Update

[ghstack-poisoned]

Author: swolchok

backends/cadence/aot/remove_ops.py

@@ -807,6 +807,72 @@ def remove_branched(
                 user.replace_all_uses_with(node.args[0])
 
 
+class RemoveCatFromSliceCopyPass(ExportPass):
+    def _remove_unused_cat(self, graph_module: torch.fx.GraphModule) -> None:
+        slice_copy_nodes = [
+            node
+            for node in graph_module.graph.nodes
+            if node.target == exir_ops.edge.aten.slice_copy.Tensor
+        ]
+        for slice_copy_node in slice_copy_nodes:
+            slice_dim, start_idx, end_idx, step = 0, 0, float("inf"), 1
+            input_node, *other_args = slice_copy_node.args
+            if len(other_args) >= 1:
+                slice_dim = other_args[0]
+            if len(other_args) >= 2:
+                start_idx = other_args[1]
+            if len(other_args) >= 3:
+                end_idx = other_args[2]
+            if len(other_args) >= 4:
+                step = other_args[3]
+            if step != 1:
+                continue
+            slice_copy_dtype = slice_copy_node.meta["val"].dtype
+            if input_node.target != exir_ops.edge.aten.cat.default:
+                continue
+            cat_dtype = input_node.meta["val"].dtype
+            if slice_copy_dtype != cat_dtype:
+                continue
+            cat_dim = input_node.args[1:]
+            if len(cat_dim) == 0:
+                cat_dim = 0
+            if cat_dim != slice_dim:
+                continue
+            cat_output_shape = input_node.meta["val"].shape
+            start_idx = (
+                cat_output_shape[cat_dim] + start_idx if start_idx < 0 else start_idx
+            )
+            end_idx = (
+                cat_output_shape[cat_dim]
+                if end_idx > cat_output_shape[cat_dim]
+                else end_idx
+            )
+            base_idx = 0
+            cat_input_to_keep = None
+            for cat_input_node in input_node.args[0]:
+                cat_input_dtype = cat_input_node.meta["val"].dtype
+                if slice_copy_dtype != cat_input_dtype:
+                    continue
+                cat_input_shape = cat_input_node.meta["val"].shape
+
+                # check if the slice range overlaps with the cat range
+                if (
+                    base_idx <= start_idx
+                    and end_idx <= list(cat_input_shape)[cat_dim] + base_idx
+                ):
+                    cat_input_to_keep = cat_input_node
+                    break
+                base_idx += list(cat_input_shape)[cat_dim]
+            if cat_input_to_keep is not None:
+                slice_copy_node.replace_input_with(input_node, cat_input_to_keep)
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        self._remove_unused_cat(graph_module)
+        graph_module.recompile()
+        graph_module.graph.eliminate_dead_code()
+        return super().call(graph_module)
+
+
 # The following class consolidates functions to remove ops that are redundant
 # in Jarvis. Currently, each function in this class iterates over each node of
 # the graph module once. In future, we could consolidate them into a monolithic

backends/cadence/aot/tests/test_remove_ops_passes.py

@@ -22,6 +22,7 @@
 from executorch.backends.cadence.aot.remove_ops import (
     RemoveAliasCopyOpPass,
     RemoveBranchedQuantDequant,
+    RemoveCatFromSliceCopyPass,
     RemoveCloneOpPass,
     RemoveContiguousOpPass,
     RemoveDetachCopyPass,
@@ -741,3 +742,54 @@ def forward(self, x):
                 },
             )
         )
+
+    def test_remove_cat_from_slice_copy_all_removal(self) -> None:
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                x1 = torch.cat((x, y), 0)  # (2, 4)
+                return torch.slice_copy(x1, dim=0, start=0, end=1)
+
+        inputs = tuple(torch.randn(2, 4) for _ in range(2))
+        graph_module = export_to_edge(M(), inputs).exported_program().graph_module
+        p = RemoveCatFromSliceCopyPass()
+        graph_module = cast(PassResult, p(graph_module)).graph_module
+
+        # Ensure both cat nodes were removed
+        self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 0)
+
+    def test_remove_cat_from_slice_copy_no_removal(self) -> None:
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                x1 = torch.cat((x, y), 0)  # (2, 4)
+                return torch.slice_copy(x1, dim=0, start=0, end=3)
+
+        inputs = tuple(torch.randn(2, 4) for _ in range(2))
+        graph_module = export_to_edge(M(), inputs).exported_program().graph_module
+        p = RemoveCatFromSliceCopyPass()
+        graph_module = cast(PassResult, p(graph_module)).graph_module
+
+        # Ensure both cat nodes were removed
+        self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 1)
+
+    def test_remove_cat_from_slice_copy_zero_range(self) -> None:
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                x1 = torch.cat((x, y), 0)  # (2, 4)
+                return torch.slice_copy(x1, dim=0, start=0, end=0)
+
+        inputs = tuple(torch.randn(2, 4) for _ in range(2))
+        graph_module = export_to_edge(M(), inputs).exported_program().graph_module
+        p = RemoveCatFromSliceCopyPass()
+        graph_module = cast(PassResult, p(graph_module)).graph_module
+
+        # Ensure both cat nodes were removed
+        self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 0)

runtime/kernel/operator_registry.h

@@ -26,17 +26,17 @@
 
 #define ET_LOG_KERNEL_KEY(k)      \
   ET_LOG(                         \
-      Error,                      \
+      Info,                       \
       "key: %s, is_fallback: %s", \
       k.data(),                   \
       k.is_fallback() ? "true" : "false");
-#define ET_LOG_TENSOR_META(meta_list)                                 \
-  for (const auto& meta : meta_list) {                                \
-    ET_LOG(Error, "dtype: %d | dim order: [", int(meta.dtype_));      \
-    for (size_t i = 0; i < meta.dim_order_.size(); i++) {             \
-      ET_LOG(Error, "%d,", static_cast<int32_t>(meta.dim_order_[i])); \
-    }                                                                 \
-    ET_LOG(Error, "]");                                               \
+#define ET_LOG_TENSOR_META(meta_list)                                \
+  for (const auto& meta : meta_list) {                               \
+    ET_LOG(Info, "dtype: %d | dim order: [", int(meta.dtype_));      \
+    for (size_t i = 0; i < meta.dim_order_.size(); i++) {            \
+      ET_LOG(Info, "%d,", static_cast<int32_t>(meta.dim_order_[i])); \
+    }                                                                \
+    ET_LOG(Info, "]");                                               \
   }
 
 namespace executorch {

setup.py

@@ -463,7 +463,8 @@ def build_extension(self, ext: _BaseExtension) -> None:
         dst_file: Path = ext.dst_path(self)
 
         # Ensure that the destination directory exists.
-        self.mkpath(os.fspath(dst_file.parent))
+        if not dst_file.parent.exists():
+            self.mkpath(os.fspath(dst_file.parent))
 
         # Copy the file.
         self.copy_file(os.fspath(src_file), os.fspath(dst_file))