Rename SymShapeEval pass to HintBasedSymShapeEval pass and add warning for it.

Summary: As titled. The original SymShapeEvalPass doesn't respect user constraints and is based on hints of the symbolics. This diff changes its name to HintBasedSymShapeEval pass to make it clearer.

Differential Revision: D49335965

Author: ydwu4

exir/passes/__init__.py

@@ -51,7 +51,7 @@
 from executorch.exir.passes.replace_sym_size_op_pass import ReplaceSymSizeOpPass
 from executorch.exir.passes.scalar_to_tensor_pass import ScalarToTensorPass
 from executorch.exir.passes.spec_prop_pass import SpecPropPass
-from executorch.exir.passes.sym_shape_eval_pass import SymShapeEvalPass
+from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass
 from executorch.exir.passes.sym_to_tensor_pass import SymToTensorPass
 from torch import fx
 from torch._subclasses import FakeTensor
@@ -65,7 +65,7 @@
     "OpReplacePass",
     "EdgeToBackendOpsPass",
     "MemoryFormatOpsPass",
-    "SymShapeEvalPass",
+    "HintBasedSymShapeEvalPass",
 ]
 
 Argument = Optional[
@@ -510,5 +510,5 @@ def propagate_dynamic_shape(
     """
     return [
         SpecPropPass(),
-        SymShapeEvalPass(),
+        HintBasedSymShapeEvalPass(),
     ]

exir/passes/sym_shape_eval_pass.py

@@ -32,12 +32,23 @@ def nonzero(args, kwargs) -> List[Optional[int]]:
     return [eval_expr(args[0].shape[0]), len(args[0].shape)]
 
 
-class SymShapeEvalPass(PassBase):
+class HintBasedSymShapeEvalPass(PassBase):
     """
     If we enable dynamic shape tracing, a tensor's shape may become a symbolic
     formula. We should convert those symbolic formula to concrete value for
     static/upperbound tensors so we can properly do memory planning for them.
 
+    HintBasedSymShapeEvalPass evalutes the symbolic expression of shapes based
+    on its hint, which is a concrete integer that backs the sym expression. The original
+    hint comes from the sizes of the inputs that user uses for tracing and hints of
+    symbolic expressions are propagated via meta tensor computation.
+    For example, when export f(x), we use x = torch.ones(3, 4) as an exmaple input to f and
+    suppose we constrain both dimensions of x as dynamic. We'll have two symbols s0, s1 created
+    and they are backed up with hints 3 and 4 respectively. If there is a y = x[0] operation in f,
+    the shape of y is inferred to be s1, which is backed up with hint 4.
+
+    Warning: if you're using torch.export with constrain API, this method doesn't respect the input constraints.
+
     Not inherit from ExportPass since we simply need a way to iterate thru
     every node's output. PassBase is easier for that purpose.
     """

exir/program/_program.py

@@ -20,7 +20,7 @@
     aten_to_edge_passes,
     EdgeToBackendOpsPass,
     OpReplacePass,
-    SymShapeEvalPass,
+    HintBasedSymShapeEvalPass,
 )
 from executorch.exir.passes.remove_assert_async_pass import RemoveAssertAsyncPass
 from executorch.exir.passes.spec_prop_pass import SpecPropPass
@@ -309,7 +309,7 @@ def edge_to_executorch_passes(config: ExecutorchBackendConfig) -> List[PassType]
         SpecPropPass(),
         EdgeToBackendOpsPass(),
         RemoveAssertAsyncPass(),
-        SymShapeEvalPass(),
+        HintBasedSymShapeEvalPass(),
         config.to_out_var_pass,
         config.memory_planning_pass,
     ]

exir/tests/test_dynamic_shape_propagation.py

@@ -7,7 +7,7 @@
 from unittest import TestCase
 
 from executorch import exir
-from executorch.exir.passes import DebugPass, SpecPropPass, SymShapeEvalPass
+from executorch.exir.passes import DebugPass, SpecPropPass, HintBasedSymShapeEvalPass
 from executorch.exir.tests.models import Repeat
 
 
@@ -23,7 +23,7 @@ def test_repeat(self):
             exir.CaptureConfig(enable_dynamic_shape=True),
         ).to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))
 
-        new_prog = prog.transform(SpecPropPass(), SymShapeEvalPass())
+        new_prog = prog.transform(SpecPropPass(), HintBasedSymShapeEvalPass())
 
         gm = new_prog.exported_program.graph_module
 

exir/tests/test_passes.py

@@ -28,7 +28,7 @@
     propagate_dynamic_shape,
     RemoveNoopPass,
     ReplaceSymSizeOpPass,
-    SymShapeEvalPass,
+    HintBasedSymShapeEvalPass,
     ToOutVarPass,
 )
 from executorch.exir.passes.const_prop_pass import ConstPropPass
@@ -586,7 +586,7 @@ def test_alloc_node_spec(self) -> None:
         ).to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))
         passes = [
             SpecPropPass(),
-            SymShapeEvalPass(),
+            HintBasedSymShapeEvalPass(),
             ToOutVarPass(),
             MemoryPlanningPass("greedy"),
         ]