Getting Started¶
Before you read this section, make sure to read the torch.compiler.
Let’s start by looking at a simple torch.compile example that demonstrates
how to use torch.compile for inference. This example demonstrates the
torch.cos() and torch.sin() features which are examples of pointwise
operators as they operate element by element on a vector. This example might
not show significant performance gains but should help you form an intuitive
understanding of how you can use torch.compile in your own programs.
Note
To run this script, you need to have at least one GPU on your machine.
If you do not have a GPU, you can remove the .to(device="cuda:0") code
in the snippet below and it will run on CPU. You can also set device to
xpu:0 to run on Intel® GPUs.
import torch
def fn(x):
a = torch.cos(x)
b = torch.sin(a)
return b
new_fn = torch.compile(fn, backend="inductor")
input_tensor = torch.randn(10000).to(device="cuda:0")
a = new_fn(input_tensor)
A more famous pointwise operator you might want to use would
be something like torch.relu(). Pointwise ops in eager mode are
suboptimal because each one would need to read a tensor from the
memory, make some changes, and then write back those changes. The single
most important optimization that inductor performs is fusion. In the
example above we can turn 2 reads (x, a) and
2 writes (a, b) into 1 read (x) and 1 write (b), which
is crucial especially for newer GPUs where the bottleneck is memory
bandwidth (how quickly you can send data to a GPU) rather than compute
(how quickly your GPU can crunch floating point operations).
Another major optimization that inductor provides is automatic support for CUDA graphs. CUDA graphs help eliminate the overhead from launching individual kernels from a Python program which is especially relevant for newer GPUs.
TorchDynamo supports many different backends, but TorchInductor specifically works
by generating Triton kernels. Let’s save
our example above into a file called example.py. We can inspect the code
generated Triton kernels by running TORCH_COMPILE_DEBUG=1 python example.py.
As the script executes, you should see DEBUG messages printed to the
terminal. Closer to the end of the log, you should see a path to a folder
that contains torchinductor_<your_username>. In that folder, you can find
the output_code.py file that contains the generated kernel code similar to
the following:
@pointwise(size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': 0, 'constants': {}, 'mutated_arg_names': [], 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]})
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 10000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask, other=0.0)
tmp1 = tl.cos(tmp0)
tmp2 = tl.sin(tmp1)
tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp2, xmask)
Note
The above code snippet is an example. Depending on your hardware, you might see different code generated.
And you can verify that fusing the cos and sin did actually occur
because the cos and sin operations occur within a single Triton kernel
and the temporary variables are held in registers with very fast access.
Read more on Triton’s performance here. Because the code is written in Python, it’s fairly easy to understand even if you have not written all that many CUDA kernels.
Next, let’s try a real model like resnet50 from the PyTorch hub.
import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
opt_model = torch.compile(model, backend="inductor")
opt_model(torch.randn(1,3,64,64))
And that is not the only available backend, you can run in a REPL
torch.compiler.list_backends() to see all the available backends. Try out the
cudagraphs next as inspiration.
Using a pretrained model¶
PyTorch users frequently leverage pretrained models from transformers or TIMM and one of the design goals is TorchDynamo and TorchInductor is to work out of the box with any model that people would like to author.
Let’s download a pretrained model directly from the HuggingFace hub and optimize it:
import torch
from transformers import BertTokenizer, BertModel
# Copy pasted from here https://huggingface.co/bert-base-uncased
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased").to(device="cuda:0")
model = torch.compile(model, backend="inductor") # This is the only line of code that we changed
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt').to(device="cuda:0")
output = model(**encoded_input)
If you remove the to(device="cuda:0") from the model and
encoded_input, then Triton will generate C++ kernels that will be
optimized for running on your CPU. You can inspect both Triton or C++
kernels for BERT. They are more complex than the trigonometry
example we tried above but you can similarly skim through it and see if you
understand how PyTorch works.
Similarly, let’s try out a TIMM example:
import timm
import torch
model = timm.create_model('resnext101_32x8d', pretrained=True, num_classes=2)
opt_model = torch.compile(model, backend="inductor")
opt_model(torch.randn(64,3,7,7))
Next Steps¶
In this section, we have reviewed a few inference examples and developed a basic understanding of how torch.compile works. Here is what you check out next:
