[export] backed_size_oblivious tutorial

intermediate_source/torch_export_tutorial.py

@@ -489,6 +489,7 @@ def forward(self, w, x, y, z):
 # specify 0/1 sample inputs when you'd like your program to hardcode them, and non-0/1 sample inputs when dynamic behavior is desirable. See what happens
 # at runtime when we export this linear layer:
 
+torch._logging.set_logs(dynamic=0)
 ep = export(
     torch.nn.Linear(4, 3),
     (torch.randn(1, 4),),
@@ -591,6 +592,30 @@ def forward(self, x, y):
     "bool_val": None,
 }
 
+######################################################################
+# (experimental) Avoiding 0/1 specialization
+# ^^^^^^^^^^^^^^^^^^
+#
+# Export provides an experimental option to avoid specializing on size 0/1 sample inputs. Users can turn on `torch.fx.experimental._config.backed_size_oblivious = True` to enable this behavior.
+# This allows the compiler to allocate a [0, inf] range for symbols, and assume general-case semantics in compiler decisions between semantics for size 0/1 and >= 2 sizes.
+# This can lead to behavior divergence between eager mode and the exported program on size 0/1 inputs - for example, in broadcasting decisions, we will assume input shapes are not 1-specialized,
+# and therefore assume broadcasting does not apply (even if it does on the particular sample inputs). The same logic applies for other semantics (e.g. contiguity), and size 0 tensors.
+#
+# The exact semantics under this flag are a work in progress, and usage is recommended only when the user is certain their model does not rely on 0/1-specialized semantics.
+# For now, export users can enable this with:
+
+class Foo(torch.nn.Module):
+    def forward(self, x, y):
+        return x + y  # nothing special about size 0/1 here
+
+x = torch.randn(0, 1)
+y = torch.randn(1)
+dynamic_shapes = {"x": (Dim.AUTO, Dim.AUTO), "y": (Dim.AUTO,)}
+with torch.fx.experimental._config.patch(backed_size_oblivious=True):
+    ep = export(Foo(), (x, y), dynamic_shapes=dynamic_shapes)
+ep.module()(torch.randn(8, 1), torch.randn(1))
+ep.module()(torch.randn(5, 6), torch.randn(6))
+
 ######################################################################
 # Data-dependent errors
 # ---------------------