tzhouam
efda9369a8
Reuse upstream weight mapping and multimodal processing, consolidate Omni processor bridges, and trim unused Hunyuan internals. Route no-media kwargs through the active renderer and preserve GLM generation prompt formatting. Validation: 453 CPU tests passed, 7 skipped; processor imports, Ruff, and diff checks passed. GPU and end-to-end CI not rerun. Signed-off-by: tzhouam <tzhouam@connect.ust.hk>
100 lines
4.1 KiB
Python
100 lines
4.1 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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"""Exercise Omni routing through the active upstream rendering pipeline."""
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import asyncio
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, Mock
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import pytest
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import torch
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from vllm.inputs import tokens_input
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from vllm.pooling_params import PoolingParams
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from vllm.renderers import BaseRenderer
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from vllm.renderers.params import TokenizeParams
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from vllm.v1.engine.input_processor import InputProcessor
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from vllm_omni.inputs.preprocess import OmniRenderer
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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class _Renderer(BaseRenderer):
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def __init__(self):
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self.model_config = SimpleNamespace(is_encoder_decoder=False, enable_prompt_embeds=True)
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self.tokenizer = None
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self.default_cmpl_tok_params = TokenizeParams(max_total_tokens=128)
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self._tokenize_prompt = lambda prompt, params: {**prompt, "prompt_token_ids": [1, 2, 3]}
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self._tokenize_prompt_async = AsyncMock(side_effect=self._tokenize_prompt)
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self._process_multimodal = Mock(return_value=tokens_input([1, 2, 3, 99]))
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self._process_multimodal_async = AsyncMock(side_effect=self._process_multimodal)
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def render_messages(self, messages, params):
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return messages, {"prompt": "hello"}
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@pytest.fixture
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def renderer():
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return OmniRenderer(_Renderer())
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@pytest.mark.parametrize("kwargs", [{}, {"target_h": 512, "target_w": 768}])
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@pytest.mark.parametrize("tokenized", [False, True])
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def test_process_inputs_routes_no_media_processor_kwargs(renderer, kwargs, tokenized):
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# Keep real process_inputs/render_cmpl; stub unrelated config validation.
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processor = object.__new__(InputProcessor)
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processor.renderer = renderer
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processor.model_config = renderer.model_config
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processor.vllm_config = SimpleNamespace(
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parallel_config=SimpleNamespace(
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data_parallel_size=1,
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data_parallel_size_local=1,
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local_engines_only=False,
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)
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)
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processor._validate_params = Mock()
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processor._validate_lora = Mock()
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processor._validate_model_inputs = Mock()
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prompt = {"prompt_token_ids": [1, 2, 3]} if tokenized else {"prompt": "hello"}
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prompt.update(mm_processor_kwargs=kwargs, cache_salt="salt")
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request = processor.process_inputs("image-request", prompt, PoolingParams(), ("embed",))
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assert request.prompt_token_ids == [1, 2, 3, 99]
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assert request.cache_salt == "salt"
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renderer._renderer._process_multimodal.assert_called_once_with(
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[1, 2, 3],
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{},
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mm_processor_kwargs=kwargs,
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mm_uuids=None,
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skip_mm_cache=False,
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)
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@pytest.mark.parametrize("async_mode", [False, True])
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@pytest.mark.parametrize("kind", ["text", "tokens", "media", "kwargs", "embeds"])
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def test_renderer_preserves_routes_extras_and_cache_policy(renderer, async_mode, kind):
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prompt = {"prompt": "hello", "additional_information": {"speaker": 1}, "model_intermediate_buffer": {"x": 2}}
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if kind == "tokens":
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prompt["prompt_token_ids"] = [1, 2, 3]
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elif kind == "media":
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prompt["multi_modal_data"] = {"image": "image"}
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elif kind == "kwargs":
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prompt["mm_processor_kwargs"] = {}
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elif kind == "embeds":
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prompt["prompt_embeds"] = torch.ones(3, 4)
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if async_mode:
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(result,) = asyncio.run(renderer.render_cmpl_async([prompt], skip_mm_cache=True))
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else:
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(result,) = renderer.render_cmpl([prompt], skip_mm_cache=True)
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assert result["additional_information"] == {"speaker": 1}
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assert result["model_intermediate_buffer"] == {"x": 2}
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assert result["prompt"] == "hello"
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if kind in ("media", "kwargs"):
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assert result["prompt_token_ids"] == [1, 2, 3, 99]
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assert renderer._renderer._process_multimodal.call_args.kwargs["skip_mm_cache"] is True
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else:
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renderer._renderer._process_multimodal.assert_not_called()
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if kind == "embeds":
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torch.testing.assert_close(result["prompt_embeds"], prompt["prompt_embeds"])
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else:
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assert result["prompt_token_ids"] == [1, 2, 3]
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