ggml.go 44.4 KB
Newer Older
Michael Yang's avatar
Michael Yang committed
1
2
package ggml

3
4
// #cgo linux LDFLAGS: -lrt -lpthread -ldl -lstdc++ -lm
// #cgo windows LDFLAGS: -lpthread
5
6
7
8
9
10
// #cgo CPPFLAGS: -I${SRCDIR}/ggml/include
// #include <stdlib.h>
// #include <stdint.h>
// #include "ggml.h"
// #include "ggml-cpu.h"
// #include "ggml-backend.h"
Michael Yang's avatar
Michael Yang committed
11
12
13
import "C"

import (
Michael Yang's avatar
Michael Yang committed
14
	"cmp"
15
	"context"
Michael Yang's avatar
Michael Yang committed
16
	"encoding/binary"
Jesse Gross's avatar
Jesse Gross committed
17
	"errors"
Michael Yang's avatar
Michael Yang committed
18
19
20
	"fmt"
	"io"
	"log/slog"
21
	"maps"
Michael Yang's avatar
Michael Yang committed
22
	"os"
23
	"runtime"
24
25
26
	"slices"
	"strconv"
	"strings"
Jesse Gross's avatar
Jesse Gross committed
27
	"sync"
28
	"sync/atomic"
29
	"unicode"
Michael Yang's avatar
Michael Yang committed
30
31
32
	"unsafe"

	"github.com/ollama/ollama/format"
33
34
	"github.com/ollama/ollama/fs"
	fsggml "github.com/ollama/ollama/fs/ggml"
35
	"github.com/ollama/ollama/logutil"
Michael Yang's avatar
Michael Yang committed
36
	"github.com/ollama/ollama/ml"
37
	ggml "github.com/ollama/ollama/ml/backend/ggml/ggml/src"
38
	"github.com/ollama/ollama/ml/nn/rope"
Michael Yang's avatar
Michael Yang committed
39
40
41
	"golang.org/x/sync/errgroup"
)

Jesse Gross's avatar
Jesse Gross committed
42
43
44
45
46
47
var (
	cpus, accels, gpus []C.ggml_backend_dev_t
	backends           map[C.ggml_backend_dev_t]C.ggml_backend_t
)

var initDevices = sync.OnceFunc(func() {
Michael Yang's avatar
Michael Yang committed
48
49
	ggml.OnceLoad()

Jesse Gross's avatar
Jesse Gross committed
50
51
52
53
54
55
56
57
58
59
60
61
	backends = make(map[C.ggml_backend_dev_t]C.ggml_backend_t)
	for i := range C.ggml_backend_dev_count() {
		d := C.ggml_backend_dev_get(i)

		switch C.ggml_backend_dev_type(d) {
		case C.GGML_BACKEND_DEVICE_TYPE_CPU:
			if len(cpus) == 0 {
				// only the first cpu device should be used
				cpus = append(cpus, d)
			}
		case C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
			accels = append(accels, d)
62
63
		case C.GGML_BACKEND_DEVICE_TYPE_GPU,
			C.GGML_BACKEND_DEVICE_TYPE_IGPU:
Jesse Gross's avatar
Jesse Gross committed
64
65
66
67
68
69
			gpus = append(gpus, d)
		}

		backends[d] = C.ggml_backend_dev_init(d, nil)
	}
})
Michael Yang's avatar
Michael Yang committed
70

Jesse Gross's avatar
Jesse Gross committed
71
72
73
74
75
type layerDevice struct {
	d  C.ggml_backend_dev_t
	bt C.ggml_backend_buffer_type_t
}

Michael Yang's avatar
Michael Yang committed
76
type Backend struct {
77
78
79
	// modelPath is the location of the model data
	modelPath string

80
81
	meta *fsggml.GGML

Jesse Gross's avatar
Jesse Gross committed
82
83
84
85
	// allocMemory means that memory should be allocated for tensors and not
	// just a dry run
	allocMemory bool

86
87
88
89
	// tensorLoadTargets maps from the name of the tensor in the file
	// to the name that is used by the model definition
	tensorLoadTargets map[string][]string

90
	schedMu       sync.Mutex // Only one Compute can run at a time
91
92
93
	sched         C.ggml_backend_sched_t
	schedBackends []C.ggml_backend_t
	schedBufts    []C.ggml_backend_buffer_type_t
94

95
	tensors map[string]*C.struct_ggml_tensor
Michael Yang's avatar
Michael Yang committed
96

Jesse Gross's avatar
Jesse Gross committed
97
	// input is the backend buffer type used for inputs
98
	input C.ggml_backend_buffer_type_t
Michael Yang's avatar
Michael Yang committed
99

Jesse Gross's avatar
Jesse Gross committed
100
101
102
	// output is the backend device used for outputs
	output C.ggml_backend_dev_t

Michael Yang's avatar
Michael Yang committed
103
	// layers is the backend used for repeating layers
Jesse Gross's avatar
Jesse Gross committed
104
	layers map[int]layerDevice
105

106
107
108
109
	// requiredMemory is the cumulative memory allocations needed by the backend
	requiredMemory *ml.BackendMemory

	// btDeviceMemory maps from a buffer type to the memory allocations associated with that device
110
	btDeviceMemory map[C.ggml_backend_buffer_type_t]*ml.DeviceMemory
111

112
	flashAttention bool
Michael Yang's avatar
Michael Yang committed
113
114
115

	// maxGraphNodes is the maximum allowed number of graph nodes in this scheduler
	maxGraphNodes int
Jesse Gross's avatar
Jesse Gross committed
116
117
118

	// weightBuffers are the GGML contexts and buffers for allocating weights
	weightBuffers map[*C.struct_ggml_context]C.ggml_backend_buffer_t
Michael Yang's avatar
Michael Yang committed
119
120
}

Jesse Gross's avatar
Jesse Gross committed
121
122
var once sync.Once

123
124
125
126
127
128
129
130
func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
	r, err := os.Open(modelPath)
	if err != nil {
		return nil, err
	}
	defer r.Close()

	meta, err := fsggml.Decode(r, -1)
Michael Yang's avatar
Michael Yang committed
131
132
133
134
	if err != nil {
		return nil, err
	}

Jesse Gross's avatar
Jesse Gross committed
135
136
137
138
139
140
141
142
143
144
145
	once.Do(func() {
		slog.Info(
			"",
			"architecture", meta.KV().Architecture(),
			"file_type", meta.KV().FileType(),
			"name", meta.KV().String("general.name"),
			"description", meta.KV().String("general.description"),
			"num_tensors", len(meta.Tensors().Items()),
			"num_key_values", len(meta.KV()),
		)
	})
Michael Yang's avatar
Michael Yang committed
146

Jesse Gross's avatar
Jesse Gross committed
147
148
	initDevices()

149
	var requiredMemory ml.BackendMemory
150
	btDeviceMemory := make(map[C.ggml_backend_buffer_type_t]*ml.DeviceMemory)
151

152
	type deviceBufferType struct {
153
154
		d   C.ggml_backend_dev_t
		bts []C.ggml_backend_buffer_type_t
155
156
	}

157
158
	blocks := int(meta.KV().BlockCount())

Michael Yang's avatar
Michael Yang committed
159
	// create list of buffer types for the cpu
Michael Yang's avatar
Michael Yang committed
160
	cpuDeviceBufferType := deviceBufferType{d: C.ggml_backend_dev_by_type(C.GGML_BACKEND_DEVICE_TYPE_CPU)}
161
162
163
164
	for _, d := range append(accels, append(gpus, cpus...)...) {
		switch C.ggml_backend_dev_type(d) {
		case C.GGML_BACKEND_DEVICE_TYPE_CPU,
			C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
Jesse Gross's avatar
Jesse Gross committed
165
166
167
			bt := C.ggml_backend_dev_buffer_type(d)
			cpuDeviceBufferType.bts = append(cpuDeviceBufferType.bts, bt)

168
			btDeviceMemory[C.ggml_backend_dev_buffer_type(d)] = &requiredMemory.CPU
Michael Yang's avatar
Michael Yang committed
169
		}
170
171
	}

172
	requiredMemory.CPU.Name = C.GoString(C.ggml_backend_dev_name(cpuDeviceBufferType.d))
173
174
	var props C.struct_ggml_backend_dev_props
	C.ggml_backend_dev_get_props(cpuDeviceBufferType.d, &props)
175
	requiredMemory.CPU.ID = C.GoString(props.id)
176
	requiredMemory.CPU.Library = C.GoString(props.library)
177
178
	requiredMemory.CPU.Weights = make([]uint64, blocks+1)
	requiredMemory.CPU.Cache = make([]uint64, blocks+1)
179

Michael Yang's avatar
Michael Yang committed
180
	// create list of buffer types for each gpu
181
	var gpuDeviceBufferTypes []deviceBufferType
182
183
	requiredMemory.GPUs = make([]ml.DeviceMemory, len(gpus))
	for i, d := range gpus {
184
		bt := C.ggml_backend_dev_buffer_type(d)
185
		gpuDeviceBufferTypes = append(gpuDeviceBufferTypes, deviceBufferType{
186
			d:   d,
187
			bts: append([]C.ggml_backend_buffer_type_t{bt}, cpuDeviceBufferType.bts...),
188
		})
Jesse Gross's avatar
Jesse Gross committed
189

190
191
		btDeviceMemory[bt] = &requiredMemory.GPUs[i]
		requiredMemory.GPUs[i].Name = C.GoString(C.ggml_backend_dev_name(d))
192
193
		var props C.struct_ggml_backend_dev_props
		C.ggml_backend_dev_get_props(d, &props)
194
		requiredMemory.GPUs[i].ID = C.GoString(props.id)
195
		requiredMemory.GPUs[i].Library = C.GoString(props.library)
196
197
		requiredMemory.GPUs[i].Weights = make([]uint64, blocks+1)
		requiredMemory.GPUs[i].Cache = make([]uint64, blocks+1)
Michael Yang's avatar
Michael Yang committed
198
199
	}

Michael Yang's avatar
Michael Yang committed
200
	// inputs always use cpu
Michael Yang's avatar
Michael Yang committed
201
	input := cpuDeviceBufferType
202

Jesse Gross's avatar
Jesse Gross committed
203
204
205
206
207
	assignLayer := func(layer int) deviceBufferType {
		for _, p := range params.GPULayers {
			for _, l := range p.Layers {
				if l == layer {
					for i := range requiredMemory.GPUs {
208
						if requiredMemory.GPUs[i].DeviceID == p.DeviceID {
Jesse Gross's avatar
Jesse Gross committed
209
210
211
							return gpuDeviceBufferTypes[i]
						}
					}
212

Jesse Gross's avatar
Jesse Gross committed
213
214
215
					return cpuDeviceBufferType
				}
			}
216
217
		}

Jesse Gross's avatar
Jesse Gross committed
218
		return cpuDeviceBufferType
219
220
	}

Michael Yang's avatar
Michael Yang committed
221
	// repeating layers are assigned based on their index in reverse order, e.g. i / (block_count + 1)
222
	layers := make([]deviceBufferType, blocks)
223
	for i := range layers {
224
		layers[i] = assignLayer(i)
225
226
	}

Michael Yang's avatar
Michael Yang committed
227
	// outputs are assigned iff allowed by splits and configured number of gpu layers
228
	output := assignLayer(blocks)
229
230
231

	maxTensors := len(meta.Tensors().Items())
	maxTensors += 1
Michael Yang's avatar
Michael Yang committed
232
	// each layer has at most 2 extra tensors for rope operations
233
234
	maxTensors += blocks * 2

235
	type tensor struct {
236
		source *fsggml.Tensor
237
238
239
		target string
	}

Michael Yang's avatar
Michael Yang committed
240
	// some tensors are mapped to different names so keep a list
241
242
	targets := make(map[string][]string)

Michael Yang's avatar
Michael Yang committed
243
	// contexts are shared by tensors of the same buffer type
244
245
	ctxs := make(map[C.ggml_backend_buffer_type_t]*C.struct_ggml_context)
	createTensor := func(t tensor, bts []C.ggml_backend_buffer_type_t, layer int) *C.struct_ggml_tensor {
246
247
248
249
250
251
252
		for _, bt := range bts {
			if _, ok := ctxs[bt]; !ok {
				ctxs[bt] = C.ggml_init(C.struct_ggml_init_params{
					mem_size: C.ggml_tensor_overhead() * C.size_t(maxTensors),
					no_alloc: true,
				})
			}
Michael Yang's avatar
Michael Yang committed
253

254
255
256
257
258
259
260
261
			targets[t.source.Name] = append(targets[t.source.Name], t.target)

			name := t.source.Name
			if t.target != "" {
				name = t.target
			}

			cname := C.CString(name)
Michael Yang's avatar
Michael Yang committed
262
			defer C.free(unsafe.Pointer(cname))
263
264
265
266
			if tt := C.ggml_get_tensor(ctxs[bt], cname); tt != nil {
				return tt
			}

267
268
269
270
271
272
273
274
275
276
			kind := t.source.Kind
			if t.source.Kind == 4 {
				// transform raw mxfp4 stream to ggml mxfp4 format
				kind = 39
			} else if t.source.Kind == uint32(fsggml.TensorTypeBF16) && strings.HasSuffix(t.source.Name, "_exps.bias") {
				// transform "_exps.bias" from bf16 to fp32; add_ids only supports fp32 tensors
				kind = uint32(fsggml.TensorTypeF32)
			}

			tt := C.ggml_new_tensor(ctxs[bt], kind, C.int(len(t.source.Shape)), (*C.int64_t)(unsafe.Pointer(&t.source.Shape[0])))
Michael Yang's avatar
Michael Yang committed
277
278
			C.ggml_set_name(tt, cname)

279
			logutil.Trace("created tensor", "name", name, "shape", t.source.Shape, "dtype", t.source.Kind, "buffer_type", C.GoString(C.ggml_backend_buft_name(bt)))
280
281
282

			size := pad(C.ggml_backend_buft_get_alloc_size(bt, tt), C.ggml_backend_buft_get_alignment(bt))
			if layer == -1 {
283
				requiredMemory.InputWeights += uint64(size)
284
			} else {
285
				btDeviceMemory[bt].Weights[layer] += uint64(size)
286
287
			}

288
289
290
291
292
			//nolint:staticcheck // TODO: check if buffer type supports this tensor
			return tt
		}

		return nil
Michael Yang's avatar
Michael Yang committed
293
294
	}

295
	contains := func(s string, parts ...string) bool {
296
297
298
299
300
301
302
303
		split := strings.Split(s, ".")
		for _, part := range parts {
			if slices.Contains(split, part) {
				return true
			}
		}

		return false
Michael Yang's avatar
Michael Yang committed
304
305
	}

306
307
	for _, t := range meta.Tensors().Items() {
		switch {
308
		case contains(t.Name, "position_embd", "token_embd", "token_norm_embd", "token_types"):
309
			createTensor(tensor{source: t}, input.bts, -1)
Michael Yang's avatar
Michael Yang committed
310
			if _, ok := meta.Tensors().GroupLayers()["output"]; !ok && t.Name == "token_embd.weight" {
311
				createTensor(tensor{source: t, target: "output.weight"}, output.bts, blocks)
Michael Yang's avatar
Michael Yang committed
312
			}
Michael Yang's avatar
Michael Yang committed
313
314
315
		case contains(t.Name, "cls", "output", "output_norm",
			"altup_proj", "altup_unembd_proj",
			"per_layer_token_embd", "per_layer_model_proj", "per_layer_proj_norm"):
316
			createTensor(tensor{source: t}, output.bts, blocks)
317
		case strings.HasPrefix(t.Name, "v.") || strings.HasPrefix(t.Name, "mm."):
Michael Yang's avatar
Michael Yang committed
318
			// TODO: assign vision tensors to the gpu if possible
319
			createTensor(tensor{source: t}, output.bts, blocks)
Michael Yang's avatar
Michael Yang committed
320
321
322
323
324
325
		case contains(t.Name, "rope_freqs", "rope_factors_long", "rope_factors_short"):
			// these tensors should be repeated per layer
			for i, layer := range layers {
				createTensor(tensor{
					source: t,
					target: "blk." + strconv.Itoa(i) + "." + t.Name,
326
				}, layer.bts, i)
Michael Yang's avatar
Michael Yang committed
327
			}
328
		default:
Michael Yang's avatar
Michael Yang committed
329
330
331
332
			layerIndex := -1
			if fields := strings.FieldsFunc(t.Name, func(r rune) bool { return !unicode.IsNumber(r) }); len(fields) > 0 {
				if i, err := strconv.Atoi(fields[0]); err == nil {
					layerIndex = i
333
				}
Michael Yang's avatar
Michael Yang committed
334
			}
335

Michael Yang's avatar
Michael Yang committed
336
			if layerIndex >= 0 {
337
				createTensor(tensor{source: t}, layers[layerIndex].bts, layerIndex)
338
			} else {
Michael Yang's avatar
Michael Yang committed
339
				// load all other tensors on the cpu
340
				createTensor(tensor{source: t}, input.bts, -1)
341
342
343
			}
		}
	}
Michael Yang's avatar
Michael Yang committed
344

Michael Yang's avatar
Michael Yang committed
345
	// map tensor names to tensors for easy lookup later
346
347
348
349
350
351
352
	tensors := make(map[string]*C.struct_ggml_tensor)
	for _, c := range ctxs {
		for t := C.ggml_get_first_tensor(c); t != nil; t = C.ggml_get_next_tensor(c, t) {
			tensors[C.GoString(C.ggml_get_name(t))] = t
		}
	}

353
	// map devices to backend buffer types so new tensors can be assigned to the correct device
354
	deviceBufferTypes := make(map[C.ggml_backend_dev_t]C.ggml_backend_buffer_type_t)
355
356

	// create backends and buffer types used for the compute graph scheduler
357
358
	var schedBackends []C.ggml_backend_t
	var schedBufts []C.ggml_backend_buffer_type_t
359
	for _, d := range append(gpus, append(accels, cpus...)...) {
Jesse Gross's avatar
Jesse Gross committed
360
		b := backends[d]
361
362
		bt := C.ggml_backend_get_default_buffer_type(b)

Jesse Gross's avatar
Jesse Gross committed
363
364
365
366
367
368
369
		// Always include CPU as a fallback but otherwise, just use the devices where we assigned layers
		if !slices.Contains(cpuDeviceBufferType.bts, bt) {
			if c, ok := ctxs[bt]; !ok || C.ggml_get_first_tensor(c) == nil {
				continue
			}
		}

370
371
372
373
374
375
376
377
378
379
380
		deviceBufferTypes[d] = bt

		schedBackends = append(schedBackends, b)
		schedBufts = append(schedBufts, bt)

		if C.ggml_backend_is_cpu(b) {
			// set number of threads for cpu backend
			C.ggml_backend_cpu_set_n_threads(b, C.int(Threads(params.NumThreads)))
		}
	}

381
	maxGraphNodes := max(1024, len(meta.Tensors().Items())*8)
382
383
384
385
386
387
388

	sched := C.ggml_backend_sched_new_ext(
		(*C.ggml_backend_t)(unsafe.Pointer(&schedBackends[0])),
		(*C.ggml_backend_buffer_type_t)(unsafe.Pointer(&schedBufts[0])),
		C.int(len(schedBackends)),
		C.size_t(maxGraphNodes),
		C._Bool(false),
389
		C._Bool(true),
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
		C._Bool(params.AllocMemory),
	)

	// allocate buffers for each context
	bbs := make(map[*C.struct_ggml_context]C.ggml_backend_buffer_t, len(ctxs))
	for bt, c := range ctxs {
		if C.ggml_get_first_tensor(c) == nil {
			continue
		}

		b := C.ggml_backend_alloc_ctx_tensors_from_buft(c, bt)
		if b == nil {
			for _, b := range bbs {
				C.ggml_backend_buffer_free(b)
			}

			for _, ctx := range ctxs {
				C.ggml_free(ctx)
			}

			panic(ml.ErrNoMem{BackendMemory: requiredMemory})
		}

		C.ggml_backend_buffer_set_usage(b, C.GGML_BACKEND_BUFFER_USAGE_WEIGHTS)
		bbs[c] = b
	}

	for bs := range maps.Values(bbs) {
		logutil.Trace("model weights", "buffer", C.GoString(C.ggml_backend_buffer_name(bs)),
			"size", format.HumanBytes2(uint64(C.ggml_backend_buffer_get_size(bs))))
	}

422
423
	return &Backend{
		modelPath:         modelPath,
Jesse Gross's avatar
Jesse Gross committed
424
		allocMemory:       params.AllocMemory,
425
426
427
428
		flashAttention:    params.FlashAttention,
		meta:              meta,
		tensorLoadTargets: targets,
		tensors:           tensors,
429
430
431
432
433
		sched:             sched,
		schedBackends:     schedBackends,
		schedBufts:        schedBufts,
		input:             deviceBufferTypes[input.d],
		output:            output.d,
Jesse Gross's avatar
Jesse Gross committed
434
435
		layers: func() map[int]layerDevice {
			m := make(map[int]layerDevice)
436
			for i, layer := range layers {
Jesse Gross's avatar
Jesse Gross committed
437
438
439
440
				m[i] = layerDevice{
					d:  layer.d,
					bt: deviceBufferTypes[layer.d],
				}
441
442
443
			}
			return m
		}(),
444
445
446
		requiredMemory: &requiredMemory,
		btDeviceMemory: btDeviceMemory,
		maxGraphNodes:  maxGraphNodes,
Jesse Gross's avatar
Jesse Gross committed
447
		weightBuffers:  bbs,
448
449
450
451
452
453
454
	}, nil
}

func init() {
	ml.RegisterBackend("ggml", New)
}

Jesse Gross's avatar
Jesse Gross committed
455
456
457
458
459
460
461
462
463
464
465
466
467
func (b *Backend) Close() {
	if b == nil {
		return
	}

	for ctx, b := range b.weightBuffers {
		C.ggml_backend_buffer_free(b)
		C.ggml_free(ctx)
	}

	C.ggml_backend_sched_free(b.sched)
}

468
func (b *Backend) Load(ctx context.Context, progress func(float32)) error {
Jesse Gross's avatar
Jesse Gross committed
469
470
471
472
473
474
475
	if !b.allocMemory {
		return errors.New("cannot load model without memory allocation")
	}

	// Mimic llama runner logs summarizing layers and memory
	gpuLayers := 0
	for layer := range maps.Values(b.layers) {
476
477
478
		switch C.ggml_backend_dev_type(layer.d) {
		case C.GGML_BACKEND_DEVICE_TYPE_GPU,
			C.GGML_BACKEND_DEVICE_TYPE_IGPU:
Jesse Gross's avatar
Jesse Gross committed
479
480
481
482
483
484
485
486
			gpuLayers++
		}
	}
	slog.Info(fmt.Sprintf("offloading %d repeating layers to GPU", gpuLayers))

	switch C.ggml_backend_dev_type(b.output) {
	case C.GGML_BACKEND_DEVICE_TYPE_CPU:
		slog.Info("offloading output layer to CPU")
487
488
	case C.GGML_BACKEND_DEVICE_TYPE_GPU,
		C.GGML_BACKEND_DEVICE_TYPE_IGPU:
Jesse Gross's avatar
Jesse Gross committed
489
490
491
492
493
494
495
		slog.Info("offloading output layer to GPU")
		gpuLayers++
	case C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
		slog.Info("offloading output layer to ACCEL")
	}
	slog.Info(fmt.Sprintf("offloaded %d/%d layers to GPU", gpuLayers, len(b.layers)+1))

496
	var doneBytes atomic.Uint64
497
	totalBytes := uint64(b.meta.Length) - b.meta.Tensors().Offset
498
499
500

	g, ctx := errgroup.WithContext(ctx)
	g.SetLimit(runtime.GOMAXPROCS(0))
501
	for _, t := range b.meta.Tensors().Items() {
502
		t := t
503
		g.Go(func() error {
504
			tts := make([]*C.struct_ggml_tensor, max(1, len(b.tensorLoadTargets[t.Name])))
505
			for i := range tts {
506
				target := b.tensorLoadTargets[t.Name][i]
507
508
509
				if target == "" {
					target = t.Name
				}
510

511
				tt, ok := b.tensors[target]
512
513
514
				if !ok {
					return fmt.Errorf("unassigned tensor: %s", t.Name)
				}
Michael Yang's avatar
Michael Yang committed
515

516
517
518
				tts[i] = tt
			}

519
520
			// Create a new FD for each goroutine so that each FD is read sequentially, rather than
			// seeking around within an FD shared between all goroutines.
521
			file, err := os.Open(b.modelPath)
522
			if err != nil {
523
				slog.Warn("file open error", "file", b.modelPath, "error", err)
524
525
526
				return err
			}
			defer file.Close()
527
			sr := io.NewSectionReader(file, int64(b.meta.Tensors().Offset+t.Offset), int64(t.Size()))
528
529
530
531
532
533
534

			if t.Kind == 4 && tts[0]._type == 39 {
				// source is mxfp4, target is ggml mxfp4

				const BS = 17                             // MXFP4 block size
				bts := make([]byte, 8*BS*format.KibiByte) // ~128k block aligned
				var s uint64
535
				var tmp [16]byte
536
537
538
539
540
541
542
543
544
545
546
547
				for s < t.Size() {
					// Stop if either the parent context has been canceled or if any of the other tensors returned an error
					if err := ctx.Err(); err != nil {
						return err
					}
					n, err := io.ReadFull(sr, bts[:min(len(bts), int(t.Size()-s))])
					if err != nil {
						slog.Warn("file read error", "file", b.modelPath, "error", err)
						return err
					}
					for j := range n / BS {
						for i := 1; i < 9; i++ {
548
549
550
551
							// transform a1b2c3 ... x7y8z9 -> 71xa82yb93zc
							a, b := bts[j*BS+i], bts[j*BS+i+8]
							tmp[2*(i-1)] = (a & 0x0F) | (b << 4)
							tmp[2*(i-1)+1] = (a >> 4) | (b & 0xF0)
552
						}
553
						copy(bts[j*BS+1:j*BS+17], tmp[:])
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
					}

					for _, tt := range tts {
						C.ggml_backend_tensor_set(tt, unsafe.Pointer(&bts[0]), C.size_t(s), C.size_t(n))
					}

					s += uint64(n)

					if progress != nil {
						done := doneBytes.Add(uint64(n))
						progress(float32(done) / float32(totalBytes))
					}
				}
				return nil
			} else if strings.HasSuffix(t.Name, "_exps.bias") && t.Kind == 30 && tts[0]._type == 0 {
				// source is bf16, target is ggml fp32

				// data is bf16 but we need to convert to fp32
				bts := make([]byte, 128*format.KibiByte)
				var e uint64
				for e < t.Elements() {
					// Stop if either the parent context has been canceled or if any of the other tensors returned an error
					if err := ctx.Err(); err != nil {
						return err
					}
					n, err := io.ReadFull(sr, bts[:min(len(bts), int(t.Elements()-e)*2)])
					if err != nil {
						slog.Warn("file read error", "file", b.modelPath, "error", err)
						return err
					}
					fp32 := ConvertToF32(bts, uint32(fsggml.TensorTypeBF16), uint64(n/2))

					for _, tt := range tts {
						C.ggml_backend_tensor_set(tt, unsafe.Pointer(&fp32[0]), C.size_t(e*4), C.size_t(n*2))
					}
					e += uint64(n / 2)
					if progress != nil {
						done := doneBytes.Add(uint64(n))
						progress(float32(done) / float32(totalBytes))
					}
				}
				return nil
			}

598
599
600
601
			bts := make([]byte, 128*format.KibiByte)

			var s uint64
			for s < t.Size() {
602
603
604
605
606
				// Stop if either the parent context has been canceled or if any of the other tensors returned an error
				if err := ctx.Err(); err != nil {
					return err
				}

607
608
				n, err := io.ReadFull(sr, bts[:min(len(bts), int(t.Size()-s))])
				if err != nil {
609
					slog.Warn("file read error", "file", b.modelPath, "error", err)
610
					return err
611
				}
Michael Yang's avatar
Michael Yang committed
612

613
614
				for _, tt := range tts {
					C.ggml_backend_tensor_set(tt, unsafe.Pointer(&bts[0]), C.size_t(s), C.size_t(n))
615
				}
Michael Yang's avatar
Michael Yang committed
616

617
618
				s += uint64(n)

619
				if progress != nil {
620
					done := doneBytes.Add(uint64(n))
621
					progress(float32(done) / float32(totalBytes))
622
623
624
625
626
				}
			}

			return nil
		})
Michael Yang's avatar
Michael Yang committed
627
628
	}

629
630
631
632
633
634
635
636
637
638
639
640
	// Cleanup any backend state from devices that we didn't end up using
nextDevice:
	for _, d := range append(gpus, append(accels, cpus...)...) {
		for _, backend := range b.schedBackends {
			if d == C.ggml_backend_get_device(backend) {
				continue nextDevice
			}
		}

		C.ggml_backend_dev_reset(d)
	}

641
	if err := g.Wait(); err != nil {
642
		return err
643
644
	}

645
	return nil
Michael Yang's avatar
Michael Yang committed
646
647
}

648
649
650
651
func (b *Backend) BackendMemory() ml.BackendMemory {
	return *b.requiredMemory
}

652
func (b *Backend) Config() fs.Config {
Michael Yang's avatar
Michael Yang committed
653
654
655
656
	return b.meta.KV()
}

func (b *Backend) Get(name string) ml.Tensor {
657
658
	if t, ok := b.tensors[name]; ok {
		return &Tensor{b: b, t: t}
Michael Yang's avatar
Michael Yang committed
659
660
661
662
663
664
	}

	return nil
}

func (b *Backend) NewContext() ml.Context {
Michael Yang's avatar
Michael Yang committed
665
	return b.NewContextSize(b.maxGraphNodes)
666
667
668
}

func (b *Backend) NewContextSize(n int) ml.Context {
Jesse Gross's avatar
Jesse Gross committed
669
670
671
672
	if n > b.maxGraphNodes {
		panic(fmt.Errorf("requested number of graph nodes (%v) for new context exceeds maximum (%v)", n, b.maxGraphNodes))
	}

673
	var allocatedBuffers []C.ggml_backend_buffer_t
674

Michael Yang's avatar
Michael Yang committed
675
	return &Context{
676
677
		b:             b,
		maxGraphNodes: n,
678
		ctx: C.ggml_init(C.struct_ggml_init_params{
679
			mem_size: C.size_t(n)*C.ggml_tensor_overhead() + C.ggml_graph_overhead_custom(C.size_t(n), false),
680
681
			no_alloc: true,
		}),
682
		allocatedBuffers: &allocatedBuffers,
683
		layer:            -1,
Michael Yang's avatar
Michael Yang committed
684
685
686
	}
}

687
func (b *Backend) CacheConfig() ml.CacheConfig {
688
689
690
691
692
	if b.flashAttention {
		return ml.CacheConfig{CachePadding: 256, MaskDType: ml.DTypeF16, MaskBatchPadding: C.GGML_KQ_MASK_PAD}
	} else {
		return ml.CacheConfig{CachePadding: 32, PermutedV: true}
	}
693
694
}

695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
func (b *Backend) BackendDevices() []ml.DeviceInfo {
	deviceInfos := []ml.DeviceInfo{}
	for _, dev := range gpus {
		// If we have a model loaded, and it's only loaded on a subset of the devices
		// skip idle/unused devices to avoid initializing them and causing VRAM allocations
		if b.allocMemory {
			idleDev := true
			for _, backend := range b.schedBackends {
				if dev == C.ggml_backend_get_device(backend) {
					idleDev = false
					break
				}
			}
			if idleDev {
				slog.Debug("skipping unused backend device", "description", C.GoString(C.ggml_backend_dev_description(dev)))
				continue
			}
		}

		info := ml.DeviceInfo{}
		props := C.struct_ggml_backend_dev_props{}
		C.ggml_backend_dev_get_props(dev, &props)
		info.Name = C.GoString(props.name)
		info.Description = C.GoString(props.description)
		info.ID = C.GoString(props.id)
		info.Library = C.GoString(props.library)
		info.ComputeMajor = (int)(props.compute_major)
		info.ComputeMinor = (int)(props.compute_minor)
		info.DriverMajor = (int)(props.driver_major)
		info.DriverMinor = (int)(props.driver_minor)
		info.Integrated = props.integrated != 0
		if props.library != nil {
			info.Library = C.GoString(props.library)
		}
729
730
731
		if props.device_id != nil {
			info.PCIID = C.GoString(props.device_id)
		}
732
733
734
735
736
737
738
739
740
741
		info.LibraryPath = ggml.LibPaths()
		C.ggml_backend_dev_memory(dev, &props.memory_free, &props.memory_total)
		info.TotalMemory = (uint64)(props.memory_total)
		info.FreeMemory = (uint64)(props.memory_free)

		deviceInfos = append(deviceInfos, info)
	}
	return deviceInfos
}

Michael Yang's avatar
Michael Yang committed
742
type Context struct {
743
	b *Backend
Michael Yang's avatar
Michael Yang committed
744

745
	ctx   *C.struct_ggml_context
Michael Yang's avatar
Michael Yang committed
746
	graph *C.struct_ggml_cgraph
747

748
749
750
	// batchSize is a hint to optimize processing
	batchSize int

751
	// buft is the buffer type used for new tensors
752
	buft C.ggml_backend_buffer_type_t
753

754
755
	// allocatedBuffers are buffers for tensors that we have allocated in this context
	// so that we can free them when we close the context
756
	allocatedBuffers *[]C.ggml_backend_buffer_t
757

Michael Yang's avatar
Michael Yang committed
758
	// maxGraphNodes is the maximum allowed number of graph nodes in this context
759
	maxGraphNodes int
760
761
762

	// layer is the graph layer that this context is allocating for - assumed to be cache
	layer int
Michael Yang's avatar
Michael Yang committed
763
764
}

765
func (c *Context) Input() ml.Context {
Michael Yang's avatar
Michael Yang committed
766
	if c.b.input != nil {
767
		return &Context{
768
769
770
771
772
			b:                c.b,
			ctx:              c.ctx,
			buft:             c.b.input,
			allocatedBuffers: c.allocatedBuffers,
			maxGraphNodes:    c.maxGraphNodes,
773
			layer:            -1,
774
775
776
		}
	}

777
	return c
778
779
}

780
func (c *Context) Layer(i int) ml.Context {
Jesse Gross's avatar
Jesse Gross committed
781
	if layer, ok := c.b.layers[i]; ok {
782
		return &Context{
783
784
			b:                c.b,
			ctx:              c.ctx,
Jesse Gross's avatar
Jesse Gross committed
785
			buft:             layer.bt,
786
787
			allocatedBuffers: c.allocatedBuffers,
			maxGraphNodes:    c.maxGraphNodes,
788
			layer:            i,
789
790
791
		}
	}

792
	return c
793
794
}

795
func (c *Context) Forward(tensors ...ml.Tensor) ml.Context {
Michael Yang's avatar
Michael Yang committed
796
	if c.graph == nil {
797
		c.graph = C.ggml_new_graph_custom(c.ctx, C.size_t(c.maxGraphNodes), false)
Michael Yang's avatar
Michael Yang committed
798
799
	}

800
801
802
803
804
	for _, tensor := range tensors {
		C.ggml_build_forward_expand(c.graph, tensor.(*Tensor).t)
	}

	return c
Michael Yang's avatar
Michael Yang committed
805
806
}

807
808
809
810
func (c *Context) SetBatchSize(batchSize int) {
	c.batchSize = batchSize
}

811
func (c *Context) Compute(tensors ...ml.Tensor) {
812
813
814
815
816
817
818
819
820
	c.ComputeWithNotify(nil, tensors...)
}

func (c *Context) ComputeWithNotify(cb func(), tensors ...ml.Tensor) {
	c.b.schedMu.Lock()
	defer c.b.schedMu.Unlock()
	if cb != nil {
		go cb()
	}
821
822
823
824
825

	if c.batchSize > 0 {
		C.ggml_backend_sched_set_batch_size(c.b.sched, C.int(c.batchSize))
	}

826
827
828
	if status := C.ggml_backend_sched_graph_compute_async(c.b.sched, c.graph); status != C.GGML_STATUS_SUCCESS {
		panic(fmt.Errorf("error computing ggml graph: %v", status))
	}
Michael Yang's avatar
Michael Yang committed
829
	C.ggml_backend_sched_reset(c.b.sched)
Michael Yang's avatar
Michael Yang committed
830

831
832
833
	needSync := true
	sync := func() {
		if needSync {
834
			C.ggml_backend_sched_synchronize(c.b.sched)
835
836
837
			needSync = false
		}
	}
Michael Yang's avatar
Michael Yang committed
838

839
840
841
	for _, t := range tensors {
		if C.ggml_nbytes(t.(*Tensor).t) > 0 {
			t.(*Tensor).sync = sync
842
843
		}
	}
Michael Yang's avatar
Michael Yang committed
844
845
}

846
func (c *Context) Reserve() {
847
848
849
850
	if c.batchSize > 0 {
		C.ggml_backend_sched_set_batch_size(c.b.sched, C.int(c.batchSize))
	}

851
	reserved := C.ggml_backend_sched_reserve(c.b.sched, c.graph)
852
853

	slog.Debug("compute graph", "nodes", C.ggml_graph_n_nodes(c.graph), "splits", C.ggml_backend_sched_get_n_splits(c.b.sched))
854
855
856

	// Reserve may get called multiple times for different graphs - we just want the last run, which will contain the max allocations
	for _, bt := range c.b.schedBufts {
857
		c.b.btDeviceMemory[bt].Graph = 0
858
859
	}

860
	for i := range c.b.schedBackends {
861
862
		bufferSize := C.ggml_backend_sched_get_attempted_buffer_size(c.b.sched, c.b.schedBackends[i])
		c.b.btDeviceMemory[c.b.schedBufts[i]].Graph += uint64(bufferSize)
863

864
		logutil.Trace("compute graph", "backend", C.GoString(C.ggml_backend_name(c.b.schedBackends[i])),
865
			"buffer_type", C.GoString(C.ggml_backend_buft_name(c.b.schedBufts[i])), "size", format.HumanBytes2(uint64(bufferSize)))
866
867
	}

868
869
870
	if !reserved {
		panic(ml.ErrNoMem{BackendMemory: *c.b.requiredMemory})
	}
871
872
}

873
func (c *Context) MaxGraphNodes() int {
874
	return c.maxGraphNodes
Jesse Gross's avatar
Jesse Gross committed
875
876
}

877
878
879
func shapeToGGML(shape []int) *C.int64_t {
	sh := make([]C.int64_t, len(shape))
	for i, s := range shape {
880
		sh[i] = C.int64_t(s)
881
882
883
884
885
	}

	return &sh[0]
}

886
887
888
889
func pad(length, pad C.size_t) C.size_t {
	return ((length + pad - 1) / pad) * pad
}

Michael Yang's avatar
Michael Yang committed
890
func (c *Context) newTensor(dtype ml.DType, shape []int) *Tensor {
891
	if c.buft == nil {
892
		panic("set Input or Layer before creating tensors")
893
894
	}

895
	cdtype := ggmlDType(dtype)
Michael Yang's avatar
Michael Yang committed
896

Jesse Gross's avatar
Jesse Gross committed
897
	if len(shape) < 1 || shape[0] == 0 {
Michael Yang's avatar
Michael Yang committed
898
		var shape C.int64_t = 0
899
		return &Tensor{b: c.b, t: C.ggml_new_tensor(c.ctx, cdtype, 1, &shape)}
Michael Yang's avatar
Michael Yang committed
900
	} else if len(shape) > 4 {
Michael Yang's avatar
Michael Yang committed
901
902
903
904
905
906
907
908
909
		panic("unsupported number of dimensions")
	}

	for _, dim := range shape {
		if dim < 1 {
			panic("invalid shape")
		}
	}

Michael Yang's avatar
Michael Yang committed
910
	t := C.ggml_new_tensor(c.ctx, cdtype, C.int(len(shape)), shapeToGGML(shape))
911
	size := pad(C.ggml_backend_buft_get_alloc_size(c.buft, t), C.ggml_backend_buft_get_alignment(c.buft))
912

913
	b := C.ggml_backend_buft_alloc_buffer(c.buft, size)
914
	if c.layer >= 0 {
915
		c.b.btDeviceMemory[c.buft].Cache[c.layer] += uint64(size)
916
917
	}

918
	if b == nil {
919
		panic(ml.ErrNoMem{BackendMemory: *c.b.requiredMemory})
920
921
	}

922
	*c.allocatedBuffers = append(*c.allocatedBuffers, b)
Michael Yang's avatar
Michael Yang committed
923
	C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
924
	return &Tensor{b: c.b, t: t}
925
926
}

927
func (c *Context) Empty(dtype ml.DType, shape ...int) ml.Tensor {
928
	return c.newTensor(dtype, shape)
929
930
}

931
func (c *Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
932
	t := c.newTensor(dtype, shape)
Jesse Gross's avatar
Jesse Gross committed
933
	if c.b.allocMemory {
Michael Yang's avatar
Michael Yang committed
934
		C.ggml_set_zero(t.t)
Jesse Gross's avatar
Jesse Gross committed
935
	}
936
	return t
Michael Yang's avatar
Michael Yang committed
937
938
}

939
func checkShape[S ~[]E, E any](s S, shape ...int) {
Michael Yang's avatar
Michael Yang committed
940
	n := len(s)
Jesse Gross's avatar
Jesse Gross committed
941
942

	if n == 0 {
943
		return
Jesse Gross's avatar
Jesse Gross committed
944
945
	}

Michael Yang's avatar
Michael Yang committed
946
947
948
949
950
	for _, v := range shape {
		n /= v
	}

	if n != 1 {
951
		panic(fmt.Errorf("invalid shape: %v", shape))
Michael Yang's avatar
Michael Yang committed
952
953
954
	}
}

Michael Yang's avatar
Michael Yang committed
955
956
957
958
959
960
961
962
963
964
965
func (c Context) FromBytes(dtype ml.DType, s []uint8, shape ...int) ml.Tensor {
	// Unchecked to handle quantized types
	t := c.newTensor(dtype, shape)
	if c.b.allocMemory {
		t.FromBytes(s)
	}

	return t
}

func (c *Context) FromFloats(s []float32, shape ...int) ml.Tensor {
966
	checkShape(s, shape...)
967

968
	t := c.newTensor(ml.DTypeF32, shape)
969

Michael Yang's avatar
Michael Yang committed
970
971
	if c.b.allocMemory {
		t.FromFloats(s)
Jesse Gross's avatar
Jesse Gross committed
972
973
	}

974
	return t
Michael Yang's avatar
Michael Yang committed
975
976
}

Michael Yang's avatar
Michael Yang committed
977
func (c *Context) FromInts(s []int32, shape ...int) ml.Tensor {
978
	checkShape(s, shape...)
979

980
	t := c.newTensor(ml.DTypeI32, shape)
Michael Yang's avatar
Michael Yang committed
981
982
	if c.b.allocMemory {
		t.FromInts(s)
Jesse Gross's avatar
Jesse Gross committed
983
984
	}

985
	return t
Michael Yang's avatar
Michael Yang committed
986
987
}

Michael Yang's avatar
arange  
Michael Yang committed
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
func (c Context) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
	switch dtype {
	case ml.DTypeF32:
		// ggml_arange creates a float32 tensor
		return &Tensor{
			b: c.b,
			t: C.ggml_arange(c.ctx, C.float(start), C.float(stop), C.float(step)),
		}
	case ml.DTypeI32:
		// ggml_cast does not support float32 to int32 conversion
		arange := make([]int32, 0, int((stop-start)/step))
		for i := start; i < stop; i += step {
			arange = append(arange, int32(i))
		}

Michael Yang's avatar
Michael Yang committed
1003
		return c.Input().FromInts(arange, len(arange))
Michael Yang's avatar
arange  
Michael Yang committed
1004
1005
1006
1007
1008
	default:
		panic("unsupported dtype for arange")
	}
}

Michael Yang's avatar
Michael Yang committed
1009
1010
func (c *Context) Close() {
	if c != nil {
1011
1012
1013
1014
1015
		for _, b := range *c.allocatedBuffers {
			C.ggml_backend_buffer_free(b)
		}
		*c.allocatedBuffers = nil

1016
1017
		C.ggml_free(c.ctx)
	}
Michael Yang's avatar
Michael Yang committed
1018
1019
1020
}

type Tensor struct {
1021
	b    *Backend
Michael Yang's avatar
Michael Yang committed
1022
	t    *C.struct_ggml_tensor
1023
	sync func()
Michael Yang's avatar
Michael Yang committed
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
}

func (t *Tensor) LogValue() slog.Value {
	return slog.GroupValue(
		slog.String("name", C.GoString(C.ggml_get_name(t.t))),
		slog.String("type", C.GoString(C.ggml_type_name(t.t._type))),
		slog.Any("shape", t.Shape()),
	)
}

1034
1035
func (t *Tensor) Dim(n int) int {
	return int(t.t.ne[n])
Michael Yang's avatar
Michael Yang committed
1036
1037
}

1038
1039
func (t *Tensor) Stride(n int) int {
	return int(t.t.nb[n])
Michael Yang's avatar
Michael Yang committed
1040
1041
}

1042
1043
func (t *Tensor) Shape() []int {
	shape := make([]int, C.ggml_n_dims(t.t))
Michael Yang's avatar
Michael Yang committed
1044
1045
1046
1047
1048
1049
1050
	for i := range shape {
		shape[i] = t.Dim(i)
	}

	return shape
}

1051
1052
1053
1054
1055
1056
1057
1058
1059
func (t *Tensor) Bytes() (data []byte) {
	if t.sync != nil {
		data = make([]byte, C.ggml_nbytes(t.t))

		t.sync()
		C.ggml_backend_tensor_get(t.t, unsafe.Pointer(&data[0]), 0, C.ggml_nbytes(t.t))
	}

	return
Michael Yang's avatar
Michael Yang committed
1060
1061
}

1062
1063
1064
1065
1066
1067
func (t *Tensor) Floats() (data []float32) {
	if t.sync != nil {
		data = make([]float32, C.ggml_nelements(t.t))

		t.sync()
		C.ggml_backend_tensor_get(t.t, unsafe.Pointer(&data[0]), 0, C.ggml_nbytes(t.t))
Michael Yang's avatar
Michael Yang committed
1068
1069
1070
1071
1072
	}

	return
}

Michael Yang's avatar
Michael Yang committed
1073
1074
1075
1076
1077
1078
func tensorSet[S ~[]E, E byte | float32 | int32](t *Tensor, s S) {
	if len(s) == 0 {
		return
	}
	if int(C.ggml_nbytes(t.t)) != len(s)*binary.Size(s[0]) {
		panic("data size does not match tensor size")
1079
	}
Michael Yang's avatar
Michael Yang committed
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
	C.ggml_backend_tensor_set(t.t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.t))
}

func (t *Tensor) FromBytes(s []byte) {
	tensorSet(t, s)
}

func (t *Tensor) FromFloats(s []float32) {
	tensorSet(t, s)
}

func (t *Tensor) FromInts(s []int32) {
	tensorSet(t, s)
1093
1094
}

Michael Yang's avatar
Michael Yang committed
1095
1096
1097
1098
func (t *Tensor) DType() ml.DType {
	switch t.t._type {
	case C.GGML_TYPE_F32:
		return ml.DTypeF32
Jesse Gross's avatar
Jesse Gross committed
1099
1100
	case C.GGML_TYPE_F16:
		return ml.DTypeF16
1101
1102
1103
1104
	case C.GGML_TYPE_Q8_0:
		return ml.DTypeQ80
	case C.GGML_TYPE_Q4_0:
		return ml.DTypeQ40
Michael Yang's avatar
Michael Yang committed
1105
1106
	case C.GGML_TYPE_I32:
		return ml.DTypeI32
Michael Yang's avatar
Michael Yang committed
1107
1108
	case C.GGML_TYPE_MXFP4:
		return ml.DTypeMXFP4
Michael Yang's avatar
Michael Yang committed
1109
1110
1111
1112
1113
	default:
		return ml.DTypeOther
	}
}

1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
func ggmlDType(dtype ml.DType) uint32 {
	switch dtype {
	case ml.DTypeF32:
		return C.GGML_TYPE_F32
	case ml.DTypeF16:
		return C.GGML_TYPE_F16
	case ml.DTypeQ80:
		return C.GGML_TYPE_Q8_0
	case ml.DTypeQ40:
		return C.GGML_TYPE_Q4_0
	case ml.DTypeI32:
		return C.GGML_TYPE_I32
	case ml.DTypeMXFP4:
		return C.GGML_TYPE_MXFP4
	default:
		panic("unsupported dtype")
	}
}

func (t *Tensor) Cast(ctx ml.Context, dtype ml.DType) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_cast(ctx.(*Context).ctx, t.t, ggmlDType(dtype)),
	}
}

1140
1141
1142
1143
1144
1145
1146
func (t *Tensor) Neg(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_neg(ctx.(*Context).ctx, t.t),
	}
}

Michael Yang's avatar
Michael Yang committed
1147
1148
func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
1149
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1150
1151
1152
1153
		t: C.ggml_add(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

Michael Yang's avatar
Michael Yang committed
1154
1155
1156
1157
1158
1159
1160
func (t *Tensor) Sub(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sub(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
func (t *Tensor) Repeat(ctx ml.Context, dim, n int) ml.Tensor {
	if dim < 0 || dim >= C.GGML_MAX_DIMS {
		panic("invalid dimension")
	}

	shape := make([]C.int64_t, C.GGML_MAX_DIMS)
	for i := range C.GGML_MAX_DIMS {
		if i == dim {
			shape[i] = C.int64_t(t.Dim(i) * n)
		} else {
			shape[i] = C.int64_t(t.Dim(i))
		}
	}

	tmpl := C.ggml_new_tensor(ctx.(*Context).ctx, t.t._type, C.int(len(shape)), unsafe.SliceData(shape))
	return &Tensor{
		b: t.b,
		t: C.ggml_repeat(ctx.(*Context).ctx, t.t, tmpl),
	}
}

Michael Yang's avatar
Michael Yang committed
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
func (t *Tensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
	if len(s) > 0 {
		return t.Concat(ctx, s[0].Stack(ctx, dim, s[1:]...), dim)
	}

	return t
}

func (t *Tensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
	return &Tensor{
1192
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1193
1194
1195
1196
		t: C.ggml_concat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(dim)),
	}
}

Michael Yang's avatar
Michael Yang committed
1197
func (t *Tensor) Contiguous(ctx ml.Context, shape ...int) ml.Tensor {
1198
1199
1200
1201
	if slices.Contains(shape, -1) {
		inferShape(t, shape)
	}

Michael Yang's avatar
Michael Yang committed
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
	switch len(shape) {
	case 0:
		return &Tensor{
			b: t.b,
			t: C.ggml_cont(ctx.(*Context).ctx, t.t),
		}
	case 1:
		return &Tensor{
			b: t.b,
			t: C.ggml_cont_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0])),
		}
	case 2:
		return &Tensor{
			b: t.b,
			t: C.ggml_cont_2d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1])),
		}
	case 3:
		return &Tensor{
			b: t.b,
			t: C.ggml_cont_3d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2])),
		}
	case 4:
		return &Tensor{
			b: t.b,
			t: C.ggml_cont_4d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2]), C.int64_t(shape[3])),
		}
	default:
		panic("unsupported number of dimensions")
Michael Yang's avatar
Michael Yang committed
1230
1231
1232
1233
1234
	}
}

func (t *Tensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
1235
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1236
1237
1238
1239
		t: C.ggml_mul(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

1240
1241
1242
1243
1244
1245
1246
func (t *Tensor) Div(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_div(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

1247
1248
1249
1250
1251
// Mulmat performs matrix multiplication between two tensors.
// If t has shape [m, p, ...] and t2 has shape [m, n, ...],
// Mulmat returns a new Tensor with shape [p, n, ...].
//
// Note: this is similar to matmul(t2, t.tranpose(-1, -2)) in other libraries.
Michael Yang's avatar
Michael Yang committed
1252
1253
func (t *Tensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
1254
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1255
1256
1257
1258
		t: C.ggml_mul_mat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

1259
1260
1261
1262
1263
func (t *Tensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	mul := C.ggml_mul_mat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t)
	C.ggml_mul_mat_set_prec(mul, C.GGML_PREC_F32)

	return &Tensor{
1264
		b: t.b,
1265
1266
1267
1268
		t: mul,
	}
}

Michael Yang's avatar
llama4  
Michael Yang committed
1269
1270
1271
1272
1273
1274
1275
func (t *Tensor) MulmatID(ctx ml.Context, t2, ids ml.Tensor) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_mul_mat_id(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, ids.(*Tensor).t),
	}
}

1276
1277
1278
1279
1280
1281
1282
func (t *Tensor) AddID(ctx ml.Context, t2, ids ml.Tensor) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_add_id(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, ids.(*Tensor).t),
	}
}

1283
1284
1285
1286
1287
1288
1289
func (t *Tensor) L2Norm(ctx ml.Context, eps float32) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_l2_norm(ctx.(*Context).ctx, t.t, C.float(eps)),
	}
}

Michael Yang's avatar
Michael Yang committed
1290
func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tensor {
Michael Yang's avatar
llama4  
Michael Yang committed
1291
1292
1293
1294
1295
1296
	tt := C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))
	if w != nil {
		tt = C.ggml_mul(ctx.(*Context).ctx, tt, w.(*Tensor).t)
		if b != nil {
			tt = C.ggml_add(ctx.(*Context).ctx, tt, b.(*Tensor).t)
		}
Michael Yang's avatar
Michael Yang committed
1297
1298
	}

Michael Yang's avatar
llama4  
Michael Yang committed
1299
	return &Tensor{b: t.b, t: tt}
Michael Yang's avatar
Michael Yang committed
1300
1301
1302
}

func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
Michael Yang's avatar
llama4  
Michael Yang committed
1303
1304
1305
1306
1307
1308
	tt := C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))
	if w != nil {
		tt = C.ggml_mul(ctx.(*Context).ctx, tt, w.(*Tensor).t)
	}

	return &Tensor{b: t.b, t: tt}
Michael Yang's avatar
Michael Yang committed
1309
1310
}

1311
func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
Michael Yang's avatar
Michael Yang committed
1312
1313
	if len(shape) != 4 {
		panic("expected 4 dimensions")
1314
1315
	} else if shape[3] != 0 {
		panic("cuda does not support 4d tensors")
Michael Yang's avatar
Michael Yang committed
1316
1317
1318
	}

	return &Tensor{
1319
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1320
1321
1322
1323
		t: C.ggml_pad(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
	}
}

1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
// Permute permutes t according to order. Permute panics if the number of dimensions
// in order does not match the number of dimensions in t.
func (t *Tensor) Permute(ctx ml.Context, order ...int) ml.Tensor {
	if len(order) != len(t.Shape()) && len(order) != 4 {
		panic("invalid number of dimensions for permute")
	}

	// ggml_permute requires 4 dimensions so fill in the rest
	for i := len(order); i < 4; i++ {
		order = append(order, i)
Michael Yang's avatar
Michael Yang committed
1334
1335
1336
	}

	return &Tensor{
1337
		b: t.b,
1338
		t: C.ggml_permute(ctx.(*Context).ctx, t.t, C.int(order[0]), C.int(order[1]), C.int(order[2]), C.int(order[3])),
Michael Yang's avatar
Michael Yang committed
1339
1340
1341
1342
1343
	}
}

func (t *Tensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
1344
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1345
1346
1347
1348
1349
1350
		t: C.ggml_get_rows(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

func (t *Tensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
	return &Tensor{
1351
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1352
1353
1354
1355
		t: C.ggml_cpy(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
	}
}

1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
// inferShape updates shape in place to automatically set a single -1 dimesion
// based on the input tensor and the other dimensions
func inferShape(t *Tensor, shape []int) {
	total := 1
	for _, dim := range t.Shape() {
		total *= dim
	}

	dim := -1
	for i := range shape {
		switch shape[i] {
		case -1:
			if dim != -1 {
				panic("only one dimension can be inferred")
			}
			dim = i
		case 0:
			panic("dimension cannot be zero")
		default:
			if total%shape[i] != 0 {
				panic("cannot infer dimension")
			}

			total /= shape[i]
		}
	}

	if dim != -1 {
		shape[dim] = total
	}
}

1388
func (t *Tensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
1389
1390
1391
1392
	if slices.Contains(shape, -1) {
		inferShape(t, shape)
	}

Michael Yang's avatar
Michael Yang committed
1393
1394
1395
	switch len(shape) {
	case 1:
		return &Tensor{
1396
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1397
1398
1399
1400
			t: C.ggml_reshape_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0])),
		}
	case 2:
		return &Tensor{
1401
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1402
1403
1404
1405
			t: C.ggml_reshape_2d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1])),
		}
	case 3:
		return &Tensor{
1406
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1407
1408
1409
1410
			t: C.ggml_reshape_3d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2])),
		}
	case 4:
		return &Tensor{
1411
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1412
1413
1414
1415
1416
1417
1418
1419
1420
			t: C.ggml_reshape_4d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2]), C.int64_t(shape[3])),
		}
	default:
		panic("unsupported number of dimensions")
	}
}

func (t *Tensor) Scale(ctx ml.Context, s float64) ml.Tensor {
	return &Tensor{
1421
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1422
1423
1424
1425
		t: C.ggml_scale(ctx.(*Context).ctx, t.t, (C.float)(s)),
	}
}

1426
1427
1428
1429
1430
1431
1432
func (t *Tensor) SumRows(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sum_rows(ctx.(*Context).ctx, t.t),
	}
}

Michael Yang's avatar
Michael Yang committed
1433
1434
func (t *Tensor) Softmax(ctx ml.Context) ml.Tensor {
	return &Tensor{
1435
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1436
1437
1438
1439
		t: C.ggml_soft_max(ctx.(*Context).ctx, t.t),
	}
}

1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
func (t *Tensor) Sin(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sin(ctx.(*Context).ctx, t.t),
	}
}

func (t *Tensor) Cos(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_cos(ctx.(*Context).ctx, t.t),
	}
}

Michael Yang's avatar
Michael Yang committed
1454
1455
func (t *Tensor) Tanh(ctx ml.Context) ml.Tensor {
	return &Tensor{
1456
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1457
1458
1459
1460
		t: C.ggml_tanh_inplace(ctx.(*Context).ctx, t.t),
	}
}

Michael Yang's avatar
llama4  
Michael Yang committed
1461
1462
1463
1464
1465
1466
1467
func (t *Tensor) Sigmoid(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sigmoid_inplace(ctx.(*Context).ctx, t.t),
	}
}

Michael Yang's avatar
Michael Yang committed
1468
1469
1470
1471
func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
	switch len(shape) {
	case 1:
		return &Tensor{
1472
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1473
1474
1475
1476
			t: C.ggml_view_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.size_t(offset)),
		}
	case 3:
		return &Tensor{
1477
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1478
1479
1480
1481
1482
1483
1484
			t: C.ggml_view_2d(ctx.(*Context).ctx, t.t,
				C.int64_t(shape[0]), C.int64_t(shape[2]),
				C.size_t(shape[1]),
				C.size_t(offset)),
		}
	case 5:
		return &Tensor{
1485
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1486
1487
1488
1489
1490
1491
1492
			t: C.ggml_view_3d(ctx.(*Context).ctx, t.t,
				C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]),
				C.size_t(shape[1]), C.size_t(shape[3]),
				C.size_t(offset)),
		}
	case 7:
		return &Tensor{
1493
			b: t.b,
Michael Yang's avatar
Michael Yang committed
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
			t: C.ggml_view_4d(ctx.(*Context).ctx, t.t,
				C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]), C.int64_t(shape[6]),
				C.size_t(shape[1]), C.size_t(shape[3]), C.size_t(shape[5]),
				C.size_t(offset)),
		}
	default:
		panic("unsupported number of dimensions")
	}
}

1504
func (t *Tensor) RoPE(ctx ml.Context, positions ml.Tensor, ropeDim int, ropeBase, ropeScale float32, options ...func(*rope.Options)) ml.Tensor {
1505
	// Default options
Michael Yang's avatar
Michael Yang committed
1506
	opts := rope.Options{Factors: &Tensor{}}
1507
1508
1509

	// Apply any provided options
	for _, option := range options {
Michael Yang's avatar
Michael Yang committed
1510
		option(&opts)
1511
1512
	}

Jesse Gross's avatar
Jesse Gross committed
1513
1514
1515
1516
1517
	dequant := t.t
	if C.ggml_is_quantized(t.t._type) {
		dequant = C.ggml_cast(ctx.(*Context).ctx, t.t, C.GGML_TYPE_F32)
	}

Michael Yang's avatar
Michael Yang committed
1518
1519
1520
1521
1522
1523
1524
1525
	var tt *C.struct_ggml_tensor
	if len(opts.MRoPE.Sections) > 0 {
		mropeSections := make([]C.int32_t, 4)
		for i, section := range opts.MRoPE.Sections {
			mropeSections[i] = C.int32_t(section)
		}

		tt = C.ggml_rope_multi(
1526
1527
			ctx.(*Context).ctx,
			dequant,
1528
1529
			positions.(*Tensor).t,
			opts.Factors.(*Tensor).t,
Michael Yang's avatar
Michael Yang committed
1530
			C.int(ropeDim),
Michael Yang's avatar
Michael Yang committed
1531
			unsafe.SliceData(mropeSections),
1532
			C.int(opts.Type),
Michael Yang's avatar
Michael Yang committed
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
			cmp.Or(C.int(opts.YaRN.OriginalContextLength), 128<<10),
			C.float(ropeBase), C.float(ropeScale),
			C.float(opts.YaRN.ExtrapolationFactor),
			cmp.Or(C.float(opts.YaRN.AttentionFactor), 1),
			cmp.Or(C.float(opts.YaRN.BetaFast), 32),
			cmp.Or(C.float(opts.YaRN.BetaSlow), 1),
		)
	} else {
		tt = C.ggml_rope_ext(
			ctx.(*Context).ctx,
			dequant,
			positions.(*Tensor).t,
			opts.Factors.(*Tensor).t,
			C.int(ropeDim), C.int(opts.Type),
			cmp.Or(C.int(opts.YaRN.OriginalContextLength), 128<<10),
			C.float(ropeBase), C.float(ropeScale),
			C.float(opts.YaRN.ExtrapolationFactor),
			cmp.Or(C.float(opts.YaRN.AttentionFactor), 1),
			cmp.Or(C.float(opts.YaRN.BetaFast), 32),
			cmp.Or(C.float(opts.YaRN.BetaSlow), 1),
		)
Michael Yang's avatar
Michael Yang committed
1554
	}
Michael Yang's avatar
Michael Yang committed
1555
	return &Tensor{b: t.b, t: tt}
Michael Yang's avatar
Michael Yang committed
1556
1557
}

1558
1559
1560
1561
1562
1563
1564
func (t *Tensor) IM2Col(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_im2col(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1), true, C.GGML_TYPE_F32),
	}
}

1565
1566
1567
1568
1569
1570
1571
func (t *Tensor) GELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
	if len(t2) > 0 {
		return &Tensor{
			b: t.b,
			t: C.ggml_geglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
		}
	}
Michael Yang's avatar
Michael Yang committed
1572
	return &Tensor{
1573
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1574
1575
1576
1577
		t: C.ggml_gelu_inplace(ctx.(*Context).ctx, t.t),
	}
}

1578
1579
1580
1581
1582
1583
func (t *Tensor) SILU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
	if len(t2) > 0 {
		return &Tensor{
			b: t.b,
			t: C.ggml_swiglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
		}
Michael Yang's avatar
Michael Yang committed
1584
	}
Michael Yang's avatar
Michael Yang committed
1585
	return &Tensor{
1586
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1587
1588
1589
1590
		t: C.ggml_silu_inplace(ctx.(*Context).ctx, t.t),
	}
}

1591
1592
1593
1594
1595
1596
1597
func (t *Tensor) RELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
	if len(t2) > 0 {
		return &Tensor{
			b: t.b,
			t: C.ggml_reglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
		}
	}
Michael Yang's avatar
Michael Yang committed
1598
1599
1600
1601
1602
1603
	return &Tensor{
		b: t.b,
		t: C.ggml_relu_inplace(ctx.(*Context).ctx, t.t),
	}
}

1604
func (t *Tensor) SILUAlphaLimit(ctx ml.Context, up ml.Tensor, alpha, limit float32) ml.Tensor {
1605
1606
1607
1608
1609
1610
	return &Tensor{
		b: t.b,
		t: C.ggml_swiglu_oai(ctx.(*Context).ctx, t.t, up.(*Tensor).t, C.float(alpha), C.float(limit)),
	}
}

Michael Yang's avatar
Michael Yang committed
1611
1612
func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
	return &Tensor{
1613
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1614
1615
1616
		t: C.ggml_conv_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1)),
	}
}
1617

1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
func (t *Tensor) Conv3D(ctx ml.Context, t2 ml.Tensor, c, s0, s1, s2, p0, p1, p2, d0, d1, d2 int) ml.Tensor {
	var tt ml.Tensor = &Tensor{
		b: t.b,
		t: C.ggml_conv_3d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int64_t(c), C.int(s0), C.int(s1), C.int(s2), C.int(p0), C.int(p1), C.int(p2), C.int(d0), C.int(d1), C.int(d2)),
	}

	tt = tt.Reshape(ctx, t.Dim(3)/c, t2.Dim(3)/c)
	return tt
}

Michael Yang's avatar
Michael Yang committed
1628
func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
Michael Yang's avatar
Michael Yang committed
1629
1630
	return &Tensor{
		b: t.b,
Michael Yang's avatar
Michael Yang committed
1631
		t: C.ggml_pool_2d(ctx.(*Context).ctx, t.t, C.GGML_OP_POOL_AVG, C.int(k), C.int(k), C.int(s), C.int(s), C.float(p), C.float(p)),
Michael Yang's avatar
Michael Yang committed
1632
1633
1634
	}
}

Michael Yang's avatar
Michael Yang committed
1635
1636
1637
1638
func (t *Tensor) Set(ctx ml.Context, t2 ml.Tensor, offset int, strides ...int) ml.Tensor {
	var tt *C.struct_ggml_tensor
	switch len(strides) {
	case 0:
Michael Yang's avatar
Michael Yang committed
1639
		tt = C.ggml_set_1d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.size_t(offset))
Michael Yang's avatar
Michael Yang committed
1640
	case 1:
Michael Yang's avatar
Michael Yang committed
1641
		tt = C.ggml_set_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.size_t(offset), C.size_t(strides[0]))
Michael Yang's avatar
Michael Yang committed
1642
1643
1644
1645
1646
1647
1648
	default:
		panic("unsupported number of dimensions")
	}

	return &Tensor{b: t.b, t: tt}
}

1649
func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask, sinks ml.Tensor, scale float64) ml.Tensor {
1650
1651
1652
1653
1654
	var kqMask *C.struct_ggml_tensor
	if mask != nil {
		kqMask = mask.(*Tensor).t
	}

1655
1656
1657
	query := t.Permute(ctx, 0, 2, 1, 3)
	key = key.Permute(ctx, 0, 2, 1, 3)

1658
1659
	if t.b.flashAttention {
		value = value.Permute(ctx, 0, 2, 1, 3)
1660

1661
		kqv := C.ggml_flash_attn_ext(ctx.(*Context).ctx, query.(*Tensor).t, key.(*Tensor).t, value.(*Tensor).t, kqMask, C.float(scale), 0, 0)
1662
1663
1664
		if sinks != nil {
			C.ggml_flash_attn_ext_add_sinks(kqv, sinks.(*Tensor).t)
		}
1665
1666
1667
1668
1669
1670
1671
1672
		C.ggml_flash_attn_ext_set_prec(kqv, C.GGML_PREC_F32)
		return &Tensor{b: t.b, t: kqv}
	} else {
		kq := key.MulmatFullPrec(ctx, query)
		kq = &Tensor{
			b: t.b,
			t: C.ggml_soft_max_ext(ctx.(*Context).ctx, kq.(*Tensor).t, kqMask, C.float(scale), 0),
		}
1673
1674
1675
		if sinks != nil {
			C.ggml_soft_max_add_sinks(kq.(*Tensor).t, sinks.(*Tensor).t)
		}
1676
1677
1678
1679

		kqv := value.Mulmat(ctx, kq)
		return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
	}
1680
}
1681
1682
1683
1684
1685
1686
1687

func (t *Tensor) Duplicate(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_dup(ctx.(*Context).ctx, t.t),
	}
}
Michael Yang's avatar
llama4  
Michael Yang committed
1688
1689
1690
1691
1692
1693
1694

func (t *Tensor) TopK(ctx ml.Context, k int) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_top_k(ctx.(*Context).ctx, t.t, C.int(k)),
	}
}
1695
1696
1697
1698
1699
1700
1701

func (t *Tensor) Argsort(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_argsort(ctx.(*Context).ctx, t.t, C.GGML_SORT_ORDER_ASC),
	}
}
Michael Yang's avatar
Michael Yang committed
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740

func (t *Tensor) Mean(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_mean(ctx.(*Context).ctx, t.t),
	}
}

func (t *Tensor) Variance(ctx ml.Context) ml.Tensor {
	return t.Add(ctx, t.Mean(ctx).Scale(ctx, -1)).
		Sqr(ctx).
		SumRows(ctx).
		Scale(ctx, 1/float64(t.Dim(0)))
}

func (t *Tensor) Stddev(ctx ml.Context) ml.Tensor {
	return t.Variance(ctx).Sqrt(ctx)
}

func (t *Tensor) Sqr(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sqr(ctx.(*Context).ctx, t.t),
	}
}

func (t *Tensor) Sqrt(ctx ml.Context) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_sqrt(ctx.(*Context).ctx, t.t),
	}
}

func (t *Tensor) Clamp(ctx ml.Context, min, max float32) ml.Tensor {
	return &Tensor{
		b: t.b,
		t: C.ggml_clamp(ctx.(*Context).ctx, t.t, C.float(min), C.float(max)),
	}
}