added all sequential tests

This commit is contained in:
Xuan Son Nguyen 2024-11-20 17:57:20 +01:00
parent eb02373f76
commit 472e128c0b
8 changed files with 332 additions and 6 deletions

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@ -120,3 +120,5 @@ def test_consistent_result_different_batch_size(n_batch: int, temperature: float
if last_res is not None:
assert res.body["content"] == last_res.body["content"]
last_res = res
# TODO: add completion with tokens as input, mixed token+string input

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@ -0,0 +1,67 @@
import pytest
from utils import *
server = ServerPreset.tinyllama2()
LONG_TEXT = """
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.
""".strip()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
server.n_ctx = 256
server.n_slots = 2
def test_ctx_shift_enabled():
# the prompt is 301 tokens
# the slot context is 256/2 = 128 tokens
# the prompt is truncated to keep the last 109 tokens
# 64 tokens are generated thanks to shifting the context when it gets full
global server
server.start()
res = server.make_request("POST", "/completion", data={
"n_predict": 64,
"prompt": LONG_TEXT,
})
assert res.status_code == 200
assert res.body["timings"]["prompt_n"] == 109
assert res.body["timings"]["predicted_n"] == 64
assert res.body["truncated"] is True
@pytest.mark.parametrize("n_predict,n_token_output,truncated", [
(64, 64, False),
(-1, 120, True),
])
def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool):
global server
server.disable_ctx_shift = True
server.n_predict = -1
server.start()
res = server.make_request("POST", "/completion", data={
"n_predict": n_predict,
"prompt": "Hi how are you",
})
assert res.status_code == 200
assert res.body["timings"]["predicted_n"] == n_token_output
assert res.body["truncated"] == truncated
def test_ctx_shift_disabled_long_prompt():
global server
server.disable_ctx_shift = True
server.start()
res = server.make_request("POST", "/completion", data={
"n_predict": 64,
"prompt": LONG_TEXT,
})
assert res.status_code != 200
assert "error" in res.body
assert "exceeds the available context size" in res.body["error"]["message"]

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@ -4,6 +4,7 @@ from utils import *
server = ServerPreset.bert_bge_small()
EPSILON = 1e-3
@pytest.fixture(scope="module", autouse=True)
def create_server():
@ -23,7 +24,7 @@ def test_embedding_single():
assert len(res.body['data'][0]['embedding']) > 1
# make sure embedding vector is normalized
assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < 1e-5
assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < EPSILON
def test_embedding_multiple():
@ -95,4 +96,4 @@ def test_same_prompt_give_same_result():
v0 = res.body['data'][0]['embedding']
vi = res.body['data'][i]['embedding']
for x, y in zip(v0, vi):
assert abs(x - y) < 1e-5
assert abs(x - y) < EPSILON

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@ -0,0 +1,35 @@
import pytest
from utils import *
server = ServerPreset.tinyllama_infill()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama_infill()
def test_infill_without_input_extra():
global server
server.start()
res = server.make_request("POST", "/infill", data={
"prompt": "Complete this",
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 200
assert match_regex("(One|day|she|saw|big|scary|bird)+", res.body["content"])
def test_infill_with_input_extra():
global server
server.start()
res = server.make_request("POST", "/infill", data={
"prompt": "Complete this",
"input_extra": [{
"filename": "llama.h",
"text": "LLAMA_API int32_t llama_n_threads();\n"
}],
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 200
assert match_regex("(cuts|Jimmy|mom|came|into|the|room)+", res.body["content"])

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@ -0,0 +1,42 @@
import pytest
import os
from utils import *
server = ServerPreset.stories15m_moe()
LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf"
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.stories15m_moe()
# download lora file if needed
file_name = LORA_FILE_URL.split('/').pop()
lora_file = f'../../../{file_name}'
if not os.path.exists(lora_file):
print(f"Downloading {LORA_FILE_URL} to {lora_file}")
with open(lora_file, 'wb') as f:
f.write(requests.get(LORA_FILE_URL).content)
print(f"Done downloading lora file")
server.lora_files = [lora_file]
@pytest.mark.parametrize("scale,re_content", [
# without applying lora, the model should behave like a bedtime story generator
(0.0, "(little|girl|three|years|old)+"),
# with lora, the model should behave like a Shakespearean text generator
(1.0, "(eye|love|glass|sun)+"),
])
def test_lora(scale: float, re_content: str):
global server
server.start()
res_lora_control = server.make_request("POST", "/lora-adapters", data=[
{"id": 0, "scale": scale}
])
assert res_lora_control.status_code == 200
res = server.make_request("POST", "/completion", data={
"prompt": "Look in thy glass",
})
assert res.status_code == 200
assert match_regex(re_content, res.body["content"])

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@ -0,0 +1,38 @@
import pytest
from utils import *
server = ServerPreset.jina_reranker_tiny()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.jina_reranker_tiny()
def test_rerank():
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": "Machine learning is",
"documents": [
"A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.",
"Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.",
"Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.",
"Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine."
]
})
assert res.status_code == 200
assert len(res.body["results"]) == 4
most_relevant = res.body["results"][0]
least_relevant = res.body["results"][0]
for doc in res.body["results"]:
if doc["relevance_score"] > most_relevant["relevance_score"]:
most_relevant = doc
if doc["relevance_score"] < least_relevant["relevance_score"]:
least_relevant = doc
assert most_relevant["relevance_score"] > least_relevant["relevance_score"]
assert most_relevant["index"] == 2
assert least_relevant["index"] == 3

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@ -0,0 +1,97 @@
import pytest
from utils import *
server = ServerPreset.tinyllama2()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
server.slot_save_path = "./tmp"
def test_slot_save_restore():
global server
server.start()
# First prompt in slot 1 should be fully processed
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Lily|cake)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed
# Save state of slot 1
res = server.make_request("POST", "/slots/1?action=save", data={
"filename": "slot1.bin",
})
assert res.status_code == 200
assert res.body["n_saved"] == 84
# Since we have cache, this should only process the last tokens
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 6 # only different part is processed
# Loading the saved cache into slot 0
res = server.make_request("POST", "/slots/0?action=restore", data={
"filename": "slot1.bin",
})
assert res.status_code == 200
assert res.body["n_restored"] == 84
# Since we have cache, slot 0 should only process the last tokens
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 0,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 6 # only different part is processed
# For verification that slot 1 was not corrupted during slot 0 load, same thing should work
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 1
def test_slot_erase():
global server
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Lily|cake)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed
# erase slot 1
res = server.make_request("POST", "/slots/1?action=erase")
assert res.status_code == 200
# re-run the same prompt, it should process all tokens again
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Lily|cake)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed

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@ -17,6 +17,7 @@ from typing import (
ContextManager,
Iterable,
Iterator,
List,
Literal,
Sequence,
Set,
@ -65,7 +66,7 @@ class ServerProcess:
draft: int | None = None
api_key: str | None = None
response_format: str | None = None
lora_file: str | None = None
lora_files: List[str] | None = None
disable_ctx_shift: int | None = False
# session variables
@ -134,8 +135,9 @@ class ServerProcess:
server_args.extend(["--grp-attn-w", self.n_ga_w])
if self.debug:
server_args.append("--verbose")
if self.lora_file:
server_args.extend(["--lora", self.lora_file])
if self.lora_files:
for lora_file in self.lora_files:
server_args.extend(["--lora", lora_file])
if self.disable_ctx_shift:
server_args.extend(["--no-context-shift"])
if self.api_key:
@ -202,7 +204,7 @@ class ServerProcess:
self,
method: str,
path: str,
data: dict | None = None,
data: dict | Any | None = None,
headers: dict | None = None,
) -> ServerResponse:
url = f"http://{self.server_host}:{self.server_port}{path}"
@ -277,6 +279,48 @@ class ServerPreset:
server.server_embeddings = True
return server
@staticmethod
def tinyllama_infill() -> ServerProcess:
server = ServerProcess()
server.model_hf_repo = "ggml-org/models"
server.model_hf_file = "tinyllamas/stories260K-infill.gguf"
server.model_alias = "tinyllama-infill"
server.n_ctx = 2048
server.n_batch = 1024
server.n_slots = 1
server.n_predict = 64
server.temperature = 0.0
server.seed = 42
return server
@staticmethod
def stories15m_moe() -> ServerProcess:
server = ServerProcess()
server.model_hf_repo = "ggml-org/stories15M_MOE"
server.model_hf_file = "stories15M_MOE-F16.gguf"
server.model_alias = "stories15m-moe"
server.n_ctx = 2048
server.n_batch = 1024
server.n_slots = 1
server.n_predict = 64
server.temperature = 0.0
server.seed = 42
return server
@staticmethod
def jina_reranker_tiny() -> ServerProcess:
server = ServerProcess()
server.model_hf_repo = "ggml-org/models"
server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf"
server.model_alias = "jina-reranker"
server.model_file = "./tmp/jina-reranker-v1-tiny-en.gguf"
server.n_ctx = 512
server.n_batch = 512
server.n_slots = 1
server.seed = 42
server.server_reranking = True
return server
def multiple_post_requests(
server: ServerProcess, path: str, data: Sequence[dict], headers: dict | None = None