NXP backend: Add context dependent cat partitioning.

Author: MartinPavella

backends/nxp/backend/edge_helper.py

@@ -22,14 +22,6 @@
 ]
 
 
-def _is_dequantize(node_: Node) -> bool:
-    return node_.op == "call_function" and node_.target in DEQUANTIZE_OPERATORS
-
-
-def _is_quantize(node_: Node) -> bool:
-    return node_.op == "call_function" and node_.target in QUANTIZE_OPERATORS
-
-
 def input_tensor(node: Node, input_index: int) -> torch.Tensor:
     if len(node.all_input_nodes) <= input_index:
         raise IndexError
@@ -103,3 +95,33 @@ def try_get_tensor_constant_from_node(
             return None
         attr_itr = getattr(attr_itr, atom)
     return attr_itr
+
+
+def _is_dequantize(node_: Node) -> bool:
+    return node_.op == "call_function" and node_.target in [
+        exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+        exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    ]
+
+
+def _is_quantize(node_: Node) -> bool:
+    return node_.op == "call_function" and node_.target in [
+        exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+        exir_ops.edge.quantized_decomposed.quantize_per_channel.default,
+        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+        torch.ops.quantized_decomposed.quantize_per_channel.default,
+    ]
+
+
+def previous_non_qdq_node(node: Node, input_index: int = 0) -> Node | None:
+    """Return the first node which is not a `quantize` or `dequantize`, found by traversing the graph backwards
+    starting with the `node.args[input_index]`,
+    """
+    current_node = node.args[input_index]
+    while True:
+        if _is_quantize(current_node) or _is_dequantize(current_node):
+            current_node = current_node.args[0]
+        else:
+            return current_node

backends/nxp/backend/ir/converter/node_converters/ops_converters/cat_converter.py

@@ -8,6 +8,7 @@
 from executorch.backends.nxp.backend.custom_delegation_options import (
     CustomDelegationOptions,
 )
+from executorch.backends.nxp.backend.edge_helper import previous_non_qdq_node
 from executorch.backends.nxp.backend.ir.converter.conversion import translator
 from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
     apply_permutation_to,
@@ -24,6 +25,7 @@
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from executorch.backends.nxp.backend.node_format import NXP_NODE_FORMAT
 from torch.fx import Node
+from torch.fx.passes.infra.partitioner import Partition
 from torch.nn import Parameter
 
 
@@ -86,30 +88,6 @@ def _is_supported_on_target(
 
         dim = CatConverter._get_normalized_dim(node)
 
-        # There is a bug in the NeutronConverter, where if none of the dimensions before the one referenced by
-        #  `dim` are `!= 1`, the `Concat` is not delegated.
-        # This only happens when the inputs to the `Concat` are model inputs, and not outputs of other
-        #  operators. However, such a requirement cannot be currently enforced during partitioning, so we must
-        #  take the conservative approach and decline delegation.
-        input_shapes = [list(n.meta["val"].shape) for n in node.args[0]]
-        if node.meta[NXP_NODE_FORMAT].is_channels_first():
-            # Transform the shapes to channels last.
-            to_nhwc_perm = create_channels_first_to_channels_last_permutation(
-                len(node.meta["val"].shape), True
-            )
-            input_shapes = [
-                apply_permutation_to(shape, to_nhwc_perm)
-                for shape in input_shapes
-            ]
-
-            # Transform the `dim` to refer to a channels last dimension.
-            dim = to_nhwc_perm.index(dim)
-
-        for input_shape in input_shapes:
-            if not any(d != 1 for d in input_shape[:dim]):
-                # Do not delegate if there are no "non-1" dimensions in the shape before the `dim` dimension.
-                return False
-
         # Neutron requires the channels to be a multiple of `num_macs`. The channels could either be the second or the
         #  last dimension, depending on the formats of the node.
         if node.meta[NXP_NODE_FORMAT].is_channels_first():
@@ -172,6 +150,46 @@ def _is_supported_in_IR(
 
         return True
 
+    @classmethod
+    def supports_partitioning_result(
+        cls,
+        node: Node,
+        partition_list: list[Partition],
+        custom_delegation_options: CustomDelegationOptions,
+    ):
+        # There is a bug in the NeutronConverter, where if none of the input dimensions before the one referenced by
+        #  `dim` are `!= 1`, the `Concat` is not delegated.
+        # This only happens when the inputs to the `Concat` are model inputs, and not outputs of other
+        #  operators.
+        cat_partition = [p for p in partition_list if node in p.nodes][0]
+        cat_inputs = map(previous_non_qdq_node, node.args[0])
+
+        if not all(
+            input_.op == "call_function" and input_ in cat_partition.nodes
+            for input_ in cat_inputs
+        ):
+            # Some inputs of the `cat` are NOT in the same partition as `cat`.
+            dim = CatConverter._get_normalized_dim(node)
+            input_shapes = [list(n.meta["val"].shape) for n in node.args[0]]
+            if node.meta[NXP_NODE_FORMAT].is_channels_first():
+                # Transform the shapes to channels last.
+                to_nhwc_perm = create_channels_first_to_channels_last_permutation(
+                    len(node.meta["val"].shape), True
+                )
+                input_shapes = [
+                    apply_permutation_to(shape, to_nhwc_perm) for shape in input_shapes
+                ]
+
+                # Transform the `dim` to refer to a channels last dimension.
+                dim = to_nhwc_perm.index(dim)
+
+            for input_shape in input_shapes:
+                if not any(d != 1 for d in input_shape[:dim]):
+                    # Do not delegate if there are no "non-1" dimensions in the shape before the `dim` dimension.
+                    return False
+
+        return True
+
     def convert(self, node: Node):
         """Convert the 'aten.cat' operator to TFLite 'Concatenation'."""
         self.assert_convertible(node)

backends/nxp/tests/ir/converter/node_converter/test_cat_converter.py

@@ -44,6 +44,18 @@ def forward(self, *inputs: torch.Tensor):
         return torch.cat(list(inputs), self.dim)
 
 
+class AddCatModule(torch.nn.Module):
+
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, *inputs: torch.Tensor):
+        inputs = [input_ + input_ for input_ in inputs]
+
+        return torch.cat(list(inputs), self.dim)
+
+
 class CatConvModule(torch.nn.Module):
 
     def __init__(self, dim: int, channels: int = 4):
@@ -147,6 +159,9 @@ def test_cat__channels_first__same_shapes(dim, num_inputs, mocker):
     ],
 )
 def test_cat__unsupported__imxrt700(dim, input_shape):
+    """This test is conjoined with the one below (`test_cat__context_dependent__imxrt700`).
+    In this case, the inputs of the `cat` are NOT compute ops, so the `cat` is NOT delegated.
+    """
     num_inputs = 2
     quantized_program = to_quantized_edge_program(
         CatModule(dim), [input_shape] * num_inputs, target="imxrt700"
@@ -161,6 +176,32 @@ def test_cat__unsupported__imxrt700(dim, input_shape):
     )
 
 
+@pytest.mark.parametrize(
+    "dim, input_shape",
+    [
+        pytest.param(0, (1, 8, 8, 8), id="axis = 0"),
+        pytest.param(0, (8, 8, 8, 8), id="axis = 0, no `1s` in the shape."),
+        pytest.param(-4, (1, 8, 8, 8), id="axis = -4"),
+        pytest.param(1, (1, 1, 8, 8), id="axis = 1"),
+        pytest.param(-3, (1, 1, 8, 8), id="axis = -3"),
+        pytest.param(2, (1, 1, 1, 8), id="axis = 2"),
+        pytest.param(-2, (1, 1, 1, 8), id="axis = -2"),
+    ],
+)
+def test_cat__context_dependent__imxrt700(dim, input_shape):
+    """This test is conjoined with the one above (`test_cat__unsupported__imxrt700`).
+    In this case, the inputs of the `cat` are compute ops, so the `cat` is delegated.
+    """
+    num_inputs = 2
+    ep = to_quantized_edge_program(
+        AddCatModule(dim), [input_shape] * num_inputs, target="imxrt700"
+    ).exported_program()
+
+    # Make sure the `Cat` was delegated.
+    assert not graph_contains_any_of_ops(ep.graph, [exir_ops.edge.aten.cat.default])
+    assert any("lowered_module" in node.name for node in ep.graph.nodes)
+
+
 @pytest.mark.parametrize(
     "rank, num_inputs, dim",
     [