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tensorflow.js
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({
requires: [
{ "import-type": "builtin", name: "valueskeleton" }
],
nativeRequires: ["@tensorflow/tfjs"],
provides: {
shorthands: {
"Tensor": ["local", "Tensor"],
"TensorBuffer": ["local", "TensorBuffer"],
"Model": ["local", "Model"],
"Sequential": ["local", "Sequential"],
"SymbolicTensor": ["local", "SymbolicTensor"],
"Layer": ["local", "Layer"],
"Optimizer": ["local", "Optimizer"],
"VS": {
tag: "name",
origin: { "import-type": "uri", uri: "builtin://valueskeleton" },
name: "ValueSkeleton"
},
"Object": {
tag: "name",
origin: { "import-type": "uri", uri: "builtin://global" },
name: "Object"
},
"NumInteger": {
tag: "name",
origin: { "import-type": "uri", uri: "builtin://global" },
name: "NumInteger"
},
"NumPositive": {
tag: "name",
origin: { "import-type": "uri", uri: "builtin://global" },
name: "NumPositive"
},
"Roughnum": {
tag: "name",
origin: { "import-type": "uri", uri: "builtin://global" },
name: "Roughnum"
},
"TensorBinOp": ["arrow", [["local", "Tensor"], ["local", "Tensor"]], ["local", "Tensor"]],
"TensorUnOp": ["arrow", [["local", "Tensor"]], ["local", "Tensor"]],
},
values: {
// Tensors
"is-tensor": ["arrow", ["Any"], "Boolean"],
"list-to-tensor": ["arrow", [["List", "Number"]], "Tensor"],
"make-scalar": ["arrow", ["Number"], "Tensor"],
"fill": ["arrow", [["List", "NumInteger"], "Number"], "Tensor"],
"tensor": ["Maker", "Number", ["local", "Tensor"]],
"linspace": ["arrow", ["Number", "Number", "Number"], "Tensor"],
"ones": ["arrow", [["List", "NumInteger"]], "Tensor"],
"zeros": ["arrow", [["List", "NumInteger"]], "Tensor"],
"multinomial": ["arrow", ["Tensor", "Number", ["Option", "Number"], "Boolean"], "Tensor"],
"random-normal": ["arrow", [["List", "NumInteger"], ["Option", "Number"], ["Option", "Number"]], "Tensor"],
"random-uniform": ["arrow", [["List", "NumInteger"], ["Option", "Number"], ["Option", "Number"]], "Tensor"],
"make-variable": ["arrow", ["Tensor"], "Tensor"],
// TensorBuffers
"is-tensor-buffer": ["arrow", ["Any"], "Boolean"],
"make-buffer": ["arrow", [["List", "NumInteger"]], "TensorBuffer"],
// Tensor Operations (Arithmetic)
"add-tensors": "TensorBinOp",
"subtract-tensors": "TensorBinOp",
"multiply-tensors": "TensorBinOp",
"divide-tensors": "TensorBinOp",
"floor-divide-tensors": "TensorBinOp",
"tensor-max": "TensorBinOp",
"tensor-min": "TensorBinOp",
"tensor-modulo": "TensorBinOp",
"tensor-expt": "TensorBinOp",
"squared-difference": "TensorBinOp",
"strict-add-tensors": "TensorBinOp",
"strict-subtract-tensors": "TensorBinOp",
"strict-multiply-tensors": "TensorBinOp",
"strict-divide-tensors": "TensorBinOp",
"strict-tensor-max": "TensorBinOp",
"strict-tensor-min": "TensorBinOp",
"strict-tensor-modulo": "TensorBinOp",
"strict-tensor-expt": "TensorBinOp",
"strict-squared-difference": "TensorBinOp",
// Tensor Operations (Basic Math)
"tensor-abs": "TensorUnOp",
"tensor-acos": "TensorUnOp",
"tensor-acosh": "TensorUnOp",
"tensor-asin": "TensorUnOp",
"tensor-asinh": "TensorUnOp",
"tensor-atan": "TensorUnOp",
"tensor-atan2": "TensorBinOp",
"tensor-atanh": "TensorUnOp",
"tensor-ceil": "TensorUnOp",
"clip-by-value": ["arrow", ["Tensor", "Number", "Number"], "Tensor"],
"tensor-cos": "TensorUnOp",
"tensor-cosh": "TensorUnOp",
"elu": "TensorUnOp",
"exponential-linear-units": "TensorUnOp",
"erf": "TensorUnOp",
"gauss-error": "TensorUnOp",
"tensor-exp": "TensorUnOp",
"tensor-exp-min1": "TensorUnOp",
"tensor-floor": "TensorUnOp",
"leaky-relu": ["arrow", ["Tensor", "Number"], "Tensor"],
"tensor-log": "TensorUnOp",
"tensor-log-plus1": "TensorUnOp",
"log-sigmoid": "TensorUnOp",
"tensor-negate": "TensorUnOp",
"parametric-relu": ["arrow", ["Tensor", "Number"], "Tensor"],
"tensor-reciprocal": "TensorUnOp",
"relu": "TensorUnOp",
"tensor-round": "TensorUnOp",
"reciprocal-sqrt": "TensorUnOp",
"scaled-elu": "TensorUnOp",
"sigmoid": "TensorUnOp",
"signed-ones": "TensorUnOp",
"tensor-sin": "TensorUnOp",
"tensor-sinh": "TensorUnOp",
"softplus": "TensorUnOp",
"tensor-sqrt": "TensorUnOp",
"tensor-square": "TensorUnOp",
"step": "TensorUnOp",
"tensor-tan": "TensorUnOp",
"tensor-tanh": "TensorUnOp",
// Operations (Reduction)
"reduce-all": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"reduce-any": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"arg-max": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"arg-min": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"log-sum-exp": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"reduce-max": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"reduce-mean": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"reduce-min": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
"reduce-sum": ["arrow", ["Tensor", ["Option", "Number"]], "Tensor"],
// Operations (Slicing and Joining)
"reshape": ["arrow", ["Tensor", ["List", "NumInteger"]], "Tensor"],
"concatenate": ["arrow", [["List", "Tensor"], "NumInteger"], "Tensor"],
"gather": ["arrow", ["Tensor", "Tensor", "NumInteger"], "Tensor"],
"reverse": ["arrow", ["Tensor", ["Option", ["List", "NumInteger"]]], "Tensor"],
"slice": ["arrow", ["Tensor", ["List", "NumInteger"], ["Option", ["List", "NumInteger"]]], "Tensor"],
"split": ["arrow", ["Tensor", ["List", "NumInteger"], "NumInteger"], ["List", "Tensor"]],
"stack": ["arrow", [["List", "Tensor"], "NumInteger"], "Tensor"],
"tile": ["arrow", ["Tensor", ["List", "Number"]], "Tensor"],
"unstack": ["arrow", ["Tensor", "NumInteger"], ["List", "Tensor"]],
"strided-slice": ["arrow", ["Tensor", ["List", "NumInteger"], ["List", "NumInteger"], ["List", "Number"]], "Tensor"],
// Models (Generic)
"is-model": ["arrow", ["Any"], "Boolean"],
"make-model": ["arrow", ["Object"], "Model"],
// Models (Sequential)
"is-sequential": ["arrow", ["Any"], "Boolean"],
"make-sequential": ["arrow", ["Sequential"], "Model"],
// Models (Inputs / SymbolicTensors)
"is-symbolic-tensor": ["arrow", ["Any"], "Boolean"],
"make-input": ["arrow", [["List", ["Option", "Number"]]], "SymbolicTensor"],
"make-batch-input": ["arrow", [["List", ["Option", "Number"]]], "SymbolicTensor"],
// Layers
"is-layer": ["arrow", ["Any"], "Boolean"],
// Optimizers
"is-optimizer": ["arrow", ["Any"], "Boolean"],
"train-sgd": ["arrow", ["Number"], "Optimizer"],
"train-momentum": ["arrow", ["Number", "Number"], "Optimizer"],
"train-adagrad": ["arrow", ["Number", ["Option", "NumPositive"]], "Optimizer"],
"train-adadelta": ["arrow", [["Option", "Number"], ["Option", "Number"], ["Option", "Number"]], "Optimizer"],
"train-adam": ["arrow", [["Option", "Number"], ["Option", "Number"], ["Option", "Number"], ["Option", "Number"]], "Optimizer"],
"train-adamax": ["arrow", [["Option", "Number"], ["Option", "Number"], ["Option", "Number"], ["Option", "Number"], ["Option", "Number"]], "Optimizer"],
"train-rmsprop": ["arrow", ["Number", ["Option", "Number"], ["Option", "Number"], ["Option", "Number"], "Boolean"], "Optimizer"],
},
aliases: {
"Tensor": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "Tensor"
},
"TensorBuffer": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "TensorBuffer"
},
"Model": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "Model"
},
"Sequential": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "Sequential"
},
"SymbolicTensor": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "SymbolicTensor"
},
"Layer": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "Layer"
},
"Optimizer": {
tag: "name",
origin: { "import-type": "$ELF" },
name: "Optimizer"
},
},
datatype: {
"Tensor": ["data", "Tensor", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
"size": ["arrow", [], "Number"],
"shape": ["arrow", [], ["List", "NumInteger"]],
"flatten": ["arrow", [], "Tensor"],
"as-scalar": ["arrow", [], "Tensor"],
"as-1d": ["arrow", [], "Tensor"],
"as-2d": ["arrow", ["NumInteger", "NumInteger"], "Tensor"],
"as-3d": ["arrow", ["NumInteger", "NumInteger", "NumInteger"], "Tensor"],
"as-4d": ["arrow", ["NumInteger", "NumInteger", "NumInteger", "NumInteger"], "Tensor"],
"as-type": ["arrow", ["String"], "Tensor"],
"data-now": ["arrow", [], ["List", "Roughnum"]],
"to-float": ["arrow", [], "Tensor"],
"to-int": ["arrow", [], "Tensor"],
"to-bool": ["arrow", [], "Tensor"],
"to-buffer": ["arrow", [], "Tensor"],
"to-variable": ["arrow", [], "Tensor"],
"reshape": ["arrow", [["List", "NumInteger"]], "Tensor"],
"expand-dims": ["arrow", [["Option", "NumInteger"]], "Tensor"],
"squeeze": ["arrow", [["Option", ["List", "NumInteger"]]], "Tensor"],
"clone": ["arrow", [], "Tensor"],
"add": ["arrow", ["Tensor"], "Tensor"],
"subtract": ["arrow", ["Tensor"], "Tensor"],
"multiply": ["arrow", ["Tensor"], "Tensor"],
"divide": ["arrow", ["Tensor"], "Tensor"],
"floor-divide": ["arrow", ["Tensor"], "Tensor"],
"max": ["arrow", ["Tensor"], "Tensor"],
"min": ["arrow", ["Tensor"], "Tensor"],
"modulo": ["arrow", ["Tensor"], "Tensor"],
"expt": ["arrow", ["Tensor"], "Tensor"],
"squared-difference": ["arrow", ["Tensor"], "Tensor"],
}],
"TensorBuffer": ["data", "TensorBuffer", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
"size": ["arrow", [], "Number"],
"shape": ["arrow", [], ["List", "NumInteger"]],
"set-now": ["arrow", ["Number", ["List", "NumInteger"]], "TensorBuffer"],
"get-now": ["arrow", [["List", "NumInteger"]], "TensorBuffer"],
"get-all-now": ["arrow", [], ["List", "Roughnum"]],
"to-tensor": ["arrow", [], "Tensor"],
}],
"Model": ["data", "Model", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
}],
"Sequential": ["data", "Sequential", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
"add": ["arrow", ["Layer"], "Nothing"],
"compile": ["arrow", ["Object"], "Nothing"],
"evaluate": ["arrow", ["Tensor", "Tensor", "Object"], "Tensor"],
"predict": ["arrow", ["Tensor", "Object"], "Tensor"],
"predict-on-batch": ["arrow", ["Tensor"], "Tensor"],
"fit": ["arrow", ["Tensor", "Tensor", "Object", ["arrow", ["Number", "Object"], "Nothing"]], "Nothing"],
}],
"SymbolicTensor": ["data", "SymbolicTensor", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
"shape": ["arrow", [], ["List", ["Option", "Number"]]],
}],
"Layer": ["data", "Layer", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
}],
"Optimizer": ["data", "Optimizer", [], [], {
"_output": ["arrow", [["arrow", ["Any"], "VS"]], "VS"],
"minimize": ["arrow", [["arrow", [], "Tensor"], ["List", "Tensor"]], "Tensor"],
}],
}
},
theModule: function(runtime, namespace, uri, VSlib, tf) {
/**
* Tensorflow Brands and Annotations
*/
/**
* The Pyret version of a TensorFlow.js Tensor.
* @typedef {Object} PyretTensor
*/
const brandTensor = runtime.namedBrander("tensor", ["tensor: tensor brander"]);
const annTensor = runtime.makeBranderAnn(brandTensor, "Tensor");
/**
* The Pyret version of a TensorFlow.js TensorBuffer.
* @typedef {Object} PyretTensorBuffer
*/
const brandTensorBuffer = runtime.namedBrander(
"tensor-buffer",
["tensor-buffer: tensor-buffer brander"]);
const annTensorBuffer = runtime.makeBranderAnn(brandTensorBuffer, "TensorBuffer");
/**
* The Pyret version of a TensorFlow.js Model.
* @typedef {Object} PyretModel
*/
const brandModel = runtime.namedBrander("model", ["model: model brander"]);
const annModel = runtime.makeBranderAnn(brandModel, "Model");
/**
* The Pyret version of a TensorFlow.js Sequential.
* @typedef {Object} PyretSequential
*/
const brandSequential = runtime.namedBrander(
"sequential",
["sequential: sequential brander"]);
const annSequential = runtime.makeBranderAnn(brandSequential, "Sequential");
/**
* The Pyret version of a TensorFlow.js SymbolicTensor.
* @typedef {Object} PyretSymbolicTensor
*/
const brandSymbolicTensor = runtime.namedBrander(
"symbolic-tensor",
["symbolic-tensor: symbolic-tensor brander"]);
const annSymbolicTensor = runtime.makeBranderAnn(brandSymbolicTensor, "SymbolicTensor");
/**
* The Pyret version of a TensorFlow.js Layer.
* @typedef {Object} PyretLayer
*/
const brandLayer = runtime.namedBrander("layer", ["layer: layer brander"]);
const annLayer = runtime.makeBranderAnn(brandLayer, "Layer");
/**
* The Pyret version of a TensorFlow.js Optimizer.
* @typedef {Object} PyretOptimizer
*/
const brandOptimizer = runtime.namedBrander(
"optimizer",
["optimizer: optimizer brander"]);
const annOptimizer = runtime.makeBranderAnn(brandOptimizer, "Optimizer");
/**
* Runtime Helpers
*/
const O = runtime.makeObject;
const F = runtime.makeFunction;
const arity = runtime.checkArity;
const get = runtime.getField;
const unwrap = runtime.unwrap;
const VS = get(VSlib, "values");
function applyBrand(brand, val) {
return get(brand, "brand").app(val);
}
function hasBrand(brand, val) {
return get(brand, "test").app(val);
}
function assertOption(val) {
if (!runtime.ffi.isOption(val)) {
runtime.ffi.throwTypeMismatch(val, "Option");
}
}
/**
* Helpers
*/
const unwrapObject = (obj) => { return unwrap(obj).dict; };
const checkTensor = (val) => { runtime._checkAnn(["tensor"], annTensor, val); };
const checkTensorBuffer = (val) => { runtime._checkAnn(["tensor-buffer"], annTensorBuffer, val); };
const checkSequential = (val) => { runtime._checkAnn(["sequential"], annSequential, val); };
const checkModel = (val) => { runtime._checkAnn(["model"], annModel, val); };
const checkSymbolicTensor = (val) => { runtime._checkAnn(["symbolic-tensor"], annSymbolicTensor, val); };
const checkLayer = (val) => { runtime._checkAnn(["layer"], annLayer, val); };
const checkOptimizer = (val) => { runtime._checkAnn(["optimizer"], annOptimizer, val); };
function checkMethodArity(arity, args, methodName) {
if (args.length !== arity) {
let $a = new Array(args.length);
for (let $i = 0; $i < args.length; $i++) {
$a[$i] = args[$i];
}
throw runtime.ffi.throwArityErrorC([methodName], arity, $a, true);
}
}
/**
* Helper function to unwrap a Option<Number>. If `some`, then returns a
* fixnum variant of the value of the Option. If `none`, returns undefined.
* The undefined return value is required for certain TensorFlow.js
* functions.
* @param {Option<Number>} option The Option to unwrap
* @returns {Number|undefined} The fixnum variant of the Option's value
* or undefined
*/
function unwrapFixnumOption(option) {
return runtime.ffi.cases(runtime.ffi.isOption, "is-Option", option, {
some: (v) => { return runtime.num_to_fixnum(v); },
none: () => { return undefined; }
});
}
/**
* Helper function to convert a List<PyretTensor> to an Array[TFTensor].
* Raises an error if any element in the input list is not a PyretTensor,
* or if the input is not a List.
* @param {List<PyretTensor>} listOfTensors The tensors to unwrap
* @returns {Array[TFTensor]} The underlying TensorFlow.js representations
* of each of the input Tensors
*/
function checkAndUnwrapListToArray(pyretList, checkType, converter) {
runtime.checkList(pyretList);
const array = runtime.ffi.toArray(pyretList);
return array.map(x => {
checkType(x);
return converter(x);
});
}
/**
* Helper function to convert a List<PyretTensor> to an Array[TFTensor].
* Raises an error if any element in the input list is not a PyretTensor,
* or if the input is not a List.
* @param {List<PyretTensor>} listOfTensors The tensors to unwrap
* @returns {Array[TFTensor]} The underlying TensorFlow.js representations
* of each of the input Tensors
*/
function unwrapListOfTensorsToArray(listOfTensors) {
const typeChecker = checkTensor;
const converter = unwrapTensor;
return checkAndUnwrapListToArray(listOfTensors, checkTensor, unwrapTensor);
}
/**
* Helper function to convert a List<PyretLayer> to an Array[TFLayer].
* Raises an error if any element in the input list is not a PyretLayer,
* or if the input is not a List.
* @param {List<PyretLayer>} listOfTensors The tensors to unwrap
* @returns {Array[TFLayer]} The underlying TensorFlow.js representations
* of each of the input Layers
*/
function unwrapListOfLayersToArray(listOfLayers) {
const typeChecker = checkLayer;
const converter = unwrapLayer;
return checkAndUnwrapListToArray(v, checkLayer, unwrapLayer);
}
/**
* Helper function to convert a List<NumInteger> to an Array[Number].
* Raises an error if any element in the input list is not a NumInteger,
* or if the input is not a List.
* @param {List<PyretTensor>} listOfIntegers The integers to unwrap
* @param {Function(PyretNumber):JSNumber} [typeChecker=runtime.checkNumber]
* Optional parameter to specify which type checking function should be
* called on every element of the input list
* @returns {Array[Number]} An array of numbers
*/
function unwrapListOfNumbersToArray(listOfIntegers, typeChecker) {
if (!typeChecker) {
typeChecker = runtime.checkNumber;
}
const converter = runtime.num_to_fixnum;
return checkAndUnwrapListToArray(listOfIntegers, typeChecker, converter);
}
/**
* Helper function to convert a List<Option<Number>> to an Array. Raises
* an error if any element in the input list is not an Option, or if the
* input is not a List.
* @param {List<PyretTensor>} listOfOptions The Options to unwrap
* @param {Any} noneValue The value to include in the final array when an
* Option is of the `none` variant
* @returns {Array[Number|Any]} An array of numbers (and possibly
* `noneValue`s)
*/
function checkAndUnwrapListOfOptionNumbers(listOfOptions, noneValue) {
return checkAndUnwrapListToArray(listOfOptions, _ => {}, option => {
let num = unwrapFixnumOption(option);
if (num === undefined) {
num = noneValue;
}
return num;
});
}
/**
* Tensors
*/
/**
* Returns PyretTrue if the input `obj` is a PyretTensor; otherwise,
* returns PyretFalse.
* @param {Any} obj Some Pyret value
* @returns {PBoolean} A Pyret object representing true or false
*/
function isTensor(obj) {
arity(1, arguments, "is-tensor", false);
return runtime.makeBoolean(hasBrand(brandTensor, obj));
}
/**
* Consumes a PyretTensor and returns its underlying TensorFlow.js
* Tensor.
* @param {PyretTensor} pyretTensor
* @returns {TFTensor} The underlying TensorFlow.js Tensor of the
* input PyretTensor
*/
function unwrapTensor(pyretTensor) {
return pyretTensor.$underlyingTensor;
}
/**
* Helper function to type-check and unwrap a PyretTensor to a
* TFTensor. Raises an error if the input is not a PyretTensor.
* @param {PyretTensor} pyretTensor
* @returns {TFTensor} The underlying TensorFlow.js Tensor of the
* input PyretTensor
*/
function checkAndUnwrapTensor(pyretTensor) {
checkTensor(pyretTensor);
return unwrapTensor(pyretTensor);
}
/**
* The methods for PyretTensors.
* @type {Object}
* @constant
*/
const TENSOR_METHODS = {
/**
* Returns a ValueSkeleton for printing to the Pyret console.
* @param {PyretTensor} self
* @return {ValueSkeleton} A vs-collection of the data currently in the
* PyretTensor
*/
"_output": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "_output");
// value-skeleton functions:
const vsValue = get(VS, "vs-value");
const vsCollection = get(VS, "vs-collection");
// Extract tensor information:
const selfTensor = unwrapTensor(self);
const tensorData = Array.from(selfTensor.dataSync());
// Create an array of value-skeleton values for each data point in
// this tensor:
let tensorElts = [];
for (let i = 0; i < tensorData.length; i++) {
const pyretNum = runtime.num_to_roughnum(tensorData[i]);
tensorElts.push(vsValue.app(pyretNum));
}
// Output value-skeleton collection:
const tensorName = runtime.makeString("tensor");
const eltList = runtime.ffi.makeList(tensorElts);
return vsCollection.app(tensorName, eltList);
}),
/**
* Returns the size of the PyretTensor.
* @param {PyretTensor} self
* @return {Number} The size of the Tensor
*/
"size": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "size");
const selfTensor = unwrapTensor(self);
return runtime.makeNumber(selfTensor.size);
}),
/**
* Returns the shape of the PyretTensor.
* @param {PyretTensor} self
* @return {List<NumInteger>} The shape of the Tensor
*/
"shape": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "shape");
const selfTensor = unwrapTensor(self);
return runtime.ffi.makeList(selfTensor.shape);
}),
/**
* Constructs a new, one-dimensional PyretTensor from the values of
* the original PyretTensor.
* @param {PyretTensor} self
* @return {PyretTensor} The newly constructed PyretTensor
*/
"flatten": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "flatten");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.flatten());
}),
/**
* Constructs a new, zero-dimensional Tensor from the values of the
* original, size-1 Tensor. Raises an error if the calling Tensor is
* not size-1.
* @param {PyretTensor} self
* @return {PyretTensor} The newly constructed PyretTensor
*/
"as-scalar": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "as-scalar");
const selfTensor = unwrapTensor(self);
if (selfTensor.size !== 1) {
runtime.ffi.throwMessageException("Tensor was size-" +
selfTensor.size + " but `as-scalar` requires the " +
"tensor to be size-1");
}
return buildTensorObject(selfTensor.asScalar());
}),
/**
* Constructs a new, one-dimensional PyretTensor from the values of
* the original PyretTensor.
* @param {PyretTensor} self
* @return {PyretTensor} The newly constructed PyretTensor
*/
"as-1d": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "as-1d");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.as1D());
}),
/**
* Constructs a new, rank-2 PyretTensor from the values of the original
* PyretTensor. The number of elements implied by the input dimensions
* must be the same as the number of elements in the calling Tensor.
* Otherwise, the method raises an error.
* @param {PyretTensor} self
* @param {NumInteger} rows The number of elements in the first dimension
* @param {NumInteger} columns The number of elements in the second
* dimension
* @return {PyretTensor} The newly constructed PyretTensor
*/
"as-2d": runtime.makeMethod2(function(self, rows, columns) {
checkMethodArity(3, arguments, "as-2d");
const jsShapeArray = [rows, columns];
const pyretShapeList = runtime.ffi.makeList(jsShapeArray);
return reshapeTensor(self, pyretShapeList);
}),
/**
* Constructs a new, rank-3 PyretTensor from the values of the original
* PyretTensor. The number of elements implied by the input dimensions
* must be the same as the number of elements in the calling Tensor.
* Otherwise, the method raises an error.
* @param {PyretTensor} self
* @param {NumInteger} rows The number of elements in the first dimension
* @param {NumInteger} columns The number of elements in the second
* dimension
* @param {NumInteger} depth The number of elements in the third
* dimension
* @return {PyretTensor} The newly constructed PyretTensor
*/
"as-3d": runtime.makeMethod3(function(self, rows, columns, depth) {
checkMethodArity(4, arguments, "as-3d");
const jsShapeArray = [rows, columns, depth];
const pyretShapeList = runtime.ffi.makeList(jsShapeArray);
return reshapeTensor(self, pyretShapeList);
}),
/**
* Constructs a new, rank-4 PyretTensor from the values of the original
* PyretTensor. The number of elements implied by the input dimensions
* must be the same as the number of elements in the calling Tensor.
* Otherwise, the method raises an error.
* @param {PyretTensor} self
* @param {NumInteger} rows The number of elements in the first dimension
* @param {NumInteger} columns The number of elements in the second
* dimension
* @param {NumInteger} depth1 The number of elements in the third
* dimension
* @param {NumInteger} depth2 The number of elements in the fourth
* dimension
* @return {PyretTensor} The newly constructed PyretTensor
*/
"as-4d": runtime.makeMethod4(function(self, rows, columns, depth1, depth2) {
checkMethodArity(5, arguments, "as-4d");
const jsShapeArray = [rows, columns, depth1, depth2];
const pyretShapeList = runtime.ffi.makeList(jsShapeArray);
return reshapeTensor(self, pyretShapeList);
}),
/**
* Constructs a new Tensor from the values of the original Tensor with
* all of the values cast to the input datatype.
*
* The possible data-types are "float32", "int32", or "bool". Any other
* dataType will raise an error.
*
* @param {PyretTensor} self
* @param {String} datatype The name of the data type to cast to
* @return {PyretTensor}
*/
"as-type": runtime.makeMethod1(function(self, datatype) {
checkMethodArity(2, arguments, "as-type");
runtime.checkString(datatype);
const type = unwrap(datatype);
if (type !== "float32" && type !== "int32" && type !== "bool") {
runtime.ffi.throwMessageException("Attempted to cast tensor to " +
"invalid type ('" + type + "'); valid types are 'float32', " +
"'int32', or 'bool'");
}
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.asType(type));
}),
/**
* Constructs a new PyretTensorBuffer from the values of the original
* Tensor.
* @param {PyretTensor} self
* @return {PyretTensorBuffer} A TensorBuffer containing all of the
* values in the calling Tensor
*/
"to-buffer": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "to-buffer");
const selfTensor = unwrapTensor(self);
const newBuffer = selfTensor.buffer();
return buildTensorBufferObject(newBuffer);
}),
/**
* Returns a List containing the data in the Tensor.
* @param {PyretTensor} self
* @return {List<Roughnum>} The data in the Tensor as a one-dimensional
* List
*/
"data-now": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "data-now");
const selfTensor = unwrapTensor(self);
// .dataSync returns a TypedArray, so convert it to a normal JSArray
// so we can then convert it to a Pyret List:
const tensorData = Array.from(selfTensor.dataSync());
// Convert to Roughnums, since the numbers returned from a Tensor are
// floating point:
const roughnumData = tensorData.map(x => runtime.num_to_roughnum(x));
return runtime.ffi.makeList(roughnumData);
}),
/**
* Constructs a new Tensor from the values of the original Tensor with
* all of the values cast to the "float32" datatype.
* @param {PyretTensor} self
* @return {PyretTensor} A newly constructed Tensor cast to the "float32"
* datatype
*/
"to-float": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "to-float");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.toFloat());
}),
/**
* Constructs a new Tensor from the values of the original Tensor with
* all of the values cast to the "int32" datatype.
* @param {PyretTensor} self
* @return {PyretTensor} A newly constructed Tensor cast to the "int32"
* datatype
*/
"to-int": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "to-int");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.toInt());
}),
/**
* Constructs a new Tensor from the values of the original Tensor with
* all of the values cast to the "bool" datatype.
* @param {PyretTensor} self
* @return {PyretTensor} A newly constructed Tensor cast to the "bool"
* datatype
*/
"to-bool": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "to-bool");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.toBool());
}),
/**
* Constructs a new, mutable Tensor from the values of the original
* Tensor.
* @param {PyretTensor} self
* @return {PyretTensor} A mutable Tensor
*/
"to-variable": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "to-variable");
return makeVariable(self);
}),
/**
* Constructs a new Tensor with the input dimensions `newShape` from
* the values of the original Tensor.
*
* The number of elements implied by `newShape` must be the same as the
* number of elements in the calling Tensor. Otherwise, the method raises
* an error.
* @param {PyretTensor} self
* @param {List<NumInteger>} newShape The shape to which to attempt to
* reshape the calling Tensor
* @return {PyretTensor} The newly constructed Tensor with the given
* shape
*/
"reshape": runtime.makeMethod0(function(self, newShape) {
checkMethodArity(2, arguments, "reshape");
return reshapeTensor(self, newShape);
}),
/**
* Returns a Tensor that has expanded rank, by inserting a dimension into
* the Tensor’s shape at the given dimension index `axis`. If `axis` is
* `none`, the method inserts a dimension at index 0 by default.
* @param {PyretTensor} self
* @param {Option<NumInteger>} axis The axis at which to insert a new
* dimension
* @return {PyretTensor} A newly constructed Tensor
*/
"expand-dims": runtime.makeMethod1(function(self, axis) {
checkMethodArity(2, arguments, "expand-dims");
const selfTensor = unwrapTensor(self);
const jsAxis = unwrapFixnumOption(axis);
// The rank of the tensor is equal to its dimensions:
const tensorRank = selfTensor.shape.length;
// Check that, if the axis was specified, that the input axis is less
// than or equal to the dimensions of the current tensor:
if (jsAxis && jsAxis > tensorRank) {
runtime.ffi.throwMessageException("Cannot expand dimensions " +
"because the input axis must be less than or equal to the rank " +
"of the tensor (tensor was rank-" + tensorRank + " but the " +
"input axis was " + jsAxis + ")");
}
return buildTensorObject(selfTensor.expandDims(jsAxis));
}),
/**
* Returns a Tensor with dimensions of size 1 removed from the shape. If
* `axes` is not `none`, the method only squeezes the dimensions listed
* as indices in `axes`. The method will raise an error if one of the
* dimensions specified in `axes` is not of size 1.
* @param {PyretTensor} self
* @param {Option<List<NumInteger>>} axes The axes at which to squeeze
* @return {PyretTensor} A newly constructed Tensor
*/
"squeeze": runtime.makeMethod1(function(self, axes) {
checkMethodArity(2, arguments, "squeeze");
const selfTensor = unwrapTensor(self);
const tensorShape = selfTensor.shape;
const tensorRank = tensorShape.length;
const jsAxes =
runtime.ffi.cases(runtime.ffi.isOption, "is-Option", axes, {
some: (v) => {
runtime.checkList(v);
return runtime.ffi.toArray(v).map((x) => {
runtime.checkNumInteger(x);
const axis = runtime.num_to_fixnum(x);
// Axes are zero-indexed, so offset by 1:
if ((axis + 1) > tensorRank) {
runtime.ffi.throwMessageException("Cannot squeeze axis " +
axis + " since the axis does not exist in a tensor of " +
"rank " + tensorRank);
}
else if (tensorShape[axis] !== 1) {
runtime.ffi.throwMessageException("Cannot squeeze axis " +
axis + " since the dimension of that axis is " +
tensorShape[axis] + ", not 1");
}
return axis;
});
},
none: () => { return undefined; }
});
return buildTensorObject(selfTensor.squeeze(jsAxes));
}),
/**
* Constructs a new Tensor that is a copy of the original Tensor.
* @param {PyretTensor} self
* @return {PyretTensor} A copy of the calling Tensor
*/
"clone": runtime.makeMethod0(function(self) {
checkMethodArity(1, arguments, "clone");
const selfTensor = unwrapTensor(self);
return buildTensorObject(selfTensor.clone());
}),
/**
* Constructs a new Tensor that is the result of the calling Tensor
* added to b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to add to self
* @return {PyretTensor} The result
*/
"add": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "add");
return addTensors(self, b);
}),
/**
* Constructs a new Tensor that is the result of the calling Tensor
* minus b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to subtract from self
* @return {PyretTensor} The result
*/
"subtract": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "subtract");
return subtractTensors(self, b);
}),
/**
* Constructs a new Tensor that is the result of the calling Tensor
* multiplied by b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to multiply with self
* @return {PyretTensor} The result
*/
"multiply": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "multiply");
return multiplyTensors(self, b);
}),
/**
* Constructs a new Tensor that is the result of the calling Tensor
* divided by b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to divide self by
* @return {PyretTensor} The result
*/
"divide": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "divide");
return divideTensors(self, b);
}),
/**
* Constructs a new Tensor that is the result of applying the floor
* function to the result of the calling Tensor divided by b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to divide self by
* @return {PyretTensor} The result
*/
"floor-divide": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "floor-divide");
return floorDivideTensors(self, b);
}),
/**
* Constructs a new Tensor that is the maximum of the Tensor and b,
* element-wise.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to compare self against
* @return {PyretTensor} The result
*/
"max": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "max");
return maxTensor(self, b);
}),
/**
* Constructs a new Tensor that is the minimum of the Tensor and b,
* element-wise.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to compare self against
* @return {PyretTensor} The result
*/
"min": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "min");
return minTensor(self, b);
}),
/**
* Constructs a new Tensor that is the result of the modulo of the Tensor
* and b.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to mod self with
* @return {PyretTensor} The result
*/
"modulo": runtime.makeMethod1(function(self, b) {
checkMethodArity(2, arguments, "modulo");
return moduloTensor(self, b);
}),
/**
* Constructs a new Tensor that is the result of the power of the Tensor
* to exp, element-wise.
* @param {PyretTensor} self
* @param {PyretTensor} exp The Tensor to raise self to
* @return {PyretTensor} The result
*/
"expt": runtime.makeMethod1(function(self, exp) {
checkMethodArity(2, arguments, "expt");
return exptTensor(self, exp);
}),
/**
* Constructs a new Tensor that is the result of (self - b) * (self - b),
* element-wise.
* @param {PyretTensor} self
* @param {PyretTensor} b The Tensor to compute the squared difference
* with
* @return {PyretTensor} The result
*/
"squared-difference": runtime.makeMethod1(function(self, b) {