run_decomp2

Differential Revision: D88223584

Author: lucylq

examples/models/llama/export_llama_lib.py

@@ -879,16 +879,21 @@ def _to_edge_and_lower_llama_xnnpack(
         builder_exported.generate_etrecord = True
 
     builder = builder_exported.pt2e_quantize(quantizers)
+
+    # re-export required here?
+    # run decomps
+    builder = builder.run_decompositions()
+    # tag EP
     if gen_tag_fn is not None:
         from executorch.exir.passes.external_constants_pass import (
-            delegate_external_constants_pass_unlifted,
+            delegate_external_constants_pass_lifted,
         )
 
         assert (
             builder_exported.pre_autograd_graph_module is not None
         ), "pre_autograd_graph_module shouldn't be None here"
-        delegate_external_constants_pass_unlifted(
-            module=builder_exported.pre_autograd_graph_module,
+        delegate_external_constants_pass_lifted(
+            ep=builder_exported.exported_module,
             gen_tag_fn=gen_tag_fn,
         )
 

exir/passes/external_constants_pass.py

@@ -111,3 +111,23 @@ def delegate_external_constants_pass_unlifted(
                     node.meta["custom"]["delegate_constant_tag"] = gen_tag_fn(node)
                     mutated = True
     return PassResult(module, mutated)
+
+def delegate_external_constants_pass_lifted(
+      ep: ExportedProgram,
+      gen_tag_fn: Optional[Callable[[torch.fx.Node], Optional[str]]] = None,
+  ) -> PassResult:
+      """
+      Tag constants in an ExportedProgram for external storage.
+      Works on the lifted graph directly, no re-export needed.
+      """
+      mutated = False
+      gm = ep.graph_module
+
+      for node in gm.graph.nodes:
+          if node.op == "placeholder" and is_param_node(ep, node):
+              if gen_tag_fn is not None:
+                  node.meta.setdefault("custom", {})
+                  node.meta["custom"]["delegate_constant_tag"] = gen_tag_fn(node)
+                  mutated = True
+
+      return PassResult(gm, mutated)

extension/llm/export/builder.py

@@ -20,7 +20,7 @@
     DuplicateDynamicQuantChainPass,
 )
 from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
-from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
+from executorch.exir import EdgeProgramManager, to_edge, to_edge_transform_and_lower
 from executorch.exir.backend.partitioner import Partitioner
 
 from executorch.exir.backend.utils import format_delegated_graph
@@ -125,6 +125,7 @@ def __init__(
         # make sure to re-export this graph module to persist any changes. See
         # https://github.com/pytorch/pytorch/blob/main/torch/export/exported_program.py#L921
         self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
+        self.exported_module: Optional[torch.export.ExportedProgram] = None
         self.edge_manager: Optional[EdgeProgramManager] = None
         self.canonical_passes = [
             RemoveRedundantTransposes()
@@ -235,17 +236,23 @@ def export(self) -> "LLMEdgeManager":
         The full torch.export() if called later on during to_edge() or
         to_edge_transform_and_lower().
         """
-        exported_module = self._export()
+        self.exported_module = self._export()
         # Need to store the graph module to record transformation passes.
         # Persisting those changes back to an ExportedProgram will require
         # an additional export().
-        self.pre_autograd_graph_module = exported_module.module()
+        self.pre_autograd_graph_module = self.exported_module.module()
         if self.save_exported_program:
             export_output = f"{self.modelname}.pt2"
             logging.info(f"Saving torch.export() result to {export_output}")
             torch.export.save(exported_module, export_output)
         return self
 
+    def run_decompositions(self) -> "LLMEdgeManager":
+        # Re-export to capture any pending changes to pre_autograd_graph_module
+        self.exported_module = self._export(self.pre_autograd_graph_module)
+        self.exported_module = self.exported_module.run_decompositions({})
+        return self
+
     def run_canonical_optimizations(self):
         """
         Run canonical optimizations (at the moment removing redundant permutes) on the model.
@@ -256,6 +263,8 @@ def run_canonical_optimizations(self):
             res = pass_instance(self.pre_autograd_graph_module)
             assert res.graph_module is not None, "Pass returned None"
             self.pre_autograd_graph_module = res.graph_module
+        # Re-export to capture changes to pre_autograd_graph_module
+        self.exported_module = self._export(self.pre_autograd_graph_module)
 
     def pt2e_calibrate(
         self,
@@ -389,6 +398,8 @@ def pt2e_quantize(self, quantizers: Optional[List[Quantizer]]) -> "LLMEdgeManage
                 m = convert_pt2e(m)
                 DuplicateDynamicQuantChainPass()(m)
                 self.pre_autograd_graph_module = m
+            # Re-export to capture changes to pre_autograd_graph_module
+            self.exported_module = self._export(self.pre_autograd_graph_module)
             return self
         else:
             logging.info("No quantizer provided, passing...")
@@ -398,7 +409,6 @@ def export_to_edge(self) -> "LLMEdgeManager":
         """
         Export the model to Edge dialect and retrieve a LLMEdgeManager.
         """
-        dynamic_shape = self._get_dynamic_shape()
         edge_config = self._get_edge_config()
 
         # 1. torch.nn.attention.sdpa_kernel([SDPBackend.MATH]) is for bypassing the dynamo error when tracing
@@ -408,17 +418,11 @@ def export_to_edge(self) -> "LLMEdgeManager":
                 # Run export() if it didn't run
                 self.export()
 
-            override_export_behaviour = contextlib.nullcontext()
-            with override_export_behaviour:
-                self.edge_manager = export_to_edge(
-                    self.pre_autograd_graph_module,  # pyre-fixme[6]
-                    self.example_inputs,
-                    example_kwarg_inputs=self.example_kwarg_inputs,
-                    dynamic_shapes=dynamic_shape,
-                    edge_constant_methods=self.metadata,
-                    edge_compile_config=edge_config,
-                    verbose=self.verbose,
-                )
+            self.edge_manager = to_edge(
+                self.exported_module,
+                constant_methods=self.metadata,
+                compile_config=edge_config,
+            )
         return self
 
     def to_backend(self, partitioners: Optional[List[Partitioner]]) -> "LLMEdgeManager":
@@ -457,12 +461,9 @@ def to_edge_transform_and_lower(
         if partitioners is None:
             logging.info("No partitioner provided, skipping backend lowering...")
 
-        # Need to construct ExportedProgram with the new transformed graph module.
-        exported_module = self._export(self.pre_autograd_graph_module)
-
         edge_config = self._get_edge_config()
         self.edge_manager = to_edge_transform_and_lower(
-            exported_module,
+            self.exported_module,
             partitioner=partitioners,
             compile_config=edge_config,
             constant_methods=self.metadata,