# Low-Memory Dropout¶

In this tutorial, you will write a memory-efficient implementation of dropout whose state will be composed of a single int32 seed. This differs from more traditional implementations of dropout, whose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:

• The limitations of naive implementations of Dropout with PyTorch

• Parallel pseudo-random number generation in Triton

## Baseline¶

The dropout operator was first introduced in [SRIVASTAVA2014] as a way to improve the performance of deep neural networks in low-data regime (i.e. regularization).

It takes a vector as input and produces a vector of the same shape as output. Each scalar in the output has a probability $$p$$ of being changed to zero and otherwise it is copied from the input. This forces the network to perform well even when only $$1 - p$$ scalars from the input are available.

At evaluation time we want to use the full power of the network so we set $$p=0$$. Naively this would increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease in the output softmax temperature). To prevent this we multiply the output by $$\frac{1}{1 - p}$$, which keeps the norm consistent regardless of the dropout probability.

Let’s first take a look at the baseline implementation.

import tabulate
import torch

import triton
import triton.language as tl

@triton.jit
def _dropout(
x_ptr,  # pointer to the input
x_keep_ptr,  # pointer to a mask of 0s and 1s
output_ptr,  # pointer to the output
n_elements,  # number of elements in the x tensor
p,  # probability that an element of x is changed to zero
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
# The line below is the crucial part, described in the paragraph above!
output = tl.where(x_keep, x / (1 - p), 0.0)
# Write-back output

def dropout(x, x_keep, p):
output = torch.empty_like(x)
assert x.is_contiguous()
n_elements = x.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_dropout[grid](x, x_keep, output, n_elements, p, BLOCK_SIZE=1024)
return output

# Input tensor
x = torch.randn(size=(10,)).cuda()
p = 0.5
x_keep = (torch.rand(size=(10,)) > p).to(torch.int32).cuda()
#
output = dropout(x, x_keep=x_keep, p=p)
print(tabulate.tabulate([
["input"] + x.tolist(),
["keep mask"] + x_keep.tolist(),
["output"] + output.tolist()
]))


Out:

---------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------
input      1.541    -0.293429  -2.17879  0.568431  -1.08452  -1.3986   0.403347  0.838026  -0.719258  -0.403344
keep mask  1         1          0        1          0         1        1         0          0          0
output     3.08199  -0.586858   0        1.13686    0        -2.79719  0.806694  0          0          0
---------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------


## Seeded dropout¶

Above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly we need to store the dropout mask for backpropagation. Secondly, dropout state management can get very tricky when using recompute/checkpointing (e.g. see all the notes about preserve_rng_state in https://pytorch.org/docs/1.9.0/checkpoint.html). In this tutorial we’ll describe an alternative implementation that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management of persisting randomness across multiple invocations of the kernel.

Pseudorandom number generation in Triton is simple! In this tutorial we will use the triton.language.rand function which generates a block of uniformly distributed float32 values in [0, 1), given a seed and a block of int32 offsets. But if you need it, Triton also provides other random number generation strategies.

Note

Triton’s implementation of PRNG is based on the Philox algorithm (described on [SALMON2011]).

Let’s put it all together.

@triton.jit
def _seeded_dropout(
x_ptr,
output_ptr,
n_elements,
p,
seed,
BLOCK_SIZE: tl.constexpr,
):
# compute memory offsets of elements handled by this instance
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
# load data from x
mask = offsets < n_elements
# randomly prune it
random = tl.rand(seed, offsets)
x_keep = random > p
# write-back
output = tl.where(x_keep, x / (1 - p), 0.0)

def seeded_dropout(x, p, seed):
output = torch.empty_like(x)
assert x.is_contiguous()
n_elements = x.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_seeded_dropout[grid](x, output, n_elements, p, seed, BLOCK_SIZE=1024)
return output

x = torch.randn(size=(10,)).cuda()
# Compare this to the baseline - dropout mask is never instantiated!
output = seeded_dropout(x, p=0.5, seed=123)
output2 = seeded_dropout(x, p=0.5, seed=123)
output3 = seeded_dropout(x, p=0.5, seed=512)

print(tabulate.tabulate([
["input"] + x.tolist(),
["output (seed = 123)"] + output.tolist(),
["output (seed = 123)"] + output2.tolist(),
["output (seed = 512)"] + output3.tolist()
]))


Out:

-------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------
input                -0.952835  0.371721  0.408716  1.42142  0.149397  -0.67086  -0.214186  -0.431969  -0.707878  -0.106434
output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
output (seed = 512)   0         0         0.817432  2.84284  0         -1.34172  -0.428372   0          0          0
-------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------


Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same! If you’d like explore further applications of pseudorandomness in GPU programming, we encourage you to explore the triton/language/random folder!

## Exercises¶

1. Extend the kernel to operate over a matrix and use a vector of seeds - one per row.

2. Add support for striding.

3. (challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.

## References¶

SALMON2011

John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, “Parallel Random Numbers: As Easy as 1, 2, 3”, 2011

SRIVASTAVA2014

Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014

Total running time of the script: ( 0 minutes 0.287 seconds)

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