Awesome
❤️ Lovely Tensors
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More lovely stuff
Working with numbers
- Numpy: ❤️ Lovely NumPy
- JAX: 💘 Lovely
JAX - TinyGrad: 🫀 Lovely Grad
Community
Install
pip install lovely-tensors
or
mamba install lovely-tensors
or
conda install -c conda-forge lovely-tensors
How to use
How often do you find yourself debugging PyTorch code? You dump a tensor to the cell output, and see this:
numbers
tensor([[[-0.3541, -0.3369, -0.4054, ..., -0.5596, -0.4739, 2.2489],
[-0.4054, -0.4226, -0.4911, ..., -0.9192, -0.8507, 2.1633],
[-0.4739, -0.4739, -0.5424, ..., -1.0390, -1.0390, 2.1975],
...,
[-0.9020, -0.8335, -0.9363, ..., -1.4672, -1.2959, 2.2318],
[-0.8507, -0.7822, -0.9363, ..., -1.6042, -1.5014, 2.1804],
[-0.8335, -0.8164, -0.9705, ..., -1.6555, -1.5528, 2.1119]],
[[-0.1975, -0.1975, -0.3025, ..., -0.4776, -0.3725, 2.4111],
[-0.2500, -0.2325, -0.3375, ..., -0.7052, -0.6702, 2.3585],
[-0.3025, -0.2850, -0.3901, ..., -0.7402, -0.8102, 2.3761],
...,
[-0.4251, -0.2325, -0.3725, ..., -1.0903, -1.0203, 2.4286],
[-0.3901, -0.2325, -0.4251, ..., -1.2304, -1.2304, 2.4111],
[-0.4076, -0.2850, -0.4776, ..., -1.2829, -1.2829, 2.3410]],
[[-0.6715, -0.9853, -0.8807, ..., -0.9678, -0.6890, 2.3960],
[-0.7238, -1.0724, -0.9678, ..., -1.2467, -1.0201, 2.3263],
[-0.8284, -1.1247, -1.0201, ..., -1.2641, -1.1596, 2.3786],
...,
[-1.2293, -1.4733, -1.3861, ..., -1.5081, -1.2641, 2.5180],
[-1.1944, -1.4559, -1.4210, ..., -1.6476, -1.4733, 2.4308],
[-1.2293, -1.5256, -1.5081, ..., -1.6824, -1.5256, 2.3611]]])
Was it really useful for you, as a human, to see all these numbers?
What is the shape? The size?
What are the statistics?
Are any of the values nan or inf?
Is it an image of a man holding a tench?
import lovely_tensors as lt
lt.monkey_patch()
Summary
numbers # torch.Tensor
tensor[3, 196, 196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
numbers.rgb
numbers.plt
Better, huh?
numbers[1,:6,1] # Still shows values if there are not too many.
tensor[6] x∈[-0.443, -0.197] μ=-0.311 σ=0.091 [-0.197, -0.232, -0.285, -0.373, -0.443, -0.338]
spicy = numbers[0,:12,0].clone()
spicy[0] *= 10000
spicy[1] /= 10000
spicy[2] = float('inf')
spicy[3] = float('-inf')
spicy[4] = float('nan')
spicy = spicy.reshape((2,6))
spicy # Spicy stuff
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
torch.zeros(10, 10) # A zero tensor - make it obvious
tensor[10, 10] n=100 all_zeros
spicy.v # Verbose
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
spicy.p # The plain old way
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
Named dimensions
named_numbers = numbers.rename("C", "H","W")
named_numbers
/home/xl0/mambaforge/envs/lovely-py31-torch25/lib/python3.10/site-packages/torch/_tensor.py:1420: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1925.)
return super().rename(names)
tensor[C=3, H=196, W=196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
Going .deeper
numbers.deeper
tensor[3, 196, 196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
tensor[196, 196] n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
tensor[196, 196] n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
tensor[196, 196] n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178
# You can go deeper if you need to
# And we can use `.deeper` with named dimensions.
named_numbers.deeper(2)
tensor[C=3, H=196, W=196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
tensor[H=196, W=196] n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
tensor[W=196] x∈[-1.912, 2.249] μ=-0.673 σ=0.522
tensor[W=196] x∈[-1.861, 2.163] μ=-0.738 σ=0.418
tensor[W=196] x∈[-1.758, 2.198] μ=-0.806 σ=0.397
tensor[W=196] x∈[-1.656, 2.249] μ=-0.849 σ=0.369
tensor[W=196] x∈[-1.673, 2.198] μ=-0.857 σ=0.357
tensor[W=196] x∈[-1.656, 2.146] μ=-0.848 σ=0.372
tensor[W=196] x∈[-1.433, 2.215] μ=-0.784 σ=0.397
tensor[W=196] x∈[-1.279, 2.249] μ=-0.695 σ=0.486
tensor[W=196] x∈[-1.364, 2.249] μ=-0.637 σ=0.539
...
tensor[H=196, W=196] n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
tensor[W=196] x∈[-1.861, 2.411] μ=-0.529 σ=0.556
tensor[W=196] x∈[-1.826, 2.359] μ=-0.562 σ=0.473
tensor[W=196] x∈[-1.756, 2.376] μ=-0.622 σ=0.458
tensor[W=196] x∈[-1.633, 2.429] μ=-0.664 σ=0.430
tensor[W=196] x∈[-1.651, 2.376] μ=-0.669 σ=0.399
tensor[W=196] x∈[-1.633, 2.376] μ=-0.701 σ=0.391
tensor[W=196] x∈[-1.563, 2.429] μ=-0.670 σ=0.380
tensor[W=196] x∈[-1.475, 2.429] μ=-0.616 σ=0.386
tensor[W=196] x∈[-1.511, 2.429] μ=-0.593 σ=0.399
...
tensor[H=196, W=196] n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178
tensor[W=196] x∈[-1.717, 2.396] μ=-0.982 σ=0.350
tensor[W=196] x∈[-1.752, 2.326] μ=-1.034 σ=0.314
tensor[W=196] x∈[-1.648, 2.379] μ=-1.086 σ=0.314
tensor[W=196] x∈[-1.630, 2.466] μ=-1.121 σ=0.305
tensor[W=196] x∈[-1.717, 2.448] μ=-1.120 σ=0.302
tensor[W=196] x∈[-1.717, 2.431] μ=-1.166 σ=0.314
tensor[W=196] x∈[-1.560, 2.448] μ=-1.124 σ=0.326
tensor[W=196] x∈[-1.421, 2.431] μ=-1.064 σ=0.383
tensor[W=196] x∈[-1.526, 2.396] μ=-1.047 σ=0.417
...
Now in .rgb color
The important queston - is it our man?
numbers.rgb
Maaaaybe? Looks like someone normalized him.
in_stats = ( (0.485, 0.456, 0.406), # mean
(0.229, 0.224, 0.225) ) # std
# numbers.rgb(in_stats, cl=True) # For channel-last input format
numbers.rgb(in_stats)
It’s indeed our hero, the Tenchman!
.plt the statistics
(numbers+3).plt(center="mean", max_s=1000)
(numbers).plt
(numbers+3).plt(center="range")
See the .chans
# .chans will map values betwen [-1,1] to colors.
# Make our values fit into that range to avoid clipping.
mean = torch.tensor(in_stats[0])[:,None,None]
std = torch.tensor(in_stats[1])[:,None,None]
numbers_01 = (numbers*std + mean)
numbers_01
tensor[3, 196, 196] n=115248 (0.4Mb) x∈[0., 1.000] μ=0.361 σ=0.248
numbers_01.chans
Let’s try with a Convolutional Neural Network
from torchvision.models import vgg11
features: torch.nn.Sequential = vgg11().features
# I saved the first 5 layers in "features.pt"
_ = features.load_state_dict(torch.load("../features.pt", weights_only=True), strict=False)
# Activatons of the second max pool layer of VGG11
acts = (features[:6](numbers[None])[0]/2) # /2 to reduce clipping
acts
tensor[128, 49, 49] n=307328 (1.2Mb) x∈[0., 12.508] μ=0.367 σ=0.634 grad DivBackward0
acts[:4].chans(cmap="coolwarm", scale=4)
Grouping
# Make 8 images with progressively higher brightness and stack them 2x2x2.
eight_images = (torch.stack([numbers]*8)
.add(torch.linspace(-3, 3, 8)[:,None,None,None])
.mul(torch.tensor(in_stats[1])[:,None,None])
.add(torch.tensor(in_stats[0])[:,None,None])
.clamp(0,1)
.view(2,2,2,3,196,196)
)
eight_images
tensor[2, 2, 2, 3, 196, 196] n=921984 (3.5Mb) x∈[0., 1.000] μ=0.411 σ=0.369
eight_images.rgb
# Weights of the second conv layer of VGG11
features[3].weight
Parameter[128, 64, 3, 3] n=73728 (0.3Mb) x∈[-0.783, 0.776] μ=-0.004 σ=0.065 grad
I want +/- 2σ to fall in the range [-1..1]
weights = features[3].weight.data
weights = weights / (2*2*weights.std()) # *2 because we want 2σ on both sides, so 4σ
# weights += weights.std() * 2
weights.plt
# Weights of the second conv layer (64ch -> 128ch) of VGG11,
# grouped per output channel.
weights.chans(frame_px=1, gutter_px=0)
It’s a bit hard to see. Scale up 10x, but onyl show the first 4 filters.
weights[:4].chans(frame_px=1, gutter_px=0, scale=10)
Options | Docs
from lovely_tensors import set_config, config, lovely, get_config
set_config(precision=1, sci_mode=True, color=False)
torch.tensor([1, 2, torch.nan])
tensor[3] μ=1.5e+00 σ=7.1e-01 NaN! [1.0e+00, 2.0e+00, nan]
set_config(precision=None, sci_mode=None, color=None) # None -> Reset to defaults
print(torch.tensor([1., 2]))
# Or with config context manager.
with config(sci_mode=True, precision=5):
print(torch.tensor([1., 2]))
print(torch.tensor([1., 2]))
tensor[2] μ=1.500 σ=0.707 [1.000, 2.000]
tensor[2] μ=1.50000e+00 σ=7.07107e-01 [1.00000e+00, 2.00000e+00]
tensor[2] μ=1.500 σ=0.707 [1.000, 2.000]
Without .monkey_patch
lt.lovely(spicy)
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
lt.lovely(spicy, verbose=True)
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
lt.lovely(numbers, depth=1)
tensor[3, 196, 196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
tensor[196, 196] n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
tensor[196, 196] n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
tensor[196, 196] n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178
lt.rgb(numbers, in_stats)
lt.plot(numbers, center="mean")
lt.chans(numbers_01)
Matplotlib integration | Docs
numbers.rgb(in_stats).fig # matplotlib figure
(numbers*0.3+0.5).chans.fig # matplotlib figure
numbers.plt.fig.savefig('pretty.svg') # Save it
!file pretty.svg; rm pretty.svg
pretty.svg: SVG Scalable Vector Graphics image
Add content to existing Axes
fig = plt.figure(figsize=(8,3))
fig.set_constrained_layout(True)
gs = fig.add_gridspec(2,2)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1,1:])
ax2.set_axis_off()
ax3.set_axis_off()
numbers_01.plt(ax=ax1)
numbers_01.rgb(ax=ax2)
numbers_01.chans(ax=ax3);
torch.compile()
Just works.
def func(x):
return x*2
if torch.__version__ >= "2.0":
func = torch.compile(func)
func(torch.tensor([1,2,3]))
tensor[3] i64 x∈[2, 6] μ=4.000 σ=2.000 [2, 4, 6]
Inport hook
Lovely tensors installes an import hook. Set LOVELY_TENSORS=1, and it
will load automatically, no need to modify the code: > Note: Don’t set
it globally, or all python scripts you run will import LT and PyTorch,
which will slow things down.
import torch
x = torch.randn(4, 16)
print(x)
LOVELY_TENSORS=1 python test.py
x: tensor[4, 16] n=64 x∈[-1.652, 1.813] μ=-0.069 σ=0.844
This is especially useful in combination with Better Exceptions:
import torch
x = torch.randn(4, 16)
print(f"x: {x}")
w = torch.randn(15, 8)
y = torch.matmul(x, w) # Dimension mismatch
BETTER_EXCEPTIONS=1 LOVELY_TENSORS=1 python test.py
x: tensor[4, 16] n=64 x∈[-1.834, 2.421] μ=0.103 σ=0.896
Traceback (most recent call last):
File "/home/xl0/work/projects/lovely-tensors/test.py", line 7, in <module>
y = torch.matmul(x, w)
│ │ └ tensor[15, 8] n=120 x∈[-2.355, 2.165] μ=0.142 σ=0.989
│ └ tensor[4, 16] n=64 x∈[-1.834, 2.421] μ=0.103 σ=0.896
└ <module 'torch' from '/home/xl0/mambaforge/envs/torch25-py313/lib/python3.12/site-packages/torch/__init__.py'>
RuntimeError: mat1 and mat2 shapes cannot be multiplied (4x16 and 15x8)