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270 lines (228 loc) · 8.21 KB
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package gorgonia
import (
"fmt"
"github.com/chewxy/gorgonia/tensor"
"github.com/chewxy/hm"
"github.com/pkg/errors"
)
// Functions in this file returns *Node and panics if an error happens
/* Helper functions to create new input nodes */
// Must indicates a node must be created. If there isn't a node created, or there was an error,
// it subsumes the error, and immediately panics
func Must(n *Node, err error, opts ...NodeConsOpt) *Node {
if err != nil || n == nil {
panic(err)
}
return n
}
// NodeFromAny creates a Node from a tensor.Tensor, automatically filling in shape and type info
func NodeFromAny(g *ExprGraph, any interface{}, opts ...NodeConsOpt) *Node {
v, t, dt, err := anyToValue(any)
if err != nil {
panic(err)
}
opts = append(opts, WithValue(v))
switch t.(type) {
case tensor.Dtype:
return NewScalar(g, dt, opts...)
case TensorType:
opts = append(opts, nil)
copy(opts[1:], opts[0:len(opts)-1])
opts[0] = WithShape(v.Shape()...)
return NewTensor(g, dt, v.Shape().Dims(), opts...)
default:
panic(nyi("NewNodeFromAny", any))
}
}
// NewScalar creates a Node representing a variable that holds a scalar value
func NewScalar(g *ExprGraph, t tensor.Dtype, opts ...NodeConsOpt) *Node {
curOpts := []NodeConsOpt{WithType(t), In(g), WithShape()}
curOpts = append(curOpts, opts...)
return NewUniqueNode(curOpts...)
}
// NewVector creates a Node representing a variable that holds a vector (nx1 matrix)
func NewVector(g *ExprGraph, t tensor.Dtype, opts ...NodeConsOpt) *Node {
tt := newTensorType(1, t)
curOpts := []NodeConsOpt{WithType(tt), In(g)}
curOpts = append(curOpts, opts...)
return NewUniqueNode(curOpts...)
}
// NewMatrix creates a Node representing a variable that holds a matrix (nxm)
func NewMatrix(g *ExprGraph, t tensor.Dtype, opts ...NodeConsOpt) *Node {
tt := newTensorType(2, t)
curOpts := []NodeConsOpt{WithType(tt), In(g)}
curOpts = append(curOpts, opts...)
return NewUniqueNode(curOpts...)
}
// NewTensor creates a Node representing a variable that holds a tensor (any n-dimensional array with dimensions greater than 2)
func NewTensor(g *ExprGraph, t tensor.Dtype, dims int, opts ...NodeConsOpt) *Node {
tt := newTensorType(dims, t)
curOpts := []NodeConsOpt{WithType(tt), In(g)}
curOpts = append(curOpts, opts...)
return NewUniqueNode(curOpts...)
}
// NewConstant takes in any reasonable value and makes it a constant node.
func NewConstant(v interface{}, opts ...NodeConsOpt) *Node {
var op Op
var t hm.Type
var name string
var s tensor.Shape
var val Value
name = fmt.Sprintf("%v", v)
val, t, _, err := anyToValue(v)
if err != nil {
panic(err)
}
switch vt := val.(type) {
case Scalar:
op = constantScalar{vt}
s = scalarShape
case tensor.Tensor:
op = constantTensor{vt}
s = vt.Shape()
}
if op == nil || t == nil {
panic(fmt.Sprintf("HELP. Op: %v, t: %v", op, t))
}
consOpts := []NodeConsOpt{WithOp(op), WithType(t), WithName(name), WithShape(s...), WithValue(val)}
consOpts = append(consOpts, opts...)
return newNode(consOpts...)
}
// UniformRandomNode creates an input node that has a random op so everytime the node is passed, random values will be plucked from
// a uniform distribution. The type of the node depends on the
// shape passed in. To get a scalar value at run time, don't pass in any shapes
func UniformRandomNode(g *ExprGraph, dt tensor.Dtype, low, high float64, shape ...int) *Node {
op := makeRandomOp(uniform, dt, low, high, shape...)
s := tensor.Shape(shape)
var t hm.Type
if s.Eq(scalarShape) {
t = dt
} else {
t = newTensorType(s.Dims(), dt)
}
retVal := NewUniqueNode(WithType(t), WithOp(op), In(g), WithShape(shape...))
return retVal
}
// GaussianRandomNode creates an input node that has a random op so everytime the node is passed, random values will be plucked from
// a gaussian distribution with the mean and stdev provided. The type of the node depends on the
// shape passed in. To get a scalar value at run time, don't pass in any shapes
func GaussianRandomNode(g *ExprGraph, dt tensor.Dtype, mean, stdev float64, shape ...int) *Node {
op := makeRandomOp(gaussian, dt, mean, stdev, shape...)
s := tensor.Shape(shape)
var t hm.Type
if s.Eq(scalarShape) {
t = dt
} else {
t = newTensorType(s.Dims(), dt)
}
retVal := NewUniqueNode(WithType(t), WithOp(op), In(g), WithShape(shape...))
return retVal
}
// BinomialRandomNode creates an input node that has a random op so that everytime the node is passed, random values will be plucked from
// a binomial distribution with the mean and stdev provided. The type of the node depends on the
// shape passed in. To get a scalar value at run time, don't pass in any shapes
//
// Whilst technically the number of trials of a binomal distribution should be a discrete value (you can't have half a trial), to keep with
// API uniformity, trials is passed in as a float64, but will be truncated to an int at runtime.
func BinomialRandomNode(g *ExprGraph, dt tensor.Dtype, trials, prob float64, shape ...int) *Node {
op := makeRandomOp(binomial, dt, trials, prob, shape...)
s := tensor.Shape(shape)
var t hm.Type
if s.Eq(scalarShape) {
t = dt
} else {
t = newTensorType(s.Dims(), dt)
}
retVal := NewUniqueNode(WithType(t), WithOp(op), In(g), WithShape(shape...))
return retVal
}
// OneHotVector creates a node representing a one hot vector
func OneHotVector(id, classes int, t tensor.Dtype, opts ...NodeConsOpt) *Node {
T := tensor.Ones(t, classes)
return NewConstant(T, opts...)
}
// Grad takes a scalar cost node and a list of with-regards-to, and returns the gradient
func Grad(cost *Node, WRTs ...*Node) (retVal []*Node, err error) {
symdiffLogf("Cost:%v", cost)
if !cost.IsScalar() {
return nil, errors.Errorf("Expected Cost to be a scalar. Got %v instead", cost)
}
for i, n := range WRTs {
if !n.isInput() {
err = errors.Errorf("Can only differentiate with regards to input nodes. %dth Node %v isn't an input", i, n)
return nil, err
}
}
var dt tensor.Dtype
var ok bool
if dt, ok = cost.t.(tensor.Dtype); !ok {
err = errors.Wrap(err, "Expected a scalar dtype for cost")
return
}
var gradOut *Node
switch dt {
case Float64:
gradOut = onef64
case Float32:
gradOut = onef32
default:
return nil, errors.Wrapf(err, "%s not yet implemented for %v of %T", dt.String(), "Grad()'s gradOut")
}
gradOut = cost.g.AddNode(gradOut)
return Backpropagate(Nodes{cost}, Nodes{gradOut}, Nodes(WRTs))
}
// Let binds a Value to a node that is a variable. A variable is represented as a *Node with no Op.
// It is equivalent to :
// x = 2
func Let(n *Node, be interface{}) error {
if !n.isInput() {
return errors.New("Cannot bind a value to a non input node")
}
return UnsafeLet(n, be)
}
// UnsafeLet binds a Value to any node, not just a variable node. This means that you can use it to change any node's value at the runtime of the graph. UNSAFE!
//
// Additional notes: if `be` is a tensor.Slice, and the node's op is a sliceOp or sliceIncrOp, the op's slice will be replaced with the new slice.
func UnsafeLet(n *Node, be interface{}) error {
switch v := be.(type) {
case tensor.Slice:
switch so := n.op.(type) {
case *sliceOp:
so.Slice = v
n.op = so
case sliceIncrOp:
so.Slice = v
n.op = so
default:
return errors.Errorf("Trying to Let() a node with a slice. Node's op is %v, not sliceOp", n.op)
}
case Value:
n.bind(v)
default:
var val Value
var err error
if val, _, _, err = anyToValue(be); err != nil {
return errors.Wrapf(err, anyToValueFail, be, be)
}
n.bind(val)
}
return nil
}
// Set is the equivalent of doing this:
// a = b
// where a and b are both variables
func Set(a, b *Node) (retVal *Node) {
op := letOp{}
name := fmt.Sprintf("%v %s %v", a, op, b)
return NewUniqueNode(WithOp(op), WithChildren(Nodes{a, b}), WithName(name), In(a.g))
}
// Read is one of those special snowflake tumblrina *Nodes. It allows for extraction of the value of the *Node at runtime
// into a Value. Note that a *Value (a pointer to a Value) is passed into this function, not a Value.
func Read(n *Node, into *Value) (retVal *Node) {
op := readOp{into}
name := fmt.Sprintf("read %v into %v", n, into)
retVal = NewUniqueNode(WithOp(op), WithChildren(Nodes{n}), WithName(name), In(n.g))
retVal.op = op // this ensures the correct pointer is written
retVal.name = name
return
}