feat: initial release

Assisted-by: GLM 5.3 Flash

grad/data_ops_test.go

@@ -0,0 +1,200 @@
+// 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)
+		}
+	}
+}