Merge branch 'master' into concedo_experimental

# Conflicts:
#	.github/workflows/build.yml
#	CMakeLists.txt
#	Makefile
#	README-sycl.md
#	README.md
#	flake.lock
#	scripts/sync-ggml-am.sh
#	scripts/sync-ggml.last
#	scripts/sync-ggml.sh
#	tests/.gitignore
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo
2024-03-11 23:00:47 +08:00
41 changed files with 4274 additions and 4304 deletions
+6
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@@ -1,5 +1,6 @@
set(TARGET server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
install(TARGETS ${TARGET} RUNTIME)
@@ -7,6 +8,11 @@ target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
if (LLAMA_SERVER_SSL)
find_package(OpenSSL REQUIRED)
target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
endif()
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()
+80 -8
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@@ -42,7 +42,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default examples/server/public)
- `--path`: path from which to serve static files (default: disabled)
- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
- `--embedding`: Enable embedding extraction, Default: disabled.
@@ -59,6 +59,10 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `--log-disable`: Output logs to stdout only, default: enabled.
- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
**If compiled with `LLAMA_SERVER_SSL=ON`**
- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
## Build
server is build alongside everything else from the root of the project
@@ -75,6 +79,28 @@ server is build alongside everything else from the root of the project
cmake --build . --config Release
```
## Build with SSL
server can also be built with SSL support using OpenSSL 3
- Using `make`:
```bash
# NOTE: For non-system openssl, use the following:
# CXXFLAGS="-I /path/to/openssl/include"
# LDFLAGS="-L /path/to/openssl/lib"
make LLAMA_SERVER_SSL=true server
```
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_SERVER_SSL=ON
make server
```
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
@@ -97,10 +123,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can
### Docker
```bash
docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
# or, with CUDA:
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
```
## Testing with CURL
@@ -169,7 +195,11 @@ node index.js
*Options:*
`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
- The prompt is a string or an array with the first element given as a string
- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
- The system prompt is empty
`temperature`: Adjust the randomness of the generated text (default: 0.8).
@@ -282,7 +312,7 @@ Notice that each `probs` is an array of length `n_probs`.
`content`: Set the text to tokenize.
Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
Note that a special `BOS` token is never inserted.
- **POST** `/detokenize`: Convert tokens to text.
@@ -526,13 +556,55 @@ Run with bash:
bash chat.sh
```
### API like OAI
### OAI-like API
The HTTP server supports OAI-like API
The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
### API errors
Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
Example of an error:
```json
{
"error": {
"code": 401,
"message": "Invalid API Key",
"type": "authentication_error"
}
}
```
Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
**When /metrics or /slots endpoint is disabled**
```json
{
"error": {
"code": 501,
"message": "This server does not support metrics endpoint.",
"type": "not_supported_error"
}
}
```
**When the server receives invalid grammar via */completions endpoint**
```json
{
"error": {
"code": 400,
"message": "Failed to parse grammar",
"type": "invalid_request_error"
}
}
```
### Extending or building alternative Web Front End
The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
Read the documentation in `/completion.js` to see convenient ways to access llama.
+88
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@@ -0,0 +1,88 @@
### Server benchmark tools
Benchmark is using [k6](https://k6.io/).
##### Install k6
Follow instruction from: https://k6.io/docs/get-started/installation/
Example for ubuntu:
```shell
snap install k6
```
#### Download a dataset
This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
```shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
#### Download a model
Example for PHI-2
```shell
../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
```
#### Start the server
The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
Example:
```shell
server --host localhost --port 8080 \
--model ggml-model-q4_0.gguf \
--cont-batching \
--metrics \
--parallel 8 \
--batch-size 512 \
--ctx-size 4096 \
--log-format text \
-ngl 33
```
#### Run the benchmark
For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
```shell
k6 run script.js --duration 10m --iterations 500 --vus 8
```
The benchmark values can be overridden with:
- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
- `SERVER_BENCH_DATASET` path to the benchmark dataset file
- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
Note: the local tokenizer is just a string space split, real number of tokens will differ.
Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
```shell
SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
```
To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
#### Metrics
Following metrics are available computed from the OAI chat completions response `usage`:
- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
K6 metrics might be compared against [server metrics](../README.md), with:
```shell
curl http://localhost:8080/metrics
```
+120
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@@ -0,0 +1,120 @@
import http from 'k6/http'
import {check, sleep} from 'k6'
import {SharedArray} from 'k6/data'
import {Counter, Rate, Trend} from 'k6/metrics'
import exec from 'k6/execution';
// Server chat completions prefix
const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
// Model name to request
const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
// Dataset path
const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
// Max tokens to predict
const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
// Max prompt tokens
const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
// Max slot context
const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
export function setup() {
console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
}
const data = new SharedArray('conversations', function () {
const tokenizer = (message) => message.split(/[\s,'".?]/)
return JSON.parse(open(dataset_path))
// Filter out the conversations with less than 2 turns.
.filter(data => data["conversations"].length >= 2)
.filter(data => data["conversations"][0]["from"] === "human")
.map(data => {
return {
prompt: data["conversations"][0]["value"],
n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
}
})
// Filter out too short sequences
.filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
// Filter out too long sequences.
.filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
// Keep only first n prompts
.slice(0, n_prompt)
})
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
export const options = {
thresholds: {
llamacpp_completions_truncated_rate: [
// more than 80% of truncated input will abort the test
{threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
],
},
duration: '10m',
vus: 8,
}
export default function () {
const conversation = data[exec.scenario.iterationInInstance % data.length]
const payload = {
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant.",
},
{
"role": "user",
"content": conversation.prompt,
}
],
"model": model,
"stream": false,
"max_tokens": max_tokens
}
const body = JSON.stringify(payload)
let res = http.post(`${server_url}/chat/completions`, body, {
headers: {'Content-Type': 'application/json'},
timeout: '300s'
})
check(res, {'success completion': (r) => r.status === 200})
if (res.status === 200) {
const completions = res.json()
llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
llamacpp_completion_tokens.add(completions.usage.completion_tokens)
llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
} else {
console.error(`response: ${res.body} request=${payload}`)
}
sleep(0.3)
}
+235 -234
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};
unsigned int completion_js_len = 5782;
unsigned int completion_js_len = 5796;
+10 -10
View File
@@ -96,18 +96,18 @@ export async function* llama(prompt, params = {}, config = {}) {
}
}
if (result.error) {
result.error = JSON.parse(result.error);
if (result.error.content.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error: ${result.error.content}`);
try {
result.error = JSON.parse(result.error);
if (result.error.message.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
}
} catch(e) {
console.error(`llama.cpp error ${result.error}`)
}
}
if (result.error) {
result.error = JSON.parse(result.error);
console.error(`llama.cpp error: ${result.error.content}`);
}
}
}
}
+568 -424
View File
File diff suppressed because it is too large Load Diff
+47 -19
View File
@@ -1,9 +1,10 @@
import errno
import os
import socket
import subprocess
import time
from contextlib import closing
from signal import SIGKILL
import signal
def before_scenario(context, scenario):
@@ -29,44 +30,71 @@ def after_scenario(context, scenario):
for line in f:
print(line)
if not is_server_listening(context.server_fqdn, context.server_port):
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n")
if not pid_exists(context.server_process.pid):
assert False, f"Server not running pid={context.server_process.pid} ..."
print(f"stopping server pid={context.server_process.pid} ...")
context.server_process.kill()
server_graceful_shutdown(context)
# Wait few for socket to free up
time.sleep(0.05)
attempts = 0
while is_server_listening(context.server_fqdn, context.server_port):
print(f"stopping server pid={context.server_process.pid} ...")
os.kill(context.server_process.pid, SIGKILL)
while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port):
server_kill(context)
time.sleep(0.1)
attempts += 1
if attempts > 5:
print(f"Server dangling exits, killing all {context.server_path} ...")
process = subprocess.run(['killall', '-9', context.server_path],
stderr=subprocess.PIPE,
universal_newlines=True)
print(process)
server_kill_hard(context)
def server_graceful_shutdown(context):
print(f"shutting down server pid={context.server_process.pid} ...\n")
if os.name == 'nt':
os.kill(context.server_process.pid, signal.CTRL_C_EVENT)
else:
os.kill(context.server_process.pid, signal.SIGINT)
def server_kill(context):
print(f"killing server pid={context.server_process.pid} ...\n")
context.server_process.kill()
def server_kill_hard(context):
pid = context.server_process.pid
path = context.server_path
print(f"Server dangling exits, hard killing force {pid}={path}...\n")
if os.name == 'nt':
process = subprocess.check_output(['taskkill', '/F', '/pid', str(pid)]).decode()
print(process)
else:
os.kill(-pid, signal.SIGKILL)
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))
return result == 0
_is_server_listening = result == 0
if _is_server_listening:
print(f"server is listening on {server_fqdn}:{server_port}...\n")
return _is_server_listening
def pid_exists(pid):
"""Check whether pid exists in the current process table."""
import errno
if pid < 0:
return False
try:
os.kill(pid, 0)
except OSError as e:
return e.errno == errno.EPERM
if os.name == 'nt':
output = subprocess.check_output(['TASKLIST', '/FI', f'pid eq {pid}']).decode()
print(output)
return "No tasks are running" not in output
else:
return True
try:
os.kill(pid, 0)
except OSError as e:
return e.errno == errno.EPERM
else:
return True
@@ -6,8 +6,8 @@ Feature: Parallel
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And 42 as server seed
And 512 as batch size
And 64 KV cache size
And 128 as batch size
And 256 KV cache size
And 2 slots
And continuous batching
Then the server is starting
@@ -76,6 +76,7 @@ Feature: Parallel
| disabled | 128 |
| enabled | 64 |
Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
Given a prompt:
"""
@@ -39,8 +39,9 @@ Feature: Security
Scenario Outline: CORS Options
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>
Given a user api key llama.cpp
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>
Examples: Headers
| origin | cors_header | cors_header_value |
+30 -11
View File
@@ -10,11 +10,10 @@ Feature: llama.cpp server
# KV Cache corresponds to the total amount of tokens
# that can be stored across all independent sequences: #4130
# see --ctx-size and #5568
And 32 KV cache size
And 512 as batch size
And 1 slots
And embeddings extraction
And 32 server max tokens to predict
And 256 KV cache size
And 32 as batch size
And 2 slots
And 64 server max tokens to predict
And prometheus compatible metrics exposed
Then the server is starting
Then the server is healthy
@@ -23,18 +22,35 @@ Feature: llama.cpp server
Then the server is ready
And all slots are idle
Scenario Outline: Completion
Given a prompt <prompt>
And <n_predict> max tokens to predict
And a completion request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
And the completion is <truncated> truncated
And <n_prompt> prompt tokens are processed
And prometheus metrics are exposed
And metric llamacpp:tokens_predicted is <n_predicted>
Examples: Prompts
| prompt | n_predict | re_content | n_predicted |
| I believe the meaning of life is | 8 | (read\|going)+ | 8 |
| Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 |
| prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
| I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not |
Scenario: Completion prompt truncated
Given a prompt:
"""
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
"""
And a completion request with no api error
Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry
And the completion is truncated
And 109 prompt tokens are processed
Scenario Outline: OAI Compatibility
Given a model <model>
@@ -44,11 +60,14 @@ Feature: llama.cpp server
And streaming is <enable_streaming>
Given an OAI compatible chat completions request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
And <n_prompt> prompt tokens are processed
And the completion is <truncated> truncated
Examples: Prompts
| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
| llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled |
| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | |
Scenario: Tokenize / Detokenize
When tokenizing:
+127 -83
View File
@@ -18,7 +18,7 @@ from huggingface_hub import hf_hub_download
from prometheus_client import parser
@step(u"a server listening on {server_fqdn}:{server_port}")
@step("a server listening on {server_fqdn}:{server_port}")
def step_server_config(context, server_fqdn, server_port):
context.server_fqdn = server_fqdn
context.server_port = int(server_port)
@@ -57,24 +57,24 @@ def step_server_config(context, server_fqdn, server_port):
context.prompts = []
@step(u'a model file {hf_file} from HF repo {hf_repo}')
@step('a model file {hf_file} from HF repo {hf_repo}')
def step_download_hf_model(context, hf_file, hf_repo):
context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
if context.debug:
print(f"model file: {context.model_file}\n")
@step(u'a model alias {model_alias}')
@step('a model alias {model_alias}')
def step_model_alias(context, model_alias):
context.model_alias = model_alias
@step(u'{seed:d} as server seed')
@step('{seed:d} as server seed')
def step_seed(context, seed):
context.server_seed = seed
@step(u'{ngl:d} GPU offloaded layers')
@step('{ngl:d} GPU offloaded layers')
def step_n_gpu_layer(context, ngl):
if 'N_GPU_LAYERS' in os.environ:
new_ngl = int(os.environ['N_GPU_LAYERS'])
@@ -84,37 +84,37 @@ def step_n_gpu_layer(context, ngl):
context.n_gpu_layer = ngl
@step(u'{n_ctx:d} KV cache size')
@step('{n_ctx:d} KV cache size')
def step_n_ctx(context, n_ctx):
context.n_ctx = n_ctx
@step(u'{n_slots:d} slots')
@step('{n_slots:d} slots')
def step_n_slots(context, n_slots):
context.n_slots = n_slots
@step(u'{n_predict:d} server max tokens to predict')
@step('{n_predict:d} server max tokens to predict')
def step_server_n_predict(context, n_predict):
context.n_server_predict = n_predict
@step(u'continuous batching')
@step('continuous batching')
def step_server_continuous_batching(context):
context.server_continuous_batching = True
@step(u'embeddings extraction')
@step('embeddings extraction')
def step_server_embeddings(context):
context.server_embeddings = True
@step(u'prometheus compatible metrics exposed')
@step('prometheus compatible metrics exposed')
def step_server_metrics(context):
context.server_metrics = True
@step(u"the server is starting")
@step("the server is starting")
def step_start_server(context):
start_server_background(context)
attempts = 0
@@ -131,7 +131,7 @@ def step_start_server(context):
time.sleep(0.1)
@step(u"the server is {expecting_status}")
@step("the server is {expecting_status}")
@async_run_until_complete
async def step_wait_for_the_server_to_be_started(context, expecting_status):
match expecting_status:
@@ -160,7 +160,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
assert False, "unknown status"
@step(u'all slots are {expected_slot_status_string}')
@step('all slots are {expected_slot_status_string}')
@async_run_until_complete
async def step_all_slots_status(context, expected_slot_status_string):
match expected_slot_status_string:
@@ -176,7 +176,7 @@ async def step_all_slots_status(context, expected_slot_status_string):
await request_slots_status(context, expected_slots)
@step(u'a completion request with {api_error} api error')
@step('a completion request with {api_error} api error')
@async_run_until_complete
async def step_request_completion(context, api_error):
expect_api_error = api_error == 'raised'
@@ -194,115 +194,133 @@ async def step_request_completion(context, api_error):
assert completion == 401, f"completion must be an 401 status code: {completion}"
@step(u'{predicted_n:d} tokens are predicted matching {re_content}')
@step('{predicted_n:d} tokens are predicted matching {re_content}')
def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content)
context.completion = context.tasks_result.pop()
assert_n_tokens_predicted(context.completion, predicted_n, re_content)
@step(u'{predicted_n:d} tokens are predicted')
@step('{predicted_n:d} tokens are predicted')
def step_n_tokens_predicted(context, predicted_n):
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n)
context.completion = context.tasks_result.pop()
assert_n_tokens_predicted(context.completion, predicted_n)
@step(u'a user prompt {user_prompt}')
@step('the completion is truncated')
def step_assert_completion_truncated(context):
step_assert_completion_truncated(context, '')
@step('the completion is {truncated} truncated')
def step_assert_completion_truncated(context, truncated):
truncated = truncated != "not"
assert context.completion['truncated'] == truncated, f'{context.completion}'
@step('{n_prompt:d} prompt tokens are processed')
def step_impl(context, n_prompt):
assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
@step('a user prompt {user_prompt}')
def step_user_prompt(context, user_prompt):
context.prompts.append(user_prompt)
context.n_prompts = len(context.prompts)
@step(u'a system prompt {system_prompt}')
@step('a system prompt {system_prompt}')
def step_system_prompt(context, system_prompt):
context.system_prompt = system_prompt
@step(u'a model {model}')
@step('a model {model}')
def step_model(context, model):
context.model = model
@step(u'{max_tokens:d} max tokens to predict')
@step('{max_tokens:d} max tokens to predict')
def step_max_tokens(context, max_tokens):
context.n_predict = max_tokens
@step(u'streaming is {enable_streaming}')
@step('streaming is {enable_streaming}')
def step_streaming(context, enable_streaming):
context.enable_streaming = enable_streaming == 'enabled'
@step(u'a user api key {user_api_key}')
@step('a user api key {user_api_key}')
def step_user_api_key(context, user_api_key):
context.user_api_key = user_api_key
@step(u'no user api key')
@step('no user api key')
def step_no_user_api_key(context):
context.user_api_key = None
@step(u'a user api key ')
@step('a user api key ')
def step_no_user_api_key_space(context):
context.user_api_key = None
@step(u'a server api key {server_api_key}')
@step('a server api key {server_api_key}')
def step_server_api_key(context, server_api_key):
context.server_api_key = server_api_key
@step(u'{n_junk:d} as number of junk')
@step('{n_junk:d} as number of junk')
def step_n_junk(context, n_junk):
context.n_junk = n_junk
@step(u'{n_batch:d} as batch size')
@step('{n_batch:d} as batch size')
def step_n_batch(context, n_batch):
context.n_batch = n_batch
@step(u'{seed:d} as seed')
@step('{seed:d} as seed')
def step_seed(context, seed):
context.seed = seed
@step(u'a prefix prompt')
@step('a prefix prompt')
def step_prompt_prefix(context):
context.prompt_prefix = context.text
context.prompt_prefix = context_text(context)
@step(u'a junk suffix prompt')
@step('a junk suffix prompt')
def step_prompt_junk_suffix(context):
context.prompt_junk_suffix = context.text
context.prompt_junk_suffix = context_text(context)
@step(u'a suffix prompt')
@step('a suffix prompt')
def step_prompt_suffix(context):
context.prompt_suffix = context.text
context.prompt_suffix = context_text(context)
@step(u'{n_ga:d} group attention factor'
u' to extend context size through self-extend')
@step('{n_ga:d} group attention factor'
' to extend context size through self-extend')
def step_impl(context, n_ga):
context.n_ga = n_ga
@step(u'{n_ga_w:d} group attention width to extend context size through self-extend')
@step('{n_ga_w:d} group attention width to extend context size through self-extend')
def step_impl(context, n_ga_w):
context.n_ga_w = n_ga_w
@step(u'a passkey prompt template')
@step('a passkey prompt template')
def step_prompt_passkey(context):
context.prompt_passkey = context.text
context.prompt_passkey = context_text(context)
@step(u'{n_prompts:d} fixed prompts')
@step('{n_prompts:d} fixed prompts')
def step_fixed_prompts(context, n_prompts):
context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
context.n_prompts = n_prompts
@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
def step_prompt_passkey(context, passkey, i_pos):
prompt = ""
for i in range(context.n_junk):
@@ -316,7 +334,7 @@ def step_prompt_passkey(context, passkey, i_pos):
context.n_prompts = len(context.prompts)
@step(u'an OAI compatible chat completions request with {api_error} api error')
@step('an OAI compatible chat completions request with {api_error} api error')
@async_run_until_complete
async def step_oai_chat_completions(context, api_error):
if context.debug:
@@ -351,19 +369,19 @@ async def step_oai_chat_completions(context, api_error):
print(f"Completion response: {completion}")
@step(u'a prompt')
@step('a prompt')
def step_a_prompt(context):
context.prompts.append(context.text)
context.prompts.append(context_text(context))
context.n_prompts = len(context.prompts)
@step(u'a prompt {prompt}')
@step('a prompt {prompt}')
def step_a_prompt_prompt(context, prompt):
context.prompts.append(prompt)
context.n_prompts = len(context.prompts)
@step(u'concurrent completion requests')
@step('concurrent completion requests')
@async_run_until_complete()
async def step_concurrent_completion_requests(context):
await concurrent_requests(context,
@@ -379,7 +397,7 @@ async def step_concurrent_completion_requests(context):
'user_api_key') else None)
@step(u'concurrent OAI completions requests')
@step('concurrent OAI completions requests')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_requests(context, oai_chat_completions,
@@ -399,7 +417,7 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'user_api_key') else None)
@step(u'concurrent OAI completions requests no v1')
@step('concurrent OAI completions requests no v1')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_requests(context, oai_chat_completions,
@@ -422,13 +440,13 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'user_api_key') else None)
@step(u'all prompts are predicted')
@step('all prompts are predicted')
@async_run_until_complete
async def step_all_prompts_are_predicted(context):
await all_prompts_are_predicted(context)
@step(u'all prompts are predicted with {n_expected_predicted:d} tokens')
@step('all prompts are predicted with {n_expected_predicted:d} tokens')
@async_run_until_complete
async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
await all_prompts_are_predicted(context, n_expected_predicted)
@@ -442,14 +460,14 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
@step(u'embeddings are computed for')
@step('embeddings are computed for')
@async_run_until_complete
async def step_compute_embedding(context):
context.n_prompts = 1
context.embeddings = await request_embedding(context.text, base_url=context.base_url)
context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
@step(u'all embeddings are the same')
@step('all embeddings are the same')
@async_run_until_complete
async def step_all_embeddings_are_the_same(context):
n_embedding_requests = await gather_tasks_results(context)
@@ -473,7 +491,8 @@ async def step_all_embeddings_are_the_same(context):
print(f"{msg}\n")
assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
@step(u'embeddings are generated')
@step('embeddings are generated')
def step_assert_embeddings(context):
assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
f"context.n_prompts={context.n_prompts}\n"
@@ -482,17 +501,17 @@ def step_assert_embeddings(context):
assert_embeddings(embedding)
@step(u'an OAI compatible embeddings computation request for')
@step('an OAI compatible embeddings computation request for')
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.n_prompts = 1
context.embeddings = await request_oai_embeddings(context.text,
context.embeddings = await request_oai_embeddings(context_text(context),
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@step(u'an OAI compatible embeddings computation request for multiple inputs')
@step('an OAI compatible embeddings computation request for multiple inputs')
@async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts,
@@ -502,7 +521,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context):
context.prompts.clear()
@step(u'concurrent embedding requests')
@step('concurrent embedding requests')
@async_run_until_complete()
async def step_concurrent_embedding_requests(context):
await concurrent_requests(context,
@@ -511,7 +530,7 @@ async def step_concurrent_embedding_requests(context):
base_url=context.base_url)
@step(u'concurrent OAI embedding requests')
@step('concurrent OAI embedding requests')
@async_run_until_complete()
async def step_concurrent_oai_embedding_requests(context):
await concurrent_requests(context,
@@ -522,7 +541,7 @@ async def step_concurrent_oai_embedding_requests(context):
model=context.model)
@step(u'all embeddings are generated')
@step('all embeddings are generated')
@async_run_until_complete()
async def all_embeddings_are_generated(context):
n_embedding_requests = await gather_tasks_results(context)
@@ -531,10 +550,10 @@ async def all_embeddings_are_generated(context):
assert_embeddings(context.tasks_result.pop().pop())
@step(u'tokenizing')
@step('tokenizing')
@async_run_until_complete
async def step_tokenize(context):
context.tokenized_text = context.text
context.tokenized_text = context_text(context)
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/tokenize',
json={
@@ -545,7 +564,7 @@ async def step_tokenize(context):
context.tokens = tokenize_json['tokens']
@step(u'tokens can be detokenize')
@step('tokens can be detokenize')
@async_run_until_complete
async def step_detokenize(context):
assert len(context.tokens) > 0
@@ -560,22 +579,23 @@ async def step_detokenize(context):
assert context.tokenized_text == detokenize_json['content'].strip()
@step(u'an OPTIONS request is sent from {origin}')
@step('an OPTIONS request is sent from {origin}')
@async_run_until_complete
async def step_options_request(context, origin):
async with aiohttp.ClientSession() as session:
headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
async with session.options(f'{context.base_url}/v1/chat/completions',
headers={"Origin": origin}) as response:
headers=headers) as response:
assert response.status == 200
context.options_response = response
@step(u'CORS header {cors_header} is set to {cors_header_value}')
@step('CORS header {cors_header} is set to {cors_header_value}')
def step_check_options_header_value(context, cors_header, cors_header_value):
assert context.options_response.headers[cors_header] == cors_header_value
@step(u'prometheus metrics are exposed')
@step('prometheus metrics are exposed')
@async_run_until_complete
async def step_prometheus_metrics_exported(context):
async with aiohttp.ClientSession() as session:
@@ -597,14 +617,14 @@ async def step_prometheus_metrics_exported(context):
assert metric_exported, "No metrics exported"
@step(u'metric {metric_name} is {metric_value:d}')
@step('metric {metric_name} is {metric_value:d}')
def step_assert_metric_value(context, metric_name, metric_value):
if metric_name not in context.metrics:
assert False, f"no metric {metric_name} in {context.metrics.keys()}"
assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
@step(u'available models')
@step('available models')
def step_available_models(context):
# openai client always expects an api_key
openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
@@ -612,14 +632,14 @@ def step_available_models(context):
context.models = openai.Model.list().data
@step(u'{n_model:d} models are supported')
@step('{n_model:d} models are supported')
def step_supported_models(context, n_model):
if context.debug:
print("server models available:", context.models)
assert len(context.models) == n_model
@step(u'model {i_model:d} is {param} {preposition} {param_value}')
@step('model {i_model:d} is {param} {preposition} {param_value}')
def step_supported_models(context, i_model, param, preposition, param_value):
assert i_model < len(context.models)
model = context.models[i_model]
@@ -722,7 +742,8 @@ async def oai_chat_completions(user_prompt,
completion_response = {
'content': '',
'timings': {
'predicted_n': 0
'predicted_n': 0,
'prompt_n': 0
}
}
if async_client:
@@ -763,7 +784,8 @@ async def oai_chat_completions(user_prompt,
completion_response = {
'content': chat_completion_raw['choices'][0]['message'],
'timings': {
'predicted_n': chat_completion_raw['usage']['completion_tokens']
'predicted_n': chat_completion_raw['usage']['completion_tokens'],
'prompt_n': chat_completion_raw['usage']['prompt_tokens']
}
}
else:
@@ -779,7 +801,7 @@ async def oai_chat_completions(user_prompt,
stream=enable_streaming,
seed=seed
)
except openai.error.APIError as e:
except openai.error.AuthenticationError as e:
if expect_api_error is not None and expect_api_error:
return 401
else:
@@ -792,13 +814,16 @@ async def oai_chat_completions(user_prompt,
if 'content' in delta:
completion_response['content'] += delta['content']
completion_response['timings']['predicted_n'] += 1
completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
else:
assert len(chat_completion.choices) == 1
completion_response = {
'content': chat_completion.choices[0].message.content,
'timings': {
'predicted_n': chat_completion.usage.completion_tokens
}
'predicted_n': chat_completion.usage.completion_tokens,
'prompt_n': chat_completion.usage.prompt_tokens
},
'truncated': chat_completion.choices[0].finish_reason != 'stop'
}
if debug:
print("OAI response formatted to llama.cpp:", completion_response)
@@ -983,12 +1008,22 @@ async def completions_seed(context):
else context.server_seed if hasattr(context, 'server_seed') else None
def context_text(context):
return context.text.replace('\r', '')
def start_server_background(context):
context.server_path = '../../../build/bin/server'
if os.name == 'nt':
context.server_path = '../../../build/bin/Release/server.exe'
else:
context.server_path = '../../../build/bin/server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_listen_addr = context.server_fqdn
if os.name == 'nt':
server_listen_addr = '0.0.0.0'
server_args = [
'--host', context.server_fqdn,
'--host', server_listen_addr,
'--port', context.server_port,
'--model', context.model_file
]
@@ -1021,7 +1056,16 @@ def start_server_background(context):
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}\n")
flags = 0
if 'nt' == os.name:
flags |= subprocess.DETACHED_PROCESS
flags |= subprocess.CREATE_NEW_PROCESS_GROUP
flags |= subprocess.CREATE_NO_WINDOW
pkwargs = {
'creationflags': flags,
}
context.server_process = subprocess.Popen(
[str(arg) for arg in [context.server_path, *server_args]],
close_fds=True)
print(f"server pid={context.server_process.pid}")
**pkwargs)
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
+57 -6
View File
@@ -14,6 +14,17 @@
using json = nlohmann::json;
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
ERROR_TYPE_INVALID_REQUEST,
ERROR_TYPE_AUTHENTICATION,
ERROR_TYPE_SERVER,
ERROR_TYPE_NOT_FOUND,
ERROR_TYPE_PERMISSION,
ERROR_TYPE_UNAVAILABLE, // custom error
ERROR_TYPE_NOT_SUPPORTED, // custom error
};
extern bool server_verbose;
extern bool server_log_json;
@@ -378,7 +389,7 @@ static json oaicompat_completion_params_parse(
return llama_params;
}
static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) {
static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
bool stopped_word = result.count("stopped_word") != 0;
bool stopped_eos = json_value(result, "stopped_eos", false);
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
@@ -412,7 +423,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
}},
{"id", gen_chatcmplid()}
{"id", completion_id}
};
if (server_verbose) {
@@ -427,7 +438,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
}
// return value is vector as there is one case where we might need to generate two responses
static std::vector<json> format_partial_response_oaicompat(json result) {
static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({result});
}
@@ -471,7 +482,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
{"role", "assistant"}
}}}})},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}};
@@ -482,7 +493,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
{"content", content}}}
}})},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}};
@@ -509,7 +520,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
json ret = json {
{"choices", choices},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}
};
@@ -542,3 +553,43 @@ static json format_detokenized_response(const std::string & content) {
{"content", content}
};
}
static json format_error_response(const std::string & message, const enum error_type type) {
std::string type_str;
int code = 500;
switch (type) {
case ERROR_TYPE_INVALID_REQUEST:
type_str = "invalid_request_error";
code = 400;
break;
case ERROR_TYPE_AUTHENTICATION:
type_str = "authentication_error";
code = 401;
break;
case ERROR_TYPE_NOT_FOUND:
type_str = "not_found_error";
code = 404;
break;
case ERROR_TYPE_SERVER:
type_str = "server_error";
code = 500;
break;
case ERROR_TYPE_PERMISSION:
type_str = "permission_error";
code = 403;
break;
case ERROR_TYPE_NOT_SUPPORTED:
type_str = "not_supported_error";
code = 501;
break;
case ERROR_TYPE_UNAVAILABLE:
type_str = "unavailable_error";
code = 503;
break;
}
return json {
{"code", code},
{"message", message},
{"type", type_str},
};
}