support argmax/argmin without dim kwargs and fix adaptive_max_pool3d

backends/qualcomm/_passes/__init__.py

@@ -33,6 +33,7 @@
 from .i64_to_i32 import I64toI32
 from .insert_io_qdq import InsertIOQDQ
 from .insert_requantize import InsertRequantize
+from .insert_reshape_for_reduce_ops import InsertReshapeForReduceOps
 from .layout_transform import LayoutTransform
 from .lift_constant_scalar_operands import LiftConstantScalarOperands
 from .recompose_pixel_unshuffle import RecomposePixelUnshuffle
@@ -45,7 +46,6 @@
 from .seq_mse import SeqMSE
 from .tag_quant_io import TagQuantIO
 
-
 __all__ = [
     AnnotateAdaptiveAvgPool1D,
     AnnotateQuantAttrs,
@@ -75,6 +75,7 @@
     FuseConsecutiveTranspose,
     I64toI32,
     InsertIOQDQ,
+    InsertReshapeForReduceOps,
     InsertRequantize,
     LayoutTransform,
     LiftConstantScalarOperands,

backends/qualcomm/_passes/insert_reshape_for_reduce_ops.py

@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from executorch.exir.pass_base import ExportPass, PassResult
+from executorch.exir.passes import dead_code_elimination_pass
+
+
+class InsertReshapeForReduceOps(ExportPass):
+    """
+    Rewrite `aten.argmax.default` with `dim=None` into
+    a reshape-to-1D followed by argmax(dim=0).
+
+    PyTorch semantics:
+      torch.argmax(x, dim=None) -> flatten(x) then argmax along axis=0
+
+    QNN requires an explicit axis, so we insert the reshape.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.op_map = {torch.ops.aten.argmax.default, torch.ops.aten.argmin.default}
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        graph = graph_module.graph
+        modified = False
+
+        for n in graph.nodes:
+            if n.target in self.op_map:
+                dim_arg = None if len(n.args) == 1 else n.args[1]
+
+                if dim_arg is None:
+                    inp = n.args[0]
+
+                    # Insert reshape before argmax
+                    with graph.inserting_before(n):
+                        reshape_node = graph.create_node(
+                            "call_function",
+                            torch.ops.aten.reshape.default,
+                            (inp, [-1]),
+                            {},
+                        )
+                        reshape_node.meta = dict(inp.meta)
+                        if "val" in inp.meta:
+                            reshape_node.meta["val"] = inp.meta["val"].reshape(-1)
+
+                    # Rewrite argmax: take reshape_node as input, set dim=0
+                    n.args = (reshape_node, 0, *n.args[2:])
+
+                modified = True
+
+        if modified:
+            graph_module.recompile()
+            dead_code_elimination_pass(graph_module)
+
+        return PassResult(graph_module, modified)

backends/qualcomm/_passes/qnn_pass_manager.py

@@ -38,6 +38,7 @@
     I64toI32,
     InsertIOQDQ,
     InsertRequantize,
+    InsertReshapeForReduceOps,
     LayoutTransform,
     LiftConstantScalarOperands,
     RecomposePixelUnshuffle,
@@ -209,6 +210,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
         self.add_pass(ReplaceInfValues())
         self.add_pass(LiftConstantScalarOperands())
+        self.add_pass(InsertReshapeForReduceOps())
         return self._transform(graph_module)
 
     def transform_for_export_pipeline(
@@ -229,6 +231,7 @@ def transform_for_export_pipeline(
             self.add_pass(ConvertLinearToConv2d(exported_program))
         self.add_pass(ConvertSquareToPow())
         self.add_pass(LiftConstantScalarOperands())
+        self.add_pass(InsertReshapeForReduceOps())
         self._transform(exported_program.graph_module)
         ep = lift_constant_tensor_pass(exported_program)
         return ep

backends/qualcomm/partition/common_defs.py

@@ -17,6 +17,7 @@
 to_be_implemented_operator = [
     exir_ops.edge.aten._adaptive_avg_pool3d.default,
     exir_ops.edge.aten.adaptive_max_pool2d.default,
+    exir_ops.edge.aten.adaptive_max_pool3d.default,
     exir_ops.edge.aten.avg_pool3d.default,
     exir_ops.edge.aten.div.Tensor_mode,
     exir_ops.edge.aten.log10.default,

backends/qualcomm/tests/TARGETS

@@ -47,3 +47,17 @@ runtime.python_library(
         ":test_qnn_delegate"
     ]
 )
+
+runtime.python_test(
+    name = "test_passes",
+    srcs = [
+        "test_passes.py",
+    ],
+    deps = [
+        "fbsource//third-party/pypi/expecttest:expecttest",  # @manual
+        "//caffe2:torch",
+        "//executorch/exir:lib",
+        "//executorch/backends/qualcomm/_passes:passes",
+        "//executorch/backends/qualcomm/builders:builders",
+    ],
+)

backends/qualcomm/tests/models.py

@@ -171,21 +171,23 @@ def forward(self, y):
 
 
 class Argmax(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, dim: Optional[int] = None, keepdim: bool = False):
         super().__init__()
+        self.dim = dim
+        self.keepdim = keepdim
 
     def forward(self, x):
-        x = torch.argmax(x, dim=0, keepdim=True)
-        return x
+        return torch.argmax(x, dim=self.dim, keepdim=self.keepdim)
 
 
 class Argmin(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, dim: Optional[int] = None, keepdim: bool = False):
         super().__init__()
+        self.dim = dim
+        self.keepdim = keepdim
 
     def forward(self, x):
-        x = torch.argmin(x, dim=0, keepdim=True)
-        return x
+        return torch.argmin(x, dim=self.dim, keepdim=self.keepdim)
 
 
 class ArgminViewSqueezeConv2D(torch.nn.Module):

backends/qualcomm/tests/test_passes.py

@@ -0,0 +1,54 @@
+import unittest
+
+import torch
+from executorch.backends.qualcomm._passes import InsertReshapeForReduceOps
+
+
+class TestPasses(unittest.TestCase):
+    def test_insert_reshape_for_argmax(self):
+        class ArgmaxModule(torch.nn.Module):
+            def forward(self, x):
+                return torch.argmax(x, dim=None)
+
+        mod = ArgmaxModule()
+
+        x = torch.tensor([[1.0, 5.0], [3.0, 2.0]])
+        ep = torch.export.export(mod, (x,))
+        # Run original module for reference
+        ref = mod(x)
+
+        reshape_nodes = [
+            n for n in ep.graph.nodes if n.target == torch.ops.aten.reshape.default
+        ]
+        argmax_nodes = [
+            n for n in ep.graph.nodes if n.target == torch.ops.aten.argmax.default
+        ]
+        self.assertTrue(len(reshape_nodes) == 0, "Reshape node not inserted")
+        self.assertTrue(len(argmax_nodes) == 1, "Argmax node missing")
+
+        InsertReshapeForReduceOps()(ep.graph_module)
+
+        out = ep.graph_module(x)
+
+        # Check graph structure: argmax should take a reshape as input
+        reshape_nodes = [
+            n for n in ep.graph.nodes if n.target == torch.ops.aten.reshape.default
+        ]
+        argmax_nodes = [
+            n for n in ep.graph.nodes if n.target == torch.ops.aten.argmax.default
+        ]
+        self.assertTrue(len(reshape_nodes) == 1, "Reshape node should be inserted")
+        self.assertTrue(len(argmax_nodes) == 1, "Argmax node missing")
+
+        argmax_node = argmax_nodes[0]
+        self.assertEqual(argmax_node.args[1], 0, "Argmax dim not set to 0")
+
+        # Execute new graph and compare with reference
+        out = ep.graph_module(x)
+        self.assertTrue(
+            torch.equal(*out, ref), f"Output mismatch: got {out}, expected {ref}"
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -173,14 +173,64 @@ def test_qnn_backend_arange(self):
                 self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_argmax(self):
-        module = Argmax()  # noqa: F405
-        sample_input = (torch.randn(16, 3, 4, 4),)
-        self.lower_module_and_test_output(module, sample_input)
+        test_cases = [
+            {
+                QCOM_MODULE: Argmax(),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=0, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=1, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(8, 5),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=None, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.tensor([5.0]),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=2, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4),),
+            },
+        ]
+
+        for i, case in enumerate(test_cases):
+            with self.subTest(i=i):
+                self.lower_module_and_test_output(
+                    case[QCOM_MODULE], case[QCOM_SAMPLE_INPUTS]
+                )
 
     def test_qnn_backend_argmin(self):
-        module = Argmin()  # noqa: F405
-        sample_input = (torch.rand(3, 4),)
-        self.lower_module_and_test_output(module, sample_input)
+        test_cases = [
+            {
+                QCOM_MODULE: Argmin(),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=0, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=1, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(8, 5),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=None, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.tensor([5.0]),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=2, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4),),
+            },
+        ]
+
+        for i, case in enumerate(test_cases):
+            with self.subTest(i=i):
+                self.lower_module_and_test_output(
+                    case[QCOM_MODULE], case[QCOM_SAMPLE_INPUTS]
+                )
 
     @unittest.expectedFailure
     def test_qnn_backend_asin(self):
@@ -1797,16 +1847,66 @@ def test_qnn_backend_arange(self):
                 self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_argmax(self):
-        module = Argmax()  # noqa: F405
-        sample_input = (torch.randn(16, 3, 4, 4),)
-        module = self.get_qdq_module(module, sample_input)
-        self.lower_module_and_test_output(module, sample_input)
+        test_cases = [
+            {
+                QCOM_MODULE: Argmax(),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=0, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=1, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(8, 5),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=None, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.tensor([5.0]),),
+            },
+            {
+                QCOM_MODULE: Argmax(dim=2, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4),),
+            },
+        ]
+
+        for i, case in enumerate(test_cases):
+            with self.subTest(i=i):
+                module = self.get_qdq_module(
+                    case[QCOM_MODULE], case[QCOM_SAMPLE_INPUTS]
+                )
+                self.lower_module_and_test_output(module, case[QCOM_SAMPLE_INPUTS])
 
     def test_qnn_backend_argmin(self):
-        module = Argmin()  # noqa: F405
-        sample_input = (torch.randn(16, 3, 4, 4),)
-        module = self.get_qdq_module(module, sample_input)
-        self.lower_module_and_test_output(module, sample_input)
+        test_cases = [
+            {
+                QCOM_MODULE: Argmin(),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=0, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(16, 3, 4, 4),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=1, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(8, 5),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=None, keepdim=False),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.tensor([5.0]),),
+            },
+            {
+                QCOM_MODULE: Argmin(dim=2, keepdim=True),  # noqa: F405
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4),),
+            },
+        ]
+
+        for i, case in enumerate(test_cases):
+            with self.subTest(i=i):
+                module = self.get_qdq_module(
+                    case[QCOM_MODULE], case[QCOM_SAMPLE_INPUTS]
+                )
+                self.lower_module_and_test_output(module, case[QCOM_SAMPLE_INPUTS])
 
     def test_qnn_backend_asin(self):
         module = Asin()  # noqa: F405