Swap to better default symshapeevalue pass

examples/models/llava/export_llava.py

@@ -33,7 +33,10 @@
 
 from executorch.exir.passes import MemoryPlanningPass
 from executorch.exir.passes.quant_fusion_pass import QuantFusionPass
-from executorch.exir.passes.sym_shape_eval_pass import ConstraintBasedSymShapeEvalPass
+from executorch.exir.passes.sym_shape_eval_pass import (
+    ConstraintBasedSymShapeEvalPass,
+    HintBasedSymShapeEvalPass,
+)
 
 from executorch.extension.llm.export.builder import DType, LLMEdgeManager
 from executorch.extension.llm.tokenizer.tokenizer import Tokenizer
@@ -227,6 +230,8 @@ def export_all(llava_model: LlavaModel):
             memory_planning_pass=MemoryPlanningPass("greedy", alloc_graph_input=False),
             sym_shape_eval_pass={
                 "image_encoder": ConstraintBasedSymShapeEvalPass(),
+                "text_model": ConstraintBasedSymShapeEvalPass(),
+                "token_embedding": HintBasedSymShapeEvalPass(),
             },
         )
     )

exir/capture/_config.py

@@ -12,7 +12,7 @@
 from executorch.exir.dynamic_shape import DynamicMemoryPlanningMode
 from executorch.exir.pass_manager import PassType
 from executorch.exir.passes import MemoryPlanningPass, ToOutVarPass
-from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass
+from executorch.exir.passes.sym_shape_eval_pass import ConstraintBasedSymShapeEvalPass
 from executorch.exir.tracer import ExirDynamoConfig
 from torch.fx._compatibility import compatibility
 
@@ -86,7 +86,7 @@ class ExecutorchBackendConfig:
     # A single sym shape eval pass can be defined for all the programs in the
     # EdgeProgramManager or can be defined per program.
     sym_shape_eval_pass: Union[PassType, Dict[str, PassType]] = (
-        HintBasedSymShapeEvalPass()
+        ConstraintBasedSymShapeEvalPass()
     )
 
     # If set to true, view_copy operations will be converted to lightweight

exir/passes/TARGETS

@@ -202,6 +202,7 @@ python_library(
     ],
     deps = [
         "//caffe2:torch",
+        "//executorch/exir:_warnings",
         "//executorch/exir:pass_base",
         "//executorch/exir:sym_util",
         "//executorch/exir:tensor",

exir/passes/sym_shape_eval_pass.py

@@ -10,6 +10,8 @@
 
 import torch
 import torch.utils._pytree as pytree
+
+from executorch.exir._warnings import deprecated
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import PassBase, PassResult
 from executorch.exir.sym_util import eval_expr, eval_shape, eval_upper_bound
@@ -164,8 +166,21 @@ def index_Tensor(args, kwargs) -> List[Optional[int]]:  # noqa: C901
     return out_sizes
 
 
+@deprecated(
+    "`HintBasedSymShapeEvalPass` is deprecated "
+    "and will be removed in a future version of ExecuTorch. "
+    "Please use `ConstraintBasedSymShapeEvalPass` instead.",
+    category=FutureWarning,
+)
 class HintBasedSymShapeEvalPass(PassBase):
     """
+
+    .. warning::
+
+        'HintBasedSymShapeEvalPass` is deprecated
+        and will be removed in a future version of ExecuTorch.
+        Please use `ConstraintBasedSymShapeEvalPass` instead.
+
     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.