runner.go 37.4 KB
Newer Older
Jesse Gross's avatar
Jesse Gross committed
1
package ollamarunner
2
3

import (
4
	"bytes"
5
6
7
8
9
	"context"
	"encoding/json"
	"errors"
	"flag"
	"fmt"
10
	"hash/maphash"
11
	"image"
12
13
14
15
16
	"log"
	"log/slog"
	"net"
	"net/http"
	"os"
Jesse Gross's avatar
Jesse Gross committed
17
	"reflect"
18
19
20
21
22
23
	"regexp"
	"runtime"
	"strconv"
	"strings"
	"sync"
	"time"
24
	"unicode/utf8"
25

26
	"golang.org/x/image/bmp"
27
28
	"golang.org/x/sync/semaphore"

29
	"github.com/ollama/ollama/api"
30
	"github.com/ollama/ollama/envconfig"
31
	"github.com/ollama/ollama/fs/ggml"
32
	"github.com/ollama/ollama/llm"
33
	"github.com/ollama/ollama/logutil"
34
	"github.com/ollama/ollama/ml"
Michael Yang's avatar
Michael Yang committed
35
	"github.com/ollama/ollama/ml/nn/pooling"
Jesse Gross's avatar
Jesse Gross committed
36
	"github.com/ollama/ollama/model"
37
	"github.com/ollama/ollama/model/input"
Jesse Gross's avatar
Jesse Gross committed
38
39
40
41
	"github.com/ollama/ollama/runner/common"
	"github.com/ollama/ollama/sample"

	_ "github.com/ollama/ollama/model/models"
42
43
44
)

type Sequence struct {
45
	// ctxs are used for allocating tensors that last the lifetime of the sequence, such as
46
	// multimodal embeddings
47
	ctxs []ml.Context
48

49
50
51
	// mmStore holds multimodal embeddings to mange memory and enable splitting across batches
	mmStore multimodalStore

52
53
54
55
	// batch index
	iBatch int

	// prompt inputs left to evaluate
56
	inputs []*input.Input
57

Jesse Gross's avatar
Jesse Gross committed
58
	// inputs that have been added to a batch but not yet submitted to Forward
59
	pendingInputs []*input.Input
60

61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
	// tokens that have been generated but not returned yet (e.g. for stop sequences)
	pendingResponses []string

	// input cache being used by this sequence
	cache *InputCacheSlot

	// channel to send responses over
	responses chan string

	// channel to stop decoding (such as if the remote connection is closed)
	quit chan bool

	// number of tokens to predict
	numPredict int

76
77
	// sampler with transforms to run on generated logits
	sampler sample.Sampler
78
79
80
81
82
83
84
85

	// channel to send back the embedding if embedding only
	embedding chan []float32

	// stop sequences
	stop []string

	// number of inputs to keep at the beginning when shifting context window
Jesse Gross's avatar
Jesse Gross committed
86
	numKeep int32
87
88
89
90

	// true if an embedding are to be returned instead of text generation
	embeddingOnly bool

91
	doneReason llm.DoneReason
92
93

	// Metrics
94
95
96
97
98
	startedAt, lastUpdatedAt time.Time
	processingDuration       time.Duration
	samplingDuration         time.Duration
	numPredicted             int
	numPromptInputs          int
99
100
101
}

type NewSequenceParams struct {
Jesse Gross's avatar
Jesse Gross committed
102
103
104
	numPredict int
	stop       []string
	numKeep    int32
105
	sampler    sample.Sampler
Jesse Gross's avatar
Jesse Gross committed
106
	embedding  bool
107
108
}

109
func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSequenceParams) (*Sequence, error) {
110
111
	s.ready.Wait()

112
	inputs, ctxs, mmStore, err := s.inputs(prompt, images)
113
114
115
116
117
118
119
	if err != nil {
		return nil, fmt.Errorf("failed to process inputs: %w", err)
	} else if len(inputs) == 0 {
		return nil, errors.New("no input provided")
	}

	if params.numKeep < 0 {
Jesse Gross's avatar
Jesse Gross committed
120
		params.numKeep = int32(len(inputs))
121
122
	}

123
124
125
	// Ensure that at least 1 input can be discarded during shift
	params.numKeep = min(params.numKeep, s.cache.numCtx-1)

Jesse Gross's avatar
Jesse Gross committed
126
127
	if int32(len(inputs)) > s.cache.numCtx {
		discard := int32(len(inputs)) - s.cache.numCtx
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
		promptStart := params.numKeep + discard

		// If we need to truncate in the middle of a unbreakable batch, remove the entire batch
		sameBatch := 0
		for i, inp := range inputs {
			if sameBatch > 0 {
				sameBatch--

				if promptStart == int32(i) {
					promptStart++
				}
			} else if promptStart == int32(i) {
				break
			}

			if inp.SameBatch != 0 {
				if int32(i) < params.numKeep {
					return nil, fmt.Errorf("SameBatch may not be specified within numKeep (index: %v numKeep: %v SameBatch: %v)", i, params.numKeep, inp.SameBatch)
				}

				sameBatch = inp.SameBatch
			}
		}

		if promptStart >= int32(len(inputs)) {
			return nil, errors.New("entire prompt removed by truncation")
		}

156
		newInputs := inputs[:params.numKeep]
157
		newInputs = append(newInputs, inputs[promptStart:]...)
158
159

		slog.Warn("truncating input prompt", "limit", s.cache.numCtx, "prompt", len(inputs), "keep", params.numKeep, "new", len(newInputs))
160
		inputs = newInputs
161
162
	}

Jesse Gross's avatar
Jesse Gross committed
163
	// TODO(jessegross): Ingest cached history for grammar
164
165

	return &Sequence{
166
167
168
169
170
171
172
173
174
175
176
177
178
		ctxs:             ctxs,
		mmStore:          mmStore,
		inputs:           inputs,
		numPromptInputs:  len(inputs),
		numPredict:       params.numPredict,
		pendingResponses: make([]string, 0),
		responses:        make(chan string, 100),
		quit:             make(chan bool, 1),
		embedding:        make(chan []float32, 1),
		sampler:          params.sampler,
		embeddingOnly:    params.embedding,
		stop:             params.stop,
		numKeep:          params.numKeep,
179
180
181
182
183
	}, nil
}

// inputs processes the prompt and images into a list of inputs
// by splitting the prompt on [img-<n>] tags, tokenizing text and
Jesse Gross's avatar
Jesse Gross committed
184
// decoding images
185
186
func (s *Server) inputs(prompt string, images []llm.ImageData) ([]*input.Input, []ml.Context, multimodalStore, error) {
	var inputs []*input.Input
187
	var ctxs []ml.Context
188
	var mmStore multimodalStore
189

190
191
192
	var parts []string
	var matches [][]string

193
	multimodalProcessor, visionModel := s.model.(model.MultimodalProcessor)
194

195
196
197
198
	if visionModel {
		re := regexp.MustCompile(`\[img-(\d+)\]`)
		parts = re.Split(prompt, -1)
		matches = re.FindAllStringSubmatch(prompt, -1)
199
		mmStore = newMultimodalStore()
200
201
202
203
204
	} else {
		parts = []string{prompt}
	}

	postTokenize := false
205
206
	for i, part := range parts {
		// text - tokenize
207
		tokens, err := s.model.(model.TextProcessor).Encode(part, i == 0)
208
		if err != nil {
209
			return nil, nil, nil, err
210
		}
211

212
		for _, t := range tokens {
213
			inputs = append(inputs, &input.Input{Token: t})
214
215
		}

Jesse Gross's avatar
Jesse Gross committed
216
		// image - decode and store
217
218
219
220
221
222
223
224
225
226
227
228
		if i < len(matches) {
			n, _ := strconv.Atoi(matches[i][1])

			imageIndex := -1
			for j := range images {
				if images[j].ID == n {
					imageIndex = j
					break
				}
			}

			if imageIndex < 0 {
229
				return nil, nil, nil, fmt.Errorf("invalid image index: %d", n)
230
231
			}

232
			ctx := s.model.Backend().NewContext()
233
234
			runtime.SetFinalizer(ctx, func(c ml.Context) { c.Close() })
			ctxs = append(ctxs, ctx)
235
			imageEmbeddings, err := multimodalProcessor.EncodeMultimodal(ctx, images[imageIndex].Data)
Jesse Gross's avatar
Jesse Gross committed
236
			if err != nil {
237
				return nil, nil, nil, err
Jesse Gross's avatar
Jesse Gross committed
238
239
			}

240
241
242
243
			s.multimodalHash.Reset()
			_, _ = s.multimodalHash.Write(images[imageIndex].Data)
			imageHash := s.multimodalHash.Sum64()

244
245
			mmStore.addMultimodal(imageEmbeddings)

246
			inputs = append(inputs, &input.Input{Multimodal: imageEmbeddings, MultimodalHash: imageHash})
247
248
249
250
251
252
			postTokenize = true
		}
	}

	if visionModel && postTokenize {
		var err error
253
		inputs, err = multimodalProcessor.PostTokenize(inputs)
254
		if err != nil {
255
			return nil, nil, nil, err
256
257
258
		}
	}

259
	return inputs, ctxs, mmStore, nil
260
261
}

262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
type batchState struct {
	// id provides a counter for trace logging batches
	id int

	// ctx holds the backend context used for this batch
	ctx ml.Context

	// modelOutput holds the outputs from this batch
	modelOutput ml.Tensor

	// batchInputs holds the input token pointers which may start as
	// placeholders later filled in before calling ctx.Compute
	batchInputs []*input.Input

	// batch contains the inputs for a model forward pass
	batch input.Batch

	// full set of seqs at the time this batch was initiated
	seqs []*Sequence

	// Signaled when this batches inputs are ready and compute can proceed
	inputsReadyCh chan struct{}

	// Signaling when Compute is about to begin on this batch, and
	// seqs have been updated to prepare for the next batch
	computeStartedCh chan struct{}

	// Signaled when this batches outputs are complete and the next batch can proceed
	outputsReadyCh chan struct{}
}

293
type Server struct {
Jesse Gross's avatar
Jesse Gross committed
294
295
296
297
298
299
300
301
302
303
	// modelPath is the location of the model to be loaded
	modelPath string

	// loadMu prevents more than one load attempt from occurring at a time
	loadMu sync.Mutex

	// lastLoad is the load request from the previous load attempt. Used to
	// detect if we can reuse an existing memory allocation.
	lastLoad llm.LoadRequest

304
305
306
307
308
	// is the server ready to process requests?
	// protects access to model and image
	ready sync.WaitGroup

	// loaded model
Jesse Gross's avatar
Jesse Gross committed
309
	model model.Model
310

311
	// status for external health reporting - loading, ready to serve, etc.
312
	status llm.ServerStatus
313
314
315
316
317
318
319
320

	// current progress on loading the model
	progress float32

	// number of simultaneous requests to handle
	parallel int

	// maximum number of elements in a batch (per sequence)
321
	// TODO (jmorganca): make this n_batch
322
323
	batchSize int

324
325
326
327
328
329
	// Used to signal a hard failure during async processing which will panic the runner
	hardErrCh chan error

	// Simple counter used only for trace logging batches
	batchID int

330
331
332
333
334
335
336
337
	// protects access to everything below this line
	// this is context state needed for decoding
	mu sync.Mutex

	// indicates that data is ready for processing
	cond *sync.Cond

	// the list of simultaneous sequences being evaluated
338
339
	seqs []*Sequence

340
341
342
343
	// seqs can have a maximum of parallel entries, which
	// is enfoced by seqSem
	seqsSem *semaphore.Weighted

344
345
346
	// KV cache
	cache *InputCache

347
348
349
	// next sequence for prompt processing to avoid starvation
	nextSeq int

350
351
352
	// multimodalHash generates hashes for comparing equality
	// of non-text data
	multimodalHash maphash.Hash
353
354
355
356
357
358
359
360
361
362
363
364
}

func (s *Server) allNil() bool {
	for _, item := range s.seqs {
		if item != nil {
			return false
		}
	}
	return true
}

func flushPending(seq *Sequence) bool {
365
366
367
368
369
370
371
372
373
374
375
	joined := strings.Join(seq.pendingResponses, "")
	seq.pendingResponses = []string{}

	// Check if there are any partial UTF-8 characters remaining.
	// We already check and queue as we are generating but some may
	// still make it here:
	// - Sequence is ending, e.g. generation limit has been hit
	// - Invalid characters in the middle of a string
	// This is a stricter check to ensure we never output invalid Unicode.
	for !utf8.ValidString(joined) {
		joined = joined[:len(joined)-1]
376
377
	}

378
379
380
381
382
383
384
385
386
387
	if len(joined) == 0 {
		return true
	}

	select {
	case seq.responses <- joined:
		return true
	case <-seq.quit:
		return false
	}
388
389
}

390
func (s *Server) removeSequence(seqIndex int, reason llm.DoneReason) {
391
392
393
394
395
396
397
398
	seq := s.seqs[seqIndex]

	flushPending(seq)
	seq.doneReason = reason
	close(seq.responses)
	close(seq.embedding)
	seq.cache.InUse = false
	s.seqs[seqIndex] = nil
399
	s.seqsSem.Release(1)
400
401
}

402
403
// track batch state between forwardBatch, computeBatch and predictForwardBatch

404
405
406
func (s *Server) run(ctx context.Context) {
	s.ready.Wait()

Michael Yang's avatar
Michael Yang committed
407
	supportsAsync := pooling.Type(s.model.Backend().Config().Uint("pooling_type")) == pooling.TypeNone
Michael Yang's avatar
Michael Yang committed
408

409
	var previousBatch batchState
410
411
412
413
	for {
		select {
		case <-ctx.Done():
			return
414
415
		case err := <-s.hardErrCh:
			panic(err)
416
		default:
417
			var err error
418
			nextBatch, err := s.forwardBatch(previousBatch)
419
420
421
			if err != nil {
				panic(err)
			}
Michael Yang's avatar
Michael Yang committed
422
423

			if supportsAsync {
424
				go s.computeBatch(nextBatch)
Michael Yang's avatar
Michael Yang committed
425
			} else {
426
				s.computeBatch(nextBatch)
Michael Yang's avatar
Michael Yang committed
427
			}
428
429

			previousBatch = nextBatch
430
431
432
433
		}
	}
}

434
435
436
437
438
439
// forwardBatch will calculate a batch.
func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, err error) {
	// If we have a pending batch still processing, wait until Compute has started
	// before setting up the next batch so the seqs inputs are ready to receive their
	// token values and we get the correct input pointers for the batchInputs
	if pendingBatch.ctx != nil {
Michael Yang's avatar
Michael Yang committed
440
		logutil.Trace("forwardBatch waiting for compute to start", "pendingBatch.id", pendingBatch.id)
441
		<-pendingBatch.computeStartedCh
Michael Yang's avatar
Michael Yang committed
442
		logutil.Trace("forwardBatch compute started, setting up next batch", "pendingBatch.id", pendingBatch.id, "id", s.batchID)
443
444
		nextBatch.inputsReadyCh = pendingBatch.outputsReadyCh // Chain the ouputs from the pending batch to the next inputs batch
	} else {
Michael Yang's avatar
Michael Yang committed
445
		logutil.Trace("forwardBatch no pending batch detected", "batchID", s.batchID)
446
447
448
449
450
		// No pendingBatch, so the inputs will be ready in the seqs immediately
		nextBatch.inputsReadyCh = make(chan struct{}, 1)
		nextBatch.inputsReadyCh <- struct{}{}
	}

451
452
453
454
455
456
	s.mu.Lock()
	for s.allNil() {
		s.cond.Wait() // Wait until an item is added
	}
	defer s.mu.Unlock()

457
458
459
460
461
462
463
464
465
466
467
	nextBatch.ctx = s.model.Backend().NewContext()
	defer func() {
		if err != nil {
			nextBatch.ctx.Close()
			nextBatch.ctx = nil
		}
	}()
	nextBatch.id = s.batchID
	nextBatch.seqs = append([]*Sequence{}, s.seqs...)
	nextBatch.computeStartedCh = make(chan struct{}, 1)
	nextBatch.outputsReadyCh = make(chan struct{}, 1)
468

469
470
	// Prepare the seqs and batch, but defer the input token values as we may not be ready yet
	var batchInputs []*input.Input
471
	var batchOutputs []int32
Jesse Gross's avatar
Jesse Gross committed
472
	var batch input.Batch
473

474
475
476
477
478
	resumeSeq := -1
	seqIdx := s.nextSeq - 1
	for range s.seqs {
		seqIdx = (seqIdx + 1) % len(s.seqs)
		seq := s.seqs[seqIdx]
479
480
481
482
483
		if seq == nil {
			continue
		}

		// if past the num predict limit
484
		if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict {
485
			s.removeSequence(seqIdx, llm.DoneReasonLength)
486
			nextBatch.seqs[seqIdx] = nil
487
488
489
			continue
		}

Jesse Gross's avatar
Jesse Gross committed
490
491
		if !s.cache.enabled {
			seq.inputs = append(seq.cache.Inputs, seq.inputs...)
492
			seq.cache.Inputs = []*input.Input{}
Jesse Gross's avatar
Jesse Gross committed
493
494
		}

495
496
		batchSize := s.batchSize

497
		for i, inp := range seq.inputs {
498
499
			// If we are required to put following inputs into a single batch then extend the
			// batch size. Since we are only extending the size the minimum amount possible, this
500
			// will cause a break if we have existing inputs.
501
502
503
504
505
			minBatch := 1 + inp.SameBatch
			if minBatch > batchSize {
				batchSize = minBatch
			}

506
507
508
509
510
511
512
513
			// Stop if the required batch would put us over the total batch size (including tokens
			// added by other sequences). If we haven't been able to add anything yet then pick up
			// here again for the next batch to avoid starvation, though we can opportunistically
			// check if other sequences can still squeeze something in.
			if len(batchInputs)+minBatch > batchSize {
				if len(seq.pendingInputs) == 0 && resumeSeq == -1 {
					resumeSeq = seqIdx
				}
514
515
				break
			}
Jesse Gross's avatar
Jesse Gross committed
516

517
518
519
520
521
522
523
524
			// If the sum of our working set (already processed tokens, tokens we added to this
			// batch, required following tokens) exceeds the context size, then trigger a shift
			// now so we don't have to do one later when we can't break the batch.
			if int32(len(seq.cache.Inputs)+len(seq.pendingInputs)+minBatch) > s.cache.numCtx {
				if len(seq.pendingInputs) != 0 {
					break
				}

525
				err = s.cache.ShiftCacheSlot(seq.cache, seq.numKeep)
526
				if err != nil {
527
528
529
530
531
					var reprocess *ErrReprocessInputs
					if errors.As(err, &reprocess) {
						// Prepend these inputs to the sequence's inputs queue for reprocessing
						seq.inputs = append(reprocess.Inputs, seq.inputs...)
						// Skip this sequence but continue processing the rest
532
533
						nextBatch.seqs[seqIdx] = nil // clear this sequence for this batch
						err = nil
534
535
						continue
					} else {
536
						return
537
					}
538
539
540
				}
			}

541
			batchInputs = append(batchInputs, seq.inputs[i])
542
			if inp.Multimodal != nil {
543
544
				var mm []input.Multimodal
				mm, err = seq.mmStore.getMultimodal(s.model.Backend(), nextBatch.ctx, inp.Multimodal, false)
545
				if err != nil {
546
					return
547
548
				}
				batch.Multimodal = append(batch.Multimodal, input.MultimodalIndex{Index: len(batchInputs) - 1, Multimodal: mm})
549
550
			}

Jesse Gross's avatar
Jesse Gross committed
551
552
			batch.Positions = append(batch.Positions, int32(len(seq.cache.Inputs)+len(seq.pendingInputs)))
			batch.Sequences = append(batch.Sequences, seq.cache.Id)
Jesse Gross's avatar
Jesse Gross committed
553

554
555
556
			seq.iBatch = len(batchOutputs)
			if i+1 == len(seq.inputs) || seq.embeddingOnly {
				batchOutputs = append(batchOutputs, int32(len(batchInputs)-1))
Jesse Gross's avatar
Jesse Gross committed
557
			}
Michael Yang's avatar
Michael Yang committed
558
			logutil.Trace("forwardBatch iBatch", "batchID", s.batchID, "seqIdx", seqIdx, "seq.iBatch", seq.iBatch, "i+1", i+1, "len(seq.inputs)", len(seq.inputs))
559
			seq.pendingInputs = append(seq.pendingInputs, inp)
560
		}
561
562

		seq.inputs = seq.inputs[len(seq.pendingInputs):]
563
564
	}

565
566
567
568
569
570
571
	startedAt := time.Now()
	for i := range nextBatch.seqs {
		if nextBatch.seqs[i] != nil && nextBatch.seqs[i].startedAt.IsZero() {
			nextBatch.seqs[i].startedAt = startedAt
		}
	}

572
573
574
575
576
577
	if resumeSeq != -1 {
		s.nextSeq = resumeSeq
	} else {
		s.nextSeq = seqIdx + 1
	}

578
	if len(batchInputs) == 0 {
Michael Yang's avatar
Michael Yang committed
579
		logutil.Trace("forwardBatch no batchInputs, going idle", "batchID", s.batchID)
580
581
582
		nextBatch.ctx.Close()
		nextBatch.ctx = nil
		return
583
	}
584
	s.batchID++
585

586
587
	// Actual batchInputs values will be injected into the batch.Inputs tensor before calling Compute
	batch.Inputs = nextBatch.ctx.Input().Empty(ml.DTypeI32, len(batchInputs))
588
	batch.Outputs = nextBatch.ctx.Input().FromIntSlice(batchOutputs, len(batchOutputs))
589
	nextBatch.modelOutput, err = model.Forward(nextBatch.ctx, s.model, batch)
590
	if err != nil {
591
592
		err = fmt.Errorf("failed to build graph: %w", err)
		return
593
	}
594
595
	nextBatch.batchInputs = batchInputs
	nextBatch.batch = batch
596

597
598
599
600
601
602
603
604
605
606
607
608
	return
}

// Async processing of the next batch
func (s *Server) computeBatch(activeBatch batchState) {
	if activeBatch.ctx == nil {
		// Nothing to compute
		return
	}
	defer activeBatch.ctx.Close()

	// Wait until inputs are ready
Michael Yang's avatar
Michael Yang committed
609
	logutil.Trace("computeBatch: waiting for inputs to be ready", "batchID", activeBatch.id)
610
	<-activeBatch.inputsReadyCh
Michael Yang's avatar
Michael Yang committed
611
	logutil.Trace("computeBatch: inputs are ready", "batchID", activeBatch.id)
612

613
614
615
	// Once we complete, signal the next batch of inputs are ready
	// This will unblock the next computeBatch, or forwardBatch if new seqs come in
	defer func() {
Michael Yang's avatar
Michael Yang committed
616
		logutil.Trace("computeBatch: outputs are ready", "batchID", activeBatch.id)
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
		activeBatch.outputsReadyCh <- struct{}{}
	}()

	s.mu.Lock()

	// Gather the actual input token values now that they're ready
	batchInputs := make([]int32, len(activeBatch.batchInputs))
	for i := range batchInputs {
		batchInputs[i] = activeBatch.batchInputs[i].Token
	}

	// Now we run part of the decoding algorithm to adjust the seq.inputs with placeholder tokens
	// so that forwardBatch can build a batchInputs set which will eventually contain the actual
	// decoded tokens.
	nextBatchTokens := make([]*input.Input, len(s.seqs))
	iBatches := make([]int, len(s.seqs)) // Record the iBatch values before releasing the lock
633
	for i, seq := range s.seqs {
634
		iBatches[i] = -1
635
636
637
		if seq == nil {
			continue
		}
638
639
640
641
		// Skip over any newly added or skipped sequences
		if activeBatch.seqs[i] == nil {
			continue
		}
642

643
644
645
		// Detect if the sequence we're processing has already been completed and replaced
		// with a new sequence
		if seq != activeBatch.seqs[i] {
Michael Yang's avatar
Michael Yang committed
646
			logutil.Trace("computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
647
648
649
650
651
652
653
654
655
656
657
			continue
		}

		// Pending inputs will actually be in the cache after we call Compute.
		// However, we have already resolved any placeholder tokens.
		//
		// It's possible for incoming sequences to look at the values that we've
		// added to the cache here and start relying on them before we've done
		// the computation. This is OK as long as we ensure that this batch's
		// computation happens before any future batch's and we never fail
		// (unless we take down the whole runner).
658
659
		if len(seq.pendingInputs) > 0 {
			seq.cache.Inputs = append(seq.cache.Inputs, seq.pendingInputs...)
660
			seq.pendingInputs = []*input.Input{}
661
662
		}

663
664
		// don't sample prompt processing
		if len(seq.inputs) != 0 {
Jesse Gross's avatar
Jesse Gross committed
665
			if !s.cache.enabled {
666
667
668
				s.hardErrCh <- fmt.Errorf("caching disabled but unable to fit entire input in a batch")
				s.mu.Unlock()
				return
Jesse Gross's avatar
Jesse Gross committed
669
			}
670
671
672
			continue
		}

Jesse Gross's avatar
Jesse Gross committed
673
		seq.numPredicted++
674
675
676
677
678
679
680
681
682
683
684
685
		nextToken := &input.Input{Token: 0} // placeholder we'll fill in after Compute/Floats
		seq.inputs = []*input.Input{nextToken}
		nextBatchTokens[i] = nextToken
		iBatches[i] = seq.iBatch
	}

	// At this point the seqs are ready for forwardBatch to move forward so unblock
	s.mu.Unlock()

	activeBatch.batch.Inputs.SetValueFromIntSlice(batchInputs)
	activeBatch.ctx.ComputeWithNotify(
		func() {
Michael Yang's avatar
Michael Yang committed
686
			logutil.Trace("computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
687
688
689
			activeBatch.computeStartedCh <- struct{}{}
		},
		activeBatch.modelOutput)
Michael Yang's avatar
Michael Yang committed
690
691

	outputs := activeBatch.modelOutput.Floats()
692
	t := time.Now()
693

Michael Yang's avatar
Michael Yang committed
694
	logutil.Trace("computeBatch: logits ready", "batchID", activeBatch.id)
695
696
697
698

	s.mu.Lock()
	defer s.mu.Unlock()

Michael Yang's avatar
Michael Yang committed
699
	logutil.Trace("computeBatch: decoding", "batchID", activeBatch.id)
700
701
702
703
704
	for i, seq := range s.seqs {
		if seq == nil || nextBatchTokens[i] == nil {
			continue
		}

705
		seq.lastUpdatedAt = t
Jesse Gross's avatar
Jesse Gross committed
706
		if seq.numPredicted == 1 {
707
708
			seq.processingDuration = seq.lastUpdatedAt.Sub(seq.startedAt)
			seq.startedAt = seq.lastUpdatedAt
709
710
711
712
		}

		// if done processing the prompt, generate an embedding and return
		if seq.embeddingOnly {
Michael Yang's avatar
Michael Yang committed
713
			seq.embedding <- outputs
714
			s.removeSequence(i, llm.DoneReasonStop)
715
			continue
716
717
718
		}

		// sample a token
719
720
		vocabSize := len(outputs) / activeBatch.batch.Outputs.Dim(0)
		logutil.Trace("computeBatch: vocab details", "batchID", activeBatch.id, "seqIdx", i, "len(logits)", len(outputs), "len(activeBatch.batch.Outputs)", activeBatch.batch.Outputs.Dim(0), "vocabSize", vocabSize, "iBatches", iBatches)
Michael Yang's avatar
Michael Yang committed
721
		token, err := seq.sampler.Sample(outputs[iBatches[i]*vocabSize : (iBatches[i]+1)*vocabSize])
Jesse Gross's avatar
Jesse Gross committed
722
		if err != nil {
723
724
			s.hardErrCh <- fmt.Errorf("failed to sample token: %w", err)
			return
Jesse Gross's avatar
Jesse Gross committed
725
		}
726

727
728
		nextBatchTokens[i].Token = token

729
		// if it's an end of sequence token, break
Jesse Gross's avatar
Jesse Gross committed
730
		if s.model.(model.TextProcessor).Is(token, model.SpecialEOS) {
731
732
733
			// TODO (jmorganca): we should send this back
			// as it's important for the /api/generate context
			// seq.responses <- piece
Michael Yang's avatar
Michael Yang committed
734
			logutil.Trace("computeBatch: EOS", "batchID", activeBatch.id, "seqIdx", i)
735
			s.removeSequence(i, llm.DoneReasonStop)
736
737
738
			continue
		}

Jesse Gross's avatar
Jesse Gross committed
739
740
		piece, err := s.model.(model.TextProcessor).Decode([]int32{token})
		if err != nil {
741
742
			s.hardErrCh <- fmt.Errorf("failed to decode token: %w", err)
			return
Jesse Gross's avatar
Jesse Gross committed
743
744
		}

745
746
747
		seq.pendingResponses = append(seq.pendingResponses, piece)
		sequence := strings.Join(seq.pendingResponses, "")

Jesse Gross's avatar
Jesse Gross committed
748
		if ok, stop := common.FindStop(sequence, seq.stop); ok {
749
750
751
752
			slog.Debug("hit stop token", "pending", seq.pendingResponses, "stop", stop)

			var tokenTruncated bool
			origLen := len(seq.pendingResponses)
Jesse Gross's avatar
Jesse Gross committed
753
			seq.pendingResponses, tokenTruncated = common.TruncateStop(seq.pendingResponses, stop)
754
755
756
757
758
759
760
761
762
763
764
765
766
767
			newLen := len(seq.pendingResponses)

			// Update the cache based on the tokens that will be returned:
			// - We have 1 token more than is currently in the cache because
			// the last one generated wasn't submitted to Decode
			// - Remove any stop sequences that we stripped out
			// - If truncateStop removed a portion of a token, drop that
			// - As defense-in-depth, if truncatedToken didn't find a stop token
			// remove the extra one that we added to the cache len
			tokenLen := len(seq.cache.Inputs) + 1
			tokenLen -= origLen - newLen
			if tokenTruncated || origLen == newLen {
				tokenLen--
			}
768

769
			seq.cache.Inputs = seq.cache.Inputs[:tokenLen]
770

771
			s.removeSequence(i, llm.DoneReasonStop)
772
773
774
			continue
		}

Jesse Gross's avatar
Jesse Gross committed
775
		if common.ContainsStopSuffix(sequence, seq.stop) {
776
777
778
			continue
		}

Jesse Gross's avatar
Jesse Gross committed
779
		if common.IncompleteUnicode(sequence) {
780
781
782
783
			continue
		}

		if !flushPending(seq) {
784
			s.removeSequence(i, llm.DoneReasonConnectionClosed)
785
786
		}
	}
787
788
789
790
791
792
793

	samplingDuration := time.Since(t)
	for i, seq := range s.seqs {
		if seq != nil && nextBatchTokens[i] != nil {
			s.seqs[i].samplingDuration += samplingDuration
		}
	}
794
795
796
}

func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
797
	var req llm.CompletionRequest
798
799
800
801
802
	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
		http.Error(w, "Bad request", http.StatusBadRequest)
		return
	}

803
804
805
806
807
	if req.Options == nil {
		opts := api.DefaultOptions()
		req.Options = &opts
	}

808
809
810
811
812
813
814
815
816
817
	// Set the headers to indicate streaming
	w.Header().Set("Content-Type", "application/json")
	w.Header().Set("Transfer-Encoding", "chunked")

	flusher, ok := w.(http.Flusher)
	if !ok {
		http.Error(w, "Streaming not supported", http.StatusInternalServerError)
		return
	}

818
	var grammar *sample.GrammarSampler
819
820
	var err error
	if req.Grammar != "" {
821
		grammar, err = sample.NewGrammarSampler(s.model.(model.TextProcessor), req.Grammar)
822
823
824
825
		if err != nil {
			http.Error(w, "failed to load model vocabulary required for format", http.StatusInternalServerError)
			return
		}
826
		defer grammar.Free()
827
828
	}

829
	sampler := sample.NewSampler(
830
831
832
833
834
		req.Options.Temperature,
		req.Options.TopK,
		req.Options.TopP,
		req.Options.MinP,
		req.Options.Seed,
835
		grammar,
836
837
	)

838
	seq, err := s.NewSequence(req.Prompt, req.Images, NewSequenceParams{
839
840
841
		numPredict: req.Options.NumPredict,
		stop:       req.Options.Stop,
		numKeep:    int32(req.Options.NumKeep),
842
		sampler:    sampler,
Jesse Gross's avatar
Jesse Gross committed
843
		embedding:  false,
844
845
846
847
848
849
	})
	if err != nil {
		http.Error(w, fmt.Sprintf("Failed to create new sequence: %v", err), http.StatusInternalServerError)
		return
	}

850
	// Ensure there is a place to put the sequence, released when removed from s.seqs
851
	if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
852
853
854
		if errors.Is(err, context.Canceled) {
			slog.Info("aborting completion request due to client closing the connection")
		} else {
855
			http.Error(w, fmt.Sprintf("Failed to acquire semaphore: %v", err), http.StatusInternalServerError)
856
		}
857
858
859
		return
	}

860
	s.mu.Lock()
861
	found := false
862
863
	for i, sq := range s.seqs {
		if sq == nil {
Michael Yang's avatar
Michael Yang committed
864
			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, true)
865
866
			if err != nil {
				s.mu.Unlock()
867
				s.seqsSem.Release(1)
868
869
870
				http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
				return
			}
871

872
873
			s.seqs[i] = seq
			s.cond.Signal()
874
			found = true
875
876
877
878
879
			break
		}
	}
	s.mu.Unlock()

880
	if !found {
881
		s.seqsSem.Release(1)
882
883
884
885
		http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
		return
	}

886
887
888
889
890
891
892
	for {
		select {
		case <-r.Context().Done():
			close(seq.quit)
			return
		case content, ok := <-seq.responses:
			if ok {
893
				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
894
					Content: content,
895
896
897
898
899
900
901
902
				}); err != nil {
					http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
					close(seq.quit)
					return
				}

				flusher.Flush()
			} else {
903
904
				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
					Done:               true,
905
					DoneReason:         seq.doneReason,
906
					PromptEvalCount:    seq.numPromptInputs,
907
					PromptEvalDuration: seq.processingDuration,
908
					EvalCount:          seq.numPredicted,
909
					EvalDuration:       seq.lastUpdatedAt.Sub(seq.startedAt) - seq.samplingDuration,
910
911
912
913
914
915
916
917
918
919
				}); err != nil {
					http.Error(w, fmt.Sprintf("failed to encode final response: %v", err), http.StatusInternalServerError)
				}

				return
			}
		}
	}
}

Michael Yang's avatar
Michael Yang committed
920
func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
Michael Yang's avatar
Michael Yang committed
921
	if pooling.Type(s.model.Backend().Config().Uint("pooling_type")) == pooling.TypeNone {
Michael Yang's avatar
Michael Yang committed
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
		http.Error(w, "this model does not support embeddings", http.StatusNotImplemented)
		return
	}

	var req llm.EmbeddingRequest
	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
		http.Error(w, fmt.Sprintf("bad request: %s", err), http.StatusBadRequest)
		return
	}

	w.Header().Set("Content-Type", "application/json")
	seq, err := s.NewSequence(req.Content, nil, NewSequenceParams{embedding: true})
	if err != nil {
		http.Error(w, fmt.Sprintf("failed to create new sequence: %v", err), http.StatusInternalServerError)
		return
	}

	if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
		if errors.Is(err, context.Canceled) {
			slog.Info("aborting embedding request due to client closing the connection")
		} else {
			http.Error(w, fmt.Sprintf("failed to acquire semaphore: %v", err), http.StatusInternalServerError)
		}
		return
	}

	s.mu.Lock()
	found := false
	for i, sq := range s.seqs {
		if sq == nil {
			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, false)
			if err != nil {
				s.mu.Unlock()
				s.seqsSem.Release(1)
				http.Error(w, fmt.Sprintf("failed to load cache: %v", err), http.StatusInternalServerError)
				return
			}

			s.seqs[i] = seq
			s.cond.Signal()
			found = true
			break
		}
	}
	s.mu.Unlock()

	if !found {
		s.seqsSem.Release(1)
		http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
		return
	}

	if err := json.NewEncoder(w).Encode(&llm.EmbeddingResponse{
		Embedding: <-seq.embedding,
	}); err != nil {
		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
	}
}

981
982
func (s *Server) health(w http.ResponseWriter, r *http.Request) {
	w.Header().Set("Content-Type", "application/json")
983
984
	if err := json.NewEncoder(w).Encode(&llm.ServerStatusResponse{
		Status:   s.status,
985
986
987
988
989
990
		Progress: s.progress,
	}); err != nil {
		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
	}
}

991
func (s *Server) reserveWorstCaseGraph() error {
992
993
994
	ctx := s.model.Backend().NewContext()
	defer ctx.Close()

995
	var err error
996
997
998
999
	inputs := make([]*input.Input, s.batchSize)
	for i := range inputs {
		inputs[i] = &input.Input{}
	}
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
	mmStore := newMultimodalStore()

	// Multimodal strategy:
	// - Encode a 2048x2048 image. This assumes that a single image of this
	//   size is sufficient to trigger the worst case. This is currently true
	//   because for existing models, only a single image fits in a batch.
	// - Add the embedding to a full batch of tokens - this is necessary because
	//   the model may be looking for non-image data, such as <image> tags.
	// - Run PostTokenize to execute any transformations between generated
	//   embeddings and what the forward pass expects.
	// - The result may now be larger than a batch (images may not fit in a
	//   single batch), so trim based on what will fit and must be grouped together.
	// - Fill out the rest of the space with text tokens.
	if multimodalProcessor, ok := s.model.(model.MultimodalProcessor); ok {
		mmCtx := s.model.Backend().NewContext()
		defer mmCtx.Close()

		img := image.NewGray(image.Rect(0, 0, 2048, 2048))
		var buf bytes.Buffer
		bmp.Encode(&buf, img)

		if inputs[0].Multimodal, err = multimodalProcessor.EncodeMultimodal(mmCtx, buf.Bytes()); err == nil {
			mmStore.addMultimodal(inputs[0].Multimodal)

			inputs, err = multimodalProcessor.PostTokenize(inputs)
			if err != nil {
				return err
			}

			for i, inp := range inputs {
				minBatch := 1 + inp.SameBatch
				if minBatch > s.batchSize {
					inputs = inputs[i:min(i+minBatch, len(inputs))]
					break
				} else if i+minBatch > s.batchSize {
					inputs = inputs[:i]
					break
				}
			}

			if len(inputs) < s.batchSize {
1041
				newInputs := make([]*input.Input, s.batchSize)
1042
				copy(newInputs, inputs)
1043
1044
1045
				for i := len(inputs); i < s.batchSize; i++ {
					newInputs[i] = &input.Input{}
				}
1046
1047
1048
1049
1050
				inputs = newInputs
			}
		}
	}

1051
1052
	var batch input.Batch

1053
	batchInputs := make([]int32, len(inputs))
1054
1055
	batch.Positions = make([]int32, len(inputs))
	batch.Sequences = make([]int, len(inputs))
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
	for i, inp := range inputs {
		batchInputs[i] = inp.Token
		if inp.Multimodal != nil {
			mm, err := mmStore.getMultimodal(s.model.Backend(), ctx, inp.Multimodal, true)
			if err != nil {
				return err
			}
			batch.Multimodal = append(batch.Multimodal, input.MultimodalIndex{Index: i, Multimodal: mm})
		}

1066
1067
1068
		batch.Positions[i] = int32(i)
	}

1069
	batch.Inputs = ctx.Input().FromIntSlice(batchInputs, len(batchInputs))
1070
	batch.Outputs = ctx.Input().Empty(ml.DTypeI32, s.parallel)
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084

	cache := s.model.Config().Cache
	if cache != nil {
		err := cache.StartForward(ctx, batch, true)
		if err != nil {
			return err
		}
	}

	t, err := s.model.Forward(ctx, batch)
	if err != nil {
		return err
	}

1085
	ctx.Forward(t).Reserve()
1086
1087

	return nil
1088
}
1089

Jesse Gross's avatar
Jesse Gross committed
1090
1091
1092
// allocModel pre-allocates the maximum needed memory for a model
// based on the given parameters
func (s *Server) allocModel(
1093
	mpath string,
1094
	params ml.BackendParams,
Jesse Gross's avatar
Jesse Gross committed
1095
	loraPath []string,
Jesse Gross's avatar
Jesse Gross committed
1096
	parallel int,
1097
	kvCacheType string,
Jesse Gross's avatar
Jesse Gross committed
1098
	kvSize int,
1099
	multiUserCache bool,
Jesse Gross's avatar
Jesse Gross committed
1100
1101
1102
1103
1104
) (panicErr error) {
	// Convert memory allocation panics to errors
	defer func() {
		if r := recover(); r != nil {
			if err, ok := r.(error); ok {
1105
1106
1107
1108
1109
1110
				var noMem ml.ErrNoMem
				if errors.As(err, &noMem) {
					panicErr = noMem
				} else {
					panic(r)
				}
Jesse Gross's avatar
Jesse Gross committed
1111
1112
1113
1114
1115
1116
			} else {
				panic(r)
			}
		}
	}()

1117
	var err error
1118
	s.model, err = model.New(mpath, params)
1119
	if err != nil {
1120
		return err
1121
	}
1122

Jesse Gross's avatar
Jesse Gross committed
1123
	// TODO(jessegross): LoRA loading
Jesse Gross's avatar
Jesse Gross committed
1124
	if len(loraPath) > 0 {
1125
		return errors.New("loras are not yet implemented")
1126
1127
	}

1128
	s.cache, err = NewInputCache(s.model, kvCacheType, int32(kvSize), parallel, s.batchSize, multiUserCache)
1129
	if err != nil {
1130
		return err
1131
	}
1132

Jesse Gross's avatar
Jesse Gross committed
1133
1134
1135
1136
1137
1138
1139
1140
1141
	if !s.cache.enabled && parallel > 1 {
		parallel = 1
		slog.Warn("model does not support caching, disabling parallel processing")
	}

	s.parallel = parallel
	s.seqs = make([]*Sequence, s.parallel)
	s.seqsSem = semaphore.NewWeighted(int64(s.parallel))

1142
1143
1144
	return s.reserveWorstCaseGraph()
}

Jesse Gross's avatar
Jesse Gross committed
1145
1146
1147
1148
1149
1150
1151
// closeModel frees all memory associated with a model
func (s *Server) closeModel() {
	s.cache.Close()
	s.cache = nil
	if s.model != nil {
		s.model.Backend().Close()
		s.model = nil
1152
	}
Jesse Gross's avatar
Jesse Gross committed
1153
}
1154

Jesse Gross's avatar
Jesse Gross committed
1155
1156
1157
1158
// loadModel loads the weights for a model. The memory must already
// have been allocated with allocModel
func (s *Server) loadModel() {
	err := s.model.Backend().Load(context.TODO(),
1159
1160
1161
1162
		func(progress float32) {
			s.progress = progress
		})
	if err != nil {
Jesse Gross's avatar
Jesse Gross committed
1163
		panic(fmt.Errorf("failed to load model: %v", err))
1164
1165
	}

1166
	s.status = llm.ServerStatusReady
1167
1168
1169
	s.ready.Done()
}

Jesse Gross's avatar
Jesse Gross committed
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
// load is the handler called by the Ollama server to process different
// load operations
func (s *Server) load(w http.ResponseWriter, r *http.Request) {
	s.loadMu.Lock()
	defer s.loadMu.Unlock()

	w.Header().Set("Content-Type", "application/json")

	if s.status != llm.ServerStatusLaunched {
		http.Error(w, "model already loaded", http.StatusInternalServerError)
		return
	}

	var req llm.LoadRequest
	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
		http.Error(w, "bad request", http.StatusBadRequest)
		return
	}

	slog.Info("load", "request", req)

	if req.Operation == llm.LoadOperationClose {
		s.closeModel()
		if err := json.NewEncoder(w).Encode(&llm.LoadResponse{}); err != nil {
			http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
		}
		return
	}

	s.lastLoad.Operation = req.Operation
	loadModel := s.model == nil || !reflect.DeepEqual(req, s.lastLoad)

	s.lastLoad = req

	if loadModel {
		s.closeModel()

		params := ml.BackendParams{
			AllocMemory:    req.Operation != llm.LoadOperationFit,
			NumThreads:     req.NumThreads,
			GPULayers:      req.GPULayers,
			FlashAttention: req.FlashAttention,
		}

		s.batchSize = req.BatchSize

		err := s.allocModel(s.modelPath, params, req.LoraPath, req.Parallel, req.KvCacheType, req.KvSize, req.MultiUserCache)
		if err != nil {
			s.closeModel()

			var noMem ml.ErrNoMem
			if errors.As(err, &noMem) {
				resp := llm.LoadResponse{Success: false, Memory: noMem.BackendMemory}
				if err := json.NewEncoder(w).Encode(&resp); err != nil {
					http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
				}

				return
			}

			http.Error(w, fmt.Sprintf("failed to initialize model: %v", err), http.StatusInternalServerError)
			return
		}
	}

	mem := s.model.Backend().BackendMemory()

	switch req.Operation {
	case llm.LoadOperationFit:
		// LoadOperationFit can't be used for anything else, so just close it
		s.closeModel()

	// LoadOperationAlloc should stay open for future operations

	case llm.LoadOperationCommit:
		s.status = llm.ServerStatusLoadingModel
		go s.loadModel()
	}

	resp := llm.LoadResponse{Success: true, Memory: mem}
	if err := json.NewEncoder(w).Encode(&resp); err != nil {
		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
		return
	}
}

1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
// info is the handler called by the Ollama server to report information
// about the GPU devices in use by this runner
func (s *Server) info(w http.ResponseWriter, r *http.Request) {
	s.loadMu.Lock()
	defer s.loadMu.Unlock()

	w.Header().Set("Content-Type", "application/json")

	m := s.model

	if m == nil {
		startLoad := time.Now()

		// Dummy load to get the backend wired up
		f, err := os.CreateTemp("", "*.bin")
		if err != nil {
			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
			return
		}
		defer f.Close()
		defer os.Remove(f.Name())

		if err := ggml.WriteGGUF(f, ggml.KV{
			"general.architecture": "llama",
			"tokenizer.ggml.model": "gpt2",
		}, nil); err != nil {
			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
			return
		}

		m, err = model.New(f.Name(), ml.BackendParams{NumThreads: runtime.NumCPU(), AllocMemory: false, GPULayers: ml.GPULayersList{{}}})
		if err != nil {
			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
			return
		}
		slog.Debug("dummy model load took", "duration", time.Since(startLoad))
	}

	startDevices := time.Now()
	infos := m.Backend().BackendDevices()
	slog.Debug("gathering device infos took", "duration", time.Since(startDevices))
	if err := json.NewEncoder(w).Encode(&infos); err != nil {
		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
	}
}

1302
1303
1304
1305
func Execute(args []string) error {
	fs := flag.NewFlagSet("runner", flag.ExitOnError)
	mpath := fs.String("model", "", "Path to model binary file")
	port := fs.Int("port", 8080, "Port to expose the server on")
1306
	_ = fs.Bool("verbose", false, "verbose output (default: disabled)")
1307

1308
1309
1310
1311
1312
1313
	fs.Usage = func() {
		fmt.Fprintf(fs.Output(), "Runner usage\n")
		fs.PrintDefaults()
	}
	if err := fs.Parse(args); err != nil {
		return err
1314
	}
1315
	slog.SetDefault(logutil.NewLogger(os.Stderr, envconfig.LogLevel()))
Jesse Gross's avatar
Jesse Gross committed
1316
	slog.Info("starting ollama engine")
1317

1318
1319
1320
	ctx, cancel := context.WithCancel(context.Background())
	defer cancel()

Jesse Gross's avatar
Jesse Gross committed
1321
1322
1323
	server := &Server{
		modelPath: *mpath,
		status:    llm.ServerStatusLaunched,
1324
		hardErrCh: make(chan error, 1),
1325
1326
	}

Jesse Gross's avatar
Jesse Gross committed
1327
1328
	server.cond = sync.NewCond(&server.mu)
	server.ready.Add(1)
1329
1330
1331
1332
1333
1334
1335

	go server.run(ctx)

	addr := "127.0.0.1:" + strconv.Itoa(*port)
	listener, err := net.Listen("tcp", addr)
	if err != nil {
		fmt.Println("Listen error:", err)
1336
		return err
1337
1338
1339
1340
	}
	defer listener.Close()

	mux := http.NewServeMux()
1341
	// TODO: support embeddings
1342
	mux.HandleFunc("GET /info", server.info)
Jesse Gross's avatar
Jesse Gross committed
1343
	mux.HandleFunc("POST /load", server.load)
Michael Yang's avatar
Michael Yang committed
1344
	mux.HandleFunc("POST /embedding", server.embeddings)
1345
1346
	mux.HandleFunc("POST /completion", server.completion)
	mux.HandleFunc("GET /health", server.health)
1347
1348
1349
1350
1351
1352
1353
1354

	httpServer := http.Server{
		Handler: mux,
	}

	log.Println("Server listening on", addr)
	if err := httpServer.Serve(listener); err != nil {
		log.Fatal("server error:", err)
1355
		return err
1356
1357
	}

1358
	return nil
1359
}