Fix shape_env handling in SpecPropPass (WIP) (pytorch#15485)

Summary: Pull Request resolved: pytorch#15485

Differential Revision: D85913581

Author: GregoryComer

exir/passes/spec_prop_pass.py

@@ -6,14 +6,16 @@
 
 # pyre-strict
 
-from typing import List, Optional
+import operator
+from typing import Optional
 
 import torch
 from executorch.exir.delegate import executorch_call_delegate
-from executorch.exir.pass_base import ExportPass, NodeMetadata, ProxyValue
+from executorch.exir.pass_base import ExportPass, ProxyValue
 from executorch.exir.tensor import TensorSpec
 from torch.export.exported_program import ExportGraphSignature
 from torch.fx.node import Node
+from torch.fx.passes.infra.pass_base import PassResult
 from torch.utils import _pytree as pytree
 
 
@@ -52,6 +54,42 @@ class SpecPropPass(ExportPass):
     def __init__(self) -> None:
         super().__init__()
 
+    def __call__(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        # Re-trace metadata to ensure it's up to date.
+        res = ExportPass()(graph_module)
+        assert res is not None
+        gm = res.graph_module
+
+        def get_spec(x):
+            if hasattr(x, "meta"):
+                return x.meta.get("spec", None)
+            else:
+                return None
+
+        for module in gm.modules():
+            if isinstance(module, torch.fx.GraphModule):
+                for node in module.graph.nodes:
+                    meta_val = node.meta.get("val", None)
+
+                    if node.op == "output":
+                        node.meta["spec"] = pytree.tree_map(get_spec, node.args[0])
+                    elif node.op == "call_function" and node.target == operator.getitem:
+                        value_spec = pytree.tree_map(get_spec, node.args[0])
+                        node.meta["spec"] = value_spec[node.args[1]]
+                    elif (
+                        node.op == "call_function"
+                        and node.target == executorch_call_delegate
+                    ):
+                        if "spec" not in node.meta:
+                            node.meta["spec"] = pytree.tree_map(make_spec, meta_val)
+                    else:
+                        if meta_val is not None:
+                            node.meta["spec"] = pytree.tree_map(make_spec, meta_val)
+        return res
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        return self(graph_module)
+
     def on_attr(self, attr: ProxyValue) -> None:
         attr.node.meta["spec"] = pytree.tree_map_only(
             torch.Tensor,
@@ -84,85 +122,3 @@ def update_placeholder_tensor_specs(
                 in exported_program.graph_signature.inputs_to_lifted_tensor_constants
             ):
                 spec.const = True
-
-    # pyre-ignore
-    def placeholder(self, name: str, arg, meta):
-        meta["spec"] = make_spec(arg)
-        return super().placeholder(name, arg, meta)
-
-    # pyre-ignore
-    def call_operator(self, op, args, kwargs, meta):
-        args_data, kwargs_data = pytree.tree_map_only(
-            ProxyValue, lambda x: x.data, (args, kwargs)
-        )
-        meta["spec"] = pytree.tree_map(make_spec, op(*args_data, **kwargs_data))
-        return super().call_operator(op, args, kwargs, meta)
-
-    # pyre-ignore
-    def call_getitem(self, value, key: int, meta):
-        meta["spec"] = value.node.meta["spec"][key]
-        return super().call_getitem(value, key, meta)
-
-    # pyre-ignore
-    def call_cond(self, pred, true_fn, false_fn, inputs, meta):
-        # true_fn/false_fn return tensors of the same shape, so we can pick
-        # either one here.
-        *_, true_out_node = true_fn.graph.nodes
-        meta["spec"] = pytree.tree_map(make_spec, true_out_node.meta["val"])
-        return super().call_cond(pred, true_fn, false_fn, inputs, meta)
-
-    def call_while(
-        self,
-        cond_fn: torch.fx.GraphModule,
-        body_fn: torch.fx.GraphModule,
-        carried_inputs: List[ProxyValue],
-        additional_inputs: List[ProxyValue],
-        meta: NodeMetadata,
-    ):
-        meta["spec"] = pytree.tree_map(make_spec, carried_inputs)
-        return super().call_while(
-            cond_fn, body_fn, carried_inputs, additional_inputs, meta
-        )
-
-    def call_map(
-        self,
-        f: torch.fx.GraphModule,
-        mapped_args: List[ProxyValue],
-        operands: List[ProxyValue],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        mapped_dim_size = [arg.data for arg in mapped_args][0].size(0)
-        *_, body_out_node = f.graph.nodes
-        body_out_node_fake_tensor = body_out_node.meta["val"]
-        map_fake_tensor = pytree.tree_map_only(
-            torch.Tensor,
-            lambda x: x.new_empty(mapped_dim_size, *x.shape),
-            body_out_node_fake_tensor,
-        )
-        meta["spec"] = pytree.tree_map(make_spec, map_fake_tensor)
-        return super().call_map(f, mapped_args, operands, meta)
-
-    # pyre-ignore
-    def call_delegate(self, lowered_module, args, kwargs, meta):
-        args_data, kwargs_data = pytree.tree_map_only(
-            ProxyValue, lambda x: x.data, (args, kwargs)
-        )
-        # If spec is missing, re-genenrate it with args data
-        if "spec" not in meta:
-            meta["spec"] = pytree.tree_map(
-                make_spec,
-                executorch_call_delegate(lowered_module, *args_data),
-            )
-        return super().call_delegate(lowered_module, args, kwargs, meta)
-
-    # pyre-ignore
-    def output(self, results, meta):
-        # pyre-ignore
-        def get_spec(x):
-            if isinstance(x, ProxyValue):
-                return x.node.meta["spec"]
-            else:
-                return make_spec(x)
-
-        meta["spec"] = pytree.tree_map(get_spec, results)
-        return super().output(results, meta)

exir/tests/test_passes.py

@@ -74,6 +74,7 @@
 from executorch.exir.passes.sym_to_tensor_pass import SymToTensorPass
 from executorch.exir.program._program import lift_constant_tensor_pass
 from executorch.exir.schema import TensorShapeDynamism
+from executorch.exir.sym_util import eval_upper_bound
 from executorch.exir.tensor import TensorSpec
 from executorch.exir.tests.common import register_additional_test_aten_ops
 from executorch.exir.tests.control_flow_models import FTCondDeadCode, FTMapBasic
@@ -113,6 +114,7 @@ def collect_ops(gm: torch.fx.GraphModule):
 
 lib.define("foo(Tensor self) -> (Tensor, Tensor)")
 lib.define("add_relu(Tensor self, Tensor other) -> Tensor")
+lib.define("unbacked(Tensor self) -> Tensor")
 
 
 @impl(lib, "foo", "CompositeExplicitAutograd")
@@ -132,6 +134,29 @@ def foo_out(
     return a + 1, None
 
 
+@impl(lib, "unbacked", "CPU")
+def unbacked(a: torch.Tensor) -> torch.Tensor:
+    return a[: a[0]]
+
+
+@torch.library.register_fake(f"{lib.ns}::unbacked")
+def meta_unbacked(x):
+    ctx = torch._custom_ops.get_ctx()
+    out_size = ctx.create_unbacked_symint()
+    return x.new_empty(out_size)
+
+
+lib.define("unbacked.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+
+
+@impl(lib, "unbacked.out", "CPU")
+def unbacked_out(
+    x: torch.Tensor,
+    out: torch.Tensor,
+) -> torch.Tensor:
+    out.copy_(x[x[0]])
+
+
 def simple_promote_dtype(
     dtype: torch.dtype, promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND
 ) -> torch.dtype:
@@ -611,6 +636,37 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
 
         self.assertEqual(counter, 1)
 
+    def test_spec_prop_pass_unbacked_symint(self) -> None:
+        # Verify that the spec prop pass picks up on guards for
+        # unbacked symints.
+        class Unbacked(torch.nn.Module):
+            def forward(self, x):
+                output = torch.ops.DO_NOT_USE_TEST_ONLY.unbacked(x)
+                torch._constrain_as_size(output.shape[0], max=10)
+                return output
+
+        model = Unbacked()
+        gm = (
+            to_edge(export(model, (torch.LongTensor([5, 4, 3, 2, 1, 0, 1, 2]),)))
+            .exported_program()
+            .graph_module
+        )
+        new_gm = SpecPropPass()(gm)
+        self.assertIsNotNone(new_gm)
+
+        # Check the spec for the custom op node. It should have a max size of 10.
+        op_node = next(
+            n
+            for n in new_gm.graph_module.graph.nodes
+            if n.target == exir_ops.edge.DO_NOT_USE_TEST_ONLY.unbacked.default
+        )
+        self.assertIsNotNone(op_node)
+
+        spec: TensorSpec = op_node.meta["spec"]
+        self.assertEqual(len(spec.shape), 1)  # Should be rank 1
+        upper_bound = eval_upper_bound(spec.shape[0])
+        self.assertEqual(upper_bound, 10)  # Should be a concrete value
+
     def test_compile_fix_broken_ops(self) -> None:
         class ExportableLoop(nn.Module):
             def __init__(self, hidden_size, out_channels):