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https://github.com/LostRuins/koboldcpp.git
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- Use server_tokens in more places in server and util.cpp - Convert most functions that used llama_tokens to server_tokens - Modify input tokenizer to handle JSON objects as subprompts - Break out MTMD prompt parsing into utility function - Support JSON objects with multimodal_data arrays for MTMD prompts along with other existing types - Add capability to model endpoint to indicate if client can send multimodal data - Add tests.
145 lines
6 KiB
Python
145 lines
6 KiB
Python
import pytest
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from utils import *
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import base64
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import requests
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server: ServerProcess
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IMG_URL_0 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/11_truck.png"
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IMG_URL_1 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png"
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response = requests.get(IMG_URL_0)
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response.raise_for_status() # Raise an exception for bad status codes
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IMG_BASE64_URI_0 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
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IMG_BASE64_0 = base64.b64encode(response.content).decode("utf-8")
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response = requests.get(IMG_URL_1)
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response.raise_for_status() # Raise an exception for bad status codes
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IMG_BASE64_URI_1 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
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IMG_BASE64_1 = base64.b64encode(response.content).decode("utf-8")
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JSON_MULTIMODAL_KEY = "multimodal_data"
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JSON_PROMPT_STRING_KEY = "prompt_string"
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@pytest.fixture(autouse=True)
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def create_server():
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global server
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server = ServerPreset.tinygemma3()
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def test_models_supports_multimodal_capability():
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global server
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server.start() # vision model may take longer to load due to download size
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res = server.make_request("GET", "/models", data={})
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assert res.status_code == 200
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model_info = res.body["models"][0]
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print(model_info)
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assert "completion" in model_info["capabilities"]
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assert "multimodal" in model_info["capabilities"]
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def test_v1_models_supports_multimodal_capability():
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global server
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server.start() # vision model may take longer to load due to download size
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res = server.make_request("GET", "/v1/models", data={})
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assert res.status_code == 200
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model_info = res.body["models"][0]
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print(model_info)
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assert "completion" in model_info["capabilities"]
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assert "multimodal" in model_info["capabilities"]
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@pytest.mark.parametrize(
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"prompt, image_url, success, re_content",
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[
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# test model is trained on CIFAR-10, but it's quite dumb due to small size
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("What is this:\n", IMG_URL_0, True, "(cat)+"),
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("What is this:\n", "IMG_BASE64_URI_0", True, "(cat)+"), # exceptional, so that we don't cog up the log
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("What is this:\n", IMG_URL_1, True, "(frog)+"),
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("Test test\n", IMG_URL_1, True, "(frog)+"), # test invalidate cache
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("What is this:\n", "malformed", False, None),
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("What is this:\n", "https://google.com/404", False, None), # non-existent image
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("What is this:\n", "https://ggml.ai", False, None), # non-image data
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# TODO @ngxson : test with multiple images, no images and with audio
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]
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)
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def test_vision_chat_completion(prompt, image_url, success, re_content):
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global server
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server.start(timeout_seconds=60) # vision model may take longer to load due to download size
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if image_url == "IMG_BASE64_URI_0":
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image_url = IMG_BASE64_URI_0
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res = server.make_request("POST", "/chat/completions", data={
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"temperature": 0.0,
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"top_k": 1,
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"messages": [
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{"role": "user", "content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {
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"url": image_url,
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}},
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]},
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],
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})
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if success:
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assert res.status_code == 200
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choice = res.body["choices"][0]
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assert "assistant" == choice["message"]["role"]
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assert match_regex(re_content, choice["message"]["content"])
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else:
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assert res.status_code != 200
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@pytest.mark.parametrize(
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"prompt, image_data, success, re_content",
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[
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# test model is trained on CIFAR-10, but it's quite dumb due to small size
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("What is this: <__media__>\n", IMG_BASE64_0, True, "(cat)+"),
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("What is this: <__media__>\n", IMG_BASE64_1, True, "(frog)+"),
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("What is this: <__media__>\n", "malformed", False, None), # non-image data
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("What is this:\n", "", False, None), # empty string
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]
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)
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def test_vision_completion(prompt, image_data, success, re_content):
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global server
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server.start() # vision model may take longer to load due to download size
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res = server.make_request("POST", "/completions", data={
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"temperature": 0.0,
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"top_k": 1,
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"prompt": { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
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})
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if success:
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assert res.status_code == 200
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content = res.body["content"]
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assert match_regex(re_content, content)
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else:
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assert res.status_code != 200
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@pytest.mark.parametrize(
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"prompt, image_data, success",
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[
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# test model is trained on CIFAR-10, but it's quite dumb due to small size
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("What is this: <__media__>\n", IMG_BASE64_0, True), # exceptional, so that we don't cog up the log
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("What is this: <__media__>\n", IMG_BASE64_1, True),
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("What is this: <__media__>\n", "malformed", False), # non-image data
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("What is this:\n", "base64", False), # non-image data
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]
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)
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def test_vision_embeddings(prompt, image_data, success):
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global server
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server.server_embeddings=True
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server.n_batch=512
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server.start() # vision model may take longer to load due to download size
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res = server.make_request("POST", "/embeddings", data={
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"content": [
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{ JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
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{ JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
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{ JSON_PROMPT_STRING_KEY: prompt, },
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],
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})
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if success:
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assert res.status_code == 200
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content = res.body
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# Ensure embeddings are stable when multimodal.
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assert content[0]['embedding'] == content[1]['embedding']
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# Ensure embeddings without multimodal but same prompt do not match multimodal embeddings.
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assert content[0]['embedding'] != content[2]['embedding']
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else:
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assert res.status_code != 200
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