forked from phoenix/litellm-mirror
045ecf3ffb
* define all slack alert types * use correct type hints for alert type * use correct defaults on slack alerting * add readme for slack alerting * fix linting error * update readme * docs all alert types * update slack alerting docs * fix slack alerting docs * handle new testing dir structure * fix config for testing * fix testing folder related imports * fix /tests import errors * fix import stream_chunk_testdata * docs alert types * fix test test_langfuse_trace_id * fix type checks for slack alerting * fix outage alerting test slack
377 lines
15 KiB
Python
377 lines
15 KiB
Python
#### What this tests ####
|
||
# This tests litellm.token_counter() function
|
||
|
||
import os
|
||
import sys
|
||
import time
|
||
from unittest.mock import MagicMock
|
||
|
||
import pytest
|
||
|
||
sys.path.insert(
|
||
0, os.path.abspath("../..")
|
||
) # Adds the parent directory to the system path
|
||
from unittest.mock import AsyncMock, MagicMock, patch
|
||
|
||
import litellm
|
||
from litellm import (
|
||
create_pretrained_tokenizer,
|
||
decode,
|
||
encode,
|
||
get_modified_max_tokens,
|
||
token_counter,
|
||
)
|
||
from large_text import text
|
||
from messages_with_counts import (
|
||
MESSAGES_TEXT,
|
||
MESSAGES_WITH_IMAGES,
|
||
MESSAGES_WITH_TOOLS,
|
||
)
|
||
|
||
|
||
def test_token_counter_normal_plus_function_calling():
|
||
try:
|
||
messages = [
|
||
{"role": "system", "content": "System prompt"},
|
||
{"role": "user", "content": "content1"},
|
||
{"role": "assistant", "content": "content2"},
|
||
{"role": "user", "content": "conten3"},
|
||
{
|
||
"role": "assistant",
|
||
"content": None,
|
||
"tool_calls": [
|
||
{
|
||
"id": "call_E0lOb1h6qtmflUyok4L06TgY",
|
||
"function": {
|
||
"arguments": '{"query":"search query","domain":"google.ca","gl":"ca","hl":"en"}',
|
||
"name": "SearchInternet",
|
||
},
|
||
"type": "function",
|
||
}
|
||
],
|
||
},
|
||
{
|
||
"tool_call_id": "call_E0lOb1h6qtmflUyok4L06TgY",
|
||
"role": "tool",
|
||
"name": "SearchInternet",
|
||
"content": "tool content",
|
||
},
|
||
]
|
||
tokens = token_counter(model="gpt-3.5-turbo", messages=messages)
|
||
print(f"tokens: {tokens}")
|
||
except Exception as e:
|
||
pytest.fail(f"An exception occurred - {str(e)}")
|
||
|
||
|
||
# test_token_counter_normal_plus_function_calling()
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"message_count_pair",
|
||
MESSAGES_TEXT,
|
||
)
|
||
def test_token_counter_textonly(message_count_pair):
|
||
counted_tokens = token_counter(
|
||
model="gpt-35-turbo", messages=[message_count_pair["message"]]
|
||
)
|
||
assert counted_tokens == message_count_pair["count"]
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"message_count_pair",
|
||
MESSAGES_WITH_IMAGES,
|
||
)
|
||
def test_token_counter_with_images(message_count_pair):
|
||
counted_tokens = token_counter(
|
||
model="gpt-4o", messages=[message_count_pair["message"]]
|
||
)
|
||
assert counted_tokens == message_count_pair["count"]
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"message_count_pair",
|
||
MESSAGES_WITH_TOOLS,
|
||
)
|
||
def test_token_counter_with_tools(message_count_pair):
|
||
counted_tokens = token_counter(
|
||
model="gpt-35-turbo",
|
||
messages=[message_count_pair["system_message"]],
|
||
tools=message_count_pair["tools"],
|
||
tool_choice=message_count_pair["tool_choice"],
|
||
)
|
||
expected_tokens = message_count_pair["count"]
|
||
diff = counted_tokens - expected_tokens
|
||
assert (
|
||
diff >= 0 and diff <= 3
|
||
), f"Expected {expected_tokens} tokens, got {counted_tokens}. Counted tokens is only allowed to be off by 3 in the over-counting direction."
|
||
|
||
|
||
def test_tokenizers():
|
||
try:
|
||
### test the openai, claude, cohere and llama2 tokenizers.
|
||
### The tokenizer value should be different for all
|
||
sample_text = "Hellö World, this is my input string! My name is ishaan CTO"
|
||
|
||
# openai tokenizer
|
||
openai_tokens = token_counter(model="gpt-3.5-turbo", text=sample_text)
|
||
|
||
# claude tokenizer
|
||
claude_tokens = token_counter(model="claude-instant-1", text=sample_text)
|
||
|
||
# cohere tokenizer
|
||
cohere_tokens = token_counter(model="command-nightly", text=sample_text)
|
||
|
||
# llama2 tokenizer
|
||
llama2_tokens = token_counter(
|
||
model="meta-llama/Llama-2-7b-chat", text=sample_text
|
||
)
|
||
|
||
# llama3 tokenizer (also testing custom tokenizer)
|
||
llama3_tokens_1 = token_counter(
|
||
model="meta-llama/llama-3-70b-instruct", text=sample_text
|
||
)
|
||
|
||
llama3_tokenizer = create_pretrained_tokenizer("Xenova/llama-3-tokenizer")
|
||
llama3_tokens_2 = token_counter(
|
||
custom_tokenizer=llama3_tokenizer, text=sample_text
|
||
)
|
||
|
||
print(
|
||
f"openai tokens: {openai_tokens}; claude tokens: {claude_tokens}; cohere tokens: {cohere_tokens}; llama2 tokens: {llama2_tokens}; llama3 tokens: {llama3_tokens_1}"
|
||
)
|
||
|
||
# assert that all token values are different
|
||
assert (
|
||
openai_tokens != llama2_tokens != llama3_tokens_1
|
||
), "Token values are not different."
|
||
|
||
assert (
|
||
llama3_tokens_1 == llama3_tokens_2
|
||
), "Custom tokenizer is not being used! It has been configured to use the same tokenizer as the built in llama3 tokenizer and the results should be the same."
|
||
|
||
print("test tokenizer: It worked!")
|
||
except Exception as e:
|
||
pytest.fail(f"An exception occured: {e}")
|
||
|
||
|
||
# test_tokenizers()
|
||
|
||
|
||
def test_encoding_and_decoding():
|
||
try:
|
||
sample_text = "Hellö World, this is my input string!"
|
||
# openai encoding + decoding
|
||
openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text)
|
||
openai_text = decode(model="gpt-3.5-turbo", tokens=openai_tokens)
|
||
|
||
assert openai_text == sample_text
|
||
|
||
# claude encoding + decoding
|
||
claude_tokens = encode(model="claude-instant-1", text=sample_text)
|
||
claude_text = decode(model="claude-instant-1", tokens=claude_tokens.ids)
|
||
|
||
assert claude_text == sample_text
|
||
|
||
# cohere encoding + decoding
|
||
cohere_tokens = encode(model="command-nightly", text=sample_text)
|
||
cohere_text = decode(model="command-nightly", tokens=cohere_tokens)
|
||
|
||
assert cohere_text == sample_text
|
||
|
||
# llama2 encoding + decoding
|
||
llama2_tokens = encode(model="meta-llama/Llama-2-7b-chat", text=sample_text)
|
||
llama2_text = decode(
|
||
model="meta-llama/Llama-2-7b-chat", tokens=llama2_tokens.ids
|
||
)
|
||
|
||
assert llama2_text == sample_text
|
||
except Exception as e:
|
||
pytest.fail(f"An exception occured: {e}")
|
||
|
||
|
||
# test_encoding_and_decoding()
|
||
|
||
|
||
def test_gpt_vision_token_counting():
|
||
messages = [
|
||
{
|
||
"role": "user",
|
||
"content": [
|
||
{"type": "text", "text": "What’s in this image?"},
|
||
{
|
||
"type": "image_url",
|
||
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
|
||
},
|
||
],
|
||
}
|
||
]
|
||
tokens = token_counter(model="gpt-4-vision-preview", messages=messages)
|
||
print(f"tokens: {tokens}")
|
||
|
||
|
||
# test_gpt_vision_token_counting()
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"model",
|
||
[
|
||
"gpt-4-vision-preview",
|
||
"gpt-4o",
|
||
"claude-3-opus-20240229",
|
||
"command-nightly",
|
||
"mistral/mistral-tiny",
|
||
],
|
||
)
|
||
def test_load_test_token_counter(model):
|
||
"""
|
||
Token count large prompt 100 times.
|
||
|
||
Assert time taken is < 1.5s.
|
||
"""
|
||
import tiktoken
|
||
|
||
messages = [{"role": "user", "content": text}] * 10
|
||
|
||
start_time = time.time()
|
||
for _ in range(10):
|
||
_ = token_counter(model=model, messages=messages)
|
||
# enc.encode("".join(m["content"] for m in messages))
|
||
|
||
end_time = time.time()
|
||
|
||
total_time = end_time - start_time
|
||
print("model={}, total test time={}".format(model, total_time))
|
||
assert total_time < 10, f"Total encoding time > 10s, {total_time}"
|
||
|
||
|
||
def test_openai_token_with_image_and_text():
|
||
model = "gpt-4o"
|
||
full_request = {
|
||
"model": "gpt-4o",
|
||
"tools": [
|
||
{
|
||
"type": "function",
|
||
"function": {
|
||
"name": "json",
|
||
"parameters": {
|
||
"type": "object",
|
||
"required": ["clause"],
|
||
"properties": {"clause": {"type": "string"}},
|
||
},
|
||
"description": "Respond with a JSON object.",
|
||
},
|
||
}
|
||
],
|
||
"logprobs": False,
|
||
"messages": [
|
||
{
|
||
"role": "user",
|
||
"content": [
|
||
{
|
||
"text": "\n Just some long text, long long text, and you know it will be longer than 7 tokens definetly.",
|
||
"type": "text",
|
||
}
|
||
],
|
||
}
|
||
],
|
||
"tool_choice": {"type": "function", "function": {"name": "json"}},
|
||
"exclude_models": [],
|
||
"disable_fallback": False,
|
||
"exclude_providers": [],
|
||
}
|
||
messages = full_request.get("messages", [])
|
||
|
||
token_count = token_counter(model=model, messages=messages)
|
||
print(token_count)
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"model, base_model, input_tokens, user_max_tokens, expected_value",
|
||
[
|
||
("random-model", "random-model", 1024, 1024, 1024),
|
||
("command", "command", 1000000, None, None), # model max = 4096
|
||
("command", "command", 4000, 256, 96), # model max = 4096
|
||
("command", "command", 4000, 10, 10), # model max = 4096
|
||
("gpt-3.5-turbo", "gpt-3.5-turbo", 4000, 5000, 4096), # model max output = 4096
|
||
],
|
||
)
|
||
def test_get_modified_max_tokens(
|
||
model, base_model, input_tokens, user_max_tokens, expected_value
|
||
):
|
||
"""
|
||
- Test when max_output is not known => expect user_max_tokens
|
||
- Test when max_output == max_input,
|
||
- input > max_output, no max_tokens => expect None
|
||
- input + max_tokens > max_output => expect remainder
|
||
- input + max_tokens < max_output => expect max_tokens
|
||
- Test when max_tokens > max_output => expect max_output
|
||
"""
|
||
args = locals()
|
||
import litellm
|
||
|
||
litellm.token_counter = MagicMock()
|
||
|
||
def _mock_token_counter(*args, **kwargs):
|
||
return input_tokens
|
||
|
||
litellm.token_counter.side_effect = _mock_token_counter
|
||
print(f"_mock_token_counter: {_mock_token_counter()}")
|
||
messages = [{"role": "user", "content": "Hello world!"}]
|
||
|
||
calculated_value = get_modified_max_tokens(
|
||
model=model,
|
||
base_model=base_model,
|
||
messages=messages,
|
||
user_max_tokens=user_max_tokens,
|
||
buffer_perc=0,
|
||
buffer_num=0,
|
||
)
|
||
|
||
if expected_value is None:
|
||
assert calculated_value is None
|
||
else:
|
||
assert (
|
||
calculated_value == expected_value
|
||
), "Got={}, Expected={}, Params={}".format(
|
||
calculated_value, expected_value, args
|
||
)
|
||
|
||
|
||
def test_empty_tools():
|
||
messages = [{"role": "user", "content": "hey, how's it going?", "tool_calls": None}]
|
||
|
||
result = token_counter(
|
||
messages=messages,
|
||
)
|
||
|
||
print(result)
|
||
|
||
|
||
def test_gpt_4o_token_counter():
|
||
with patch.object(
|
||
litellm.utils, "openai_token_counter", new=MagicMock()
|
||
) as mock_client:
|
||
token_counter(
|
||
model="gpt-4o-2024-05-13", messages=[{"role": "user", "content": "Hey!"}]
|
||
)
|
||
|
||
mock_client.assert_called()
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"img_url",
|
||
[
|
||
"https://blog.purpureus.net/assets/blog/personal_key_rotation/simplified-asset-graph.jpg",
|
||
"data:image/png;base64,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",
|
||
],
|
||
)
|
||
def test_img_url_token_counter(img_url):
|
||
|
||
from litellm.utils import get_image_dimensions
|
||
|
||
width, height = get_image_dimensions(data=img_url)
|
||
|
||
print(width, height)
|
||
|
||
assert width is not None
|
||
assert height is not None
|