MIL Ops

Operators supported by the Model Intermediate Language (MIL):

activation (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.clamped_relu(**kwargs)

If x >= 0 return elementwise min(beta, x), otherwise return min(beta, alpha * x).

Parameters:

x: tensor<*?, T> (Required)

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same type and shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.elu(**kwargs)

If x > 0 return elementwise x, otherwise return alpha * (e^x - 1).

Parameters:

x: tensor<*?, T> (Required)

alpha: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.gelu(**kwargs)

Return the elementwise Gaussian error linear unit activation function for x.

You can use EXACT, TANH_APPROXIMATION, or SIGMOID_APPROXIMATION values based on the following formulas:

• EXACT:

\[f(x) = 0.5x\left ( 1+\rm{erf}\left ( \frac{x}{\sqrt{2}} \right ) \right )\]

• TANH_APPROXIMATION:

\[f(x) = 0.5x\left ( 1+\rm{tanh}\left ( \sqrt{2/\pi}\left ( x + 0.044715x^3 \right ) \right ) \right )\]

• SIGMOID_APPROXIMATION:

\[f(x) = x*\rm{sigmoid}(1.702x)\]

Parameters:

x: tensor<*?, T> (Required)

mode: const str (Optional)

• Use 'EXACT', 'TANH_APPROXIMATION', or 'SIGMOID_APPROXIMATION' for str.

• Default is 'EXACT'.

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.leaky_relu(**kwargs)

If x >= 0 apply x elementwise, otherwise apply alpha * x elementwise.

Parameters:

x: <*?, T> (Required)

alpha: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.linear_activation(**kwargs)

Apply elementwise x * alpha + beta.

Parameters:

x: tensor<*?, T> (Required)

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.prelu(**kwargs)

Where i = 1 ... C, if x_i > 0, return x_i , otherwise return alpha_i * x_i.

Parameters:

x: tensor<[B, C, 1..3], T> (Required)

• x must have rank 4, rank 3, or rank 5; that is, a shape of (B,C,H), (B,C,H,W), or (B,C,D,H,W).

alpha: const tensor<[C], T>, (Required)

• The length of alpha must match the second dimension of x (channel dimension).

Attributes:

T: fp32, fp16

Returns:

tensor<[B, C, 1..3], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.relu(**kwargs)

Return elementwise-applied rectified linear activation: max(x, 0).

Parameters:

x: tensor<*?, T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.relu6(**kwargs)

Return elementwise-applied rectified linear activation: min(max(x, 0), 6).

Parameters:

x: tensor<*?, T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.scaled_tanh(**kwargs)

Return alpha * tanh(beta * x) elementwise.

Parameters:

x: tensor<*?, T> (Required)

• Input range is (-inf, inf).

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.sigmoid(**kwargs)

Return sigmoid(x) elementwise.

Parameters:

x: tensor<*?, T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.sigmoid_hard(**kwargs)

Return min( max( alpha * x + beta, 0 ), 1 ) elementwise.

Parameters:

x: tensor<*?, T> (Required)

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.silu(**kwargs)

Sigmoid Linear Unit, elementwise apply the SiLU or Swish operation x * sigmoid(x).

Parameters:

x: tensor<*, T>

Attributes:

T: fp16, fp32

Returns:

tensor<*, T>

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.softplus(**kwargs)

Return log( 1 + e^x ) elementwise.

Parameters:

x: tensor<*?, T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.softplus_parametric(**kwargs)

Return alpha_i * log( 1 + e^( beta_i * x_i ) ), where i = 1 ... C.

Parameters:

x: tensor<[b, C, n, m], T> (Required)

alpha: const tensor<[C], T> (Required)

beta: const tensor<[C], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[b, C, n, m], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.softmax(**kwargs)

Return exp(x) / tf.reduce_sum(tf.exp(x), axis).

Parameters:

x: tensor<*?, T> (Required)

axis: const i32 (Optional)

• Default is -1.

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.softsign(**kwargs)

Return x / ( 1 + |x| ) applied elementwise.

Parameters:

x: tensor<*?, T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.activation.thresholded_relu(**kwargs)

Return x if x >= alpha, otherwise return 0.

Parameters:

x: tensor<*?, T> (Required)

alpha: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<*, T>

• A tensor of the same shape and type as x.

activation (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.clamped_relu(**kwargs)

If x >= 0 return elementwise min(beta, x), otherwise return min(beta, alpha * x).

The major difference between this version and the iOS 15 clamped_relu is that the alpha and beta may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

alpha: const U (Required)

beta: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same type and shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.elu(**kwargs)

If x > 0 return elementwise x, otherwise return alpha * (e^x - 1).

The major difference between this version and the iOS 15 elu is that the alpha may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

alpha: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.leaky_relu(**kwargs)

If x >= 0 apply x elementwise, otherwise apply alpha * x elementwise.

The major difference between this version and the iOS 15 leaky_relu is that the alpha may have a different dtype than the input/output.

Parameters:

x: <*?, T> (Required)

alpha: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.linear_activation(**kwargs)

Apply elementwise x * alpha + beta.

The major difference between this version and the iOS 15 linear_activation is that the alpha and beta may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

alpha: const U (Required)

beta: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.prelu(**kwargs)

Where i = 1 ... C, if x_i > 0, return x_i , otherwise return alpha_i * x_i.

The major difference between this version and the iOS 15 prelu is that the alpha may have a different dtype than the input/output.

Parameters:

x: tensor<[B, C, 1..3], T> (Required)

• x must have rank 4, rank 3, or rank 5; that is, a shape of (B,C,H), (B,C,H,W), or (B,C,D,H,W).

alpha: const tensor<[C], U>, (Required)

• The length of alpha must match the second dimension of x (channel dimension).

Attributes:

T: fp32, fp16

U: fp16, fp32

Returns:

tensor<[B, C, 1..3], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.scaled_tanh(**kwargs)

Return alpha * tanh(beta * x) elementwise.

The major difference between this version and the iOS 15 scaled_tanh is that the alpha and beta may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

• Input range is (-inf, inf).

alpha: const U (Required)

beta: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.sigmoid_hard(**kwargs)

Return min( max( alpha * x + beta, 0 ), 1 ) elementwise.

The major difference between this version and the iOS 15 sigmoid_hard is that the alpha and beta may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

alpha: const U (Required)

beta: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*?, T>

• A tensor of the same shape and type as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.softplus_parametric(**kwargs)

Return alpha_i * log( 1 + e^( beta_i * x_i ) ), where i = 1 ... C.

Parameters:

x: tensor<[b, C, n, m], T> (Required)

alpha: const tensor<[C], U> (Required)

beta: const tensor<[C], U> (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<[b, C, n, m], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.activation.thresholded_relu(**kwargs)

Return x if x >= alpha, otherwise return 0.

The major difference between this version and the iOS 15 thresholded_relu is that the alpha may have a different dtype than the input/output.

Parameters:

x: tensor<*?, T> (Required)

alpha: const U (Required)

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<*, T>

• A tensor of the same shape and type as x.

classify

class coremltools.converters.mil.mil.ops.defs.iOS15.classify.classify(**kwargs)

The presence of this op indicates that the model is of type classifier. The op constructs the model output accordingly; that is, the predicted class label and the output probability dictionary. The parameters of this op are set based on the attributes set for the coremltools.ClassifierConfig class by the user. The outputs of this op cannot be used by another op.

Parameters:

probabilities: tensor<[* , ProbT]> (Required)

A tensor in the graph, which is used to compute the classifier output(s). This is the tensor whose values are mapped to the class labels and used for constructing the predicted class label and the output dictionary of class names and values.

classes: list<*, ClassT> (Required)

List of classes.

Attributes:

ProbT: fp32

ClassT: i64, str

Returns:

<classT>

Dict[classT, probT]

constexpr_ops (iOS 16+)

class coremltools.converters.mil.mil.ops.defs.iOS16.constexpr_ops.constexpr_affine_dequantize(**kwargs)

A compile-time operation that returns a constant output value upon dequantizing its constant inputs.

This operation is used to represent constant 8-bit quantized data with affine/linear quantization. The quantized data is stored in the parameter quantized_data. The other parameters – scale, zero_point, and axis – describe how unquantized values can be extracted from it, using the equation for affine/linear quantization:

unquantized_data = scale * (quantized_data - zero_point)

Although all of the parameters of this op are constants, this op is not constant folded to a single const op at the time of model serialization. The unquantized output will be decompressed later, based on the implementation detail (either at model load time or runtime).

Parameters:

quantized_data: const tensor<SrcT, [1..]> (Required)

zero_point: const tensor<ZeroPointT, [0..1]> (Required)

• zero_point can be either a scalar or a vector.

• zero_point follows similar broadcasting rules and size constraints as scale.

scale: const tensor<DstT, [0..1]> (Required)

• scale can be either a scalar or a vector.

• If scale is a vector, for implementation it is broadcast to the following shape:

  • The rank of scale becomes the same as the rank of quantized_data.

  • The constraint: size(scale-vector) == quantized_data.shape[axis].

  • For i == axis, scale.shape[i] == quantized_data.shape[i].

  • For i != axis, scale.shape == 1. For example, assume quantized_data.shape = (2, 3, 4, 5) and axis = 1. If scale is a vector, then scale.size needs to be equal to quantized_data.shape[axis] i.e = 3, which would be broadcast to (1, 3, 1, 1).

axis: const tensor<int32, []> (Required)

Attributes:

SrcT: uint8, int8

ZeroPointT: uint8, int8, fp32

DstT: fp16, fp32

Returns:

const tensor<DstT, [1..]>

class coremltools.converters.mil.mil.ops.defs.iOS16.constexpr_ops.constexpr_cast(**kwargs)

A compile-time operation that returns a constant output value upon casting its constant input.

Expression: output = constexpr_cast(source_val, output_dtype="fp32")

Parameters:

source_val: const tensor<SrcT, […]> (Required)

output_dtype: const tensor<string, []> (Required)

Attributes:

SrcT: fp16

DstT: fp32

Returns:

const tensor<DstT, […]>

class coremltools.converters.mil.mil.ops.defs.iOS16.constexpr_ops.constexpr_lut_to_dense(**kwargs)

A compile-time operation that returns a constant output value upon decompressing a look-up table (LUT) to a dense tensor.

This operation is used to store constant weights in a LUT format (also known as palettized weights). A LUT is a mapping from index to values. Weights are quantized and stored as indices (or keys) into the LUT. Before computation, these keys are mapped to corresponding values in the LUT.

Parameters:

indices: const tensor<uint8, [M]> (Required)

lut: const tensor<T, [NUM_PALETTES]> (Required)

shape: const tensor<uint32, [K]> (Required)

Attributes:

T: uint8, int8, fp16, fp32

Returns:

const tensor<T, […]>

Notes

• Any data is packed and read in a row-major order.

• NUM_PALETTES can be one of {2, 4, 16, 64 or 256}.

• n_bits = log2(NUM_PALETTES) can thus be one of {1, 2, 4, 6, 8}.

• Indices are packed in bytes of size M, where M = ceil(n_bits * product(shape) / 8).

The bit fields are packed one byte at a time, starting with the least significant bit (LSB) and moving upward to the most significant bit (MSB). It follows, naturally, that if an index is split across two bytes, the LSBs of that index is filled over the MSBs of current byte, and the remaining bits of the same index are filled in the LSBs of the next byte.

For example:

if n_bits = 2, shape = (5,) => M = 2 bytes

            MSB             LSB
             |               |
indices =  | 01   10   11   00 | xx   xx   xx   11 |      <== packed elements
           | i3 | i2 | i1 | i0 | -- | -- | -- | i4 |      <== tagged element ids
           |      byte 0       |       byte 1      |      <== tagged bytes

class coremltools.converters.mil.mil.ops.defs.iOS16.constexpr_ops.constexpr_sparse_to_dense(**kwargs)

A compile-time operation that returns a constant output value upon de-sparsification of its constant inputs.

This operation represents unstructured sparsity and uses bit mask binary representation. If a bit is set, then the corresponding element in the output tensor is non-zero and the value is read from the nonzero_data attribute. Likewise, if the bit is not set, then the corresponding element in the output tensor is zero.

Parameters:

nonzero_data: const tensor<T, [D]> (Required)

mask: const tensor<uint8, [M]> (Required)

shape: const tensor<uint32, [K]> (Required)

Attributes:

T: uint8, int8, fp16, fp32

Returns:

const tensor<T, […]>

Notes

• Any data is packed and read in a row-major order.

• mask contains M bytes, where M = ceil( product(shape) / 8). That is, each bit field corresponds to one element in the output tensor.

• D == the total number of set bits in mask.

The bit fields are packed one byte at a time, starting with the least significant bit and moving up to the most significant bit.

For example:

shape = (5,) => M = 1 bytes

           MSB                  LSB
            |                    |
mask    =  |x  x  x  0  1  1  0  0 |      <== packed elements
           |--|--|--|i4|i3|i2|i1|i0|      <== tagged element ids
           |      byte 0           |      <== tagged bytes

constexpr_ops (iOS 18+)

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_blockwise_shift_scale(**kwargs)

A compile-time operation that returns a constant output value upon dequantizing its constant inputs.

It’s similar to iOS 16 constexpr_affine_dequantize, but supports block-wise quantization for int4 and int8.

Although all parameters of this op are constants, this op is not constant-folded to a single const op at the time of model serialization. The unquantized output will be decompressed later, based on the implementation detail (either at model load time or runtime).

Generic expression: output = scale * (data - offset)

Algorithm:

Assuming Rank 3 scenario:

output_data[i, j, k] = scale[i0, j0, k0] * (data[i, j, k] - offset[i0, j0, k0]) where

i0 = floor(i/block_size[0]), j0 = floor(j/block_size[1]), k0 = floor(k/block_size[2])

The block size is implied by block_size[m] = data.shape[m] / scale.shape[m]

Constraints: - All tensors: scale, data, offset and output have same rank. - Inputs: scale and offset (if provided) have same shape. - Output shape is same as the shape of input argument: data. - Number of scales along each dimension should be a factor of corresponding dimension size of

data. That is, block_size[i] should be an integer where block_size[i] = data.shape[i] / scale.shape[i]

Parameters:

data: const tensor<SrcT, [1..]> (Required)

scale: const tensor<DstT, [1..]> (Required)

offset: const tensor<OffsetT, [1..]> (Optional)

• If provided, must have the same shape as the scale.

• If dtype is not fp16 or fp32, it must be the same as SrcT.

Attributes:

SrcT: int4, uint4, int8, uint8, fp16, fp32

DstT: fp16, fp32

OffsetT: int4, uint4, int8, uint8, fp16, fp32

Returns:

const tensor<DstT, [1..]>

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_lut_to_dense(**kwargs)

A compile-time operation that returns a constant output value upon dequantizing its constant inputs.

This operator is used to store constant weights in lookup tables format (aka palettized weights). It’s similar to iOS 16 constexpr_lut_to_dense, but supports block-wise / vector palettization.

LUT’s rank is K + 2, where K is the rank of indices. Each dimension of LUT’s first K dimensions should be divisible by each corresponding dimension of the decompressed tensor. e.g., when indices_shape = [2, 3, 4], lut_shape[:3] = [1, 1, 2], it means that there are two lookup tables over the last axis. And each of them have their own LUT values. See Case 1 below for details.

VECTOR_SIZE is added to support vector palettization. - When VECTOR_SIZE is 1, it is scalar palettization. - When VECTOR_SIZE is larger than 1, it retrieves a vector instead of a single value from the

lookup table, and fill the result continuously.

The vector_axis is used to define which axis the vectored elements in the lookup table be filled across the output tensor. vector_axis is only optional if VECTOR_SIZE is 1. As a result:

output_shape[i] = indices_shape[i] , i != vector_axis output_shape[i] = indices_shape[i] * VECTOR_SIZE, i == vector_axis

See Case 2 below for details.

Examples

Case 1: per-group scalar palettization:

e.g.: - indices = tensor<uint2, [6, 2]>>([2, 3, 3, 0, 1, 0, 3, 0, 2, 1, 0, 3]) - lut = tensor<fp16, [2, 1, 4, 1]>([1.0, 5.0, 9.0, 13.0, 2.0, 10.0, 18.0, 26.0])

It is effectively a 2-group 2-bit scalar palettization. The output shape would be [6, 2], which is the same as the indices shape. The output tensor values are: [[lut0[2]->9.0, lut0[3]->13.0],

[lut0[3]->13.0, lut0[0]->1.0], [lut0[1]->5.0, lut0[0]->1.0], [lut1[3]->26.0, lut1[0]->2.0], [lut1[2]->18.0, lut1[1]->10.0], [lut1[0]->2.0, lut1[3]->26.0]]

where lut0 is the first lookup table (lut[0, :, :, :]) and lut1 is the second lookup table.

Case 2: per-tensor vector palettization:

e.g.: - indices = tensor<uint1, [2, 2, 2]>>. The indices values are:

[

[

[0, 0], [1, 0]

], [

[1, 1], [0, 0]

]

]

• lut = tensor<int8, [1, 1, 1, 2, 3]>([a0, a1, a2,

  b0, b1, b2])

  which means the two centroids are [a1, a2, a3] and [b1, b2, b3].

Case 2.1: vector_axis = 1

It is effectively a 1-bit vector palettization. The output shape would be [2, 2*3, 2], where each index in the indices would be effectively replaced with the 3 elements in the vector over the 1st dimension to construct the output tensor. The output values are: [

[

[a0, a0], [a1, a1], [a2, a2], [b0, a0], [b1, a1], [b2, a2],

], [

[b0, b0], [b1, b1], [b2, b2], [a0, a0], [a1, a1], [a2, a2],

]

]

Case 2.2: vector_axis = 2

The output shape would be [2, 2, 2*3], where each index in the indices would be effectively replaced with the 3 elements in the vector over the last dimension to construct the output tensor. The output values are: [

[

[a0, a1, a2, a0, a1, a2], [b0, b1, b2, a0, a1, a2],

], [

[b0, b1, b2, b0, b1, b2], [a0, a1, a2, a0, a1, a2],

]

]

Parameters:

indices: const tensor<IndicesT, [1..]> (Required)

lut: const tensor<T, [1.., NUM_PALETTES, VECTOR_SIZE]> (Required)

• NUM_PALETTES needs to be 2^nbits where nbits is indicated by IndicesT.

vector_axis: const tensor<int32, []> (Optional)

• vector_axis can be optional if VECTOR_SIZE is 1.

Attributes:

IndicesT: uint1, uint2, uint3, uint4, uint6, uint8

T: uint8, int8, fp16, fp32

Returns:

const tensor<T, [1..]>

• output_shape = indices_shape * [1..1, VECTOR_SIZE, 1..1] (all 1 but VECTOR_SIZE at vector_axis dimension).

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_sparse_to_dense(**kwargs)

A compile-time operation that returns a constant output value upon de-sparsification of its constant inputs.

The differences from iOS16 constexpr_sparse_to_dense are: - In iOS16, the mask parameter is ‘const tensor<uint8, [M]>’, which is a flat tensor with length

M, so it requires a parameter shape to determine the output shape. In iOS18, we use uint1 (0 or 1) to represent bitmask, which packs the bitmask data and costs the same memory as the uint8 mask in iOS16, but can explicitly tell the tensor shape. We use uint1 instead of bool because bool in MIL uses uint8 as the storage dtype, which costs 8x memory compared to uint1.

• Support more dtypes (int4 and uint4) for the input/output data.

Parameters:

nonzero_data: const tensor<T, [D]> (Required)

mask: const tensor<uint1, [1..]> (Required)

Attributes:

T: int4, uint4, int8, uint8, fp16, fp32

Returns:

const tensor<T, [1..]>

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_lut_to_sparse(**kwargs)

A compile-time operation that returns a constant output value upon de-palettizing its constant inputs.

This op is a sparse-to-sparse op to support constexpr_lut_to_dense on sparse data, where the de-palettization is only applied on the nonzero data. Usually it would be followed by a constexpr_sparse_to_dense op to get the dense tensor. So, parameters of this op are similar to constexpr_sparse_to_dense and constexpr_lut_to_dense. For detailed descriptions about its parameters, please refer to iOS 18 constexpr_sparse_to_dense and constexpr_lut_to_dense.

This op has two outputs:

1. the mask of the de-palettized nonzero_data.

2. the de-palettized nonzero_data.

Parameters:

indices_mask: const tensor<uint1, [1..]> (Required)

indices_nonzero_data: const tensor<IndicesT, [D]> (Required)

lut: const tensor<T, [1.., NUM_PALETTES, VECTOR_SIZE]> (Required)

• NUM_PALETTES needs to be 2^nbits where nbits is indicated by IndicesT.

vector_axis: const tensor<int32, []> (Optional)

• vector_axis can be optional if VECTOR_SIZE is 1.

Attributes:

IndicesT: uint1, uint2, uint3, uint4, uint6, uint8

T: uint8, int8, fp16, fp32

Returns:

const tensor<uint1, [1..]>

• the mask of the de-palettized nonzero_data. For scalar palettization, it’s the same as the input indices_mask. For vector palettization, it’s expanded of the indices_mask over axis=vector_axis.

const tensor<T, [VD]>

• the de-palettized nonzero_data. For scalar palettization, VD=D (same size as indices_nonzero_data). For vector palettization, VD=VECTOR_SIZE * D (each entry is expanded by a vector).

Examples

Assume we have the following inputs:

indices_mask<uint1, [4, 6]> = [[1, 1, 0, 0, 0, 0],

[1, 1, 0, 0, 0, 1], [0, 1, 1, 0, 1, 0], [0, 0, 0, 1, 0, 0]]

indices_nonzero_data<uint1, [9]> = [0, 1, 1, 0, 1, 1, 0, 0, 1]

Notice that: - The uint1 in indices_mask and indices_nonzero_data has different meanings. For

indices_mask the dtype is always uint1 to represent bit mask. For indices_nonzero_data the uint1 means the LUT only has two entries, so only 1 bit is needed to represent indices.

• The 0 in indices_mask and indices_nonzero_data has different meanings. For indices_mask the 0 means empty entry in sparse representation. For indices_nonzero_data the 0 means index 0 in LUT.

With the given indices_mask and indices_nonzero_data, an example for “Scalar Palettization”:

lut<fp16, [1, 1, 2, 1]> = [2.0, 3.0] (indices-to-values mapping is {0: 2.0, 1: 3.0})

The sparse indices in the dense layout would look like: 0 1 . . . . 1 0 . . . 1 . 1 0 . 0 . . . . 1 . . (here “.” means spare elements in sparse representation)

When we apply per-tensor de-palettization with this sparse indices, the indices_nonzero_data is used to read the values from the LUT as in the dense layout. The output sparse tensor in the dense layout would be: 2.0 3.0 . . . . 3.0 2.0 . . . 3.0

. 3.0 2.0 . 2.0 . . . . 3.0 . .

The first output would be the same as the indices_mask. The second output would be [2.0, 3.0, 3.0, 2.0, 3.0, 3.0, 2.0, 2.0, 3.0]

With the given indices_mask and indices_nonzero_data, an example for “Vector Palettization”:

lut<fp16, [1, 1, 2, 2] = [[2.0, 2.0], [3.0, 3.0]] vector_axis = 0

The first output would be the expanded mask of the indices_mask over axis=0, which is: output<uint1, [8, 6]> = [

[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 1], [1, 1, 0, 0, 0, 1], [0, 1, 1, 0, 1, 0], [0, 1, 1, 0, 1, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0],

] The second output in the dense layout would be: 2.0 3.0 . . . . 2.0 3.0 . . . . 3.0 2.0 . . . 3.0 3.0 2.0 . . . 3.0

. 3.0 2.0 . 2.0 . . 3.0 2.0 2.0 . . . . 3.0 . . . . . 3.0 . .

It is created by fetching the vector entry from the lut for every bit 1 in the data_mask, and filling the vector over axis=0.

Those two outputs of this op could be passed as inputs to a following sparse_to_dense op in order to recover the dense weights.

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_sparse_blockwise_shift_scale(**kwargs)

A compile-time operation that returns a constant output value upon de-quantize (shift-scale) its constant inputs. This op is a sparse-to-sparse op to support constexpr_blockwise_shift_scale on sparse data, where the de-quantization is only applied on the nonzero data. Usually it would be followed by a constexpr_sparse_to_dense op to get the dense tensor. So, parameters of this op are similar to constexpr_sparse_to_dense and constexpr_blockwise_shift_scale. For detailed descriptions about its parameters, please refer to iOS 18 constexpr_sparse_to_dense and constexpr_blockwise_shift_scale.

This op has two outputs:

1. the mask of the de-quantized nonzero_data.

2. the de-quantized nonzero_data.

Parameters:

• data_mask (const tensor<uint1, [1..]> (Required))

• nonzero_data (const tensor<SrcT, [D]> (Required))

• scale (const tensor<DstT, [1..]> (Required))

• offset (const tensor<OffsetT, [1..]> (Optional)) –

  • If provided, must have the same shape as the scale.

Returns:

const tensor<uint1, [1..]>

• the mask of the shift-scaled nonzero_data.

const tensor<DstT, [D]>

• the shift-scaled nonzero_data.

Attributes

SrcT: int4, uint4, int8, uint8, fp16, fp32

DstT: fp16, fp32

OffsetT: int4, uint4, int8, uint8, fp16, fp32

Examples

For example:

data_mask = [[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [1, 1, 0, 0]] nonzero_data = [10, 11, 3, 4, 5, 6, 7, 8, 9] The sparse tensor in the dense layout would look like:

10 11 . .

3 4 5 . . . 6 7 8 9 . .

When we apply per-channel de-quantization on this sparse tensor, where: scale = [[0.1, 0.2, 0.3, 0.4]] offset = [[1, 2, 3, 4]] The input nonzero_data would be dequantized per-column as in the dense layout, and the output sparse tensor in the dense layout would be:

(10-1)*0.1 (11-2)*0.2 . . (10-1)*0.1 (11-2)*0.2 . .

(3-1)*0.1 (4-2)*0.2 (5-3)*0.3 .

. . (6-3)*0.3 (7-4)*0.4

(8-1)*0.1 (9-2)*0.2 . .

The first output would be the same as the data_mask, The second output would be [0.9, 1.8, 0.2, 0.4, 0.6, 0.9, 1.2, 0.7, 1.4]. The two outputs could be passed as inputs to the following sparse_to_dense op in order to get the dense weights.

class coremltools.converters.mil.mil.ops.defs.iOS18.compression.constexpr_cast(**kwargs)

A compile-time operation that returns a constant output value upon casting its constant input.

The only difference between this version and the iOS 16 constexpr_cast is the parameters are treated as inputs, instead of attributes in the MIL backend framework.

control_flow

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.cond(**kwargs)

Perform a conditional execution. The return types must be identical between the true and false branches.

Parameters:

pred: tensor<[], bool> (Required)

• 0-D tensor (scalar) predicate to switch between true and false branches.

_true_fn: function (Required)

• A Python function that executes if pred evaluates to True.

• It must take zero input (i.e, no input), and return one or more values whose type becomes the operation’s return type.

_false_fn: function (Required)

• A Python function that executes if pred evaluates to False.

• It must take zero input (i.e. no input), and have return types that match those of the if branch.

_existing_blocks: list[Block] (Optional)

• Python list of Block.

• For internal use only. When converting a milproto, we already got existing blocks, and the build_nested_blocks function can use them directly.

• When _existing_blocks is set, _true_fn and _false_fn must be dummy functions which returns None.

Returns:

tuple

• Python tuple of Variables from one of the branches.

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.Const(**kwargs)

A base class that returns constant values.

Parameters:

val: const<*,T> (Required)

mode: immediate_value, file_value (Optional)

• Determines how the constant value is stored in the internal MIL format.

• For large constants such as convolution weights, use file_value.

• For smaller-size constants such as values of a stride, use immediate_value.

Attributes:

T: fp16, fp32, i32, str, bool

Returns:

const<*,T>

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.select(**kwargs)

Return the elements selected from either a or b depending on the cond.

You must provide a, b and cond. The shape of cond, a, and b must be broadcastable.

Parameters:

cond: tensor<[*D1], B> (Required)

• Tensor. When True, select element from x, otherwise, y.

a: tensor<[*D2], T> (Optional)

• Values selected at indices where cond is True.

• Default is None.

b: tensor<[*D3], T> (Optional)

• Values selected at indices where cond is False.

• Default is None.

Attributes:

B: bool

T: fp16, fp32, i32, bool

Returns:

tensor<[*D_out], T> or tensor<[n, len(D1)], int32>

• If a, b are both provided, the return shape is based on broadcast rules from cond, a, b.

• If a, b are None, the return shape is 2-D, where the first dimension n is the number of matching indices in cond, and len(D1) is the rank of cond.

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.while_loop(**kwargs)

Perform the body repeatedly while the condition cond is true.

Parameters:

_cond: function (Required)

• A Python function that takes loop_vars as positional arguments.

• The function must return a bool Var.

_body: function (Required)

• A Python function that takes loop_vars as positional arguments.

• The function must return the same number of output vars as loop_vars with the same types.

loop_vars: tuple (Required)

• Python tuple of Variables.

_existing_blocks: list[Block] (Optional)

• Python list of Block.

• For internal use only. When converting a milproto, we already got existing blocks, and the build_nested_blocks function can use them directly.

• When _existing_blocks is set, _cond and _body must be dummy functions which returns None.

Returns:

tuple

• Python tuple (same type as loop_vars).

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.make_list(**kwargs)

Create a list of tensor elements. The elements should have the same shape. The list is similar to an auto-resizing array.

Parameters:

init_length: <i32> (Optional, Default=1)

• Initial length for the list.

• If dynamic_length is False, init_length is the fixed length of the list throughout runtime.

dynamic_length: <bool> (Optional, Default is True)

elem_shape: Tuple[const<T>] (Required)

• 1-D vector denoting the shape of elements.

• If T = int32, the element shape is known at compile time.

• T = string denotes the symbolic shape, in which the shape is determined at runtime.

• If not provided, the resulting List won’t have the elementary shape info, which may cause backend errors. Remedy this with SSA passes.

dtype: const (Optional, Default is fp32)

• Possible values: {"bool", "fp16", "fp32", "int32"}

• Element tensor’s dtype.

Attributes:

T: i32, string

Returns:

List[*]

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.list_length(**kwargs)

Return the length of ls.

Parameters:

ls: List[*] (Required)

Attributes:

T: fp16, fp32, i32, bool

Returns:

<i32>

• Length of ls.

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.list_write(**kwargs)

Write a value into index index of ls.

Parameters:

ls: List (Required)

index: <i32> (Required)

• Size of the list.

value: <*,T> (Optional)

• Element value to write, which must match the element shape of ls.

• Default is None.

Attributes:

T: fp16, fp32, i32, bool

Returns:

List[*]

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.list_read(**kwargs)

Read the value at location index of ls.

Parameters:

ls: List[*] (Required)

index: <i32> (Required)

• Size of the list.

Attributes:

T: fp16, fp32, i32, bool

Returns:

<*,T>

• The element’s value.

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.list_gather(**kwargs)

Return selected values in ls as a packed Tensor.

Parameters:

ls: List[*] (Required)

indices: <K, i32> (Required)

• Gather from indices, whose element must be in [0, ls.length) at runtime.

Attributes:

T: fp16, fp32, i32, bool

Returns:

<*K, T>

• Selected tensors packed into a len(ls.elem_shape)+1 rank tensor.

• K[0] == len(indices).

class coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.list_scatter(**kwargs)

Scatter values to ls at locations indices.

Parameters:

ls: List[*] (Required)

indices: tensor<num_updates, i32> (Required)

• Indices of ls to scatter to.

• Elements of indices must be in [0, ls.length) at runtime.

• If indices are greater than or equal to the list length, the list is dynamically resized.

value: <*,T> (Optional)

• Element value to write, which must match the element shape of ls.

• Default is None.

Attributes:

T: fp16, fp32, i32, bool

Returns:

List[*]

• Updated list.

conv (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.conv.conv(**kwargs)

Perform convolution over input. Supports 1-D, 2-D, and 3-D convolution.

Parameters:

x: tensor<[n, C_in, *d_in], T> (Required)

• d_in are (possibly runtime-determined) spatial dimensions. For example, d_in = [224, 224] for 2D convolution.

• 1 <= len(d_in) <= 3.

• C_in is the number of input channels or depth dimensions.

• n is the batch dimension.

weight: tensor<[C_out, C_in/groups, *K], T> (Required)

• Filter weights.

• C_in is the number of input channels.

• C_in must be divisible by groups.

• K are kernel sizes. For example, K = [KH, KW] for 2-D convolution.

• When dilations is not all 1, weight has to be const at compile time

strides: const tensor<[S], i32> (Optional)

• Default to one vector of length equal to the number of spatial dimensions.

• Strides along each of the spatial dimensions.

• S == len(d_in).

pad_type: const str (Required)

Must be one of the following:

• valid: No padding. This is equivalent to custom pad with pad[2*i] == pad[2*i+1] == 0, for i=0,...,len(d_in)-1.

• custom: Specify custom padding in the parameter pad.

• same: Input is padded such that out spatial shapes are d_out[i] = ceil(d_in[i] / strides[i]).

• same_lower: Similar to same but the padding will place extra rows/cols on the top/left if the padding amount is odd.

Specifically, for i = 0,..,,len(d_in)-1, the equivalent paddings are calculated as follows:

• dilated_kernel = (K[i] - 1) * dilate[i] + 1

• If dilated_kernel is odd, padding[2*i] = padding[2*i+1] = floor(dilated_kernel / 2)

• Otherwise: padding[2*i] = ceil((dilated_kernel - 1) / 2), padding[2*i+1] = floor((dilated_kernel - 1) / 2)

pad: const tensor<[P], i32> (Optional. Default to all zeros)

• len(P) = 2 * len(d_in)

• pad should be specified if and only if pad_type == custom, otherwise errors occur.

• pad represents the number of elements to pad before and after each dimension. Specifically, pad[0], pad[1] are the pad size before / after spatial dimension 0, pad[2], pad[3] are the pad size before / after spatial dimension 1, etc.

dilations: const tensor<[S], i32> (Optional. Default to all 1s)

• Dilation value along each spatial dimension in d_in. See visualization.

• S == len(d_in).

groups: const tensor<[], i32> (Optional, default to 1)

• Input and output channels are split by groups.

• C_in must be divisible by groups.

• Maximum value for group is C_in, in which case it is a depthwise convolution.

For examples (assuming C_in = 16, C_out = 32):

• groups == 1, weight has shape [32, 16, KH, KW]: All input channels are convolved with the weight kernel to produce all output channels.

• groups == 2, weight has shape [32, 8, KH, KW]: Input channels 0~7 are convolved with half of the weight kernel to produce output channels 0~15. Similarly, input channels 8~15 are convolved with the other half of weight to product output channels 16~31.

• groups == C_in, weight has shape [32, 1, KH, KW]: Each input channel is convolved with its own set of filters and each produce C_out / C_in = 2 channels. This is equivalent to depthwise convolution.

bias: const tensor<[C_out],T> (Optional, default to all 0)

• Bias along output channels.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C_out, *d_out], T>

• Output activation has the same rank and spatial dimension as the input. That is, len(d_out) == len(d_in).

• For i=0,..,len(d_in)-1, d_out[i] = floor [(D_in[i] + pad[2*i] + pad[2*i+1] - (K[i]-1)*dilations[i] - 1) / strides[i] ] + 1.

See also

conv_transpose

class coremltools.converters.mil.mil.ops.defs.iOS15.conv.conv_transpose(**kwargs)

Perform transposed convolution (also known as deconvolution and fractionally stride convolution) over input. conv_transpose can also be used to compute the gradient of conv. Supports 1-D, 2-D, and 3-D convolution.

Parameters:

x: tensor<[n,C_in,*D_in],T> (Required)

• Input data.

• D_in are spatial dimensions.

• 1 <= len(D_in) <= 3.

• C_in is the number of input channels.

weight: const tensor<[C_in,C_out/groups,*D_in], T> (Required)

• Filter weights. C_in, C_out are the number of input and output channels respectively.

• D_in are spatial dimensions. 1 <= len(D_in) <= 2.

bias: const tensor<[C_out],T> (Optional, default to all 0)

• Bias added along output channels.

pad: const tensor<[P],i32> (Optional, default to all 0s)

• Number of elements to pad before and after each dimension.

• P == 2 * len(D_in).

• pad[2*i], pad[2*i+1] are pad sizes before and after dimension i, where 0 <= i < len(D_in).

output_shape: const tensor<[P],i32> (Optional, default None)

• Expected output shape. The first two dimensions must be [n, C_out].

• The output shape of conv_transpose is underdetermined in general, because conv can map multiple input shapes to a single output shape. For example, for same padding mode, conv_out = ceil(conv_in/stride). Hence we need output_shape when this occurs.

pad_type: const tensor<[P],i32> (Optional, default valid)

• One of same, valid, or custom.

strides: const tensor<[S],i32> (Optional. Default to all 1s)

• Stride along each of the spatial dimensions.

• S == len(D_in).

dilations: const tensor<[S],i32> (Optional. Default to all 1s)

• Dilation value along each spatial dimension in d_in. See conv.

• S == len(D_in).

groups: const tensor<[], i32> (Optional. Default to 1)

• Input and output channels are separated into groups.

• C_in and C_out must be divisible by the number of groups. See conv for examples.

Attributes:

T: fp16, fp32

Returns:

tensor<[n,C_out,*D_out],T>

• If output_shape is not None:

  Dout = output_shape

• If pad_type == "custom":

  Dout[i] = (D_in[i]-1)*stride[i] + (K[i]-1) * dilation[i] + 1 - pad[2*i] - pad[2*i-1]

• If pad_type == "valid":

  Dout[i] = (D_in[i]-1)*stride[i] + (K[i]-1) * dilation[i] + 1

• If pad_type == "same":

  Dout[i] = D_in[i] * stride[i]

See also

conv

conv (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.conv.conv(**kwargs)

Perform convolution over input. Supports 1-D, 2-D, and 3-D convolution.

The difference between this version and the iOS 15 conv is that the weight and bias may have a different dtype than the input/output.

class coremltools.converters.mil.mil.ops.defs.iOS17.conv.conv_transpose(**kwargs)

Perform transposed convolution (also known as deconvolution and fractionally stride convolution) over input. conv_transpose can also be used to compute the gradient of conv. Supports 1-D, 2-D, and 3-D convolution.

The differences between this version and the iOS 15 conv_transpose are: - weight and bias may have a different dtype than the input/output. - weight doesn’t have to be const.

coreml_update_state

class coremltools.converters.mil.mil.ops.defs.coreml_dialect.ops.coreml_update_state(**kwargs)

Copy the content of a variable into a state and return the copy of the variable. The type of the variable must match the type that is wrapped inside the state. This is a coreml dialect op to simplify the program. When loading into MIL, the following transformation is done:

%x = coreml_update_state(state=%state, value=%value)

-->

write_state(state=%state, value=%value)
%x = read_state(input=%state)

Parameters:

state: state<ST> (Required)

value: ST (Required)

Attributes:

ST: tensor

Returns:

ST

elementwise_binary

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.add(**kwargs)

Return x + y element-wise with broadcasting.

Parameters:

x: <*, T> (Required)

• Shape must be compatible with y in broadcast.

y: <*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

<*, T>

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.equal(**kwargs)

Return the truth value of x == y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: <*, T> (Required)

• Shape must be compatible with y in broadcast.

y: <*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

<*, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.floor_div(**kwargs)

Return x / y element-wise with broadcasting, rounded towards negative infinity.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*, T>

• A tensor of the same type and shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.greater(**kwargs)

Return the truth value of x > y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.greater_equal(**kwargs)

Return the truth value of x >= y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.less(**kwargs)

Return the truth value of x < y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.less_equal(**kwargs)

Return the truth value of x <= y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.logical_and(**kwargs)

Return the truth value of x AND y element-wise with broadcasting

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: bool

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.logical_or(**kwargs)

Return the truth value of x OR y element-wise with broadcasting

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: bool

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.logical_xor(**kwargs)

Return the truth value of x XOR y element-wise with broadcasting

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: bool

Returns:

tensor<*?, bool>

• A boolean tensor with the same shape as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.maximum(**kwargs)

Return x > y ? x : y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.minimum(**kwargs)

Return x > y ? y : x element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.mod(**kwargs)

Return x % y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.mul(**kwargs)

Return x * y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.not_equal(**kwargs)

Return the truth value of x != y element-wise with broadcasting (1 for true, 0 for false in numeric domain).

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, bool>

• A boolean tensor with the broadcasted shape from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.real_div(**kwargs)

Return x / y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.pow(**kwargs)

Return x ^ y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_binary.sub(**kwargs)

Return x - y element-wise with broadcasting.

Parameters:

x: tensor<*, T> (Required)

• Shape must be compatible with y in broadcast.

y: tensor<*, T> (Required)

• Shape must be compatible with x in broadcast.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• A tensor with the broadcasted shape from inputs, and type is derived from inputs.

elementwise_unary (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.abs(**kwargs)

Return the absolute values of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.acos(**kwargs)

Return the inverse cosine values of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.asin(**kwargs)

Return the inverse sine of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.atan(**kwargs)

Return the inverse tangent of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.atanh(**kwargs)

Return the inverse hyperbolic tangent values of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.cast(**kwargs)

Cast the input x to the new type dtype.

Parameters:

x: tensor<[*d], T> (Required)

dtype: const str (Required)

• Can be one of the following types: int32, fp16, fp32, bool.

Attributes:

T: i32, fp16, fp32, bool.

Returns:

tensor<[*d], dtype>

• A tensor of the same shape as x, with type dtype.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.ceil(**kwargs)

Return the ceil values of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.clip(**kwargs)

Clip the values in the input x to [alpha, beta], element-wise. Any values less than alpha are set to alpha, and any values greater than beta are set to beta.

Parameters:

x: tensor<[*d], T> (Required)

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.cos(**kwargs)

Return cosine of x element-wise. Input domain is (-inf, inf) and output range is [-1,1].

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.cosh(**kwargs)

Return hyperbolic cosine of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.erf(**kwargs)

Return the gauss error function of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.exp(**kwargs)

Return e^x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.exp2(**kwargs)

Return 2^x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.floor(**kwargs)

Return the floor of the input x, element-wise, the same as rounding towards negative infinity.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.inverse(**kwargs)

Return the reciprocal value of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const T (Optional, default=1e-4)

• This is a small constant that is added to the input, before taking its inverse, for stability.

• y = 1 / (x + epsilon).

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.log(**kwargs)

Return the natural logarithm value of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const T (Optional, default=1e-45)

• This is a small constant that is added to the input, before taking log.

• y = log(x + epsilon).

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.logical_not(**kwargs)

Return the value of NOT the input x, element-wise. (1 for true, 0 for false in numeric domain.) A numeric value t is evaluated to true iff t != 0.

Parameters:

x: tensor<[*d], bool> (Required)

Attributes:

T: bool

Returns:

tensor<[*d], bool>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.round(**kwargs)

Return the round value of the input x to nearest integer, element-wise. 0.5 is rounded to 0.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.rsqrt(**kwargs)

Return the reciprocal value of the square root of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const T (Optional, default=1e-12)

• This is a small constant that is added to the input, before applying the rsqrt function, for stability.

• y = 1 / sqrt(x + epsilon).

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.sign(**kwargs)

Return the sign value of the input x, element-wise.

All elements in the output will be either -1 or 1, or zero if the input x is zero.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.sin(**kwargs)

Return the sine value of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.sinh(**kwargs)

Return the hyperbolic sine value of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.sqrt(**kwargs)

Returns the square root value of the input x, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.square(**kwargs)

Return x^2, element-wise.

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.tan(**kwargs)

Return the tangent value of the input x, element-wise. Both input and output ranges are (-inf, inf).

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.tanh(**kwargs)

Return the hyperbolic tangent value of the input x, element-wise. Both input and output ranges are (-inf, inf) while output range is [-1,  1].

Parameters:

x: tensor<[*d], T> (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.threshold(**kwargs)

Set a lower bound alpha to the values in the input x, element-wise. Any values less than alpha are set to alpha.

Parameters:

x: tensor<[*d], T> (Required)

alpha: const T (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

elementwise_unary (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.elementwise_unary.cast(**kwargs)

Cast the input x to the new type dtype. The only difference between this version and the iOS 15 cast is that it supports int8, uint8, int16, and uint16.

Parameters:

x: tensor<[*d], T> (Required)

dtype: const str (Required)

• Can be one of the following types: int8, uint8, int16, uint16, int32, fp16, fp32, or bool.

Attributes:

T: i8, ui8, i16, ui16, i32, fp16, fp32, bool.

Returns:

tensor<[*d], dtype>

• A tensor of the same shape as x, with type dtype.

class coremltools.converters.mil.mil.ops.defs.iOS17.elementwise_unary.clip(**kwargs)

Clip the values in the input x to [alpha, beta], element-wise. Any values less than alpha are set to alpha, and any values greater than beta are set to beta.

The major difference between this version and the iOS 15 clip is that it uses strict validation to ensure that alpha < beta.

Parameters:

x: tensor<[*d], T> (Required)

alpha: const T (Required)

beta: const T (Required)

Attributes:

T: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.elementwise_unary.inverse(**kwargs)

Return the reciprocal value of the input x, element-wise. The only difference between this version and the iOS 15 inverse is epsilon may have different dtypes than the inputs/outputs.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const U (Optional, default=1e-4)

• This is a small constant that is added to the input, before taking its inverse, for stability.

• y = 1 / (x + epsilon).

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.elementwise_unary.log(**kwargs)

Return the natural logarithm value of the input x, element-wise. The only difference between this version and the iOS 15 log is epsilon may have different dtypes than the inputs/outputs.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const U (Optional, default=1e-45)

• This is a small constant that is added to the input, before taking log.

• y = log(x + epsilon).

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

class coremltools.converters.mil.mil.ops.defs.iOS17.elementwise_unary.rsqrt(**kwargs)

Return the reciprocal value of the square root of the input x, element-wise. The only difference between this version and the iOS 15 rsqrt is epsilon may have different dtypes than the inputs/outputs.

Parameters:

x: tensor<[*d], T> (Required)

epsilon: const U (Optional, default=1e-12)

• This is a small constant that is added to the input, before applying the rsqrt function, for stability.

• y = 1 / sqrt(x + epsilon).

Attributes:

T: fp16, fp32

U: fp16, fp32

Returns:

tensor<[*d], T>

• A tensor of the same shape as x.

image_resizing (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.affine(**kwargs)

Apply a linear affine transform to the input 2D image tensor. The value at the (x, y) (i.e., (w, h)) coordinate of the output is computed by first computing the coordinates x’ and y’ with the following equation, and then computing the value at the coordinate (x’,y’) in the input image using either bilinear or nearest neighbor interpolation. If the (x’, y’) point falls outside the input image, then padding information is used to compute the value:

x’ = a0 * x + a1 * y + a2
y’ = b0 * x + b1 * y + b2

Parameters:

x: tensor<[B, C, H1, W1], T>

• Must be rank 4.

transform_matrix: tensor<[D, 6], T>

• Must be rank 2.

• D can be either B or 1.

  • If D == B, there is a separate transform matrix for each batch.

  • If D == 1, the same matrix is used for all input batches.

  • For each batch: [a0, a1, a2, b0, b1, b2].

output_height: const<i32>

• Target output height

output_width: const<i32>

• Target output width

sampling_mode: const<str>

• Allowed values: "bilinear"

padding_mode: const<str>

• Allowed values: "constant".

• Note that the following example is 1D case for brevity. The op supports only 2D image input.

• If padding_mode == "constant":

  • The input image is assumed to be padded with the padding_value.

  • For example, |1, 2, 3| -> |0, 0, 0, 1, 2, 3, 0, 0, 0|.

padding_value: const<T>

• Currently non-zero values are not supported.

• To be used only when padding_mode == "constant", ignored in other cases.

coordinates_mode: const<str>

• Allowed values: "normalized_minus_one_to_one".

• If coordinates_mode == "normalized_minus_one_to_one", in-image values are [-1, 1].

• For example, if coordinates_mode == "normalized_minus_one_to_one", the in-range values are [-1, 1]. That is:

  • (-1, -1), i.e. (w=-1, h=-1), corresponds to the top-left pixel.

  • (1, -1), i.e. (w=1, h=-1), corresponds to the top-right pixel.

  • (-1, 1), i.e. (w=-1, h=1), corresponds to the bottom-left pixel.

  • (1, 1), i.e. (w=1, h=1), corresponds to the bottom-right pixel.

align_corners: const<bool>

• Currently align_corners=False is not supported.

• To be used only when coordinates_mode != unnormalized, ignored otherwise.

• If align_corners == True, the extrema coordinates correspond to the center of the first and last corner pixels.

• If align_corners == False, the extrema coordinates correspond to the edge of the first and last corner pixels.

Attributes:

T: fp16, fp32

Returns:

tensor<[B, C, output_height, output_width], T>

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.crop(**kwargs)

Crop the spatial dimensions (last two dimensions) of the input by the specified amounts.

Parameters:

x: tensor<[*D, H1, W1],T> (Required)

• Must be at least rank 3.

crop_height: const<2, i32> (Required)

• Amount to be cropped from the top and bottom of the height dimension (axis=-2).

crop_width: const<2, i32> (Required)

• Amount to be cropped from the left and right sides of the width dimension (axis=-1).

Attributes:

T: fp16, fp32

Returns:

tensor<[*D, H2, W2],T>

• Tensor with same type as the input.

• H2 = H1 - crop_height[0] - crop_height[1].

• W2 = W1 - crop_width[0] - crop_width[1].

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.crop_resize(**kwargs)

Resize the spatial dimensions (last two dimensions) of the first input according to the bounding boxes specified in the second input, using bilinear interpolation.

Parameters:

x: tensor<[B, C, H, W],T> (Required)

• The input, from which patches (regions of interest) are extracted and resized using bilinear interpolation.

• Rank 4.

roi: tensor<[N,1,4,1,1], T> or tensor<[N,1,5,1,1], T> (Required)

• Regions of interest, or coordinates of the boxes. The above input represents coordinates of N boxes.

• The convention to express coordinates depends on the value of the input box_coordinate_mode.

• Rank 5.

• If tensor<[N,1,4,1,1], T>: Resized images are computed for all B input images.

• If tensor<[N,1,5,1,1], T>: The first element from axis=-3 to be resized is an index. It must be within range [0, B).

target_height: const<i32> (Optional, Default=1)

• Target height for resizing each patch.

target_width: const<i32> (Optional, Default=1)

• Target width for resizing each patch.

normalized_coordinatesconst<bool> (Optional, default=False)

• If True, the bounding box coordinates must be in the interval [0, 1]. Scaling is based on the input spatial dimensions: (H_in - 1) for height and (W_in - 1) for width.

• If False, the bounding box coordinates must be in the interval [0, H_in - 1] for height dimensions and [0, W_in - 1] for width dimensions.

spatial_scaleconst<fp32> (Optional, default=1.0)

• Additional spatial scale that multiplies the bounding box coordinates. You would use this to implement the RoI Align layer, which typically uses unnormalized RoI coordinates along with a spatial scale that is less than or equal to 1.

box_coordinate_mode: const<str> (Optional, default=”CORNERS_HEIGHT_FIRST”)

• Specifies the convention for specifying the four bounding box coordinates for an image of size (Height, Width). The (0,0) coordinate corresponds to the top-left corner of the image.

• This parameter can take one of four values:

  "CORNERS_HEIGHT_FIRST": [h_start, w_start, h_end, w_end]

  "CORNERS_WIDTH_FIRST": [w_start, h_start, w_end, h_end]

  "CENTER_SIZE_HEIGHT_FIRST": [h_center, w_center, box_height, box_width]

  "CENTER_SIZE_WIDTH_FIRST": [w_center, h_center, box_width, box_height]

sampling_modeconst<str> (Optional, default=”DEFAULT”)

• This parameter can take "STRICT_ALIGN_CORNERS", "ALIGN_CORNERS", "DEFAULT", "OFFSET_CORNERS" or UNALIGN_CORNERS as values.

• This same convention is used by the resize_bilinear op (see that op for details).

Attributes:

T: fp16, fp32

Returns:

tensor<[N, B, C, target_height, target_width],T> or tensor<[N, 1, C, target_height, target_width],T>

• Tensor with same type as the input.

• If roi : tensor<[N,1,4,1,1], T>, the output is tensor<[N, B, C, target_height, target_width],T>. Total crops = N*B; that is, N crops for each input in the batch.

• If roi : tensor<[N,1,5,1,1], T>, the output is tensor<[N, 1, C, target_height, target_width],T>. Total crops = N; that is, 1 crop for given input image index in the batch.

See also

resize_bilinear

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.resample(**kwargs)

Resample the input image tensor x at the coordinates. Resampling is required if the coordinates do not correspond to exact pixels in the input image. The sampling_mode determines the algorithm used for resampling and computing the values.

Parameters:

x: tensor<[B, C, H1, W1], T>

• Must be rank 4.

coordinates: tensor<[B, H2, W2, 2], U>

• Must be rank 4.

• Coordinates are provided in the order (x, y) (i.e. (w, h)).

• The value of each output location output[b, c, h, w] is calculated by sampling from the input image x[b, c, :, :].

• The pixel at the (x, y) location corresponds to the length-2 vector: coordinates[b, h, w, :].

• Coordinate (normalized or unnormalized) should be specified according to coordinates_mode.

sampling_mode: const<str>

• Allowed values: "bilinear" , "nearest"

padding_mode: const<str>

• Allowed values: "constant", "border", "reflection", "symmetric"

• Note that the following example is 1D case for brevity. The op supports only 2D image input.

• If padding_mode == "constant":

  • The input image is assumed to be padded with the padding_value.

  • For example: |1, 2, 3| -> |0, 0, 0, 1, 2, 3, 0, 0, 0|

• if padding_mode == "border":

  • The input image is assumed to be padded with the values replicated from the values at the edge. This is also referred to as the “clamped” or “replication” mode, since the padded values are clamped to the border values.

  • For example: |1, 2, 3| -> |1, 1, 1, 1, 2, 3, 3, 3, 3|

• If padding_mode == "reflection":

  • The border values are reflected, not including the values at the edge/border.

  • For example: |1, 2, 3| -> |2, 3, 2, 1, 2, 3, 2, 1, 2|

• If padding_mode == "symmetric":

  • Values are reflected, including the border/edge values.

  • For example: |1, 2, 3| -> |3, 2, 1 , 1, 2, 3, 3, 2, 1|

padding_value: const<T>

• To be used only when padding_mode == "constant", ignored in other cases.

coordinates_mode: const<str>

• Allowed values: "unnormalized", "normalized_minus_one_to_one", "normalized_zero_to_one"

• If coordinates_mode == "unnormalized", the coordinates input values are interpreted to be in range [0, W - 1] / [0, H - 1], which corresponds to the in-image point.

• If coordinates_mode == "normalized_minus_one_to_one", the in-image values are [-1, 1].

• If coordinates_mode == "normalized_zero_to_one", in-image values are [0, 1].

• For example, if coordinates_mode == "normalized_minus_one_to_one", the in range values are [-1, 1]. That is:

  • (-1, -1), i.e. (w=-1, h=-1), corresponds to the top-left pixel.

  • (1, -1), i.e. (w=1, h=-1), corresponds to the top-right pixel.

  • (-1, 1), i.e. (w=-1, h=1), corresponds to the bottom-left pixel.

  • (1, 1), i.e. (w=1, h=1), corresponds to the bottom-right pixel.

align_corners: const<bool>

• If align_corners == True, the extrema coordinates correspond to the center of the first and last corner pixels.

• If align_corners == False, the extrema coordinates correspond to the edge of the first and last corner pixels.

Attributes:

T: fp16, fp32

U: fp32, int32

Returns:

tensor<[B, C, H2, W2], T>

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.resize_bilinear(**kwargs)

Resize the spatial (last two) dimensions to the specified target size using bilinear interpolation. Although this op is similar to upsample_bilinear, resize_bilinear works with a target size rather than with scale factors.

Parameters:

x: tensor<[*D, H1, W1],T> (Required)

• Must be at least rank 3.

target_size_height: const<int32> (Optional, default=1)

• Target spatial size for the height dimension (axis=-2).

target_size_width: const<int32> (Optional, default=1)

• Target spatial size for the width dimension (axis=-1).

sampling_mode: const<str> (Optional, default=”DEFAULT”)

• This parameter can take "STRICT_ALIGN_CORNERS”, "ALIGN_CORNERS", "DEFAULT", "OFFSET_CORNERS" or UNALIGN_CORNERS as values. For details, see the Notes section.

Attributes:

T: fp16, fp32

Returns:

tensor<[*D, H2, W2],T>

• Tensor with same type as the input.

• H2 = target_size_height.

• W2 = target_size_width.

Notes

To understand the sampling_mode parameter, consider the 1-D case. You need to sample a grid of pixels whose values are computed using linear interpolation. This parameter controls how the grid is sampled. If the input grid is [0, Xin-1] (corresponding to an input size of Xin), and if the output size is Xout, then the grid points are sampled in the following manner:

# "STRICT_ALIGN_CORNERS":
spacing = (Xin - 1) / (Xout - 1)
grid_point[i] = min(Xin-1, max(0, i*spacing)), for i=0,1,...,Xout-1

# "ALIGN_CORNERS": Same as "STRICT_ALIGN_CORNERS" unless Xout=1,
# in which case:
grid_point[0] = (Xin-1) / 2, if Xout==1

# "DEFAULT":
spacing = (Xin - Xin/Xout) / (Xout - 1)
grid_point[i] = min(Xin-1, max(0, i*spacing)), for i=0,1,...,Xout-1

# "OFFSET_CORNERS":
delta = max(1, Xin - 1) / Xout
spacing = ((Xout - 1) * delta) / (Xout - 1)
grid_point[i] = min(Xin-1, max(0, 0.5*delta + i*spacing)), for
...   i=0,1,...,Xout-1

# "UNALIGN_CORNERS":
spacing = Xin / Xout
grid_point[i] = min(Xin - 1, max(0, i*spacing + 0.5*spacing - 0.5)), for i=0,1,...,Xout-1

For example:

Xin = 2
input_interval = [0,1]

Grid points:

[0., 0.1, 0.5, 0.9, 1.] (Xout = 5, UNALIGN_CORNERS)
[0., 0.25, 0.5, 0.75, 1.] (Xout = 5, "STRICT_ALIGN_CORNERS" / "ALIGN_CORNERS")
[0., 0.4, 0.8, 1., 1.] (Xout = 5, "DEFAULT")
[0.1, 0.3, 0.5, 0.7, 0.9] (Xout = 5, "OFFSET_CORNERS")

[0., 0., 0.33, 0.67, 1., 1.] (Xout = 6, UNALIGN_CORNERS)
[0., 0.2, 0.4, 0.6, 0.8, 1.] (Xout = 6, "STRICT_ALIGN_CORNERS" / "ALIGN_CORNERS")
[0., 0.33, 0.67, 1., 1., 1.] (Xout = 6, "DEFAULT")
[0.08, 0.25, 0.42, 0.58, 0.75, 0.92] (Xout = 6, "OFFSET_CORNERS")

Note the following similarities:

• "DEFAULT" is same as tf.raw_ops.ResizeBilinear(align_corners=False, half_pixel_centers=False).

• "STRICT_ALIGN_CORNERS" is same as tf.raw_ops.ResizeBilinear(align_corners=True, half_pixel_centers=False).

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.resize_nearest_neighbor(**kwargs)

Resize the spatial (last two) dimensions to the specified target size using nearest neighbor interpolation. Although this op is similar to upsample_nearest_neighbor, resize_nearest_neighbor works with a target size rather than with scale factors.

Parameters:

x: tensor<[*D, H1, W1], T> (Required)

• Must be at least rank 3.

target_size_height: const<int32> (Required)

• Target spatial size for the height dimension (axis=-2).

target_size_width: const<int32> (Required)

• Target spatial size for the width dimension (axis=-1).

Attributes:

T: fp16, fp32

Returns:

tensor<[*D, H2, W2], T>

• Tensor with same type as the input.

• H2 = target_size_height.

• W2 = target_size_width.

See also

resize_bilinear

Notes

See resize_bilinear for examples.

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.upsample_bilinear(**kwargs)

Upsample the spatial dimensions (last two dimensions) of the input by scale factors using bilinear interpolation. The upsample_bilinear operation in MIL corresponds to the recompute_scale_factor=True mode in the pyorch bilinear interpolation op. That is, the scale factor is recomputed by the output size. Note that when the scale_factor_height and scale_factor_width are floating point, this could result in a different scale factor due to rounding.

Parameters:

x: tensor<[*D, H1, W1], T> (Required)

• Must be at least rank 3.

scale_factor_height: const<U> (Optional, default=1)

• Scale factor for the height dimension (axis=-2).

scale_factor_width: const<U> (Optional, default=1)

• Scale factor for the width dimension (axis=-1).

align_corners: const<bool> (Optional, default=True)

• This parameter determines how samples are chosen for bilinear interpolation. For details, see the Notes section.

Attributes:

T: fp16, fp32

Ufp32, i32

Returns:

tensor<[*D, H2, W2], T>

• Tensor with same type as the input.

• H2 = floor(H1 * scale_factor_height).

• W2 = floor(W1 * scale_factor_width).

Notes

To understand the align_corners parameter, consider the 1-D case. You need to sample a grid of pixels whose values are computed using linear interpolation. This parameter controls how the grid is sampled. If the input grid is [0, Xin-1] (corresponding to an input size of Xin), and if the output size is Xout, then the grid points are sampled in the following manner:

# If align_corners == True:
spacing = (Xin - 1) / (Xout - 1)
grid_point[i] = min(Xin - 1, max(0, i*spacing)), for i=0,1,...,Xout-1

# If align_corners == False:
spacing = Xin / Xout
grid_point[i] = min(Xin - 1, max(0, i*spacing + 0.5*spacing - 0.5)),
...   for i=0,1,...,Xout-1

For example:

Xin = 2
input_interval = [0,1]

Grid points:

[0., 0.1, 0.5, 0.9, 1.] (Xout = 5, align_corners=False)
[0., 0.25, 0.5, 0.75, 1.] (Xout = 5, align_corners=True)
[0., 0., 0.33, 0.67, 1., 1.] (Xout = 6, align_corners=False)
[0., 0.2, 0.4, 0.6, 0.8, 1.] (Xout = 6, align_corners=True)

Note the following similarities:

• align_corners=False is the same as tf.raw_ops.ResizeBilinear(align_corners=False, half_pixel_centers=True).

• align_corners=True is the same as tf.raw_ops.ResizeBilinear(align_corners=True, half_pixel_centers=False).

class coremltools.converters.mil.mil.ops.defs.iOS15.image_resizing.upsample_nearest_neighbor(**kwargs)

Upsample the spatial dimensions (last two dimensions) of the input by integer scale factors using nearest-neighbor interpolation.

Parameters:

x: tensor<[*D, H1, W1],T> (Required)

• Must be at least rank 3.

scale_factor_height: const<i32> or const<fp32> (Optional, default=1)

• Scale factor for the height dimension (axis=-2).

• Can be either an integer or fractional.

scale_factor_width: const<i32> or const<fp32> (Optional, default=1)

• Scale factor for the width dimension (axis=-1).

• Can be either an integer or fractional.

Attributes:

T: fp16, fp32

U: fp32, i32

Returns:

tensor<[*D, H2, W2],T>

• Tensor with same type as the input.

• H2 = floor(H1 * scale_factor_height).

• W2 = floor(W1 * scale_factor_width).

image_resizing (iOS 16+)

class coremltools.converters.mil.mil.ops.defs.iOS16.image_resizing.crop_resize(**kwargs)

This version differs from the iOS 15 crop_resize by supporting pad_value as an additional parameter.

Parameters:

pad_valueconst<T> (Optional, default=0.0)

• If the box indexes go beyond the input boundary, the input image is padded with pad_value.

• Defaults to 0.

• It is the same as extrapolation_value in tf.image.crop_and_resize.

Attributes:

T: fp16, fp32

class coremltools.converters.mil.mil.ops.defs.iOS16.image_resizing.resample(**kwargs)

This version of resample supports float 16 coordinates.

For complete documentation, see the iOS 15 resample.

class coremltools.converters.mil.mil.ops.defs.iOS16.image_resizing.upsample_bilinear(**kwargs)

This version of upsample_bilinear supports half_pixel_centers. For complete documentation, see the iOS 15 upsample_bilinear.

Parameters:

half_pixel_centers: const<bool> (Optional)

• Defaults to !align_corners if not provided.

image_resizing (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.image_resizing.crop_resize(**kwargs)

The major differences between this version and the iOS 16 crop_resize are as follows:

• The input ROI is replaced by boxes and box_indices.

• The dtype domain of input x, boxes, and box_indices are independent.

• The output no longer has the B dim. The output is [N, C, target_height, target_width] rather than the [N, B, C, target_height, target_width] in iOS 16.

Parameters:

x: tensor<[B, C, H, W], T> (Required)

• The input, from which patches (regions of interest) are extracted and resized using bilinear interpolation.

• Rank 4.

boxes: tensor<[N, 4], BOX_T> (Required)

• Coordinates of N boxes.

• The convention to express coordinates depends on the value of box_coordinate_mode.

• If normalized_coordinates is True, only fp16 and fp32 dtypes are allowed.

box_indices: tensor<[N], BOX_INDEX_T> (Optional)

• Default is arange(N), or [0, 1, ..., N-1].

• If box_indices[i]=j, this means that boxes[i] will be applied to the j-th image. Therefore, it is invalid for box_indices[i] to be greater than B.

target_height: const<i32> (Optional, Default=1)

• Target height for resizing each patch.

target_width: const<i32> (Optional, Default=1)

• Target width for resizing each patch.

normalized_coordinatesconst<bool> (Optional, default=False)

• If True, the bounding box coordinates must be in the interval [0, 1]. Scaling is based on the input spatial dimensions: (H_in - 1) for height and (W_in - 1) for width.

• If False, the bounding box coordinates must be in the interval [0, H_in - 1] for height dimensions and [0, W_in - 1] for width dimensions.

spatial_scaleconst<fp32> (Optional, default=1.0)

• Additional spatial scale that multiplies the bounding box coordinates.

• You would use this to implement the RoI Align layer, which typically uses unnormalized RoI coordinates along with a spatial scale that is less than or equal to 1.

box_coordinate_mode: const<str> (Optional, default=”CORNERS_HEIGHT_FIRST”)

• Specifies the convention for specifying the four bounding box coordinates for an image of size (Height, Width). The (0,0) coordinate corresponds to the top-left corner of the image.

• This parameter can take one of four values:

  "CORNERS_HEIGHT_FIRST": [h_start, w_start, h_end, w_end]

  "CORNERS_WIDTH_FIRST": [w_start, h_start, w_end, h_end]

  "CENTER_SIZE_HEIGHT_FIRST": [h_center, w_center, box_height, box_width]

  "CENTER_SIZE_WIDTH_FIRST": [w_center, h_center, box_width, box_height]

sampling_modeconst<str> (Optional, default=”DEFAULT”)

• This parameter can take "STRICT_ALIGN_CORNERS", "ALIGN_CORNERS", "DEFAULT", "OFFSET_CORNERS" or UNALIGN_CORNERS as values.

• This is the same convention used by the resize_bilinear op.

pad_valueconst<T> (Optional, default=0.0)

• If the box indexes go beyond the input boundary, the input image is padded with pad_value.

• Defaults to 0.

• It is the same as extrapolation_value in tf.image.crop_and_resize.

Attributes:

T: fp16, fp32

BOX_T: fp16, fp32, uint16

BOX_INDEX_T: uint16, int32

Returns:

tensor<[N, C, target_height, target_width], T>

class coremltools.converters.mil.mil.ops.defs.iOS17.image_resizing.resample(**kwargs)

Resample the input image tensor x at the coordinates.

The major difference between this version and the iOS 16 resample is that coordinates supports int8, uint8, int16, uint16.

class coremltools.converters.mil.mil.ops.defs.iOS17.image_resizing.resize(**kwargs)

Resizes the input tensor x by choosing the right-most resized_dims dimensions from the input shape shape, and by choosing the rest from x ‘s shape.

This iOS17 resize is a superset of iOS 15 resize_bilinear and resize_nearest_neighbor. The main benefit is that this resize op allows a use-case in dynamic tensor shapes where a tensor needs to be resized to a dynamic shape as specified by another tensor.

To illustrate how output shape is inferred, the following are two examples:

• Example #1:

  x.shape: [1, 2, 3, 4]
  shape: [1, 6, 8]
  resized_dims: 2
  The output's shape will be [1, 2, 6, 8]
  

• Example #2:

  x.shape: [1, 2, 3, is0]
  shape: [1, 0, 0]
  resized_dims: 2
  The output's shape will be [1, 2, 3, is0]
  

Parameters:

x: tensor<[…], T> (Required)

shape: tensor<[K], U> (Required)

• Restriction: size(shape) <= rank(x).

• If shape[i]==0, the dimension in the output tensor will instead be inferred from the corresponding element of x.shape(). Note this might not be x.shape()[i], as size(shape), resized_dims, and size(x) may all be different sizes.

resized_dims: const tensor<[], uint32> (Required)

• Restriction: resized_dims <= size(shape).

interpolation_mode: const<str> (Optional, default=”LINEAR”)

• Available mode: LINEAR, NEAREST_NEIGHBOR.

sampling_mode: const<str> (Optional, default=”DEFAULT”)

• Available mode: DEFAULT, STRICT_ALIGN_CORNERS, ALIGN_CORNERS, OFFSET_CORNERS, UNALIGN_CORNERS.

• For details about different sampling modes, see iOS 15 resize_bilinear.

Attributes:

T: fp16, fp32, int32

U: int32, int16, uint16, uint32

Returns:

tensor<[…], T>

linear (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.linear.einsum(**kwargs)

Perform tensor multiplication expressed according to the einsum notation. The mode/equation that is currently supported is multiplying matrices that are laid out on dimensions -1 and -3, treating all the other dimensions as batch. Broadcasting is supported along batch dimensions. In particular, the inputs must be of the following shapes:

• Rank 4 input case:

  • Input 1: [B, C, H, W1].

  • Input 2: [B, W1, H, W2].

  • Output: [B, C, H, W2].

  • If, for one of the inputs, the dimensions "B" or "H" is 1, they are broadcast to match the other input.

• Rank 3 input case:

  • Input 1: [C, H, W1].

  • Input 2: [W1, H, W2].

  • Output: [C, H, W2].

  • If, for one of the inputs, the dimension "H" is 1, it is broadcast to match the other input.

Parameters:

valuesTuple(tensor_1, tensor_2)

• Where:

  • tensor_1: tensor<[*D, C, H, W1], T>.

  • Must be of rank 3 or 4.

  • tensor_2: tensor<[*D, W1, H, W2], T>.

  • Must be of rank 3 or 4.

equation: const<str>

• Supported equations are:

  • "nchw,nwhu->nchu" and its equivalent equation strings.

  • "chw,whr->chr" and its equivalent equation strings.

Attributes:

T: fp16, fp32

Returns:

tensor<[*D, C, H, W2], T>

• Same ranks as the inputs.

class coremltools.converters.mil.mil.ops.defs.iOS15.linear.linear(**kwargs)

Perform x * weight.T + bias where weight and bias are constant at compile time.

Parameters:

x: tensor<[*D, D_in], T> (Required)

• 1 <= rank <= 3.

• 0 <= rank(*D) <= 2.

weight: const tensor<[D_out,D_in], T> (Required)

bias: const tensor<[D_out],T> (Optional)

• Default to 0.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<[*D, D_out], T>

• Same rank as the input x.

class coremltools.converters.mil.mil.ops.defs.iOS15.linear.matmul(**kwargs)

Perform N-D batch matrix multiplication with NumPy-style broadcasting based on the following rules:

Rule 1. If both x, y are 1-D, return the scalar from the dot product.

Rule 2. If both x, y are 2-D or higher, perform a broadcast on the batch dimensions (all dimensions except the last 2).

For example:

• x.shape == (10, 4, 3)

• y.shape == (5, 10, 3, 2)

• matmul(x, y).shape == (5, 10, 4, 2)

Conventional matrix multiplication is a special case where both x, y are exactly 2-D. For example:

• x.shape == (4, 3)

• y.shape == (3, 2)

• matmul(x, y).shape == (4, 2)

If x is 1-D, and y is N-D where N >= 2, x is first promoted to matrix xm by prepending a 1 to its dimension, and the resulting xm is broadcast to y following Rule 2 above. After this, remove the inserted dimension. For example:

• x.shape == (4)

• y.shape == (10, 4, 3)

• xm.shape == (1, 4)

• matmul(xm, y).shape == (10, 1, 3)

• Removing the inserted dimension results in matmul(x, y).shape == (10, 3).

• Note: xm and matmul(xm, y) are for illustration only.

If x is N-D where N >= 2, and y is 1-D, y is first promoted to matrix ym by appending a 1 to its dimension, and the resulting ym is broadcast to x following Rule 2 above. After this, remove the inserted dimension. For example:

• x.shape == (10, 3, 4)

• y.shape == (4,)

• ym.shape == (4, 1)

• matmul(x, ym).shape == (10, 3, 1)

• Removing the inserted dimension results in matmul(x, y).shape == (10, 3).

• Note: xm and matmul(xm, y) are for illustration only.

Parameters:

x: tensor<[*, K1], T> (Required)

• x must be 1-D or higher.

y: tensor<[*, K2], T> (Required)

• y must be 1-D or higher.

transpose_x: const bool (Optional)

• Default to False.

• Use True to transpose the last two dimensions of x before multiplication. It has no effect when x is 1-D.

transpose_y: const bool (Optional)

• Default to False.

• Use True to transpose the last two dimensions of y before multiplication. It has no effect when y is 1-D.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*, T>

• Scalar or tensor output.

linear (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.linear.linear(**kwargs)

A version of linear for iOS 17+. The only difference between this version and the iOS 15 linear is that the weight and bias may have a different dtype than the input/output.

Parameters:

x: tensor<[*D, D_in], T> (Required)

• 1 <= rank <= 3.

• 0 <= rank(*D) <= 2.

weight: const tensor<[D_out, D_in], U> (Required)

bias: const tensor<[D_out], U> (Optional)

• Default to 0.

Attributes:

T: fp16, fp32, i32

U: fp16, fp32, i32

Returns:

tensor<[*D, D_out], T>

• Same rank as the input x.

class coremltools.converters.mil.mil.ops.defs.iOS17.linear.matmul(**kwargs)

A version of matmul for iOS 17+. The only difference between this version and the iOS 15 matmul is that the x and y can have a different dtypes when one of them is const.

Parameters:

x: tensor<[*, K1], T> (Required)

• x must be 1-D or higher.

y: tensor<[*, K2], U> (Required)

• y must be 1-D or higher.

transpose_x: const bool (Optional)

• Default to False.

• Use True to transpose the last two dimensions of x before multiplication. It has no effect when x is 1-D.

transpose_y: const bool (Optional)

• Default to False.

• Use True to transpose the last two dimensions of y before multiplication. It has no effect when y is 1-D.

Attributes:

T: fp16, fp32, i32

U: fp16, fp32, i32

Returns:

tensor<*, V>

• Scalar or tensor output.

• When x and y are both const or both non-const, it should follow ios15 behavior that x, y, and output all have the same dtype. When one of x and y is const, the output dtype should be the same as the non-const one.

normalization (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.normalization.batch_norm(**kwargs)

Normalize input tensor x by mean and variance, and optionally apply a scale gamma and an offset beta:

\[y_i = \gamma_i \dfrac{ (x_i - mean_i)}{\sqrt{variance_i + epsilon}} + beta_i \;,\;i=1,....,C\]

The mean, variance, gamma, and beta must be 1-D tensors whose lengths are equal to the second axis (the “depth” or “channel” dimension) of x.

Parameters:

x: tensor<[n,C,*D], T> (Required)

• 3 <= rank <= 5.

• *D refers to the spatial dimensions, 1 <= rank(*D) <= 3.

• n is the batch dimension.

mean: const tensor<[C], T> (Required)

variance: const tensor<[C], T> (Required)

gamma: const tensor<[C], T> (Optional)

• Optional scale applied to normalized tensor.

• Default is all ones.

beta: const tensor<[C], T> (Optional)

• Optional offset applied to normalized tensor.

• Default is all zeros.

epsilon: const T (Optional)

• Default is 1e-5.

Attributes:

T: fp16, fp32

Returns:

tensor<[n,C,*D], T>

• Output tensor has the same shape and type as the input x.

class coremltools.converters.mil.mil.ops.defs.iOS15.normalization.instance_norm(**kwargs)

Apply instance normalization to the n-dimensional input tensor.

Parameters:

x: tensor<[n,C,*D], T> (Required)

• 3 <= rank(x) <= 4.

• *D refers to the spatial dimensions, 1 <= rank(*D) <= 2.

• n is the batch dimension.

gamma: const tensor<[C], T> (Optional)

• Optional scale applied to normalized tensor.

• Default to all ones.

beta: const tensor<[C], T> (Optional)

• Optional offset applied to normalized tensor.

• Default to all zeros.

epsilon: const f32 (Optional)

• Default to 1e-5.

Attributes:

T: fp16, fp32

Returns:

tensor<[n,C,*D], T>

• Output tensor has the same shape and type as the input x.

class coremltools.converters.mil.mil.ops.defs.iOS15.normalization.l2_norm(**kwargs)

Apply L2 normalization to the n-dimensional input tensor. That is, divide the input tensor by the square root of the sum of squares of all elements of the input.

\[x_i \leftarrow \dfrac{x_i}{\sqrt{\sum{x_i^2} + \epsilon}}\]

Parameters:

x: tensor<[*B, *D], T> (Required)

• Input tensor, rank(x) >= 3.

• *B refers to the leading dimensions.

• *D refers to the spatial dimensions to be normalized. Must be rank 3: rank(*D) == 3.

• When rank(x) == 3, in which rank(*B) == 0 and rank(*D) == 3, the input is divided by the square root of the sum of squares of all elements.

• For ranks greater than 3, in which rank(*B) >= 1 and rank(*D) == 3, the leading dimensions *B, starting from 0 to -4 (inclusive), are all treated as batch. The L2 normalization are done batch-wise.

epsilon: const T (Optional)

• Small constant to avoid division by 0.

• Optional, defaults to 1e-6.

Attributes:

T: fp16, fp32

Returns:

tensor<[*B, *D], T>

• Same type and shape as the input tensor x.

class coremltools.converters.mil.mil.ops.defs.iOS15.normalization.layer_norm(**kwargs)

Apply layer normalization to the n-dimensional input tensor:

\[out = gamma * (input - E[x]) / sqrt(Var[x] + epsilon) + beta\]

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

axes: const<[K], i32> (Optional)

• Dimensions to perform layer normalization.

• Default is None (all dimensions).

gamma: const tensor<*?, T>, T> (Optional)

• if provided, the shape must be be x.shape[axes]. For instance, if input x with shape (3,4,5,6) and axes = [2,3], gamma must have shape (5,6).

• Default is all ones.

beta: const tensor<*?, T>, T> (Optional)

• Same shape as gamma.

• Default is all zeros.

epsilon: const T (Optional)

• Small constant to avoid division by 0.

• Default is 1e-5.

Attributes:

T: fp16, fp32

Returns:

tensor<*?, T>:

• Tensor with same shape and type as the input tensor x.

class coremltools.converters.mil.mil.ops.defs.iOS15.normalization.local_response_norm(**kwargs)

Apply local response normalization to the n-dimensional input tensor:

\[x_i \leftarrow \dfrac{x_i}{\left ( k + \dfrac{\alpha}{\text{size}} \sum_j x_j^2 \right )^\beta}\]

Parameters:

x: tensor<[n,C,*D], T> (Required)

• Input tensor, 3 <= rank(x) <= 4.

• *D refers to the spatial dimensions, 1 <= rank(*D) <= 2.

• n is the batch dimension.

size: const i32 (Required)

• Amount of neighboring channels to normalize.

alpha: const T (Optional)

• Scale factor.

• Default is 1e-4.

beta: const T (Optional)

• An exponent.

• Default is 0.75.

k: const T (Optional)

• Additive factor.

• Default is 1.0.

Attributes:

T: fp16, fp32

Returns:

tensor<[n,C,*D], T>

• Same type and shape as the input tensor x.

normalization (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.normalization.batch_norm(**kwargs)

Normalize input tensor x by mean and variance, and optionally apply a scale gamma and an offset beta:

\[y_i = \gamma_i \dfrac{ (x_i - mean_i)}{\sqrt{variance_i + epsilon}} + beta_i \;,\;i=1,....,C\]

The difference between this version and the iOS 15 batch_norm is that input/output can have different dtypes from other parameters.

class coremltools.converters.mil.mil.ops.defs.iOS17.normalization.instance_norm(**kwargs)

Apply instance normalization to the n-dimensional input tensor.

The difference between this version and the iOS 15 instance_norm is that input/output can have different dtypes from other parameters.

class coremltools.converters.mil.mil.ops.defs.iOS17.normalization.l2_norm(**kwargs)

Apply L2 normalization to the n-dimensional input tensor. That is, divide the input tensor by the square root of the sum of squares of all elements of the input.

\[x_i \leftarrow \dfrac{x_i}{\sqrt{\sum{x_i^2} + \epsilon}}\]

The difference between this version and the iOS 15 l2_norm is that input/output and epsilon can have different dtypes.

class coremltools.converters.mil.mil.ops.defs.iOS17.normalization.layer_norm(**kwargs)

Apply layer normalization to the n-dimensional input tensor:

\[out = gamma * (input - E[x]) / sqrt(Var[x] + epsilon) + beta\]

The difference between this version and the iOS 15 layer_norm is that input/output can have different dtypes from other parameters.

class coremltools.converters.mil.mil.ops.defs.iOS17.normalization.local_response_norm(**kwargs)

Apply local response normalization to the n-dimensional input tensor:

\[x_i \leftarrow \dfrac{x_i}{\left ( k + \dfrac{\alpha}{\text{size}} \sum_j x_j^2 \right )^\beta}\]

The difference between this version and the iOS 15 local_response_norm is that input/output can have different dtypes from other parameters.

pool

class coremltools.converters.mil.mil.ops.defs.iOS15.pool.avg_pool(**kwargs)

Perform average pooling. Supports 1-D, 2-D, and 3-D pool (1, 2, or 3 spatial dimensions).

Parameters:

x: tensor<[n,C_in, *D_in], T> (Required)

• 3 <= rank <= 5.

• D_in are spatial dimensions, 1 <= len(D_in) <= 3.

• C_in is the number of input channels or depth dimensions.

• n is the batch dimension.

kernel_sizes: const tensor<[K], T> (Required)

• The size of the window for each spatial dimension D_in of the input tensor.

• K == len(D_in)

strides: const tensor<[S],i32> (Optional, default to all 1s)

• Stride along each of the spatial dimensions.

• S == len(D_in).

pad_type: const str (Required)

Must be one of valid, same, custom or same_lower.

• valid: No padding. This is equivalent to custom pad with pad[i] = 0, for all i.

• same : This is equivalent to custom pad with pad[2*i] + pad[2*i+1] = kernel_size[i].

• custom: Specify custom padding in the parameter pad. note that same padding is equivalent to custom padding with pad[2*i] + pad[2*i+1] = kernel_size[i].

• same_lower: Similar to same but the padding will place extra rows/cols on the top/left if the padding amount is odd.

pad: const<[P],i32> (Optional. Default to all 0s)

• pad represents the number of elements to pad before and after each dimension: pad[2*i], pad[2*i+1] are the pad size before and after spatial dimension i.

• P = 2 * len(D_in).

• pad should be specified if and only if pad_type == custom

exclude_padding_from_average: const tensor<[], bool> (Optional, default to False)

• If True, padded values (0s) are excluded from the denominator count when computing the average over the kernel window.

ceil_mode: const<bool>

• Same as PyTorch’s ceil mode.

• ceil is used instead of floor in calculating the output size.

• Optional, defaults to False.

• Only applicable when pad_type is valid or custom.

• When ceil_mode is True, padding must be symmetric; that is, if specified, pad[2*i] == pad[2*i+1] must hold.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C_out, *D_out], T>

• Same rank as x.

• C_out is the number of output channels or depth dimensions.

• When ceil_mode = False:

  • D_out[i] = floor[(D_in[i] + pad[2*i] + pad[2*i+1] - kernel_sizes[i]) / strides[i]] +1, for i = 0, .., len(D_in) - 1 is mathematically the same as (when all parameters involved are integers):

    • D_out[i] = ceil [(D_in[i] + pad[2*i] + pad[2*i+1] - kernel_size[i] - 1) / stride[i]], for i = 0, .., len(D_in) - 1.

    • *D_out is all ones if global_pooling is true.

• When ceil_mode = True:

  • D_out[i] = ceil[(D_in[i] + pad[2*i] + pad[2*i+1] - kernel_sizes[i]) / strides[i]] +1, for i = 0, .., len(D_in) - 1

    • If (D_out[i] - 1) * strides[i] >= D_in[i] + pad[2*i] and (pad[2*i] + pad[2*i+1] > 0) then D_out[i] = D_out[i] - 1.

  • The first equation is same as:

    • D_out[i] = floor[(D_in[i] + pad[2*i] + pad[2*i+1] - kernel_sizes[i] + strides[i] - 1) / strides[i]] +1, for i = 0, .., len(D_in) - 1

See also

l2_pool, max_pool

class coremltools.converters.mil.mil.ops.defs.iOS15.pool.l2_pool(**kwargs)

Perform L2 pooling. Supports 1-D and 2-D pool.

Parameters:

x: tensor<[n,C_in,*D_in], T> (Required)

• Only support 1d and 2d pooling.

• See avg_pool.

kernel_sizes: const tensor<[K], T> (Required)

• See avg_pool.

strides: const tensor<[S],i32> (Optional, default to all 1s)

• See avg_pool.

pad_type: const str (Required)

• See avg_pool.

pad: const<[P],i32> (Optional, default to all 0s)

• See avg_pool.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C_out,*D_out], T>

• See avg_pool.

See also

avg_pool, max_pool

class coremltools.converters.mil.mil.ops.defs.iOS15.pool.max_pool(**kwargs)

Perform max pooling. Supports 1-D, 2-D, and 3-D pool.

Parameters:

x: tensor<[n,C_in,*D_in], T> (Required)

• See avg_pool.

kernel_sizes: const tensor<[K], T> (Required)

• See avg_pool.

strides: const tensor<[S],i32> (Optional, default to all 1s)

• See avg_pool.

pad_type: const str (Required)

• See avg_pool.

pad: const<[P],i32> (Optional, default to all 0s)

• See avg_pool.

ceil_mode: const<bool>

• see avg_pool.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C_out,*D_out], T>

• See avg_pool.

See also

avg_pool, l2_pool

quantization

class coremltools.converters.mil.mil.ops.defs.iOS17.quantization_ops.quantize(**kwargs)

Performs affine/linear quantization on an input tensor.

The original data comes from the first “input”. The other parameters – scale, zero_point, and axis – describe how quantization should occur:

quantized_data = clip(round(input / scale) + zero_point)

Parameters:

input: tensor<SrcT, [1..]> (Required)

zero_point: const tensor<DstT, [0..1]> (Optional)

• The zero_point can be either a scalar or a vector. If not provided, it is assumed to be 0.

• The zero_point follows similar broadcasting rules and size constraints as scale.

scale: const tensor<SrcT, [0..1]> (Required)

• The scale can be either a scalar or a vector.

• If scale is a vector, for implementation, it is broadcasted to the following shape:

  • The rank of scale becomes the same as the rank of the input.

  • Constraint: size(scale-vector) == input.shape[axis].

  • For i == axis, scale.shape[i] == input.shape[i].

  • For i != axis, scale.shape == 1.

  • For example:

    • Assume input.shape = (2, 3, 4, 5) and axis = 1.

    • If scale is a vector, then scale.size needs to be equal to input.shape[axis]; that is, equal to 3.

    • This is broadcasted to (1, 3, 1, 1).

output_dtype: const tensor<string, []> (Required)

• This parameter can take "uint8", "int8" as values.

• The output_dtype value must match the zero_point dtype.

axis: const tensor<int32, []> (Optional)

Attributes:

SrcT: fp16, fp32

DstT: uint8, int8

Returns:

tensor<DstT, [1..]>

class coremltools.converters.mil.mil.ops.defs.iOS17.quantization_ops.dequantize(**kwargs)

Performs dequantization on an input tensor with affine/linear quantization.

The quantized data comes from the first “input”. The other parameters – scale, zero_point, and axis – describe how unquantized values can be extracted from it, using the following equation for affine/linear quantization:

unquantized_data = scale * (input - zero_point)

Parameters:

input: tensor<SrcT, [1..]> (Required)

zero_point: const tensor<SrcT, [0..1]> (Optional)

• The zero_point can be either a scalar or a vector. If not provided, it is assumed to be 0.

• The zero_point follows similar broadcasting rules and size constraints as scale.

scale: const tensor<DstT, [0..1]> (Required)

• The scale can be either a scalar or a vector.

• If scale is a vector, for implementation, it is broadcasted to the following shape:

  • The rank of scale becomes the same as the rank of the input.

  • Constraint: size(scale-vector) == input.shape[axis].

  • For i == axis, scale.shape[i] == input.shape[i].

  • For i != axis, scale.shape == 1.

  • For example:

    • Assume input.shape = (2, 3, 4, 5) and axis = 1.

    • If scale is a vector, then scale.size needs to be equal to input.shape[axis]; that is, equal to 3.

    • This is broadcasted to (1, 3, 1, 1).

axis: const tensor<int32, []> (Optional)

Attributes:

SrcT: uint8, int8

DstT: fp16, fp32

Returns:

tensor<DstT, [1..]>

random

class coremltools.converters.mil.mil.ops.defs.iOS15.random.random_bernoulli(**kwargs)

Returns a tensor with the specified shape, with random values from a Bernoulli distribution.

\[\begin{split}f(k) = \begin{cases}1-p &\text{if } k = 0\\ p &\text{if } k = 1\end{cases}\end{split}\]

for \(k\) in \(\{0, 1\}\).

Parameters:

shape: <K, i32> (Required)

• Target output tensor shape.

• K is the rank of the output tensor. shape[k] > 0 for k = 0,..., K-1.

prob: const<T> (Optional)

• The probability of sampling 1. Defaults to 0.5.

seed: const<i32> (Optional)

• Seed to create a reproducible sequence of values across multiple invokes.

Attributes:

T: fp16, fp32

Returns:

<\*, T>

• A tensor of the given target output shape filled with random values.

See also

random_categorical, random_normal, random_uniform

class coremltools.converters.mil.mil.ops.defs.iOS15.random.random_categorical(**kwargs)

Returns random values from a categorical distribution.

Parameters:

x: <*D_in, T>

• N-dimensional tensor which represents logits (event log-probabilities) or probs (event probabilities) depending on mode. The first N - 1 dimensions specifies distributions, and the last dimension represents a vector of probabilities.

mode: const<str> (Optional)

One of ['logits', 'probs']. Defaults to logits. When set to probs, an element-wise log layer will be added to calculate logits.

size: const<i32> (Optional)

Number of samples to draw. Defaults to 1. When set as 1, it’s categorical distribution. When set larger than 1, it’s actually multinomial distribution by drawing with replacement. It means that when a sample index is drawn, it can be drawn again. The categorical distribution is a special case of the multinomial distribution, giving the probabilities of potential outcomes of a single drawing rather than multiple drawings.

seed: const<i32> (Optional)

Seed to create a reproducible sequence of values across multiple invokes.

Attributes:

T: fp16, fp32

Returns:

<*D_in[:-1] + [size], T>

• A tensor of the given target output shape filled with random values.

See also

random_bernoulli, random_normal, random_uniform

class coremltools.converters.mil.mil.ops.defs.iOS15.random.random_normal(**kwargs)

Returns a tensor with the specified shape, with random values from a normal distribution.

Parameters:

shape: <K, i32> (Required)

• Target output tensor shape.

• K is the rank of the output tensor. shape[k] > 0 for k = 0,..., K-1.

mean: const<T> (Optional)

The mean (center) of the normal distribution. Defaults to 0.0.

stddev: const<T> (Optional)

The standard deviation (width) of the normal distribution. Defaults to 1.0.

seed: const<i32> (Optional)

Seed to create a reproducible sequence of values across multiple invokes.

Attributes:

T: fp16, fp32

Returns:

<\*, T>

• A tensor of the given target output shape filled with random values.

See also

random_categorical, random_bernoulli, random_uniform

class coremltools.converters.mil.mil.ops.defs.iOS15.random.random_uniform(**kwargs)

Returns a tensor with the specified shape with random values from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

\[p(x) = \frac{1}{high - low}\]

For a real number \(x\).

When high == low, values of low will be returned. If high < low, the results are officially undefined and may eventually raise an error.

Parameters:

shape: <K, i32> (Required)

• Target output tensor shape.

• K is the rank of the output tensor. shape[k] > 0 for k = 0,..., K-1.

low: const<T> (Optional)

• Lower boundary of the output interval (inclusive). Defaults to 0.0.

high: const<T> (Optional)

• Upper boundary of the output interval (exclusive). Defaults to 1.0.

seed: const<i32> (Optional)

• Seed to create a reproducible sequence of values across multiple invokes.

Attributes:

T: fp16, fp32

Returns:

<\*, T>

• A tensor of the given target output shape filled with random values.

See also

random_categorical, random_bernoulli, random_normal

recurrent (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.recurrent.gru(**kwargs)

Gated Recurrent Unit (GRU)

\[r_t = \rm{recurrent\_activation}(W_{ir} x_t + b_{ir} + W_{hr} h_{t-1} + b_{hr})\]

\[z_t = \rm{recurrent\_activation}(W_{iz} x_t + b_{iz} + W_{hz} h_{t-1} + b_{hz})\]

\[o_t = \rm{activation}(W_{io} x_t + b_{io} + r_t * W_{ho} h_{t-1} + b_{ho})\]

\[h_t = (1 − z_t) * o_t + z_t * h_{t−1}\]

Where:

• \(W_{i[r|o|z]}\) are state input weights for reset, output and update gate, respectively.

• \(b_{i[r|o|z]}\) are input biases for reset, output and update gate, respectively.

• \(W_{h[r|o|z]}\) are recurrent/hidden weights on hidden state to reset, output, and update gates, respectively.

• \(b_{h[r|o|z]}\) are recurrent/hidden biases on hidden state to reset, output, and update gates, respectively.

• \(h_t\) is the hidden state at time t.

• \(x_t\) is the input at time t.

• \(h_{t-1}\) is the hidden state of the layer at time t-1 or the initial hidden state at time 0.

• \(r_t\), \(o_t\), and \(z_t\) are the reset, new, and update gates, respectively.

• \(*\) is elementwise product.

Parameters:

x: <s, b, I, T> (Required)

• s is the sequence length, b is the batch size, and I is the input dimension.

initial_h: <b, H, T> (Required)

• H denotes hidden size.

weight_ih: const<3*H, I, T> (Required) - Weight matrix

• weigh_ih = [W_{ir} | W_{io} | W_{iz}] where [a|b] denotes column concatenation and [a, b] denotes row concatenation. W_{ir}, W_{io}, and W_{iz} have shape (H, I).

weight_hh: const<3*H, H, T> (Required) - Weight matrix

• weight_hh =  [W_{hr} | W_{ho} | W_{hz}]: W_{hr}, W_{ho}, and W_{hz} have shape (H, H).

bias: const<3*H, T> (Optional) [Default all 0s]

• bias[0] are input-hidden and hidden-hidden bias.

• 3*H are biases for [b_{ir} | b_{io} | b_{hz}].

direction: const<str> (Optional) [Default=forward]

• Either forward or reverse.

output_sequence: const<bool> (Optional) [Default=False]

• Outputs every step if True.

recurrent_activation: const<str> (Optional) [Default=sigmoid]

• Activation applied on update and reset gate.

activation: const<str> (Optional) [Default=tanh]

• Activation applied on output gate.

Attributes:

T: fp32

Returns:

<s, b, H, T> or <1, b, H, T>

• If output_sequence == True (hidden states from every step): <s, b, H, T>.

• Else <1, b, H, T> (hidden states of the final step).

<b, H, T>

• Hidden states of the final step.

class coremltools.converters.mil.mil.ops.defs.iOS15.recurrent.lstm(**kwargs)

Long Short-Term Memory (LSTM)

\[i_t = \rm{recurrent\_activation}(W_{ii} x_t + B_{ii} + W_{hi} h_{t-1} + B_{hi})\]

\[f_t = \rm{recurrent\_activation}(W_{if} x_t + B_{if} + W_{hf} h_{t-1} + B_{hf})\]

\[z_t = \rm{cell\_activation}(W_{iz} x_t + B_{iz} + W_{hz} h_{t-1} + B_{hz})\]

\[o_t = \rm{recurrent\_activation}(W_{io} x_t + B_{io} + W_{ho} h_{t-1} + B_{ho})\]

\[c_t = f_t * c_{t-1} + i_t * z_t\]

\[h_t = o_t * \rm{activation(c_t)}\]

Where:

• \(i_t\), \(f_t\), \(o_t\), and \(z_t\) are input, forget, output, and cell gates, respectively, at time t.

• \(c_t\) is cell state at time t.

• \(h_t\) is the hidden state at time t.

• \(W_{ii}\), \(W_{if}\), \(W_{io}\), and \(W_{iz}\) are input weights for input, forget, output, and cell gate, respectively.

• \(B_{ii}\), \(B_{if}\), \(B_{io}\), and \(B_{iz}\) are input biases for input, forget, output, and cell gate, respectively.

• \(W_{hi}\), \(W_{hf}\), \(W_{ho}\), and \(W_{hz}\) are recurrent weights for input, forget, output, and cell gate, respectively.

• \(B_{hi}\), \(B_{hf}\), \(B_{ho}\), and \(B_{hz}\) are recurrent weights for input, forget, output, and cell gate, respectively.

Parameters:

x: <s, b, I, T> (Required)

• s is the sequence length, b is the batch size, and I is the input dimension.

initial_h: <b, DIRECTIONS*H, T> (Required)

• Initial hidden state. DIRECTIONS = 1 for uni-directional. DIRECTIONS = 2 for bi-directional LSTM.

• H denotes hidden size.

• [b, :H] and [b, H:] represents forward and reverse direction values, respectively.

initial_c: <b, DIRECTIONS*H, T> (Required)

• Initial cell state.

• Format is same as initial_h.

weight_ih: const<4*H, I, T> (Required)

• Input-hidden weight matrix

• Weight tensor should be in order of [input_gate, forget_gate, output_gate, cell_gate].

• If direction==”bidirectional”, this is applied in forward direction.

• If direction==”forward” or “backward” these weights are used.

weight_hh: const<4*H, H, T> (Required)

• Hidden-hidden weight matrix.

• Weight tensor should be in order of [input_gate, forget_gate, output_gate, cell_gate].

• If direction==”bidirectional”, this is applied in forward direction.

• If direction==”forward” or “backward” these weights are used.

bias: const<4*H, T> (Optional, default all 0s)

• bias = input-hidden bias + hidden-hidden bias

• If direction==”bidirectional”, this is applied in forward direction.

• If direction==”forward” or “backward” this bias are used.

peephole: const<3*H, T> (Optional, default all 0s)

• Weight tensor for peephole.

• Order is [input_gate, forget_gate, output_gate].

• Shape of each peephole vector is (H,) (H is hidden size).

• If direction==”bidirectional”, this is applied in forward direction.

• If direction==”forward” or “backward” these weights are used.

weight_ih_back: const<4*H, I, T> (Optional) -

• Input-hidden weight matrix for backward direction for bidirectinal LSTM.

• Weight tensor should be in order of [input_gate, forget_gate, output_gate, cell_gate].

• Must be provided for bidirectional LSTM.

• This is only used when direction is “bidirectional”.

• For direction=”reverse” use weight_ih instead.

weight_hh_back: const<4*H, H, T> (Optional) - Hidden-hidden weight matrix

• Hidden-hidden weight matrix for backward direction for bidirectinal LSTM.

• Weight tensor should be in order of [input_gate, forget_gate, output_gate, cell_gate].

• Must be provided for bidirectional LSTM.

• This is only used when direction is “bidirectional”.

• For direction=”reverse” use weight_hh instead.

bias_back: const<4*H, T> (Optional, default all 0s)

• bias = input-hidden bias + hidden-hidden bias.

• Bias of backward direction for bidirectional lstm

• This is only used when direction is “bidirectional”.

• For direction=”reverse” use bias instead.

peephole_back: const<3*H, T> (Optional, default all 0s)

• Weight tensor for peephole in backward direction for bidirectional LSTM.

• Order is [input_gate, forget_gate, output_gate].

• Shape of each peephole vector is (H,) (H is hidden size).

• Peephole of backward direction for bidirectional lstm

• Bias of backward direction for bidirectional lstm

• This is only used when direction is “bidirectional”.

• For direction=”reverse” use peephole instead.

direction: const<str> (Optional) [Default=forward]

• One of the following: forward, reverse, or bidirectional.

• Must match DIRECTIONAL in initial states and weight parameters.

output_sequence: const<bool> (Optional) [Default=False]

• Outputs every step if True.

recurrent_activation: const<str> (Optional) [Default=sigmoid]

• Activation applied on input, forget, and output gates.

• Supported values: hard_sigmoid, linear, relu, scaled_tanh, sigmoid, tanh

cell_activation: const<str> (Optional) [Default=tanh]

• Activation applied on cell gate.

• Supported values: hard_sigmoid, linear, relu, scaled_tanh, sigmoid, tanh

activation: const<str> (Optional) [Default=tanh]

• Activation applied on output gate.

• Supported values: hard_sigmoid, linear, relu, scaled_tanh, sigmoid, tanh

clip: const<T> (optional) [Default=None]

• Cell gate is clipped to [-clip, +clip].

Attributes:

T: fp32

Returns:

<s, b, DIRECTIONS*H, T> or <1, b, DIRECTIONS*H, T>

• If output_sequence == True (hidden states from every step): <s, b, DIRECTIONS*H, T>.

• Else <1, b, DIRECTIONS*H, T> (hidden states of the final step).

<b, DIRECTIONS*H, T>

• Hidden states of the final step.

<b, DIRECTIONS*H, T>

• Memory state of the final step.

class coremltools.converters.mil.mil.ops.defs.iOS15.recurrent.rnn(**kwargs)

Recurrent Neural Network (RNN)

\[h_t = \rm{activation}(W_{ih} x_t + b_{ih} + W_{hh} h_{t−1} + b_{hh})\]

Where:

• \(W_{ih}\) is the input weight.

• \(W_{hh}\) is the hidden/recurrent weight.

• \(h_t\) is the hidden state at time t.

• \(x_t\) is the input at time t.

• \(h_{t-1}\) is the hidden state of the layer at time t-1 or the initial hidden state at t = 0.

• \(b_{ih}\) is the input bias.

• \(b_{hh}\) if the hidden/recurrent bias.

Parameters:

x: <s, b, I, T> (Required)

• s is the sequence length, b is the batch size, and I is the input dimension.

initial_h: <b, H, T> (Required)

• H denotes hidden size.

weight_ih: const<H, I, T> (Required) - Input-hidden weight matrix

weight_hh: const<H, H, T> (Required) - Hidden-hidden weight matrix

bias: const<H, T> (Optional) [Default all 0s]

• bias for input-hidden and hidden-hidden

direction: const<str> (Optional) [Default=forward]

• Either forward or reverse.

output_sequence: const<bool> (Optional) [Default=False]

• Outputs every step if True.

activation: const<str> (Optional) [Default=tanh]

• Supported activation functions: relu, tanh, sigmoid, sigmoid_hard, scaled_tanh, and linear.

Attributes:

T: fp32

Returns:

<s, b, H, T> or <1, b, H, T>

• If output_sequence == True (hidden states from every step): <s, b, H, T>.

• Else <1, b, H, T> (hidden states of the final step).

<b, H, T>

• Hidden states of the final step.

recurrent (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.recurrent.gru(**kwargs)

Gated Recurrent Unit (GRU)

The only difference between this version and the iOS 15 gru is adding the support for fp16.

class coremltools.converters.mil.mil.ops.defs.iOS17.recurrent.lstm(**kwargs)

Long Short-Term Memory (LSTM)

The only difference between this version and the iOS 15 lstm is adding the support for fp16.

class coremltools.converters.mil.mil.ops.defs.iOS17.recurrent.rnn(**kwargs)

Recurrent Neural Network (RNN)

The only difference between this version and the iOS 15 rnn is adding the support for fp16.

recurrent (iOS 18+)

class coremltools.converters.mil.mil.ops.defs.iOS18.recurrent.gru(**kwargs)

Gated Recurrent Unit (GRU)

Two new parameters have been added to the iOS 17 gru.

reset_after - this parameter is optional and defaults to False. When True, the reset gate is applied before the elementwise matrix multiplication.

input_bias - const<3*H, T> (Optional) [Default all 0s].

reduction (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_argmax(**kwargs)

Computes the indices of the maximum value across dimensions of a tensor. In case of ties, the identity of the return value is not guaranteed.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axis: const<i32> (Optional)

• The dimension to reduce. Default is -1.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 by removing the dimension specified in axis. If True, retain reduced axis with length 1.

Attributes:

T: fp16, fp32, i32

Returns:

<*, int32>

References

See tf.math.argmax.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_argmin(**kwargs)

Computes the indices of the minimum value across dimensions of a tensor. In case of ties, the identity of the return value is not guaranteed.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axis: const<i32> (Optional)

• The dimension to reduce. Default is -1.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 by removing the dimension specified in axis, otherwise retain reduced axis with length 1.

Attributes:

T: fp16, fp32, i32

Returns:

<*, int32>

References

See tf.math.argmin.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_l1_norm(**kwargs)

Computes the L1 normalization of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K, i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

References

See reduce_mean.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_l2_norm(**kwargs)

Computes the L2 normalization of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K, i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_log_sum(**kwargs)

Computes the natural logarithm of the sum of all the elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K, i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_log_sum_exp(**kwargs)

Computes the natural logarithm of the sum of the exponentials of the elements across given dimensions of the input tensor. It is a smooth approximation of the maximum function, more numerically stable than log(sum(exp(input))). It avoids overflows caused by taking the exp of large inputs and underflows caused by taking the log of small inputs.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

References

See tf.math.reduce_logsumexp.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_max(**kwargs)

Computes the maximum of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_mean(**kwargs)

Computes the mean of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

References

For an example, see tf.math.reduce_mean.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_min(**kwargs)

Computes the minimum of elements across given dimensions of the input tensor.

Parameters:

x: <*,T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*,T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_prod(**kwargs)

Computes the product of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_sum(**kwargs)

Computes the sum of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.reduction.reduce_sum_square(**kwargs)

Computes the sum of squares of elements across given dimensions of the input tensor.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axes: const<K,i32> (Optional, default=”None”, reduce on all axes.)

• The dimensions to reduce.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 for each entry in axes, otherwise retain reduced axes with length 1.

Attributes:

T: i32, fp16, fp32

Returns:

<*, T>

• Scalar or tensor: The reduced tensor.

reduction (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.reduction.reduce_argmax(**kwargs)

Computes the indices of the maximum value across dimensions of a tensor. In case of ties, the identity of the return value is not guaranteed.

The differences between this version and the iOS 15 reduce_argmax are:

• The output supports uint16 dtype.

• New optional input output_dtype.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axis: const<i32> (Optional)

• The dimension to reduce. Default is -1.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 by removing the dimension specified in axis.

• If True, retain reduced axis with length 1.

output_dtype: const<str> (Optional)

• Possible values: uint16, int32.

• If set, then value type inference will output using that dtype.

• Default is int32.

Attributes:

T: fp16, fp32, i32

U: int32, uint16

Returns:

<*, U>

class coremltools.converters.mil.mil.ops.defs.iOS17.reduction.reduce_argmin(**kwargs)

Computes the indices of the minimum value across dimensions of a tensor. In case of ties, the identity of the return value is not guaranteed.

The differences between this version and the iOS 15 reduce_argmin are:

• The output supports uint16 dtype.

• New optional input output_dtype.

Parameters:

x: <*, T> (Required)

• Must be 1-dimensional or higher.

axis: const<i32> (Optional)

• The dimension to reduce. Default is -1.

keep_dims: const<bool> (Optional, default=False)

• If False, the rank is reduced by 1 by removing the dimension specified in axis, otherwise retain reduced axis with length 1.

output_dtype: const<str> (Optional)

• Possible values: uint16, int32.

• If set, then value type inference will output using that dtype.

• Default is int32.

Attributes:

T: fp16, fp32, i32

U: int32, uint16

Returns:

<*, U>

scatter_gather (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.gather(**kwargs)

Gather slices from input x along dimension axis according to indices, similar to tf.gather.

• If indices is scalar (0-D):

\[output[p_0, ..., p_{axis-1}, ~~~~~~~~~~~~~~~~~~~~~~~~ p_{axis+1}, ..., p_{rank(x)-1}] =\]

\[x[p_0, ..., p_{axis-1}, ~~~~~~~~~ indices, ~~~~~~~~ p_{axis+1}, ..., p_{rank(x)-1}]\]

Where rank(x) is the rank of x. The output has rank rank(x) - 1.

• If indices is 1-D tensor:

\[output[p_0, ..., p_{axis-1}, ~~~~~~~~~~~~~ i, ~~~~~~~~~~~~~ p_{axis+1}, ..., p_{rank(*D)-1}] =\]

\[x[p_0, ..., p_{axis-1}, ~~~~~~~~ indices[i], ~~~~~~~~ p_{axis+1}, ..., p_{rank(*D)-1}]\]

The output has rank rank(x).

• In general:

\[output[p_0, ..., p_{axis-1}, ~~~~~~~~ i_0, ..., i_{M-1}, ~~~~~~~~ p_{axis+1}, ..., p_{rank(x)-1}] =\]

\[x[p_0, ..., p_{axis-1}, ~~~~~~~ indices[i_0, ..., i_{M-1}], ~~~~~~~ p_{axis+1}, ..., p_{rank(x)-1}]\]

Where M = rank(indices).

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*N, i32> (Required)

• Indices values may be negative. More precisely, -D[axis]<= v < D[axis] for v in indices.

axis: const i32 (Optional. Default=``0``)

• Negative axis is supported.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*K, T>

• Where K = D[:axis] + N + D[axis+1:].

References

See tf.gather.

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.gather_along_axis(**kwargs)

Take the values along axis at locations indices.

\[idx = indices[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D]\]

\[output[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D] = = x[p_0, ..., p_{axis-1}, idx, p_{axis+1}, ..., p_D]\]

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*K, i32> (Required)

• rank(indices) == rank(x).

axis: const i32 (Optional):

• Default to 0.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>:

• Output tensor has the same shape as indices.

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.gather_nd(**kwargs)

Gather slices from x according to indices, similar to tf.gather_nd.

The indices is a K-dim tensor, where indices[i_0,...,i_{K-2}] defines a slice of x:

\[output[i_0, ..., i_{K-2}]= x[indices[i_0, ..., i_{K-2}]]\]

Where K = rank(indices) and x[indices[i_0, ..., i_{K-2}]] has rank rank(x) - indices.shape[-1].

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*K, i32> (Required)

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*V, T>

• V = K[:-1] + D[K[-1]:], where D = x.shape and K = indices.shape.

References

See tf.gather_nd.

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.scatter(**kwargs)

Scatter updates to data at locations indices at dimension axis by operation mode.

Example: mode == update.

• For i in [0, len(indices)]:

\[output[p_0, ..., p_{axis-1}, indice[i], p_{axis+1}, ..., p_D] =\]

\[updates[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D]\]

• For j != i:

\[output[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D] =\]

\[data[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D]\]

Example: mode == add.

• For i in [0, len(indices)]:

\[output[p_0, ..., p_{axis-1}, indice[i], p_{axis+1}, ..., p_D] =\]

\[updates[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D] +\]

\[x[p_0, ..., p_{axis-1}, indice[i], p_{axis+1}, ..., p_D]\]

• For j != i:

\[output[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D] =\]

\[data[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D]\]

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<[C], i32> (Required)

• 1-D tensor.

updates: tensor<*K, T> (Required)

• K = data.shape[:axis] + [len(indices)] + data.shape[axis+1:].

axis: const i32 (Optional)

• Default to 0.

mode: const string (Optional)

• Can be the following modes: update, add, sub, mul, div, max, min.

• Default value is update.

Attributes:

T: fp16, fp32, i32

For example:

data = [[1, 2, 3], [4, 5, 6]] indices = [1, 0] updates = [[5, 6, 7], [8, 9, 10]] axis = 0 mode = “add”

produces:

[[9, 11, 13], [9, 11, 13]]

Returns:

tensor<*D, T>

• With the same type and shape as input x.

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.scatter_along_axis(**kwargs)

Scatter updates to data at locations indices along axis dimension using mode operation.

Example: mode == update.

• For i in [0, len(indices)]:

\[idx = indices[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D]\]

\[output[p_0, ..., p_{axis-1}, idx, p_{axis+1}, ..., p_D] =\]

\[updates[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D]\]

• For j! = i:

\[output[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D] =\]

\[data[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D]\]

Example: mode == add.

• For i in [0, len(indices)]:

\[idx = indices[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D]\]

\[output[p_0, ..., p_{axis-1}, idx, p_{axis+1}, ..., p_D] =\]

\[updates[p_0, ..., p_{axis-1}, i, p_{axis+1}, ..., p_D] +\]

\[x[p_0, ..., p_{axis-1}, indice[i], p_{axis+1}, ..., p_D]\]

• For j! = i:

\[output[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D] =\]

\[data[p_0, ..., p_{axis-1}, j, p_{axis+1}, ..., p_D]\]

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<*K, i32> (Required)

• rank(indices) == rank(data).

updates: tensor<*K, T> (Required)

• Must be the same shape as indices.

axis: const i32 (Optional)

• Default to 0.

mode: const string (Optional)

• Default to add.

• Can be the following modes: update, add, sub, mul, div, max, min.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>

• With the same type and shape as input x.

class coremltools.converters.mil.mil.ops.defs.iOS15.scatter_gather.scatter_nd(**kwargs)

Scatter updates to data at locations indices.

The indices is a K-dim tensor, where indices[i_0,...,i_{K-2}] defines a slice of data, K = rank(indices), and data[i_0, ..., i_{K-2}] has rank rank(data) - indices.shape[-1]. Concretely, this means the index is stored in the last dim of indices, e.g. take a K == 2 example

\[indices = [[0, 1], [0, 2]]\]

where indices[0] / [0, 1] and indices[1] / [0, 2] are two indices that get applied to data

• Example: mode == update: The output is set to data initially, and the op updates output as follows:

\[output[indices[i_0, ..., i_{K-2}]]= updates[i_0, ..., i_{K-2}]\]

• Example: mode == add. The update rule is:

\[output[indices[i_0, ..., i_{K-2}]] += updates[i_0, ..., i_{K-2}]\]

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<*E, i32> (Required)

• indices.shape[-1] <= data.rank

updates: tensor<*F, T> (Required)

• Must be the shape as indices.shape[:-1] + data.shape[indices.shape[-1]:].

mode: const string (Optional)

• Default to add.

• Can be the following modes: update, add, sub, mul, div, max, min.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>

• A tensor with the same shape and type as data.

scatter_gather (iOS 16+)

class coremltools.converters.mil.mil.ops.defs.iOS16.scatter_gather.gather(**kwargs)

The iOS16 version. This section documents only the differences between this version and the iOS 15 gather.

This version supports batch_dims, similar to tf.gather. Input parameter indices now supports int16 and uint16.

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*N, I> (Required)

• Indices values may be negative. More precisely, -D[axis]<= v < D[axis] for v in indices.

axis: const i32 (Optional. Default=``0``)

• Negative axis is supported.

batch_dims: const i32 (Optional. Default=``0``)

• The number of batch dimensions.

Attributes:

T: fp16, fp32, i32

I: uint16, int16, int32

Returns:

tensor<*K, T>

• Where K = D[:axis] + N[batch_dims:] + D[axis+1:].

References

See tf.gather.

class coremltools.converters.mil.mil.ops.defs.iOS16.scatter_gather.gather_nd(**kwargs)

The iOS16 version. This section documents only the differences between this version and the iOS 15 gather_nd.

This version supports batch_dims. Input parameter indices now supports int16 and uint16.

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*K, I> (Required)

batch_dims: const i32 (Optional. Default=``0``)

• The number of batch dimensions.

Attributes:

T: fp16, fp32, i32

I: uint16, int16, int32

Returns:

tensor<*V, T>

• V = K[:-1] + D[batch_dims + K[-1]:], where D = x.shape and K = indices.shape.

References

See tf.gather_nd.

scatter_gather (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.gather(**kwargs)

Gather slices from input x along dimension axis according to indices, similar to tf.gather_nd.

This section documents only the differences between this version and the iOS 16 gather. The major differences are as follows:

• Input parameter x adds support for int16, uint16, int8, and uint8.

• Input parameter indices adds support for int8 and uint8.

• Input parameter indices now supports only positive values – negative values are considered out-of-bound. If support for negative indices is required, they must be explicitly converted to positive values, using the following:

  index = iOS17.select(index >= 0, index, index + max_index)
  

• New input parameter called validate_indices has been added to all gather ops. Its behavior is as follows:

  • If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

  • If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*N, I> (Required)

• Indices values may be negative. More precisely, -D[axis]<= v < D[axis] for v in indices.

axis: const i32 (Optional. Default=``0``)

• Negative axis is supported.

batch_dims: const i32 (Optional. Default=``0``)

• The number of batch dimensions.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, int32, int16, uint16, int8, uint8

I: int32, int16, uint16, int8, uint8

Returns:

tensor<*K, T>

• Where K = D[:axis] + N[batch_dims:] + D[axis+1:].

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.gather_along_axis(**kwargs)

Take the values along axis at locations indices.

The major differences from the previous version are illustrated in gather. For more information, see the iOS 16 gather_along_axis.

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*K, I> (Required)

• rank(indices) == rank(x).

axis: const i32 (Optional):

• Default to 0.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, int32, int16, uint16, int8, uint8

I: int32, int16, uint16, int8, uint8

Returns:

tensor<*D, T>:

• Output tensor has the same shape as indices.

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.gather_nd(**kwargs)

Gather slices from x according to indices, similar to tf.gather_nd.

The major differences from the previous version are illustrated in gather. For more information, see the iOS 16 gather_nd.

Parameters:

x: tensor<*D, T> (Required)

indices: tensor<*K, I> (Required)

batch_dims: const i32 (Optional. Default=``0``)

• The number of batch dimensions.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, int32, int16, uint16, int8, uint8

I: int32, int16, uint16, int8, uint8

Returns:

tensor<*V, T>

• V = K[:-1] + D[batch_dims + K[-1]:], where D = x.shape and K = indices.shape.

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.scatter(**kwargs)

Scatter updates to data at locations indices at dimension axis by the operation mode.

This section documents only the differences between this version and the iOS 15 scatter. The major differences are as follows:

• Input parameter indices now supports only positive values – negative values are considered out-of-bound. If support for negative indices is required, they must be explicitly converted to positive values using the following:

  index = iOS17.select(index >= 0, index, index + max_index)
  

• New input parameter called validate_indices has been added to all scatter ops. Its behavior is as follows:

  • If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

  • If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<[C], i32> (Required)

• 1-D tensor.

updates: tensor<*K, T> (Required)

• K = data.shape[:axis] + [len(indices)] + data.shape[axis+1:].

axis: const i32 (Optional)

• Default to 0.

mode: const string (Optional)

• Can be the following modes: add, div, max, min, mul, sub, update.

• Default value is update.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>

• With the same type and shape as input x.

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.scatter_along_axis(**kwargs)

Scatter updates to data at locations indices along axis dimension using the mode operation.

The major differences from the previous version are illustrated in scatter. For more information, see the iOS 15 scatter_along_axis.

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<*K, i32> (Required)

• rank(indices) == rank(data).

updates: tensor<*K, T> (Required)

• Must be the same shape as indices.

axis: const i32 (Optional)

• Default to 0.

mode: const string (Optional)

• Default to add.

• Can be the following modes: add, div, max, min, mul, sub, update.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>

• With the same type and shape as input x.

class coremltools.converters.mil.mil.ops.defs.iOS17.scatter_gather.scatter_nd(**kwargs)

Scatter updates to data at locations indices.

The major differences from the previous version are illustrated in scatter. For more information, see the iOS 15 scatter_nd.

Parameters:

data: tensor<*D, T> (Required)

indices: tensor<*K, i32> (Required)

updates: tensor<*K, T> (Required)

• Must be the shape as K[:-1]+data.shape[K[-1]:].

mode: const string (Optional)

• Default to add.

• Can be the following modes: add, div, max, min, mul, sub, update.

validate_indices: const bool (Optional)

• If True, it raises a runtime (possibly also a compile-time) exception for out-of-bound values of the indices parameter.

• If False, absolutely no checking is performed for out-of-bound values of indices either at compile or runtime. Behavior for out-of-bound indices is undefined but memory safe.

• Default value is False.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*D, T>

• A tensor with the same shape and type as data.

states (iOS 18+)

class coremltools.converters.mil.mil.ops.defs.iOS18.states.read_state(**kwargs)

Read a state, copy its content into a new variable, and return the variable. The type of the output variable depends on the type that is wrapped inside the state, which could be types.tensor.

Parameters:

input: state<ST> (Required)

Attributes:

ST: tensor

Returns:

ST

tensor_operation (iOS 15+)

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.argsort(**kwargs)

Returns a tensor containing the indices of the sorted values along a given axis of the input tensor.

Parameters:

x: <*?, T> (Required)

• Input tensor.

* axis: const<i32> (Optional)

• Defaults to -1 (the last dimension).

• Axis to perform the operation.

* ascending: const<bool> (Optional)

• Defaults to False, sort in descending order.

• True to sort in ascending order.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, int32>

• Tensor containing the indices of the sorted values

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.band_part(**kwargs)

Returns a tensor setting everything outside a center band to zeros for the innermost matrix. That is, band(m, n) = (lower < 0 || (m-n) <= lower) && (upper < 0 || (n-m) <= upper) output[i, j, k, …, m, n] = band(m, n) * input[i, j, k, …, m, n]

Special cases:

• band_part(x, 0, -1) returns upper triangular part.

• band_part(x, -1, 0) returns lower triangular part.

• band_part(x, 0, 0) returns diagonal.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

lower: const<i32> (Optional)

• Number of lower / below sub-diagonals to keep. If negative, keep entire lower triangle.

• Defaults to -1 (keep the entire lower triangle).

upper: const<i32> (Optional)

• Number of upper / above sub-diagonals to keep. If negative, keep entire lower triangle.

• Defaults to -1 (keep the entire upper triangle).

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.concat(**kwargs)

Concatenates tensors along a dimension.

Parameters:

values: Tuple[tensor<[d0, d1, …, d_axis_i, …, d_n],T>] (Required)

• The number of dimensions of the input tensors must match, and all dimensions except axis must be equal.

• The tensors may be variadic, but the number of tensors must be determined at compile time (i.e. a tuple).

axis: const<int32> (Required)

• The dimension along which to concatenate. Must be in the range [-rank(values[i]), rank(values[i])) for all i.

interleave: const<bool> (Optional, Default=False)

• If True, concatenate the inputs by interleaving them.

• If True, all the inputs to this op must have the exact same shape.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<[d0, d1,…d_axis_out, …, d_n],T>

• Where d_axis_out = sum(d_axis_i).

Examples

in1 = [[1, 2], [3, 4], [5, 6]]  # shape (3, 2)
in2 = [[7, 8], [9, 10], [11, 12]]  # shape (3, 2)
axis = 0  # output shape is (6, 2)

if interleave is False:  # default
    # output[0:3, :] = in1
    # output[3:6, :] = in2
    output = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]]

if interleave is True:
    # output[0::2, :] = in1
    # output[1::2, :] = in2
    output = [[1, 2], [7, 8], [3, 4], [9, 10], [5, 6], [11, 12]]

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.cumsum(**kwargs)

Returns the cumulative sum of the input along the given axis.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

axis: const<i32> (Optional)

• Defaults to 0.

• Axis for which the cumulative sum is computed.

exclusive: const<bool> (Optional)

• Defaults to False.

• When set to False, inclusive cumsum is computed, that is the first element of the output is identical to the first element in the input.

• When set to True, exclusive cumsum is computed, which makes the first element of output to 0.

reverse: const<bool> (Optional)

• Defaults to False.

• When set to True, perform cumsum in the reverse order.

Attributes:

T: fp16, fp32, i32

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.fill(**kwargs)

Returns a tensor with a given shape filled with a constant value.

Parameters:

shape: tensor<[K], i32> (Required)

• Target output tensor shape.

• K is the rank of the output tensor. shape[k] > 0 for k = 0,..., K-1.

value: const<T> (Optional)

• Defaults to 0.0.

• Constant value to fill in.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Tensor with shape determined by the input shape.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.flatten2d(**kwargs)

Flattens input tensor into 2d tensor by flattening dimensions before and after the provided axis.

Parameters:

x: tensor<[*d], T> (Required)

• Input tensor.

axis: const<i32> (Optional)

• Defaults to 1.

• Negative axis is supported.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<d_prior, d_post, T>

• d_prior is product of dimensions x[:axis]

• d_post is product of dimensions x[axis:]

Examples

1. input_shape = (3, ), axis = -1, output_shape = (1, 3)

2. input_shape = (3, ), axis = 1, output_shape = (3, 1)

3. input_shape = (4, 3), axis = -1, output_shape = (4, 3)

4. input_shape = (2, 3, 2), axis = -1, output_shape = (6, 2)

5. input_shape = (5, 5, 2), axis = 1, output_shape = (5, 10)

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.identity(**kwargs)

Returns a tensor with the same shape and contents as input.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.non_maximum_suppression(**kwargs)

Applies non-maximum suppression (NMS) on the input box coordinates according to their intersection-over-union (IoU).

NMS selects a subset of bounding boxes in descending order of score, and removes boxes that have high intersection-over-union (IOU) overlap with previously-selected boxes.

Parameters:

boxes: tensor<[n, B, 4], T> (Required)

• Box coordinates on which to perform NMS. The coordinates are expected in CENTER_SIZE_WIDTH_FIRST format (x, y, width, height) where (x, y) is the center.

scores: tensor<[n, B, K], T> (Required)

• Scores for each one of the boxes. K is the number of classes.

iou_threshold: const<T> (Required)

• The intersection over union (IoU) threshold over which boxes are suppressed. NMS remove all overlapping boxes with IoU > iou_threshold.

score_threshold: const<T> (Required)

• Before IoU suppression is performed, boxes with class scores below this threshold are rejected.

max_boxes: const<i32> (Required)

• Maximum number of boxes to select. If the number of surviving boxes are less, output is padded up to this number.

per_class_suppression: const<bool> (Optional)

• Defaults to False.

• If True, suppression is performed independently within boxes of each class.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, max_boxes, 4], T>

• Coordinates of selected boxes.

tensor<[n, max_boxes, K], T>

• Scores of selected boxes.

tensor<[n, max_boxes], i32>

• Indices of selected boxes.

tensor<[n], i32>

• Number of boxes selected for each batch.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.non_zero(**kwargs)

Returns the indices of the elements in the given tensor that are non-zero.

Parameters:

x: tensor<*?, T> (Required)

• Tensor, values selected at indices where its values is not equal to 0.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<[N, R], int32>

• 2-dimensional tensor contains indices of elements that are non-zero. Each row is the index for a non-zero value.

• N is the number of non-zero elements, R is the rank of the input.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.one_hot(**kwargs)

Returns one-hot vectors whose locations represented in indices take the on_value, while other locations take the off_value.

Parameters:

indices: tensor<[D], i32> (Required)

• Tensor, values indicate the locations for each one-hot vector to take the on_value.

one_hot_vector_size: i32 (Required)

• Indicates the number of returning vectors.

axis: const i32 (Optional)

• Indicates which dimension to append the new axis.

• If the input indices is rank D, the output tensor will have rank D+1.

• Defaults to -1 (the last dimension).

on_value: const T (Optional)

• Values for locations where defined in indices.

• Defaults to 1.

off_value: const T (Optional)

• Defaults to 0.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• A tensor that contains one-hot vectors.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.pad(**kwargs)

Pads a tensor.

Parameters:

x: tensor<[*D_in], T> (Required)

pad: tensor<[2*N], i32> (Required)

N <= D_in. Last N dimensions of x are padded as follows:

• For each dimension i of x if i >= D_in - N:

  • pad pad[2*i] elements before x[..,i,..].

  • pad pad[2*i+1] elements after x[..,i,..].

• If mode is “reflect” then pad[2*i] and pad[2*i+1] can be at most D[i]-1.

• If mode is “replicate” then pad[2*i] and pad[2*i+1] can be at most D[i].

• If pad is not a constant, it must be a vector of length 2 * rank(x), that is, N == D_in.

mode: const<str> (Optional)

• Defaults to constant.

• Must be one of the following values: constant, reflect, or replicate.

constant_val: const<T> (Optional)

• Defaults to 0.

• Constant value to pad. Ignored if mode != constant.

Attributes:

T: fp16, fp32

Returns:

tensor<[*D_out], T>

• Tensor with same type as the input.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.range_1d(**kwargs)

Returns a numpy-like 1-D range sequence.

Parameters:

start: <T> (Required)

• The start point of the sequence.

end: <T> (Required)

• The upper limit of the sequence, exclusive.

step: <T> (Required)

• Number that increments start.

Attributes:

T: i32, fp16, fp32

Returns:

tensor<M, T>

• A 1-D tensor, where M is the length of the sequence.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.shape(**kwargs)

Returns a 1-dimensional tensor with the shape of the input tensor.

Parameters:

x: tensor<[*?], T> (Required)

• Input tensor.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<K, i32>

• Shape of the input tensor.

• K = x.rank.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.split(**kwargs)

Split tensors into a tuple

Parameters:

x: <*?, T> (Required)

• The tensor to split.

• The tensors may be variadic, but the number of tensors must be determined at compile time (i.e. a tuple).

axis: const<i32> (Required)

• The dimension along which to concatenate. Must be in the range [-rank(x), rank(x)).

num_splits: <i32> (Optional)

If specified, divide x into num_splits tensors along axis. Its behavior depends on split_sizes:

• If split_sizes is defined, num_splits == S, and the output sizes may be uneven.

• If split_sizes is not defined, value.shape[axis] must be divisible by num_splits, and the output sizes must be even.

At least one of num_splits or split_sizes must be provided. If split_sizes length S cannot be determined at compile time, num_splits must be supplied to determine the number of outputs.

split_sizes: const<S, i32> (Optional)

• Sizes to split to. The sum of split_sizes must equal to value.shape[axis].

Attributes:

T: fp16, fp32, i32, bool

Returns:

Tuple[tensor<*?, T>]

• Where the length of the tuple is the number of splits (determined from num_splits or split_sizes).

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.stack(**kwargs)

Concatenates tensors along a dimension.

Parameters:

values: Tuple[tensor<[d0, d1,…d_axis_i, …, d_n], T>] (Required)

• All tensors must have identical shape.

axis: const<i32> (Required)

• The dimension along which to concatenate. Must be in the range [-rank(values[i]), rank(values[i])) for all i.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tenor<[d0, d1,…d_axis_out, …, d_n], T>

• Where d_axis_out = sum(d_axis_i).

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.tile(**kwargs)

Returns a new tensor by replicating input x multiples times. Dimension i of x will be replicated reps[i] times.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

reps: tensor<[rank(x)], i32> (Required)

• A 1-D tensor with length rank(x), which indicates the number to replicate the input along each dimension.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>:

• An n-D tensor with same type as the input.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_operation.topk(**kwargs)

Returns a tensor containing top or bottom k values and the corresponding indices of the input tensor along a given axis.

Parameters:

x: <*?, T> (Required)

• Input tensor.

k: const<i32> (Optional)

• Defaults to 1.

• Number of values/indices to be computed along each axis.

axis: const<i32> (Optional)

• Defaults to -1 (last dimension).

• Axis to perform the operation.

ascending: const<bool> (Optional)

• Defaults to False, sort in descending order.

• True to sort in ascending order.

Attributes:

T: fp16, fp32, int32

Returns:

tensor<*?, T>

• Values of top/bottom k elements.

tensor<*?, int32>

• Indices of the top/bottom k elements along axis.

tensor_operation (iOS 16+)

class coremltools.converters.mil.mil.ops.defs.iOS16.tensor_operation.fill_like(**kwargs)

Returns a tensor with the same shape as the input tensor filled with a constant value.

Parameters:

ref_tensor: tensor<*?, T> (Required)

• Input tensor.

value: const<U> (Optional)

• Default is 0.0.

• Constant value to fill in.

Attributes:

T: fp16, fp32, int32, bool

U: fp16, fp32, int32, bool

Returns:

tensor<*?, T>

• Tensor with shape determined by the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS16.tensor_operation.topk(**kwargs)

A version of topk for iOS 16+. This section documents the differences. The following are additional parameters for the iOS 16+ version. For the rest of the documentation, see the iOS 15 version of topk.

Parameters:

sort: const<bool> (Optional)

• Defaults to True.

• If True, top-k elements are themselves sorted. Otherwise, no particular ordering is guaranteed.

return_indices: const<bool> (Optional)

• Defaults to True.

• If True, returns both values and indices. Otherwise, returns only the top-k values.

Attributes:

T: fp32, int32

Returns:

tensor<*?, T>

• Values of top/bottom k elements.

tensor<*?, int32>

• Only returned when return_indices = True

• Indices of the top/bottom k elements along axis.

tensor_operation (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_operation.non_maximum_suppression(**kwargs)

Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).

NMS iteratively removes lower-scoring boxes which have an IoU greater than iou_threshold with another (higher-scoring) box.

The major differences between this version and the iOS 15 non_maximum_suppression are as follows:

• The input parameter score_threshold has been removed.

• The inputs boxes and scores are ordered with number of boxes in the last dimension.

• The fourth output containing number of boxes for each batch has been removed.

Parameters:

boxes: tensor<[n, 4, B], T> (Required)

• Box coordinates on which to perform NMS. The coordinates are expected in CENTER_SIZE_WIDTH_FIRST format (x, y, width, height), in which (x, y) is the center.

scores: tensor<[n, K, B], T> (Required)

• Scores for each one of the boxes. K is the number of classes.

iou_threshold: const<T> (Required)

• The intersection over union (IoU) threshold over which boxes are suppressed. NMS remove all overlapping boxes with IoU > iou_threshold.

max_boxes: const<i32> (Required)

• Maximum number of boxes to select. If the number of surviving boxes are less, the output is padded up to this number.

per_class_suppression: const<bool> (Optional)

• Defaults to False.

• If True, suppression is performed independently within boxes of each class.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, 4, max_boxes], T>

• Coordinates of selected boxes.

tensor<[n, K, max_boxes], T>

• Scores of selected boxes.

tensor<[n, max_boxes], i32>

• Indices of selected boxes.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_operation.topk(**kwargs)

A version of topk for iOS 17+. The differences between this version and the iOS 16 topk are:

• New data type support. The newly added data types are:

  • int8, uint8, int16, unint16 for x and output.

  • int8, int16 for k.

• Validation restrictions on the optional indices output must be either uint16 or int32.

• A new input parameter output_indices_dtype has been added to set the dtype of output indices.

Parameters:

x: <*?, T> (Required)

• Input tensor.

k: const<K> (Optional)

• Defaults to 1.

• Number of values/indices to be computed along each axis.

• Set to -1 to select all elements.

axis: const<i32> (Optional)

• Defaults to -1 (last dimension).

• Axis to perform the operation.

ascending: const<bool> (Optional)

• Defaults to False, sort in descending order.

• True to sort in ascending order.

sort: const<bool> (Optional)

• Defaults to True.

• If True, top-k elements are themselves sorted. Otherwise, no particular ordering is guaranteed.

return_indices: const<bool> (Optional)

• Defaults to True.

• If True, returns both values and indices. Otherwise, returns only the top-k values.

output_indices_dtype: const<str> (Optional, default=”int32”)

• It can only be set when return_indices is True.

• This parameter can take "int32" or "uint16" as values.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16

K: int8, int16, int32

Returns:

tensor<*?, T>

• Values of top/bottom k elements.

tensor<*?, U>

• Only returned when return_indices = True

• Indices of the top/bottom k elements along axis.

• U is int32 or uint16 determined by output_indices_dtype (int32 by default).

tensor_transformation (iOS 15)

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.batch_to_space(**kwargs)

Rearrange elements in a tensor from batch into spatial dimensions.

Parameters:

x: tensor<[n, C, H, W], T> (Required)

• Input tensor must have rank 4.

• The first and the second dimension are batch, channel; respectively.

• The remaining dimensions (H, W) are treated as “spatial dimensions”.

block_shape: const tensor<[2], i32> (Required)

• The length of the block_shape must be 2.

• It defines the shapes of the block in which the spatial dimensions are multiplied.

crops: const tensor<[2, 2], i32> (Required)

• It must have shape (2, 2).

• It defines the amount to crop from each spatial dimension.

Attributes:

T: fp16, fp32

Returns:

tensor<[new_n, C, new_H, new_W], T>

• new_n = n / (block_shape[0] * block_shape[1])

• new_H = (H * block_shape[0]) - paddings[0][0] - padding[0][1]

• new_W = (W * block_shape[1]) - paddings[1][0] - padding[1][1]

• The output has the same rank as the input.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.depth_to_space(**kwargs)

Rearrange elements in a tensor from depth (channel) into spatial dimensions.

Parameters:

x: tensor<[n, C, H, W], T> (Required)

• Input tensor of rank 4.

block_size: const i32 (Required)

• The size of the spatial block. Must be greater than 1 and divisible by channel dimension C.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C / block_size^2, H x block_size, W x block_size], T>

• Where b is the block size.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.expand_dims(**kwargs)

Insert a single-dimension in a 1-D or higher tensor at each axis in axes.

Parameters:

x: tensor<*?, T> (Required)

• Scalar or tensor.

axes: const tensor<[K], i32> Required

• K is the number of dimensions expanded.

• Insert single dimension at dimension index at each axes.

• Negative value to index from the end. -d-1 <= axis <= d where d is the rank of x.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*(rank(x)+K), T>

• Same type as the input x with rank rank(x)+K.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.pixel_shuffle(**kwargs)

Rearrange elements in a tensor from depth (channel) into spatial dimensions. Equivalent to PyTorch’s PixelShuffle.

Parameters:

x: tensor<[n, C x f^2, H, W], T> (Required)

• Input tensor of rank 4.

upscale_factor: const<i32>

• Factor to increase spatial resolution by.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C, H x f, W x f], T>

• Where f is the upscale factor.

References

torch.nn.PixelShuffle

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.reshape(**kwargs)

Return a tensor that has the same values as x with shape shape. shape must have the same volume (number of elements) as x.

Parameters:

x: tensor<*?, T> (Required)

• An n-D tensor or a scalar.

• If x is fixed rank (and possibly contains symbolic dimension), shape may contain elements that are not positive integers (see below).

• If x is variadic rank, shape can only contain positive integers.

shape: tensor<[K], i32> (Required)

A 1-D tensor, with elements from the following:

• Positive integers.

• Symbols: All but one symbol in shape must be present in x.shape. The new symbol that is not present in x.shape represent a dimension such that the total size remains constant. Symbol is illegal if x is variadic rank.

• -1: -1 introduces a new symbol (see Symbols above). Therefore, -1 is allowed if all symbols in the shape appear in x.shape. -1 is illegal if x is variadic rank.

• 0: If K == rank(x) then 0 means inheriting from the corresponding dimension in x.shape. 0 is illegal if x is variadic rank.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Tensor with shape determined by the input shape.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.reverse(**kwargs)

Reverse the order of the input tensor x along specified axes (dimensions).

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

axes: const<D, i32> (Optional)

• Dimension(s) to reverse. Each axis must be in the range [-rank(x), rank(x)).

• Defaults to None (reverse on all dimensions).

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

References

See tf.reverse and TORCH.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.reverse_sequence(**kwargs)

Reverse variable length slices for specified axes / dimensions of the input tensor. This op first slices input tensor along the batch_axis dimension, then partially reverses the elements along the seq_axis for the first lengths[i] elements.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

lengths: tensor<L, i32> (Required)

• 1-dimensional tensor of length x.shape[batch_axis] specifying the length of the sequence to reverse.

• Values must be in range [0, x.shape[seq_axis]].

seq_axis: const<i32> (Optional)

• The dimension to reverse.

• Defaults to 0.

batch_axis: const<i32> (Optional)

• Dimension for slicing.

• Defaults to 0.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

References

tf.reverse_sequence

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.slice_by_index(**kwargs)

Method for numpy style indexing and slicing. With a tensor x, this method achieves the following:

result = x[begin[0]: end[0]: stride[0], begin[1]: end[1]: stride[1], ...]

Note: This method does not support pure indexing. You would need to do a squeeze if indexing is intended.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor

begin: tensor<[rank(x)], i32> (Required)

• Starting index for the dimension of slicing.

end: tensor<[rank(x)], i32> (Required)

• Ending index for the dimension of slicing.

stride: tensor<[rank(x)], i32> (Optional)

• Default is all 1.

• Stride for the dimension of slicing.

begin_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If begin_mask[i]==True, ignores begin[i], and set begin[i] to 0.

end_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If end_mask[i]==True, ignores end[i], and set end[i] to x.shape[i].

squeeze_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If squeeze_mask[i]==true, ignores end[i], and do the pure index at begin[i].

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Scalar or tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.slice_by_size(**kwargs)

Slice input tensor starting from the given begin index and by the amount specified by the size input, for each dimension.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

begin: tensor<[rank(x)], i32> (Required)

• The begin index for slice.

size: tensor<[rank(x)], i32> (Required)

• The size that is to be sliced. If size is -1, all the remaining elements starting with “begin” are sliced.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Scalar or tensor.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.sliding_windows(**kwargs)

Return a tensor containing all windows of size, separated by stride along the given axis.

Parameters:

x: tensor<[*d0, d_axis, *dn], T>

• Input tensor.

axis: const<i32>

• Axis to perform the operation.

size: const<i32>

• Number of elements in the sliding window.

stride: const<i32> Optional

• Default to 1.

• The stride of the input elements in the sliding window.

Attributes:

T: fp16, fp32, int32

Returns:

tensor<[*d0, d_axis - size // stride + 1, size, *dn], T>

• The output will be a tensor of rank N+1 where N is the input tensor rank.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.space_to_batch(**kwargs)

Rearrange elements in a tensor from spatial into batch dimensions.

Parameters:

x: tensor<[n, C, H, W], T> (Required)

• Input tensor must have rank 4.

• The first and the second dimension are batch, channel; respectively.

• The remaining dimensions (H, W) are treated as “spatial dimensions”.

block_shape: const tensor<[2], i32> (Required)

• The length of the block_shape must be 2.

• It defines the shapes of the block in which the spatial dimensions are divided.

paddings: const tensor<[2, 2], i32> (Required)

• It must have shape (2, 2).

• It defines the padding for each spatial dimension.

Attributes:

T: fp16, fp32

Returns:

tensor<[new_n, C, new_H, new_W], T>

• new_n = n * block_shape[0] * block_shape[1]

• new_H = (H + paddings[0][0] + padding[0][1])/block_shape[0]

• new_W = (W + paddings[1][0] + padding[1][1])/block_shape[1]

• The output has the same rank as the input.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.space_to_depth(**kwargs)

Rearrange elements in a tensor from spatial into depth (channel) dimension.

Parameters:

x: tensor<[n, C, H, W], T> (Required)

• Input tensor of rank 4.

block_size: const<i32> (Required)

• The size of the spatial block. Must be greater than 1 and divisible by spatial dimensions H, W.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C x block_size^2, H / block_size, W / block_size], T>

• Where b is the block size.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.squeeze(**kwargs)

Remove single-dimension dimensions in a 1-D or higher tensor.

Parameters:

x: tensor<*?, T> (Required)

• Must be at least 1-D.

axes: const<K,i32> (Optional)

• Axes to squeeze out.

• The behaviour of squeezing non-single dimensions follow PyTorch instead of NumPy, where it ignores non-single dimensions instead of erroring out. More specifically, if x has shape (2, 3, 4) and axes is [0, 1], the output will be a tensor with shape (2, 3, 4).

• Default to remove all single-dimensions.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*(rank(x)-K), T>

• Tensor with same type as input x and rank rank(x)-K.

class coremltools.converters.mil.mil.ops.defs.iOS15.tensor_transformation.transpose(**kwargs)

Permute tensor x dimensions according to perm.

Parameters:

x: tensor<*?, T> (Required)

• Must be at least 1-D. x may have a symbolic shape.

perm: const<[rank(x)], i32> (Required)

• Permutation order. -rank(x) <= perm[I] < rank(x) for all perm entries.

Attributes:

T: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Tensor with same rank and type as x.

References

torch.Tensor.permute

tensor_transformation (iOS 16+)

class coremltools.converters.mil.mil.ops.defs.iOS16.tensor_transformation.pixel_unshuffle(**kwargs)

Rearrange elements in a tensor from spatial dimensions into depth (channel). It is basically the inverse operation of pixel_shuffle. Equivalent to PyTorch PixelUnshuffle.

Parameters:

x: tensor<[n, C, H / f , W / f], T> (Required)

• Input tensor of rank 4.

downscale_factor: const<i32>

• Factor to decrease spatial resolution by.

Attributes:

T: fp16, fp32

Returns:

tensor<[n, C * f^2, H, W], T>

• In which f is the downscale factor.

References

torch.nn.PixelUnshuffle

class coremltools.converters.mil.mil.ops.defs.iOS16.tensor_transformation.reshape_like(**kwargs)

Reshape a tensor to an output shape specified by some or all dimensions of a tuple of reference tensors ref_tensors.

Parameters:

x: tensor<*?, T> (Required)

• The input tensor to be reshaped.

ref_tensors: Tuple[tensor<*?, R>] (Required)

• A tuple of tensors that define the output shape.

begins: Tuple[const<int32>] (Required)

• A tuple of integers specifying the begin index into the shape vector of the corresponding ref_tensor.

ends: Tuple[const<int32>] (Required)

• A tuple of integers specifying the end index into the shape vector of the corresponding ref_tensor.

end_masks: Tuple[const<bool>] (Required)

• If True, select all axes from the begin index until the end of the corresponding ref_tensor, as in ref_tensors[i].shape[begins[i]:].

Attributes:

T: fp16, fp32, i32, bool

R: fp16, fp32, i32, bool

Returns:

tensor<*?, T>

• Same type as input tensor x.

• Output shape is computed by ref_tensors, begins, ends, and end_masks.

Notes

The output shape is computed as follows:

output_shape = []
num_of_refs = len(begins)
for i in range(num_of_refs):
    if end_masks[i]:
        output_shape.append(ref_tensor_i.shape[begins[i]:])
    else:
        output_shape.append(ref_tensor_i.shape[begins[i]:ends[i]])
output_shape = np.concat(output_shape, axis=0)

The following is an example:

ref_tensors=[tensor[2, 3, 4], tensor[1, 5, 6]]
begins=[0, 1]
ends=[2, 0]
end_masks=[False, True]

The output shape would be (2, 3, 5, 6).

tensor_transformation (iOS 17+)

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.expand_dims(**kwargs)

Insert a single-dimension in a 1-D or higher tensor at each axis in axes.

The major difference between this version and the iOS 15 expand_dims is that input x supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<*?, T> (Required)

• Scalar or tensor.

axes: const tensor<[K], int32> Required

• K is the number of dimensions expanded.

• Insert single dimension at dimension index at each axes.

• Negative value to index from the end. -d-1 <= axis <= d where d is the rank of x.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<*(rank(x)+K), T>

• Same type as the input x with rank rank(x)+K.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.reshape(**kwargs)

Return a tensor that has the same values as x with shape shape. shape must have the same volume (number of elements) as x.

The major differences between this version and the iOS 15 reshape are as follows:

• When the shape contains 0, the restriction about K == rank(x) is no longer enforced. Each 0 in shape will match the corresponding dimension in x.shape, counting from the rightmost element. So shape[i] matches input[j] if length(shape)-i == rank(input)-j. If a 0 is out of range, assign 1 (equivalent to expand_dims for x.shape).

  More specifically, when x.shape is [2, 50] and shape is [1, 0, -1, 0], it will error out in iOS 15 or iOS 16 because x has rank 2 while the len of shape is 4. In iOS 17, the result will have shape [1, 1, 2, 50], because the rightmost 0 will be changed to the rightmost dim of x.shape, which is 50. There is no other 0 that has a corresponding dim in x.shape, so it is set as 1. Finally, the -1 is calculated based on knowing dimensions that produce 2.

• Support more data types, including int8, uint8, int16, uint16 for x and int8, int16 for shape.

Parameters:

x: tensor<*?, T> (Required)

• An n-D tensor or a scalar.

• If x has a fixed rank (and possibly contains symbolic dimension), shape may contain elements that are not positive integers (see below).

• If x has a variadic rank, shape can only contain positive integers.

shape: tensor<[K], U> (Required)

A 1-D tensor, with elements from the following:

• Positive integers.

• Symbols: All but one symbol in shape must be present in x.shape. The new symbol that is not present in x.shape represents a dimension such that the total size remains constant. Symbol is illegal if x has a variadic rank.

• -1: -1 introduces a new symbol (see Symbols). Therefore, -1 is allowed if all symbols in the shape appear in x.shape. -1 is illegal if x has a variadic rank.

• 0: It will match the corresponding dimension in x.shape. See the previous description of different behaviors with iOS 17.

Attributes:

T: fp16, fp32, int8, uint8, int16, uint16, int32, bool

U: int8, int16, int32

Returns:

tensor<*?, T>

• Tensor with shape determined by the input shape.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.reshape_like(**kwargs)

Reshape a tensor to an output shape specified by some or all dimensions of a tuple of reference tensors ref_tensors.

The major difference between this version and the iOS 15 reshape_like is that input x and ref_tensors supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<*?, T> (Required)

• The input tensor to be reshaped.

ref_tensors: Tuple[tensor<*?, R>] (Required)

• A tuple of tensors that define the output shape.

begins: Tuple[const<int32>] (Required)

• A tuple of integers specifying the begin index into the shape vector of the corresponding ref_tensor.

ends: Tuple[const<int32>] (Required)

• A tuple of integers specifying the end index into the shape vector of the corresponding ref_tensor.

end_masks: Tuple[const<bool>] (Required)

• If True, select all axes from the begin index until the end of the corresponding ref_tensor, as in ref_tensors[i].shape[begins[i]:].

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

R: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<*?, T>

• Same type as input tensor x.

• Output shape is computed by ref_tensors, begins, ends, and end_masks.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.reverse(**kwargs)

Reverse the order of the input tensor x along specified axes (dimensions).

The major difference between this version and the iOS 15 reverse is that input x supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

axes: const<D, int32> (Optional)

• Dimension(s) to reverse. Each axis must be in the range [-rank(x), rank(x)).

• Defaults to None (reverse on all dimensions).

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.reverse_sequence(**kwargs)

Reverse variable length slices for specified axes / dimensions of the input tensor. This op first slices input tensor along the batch_axis dimension, then partially reverses the elements along the seq_axis for the first lengths[i] elements.

The major difference between this version and the iOS 15 reverse_sequence is that input supports more data types: - x additionally supports int8, uint8, int16, uint16 - lengths additionally supports int8, int16

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

lengths: tensor<L, U> (Required)

• 1-dimensional tensor of length x.shape[batch_axis] specifying the length of the sequence to reverse.

• Values must be in range [0, x.shape[seq_axis]].

seq_axis: const<int32> (Optional)

• The dimension to reverse.

• Defaults to 0.

batch_axis: const<int32> (Optional)

• Dimension for slicing.

• Defaults to 0.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

U: int8, int16, int32

Returns:

tensor<*?, T>

• Same type and shape as the input tensor.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.slice_by_index(**kwargs)

Method for numpy style indexing and slicing. With a tensor x, this method achieves the following:

result = x[begin[0]: end[0]: stride[0], begin[1]: end[1]: stride[1], ...]

The differences between this version and the iOS 15 slice_by_index is that additional data types are supported for x, begin, end, and stride. See Parameters and Attributes sections for details.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor

begin: tensor<[rank(x)], U> (Required)

• Starting index for the dimension of slicing.

end: tensor<[rank(x)], U> (Required)

• Ending index for the dimension of slicing.

stride: tensor<[rank(x)], U> (Optional)

• Default is all 1.

• Stride for the dimension of slicing.

begin_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If begin_mask[i]==True, ignores begin[i], and set begin[i] to 0.

end_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If end_mask[i]==True, ignores end[i], and set end[i] to x.shape[i].

squeeze_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If squeeze_mask[i]==True, ignores end[i], and do the pure index at begin[i].

Attributes:

T: bool, fp16, fp32, int8, int16, int32, uint8, uint16

U: int8, int16, int32

Returns:

tensor<*?, T>

• Scalar or tensor.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.slice_by_size(**kwargs)

Slice input tensor starting from the given begin index and by the amount specified by the size input, for each dimension.

The differences between this version and the iOS 15 slice_by_size is that additional data types are supported for x, begin, and size. See Parameters and Attributes sections for details.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

begin: tensor<[rank(x)], U> Required

• The begin index for slice.

size: tensor<[rank(x)], U> Required

• The size that is to be sliced. If size is -1, all the remaining elements starting with “begin” are sliced.

Attributes:

T: bool, fp16, fp32, int8, int16, int32, uint8, uint16

U: int8, int16, int32

Returns:

tensor<*?, T>

• Scalar or tensor.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.sliding_windows(**kwargs)

Return a tensor containing all windows of size, separated by stride along the given axis.

The major difference between this version and the iOS 15 sliding_windows is that input x supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<[*d0, d_axis, *dn], T>

• Input tensor.

axis: const<int32>

• Axis to perform the operation.

size: const<int32>

• Number of elements in the sliding window.

stride: const<int32> Optional

• Default to 1.

• The stride of the input elements in the sliding window.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<[*d0, d_axis - size // stride + 1, size, *dn], T>

• The output will be a tensor of rank N+1 where N is the input tensor rank.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.squeeze(**kwargs)

Remove single-dimension dimensions in a 1-D or higher tensor.

The major difference between this version and the iOS 15 squeeze is that input x supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<*?, T> (Required)

• Must be at least 1-D.

axes: const<K,int32> (Optional)

• Axes to squeeze out.

• Default to remove all single-dimensions.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<*(rank(x)-K), T>

• Tensor with same type as input x and rank rank(x)-K.

class coremltools.converters.mil.mil.ops.defs.iOS17.tensor_transformation.transpose(**kwargs)

Permute tensor x dimensions according to perm.

The major difference between this version and the iOS 15 transpose is that input x supports more data types: int8, uint8, int16, uint16.

Parameters:

x: tensor<*?, T> (Required)

• Must be at least 1-D. x may have a symbolic shape.

perm: const<[rank(x)], i32> (Required)

• Permutation order. -rank(x) <= perm[I] < rank(x) for all perm entries.

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

Returns:

tensor<*?,T>

• Tensor with same rank and type as x.

tensor_transformation (iOS 18+)

class coremltools.converters.mil.mil.ops.defs.iOS18.tensor_transformation.slice_update(**kwargs)

Update a custom slice of a source tensor with another tensor of the same shape, as dictated by the slice.

For example, if you have a tensor x, this method produces the following:

x[begin[0]: end[0]: stride[0], begin[1]: end[1]: stride[1], ...] = value

The arguments defining the slice (begin, end, stride, masks, and so on) should be treated the same way as iOS15 slice_by_index.

Parameters:

x: tensor<*?, T> (Required)

• Input tensor.

update: tensor<*K, T> (Required)

• Value tensor to be inserted.

• The shape of the update tensor must match the slicing result of the input data.

• rank-0 update is not supported.

begin: tensor<[rank<x>], U> (Required)

• Starting index for the dimension of slicing.

end: tensor<[rank(x)], U> (Required)

• Ending index for the dimension of slicing.

stride: tensor<[rank(x)], U> (Optional)

• Default as all 1.

• Stride for the dimension of slicing.

begin_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If begin_mask[i]==True, neglect begin[i], and set begin[i] to 0.

end_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If end_mask[i]==True, neglect end[i], and set end[i] to x.shape[i].

squeeze_mask: tensor<[rank(x)], bool> (Optional)

• Default to all False.

• If squeeze_mask[i]==True, neglect end[i], and do the pure index at begin[i].

Attributes:

T: fp16, fp32, int8, int16, int32, uint8, uint16, bool

U: int8, int16, int32

Returns:

tensor<*?, T>

• Scalar or tensor.

transformers (iOS 18+)

class coremltools.converters.mil.mil.ops.defs.iOS18.transformers.scaled_dot_product_attention(**kwargs)

Source: PyTorch scaled dot product attention. Computes the scaled dot product attention on query, key, and value tensors, using an optional attention mask if passed. In PyTorch, this is equivalent to:

attn_mask = attn_mask.masked_fill(not attn_mask, -float('inf')) if attn_mask.dtype==torch.bool else attn_mask
attn_weight = torch.softmax((Q @ K.transpose(-2, -1) / math.sqrt(Q.size(-1))) + attn_mask, dim=-1)
return attn_weight @ V

Shape key:

• B = Batch size

• S = Source sequence length

• L = Target sequence length

• E = Query/Key embedding dimension

• EV = Value embedding dimension

Numerical values can differ due to floating point fusion/accumulation between backends. Note: We currently do not support the dropout_p and is_causal.

Mask can either be bool or float matching query, key, or value. For bool, it indicates whether the element should take part in the attention. Floats are added to the attention score. Mask shape must be broadcastable to [B, \*?, L, S].

Parameters:

query: tensor<[B, *?, L, E], T> (Required)

key: tensor<[B, *?, S, E], T> (Required)

value: tensor<[B, *?, S, EV], T> (Required)

attn_mask: tensor<[*?, S], M> (Optional)

Attributes:

T: fp16, fp32

M: bool, fp16, fp32

Returns:

tensor<[B, *?, L, EV], T>