Arm backend: Fix quantized constant-folding for aten.cat lists (#18971)

FuseConstantArgsPass resolved input_qparams by flattened input-node
index, while FoldAndAnnotateQParamsPass stores them by top-level
argument index. For aten.cat with a list-valued tensor argument, this
caused only the first tensor to be dequantized before folding, which
corrupted the fused constant.

Resolve qparams by top-level argument index and propagate that qparam
through nested list and tuple arguments. Add a regression test for
quantized aten.cat constant folding with list-valued tensor inputs.

Signed-off-by: Per Held <per.held@arm.com>
Change-Id: I6e1a012d82a5dbeecb403c440a2944953dd5cba7

Author: perheld

backends/arm/_passes/fuse_constant_ops_pass.py

@@ -83,21 +83,24 @@ def _fuse_nodes(self, node) -> bool:
         input_nodes = list(node.all_input_nodes)
         qparams = node.meta.get("input_qparams", None)
 
-        def resolve_arg(arg):
+        def resolve_arg(arg, arg_index=None):
+            qparam = (
+                qparams.get(arg_index) if qparams and arg_index is not None else None
+            )
             if isinstance(arg, torch.fx.Node) and arg in input_nodes:
-                idx = input_nodes.index(arg)
                 t = get_param_tensor(self.exported_program, arg)
-                # Check if qparams exist for this arg
-                if qparams and idx in qparams.keys():
-                    t = qparams[idx].dequantize_value(t)
+                if qparam is not None:
+                    t = qparam.dequantize_value(t)
                 return t
             if isinstance(arg, tuple):
-                return tuple(resolve_arg(x) for x in arg)
+                return tuple(resolve_arg(x, arg_index) for x in arg)
             if isinstance(arg, list):
-                return [resolve_arg(x) for x in arg]
+                return [resolve_arg(x, arg_index) for x in arg]
             return arg
 
-        new_args = tuple(resolve_arg(a) for a in node.args)
+        new_args = tuple(
+            resolve_arg(arg, arg_index) for arg_index, arg in enumerate(node.args)
+        )
         new_kwargs = {k: resolve_arg(v) for k, v in node.kwargs.items()}
 
         data = node.target(*new_args, **new_kwargs)

backends/arm/test/passes/test_fuse_constant_ops_pass.py

@@ -11,8 +11,12 @@
     ComputeConstantOpsAOTPass,
     FuseConstantArgsPass,
 )
+from executorch.backends.arm._passes.quant_args import QuantArgs
 from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.arm_tester import ArmTester
 from executorch.backends.arm.test.tester.test_pipeline import PassPipeline
+from executorch.backends.arm.tosa import TosaSpecification
+from executorch.backends.test.harness.stages import StageType
 
 input_t = Tuple[torch.Tensor]  # Input x
 input_t2 = Tuple[torch.Tensor, torch.Tensor]
@@ -116,6 +120,52 @@ def forward(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
         return torch.cat((a, b), dim=0)
 
 
+class QuantizedCatConstantBuffers(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.register_buffer(
+            "horizontal_ramp",
+            torch.tensor(
+                [
+                    [
+                        [
+                            [-95, -32, 32, 95, 0],
+                            [-95, -32, 32, 95, 0],
+                            [-95, -32, 32, 95, 0],
+                            [-95, -32, 32, 95, 0],
+                        ]
+                    ]
+                ],
+                dtype=torch.int8,
+            ),
+        )
+        self.register_buffer(
+            "vertical_ramp",
+            torch.tensor(
+                [
+                    [
+                        [
+                            [-95, -95, -95, -95, -95],
+                            [-32, -32, -32, -32, -32],
+                            [32, 32, 32, 32, 32],
+                            [95, 95, 95, 95, 95],
+                        ]
+                    ]
+                ],
+                dtype=torch.int8,
+            ),
+        )
+
+    def forward(self) -> torch.Tensor:
+        return torch.cat(
+            (
+                cast(torch.Tensor, self.horizontal_ramp),
+                cast(torch.Tensor, self.vertical_ramp),
+            ),
+            dim=1,
+        )
+
+
 modules: Dict[str, ModuleWithFuseAttrs] = {
     "fuse_parameter": cast(ModuleWithFuseAttrs, FuseParameter()),
     "fuse_buffer": cast(ModuleWithFuseAttrs, FuseBuffer()),
@@ -174,3 +224,49 @@ def test_fuse_constant_args_tosa_INT_cat(module: ModuleWithFuseAttrs) -> None:
         ],
     )
     pipeline.run()
+
+
+def test_fuse_constant_args_tosa_INT_cat_uses_top_level_arg_qparams() -> None:
+    qargs = QuantArgs(
+        scale=1.0 / 127.0,
+        zp=0,
+        qmin=-127,
+        qmax=127,
+        dtype=torch.int8,
+    )
+    module = QuantizedCatConstantBuffers()
+    compile_spec = common.get_tosa_compile_spec(
+        TosaSpecification.create_from_string("TOSA-1.0+FP")
+    )
+    tester = ArmTester(module, example_inputs=(), compile_spec=compile_spec)
+    tester.export().to_edge()
+    exported_program = tester.get_artifact(StageType.TO_EDGE).exported_program()
+
+    cat_node = next(
+        node
+        for node in exported_program.graph_module.graph.nodes
+        if node.op == "call_function"
+    )
+    cat_node.meta["input_qparams"] = {0: qargs}
+    cat_node.meta["output_qparams"] = {0: qargs}
+
+    pass_result = FuseConstantArgsPass(exported_program).call(
+        exported_program.graph_module
+    )
+
+    assert list(exported_program.state_dict) == ["aten_cat_default_fused_const"]
+    torch.testing.assert_close(
+        exported_program.state_dict["aten_cat_default_fused_const"],
+        torch.cat(
+            (
+                cast(torch.Tensor, module.horizontal_ramp),
+                cast(torch.Tensor, module.vertical_ramp),
+            ),
+            dim=1,
+        ),
+    )
+    assert [
+        node.name
+        for node in pass_result.graph_module.graph.nodes
+        if node.op == "placeholder"
+    ] == ["aten_cat_default_fused_const"]