Add trunc scalar prim_op (#6580)

Summary:
Add a primitive op kernel for scalar trunc (double -> int). This corresponds to python math.trunc in the export graph and is used in some torchvision transforms for size calculations.

Note that trunc is already supported in export.

Test Plan: Added a test for trunc in prim ops tests. Also validated end to end with MSGR ODNC model.

Differential Revision: D65057149

Pulled By: GregoryComer

Author: GregoryComer

exir/passes/executorch_prim_ops_registry.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import math
 import operator
 from typing import Dict, Set, Union
 
@@ -91,7 +92,13 @@ def neg(a: _SymScalar) -> _SymScalar:
     return -a  # pyre-ignore
 
 
+@bind_pattern_to_op(executorch_prims_lib, "trunc.Scalar(Scalar a) -> Scalar")
+def trunc(a: _SymScalar) -> _SymScalar:
+    return math.trunc(a)  # pyre-ignore
+
+
 _PYTHON_SYM_OPS_TO_EXECUTORCH_SYM_OPS: Dict[OpOverload, OpOverload] = {
+    math.trunc: ops.backend.executorch_prim.trunc.Scalar,
     operator.sub: ops.backend.executorch_prim.sub.Scalar,
     operator.mul: ops.backend.executorch_prim.mul.Scalar,
     operator.add: ops.backend.executorch_prim.add.Scalar,

kernels/prim_ops/register_prim_ops.cpp

@@ -12,6 +12,8 @@
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <executorch/runtime/kernel/operator_registry.h>
 
+#include <cmath>
+
 using torch::executor::function::et_copy_index;
 
 namespace torch {
@@ -301,6 +303,20 @@ static Kernel prim_ops[] = {
           }
         }),
 
+    // trunc.Scalar(Scalar a) -> Scalar
+    Kernel(
+        "executorch_prim::trunc.Scalar",
+        [](KernelRuntimeContext& context, EValue** stack) {
+          (void)context;
+          EValue& a = *stack[0];
+          EValue& out = *stack[1];
+          if (a.isDouble()) {
+            out = EValue(static_cast<int64_t>(trunc(a.toDouble())));
+          } else {
+            ET_CHECK_MSG(false, "%zu", (size_t)a.tag);
+          }
+        }),
+
     // executorch_prim::et_copy_index.tensor(tensor, tensor) -> tensor
     Kernel("executorch_prim::et_copy_index.tensor", &et_copy_index),
     // executorch_prim::et_view.default(Tensor, int[]) -> Tensor

kernels/prim_ops/test/prim_ops_test.cpp

@@ -503,5 +503,25 @@ TEST_F(RegisterPrimOpsTest, TestETViewEmpty) {
       getOpsFn("executorch_prim::et_view.default")(context, bad_stack), "");
 }
 
+TEST_F(RegisterPrimOpsTest, TestTrunc) {
+  std::array<double, 10> inputs = {
+      0.0, 0.25, 0.5, 0.75, 1.0, 1.75, -0.5, -1.0, -1.5, 9.999999};
+  std::array<int64_t, 10> expected = {0, 0, 0, 0, 1, 1, 0, -1, -1, 9};
+
+  for (auto i = 0; i < inputs.size(); i++) {
+    EValue values[2];
+    values[0] = EValue(inputs[i]);
+    values[1] = EValue(0.0);
+
+    EValue* stack[2];
+    for (size_t i = 0; i < 2; i++) {
+      stack[i] = &values[i];
+    }
+
+    getOpsFn("executorch_prim::trunc.Scalar")(context, stack);
+    EXPECT_EQ(stack[1]->toInt(), expected[i]);
+  }
+}
+
 } // namespace executor
 } // namespace torch