Arm backend: Add TOSA dialect ARGMAX op

backends/arm/test/misc/test_tosa_dialect_argmax.py

@@ -0,0 +1,43 @@
+# Copyright 2026 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import executorch.backends.arm.tosa.dialect  # noqa: F401
+import pytest
+import torch
+from executorch.backends.arm.tosa.dialect.lib import TosaValueError
+from executorch.backends.arm.tosa.specification import (
+    TosaLoweringContext,
+    TosaSpecification,
+)
+from executorch.exir.dialects._ops import ops as exir_ops
+from torch._subclasses.fake_tensor import FakeTensorMode
+
+
+def test_argmax_tosa_fp() -> None:
+    sample_input = torch.randn((2, 3, 4), dtype=torch.float32)
+
+    with TosaLoweringContext(
+        TosaSpecification.create_from_string("TOSA-1.1+FP")
+    ), FakeTensorMode() as mode:
+        output = exir_ops.backend.tosa.ARGMAX.default(
+            mode.from_tensor(sample_input),
+            axis=1,
+        )
+
+    assert output.dtype == torch.int32
+    assert tuple(output.shape) == (2, 4)
+
+
+def test_argmax_rejects_bfloat16_without_extension() -> None:
+    sample_input = torch.randn((2, 3, 4), dtype=torch.bfloat16)
+
+    with TosaLoweringContext(
+        TosaSpecification.create_from_string("TOSA-1.1+FP")
+    ), FakeTensorMode() as mode:
+        with pytest.raises(TosaValueError, match="doesn't support bfloat16"):
+            exir_ops.backend.tosa.ARGMAX.default(
+                mode.from_tensor(sample_input),
+                axis=1,
+            )

backends/arm/tosa/dialect/__init__.py

@@ -5,6 +5,7 @@
 
 from executorch.backends.arm.tosa.dialect.ops import (  # noqa F401
     activation,
+    argmax,
     avg_pool2d,
     avg_pool2d_adaptive,
     conv2d,

backends/arm/tosa/dialect/ops/argmax.py

@@ -0,0 +1,78 @@
+# Copyright 2026 Arm Limited and/or its affiliates.
+#
+# 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.backends.arm.tosa.dialect.lib import TosaValueError
+from executorch.backends.arm.tosa.dialect.ops._common import validate_nan_mode
+from executorch.backends.arm.tosa.dialect.ops_registration import register_fake_tosa_op
+from executorch.backends.arm.tosa.specification import (
+    get_context_spec,
+    TosaSpecification,
+)
+
+
+def _validate_argmax_dtype(dtype: torch.dtype) -> None:
+    tosa_spec = get_context_spec()
+
+    if dtype == torch.int8:
+        if not tosa_spec.support_integer():
+            raise TosaValueError(
+                f"TOSA spec {tosa_spec} doesn't support int8 for ARGMAX",
+                op="ARGMAX",
+            )
+        return
+
+    if dtype == torch.int16:
+        if not (tosa_spec.support_integer() and tosa_spec.support_extension("int16")):
+            raise TosaValueError(
+                f"TOSA spec {tosa_spec} doesn't support int16 for ARGMAX",
+                op="ARGMAX",
+            )
+        return
+
+    if dtype in (torch.float16, torch.float32):
+        if not tosa_spec.support_float():
+            raise TosaValueError(
+                f"TOSA spec {tosa_spec} doesn't support {dtype} for ARGMAX",
+                op="ARGMAX",
+            )
+        return
+
+    if dtype == torch.bfloat16:
+        if not (tosa_spec.support_float() and tosa_spec.support_extension("bf16")):
+            raise TosaValueError(
+                f"TOSA spec {tosa_spec} doesn't support bfloat16 for ARGMAX",
+                op="ARGMAX",
+            )
+        return
+
+    raise TosaValueError(f"Unsupported dtype {dtype} for ARGMAX", op="ARGMAX")
+
+
+@register_fake_tosa_op(
+    'ARGMAX(Tensor input, int axis, *, str nan_mode="PROPAGATE") -> Tensor',
+    TosaSpecification.all_versions_and_profiles(),
+)
+def ARGMAX(
+    input: torch.Tensor,
+    axis: int,
+    *,
+    nan_mode: str = "PROPAGATE",
+) -> torch.Tensor:
+    validate_nan_mode(nan_mode, "ARGMAX")
+    _validate_argmax_dtype(input.dtype)
+
+    if input.dim() == 0:
+        raise TosaValueError(
+            "ARGMAX requires an input with rank at least 1", op="ARGMAX"
+        )
+    if axis < 0 or axis >= input.dim():
+        raise TosaValueError(
+            f"axis must be in [0, {input.dim() - 1}] but got {axis}",
+            op="ARGMAX",
+        )
+
+    output_shape = tuple(input.shape[:axis]) + tuple(input.shape[axis + 1 :])
+    return torch.empty(output_shape, dtype=torch.int32)