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autodocs 2017-02-21 17:19:13 +00:00
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Code can be written in a batch-agnostic way, i.e. as if working with a single data point, with batching happening independently.
Code can be written in a batch-agnostic way or be generic across batching setups. Code works with a single data point, and batching happens independently.
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"page": "Batching",
"title": "Future Work",
"category": "section",
"text": "The design of batching is still a fairly early work in progress, though it's used in a few places in the system. For example, all Flux models expect to be given Batch objects which are unwrapped into raw arrays for the computation. Models will convert their arguments if necessary, so it's convenient to call a model with a single data point like f([1,2,3]).Right now, the Batch or Seq types always stack along the left-most dimension. In future, this will be customisable, and Flux will provide implementations of common functions that are generic across the batch dimension. This brings the following benefits:Code can be written in a batch-agnostic way, i.e. as if working with a single data point, with batching happening independently.\nAutomatic batching can be done with correctness assured, reducing programmer errors when manipulating dimensions.\nOptimisations, like switching batch dimensions, can be expressed by the programmer with compiler support; fewer code changes are required and optimisations are guaranteed not to break the model.\nThis also opens the door for more automatic optimisations, e.g. having the compiler explore the search base of possible batching combinations."
"text": "The design of batching is still a fairly early work in progress, though it's used in a few places in the system. For example, all Flux models expect to be given Batch objects which are unwrapped into raw arrays for the computation. Models will convert their arguments if necessary, so it's convenient to call a model with a single data point like f([1,2,3]).Right now, the Batch or Seq types always stack along the left-most dimension. In future, this will be customisable, and Flux will provide implementations of common functions that are generic across the batch dimension. This brings the following benefits:Code can be written in a batch-agnostic way or be generic across batching setups. Code works with a single data point, and batching happens independently.\nAutomatic batching can be done with correctness assured, reducing programmer errors when manipulating dimensions.\nOptimisations, like switching batch dimensions, can be expressed by the programmer with compiler support; fewer code changes are required and optimisations are guaranteed not to break the model.\nThis also opens the door for more automatic optimisations, e.g. having the compiler explore the search base of possible batching combinations."
},
{