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fix minor typos #786

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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ Audience: ML Engineers, who are interested in applying delegates to accelerate t
Backend delegation is an entry point for backends to process and execute PyTorch
programs to leverage performance and efficiency benefits of specialized
backends and hardware, while still providing PyTorch users with an experience
close to that of the PyTorch runtime. The backend delegate is usually either provited by
close to that of the PyTorch runtime. The backend delegate is usually either provided by
ExecuTorch or vendors. The way to leverage delegate in your program is via a standard entry point `to_backend`.


Expand Down Expand Up @@ -135,7 +135,7 @@ This function takes in a `Partitioner` which adds a tag to all the nodes that
are meant to be lowered. It will return a `partition_tags` mapping tags to
backend names and module compile specs. The tagged nodes will then be
partitioned and lowered to their mapped backends using Flow 1's process.
Available helper partitioner are documented
Available helper partitioners are documented
[here](./compiler-custom-compiler-passes.md). These lowered modules
will be inserted into the top-level module and serialized.

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2 changes: 1 addition & 1 deletion docs/source/sdk-delegate-integration.md
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ For example:
- **{ “Fancy Fusion”: (11, 12, 15) }**: Identifier “Fancy Fusion” in the runtime corresponds to operators with debug handles (11, 12, 15).

```{Note}
Identifiers are a means of connecting runtime results to the model graph; the interpretation of the identifiers is defined by the delegate author.*
Identifiers are a means of connecting runtime results to the model graph; the interpretation of the identifiers is defined by the delegate author.
```

**Debug Handle Maps** are constructed through the use of **DelegateMappingBuilder** and returned as a part of `PreprocessResult`.
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