samskalicky commented on a change in pull request #19543:
URL: https://github.com/apache/incubator-mxnet/pull/19543#discussion_r524869683
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File path: example/extensions/lib_subgraph/README.md
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@@ -102,35 +96,35 @@ Partitioning APIs in MXNet are available in both Symbol
and Gluon APIs. For the
sym.optimize_for(backend, args=None, aux=None, ctx=None, **kwargs)
```
-The `optimize_for` API takes at least 1 argument, `backend` which is a string
that identifies which backend to partition the model for. The `args` and `aux`
arguments are optional and take a list of NDArray or dict of str to NDArray.
They are used to infer shapes and types and before partitioning, and passed to
the backend to use during compilation. The `ctx` argument is optional and takes
a device context to infer storage types. It also takes any other user-specified
options that will be passed to the backend partitioning APIs.
+The `optimize_for` API takes at least 1 argument, `backend` which is a string
that identifies which backend to partition the model for. The `args` and `aux`
arguments are optional and take a list of NDArray or dict of str to NDArray.
They are used to infer shapes and types and before partitioning, and passed to
the backend to use during compilation. The `ctx` argument is optional and takes
a device context to infer storage types. It also takes any other user-specified
options that will be passed to the backend partitioning APIs. The backend
options can be passed as kwargs.
-For the Gluon API, `hybridize` can be called on HybridBlocks to partition the
internal CachedOp Symbol.
+When the `optimize_for` API is called on a HybridBlock it partitions
immediately. This lets users export the partitioned model without running a
complete forward pass. Chaining multiple optimizations is as simple as calling
`optimize_for` multiple times.
```python
-block.hybridize(backend=None, backend_opts=None, clear=True, **kwargs)
+block.optimize_for(x, backend='myPart')
+block.optimize_for(x, backend='myOtherPart')
+block.export('partitioned')
```
-The `hybridize` function prepares the HybridBlock to be converted into a
backend symbol. The `backend` argument is a string that identifies which
backend that will partition the model. The `backend_opts` are other
user-specified options (as a Python dictionary of strings mapped to strings)
that will be passed to the backend partitioning APIs. The `clear` argument
defaults to `True` and clears any previous optimizations done on the block. If
you want to chain optimizations together, set `clear` to `False`. The actual
partitioning takes place during the forward pass. If you want to use
`hybridize` to chain multiple optimizations, be sure to execute a forward pass
after each call to `hybridize`.
-
-If you just want to partition the HybridBlock but not run a complete forward
pass, you can use the `optimize_for` API that combines the work done in the
`hybridize` API with part of the work done in the forward pass.
+For the Gluon API, hybridization is needed, so calling `optimize_for` on a
non-hybridized block will hybridize it.
+If the users need to pass some hybridization parameters, they can either call
`hybridize` explicitedly, or directly pass the arguments to `optimize_for`.
+This:
```python
-block.optimize_for(x, backend=None, backend_opts=None, clear=True, **kwargs)
+block.hybridize(static_shape=True, static_alloc=False)
+block.optimize_for(x, backend='myPart')
```
-
-When the `optimize_for` API is called on a HybridBlock it partitions
immediately. This lets users export the partitioned model without running a
complete forward pass. Chaining multiple optimizations is as simple as calling
`optimize_for` multiple times, no need to execute a forward pass (as opposed to
`hybridize`).
-
+is equivalent to:
```python
-block.optimize_for(x, backend='myPart')
-block.optimize_for(x, backend='myOtherPart', clear=False)
-block.export('partitioned')
+block.optimize_for(x, backend='myPart', static_shape=True, static_alloc=False)
```
-But you can also use `optimize_for` in place of `hybridize` and run inference
immediately after too.
+It's important to note that `hybridize` clars the CachedOp and any previous
optimization.
Review comment:
clars --> clears
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