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Remove node-hyperlink and copypaste-fallback support, tweak path copying. #68

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Jul 18, 2024
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Remove node-hyperlink and copypaste-fallback support, tweak path copying. #68

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1 change: 0 additions & 1 deletion docs/_autogen_root.rst
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Expand Up @@ -20,7 +20,6 @@

penzai.treescope.arrayviz
penzai.treescope.autovisualize
penzai.treescope.copypaste_fallback
penzai.treescope.default_renderer
penzai.treescope.figures
penzai.treescope.repr_lib
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7 changes: 0 additions & 7 deletions docs/api/treescope.rst
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Expand Up @@ -195,10 +195,3 @@ Rendering to Strings
default_renderer.render_to_text
default_renderer.render_to_html


Utility Types
^^^^^^^^^^^^^

.. autosummary::

copypaste_fallback.NotRoundtrippable
24 changes: 19 additions & 5 deletions notebooks/how_to_think_in_penzai.ipynb
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Expand Up @@ -77,6 +77,7 @@
"import dataclasses\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"from typing import Any, Callable, Sequence"
]
},
Expand Down Expand Up @@ -177,7 +178,20 @@
"source": [
"Try clicking to expand or collapse different sublayers! We've turned on automatic array visualization, so if you expand one of the parameters, you can immediately visualize its shape and array data.\n",
"\n",
"Importantly, this isn't just a pretty visualization of the model, it's actually a **fully-roundtrippable specification of the model structure**. You can press `r` to enable roundtrip mode, and then directly copy and execute the pretty-printed output:\n"
"Importantly, this isn't just a pretty visualization of the model, it's actually a **fully-roundtrippable specification of the model structure**. You can press `r` to enable roundtrip mode, which adds fully-qualified names to every type. Then, if you remove the arrays first, you can directly copy and execute the pretty-printed output:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I7OJhe-RfNT2"
},
"outputs": [],
"source": [
"# We can use `eval_shape` to remove array data and just keep the shapes.\n",
"# Try pressing `r` and copying the below output:\n",
"jax.eval_shape(lambda: mlp)"
]
},
{
Expand All @@ -192,18 +206,18 @@
" sublayers=[\n",
" penzai.nn.linear_and_affine.Affine( # Sequential\n",
" sublayers=[\n",
" penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 8, 'features_out': 32}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='<jax.Array float32(8, 32) ≈-0.0019 ±0.22 [≥-0.38, ≤0.38] nonzero:256>', original_id=23148094748192, original_type=jax.Array)), name='Affine_0.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)),\n",
" penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 8, 'features_out': 32}), data_array=jax.ShapeDtypeStruct(shape=(8, 32), dtype=np.dtype('float32'))), name='Affine_0.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)),\n",
" penzai.nn.linear_and_affine.RenameAxes(old=('features_out',), new=('features',)),\n",
" penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='<jax.Array float32(32,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:32>', original_id=23148094743968, original_type=jax.Array)), name='Affine_0.AddBias.bias'), new_axis_names=()),\n",
" penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32}), data_array=jax.ShapeDtypeStruct(shape=(32,), dtype=np.dtype('float32'))), name='Affine_0.AddBias.bias'), new_axis_names=()),\n",
" ],\n",
" ),\n",
" penzai.nn.basic_ops.Elementwise(fn=jax.nn.relu),\n",
" penzai.nn.linear_and_affine.Affine( # Sequential\n",
" sublayers=[penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32, 'features_out': 32}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='<jax.Array float32(32, 32) ≈-0.0037 ±0.18 [≥-0.31, ≤0.31] nonzero:1_024>', original_id=23147983056352, original_type=jax.Array)), name='Affine_1.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)), penzai.nn.linear_and_affine.RenameAxes(old=('features_out',), new=('features',)), penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='<jax.Array float32(32,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:32>', original_id=23148002433952, original_type=jax.Array)), name='Affine_1.AddBias.bias'), new_axis_names=())],\n",
" sublayers=[penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32, 'features_out': 32}), data_array=jax.ShapeDtypeStruct(shape=(32, 32), dtype=np.dtype('float32'))), name='Affine_1.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)), penzai.nn.linear_and_affine.RenameAxes(old=('features_out',), new=('features',)), penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32}), data_array=jax.ShapeDtypeStruct(shape=(32,), dtype=np.dtype('float32'))), name='Affine_1.AddBias.bias'), new_axis_names=())],\n",
" ),\n",
" penzai.nn.basic_ops.Elementwise(fn=jax.nn.relu),\n",
" penzai.nn.linear_and_affine.Affine( # Sequential\n",
" sublayers=[penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32, 'features_out': 8}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='<jax.Array float32(32, 8) ≈-0.0052 ±0.23 [≥-0.38, ≤0.39] nonzero:256>', original_id=23148002427616, original_type=jax.Array)), name='Affine_2.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)), penzai.nn.linear_and_affine.RenameAxes(old=('features_out',), new=('features',)), penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 8}), data_array=penzai.treescope.copypaste_fallback.NotRoundtrippable(original_repr='Array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)', original_id=23147983059168, original_type=jax.Array)), name='Affine_2.AddBias.bias'), new_axis_names=())],\n",
" sublayers=[penzai.nn.linear_and_affine.Linear(weights=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 32, 'features_out': 8}), data_array=jax.ShapeDtypeStruct(shape=(32, 8), dtype=np.dtype('float32'))), name='Affine_2.Linear.weights'), in_axis_names=('features',), out_axis_names=('features_out',)), penzai.nn.linear_and_affine.RenameAxes(old=('features_out',), new=('features',)), penzai.nn.linear_and_affine.AddBias(bias=penzai.nn.parameters.Parameter(value=penzai.core.named_axes.NamedArray(named_axes=collections.OrderedDict({'features': 8}), data_array=jax.ShapeDtypeStruct(shape=(8,), dtype=np.dtype('float32'))), name='Affine_2.AddBias.bias'), new_axis_names=())],\n",
" ),\n",
" ],\n",
")\n",
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