Add idma Fake operators for AoT.

backends/cadence/aot/TARGETS

@@ -518,3 +518,16 @@ python_unittest(
         "//executorch/exir/tests:models",
     ],
 )
+
+python_unittest(
+    name = "test_idma_ops",
+    srcs = [
+        "tests/test_idma_ops.py",
+    ],
+    typing = True,
+    deps = [
+        "//executorch/backends/cadence/aot:graph_builder",
+        "//executorch/backends/cadence/aot:ops_registrations",
+        "//later:lib",
+    ],
+)

backends/cadence/aot/ops_registrations.py

@@ -174,6 +174,21 @@
     "rope.out(Tensor input, Tensor sin_tensor, Tensor cos_tensor, Tensor? pos, *, Tensor(a!) out) -> Tensor(a!)"
 )
 
+# Load/store with iDMA. These only exist before memory planning.
+# Post memory planning, we check that outputs/inputs for the load/store are in
+# DTCM and replace idma_load/idma_store with idma_copy.
+lib.define("idma_load(Tensor src, int task_num=0, int channel=0) -> Tensor")
+lib.define("idma_store(Tensor src, int task_num=0, int channel=0) -> Tensor")
+
+# Non-blocking iDMA copy.
+lib.define("idma_copy(Tensor src, int task_num=0, int channel=0) -> Tensor")
+lib.define(
+    "idma_copy.out(Tensor src, int task_num=0, int channel=0, *, Tensor(a!) out) -> Tensor(a!)"
+)
+# iDMA wait.
+lib.define("idma_wait(Tensor src, int task_num=0) -> Tensor")
+lib.define("idma_wait.out(Tensor src, int task_num=0, *, Tensor(a!) out) -> Tensor(a!)")
+
 # ------------------------------------ #
 #   Migrated from custom_ops.yaml      #
 # ------------------------------------ #
@@ -983,7 +998,43 @@ def rope_meta(
     assert (
         len(sin_shape) == 2 and sin_shape[-1] == hd // 2
     ), f"{sin_shape=} must be [seq, hd/2]"
-    assert (
-        pos is None or len(pos.shape) == 1 and pos.shape[0] == seq
-    ), f"{pos.shape} must be [{seq}]"
+    if pos is not None:
+        assert (
+            len(pos.shape) == 1 and pos.shape[0] == seq
+        ), f"{pos.shape} must be [{seq}]"
     return input.new_empty(input.shape, dtype=input.dtype)
+
+
+@register_fake("cadence::idma_copy")
+def copy_idma_copy_impl(
+    src: torch.Tensor,
+    task_num: int = 0,
+    channel: int = 0,
+) -> torch.Tensor:
+    return src.new_empty(*src.shape, dtype=src.dtype)
+
+
+@register_fake("cadence::idma_wait")
+def copy_idma_wait_impl(
+    src: torch.Tensor,
+    task_num: int = 0,
+) -> torch.Tensor:
+    return src.new_empty(*src.shape, dtype=src.dtype)
+
+
+@register_fake("cadence::idma_load")
+def idma_load_impl(
+    src: torch.Tensor,
+    task_num: int = 0,
+    channel: int = 0,
+) -> torch.Tensor:
+    return copy_idma_copy_impl(src, task_num, channel)
+
+
+@register_fake("cadence::idma_store")
+def idma_store_impl(
+    src: torch.Tensor,
+    task_num: int = 0,
+    channel: int = 0,
+) -> torch.Tensor:
+    return copy_idma_copy_impl(src, task_num, channel)

backends/cadence/aot/tests/test_idma_ops.py

@@ -0,0 +1,31 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# pyre-strict
+
+import executorch.backends.cadence.aot.ops_registrations  # noqa
+import torch
+
+from executorch.backends.cadence.aot.graph_builder import GraphBuilder
+from executorch.exir.dialects._ops import ops as exir_ops
+
+from later.unittest import TestCase
+
+
+class TestIdmaOps(TestCase):
+    def test_idma_load_store_wait(self) -> None:
+        """Check that the idma load/store/wait ops are registered correctly."""
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.ones(2, 7, dtype=torch.float32))
+        load = builder.call_operator(
+            op=exir_ops.edge.cadence.idma_load.default, args=(x,)
+        )
+        wait = builder.call_operator(
+            op=exir_ops.edge.cadence.idma_wait.default, args=(load,)
+        )
+        store = builder.call_operator(
+            op=exir_ops.edge.cadence.idma_store.default, args=(wait,)
+        )
+        wait2 = builder.call_operator(
+            op=exir_ops.edge.cadence.idma_wait.default, args=(store,)
+        )
+        builder.output([wait2])