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server: continue to update other slots on embedding concurrent request (#5699)

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* server: #5655 - continue to update other slots on embedding concurrent request.

* server: tests: add multi users embeddings as fixed

* server: tests: adding OAI compatible embedding concurrent endpoint

* server: tests: adding OAI compatible embedding with multiple inputs
This commit is contained in:
Pierrick Hymbert 2024-02-24 19:16:04 +01:00 committed by GitHub
parent 4c4cb30736
commit 9e359a4f47
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5 changed files with 168 additions and 78 deletions
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2
examples/server/server.cpp
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@ -1836,7 +1836,7 @@ struct llama_server_context
send_embedding(slot);
slot.release();
slot.i_batch = -1;
return true;
continue;
}
completion_token_output result;

34
examples/server/tests/features/issues.feature
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@ -1,36 +1,4 @@
# List of ongoing issues
@bug
Feature: Issues
# Issue #5655
Scenario: Multi users embeddings
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model alias tinyllama-2
And 42 as server seed
And 64 KV cache size
And 2 slots
And continuous batching
And embeddings extraction
Then the server is starting
Then the server is healthy
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
Given concurrent embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
# No confirmed issue at the moment

46
examples/server/tests/features/parallel.feature
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@ -8,6 +8,7 @@ Feature: Parallel
And 42 as server seed
And 64 KV cache size
And 2 slots
And embeddings extraction
And continuous batching
Then the server is starting
Then the server is healthy
@ -75,3 +76,48 @@ Feature: Parallel
Then the server is busy
Then the server is idle
Then all prompts are predicted
Scenario: Multi users embeddings
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
Given concurrent embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
Scenario: Multi users OAI compatibility embeddings
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
And a prompt:
"""
What is the biggest US city ?
"""
And a prompt:
"""
What is the capital of Bulgaria ?
"""
And a model tinyllama-2
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated

13
examples/server/tests/features/server.feature
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@ -60,6 +60,19 @@ Feature: llama.cpp server
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility with multiple inputs
Given a model tinyllama-2
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
When an OAI compatible embeddings computation request for multiple inputs
Then embeddings are generated
Scenario: Tokenize / Detokenize
When tokenizing:

151
examples/server/tests/features/steps/steps.py
View File

@ -1,4 +1,5 @@
import asyncio
import collections
import json
import os
import re
@ -261,35 +262,35 @@ def step_a_prompt_prompt(context, prompt):
@step(u'concurrent completion requests')
@async_run_until_complete()
async def step_concurrent_completion_requests(context):
await concurrent_completion_requests(context,
request_completion,
# prompt is inserted automatically
context.base_url,
debug=context.debug,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key if hasattr(context,
'user_api_key') else None)
await concurrent_requests(context,
request_completion,
# prompt is inserted automatically
context.base_url,
debug=context.debug,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key if hasattr(context,
'user_api_key') else None)
@step(u'concurrent OAI completions requests')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_completion_requests(context, oai_chat_completions,
# user_prompt is inserted automatically
context.system_prompt,
context.base_url,
True, # async_client
model=context.model
if hasattr(context, 'model') else None,
n_predict=context.n_predict
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
await concurrent_requests(context, oai_chat_completions,
# user_prompt is inserted automatically
context.system_prompt,
context.base_url,
True, # async_client
model=context.model
if hasattr(context, 'model') else None,
n_predict=context.n_predict
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@step(u'all prompts are predicted')
@ -316,36 +317,58 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
@step(u'embeddings are computed for')
@async_run_until_complete
async def step_compute_embedding(context):
content = context.text
base_url = context.base_url
context.embeddings = await request_embedding(content, base_url)
context.embeddings = await request_embedding(context.text, base_url=context.base_url)
@step(u'embeddings are generated')
def step_assert_embeddings(context):
assert_embeddings(context.embeddings)
if len(context.prompts) == 0:
assert_embeddings(context.embeddings)
else:
assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
f"context.prompts={context.prompts}\n"
f"context.embeddings={context.embeddings}")
for embedding in context.embeddings:
context.prompts.pop()
assert_embeddings(embedding)
@step(u'an OAI compatible embeddings computation request for')
def step_oai_compute_embedding(context):
openai.api_key = 'nope' # openai client always expects an api_keu
if context.user_api_key is not None:
openai.api_key = context.user_api_key
openai.api_base = f'{context.base_url}/v1'
embeddings = openai.Embedding.create(
model=context.model,
input=context.text,
)
context.embeddings = embeddings
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.embeddings = await request_oai_embeddings(context.text,
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')
@async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts,
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@step(u'concurrent embedding requests')
@async_run_until_complete()
async def step_concurrent_embedding_requests(context):
await concurrent_completion_requests(context,
request_embedding,
# prompt is inserted automatically
context.base_url)
await concurrent_requests(context,
request_embedding,
# prompt is inserted automatically
base_url=context.base_url)
@step(u'concurrent OAI embedding requests')
@async_run_until_complete()
async def step_concurrent_oai_embedding_requests(context):
await concurrent_requests(context,
request_oai_embeddings,
# prompt is inserted automatically
base_url=context.base_url,
async_client=True,
model=context.model)
@step(u'all embeddings are generated')
@ -401,7 +424,7 @@ def step_check_options_header_value(context, cors_header, cors_header_value):
assert context.options_response.headers[cors_header] == cors_header_value
async def concurrent_completion_requests(context, f_completion, *args, **kwargs):
async def concurrent_requests(context, f_completion, *args, **kwargs):
n_prompts = len(context.prompts)
if context.debug:
print(f"starting {n_prompts} concurrent completion requests...")
@ -565,7 +588,7 @@ async def oai_chat_completions(user_prompt,
return completion_response
async def request_embedding(content, base_url):
async def request_embedding(content, base_url=None):
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/embedding',
json={
@ -576,6 +599,46 @@ async def request_embedding(content, base_url):
return response_json['embedding']
async def request_oai_embeddings(input,
base_url=None, user_api_key=None,
model=None, async_client=False):
# openai client always expects an api_key
user_api_key = user_api_key if user_api_key is not None else 'nope'
if async_client:
origin = 'llama.cpp'
if user_api_key is not None:
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/v1/embeddings',
json={
"input": input,
"model": model,
},
headers=headers) as response:
assert response.status == 200, f"received status code not expected: {response.status}"
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
response_json = await response.json()
assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
assert response_json['object'] == 'list'
return response_json['data']
else:
openai.api_key = user_api_key
openai.api_base = f'{base_url}/v1'
oai_embeddings = openai.Embedding.create(
model=model,
input=input,
)
if isinstance(input, collections.abc.Sequence):
embeddings = []
for an_oai_embeddings in oai_embeddings.data:
embeddings.append(an_oai_embeddings.embedding)
else:
embeddings = oai_embeddings.data.embedding
return embeddings
def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
content = completion_response['content']
n_predicted = completion_response['timings']['predicted_n']