Tensor Guide

Vespa provides a tensor data model and computation engine to support advanced computations over data, such as for example computing neural network ranking models. In this guide, tensors are introduced with some examples of use. For details, refer to the tensor reference. This guide goes through:

  • Setting up tensor fields in schemas
  • Feeding tensors to Vespa
  • Querying Vespa with tensors
  • Ranking with tensors
  • Constant tensors
  • Tensor Java API
  • Tensor concepts
For a quick introduction to tensors, refer to tensor concepts and the tensor reference guide. See also using TensorFlow models or using ONNX models which explains how to use TensorFlow or ONNX models directly in Vespa, and a collection of examples of usages of tensors to perform various computations.

Tensor document fields

In typical use, a document contains one or more tensor fields to be used for ranking - this example sets up a tensor field called tensor_attribute:

field tensor_attribute type tensor<float>(x[4]) {
    indexing: attribute | summary
}
A tensor requires a type - x[4] means indexed dimension of size 4, where x{} means mapped dimension - see tensor field in schemas. For details on tensor types, refer to the tensor type reference.

Feeding tensors

There are two options when feeding tensors.

Tensors can be updated - one can add, remove and modify tensor cells, or assign a completely new tensor value.

Querying with tensors

Tensors can only be used for ranking, not searching. The tensor can either be supplied in the query request, or constructed from some other data or data source. In the latter case, refer to the Tensor Java API for details on how to construct tensors programmatically. Query/Context tensors must be defined in the application package (name, type and dimension), see defining query feature types.

Ranking with tensors

Tensors are used during ranking to modify a document's rank score given the query. Typical operations are dot products between tensors of order 1 (vectors), or matrix products between tensors of order 2 (matrices). Tensors are used in rank expressions as rank features. Two rank features are defined above:

  • attribute(tensor_attribute): the tensor associated with the document
  • query(tensor): the tensor sent with the query request
These can be used in rank expressions. Note that the final rank score of a document must be a single double value - example:
rank-profile dot_product {
    first-phase {
        expression: sum(query(tensor)*attribute(tensor_attribute))
    }
}
This takes the product of the query tensor and the document tensor, and sums all fields thus resolving into a single value which is used as the rank score. In the case above, the value is 39.0 (1*1 + 2*2 + 3*3 + 5*5).

There are some ranking functions that are specific for tensors:

map(tensor, f(x)(...)) Returns a new tensor with the lambda function defined in f(x)(...) applied to each cell.
reduce(tensor, aggregator, dim1, dim2, ...) Returns a new tensor with the aggregator applied across dimensions dim1, dim2, etc. If no dimensions are specified, reduce over all dimensions.
join(tensor1, tensor2, f(x,y)(...)) Returns a new tensor constructed from the natural join between tensor1 and tensor2, with the resulting cells having the value as calculated from f(x,y)(...), where x is the cell value from tensor1 and y from tensor2.
These primitives allow for a great deal of flexibility when combined. The above rank expression is equivalently:
rank-profile dot_product {
    first-phase {
        expression {
            reduce(
                join(
                    query(tensor),
                    attribute(tensor_attribute),
                    f(x,y)(x * y)
                ),
                sum
            )
        }
    }
}
...and represents the general dot product for tensors of any order. Details about tensor ranking functions including lambda expression and available aggregators can be found in the tensor reference documentation.

Create a tensor on the fly from another field type:

  • From a single-valued source field:
    tensor(x{}):{x1:attribute(foo)}
    
  • Create a tensor in the ranking function from arrays or weighted sets using tensorFrom... functions - see document features.

Performance considerations

Tensor expressions are fairly concise, and since the expressions themselves are independent of the data size, the actual workload during ranking can be significant for large tensors.

When using tensors in ranking it is important to have an understanding of the potential computational cost for each query. As an example, assume the dot product of two tensors with 1000 values each, e.g. tensor<double>(x[1000]). Assuming one query tensor and one document tensor, the operation is:

sum(query(tensor1) * attribute(tensor2))
If 8 bytes is used to store each value (e.g. using a double), each tensor is approximately 8KB. With for instance a Haswell architecture the theoretical upper memory bandwidth is 68GB/s, which is around 9 million document ranking evaluations per second. With 1 million documents, this means the maximum throughput, with regards to pure memory bandwidth, is 9 queries per second (per node).

Even though you would typically not do the above without reducing the search space first (using matching and first phase), it is important to consider the memory bandwidth and other hardware limitations when developing ranking expressions with tensors.

Performance considerations for cell value types

double The 64-bit floating-point "double" format is the default cell type. It gives best precision at the cost of high memory usage and somewhat slower calculations. Using a smaller value type increases performance, trading off precision, so consider changing to one of the cell types below before scaling your application.
float The usual 32-bit floating-point format "float" should usually be used for all tensors when scaling for production. (Note that other frameworks, like tensorflow, will also prefer 32-bit floats.) A vector with 1000 dimensions, tensor<float>(x[1000]) would then use approx 4K memory per tensor value.
bfloat16

If memory (or memory bandwidth) is still a concern, it's possible to change the most space-consuming tensors to use the bfloat16 cell type. This type has the range as a normal 32-bit float but only 8 bits of precision, and can be thought of as "float with lossy compression". See also bfloat16 floating-point format on Wikipedia. Some careful analysis of your data is required before using this type.

Note that when doing calculations bfloat16 will act as if it was a 32-bit float, but the smaller size comes with a potential computation overhead. In most cases, the bfloat16 needs to be converted to a 32-bit float before the actual calculation can take place; adding an extra conversion step.

In some cases, having tensors with bfloat16 cells might bypass some build-in optimizations in the back-end (like matrix multiplication) that will be hardware accelerated only if the cells are the same type. To avoid this last case, you can use the cell_cast tensor operation to make sure the cells are of the appropriate type before doing the more expensive operations.

int8 If one uses machine-learning to generate a model with data quantization you can target the int8 cell value type, which is a signed integer with range from -128 to +127 only. This is also treated like a "float with limited range and lossy compression" by the Vespa tensor framework, and gives results as if it was a 32-bit float when any calculation is done. This type is also suitable when representing boolean values (0 or 1). Note that if the input for an int8 cell is not directly representable, the resulting cell value is undefined, so you should take care to only input numbers in the [-128,127] range. It's also possible to use int8 representing binary data for hamming distance Nearest-Neighbor search.

Constant tensors

In addition to document tensors and query tensors, constant tensors can be put in the application package. This is useful when constant tensors are used in ranking expressions, for instance machine learned models. Example:

constant tensor_constant {
    file: constants/constant_tensor_file.json
    type: tensor<float>(x[4])
}
This defines a new tensor rank feature with the type as defined and the contents distributed with the application package in the file constants/constant_tensor_file.json. The format of this file is the tensor JSON format, it can be compressed, see the reference for examples.

To use this tensor in a rank expression, encapsulate the constant name with constant(...):

rank-profile use_constant_tensor {
    first-phase {
        expression: sum(query(tensor) * attribute(tensor_attribute) * constant(tensor_constant))
    }
}
The above expression combines three tensors: the query tensor, the document tensor and a constant tensor.

Tensor concepts

In Vespa, a tensor is a data structure which is a generalization of scalars, vectors and matrices. Tensors can have any order:

  • A scalar is a tensor of order 0
  • A vector is a tensor of order 1
  • A matrix is a tensor of order 2
Tensors consist of a set of double valued cells, with each cell having a unique address. A cell's address is specified by its index or label along all dimensions. The number of dimensions in a tensor is the rank of the tensor. Each dimension can be either mapped or indexed. Mapped dimensions are sparse and allow any label (string identifier) designating their address, while indexed dimensions use dense numberic indices starting at 0.

Example: Using literal form, the tensor:

{
    {x:2, y:1}:1.0,
    {x:0, y:2}:1.0
}
has two dimensions named x and y, and has two cells with defined values:

Tensor graphical representation

A type declaration is needed for tensors. This defines a 2-dimensional mapped tensor (matrix) of float:

tensor<float>(x{},y{})
This is a 2-dimensional indexed tensor (a 2x3 matrix) of double:
tensor<double>(x[2],y[3])
A combination of mapped and indexed dimensions is a mixed tensor:
tensor<float>(x{},y[3])

Vespa uses the type information to optimize execution plans at configuration time. For dense data the best performance is achieved with indexed dimensions.