wehub-resource-sync
102 lines
4.0 KiB
Go
102 lines
4.0 KiB
Go
package config
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import (
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gguf "github.com/gpustack/gguf-parser-go"
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. "github.com/onsi/ginkgo/v2"
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. "github.com/onsi/gomega"
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)
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// These specs exercise the auto-derived default context. The detection seams
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// (perDeviceVRAM, estimateContextVRAM) are package vars so a deterministic VRAM
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// ceiling and footprint can be injected without a real GPU or model file — the
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// same pattern hardware_defaults_internal_test.go uses for localGPU.
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var _ = Describe("Auto-derived default context (VRAM-aware cap)", func() {
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const gib = uint64(1) << 30
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var (
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origVRAM func() uint64
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origEstimate func(f *gguf.GGUFFile, ctx int) uint64
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)
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BeforeEach(func() {
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origVRAM = perDeviceVRAM
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origEstimate = estimateContextVRAM
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})
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AfterEach(func() {
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perDeviceVRAM = origVRAM
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estimateContextVRAM = origEstimate
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})
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Context("autoContextSize", func() {
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It("caps a long-context model at DefaultAutoContextSize when VRAM is ample", func() {
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// 1M-context model on an 80 GiB card: we do NOT chase the trained max,
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// we keep the conservative 8k cap (users opt into more via context_size).
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perDeviceVRAM = func() uint64 { return 80 * gib }
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estimateContextVRAM = func(_ *gguf.GGUFFile, _ int) uint64 { return gib } // trivially fits
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Expect(autoContextSize(nil, 1048576)).To(Equal(DefaultAutoContextSize))
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})
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It("keeps a small model's trained window instead of inflating it", func() {
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// trained 4096 < 8192: min() keeps 4096, it is not raised to the cap.
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perDeviceVRAM = func() uint64 { return 80 * gib }
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estimateContextVRAM = func(_ *gguf.GGUFFile, _ int) uint64 { return gib }
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Expect(autoContextSize(nil, 4096)).To(Equal(4096))
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})
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It("steps below the cap when even 8k would not fit a tiny card", func() {
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// A large model on a 2 GiB card where the 8k footprint overflows but a
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// smaller context fits: choose the largest that fits, never below the
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// floor. Footprint grows with context so the walk finds a fit.
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perDeviceVRAM = func() uint64 { return 2 * gib }
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estimateContextVRAM = func(_ *gguf.GGUFFile, ctx int) uint64 {
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return gib + uint64(ctx)*100000
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}
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chosen := autoContextSize(nil, 1048576)
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Expect(chosen).To(BeNumerically("<", DefaultAutoContextSize))
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Expect(chosen).To(BeNumerically(">=", DefaultContextSize))
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// The chosen context's footprint must actually fit the card with headroom.
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Expect(contextFitsVRAM(estimateContextVRAM(nil, chosen), 2*gib)).To(BeTrue())
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})
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It("falls back to the floor when nothing fits", func() {
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// Even DefaultContextSize does not fit: return the floor and let the
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// backend clamp n_gpu_layers to what it can (partial offload) rather
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// than defaulting to a window guaranteed to abort.
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perDeviceVRAM = func() uint64 { return 1 * gib }
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estimateContextVRAM = func(_ *gguf.GGUFFile, _ int) uint64 { return 100 * gib }
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Expect(autoContextSize(nil, 1048576)).To(Equal(DefaultContextSize))
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})
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It("does not clamp when per-device VRAM is unknown", func() {
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// CPU-only / detection gap: no GPU budget to reason about, so we must
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// not regress — keep the conservative base cap regardless of estimate.
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perDeviceVRAM = func() uint64 { return 0 }
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estimateContextVRAM = func(_ *gguf.GGUFFile, _ int) uint64 { return 999 * gib }
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Expect(autoContextSize(nil, 1048576)).To(Equal(DefaultAutoContextSize))
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})
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})
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Context("guessGGUFFromFile", func() {
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It("never overrides an explicitly configured context_size", func() {
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// A fabricated GGUF is enough: the context branch is skipped entirely
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// when the user pinned context_size, so the estimate is never consulted.
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explicit := 262144
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cfg := &ModelConfig{LLMConfig: LLMConfig{ContextSize: &explicit}}
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f := &gguf.GGUFFile{
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Header: gguf.GGUFHeader{
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MetadataKV: gguf.GGUFMetadataKVs{
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{
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Key: "general.architecture",
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ValueType: gguf.GGUFMetadataValueTypeString,
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Value: "llama",
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},
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},
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},
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}
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guessGGUFFromFile(cfg, f, 0)
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Expect(cfg.ContextSize).ToNot(BeNil())
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Expect(*cfg.ContextSize).To(Equal(262144))
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})
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})
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})
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