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Add instruction for exporting inlined constant #8707

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Add instruction for exporting inlined constant
qihqi Feb 13, 2025
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mikegre/s chagne
qihqi Feb 13, 2025
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27 changes: 25 additions & 2 deletions docs/source/features/stablehlo.md
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,8 @@ There are 2 ways to accomplish this:
from torch.export import export
import torchvision
import torch
import torch_xla2 as tx
import torch_xla2.export
import torchax as tx
import torchax.export

resnet18 = torchvision.models.resnet18()
# Sample input is a tuple
Expand Down Expand Up @@ -64,6 +64,29 @@ print(stablehlo.mlir_module())
The second to last line we used `jax.ShapedDtypeStruct` to specify the input shape.
You can also pass a numpy array here.

### Inline some weights in generated stablehlo

You can inline some or all of your model's weights into the StableHLO graph as constants by exporting a separate function that calls your model.

The convention used in `jax.jit` is all the input of the `jit`ed Python
functions are exported as parameters, everything else are inlined as constants.

So as above, the function we exported `jfunc` takes `weights` and `args` as input, so
they appear as paramters.

If you do this instead:

```
def jfunc_inlined(args):
return jfunc(weights, args)
```
and export / print out stablehlo for that:

```
print(jax.jit(jfunc_inlined).lower((jax.ShapedDtypeStruct((4, 3, 224, 224), jnp.float32.dtype, ))))
```
Then, you will see inlined constants.


## Preserving High-Level PyTorch Operations in StableHLO by generating `stablehlo.composite`

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