ChaiBapchya commented on a change in pull request #13597: [MXNET-1255] update
hybridize documentation
URL: https://github.com/apache/incubator-mxnet/pull/13597#discussion_r241998523
##########
File path: docs/tutorials/gluon/hybrid.md
##########
@@ -137,4 +137,94 @@ to gluon with `SymbolBlock`:
net2 = gluon.SymbolBlock.imports('model-symbol.json', ['data'],
'model-0001.params')
```
+## Operators that does not work with hybridize
+
+While most APIs are the same in NDArray and Symbol, there are some
differences. Writting `F.operator` and call `hybridize` may not work all the
time.
+Here we list all the APIs that do not work and provide you the work arounds.
+
+### Element-wise Operators
+
+The following arithmetic and comparison APIs are automatically broadcasted if
the input NDArrays have different shapes.
+However, that's not the case in Symbol API, it's not automatically broadcasted
and you have to manually specify whether to use element-wise operator or
broadcast operators.
+
+
+| NDArray APIs | Description |
+|---|---|
+|
[*NDArray.__add__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__add__)
| x.__add__(y) <=> x+y <=> mx.nd.add(x, y) |
+|
[*NDArray.__sub__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__sub__)
| x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y) |
+|
[*NDArray.__mul__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mul__)
| x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y) |
+|
[*NDArray.__div__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__div__)
| x.__div__(y) <=> x/y <=> mx.nd.divide(x, y) |
+|
[*NDArray.__mod__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mod__)
| x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y) |
+|
[*NDArray.__lt__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__lt__)
| x.__lt__(y) <=> x mx.nd.lesser(x, y) |
+|
[*NDArray.__le__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__le__)
| x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y) |
+|
[*NDArray.__gt__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__gt__)
| x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) |
+|
[*NDArray.__ge__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ge__)
| x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y)|
+|
[*NDArray.__eq__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__eq__)
| x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y) |
+|
[*NDArray.__ne__*](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ne__)
| x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y) |
+
+The current workaround is to use corecponding broadcast operators for
arithmetic and comparison to avoid potential hybridization failure when input
shapes are different.
+
+| Symbol APIs | Description |
+|---|---|
+|[*broadcast_add*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_add)
| Returns element-wise sum of the input arrays with broadcasting. |
+|[*broadcast_sub*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_sub)
| Returns element-wise difference of the input arrays with broadcasting. |
+|[*broadcast_mul*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_mul)
| Returns element-wise product of the input arrays with broadcasting. |
+|[*broadcast_div*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_div)
| Returns element-wise division of the input arrays with broadcasting. |
+|[*broadcast_mod*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_mod)
| Returns element-wise modulo of the input arrays with broadcasting. |
+|[*broadcast_equal*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_equal)
| Returns the result of element-wise *equal to* (==) comparison operation with
broadcasting. |
+|[*broadcast_not_equal*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_not_equal)
| Returns the result of element-wise *not equal to* (!=) comparison operation
with broadcasting. |
+|[*broadcast_greater*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_greater)
| Returns the result of element-wise *greater than* (>) comparison operation
with broadcasting. |
+|[*broadcast_greater_equal*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_greater_equal)
| Returns the result of element-wise *greater than or equal to* (>=)
comparison operation with broadcasting. |
+|[*broadcast_lesser*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_lesser)
| Returns the result of element-wise *lesser than* (<) comparison
operation with broadcasting. |
+|[*broadcast_lesser_equal*](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_lesser_equal)
| Returns the result of element-wise *lesser than or equal to* (<=) comparison
operation with broadcasting. |
+
+For example, if you wan to add a NDarray to your input x, use `broadcast_add`
instead of `+`:
+
+```python
+def hybrid_forward(self, F, x):
+ # avoid writting: return x + F.ones((1, 1))
+ return F.broadcast_add(x, F.ones((1, 1)))
+```
+
+### Shape
+
+Gluon's imperative interface is very flexible and allows you to print shape of
the NDArray. However, Symbol does not have shape attributes. As a result, you
need to avoid printing shapes in `hybrid_forward`.
+Otherwise, you will get the following error:
+```bash
+AttributeError: 'Symbol' object has no attribute 'shape'
+```
+
+### Slice
+[] in NDArray is to get a slice from the array. [] in Symbol is to get an
output from a grouped symbol.
+For example, you will get different result for the following method before and
after hybridization.
+
+```python
+def hybrid_forward(self, F, x):
+ return x[0]
+```
+
+The current workaround is explicitly call
[`slice`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.slice)
operators in `hybrid_forwar`d.
Review comment:
nit: _to_ explicitly call
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