tensor/grad/data_ops_test.go

// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
// SPDX-License-Identifier: MIT

package grad

import (
	"testing"

	"sourcedock.dev/petrbalvin/tensor/internal/core"
)

func TestTensorSqueezeUnsqueezeClip(t *testing.T) {
	// Squeeze/Unsqueeze round-trip with gradient.
	x, _ := core.FromFloats([]float64{1, 2, 3, 4}, 1, 4, 1)
	xt := FromArray(x, true)
	sq, err := xt.Squeeze(2)
	if err != nil {
		t.Fatal(err)
	}
	if sq.Data().NDim() != 2 {
		t.Fatalf("Squeeze ndim: %d", sq.Data().NDim())
	}
	back, err := sq.Unsqueeze(2)
	if err != nil {
		t.Fatal(err)
	}
	s, _ := back.Sum()
	if err := s.Backward(); err != nil {
		t.Fatal(err)
	}
	for i := range 4 {
		if g := xt.Grad().FloatAt(i); g != 1 {
			t.Errorf("Squeeze/Unsqueeze grad[%d]: %v, want 1", i, g)
		}
	}

	// Clip gradient: 1 inside [lo, hi], 0 outside.
	c, _ := core.FromFloats([]float64{-1, 0.5, 2}, 3)
	ct := FromArray(c, true)
	cl, err := ct.Clip(0, 1)
	if err != nil {
		t.Fatal(err)
	}
	s2, _ := cl.Sum()
	if err := s2.Backward(); err != nil {
		t.Fatal(err)
	}
	want := []float64{0, 1, 0}
	for i := range 3 {
		if g := ct.Grad().FloatAt(i); g != want[i] {
			t.Errorf("Clip grad[%d]: %v, want %v", i, g, want[i])
		}
	}

}
func TestAxisReductionAutograd(t *testing.T) {
	x, _ := core.FromFloats([]float64{1, 2, 3, 4, 5, 6}, 2, 3)
	xt := FromArray(x, true)

	s, err := xt.SumAxis(1)
	if err != nil {
		t.Fatal(err)
	}
	if s.Data().Len() != 2 {
		t.Fatalf("SumAxis len: %d", s.Data().Len())
	}
	if err := s.Backward(); err != nil {
		t.Fatal(err)
	}
	for i := range x.Len() {
		if g := xt.Grad().FloatAt(i); g != 1 {
			t.Errorf("SumAxis grad[%d]: %v, want 1", i, g)
		}
	}

	mean, err := FromArray(x, true).MeanAxis(1)
	if err != nil {
		t.Fatal(err)
	}
	if err := mean.Backward(); err != nil {
		t.Fatal(err)
	}
}

func TestL2NormAxisAutogradGradient(t *testing.T) {
	xv := []float64{3, 4, 0.5, 0.5}
	x, _ := core.FromFloats(xv, 1, 1, 2, 2)
	xt := FromArray(x, true)
	out, err := xt.L2NormAxis(1)
	if err != nil {
		t.Fatal(err)
	}
	s, _ := out.Sum()
	if err := s.Backward(); err != nil {
		t.Fatal(err)
	}
	analytic := make([]float64, x.Len())
	for i := range x.Len() {
		analytic[i] = xt.Grad().FloatAt(i)
	}
	ref := numericGrad(func(a *core.Array) float64 {
		o, err := FromArray(a, false).L2NormAxis(1)
		if err != nil {
			t.Fatal(err)
		}
		ss, _ := o.Sum()
		return ss.Data().FloatAt(0)
	}, x)
	if d := maxAbsDiff(analytic, ref); d > 1e-6 {
		t.Errorf("L2NormAxis grad: max diff %v", d)
	}
}

func TestBroadcastToAutograd(t *testing.T) {
	x, _ := core.FromFloats([]float64{1, 2, 3}, 1, 3)
	xt := FromArray(x, true)
	out, err := xt.BroadcastTo(2, 3)
	if err != nil {
		t.Fatal(err)
	}
	if out.Data().Shape()[0] != 2 {
		t.Fatalf("BroadcastTo shape: %v", out.Data().Shape())
	}
	onesArr, _ := core.Ones(core.Float, 2, 3)
	loss, _ := out.Mul(FromArray(onesArr, false))
	s, _ := loss.Sum()
	if err := s.Backward(); err != nil {
		t.Fatal(err)
	}
	// Gradient sums over replicated rows.
	for i := range 3 {
		if g := xt.Grad().FloatAt(i); g != 2 {
			t.Errorf("BroadcastTo grad[%d]: %v, want 2", i, g)
		}
	}
}

func TestPowAbsSqrtFloorAutogradGradient(t *testing.T) {
	cases := []struct {
		name string
		vals []float64
		fn   func(*Tensor) (*Tensor, error)
	}{
		{"Pow3", []float64{0.5, 1.5}, func(x *Tensor) (*Tensor, error) { return x.Pow(3) }},
		{"Abs", []float64{0.5, -1.5}, func(x *Tensor) (*Tensor, error) { return x.Abs() }},
		{"Sqrt", []float64{0.25, 2.25}, func(x *Tensor) (*Tensor, error) { return x.Sqrt() }},
	}
	for _, tc := range cases {
		t.Run(tc.name, func(t *testing.T) {
			a, _ := core.FromFloats(tc.vals, 2)
			at := FromArray(a, true)
			out, err := tc.fn(at)
			if err != nil {
				t.Fatal(err)
			}
			s, err := out.Sum()
			if err != nil {
				t.Fatal(err)
			}
			if err := s.Backward(); err != nil {
				t.Fatal(err)
			}
			analytic := make([]float64, a.Len())
			for i := range a.Len() {
				analytic[i] = at.Grad().FloatAt(i)
			}
			ref := numericGrad(func(v *core.Array) float64 {
				o, err := tc.fn(FromArray(v, false))
				if err != nil {
					t.Fatal(err)
				}
				ss, err := o.Sum()
				if err != nil {
					t.Fatal(err)
				}
				return ss.Data().FloatAt(0)
			}, a)
			if d := maxAbsDiff(analytic, ref); d > 1e-6 {
				t.Errorf("%s grad: max diff %v", tc.name, d)
			}
		})
	}

	// Floor contributes no gradient.
	a, _ := core.FromFloats([]float64{1.4, 2.6}, 2)
	at := FromArray(a, true)
	fl, err := at.Floor()
	if err != nil {
		t.Fatal(err)
	}
	s, _ := fl.Sum()
	if err := s.Backward(); err != nil {
		t.Fatal(err)
	}
	for i := range a.Len() {
		if g := at.Grad().FloatAt(i); g != 0 {
			t.Errorf("Floor grad[%d]: %v, want 0", i, g)
		}
	}
}