Sy03andsy03
432b2c6ca3
Signed-off-by: Sysy <1370724210@qq.com> Signed-off-by: sy03 <137072421@qq.com> Co-authored-by: sy03 <137072421@qq.com>
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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from __future__ import annotations
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import base64
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from types import SimpleNamespace
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import numpy as np
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import pytest
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from vllm.sampling_params import SamplingParams
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from vllm_omni.experimental.fullduplex.engine.contracts import DuplexInputMode
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from vllm_omni.experimental.fullduplex.engine.messages import DuplexFence
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from vllm_omni.experimental.fullduplex.nemotron_voicechat.runtime import (
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NemotronVoiceChatDuplexRuntimeExtension,
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)
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from vllm_omni.experimental.fullduplex.nemotron_voicechat.serving_adapter import (
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NemotronVoiceChatServingRuntimeAdapter,
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_render_tool_response,
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)
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from vllm_omni.model_executor.models.nemotron_voicechat.nemotron_voicechat_thinker import (
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NemotronVoiceChatThinkerForConditionalGeneration,
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)
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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def _frame() -> dict[str, object]:
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raw = np.zeros(1280, dtype=np.float32).tobytes()
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return {
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"type": "audio",
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"audio": base64.b64encode(raw).decode("ascii"),
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"format": "pcm_f32le",
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"sample_rate_hz": 16000,
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}
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def _plan(extension, *, input_seq: int):
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return extension.plan_append(
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request_id="req",
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fence=DuplexFence("sid", incarnation=2, epoch=3),
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session_config={},
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runtime_config={
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"nvc_prompt_token_ids": [0, 42, 1],
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"nvc_text_pad_id": 12,
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"nvc_max_model_len": 8192,
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},
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seq=input_seq,
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turn_seq=input_seq,
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mode=DuplexInputMode.APPEND_AUDIO_CHUNK,
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payload=_frame(),
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final=False,
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sampling_params=SamplingParams(),
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)
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def test_first_append_prefills_prompt_then_each_append_consumes_one_frame() -> None:
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extension = NemotronVoiceChatDuplexRuntimeExtension()
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params = extension.configure_sampling_params(
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runtime_config={}, defaults=(SamplingParams(temperature=0.7, max_tokens=4),)
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)[0]
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assert params.temperature == 0.0 and params.max_tokens == 1 and params.top_p == 1.0 and params.top_k == 0
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first = _plan(extension, input_seq=1)
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later = _plan(extension, input_seq=2)
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assert first.prompt["prompt_token_ids"] == [0, 42, 1, 12]
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assert later.prompt["prompt_token_ids"] == [12]
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assert first.prompt["model_intermediate_buffer"]["duplex"]["source_input_seq"] == 1
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assert later.prompt["model_intermediate_buffer"]["duplex"]["source_input_seq"] == 2
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def test_append_rejects_stage0_context_overflow() -> None:
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with pytest.raises(ValueError, match="max_model_len"):
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_plan(NemotronVoiceChatDuplexRuntimeExtension(), input_seq=8190)
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def test_function_output_becomes_versioned_nvidia_channel_tokens() -> None:
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encoded: list[str] = []
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adapter = NemotronVoiceChatServingRuntimeAdapter(lambda *_: None)
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adapter._tokenizer = SimpleNamespace(
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encode=lambda text, **_kwargs: encoded.append(text) or [31, 32, 33],
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)
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first = adapter.runtime_config_for_function_output(
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{"type": "function_call_output", "call_id": "call-1", "output": '{"result":20}'},
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{},
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)
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second = adapter.runtime_config_for_function_output(
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{"type": "function_call_output", "call_id": "call-2", "output": "plain text"},
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first,
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)
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assert _render_tool_response('{"result":20}') == '<TOOL_RESPONSE>[{"result":20}]</TOOL_RESPONSE>'
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assert encoded == [
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'<TOOL_RESPONSE>[{"result":20}]</TOOL_RESPONSE>',
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'<TOOL_RESPONSE>["plain text"]</TOOL_RESPONSE>',
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]
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assert first["nvc_function_response_generation"] == 1
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assert second["nvc_function_response_generation"] == 2
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assert second["nvc_function_response_token_ids"] == [31, 32, 33]
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assert second["nvc_function_response_call_id"] == "call-2"
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assert second["nvc_function_response_batches"] == [
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{"generation": 1, "call_id": "call-1", "token_ids": [31, 32, 33]},
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{"generation": 2, "call_id": "call-2", "token_ids": [31, 32, 33]},
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]
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session = {"function_response_generation": 0, "forced_function_tokens": []}
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NemotronVoiceChatThinkerForConditionalGeneration._sync_forced_function_response(session, second)
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assert session["function_response_generation"] == 2
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assert session["forced_function_token"] == 31
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assert session["forced_function_tokens"] == [31, 32, 33, 31, 32, 33]
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