Handle torch.jit scripted modules in layer summary

CHANGELOG.md

@@ -137,6 +137,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed LightningModule `all_gather` on cpu tensors ([#6416](https://github.com/PyTorchLightning/pytorch-lightning/pull/6416))
 
 
+- Fixed an exception in the layer summary when the model contains torch.jit scripted submodules ([#6511](https://github.com/PyTorchLightning/pytorch-lightning/pull/6511))
+
+
 ## [1.2.3] - 2021-03-09
 
 ### Fixed

pytorch_lightning/core/memory.py

@@ -16,7 +16,7 @@
 import shutil
 import subprocess
 from collections import OrderedDict
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -71,14 +71,15 @@ def __init__(self, module: nn.Module):
     def __del__(self):
         self.detach_hook()
 
-    def _register_hook(self) -> RemovableHandle:
+    def _register_hook(self) -> Optional[RemovableHandle]:
         """
         Registers a hook on the module that computes the input- and output size(s) on the first forward pass.
         If the hook is called, it will remove itself from the from the module, meaning that
         recursive models will only record their input- and output shapes once.
+        Registering hooks on :class:`~torch.jit.ScriptModule` is not supported.
 
         Return:
-            A handle for the installed hook.
+            A handle for the installed hook, or ``None`` if registering the hook is not possible.
         """
 
         def hook(module, inp, out):
@@ -88,7 +89,10 @@ def hook(module, inp, out):
             self._out_size = parse_batch_shape(out)
             self._hook_handle.remove()
 
-        return self._module.register_forward_hook(hook)
+        handle = None
+        if not isinstance(self._module, torch.jit.ScriptModule):
+            handle = self._module.register_forward_hook(hook)
+        return handle
 
     def detach_hook(self):
         """

tests/core/test_memory.py

@@ -88,6 +88,19 @@ def forward(self, x):
         return self.reduce(self.embed(x))
 
 
+class PartialScriptModel(LightningModule):
+    """ A model which contains scripted layers. """
+
+    def __init__(self):
+        super().__init__()
+        self.layer1 = torch.jit.script(nn.Linear(5, 3))
+        self.layer2 = nn.Linear(3, 2)
+        self.example_input_array = torch.rand(2, 5)
+
+    def forward(self, x):
+        return self.layer2(self.layer1(x))
+
+
 def test_invalid_weights_summmary():
     """ Test that invalid value for weights_summary raises an error. """
     with pytest.raises(MisconfigurationException, match='`mode` can be None, .* got temp'):
@@ -214,6 +227,15 @@ def test_summary_layer_types(mode):
     ]
 
 
+@pytest.mark.parametrize('mode', [ModelSummary.MODE_FULL, ModelSummary.MODE_TOP])
+def test_summary_with_scripted_modules(mode):
+    model = PartialScriptModel()
+    summary = model.summarize(mode=mode)
+    assert summary.layer_types == ["RecursiveScriptModule", "Linear"]
+    assert summary.in_sizes == [UNKNOWN_SIZE, [2, 3]]
+    assert summary.out_sizes == [UNKNOWN_SIZE, [2, 2]]
+
+
 @pytest.mark.parametrize('mode', [ModelSummary.MODE_FULL, ModelSummary.MODE_TOP])
 @pytest.mark.parametrize(['example_input', 'expected_size'], [
     pytest.param([], UNKNOWN_SIZE),
@@ -265,7 +287,7 @@ def test_empty_model_size(mode):
 
 
 @RunIf(min_gpus=1, amp_native=True)
-def test_model_size_precision(monkeypatch, tmpdir):
+def test_model_size_precision(tmpdir):
     """ Test model size for half and full precision. """
     model = PreCalculatedModel()