| /* |
| * Copyright (c) Meta Platforms, Inc. and affiliates. |
| * All rights reserved. |
| * |
| * This source code is licensed under the BSD-style license found in the |
| * LICENSE file in the root directory of this source tree. |
| */ |
| |
| #include <executorch/kernels/test/FunctionHeaderWrapper.h> // Declares the operator |
| #include <executorch/kernels/test/TestUtil.h> |
| #include <executorch/kernels/test/supported_features.h> |
| #include <executorch/runtime/core/exec_aten/exec_aten.h> |
| #include <executorch/runtime/core/exec_aten/testing_util/tensor_factory.h> |
| #include <executorch/runtime/core/exec_aten/testing_util/tensor_util.h> |
| |
| #include <gtest/gtest.h> |
| |
| using namespace ::testing; |
| using exec_aten::ScalarType; |
| using exec_aten::Tensor; |
| using torch::executor::testing::TensorFactory; |
| |
| class OpTrilTest : public OperatorTest { |
| protected: |
| Tensor& op_tril_out(const Tensor& self, int64_t diagonal, Tensor& out) { |
| return torch::executor::aten::tril_outf(context_, self, diagonal, out); |
| } |
| |
| // Assert `self` and `out` as zero tensors is a no-op. |
| template <ScalarType DTYPE> |
| void test_tril_out_zeros() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `out` as a non-zero tensor yields correct results. |
| template <ScalarType DTYPE> |
| void test_tril_out_ones() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.ones({3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with multiple empty dims. |
| template <ScalarType DTYPE> |
| void test_tril_out_empty_dims() { |
| TensorFactory<DTYPE> tf; |
| Tensor out = tf.zeros({1, 1, 1, 1}); |
| |
| // tensor([[[[1]]]]) |
| Tensor self = tf.ones({1, 1, 1, 1}); |
| |
| op_tril_out(self, 0, out); |
| |
| // tensor([[[[1]]]]) |
| Tensor result = tf.ones({1, 1, 1, 1}); |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a square tensor. |
| template <ScalarType DTYPE> |
| void test_tril_out_square() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 1, 1, // tensor([[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 0, 0, // tensor([[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0], |
| 1, 1, 1, // [ 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a rectangular tensor. |
| template <ScalarType DTYPE> |
| void test_tril_out_rectangle() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 5}, |
| /*data=*/ |
| { |
| 1, 1, 1, 1, 1, // tensor([[ 1, 1, 1, 1, 1], |
| 1, 1, 1, 1, 1, // [ 1, 1, 1, 1, 1], |
| 1, 1, 1, 1, 1, // [ 1, 1, 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 5}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 5}, |
| /*data=*/ |
| { |
| 1, 0, 0, 0, 0, // tensor([[ 1, 0, 0, 0, 0], |
| 1, 1, 0, 0, 0, // [ 1, 1, 0, 0, 0], |
| 1, 1, 1, 0, 0, // [ 1, 1, 1, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a positive diagonal value. |
| template <ScalarType DTYPE> |
| void test_tril_out_pos_diag() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 1, 1, // tensor([[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3}); |
| |
| op_tril_out(self, 1, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 1, 0, // tensor([[ 1, 1, 0], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a negative diagonal value. |
| template <ScalarType DTYPE> |
| void test_tril_out_neg_diag() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 1, 1, // tensor([[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3}); |
| |
| op_tril_out(self, -1, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 1, 0, 0, // [ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a batch of tensors, where dims are equal. |
| template <ScalarType DTYPE> |
| void test_tril_out_multi_equal_dim() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3, 3}, |
| /*data=*/ |
| { |
| 1, 1, 1, // tensor([[[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]], |
| |
| 1, 1, 1, // [[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]], |
| |
| 1, 1, 1, // [[ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1], |
| 1, 1, 1, // [ 1, 1, 1]]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3, 3}, |
| /*data=*/ |
| { |
| 1, 0, 0, // tensor([[[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0], |
| 1, 1, 1, // [ 1, 1, 1]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0], |
| 1, 1, 1, // [ 1, 1, 1]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0], |
| 1, 1, 1, // [ 1, 1, 1]]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with a batch of tensors, where dims are unequal. |
| template <ScalarType DTYPE> |
| void test_tril_out_multi_unequal_dim() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format offF |
| Tensor self = tf.make( |
| /*sizes=*/{3, 2, 3}, |
| /*data=*/ |
| { |
| 1, |
| 1, |
| 1, // tensor([[[ 1, 1, 1], |
| 1, |
| 1, |
| 1, // [ 1, 1, 1]], |
| |
| 1, |
| 1, |
| 1, // [[ 1, 1, 1], |
| 1, |
| 1, |
| 1, // [ 1, 1, 1]], |
| |
| 1, |
| 1, |
| 1, // [[ 1, 1, 1], |
| 1, |
| 1, |
| 1, // [ 1, 1, 1]]]) |
| }); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 2, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 2, 3}, |
| /*data=*/ |
| { |
| 1, 0, 0, // tensor([[[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 1, 1, 0, // [ 1, 1, 0]]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with non-0/1 values on regular diagonal. |
| template <ScalarType DTYPE> |
| void test_tril_out_arange_reg_diag() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 2, 3, // tensor([[ 1, 2, 3], |
| 4, 5, 6, // [ 4, 5, 6], |
| 7, 8, 9, // [ 7, 8, 9]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 0, 0, // tensor([[ 1, 0, 0], |
| 4, 5, 0, // [ 4, 5, 0], |
| 7, 8, 9, // [ 7, 8, 9]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works with non-0/1 values on positive diagonal values. |
| // An edge case with a far-out positive diagonal is also included. |
| template <ScalarType DTYPE> |
| void test_tril_out_arange_pos_diag() { |
| TensorFactory<DTYPE> tf; |
| |
| // Case: diag = 1 |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 2, 3, // tensor([[ 1, 2, 3], |
| 4, 5, 6, // [ 4, 5, 6], |
| 7, 8, 9, // [ 7, 8, 9]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out1 = tf.zeros({3, 3}); |
| |
| op_tril_out(self, 1, out1); |
| |
| // clang-format off |
| Tensor result1 = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 2, 0, // tensor([[ 1, 2, 0], |
| 4, 5, 6, // [ 4, 5, 6], |
| 7, 8, 9, // [ 7, 8, 9]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out1, result1); |
| |
| // Case: diag = 2 |
| |
| Tensor out2 = tf.zeros({3, 3}); |
| op_tril_out(self, 2, out2); |
| EXPECT_TENSOR_EQ(out2, self); |
| |
| // Case: diag = 10 |
| |
| Tensor out3 = tf.zeros({3, 3}); |
| op_tril_out(self, 10, out3); |
| EXPECT_TENSOR_EQ(out3, self); |
| } |
| |
| // Assert `tril` works with non-0/1 values on negative diagonal values. |
| // An edge case with a far-out negative diagonal is also included. |
| template <ScalarType DTYPE> |
| void test_tril_out_arange_neg_diag() { |
| TensorFactory<DTYPE> tf; |
| |
| // Case: diag = -1 |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 1, 2, 3, // tensor([[ 1, 2, 3], |
| 4, 5, 6, // [ 4, 5, 6], |
| 7, 8, 9, // [ 7, 8, 9]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out1 = tf.zeros({3, 3}); |
| |
| op_tril_out(self, -1, out1); |
| |
| // clang-format off |
| Tensor result1 = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 4, 0, 0, // [ 4, 0, 0], |
| 7, 8, 0, // [ 7, 8, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out1, result1); |
| |
| // Case: diag = 2 |
| |
| Tensor out2 = tf.zeros({3, 3}); |
| |
| op_tril_out(self, -2, out2); |
| |
| // clang-format off |
| Tensor result2 = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 7, 0, 0, // [ 7, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out2, result2); |
| |
| // Case: diag = 10 |
| |
| Tensor out3 = tf.zeros({3, 3}); |
| |
| op_tril_out(self, -10, out3); |
| |
| // clang-format off |
| Tensor result3 = tf.make( |
| /*sizes=*/{3, 3}, |
| /*data=*/ |
| { |
| 0, 0, 0, // tensor([[ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0], |
| 0, 0, 0, // [ 0, 0, 0]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out3, result3); |
| } |
| |
| // Assert `tril` works on a batch of tensors with random integers, where dims |
| // are equal. |
| template <ScalarType DTYPE> |
| void test_tril_out_randint_multi_equal() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 3, 3, 3}, |
| /*data=*/ |
| { |
| 9, 5, 4, // tensor([[[[ 9, 5, 4], |
| 3, 9, 6, // [ 3, 9, 6], |
| 9, 9, 5, // [ 9, 9, 5]], |
| |
| 7, 2, 6, // [[ 7, 2, 6], |
| 8, 5, 5, // [ 8, 5, 5], |
| 9, 3, 9, // [ 9, 3, 9]], |
| |
| 1, 2, 1, // [[ 1, 2, 1], |
| 6, 2, 6, // [ 6, 2, 6], |
| 1, 1, 8, // [ 1, 1, 8]]], |
| |
| 3, 2, 5, // [[[ 3, 2, 5], |
| 4, 4, 1, // [ 4, 4, 1], |
| 7, 1, 1, // [ 7, 1, 1]], |
| |
| 5, 7, 8, // [[ 5, 7, 8], |
| 1, 5, 7, // [ 1, 5, 7], |
| 7, 6, 3, // [ 7, 6, 3]]], |
| |
| 3, 5, 9, // [[ 3, 5, 9], |
| 4, 2, 2, // [ 4, 2, 2], |
| 9, 5, 2, // [ 9, 5, 2]]], |
| |
| 8, 4, 7, // [[[ 8, 4, 7], |
| 8, 7, 5, // [ 8, 7, 5], |
| 7, 3, 8, // [ 7, 3, 8]], |
| |
| 9, 5, 5, // [[ 9, 5, 5], |
| 6, 1, 8, // [ 6, 1, 8], |
| 8, 9, 7, // [ 8, 9, 7]]], |
| |
| 1, 2, 3, // [[ 1, 2, 3], |
| 7, 9, 1, // [ 7, 9, 1], |
| 5, 2, 2, // [ 5, 2, 2]]]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 3, 3, 3}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 3, 3, 3}, |
| /*data=*/ |
| { |
| 9, 0, 0, // tensor([[[[ 9, 0, 0], |
| 3, 9, 0, // [ 3, 9, 0], |
| 9, 9, 5, // [ 9, 9, 5]], |
| |
| 7, 0, 0, // [[ 7, 0, 0], |
| 8, 5, 0, // [ 8, 5, 0], |
| 9, 3, 9, // [ 9, 3, 9]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 6, 2, 0, // [ 6, 2, 0], |
| 1, 1, 8, // [ 1, 1, 8]]], |
| |
| 3, 0, 0, // [[[ 3, 0, 0], |
| 4, 4, 0, // [ 4, 4, 0], |
| 7, 1, 1, // [ 7, 1, 1]], |
| |
| 5, 0, 0, // [[ 5, 0, 0], |
| 1, 5, 0, // [ 1, 5, 0], |
| 7, 6, 3, // [ 7, 6, 3]]], |
| |
| 3, 0, 0, // [[ 3, 0, 0], |
| 4, 2, 0, // [ 4, 2, 0], |
| 9, 5, 2, // [ 9, 5, 2]]], |
| |
| 8, 0, 0, // [[[ 8, 0, 0], |
| 8, 7, 0, // [ 8, 7, 0], |
| 7, 3, 8, // [ 7, 3, 8]], |
| |
| 9, 0, 0, // [[ 9, 0, 0], |
| 6, 1, 0, // [ 6, 1, 0], |
| 8, 9, 7, // [ 8, 9, 7]]], |
| |
| 1, 0, 0, // [[ 1, 0, 0], |
| 7, 9, 0, // [ 7, 9, 0], |
| 5, 2, 2, // [ 5, 2, 2]]]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| |
| // Assert `tril` works on a batch of tensors with random integers, where dims |
| // are unequal. |
| template <ScalarType DTYPE> |
| void test_tril_out_randint_multi_unequal() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor self = tf.make( |
| /*sizes=*/{3, 2, 3, 2}, |
| /*data=*/ |
| { |
| 1, 1, // tensor([[[[ 1, 1], |
| 1, 1, // [ 1, 1], |
| 9, 1, // [ 9, 1]], |
| |
| 1, 6, // [[ 1, 6], |
| 6, 2, // [ 6, 2], |
| 7, 2, // [ 7, 2]], |
| |
| 2, 4, // [[[ 2, 4], |
| 8, 3, // [ 8, 3], |
| 4, 2, // [ 4, 2]]], |
| |
| 7, 6, // [[ 7, 6], |
| 1, 8, // [ 1, 8], |
| 4, 3, // [ 4, 3]], |
| |
| 2, 2, // [[[ 2, 2], |
| 7, 4, // [ 7, 4], |
| 3, 7, // [ 3, 7]]], |
| |
| 7, 8, // [[ 7, 8], |
| 4, 9, // [ 4, 9], |
| 1, 6, // [ 1, 6]]]]) |
| } |
| ); |
| // clang-format on |
| |
| Tensor out = tf.zeros({3, 2, 3, 2}); |
| |
| op_tril_out(self, 0, out); |
| |
| // clang-format off |
| Tensor result = tf.make( |
| /*sizes=*/{3, 2, 3, 2}, |
| /*data=*/ |
| { |
| 1, 0, // tensor([[[[ 1, 0], |
| 1, 1, // [ 1, 1], |
| 9, 1, // [ 9, 1]], |
| |
| 1, 0, // [[ 1, 0], |
| 6, 2, // [ 6, 2], |
| 7, 2, // [ 7, 2]], |
| |
| 2, 0, // [[[ 2, 0], |
| 8, 3, // [ 8, 3], |
| 4, 2, // [ 4, 2]]], |
| |
| 7, 0, // [[ 7, 0], |
| 1, 8, // [ 1, 8], |
| 4, 3, // [ 4, 3]], |
| |
| 2, 0, // [[[ 2, 0], |
| 7, 4, // [ 7, 4], |
| 3, 7, // [ 3, 7]]], |
| |
| 7, 0, // [[ 7, 0], |
| 4, 9, // [ 4, 9], |
| 1, 6, // [ 1, 6]]]]) |
| } |
| ); |
| // clang-format on |
| |
| EXPECT_TENSOR_EQ(out, result); |
| } |
| }; |
| |
| // Create generic tests for all dtypes. Tensors contain 0s or 1s. |
| #define GENERATE_GENERIC_TEST(_, DTYPE) \ |
| TEST_F(OpTrilTest, DTYPE##GenericTest) { \ |
| test_tril_out_zeros<ScalarType::DTYPE>(); \ |
| test_tril_out_ones<ScalarType::DTYPE>(); \ |
| test_tril_out_empty_dims<ScalarType::DTYPE>(); \ |
| test_tril_out_square<ScalarType::DTYPE>(); \ |
| test_tril_out_rectangle<ScalarType::DTYPE>(); \ |
| test_tril_out_pos_diag<ScalarType::DTYPE>(); \ |
| test_tril_out_neg_diag<ScalarType::DTYPE>(); \ |
| test_tril_out_multi_equal_dim<ScalarType::DTYPE>(); \ |
| test_tril_out_multi_unequal_dim<ScalarType::DTYPE>(); \ |
| } |
| |
| ET_FORALL_REAL_TYPES_AND(Bool, GENERATE_GENERIC_TEST) |
| |
| // Create generic tests for real dtypes. Tensors have diverse values. |
| #define GENERATE_REAL_TEST(_, DTYPE) \ |
| TEST_F(OpTrilTest, DTYPE##RealTest) { \ |
| test_tril_out_arange_pos_diag<ScalarType::DTYPE>(); \ |
| test_tril_out_arange_neg_diag<ScalarType::DTYPE>(); \ |
| test_tril_out_randint_multi_equal<ScalarType::DTYPE>(); \ |
| test_tril_out_randint_multi_unequal<ScalarType::DTYPE>(); \ |
| } |
| |
| ET_FORALL_REAL_TYPES(GENERATE_REAL_TEST) |
| |
| TEST_F(OpTrilTest, InvalidInputShapesDies) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| // `self` and `out` invalid shapes: ndims = 0 is <2. |
| Tensor self1 = tf.zeros({}); |
| Tensor out1 = tf.zeros({}); |
| |
| // Assert `out` can't be filled due to incompatible shapes. |
| ET_EXPECT_KERNEL_FAILURE(context_, op_tril_out(self1, 0, out1)); |
| |
| // `self` and `out` invalid shapes: ndims = 1 is <2. |
| Tensor self2 = tf.zeros({1}); |
| Tensor out2 = tf.zeros({1}); |
| |
| // Assert `out` can't be filled due to incompatible shapes. |
| ET_EXPECT_KERNEL_FAILURE(context_, op_tril_out(self2, 0, out2)); |
| } |
| |
| TEST_F(OpTrilTest, MismatchedOutputShapesDies) { |
| // Skip ATen test since it supports `self` and `out` having different shapes. |
| if (torch::executor::testing::SupportedFeatures::get()->is_aten) { |
| GTEST_SKIP() << "ATen kernel can handle mismatched output shape"; |
| } |
| |
| TensorFactory<ScalarType::Int> tf; |
| |
| // `self` and `out` have different shapes but same dtype. |
| Tensor self = tf.zeros({2, 1}); |
| Tensor out = tf.zeros({2, 2}); |
| |
| // Assert `out` can't be filled due to incompatible shapes. |
| ET_EXPECT_KERNEL_FAILURE(context_, op_tril_out(self, 0, out)); |
| } |
| |
| TEST_F(OpTrilTest, MismatchedOutputDtypeDies) { |
| TensorFactory<ScalarType::Byte> tf_byte; |
| TensorFactory<ScalarType::Float> tf_float; |
| |
| // `self` and `out` have different dtypes but same shape. |
| Tensor self = tf_byte.zeros({2, 2}); |
| Tensor out = tf_float.zeros({2, 2}); |
| |
| // Assert `out` can't be filled due to incompatible dtype. |
| ET_EXPECT_KERNEL_FAILURE(context_, op_tril_out(self, 0, out)); |
| } |
| |
| TEST_F(OpTrilTest, InvalidTensorDims) { |
| // Skip ATen test since it supports `self` and `out` having different shapes. |
| if (torch::executor::testing::SupportedFeatures::get()->is_aten) { |
| GTEST_SKIP() << "ATen kernel can handle mismatched output shape"; |
| } |
| |
| TensorFactory<ScalarType::Int> tf; |
| |
| // Create `self` and `out` with 25 dims. |
| std::vector<int32_t> sizes(25, 1); |
| Tensor self = tf.zeros(sizes); |
| Tensor out = tf.zeros(sizes); |
| |
| // Assert `out` can't be filled due to too many tensor dims. |
| ET_EXPECT_KERNEL_FAILURE(context_, op_tril_out(self, 0, out)); |
| } |
