Keep the shape operand of view_as and expand_as out of shared qparams (#24153)

backends/arm/quantizer/arm_quantizer_utils.py

@@ -411,6 +411,20 @@ def validate(self, model: torch.fx.GraphModule) -> bool:  # type: ignore[overrid
         return True
 
 
+# Ops whose second operand contributes only its shape, never its values, so it
+# must not be tied into the shared qparams of the first.
+_SHAPE_AS_OPS = {
+    torch.ops.aten.expand_as.default,
+    torch.ops.aten.view_as.default,
+}
+
+
+# Whether ``node`` shares qparams with its input ``input_node``: always for an
+# ordinary shared op, but for a shape-as op only through its data operand.
+def _shares_qparams_with(node: Node, input_node: Node) -> bool:
+    return node.target not in _SHAPE_AS_OPS or input_node is node.args[0]
+
+
 class SharedQspecQuantizer(Quantizer, QuantizerReporterUser):
     """Assures that specific ops share quantization parameters on all
     inputs/outputs.
@@ -442,6 +456,7 @@ class SharedQspecQuantizer(Quantizer, QuantizerReporterUser):
         torch.ops.aten.repeat_interleave.self_int,
         torch.ops.aten.expand_copy.default,
         torch.ops.aten.expand.default,
+        torch.ops.aten.expand_as.default,
         torch.ops.aten.select.int,
         torch.ops.aten.select_copy.int,
         torch.ops.aten.slice.Tensor,
@@ -543,7 +558,11 @@ def _is_annotated(self, node: Node) -> bool:
         return Q_ANNOTATION_KEY in node.meta
 
     def _get_input_nodes_with_float_output(self, node: Node) -> list[Node]:
-        return [n for n in node.all_input_nodes if has_float_output(n)]
+        return [
+            n
+            for n in node.all_input_nodes
+            if has_float_output(n) and _shares_qparams_with(node, n)
+        ]
 
     def _get_user_nodes_with_float_input(self, node: Node) -> list[Node]:
         return [n for n in node.users.keys() if has_float_output(node)]
@@ -640,6 +659,8 @@ def _get_shared_clique(
             shared_nodes.add(node)
 
             for input_node in node.all_input_nodes:
+                if not _shares_qparams_with(node, input_node):
+                    continue
                 self._maybe_enqueue_shared_node(input_node, shared_nodes, bfs_queue)
                 self._append_output_qspec(input_node, adjacent_qspecs)
                 touches_quantized_io |= self._is_quantized_io_boundary(input_node)
@@ -648,6 +669,8 @@ def _get_shared_clique(
                 )
 
             for output_node in node.users.keys():
+                if not _shares_qparams_with(output_node, node):
+                    continue
                 self._maybe_enqueue_shared_node(output_node, shared_nodes, bfs_queue)
                 self._append_input_qspec(output_node, node, adjacent_qspecs)
                 touches_quantized_io |= self._is_quantized_io_boundary(output_node)

backends/arm/test/misc/test_shared_qspecs.py

@@ -16,6 +16,8 @@
 from executorch.backends.arm.test.tester.test_pipeline import QuantizationPipeline
 from executorch.backends.arm.tosa import TosaSpecification
 from executorch.backends.cortex_m.test.tester import McuTestCase, ramp_tensor
+from torch.export import export
+from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
 
 
 _QUANT_CONFIG_INT8 = get_symmetric_quantization_config()
@@ -734,3 +736,36 @@ def test_silu_does_not_share_input_qspec():
     )
     pipeline.run()
     _check_quant_params(pipeline, model.quant_params)
+
+
+class ShapeAsOperand(torch.nn.Module):
+    """``op(x + x, other)`` reads only the shape of ``other``, so its qparams
+    must stay ``other``'s own rather than join those of ``x + x``.
+    """
+
+    def __init__(self, op: str) -> None:
+        super().__init__()
+        self.op = op
+
+    def forward(self, x, y):
+        other = y + y
+        return getattr(x + x, self.op)(other) + other
+
+
+@parametrize(
+    "test_data",
+    {"view_as": ("view_as", (2, 16)), "expand_as": ("expand_as", (1, 8))},
+)
+def test_shape_operand_does_not_share_qspec(test_data):
+    op, x_shape = test_data
+    inputs = (ramp_tensor(-1, 1, x_shape), ramp_tensor(-50, 50, (4, 8)))
+    model = ShapeAsOperand(op).eval()
+    prepared = prepare_pt2e(export(model, inputs).module(), _get_quantizer())
+    prepared(*inputs)
+    quantized = convert_pt2e(prepared)
+
+    (node,) = quantized.graph.find_nodes(
+        op="call_function", target=getattr(torch.ops.aten, op).default
+    )
+    data_dequantize, other_dequantize = node.args
+    assert data_dequantize.args[1] != other_dequantize.args[1]

backends/arm/test/models/test_swin2sr_arm.py

@@ -29,7 +29,7 @@
 ]
 
 # TODO/MLETORCH-2163: Investigate Swin2SR delegation gaps around index/view
-# in FP and Q/DQ, clamp, and expand_copy in INT.
+# in FP and Q/DQ and clamp in INT.
 swin2sr_fp_lowered_outer_graph_ops = {
     "torch.ops.higher_order.executorch_call_delegate": 2,
     "executorch_exir_dialects_edge__ops_aten_index_Tensor": 2,
@@ -38,7 +38,6 @@
 swin2sr_int_lowered_outer_graph_ops = {
     "torch.ops.higher_order.executorch_call_delegate": 3,
     "executorch_exir_dialects_edge__ops_aten_clamp_default": 4,
-    "executorch_exir_dialects_edge__ops_aten_expand_copy_default": 4,
     "executorch_exir_dialects_edge__ops_quantized_decomposed_dequantize_per_tensor_default": 5,
     "executorch_exir_dialects_edge__ops_quantized_decomposed_quantize_per_tensor_default": 5,
 }

backends/cortex_m/test/misc/test_portable_int8.py

@@ -221,6 +221,12 @@ def _quantize_and_export(
         (torch.randn(1, 3, 4, 5), torch.randn(1, 3, 4, 5)),
         None,
     ),
+    "expand_as": OpCase(
+        torch.ops.aten.expand_as.default,
+        _build_module(lambda x, y: torch.ops.aten.expand_as.default(x, y)),
+        (torch.randn(1, 3, 4, 5), torch.randn(2, 3, 4, 5)),
+        None,
+    ),
     "select": OpCase(
         torch.ops.aten.select.int,
         _build_module(lambda x, y: torch.ops.aten.select.int(x, 1, 0)),