-
Notifications
You must be signed in to change notification settings - Fork 8
/
Tensor.h
470 lines (376 loc) · 12.7 KB
/
Tensor.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
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
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
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
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
/*
* TinyGPT
* @author : keith@robot9.me
*
*/
#pragma once
#include <cstdint>
#include <vector>
#include <functional>
#include <limits>
#include <cmath>
namespace TinyGPT {
#define TENSOR_MAX_DIMS 16
typedef enum TensorError_ {
TensorError_None = 0,
TensorError_EmptyTensor,
TensorError_InvalidShape,
TensorError_InvalidAxis,
TensorError_InvalidSections,
TensorError_ShapeNotAligned,
TensorError_NotSupport,
} TensorError;
typedef enum ShapeCompatible_ {
ShapeCompatible_Error = 0,
ShapeCompatible_SameShape,
ShapeCompatible_Broadcast,
} ShapeCompatible;
typedef std::vector<uint32_t> Shape;
typedef std::vector<float> Array1d;
typedef std::vector<std::vector<float>> Array2d;
typedef std::vector<std::vector<std::vector<float>>> Array3d;
// one axis only
class Axis {
public:
Axis() = delete;
Axis(int32_t axis) : axis_(axis) {}
uint32_t get(uint32_t axisCnt) const {
return axis_ >= 0 ? axis_ : axis_ + axisCnt;
}
private:
int32_t axis_ = 0;
};
class UFunc {
public:
virtual void op(const float &val) {
idx_++;
};
virtual float result() {
return tmp;
};
virtual void reset() {
idx_ = 0;
tmp = 0.f;
}
protected:
uint32_t idx_ = 0;
float tmp = 0.f;
};
class UFuncSum : public UFunc {
public:
void op(const float &val) override {
tmp += val;
}
};
class UFuncMean : public UFunc {
public:
void op(const float &val) override {
idx_++;
tmp += val;
}
float result() override {
return tmp / (float) idx_;
}
};
class UFuncVar : public UFunc {
public:
void op(const float &val) override {
idx_++;
tmp += val;
squareSum_ += val * val;
}
float result() override {
float mean = tmp / (float) idx_;
return squareSum_ / (float) idx_ - mean * mean;
}
void reset() override {
idx_ = 0;
tmp = 0;
squareSum_ = 0;
}
private:
float squareSum_ = 0;
};
class UFuncMin : public UFunc {
public:
void op(const float &val) override {
if (val < tmp) {
tmp = val;
}
}
void reset() override {
tmp = std::numeric_limits<float>::max();
}
};
class UFuncMax : public UFunc {
public:
void op(const float &val) override {
if (val > tmp) {
tmp = val;
}
}
void reset() override {
tmp = -std::numeric_limits<float>::max();
}
};
class UFuncArgMin : public UFunc {
public:
void op(const float &val) override {
if (val < tmp) {
tmp = val;
minIdx_ = idx_;
}
idx_++;
}
float result() override {
return (float) minIdx_;
}
void reset() override {
tmp = std::numeric_limits<float>::max();
idx_ = 0;
minIdx_ = 0;
}
private:
uint32_t minIdx_ = 0;
};
class UFuncArgMax : public UFunc {
public:
void op(const float &val) override {
if (val > tmp) {
tmp = val;
maxIdx_ = idx_;
}
idx_++;
}
float result() override {
return (float) maxIdx_;
}
void reset() override {
tmp = -std::numeric_limits<float>::max();
idx_ = 0;
maxIdx_ = 0;
}
private:
uint32_t maxIdx_ = 0;
};
// float type elements only
class Tensor {
public:
// constructor
Tensor() = default;
static Tensor shape(const Shape &shape);
static Tensor scalar(const float &value);
static Tensor ones(const Shape &shape);
static Tensor zeros(const Shape &shape);
static Tensor tri(uint32_t n, uint32_t m = 0, int32_t k = 0);
// 1d array
explicit Tensor(const Array1d &values1d);
// 2d array
explicit Tensor(const Array2d &values2d);
// 3d array
explicit Tensor(const Array3d &values3d);
Tensor reshape(const Shape &shape);
inline bool empty() const {
return data_.empty();
}
inline bool isScalar() const {
return dimCount_ == 0 && elemCount_ == 1;
}
inline uint32_t dim() const {
return dimCount_;
}
inline uint32_t size() const {
return elemCount_;
}
inline const Shape &shape() const {
return shape_;
}
inline const std::vector<int32_t> &strides() const {
return strides_;
}
inline std::vector<float> &data() {
return data_;
}
inline const std::vector<float> &data() const {
return data_;
}
// range
static std::vector<int32_t> range(int32_t start, int32_t stop, int32_t step = 1);
static Tensor arange(float start, float stop, float step = 1.f);
// indexing
Tensor operator[](const std::vector<int32_t> &idx) const;
// transpose
Tensor transpose(const std::vector<uint32_t> &axis = {}) const;
inline static Tensor transpose(const Tensor &t, const std::vector<uint32_t> &axis = {}) {
return t.transpose(axis);
}
// split
std::vector<Tensor> split(uint32_t sections, const Axis &axis = 0) const;
inline std::vector<Tensor> vsplit(uint32_t sections) const { return split(sections, 0); }
inline std::vector<Tensor> hsplit(uint32_t sections) const { return split(sections, 1); }
inline std::vector<Tensor> dsplit(uint32_t sections) const { return split(sections, 2); }
std::vector<Tensor> split(const std::vector<uint32_t> &indices, const Axis &axis = 0) const;
inline std::vector<Tensor> vsplit(const std::vector<uint32_t> &indices) { return split(indices, 0); }
inline std::vector<Tensor> hsplit(const std::vector<uint32_t> &indices) { return split(indices, 1); }
inline std::vector<Tensor> dsplit(const std::vector<uint32_t> &indices) { return split(indices, 2); }
inline static std::vector<Tensor> split(const Tensor &t, uint32_t sections, const Axis &axis = 0) {
return t.split(sections, axis);
}
inline static std::vector<Tensor> vsplit(const Tensor &t, uint32_t sections) { return t.split(sections, 0); }
inline static std::vector<Tensor> hsplit(const Tensor &t, uint32_t sections) { return t.split(sections, 1); }
inline static std::vector<Tensor> dsplit(const Tensor &t, uint32_t sections) { return t.split(sections, 2); }
inline static std::vector<Tensor> split(const Tensor &t, const std::vector<uint32_t> &indices, const Axis &axis = 0) {
return t.split(indices, axis);
}
inline static std::vector<Tensor> vsplit(const Tensor &t, const std::vector<uint32_t> &indices) { return t.split(indices, 0); }
inline static std::vector<Tensor> hsplit(const Tensor &t, const std::vector<uint32_t> &indices) { return t.split(indices, 1); }
inline static std::vector<Tensor> dsplit(const Tensor &t, const std::vector<uint32_t> &indices) { return t.split(indices, 2); }
// concatenate
static Tensor concatenate(const std::vector<std::reference_wrapper<Tensor>> &arrays);
static Tensor concatenate(const std::vector<std::reference_wrapper<Tensor>> &arrays, const Axis &axis);
// stack
static Tensor stack(const std::vector<std::reference_wrapper<Tensor>> &arrays, const Axis &axis = 0);
static Tensor vstack(const std::vector<std::reference_wrapper<Tensor>> &arrays);
static Tensor hstack(const std::vector<std::reference_wrapper<Tensor>> &arrays);
static Tensor dstack(const std::vector<std::reference_wrapper<Tensor>> &arrays);
// math
Tensor operator+(const Tensor &other) const;
Tensor operator-(const Tensor &other) const;
Tensor operator*(const Tensor &other) const;
Tensor operator/(const Tensor &other) const;
Tensor operator+(const float &other) const;
Tensor operator-(const float &other) const;
Tensor operator*(const float &other) const;
Tensor operator/(const float &other) const;
void operator+=(const Tensor &other);
void operator-=(const Tensor &other);
void operator*=(const Tensor &other);
void operator/=(const Tensor &other);
void operator+=(const float &other);
void operator-=(const float &other);
void operator*=(const float &other);
void operator/=(const float &other);
friend Tensor operator+(const float &other, const Tensor &obj);
friend Tensor operator-(const float &other, const Tensor &obj);
friend Tensor operator*(const float &other, const Tensor &obj);
friend Tensor operator/(const float &other, const Tensor &obj);
static Tensor sqrt(const Tensor &t);
static Tensor tanh(const Tensor &t);
static Tensor exp(const Tensor &t);
Tensor sqrt() const { return Tensor::sqrt(*this); }
Tensor tanh() const { return Tensor::tanh(*this); }
Tensor exp() const { return Tensor::exp(*this); }
// linear algebra
static float dot(const float &a, const float &b);
static Tensor dot(const Tensor &a, const float &b);
static Tensor dot(const float &a, const Tensor &b);
static Tensor dot(const Tensor &a, const Tensor &b);
static Tensor matmul(const Tensor &a, const Tensor &b);
static Tensor matmulTrans(const Tensor &a, const Tensor &b);
// aggregation
static float min(const Tensor &t);
static float max(const Tensor &t);
static float mean(const Tensor &t);
static float sum(const Tensor &t);
static float var(const Tensor &t);
static float argmin(const Tensor &t);
static float argmax(const Tensor &t);
inline float min() const { return Tensor::min(*this); };
inline float max() const { return Tensor::max(*this); };
inline float mean() const { return Tensor::mean(*this); };
inline float sum() const { return Tensor::sum(*this); };
inline float var() const { return Tensor::var(*this); };
inline float argmin() const { return Tensor::argmin(*this); };
inline float argmax() const { return Tensor::argmax(*this); };
static Tensor min(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor max(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor mean(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor sum(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor var(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor argmin(const Tensor &t, const Axis &axis, bool keepDims = false);
static Tensor argmax(const Tensor &t, const Axis &axis, bool keepDims = false);
inline Tensor min(const Axis &axis, bool keepDims = false) const {
return Tensor::min(*this, axis, keepDims);
};
inline Tensor max(const Axis &axis, bool keepDims = false) const {
return Tensor::max(*this, axis, keepDims);
};
inline Tensor mean(const Axis &axis, bool keepDims = false) const {
return Tensor::mean(*this, axis, keepDims);
};
inline Tensor sum(const Axis &axis, bool keepDims = false) const {
return Tensor::sum(*this, axis, keepDims);
};
inline Tensor var(const Axis &axis, bool keepDims = false) const {
return Tensor::var(*this, axis, keepDims);
};
inline Tensor argmin(const Axis &axis, bool keepDims = false) const {
return Tensor::argmin(*this, axis, keepDims);
};
inline Tensor argmax(const Axis &axis, bool keepDims = false) const {
return Tensor::argmax(*this, axis, keepDims);
};
protected:
inline void initMeta();
inline void initData();
inline float &operator[](uint32_t idx) {
return data_[idx];
}
inline const float &operator[](uint32_t idx) const {
return data_[idx];
}
inline void traverse(UFunc &func, uint32_t start, uint32_t stride, uint32_t cnt) const;
Tensor reduce(UFunc &func, uint32_t axis, bool keepDims = false) const;
void splitAxis(std::vector<Tensor> &retTensors, std::vector<uint32_t> &splitIndices, uint32_t axis) const;
static Tensor arraysConcat(const std::vector<std::reference_wrapper<Tensor>> &arrays, const Shape &retShape,
const std::vector<uint32_t> &concatIndices, uint32_t axis);
static ShapeCompatible checkCompatible(const Shape &t0, const Shape &t1, Shape &retShape, uint32_t skipLast = 0);
static bool checkShapeEqual(const std::vector<std::reference_wrapper<Tensor>> &arrays, uint32_t exceptAxis);
static void error(const char *where, TensorError error);
private:
static float fastTanh(float x);
protected:
uint32_t dimCount_ = 0;
uint32_t elemCount_ = 0;
Shape shape_;
std::vector<int32_t> strides_;
std::vector<float> data_;
};
class TensorIter {
public:
explicit TensorIter(const Shape &shape);
// get shape
Shape shape();
// reshape
void reshape(const Shape &shape);
// get size
inline uint32_t size() const { return size_; }
// get current coordinates
inline const int32_t *coordinates() const { return coordinates_; };
// return -1 if not available
int32_t next();
// reset to init states
void reset();
// broadcast to shape (no broadcast rules check)
void broadcast(const Shape &shape);
// transpose
void transpose(const std::vector<uint32_t> &axis);
protected:
// reorder array
static void reorder(int32_t *v, std::vector<uint32_t> const &order) {
for (uint32_t s = 1, d; s < order.size(); ++s) {
for (d = order[s]; d < s; d = order[d]);
if (d == s) while (d = order[d], d != s) std::swap(v[s], v[d]);
}
}
protected:
int32_t ndM1_;
int32_t size_;
int32_t dimsM1_[TENSOR_MAX_DIMS];
int32_t strides_[TENSOR_MAX_DIMS];
int32_t backStrides_[TENSOR_MAX_DIMS];
int32_t coordinates_[TENSOR_MAX_DIMS];
int32_t index_;
int32_t itCnt_;
};
}