Helper module for Tensor
processing.
These functions and classes are only used internally, meaning an end-user shouldn’t need to access anything here.
new Tensor(...args)
.dims
: Array.<number>
.type
: DataType
.data
: DataArray
.size
: number
.location
: string
.Symbol.iterator()
⇒ Iterator
._getitem(index)
⇒ Tensor
.indexOf(item)
⇒ number
._subarray(index, iterSize, iterDims)
⇒ Tensor
.item()
⇒ number
| bigint
.tolist()
⇒ Array
.sigmoid()
⇒ Tensor
.sigmoid_()
⇒ Tensor
.map(callback)
⇒ Tensor
.map_(callback)
⇒ Tensor
.mul(val)
⇒ Tensor
.mul_(val)
⇒ Tensor
.div(val)
⇒ Tensor
.div_(val)
⇒ Tensor
.add(val)
⇒ Tensor
.add_(val)
⇒ Tensor
.sub(val)
⇒ Tensor
.sub_(val)
⇒ Tensor
.permute(...dims)
⇒ Tensor
.sum([dim], keepdim)
⇒.norm([p], [dim], [keepdim])
⇒ Tensor
.normalize_([p], [dim])
⇒ Tensor
.normalize([p], [dim])
⇒ Tensor
.stride()
⇒ Array.<number>
.squeeze([dim])
⇒ Tensor
.squeeze_()
.unsqueeze(dim)
⇒ Tensor
.unsqueeze_()
.flatten_()
.flatten(start_dim, end_dim)
⇒ Tensor
.view(...dims)
⇒ Tensor
.clamp_()
.clamp(min, max)
⇒ Tensor
.round_()
.round()
⇒ Tensor
.to(type)
⇒ Tensor
.permute(tensor, axes)
⇒ Tensor
.interpolate(input, size, mode, align_corners)
⇒ Tensor
.interpolate_4d(input, options)
⇒ Promise.<Tensor>
.matmul(a, b)
⇒ Promise.<Tensor>
.rfft(x, a)
⇒ Promise.<Tensor>
.topk(x, k)
⇒ *
.mean_pooling(last_hidden_state, attention_mask)
⇒ Tensor
.layer_norm(input, normalized_shape, options)
⇒ Tensor
.cat(tensors, dim)
⇒ Tensor
.stack(tensors, dim)
⇒ Tensor
.std_mean(input, dim, correction, keepdim)
⇒ Array.<Tensor>
.mean(input, dim, keepdim)
⇒ Tensor
.full(size, fill_value)
⇒ Tensor
.ones(size)
⇒ Tensor
.ones_like(tensor)
⇒ Tensor
.zeros(size)
⇒ Tensor
.zeros_like(tensor)
⇒ Tensor
.quantize_embeddings(tensor, precision)
⇒ Tensor
~args[0]
: ONNXTensor
~reshape(data, dimensions)
⇒ *
~reshapedArray
: any
~DataArray
: *
~NestArray
: *
Kind: static class of utils/tensor
new Tensor(...args)
.dims
: Array.<number>
.type
: DataType
.data
: DataArray
.size
: number
.location
: string
.Symbol.iterator()
⇒ Iterator
._getitem(index)
⇒ Tensor
.indexOf(item)
⇒ number
._subarray(index, iterSize, iterDims)
⇒ Tensor
.item()
⇒ number
| bigint
.tolist()
⇒ Array
.sigmoid()
⇒ Tensor
.sigmoid_()
⇒ Tensor
.map(callback)
⇒ Tensor
.map_(callback)
⇒ Tensor
.mul(val)
⇒ Tensor
.mul_(val)
⇒ Tensor
.div(val)
⇒ Tensor
.div_(val)
⇒ Tensor
.add(val)
⇒ Tensor
.add_(val)
⇒ Tensor
.sub(val)
⇒ Tensor
.sub_(val)
⇒ Tensor
.permute(...dims)
⇒ Tensor
.sum([dim], keepdim)
⇒.norm([p], [dim], [keepdim])
⇒ Tensor
.normalize_([p], [dim])
⇒ Tensor
.normalize([p], [dim])
⇒ Tensor
.stride()
⇒ Array.<number>
.squeeze([dim])
⇒ Tensor
.squeeze_()
.unsqueeze(dim)
⇒ Tensor
.unsqueeze_()
.flatten_()
.flatten(start_dim, end_dim)
⇒ Tensor
.view(...dims)
⇒ Tensor
.clamp_()
.clamp(min, max)
⇒ Tensor
.round_()
.round()
⇒ Tensor
.to(type)
⇒ Tensor
Create a new Tensor or copy an existing Tensor.
Param | Type |
---|---|
...args | * |
Dimensions of the tensor.
Kind: instance property of Tensor
Type of the tensor.
Kind: instance property of Tensor
The data stored in the tensor.
Kind: instance property of Tensor
The number of elements in the tensor.
Kind: instance property of Tensor
The location of the tensor data.
Kind: instance property of Tensor
Returns an iterator object for iterating over the tensor data in row-major order. If the tensor has more than one dimension, the iterator will yield subarrays.
Kind: instance method of Tensor
Returns: Iterator
- An iterator object for iterating over the tensor data in row-major order.
Index into a Tensor object.
Kind: instance method of Tensor
Returns: Tensor
- The data at the specified index.
Param | Type | Description |
---|---|---|
index | number | The index to access. |
Kind: instance method of Tensor
Returns: number
- The index of the first occurrence of item in the tensor data.
Param | Type | Description |
---|---|---|
item | number | bigint | The item to search for in the tensor |
Kind: instance method of Tensor
Param | Type |
---|---|
index | number |
iterSize | number |
iterDims | any |
Returns the value of this tensor as a standard JavaScript Number. This only works
for tensors with one element. For other cases, see Tensor.tolist()
.
Kind: instance method of Tensor
Returns: number
| bigint
- The value of this tensor as a standard JavaScript Number.
Throws:
Error
If the tensor has more than one element.Convert tensor data to a n-dimensional JS list
Kind: instance method of Tensor
Return a new Tensor with the sigmoid function applied to each element.
Kind: instance method of Tensor
Returns: Tensor
- The tensor with the sigmoid function applied.
Applies the sigmoid function to the tensor in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Return a new Tensor with a callback function applied to each element.
Kind: instance method of Tensor
Returns: Tensor
- A new Tensor with the callback function applied to each element.
Param | Type | Description |
---|---|---|
callback | function | The function to apply to each element. It should take three arguments: the current element, its index, and the tensor's data array. |
Apply a callback function to each element of the tensor in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Param | Type | Description |
---|---|---|
callback | function | The function to apply to each element. It should take three arguments: the current element, its index, and the tensor's data array. |
Return a new Tensor with every element multiplied by a constant.
Kind: instance method of Tensor
Returns: Tensor
- The new tensor.
Param | Type | Description |
---|---|---|
val | number | The value to multiply by. |
Multiply the tensor by a constant in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Param | Type | Description |
---|---|---|
val | number | The value to multiply by. |
Return a new Tensor with every element divided by a constant.
Kind: instance method of Tensor
Returns: Tensor
- The new tensor.
Param | Type | Description |
---|---|---|
val | number | The value to divide by. |
Divide the tensor by a constant in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Param | Type | Description |
---|---|---|
val | number | The value to divide by. |
Return a new Tensor with every element added by a constant.
Kind: instance method of Tensor
Returns: Tensor
- The new tensor.
Param | Type | Description |
---|---|---|
val | number | The value to add by. |
Add the tensor by a constant in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Param | Type | Description |
---|---|---|
val | number | The value to add by. |
Return a new Tensor with every element subtracted by a constant.
Kind: instance method of Tensor
Returns: Tensor
- The new tensor.
Param | Type | Description |
---|---|---|
val | number | The value to subtract by. |
Subtract the tensor by a constant in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
Param | Type | Description |
---|---|---|
val | number | The value to subtract by. |
Return a permuted version of this Tensor, according to the provided dimensions.
Kind: instance method of Tensor
Returns: Tensor
- The permuted tensor.
Param | Type | Description |
---|---|---|
...dims | number | Dimensions to permute. |
Returns the sum of each row of the input tensor in the given dimension dim.
Kind: instance method of Tensor
Returns: The summed tensor
Param | Type | Default | Description |
---|---|---|---|
[dim] | number |
| The dimension or dimensions to reduce. If |
keepdim | boolean | false | Whether the output tensor has |
Returns the matrix norm or vector norm of a given tensor.
Kind: instance method of Tensor
Returns: Tensor
- The norm of the tensor.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | string | 'fro' | The order of norm |
[dim] | number |
| Specifies which dimension of the tensor to calculate the norm across. If dim is None, the norm will be calculated across all dimensions of input. |
[keepdim] | boolean | false | Whether the output tensors have dim retained or not. |
Performs L_p
normalization of inputs over specified dimension. Operates in place.
Kind: instance method of Tensor
Returns: Tensor
- this
for operation chaining.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | 2 | The exponent value in the norm formulation |
[dim] | number | 1 | The dimension to reduce |
Performs L_p
normalization of inputs over specified dimension.
Kind: instance method of Tensor
Returns: Tensor
- The normalized tensor.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | 2 | The exponent value in the norm formulation |
[dim] | number | 1 | The dimension to reduce |
Compute and return the stride of this tensor. Stride is the jump necessary to go from one element to the next one in the specified dimension dim.
Kind: instance method of Tensor
Returns: Array.<number>
- The stride of this tensor.
Returns a tensor with all specified dimensions of input of size 1 removed.
NOTE: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.
If you would like a copy, use tensor.clone()
before squeezing.
Kind: instance method of Tensor
Returns: Tensor
- The squeezed tensor
Param | Type | Default | Description |
---|---|---|---|
[dim] | number |
| If given, the input will be squeezed only in the specified dimensions. |
In-place version of @see Tensor.squeeze
Kind: instance method of Tensor
Returns a new tensor with a dimension of size one inserted at the specified position.
NOTE: The returned tensor shares the same underlying data with this tensor.
Kind: instance method of Tensor
Returns: Tensor
- The unsqueezed tensor
Param | Type | Default | Description |
---|---|---|---|
dim | number |
| The index at which to insert the singleton dimension |
In-place version of @see Tensor.unsqueeze
Kind: instance method of Tensor
In-place version of @see Tensor.flatten
Kind: instance method of Tensor
Flattens input by reshaping it into a one-dimensional tensor.
If start_dim
or end_dim
are passed, only dimensions starting with start_dim
and ending with end_dim
are flattened. The order of elements in input is unchanged.
Kind: instance method of Tensor
Returns: Tensor
- The flattened tensor.
Param | Type | Default | Description |
---|---|---|---|
start_dim | number | 0 | the first dim to flatten |
end_dim | number | the last dim to flatten |
Returns a new tensor with the same data as the self
tensor but of a different shape
.
Kind: instance method of Tensor
Returns: Tensor
- The tensor with the same data but different shape
Param | Type | Description |
---|---|---|
...dims | number | the desired size |
In-place version of @see Tensor.clamp
Kind: instance method of Tensor
Clamps all elements in input into the range [ min, max ]
Kind: instance method of Tensor
Returns: Tensor
- the output tensor.
Param | Type | Description |
---|---|---|
min | number | lower-bound of the range to be clamped to |
max | number | upper-bound of the range to be clamped to |
In-place version of @see Tensor.round
Kind: instance method of Tensor
Rounds elements of input to the nearest integer.
Kind: instance method of Tensor
Returns: Tensor
- the output tensor.
Performs Tensor dtype conversion.
Kind: instance method of Tensor
Returns: Tensor
- The converted tensor.
Param | Type | Description |
---|---|---|
type | DataType | The desired data type. |
Permutes a tensor according to the provided axes.
Kind: static method of utils/tensor
Returns: Tensor
- The permuted tensor.
Param | Type | Description |
---|---|---|
tensor | any | The input tensor to permute. |
axes | Array | The axes to permute the tensor along. |
Interpolates an Tensor to the given size.
Kind: static method of utils/tensor
Returns: Tensor
- The interpolated tensor.
Param | Type | Description |
---|---|---|
input | Tensor | The input tensor to interpolate. Data must be channel-first (i.e., [c, h, w]) |
size | Array.<number> | The output size of the image |
mode | string | The interpolation mode |
align_corners | boolean | Whether to align corners. |
Down/up samples the input. Inspired by https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html.
Kind: static method of utils/tensor
Returns: Promise.<Tensor>
- The interpolated tensor.
Param | Type | Default | Description |
---|---|---|---|
input | Tensor | the input tensor | |
options | Object | the options for the interpolation | |
[options.size] | * |
| output spatial size. |
[options.mode] | "bilinear" | "bicubic" | 'bilinear' | algorithm used for upsampling |
Matrix product of two tensors. Inspired by https://pytorch.org/docs/stable/generated/torch.matmul.html
Kind: static method of utils/tensor
Returns: Promise.<Tensor>
- The matrix product of the two tensors.
Param | Type | Description |
---|---|---|
a | Tensor | the first tensor to be multiplied |
b | Tensor | the second tensor to be multiplied |
Computes the one dimensional Fourier transform of real-valued input. Inspired by https://pytorch.org/docs/stable/generated/torch.fft.rfft.html
Kind: static method of utils/tensor
Returns: Promise.<Tensor>
- the output tensor.
Param | Type | Description |
---|---|---|
x | Tensor | the real input tensor |
a | Tensor | The dimension along which to take the one dimensional real FFT. |
Returns the k largest elements of the given input tensor. Inspired by https://pytorch.org/docs/stable/generated/torch.topk.html
Kind: static method of utils/tensor
Returns: *
- the output tuple of (Tensor, LongTensor) of top-k elements and their indices.
Param | Type | Description |
---|---|---|
x | Tensor | the input tensor |
k | number | the k in "top-k" |
Perform mean pooling of the last hidden state followed by a normalization step.
Kind: static method of utils/tensor
Returns: Tensor
- Returns a new Tensor of shape [batchSize, embedDim].
Param | Type | Description |
---|---|---|
last_hidden_state | Tensor | Tensor of shape [batchSize, seqLength, embedDim] |
attention_mask | Tensor | Tensor of shape [batchSize, seqLength] |
Apply Layer Normalization for last certain number of dimensions.
Kind: static method of utils/tensor
Returns: Tensor
- The normalized tensor.
Param | Type | Default | Description |
---|---|---|---|
input | Tensor | The input tensor | |
normalized_shape | Array.<number> | input shape from an expected input of size | |
options | Object | The options for the layer normalization | |
[options.eps] | number | 1e-5 | A value added to the denominator for numerical stability. |
Concatenates an array of tensors along a specified dimension.
Kind: static method of utils/tensor
Returns: Tensor
- The concatenated tensor.
Param | Type | Description |
---|---|---|
tensors | Array.<Tensor> | The array of tensors to concatenate. |
dim | number | The dimension to concatenate along. |
Stack an array of tensors along a specified dimension.
Kind: static method of utils/tensor
Returns: Tensor
- The stacked tensor.
Param | Type | Description |
---|---|---|
tensors | Array.<Tensor> | The array of tensors to stack. |
dim | number | The dimension to stack along. |
Calculates the standard deviation and mean over the dimensions specified by dim. dim can be a single dimension or null
to reduce over all dimensions.
Kind: static method of utils/tensor
Returns: Array.<Tensor>
- A tuple of (std, mean) tensors.
Param | Type | Description |
---|---|---|
input | Tensor | the input tenso |
dim | number | null | the dimension to reduce. If None, all dimensions are reduced. |
correction | number | difference between the sample size and sample degrees of freedom. Defaults to Bessel's correction, correction=1. |
keepdim | boolean | whether the output tensor has dim retained or not. |
Returns the mean value of each row of the input tensor in the given dimension dim.
Kind: static method of utils/tensor
Returns: Tensor
- A new tensor with means taken along the specified dimension.
Param | Type | Description |
---|---|---|
input | Tensor | the input tensor. |
dim | number | null | the dimension to reduce. |
keepdim | boolean | whether the output tensor has dim retained or not. |
Creates a tensor of size size filled with fill_value. The tensor’s dtype is inferred from fill_value.
Kind: static method of utils/tensor
Returns: Tensor
- The filled tensor.
Param | Type | Description |
---|---|---|
size | Array.<number> | A sequence of integers defining the shape of the output tensor. |
fill_value | number | bigint | The value to fill the output tensor with. |
Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size.
Kind: static method of utils/tensor
Returns: Tensor
- The ones tensor.
Param | Type | Description |
---|---|---|
size | Array.<number> | A sequence of integers defining the shape of the output tensor. |
Returns a tensor filled with the scalar value 1, with the same size as input.
Kind: static method of utils/tensor
Returns: Tensor
- The ones tensor.
Param | Type | Description |
---|---|---|
tensor | Tensor | The size of input will determine size of the output tensor. |
Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size.
Kind: static method of utils/tensor
Returns: Tensor
- The zeros tensor.
Param | Type | Description |
---|---|---|
size | Array.<number> | A sequence of integers defining the shape of the output tensor. |
Returns a tensor filled with the scalar value 0, with the same size as input.
Kind: static method of utils/tensor
Returns: Tensor
- The zeros tensor.
Param | Type | Description |
---|---|---|
tensor | Tensor | The size of input will determine size of the output tensor. |
Quantizes the embeddings tensor to binary or unsigned binary precision.
Kind: static method of utils/tensor
Returns: Tensor
- The quantized tensor.
Param | Type | Description |
---|---|---|
tensor | Tensor | The tensor to quantize. |
precision | 'binary' | 'ubinary' | The precision to use for quantization. |
Kind: inner property of utils/tensor
Reshapes a 1-dimensional array into an n-dimensional array, according to the provided dimensions.
Kind: inner method of utils/tensor
Returns: *
- The reshaped array.
Param | Type | Description |
---|---|---|
data | Array<T> | DataArray | The input array to reshape. |
dimensions | DIM | The target shape/dimensions. |
Example
reshape([10 ], [1 ]); // Type: number[] Value: [10]
reshape([1, 2, 3, 4 ], [2, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4]]
reshape([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]); // Type: number[][][] Value: [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
reshape([1, 2, 3, 4, 5, 6, 7, 8], [4, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4], [5, 6], [7, 8]]
Kind: inner property of reshape
Kind: inner typedef of utils/tensor
This creates a nested array of a given type and depth (see examples).
Kind: inner typedef of utils/tensor
Example
NestArray<string, 1>; // string[]
Example
NestArray<number, 2>; // number[][]
Example
NestArray<string, 3>; // string[][][] etc.