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import pytest
from openai import OpenAI
from utils import *
server: ServerProcess
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
@pytest.mark.parametrize(
"model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
[
(None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None),
(None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None),
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'),
(None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
]
)
def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
global server
server.jinja = jinja
server.chat_template = chat_template
server.start()
res = server.make_request("POST", "/chat/completions", data={
"model": model,
"max_tokens": max_tokens,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
})
assert res.status_code == 200
assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
assert res.body["system_fingerprint"].startswith("b")
assert res.body["model"] == model if model is not None else server.model_alias
assert res.body["usage"]["prompt_tokens"] == n_prompt
assert res.body["usage"]["completion_tokens"] == n_predicted
choice = res.body["choices"][0]
assert "assistant" == choice["message"]["role"]
assert match_regex(re_content, choice["message"]["content"])
assert choice["finish_reason"] == finish_reason
@pytest.mark.parametrize(
"system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
[
("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
]
)
def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
global server
server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL
server.start()
res = server.make_stream_request("POST", "/chat/completions", data={
"max_tokens": max_tokens,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
"stream": True,
})
content = ""
last_cmpl_id = None
for data in res:
choice = data["choices"][0]
assert data["system_fingerprint"].startswith("b")
assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
if last_cmpl_id is None:
last_cmpl_id = data["id"]
assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
if choice["finish_reason"] in ["stop", "length"]:
assert data["usage"]["prompt_tokens"] == n_prompt
assert data["usage"]["completion_tokens"] == n_predicted
assert "content" not in choice["delta"]
assert match_regex(re_content, content)
assert choice["finish_reason"] == finish_reason
else:
assert choice["finish_reason"] is None
content += choice["delta"]["content"]
def test_chat_completion_with_openai_library():
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
res = client.chat.completions.create(
model="gpt-3.5-turbo-instruct",
messages=[
{"role": "system", "content": "Book"},
{"role": "user", "content": "What is the best book"},
],
max_tokens=8,
seed=42,
temperature=0.8,
)
assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
assert res.choices[0].finish_reason == "length"
assert res.choices[0].message.content is not None
assert match_regex("(Suddenly)+", res.choices[0].message.content)
def test_chat_template():
global server
server.chat_template = "llama3"
server.debug = True # to get the "__verbose" object in the response
server.start()
res = server.make_request("POST", "/chat/completions", data={
"max_tokens": 8,
"messages": [
{"role": "system", "content": "Book"},
{"role": "user", "content": "What is the best book"},
]
})
assert res.status_code == 200
assert "__verbose" in res.body
assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
def test_apply_chat_template():
global server
server.chat_template = "command-r"
server.start()
res = server.make_request("POST", "/apply-template", data={
"messages": [
{"role": "system", "content": "You are a test."},
{"role": "user", "content":"Hi there"},
]
})
assert res.status_code == 200
assert "prompt" in res.body
assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
@pytest.mark.parametrize("response_format,n_predicted,re_content", [
({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),
({"type": "json_object"}, 10, "(\\{|John)+"),
({"type": "sound"}, 0, None),
# invalid response format (expected to fail)
({"type": "json_object", "schema": 123}, 0, None),
({"type": "json_object", "schema": {"type": 123}}, 0, None),
({"type": "json_object", "schema": {"type": "hiccup"}}, 0, None),
])
def test_completion_with_response_format(response_format: dict, n_predicted: int, re_content: str | None):
global server
server.start()
res = server.make_request("POST", "/chat/completions", data={
"max_tokens": n_predicted,
"messages": [
{"role": "system", "content": "You are a coding assistant."},
{"role": "user", "content": "Write an example"},
],
"response_format": response_format,
})
if re_content is not None:
assert res.status_code == 200
choice = res.body["choices"][0]
assert match_regex(re_content, choice["message"]["content"])
else:
assert res.status_code != 200
assert "error" in res.body
@pytest.mark.parametrize("messages", [
None,
"string",
[123],
[{}],
[{"role": 123}],
[{"role": "system", "content": 123}],
# [{"content": "hello"}], # TODO: should not be a valid case
[{"role": "system", "content": "test"}, {}],
])
def test_invalid_chat_completion_req(messages):
global server
server.start()
res = server.make_request("POST", "/chat/completions", data={
"messages": messages,
})
assert res.status_code == 400 or res.status_code == 500
assert "error" in res.body
def test_chat_completion_with_timings_per_token():
global server
server.start()
res = server.make_stream_request("POST", "/chat/completions", data={
"max_tokens": 10,
"messages": [{"role": "user", "content": "test"}],
"stream": True,
"timings_per_token": True,
})
for data in res:
assert "timings" in data
assert "prompt_per_second" in data["timings"]
assert "predicted_per_second" in data["timings"]
assert "predicted_n" in data["timings"]
assert data["timings"]["predicted_n"] <= 10
def test_logprobs():
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
res = client.chat.completions.create(
model="gpt-3.5-turbo-instruct",
temperature=0.0,
messages=[
{"role": "system", "content": "Book"},
{"role": "user", "content": "What is the best book"},
],
max_tokens=5,
logprobs=True,
top_logprobs=10,
)
output_text = res.choices[0].message.content
aggregated_text = ''
assert res.choices[0].logprobs is not None
assert res.choices[0].logprobs.content is not None
for token in res.choices[0].logprobs.content:
aggregated_text += token.token
assert token.logprob <= 0.0
assert token.bytes is not None
assert len(token.top_logprobs) > 0
assert aggregated_text == output_text
def test_logprobs_stream():
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
res = client.chat.completions.create(
model="gpt-3.5-turbo-instruct",
temperature=0.0,
messages=[
{"role": "system", "content": "Book"},
{"role": "user", "content": "What is the best book"},
],
max_tokens=5,
logprobs=True,
top_logprobs=10,
stream=True,
)
output_text = ''
aggregated_text = ''
for data in res:
choice = data.choices[0]
if choice.finish_reason is None:
if choice.delta.content:
output_text += choice.delta.content
assert choice.logprobs is not None
assert choice.logprobs.content is not None
for token in choice.logprobs.content:
aggregated_text += token.token
assert token.logprob <= 0.0
assert token.bytes is not None
assert token.top_logprobs is not None
assert len(token.top_logprobs) > 0
assert aggregated_text == output_text
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