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113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# imports for structured outputs tests
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import json
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import pytest
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from ...utils import RemoteOpenAIServer
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from .conftest import add_attention_backend
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MISTRAL_FORMAT_ARGS = [
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"--tokenizer_mode",
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"mistral",
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"--config_format",
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"mistral",
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"--load_format",
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"mistral",
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]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", ["mistralai/Voxtral-Mini-3B-2507"])
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async def test_basic_audio(mary_had_lamb, model_name, rocm_aiter_fa_attention):
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server_args = ["--enforce-eager"]
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if model_name.startswith("mistralai"):
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server_args += MISTRAL_FORMAT_ARGS
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add_attention_backend(server_args, rocm_aiter_fa_attention)
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# Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
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with RemoteOpenAIServer(model_name, server_args) as remote_server:
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client = remote_server.get_async_client()
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transcription = await client.audio.transcriptions.create(
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model=model_name,
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file=mary_had_lamb,
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language="en",
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response_format="text",
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temperature=0.0,
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)
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out = json.loads(transcription)
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out_text = out["text"]
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out_usage = out["usage"]
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assert "Mary had a little lamb," in out_text
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assert out_usage["seconds"] == 16, out_usage["seconds"]
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@pytest.mark.asyncio
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async def test_basic_audio_with_lora(mary_had_lamb, rocm_aiter_fa_attention):
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"""Ensure STT (transcribe) requests can pass LoRA through to generate."""
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# ROCm SPECIFIC CONFIGURATION:
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# To ensure the test passes on ROCm, we modify the max model length to 512.
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# We DO NOT apply this to other platforms to maintain strict upstream parity.
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from vllm.platforms import current_platform
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model_name = "ibm-granite/granite-speech-3.3-2b"
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lora_model_name = "speech"
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server_args = [
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"--enforce-eager",
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"--enable-lora",
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"--max-lora-rank",
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"64",
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"--lora-modules",
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f"{lora_model_name}={model_name}",
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"--max-model-len",
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"512" if current_platform.is_rocm() else "2048",
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"--max-num-seqs",
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"1",
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]
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add_attention_backend(server_args, rocm_aiter_fa_attention)
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# Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
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with RemoteOpenAIServer(model_name, server_args) as remote_server:
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client = remote_server.get_async_client()
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transcription = await client.audio.transcriptions.create(
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model=lora_model_name,
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file=mary_had_lamb,
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language="en",
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response_format="text",
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temperature=0.0,
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)
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out = json.loads(transcription)
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out_text = out["text"]
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out_usage = out["usage"]
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assert "mary had a little lamb" in out_text
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assert out_usage["seconds"] == 16, out_usage["seconds"]
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@pytest.mark.asyncio
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async def test_basic_audio_gemma(foscolo, rocm_aiter_fa_attention):
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# Gemma accuracy on some of the audio samples we use is particularly bad,
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# hence we use a different one here. WER is evaluated separately.
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model_name = "google/gemma-3n-E2B-it"
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server_args = ["--enforce-eager"]
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add_attention_backend(server_args, rocm_aiter_fa_attention)
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with RemoteOpenAIServer(
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model_name, server_args, max_wait_seconds=480
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) as remote_server:
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client = remote_server.get_async_client()
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transcription = await client.audio.transcriptions.create(
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model=model_name,
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file=foscolo,
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language="it",
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response_format="text",
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temperature=0.0,
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)
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out = json.loads(transcription)["text"]
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assert "da cui vergine nacque Venere" in out
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