[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #19543: Separate backend from hybridize and refactor optimize_for kwargs

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mseth10 commented on a change in pull request #19543:
URL: https://github.com/apache/incubator-mxnet/pull/19543#discussion_r524810384



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File path: example/extensions/lib_subgraph/README.md
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@@ -102,27 +102,27 @@ 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.
 
 ```python
-block.hybridize(backend=None, backend_opts=None, clear=True, **kwargs)
+block.hybridize(backend=None, clear=True)
 ```
 
-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`. 
+The `hybridize` function prepares the HybridBlock to be converted into a backend symbol. 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.

Review comment:
       remove "If you want to chain optimizations together, set `clear` to `False`."




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