// Package kvcache implements a paged KV cache with global prefix caching.
package kvcache

import (
	"fmt"
	"math"
	"sync"
	"unsafe"

	"makarna/pkg/backend/cpu"
	"makarna/pkg/backend/cuda"
	"makarna/pkg/tensor"
)

func float32ToFloat16Bits(f float32) uint16 {
	bits := math.Float32bits(f)
	sign := uint16((bits >> 16) & 0x8000)
	exp := int((bits >> 23) & 0xFF)
	mant := bits & 0x007FFFFF

	// NaN / Inf
	if exp == 255 {
		if mant == 0 {
			return sign | 0x7C00
		}
		m := uint16(mant >> 13)
		if m == 0 {
			m = 1
		}
		return sign | 0x7C00 | m
	}

	// Bias adjust: float32 bias=127, float16 bias=15
	exp16 := exp - 127 + 15

	// Overflow -> Inf
	if exp16 >= 31 {
		return sign | 0x7C00
	}

	// Subnormal / underflow
	if exp16 <= 0 {
		if exp16 < -10 {
			return sign
		}
		// Add implicit leading 1.
		mant |= 0x00800000
		// Shift to 10-bit mantissa (plus 13-bit alignment), with round-to-nearest-even.
		shift := uint32(1-exp16) + 13
		m16 := mant >> shift
		rem := mant & ((uint32(1) << shift) - 1)
		half := uint32(1) << (shift - 1)
		if rem > half || (rem == half && (m16&1) == 1) {
			m16++
		}
		return sign | uint16(m16)
	}

	// Normalized: round mantissa to 10 bits (round-to-nearest-even).
	m16 := mant >> 13
	rem := mant & 0x1FFF
	if rem > 0x1000 || (rem == 0x1000 && (m16&1) == 1) {
		m16++
		if m16 == 0x400 {
			m16 = 0
			exp16++
			if exp16 >= 31 {
				return sign | 0x7C00
			}
		}
	}
	return sign | uint16(exp16<<10) | uint16(m16)
}

func float32ToBFloat16Bits(f float32) uint16 {
	bits := math.Float32bits(f)
	upper := uint16(bits >> 16)
	lower := uint16(bits & 0xFFFF)
	if lower > 0x8000 || (lower == 0x8000 && (upper&1) == 1) {
		upper++
	}
	return upper
}

// PagedKVCache is a paged KV cache that uses a global BlockPool.
// Unlike the old Cache which allocated per-request, this shares blocks across
// requests and enables prefix caching via block hashing.
type PagedKVCache struct {
	pool *BlockPool
	cfg  PagedCacheConfig
	mu   sync.RWMutex

	// Per-request state
	requestID string
	tokenIDs  []int

	// Block allocation per layer: blockIDs[layer] = list of block IDs
	blockIDs [][]int

	// How many tokens have computed KV
	numComputed int
	// How many tokens have written KV (may be ahead of numComputed within a step).
	numWritten int

	// Block hashes for prefix matching
	blockHashes []BlockHash

	// Whether this cache owns its blocks (vs borrowed from prefix cache)
	ownedBlocks [][]bool

	// Cached device pointer tables for paged attention (per layer).
	// These are (re)built only when the number of blocks grows.
	ptrTables []devicePtrTable
}

type devicePtrTable struct {
	kDev   unsafe.Pointer
	vDev   unsafe.Pointer
	len    int
	gpu    int
	kvType tensor.DType
}

func (c *PagedKVCache) clearPtrTablesLocked() {
	if c.ptrTables == nil {
		return
	}
	for i := range c.ptrTables {
		if c.ptrTables[i].kDev != nil {
			cuda.FreeDevicePtr(c.ptrTables[i].kDev)
		}
		if c.ptrTables[i].vDev != nil {
			cuda.FreeDevicePtr(c.ptrTables[i].vDev)
		}
		c.ptrTables[i] = devicePtrTable{}
	}
}

func (c *PagedKVCache) ensureCapacityLocked(requiredBlocks int) error {
	if requiredBlocks <= 0 {
		return nil
	}
	if len(c.blockIDs) == 0 {
		return fmt.Errorf("cache not initialized")
	}
	// All layers are expected to have the same number of blocks.
	cur := 0
	if len(c.blockIDs[0]) > 0 {
		cur = len(c.blockIDs[0])
	}
	if requiredBlocks <= cur {
		return nil
	}
	need := requiredBlocks - cur

	allocatedByLayer := make([][]int, c.cfg.NumLayers)
	for layer := 0; layer < c.cfg.NumLayers; layer++ {
		newBlocks, err := c.pool.AllocateBlocks(layer, need)
		if err != nil {
			// rollback
			for l := 0; l < layer; l++ {
				c.pool.FreeBlocks(l, allocatedByLayer[l])
			}
			return err
		}
		allocatedByLayer[layer] = newBlocks
		c.blockIDs[layer] = append(c.blockIDs[layer], newBlocks...)
		for range newBlocks {
			c.ownedBlocks[layer] = append(c.ownedBlocks[layer], true)
		}
	}
	return nil
}

// AppendToken updates the token history for this request.
// This is used to extend block hashing/caching beyond the initial prompt.
func (c *PagedKVCache) AppendToken(tokenID int) {
	c.mu.Lock()
	defer c.mu.Unlock()

	c.tokenIDs = append(c.tokenIDs, tokenID)

	// Only add hashes for fully completed blocks.
	bs := c.pool.cfg.BlockSize
	if bs <= 0 {
		return
	}
	if len(c.tokenIDs)%bs != 0 {
		return
	}

	blockIdx := len(c.tokenIDs)/bs - 1
	if blockIdx < 0 {
		return
	}
	// Ensure blockIDs can hold this block.
	_ = c.ensureCapacityLocked(blockIdx + 1)

	start := blockIdx * bs
	end := start + bs
	if end > len(c.tokenIDs) {
		end = len(c.tokenIDs)
	}
	var parent *BlockHash
	if blockIdx-1 >= 0 && blockIdx-1 < len(c.blockHashes) {
		parent = &c.blockHashes[blockIdx-1]
	}
	h := ComputeBlockHash(c.tokenIDs[start:end], parent)
	// Extend or overwrite
	if blockIdx < len(c.blockHashes) {
		c.blockHashes[blockIdx] = h
	} else {
		c.blockHashes = append(c.blockHashes, h)
	}
}

// PagedCacheConfig configures a paged KV cache.
type PagedCacheConfig struct {
	NumLayers  int
	NumKVHeads int
	HeadDim    int
	BlockSize  int
	MaxSeqLen  int
	Device     tensor.DeviceType
	GPU        int
}

// NewPagedKVCache creates a new paged cache backed by the given block pool.
func NewPagedKVCache(pool *BlockPool, cfg PagedCacheConfig, requestID string) *PagedKVCache {
	return &PagedKVCache{
		pool:        pool,
		cfg:         cfg,
		requestID:   requestID,
		blockIDs:    make([][]int, cfg.NumLayers),
		ownedBlocks: make([][]bool, cfg.NumLayers),
	}
}

// AllocateForTokens allocates blocks for the given token sequence.
// Returns number of tokens that are already cached (prefix hit).
func (c *PagedKVCache) AllocateForTokens(tokens []int) (int, error) {
	c.mu.Lock()
	defer c.mu.Unlock()

	if c.cfg.MaxSeqLen > 0 && len(tokens) > c.cfg.MaxSeqLen {
		return 0, fmt.Errorf("prompt length %d exceeds MaxSeqLen %d", len(tokens), c.cfg.MaxSeqLen)
	}

	c.tokenIDs = tokens
	c.blockHashes = ComputeBlockHashes(tokens, c.pool.cfg.BlockSize)
	c.clearPtrTablesLocked()

	numBlocks := len(c.blockHashes)
	if numBlocks == 0 {
		return 0, nil
	}

	// Find cached prefix in layer 0 (all layers have same structure)
	cachedBlockIDs, cachedTokens := c.pool.FindCachedBlocks(0, c.blockHashes)
	numCachedBlocks := len(cachedBlockIDs)

	// Allocate blocks for all layers
	for layer := 0; layer < c.cfg.NumLayers; layer++ {
		c.blockIDs[layer] = make([]int, 0, numBlocks)
		c.ownedBlocks[layer] = make([]bool, 0, numBlocks)

		// First, get cached blocks for this layer
		if numCachedBlocks > 0 {
			layerCached, _ := c.pool.FindCachedBlocks(layer, c.blockHashes[:numCachedBlocks])
			c.pool.TouchBlocks(layer, layerCached)
			c.blockIDs[layer] = append(c.blockIDs[layer], layerCached...)
			for range layerCached {
				c.ownedBlocks[layer] = append(c.ownedBlocks[layer], false) // borrowed
			}
		}

		// Allocate new blocks for uncached portion
		numNewBlocks := numBlocks - numCachedBlocks
		if numNewBlocks > 0 {
			newBlocks, err := c.pool.AllocateBlocks(layer, numNewBlocks)
			if err != nil {
				// Rollback on failure
				for l := 0; l < layer; l++ {
					c.pool.FreeBlocks(l, c.blockIDs[l])
				}
				return 0, fmt.Errorf("layer %d allocation failed: %w", layer, err)
			}
			c.blockIDs[layer] = append(c.blockIDs[layer], newBlocks...)
			for range newBlocks {
				c.ownedBlocks[layer] = append(c.ownedBlocks[layer], true) // owned
			}
		}
	}

	// Don't cache the very last token so we always compute logits for the final position.
	c.numComputed = cachedTokens
	c.numWritten = c.numComputed
	if c.numComputed >= len(tokens) {
		c.numComputed = len(tokens) - 1
		if c.numComputed < 0 {
			c.numComputed = 0
		}
		c.numWritten = c.numComputed
	}

	return c.numComputed, nil
}

// LayerDevicePtrTables returns device pointers to contiguous pointer tables for this layer.
// The returned pointers live until the cache is freed (or reallocated) and are updated only
// when the required number of blocks increases.
func (c *PagedKVCache) LayerDevicePtrTables(layer int, numBlocks int) (unsafe.Pointer, unsafe.Pointer, int, tensor.DType, error) {
	c.mu.Lock()
	defer c.mu.Unlock()

	if c.pool == nil {
		return nil, nil, 0, 0, fmt.Errorf("cache not initialized")
	}
	if layer < 0 || layer >= len(c.blockIDs) {
		return nil, nil, 0, 0, fmt.Errorf("invalid layer %d", layer)
	}
	if numBlocks <= 0 {
		return nil, nil, 0, 0, fmt.Errorf("numBlocks must be > 0")
	}
	layerDev := c.pool.LayerDevice(layer).Normalize()
	if layerDev.Type != tensor.CUDA {
		return nil, nil, 0, 0, fmt.Errorf("layer %d is not on CUDA (got %v)", layer, layerDev)
	}

	if numBlocks > len(c.blockIDs[layer]) {
		numBlocks = len(c.blockIDs[layer])
	}
	if numBlocks <= 0 {
		return nil, nil, 0, 0, fmt.Errorf("no blocks")
	}

	if c.ptrTables == nil || len(c.ptrTables) != c.cfg.NumLayers {
		c.ptrTables = make([]devicePtrTable, c.cfg.NumLayers)
	}
	pt := &c.ptrTables[layer]
	if pt.kDev != nil && pt.vDev != nil && pt.len == numBlocks && pt.gpu == layerDev.GPU {
		return pt.kDev, pt.vDev, c.pool.cfg.BlockSize, pt.kvType, nil
	}

	// Rebuild pointer tables on growth or device mismatch.
	kPtrs := make([]uintptr, numBlocks)
	vPtrs := make([]uintptr, numBlocks)
	var kvType tensor.DType
	for i := 0; i < numBlocks; i++ {
		bid := c.blockIDs[layer][i]
		b := c.pool.GetBlock(layer, bid)
		if b == nil {
			return nil, nil, 0, 0, fmt.Errorf("block %d not found", bid)
		}
		kT, ok := b.K.(*cuda.Tensor)
		if !ok {
			return nil, nil, 0, 0, fmt.Errorf("block K is not CUDA tensor")
		}
		vT, ok := b.V.(*cuda.Tensor)
		if !ok {
			return nil, nil, 0, 0, fmt.Errorf("block V is not CUDA tensor")
		}
		if kvType == 0 {
			kvType = kT.DType()
		}
		if kT.DType() != kvType || vT.DType() != kvType {
			return nil, nil, 0, 0, fmt.Errorf("mixed KV dtypes in blocks")
		}
		kPtrs[i] = uintptr(kT.Data().(unsafe.Pointer))
		vPtrs[i] = uintptr(vT.Data().(unsafe.Pointer))
	}

	kDev, err := cuda.AllocAndCopyPtrTable(kPtrs, layerDev.GPU)
	if err != nil {
		return nil, nil, 0, 0, err
	}
	vDev, err := cuda.AllocAndCopyPtrTable(vPtrs, layerDev.GPU)
	if err != nil {
		cuda.FreeDevicePtr(kDev)
		return nil, nil, 0, 0, err
	}

	if pt.kDev != nil {
		cuda.FreeDevicePtr(pt.kDev)
	}
	if pt.vDev != nil {
		cuda.FreeDevicePtr(pt.vDev)
	}
	pt.kDev = kDev
	pt.vDev = vDev
	pt.len = numBlocks
	pt.gpu = layerDev.GPU
	pt.kvType = kvType

	return pt.kDev, pt.vDev, c.pool.cfg.BlockSize, pt.kvType, nil
}

func writePackedBlockU32(dstK, dstV []float32, numKVHeads, headDim, blockSize, blockOffset, writeCount int, kSrc, vSrc []float32) {
	// kSrc/vSrc are token-major: [t][kvHead][d]
	// dstK/dstV are head-major: [kvHead][t][d]
	for t := 0; t < writeCount; t++ {
		baseTok := t * (numKVHeads * headDim)
		dstTok := blockOffset + t
		for h := 0; h < numKVHeads; h++ {
			srcBase := baseTok + h*headDim
			dstBase := h*(blockSize*headDim) + dstTok*headDim
			copy(dstK[dstBase:dstBase+headDim], kSrc[srcBase:srcBase+headDim])
			copy(dstV[dstBase:dstBase+headDim], vSrc[srcBase:srcBase+headDim])
		}
	}
}

// GetBlockForPosition returns the KV block for writing at a token position.
func (c *PagedKVCache) GetBlockForPosition(layer, tokenPos int) *KVBlock {
	c.mu.RLock()
	defer c.mu.RUnlock()

	if layer < 0 || layer >= len(c.blockIDs) {
		return nil
	}

	blockIdx := tokenPos / c.pool.cfg.BlockSize
	if blockIdx < 0 || blockIdx >= len(c.blockIDs[layer]) {
		return nil
	}

	blockID := c.blockIDs[layer][blockIdx]
	return c.pool.GetBlock(layer, blockID)
}

// GetBlockOffset returns the offset within a block for a token position.
func (c *PagedKVCache) GetBlockOffset(tokenPos int) int {
	return tokenPos % c.pool.cfg.BlockSize
}

func (c *PagedKVCache) LayerBlockIDs(layer int, numBlocks int) []int {
	c.mu.RLock()
	defer c.mu.RUnlock()
	if layer < 0 || layer >= len(c.blockIDs) {
		return nil
	}
	if numBlocks <= 0 {
		return nil
	}
	if numBlocks > len(c.blockIDs[layer]) {
		numBlocks = len(c.blockIDs[layer])
	}
	out := make([]int, numBlocks)
	copy(out, c.blockIDs[layer][:numBlocks])
	return out
}

func (c *PagedKVCache) LayerBlockPtrTables(layer int, numBlocks int) ([]uintptr, []uintptr, int, tensor.DType, error) {
	if numBlocks <= 0 {
		return nil, nil, 0, 0, fmt.Errorf("numBlocks must be > 0")
	}
	blockIDs := c.LayerBlockIDs(layer, numBlocks)
	if len(blockIDs) == 0 {
		return nil, nil, 0, 0, fmt.Errorf("no blocks")
	}
	kPtrs := make([]uintptr, len(blockIDs))
	vPtrs := make([]uintptr, len(blockIDs))
	var kvType tensor.DType
	for i, bid := range blockIDs {
		b := c.pool.GetBlock(layer, bid)
		if b == nil {
			return nil, nil, 0, 0, fmt.Errorf("block %d not found", bid)
		}
		kT, ok := b.K.(*cuda.Tensor)
		if !ok {
			return nil, nil, 0, 0, fmt.Errorf("block K is not CUDA tensor")
		}
		vT, ok := b.V.(*cuda.Tensor)
		if !ok {
			return nil, nil, 0, 0, fmt.Errorf("block V is not CUDA tensor")
		}
		if kvType == 0 {
			kvType = kT.DType()
		}
		if kT.DType() != kvType || vT.DType() != kvType {
			return nil, nil, 0, 0, fmt.Errorf("mixed KV dtypes in blocks")
		}
		kPtrs[i] = uintptr(kT.Data().(unsafe.Pointer))
		vPtrs[i] = uintptr(vT.Data().(unsafe.Pointer))
	}
	return kPtrs, vPtrs, c.pool.cfg.BlockSize, kvType, nil
}

// Append writes new K/V tokens into the cache for a layer.
// Implements KVCacheInterface.
func (c *PagedKVCache) Append(layer int, k, v tensor.Tensor) ([]View, int, error) {
	c.mu.Lock()
	defer c.mu.Unlock()

	if layer < 0 || layer >= len(c.blockIDs) {
		return nil, 0, fmt.Errorf("invalid layer %d", layer)
	}

	layerPlacement := c.LayerDevice(layer).Normalize()

	startPos := c.numComputed
	newTokens := k.Shape()[0]
	kvDim := k.Shape()[1]
	endPos := startPos + newTokens

	if c.cfg.MaxSeqLen > 0 && endPos > c.cfg.MaxSeqLen {
		return nil, 0, fmt.Errorf("KV cache overflow: need %d tokens (start %d + new %d) > MaxSeqLen %d", endPos, startPos, newTokens, c.cfg.MaxSeqLen)
	}

	if layerPlacement.Type == tensor.CUDA {
		kSrc, kIsCUDA := k.(*cuda.Tensor)
		vSrc, vIsCUDA := v.(*cuda.Tensor)
		if !kIsCUDA || !vIsCUDA {
			return nil, 0, fmt.Errorf("PagedKVCache layer %d requires CUDA tensors, got %T/%T", layer, k, v)
		}
		if layerPlacement.GPU >= 0 {
			if kSrc.GPU() != layerPlacement.GPU || vSrc.GPU() != layerPlacement.GPU {
				return nil, 0, fmt.Errorf("PagedKVCache layer %d on GPU %d, got k/v on GPU %d/%d", layer, layerPlacement.GPU, kSrc.GPU(), vSrc.GPU())
			}
		}

		// Ensure we have enough blocks to cover the write range.
		requiredBlocks := (endPos + c.pool.cfg.BlockSize - 1) / c.pool.cfg.BlockSize
		if err := c.ensureCapacityLocked(requiredBlocks); err != nil {
			return nil, 0, fmt.Errorf("ensure capacity: %w", err)
		}

		written := 0
		for written < newTokens {
			globalPos := startPos + written
			blockIdx := globalPos / c.pool.cfg.BlockSize
			blockOffset := globalPos % c.pool.cfg.BlockSize
			// Capacity is ensured above.

			block := c.pool.GetBlock(layer, c.blockIDs[layer][blockIdx])
			if block == nil {
				return nil, 0, fmt.Errorf("block %d not found", c.blockIDs[layer][blockIdx])
			}

			// How many tokens can we write to this block?
			writeCount := min(newTokens-written, c.pool.cfg.BlockSize-blockOffset)

			// Copy K/V to block
			srcStart := written * kvDim
			dstStart := blockOffset * kvDim
			length := writeCount * kvDim

			kDst, ok := block.K.(*cuda.Tensor)
			if !ok {
				return nil, 0, fmt.Errorf("block K is not CUDA tensor")
			}
			vDst, ok := block.V.(*cuda.Tensor)
			if !ok {
				return nil, 0, fmt.Errorf("block V is not CUDA tensor")
			}

			if kDst.DType() == tensor.Float16 && kSrc.DType() == tensor.Float32 {
				srcPtr := unsafe.Pointer(uintptr(kSrc.Data().(unsafe.Pointer)) + uintptr(srcStart*4))
				dstPtr := unsafe.Pointer(uintptr(kDst.Data().(unsafe.Pointer)) + uintptr(dstStart*2))
				if err := cuda.CastF32ToF16(srcPtr, dstPtr, length, kDst.GPU()); err != nil {
					return nil, 0, fmt.Errorf("cast K f32->f16 failed: %w", err)
				}
			} else {
				if err := kDst.CopyPartialFromDevice(dstStart, kSrc, srcStart, length); err != nil {
					return nil, 0, fmt.Errorf("copy K failed: %w", err)
				}
			}
			if vDst.DType() == tensor.Float16 && vSrc.DType() == tensor.Float32 {
				srcPtr := unsafe.Pointer(uintptr(vSrc.Data().(unsafe.Pointer)) + uintptr(srcStart*4))
				dstPtr := unsafe.Pointer(uintptr(vDst.Data().(unsafe.Pointer)) + uintptr(dstStart*2))
				if err := cuda.CastF32ToF16(srcPtr, dstPtr, length, vDst.GPU()); err != nil {
					return nil, 0, fmt.Errorf("cast V f32->f16 failed: %w", err)
				}
			} else {
				if err := vDst.CopyPartialFromDevice(dstStart, vSrc, srcStart, length); err != nil {
					return nil, 0, fmt.Errorf("copy V failed: %w", err)
				}
			}

			written += writeCount
		}

		if endPos > c.numWritten {
			c.numWritten = endPos
		}
		return c.viewsLockedAt(layer, endPos), startPos, nil
	}

	kSrc, kIsCPU := k.(*cpu.Tensor)
	vSrc, vIsCPU := v.(*cpu.Tensor)
	if !kIsCPU || !vIsCPU {
		return nil, 0, fmt.Errorf("PagedKVCache layer %d requires CPU tensors, got %T/%T", layer, k, v)
	}

	// Ensure we have enough blocks to cover the write range.
	requiredBlocks := (endPos + c.pool.cfg.BlockSize - 1) / c.pool.cfg.BlockSize
	if err := c.ensureCapacityLocked(requiredBlocks); err != nil {
		return nil, 0, fmt.Errorf("ensure capacity: %w", err)
	}

	var kF32, vF32 []float32
	if kSrc.DType() == tensor.Float32 {
		kF32 = kSrc.DataFloat32()
	}
	if vSrc.DType() == tensor.Float32 {
		vF32 = vSrc.DataFloat32()
	}

	written := 0
	for written < newTokens {
		globalPos := startPos + written
		blockIdx := globalPos / c.pool.cfg.BlockSize
		blockOffset := globalPos % c.pool.cfg.BlockSize
		// Capacity is ensured above.

		block := c.pool.GetBlock(layer, c.blockIDs[layer][blockIdx])
		if block == nil {
			return nil, 0, fmt.Errorf("block %d not found", c.blockIDs[layer][blockIdx])
		}

		// How many tokens can we write to this block?
		writeCount := min(newTokens-written, c.pool.cfg.BlockSize-blockOffset)

		// Copy K/V to block
		srcStart := written * kvDim
		dstStart := blockOffset * kvDim
		length := writeCount * kvDim

		kDst, ok := block.K.(*cpu.Tensor)
		if !ok {
			return nil, 0, fmt.Errorf("block K is not CPU tensor")
		}
		vDst, ok := block.V.(*cpu.Tensor)
		if !ok {
			return nil, 0, fmt.Errorf("block V is not CPU tensor")
		}

		switch kDst.DType() {
		case tensor.Float16:
			kOut := kDst.DataUint16()[dstStart : dstStart+length]
			switch kSrc.DType() {
			case tensor.Float32:
				kIn := kSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					kOut[i] = float32ToFloat16Bits(kIn[i])
				}
			case tensor.Float16:
				copy(kOut, kSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return nil, 0, fmt.Errorf("unsupported CPU K src dtype: %v", kSrc.DType())
			}
		case tensor.BFloat16:
			kOut := kDst.DataUint16()[dstStart : dstStart+length]
			switch kSrc.DType() {
			case tensor.Float32:
				kIn := kSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					kOut[i] = float32ToBFloat16Bits(kIn[i])
				}
			case tensor.BFloat16:
				copy(kOut, kSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return nil, 0, fmt.Errorf("unsupported CPU K src dtype: %v", kSrc.DType())
			}
		default:
			return nil, 0, fmt.Errorf("unsupported CPU K dst dtype: %v", kDst.DType())
		}

		switch vDst.DType() {
		case tensor.Float16:
			vOut := vDst.DataUint16()[dstStart : dstStart+length]
			switch vSrc.DType() {
			case tensor.Float32:
				vIn := vSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					vOut[i] = float32ToFloat16Bits(vIn[i])
				}
			case tensor.Float16:
				copy(vOut, vSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return nil, 0, fmt.Errorf("unsupported CPU V src dtype: %v", vSrc.DType())
			}
		case tensor.BFloat16:
			vOut := vDst.DataUint16()[dstStart : dstStart+length]
			switch vSrc.DType() {
			case tensor.Float32:
				vIn := vSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					vOut[i] = float32ToBFloat16Bits(vIn[i])
				}
			case tensor.BFloat16:
				copy(vOut, vSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return nil, 0, fmt.Errorf("unsupported CPU V src dtype: %v", vSrc.DType())
			}
		default:
			return nil, 0, fmt.Errorf("unsupported CPU V dst dtype: %v", vDst.DType())
		}

		// Populate packed CPU layout when available and source is float32.
		if block.pk != nil && block.pv != nil && kF32 != nil && vF32 != nil {
			srcStartTok := written * kvDim
			srcEndTok := srcStartTok + length
			writePackedBlockU32(block.pk, block.pv, c.cfg.NumKVHeads, c.cfg.HeadDim, c.pool.cfg.BlockSize, blockOffset, writeCount, kF32[srcStartTok:srcEndTok], vF32[srcStartTok:srcEndTok])
		}

		written += writeCount
	}
	if endPos > c.numWritten {
		c.numWritten = endPos
	}
	return c.viewsLockedAt(layer, endPos), startPos, nil
}

// Views returns the live KV block views for a layer.
func (c *PagedKVCache) Views(layer int) []View {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return c.viewsLockedAt(layer, c.numComputed)
}

func (c *PagedKVCache) ViewsPacked(layer int) []PackedView {
	c.mu.RLock()
	defer c.mu.RUnlock()
	if c.LayerDevice(layer).Type != tensor.CPU {
		return nil
	}
	computed := c.numWritten
	if computed <= 0 {
		return nil
	}
	if layer < 0 || layer >= len(c.blockIDs) {
		return nil
	}
	numBlocks := (computed + c.pool.cfg.BlockSize - 1) / c.pool.cfg.BlockSize
	views := make([]PackedView, 0, numBlocks)
	for i := 0; i < numBlocks && i < len(c.blockIDs[layer]); i++ {
		block := c.pool.GetBlock(layer, c.blockIDs[layer][i])
		if block == nil || block.pk == nil || block.pv == nil {
			continue
		}
		start := i * c.pool.cfg.BlockSize
		length := c.pool.cfg.BlockSize
		if start+length > computed {
			length = computed - start
		}
		if length <= 0 {
			continue
		}
		views = append(views, PackedView{
			K:          block.pk,
			V:          block.pv,
			Start:      start,
			Length:     length,
			BlockSize:  c.pool.cfg.BlockSize,
			HeadDim:    c.cfg.HeadDim,
			NumKVHeads: c.cfg.NumKVHeads,
		})
	}
	return views
}

// viewsLocked returns live views for the currently committed KV length.
// Kept for internal call sites.
func (c *PagedKVCache) viewsLocked(layer int) []View {
	return c.viewsLockedAt(layer, c.numComputed)
}

func (c *PagedKVCache) viewsLockedAt(layer int, computed int) []View {
	if layer < 0 || layer >= len(c.blockIDs) {
		return nil
	}
	if computed <= 0 {
		return nil
	}

	numBlocks := (computed + c.pool.cfg.BlockSize - 1) / c.pool.cfg.BlockSize
	views := make([]View, 0, numBlocks)
	layerDev := c.pool.LayerDevice(layer).Normalize()

	for i := 0; i < numBlocks && i < len(c.blockIDs[layer]); i++ {
		block := c.pool.GetBlock(layer, c.blockIDs[layer][i])
		if block == nil {
			continue
		}

		start := i * c.pool.cfg.BlockSize
		length := c.pool.cfg.BlockSize
		if start+length > computed {
			length = computed - start
		}
		if length <= 0 {
			continue
		}

		views = append(views, View{
			K:      block.K,
			V:      block.V,
			Start:  start,
			Length: length,
			Device: layerDev.Type,
			GPU:    layerDev.GPU,
		})
	}

	return views
}

// AppendKV writes K/V values for new tokens into the cache.
// This is the main write path for prefill and decode.
func (c *PagedKVCache) AppendKV(layer int, k, v tensor.Tensor, startPos int) error {
	c.mu.Lock()
	defer c.mu.Unlock()

	if layer < 0 || layer >= len(c.blockIDs) {
		return fmt.Errorf("invalid layer %d", layer)
	}

	layerPlacement := c.LayerDevice(layer).Normalize()

	newTokens := k.Shape()[0]
	kvDim := k.Shape()[1]

	if c.cfg.MaxSeqLen > 0 && startPos+newTokens > c.cfg.MaxSeqLen {
		return fmt.Errorf("KV cache overflow: need %d tokens (start %d + new %d) > MaxSeqLen %d", startPos+newTokens, startPos, newTokens, c.cfg.MaxSeqLen)
	}

	if layerPlacement.Type == tensor.CUDA {
		kSrc, kIsCUDA := k.(*cuda.Tensor)
		vSrc, vIsCUDA := v.(*cuda.Tensor)
		if !kIsCUDA || !vIsCUDA {
			return fmt.Errorf("PagedKVCache layer %d requires CUDA tensors, got %T/%T", layer, k, v)
		}

		written := 0
		for written < newTokens {
			globalPos := startPos + written
			blockIdx := globalPos / c.pool.cfg.BlockSize
			blockOffset := globalPos % c.pool.cfg.BlockSize

			if blockIdx >= len(c.blockIDs[layer]) {
				return fmt.Errorf("block index %d out of range", blockIdx)
			}

			block := c.pool.GetBlock(layer, c.blockIDs[layer][blockIdx])
			if block == nil {
				return fmt.Errorf("block %d not found", c.blockIDs[layer][blockIdx])
			}

			// How many tokens can we write to this block?
			writeCount := min(newTokens-written, c.pool.cfg.BlockSize-blockOffset)

			// Copy K/V to block
			srcStart := written * kvDim
			dstStart := blockOffset * kvDim
			length := writeCount * kvDim

			kDst, ok := block.K.(*cuda.Tensor)
			if !ok {
				return fmt.Errorf("block K is not CUDA tensor")
			}
			vDst, ok := block.V.(*cuda.Tensor)
			if !ok {
				return fmt.Errorf("block V is not CUDA tensor")
			}

			if kDst.DType() == tensor.Float16 && kSrc.DType() == tensor.Float32 {
				srcPtr := unsafe.Pointer(uintptr(kSrc.Data().(unsafe.Pointer)) + uintptr(srcStart*4))
				dstPtr := unsafe.Pointer(uintptr(kDst.Data().(unsafe.Pointer)) + uintptr(dstStart*2))
				if err := cuda.CastF32ToF16(srcPtr, dstPtr, length, kDst.GPU()); err != nil {
					return fmt.Errorf("cast K f32->f16 failed: %w", err)
				}
			} else {
				if err := kDst.CopyPartialFromDevice(dstStart, kSrc, srcStart, length); err != nil {
					return fmt.Errorf("copy K failed: %w", err)
				}
			}
			if vDst.DType() == tensor.Float16 && vSrc.DType() == tensor.Float32 {
				srcPtr := unsafe.Pointer(uintptr(vSrc.Data().(unsafe.Pointer)) + uintptr(srcStart*4))
				dstPtr := unsafe.Pointer(uintptr(vDst.Data().(unsafe.Pointer)) + uintptr(dstStart*2))
				if err := cuda.CastF32ToF16(srcPtr, dstPtr, length, vDst.GPU()); err != nil {
					return fmt.Errorf("cast V f32->f16 failed: %w", err)
				}
			} else {
				if err := vDst.CopyPartialFromDevice(dstStart, vSrc, srcStart, length); err != nil {
					return fmt.Errorf("copy V failed: %w", err)
				}
			}

			written += writeCount
		}

		return nil
	}

	kSrc, kIsCPU := k.(*cpu.Tensor)
	vSrc, vIsCPU := v.(*cpu.Tensor)
	if !kIsCPU || !vIsCPU {
		return fmt.Errorf("PagedKVCache layer %d requires CPU tensors, got %T/%T", layer, k, v)
	}

	written := 0
	for written < newTokens {
		globalPos := startPos + written
		blockIdx := globalPos / c.pool.cfg.BlockSize
		blockOffset := globalPos % c.pool.cfg.BlockSize

		if blockIdx >= len(c.blockIDs[layer]) {
			return fmt.Errorf("block index %d out of range", blockIdx)
		}

		block := c.pool.GetBlock(layer, c.blockIDs[layer][blockIdx])
		if block == nil {
			return fmt.Errorf("block %d not found", c.blockIDs[layer][blockIdx])
		}

		// How many tokens can we write to this block?
		writeCount := min(newTokens-written, c.pool.cfg.BlockSize-blockOffset)

		// Copy K/V to block
		srcStart := written * kvDim
		dstStart := blockOffset * kvDim
		length := writeCount * kvDim

		kDst, ok := block.K.(*cpu.Tensor)
		if !ok {
			return fmt.Errorf("block K is not CPU tensor")
		}
		vDst, ok := block.V.(*cpu.Tensor)
		if !ok {
			return fmt.Errorf("block V is not CPU tensor")
		}

		switch kDst.DType() {
		case tensor.Float16:
			kOut := kDst.DataUint16()[dstStart : dstStart+length]
			switch kSrc.DType() {
			case tensor.Float32:
				kIn := kSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					kOut[i] = float32ToFloat16Bits(kIn[i])
				}
			case tensor.Float16:
				copy(kOut, kSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return fmt.Errorf("unsupported CPU K src dtype: %v", kSrc.DType())
			}
		case tensor.BFloat16:
			kOut := kDst.DataUint16()[dstStart : dstStart+length]
			switch kSrc.DType() {
			case tensor.Float32:
				kIn := kSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					kOut[i] = float32ToBFloat16Bits(kIn[i])
				}
			case tensor.BFloat16:
				copy(kOut, kSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return fmt.Errorf("unsupported CPU K src dtype: %v", kSrc.DType())
			}
		default:
			return fmt.Errorf("unsupported CPU K dst dtype: %v", kDst.DType())
		}

		switch vDst.DType() {
		case tensor.Float16:
			vOut := vDst.DataUint16()[dstStart : dstStart+length]
			switch vSrc.DType() {
			case tensor.Float32:
				vIn := vSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					vOut[i] = float32ToFloat16Bits(vIn[i])
				}
			case tensor.Float16:
				copy(vOut, vSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return fmt.Errorf("unsupported CPU V src dtype: %v", vSrc.DType())
			}
		case tensor.BFloat16:
			vOut := vDst.DataUint16()[dstStart : dstStart+length]
			switch vSrc.DType() {
			case tensor.Float32:
				vIn := vSrc.DataFloat32()[srcStart : srcStart+length]
				for i := 0; i < length; i++ {
					vOut[i] = float32ToBFloat16Bits(vIn[i])
				}
			case tensor.BFloat16:
				copy(vOut, vSrc.DataUint16()[srcStart:srcStart+length])
			default:
				return fmt.Errorf("unsupported CPU V src dtype: %v", vSrc.DType())
			}
		default:
			return fmt.Errorf("unsupported CPU V dst dtype: %v", vDst.DType())
		}

		written += writeCount
	}

	return nil
}

// Commit marks tokens as computed and caches completed blocks.
func (c *PagedKVCache) Commit(newTokens int) {
	c.mu.Lock()
	defer c.mu.Unlock()

	oldComputed := c.numComputed
	c.numComputed += newTokens
	if c.numWritten < c.numComputed {
		c.numWritten = c.numComputed
	}

	// Cache newly completed blocks (only those we have hashes for).
	oldBlocks := oldComputed / c.pool.cfg.BlockSize
	newBlocks := c.numComputed / c.pool.cfg.BlockSize
	maxHashBlocks := len(c.blockHashes)
	if newBlocks > maxHashBlocks {
		newBlocks = maxHashBlocks
	}
	if newBlocks > oldBlocks {
		for layer := 0; layer < c.cfg.NumLayers; layer++ {
			blockIDs := c.blockIDs[layer][oldBlocks:newBlocks]
			hashes := c.blockHashes[oldBlocks:newBlocks]
			c.pool.CacheBlocks(layer, blockIDs, hashes)
		}
	}
}

// SeqLen returns the number of computed tokens.
func (c *PagedKVCache) SeqLen() int {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return c.numComputed
}

// ContiguousKV returns a contiguous view of K/V for attention.
// For paged cache, this gathers from blocks into a contiguous buffer.
func (c *PagedKVCache) ContiguousKV(layer, kvLen, kvDim int) (tensor.Tensor, tensor.Tensor, bool, error) {
	c.mu.RLock()
	defer c.mu.RUnlock()

	// NOTE: PagedKVCache does not currently provide a contiguous K/V view.
	// Building one by allocating [kvLen, kvDim] tensors per layer causes large
	// transient CUDA allocations and can trigger GPU OOM under load.
	// Callers should fall back to Views()+concatKVOnDevice.
	return nil, nil, false, nil
}

// Free releases all blocks back to the pool.
func (c *PagedKVCache) Free() {
	c.mu.Lock()
	defer c.mu.Unlock()

	c.clearPtrTablesLocked()

	for layer := 0; layer < len(c.blockIDs); layer++ {
		c.pool.FreeBlocks(layer, c.blockIDs[layer])
	}
	c.blockIDs = nil
	c.ownedBlocks = nil
	c.tokenIDs = nil
	c.blockHashes = nil
}

// RequestID returns the request ID.
func (c *PagedKVCache) RequestID() string {
	return c.requestID
}

// NumTokens returns total tokens in sequence.
func (c *PagedKVCache) NumTokens() int {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return len(c.tokenIDs)
}

// BlockSize returns the block size.
func (c *PagedKVCache) BlockSize() int {
	return c.pool.cfg.BlockSize
}

// Truncate rewinds the cache to a specific sequence length.
func (c *PagedKVCache) Truncate(seqLen int) {
	c.mu.Lock()
	defer c.mu.Unlock()

	if seqLen < 0 {
		seqLen = 0
	}
	if seqLen >= c.numComputed {
		return
	}
	c.numComputed = seqLen
	if c.numWritten > c.numComputed {
		c.numWritten = c.numComputed
	}
}

// LayerDevice returns the device placement for a layer.
func (c *PagedKVCache) LayerDevice(layer int) tensor.DevicePlacement {
	if c.pool != nil {
		return c.pool.LayerDevice(layer)
	}
	return tensor.DevicePlacement{Type: c.cfg.Device, GPU: c.cfg.GPU}
}

// MaxSeqLen returns the maximum sequence length.
func (c *PagedKVCache) MaxSeqLen() int {
	return c.cfg.MaxSeqLen
}

// IsOnGPU returns true if the cache is on GPU.
func (c *PagedKVCache) IsOnGPU() bool {
	if c == nil {
		return false
	}
	if c.pool != nil {
		for i := 0; i < c.cfg.NumLayers; i++ {
			if c.pool.LayerDevice(i).Type == tensor.CUDA {
				return true
			}
		}
		return false
	}
	return c.cfg.Device == tensor.CUDA
}