eb086514da
Add support for model-specific RoPE configuration parameters by: 1. Creating a new `RopeConfig` struct to encapsulate all RoPE parameters 2. Adding `RopeType` enum to specify different RoPE variants (Standard/NeoX) 3. Extracting original context length from model config 4. Refactoring `RoPE()` interface to use the new config struct 5. Updating llama and mllama models to use new RoPE configuration This change allows models to specify their RoPE implementation type and original context length, which is important for proper position embedding calculation and model compatibility.
507 lines
17 KiB
Go
507 lines
17 KiB
Go
package kvcache
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import (
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"math"
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"slices"
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"testing"
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"github.com/ollama/ollama/ml"
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)
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type testCase struct {
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name string
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in []float32
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inShape []int
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seqs []int
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pos []int32
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expected []float32
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expectedShape []int
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expectedMask []float32
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}
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func TestStore(t *testing.T) {
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backend := &testBackend{}
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cache := NewCausalCache(nil)
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defer cache.Close()
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cache.Init(backend, ml.DTypeF16, 16)
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tests := []testCase{
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{
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name: "FirstBatch",
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in: []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
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inShape: []int{2, 3, 4},
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seqs: []int{0, 0, 0, 0},
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pos: []int32{0, 1, 2, 3},
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expected: []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
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expectedShape: []int{2, 3, 4},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
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},
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{
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name: "SecondBatch",
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in: []float32{115, 215, 125, 225, 135, 235},
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inShape: []int{2, 3, 1},
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seqs: []int{0},
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pos: []int32{4},
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expected: []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234, 115, 215, 125, 225, 135, 235},
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expectedShape: []int{2, 3, 5},
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expectedMask: []float32{0, 0, 0, 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func TestSWA(t *testing.T) {
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backend := &testBackend{}
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cache := NewSWACache(1, nil)
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defer cache.Close()
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cache.Init(backend, ml.DTypeF32, 16)
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tests := []testCase{
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{
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name: "SlidingWindow",
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in: []float32{1, 2, 3, 4},
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inShape: []int{1, 1, 4},
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seqs: []int{0, 0, 0, 0},
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pos: []int32{0, 1, 2, 3},
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expected: []float32{1, 2, 3, 4},
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expectedShape: []int{1, 1, 4},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func TestSequences(t *testing.T) {
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backend := &testBackend{}
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cache := NewCausalCache(nil)
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defer cache.Close()
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cache.Init(backend, ml.DTypeF16, 16)
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tests := []testCase{
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{
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name: "FirstBatch",
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in: []float32{1, 2, 3, 4},
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inShape: []int{1, 1, 4},
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seqs: []int{0, 0, 1, 1},
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pos: []int32{0, 1, 0, 1},
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expected: []float32{1, 2, 3, 4},
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expectedShape: []int{1, 1, 4},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
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},
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{
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name: "SecondBatch",
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in: []float32{5, 6},
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inShape: []int{1, 1, 2},
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seqs: []int{0, 1},
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pos: []int32{2, 2},
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expected: []float32{1, 2, 3, 4, 5, 6},
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expectedShape: []int{1, 1, 6},
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expectedMask: []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func TestRemove(t *testing.T) {
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backend := &testBackend{}
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cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
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return key.Add(ctx, shift), nil
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})
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defer cache.Close()
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cache.Init(backend, ml.DTypeF16, 16)
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tests := []testCase{
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{
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name: "FirstBatch",
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in: []float32{1, 2, 3, 4},
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inShape: []int{1, 1, 4},
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seqs: []int{0, 0, 1, 1},
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pos: []int32{0, 1, 0, 1},
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expected: []float32{1, 2, 3, 4},
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expectedShape: []int{1, 1, 4},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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err := cache.Remove(0, 1, math.MaxInt32)
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if err != nil {
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panic(err)
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}
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tests = []testCase{
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{
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name: "RemoveEnd",
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in: []float32{5, 6},
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inShape: []int{1, 1, 2},
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seqs: []int{0, 1},
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pos: []int32{1, 2},
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expected: []float32{1, 2, 3, 4, 5, 6},
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expectedShape: []int{1, 1, 6},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
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},
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}
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testCache(t, backend, cache, tests)
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err = cache.Remove(0, 0, 1)
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if err != nil {
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panic(err)
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}
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tests = []testCase{
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{
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name: "RemoveMiddle",
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in: []float32{7, 8},
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inShape: []int{1, 1, 2},
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seqs: []int{0, 0},
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pos: []int32{1, 2},
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expected: []float32{7, 8, 3, 4, 4},
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expectedShape: []int{1, 1, 5},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func TestDefrag(t *testing.T) {
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backend := &testBackend{}
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cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
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return key.Add(ctx, shift), nil
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})
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defer cache.Close()
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cache.Init(backend, ml.DTypeF16, 16)
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tests := []testCase{
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{
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name: "FirstBatch",
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in: []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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inShape: []int{1, 1, 16},
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seqs: []int{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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pos: []int32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15},
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expected: []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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expectedShape: []int{1, 1, 16},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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err := cache.Remove(0, 2, 4)
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if err != nil {
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panic(err)
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}
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err = cache.Remove(0, 13, math.MaxInt32)
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if err != nil {
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panic(err)
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}
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tests = []testCase{
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{
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name: "Defrag",
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in: []float32{17, 18, 19},
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inShape: []int{1, 1, 3},
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seqs: []int{0, 0, 0},
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pos: []int32{16, 17, 18},
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expected: []float32{1, 2, 12, 13, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19},
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expectedShape: []int{1, 1, 16},
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expectedMask: []float32{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func TestCopy(t *testing.T) {
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backend := &testBackend{}
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cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { return key, nil })
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defer cache.Close()
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cache.Init(backend, ml.DTypeF16, 16)
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tests := []testCase{
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{
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name: "FirstBatch",
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in: []float32{1, 2, 3, 4},
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inShape: []int{1, 1, 4},
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seqs: []int{0, 0, 0, 0},
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pos: []int32{0, 1, 2, 3},
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expected: []float32{1, 2, 3, 4},
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expectedShape: []int{1, 1, 4},
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expectedMask: []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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cache.CopyPrefix(0, 1, 2)
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tests = []testCase{
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{
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name: "Copy",
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in: []float32{5, 6},
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inShape: []int{1, 1, 2},
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seqs: []int{1, 1},
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pos: []int32{3, 4},
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expected: []float32{1, 2, 3, 4, 5, 6},
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expectedShape: []int{1, 1, 6},
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expectedMask: []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
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},
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}
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testCache(t, backend, cache, tests)
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}
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func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase) {
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for _, test := range tests {
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t.Run(test.name, func(t *testing.T) {
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context := backend.NewContext()
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defer context.Close()
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err := cache.StartForward(context, test.pos, test.seqs)
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if err != nil {
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panic(err)
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}
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cache.SetLayer(0)
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tensor, _ := context.FromFloatSlice(test.in, test.inShape...)
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cache.Put(context, tensor, tensor)
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out, _, mask := cache.Get(context)
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context.Forward(out)
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context.Forward(mask)
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context.Compute(out, mask)
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if !slices.Equal(out.Floats(), test.expected) || !slices.Equal(out.Shape(), test.expectedShape) || !slices.Equal(mask.Floats(), test.expectedMask) {
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t.Errorf("TestCache: have %v (shape %v); want %v (shape %v); mask: have %v (shape %v) want %v", out.Floats(), out.Shape(), test.expected, test.expectedShape, mask.Floats(), mask.Shape(), test.expectedMask)
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}
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})
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}
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}
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type testBackend struct{}
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func (b *testBackend) Config() ml.Config {
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panic("not implemented")
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}
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func (b *testBackend) Get(name string) ml.Tensor {
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panic("not implemented")
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}
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func (b *testBackend) NewContext() ml.Context {
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return &testContext{}
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}
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type testContext struct{}
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func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
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total := 0
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if len(shape) > 0 {
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total = 1
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for _, s := range shape {
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total *= s
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}
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}
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return &testTensor{dtype: dtype, elementSize: 4, data: make([]float32, total), shape: shape}
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}
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func (c *testContext) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
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t := c.Zeros(ml.DTypeF32, shape...).(*testTensor)
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copy(t.data, s)
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return t, nil
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}
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func (c *testContext) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
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f := make([]float32, len(s))
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for i := range f {
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f[i] = float32(s[i])
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}
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out, _ := c.FromFloatSlice(f, shape...)
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out.(*testTensor).dtype = ml.DTypeI32
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return out, nil
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}
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func (c *testContext) Forward(ml.Tensor) {}
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func (c *testContext) Compute(...ml.Tensor) {}
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func (c *testContext) MaxTensors() int {
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return 10
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}
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func (c *testContext) Close() {}
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type testTensor struct {
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dtype ml.DType
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elementSize int
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data []float32
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shape []int
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}
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func (t *testTensor) Dim(n int) int {
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return t.shape[n]
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}
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func (t *testTensor) Stride(n int) int {
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stride := t.elementSize
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for i := range n {
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stride *= t.shape[i]
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}
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return stride
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}
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func (t *testTensor) Shape() []int {
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return t.shape
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}
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func (t *testTensor) DType() ml.DType {
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return t.dtype
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}
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func (t *testTensor) Bytes() []byte {
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panic("not implemented")
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}
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func (t *testTensor) Floats() []float32 {
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out := make([]float32, len(t.data))
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copy(out, t.data)
|
|
return out
|
|
}
|
|
|
|
func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
out := ctx.Zeros(t.DType(), t.Shape()...).(*testTensor)
|
|
|
|
for i := range out.data {
|
|
out.data[i] = t.data[i] + t2.(*testTensor).data[i]
|
|
}
|
|
|
|
return out
|
|
}
|
|
|
|
func (t *testTensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Softmax(ctx ml.Context) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) LayerNorm(ctx ml.Context, weight, bias ml.Tensor, eps float32) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) RMSNorm(ctx ml.Context, weight ml.Tensor, eps float32) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Scale(ctx ml.Context, s float64) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Conv2D(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) RoPE(ctx ml.Context, rc ml.RopeConfig) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Tanh(ctx ml.Context) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) GELU(ctx ml.Context) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) SILU(ctx ml.Context) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
|
|
offset /= t.elementSize
|
|
|
|
var s []int
|
|
|
|
switch len(shape) {
|
|
case 1:
|
|
s = []int{shape[0]}
|
|
case 5:
|
|
s = []int{shape[0], shape[2], shape[4]}
|
|
default:
|
|
panic("unsupported number of dimensions")
|
|
}
|
|
|
|
context := &testContext{}
|
|
|
|
view := context.Zeros(t.dtype, s...).(*testTensor)
|
|
view.data = t.data[offset : offset+len(view.data)]
|
|
|
|
return view
|
|
}
|
|
|
|
func (t *testTensor) Permute(ctx ml.Context, shape ...int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Contiguous(ctx ml.Context) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Unpad(ctx ml.Context, shape ...int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
panic("not implemented")
|
|
}
|
|
|
|
func (t *testTensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
|
|
copy(t2.(*testTensor).data, t.data)
|
|
return nil
|
|
}
|