View Source Nx.Batch (Nx v0.4.1)
Creates a batch of tensors (and containers).
A batch is lazily traversed, concatenated, and padded upon defn invocation.
Link to this section Summary
Functions
A Nx.Batch struct.
Concatenates the given entries to the batch.
Merges a list of batches.
Merges two batches.
Returns a new empty batch.
Configures the batch with the given padding.
Splits a batch in two, where the first one has at most n elements.
Stacks the given entries to the batch.
Link to this section Types
@type t() :: %Nx.Batch{ pad: non_neg_integer(), size: non_neg_integer(), stack: list(), template: Nx.Container.t() | Nx.Tensor.t() | nil }
Link to this section Functions
A Nx.Batch struct.
The :size field is public.
Concatenates the given entries to the batch.
Entries are concatenated based on their first axis. If the first axis has multiple entries, each entry is added to the size of the batch.
You can either concatenate to an existing batch or skip the batch argument to create a new batch.
See stack/2 if you want to stack entries instead
of concatenating them.
examples
Examples
If no batch is given, one is automatically created:
iex> batch = Nx.Batch.concatenate([Nx.tensor([1]), Nx.tensor([2]), Nx.tensor([3])])
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[3]
[1, 2, 3]
>But you can also concatenate to existing batches:
iex> batch = Nx.Batch.concatenate([Nx.tensor([1]), Nx.tensor([2])])
iex> batch = Nx.Batch.concatenate(batch, [Nx.tensor([3]), Nx.tensor([4])])
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[4]
[1, 2, 3, 4]
>If the first axis has multiple entries, each entry counts towards the size of the batch:
iex> batch = Nx.Batch.concatenate([Nx.tensor([1, 2]), Nx.tensor([3, 4, 5])])
iex> batch.size
5
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[5]
[1, 2, 3, 4, 5]
>What makes batches powerful is that they can concatenate across containers:
iex> container1 = {Nx.tensor([11]), Nx.tensor([21])}
iex> container2 = {Nx.tensor([12]), Nx.tensor([22])}
iex> batch = Nx.Batch.concatenate([container1, container2])
iex> {batched1, batched2} = Nx.Defn.jit_apply(&Function.identity/1, [batch])
iex> batched1
#Nx.Tensor<
s64[2]
[11, 12]
>
iex> batched2
#Nx.Tensor<
s64[2]
[21, 22]
>Merges a list of batches.
See merge/2.
Merges two batches.
The tensors on the left will appear before the tensors on the right.
The size and padding of both batches are summed. The padding still applies only at the end of batch.
It will raise if the batch templates are incompatible.
examples
Examples
iex> batch1 = Nx.Batch.stack([Nx.tensor(1), Nx.tensor(2), Nx.tensor(3)])
iex> batch2 = Nx.Batch.concatenate([Nx.tensor([4, 5]), Nx.tensor([6, 7, 8])])
iex> batch = Nx.Batch.merge(batch1, batch2)
iex> batch.size
8
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[8]
[1, 2, 3, 4, 5, 6, 7, 8]
>Returns a new empty batch.
Configures the batch with the given padding.
The batch will be padded when consumed:
iex> batch = Nx.Batch.stack([Nx.tensor(1), Nx.tensor(2), Nx.tensor(3)])
iex> Nx.Defn.jit_apply(&Function.identity/1, [Nx.Batch.pad(batch, 2)])
#Nx.Tensor<
s64[5]
[1, 2, 3, 0, 0]
>Splits a batch in two, where the first one has at most n elements.
If there is any padding and the batch is not full, the amount of padding necessary will be moved to the first batch and the remaining stays in the second batch.
examples
Examples
iex> batch = Nx.Batch.concatenate([Nx.tensor([1, 2]), Nx.tensor([3, 4, 5])])
iex> {left, right} = Nx.Defn.jit_apply(&Function.identity/1, [Nx.Batch.split(batch, 3)])
iex> left
#Nx.Tensor<
s64[3]
[1, 2, 3]
>
iex> right
#Nx.Tensor<
s64[2]
[4, 5]
>Stacks the given entries to the batch.
Each entry counts exactly as a single entry. You can either stack to an existing batch or skip the batch argument to create a new batch.
See concatenate/2 if you want to concatenate entries
instead of stacking them.
examples
Examples
If no batch is given, one is automatically created:
iex> batch = Nx.Batch.stack([Nx.tensor(1), Nx.tensor(2), Nx.tensor(3)])
iex> batch.size
3
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[3]
[1, 2, 3]
>But you can also stack an existing batch:
iex> batch = Nx.Batch.stack([Nx.tensor(1), Nx.tensor(2)])
iex> batch = Nx.Batch.stack(batch, [Nx.tensor(3), Nx.tensor(4)])
iex> batch.size
4
iex> Nx.Defn.jit_apply(&Function.identity/1, [batch])
#Nx.Tensor<
s64[4]
[1, 2, 3, 4]
>What makes batches powerful is that they can concatenate across containers:
iex> container1 = {Nx.tensor(11), Nx.tensor(21)}
iex> container2 = {Nx.tensor(12), Nx.tensor(22)}
iex> batch = Nx.Batch.stack([container1, container2])
iex> {batched1, batched2} = Nx.Defn.jit_apply(&Function.identity/1, [batch])
iex> batched1
#Nx.Tensor<
s64[2]
[11, 12]
>
iex> batched2
#Nx.Tensor<
s64[2]
[21, 22]
>