Support sym round and ceil

Summary: Add prim op implementations for sym round and ceil. Support round in pass_base interpreter.

Differential Revision: D65382714

Author: GregoryComer

exir/pass_base.py

@@ -318,7 +318,11 @@ def call_function(
             if target == operator.getitem:
                 value, key = args
                 return self.callback.call_getitem(value, key, meta)
-            elif getattr(target, "__module__", None) in {"_operator", "math"}:
+            elif getattr(target, "__module__", None) in {
+                "_operator",
+                "builtins",
+                "math",
+            }:
                 assert callable(target)
                 return self.callback.call_sym(target, args, meta)
             elif target in _TORCH_SYM_OPS:

exir/passes/__init__.py

@@ -339,7 +339,7 @@ def get_submodule(node: torch.fx.Node) -> torch.fx.GraphModule:
                 self.call(get_submodule(node.args[0]))
                 self.call(get_submodule(node.args[1]))
                 continue
-            elif getattr(target, "__module__", None) == "_operator":
+            elif getattr(target, "__module__", None) in ("builtins", "_operator"):
                 continue
             elif target in to_out_var_skiplist:
                 continue

exir/passes/executorch_prim_ops_registry.py

@@ -4,9 +4,10 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import builtins
 import math
 import operator
-from typing import Dict, Set, Union
+from typing import Any, Dict, Set, Union
 
 # necessary to ensure the ops are registered
 import torch
@@ -94,12 +95,24 @@ def neg(a: _SymScalar) -> _SymScalar:
     return -a  # pyre-ignore
 
 
+@bind_pattern_to_op(executorch_prims_lib, "ceil.Scalar(Scalar a) -> Scalar")
+def ceil(a: _SymScalar) -> _SymScalar:
+    return math.ceil(a)  # pyre-ignore
+
+
+@bind_pattern_to_op(executorch_prims_lib, "round.Scalar(Scalar a) -> Scalar")
+def builtin_round(a: _SymScalar) -> _SymScalar:
+    return round(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] = {
+_PYTHON_SYM_OPS_TO_EXECUTORCH_SYM_OPS: Dict[Any, OpOverload] = {
+    builtins.round: ops.backend.executorch_prim.round.Scalar,
+    math.ceil: ops.backend.executorch_prim.ceil.Scalar,
     math.trunc: ops.backend.executorch_prim.trunc.Scalar,
     operator.sub: ops.backend.executorch_prim.sub.Scalar,
     operator.mul: ops.backend.executorch_prim.mul.Scalar,

kernels/prim_ops/register_prim_ops.cpp

@@ -12,6 +12,7 @@
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <executorch/runtime/kernel/operator_registry.h>
 
+#include <cfenv>
 #include <cmath>
 
 using torch::executor::function::et_copy_index;
@@ -303,6 +304,51 @@ static Kernel prim_ops[] = {
           }
         }),
 
+    // ceil.Scalar(Scalar a) -> Scalar
+    Kernel(
+        "executorch_prim::ceil.Scalar",
+        [](KernelRuntimeContext& context, EValue** stack) {
+          (void)context;
+          EValue& a = *stack[0];
+          EValue& out = *stack[1];
+          if (a.isDouble()) {
+            out = EValue(static_cast<int64_t>(ceil(a.toDouble())));
+          } else {
+            ET_CHECK_MSG(false, "%zu", (size_t)a.tag);
+          }
+        }),
+
+    // round.Scalar(Scalar a) -> Scalar
+    Kernel(
+        "executorch_prim::round.Scalar",
+        [](KernelRuntimeContext& context, EValue** stack) {
+          (void)context;
+          EValue& a = *stack[0];
+          EValue& out = *stack[1];
+          if (a.isDouble()) {
+            // Round half to even to match Python round(). Need an explicit
+            // implementation as not all platforms support fenv rounding modes.
+            // See
+            // https://codeyarns.com/tech/2018-08-17-how-to-round-half-to-even.html
+            const auto val = a.toDouble();
+            const auto r = round(val);
+            const auto d = r - val;
+            auto res = 0.0;
+
+            if ((d != 0.5) && (d != -0.5)) {
+              res = r;
+            } else if (fmod(r, 2.0) == 0.0) {
+              res = r;
+            } else {
+              res = val - d;
+            }
+
+            out = EValue(static_cast<int64_t>(res));
+          } else {
+            ET_CHECK_MSG(false, "%zu", (size_t)a.tag);
+          }
+        }),
+
     // trunc.Scalar(Scalar a) -> Scalar
     Kernel(
         "executorch_prim::trunc.Scalar",

kernels/prim_ops/test/prim_ops_test.cpp

@@ -503,6 +503,47 @@ TEST_F(RegisterPrimOpsTest, TestETViewEmpty) {
       getOpsFn("executorch_prim::et_view.default")(context, bad_stack), "");
 }
 
+TEST_F(RegisterPrimOpsTest, TestCeil) {
+  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, 1, 1, 1, 1, 2, 0, -1, -1, 10};
+
+  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 j = 0; j < 2; j++) {
+      stack[j] = &values[j];
+    }
+
+    getOpsFn("executorch_prim::ceil.Scalar")(context, stack);
+    EXPECT_EQ(stack[1]->toInt(), expected[i]);
+  }
+}
+
+TEST_F(RegisterPrimOpsTest, TestRound) {
+  // Note that Python uses round-to-even for halfway values.
+  std::array<double, 10> inputs = {
+      0.0, 0.25, 0.5, 0.75, 1.0, 1.5, -0.5, -1.0, -1.5, 9.999999};
+  std::array<int64_t, 10> expected = {0, 0, 0, 1, 1, 2, 0, -1, -2, 10};
+
+  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 j = 0; j < 2; j++) {
+      stack[j] = &values[j];
+    }
+
+    getOpsFn("executorch_prim::round.Scalar")(context, stack);
+    EXPECT_EQ(stack[1]->toInt(), expected[i]);
+  }
+}
+
 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};