gotensor/optimizer_test.go

package gotensor

import (
	"math"
	"testing"

	"git.kingecg.top/kingecg/gomatrix"
)

func NewMatrix(data [][]float64) (*gomatrix.Matrix, error) {
	c := make([]float64, len(data)*len(data[0]))
	for i := 0; i < len(c); i++ {
		c[i] = data[i/len(data[0])][i%len(data[0])]
	}
	return gomatrix.NewMatrix(c, []int{len(data), len(data[0])})
}

// TestSGD 测试SGD优化器
func TestSGD(t *testing.T) {
	// 创建一些参数用于测试
	weightData, _ := NewMatrix([][]float64{{1.0, 2.0}, {3.0, 4.0}})
	weightGrad, _ := NewMatrix([][]float64{{0.1, 0.2}, {0.3, 0.4}})

	params := []*Tensor{
		{
			Data: Must(gomatrix.NewMatrix([]float64{1.0, 2.0, 3.0}, []int{3, 1})),
			Grad: Must(gomatrix.NewMatrix([]float64{0.1, 0.2, 0.3}, []int{3, 1})),
		},
		{
			Data: weightData,
			Grad: weightGrad,
		},
	}

	// 创建SGD优化器
	lr := 0.1
	sgd := NewSGD(params, lr)

	// 保存原始参数值
	origVec0, _ := params[0].Data.Get(0, 0)
	origMat00, _ := params[1].Data.Get(0, 0)

	// 执行一步优化
	sgd.Step()

	// 检查参数是否已更新
	newVec0, _ := params[0].Data.Get(0, 0)
	newMat00, _ := params[1].Data.Get(0, 0)

	expectedVec0 := origVec0 - lr*0.1
	expectedMat00 := origMat00 - lr*0.1

	if math.Abs(newVec0-expectedVec0) > 1e-9 {
		t.Errorf("Expected updated param[0][0] to be %v, got %v", expectedVec0, newVec0)
	}

	if math.Abs(newMat00-expectedMat00) > 1e-9 {
		t.Errorf("Expected updated param[1][0,0] to be %v, got %v", expectedMat00, newMat00)
	}

	// 测试ZeroGrad
	sgd.ZeroGrad()
	for _, param := range params {
		shape := param.Shape()
		for i := 0; i < param.Size(); i++ {
			var gradVal float64
			if len(shape) == 1 {
				gradVal, _ = param.Grad.Get(i)
			} else if len(shape) == 2 {
				cols := shape[1]
				gradVal, _ = param.Grad.Get(i/cols, i%cols)
			}
			if math.Abs(gradVal) > 1e-9 {
				t.Errorf("Expected gradient to be zero after ZeroGrad, got %v", gradVal)
			}
		}
	}
}

// TestAdam 测试Adam优化器
func TestAdam(t *testing.T) {
	// 创建一些参数用于测试
	params := []*Tensor{
		{
			Data: Must(gomatrix.NewMatrix([]float64{1.0, 2.0}, []int{2, 1})),
			Grad: Must(gomatrix.NewMatrix([]float64{0.1, 0.2}, []int{2, 1})),
		},
	}

	// 创建Adam优化器
	lr := 0.001
	beta1 := 0.9
	beta2 := 0.999
	epsilon := 1e-8
	adam := NewAdam(params, lr, beta1, beta2, epsilon)

	// 保存原始参数值
	origVec0, _ := params[0].Data.Get(0, 0)

	// 执行几步优化
	for i := 0; i < 3; i++ {
		adam.Step()
	}

	// 检查参数是否已更新
	newVec0, _ := params[0].Data.Get(0, 0)

	if math.Abs(newVec0-origVec0) < 1e-9 {
		t.Errorf("Expected parameter to be updated, but it wasn't. Original: %v, New: %v", origVec0, newVec0)
	}

	// 验证内部状态是否已创建
	if len(adam.M) != len(params) || len(adam.V) != len(params) {
		t.Error("Adam internal states M and V not properly initialized")
	}

	// 测试ZeroGrad
	adam.ZeroGrad()
	for _, param := range params {
		shape := param.Shape()
		for i := 0; i < param.Size(); i++ {
			var gradVal float64
			if len(shape) == 1 {
				gradVal, _ = param.Grad.Get(i)
			} else if len(shape) == 2 {
				cols := shape[1]
				gradVal, _ = param.Grad.Get(i/cols, i%cols)
			}
			if math.Abs(gradVal) > 1e-9 {
				t.Errorf("Expected gradient to be zero after ZeroGrad, got %v", gradVal)
			}
		}
	}
}

// TestAdamWithMatrix 测试Adam优化器处理矩阵参数
func TestAdamWithMatrix(t *testing.T) {
	matrixData, _ := NewMatrix([][]float64{{1.0, 2.0}, {3.0, 4.0}})
	matrixGrad, _ := NewMatrix([][]float64{{0.1, 0.2}, {0.3, 0.4}})

	// 创建矩阵参数用于测试
	params := []*Tensor{
		{
			Data: matrixData,
			Grad: matrixGrad,
		},
	}

	// 创建Adam优化器
	lr := 0.001
	adam := NewAdam(params, lr, 0.9, 0.999, 1e-8)

	// 验证内部状态是否已正确创建
	if len(adam.M) != len(params) || len(adam.V) != len(params) {
		t.Fatalf("Adam internal states M and V not properly initialized. Expected %d states, got M:%d, V:%d",
			len(params), len(adam.M), len(adam.V))
	}

	// 验证内部状态矩阵的形状与参数一致
	mShape := adam.M[0]["tensor"].Shape()
	vShape := adam.V[0]["tensor"].Shape()
	paramShape := params[0].Shape()
	if mShape[0] != paramShape[0] || mShape[1] != paramShape[1] ||
		vShape[0] != paramShape[0] || vShape[1] != paramShape[1] {
		t.Errorf("Adam internal state shapes don't match parameter shape. "+
			"Param: %v, M: %v, V: %v", paramShape, mShape, vShape)
	}

	// 保存原始参数值的副本
	originalData := make([][]float64, paramShape[0])
	for i := 0; i < paramShape[0]; i++ {
		originalData[i] = make([]float64, paramShape[1])
		for j := 0; j < paramShape[1]; j++ {
			originalData[i][j], _ = params[0].Data.Get(i, j)
		}
	}

	// 执行几步优化
	for i := 0; i < 5; i++ {
		adam.Step()
	}

	// 检查所有参数是否已更新
	updated := false
	for i := 0; i < paramShape[0]; i++ {
		for j := 0; j < paramShape[1]; j++ {
			newVal, _ := params[0].Data.Get(i, j)
			if math.Abs(newVal-originalData[i][j]) > 1e-9 {
				updated = true
				return
			}
		}
	}

	if !updated {
		t.Errorf("Expected parameters to be updated, but none were changed")
	}

	// 额外验证更新值是否合理（应该向梯度相反方向移动）
	firstOrig := originalData[0][0]
	firstNew, _ := params[0].Data.Get(0, 0)
	firstGrad, _ := params[0].Grad.Get(0, 0)

	// 参数应该沿着梯度的反方向更新
	if (firstNew-firstOrig)*firstGrad > 0 {
		t.Errorf("Parameter updated in wrong direction. delta=%v, grad=%v",
			firstNew-firstOrig, firstGrad)
	}
}