forked from Karylab-cklius/vllm
f57ac274b2
Signed-off-by: aoshen524 <aoshen524@gmail.com> Co-authored-by: Martin Hickey <martin.hickey@ie.ibm.com>
925 lines
30 KiB
Python
925 lines
30 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
"""Tests for the /derender endpoints (postprocessing counterpart to /render)."""
|
|
|
|
import httpx
|
|
import pytest
|
|
import pytest_asyncio
|
|
|
|
from tests.utils import RemoteLaunchRenderServer
|
|
from vllm.tokenizers import get_tokenizer
|
|
|
|
MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def server():
|
|
with RemoteLaunchRenderServer(MODEL_NAME, []) as remote_server:
|
|
yield remote_server
|
|
|
|
|
|
@pytest_asyncio.fixture
|
|
async def client(server):
|
|
async with httpx.AsyncClient(
|
|
base_url=server.url_for(""), timeout=30.0
|
|
) as http_client:
|
|
yield http_client
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helpers
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
async def _render_chat(client: httpx.AsyncClient) -> dict:
|
|
"""Render a minimal chat request and return the GenerateRequest dict."""
|
|
resp = await client.post(
|
|
"/v1/chat/completions/render",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"messages": [{"role": "user", "content": "Hello"}],
|
|
},
|
|
)
|
|
assert resp.status_code == 200
|
|
return resp.json()
|
|
|
|
|
|
def _make_generate_response(
|
|
token_ids: list[int] | None,
|
|
request_id: str = "chatcmpl-test-id",
|
|
finish_reason: str = "stop",
|
|
logprobs: dict | None = None,
|
|
prompt_logprobs: list | None = None,
|
|
kv_transfer_params: dict | None = None,
|
|
) -> dict:
|
|
choice: dict = {
|
|
"index": 0,
|
|
"token_ids": token_ids,
|
|
"finish_reason": finish_reason,
|
|
"logprobs": logprobs,
|
|
}
|
|
return {
|
|
"request_id": request_id,
|
|
"choices": [choice],
|
|
"prompt_logprobs": prompt_logprobs,
|
|
"kv_transfer_params": kv_transfer_params,
|
|
}
|
|
|
|
|
|
def _make_logprobs_with_placeholders(token_id: int = 1234) -> dict:
|
|
entry = {
|
|
"token": f"token_id:{token_id}",
|
|
"logprob": -1.0,
|
|
"bytes": None,
|
|
"top_logprobs": [
|
|
{"token": f"token_id:{token_id + 1}", "logprob": -2.0, "bytes": None}
|
|
],
|
|
}
|
|
return {"content": [entry]}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Chat derender tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_roundtrip(client):
|
|
"""Render then derender: decoded content should be a non-empty string."""
|
|
gen_req = await _render_chat(client)
|
|
# Use the first 5 rendered token IDs as synthetic "generated" tokens.
|
|
synthetic_ids = gen_req["token_ids"][:5]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(synthetic_ids),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
data = response.json()
|
|
assert data["object"] == "chat.completion"
|
|
assert len(data["choices"]) == 1
|
|
assert data["choices"][0]["message"]["content"]
|
|
assert data["choices"][0]["message"]["role"] == "assistant"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_usage(client):
|
|
"""Supplied prompt_tokens flows through into usage correctly."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(synthetic_ids),
|
|
"prompt_tokens": 10,
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
usage = response.json()["usage"]
|
|
assert usage["prompt_tokens"] == 10
|
|
assert usage["completion_tokens"] == len(synthetic_ids)
|
|
assert usage["total_tokens"] == 10 + len(synthetic_ids)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_usage_default(client):
|
|
"""Omitting prompt_tokens gives usage.prompt_tokens == 0."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(synthetic_ids),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
usage = response.json()["usage"]
|
|
assert usage["prompt_tokens"] == 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_logprobs(client):
|
|
"""token_id:N placeholders in content.token are resolved to real strings."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
token_id = synthetic_ids[0]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(
|
|
synthetic_ids,
|
|
logprobs=_make_logprobs_with_placeholders(token_id),
|
|
),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
data = response.json()
|
|
logprobs = data["choices"][0]["logprobs"]
|
|
assert logprobs is not None
|
|
content = logprobs["content"]
|
|
assert content is not None and len(content) == 1
|
|
token_str = content[0]["token"]
|
|
assert not token_str.startswith("token_id:"), (
|
|
f"Placeholder was not resolved: {token_str!r}"
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_logprobs_bytes(client):
|
|
"""Resolved logprob entries have bytes populated as list[int]."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
token_id = synthetic_ids[0]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(
|
|
synthetic_ids,
|
|
logprobs=_make_logprobs_with_placeholders(token_id),
|
|
),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
content = response.json()["choices"][0]["logprobs"]["content"]
|
|
bytes_field = content[0]["bytes"]
|
|
assert isinstance(bytes_field, list)
|
|
assert len(bytes_field) > 0
|
|
assert all(isinstance(b, int) for b in bytes_field)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_top_logprobs(client):
|
|
"""top_logprobs entries also have their placeholders resolved."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
token_id = synthetic_ids[0]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(
|
|
synthetic_ids,
|
|
logprobs=_make_logprobs_with_placeholders(token_id),
|
|
),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
content = response.json()["choices"][0]["logprobs"]["content"]
|
|
top = content[0]["top_logprobs"]
|
|
assert len(top) == 1
|
|
assert not top[0]["token"].startswith("token_id:"), (
|
|
f"top_logprobs placeholder not resolved: {top[0]['token']!r}"
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_prompt_logprobs_passthrough(client):
|
|
"""prompt_logprobs on GenerateResponse passes through unchanged."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
# prompt_logprobs is a list[dict[int, Logprob] | None]; use None entries.
|
|
prompt_logprobs = [None, None]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(
|
|
synthetic_ids, prompt_logprobs=prompt_logprobs
|
|
),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
assert response.json()["prompt_logprobs"] == prompt_logprobs
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_kv_transfer_params_passthrough(client):
|
|
"""kv_transfer_params passes through to the ChatCompletionResponse."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
kv = {"key": "value"}
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(
|
|
synthetic_ids, kv_transfer_params=kv
|
|
),
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
assert response.json()["kv_transfer_params"] == kv
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_empty_token_ids(client):
|
|
"""Empty token_ids list returns 400."""
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response([]),
|
|
},
|
|
)
|
|
assert response.status_code == 400
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_null_token_ids(client):
|
|
"""Null token_ids returns 400."""
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_response": _make_generate_response(None),
|
|
},
|
|
)
|
|
assert response.status_code == 400
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_chat_unknown_model(client):
|
|
"""Unknown model returns 404."""
|
|
gen_req = await _render_chat(client)
|
|
synthetic_ids = gen_req["token_ids"][:3]
|
|
|
|
response = await client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": "does-not-exist",
|
|
"generate_response": _make_generate_response(synthetic_ids),
|
|
},
|
|
)
|
|
assert response.status_code == 404
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Completion derender tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
async def _render_completion(client: httpx.AsyncClient, prompt: str) -> dict:
|
|
"""Render a completion prompt and return the first GenerateRequest dict."""
|
|
resp = await client.post(
|
|
"/v1/completions/render",
|
|
json={"model": MODEL_NAME, "prompt": prompt},
|
|
)
|
|
assert resp.status_code == 200
|
|
data = resp.json()
|
|
assert isinstance(data, list) and len(data) >= 1
|
|
return data[0]
|
|
|
|
|
|
def _make_completion_generate_response(
|
|
token_ids: list[int],
|
|
request_id: str,
|
|
kv_transfer_params: dict | None = None,
|
|
logprobs: dict | None = None,
|
|
) -> dict:
|
|
return {
|
|
"request_id": request_id,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"token_ids": token_ids,
|
|
"finish_reason": "stop",
|
|
"logprobs": logprobs,
|
|
}
|
|
],
|
|
"prompt_logprobs": None,
|
|
"kv_transfer_params": kv_transfer_params,
|
|
}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_roundtrip(client):
|
|
"""Two prompts rendered, two GenerateResponses → two choices with indices 0, 1."""
|
|
gr1 = await _render_completion(client, "Hello world")
|
|
gr2 = await _render_completion(client, "Goodbye world")
|
|
|
|
ids1 = gr1["token_ids"][:4]
|
|
ids2 = gr2["token_ids"][:4]
|
|
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_responses": [
|
|
_make_completion_generate_response(ids1, gr1["request_id"]),
|
|
_make_completion_generate_response(ids2, gr2["request_id"]),
|
|
],
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
data = response.json()
|
|
assert data["object"] == "text_completion"
|
|
choices = data["choices"]
|
|
assert len(choices) == 2
|
|
assert choices[0]["index"] == 0
|
|
assert choices[1]["index"] == 1
|
|
assert choices[0]["text"]
|
|
assert choices[1]["text"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_usage_aggregation(client):
|
|
"""prompt_tokens=[5, 10] is aggregated correctly into usage."""
|
|
gr1 = await _render_completion(client, "Hello")
|
|
gr2 = await _render_completion(client, "World")
|
|
|
|
ids1 = gr1["token_ids"][:3]
|
|
ids2 = gr2["token_ids"][:4]
|
|
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_responses": [
|
|
_make_completion_generate_response(ids1, gr1["request_id"]),
|
|
_make_completion_generate_response(ids2, gr2["request_id"]),
|
|
],
|
|
"prompt_tokens": [5, 10],
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
usage = response.json()["usage"]
|
|
assert usage["prompt_tokens"] == 15
|
|
assert usage["completion_tokens"] == len(ids1) + len(ids2)
|
|
assert usage["total_tokens"] == 15 + len(ids1) + len(ids2)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_prompt_tokens_length_mismatch(client):
|
|
"""len(prompt_tokens) != len(generate_responses) returns 400."""
|
|
gr1 = await _render_completion(client, "Hello")
|
|
ids1 = gr1["token_ids"][:3]
|
|
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_responses": [
|
|
_make_completion_generate_response(ids1, gr1["request_id"]),
|
|
],
|
|
"prompt_tokens": [5, 10],
|
|
},
|
|
)
|
|
assert response.status_code == 400
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_empty_generate_responses(client):
|
|
"""Empty generate_responses list returns 400."""
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={"model": MODEL_NAME, "generate_responses": []},
|
|
)
|
|
assert response.status_code == 400
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_logprobs(client):
|
|
"""token_id:N placeholders in logprobs are resolved; CompletionLogProbs
|
|
flat-list structure is returned with non-empty tokens and text_offsets."""
|
|
gr1 = await _render_completion(client, "Hello world")
|
|
ids1 = gr1["token_ids"][:3]
|
|
token_id = ids1[0]
|
|
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_responses": [
|
|
_make_completion_generate_response(
|
|
ids1,
|
|
gr1["request_id"],
|
|
logprobs=_make_logprobs_with_placeholders(token_id),
|
|
),
|
|
],
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
logprobs = response.json()["choices"][0]["logprobs"]
|
|
assert logprobs is not None
|
|
tokens = logprobs["tokens"]
|
|
assert len(tokens) == 1
|
|
assert not tokens[0].startswith("token_id:"), (
|
|
f"Placeholder was not resolved: {tokens[0]!r}"
|
|
)
|
|
assert len(logprobs["token_logprobs"]) == 1
|
|
assert isinstance(logprobs["token_logprobs"][0], float)
|
|
assert len(logprobs["text_offset"]) == 1
|
|
assert logprobs["text_offset"][0] == 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_derender_completion_kv_transfer_params_passthrough(client):
|
|
"""kv_transfer_params passes through to CompletionResponse."""
|
|
gr1 = await _render_completion(client, "Hello")
|
|
ids1 = gr1["token_ids"][:3]
|
|
kv = {"node": "abc"}
|
|
|
|
response = await client.post(
|
|
"/v1/completions/derender",
|
|
json={
|
|
"model": MODEL_NAME,
|
|
"generate_responses": [
|
|
_make_completion_generate_response(
|
|
ids1, gr1["request_id"], kv_transfer_params=kv
|
|
),
|
|
],
|
|
},
|
|
)
|
|
assert response.status_code == 200
|
|
assert response.json()["kv_transfer_params"] == kv
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# E2E: render -> derender roundtrip with parser (reasoning + tool calls)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
PARSER_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
|
|
|
|
_E2E_TOOLS = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_weather",
|
|
"description": "Get weather for a city",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {"city": {"type": "string"}},
|
|
},
|
|
},
|
|
}
|
|
]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def parser_server():
|
|
args = [
|
|
"--enable-auto-tool-choice",
|
|
"--tool-call-parser",
|
|
"hermes",
|
|
"--reasoning-parser",
|
|
"deepseek_r1",
|
|
]
|
|
with RemoteLaunchRenderServer(PARSER_MODEL, args) as remote_server:
|
|
yield remote_server
|
|
|
|
|
|
@pytest_asyncio.fixture
|
|
async def parser_client(parser_server):
|
|
async with httpx.AsyncClient(
|
|
base_url=parser_server.url_for(""), timeout=60.0
|
|
) as http_client:
|
|
yield http_client
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def parser_tokenizer():
|
|
return get_tokenizer(PARSER_MODEL)
|
|
|
|
|
|
def _encode(tokenizer, text: str) -> list[int]:
|
|
return tokenizer.encode(text, add_special_tokens=False)
|
|
|
|
|
|
def _decoded(tokenizer, token_ids: list[int]) -> str:
|
|
return tokenizer.decode(token_ids, skip_special_tokens=True)
|
|
|
|
|
|
def _require_markers_survive(tokenizer, text: str, *markers: str) -> list[int]:
|
|
"""Encode text and skip the test if any marker is lost in roundtrip."""
|
|
ids = _encode(tokenizer, text)
|
|
decoded = tokenizer.decode(ids, skip_special_tokens=False)
|
|
for m in markers:
|
|
if m not in decoded:
|
|
pytest.skip(f"Marker {m!r} lost in encode->decode roundtrip")
|
|
return ids
|
|
|
|
|
|
async def _e2e_render_chat(
|
|
client: httpx.AsyncClient,
|
|
model: str,
|
|
messages: list[dict],
|
|
) -> dict:
|
|
resp = await client.post(
|
|
"/v1/chat/completions/render",
|
|
json={"model": model, "messages": messages},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
return resp.json()
|
|
|
|
|
|
def _e2e_generate_response(
|
|
token_ids: list[int],
|
|
request_id: str = "chatcmpl-e2e-test",
|
|
) -> dict:
|
|
return {
|
|
"request_id": request_id,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"token_ids": token_ids,
|
|
"finish_reason": "stop",
|
|
}
|
|
],
|
|
}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_plain_roundtrip(parser_client, parser_tokenizer):
|
|
"""Plain text without reasoning markers roundtrips correctly."""
|
|
messages = [{"role": "user", "content": "What is 2+2?"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
answer = "The answer is four."
|
|
output_ids = _encode(parser_tokenizer, answer)
|
|
expected = _decoded(parser_tokenizer, output_ids)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
content = resp.json()["choices"][0]["message"]["content"]
|
|
assert content == expected
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_token_identity(parser_client, parser_tokenizer):
|
|
"""encode(derender(token_ids)) == token_ids (RL invariant)."""
|
|
messages = [{"role": "user", "content": "Hi"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
answer = "Hello! How can I help?"
|
|
output_ids = _encode(parser_tokenizer, answer)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
},
|
|
)
|
|
assert resp.status_code == 200
|
|
content = resp.json()["choices"][0]["message"]["content"]
|
|
re_encoded = _encode(parser_tokenizer, content)
|
|
assert output_ids == re_encoded
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_non_ascii_roundtrip(parser_client, parser_tokenizer):
|
|
"""CJK + emoji roundtrip without U+FFFD."""
|
|
messages = [{"role": "user", "content": "Reply in Chinese"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
answer = "你好世界 😀"
|
|
output_ids = _encode(parser_tokenizer, answer)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
},
|
|
)
|
|
assert resp.status_code == 200
|
|
content = resp.json()["choices"][0]["message"]["content"]
|
|
assert "�" not in content
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_parsed_reasoning(parser_client, parser_tokenizer):
|
|
"""<think>...</think> splits into reasoning + content."""
|
|
messages = [{"role": "user", "content": "What is 2+3?"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
reasoning_text = "The user wants 2 plus 3. That is 5."
|
|
answer_text = "The answer is 5."
|
|
output_text = f"<think>{reasoning_text}</think>{answer_text}"
|
|
output_ids = _require_markers_survive(parser_tokenizer, output_text, "</think>")
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
"chat_request": {
|
|
"model": PARSER_MODEL,
|
|
"messages": messages,
|
|
"include_reasoning": True,
|
|
},
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
msg = resp.json()["choices"][0]["message"]
|
|
assert msg["reasoning"] is not None
|
|
assert reasoning_text in msg["reasoning"]
|
|
assert answer_text in msg["content"]
|
|
assert "<think>" not in msg["content"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_parsed_tool_call(parser_client, parser_tokenizer):
|
|
"""<tool_call> extracted into tool_calls field."""
|
|
messages = [{"role": "user", "content": "Weather in Paris?"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
output_text = (
|
|
"<think>Let me check the weather.</think>"
|
|
'<tool_call>\n{"name": "get_weather", '
|
|
'"arguments": {"city": "Paris"}}\n</tool_call>'
|
|
)
|
|
output_ids = _require_markers_survive(
|
|
parser_tokenizer,
|
|
output_text,
|
|
"</think>",
|
|
"<tool_call>",
|
|
"</tool_call>",
|
|
)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
"chat_request": {
|
|
"model": PARSER_MODEL,
|
|
"messages": messages,
|
|
"tools": _E2E_TOOLS,
|
|
"tool_choice": "auto",
|
|
},
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
choice = resp.json()["choices"][0]
|
|
assert choice["message"]["tool_calls"]
|
|
assert choice["message"]["tool_calls"][0]["function"]["name"] == "get_weather"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_parsed_reasoning_and_tool_call(parser_client, parser_tokenizer):
|
|
"""Reasoning + tool call in the same output."""
|
|
messages = [{"role": "user", "content": "Weather in Paris?"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
reasoning_text = "I should look up the weather."
|
|
tool_text = (
|
|
'<tool_call>\n{"name": "get_weather", '
|
|
'"arguments": {"city": "Paris"}}\n</tool_call>'
|
|
)
|
|
output_text = f"<think>{reasoning_text}</think>{tool_text}"
|
|
output_ids = _require_markers_survive(
|
|
parser_tokenizer, output_text, "</think>", "<tool_call>"
|
|
)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
"chat_request": {
|
|
"model": PARSER_MODEL,
|
|
"messages": messages,
|
|
"tools": _E2E_TOOLS,
|
|
"tool_choice": "auto",
|
|
"include_reasoning": True,
|
|
},
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
choice = resp.json()["choices"][0]
|
|
assert choice["message"]["reasoning"] is not None
|
|
assert reasoning_text in choice["message"]["reasoning"]
|
|
assert choice["message"]["tool_calls"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_no_chat_request_fallback(parser_client, parser_tokenizer):
|
|
"""Without chat_request, derender falls back to plain detokenization."""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
gen_req = await _e2e_render_chat(parser_client, PARSER_MODEL, messages)
|
|
|
|
answer = "Hi there!"
|
|
output_ids = _encode(parser_tokenizer, answer)
|
|
|
|
resp = await parser_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": PARSER_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
},
|
|
)
|
|
assert resp.status_code == 200
|
|
content = resp.json()["choices"][0]["message"]["content"]
|
|
assert "Hi" in content
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# E2E: HarmonyParser + GPT-OSS
|
|
# ---------------------------------------------------------------------------
|
|
|
|
HARMONY_MODEL = "openai/gpt-oss-20b"
|
|
|
|
|
|
def _ensure_harmony_vocab():
|
|
"""Pre-cache the o200k_base BPE file needed by openai-harmony.
|
|
|
|
The Rust tiktoken-rs backend downloads from Azure Blob Storage, which
|
|
may be unreachable in some environments. When the cache is cold we
|
|
fetch the file ourselves and place it in ``/tmp/tiktoken-rs-cache/``
|
|
using the SHA-1(URL) filename that tiktoken-rs expects.
|
|
"""
|
|
import hashlib
|
|
import urllib.request
|
|
from pathlib import Path
|
|
|
|
url = "https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken"
|
|
cache_dir = Path("/tmp/tiktoken-rs-cache")
|
|
cache_key = hashlib.sha1(url.encode()).hexdigest()
|
|
cache_file = cache_dir / cache_key
|
|
if not cache_file.exists():
|
|
cache_dir.mkdir(parents=True, exist_ok=True)
|
|
urllib.request.urlretrieve(url, cache_file)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def harmony_server():
|
|
_ensure_harmony_vocab()
|
|
args = [
|
|
"--trust-remote-code",
|
|
"--enable-auto-tool-choice",
|
|
"--tool-call-parser",
|
|
"openai",
|
|
"--reasoning-parser",
|
|
"openai_gptoss",
|
|
]
|
|
with RemoteLaunchRenderServer(HARMONY_MODEL, args) as remote_server:
|
|
yield remote_server
|
|
|
|
|
|
@pytest_asyncio.fixture
|
|
async def harmony_client(harmony_server):
|
|
async with httpx.AsyncClient(
|
|
base_url=harmony_server.url_for(""), timeout=60.0
|
|
) as http_client:
|
|
yield http_client
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def harmony_tokenizer():
|
|
return get_tokenizer(HARMONY_MODEL, trust_remote_code=True)
|
|
|
|
|
|
def _harmony_extract_assistant_ids(
|
|
tokenizer, assistant_msg: dict, user_content: str = "test"
|
|
) -> list[int]:
|
|
"""Extract assistant token IDs via apply_chat_template diff."""
|
|
prompt = [{"role": "user", "content": user_content}]
|
|
full = prompt + [assistant_msg]
|
|
text_prompt = tokenizer.apply_chat_template(
|
|
prompt, add_generation_prompt=True, tokenize=False
|
|
)
|
|
text_full = tokenizer.apply_chat_template(
|
|
full, add_generation_prompt=False, tokenize=False
|
|
)
|
|
prompt_ids = tokenizer.encode(text_prompt)
|
|
full_ids = tokenizer.encode(text_full)
|
|
assistant_ids = list(full_ids[len(prompt_ids) :])
|
|
if not assistant_ids:
|
|
pytest.skip("Could not extract assistant tokens for Harmony")
|
|
return assistant_ids
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_harmony_plain_roundtrip(harmony_client, harmony_tokenizer):
|
|
"""GPT-OSS content-only roundtrip."""
|
|
messages = [{"role": "user", "content": "What is 2+2?"}]
|
|
gen_req = await _e2e_render_chat(harmony_client, HARMONY_MODEL, messages)
|
|
|
|
assistant_msg = {"role": "assistant", "content": "Four."}
|
|
output_ids = _harmony_extract_assistant_ids(harmony_tokenizer, assistant_msg)
|
|
|
|
resp = await harmony_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": HARMONY_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
"chat_request": {
|
|
"model": HARMONY_MODEL,
|
|
"messages": messages,
|
|
},
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
content = resp.json()["choices"][0]["message"]["content"]
|
|
assert content is not None and len(content) > 0
|
|
assert "Four" in content
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_e2e_harmony_reasoning(harmony_client, harmony_tokenizer):
|
|
"""GPT-OSS reasoning: analysis channel extracted."""
|
|
messages = [{"role": "user", "content": "Add 2 and 3."}]
|
|
gen_req = await _e2e_render_chat(harmony_client, HARMONY_MODEL, messages)
|
|
|
|
reasoning_text = "The user wants 2 plus 3."
|
|
answer_text = "The answer is 5."
|
|
assistant_msg = {
|
|
"role": "assistant",
|
|
"thinking": reasoning_text,
|
|
"content": answer_text,
|
|
}
|
|
output_ids = _harmony_extract_assistant_ids(harmony_tokenizer, assistant_msg)
|
|
|
|
decoded = harmony_tokenizer.decode(output_ids)
|
|
if reasoning_text not in decoded:
|
|
pytest.skip("Harmony template did not render thinking")
|
|
|
|
resp = await harmony_client.post(
|
|
"/v1/chat/completions/derender",
|
|
json={
|
|
"model": HARMONY_MODEL,
|
|
"generate_response": _e2e_generate_response(output_ids),
|
|
"prompt_tokens": len(gen_req["token_ids"]),
|
|
"chat_request": {
|
|
"model": HARMONY_MODEL,
|
|
"messages": messages,
|
|
"include_reasoning": True,
|
|
},
|
|
},
|
|
)
|
|
assert resp.status_code == 200, resp.text
|
|
msg = resp.json()["choices"][0]["message"]
|
|
assert msg["reasoning"] is not None
|
|
assert reasoning_text in msg["reasoning"]
|
|
assert answer_text in (msg["content"] or "")
|