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mark shard_map as implemented in transforms guide #4738

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4 changes: 1 addition & 3 deletions docs_nnx/guides/transforms.ipynb
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Expand Up @@ -406,11 +406,9 @@
"| Lift type | JAX transforms |\n",
"|------------------|-----------------------------------------|\n",
"| `StateAxes` | `vmap`, `pmap`, `scan` |\n",
"| `StateSharding` | `jit`, `shard_map`* |\n",
"| `StateSharding` | `jit`, `shard_map` |\n",
"| `DiffState` | `grad`, `value_and_grad`, `custom_vjp` |\n",
"\n",
"> **Note:** * Flax NNX `shard_map` has not been implemented yet at the time of writing this version of the document.\n",
"\n",
"To specify how to vectorize different sub-states of an object in `nnx.vmap`, the Flax team created a `nnx.StateAxes`. `StateAxes` maps a set of sub-states via Flax NNX [Filters](https://flax.readthedocs.io/en/latest/guides/filters_guide.html) to their corresponding axes, and you can pass the `nnx.StateAxes` to `in_axes` and `out_axes` as if it/they were a pytree prefix.\n",
"\n",
"Let's use the previous `stateful_vector_dot` example and vectorize only the `nnx.Param` variables and broadcast the `count` variable so we only keep a single count for all the batch elements.\n",
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4 changes: 1 addition & 3 deletions docs_nnx/guides/transforms.md
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Expand Up @@ -200,11 +200,9 @@ Certain JAX transforms allow the use of pytree prefixes to specify how different
| Lift type | JAX transforms |
|------------------|-----------------------------------------|
| `StateAxes` | `vmap`, `pmap`, `scan` |
| `StateSharding` | `jit`, `shard_map`* |
| `StateSharding` | `jit`, `shard_map` |
| `DiffState` | `grad`, `value_and_grad`, `custom_vjp` |

> **Note:** * Flax NNX `shard_map` has not been implemented yet at the time of writing this version of the document.

To specify how to vectorize different sub-states of an object in `nnx.vmap`, the Flax team created a `nnx.StateAxes`. `StateAxes` maps a set of sub-states via Flax NNX [Filters](https://flax.readthedocs.io/en/latest/guides/filters_guide.html) to their corresponding axes, and you can pass the `nnx.StateAxes` to `in_axes` and `out_axes` as if it/they were a pytree prefix.

Let's use the previous `stateful_vector_dot` example and vectorize only the `nnx.Param` variables and broadcast the `count` variable so we only keep a single count for all the batch elements.
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