[Bugfix] Fix OpenPangu multimodal embedding merge (#55941)
Signed-off-by: Taneem Ibrahim <taneem.ibrahim@gmail.com>
This commit is contained in:
@@ -0,0 +1,55 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from types import SimpleNamespace
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import pytest
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import torch
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from torch import nn
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from vllm.model_executor.models.openpangu_vl import (
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OpenPanguVLForConditionalGeneration,
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)
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class _LanguageModel(nn.Module):
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def __init__(self, hidden_size: int) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return input_ids.float().unsqueeze(-1).expand(-1, self.hidden_size).clone()
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class _OpenPanguVL(OpenPanguVLForConditionalGeneration):
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def __init__(self, hidden_size: int) -> None:
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nn.Module.__init__(self)
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self.config = SimpleNamespace(image_token_id=41, video_token_id=42)
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self.language_model = _LanguageModel(hidden_size)
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def get_language_model(self) -> _LanguageModel:
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return self.language_model
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@pytest.mark.parametrize("device_type", ["cpu", "cuda"])
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def test_get_input_embeddings_merges_image_and_video_embeddings(
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device_type: str,
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) -> None:
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if device_type == "cuda" and not torch.cuda.is_available():
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pytest.skip("CUDA is not available")
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device = torch.device(device_type)
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hidden_size = 4
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model = _OpenPanguVL(hidden_size).to(device)
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input_ids = torch.tensor([7, 41, 41, 8, 42, 9], device=device)
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multimodal_embeddings = (
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torch.full((2, hidden_size), -1.0, device=device),
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torch.full((1, hidden_size), -2.0, device=device),
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)
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actual = model.get_input_embeddings(input_ids, multimodal_embeddings)
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expected = model.language_model.embed_input_ids(input_ids)
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expected[1:3] = -1.0
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expected[4] = -2.0
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torch.testing.assert_close(actual, expected)
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@@ -1039,17 +1039,20 @@ class OpenPanguVLForConditionalGeneration(
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings=None,
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multimodal_embeddings: MultiModalEmbeddings | None = None,
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) -> torch.Tensor:
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inputs_embeds = self.language_model.embed_input_ids(input_ids)
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if multimodal_embeddings is not None:
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inputs_embeds = self.embed_input_ids(
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input_ids,
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inputs_embeds,
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multimodal_embeddings,
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[self.config.image_token_id, self.config.video_token_id],
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)
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return inputs_embeds
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if multimodal_embeddings is None:
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return self.language_model.embed_input_ids(input_ids)
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mm_token_ids = input_ids.new_tensor(
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[self.config.image_token_id, self.config.video_token_id]
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)
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is_multimodal = torch.isin(input_ids, mm_token_ids)
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return self.embed_input_ids(
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input_ids,
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multimodal_embeddings,
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is_multimodal=is_multimodal,
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)
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def _process_image_input(self, image_input) -> tuple[torch.Tensor, ...]:
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grid_thw = image_input["image_grid_thw"]
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