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* feat(grpc): add a Forecast RPC for time-series models Add Forecast to the backend service with columnar request and response messages: flat row-major arrays, NaN for missing values, and one message per series so a caller can send series of different shapes in one call. The response keeps the request order. Wire the RPC through the server, client, embedded client, connection evicting client, in-flight tracking client and the base backend, which answers unimplemented. core/backend gains a Forecast helper that stamps the model identity and records a trace, like the other single-purpose RPCs. Assisted-by: Claude:claude-sonnet-5-5 go-vet * feat(config): add the forecast usecase FLAG_FORECAST marks a model as a time-series forecaster. The timestral-cpp backend declares it as its default usecase, so a config with that backend needs no extra lines. The backend is also listed as a non-text backend, so a forecast model is never offered as a chat model. It shows up in the gallery filter and in the capability list. Assisted-by: Claude:claude-sonnet-5-5 go-vet * feat(api): add POST /v1/forecast Take one or more numeric series and return quantile trajectories for the next prediction_length steps. Targets are [variates][steps] or a flat array for one variate; JSON null marks a missing value. Past and known covariates and categorical codes are checked for length before the backend sees them. Quantiles default to 0.1, 0.5 and 0.9 in the backend. The route is gated by a new default-on forecast API feature. The endpoint is listed in the swagger spec, the /api/instructions index, the UI model filter and the capability symbols. Assisted-by: Claude:claude-sonnet-5-5 go-vet * feat(backends): add the timestral-cpp forecast backend Wrap the timestral.cpp C ABI with purego, the way the other ggml backends do: libtimestral is built from a pinned commit with ggml embedded, and the Go binary loads it at start. Forecast validates the counts against the array lengths, groups the series by shape, makes one library call per group and puts the results back in the order the caller sent them. Quantiles default to 0.1, 0.5 and 0.9, and a level the model does not have is refused with the list of valid ones. A mutex guards the handle because the library is not reentrant. Request size is bounded by two model options. The unit specs use a fake library. The integration specs and a gRPC spec run against the 5M F32 model that test.sh downloads and checks against a fixed SHA-256. Assisted-by: Claude:claude-sonnet-5-5 go-vet * build(backends): register timestral-cpp in the build and the library Add the CPU image to the backend matrix for amd64 and arm64, plus an Apple silicon entry that is not built or tested yet. The image has no GPU keys because the engine runs on the CPU only. Wire the make targets, the path filter and the dependency bump bot. The library entries were generated with backendcatalog. The images are not published yet, so the release entries are unpinned until a maintainer runs --repin. The gallery reader test now counts the names in the library instead of a fixed number, so adding a backend does not break it. Assisted-by: Claude:claude-sonnet-5-5 go-vet * feat(gallery): add the Timestral forecast models Add Timestral 5M, 28M, 123M and 209M as F32 entries, each with its F16 build as a variant. Q8_0 and the K-quants are left out until the quality question is settled. Each entry runs on the timestral-cpp backend, on the CPU only. The weights use Mistral's Modified MIT licence, which bars use by companies above a monthly revenue threshold. The first paragraph after the summary says so, the licence file is installed next to the weights, and the revenue-restricted-license tag makes the entries filterable. The memory each build needs has not been measured, so no size is set. Assisted-by: Claude:claude-sonnet-5-5 * docs(forecast): document /v1/forecast and the Timestral models Add a feature page with the request and response shapes, the error cases, the model options and the licence note. List the engine in the README, the compatibility table and the backend categories. Assisted-by: Claude:claude-sonnet-5-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
219 lines
6.2 KiB
Go
219 lines
6.2 KiB
Go
package main
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import (
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"fmt"
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"os"
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"path/filepath"
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"runtime"
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"sync"
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"unsafe"
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"github.com/mudler/LocalAI/pkg/grpc/base"
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pb "github.com/mudler/LocalAI/pkg/grpc/proto"
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"github.com/mudler/xlog"
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"google.golang.org/grpc/codes"
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"google.golang.org/grpc/status"
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)
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// TimestralCpp implements the Forecast RPC of the Backend gRPC service over
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// libtimestral.so. The C handle is not reentrant: the server serializes calls
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// through base.SingleThread, and mu repeats that guard for Forecast and Free so
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// the handle is safe even when the type is used without the server.
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type TimestralCpp struct {
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base.SingleThread
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mu sync.Mutex
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ctxPtr uintptr
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opts loadOptions
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levels []float64 // quantile levels of the loaded model, ascending
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}
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func (t *TimestralCpp) Load(opts *pb.ModelOptions) error {
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model := opts.ModelFile
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if model == "" {
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model = opts.ModelPath
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}
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if !filepath.IsAbs(model) && opts.ModelPath != "" && model != opts.ModelPath {
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model = filepath.Join(opts.ModelPath, model)
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}
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if model == "" {
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return fmt.Errorf("timestral-cpp: ModelFile is required")
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}
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if st, err := os.Stat(model); err != nil || st.IsDir() {
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return fmt.Errorf("timestral-cpp: model file %q is not readable", model)
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}
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if v := CppAbiVersion(); v != abiVersionRequired {
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return fmt.Errorf("timestral-cpp: libtimestral ABI version %d, this backend needs %d", v, abiVersionRequired)
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}
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threads := opts.Threads
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if threads <= 0 {
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threads = defaultThreads
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}
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t.mu.Lock()
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defer t.mu.Unlock()
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t.freeLocked()
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// last_error(NULL) is thread-local, and a goroutine may move to another OS
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// thread between two calls into C. Pin the thread across the load and the
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// error read.
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runtime.LockOSThread()
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ctx := CppLoad(model, threads)
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if ctx == 0 {
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msg := CppLastError(0)
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runtime.UnlockOSThread()
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return fmt.Errorf("timestral-cpp: load %q failed: %s", filepath.Base(model), msg)
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}
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runtime.UnlockOSThread()
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n := int(CppNumQuantiles(ctx))
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if n <= 0 {
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msg := CppLastError(ctx)
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CppFree(ctx)
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return fmt.Errorf("timestral-cpp: model reports no quantile levels: %s", msg)
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}
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levels := make([]float64, n)
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for i := range levels {
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levels[i] = CppQuantileLevel(ctx, int32(i))
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}
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t.ctxPtr = ctx
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t.levels = levels
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t.opts = parseOptions(opts.Options)
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xlog.Info("timestral-cpp: model loaded", "model", filepath.Base(model), "threads", threads,
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"quantile_levels", len(levels), "abi", abiVersionRequired)
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return nil
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}
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func (t *TimestralCpp) Free() error {
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t.mu.Lock()
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defer t.mu.Unlock()
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t.freeLocked()
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return nil
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}
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func (t *TimestralCpp) freeLocked() {
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if t.ctxPtr != 0 {
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CppFree(t.ctxPtr)
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t.ctxPtr = 0
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}
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t.levels = nil
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}
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// Forecast groups the series by shape, runs one ABI call per group and returns
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// the results in the order the series were sent.
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func (t *TimestralCpp) Forecast(req *pb.ForecastRequest) (pb.ForecastResponse, error) {
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t.mu.Lock()
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defer t.mu.Unlock()
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if t.ctxPtr == 0 {
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return pb.ForecastResponse{}, status.Error(codes.FailedPrecondition, "timestral-cpp: no model loaded")
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}
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if req.GetPredictionLength() < 1 {
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return pb.ForecastResponse{}, invalidArgument("prediction_length must be at least 1")
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}
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if req.GetMaxContext() < 0 {
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return pb.ForecastResponse{}, invalidArgument("max_context must not be negative")
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}
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h := req.GetPredictionLength()
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if err := validateSeries(req.GetSeries(), h, t.opts); err != nil {
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return pb.ForecastResponse{}, err
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}
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sel, levels, err := selectQuantiles(req.GetQuantiles(), t.opts.defaultQuantiles, t.levels)
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if err != nil {
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return pb.ForecastResponse{}, err
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}
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maxContext := req.GetMaxContext()
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if maxContext == 0 {
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maxContext = t.opts.maxContext
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}
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results := make([]*pb.ForecastSeriesResult, len(req.GetSeries()))
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for _, g := range planGroups(req.GetSeries()) {
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out, err := t.forecastGroup(req, g, h, maxContext)
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if err != nil {
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return pb.ForecastResponse{}, err
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}
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for i, r := range reshape(out, req.GetSeries(), g, int(h), len(t.levels), sel) {
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results[i] = r
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}
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}
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return pb.ForecastResponse{PredictionLength: h, QuantileLevels: levels, Series: results}, nil
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}
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// flagValue maps an optional bool to the ABI flag: -1 model default, 0 or 1.
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func flagValue(b *bool) int32 {
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switch {
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case b == nil:
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return -1
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case *b:
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return 1
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default:
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return 0
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}
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}
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func floatPtr(s []float32) unsafe.Pointer {
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if len(s) == 0 {
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return nil
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}
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return unsafe.Pointer(&s[0])
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}
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func doublePtr(s []float64) unsafe.Pointer {
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if len(s) == 0 {
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return nil
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}
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return unsafe.Pointer(&s[0])
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}
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// forecastGroup runs one ABI call and returns a Go copy of the [B,V,H,Q]
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// output. The C buffer is freed before it returns.
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func (t *TimestralCpp) forecastGroup(req *pb.ForecastRequest, g group, h, maxContext int32) ([]float32, error) {
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b := buildBatch(req.GetSeries(), g)
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cr := forecastRequest{
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context: floatPtr(b.context),
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B: int32(len(g.idx)),
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V: g.shape.V,
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T: g.shape.T,
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predictionLen: h,
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pastCov: floatPtr(b.pastCov),
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cPast: g.shape.cPast,
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knownCov: floatPtr(b.knownCov),
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cKnown: g.shape.cKnown,
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catPast: doublePtr(b.catPast),
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ccPast: g.shape.ccPast,
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catKnown: doublePtr(b.catKnown),
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ccKnown: g.shape.ccKnown,
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symmetricAvg: flagValue(req.SymmetricAveraging),
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clipNonneg: flagValue(req.ClipNonnegative),
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maxContext: maxContext,
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}
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var q int32
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ptr := CppForecast(t.ctxPtr, unsafe.Pointer(&cr), unsafe.Pointer(&q))
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// The buffers behind cr must outlive the call: they are only reachable from
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// the C side while it runs.
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runtime.KeepAlive(b)
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runtime.KeepAlive(&cr)
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if ptr == 0 {
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// The ABI rejects what it cannot serve (for example covariates the model
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// has no input for), so the message is about the request.
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return nil, invalidArgument("forecast failed: %s", CppLastError(t.ctxPtr))
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}
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defer CppFreeBuffer(ptr)
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if int(q) != len(t.levels) {
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return nil, status.Errorf(codes.Internal, "timestral-cpp: library returned %d quantiles, model reports %d", q, len(t.levels))
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}
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n := len(g.idx) * int(g.shape.V) * int(h) * int(q)
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src := unsafe.Slice((*float32)(unsafe.Pointer(ptr)), n) //nolint:govet // C-owned malloc'd buffer, not Go-GC memory; freed by CppFreeBuffer above
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out := make([]float32, n)
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copy(out, src)
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return out, nil
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}
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