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[Voxtral] Streaming example #33042
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,6 @@ | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # SPDX-FileCopyrightText: Copyright contributors to the vLLM project | ||
| import asyncio | ||
| from dataclasses import asdict | ||
|
|
||
| import pytest | ||
|
|
@@ -9,33 +10,71 @@ | |
| StreamingMode, | ||
| TranscriptionRequest, | ||
| ) | ||
| from mistral_common.tokens.tokenizers.audio import AudioConfig | ||
| from mistral_common.tokens.tokenizers.mistral import MistralTokenizer | ||
|
|
||
| from vllm import LLM, EngineArgs, SamplingParams | ||
| from vllm.assets.audio import AudioAsset | ||
| from vllm.engine.arg_utils import AsyncEngineArgs | ||
| from vllm.inputs.data import TokensPrompt | ||
| from vllm.v1.engine.async_llm import AsyncLLM, StreamingInput | ||
|
|
||
| MODEL_NAME = "mistralai/Voxtral-Mini-3B-Realtime-2602" | ||
| ENGINE_CONFIG = dict( | ||
| model=MODEL_NAME, | ||
| max_model_len=8192, | ||
| max_num_seqs=4, | ||
| limit_mm_per_prompt={"audio": 1}, | ||
| config_format="mistral", | ||
| load_format="mistral", | ||
| tokenizer_mode="mistral", | ||
| enforce_eager=True, | ||
| gpu_memory_utilization=0.4, | ||
| ) | ||
|
|
||
| def _get_engine(path: str) -> LLM: | ||
| engine_args = EngineArgs( | ||
| model=path, | ||
| max_model_len=8192, | ||
| max_num_seqs=1, | ||
| limit_mm_per_prompt={"audio": 1}, | ||
| config_format="mistral", | ||
| load_format="mistral", | ||
| tokenizer_mode="mistral", | ||
| enforce_eager=True, | ||
| gpu_memory_utilization=0.4, | ||
| ) | ||
|
|
||
| EXPECTED_TEXT = [ | ||
| ( | ||
| " First words I spoke in the original phonograph. " | ||
| "A little piece of practical poetry. Mary had a little lamb," | ||
| " its fleece was quite a slow, and everywhere that Mary went, " | ||
| "the lamb was sure to go." | ||
| ), | ||
| ( | ||
| " And the 0-1 pitch on the way to Edgar Martinez. Swung on" | ||
| " the line. Down the left field line for OBS. Here comes Joy. " | ||
| "Here is Junior to third base. They're going to wave him in. " | ||
| "The throw to the plate will be late. The Mariners are going" | ||
| " to play. For the American League Championship, " | ||
| "I don't believe it. It just continues. My, oh, my." | ||
| ), | ||
| ] | ||
|
|
||
|
|
||
| @pytest.fixture | ||
| def audio_assets() -> list[AudioAsset]: | ||
| return [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")] | ||
|
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|
|
||
| @pytest.fixture | ||
| def tokenizer() -> MistralTokenizer: | ||
| return MistralTokenizer.from_hf_hub(MODEL_NAME) | ||
|
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||
|
|
||
| @pytest.fixture | ||
| def engine() -> LLM: | ||
| engine_args = EngineArgs(**ENGINE_CONFIG) | ||
| return LLM(**asdict(engine_args)) | ||
|
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||
|
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| @pytest.mark.skip(reason="Voxtral streaming is not yet public") | ||
| def test_voxtral_streaming_forward(): | ||
| audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")] | ||
| @pytest.fixture | ||
| def async_engine() -> AsyncLLM: | ||
| engine_args = AsyncEngineArgs(**ENGINE_CONFIG) | ||
| return AsyncLLM.from_engine_args(engine_args) | ||
|
|
||
| model_name = "mistralai/Voxtral-Mini-3B-Realtime-2602" | ||
| tokenizer = MistralTokenizer.from_hf_hub(model_name) | ||
|
|
||
| @pytest.mark.skip(reason="Voxtral streaming is not yet public") | ||
| def test_voxtral_streaming_forward(audio_assets, tokenizer, engine): | ||
| audio_config = tokenizer.instruct_tokenizer.tokenizer.audio | ||
|
|
||
| def from_file(file_path: str): | ||
|
|
@@ -58,11 +97,7 @@ def from_file(file_path: str): | |
|
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| for tokens, audio_array in tokenized_list: | ||
| num_samples = audio_array.shape[0] | ||
| max_tokens = ( | ||
| audio_config.num_audio_tokens(num_samples) | ||
| - audio_config.num_delay_tokens | ||
| - 1 | ||
| ) | ||
| max_tokens = audio_config.num_audio_tokens(num_samples) - len(tokens) - 1 | ||
| sampling_params.append(SamplingParams(temperature=0.0, max_tokens=max_tokens)) | ||
|
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| input_dict = { | ||
|
|
@@ -71,27 +106,150 @@ def from_file(file_path: str): | |
| } | ||
| inputs.append(input_dict) | ||
|
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| llm = _get_engine(model_name) | ||
| outputs = llm.generate( | ||
| outputs = engine.generate( | ||
| inputs, | ||
| sampling_params=sampling_params, | ||
| ) | ||
|
|
||
| texts = [out.outputs[0].text for out in outputs] | ||
| expected = [ | ||
| ( | ||
| " First words I spoke in the original phonograph. " | ||
| "A little piece of practical poetry. Mary had a little lamb," | ||
| " it sleeps with quite a snow, and everywhere that Mary went, " | ||
| "the lamb was sure to go." | ||
| ), | ||
| ( | ||
| " And the 0-1 pitch on the way to Edgar Martinez. Swung on" | ||
| " the line. Down the left field line for OBS. Here comes Joy. " | ||
| "Here is Junior to third base. They're going to wave him in. " | ||
| "The throw to the plate will be late. The Mariners are going" | ||
| " to play. For the American League Championship, " | ||
| "I don't believe it. It just continues. My oh, my." | ||
| ), | ||
| ] | ||
| assert texts == expected | ||
| assert texts == EXPECTED_TEXT | ||
|
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||
|
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| class RealTimeAudioInput: | ||
| """ | ||
| This class is used to stream an audio file just as | ||
| if it would be streamed in real-time. | ||
| """ | ||
|
|
||
| def __init__(self, tokenizer: MistralTokenizer) -> None: | ||
| # TODO(Patrick) - put these into the tokenizer config | ||
| self._look_ahead_in_ms = 2.5 | ||
|
Collaborator
Author
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. audio input takes care of look ahead and look back |
||
| self._look_back_in_ms = 52.5 | ||
|
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||
| self._tokenizer = tokenizer | ||
| self._config: AudioConfig = ( | ||
| self._tokenizer.instruct_tokenizer.audio_encoder.audio_config | ||
| ) | ||
| self._sampling_rate = self._config.sampling_rate | ||
|
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| self._audio: Audio | None = None | ||
|
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| # mutable objects | ||
| self._start = 0 | ||
|
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| n_left_pad_samples = ( | ||
| self._config.raw_audio_length_per_tok * self._config.n_left_pad_tokens | ||
| ) | ||
| self._end = self.streaming_delay + n_left_pad_samples + self.streaming_size | ||
| self._queue: asyncio.Queue[StreamingInput | None] = asyncio.Queue() | ||
|
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| @classmethod | ||
| async def create(cls, audio: Audio, tokenizer: MistralTokenizer): | ||
| self = cls(tokenizer) | ||
|
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| # we're doing "OFFLINE" encoding here to right & left pad the audio since | ||
| # we have access to the whole audio | ||
| # if we'd do an actual online realtime streaming application we | ||
| # should instead pass `StreamingMode.ONLINE` | ||
| req = TranscriptionRequest( | ||
| streaming=StreamingMode.OFFLINE, | ||
| audio=RawAudio.from_audio(audio), | ||
| language=None, | ||
| ) | ||
| audio_enc = self._tokenizer.encode_transcription(req) | ||
| self._audio = audio_enc.audios[0] | ||
|
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| # add first request | ||
| await self.add_tokens(audio_enc.tokens) | ||
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| return self | ||
|
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| @property | ||
| def look_ahead(self) -> int: | ||
| return self._get_len_in_samples(self._look_ahead_in_ms) | ||
|
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| @property | ||
| def look_back(self) -> int: | ||
| return self._get_len_in_samples(self._look_back_in_ms) | ||
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| @property | ||
| def streaming_delay(self) -> int: | ||
| return self._get_len_in_samples(self._config.transcription_delay_ms) | ||
|
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| @property | ||
| def streaming_size(self) -> int: | ||
| stream_size_in_ms = 1000 / self._config.frame_rate | ||
| return self._get_len_in_samples(stream_size_in_ms) | ||
|
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| def _get_len_in_samples(self, len_in_ms: float) -> int: | ||
| _len_in_s = self._sampling_rate * len_in_ms / 1000 | ||
| assert _len_in_s.is_integer(), _len_in_s | ||
| len_in_s = int(_len_in_s) | ||
|
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| return len_in_s | ||
|
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| async def add_tokens(self, tokens: list[int]) -> None: | ||
| assert self._audio is not None | ||
| if self._start >= len(self._audio.audio_array): | ||
| self.stop() | ||
| return | ||
|
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| _end = self._end + self.look_ahead | ||
| _start = max(0, self._start - self.look_back) | ||
|
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| multi_modal_data = {"audio": (self._audio.audio_array[_start:_end], None)} | ||
|
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| prompt = TokensPrompt( | ||
| prompt_token_ids=tokens, multi_modal_data=multi_modal_data | ||
| ) | ||
|
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| await self._queue.put(StreamingInput(prompt)) | ||
|
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| # increase | ||
| self._start = self._end | ||
| self._end = self._end + self.streaming_size | ||
|
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| def stop(self): | ||
| self._queue.put_nowait(None) | ||
|
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| async def generator(self): | ||
| while (item := await self._queue.get()) is not None: | ||
| yield item | ||
|
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| @pytest.mark.asyncio | ||
| @pytest.mark.skip(reason="Voxtral streaming is not yet public") | ||
|
Collaborator
Author
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. skip for now - important to make sure async generator works as expected |
||
| async def test_voxtral_streaming_generator(audio_assets, tokenizer, async_engine): | ||
| sampling_params = SamplingParams(temperature=0.0, max_tokens=1) | ||
|
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| output_tokens_list = [] | ||
| for i, audio_asset in enumerate(audio_assets): | ||
| output_tokens = [] | ||
| audio = Audio.from_file(audio_asset.get_local_path(), strict=False) | ||
| streaming_input = await RealTimeAudioInput.create( | ||
| audio=audio, tokenizer=tokenizer | ||
| ) | ||
|
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| request_id = f"session-{i}" | ||
|
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| async for resp in async_engine.generate( | ||
| prompt=streaming_input.generator(), | ||
| sampling_params=sampling_params, | ||
| request_id=request_id, | ||
| ): | ||
| tokens = resp.outputs[0].token_ids[-1:] | ||
|
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| output_tokens.extend(tokens) | ||
| await streaming_input.add_tokens(tokens) | ||
|
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| output_tokens_list.append(output_tokens) | ||
|
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| texts = [tokenizer.decode(output_tokens) for output_tokens in output_tokens_list] | ||
|
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| # 'true' streaming and 'offline' streaming differ a bit because log-mels are | ||
| # differently noramalized | ||
| # TODO(Patrick) - check if we want to align or not | ||
| texts[0] = texts[0].replace("He has f", "F") | ||
|
Collaborator
Author
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. small diff because of log-mel |
||
| texts[1] = texts[1].replace("a base hit", "OBS").replace("oh my", "oh, my") | ||
|
|
||
| assert texts == EXPECTED_TEXT | ||
There was a problem hiding this comment.
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skip for now