Add vLLM v0.18.1 source tree with KV transfer abort fix
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
0
third_party/vllm/tests/entrypoints/pooling/pooling/__init__.py
vendored
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0
third_party/vllm/tests/entrypoints/pooling/pooling/__init__.py
vendored
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569
third_party/vllm/tests/entrypoints/pooling/pooling/test_online.py
vendored
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569
third_party/vllm/tests/entrypoints/pooling/pooling/test_online.py
vendored
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@@ -0,0 +1,569 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import base64
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import json
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import numpy as np
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import pytest
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import requests
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import torch
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from tests.models.utils import check_embeddings_close
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from tests.utils import RemoteOpenAIServer
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from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
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from vllm.entrypoints.pooling.utils import (
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MetadataItem,
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build_metadata_items,
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decode_pooling_output,
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)
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from vllm.tokenizers import get_tokenizer
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from vllm.utils.serial_utils import EMBED_DTYPES, ENDIANNESS, binary2tensor
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MODEL_NAME = "internlm/internlm2-1_8b-reward"
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DUMMY_CHAT_TEMPLATE = """{% for message in messages %}{{message['role'] + ': ' + message['content'] + '\\n'}}{% endfor %}""" # noqa: E501
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input_text = "The chef prepared a delicious meal."
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input_tokens = [1, 918, 29981, 10166, 395, 18067, 15265, 281]
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@pytest.fixture(scope="module")
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def server():
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args = [
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"--runner",
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"pooling",
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# use half precision for speed and memory savings in CI environment
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"--dtype",
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"bfloat16",
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"--enforce-eager",
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"--max-model-len",
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"512",
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"--chat-template",
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DUMMY_CHAT_TEMPLATE,
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"--trust-remote-code",
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]
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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yield remote_server
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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def test_basic(server: RemoteOpenAIServer, model_name: str):
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# test /v1/models
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response = requests.get(server.url_for("/v1/models"))
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served_model = response.json()["data"][0]["id"]
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assert served_model == MODEL_NAME
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# test /tokenize
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response = requests.post(
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server.url_for("/tokenize"),
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json={"model": model_name, "prompt": input_text},
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)
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assert response.json()["tokens"] == input_tokens
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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def test_completion_request(server: RemoteOpenAIServer, model_name: str):
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# test input: str
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response = requests.post(
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server.url_for("pooling"),
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json={"model": model_name, "input": input_text, "encoding_format": "float"},
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)
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response.raise_for_status()
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poolings = PoolingResponse.model_validate(response.json())
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assert poolings.id is not None
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assert len(poolings.data) == 1
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assert len(poolings.data[0].data) == len(input_tokens)
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assert poolings.usage.completion_tokens == 0
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assert poolings.usage.prompt_tokens == len(input_tokens)
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assert poolings.usage.total_tokens == len(input_tokens)
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# test input: list[int]
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response = requests.post(
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server.url_for("pooling"),
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json={"model": model_name, "input": input_tokens, "encoding_format": "float"},
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)
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response.raise_for_status()
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poolings = PoolingResponse.model_validate(response.json())
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assert poolings.id is not None
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assert len(poolings.data) == 1
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assert len(poolings.data[0].data) == len(input_tokens)
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assert poolings.usage.completion_tokens == 0
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assert poolings.usage.prompt_tokens == len(input_tokens)
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assert poolings.usage.total_tokens == len(input_tokens)
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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def test_completion_request_batched(server: RemoteOpenAIServer, model_name: str):
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N = 10
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input_texts = [input_text] * N
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response = requests.post(
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server.url_for("pooling"),
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json={"model": model_name, "input": input_texts, "encoding_format": "float"},
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)
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response.raise_for_status()
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poolings = PoolingResponse.model_validate(response.json())
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assert poolings.id is not None
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assert len(poolings.data) == N
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assert len(poolings.data[0].data) == len(input_tokens)
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assert poolings.usage.completion_tokens == 0
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assert poolings.usage.prompt_tokens == len(input_tokens) * N
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assert poolings.usage.total_tokens == len(input_tokens) * N
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# test list[list[int]]
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response = requests.post(
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server.url_for("pooling"),
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json={
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"model": model_name,
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"input": [input_tokens] * N,
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"encoding_format": "float",
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},
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)
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response.raise_for_status()
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poolings = PoolingResponse.model_validate(response.json())
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assert poolings.id is not None
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assert len(poolings.data) == N
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assert len(poolings.data[0].data) == len(input_tokens)
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assert poolings.usage.completion_tokens == 0
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assert poolings.usage.prompt_tokens == len(input_tokens) * N
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assert poolings.usage.total_tokens == len(input_tokens) * N
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_chat_request(server: RemoteOpenAIServer, model_name: str):
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messages = [
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{
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"role": "user",
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"content": "The cat sat on the mat.",
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},
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{
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"role": "assistant",
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"content": "A feline was resting on a rug.",
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},
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{
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"role": "user",
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"content": "Stars twinkle brightly in the night sky.",
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},
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]
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# test chat request basic usage
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chat_response = requests.post(
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server.url_for("pooling"),
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json={
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"model": model_name,
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"messages": messages,
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"encoding_format": "float",
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},
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)
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chat_response.raise_for_status()
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chat_poolings = PoolingResponse.model_validate(chat_response.json())
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tokenizer = get_tokenizer(tokenizer_name=model_name, trust_remote_code=True)
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prompt = tokenizer.apply_chat_template(
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messages,
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chat_template=DUMMY_CHAT_TEMPLATE,
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add_generation_prompt=True,
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continue_final_message=False,
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tokenize=False,
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)
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completions_response = requests.post(
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server.url_for("pooling"),
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json={
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"model": model_name,
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"input": prompt,
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"encoding_format": "float",
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# To be consistent with chat
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"add_special_tokens": False,
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},
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)
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completions_response.raise_for_status()
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completion_poolings = PoolingResponse.model_validate(completions_response.json())
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assert chat_poolings.id is not None
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assert completion_poolings.id is not None
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assert chat_poolings.created <= completion_poolings.created
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assert chat_poolings.model_dump(exclude={"id", "created"}) == (
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completion_poolings.model_dump(exclude={"id", "created"})
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)
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# test add_generation_prompt
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response = requests.post(
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server.url_for("pooling"),
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json={"model": model_name, "messages": messages, "add_generation_prompt": True},
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)
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response.raise_for_status()
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output = PoolingResponse.model_validate(response.json())
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assert output.object == "list"
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assert len(output.data) == 1
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assert output.model == MODEL_NAME
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assert output.usage.prompt_tokens == 33
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# test continue_final_message
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# The continue_final_message parameter doesn't seem to be working with this model.
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response = requests.post(
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server.url_for("pooling"),
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json={
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"model": model_name,
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"messages": messages,
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"continue_final_message": True,
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},
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)
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response.raise_for_status()
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output = PoolingResponse.model_validate(response.json())
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assert output.object == "list"
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assert len(output.data) == 1
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assert output.model == MODEL_NAME
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assert output.usage.prompt_tokens == 33
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# test add_special_tokens
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response = requests.post(
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server.url_for("pooling"),
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json={"model": model_name, "messages": messages, "add_special_tokens": True},
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)
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response.raise_for_status()
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output = PoolingResponse.model_validate(response.json())
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assert output.object == "list"
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assert len(output.data) == 1
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assert output.model == MODEL_NAME
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assert output.usage.prompt_tokens == 34
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# test continue_final_message with add_generation_prompt
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response = requests.post(
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server.url_for("pooling"),
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json={
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"model": model_name,
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"messages": messages,
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"continue_final_message": True,
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"add_generation_prompt": True,
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},
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)
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assert (
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"Cannot set both `continue_final_message` and `add_generation_prompt` to True."
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in response.json()["error"]["message"]
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)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_batch_base64_pooling(server: RemoteOpenAIServer, model_name: str):
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input_texts = [
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"Hello my name is",
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"The best thing about vLLM is that it supports many different models",
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]
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float_response = requests.post(
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server.url_for("pooling"),
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json={
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"input": input_texts,
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"model": model_name,
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"encoding_format": "float",
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},
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)
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float_response.raise_for_status()
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responses_float = PoolingResponse.model_validate(float_response.json())
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float_data = [np.array(d.data).squeeze(-1).tolist() for d in responses_float.data]
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base64_response = requests.post(
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server.url_for("pooling"),
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json={
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"input": input_texts,
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"model": model_name,
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"encoding_format": "base64",
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},
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)
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base64_response.raise_for_status()
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responses_base64 = PoolingResponse.model_validate(base64_response.json())
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decoded_responses_base64_data = []
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for data in responses_base64.data:
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decoded_responses_base64_data.append(
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np.frombuffer(base64.b64decode(data.data), dtype="float32").tolist()
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)
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check_embeddings_close(
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embeddings_0_lst=float_data,
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embeddings_1_lst=decoded_responses_base64_data,
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name_0="float32",
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name_1="base64",
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)
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# Default response is float32 decoded from base64 by OpenAI Client
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default_response = requests.post(
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server.url_for("pooling"),
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json={
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"input": input_texts,
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"model": model_name,
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},
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)
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default_response.raise_for_status()
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responses_default = PoolingResponse.model_validate(default_response.json())
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default_data = [
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np.array(d.data).squeeze(-1).tolist() for d in responses_default.data
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]
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check_embeddings_close(
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embeddings_0_lst=float_data,
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embeddings_1_lst=default_data,
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name_0="float32",
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name_1="default",
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)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_base64_embed_dtype_and_endianness(
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server: RemoteOpenAIServer, model_name: str
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):
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input_texts = [input_text] * 3
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url = server.url_for("pooling")
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float_response = requests.post(
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url,
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json={
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"model": model_name,
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"input": input_texts,
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"encoding_format": "float",
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},
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)
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responses_float = PoolingResponse.model_validate(float_response.json())
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float_data = [np.array(d.data).squeeze(-1).tolist() for d in responses_float.data]
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for embed_dtype in EMBED_DTYPES:
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for endianness in ENDIANNESS:
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responses_base64 = requests.post(
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url,
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json={
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"model": model_name,
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"input": input_texts,
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"encoding_format": "base64",
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"embed_dtype": embed_dtype,
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"endianness": endianness,
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},
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)
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base64_data = []
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for data in responses_base64.json()["data"]:
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binary = base64.b64decode(data["data"])
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tensor = binary2tensor(binary, (-1,), embed_dtype, endianness)
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base64_data.append(tensor.to(torch.float32).tolist())
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check_embeddings_close(
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embeddings_0_lst=float_data,
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embeddings_1_lst=base64_data,
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name_0="float_data",
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name_1="base64_data",
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tol=1e-2,
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)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_bytes_embed_dtype_and_endianness(
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server: RemoteOpenAIServer, model_name: str
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):
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input_texts = [input_text] * 3
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|
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url = server.url_for("pooling")
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float_response = requests.post(
|
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url,
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json={
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"model": model_name,
|
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"input": input_texts,
|
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"encoding_format": "float",
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},
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)
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responses_float = PoolingResponse.model_validate(float_response.json())
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float_data = [np.array(d.data).squeeze(-1).tolist() for d in responses_float.data]
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for embed_dtype in EMBED_DTYPES:
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for endianness in ENDIANNESS:
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responses_bytes = requests.post(
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url,
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json={
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"model": model_name,
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"input": input_texts,
|
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"encoding_format": "bytes",
|
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"embed_dtype": embed_dtype,
|
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"endianness": endianness,
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},
|
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)
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metadata = json.loads(responses_bytes.headers["metadata"])
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body = responses_bytes.content
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items = [MetadataItem(**x) for x in metadata["data"]]
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bytes_data = decode_pooling_output(items=items, body=body)
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bytes_data = [x.to(torch.float32).view(-1).tolist() for x in bytes_data]
|
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|
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check_embeddings_close(
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embeddings_0_lst=float_data,
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embeddings_1_lst=bytes_data,
|
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name_0="float_data",
|
||||
name_1="bytes_data",
|
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tol=1e-2,
|
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)
|
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|
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|
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_bytes_only_embed_dtype_and_endianness(
|
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server: RemoteOpenAIServer, model_name: str
|
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):
|
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input_texts = [input_text] * 3
|
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|
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url = server.url_for("pooling")
|
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float_response = requests.post(
|
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url,
|
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json={
|
||||
"model": model_name,
|
||||
"input": input_texts,
|
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"encoding_format": "float",
|
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},
|
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)
|
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responses_float = PoolingResponse.model_validate(float_response.json())
|
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float_data = [np.array(d.data).squeeze(-1).tolist() for d in responses_float.data]
|
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n_tokens = responses_float.usage.prompt_tokens // len(input_texts)
|
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|
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for embed_dtype in EMBED_DTYPES:
|
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for endianness in ENDIANNESS:
|
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responses_bytes = requests.post(
|
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url,
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": input_texts,
|
||||
"encoding_format": "bytes_only",
|
||||
"embed_dtype": embed_dtype,
|
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"endianness": endianness,
|
||||
},
|
||||
)
|
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|
||||
assert "metadata" not in responses_bytes.headers
|
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body = responses_bytes.content
|
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items = build_metadata_items(
|
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embed_dtype=embed_dtype,
|
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endianness=endianness,
|
||||
shape=(n_tokens, 1),
|
||||
n_request=len(input_texts),
|
||||
)
|
||||
bytes_data = decode_pooling_output(items=items, body=body)
|
||||
bytes_data = [x.to(torch.float32).view(-1).tolist() for x in bytes_data]
|
||||
|
||||
check_embeddings_close(
|
||||
embeddings_0_lst=float_data,
|
||||
embeddings_1_lst=bytes_data,
|
||||
name_0="float_data",
|
||||
name_1="bytes_data",
|
||||
tol=1e-2,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
@pytest.mark.parametrize("param_name", ["encoding_format", "embed_dtype", "endianness"])
|
||||
async def test_params_not_supported(
|
||||
server: RemoteOpenAIServer, model_name: str, param_name: str
|
||||
):
|
||||
responses_base64 = requests.post(
|
||||
server.url_for("pooling"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": input_text,
|
||||
"encoding_format": "base64",
|
||||
param_name: f"bad_{param_name}",
|
||||
},
|
||||
)
|
||||
|
||||
assert responses_base64.status_code == 400
|
||||
assert "literal_error" in responses_base64.json()["error"]["message"]
|
||||
assert f"bad_{param_name}" in responses_base64.json()["error"]["message"]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_invocations_chat_request(server: RemoteOpenAIServer):
|
||||
request_args = {
|
||||
"model": MODEL_NAME,
|
||||
"input": input_text,
|
||||
"encoding_format": "float",
|
||||
}
|
||||
|
||||
completion_response = requests.post(server.url_for("pooling"), json=request_args)
|
||||
completion_response.raise_for_status()
|
||||
|
||||
invocation_response = requests.post(
|
||||
server.url_for("invocations"), json=request_args
|
||||
)
|
||||
invocation_response.raise_for_status()
|
||||
|
||||
completion_output = completion_response.json()
|
||||
invocation_output = invocation_response.json()
|
||||
|
||||
assert completion_output.keys() == invocation_output.keys()
|
||||
for completion_data, invocation_data in zip(
|
||||
completion_output["data"], invocation_output["data"]
|
||||
):
|
||||
assert completion_data.keys() == invocation_data.keys()
|
||||
check_embeddings_close(
|
||||
embeddings_0_lst=completion_data["data"],
|
||||
embeddings_1_lst=invocation_data["data"],
|
||||
name_0="completion",
|
||||
name_1="invocation",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_invocations_conversation_chat_request(server: RemoteOpenAIServer):
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "The cat sat on the mat.",
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "A feline was resting on a rug.",
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Stars twinkle brightly in the night sky.",
|
||||
},
|
||||
]
|
||||
|
||||
request_args = {
|
||||
"model": MODEL_NAME,
|
||||
"messages": messages,
|
||||
"encoding_format": "float",
|
||||
}
|
||||
|
||||
chat_response = requests.post(server.url_for("pooling"), json=request_args)
|
||||
chat_response.raise_for_status()
|
||||
|
||||
invocation_response = requests.post(
|
||||
server.url_for("invocations"), json=request_args
|
||||
)
|
||||
invocation_response.raise_for_status()
|
||||
|
||||
chat_output = chat_response.json()
|
||||
invocation_output = invocation_response.json()
|
||||
|
||||
assert chat_output.keys() == invocation_output.keys()
|
||||
for chat_data, invocation_data in zip(
|
||||
chat_output["data"], invocation_output["data"]
|
||||
):
|
||||
assert chat_data.keys() == invocation_data.keys()
|
||||
check_embeddings_close(
|
||||
embeddings_0_lst=chat_data["data"],
|
||||
embeddings_1_lst=invocation_data["data"],
|
||||
name_0="chat",
|
||||
name_1="invocation",
|
||||
)
|
||||
Reference in New Issue
Block a user