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Update 2021-3-4-pytorch-1.8-released.md #598

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Expand Up @@ -31,29 +31,6 @@ This kind of functionality is applicable in many scenarios. For example, the FX-

Because FX transforms consume and produce nn.Module instances, they can be used within many existing PyTorch workflows. This includes workflows that, for example, train in Python then deploy via TorchScript.

Below is an FX transform example:

```python
import torch
import torch.fx

def transform(m: nn.Module,
tracer_class : type = torch.fx.Tracer) -> torch.nn.Module:
# Step 1: Acquire a Graph representing the code in `m`

# NOTE: torch.fx.symbolic_trace is a wrapper around a call to
# fx.Tracer.trace and constructing a GraphModule. We'll
# split that out in our transform to allow the caller to
# customize tracing behavior.
graph : torch.fx.Graph = tracer_class().trace(m)

# Step 2: Modify this Graph or create a new one
graph = ...

# Step 3: Construct a Module to return
return torch.fx.GraphModule(m, graph)

```
You can read more about FX in the official [documentation](https://pytorch.org/docs/master/fx.html). You can also find several examples of program transformations implemented using ```torch.fx``` [here](https://github.com/pytorch/examples/tree/master/fx). We are constantly improving FX and invite you to share any feedback you have about the toolkit on the [forums](https://discuss.pytorch.org/) or [issue tracker](https://github.com/pytorch/pytorch/issues).

# Distributed Training
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