pytorch/test/cpp/jit/test_custom_operators.cpp

#include "test/cpp/jit/test_base.h"
#include "test/cpp/jit/test_utils.h"

#include "torch/csrc/jit/custom_operator.h"
#include "torch/csrc/jit/irparser.h"
#include "torch/csrc/jit/passes/alias_analysis.h"
#include "torch/csrc/jit/passes/dead_code_elimination.h"
#include "torch/jit.h"

namespace torch {
namespace jit {

void testCustomOperators() {
  {
    torch::RegisterOperators reg("foo::bar", [](double a, at::Tensor b) { return a + b; });
    auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::bar"));
    ASSERT_EQ(ops.size(), 1);

    auto& op = ops.front();
    ASSERT_EQ(op->schema().name(), "foo::bar");

    ASSERT_EQ(op->schema().arguments().size(), 2);
    ASSERT_EQ(op->schema().arguments()[0].name(), "_0");
    ASSERT_EQ(op->schema().arguments()[0].type()->kind(), TypeKind::FloatType);
    ASSERT_EQ(op->schema().arguments()[1].name(), "_1");
    ASSERT_EQ(op->schema().arguments()[1].type()->kind(), TypeKind::TensorType);

    ASSERT_EQ(op->schema().returns()[0].type()->kind(), TypeKind::TensorType);

    Stack stack;
    push(stack, 2.0f, autograd::make_variable(at::ones(5)));
    op->getOperation()(stack);
    at::Tensor output;
    pop(stack, output);

    ASSERT_TRUE(output.allclose(autograd::make_variable(at::full(5, 3.0f))));
  }
  {
    torch::RegisterOperators reg("foo::bar_with_schema(float a, Tensor b) -> Tensor",
        [](double a, at::Tensor b) { return a + b; });

    auto& ops =
        getAllOperatorsFor(Symbol::fromQualString("foo::bar_with_schema"));
    ASSERT_EQ(ops.size(), 1);

    auto& op = ops.front();
    ASSERT_EQ(op->schema().name(), "foo::bar_with_schema");

    ASSERT_EQ(op->schema().arguments().size(), 2);
    ASSERT_EQ(op->schema().arguments()[0].name(), "a");
    ASSERT_EQ(op->schema().arguments()[0].type()->kind(), TypeKind::FloatType);
    ASSERT_EQ(op->schema().arguments()[1].name(), "b");
    ASSERT_EQ(op->schema().arguments()[1].type()->kind(), TypeKind::TensorType);

    ASSERT_EQ(op->schema().returns().size(), 1);
    ASSERT_EQ(op->schema().returns()[0].type()->kind(), TypeKind::TensorType);

    Stack stack;
    push(stack, 2.0f, autograd::make_variable(at::ones(5)));
    op->getOperation()(stack);
    at::Tensor output;
    pop(stack, output);

    ASSERT_TRUE(output.allclose(autograd::make_variable(at::full(5, 3.0f))));
  }
  {
    // Check that lists work well.
    torch::RegisterOperators reg(
        "foo::lists(int[] ints, float[] floats, Tensor[] tensors) -> float[]",
        [](torch::List<int64_t> ints,
           torch::List<double> floats,
           torch::List<at::Tensor> tensors) { return floats; });

    auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists"));
    ASSERT_EQ(ops.size(), 1);

    auto& op = ops.front();
    ASSERT_EQ(op->schema().name(), "foo::lists");

    ASSERT_EQ(op->schema().arguments().size(), 3);
    ASSERT_EQ(op->schema().arguments()[0].name(), "ints");
    ASSERT_TRUE(
        op->schema().arguments()[0].type()->isSubtypeOf(ListType::ofInts()));
    ASSERT_EQ(op->schema().arguments()[1].name(), "floats");
    ASSERT_TRUE(
        op->schema().arguments()[1].type()->isSubtypeOf(ListType::ofFloats()));
    ASSERT_EQ(op->schema().arguments()[2].name(), "tensors");
    ASSERT_TRUE(
        op->schema().arguments()[2].type()->isSubtypeOf(ListType::ofTensors()));

    ASSERT_EQ(op->schema().returns().size(), 1);
    ASSERT_TRUE(
        op->schema().returns()[0].type()->isSubtypeOf(ListType::ofFloats()));

    Stack stack;
    push(stack, c10::List<int64_t>({1, 2}));
    push(stack, c10::List<double>({1.0, 2.0}));
    push(stack, c10::List<at::Tensor>({autograd::make_variable(at::ones(5))}));
    op->getOperation()(stack);
    c10::List<double> output;
    pop(stack, output);

    ASSERT_EQ(output.size(), 2);
    ASSERT_EQ(output.get(0), 1.0);
    ASSERT_EQ(output.get(1), 2.0);
  }
  {
    torch::RegisterOperators reg(
        "foo::lists2(Tensor[] tensors) -> Tensor[]",
        [](torch::List<at::Tensor> tensors) { return tensors; });

    auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists2"));
    ASSERT_EQ(ops.size(), 1);

    auto& op = ops.front();
    ASSERT_EQ(op->schema().name(), "foo::lists2");

    ASSERT_EQ(op->schema().arguments().size(), 1);
    ASSERT_EQ(op->schema().arguments()[0].name(), "tensors");
    ASSERT_TRUE(
        op->schema().arguments()[0].type()->isSubtypeOf(ListType::ofTensors()));

    ASSERT_EQ(op->schema().returns().size(), 1);
    ASSERT_TRUE(
        op->schema().returns()[0].type()->isSubtypeOf(ListType::ofTensors()));

    Stack stack;
    push(stack, c10::List<at::Tensor>({autograd::make_variable(at::ones(5))}));
    op->getOperation()(stack);
    c10::List<at::Tensor> output;
    pop(stack, output);

    ASSERT_EQ(output.size(), 1);
    ASSERT_TRUE(output.get(0).allclose(autograd::make_variable(at::ones(5))));
  }
}

void testCustomOperatorAliasing() {
  torch::RegisterOperators reg(
      "foo::aliasing", [](at::Tensor a, at::Tensor b) -> at::Tensor {
        a.add_(b);
        return a;
      });
  getAllOperatorsFor(Symbol::fromQualString("foo::aliasing"));

  {
    auto graph = std::make_shared<Graph>();
    script::parseIR(
        R"IR(
graph(%x: Tensor, %y: Tensor):
  %ret : Tensor = foo::aliasing(%x, %y)
  return (%ret)
  )IR",
        graph.get());

    auto opNode = *graph->block()->nodes().begin();

    AliasDb aliasDb(graph);
    for (const auto input : opNode->inputs()) {
      // The custom op writes to all its inputs
      ASSERT_TRUE(aliasDb.writesToAlias(opNode, {input}));
      // The output should be a wildcard and thus alias all inputs
      ASSERT_TRUE(aliasDb.mayAlias(opNode->output(), input));
    }
  }
  {
    // DCE should not remove a custom op
    auto graph = std::make_shared<Graph>();
    const auto text = R"IR(
graph(%x: Tensor, %y: Tensor):
  # CHECK: foo::aliasing
  %ret : Tensor = foo::aliasing(%x, %y)
  return (%x)
  )IR";
    script::parseIR(text, graph.get());
    EliminateDeadCode(graph);

    testing::FileCheck().run(text, *graph);
  }
}

void testIValueKWargs() {
  const auto text = R"(
    def foo(a : int, b : int, c : int = 4):
      return a + 2*b + 3*c
  )";
  auto cu = compile(text);
  auto result = cu->get_function("foo")({1}, {{"b", 3}});
  ASSERT_EQ(result.toInt(), 19);
}

} // namespace jit
} // namespace torch