Merge pull request #4250 from google:enable-notebook-doctest

PiperOrigin-RevId: 682363934

docs/conf.py

@@ -137,7 +137,7 @@
 
 # -- Options for myst ----------------------------------------------
 # uncomment line below to avoid running notebooks during development
-nb_execution_mode = 'off'
+# nb_execution_mode = 'off'
 # Notebook cell execution timeout; defaults to 30.
 nb_execution_timeout = 100
 # List of patterns, relative to source directory, that match notebook
@@ -147,6 +147,8 @@
   'quick_start.ipynb',  # <-- times out
   'transfer_learning.ipynb',  # <-- transformers requires flax<=0.7.0
   'flax/nnx',  # exclude nnx
+  'guides/quantization/fp8_basics.ipynb',
+  'guides/training_techniques/use_checkpointing.ipynb',  # TODO(IvyZX): needs to be updated
 ]
 # raise exceptions on execution so CI can catch errors
 nb_execution_allow_errors = False

docs_nnx/conf.py

@@ -137,7 +137,7 @@
 
 # -- Options for myst ----------------------------------------------
 # uncomment line below to avoid running notebooks during development
-nb_execution_mode = 'off'
+# nb_execution_mode = 'off'
 # Notebook cell execution timeout; defaults to 30.
 nb_execution_timeout = 100
 # List of patterns, relative to source directory, that match notebook
@@ -147,6 +147,10 @@
   'quick_start.ipynb',  # <-- times out
   'transfer_learning.ipynb',  # <-- transformers requires flax<=0.7.0
   'flax/nnx',  # exclude nnx
+  'guides/demo.ipynb',  # TODO(cgarciae): broken, remove or update
+  'guides/why.ipynb',  # TODO(cgarciae): broken, remove in favor on the new guide
+  'guides/flax_gspmd.ipynb',  # TODO(IvyZX): broken, needs to be updated
+  'guides/surgery.ipynb',  # TODO(IvyZX): broken, needs to be updated
 ]
 # raise exceptions on execution so CI can catch errors
 nb_execution_allow_errors = False

docs_nnx/guides/demo.ipynb

@@ -88,7 +88,7 @@
     "  pass\n",
     "\n",
     "model = MLP(5, 4, rngs=nnx.Rngs(0))  # no special `init` method\n",
-    "model.set_attributes(deterministic=False, use_running_average=False)  # set flags\n",
+    "model.set_attributes(use_running_average=False)  # set flags\n",
     "y = model(jnp.ones((2, 4)))  # call methods directly\n",
     "\n",
     "print(f'{model = }'[:500] + '\\n...')"

docs_nnx/guides/demo.md

@@ -48,7 +48,7 @@ class Count(nnx.Variable):   # custom Variable types define the "collections"
   pass
 
 model = MLP(5, 4, rngs=nnx.Rngs(0))  # no special `init` method
-model.set_attributes(deterministic=False, use_running_average=False)  # set flags
+model.set_attributes(use_running_average=False)  # set flags
 y = model(jnp.ones((2, 4)))  # call methods directly
 
 print(f'{model = }'[:500] + '\n...')

docs_nnx/guides/filters_guide.ipynb

@@ -172,7 +172,8 @@
     "\n",
     "Let see the DSL in action with a `nnx.vmap` example. Lets say we want vectorized all parameters\n",
     "and `dropout` Rng(Keys|Counts) on the 0th axis, and broadcasted the rest. To do so we can\n",
-    "use the following filters:"
+    "use the following filters to define a `nnx.StateAxes` object that we can pass to `nnx.vmap`'s `in_axes`\n",
+    "to specify how `model`'s various substates should be vectorized:"
    ]
   },
   {
@@ -182,9 +183,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from functools import partial\n",
+    "state_axes = nnx.StateAxes({(nnx.Param, 'dropout'): 0, ...: None})\n",
     "\n",
-    "@partial(nnx.vmap, in_axes=(None, 0), state_axes={(nnx.Param, 'dropout'): 0, ...: None})\n",
+    "@nnx.vmap(in_axes=(state_axes, 0))\n",
     "def forward(model, x):\n",
     "  ..."
    ]
@@ -275,7 +276,7 @@
     "KeyPath = tuple[nnx.graph.Key, ...]\n",
     "\n",
     "def split(node, *filters):\n",
-    "  graphdef, state, _ = nnx.graph.flatten(node)\n",
+    "  graphdef, state = nnx.graph.flatten(node)\n",
     "  predicates = [nnx.filterlib.to_predicate(f) for f in filters]\n",
     "  flat_states: list[dict[KeyPath, Any]] = [{} for p in predicates]\n",
     "\n",

docs_nnx/guides/filters_guide.md

@@ -98,12 +98,13 @@ Here is a list of all the callable Filters included in Flax NNX and their DSL li
 
 Let see the DSL in action with a `nnx.vmap` example. Lets say we want vectorized all parameters
 and `dropout` Rng(Keys|Counts) on the 0th axis, and broadcasted the rest. To do so we can
-use the following filters:
+use the following filters to define a `nnx.StateAxes` object that we can pass to `nnx.vmap`'s `in_axes`
+to specify how `model`'s various substates should be vectorized:
 
 ```{code-cell} ipython3
-from functools import partial
+state_axes = nnx.StateAxes({(nnx.Param, 'dropout'): 0, ...: None})
 
-@partial(nnx.vmap, in_axes=(None, 0), state_axes={(nnx.Param, 'dropout'): 0, ...: None})
+@nnx.vmap(in_axes=(state_axes, 0))
 def forward(model, x):
   ...
 ```
@@ -140,7 +141,7 @@ from typing import Any
 KeyPath = tuple[nnx.graph.Key, ...]
 
 def split(node, *filters):
-  graphdef, state, _ = nnx.graph.flatten(node)
+  graphdef, state = nnx.graph.flatten(node)
   predicates = [nnx.filterlib.to_predicate(f) for f in filters]
   flat_states: list[dict[KeyPath, Any]] = [{} for p in predicates]