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server: tests: embeddings use a real embeddings model (#5908)

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Pierrick Hymbert 2024-03-06 20:24:20 +01:00 • committed by GitHub
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6 changed files with 161 additions and 93 deletions
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3
.github/workflows/server.yml vendored
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@ -58,7 +58,8 @@ jobs:
cmake \
python3-pip \
wget \
psmisc
psmisc \
language-pack-en
- name: Build
id: cmake_build

97
examples/server/tests/features/embeddings.feature Normal file
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@ -0,0 +1,97 @@
@llama.cpp
@embeddings
Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models
And a model alias bert-bge-small
And 42 as server seed
And 2 slots
And 512 as batch size
And 1024 KV cache size
And embeddings extraction
Then the server is starting
Then the server is healthy
Scenario: Embedding
When embeddings are computed for:
"""
What is the capital of Bulgaria ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility
Given a model bert-bge-small
When an OAI compatible embeddings computation request for:
"""
What is the capital of Spain ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility with multiple inputs
Given a model bert-bge-small
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: 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 bert-bge-small
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
@wip
Scenario: All embeddings should be the same
Given 20 fixed prompts
And a model bert-bge-small
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are the same

46
examples/server/tests/features/parallel.feature
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@ -9,7 +9,6 @@ Feature: Parallel
And 512 as batch size
And 64 KV cache size
And 2 slots
And embeddings extraction
And continuous batching
Then the server is starting
Then the server is healthy
@ -99,48 +98,3 @@ 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

28
examples/server/tests/features/server.feature
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@ -49,34 +49,6 @@ Feature: llama.cpp server
| 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 |
Scenario: Embedding
When embeddings are computed for:
"""
What is the capital of Bulgaria ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility
Given a model tinyllama-2
When an OAI compatible embeddings computation request for:
"""
What is the capital of Spain ?
"""
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:
"""

73
examples/server/tests/features/steps/steps.py
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@ -10,6 +10,7 @@ from contextlib import closing
from re import RegexFlag
import aiohttp
import numpy as np
import openai
from behave import step
from behave.api.async_step import async_run_until_complete
@ -34,6 +35,7 @@ def step_server_config(context, server_fqdn, server_port):
context.n_ga_w = None
context.n_gpu_layer = None
context.n_predict = None
context.n_prompts = 0
context.n_server_predict = None
context.n_slots = None
context.prompt_prefix = None
@ -202,6 +204,7 @@ def step_n_tokens_predicted(context, predicted_n):
@step(u'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}')
@ -289,6 +292,11 @@ def step_impl(context, n_ga_w):
def step_prompt_passkey(context):
context.prompt_passkey = context.text
@step(u'{n_prompts:d} fixed prompts')
def step_fixed_prompts(context, n_prompts):
context.prompts.extend([str(0)*1024 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')
def step_prompt_passkey(context, passkey, i_pos):
@ -301,6 +309,7 @@ def step_prompt_passkey(context, passkey, i_pos):
passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n")
context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
context.n_prompts = len(context.prompts)
@step(u'an OAI compatible chat completions request with {api_error} api error')
@ -341,11 +350,13 @@ async def step_oai_chat_completions(context, api_error):
@step(u'a prompt')
def step_a_prompt(context):
context.prompts.append(context.text)
context.n_prompts = len(context.prompts)
@step(u'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')
@ -430,25 +441,47 @@ 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):
context.n_prompts = 1
context.embeddings = await request_embedding(context.text, base_url=context.base_url)
@step(u'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)
assert n_embedding_requests > 0
embeddings = []
for i in range(n_embedding_requests):
embedding = context.tasks_result.pop().pop()
embeddings.append(embedding)
assert_embeddings(embedding)
n = len(embeddings)
for i in range(n-1):
for j in range(i+1, n):
embedding1 = np.array(embeddings[i])
embedding2 = np.array(embeddings[j])
if context.debug:
print(f"embedding1: {embedding1[-8:]}\n")
print(f"embedding2: {embedding2[-8:]}\n")
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
msg = f"Similarity between {i} and {j}: {similarity:.10f}"
if context.debug:
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')
def step_assert_embeddings(context):
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"
assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
f"context.n_prompts={context.n_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')
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.n_prompts = 1
context.embeddings = await request_oai_embeddings(context.text,
base_url=context.base_url,
user_api_key=context.user_api_key,
@ -462,6 +495,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context):
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
context.prompts.clear()
@step(u'concurrent embedding requests')
@ -488,9 +522,9 @@ async def step_concurrent_oai_embedding_requests(context):
@async_run_until_complete()
async def all_embeddings_are_generated(context):
n_embedding_requests = await gather_tasks_results(context)
assert n_embedding_requests > 0
assert n_embedding_requests == context.n_prompts
for i in range(n_embedding_requests):
assert_embeddings(context.tasks_result.pop())
assert_embeddings(context.tasks_result.pop().pop())
@step(u'tokenizing')
@ -588,11 +622,11 @@ def step_supported_models(context, i_model, param, preposition, param_value):
async def concurrent_requests(context, f_completion, *args, **kwargs):
n_prompts = len(context.prompts)
context.n_prompts = len(context.prompts)
if context.debug:
print(f"starting {n_prompts} concurrent completion requests...")
assert n_prompts > 0
for prompt_no in range(n_prompts):
print(f"starting {context.n_prompts} concurrent completion requests...")
assert context.n_prompts > 0
for prompt_no in range(context.n_prompts):
shifted_args = [context.prompts.pop(), *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1)
@ -765,7 +799,7 @@ async def request_embedding(content, base_url=None):
}) as response:
assert response.status == 200
response_json = await response.json()
return response_json['embedding']
return [response_json['embedding']]
async def request_oai_embeddings(input,
@ -775,6 +809,7 @@ async def request_oai_embeddings(input,
user_api_key = user_api_key if user_api_key is not None else 'nope'
if async_client:
origin = 'llama.cpp'
headers=[]
if user_api_key is not None:
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
@ -790,7 +825,13 @@ async def request_oai_embeddings(input,
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']
if isinstance(input, collections.abc.Sequence):
embeddings = []
for an_oai_embeddings in response_json['data']:
embeddings.append(an_oai_embeddings['embedding'])
else:
embeddings = [response_json['data']['embedding']]
return embeddings
else:
openai.api_key = user_api_key
openai.api_base = f'{base_url}/v1'
@ -804,7 +845,7 @@ async def request_oai_embeddings(input,
for an_oai_embeddings in oai_embeddings.data:
embeddings.append(an_oai_embeddings.embedding)
else:
embeddings = oai_embeddings.data.embedding
embeddings = [oai_embeddings.data.embedding]
return embeddings
@ -899,6 +940,8 @@ def assert_embeddings(embeddings):
assert len(embeddings) > 0
embeddings_computed = False
for emb in embeddings:
if not isinstance(emb, float):
assert False, f"Bad embeddings: {embeddings}"
if emb != 0:
embeddings_computed = True
assert embeddings_computed, f"Embeddings: {embeddings}"

1
examples/server/tests/requirements.txt
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@ -1,5 +1,6 @@
aiohttp~=3.9.3
behave~=1.2.6
huggingface_hub~=0.20.3
numpy~=1.24.4
openai~=0.25.0
prometheus-client~=0.20.0