qwen",
|
||||
MODEL_ARCH.PHI2: "phi2",
|
||||
MODEL_ARCH.PLAMO: "plamo",
|
||||
}
|
||||
|
||||
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
||||
|
|
@ -352,6 +354,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.PLAMO: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.GPT2: [
|
||||
# TODO
|
||||
],
|
||||
|
|
|
|||
|
|
@ -79,6 +79,7 @@ class TensorNameMap:
|
|||
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
|
||||
"model.layers.{bid}.ln1", # yi
|
||||
"transformer.h.{bid}.ln", # phi2
|
||||
"model.layers.layers.{bid}.norm", # plamo
|
||||
),
|
||||
|
||||
# Attention norm 2
|
||||
|
|
@ -99,26 +100,29 @@ class TensorNameMap:
|
|||
|
||||
# Attention query
|
||||
MODEL_TENSOR.ATTN_Q: (
|
||||
"model.layers.{bid}.self_attn.q_proj", # llama-hf
|
||||
"layers.{bid}.attention.wq", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.query", # bert
|
||||
"transformer.h.{bid}.attn.q_proj", # gpt-j
|
||||
"model.layers.{bid}.self_attn.q_proj", # llama-hf
|
||||
"layers.{bid}.attention.wq", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.query", # bert
|
||||
"transformer.h.{bid}.attn.q_proj", # gpt-j
|
||||
"model.layers.layers.{bid}.self_attn.q_proj", # plamo
|
||||
),
|
||||
|
||||
# Attention key
|
||||
MODEL_TENSOR.ATTN_K: (
|
||||
"model.layers.{bid}.self_attn.k_proj", # llama-hf
|
||||
"layers.{bid}.attention.wk", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.key", # bert
|
||||
"transformer.h.{bid}.attn.k_proj", # gpt-j
|
||||
"model.layers.{bid}.self_attn.k_proj", # llama-hf
|
||||
"layers.{bid}.attention.wk", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.key", # bert
|
||||
"transformer.h.{bid}.attn.k_proj", # gpt-j
|
||||
"model.layers.layers.{bid}.self_attn.k_proj", # plamo
|
||||
),
|
||||
|
||||
# Attention value
|
||||
MODEL_TENSOR.ATTN_V: (
|
||||
"model.layers.{bid}.self_attn.v_proj", # llama-hf
|
||||
"layers.{bid}.attention.wv", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.value", # bert
|
||||
"transformer.h.{bid}.attn.v_proj", # gpt-j
|
||||
"model.layers.{bid}.self_attn.v_proj", # llama-hf
|
||||
"layers.{bid}.attention.wv", # llama-pth
|
||||
"encoder.layer.{bid}.attention.self.value", # bert
|
||||
"transformer.h.{bid}.attn.v_proj", # gpt-j
|
||||
"model.layers.layers.{bid}.self_attn.v_proj", # plamo
|
||||
),
|
||||
|
||||
# Attention output
|
||||
|
|
@ -134,12 +138,14 @@ class TensorNameMap:
|
|||
"transformer.h.{bid}.attn.out_proj", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
|
||||
"transformer.h.{bid}.mixer.out_proj", # phi2
|
||||
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
|
||||
),
|
||||
|
||||
# Rotary embeddings
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD: (
|
||||
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
|
||||
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
|
||||
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
|
||||
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
|
||||
"model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
|
||||
),
|
||||
|
||||
# Feed-forward norm
|
||||
|
|
@ -174,6 +180,7 @@ class TensorNameMap:
|
|||
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
|
||||
"transformer.h.{bid}.mlp.w1", # qwen
|
||||
"transformer.h.{bid}.mlp.fc1", # phi2
|
||||
"model.layers.layers.{bid}.mlp.up_proj", # plamo
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_UP_EXP: (
|
||||
|
|
@ -191,6 +198,7 @@ class TensorNameMap:
|
|||
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
|
||||
"layers.{bid}.feed_forward.w1", # llama-pth
|
||||
"transformer.h.{bid}.mlp.w2", # qwen
|
||||
"model.layers.layers.{bid}.mlp.gate_proj", # plamo
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_EXP: (
|
||||
|
|
@ -211,6 +219,7 @@ class TensorNameMap:
|
|||
"transformer.h.{bid}.mlp.fc_out", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
|
||||
"transformer.h.{bid}.mlp.fc2", # phi2
|
||||
"model.layers.layers.{bid}.mlp.down_proj", # plamo
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_DOWN_EXP: (
|
||||
|
|
|
|||
242
llama.cpp
242
llama.cpp
|
|
@ -198,6 +198,7 @@ enum llm_arch {
|
|||
LLM_ARCH_STABLELM,
|
||||
LLM_ARCH_QWEN,
|
||||
LLM_ARCH_PHI2,
|
||||
LLM_ARCH_PLAMO,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -216,6 +217,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_STABLELM, "stablelm" },
|
||||
{ LLM_ARCH_QWEN, "qwen" },
|
||||
{ LLM_ARCH_PHI2, "phi2" },
|
||||
{ LLM_ARCH_PLAMO, "plamo" },
|
||||
};
|
||||
|
||||
enum llm_kv {
|
||||
|
|
@ -569,6 +571,24 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
|
|||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_PLAMO,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
||||
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
||||
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
||||
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
LLM_ARCH_UNKNOWN,
|
||||
|
|
@ -780,7 +800,7 @@ struct llama_file {
|
|||
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
||||
}
|
||||
if (ret != 1) {
|
||||
throw std::runtime_error(std::string("unexpectedly reached end of file"));
|
||||
throw std::runtime_error("unexpectedly reached end of file");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -933,29 +953,29 @@ struct llama_mmap {
|
|||
#elif defined(_WIN32)
|
||||
static constexpr bool SUPPORTED = true;
|
||||
|
||||
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
|
||||
(void) numa;
|
||||
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
|
||||
GGML_UNUSED(numa);
|
||||
|
||||
size = file->size;
|
||||
|
||||
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
|
||||
|
||||
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
|
||||
DWORD error = GetLastError();
|
||||
|
||||
if (hMapping == NULL) {
|
||||
DWORD error = GetLastError();
|
||||
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
|
||||
}
|
||||
|
||||
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
|
||||
error = GetLastError();
|
||||
DWORD error = GetLastError();
|
||||
CloseHandle(hMapping);
|
||||
|
||||
if (addr == NULL) {
|
||||
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
|
||||
}
|
||||
|
||||
if (prefetch) {
|
||||
if (prefetch > 0) {
|
||||
// PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
|
||||
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
|
||||
HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
|
||||
|
|
@ -967,9 +987,9 @@ struct llama_mmap {
|
|||
// advise the kernel to preload the mapped memory
|
||||
WIN32_MEMORY_RANGE_ENTRY range;
|
||||
range.VirtualAddress = addr;
|
||||
range.NumberOfBytes = (SIZE_T)size;
|
||||
range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
|
||||
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
|
||||
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
|
||||
LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
|
||||
llama_format_win_err(GetLastError()).c_str());
|
||||
}
|
||||
}
|
||||
|
|
@ -984,26 +1004,26 @@ struct llama_mmap {
|
|||
|
||||
~llama_mmap() {
|
||||
if (!UnmapViewOfFile(addr)) {
|
||||
fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
|
||||
LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
|
||||
llama_format_win_err(GetLastError()).c_str());
|
||||
}
|
||||
}
|
||||
#else
|
||||
static constexpr bool SUPPORTED = false;
|
||||
|
||||
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
|
||||
(void) file;
|
||||
(void) prefetch;
|
||||
(void) numa;
|
||||
llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
|
||||
GGML_UNUSED(file);
|
||||
GGML_UNUSED(prefetch);
|
||||
GGML_UNUSED(numa);
|
||||
|
||||
throw std::runtime_error(std::string("mmap not supported"));
|
||||
throw std::runtime_error("mmap not supported");
|
||||
}
|
||||
|
||||
void unmap(size_t offset, size_t len) {
|
||||
(void) offset;
|
||||
(void) len;
|
||||
void unmap_fragment(size_t first, size_t last) {
|
||||
GGML_UNUSED(first);
|
||||
GGML_UNUSED(last);
|
||||
|
||||
throw std::runtime_error(std::string("mmap not supported"));
|
||||
throw std::runtime_error("mmap not supported");
|
||||
}
|
||||
#endif
|
||||
};
|
||||
|
|
@ -1179,21 +1199,27 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_
|
|||
}
|
||||
|
||||
static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) {
|
||||
ggml_backend_buffer_type_t buft = nullptr;
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (n_gpu_layers > 0) {
|
||||
return ggml_backend_metal_buffer_type();
|
||||
buft = ggml_backend_metal_buffer_type();
|
||||
}
|
||||
#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
|
||||
if (n_gpu_layers > 0) {
|
||||
return ggml_backend_cuda_buffer_type(0);
|
||||
buft = ggml_backend_cuda_buffer_type(0);
|
||||
}
|
||||
#elif defined(GGML_USE_CUBLAS)
|
||||
return ggml_backend_cuda_host_buffer_type();
|
||||
buft = ggml_backend_cuda_host_buffer_type();
|
||||
#elif defined(GGML_USE_CPU_HBM)
|
||||
return ggml_backend_cpu_hbm_buffer_type();
|
||||
buft = ggml_backend_cpu_hbm_buffer_type();
|
||||
#endif
|
||||
|
||||
return ggml_backend_cpu_buffer_type();
|
||||
if (buft == nullptr) {
|
||||
buft = ggml_backend_cpu_buffer_type();
|
||||
}
|
||||
|
||||
return buft;
|
||||
|
||||
GGML_UNUSED(n_gpu_layers);
|
||||
}
|
||||
|
|
@ -1278,7 +1304,7 @@ struct llama_hparams {
|
|||
if (this->rope_finetuned != other.rope_finetuned) return true;
|
||||
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
|
||||
|
||||
const float EPSILON = 1e-9;
|
||||
const float EPSILON = 1e-9f;
|
||||
|
||||
if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
|
||||
if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
|
||||
|
|
@ -2747,6 +2773,15 @@ static void llm_load_hparams(
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_PLAMO:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 40: model.type = e_model::MODEL_13B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
|
||||
default: (void)0;
|
||||
}
|
||||
|
|
@ -3630,6 +3665,51 @@ static bool llm_load_tensors(
|
|||
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_PLAMO:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
||||
|
||||
// output
|
||||
{
|
||||
ggml_backend_type backend_norm;
|
||||
ggml_backend_type backend_output;
|
||||
|
||||
if (n_gpu_layers > int(n_layer)) {
|
||||
backend_norm = llama_backend_offload;
|
||||
backend_output = llama_backend_offload_split;
|
||||
} else {
|
||||
backend_norm = GGML_BACKEND_CPU;
|
||||
backend_output = GGML_BACKEND_CPU;
|
||||
}
|
||||
|
||||
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
||||
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
||||
}
|
||||
|
||||
const uint32_t n_ff = hparams.n_ff;
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
|
||||
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
||||
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
||||
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
||||
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
}
|
||||
|
|
@ -5570,6 +5650,109 @@ struct llm_build_context {
|
|||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_plamo() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
|
||||
cb(inpL, "inp_embd", -1);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
cb(inp_pos, "inp_pos", -1);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
||||
cb(KQ_mask, "KQ_mask", -1);
|
||||
|
||||
// shift the entire K-cache if needed
|
||||
if (do_rope_shift) {
|
||||
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
|
||||
}
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
struct ggml_tensor * attention_norm = cur;
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
struct ggml_tensor * sa_out = cur;
|
||||
|
||||
cur = attention_norm;
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, NULL,
|
||||
model.layers[il].ffn_gate, NULL,
|
||||
model.layers[il].ffn_down, NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, sa_out);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
|
|
@ -6081,6 +6264,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_phi2();
|
||||
} break;
|
||||
case LLM_ARCH_PLAMO:
|
||||
{
|
||||
result = llm.build_plamo();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -9354,7 +9541,8 @@ struct llama_context * llama_new_context_with_model(
|
|||
ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc);
|
||||
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
|
||||
if (model->n_gpu_layers > 0) {
|
||||
ggml_cuda_set_scratch_size(alloc_size);
|
||||
// the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets
|
||||
ggml_cuda_set_scratch_size(alloc_size + 64);
|
||||
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0);
|
||||
|
||||
// calculate total VRAM usage
|
||||
|
|
@ -10316,7 +10504,7 @@ int llama_token_to_piece(const struct llama_model * model, llama_token token, ch
|
|||
std::string result = model->vocab.id_to_token[token].text;
|
||||
llama_unescape_whitespace(result);
|
||||
if (length < (int) result.length()) {
|
||||
return -result.length();
|
||||
return -(int) result.length();
|
||||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
|
|
@ -10346,7 +10534,7 @@ int llama_token_to_piece(const struct llama_model * model, llama_token token, ch
|
|||
std::string result = model->vocab.id_to_token[token].text;
|
||||
result = llama_decode_text(result);
|
||||
if (length < (int) result.length()) {
|
||||
return -result.length();
|
||||
return -(int) result.length();
|
||||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
|
|
|
|||
131
scripts/sync-ggml-am.sh
Executable file
131
scripts/sync-ggml-am.sh
Executable file
|
|
@ -0,0 +1,131 @@
|
|||
#!/bin/bash
|
||||
#
|
||||
# Synchronize ggml changes to llama.cpp
|
||||
#
|
||||
# Usage:
|
||||
#
|
||||
# $ cd /path/to/llama.cpp
|
||||
# $ ./scripts/sync-ggml-am.sh
|
||||
#
|
||||
|
||||
set -e
|
||||
|
||||
sd=$(dirname $0)
|
||||
cd $sd/../
|
||||
|
||||
SRC_LLAMA=$(pwd)
|
||||
SRC_GGML=$(cd ../ggml; pwd)
|
||||
|
||||
if [ ! -d $SRC_GGML ]; then
|
||||
echo "ggml not found at $SRC_GGML"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
lc=$(cat $SRC_LLAMA/scripts/sync-ggml.last)
|
||||
echo "Syncing ggml changes since commit $lc"
|
||||
|
||||
cd $SRC_GGML
|
||||
|
||||
git log --oneline $lc..HEAD
|
||||
|
||||
git format-patch $lc --stdout -- \
|
||||
include/ggml/ggml*.h \
|
||||
src/ggml*.h \
|
||||
src/ggml*.c \
|
||||
src/ggml*.cpp \
|
||||
src/ggml*.m \
|
||||
src/ggml*.metal \
|
||||
src/ggml*.cu \
|
||||
tests/test-opt.cpp \
|
||||
tests/test-grad0.cpp \
|
||||
tests/test-quantize-fns.cpp \
|
||||
tests/test-quantize-perf.cpp \
|
||||
tests/test-backend-ops.cpp \
|
||||
> $SRC_LLAMA/ggml-src.patch
|
||||
|
||||
# delete files if empty
|
||||
if [ ! -s $SRC_LLAMA/ggml-src.patch ]; then
|
||||
rm -v $SRC_LLAMA/ggml-src.patch
|
||||
fi
|
||||
|
||||
cd $SRC_LLAMA
|
||||
|
||||
if [ -f $SRC_LLAMA/ggml-src.patch ]; then
|
||||
# replace PR numbers
|
||||
#
|
||||
# Subject: some text (#1234)
|
||||
# Subject: some text (ggml/1234)
|
||||
cat ggml-src.patch | sed -e 's/^Subject: \(.*\) (#\([0-9]*\))/Subject: \1 (ggml\/\2)/' > ggml-src.patch.tmp
|
||||
mv ggml-src.patch.tmp ggml-src.patch
|
||||
|
||||
cat ggml-src.patch | sed -e 's/^\(.*\) (#\([0-9]*\))$/\1 (ggml\/\2)/' > ggml-src.patch.tmp
|
||||
mv ggml-src.patch.tmp ggml-src.patch
|
||||
|
||||
# replace filenames:
|
||||
#
|
||||
# src/ggml.c -> ggml.c
|
||||
# src/ggml-alloc.c -> ggml-alloc.c
|
||||
# src/ggml-backend-impl.h -> ggml-backend-impl.h
|
||||
# src/ggml-backend.c -> ggml-backend.c
|
||||
# src/ggml-cuda.cu -> ggml-cuda.cu
|
||||
# src/ggml-cuda.h -> ggml-cuda.h
|
||||
# src/ggml-impl.h -> ggml-impl.h
|
||||
# src/ggml-metal.h -> ggml-metal.h
|
||||
# src/ggml-metal.m -> ggml-metal.m
|
||||
# src/ggml-metal.metal -> ggml-metal.metal
|
||||
# src/ggml-mpi.h -> ggml-mpi.h
|
||||
# src/ggml-mpi.c -> ggml-mpi.c
|
||||
# src/ggml-opencl.cpp -> ggml-opencl.cpp
|
||||
# src/ggml-opencl.h -> ggml-opencl.h
|
||||
# src/ggml-quants.c -> ggml-quants.c
|
||||
# src/ggml-quants.h -> ggml-quants.h
|
||||
# include/ggml/ggml.h -> ggml.h
|
||||
# include/ggml/ggml-alloc.h -> ggml-alloc.h
|
||||
# include/ggml/ggml-backend.h -> ggml-backend.h
|
||||
#
|
||||
# tests/test-opt.cpp -> tests/test-opt.cpp
|
||||
# tests/test-grad0.cpp -> tests/test-grad0.cpp
|
||||
# tests/test-quantize-fns.cpp -> tests/test-quantize-fns.cpp
|
||||
# tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp
|
||||
# tests/test-backend-ops.cpp -> tests/test-backend-ops.cpp
|
||||
|
||||
cat ggml-src.patch | sed \
|
||||
-e 's/src\/ggml\.c/ggml.c/g' \
|
||||
-e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \
|
||||
-e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \
|
||||
-e 's/src\/ggml-backend\.c/ggml-backend.c/g' \
|
||||
-e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \
|
||||
-e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \
|
||||
-e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
|
||||
-e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
|
||||
-e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
|
||||
-e 's/src\/ggml-metal\.metal/ggml-metal.metal/g' \
|
||||
-e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
|
||||
-e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \
|
||||
-e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \
|
||||
-e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \
|
||||
-e 's/src\/ggml-quants\.c/ggml-quants.c/g' \
|
||||
-e 's/src\/ggml-quants\.h/ggml-quants.h/g' \
|
||||
-e 's/include\/ggml\/ggml\.h/ggml.h/g' \
|
||||
-e 's/include\/ggml\/ggml-alloc\.h/ggml-alloc.h/g' \
|
||||
-e 's/include\/ggml\/ggml-backend\.h/ggml-backend.h/g' \
|
||||
-e 's/tests\/test-opt\.cpp/tests\/test-opt.cpp/g' \
|
||||
-e 's/tests\/test-grad0\.cpp/tests\/test-grad0.cpp/g' \
|
||||
-e 's/tests\/test-quantize-fns\.cpp/tests\/test-quantize-fns.cpp/g' \
|
||||
-e 's/tests\/test-quantize-perf\.cpp/tests\/test-quantize-perf.cpp/g' \
|
||||
-e 's/tests\/test-backend-ops\.cpp/tests\/test-backend-ops.cpp/g' \
|
||||
> ggml-src.patch.tmp
|
||||
mv ggml-src.patch.tmp ggml-src.patch
|
||||
|
||||
git am ggml-src.patch
|
||||
|
||||
rm -v $SRC_LLAMA/ggml-src.patch
|
||||
fi
|
||||
|
||||
# update last commit
|
||||
cd $SRC_GGML
|
||||
git log -1 --format=%H > $SRC_LLAMA/scripts/sync-ggml.last
|
||||
|
||||
echo "Done"
|
||||
|
||||
exit 0
|
||||
1
scripts/sync-ggml.last
Normal file
1
scripts/sync-ggml.last
Normal file
|
|
@ -0,0 +1 @@
|
|||
76e7f47b69e8334384dc718480c496dafbd47999
|
||||
|
|
@ -883,9 +883,6 @@ int main(int argc, const char ** argv) {
|
|||
srand(seed);
|
||||
const int nargs = 1;
|
||||
|
||||
int64_t ne2[4];
|
||||
ne2[0] = 1;
|
||||
|
||||
for (int ndims = 1; ndims <= 2; ++ndims) {
|
||||
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue