Skip to content

Elementwise

Elementwise ops operate on a per element basis. They don't change the shape of the tensor.

Unary Ops (math)¤

logical_not ¤

logical_not()

Computes the logical NOT of the tensor element-wise.

print(Tensor([False, True]).logical_not().numpy())
[ True False]
Source code in tinygrad/tensor.py
2234
2235
2236
2237
2238
2239
2240
2241
2242
def logical_not(self):
  """
  Computes the logical NOT of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([False, True]).logical_not().numpy())
  ```
  """
  return F.Neq.apply(*self.cast(dtypes.bool)._broadcasted(True))

neg ¤

neg()

Negates the tensor element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).neg().numpy())
[ 3.  2.  1. -0. -1. -2. -3.]
Source code in tinygrad/tensor.py
2243
2244
2245
2246
2247
2248
2249
2250
2251
def neg(self):
  """
  Negates the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).neg().numpy())
  ```
  """
  return self*-1 if self.dtype != dtypes.bool else self.logical_not()

log ¤

log()

Computes the natural logarithm element-wise.

See: https://en.wikipedia.org/wiki/Logarithm

print(Tensor([1., 2., 4., 8.]).log().numpy())
[0.     0.6931 1.3863 2.0794]
Source code in tinygrad/tensor.py
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
def log(self):
  """
  Computes the natural logarithm element-wise.

  See: https://en.wikipedia.org/wiki/Logarithm

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 4., 8.]).log().numpy())
  ```
  """
  return F.Log.apply(self.cast(least_upper_float(self.dtype)))

log2 ¤

log2()

Computes the base-2 logarithm element-wise.

See: https://en.wikipedia.org/wiki/Logarithm

print(Tensor([1., 2., 4., 8.]).log2().numpy())
[0. 1. 2. 3.]
Source code in tinygrad/tensor.py
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
def log2(self):
  """
  Computes the base-2 logarithm element-wise.

  See: https://en.wikipedia.org/wiki/Logarithm

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 4., 8.]).log2().numpy())
  ```
  """
  return self.log()/math.log(2)

exp ¤

exp()

Computes the exponential function element-wise.

See: https://en.wikipedia.org/wiki/Exponential_function

print(Tensor([0., 1., 2., 3.]).exp().numpy())
[ 1.      2.7183  7.3891 20.0855]
Source code in tinygrad/tensor.py
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
def exp(self):
  """
  Computes the exponential function element-wise.

  See: https://en.wikipedia.org/wiki/Exponential_function

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([0., 1., 2., 3.]).exp().numpy())
  ```
  """
  return F.Exp.apply(self.cast(least_upper_float(self.dtype)))

exp2 ¤

exp2()

Computes the base-2 exponential function element-wise.

See: https://en.wikipedia.org/wiki/Exponential_function

print(Tensor([0., 1., 2., 3.]).exp2().numpy())
[1. 2. 4. 8.]
Source code in tinygrad/tensor.py
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
def exp2(self):
  """
  Computes the base-2 exponential function element-wise.

  See: https://en.wikipedia.org/wiki/Exponential_function

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([0., 1., 2., 3.]).exp2().numpy())
  ```
  """
  return F.Exp.apply(self*math.log(2))

sqrt ¤

sqrt()

Computes the square root of the tensor element-wise.

print(Tensor([1., 2., 3., 4.]).sqrt().numpy())
[1.     1.4142 1.7321 2.    ]
Source code in tinygrad/tensor.py
2328
2329
2330
2331
2332
2333
2334
2335
2336
def sqrt(self):
  """
  Computes the square root of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 3., 4.]).sqrt().numpy())
  ```
  """
  return F.Sqrt.apply(self.cast(least_upper_float(self.dtype)))

rsqrt ¤

rsqrt()

Computes the reciprocal of the square root of the tensor element-wise.

print(Tensor([1., 2., 3., 4.]).rsqrt().numpy())
[1.     0.7071 0.5774 0.5   ]
Source code in tinygrad/tensor.py
2337
2338
2339
2340
2341
2342
2343
2344
2345
def rsqrt(self):
  """
  Computes the reciprocal of the square root of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 3., 4.]).rsqrt().numpy())
  ```
  """
  return self.reciprocal().sqrt()

sin ¤

sin()

Computes the sine of the tensor element-wise.

print(Tensor([0., math.pi/2, math.pi, 3*math.pi/2, 2*math.pi]).sin().numpy())
[ 0.  1. -0. -1.  0.]
Source code in tinygrad/tensor.py
2346
2347
2348
2349
2350
2351
2352
2353
2354
def sin(self):
  """
  Computes the sine of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([0., math.pi/2, math.pi, 3*math.pi/2, 2*math.pi]).sin().numpy())
  ```
  """
  return F.Sin.apply(self.cast(least_upper_float(self.dtype)))

cos ¤

cos()

Computes the cosine of the tensor element-wise.

print(Tensor([0., math.pi/2, math.pi, 3*math.pi/2, 2*math.pi]).cos().numpy())
[ 1.0000e+00  0.0000e+00 -1.0000e+00 -2.3842e-07  1.0000e+00]
Source code in tinygrad/tensor.py
2355
2356
2357
2358
2359
2360
2361
2362
2363
def cos(self):
  """
  Computes the cosine of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([0., math.pi/2, math.pi, 3*math.pi/2, 2*math.pi]).cos().numpy())
  ```
  """
  return ((math.pi/2)-self).sin()

tan ¤

tan()

Computes the tangent of the tensor element-wise.

print(Tensor([0., math.pi/4, math.pi/2, 3*math.pi/4, math.pi]).tan().numpy())
[ 0.  1. inf -1.  0.]
Source code in tinygrad/tensor.py
2364
2365
2366
2367
2368
2369
2370
2371
2372
def tan(self):
  """
  Computes the tangent of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([0., math.pi/4, math.pi/2, 3*math.pi/4, math.pi]).tan().numpy())
  ```
  """
  return self.sin() / self.cos()

trunc ¤

trunc() -> Tensor

Truncates the tensor element-wise.

print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).trunc().numpy())
[-3. -2. -1.  0.  0.  1.  2.  3.]
Source code in tinygrad/tensor.py
2376
2377
2378
2379
2380
2381
2382
2383
2384
def trunc(self: Tensor) -> Tensor:
  """
  Truncates the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).trunc().numpy())
  ```
  """
  return self.cast(dtypes.int32).cast(self.dtype)

ceil ¤

ceil() -> Tensor

Rounds the tensor element-wise towards positive infinity.

print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).ceil().numpy())
[-3. -2. -1.  0.  1.  2.  3.  4.]
Source code in tinygrad/tensor.py
2385
2386
2387
2388
2389
2390
2391
2392
2393
def ceil(self: Tensor) -> Tensor:
  """
  Rounds the tensor element-wise towards positive infinity.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).ceil().numpy())
  ```
  """
  return (self > (b := self.trunc())).where(b+1, b)

floor ¤

floor() -> Tensor

Rounds the tensor element-wise towards negative infinity.

print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).floor().numpy())
[-4. -3. -2. -1.  0.  1.  2.  3.]
Source code in tinygrad/tensor.py
2394
2395
2396
2397
2398
2399
2400
2401
2402
def floor(self: Tensor) -> Tensor:
  """
  Rounds the tensor element-wise towards negative infinity.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).floor().numpy())
  ```
  """
  return (self < (b := self.trunc())).where(b-1, b)

round ¤

round() -> Tensor

Rounds the tensor element-wise with rounding half to even.

print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).round().numpy())
[-4. -2. -2.  0.  0.  2.  2.  4.]
Source code in tinygrad/tensor.py
2403
2404
2405
2406
2407
2408
2409
2410
2411
def round(self: Tensor) -> Tensor:
  """
  Rounds the tensor element-wise with rounding half to even.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]).round().numpy())
  ```
  """
  return ((self > 0) == ((b := self.cast(dtypes.int32) / 2.0).cast(dtypes.int32) == b)).where((self - 0.5).ceil(), (self + 0.5).floor())

lerp ¤

lerp(end: Tensor, weight: Union[Tensor, float]) -> Tensor

Linearly interpolates between self and end by weight.

print(Tensor([1., 2., 3.]).lerp(Tensor([4., 5., 6.]), 0.5).numpy())
[2.5 3.5 4.5]
Source code in tinygrad/tensor.py
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
def lerp(self, end: Tensor, weight: Union[Tensor, float]) -> Tensor:
  """
  Linearly interpolates between `self` and `end` by `weight`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 3.]).lerp(Tensor([4., 5., 6.]), 0.5).numpy())
  ```
  """
  if self.dtype == dtypes.uint8 and isinstance(weight, Tensor):
    w_i = (weight * (1<<(W_PREC:=7)) + 0.5).cast(dtypes.int16)
    return (self+(((end - self).cast(dtypes.int8) * w_i + (1<<W_PREC-1)).cast(dtypes.uint16) >> W_PREC)).cast(dtypes.uint8)
  return self + (end - self) * weight

square ¤

square()

Squares the tensor element-wise. Equivalent to self*self.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).square().numpy())
[9. 4. 1. 0. 1. 4. 9.]
Source code in tinygrad/tensor.py
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
def square(self):
  """
  Squares the tensor element-wise.
  Equivalent to `self*self`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).square().numpy())
  ```
  """
  return self*self

clamp ¤

clamp(min_=None, max_=None)

Clips (clamps) the values in the tensor between min_ and max_ element-wise. If min_ is None, there is no lower bound. If max_ is None, there is no upper bound.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).clip(-1, 1).numpy())
[-1. -1. -1.  0.  1.  1.  1.]
Source code in tinygrad/tensor.py
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
def clamp(self, min_=None, max_=None):
  """
  Clips (clamps) the values in the tensor between `min_` and `max_` element-wise.
  If `min_` is `None`, there is no lower bound. If `max_` is None, there is no upper bound.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).clip(-1, 1).numpy())
  ```
  """
  if min_ is None and max_ is None: raise RuntimeError("at least one of 'min_' or 'max_' must not be None")
  ret = self.maximum(min_) if min_ is not None else self
  return ret.minimum(max_) if max_ is not None else ret

clip ¤

clip(min_=None, max_=None)

Alias for Tensor.clamp.

Source code in tinygrad/tensor.py
2448
2449
2450
2451
2452
def clip(self, min_=None, max_=None):
  """
  Alias for `Tensor.clamp`.
  """
  return self.clamp(min_, max_)

sign ¤

sign()

Returns the sign of the tensor element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sign().numpy())
[-1. -1. -1.  0.  1.  1.  1.]
Source code in tinygrad/tensor.py
2453
2454
2455
2456
2457
2458
2459
2460
2461
def sign(self):
  """
  Returns the sign of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sign().numpy())
  ```
  """
  return F.Sign.apply(self)

abs ¤

abs()

Computes the absolute value of the tensor element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).abs().numpy())
[3. 2. 1. 0. 1. 2. 3.]
Source code in tinygrad/tensor.py
2462
2463
2464
2465
2466
2467
2468
2469
2470
def abs(self):
  """
  Computes the absolute value of the tensor element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).abs().numpy())
  ```
  """
  return self * self.sign()

reciprocal ¤

reciprocal()

Compute 1/x element-wise.

print(Tensor([1., 2., 3., 4.]).reciprocal().numpy())
[1.     0.5    0.3333 0.25  ]
Source code in tinygrad/tensor.py
2471
2472
2473
2474
2475
2476
2477
2478
2479
def reciprocal(self):
  """
  Compute `1/x` element-wise.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1., 2., 3., 4.]).reciprocal().numpy())
  ```
  """
  return F.Reciprocal.apply(self.cast(least_upper_float(self.dtype)))

Unary Ops (activation)¤

relu ¤

relu()

Applies the Rectified Linear Unit (ReLU) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).relu().numpy())
[0. 0. 0. 0. 1. 2. 3.]
Source code in tinygrad/tensor.py
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
def relu(self):
  """
  Applies the Rectified Linear Unit (ReLU) function element-wise.

  - Described: https://paperswithcode.com/method/relu

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).relu().numpy())
  ```
  """
  return F.Relu.apply(self)

sigmoid ¤

sigmoid()

Applies the Sigmoid function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sigmoid().numpy())
[0.0474 0.1192 0.2689 0.5    0.7311 0.8808 0.9526]
Source code in tinygrad/tensor.py
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
def sigmoid(self):
  """
  Applies the Sigmoid function element-wise.

  - Described: https://en.wikipedia.org/wiki/Sigmoid_function

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sigmoid().numpy())
  ```
  """
  return F.Sigmoid.apply(self.cast(least_upper_float(self.dtype)))

elu ¤

elu(alpha=1.0)

Applies the Exponential Linear Unit (ELU) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).elu().numpy())
[-0.9502 -0.8647 -0.6321  0.      1.      2.      3.    ]
Source code in tinygrad/tensor.py
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
def elu(self, alpha=1.0):
  """
  Applies the Exponential Linear Unit (ELU) function element-wise.

  - Described: https://paperswithcode.com/method/elu
  - Paper: https://arxiv.org/abs/1511.07289v5

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).elu().numpy())
  ```
  """
  return self.relu() - alpha*(1-self.exp()).relu()

celu ¤

celu(alpha=1.0)

Applies the Continuously differentiable Exponential Linear Unit (CELU) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).celu().numpy())
[-0.9502 -0.8647 -0.6321  0.      1.      2.      3.    ]
Source code in tinygrad/tensor.py
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
def celu(self, alpha=1.0):
  """
  Applies the Continuously differentiable Exponential Linear Unit (CELU) function element-wise.

  - Described: https://paperswithcode.com/method/celu
  - Paper: https://arxiv.org/abs/1704.07483

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).celu().numpy())
  ```
  """
  return self.maximum(0) + (alpha * ((self / alpha).exp() - 1)).minimum(0)

swish ¤

swish()

See .silu()

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).swish().numpy())
[-0.1423 -0.2384 -0.2689  0.      0.7311  1.7616  2.8577]
Source code in tinygrad/tensor.py
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
def swish(self):
  """
  See `.silu()`

  - Paper: https://arxiv.org/abs/1710.05941v1

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).swish().numpy())
  ```
  """
  return self * self.sigmoid()

silu ¤

silu()

Applies the Sigmoid Linear Unit (SiLU) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).silu().numpy())
[-0.1423 -0.2384 -0.2689  0.      0.7311  1.7616  2.8577]
Source code in tinygrad/tensor.py
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
def silu(self):
  """
  Applies the Sigmoid Linear Unit (SiLU) function element-wise.

  - Described: https://paperswithcode.com/method/silu
  - Paper: https://arxiv.org/abs/1606.08415

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).silu().numpy())
  ```
  """
  return self.swish()   # The SiLU function is also known as the swish function.

relu6 ¤

relu6()

Applies the ReLU6 function element-wise.

print(Tensor([-9., -6., -3., 0., 3., 6., 9.]).relu6().numpy())
[0. 0. 0. 0. 3. 6. 6.]
Source code in tinygrad/tensor.py
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
def relu6(self):
  """
  Applies the ReLU6 function element-wise.

  - Described: https://paperswithcode.com/method/relu6
  - Paper: https://arxiv.org/abs/1704.04861v1

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-9., -6., -3., 0., 3., 6., 9.]).relu6().numpy())
  ```
  """
  return self.relu() - (self-6).relu()

hardswish ¤

hardswish()

Applies the Hardswish function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).hardswish().numpy())
[-0.     -0.3333 -0.3333  0.      0.6667  1.6667  3.    ]
Source code in tinygrad/tensor.py
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
def hardswish(self):
  """
  Applies the Hardswish function element-wise.

  - Described: https://paperswithcode.com/method/hard-swish
  - Paper: https://arxiv.org/abs/1905.02244v5

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).hardswish().numpy())
  ```
  """
  return self * (self+3).relu6() * (1/6)

tanh ¤

tanh()

Applies the Hyperbolic Tangent (tanh) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).tanh().numpy())
[-0.9951 -0.964  -0.7616  0.      0.7616  0.964   0.9951]
Source code in tinygrad/tensor.py
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
def tanh(self):
  """
  Applies the Hyperbolic Tangent (tanh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Hyperbolic_functions#Tanh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).tanh().numpy())
  ```
  """
  return 2.0 * ((2.0 * self).sigmoid()) - 1.0

sinh ¤

sinh()

Applies the Hyperbolic Sine (sinh) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sinh().numpy())
[-10.0179  -3.6269  -1.1752   0.       1.1752   3.6269  10.0179]
Source code in tinygrad/tensor.py
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
def sinh(self):
  """
  Applies the Hyperbolic Sine (sinh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Hyperbolic_functions#Sinh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).sinh().numpy())
  ```
  """
  return (self.exp() - self.neg().exp()) / 2

cosh ¤

cosh()

Applies the Hyperbolic Cosine (cosh) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).cosh().numpy())
[10.0677  3.7622  1.5431  1.      1.5431  3.7622 10.0677]
Source code in tinygrad/tensor.py
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
def cosh(self):
  """
  Applies the Hyperbolic Cosine (cosh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Hyperbolic_functions#Cosh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).cosh().numpy())
  ```
  """
  return (self.exp() + self.neg().exp()) / 2

atanh ¤

atanh()

Applies the Inverse Hyperbolic Tangent (atanh) function element-wise.

print(Tensor([-0.9, -0.6, -0.3, 0., 0.3, 0.6, 0.9]).atanh().numpy())
[-1.4722 -0.6931 -0.3095  0.      0.3095  0.6931  1.4722]
Source code in tinygrad/tensor.py
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
def atanh(self):
  """
  Applies the Inverse Hyperbolic Tangent (atanh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#atanh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-0.9, -0.6, -0.3, 0., 0.3, 0.6, 0.9]).atanh().numpy())
  ```
  """
  return ((1 + self)/(1 - self)).log() / 2

asinh ¤

asinh()

Applies the Inverse Hyperbolic Sine (asinh) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).asinh().numpy())
[-1.8184 -1.4436 -0.8814  0.      0.8814  1.4436  1.8184]
Source code in tinygrad/tensor.py
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
def asinh(self):
  """
  Applies the Inverse Hyperbolic Sine (asinh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#asinh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).asinh().numpy())
  ```
  """
  return (self + (self.square() + 1).sqrt()).log()

acosh ¤

acosh()

Applies the Inverse Hyperbolic Cosine (acosh) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).acosh().numpy())
[   nan    nan    nan    nan 0.     1.317  1.7627]
Source code in tinygrad/tensor.py
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
def acosh(self):
  """
  Applies the Inverse Hyperbolic Cosine (acosh) function element-wise.

  - Described: https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#acosh

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).acosh().numpy())
  ```
  """
  return (self + (self.square() - 1).sqrt()).log()

hardtanh ¤

hardtanh(min_val=-1, max_val=1)

Applies the Hardtanh function element-wise.

print(Tensor([-1.5, -1.0, -0.5, 0., 0.5, 1.0, 1.5]).hardtanh().numpy())
[-1.  -1.  -0.5  0.   0.5  1.   1. ]
Source code in tinygrad/tensor.py
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
def hardtanh(self, min_val=-1, max_val=1):
  """
  Applies the Hardtanh function element-wise.

  - Described: https://paperswithcode.com/method/hardtanh-activation

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1.5, -1.0, -0.5, 0., 0.5, 1.0, 1.5]).hardtanh().numpy())
  ```
  """
  return self.clip(min_val, max_val)

gelu ¤

gelu()

Applies the Gaussian Error Linear Unit (GELU) function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).gelu().numpy())
[-0.0036 -0.0454 -0.1588  0.      0.8412  1.9546  2.9964]
Source code in tinygrad/tensor.py
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
def gelu(self):
  """
  Applies the Gaussian Error Linear Unit (GELU) function element-wise.

  - Described: https://paperswithcode.com/method/gelu
  - Paper: https://arxiv.org/abs/1606.08415v5

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).gelu().numpy())
  ```
  """
  return 0.5 * self * (1 + (math.sqrt(2 / math.pi) * (self + 0.044715 * self ** 3)).tanh())

quick_gelu ¤

quick_gelu()

Applies the Sigmoid GELU approximation element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).quick_gelu().numpy())
[-0.0181 -0.0643 -0.1542  0.      0.8458  1.9357  2.9819]
Source code in tinygrad/tensor.py
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
def quick_gelu(self):
  """
  Applies the Sigmoid GELU approximation element-wise.

  - Described: https://paperswithcode.com/method/gelu

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).quick_gelu().numpy())
  ```
  """
  return self * (self * 1.702).sigmoid()

leakyrelu ¤

leakyrelu(neg_slope=0.01)

Applies the Leaky ReLU function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leakyrelu().numpy())
[-0.03 -0.02 -0.01  0.    1.    2.    3.  ]
print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leakyrelu(neg_slope=0.42).numpy())
[-1.26 -0.84 -0.42  0.    1.    2.    3.  ]

Source code in tinygrad/tensor.py
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
def leakyrelu(self, neg_slope=0.01):
  """
  Applies the Leaky ReLU function element-wise.

  - Described: https://paperswithcode.com/method/leaky-relu

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leakyrelu().numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leakyrelu(neg_slope=0.42).numpy())
  ```
  """
  return self.relu() - (-neg_slope*self).relu()

mish ¤

mish()

Applies the Mish function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).mish().numpy())
[-0.1456 -0.2525 -0.3034  0.      0.8651  1.944   2.9865]
Source code in tinygrad/tensor.py
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
def mish(self):
  """
  Applies the Mish function element-wise.

  - Described: https://paperswithcode.com/method/mish
  - Paper: https://arxiv.org/abs/1908.08681v3

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).mish().numpy())
  ```
  """
  return self * self.softplus().tanh()

softplus ¤

softplus(beta=1)

Applies the Softplus function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softplus().numpy())
[0.0486 0.1269 0.3133 0.6931 1.3133 2.1269 3.0486]
Source code in tinygrad/tensor.py
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
def softplus(self, beta=1):
  """
  Applies the Softplus function element-wise.

  - Described: https://paperswithcode.com/method/softplus

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softplus().numpy())
  ```
  """
  return (1/beta) * (1 + (self*beta).exp()).log()

softsign ¤

softsign()

Applies the Softsign function element-wise.

print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softsign().numpy())
[-0.75   -0.6667 -0.5     0.      0.5     0.6667  0.75  ]
Source code in tinygrad/tensor.py
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
def softsign(self):
  """
  Applies the Softsign function element-wise.

  - Described: https://paperswithcode.com/method/softsign

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softsign().numpy())
  ```
  """
  return self / (1 + self.abs())

Elementwise Ops (broadcasted)¤

add ¤

add(x: Union[Tensor, ConstType], reverse=False) -> Tensor

Adds self and x. Equivalent to self + x. Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

Tensor.manual_seed(42)
t = Tensor.randn(4)
print(t.numpy())
[-0.5144  1.085   0.9089 -0.0841]
print(t.add(20).numpy())
[19.4856 21.085  20.9089 19.9159]
print(t.add(Tensor([[2.0], [3.5]])).numpy())
[[1.4856 3.085  2.9089 1.9159]
 [2.9856 4.585  4.4089 3.4159]]

Source code in tinygrad/tensor.py
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
def add(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
  """
  Adds `self` and `x`.
  Equivalent to `self + x`.
  Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

  ```python exec="true" source="above" session="tensor" result="python"
  Tensor.manual_seed(42)
  t = Tensor.randn(4)
  print(t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.add(20).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.add(Tensor([[2.0], [3.5]])).numpy())
  ```
  """
  return F.Add.apply(*self._broadcasted(x, reverse))

sub ¤

sub(x: Union[Tensor, ConstType], reverse=False) -> Tensor

Subtracts x from self. Equivalent to self - x. Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

Tensor.manual_seed(42)
t = Tensor.randn(4)
print(t.numpy())
[-0.5144  1.085   0.9089 -0.0841]
print(t.sub(20).numpy())
[-20.5144 -18.915  -19.0911 -20.0841]
print(t.sub(Tensor([[2.0], [3.5]])).numpy())
[[-2.5144 -0.915  -1.0911 -2.0841]
 [-4.0144 -2.415  -2.5911 -3.5841]]

Source code in tinygrad/tensor.py
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
def sub(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
  """
  Subtracts `x` from `self`.
  Equivalent to `self - x`.
  Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

  ```python exec="true" source="above" session="tensor" result="python"
  Tensor.manual_seed(42)
  t = Tensor.randn(4)
  print(t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.sub(20).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.sub(Tensor([[2.0], [3.5]])).numpy())
  ```
  """
  a, b = self._broadcasted(x, reverse)
  return a + (-b)

mul ¤

mul(x: Union[Tensor, ConstType], reverse=False) -> Tensor

Multiplies self and x. Equivalent to self * x. Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

Tensor.manual_seed(42)
t = Tensor.randn(4)
print(t.numpy())
[-0.5144  1.085   0.9089 -0.0841]
print(t.mul(3).numpy())
[-1.5431  3.2549  2.7267 -0.2523]
print(t.mul(Tensor([[-1.0], [2.0]])).numpy())
[[ 0.5144 -1.085  -0.9089  0.0841]
 [-1.0287  2.17    1.8178 -0.1682]]

Source code in tinygrad/tensor.py
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
def mul(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
  """
  Multiplies `self` and `x`.
  Equivalent to `self * x`.
  Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.

  ```python exec="true" source="above" session="tensor" result="python"
  Tensor.manual_seed(42)
  t = Tensor.randn(4)
  print(t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.mul(3).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.mul(Tensor([[-1.0], [2.0]])).numpy())
  ```
  """
  return F.Mul.apply(*self._broadcasted(x, reverse))

div ¤

div(
    x: Union[Tensor, ConstType], reverse=False, upcast=True
) -> Tensor

Divides self by x. Equivalent to self / x. Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs. By default, div performs true division. Set upcast to False for integer division.

Tensor.manual_seed(42)
t = Tensor.randn(4)
print(t.numpy())
[-0.5144  1.085   0.9089 -0.0841]
print(t.div(3).numpy())
[-0.1715  0.3617  0.303  -0.028 ]
print(Tensor([1, 4, 10]).div(Tensor([2, 3, 4])).numpy())
[0.5    1.3333 2.5   ]
print(Tensor([1, 4, 10]).div(Tensor([2, 3, 4]), upcast=False).numpy())
[0 1 2]

Source code in tinygrad/tensor.py
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
def div(self, x:Union[Tensor, ConstType], reverse=False, upcast=True) -> Tensor:
  """
  Divides `self` by `x`.
  Equivalent to `self / x`.
  Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.
  By default, `div` performs true division. Set `upcast` to `False` for integer division.

  ```python exec="true" source="above" session="tensor" result="python"
  Tensor.manual_seed(42)
  t = Tensor.randn(4)
  print(t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(t.div(3).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1, 4, 10]).div(Tensor([2, 3, 4])).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1, 4, 10]).div(Tensor([2, 3, 4]), upcast=False).numpy())
  ```
  """
  numerator, denominator = self._broadcasted(x, reverse)
  if upcast: numerator, denominator = numerator.cast(least_upper_float(numerator.dtype)), denominator.cast(least_upper_float(denominator.dtype))
  return (numerator * denominator.reciprocal()) if dtypes.is_float(numerator.dtype) else F.IDiv.apply(numerator, denominator)

xor ¤

xor(x: Union[Tensor, ConstType], reverse=False) -> Tensor

Computes bitwise xor of self and x. Equivalent to self ^ x. Supports broadcasting to a common shape, type promotion, and integer, boolean inputs.

print(Tensor([-1, -2, 3]).xor(Tensor([1, 0, 3])).numpy())
[-2 -2  0]
print(Tensor([True, True, False, False]).xor(Tensor([True, False, True, False])).numpy())
[False  True  True False]

Source code in tinygrad/tensor.py
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
def xor(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
  """
  Computes bitwise xor of `self` and `x`.
  Equivalent to `self ^ x`.
  Supports broadcasting to a common shape, type promotion, and integer, boolean inputs.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, -2, 3]).xor(Tensor([1, 0, 3])).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([True, True, False, False]).xor(Tensor([True, False, True, False])).numpy())
  ```
  """
  return F.Xor.apply(*self._broadcasted(x, reverse))

lshift ¤

lshift(x: int)

Computes left arithmetic shift of self by x bits. self must have unsigned dtype. Equivalent to self << x.

print(Tensor([1, 3, 31], dtype=dtypes.uint8).lshift(2).numpy())
[  4  12 124]
Source code in tinygrad/tensor.py
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
def lshift(self, x:int):
  """
  Computes left arithmetic shift of `self` by `x` bits. `self` must have unsigned dtype.
  Equivalent to `self << x`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([1, 3, 31], dtype=dtypes.uint8).lshift(2).numpy())
  ```
  """
  assert dtypes.is_unsigned(self.dtype) and isinstance(x, int) and x >= 0, f"not supported {self.dtype=} {x=}"
  return self.mul(2 ** x)

rshift ¤

rshift(x: int)

Computes right arithmetic shift of self by x bits. self must have unsigned dtype. Equivalent to self >> x.

print(Tensor([4, 13, 125], dtype=dtypes.uint8).rshift(2).numpy())
[ 1  3 31]
Source code in tinygrad/tensor.py
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
def rshift(self, x:int):
  """
  Computes right arithmetic shift of `self` by `x` bits. `self` must have unsigned dtype.
  Equivalent to `self >> x`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([4, 13, 125], dtype=dtypes.uint8).rshift(2).numpy())
  ```
  """
  assert dtypes.is_unsigned(self.dtype) and isinstance(x, int) and x >= 0, f"not supported {self.dtype=} {x=}"
  return self.div(2 ** x, upcast=False)

pow ¤

pow(x: Union[Tensor, ConstType], reverse=False) -> Tensor

Computes power of self with x. Equivalent to self ** x.

print(Tensor([-1, 2, 3]).pow(2).numpy())
[1 4 9]
print(Tensor([-1, 2, 3]).pow(Tensor([-1.5, 0.5, 1.5])).numpy())
[   nan 1.4142 5.1962]
print((2 ** Tensor([-1, 2, 3])).numpy())
[0.5 4.  8. ]

Source code in tinygrad/tensor.py
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
def pow(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
  """
  Computes power of `self` with `x`.
  Equivalent to `self ** x`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).pow(2).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).pow(Tensor([-1.5, 0.5, 1.5])).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print((2 ** Tensor([-1, 2, 3])).numpy())
  ```
  """
  x = self._to_const_val(x)
  if not isinstance(x, Tensor) and not reverse:
    # simple pow identities
    if x < 0: return self.reciprocal().pow(-x)
    if x == 0: return 1 + self * 0
    if int(x - 0.5) + 0.5 == x: return self.pow(int(x - 0.5)) * self.sqrt()
    if int(x) == x: return self.pow(x // 2).square() * (1 if x % 2 == 0 else self)

  # positive const ** self
  if not isinstance(x, Tensor) and reverse and x > 0: return self.mul(math.log(x)).exp()

  base, exponent = self._broadcasted(x, reverse=reverse)
  # start with b ** e = exp(e * log(b))
  ret = base.abs().log().mul(exponent).exp()
  # correct sign of negative base with odd exponent (cos has a period of 2pi so we use it here to get the oddness of the exponent)
  negative_base = (base < 0).detach().where(1, 0)
  # 1 for non-negative base or negative even exponent, -1 for negative odd exponent, don't care about non-integer exponent
  correct_sign = 1 + negative_base * ((exponent * math.pi).cos() - 1)
  # inject nan for negative base and non-integer exponent
  inject_nan = (negative_base * (exponent != exponent.trunc())).detach().where(math.nan, 1)
  # apply correct_sign inject_nan, and fix 0 ** 0 = 1
  return ((base == 0) * (exponent == 0)).detach().where(1, ret * correct_sign * inject_nan)

maximum ¤

maximum(x: Union[Tensor, ConstType]) -> Tensor

Computes element-wise maximum of self and x.

print(Tensor([-1, 2, 3]).maximum(1).numpy())
[1 2 3]
print(Tensor([-1, 2, 3]).maximum(Tensor([-4, -2, 9])).numpy())
[-1  2  9]

Source code in tinygrad/tensor.py
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
def maximum(self, x:Union[Tensor, ConstType]) -> Tensor:
  """
  Computes element-wise maximum of `self` and `x`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).maximum(1).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).maximum(Tensor([-4, -2, 9])).numpy())
  ```
  """
  return (self<x).detach().where(x, (self==x).detach().where(((self * 0.5 + x * 0.5).cast(self.dtype)), self))

minimum ¤

minimum(x: Union[Tensor, ConstType]) -> Tensor

Computes element-wise minimum of self and x.

print(Tensor([-1, 2, 3]).minimum(1).numpy())
[-1  1  1]
print(Tensor([-1, 2, 3]).minimum(Tensor([-4, -2, 9])).numpy())
[-4 -2  3]

Source code in tinygrad/tensor.py
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
def minimum(self, x:Union[Tensor, ConstType]) -> Tensor:
  """
  Computes element-wise minimum of `self` and `x`.

  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).minimum(1).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print(Tensor([-1, 2, 3]).minimum(Tensor([-4, -2, 9])).numpy())
  ```
  """
  return -((-self).maximum(-x))

where ¤

Return a tensor of elements selected from either x or y, depending on self. output_i = x_i if self_i else y_i.

cond = Tensor([[True, True, False], [True, False, False]])
print(cond.where(1, 3).numpy())
[[1 1 3]
 [1 3 3]]
Tensor.manual_seed(42)
cond = Tensor.randn(2, 3)
print(cond.numpy())
[[ 0.9779  0.4678  0.5526]
 [-0.3288 -0.8555  0.2753]]
print((cond > 0).where(cond, -float("inf")).numpy())
[[0.9779 0.4678 0.5526]
 [  -inf   -inf 0.2753]]

Source code in tinygrad/tensor.py
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
def where(self:Tensor, x:Union[Tensor, ConstType], y:Union[Tensor, ConstType]):
  """
  Return a tensor of elements selected from either `x` or `y`, depending on `self`.
  `output_i = x_i if self_i else y_i`.

  ```python exec="true" source="above" session="tensor" result="python"
  cond = Tensor([[True, True, False], [True, False, False]])
  print(cond.where(1, 3).numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  Tensor.manual_seed(42)
  cond = Tensor.randn(2, 3)
  print(cond.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  print((cond > 0).where(cond, -float("inf")).numpy())
  ```
  """
  if isinstance(x, Tensor): x, y = x._broadcasted(y)
  elif isinstance(y, Tensor): y, x = y._broadcasted(x)
  cond, x = self._broadcasted(x, match_dtype=False)
  cond, y = cond._broadcasted(y, match_dtype=False)
  return F.Where.apply(cond.cast(dtypes.bool), *x._broadcasted(y))

Casting Ops¤

cast ¤

cast(dtype: DTypeLike) -> Tensor

Casts self to the given dtype.

t = Tensor([-1, 2.5, 3], dtype=dtypes.float)
print(t.dtype, t.numpy())
dtypes.float [-1.   2.5  3. ]
t = t.cast(dtypes.int32)
print(t.dtype, t.numpy())
dtypes.int [-1  2  3]

Source code in tinygrad/tensor.py
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
def cast(self, dtype:DTypeLike) -> Tensor:
  """
  Casts `self` to the given `dtype`.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1, 2.5, 3], dtype=dtypes.float)
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.cast(dtypes.int32)
  print(t.dtype, t.numpy())
  ```
  """
  return self if self.dtype == (dt:=to_dtype(dtype)) else F.Cast.apply(self, dtype=dt)

bitcast ¤

bitcast(dtype: DTypeLike) -> Tensor

Bitcasts self to the given dtype of the same itemsize.

self must not require a gradient.

t = Tensor([-1, 2, 3], dtype=dtypes.int32)
print(t.dtype, t.numpy())
dtypes.int [-1  2  3]
t = t.bitcast(dtypes.uint32)
print(t.dtype, t.numpy())
dtypes.uint [4294967295          2          3]

Source code in tinygrad/tensor.py
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
def bitcast(self, dtype:DTypeLike) -> Tensor:
  """
  Bitcasts `self` to the given `dtype` of the same itemsize.

  `self` must not require a gradient.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1, 2, 3], dtype=dtypes.int32)
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.bitcast(dtypes.uint32)
  print(t.dtype, t.numpy())
  ```
  """
  if self.requires_grad: raise RuntimeError("can't backprop through bitcast")
  dt = to_dtype(dtype)
  if (not isinstance(self.device, str) or not self.device.startswith("DISK")) and (ns:=dt.itemsize) != (os:=self.dtype.itemsize):
    if (self.shape[-1]*os) % ns != 0: raise RuntimeError("unsupported size in bitcast")
    new_uint, old_uint = to_dtype(f"uint{8*ns}"), to_dtype(f"uint{8*os}")
    tmp = self.bitcast(old_uint)
    if ns > os: return functools.reduce(Tensor.add, (tmp[..., i::ns//os].cast(new_uint) << 8*i*os for i in range(ns//os))).bitcast(dtype)
    return Tensor.stack(*(tmp>>8*i*ns for i in range(os//ns)), dim=-1).flatten(-2).cast(new_uint).bitcast(dtype)
  return F.Cast.apply(self, dtype=dt, bitcast=True) if self.dtype != dt else self

float ¤

float() -> Tensor

Convenience method to cast self to a float32 Tensor.

t = Tensor([-1, 2, 3], dtype=dtypes.int32)
print(t.dtype, t.numpy())
dtypes.int [-1  2  3]
t = t.float()
print(t.dtype, t.numpy())
dtypes.float [-1.  2.  3.]

Source code in tinygrad/tensor.py
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
def float(self) -> Tensor:
  """
  Convenience method to cast `self` to a `float32` Tensor.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1, 2, 3], dtype=dtypes.int32)
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.float()
  print(t.dtype, t.numpy())
  ```
  """
  return self.cast(dtypes.float32)

half ¤

half() -> Tensor

Convenience method to cast self to a float16 Tensor.

t = Tensor([-1, 2, 3], dtype=dtypes.int32)
print(t.dtype, t.numpy())
dtypes.int [-1  2  3]
t = t.half()
print(t.dtype, t.numpy())
dtypes.half [-1.  2.  3.]

Source code in tinygrad/tensor.py
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
def half(self) -> Tensor:
  """
  Convenience method to cast `self` to a `float16` Tensor.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1, 2, 3], dtype=dtypes.int32)
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.half()
  print(t.dtype, t.numpy())
  ```
  """
  return self.cast(dtypes.float16)

int ¤

int() -> Tensor

Convenience method to cast self to a int32 Tensor.

t = Tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
print(t.dtype, t.numpy())
dtypes.float [-1.5 -0.5  0.   0.5  1.5]
t = t.int()
print(t.dtype, t.numpy())
dtypes.int [-1  0  0  0  1]

Source code in tinygrad/tensor.py
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
def int(self) -> Tensor:
  """
  Convenience method to cast `self` to a `int32` Tensor.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.int()
  print(t.dtype, t.numpy())
  ```
  """
  return self.cast(dtypes.int32)

bool ¤

bool() -> Tensor

Convenience method to cast self to a bool Tensor.

t = Tensor([-1, 0, 1])
print(t.dtype, t.numpy())
dtypes.int [-1  0  1]
t = t.bool()
print(t.dtype, t.numpy())
dtypes.bool [ True False  True]

Source code in tinygrad/tensor.py
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
def bool(self) -> Tensor:
  """
  Convenience method to cast `self` to a `bool` Tensor.

  ```python exec="true" source="above" session="tensor" result="python"
  t = Tensor([-1, 0, 1])
  print(t.dtype, t.numpy())
  ```
  ```python exec="true" source="above" session="tensor" result="python"
  t = t.bool()
  print(t.dtype, t.numpy())
  ```
  """
  return self.cast(dtypes.bool)