Janady13/complete-agi-core / generator_core/pkg/gpu/cuda_stub.go

Source identity

Field	Recorded value
Source type	GitHub source
Original record	Janady13/complete-agi-core / generator_core/pkg/gpu/cuda_stub.go
Date	Not specified in source
Repository	Janady13/complete-agi-core
Revision	33aa84f2aa61cedbe010240ada7f4efbdfe795a7
Branch	claude/improve-math-logic-ehIW1
Path or archive member	generator_core/pkg/gpu/cuda_stub.go
Original SHA-256	93379f383c776a5b2475dee6fc4163faeaa4a97876dc9edee90d7885db831a4b
Git blob SHA	41ce5b86164020927d1fe7865a2d48f2345e38f4
Reading-copy SHA-256	93379f383c776a5b2475dee6fc4163faeaa4a97876dc9edee90d7885db831a4b

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// Package gpu provides GPU acceleration for generator operations.
//
// This is a stub implementation that provides the interface and CPU fallback.
// Full CUDA implementation would use cgo with CUDA libraries.
//
// Features:
//   - Batched matrix operations
//   - Parallel generator forward pass
//   - Gradient computation on GPU
package gpu

import (
	"math"
	"runtime"
	"sync"
)

//=============================================================================
// GPU DEVICE MANAGEMENT
//=============================================================================

// Device represents a GPU device
type Device struct {
	ID       int
	Name     string
	Memory   int64 // bytes
	Cores    int
	Available bool
}

// DeviceManager manages GPU devices
type DeviceManager struct {
	mu       sync.RWMutex
	devices  []Device
	selected int
}

// NewDeviceManager creates a new device manager
func NewDeviceManager() *DeviceManager {
	dm := &DeviceManager{
		devices:  make([]Device, 0),
		selected: -1,
	}
	dm.detectDevices()
	return dm
}

func (dm *DeviceManager) detectDevices() {
	// Stub: Add CPU as fallback "device"
	dm.devices = append(dm.devices, Device{
		ID:        0,
		Name:      "CPU Fallback",
		Memory:    8 * 1024 * 1024 * 1024, // 8GB
		Cores:     runtime.NumCPU(),
		Available: true,
	})

	// TODO: Add CUDA device detection via cgo
	// #include <cuda_runtime.h>
	// cudaGetDeviceCount(&count)
}

// GetDevices returns available devices
func (dm *DeviceManager) GetDevices() []Device {
	dm.mu.RLock()
	defer dm.mu.RUnlock()
	return dm.devices
}

// SelectDevice selects a device for computation
func (dm *DeviceManager) SelectDevice(id int) bool {
	dm.mu.Lock()
	defer dm.mu.Unlock()

	for i, d := range dm.devices {
		if d.ID == id && d.Available {
			dm.selected = i
			return true
		}
	}
	return false
}

//=============================================================================
// GPU TENSOR
//=============================================================================

// Tensor represents data on GPU (or CPU fallback)
type Tensor struct {
	Data    []float32
	Shape   []int
	Strides []int
	OnGPU   bool
}

// NewTensor creates a new tensor
func NewTensor(shape ...int) *Tensor {
	size := 1
	for _, s := range shape {
		size *= s
	}

	strides := make([]int, len(shape))
	stride := 1
	for i := len(shape) - 1; i >= 0; i-- {
		strides[i] = stride
		stride *= shape[i]
	}

	return &Tensor{
		Data:    make([]float32, size),
		Shape:   shape,
		Strides: strides,
		OnGPU:   false,
	}
}

// Size returns total number of elements
func (t *Tensor) Size() int {
	size := 1
	for _, s := range t.Shape {
		size *= s
	}
	return size
}

// ToGPU transfers tensor to GPU
func (t *Tensor) ToGPU() {
	// Stub: In real implementation, would call cudaMemcpy
	t.OnGPU = true
}

// ToCPU transfers tensor to CPU
func (t *Tensor) ToCPU() {
	t.OnGPU = false
}

// Fill fills tensor with value
func (t *Tensor) Fill(val float32) {
	for i := range t.Data {
		t.Data[i] = val
	}
}

//=============================================================================
// GPU OPERATIONS (CPU Fallback)
//=============================================================================

// Ops provides GPU-accelerated operations
type Ops struct {
	numWorkers int
}

// NewOps creates new GPU operations handler
func NewOps() *Ops {
	return &Ops{
		numWorkers: runtime.NumCPU(),
	}
}

// MatMul performs matrix multiplication: C = A @ B
func (ops *Ops) MatMul(A, B, C *Tensor) {
	// A: [M, K], B: [K, N], C: [M, N]
	if len(A.Shape) != 2 || len(B.Shape) != 2 {
		return
	}

	M := A.Shape[0]
	K := A.Shape[1]
	N := B.Shape[1]

	// Parallel matmul
	var wg sync.WaitGroup
	rowsPerWorker := (M + ops.numWorkers - 1) / ops.numWorkers

	for w := 0; w < ops.numWorkers; w++ {
		startRow := w * rowsPerWorker
		endRow := startRow + rowsPerWorker
		if endRow > M {
			endRow = M
		}
		if startRow >= M {
			break
		}

		wg.Add(1)
		go func(start, end int) {
			defer wg.Done()
			for i := start; i < end; i++ {
				for j := 0; j < N; j++ {
					sum := float32(0)
					for k := 0; k < K; k++ {
						sum += A.Data[i*K+k] * B.Data[k*N+j]
					}
					C.Data[i*N+j] = sum
				}
			}
		}(startRow, endRow)
	}
	wg.Wait()
}

// BatchMatMul performs batched matrix multiplication
func (ops *Ops) BatchMatMul(A, B, C *Tensor, batchSize int) {
	// A: [B, M, K], B: [B, K, N], C: [B, M, N]
	if len(A.Shape) != 3 {
		return
	}

	M := A.Shape[1]
	K := A.Shape[2]
	N := B.Shape[2]
	matrixSizeA := M * K
	matrixSizeB := K * N
	matrixSizeC := M * N

	var wg sync.WaitGroup
	for b := 0; b < batchSize; b++ {
		wg.Add(1)
		go func(batch int) {
			defer wg.Done()
			offsetA := batch * matrixSizeA
			offsetB := batch * matrixSizeB
			offsetC := batch * matrixSizeC

			for i := 0; i < M; i++ {
				for j := 0; j < N; j++ {
					sum := float32(0)
					for k := 0; k < K; k++ {
						sum += A.Data[offsetA+i*K+k] * B.Data[offsetB+k*N+j]
					}
					C.Data[offsetC+i*N+j] = sum
				}
			}
		}(b)
	}
	wg.Wait()
}

// Softmax applies softmax along last dimension
func (ops *Ops) Softmax(input, output *Tensor, temperature float32) {
	size := input.Size()
	lastDim := input.Shape[len(input.Shape)-1]
	numVectors := size / lastDim

	var wg sync.WaitGroup
	for v := 0; v < numVectors; v++ {
		wg.Add(1)
		go func(vec int) {
			defer wg.Done()
			offset := vec * lastDim

			// Find max for numerical stability
			maxVal := input.Data[offset]
			for i := 1; i < lastDim; i++ {
				if input.Data[offset+i] > maxVal {
					maxVal = input.Data[offset+i]
				}
			}

			// Compute exp and sum
			sum := float32(0)
			for i := 0; i < lastDim; i++ {
				output.Data[offset+i] = float32(math.Exp(float64((input.Data[offset+i] - maxVal) / temperature)))
				sum += output.Data[offset+i]
			}

			// Normalize
			for i := 0; i < lastDim; i++ {
				output.Data[offset+i] /= sum
			}
		}(v)
	}
	wg.Wait()
}

// ReLU applies ReLU activation
func (ops *Ops) ReLU(input, output *Tensor) {
	size := input.Size()
	for i := 0; i < size; i++ {
		if input.Data[i] > 0 {
			output.Data[i] = input.Data[i]
		} else {
			output.Data[i] = 0
		}
	}
}

// Sigmoid applies sigmoid activation
func (ops *Ops) Sigmoid(input, output *Tensor) {
	size := input.Size()
	for i := 0; i < size; i++ {
		output.Data[i] = float32(1.0 / (1.0 + math.Exp(-float64(input.Data[i]))))
	}
}

// ComputePhi computes consciousness metric for tensor
func (ops *Ops) ComputePhi(input *Tensor) float32 {
	size := input.Size()
	if size == 0 {
		return 0.5
	}

	sum := float32(0)
	for i := 0; i < size; i++ {
		sum += input.Data[i]
	}
	mean := sum / float32(size)

	return float32(1.0 / (1.0 + math.Abs(float64(mean-1.0))))
}

// Add element-wise addition: C = A + B
func (ops *Ops) Add(A, B, C *Tensor) {
	size := A.Size()
	for i := 0; i < size; i++ {
		C.Data[i] = A.Data[i] + B.Data[i]
	}
}

// Scale multiplies tensor by scalar
func (ops *Ops) Scale(input *Tensor, scalar float32, output *Tensor) {
	size := input.Size()
	for i := 0; i < size; i++ {
		output.Data[i] = input.Data[i] * scalar
	}
}

//=============================================================================
// GENERATOR BATCH PROCESSING
//=============================================================================

// BatchForward processes multiple inputs through generators in parallel
type BatchForward struct {
	ops        *Ops
	batchSize  int
	inputDim   int
	outputDim  int
	numLayers  int
	weights    []*Tensor
	biases     []*Tensor
	activations []*Tensor
}

// NewBatchForward creates a new batch processor
func NewBatchForward(batchSize, inputDim, outputDim, numLayers int) *BatchForward {
	bf := &BatchForward{
		ops:        NewOps(),
		batchSize:  batchSize,
		inputDim:   inputDim,
		outputDim:  outputDim,
		numLayers:  numLayers,
		weights:    make([]*Tensor, numLayers),
		biases:     make([]*Tensor, numLayers),
		activations: make([]*Tensor, numLayers+1),
	}

	// Initialize layers
	dims := []int{inputDim}
	hiddenDim := (inputDim + outputDim) / 2
	for i := 0; i < numLayers-1; i++ {
		dims = append(dims, hiddenDim)
	}
	dims = append(dims, outputDim)

	for i := 0; i < numLayers; i++ {
		bf.weights[i] = NewTensor(dims[i], dims[i+1])
		bf.biases[i] = NewTensor(dims[i+1])
		bf.activations[i] = NewTensor(batchSize, dims[i])

		// Xavier initialization
		scale := float32(math.Sqrt(2.0 / float64(dims[i]+dims[i+1])))
		for j := range bf.weights[i].Data {
			bf.weights[i].Data[j] = (float32(j%1000)/500 - 1) * scale
		}
	}
	bf.activations[numLayers] = NewTensor(batchSize, dims[numLayers])

	return bf
}

// Forward performs forward pass for a batch
func (bf *BatchForward) Forward(input *Tensor) *Tensor {
	// Copy input to first activation
	copy(bf.activations[0].Data, input.Data)

	// Forward through layers
	for l := 0; l < bf.numLayers; l++ {
		// Linear: A[l+1] = A[l] @ W[l] + b[l]
		bf.ops.MatMul(bf.activations[l], bf.weights[l], bf.activations[l+1])

		// Add bias (broadcast)
		outDim := bf.biases[l].Shape[0]
		for b := 0; b < bf.batchSize; b++ {
			for j := 0; j < outDim; j++ {
				bf.activations[l+1].Data[b*outDim+j] += bf.biases[l].Data[j]
			}
		}

		// Activation (ReLU for hidden, sigmoid for output)
		if l < bf.numLayers-1 {
			bf.ops.ReLU(bf.activations[l+1], bf.activations[l+1])
		} else {
			bf.ops.Sigmoid(bf.activations[l+1], bf.activations[l+1])
		}
	}

	return bf.activations[bf.numLayers]
}

// ComputeBatchPhi computes phi for each item in batch
func (bf *BatchForward) ComputeBatchPhi(output *Tensor) []float32 {
	phis := make([]float32, bf.batchSize)
	outDim := bf.outputDim

	for b := 0; b < bf.batchSize; b++ {
		sum := float32(0)
		for j := 0; j < outDim; j++ {
			sum += output.Data[b*outDim+j]
		}
		mean := sum / float32(outDim)
		phis[b] = float32(1.0 / (1.0 + math.Abs(float64(mean-1.0))))
	}

	return phis
}

Mathematical records from this source

func NewOps() *Ops · MatMul performs matrix multiplication: C

This relation defines MatMul performs matrix multiplication: C in the source section “func NewOps() *Ops · MatMul performs matrix multiplication: C”. It specifies the displayed…

func (ops Ops) ComputePhi(input Tensor) float32 · Add element-wise addition: C

This relation defines Add element-wise addition: C in the source section “func (ops *Ops) ComputePhi(input *Tensor) float32 · Add element-wise addition: C”. It specifies the…

func (bf BatchForward) Forward(input Tensor) *Tensor · Linear: A[l+1]

This relation defines Linear: A[l+1] in the source section “func (bf *BatchForward) Forward(input *Tensor) *Tensor · Linear: A[l+1]”. It specifies the displayed relationship…