Merge pull request #22 from Vivswan/next

v1.0.2: Bugfix: removed  `graph` from `Layer` class

CHANGELOG.md

@@ -1,7 +1,15 @@
 # Changelog
 
 ## 1.0.0
+
 * Public release
 
 ## 1.0.1 (Patches for Pytorch 2.0.0)
+
 * added `grad.setter` to `PseudoParameterModule` class
+
+## 1.0.2
+
+* Bugfix: removed  `graph` from `Layer` class
+    * `graph` was causing issues with nested `Model` objects
+    * Now `_use_autograd_graph` is directly set while compiling the `Model` object

analogvnn/nn/module/Layer.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import functools
-from typing import Union, Type, Callable, Sequence, Optional, TYPE_CHECKING, Set, Iterator, Tuple
+from typing import Union, Type, Callable, Sequence, Optional, Set, Iterator, Tuple
 
 from torch import nn, Tensor
 
@@ -10,9 +10,6 @@
 from analogvnn.graph.ArgsKwargs import ArgsKwargs, ArgsKwargsOutput
 from analogvnn.utils.common_types import TENSORS
 
-if TYPE_CHECKING:
-    from analogvnn.graph.ModelGraph import ModelGraph
-
 __all__ = ['Layer']
 
 
@@ -63,7 +60,6 @@ class Layer(nn.Module):
         _outputs (Union[None, Tensor, Sequence[Tensor]]): Outputs of the layer.
         _backward_module (Optional[BackwardModule]): Backward module of the layer.
         _use_autograd_graph (bool): If True, the autograd graph is used to calculate the gradients.
-        graphs (Optional[ModelGraph]): Contains Forward and Backward Graphs of the layer.
         call_super_init (bool): If True, the super class __init__ of nn.Module is called
         https://github.com/pytorch/pytorch/pull/91819
     """
@@ -72,7 +68,6 @@ class Layer(nn.Module):
     _outputs: Union[None, Tensor, Sequence[Tensor]]
     _backward_module: Optional[BackwardModule]
     _use_autograd_graph: bool
-    graphs: Optional[ModelGraph]
 
     # https://github.com/pytorch/pytorch/pull/91819
     call_super_init: bool = True
@@ -84,7 +79,6 @@ def __init__(self):
         self._outputs = None
         self._backward_module = None
         self._use_autograd_graph = False
-        self.graphs = None
 
     def __call__(self, *inputs, **kwargs):
         """Calls the forward pass of neural network layer.
@@ -110,8 +104,6 @@ def use_autograd_graph(self) -> bool:
             bool: use_autograd_graph.
         """
 
-        if self.graphs is not None:
-            return self.graphs.use_autograd_graph
         return self._use_autograd_graph
 
     @use_autograd_graph.setter
@@ -123,8 +115,6 @@ def use_autograd_graph(self, use_autograd_graph: bool):
         """
 
         self._use_autograd_graph = use_autograd_graph
-        if self.graphs is not None:
-            self.graphs.use_autograd_graph = use_autograd_graph
 
     @property
     def inputs(self) -> ArgsKwargsOutput:

analogvnn/nn/module/Model.py

@@ -158,7 +158,7 @@ def compile(self, device: Optional[torch.device] = None, layer_data: bool = True
         self.graphs.compile()
         for i in self.modules():
             if isinstance(i, Layer) and i != self:
-                i.graphs = self.graphs
+                i.use_autograd_graph = self.use_autograd_graph
 
         self.to(device=self.device)
 

pyproject.toml

@@ -14,7 +14,7 @@ py-modules = ['analogvnn']
 [project]
 # $ pip install analogvnn
 name = "analogvnn"
-version = "1.0.1"
+version = "1.0.2"
 description = "A fully modular framework for modeling and optimizing analog/photonic neural networks"  # Optional
 readme = "README.md"
 requires-python = ">=3.7"