Merge branch 'ggerganov:master' into master
This commit is contained in:
hxer7963
GitHub
commit
16a5d0a1bc
21 changed files with 3616 additions and 2883 deletions
279
.github/workflows/bench.yml
vendored
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279
.github/workflows/bench.yml
vendored
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|
|
@ -0,0 +1,279 @@
|
|||
# Benchmark
|
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name: Benchmark
|
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|
||||
on:
|
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workflow_dispatch:
|
||||
inputs:
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gpu-series:
|
||||
description: 'Azure GPU series to run with'
|
||||
required: true
|
||||
type: choice
|
||||
options:
|
||||
- Standard_NC4as_T4_v3
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||||
- Standard_NC24ads_A100_v4
|
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- Standard_NC80adis_H100_v5
|
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sha:
|
||||
description: 'Commit SHA1 to build'
|
||||
required: false
|
||||
type: string
|
||||
duration:
|
||||
description: 'Duration of the bench'
|
||||
type: string
|
||||
default: 10m
|
||||
|
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push:
|
||||
branches:
|
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- master
|
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paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
|
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pull_request:
|
||||
types: [opened, synchronize, reopened]
|
||||
paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
|
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schedule:
|
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- cron: '04 2 * * *'
|
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|
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concurrency:
|
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group: ${{ github.workflow }}-${{ github.ref }}
|
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cancel-in-progress: true
|
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|
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jobs:
|
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bench-server-baseline:
|
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runs-on: Standard_NC4as_T4_v3
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env:
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RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
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N_USERS: 8
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DURATION: 10m
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if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.event.push.ref == 'refs/heads/master' }}
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steps:
|
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- name: Clone
|
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id: checkout
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
fetch-depth: 0
|
||||
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
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|
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- name: Install python env
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id: pipenv
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run: |
|
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cd examples/server/bench
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python3 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
|
||||
|
||||
- name: Prometheus
|
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id: install_prometheus
|
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run: |
|
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wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
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tar xzf prometheus*.tar.gz --strip-components=1
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./prometheus --config.file=examples/server/bench/prometheus.yml &
|
||||
while ! nc -z localhost 9090; do
|
||||
sleep 0.1
|
||||
done
|
||||
|
||||
- name: Install k6
|
||||
id: k6_installation
|
||||
run: |
|
||||
cd examples/server/bench
|
||||
wget --quiet https://github.com/grafana/k6/releases/download/v0.49.0/k6-v0.49.0-linux-amd64.tar.gz
|
||||
tar xzf k6*.tar.gz --strip-components=1
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
set -eux
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
-DLLAMA_NATIVE=OFF \
|
||||
-DLLAMA_BUILD_SERVER=ON \
|
||||
-DLLAMA_CURL=ON \
|
||||
-DLLAMA_CUBLAS=ON \
|
||||
-DCUDAToolkit_ROOT=/usr/local/cuda \
|
||||
-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
|
||||
-DCMAKE_CUDA_ARCHITECTURES=75 \
|
||||
-DLLAMA_FATAL_WARNINGS=OFF \
|
||||
-DLLAMA_ALL_WARNINGS=OFF \
|
||||
-DCMAKE_BUILD_TYPE=Release;
|
||||
cmake --build . --config Release -j $(nproc) --target server
|
||||
|
||||
- name: Download the dataset
|
||||
id: download_dataset
|
||||
run: |
|
||||
cd examples/server/bench
|
||||
wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
|
||||
- name: Server bench
|
||||
id: server_bench
|
||||
run: |
|
||||
set -eux
|
||||
|
||||
cd examples/server/bench
|
||||
source venv/bin/activate
|
||||
BENCH_K6_BIN_PATH=./k6 python bench.py \
|
||||
--runner-label ${{ env.RUNNER_LABEL }} \
|
||||
--name ${{ github.job }} \
|
||||
--branch ${{ github.head_ref || github.ref_name }} \
|
||||
--commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
|
||||
--scenario script.js \
|
||||
--duration ${{ github.event.inputs.duration || env.DURATION }} \
|
||||
--hf-repo ggml-org/models \
|
||||
--hf-file phi-2/ggml-model-q4_0.gguf \
|
||||
--model-path-prefix /models \
|
||||
--parallel ${{ env.N_USERS }} \
|
||||
-ngl 33 \
|
||||
--batch-size 2048 \
|
||||
--ubatch-size 256 \
|
||||
--ctx-size 16384 \
|
||||
--n-prompts 1000 \
|
||||
--max-prompt-tokens 1024 \
|
||||
--max-tokens 2048
|
||||
|
||||
cat results.github.env >> $GITHUB_ENV
|
||||
|
||||
# Remove dataset as we do not want it in the artefact
|
||||
rm ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: benchmark-results
|
||||
compression-level: 9
|
||||
path: |
|
||||
examples/server/bench/*.jpg
|
||||
examples/server/bench/*.json
|
||||
examples/server/bench/*.log
|
||||
|
||||
- name: Commit status
|
||||
uses: Sibz/github-status-action@v1
|
||||
with:
|
||||
authToken: ${{secrets.GITHUB_TOKEN}}
|
||||
sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
|
||||
context: bench-server-baseline
|
||||
description: |
|
||||
${{ env.BENCH_RESULTS }}
|
||||
state: 'success'
|
||||
|
||||
- name: Upload benchmark images
|
||||
uses: devicons/public-upload-to-imgur@v2.2.2
|
||||
continue-on-error: true # Important as it looks unstable: 503
|
||||
id: imgur_step
|
||||
with:
|
||||
client_id: ${{secrets.IMGUR_CLIENT_ID}}
|
||||
path: |
|
||||
examples/server/bench/prompt_tokens_seconds.jpg
|
||||
examples/server/bench/predicted_tokens_seconds.jpg
|
||||
examples/server/bench/kv_cache_usage_ratio.jpg
|
||||
examples/server/bench/requests_processing.jpg
|
||||
|
||||
- name: Extract mermaid
|
||||
id: set_mermaid
|
||||
run: |
|
||||
set -eux
|
||||
|
||||
cd examples/server/bench
|
||||
PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
|
||||
echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
|
||||
echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
|
||||
echo "EOF" >> $GITHUB_ENV
|
||||
|
||||
PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
|
||||
echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
|
||||
echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
|
||||
echo "EOF" >> $GITHUB_ENV
|
||||
|
||||
KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
|
||||
echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
|
||||
echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
|
||||
echo "EOF" >> $GITHUB_ENV
|
||||
|
||||
REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
|
||||
echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
|
||||
echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
|
||||
echo "EOF" >> $GITHUB_ENV
|
||||
|
||||
- name: Extract image url
|
||||
id: extract_image_url
|
||||
continue-on-error: true
|
||||
run: |
|
||||
set -eux
|
||||
|
||||
echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
|
||||
echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
|
||||
echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
|
||||
echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
|
||||
|
||||
- name: Comment PR
|
||||
uses: mshick/add-pr-comment@v2
|
||||
id: comment_pr
|
||||
if: ${{ github.event.pull_request != '' }}
|
||||
with:
|
||||
message-id: bench-${{ github.job }}-${{ env.RUNNER_LABEL }}
|
||||
message: |
|
||||
📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
|
||||
|
||||
- Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
|
||||
- HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(90)=${{ env.HTTP_REQ_DURATION_P_90_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
|
||||
- Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_TOKENS_AVG }}tk/s p(90)=${{ env.LLAMACPP_PROMPT_TOKENS_P_90_ }}tk/s **total=${{ env.LLAMACPP_PROMPT_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
|
||||
- Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(90)=${{ env.LLAMACPP_TOKENS_SECOND_P_90_ }}tk/s **total=${{ env.LLAMACPP_COMPLETION_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
|
||||
- ${{ env.BENCH_GRAPH_XLABEL }}
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Time series</summary>
|
||||
|
||||
<p align="center">
|
||||
|
||||
<img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
|
||||
|
||||
<details>
|
||||
|
||||
<summary>More</summary>
|
||||
|
||||
```mermaid
|
||||
${{ env.PROMPT_TOKENS_SECONDS }}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
|
||||
|
||||
<details>
|
||||
<summary>More</summary>
|
||||
|
||||
```mermaid
|
||||
${{ env.PREDICTED_TOKENS_SECONDS }}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
</p>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Details</summary>
|
||||
|
||||
<p align="center">
|
||||
|
||||
<img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
|
||||
|
||||
<details>
|
||||
<summary>More</summary>
|
||||
|
||||
```mermaid
|
||||
${{ env.KV_CACHE_USAGE_RATIO }}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
|
||||
|
||||
<details>
|
||||
<summary>More</summary>
|
||||
|
||||
```mermaid
|
||||
${{ env.REQUESTS_PROCESSING }}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
</p>
|
||||
</details>
|
||||
</details>
|
||||
1
Makefile
1
Makefile
|
|
@ -556,6 +556,7 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
|
|||
endif # LLAMA_CUDA_NO_PEER_COPY
|
||||
OBJS += ggml-cuda.o
|
||||
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
|
||||
|
||||
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
|
||||
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
|
||||
|
||||
|
|
|
|||
|
|
@ -149,6 +149,7 @@ Typically finetunes of the base models below are supported as well.
|
|||
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
|
||||
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
|
||||
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
|
||||
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
|
||||
|
||||
**UI:**
|
||||
|
||||
|
|
|
|||
|
|
@ -1062,8 +1062,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
|
|||
params.ignore_eos = true;
|
||||
return true;
|
||||
}
|
||||
if (arg == "--no-penalize-nl") {
|
||||
sparams.penalize_nl = false;
|
||||
if (arg == "--penalize-nl") {
|
||||
sparams.penalize_nl = true;
|
||||
return true;
|
||||
}
|
||||
if (arg == "-l" || arg == "--logit-bias") {
|
||||
|
|
@ -1373,7 +1373,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
printf(" -dt N, --defrag-thold N\n");
|
||||
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
|
||||
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
||||
printf(" --no-penalize-nl do not penalize newline token\n");
|
||||
printf(" --penalize-nl penalize newline tokens\n");
|
||||
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
|
||||
printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n");
|
||||
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
|
||||
|
|
@ -2489,7 +2489,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
|
|||
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
|
||||
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
|
||||
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
|
||||
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
|
||||
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
|
||||
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
|
||||
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
|
||||
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
|
||||
|
|
|
|||
|
|
@ -880,7 +880,7 @@ int main(int argc, char ** argv) {
|
|||
TransformerWeights weights = {};
|
||||
{
|
||||
LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
|
||||
FILE *file = fopen(params.fn_llama2c_model, "r");
|
||||
FILE * file = fopen(params.fn_llama2c_model, "rb");
|
||||
if (!file) {
|
||||
LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
|
||||
return 1;
|
||||
|
|
|
|||
|
|
@ -178,25 +178,27 @@ int main(int argc, char ** argv) {
|
|||
float * out = emb + p * n_embd;
|
||||
batch_decode(ctx, batch, out, s, n_embd);
|
||||
|
||||
// print the first part of the embeddings
|
||||
// print the first part of the embeddings or for a single prompt, the full embedding
|
||||
fprintf(stdout, "\n");
|
||||
for (int j = 0; j < n_prompts; j++) {
|
||||
fprintf(stdout, "embedding %d: ", j);
|
||||
for (int i = 0; i < std::min(16, n_embd); i++) {
|
||||
for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
|
||||
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
|
||||
}
|
||||
fprintf(stdout, "\n");
|
||||
}
|
||||
|
||||
// print cosine similarity matrix
|
||||
fprintf(stdout, "\n");
|
||||
printf("cosine similarity matrix:\n\n");
|
||||
for (int i = 0; i < n_prompts; i++) {
|
||||
for (int j = 0; j < n_prompts; j++) {
|
||||
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
|
||||
fprintf(stdout, "%6.2f ", sim);
|
||||
}
|
||||
if (n_prompts > 1) {
|
||||
fprintf(stdout, "\n");
|
||||
printf("cosine similarity matrix:\n\n");
|
||||
for (int i = 0; i < n_prompts; i++) {
|
||||
for (int j = 0; j < n_prompts; j++) {
|
||||
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
|
||||
fprintf(stdout, "%6.2f ", sim);
|
||||
}
|
||||
fprintf(stdout, "\n");
|
||||
}
|
||||
}
|
||||
|
||||
// clean up
|
||||
|
|
|
|||
303
examples/server/bench/bench.py
Normal file
303
examples/server/bench/bench.py
Normal file
|
|
@ -0,0 +1,303 @@
|
|||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import signal
|
||||
import socket
|
||||
import subprocess
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import traceback
|
||||
from contextlib import closing
|
||||
from datetime import datetime
|
||||
|
||||
import matplotlib
|
||||
import matplotlib.dates
|
||||
import matplotlib.pyplot as plt
|
||||
import requests
|
||||
|
||||
|
||||
def main(args_in: list[str] | None = None) -> None:
|
||||
parser = argparse.ArgumentParser(description="Start server benchmark scenario")
|
||||
parser.add_argument("--name", type=str, help="Bench name", required=True)
|
||||
parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
|
||||
parser.add_argument("--branch", type=str, help="Branch name", default="detached")
|
||||
parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
|
||||
parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
|
||||
parser.add_argument("--port", type=int, help="Server listen host", default="8080")
|
||||
parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
|
||||
parser.add_argument("--n-prompts", type=int,
|
||||
help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
|
||||
parser.add_argument("--max-prompt-tokens", type=int,
|
||||
help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
|
||||
required=True)
|
||||
parser.add_argument("--max-tokens", type=int,
|
||||
help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
|
||||
required=True)
|
||||
parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
|
||||
parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
|
||||
parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
|
||||
parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
|
||||
parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
|
||||
parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
|
||||
parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
|
||||
parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
|
||||
parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
|
||||
|
||||
args = parser.parse_args(args_in)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# Start the server and performance scenario
|
||||
try:
|
||||
server_process = start_server(args)
|
||||
except Exception:
|
||||
print("bench: server start error :")
|
||||
traceback.print_exc(file=sys.stdout)
|
||||
sys.exit(1)
|
||||
|
||||
# start the benchmark
|
||||
try:
|
||||
start_benchmark(args)
|
||||
|
||||
iterations = 0
|
||||
with open("results.github.env", 'w') as github_env:
|
||||
# parse output
|
||||
with open('k6-results.json', 'r') as bench_results:
|
||||
# Load JSON data from file
|
||||
data = json.load(bench_results)
|
||||
for metric_name in data['metrics']:
|
||||
for metric_metric in data['metrics'][metric_name]:
|
||||
value = data['metrics'][metric_name][metric_metric]
|
||||
if isinstance(value, float) or isinstance(value, int):
|
||||
value = round(value, 2)
|
||||
data['metrics'][metric_name][metric_metric]=value
|
||||
github_env.write(
|
||||
f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
|
||||
token_seconds = data['metrics']['llamacpp_tokens_second']['avg']
|
||||
iterations = data['root_group']['checks']['success completion']['passes']
|
||||
|
||||
except Exception:
|
||||
print("bench: error :")
|
||||
traceback.print_exc(file=sys.stdout)
|
||||
|
||||
# Stop the server
|
||||
if server_process:
|
||||
try:
|
||||
print(f"bench: shutting down server pid={server_process.pid} ...")
|
||||
if os.name == 'nt':
|
||||
interrupt = signal.CTRL_C_EVENT
|
||||
else:
|
||||
interrupt = signal.SIGINT
|
||||
server_process.send_signal(interrupt)
|
||||
server_process.wait(0.5)
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
|
||||
server_process.kill() # SIGKILL
|
||||
server_process.wait()
|
||||
|
||||
while is_server_listening(args.host, args.port):
|
||||
time.sleep(0.1)
|
||||
|
||||
title = (f"llama.cpp {args.name} on {args.runner_label}\n "
|
||||
f"duration={args.duration} {iterations} iterations")
|
||||
xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
|
||||
f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
|
||||
f"branch={args.branch} commit={args.commit}")
|
||||
|
||||
# Prometheus
|
||||
end_time = time.time()
|
||||
if is_server_listening("0.0.0.0", 9090):
|
||||
metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
|
||||
'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
|
||||
|
||||
for metric in metrics:
|
||||
resp = requests.get(f"http://localhost:9090/api/v1/query_range",
|
||||
params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
|
||||
|
||||
with open(f"{metric}.json", 'w') as metric_json:
|
||||
metric_json.write(resp.text)
|
||||
|
||||
if resp.status_code != 200:
|
||||
print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
|
||||
else:
|
||||
metric_data = resp.json()
|
||||
values = metric_data['data']['result'][0]['values']
|
||||
timestamps, metric_values = zip(*values)
|
||||
metric_values = [float(value) for value in metric_values]
|
||||
timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
|
||||
plt.figure(figsize=(16, 10), dpi=80)
|
||||
plt.plot(timestamps_dt, metric_values, label=metric)
|
||||
plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
|
||||
plt.yticks(fontsize=12, alpha=.7)
|
||||
|
||||
ylabel = f"llamacpp:{metric}"
|
||||
plt.title(title,
|
||||
fontsize=14, wrap=True)
|
||||
plt.grid(axis='both', alpha=.3)
|
||||
plt.ylabel(ylabel, fontsize=22)
|
||||
plt.xlabel(xlabel, fontsize=14, wrap=True)
|
||||
plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
|
||||
plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
|
||||
plt.gcf().autofmt_xdate()
|
||||
|
||||
# Remove borders
|
||||
plt.gca().spines["top"].set_alpha(0.0)
|
||||
plt.gca().spines["bottom"].set_alpha(0.3)
|
||||
plt.gca().spines["right"].set_alpha(0.0)
|
||||
plt.gca().spines["left"].set_alpha(0.3)
|
||||
|
||||
# Save the plot as a jpg image
|
||||
plt.savefig(f'{metric}.jpg', dpi=60)
|
||||
plt.close()
|
||||
|
||||
# Mermaid format in case images upload failed
|
||||
with (open(f"{metric}.mermaid", 'w') as mermaid_f):
|
||||
mermaid = (
|
||||
f"""---
|
||||
config:
|
||||
xyChart:
|
||||
titleFontSize: 12
|
||||
width: 900
|
||||
height: 600
|
||||
themeVariables:
|
||||
xyChart:
|
||||
titleColor: "#000000"
|
||||
---
|
||||
xychart-beta
|
||||
title "{title}"
|
||||
y-axis "llamacpp:{metric}"
|
||||
x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
|
||||
line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
|
||||
""")
|
||||
mermaid_f.write(mermaid)
|
||||
|
||||
# 140 chars max for commit status description
|
||||
bench_results = {
|
||||
"req": {
|
||||
"p90": data['metrics']["http_req_duration"]["p(90)"],
|
||||
"avg": data['metrics']["http_req_duration"]["avg"],
|
||||
},
|
||||
"pp": {
|
||||
"p90": data['metrics']["llamacpp_prompt_tokens"]["p(90)"],
|
||||
"avg": data['metrics']["llamacpp_prompt_tokens"]["avg"],
|
||||
},
|
||||
"tg": {
|
||||
"p90": data['metrics']["llamacpp_tokens_second"]["p(90)"],
|
||||
"avg": data['metrics']["llamacpp_tokens_second"]["avg"],
|
||||
},
|
||||
}
|
||||
with open("results.github.env", 'a') as github_env:
|
||||
github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
|
||||
github_env.write(f"BENCH_ITERATIONS={iterations}\n")
|
||||
|
||||
title = title.replace('\n', ' ')
|
||||
xlabel = xlabel.replace('\n', ' ')
|
||||
github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
|
||||
github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
|
||||
|
||||
|
||||
def start_benchmark(args):
|
||||
k6_path = 'k6'
|
||||
if 'BENCH_K6_BIN_PATH' in os.environ:
|
||||
k6_path = os.environ['BENCH_K6_BIN_PATH']
|
||||
k6_args = [
|
||||
'run', args.scenario,
|
||||
'--no-color',
|
||||
]
|
||||
k6_args.extend(['--duration', args.duration])
|
||||
k6_args.extend(['--iterations', args.n_prompts])
|
||||
k6_args.extend(['--vus', args.parallel])
|
||||
k6_args.extend(['--summary-export', 'k6-results.json'])
|
||||
args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
|
||||
args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
|
||||
print(f"bench: starting k6 with: {args}")
|
||||
k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
|
||||
if k6_completed.returncode != 0:
|
||||
raise Exception("bench: unable to run k6")
|
||||
|
||||
|
||||
def start_server(args):
|
||||
server_process = start_server_background(args)
|
||||
|
||||
attempts = 0
|
||||
max_attempts = 20
|
||||
if 'GITHUB_ACTIONS' in os.environ:
|
||||
max_attempts *= 2
|
||||
|
||||
while not is_server_listening(args.host, args.port):
|
||||
attempts += 1
|
||||
if attempts > max_attempts:
|
||||
assert False, "server not started"
|
||||
print(f"bench: waiting for server to start ...")
|
||||
time.sleep(0.5)
|
||||
|
||||
print("bench: server started.")
|
||||
return server_process
|
||||
|
||||
|
||||
def start_server_background(args):
|
||||
# Start the server
|
||||
server_path = '../../../build/bin/server'
|
||||
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
|
||||
server_path = os.environ['LLAMA_SERVER_BIN_PATH']
|
||||
server_args = [
|
||||
'--host', args.host,
|
||||
'--port', args.port,
|
||||
]
|
||||
model_file = args.model_path_prefix + os.path.sep + args.hf_file
|
||||
model_dir = os.path.dirname(model_file)
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
server_args.extend(['--model', model_file])
|
||||
server_args.extend(['--hf-repo', args.hf_repo])
|
||||
server_args.extend(['--hf-file', args.hf_file])
|
||||
server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
|
||||
server_args.extend(['--ctx-size', args.ctx_size])
|
||||
server_args.extend(['--parallel', args.parallel])
|
||||
server_args.extend(['--batch-size', args.batch_size])
|
||||
server_args.extend(['--ubatch-size', args.ubatch_size])
|
||||
server_args.extend(['--n-predict', args.max_tokens * 2])
|
||||
server_args.extend(['--defrag-thold', "0.1"])
|
||||
server_args.append('--cont-batching')
|
||||
server_args.append('--metrics')
|
||||
server_args.extend(['--log-format', "text"])
|
||||
args = [str(arg) for arg in [server_path, *server_args]]
|
||||
print(f"bench: starting server with: {' '.join(args)}")
|
||||
pkwargs = {
|
||||
'stdout': subprocess.PIPE,
|
||||
'stderr': subprocess.PIPE
|
||||
}
|
||||
server_process = subprocess.Popen(
|
||||
args,
|
||||
**pkwargs)
|
||||
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=out_stream)
|
||||
|
||||
thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
|
||||
thread_stdout.start()
|
||||
thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
|
||||
thread_stderr.start()
|
||||
|
||||
return server_process
|
||||
|
||||
|
||||
def is_server_listening(server_fqdn, server_port):
|
||||
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
|
||||
result = sock.connect_ex((server_fqdn, server_port))
|
||||
_is_server_listening = result == 0
|
||||
if _is_server_listening:
|
||||
print(f"server is listening on {server_fqdn}:{server_port}...")
|
||||
return _is_server_listening
|
||||
|
||||
|
||||
def escape_metric_name(metric_name):
|
||||
return re.sub('[^A-Z0-9]', '_', metric_name.upper())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
9
examples/server/bench/prometheus.yml
Normal file
9
examples/server/bench/prometheus.yml
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
global:
|
||||
scrape_interval: 10s
|
||||
external_labels:
|
||||
llamacpp: 'server'
|
||||
|
||||
scrape_configs:
|
||||
- job_name: 'llama.cpp server'
|
||||
static_configs:
|
||||
- targets: ['localhost:8080']
|
||||
2
examples/server/bench/requirements.txt
Normal file
2
examples/server/bench/requirements.txt
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
matplotlib
|
||||
requests
|
||||
|
|
@ -43,444 +43,454 @@ unsigned char completion_js[] = {
|
|||
0x7d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63,
|
||||
0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20,
|
||||
0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x72,
|
||||
0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x69, 0x66,
|
||||
0x20, 0x28, 0x21, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65,
|
||||
0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e,
|
||||
0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65,
|
||||
0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72,
|
||||
0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d,
|
||||
0x0a, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f,
|
||||
0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61,
|
||||
0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61,
|
||||
0x72, 0x61, 0x6d, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x2c,
|
||||
0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20,
|
||||
0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20,
|
||||
0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f,
|
||||
0x6e, 0x73, 0x65, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20,
|
||||
0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, 0x63, 0x6f, 0x6d, 0x70,
|
||||
0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x2c, 0x20, 0x7b, 0x0a, 0x20,
|
||||
0x20, 0x20, 0x20, 0x6d, 0x65, 0x74, 0x68, 0x6f, 0x64, 0x3a, 0x20, 0x27,
|
||||
0x50, 0x4f, 0x53, 0x54, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62,
|
||||
0x6f, 0x64, 0x79, 0x3a, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x73, 0x74,
|
||||
0x72, 0x69, 0x6e, 0x67, 0x69, 0x66, 0x79, 0x28, 0x63, 0x6f, 0x6d, 0x70,
|
||||
0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73,
|
||||
0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, 0x61, 0x64, 0x65,
|
||||
0x72, 0x73, 0x3a, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
|
||||
0x27, 0x43, 0x6f, 0x6e, 0x6e, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x27,
|
||||
0x3a, 0x20, 0x27, 0x6b, 0x65, 0x65, 0x70, 0x2d, 0x61, 0x6c, 0x69, 0x76,
|
||||
0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x43,
|
||||
0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2d, 0x54, 0x79, 0x70, 0x65, 0x27,
|
||||
0x3a, 0x20, 0x27, 0x61, 0x70, 0x70, 0x6c, 0x69, 0x63, 0x61, 0x74, 0x69,
|
||||
0x6f, 0x6e, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x27, 0x2c, 0x0a, 0x20, 0x20,
|
||||
0x20, 0x20, 0x20, 0x20, 0x27, 0x41, 0x63, 0x63, 0x65, 0x70, 0x74, 0x27,
|
||||
0x3a, 0x20, 0x27, 0x74, 0x65, 0x78, 0x74, 0x2f, 0x65, 0x76, 0x65, 0x6e,
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0x20, 0x47, 0x65, 0x74, 0x20, 0x74, 0x68, 0x65, 0x20, 0x6d, 0x6f, 0x64,
|
||||
0x65, 0x6c, 0x20, 0x69, 0x6e, 0x66, 0x6f, 0x20, 0x66, 0x72, 0x6f, 0x6d,
|
||||
0x20, 0x74, 0x68, 0x65, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2e,
|
||||
0x20, 0x54, 0x68, 0x69, 0x73, 0x20, 0x69, 0x73, 0x20, 0x75, 0x73, 0x65,
|
||||
0x66, 0x75, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x67, 0x65, 0x74, 0x74,
|
||||
0x69, 0x6e, 0x67, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6e, 0x74,
|
||||
0x65, 0x78, 0x74, 0x20, 0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x20, 0x61,
|
||||
0x6e, 0x64, 0x20, 0x73, 0x6f, 0x20, 0x6f, 0x6e, 0x2e, 0x0a, 0x65, 0x78,
|
||||
0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c,
|
||||
0x6c, 0x61, 0x6d, 0x61, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66,
|
||||
0x6f, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x63,
|
||||
0x6f, 0x6e, 0x66, 0x69, 0x67, 0x20, 0x3d, 0x20, 0x7b, 0x7d, 0x29, 0x20,
|
||||
0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21,
|
||||
0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73,
|
||||
0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20,
|
||||
0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x61, 0x70, 0x69,
|
||||
0x5f, 0x75, 0x72, 0x6c, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69,
|
||||
0x67, 0x2e, 0x61, 0x70, 0x69, 0x5f, 0x75, 0x72, 0x6c, 0x20, 0x7c, 0x7c,
|
||||
0x20, 0x22, 0x22, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e,
|
||||
0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x20, 0x3d, 0x20, 0x61,
|
||||
0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x60,
|
||||
0x24, 0x7b, 0x61, 0x70, 0x69, 0x5f, 0x75, 0x72, 0x6c, 0x7d, 0x2f, 0x70,
|
||||
0x72, 0x6f, 0x70, 0x73, 0x60, 0x29, 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28,
|
||||
0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28,
|
||||
0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x67, 0x65, 0x6e, 0x65,
|
||||
0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69,
|
||||
0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e,
|
||||
0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f, 0x67, 0x65, 0x6e, 0x65,
|
||||
0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69,
|
||||
0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
|
||||
0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
|
||||
0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
|
||||
0x73, 0x3b, 0x0a, 0x7d, 0x0a
|
||||
};
|
||||
size_t completion_js_len = 5796;
|
||||
unsigned int completion_js_len = 5909;
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -1928,4 +1928,4 @@ unsigned char index_js[] = {
|
|||
0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
|
||||
0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
|
||||
};
|
||||
size_t index_js_len = 23136;
|
||||
unsigned int index_js_len = 23136;
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ let generation_settings = null;
|
|||
//
|
||||
export async function* llama(prompt, params = {}, config = {}) {
|
||||
let controller = config.controller;
|
||||
const api_url = config.api_url || "";
|
||||
|
||||
if (!controller) {
|
||||
controller = new AbortController();
|
||||
|
|
@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
|
|||
|
||||
const completionParams = { ...paramDefaults, ...params, prompt };
|
||||
|
||||
const response = await fetch("/completion", {
|
||||
const response = await fetch(`${api_url}/completion`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify(completionParams),
|
||||
headers: {
|
||||
|
|
@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
|
|||
}
|
||||
|
||||
// Get the model info from the server. This is useful for getting the context window and so on.
|
||||
export const llamaModelInfo = async () => {
|
||||
export const llamaModelInfo = async (config = {}) => {
|
||||
if (!generation_settings) {
|
||||
const props = await fetch("/props").then(r => r.json());
|
||||
const api_url = config.api_url || "";
|
||||
const props = await fetch(`${api_url}/props`).then(r => r.json());
|
||||
generation_settings = props.default_generation_settings;
|
||||
}
|
||||
return generation_settings;
|
||||
|
|
|
|||
|
|
@ -199,10 +199,10 @@
|
|||
<script type="module">
|
||||
import {
|
||||
html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
|
||||
} from '/index.js';
|
||||
} from './index.js';
|
||||
|
||||
import { llama } from '/completion.js';
|
||||
import { SchemaConverter } from '/json-schema-to-grammar.mjs';
|
||||
import { llama } from './completion.js';
|
||||
import { SchemaConverter } from './json-schema-to-grammar.mjs';
|
||||
let selected_image = false;
|
||||
var slot_id = -1;
|
||||
|
||||
|
|
@ -405,7 +405,7 @@
|
|||
throw new Error("already running");
|
||||
}
|
||||
controller.value = new AbortController();
|
||||
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
|
||||
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
|
||||
const data = chunk.data;
|
||||
|
||||
if (data.stop) {
|
||||
|
|
|
|||
|
|
@ -1114,7 +1114,10 @@ def start_server_background(context):
|
|||
server_args.append('--verbose')
|
||||
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
|
||||
server_args.extend(['--log-format', "text"])
|
||||
print(f"starting server with: {context.server_path} {server_args}")
|
||||
|
||||
args = [str(arg) for arg in [context.server_path, *server_args]]
|
||||
print(f"bench: starting server with: {' '.join(args)}")
|
||||
|
||||
flags = 0
|
||||
if 'nt' == os.name:
|
||||
flags |= subprocess.DETACHED_PROCESS
|
||||
|
|
@ -1130,16 +1133,14 @@ def start_server_background(context):
|
|||
[str(arg) for arg in [context.server_path, *server_args]],
|
||||
**pkwargs)
|
||||
|
||||
def log_stdout(process):
|
||||
for line in iter(process.stdout.readline, b''):
|
||||
print(line.decode('utf-8'), end='')
|
||||
thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=out_stream)
|
||||
|
||||
thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
|
||||
thread_stdout.start()
|
||||
|
||||
def log_stderr(process):
|
||||
for line in iter(process.stderr.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=sys.stderr)
|
||||
thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
|
||||
thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
|
||||
thread_stderr.start()
|
||||
|
||||
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
|
||||
|
|
|
|||
11
flake.nix
11
flake.nix
|
|
@ -168,9 +168,14 @@
|
|||
};
|
||||
|
||||
# Packages exposed in `.#checks` will be built by the CI and by
|
||||
# `nix flake check`. Currently we expose all packages, but we could
|
||||
# make more granular choices
|
||||
checks = config.packages;
|
||||
# `nix flake check`.
|
||||
#
|
||||
# We could test all outputs e.g. as `checks = confg.packages`.
|
||||
#
|
||||
# TODO: Build more once https://github.com/ggerganov/llama.cpp/issues/6346 has been addressed
|
||||
checks = {
|
||||
inherit (config.packages) default vulkan;
|
||||
};
|
||||
};
|
||||
};
|
||||
}
|
||||
|
|
|
|||
|
|
@ -377,13 +377,20 @@ typedef struct {
|
|||
} block_iq1_s;
|
||||
static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
|
||||
|
||||
// 1.8125 bpw
|
||||
// 1.75 bpw
|
||||
typedef struct {
|
||||
uint8_t qs[QK_K/8]; // grid index, low 8 bits
|
||||
uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8)
|
||||
uint8_t scales[QK_K/32]; // 4-bit block scales
|
||||
#if QK_K == 64
|
||||
ggml_half d;
|
||||
#endif
|
||||
uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
|
||||
} block_iq1_m;
|
||||
#if QK_K == 64
|
||||
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
|
||||
#else
|
||||
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
|
||||
#endif
|
||||
|
||||
// Used by IQ1_M quants
|
||||
typedef union {
|
||||
|
|
|
|||
|
|
@ -4497,7 +4497,9 @@ void kernel_mul_mv_iq1_m_f32_impl(
|
|||
|
||||
device const float * y4 = y + 32 * ix;
|
||||
|
||||
#if QK_K != 64
|
||||
iq1m_scale_t scale;
|
||||
#endif
|
||||
|
||||
for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
|
||||
|
||||
|
|
@ -4519,7 +4521,9 @@ void kernel_mul_mv_iq1_m_f32_impl(
|
|||
|
||||
for (int row = 0; row < N_DST; row++) {
|
||||
|
||||
#if QK_K != 64
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
#endif
|
||||
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
|
||||
|
|
@ -4535,8 +4539,14 @@ void kernel_mul_mv_iq1_m_f32_impl(
|
|||
}
|
||||
const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
#if QK_K == 64
|
||||
const float d = (float) *((device const half *)(sc - 1));
|
||||
sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
|
||||
(sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
|
||||
#else
|
||||
sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
|
||||
(sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
|
||||
#endif
|
||||
|
||||
sc += nb*sizeof(block_iq1_m)/2;
|
||||
qs += nb*sizeof(block_iq1_m);
|
||||
|
|
@ -5277,13 +5287,21 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 &
|
|||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
iq1m_scale_t scale;
|
||||
device const uint16_t * sc = (device const uint16_t *)xb->scales;
|
||||
#if QK_K == 64
|
||||
const float d = xb->d;
|
||||
#else
|
||||
iq1m_scale_t scale;
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
const float d = scale.f16;
|
||||
#endif
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * qh = xb->qh + 2*ib32 + il;
|
||||
#if QK_K == 64
|
||||
const float dl = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
|
||||
#else
|
||||
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
|
||||
#endif
|
||||
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
|
||||
|
|
|
|||
|
|
@ -3481,19 +3481,30 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in
|
|||
float delta[4];
|
||||
uint16_t idx[4];
|
||||
|
||||
#if QK_K != 64
|
||||
iq1m_scale_t scale;
|
||||
#endif
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
|
||||
const uint16_t * sc = (const uint16_t *)x[i].scales;
|
||||
#if QK_K == 64
|
||||
const float d = GGML_FP16_TO_FP32(x[i].d);
|
||||
#else
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
const float d = GGML_FP16_TO_FP32(scale.f16);
|
||||
#endif
|
||||
const uint8_t * qs = x[i].qs;
|
||||
const uint8_t * qh = x[i].qh;
|
||||
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
#if QK_K == 64
|
||||
const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
|
||||
const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
|
||||
#else
|
||||
const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
|
||||
const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
|
||||
#endif
|
||||
idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
|
||||
idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
|
||||
idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
|
||||
|
|
@ -9756,11 +9767,17 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
|
||||
const int nb = n / QK_K;
|
||||
|
||||
#if QK_K != 64
|
||||
iq1m_scale_t scale;
|
||||
#endif
|
||||
|
||||
#if defined __ARM_NEON
|
||||
|
||||
#if QK_K == 64
|
||||
const int32x4_t mask = vdupq_n_s32(0xf);
|
||||
#else
|
||||
const int32x4_t mask = vdupq_n_s32(0x7);
|
||||
#endif
|
||||
const int32x4_t mone = vdupq_n_s32(1);
|
||||
const int32x4_t mzero = vdupq_n_s32(0);
|
||||
|
||||
|
|
@ -9784,7 +9801,9 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
const uint8_t * qh = x[i].qh;
|
||||
const uint16_t * sc = (const uint16_t *)x[i].scales;
|
||||
|
||||
#if QK_K != 64
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
#endif
|
||||
|
||||
int32x4_t sumi1 = mzero;
|
||||
int32x4_t sumi2 = mzero;
|
||||
|
|
@ -9813,7 +9832,11 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
|
||||
const int32x4_t p34 = vpaddq_s32(p3, p4);
|
||||
|
||||
#if QK_K == 64
|
||||
int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
|
||||
#else
|
||||
int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
|
||||
#endif
|
||||
scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
|
||||
|
||||
sumi1 = vmlaq_s32(sumi1, scales_4, p12);
|
||||
|
|
@ -9823,14 +9846,22 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
|
||||
}
|
||||
|
||||
#if QK_K == 64
|
||||
sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
|
||||
#else
|
||||
sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = sumf;
|
||||
|
||||
#elif defined __AVX2__
|
||||
|
||||
#if QK_K == 64
|
||||
const __m256i mask = _mm256_set1_epi16(0xf);
|
||||
#else
|
||||
const __m256i mask = _mm256_set1_epi16(0x7);
|
||||
#endif
|
||||
const __m256i mone = _mm256_set1_epi16(1);
|
||||
|
||||
__m256 accum1 = _mm256_setzero_ps();
|
||||
|
|
@ -9842,7 +9873,9 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
const uint8_t * qh = x[i].qh;
|
||||
const uint16_t * sc = (const uint16_t *)x[i].scales;
|
||||
|
||||
#if QK_K != 64
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
#endif
|
||||
|
||||
__m256i sumi1 = _mm256_setzero_si256();
|
||||
__m256i sumi2 = _mm256_setzero_si256();
|
||||
|
|
@ -9872,8 +9905,13 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
|
||||
const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
|
||||
const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
|
||||
#if QK_K == 64
|
||||
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 4), _mm_set1_epi16(sc[0] >> 0));
|
||||
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
|
||||
#else
|
||||
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
|
||||
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
|
||||
#endif
|
||||
scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
|
||||
scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
|
||||
const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
|
||||
|
|
@ -9887,7 +9925,11 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
qs += 8; qh += 4;
|
||||
}
|
||||
|
||||
#if QK_K == 64
|
||||
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
|
||||
#else
|
||||
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
|
||||
#endif
|
||||
accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
|
||||
accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
|
||||
|
||||
|
|
@ -9907,7 +9949,9 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
const uint8_t * qh = x[i].qh;
|
||||
const uint16_t * sc = (const uint16_t *)x[i].scales;
|
||||
|
||||
#if QK_K != 64
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
#endif
|
||||
|
||||
int sumi1 = 0, sumi2 = 0;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
|
|
@ -9927,15 +9971,24 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void
|
|||
sum1[l/2] += lsum1;
|
||||
sum2[l/2] += lsum2*delta[l];
|
||||
}
|
||||
#if QK_K == 64
|
||||
const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
|
||||
const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
|
||||
#else
|
||||
const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
|
||||
const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
|
||||
#endif
|
||||
sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
|
||||
sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
|
||||
qs += 4;
|
||||
qh += 2;
|
||||
}
|
||||
|
||||
#if QK_K == 64
|
||||
sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
|
||||
#else
|
||||
sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
|
||||
#endif
|
||||
}
|
||||
|
||||
*s = sumf;
|
||||
|
|
@ -11986,7 +12039,9 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
|
|||
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
|
||||
//y[ibl].d = GGML_FP32_TO_FP16(0.f);
|
||||
#if QK_K == 64
|
||||
y[ibl].d = GGML_FP32_TO_FP16(0.f);
|
||||
#endif
|
||||
memset(y[ibl].qs, 0, QK_K/8);
|
||||
memset(y[ibl].qh, 0, QK_K/16);
|
||||
memset(y[ibl].scales, 0, QK_K/32);
|
||||
|
|
@ -12161,13 +12216,22 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
|
|||
}
|
||||
|
||||
uint16_t * sc = (uint16_t *)y[ibl].scales;
|
||||
#if QK_K == 64
|
||||
float d = max_scale/31;
|
||||
#else
|
||||
float d = max_scale/15;
|
||||
#endif
|
||||
float id = 1/d;
|
||||
float sumqx_f = 0, sumq2_f = 0;
|
||||
for (int ib = 0; ib < QK_K/block_size; ++ib) {
|
||||
int l = nearest_int(0.5f*(id*scales[ib+0]-1));
|
||||
#if QK_K == 64
|
||||
l = MAX(0, MIN(15, l));
|
||||
sc[ib/4] |= (l << 4*(ib%4));
|
||||
#else
|
||||
l = MAX(0, MIN(7, l));
|
||||
sc[ib/4] |= (l << 3*(ib%4));
|
||||
#endif
|
||||
y[ibl].qh[ib] |= masks[shifts[ib]];
|
||||
const float * xb = xbl + block_size*ib;
|
||||
if (quant_weights) {
|
||||
|
|
@ -12190,10 +12254,14 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
|
|||
}
|
||||
if (sumq2_f > 0) d = sumqx_f/sumq2_f;
|
||||
s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
|
||||
#if QK_K == 64
|
||||
y[ibl].d = s.f16;
|
||||
#else
|
||||
sc[0] |= ((s.u16 & 0x000f) << 12);
|
||||
sc[1] |= ((s.u16 & 0x00f0) << 8);
|
||||
sc[2] |= ((s.u16 & 0x0f00) << 4);
|
||||
sc[3] |= ((s.u16 & 0xf000) << 0);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -14986,6 +14986,9 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
|
|||
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
|
||||
dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
|
||||
|
||||
bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
|
||||
main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
|
||||
|
||||
SYCL_CHECK(
|
||||
CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));
|
||||
|
||||
|
|
@ -15016,24 +15019,38 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
|
|||
|
||||
dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
|
||||
dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
|
||||
if (no_mixed_dtypes) {
|
||||
cu_compute_type = dpct::library_data_t::real_half;
|
||||
cu_data_type = dpct::library_data_t::real_half;
|
||||
}
|
||||
|
||||
// dst strides
|
||||
size_t nbd2 = dst->nb[2];
|
||||
size_t nbd3 = dst->nb[3];
|
||||
|
||||
const float alpha_f32 = 1.0f;
|
||||
const float beta_f32 = 0.0f;
|
||||
|
||||
const sycl::half alpha_f16 = 1.0f;
|
||||
const sycl::half beta_f16 = 0.0f;
|
||||
|
||||
const float alpha_f32 = 1.0f;
|
||||
const float beta_f32 = 0.0f;
|
||||
|
||||
const void * alpha = &alpha_f32;
|
||||
const void * beta = &beta_f32;
|
||||
if (no_mixed_dtypes) {
|
||||
alpha = &alpha_f16;
|
||||
beta = &beta_f16;
|
||||
}
|
||||
|
||||
// TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
|
||||
// oneMKL open source supports half, half, float, float: datatypes
|
||||
// when oneMKL open source supports half, half, float, float: datatypes
|
||||
|
||||
dst_t = (char *) dst_ddf;
|
||||
if (no_mixed_dtypes) {
|
||||
dst_t = (char *) dst_f16.alloc(ne_dst);
|
||||
|
||||
nbd2 /= sizeof(float) / sizeof(sycl::half);
|
||||
nbd3 /= sizeof(float) / sizeof(sycl::half);
|
||||
}
|
||||
|
||||
GGML_ASSERT(ne12 % ne02 == 0);
|
||||
GGML_ASSERT(ne13 % ne03 == 0);
|
||||
|
|
@ -15119,6 +15136,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
|
|||
}
|
||||
#endif
|
||||
|
||||
if (no_mixed_dtypes) {
|
||||
const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
|
||||
to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
|
||||
}
|
||||
}
|
||||
catch (sycl::exception const &exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
|
||||
|
|
|
|||
2
ggml.c
2
ggml.c
|
|
@ -2938,7 +2938,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
|||
data_size *= ne[i];
|
||||
}
|
||||
|
||||
GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
|
||||
GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src));
|
||||
|
||||
void * data = view_src != NULL ? view_src->data : NULL;
|
||||
if (data != NULL) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue