Description

vincetrep

opened on Nov 6, 2023

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Initial Checks

• I have read and followed the docs and still think this is a bug

Description

I have found an issue with the following sequence:

Object
SubIndiceObject with an attribute Optional[AnyEmbedding]

I am running an operation on GPU and storing the TorchEmbedding on that attribute

Then I am converting the value back into a NDArrayEmbedding on CPU and storing in the same attribute.

When indexing the data into weaviate, it still tracks that attribute as a TorchTensor on GPU and tries to convert to a NDArrayEmbedding.

In order to fix it, I ran the cpu() operation prior to saving that tensor on the attribute.

I don't know if it is a bug or by design but logging the scenario nonetheless.

Example Code

No response

Python, DocArray & OS Version

docarray version:  0.39.1

Affected Components

• Vector Database / Index
• Representing
• Sending
• storing
• multi modal data type

Activity

1. JoanFM commented on Nov 6, 2023

   

   JoanFM

   on Nov 6, 2023

   Member

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   Hey @vincetrep ,

   do u have a minimal reproducible code that we can use to debug?
2. JohannesMessner commented on Nov 8, 2023

   

   JohannesMessner

   on Nov 8, 2023

   Member

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   I also cannot reproduce the issue, the following executes without problem:

   from docarray.index.backends.weaviate import EmbeddedOptions
   from docarray.index.backends.weaviate import WeaviateDocumentIndex
   from typing import Optional
   from docarray.typing import AnyEmbedding, NdArrayEmbedding
   from pydantic import Field, parse_obj_as
   from docarray import DocList, BaseDoc
   import numpy as np
   import torch
   
   class SubDoc(BaseDoc):
       embedding: Optional[AnyEmbedding] = Field(is_embedding=True, n_dim=128)
   
   
   class MyDoc(BaseDoc):
       text: str 
       subdocs: DocList[SubDoc]
   
   
   dbconfig = WeaviateDocumentIndex.DBConfig(embedded_options=EmbeddedOptions())
   doc_index = WeaviateDocumentIndex[MyDoc](db_config=dbconfig)
   
   # creating tensors on GPU
   gpu_tensors = [torch.rand(128).to('cuda:2') for i in range(3)]
   assert all(t.is_cuda for t in gpu_tensors)
   # storing them in DocList
   data = DocList[MyDoc](
       [
           MyDoc(text='hello world', subdocs=[SubDoc(embedding=t)]) for t in gpu_tensors
       ]
   )
   # converting to NdArray on CPU
   for doc in data:
       doc.subdocs[0].embedding = parse_obj_as(NdArrayEmbedding, doc.subdocs[0].embedding)
       assert isinstance(doc.subdocs[0].embedding, np.ndarray)  # not on GPU
   # insert into index
   doc_index.index(data)
   
   root_docs, sub_docs, scores = doc_index.find_subindex(np.random.rand(128), subindex='subdocs', limit=3)
   
   print(scores)
3. Sumant02 commented on Dec 23, 2023

   

   Sumant02

   on Dec 23, 2023

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   `from docarray.index.backends.weaviate import WeaviateDocumentIndex
   doc_index = WeaviateDocumentIndexMyDoc

   from docarray.index.backends.weaviate import EmbeddedOptions
   from docarray.index.backends.weaviate import WeaviateDocumentIndex
   from typing import Optional
   from docarray.typing import AnyEmbedding, NdArrayEmbedding
   from pydantic import Field, parse_obj_as
   from docarray import DocList, BaseDoc
   import numpy as np
   import torch

   class SubDoc(BaseDoc):
   embedding: Optional[AnyEmbedding] = Field(is_embedding=True, n_dim=128)

   class MyDoc(BaseDoc):
   text: str
   subdocs: DocList[SubDoc]

   dbconfig = WeaviateDocumentIndex.DBConfig(embedded_options=EmbeddedOptions())
   doc_index = WeaviateDocumentIndexMyDoc

   creating tensors on GPU

   gpu_tensors = [torch.rand(128).to('cuda:2') for i in range(3)]
   assert all(t.is_cuda for t in gpu_tensors)

   storing them in DocList

   data = DocList[MyDoc](
   [
   MyDoc(text='hello world', subdocs=[SubDoc(embedding=t)]) for t in gpu_tensors
   ]
   )

   converting to NdArray on CPU

   for doc in data:
   doc.subdocs[0].embedding = parse_obj_as(NdArrayEmbedding, doc.subdocs[0].embedding)
   assert isinstance(doc.subdocs[0].embedding, np.ndarray) # not on GPU

   insert into index

   doc_index.index(data)

   root_docs, sub_docs, scores = doc_index.find_subindex(np.random.rand(128), subindex='subdocs', limit=3)
   print(scores)`
   @JohannesMessner JohannesMessner

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