| /* |
| * 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 <executorch/runtime/core/exec_aten/util/scalar_type_util.h> |
| |
| #include <gtest/gtest.h> |
| |
| using namespace ::testing; |
| using exec_aten::ArrayRef; |
| using exec_aten::optional; |
| using exec_aten::ScalarType; |
| using exec_aten::Tensor; |
| using torch::executor::testing::TensorFactory; |
| |
| class OpSliceCopyTensorOutTest : public OperatorTest { |
| protected: |
| Tensor& op_slice_copy_tensor_out( |
| const Tensor& self, |
| int64_t dim, |
| optional<int64_t> start, |
| optional<int64_t> end, |
| int64_t step, |
| Tensor& out) { |
| return torch::executor::aten::slice_copy_outf( |
| context_, self, dim, start, end, step, out); |
| } |
| |
| template <class CTYPE, exec_aten::ScalarType DTYPE> |
| void test_dtype() { |
| TensorFactory<DTYPE> tf; |
| |
| // clang-format off |
| Tensor input = tf.make( |
| /*sizes=*/{3, 4}, |
| /*data=*/{ |
| 1, 2, 3, 4, // [0, :] |
| 5, 6, 7, 8, // [1, :] |
| 9, 10, 11, 12, // [2, :] |
| }); |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/0, /*start=*/0, /*end=*/2, /*step=*/1, out), |
| // The result should equal to input[0:2:1, :] |
| Tensor expect_ret = tf.make( |
| /*sizes=*/{2, 4}, |
| /*data=*/{ |
| 1, 2, 3, 4, // [0, :] |
| 5, 6, 7, 8, // [1, :] |
| }); |
| // clang-format on |
| |
| Tensor out = tf.zeros({2, 4}); |
| Tensor ret = op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/2, /*step=*/1, out); |
| |
| EXPECT_TENSOR_EQ(out, ret); |
| EXPECT_TENSOR_EQ(ret, expect_ret); |
| } |
| }; |
| |
| TEST_F(OpSliceCopyTensorOutTest, LegalDimSupported) { |
| TensorFactory<ScalarType::Double> tf; |
| |
| // clang-format off |
| Tensor input = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| // clang-format on |
| |
| // clang-format off |
| // The size of expected output tensor should follow these rules: |
| // - output.size(i) shall equal input.size(i) if i != dim, |
| // - output.size(i) shall equal num_values if i == dim |
| // The definition of num_values could be found at https://fburl.com/code/mnnxkowm |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/0, /*start=*/0, /*end=*/1, /*step=*/1, out), |
| // The result should equal to input[0:1:1，:, :] |
| Tensor expected_dim_0 = tf.make( |
| /*sizes=*/{1, 3, 4}, |
| /*data=*/{ |
| 1., 2., 3., 4., // [0, :] |
| 5., 6., 7., 8., // [1, :] |
| 9., 10., 11., 12., // [2, :] |
| }); |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/1, /*step=*/1, out), |
| // The result should equal to input[:，0:1:1, :] |
| Tensor expected_dim_1 = tf.make( |
| /*sizes=*/{2, 1, 4}, |
| /*data=*/{ |
| 1., 2., 3., 4., // [0, :, :] |
| -1., -2., -3., -4., // [1, :, :] |
| }); |
| // op_slice_copy_tensor_out(input, /*dim=*/2, /*start=*/0, /*end=*/1, /*step=*/1, out), |
| // The result should equal to input[:，:, 0:1:1] |
| Tensor expected_dim_2 = tf.make( |
| /*sizes=*/{2, 3, 1}, |
| /*data=*/{ |
| 1., 5., 9., // [0, :, :] |
| -1., -5., -9., // [1, :, :] |
| }); |
| // clang-format on |
| std::vector<Tensor> expected_rets = { |
| // Groud truth for dim=-3 |
| expected_dim_0, |
| // Groud truth for dim=-2 |
| expected_dim_1, |
| // Groud truth for dim=-1 |
| expected_dim_2, |
| // Groud truth for dim=0 |
| expected_dim_0, |
| // Groud truth for dim=1 |
| expected_dim_1, |
| // Groud truth for dim=2 |
| expected_dim_2, |
| }; |
| |
| for (int64_t dim = -3; dim < 3; dim++) { |
| int64_t testcase_idx = dim + 3; |
| auto expected_ret = expected_rets[testcase_idx]; |
| Tensor out = tf.zeros_like(expected_ret); |
| |
| // Slice input on dim with start=0, end = 0 and step = 1 |
| // Should always return the provided out Tensor. |
| // The ret shall meet the expectation. |
| Tensor ret = op_slice_copy_tensor_out( |
| input, dim, /*start=*/0, /*end=*/1, /*step=*/1, out); |
| EXPECT_TENSOR_EQ(out, ret); |
| EXPECT_TENSOR_EQ(ret, expected_rets[testcase_idx]); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, AllStartValsSupported) { |
| TensorFactory<ScalarType::Double> tf; |
| |
| // clang-format off |
| Tensor input = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| // clang-format on |
| |
| // clang-format off |
| // Set the end large enough to hold any start |
| |
| // The size of expected output tensor should follow these rules: |
| // - output.size(i) shall equal input.size(i) if i != dim, |
| // - output.size(i) shall equal num_values if i == dim |
| // The definition of num_values could be found at https://fburl.com/code/mnnxkowm |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/ <= 0, /*end=*/10, /*step=*/1, out), |
| // The result shall equal to input[:，0:3:1, :] |
| Tensor expected_start_0_or_below = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/1, /*end=*/10, /*step=*/1, out), |
| // The result shall equal to input[:，1:3:1, :] |
| Tensor expected_start_1 = tf.make( |
| /*sizes=*/{2, 2, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 5., 6., 7., 8., // [0, 0, :] |
| 9., 10., 11., 12., // [0, 1, :] |
| |
| // [1, :, :] |
| -5., -6., -7., -8., // [1, 0, :] |
| -9., -10., -11., -12., // [1, 1, :] |
| }); |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/2, /*end=*/10, /*step=*/1, out), |
| // The result shall equal to input[:，2:3:1, :] = input |
| Tensor expected_start_2 = tf.make( |
| /*sizes=*/{2, 1, 4}, |
| /*data=*/{ |
| 9., 10., 11., 12., // [0, 0, :] |
| -9., -10., -11., -12., // [1, 0, :] |
| }); |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/ > input.size(1) = 2, /*end=*/10, /*step=*/1, out), |
| // The result shall equal to input[:, 3:3:1, :], which is an empty tensor |
| Tensor expected_start_3_or_above = tf.make({2, 0, 4}, {}); |
| // clang-format on |
| std::vector<Tensor> expected_rets = {// start = -3 |
| expected_start_0_or_below, |
| // start = -2 |
| expected_start_1, |
| // start = -1 |
| expected_start_2, |
| // start = 0 |
| expected_start_0_or_below, |
| // start = 1 |
| expected_start_1, |
| // start = 2 |
| expected_start_2, |
| // start = 3 |
| expected_start_3_or_above}; |
| |
| // In this test, we maintain dim and step as 1 and 1, also set the end |
| // large enough to hold any start |
| int64_t dim = 1; |
| int64_t end = 10; |
| int64_t step = 1; |
| for (int64_t start = -3; start < 4; start++) { |
| int64_t testcase_idx = start + 3; |
| auto expected_ret = expected_rets[testcase_idx]; |
| Tensor out = tf.zeros_like(expected_ret); |
| |
| // Should always return the provided out Tensor. |
| // The ret shall meet the expectation. |
| Tensor ret = op_slice_copy_tensor_out(input, dim, start, end, step, out); |
| EXPECT_TENSOR_EQ(out, ret); |
| EXPECT_TENSOR_EQ(ret, expected_ret); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, AllEndValsSupported) { |
| TensorFactory<ScalarType::Double> tf; |
| |
| // clang-format off |
| Tensor input = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| |
| // The size of expected output tensor should follow these rules: |
| // - output.size(i) shall equal input.size(i) if i != dim, |
| // - output.size(i) shall equal num_values if i == dim |
| // The definition of num_values could be found at https://fburl.com/code/mnnxkowm |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/ <= 0, /*step=*/1, out), |
| // The result should equal input[:，0:0:1, :], which should be an empty tensor |
| Tensor expected_end_0_or_below = tf.make({2, 0, 4}, {}); |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/1, /*step=*/1, out), |
| // The result should equal to input[:，0:1:1, :] |
| Tensor expected_end_1 = tf.make( |
| /*sizes=*/{2, 1, 4}, |
| /*data=*/{ |
| 1., 2., 3., 4., // [0, :, :] |
| -1., -2., -3., -4., // [1, :, :] |
| }); |
| |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/2, /*step=*/1, out), |
| // The result should equal input[:，0:2:1, :] |
| Tensor expected_end_2 = tf.make( |
| /*sizes=*/{2, 2, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| }); |
| // op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/ >= 3, /*step=*/1, out), |
| // The result should equal input[:，0:3:1, :] = input for any end >= 3 |
| Tensor expected_end_3_or_above = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| // clang-format on |
| std::vector<Tensor> expected_rets = {// end = -3 |
| expected_end_0_or_below, |
| // end = -2 |
| expected_end_1, |
| // end = -1 |
| expected_end_2, |
| // end = 0 |
| expected_end_0_or_below, |
| // end = 1 |
| expected_end_1, |
| // end = 2 |
| expected_end_2, |
| // end = 3 |
| expected_end_3_or_above}; |
| |
| int64_t dim = 1; |
| int64_t start = 0; |
| int64_t step = 1; |
| for (int64_t end = -3; end < 4; end++) { |
| int64_t testcase_idx = end + 3; |
| |
| auto expected_ret = expected_rets[testcase_idx]; |
| Tensor out = tf.zeros_like(expected_ret); |
| |
| // Should always return the provided out Tensor. |
| // The ret shall meet the expectation. |
| Tensor ret = op_slice_copy_tensor_out(input, dim, start, end, step, out); |
| EXPECT_TENSOR_EQ(out, ret); |
| EXPECT_TENSOR_EQ(ret, expected_ret); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, LegalStepsSupported) { |
| TensorFactory<ScalarType::Double> tf; |
| |
| // clang-format off |
| Tensor input = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| |
| // Set the end large enough to hold any step |
| |
| // Expected ret for op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/10, /*step=*/1, out), |
| // The result should equal to input[:，0:3:1, :] |
| Tensor expected_0 = tf.make( |
| /*sizes=*/{2, 3, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 5., 6., 7., 8., // [0, 1, :] |
| 9., 10., 11., 12., // [0, 2, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -5., -6., -7., -8., // [1, 1, :] |
| -9., -10., -11., -12., // [1, 2, :] |
| }); |
| // Expected ret for op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/10, /*step=*/2, out), |
| // The result should equal to input[:，0:3:2, :] |
| Tensor expected_1 = tf.make( |
| /*sizes=*/{2, 2, 4}, |
| /*data=*/{ |
| // [0, :, :] |
| 1., 2., 3., 4., // [0, 0, :] |
| 9., 10., 11., 12., // [0, 1, :] |
| |
| // [1, :, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| -9., -10., -11., -12., // [1, 1, :] |
| }); |
| // Expected ret for op_slice_copy_tensor_out(input, /*dim=*/1, /*start=*/0, /*end=*/10, /*step=*/3, out), |
| // The result should equal to input[:，0:3:3, :] = input |
| Tensor expected_2 = tf.make( |
| /*sizes=*/{2, 1, 4}, |
| /*data=*/{ |
| 1., 2., 3., 4., // [0, 0, :] |
| -1., -2., -3., -4., // [1, 0, :] |
| }); |
| // clang-format on |
| std::vector<Tensor> expected_rets = {expected_0, expected_1, expected_2}; |
| |
| // In this test, we maintain start and dim as 0 and 1, also set the |
| // end large enough to hold any step |
| int64_t start = 0; |
| int64_t dim = 1; |
| int64_t end = 10; |
| for (int64_t step = 1; step < 4; step++) { |
| int64_t testcase_idx = step - 1; |
| |
| auto expected_ret = expected_rets[testcase_idx]; |
| Tensor out = tf.zeros_like(expected_ret); |
| |
| // Should always return the provided out Tensor. |
| // The ret shall meet the expectation. |
| Tensor ret = op_slice_copy_tensor_out(input, dim, start, end, step, out); |
| EXPECT_TENSOR_EQ(out, ret); |
| EXPECT_TENSOR_EQ(ret, expected_ret); |
| } |
| } |
| |
| /// A generic smoke test that works for any dtype that supports ones() and |
| /// zeros(). |
| TEST_F(OpSliceCopyTensorOutTest, AllDtypesSupported) { |
| if (torch::executor::testing::SupportedFeatures::get()->is_aten) { |
| GTEST_SKIP() << "ATen kernel test fails"; |
| } |
| #define TEST_ENTRY(ctype, dtype) test_dtype<ctype, ScalarType::dtype>(); |
| ET_FORALL_REAL_TYPES_AND(Bool, TEST_ENTRY); |
| #undef TEST_ENTRY |
| // TODO: Also add tests for half, complex, quantized, and other types. Easiest |
| // way to do that would be to make TensorFactory support zeros() and ones() |
| // for those types. |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, EmptyInputSupported) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.ones({1, 0, 1}); |
| Tensor out = tf.zeros({1, 0, 1}); |
| |
| Tensor expect = tf.ones({1, 0, 1}); |
| |
| // Some invalid dim values. |
| for (int64_t dim = 0; dim > input.dim(); dim++) { |
| Tensor ret = op_slice_copy_tensor_out( |
| input, dim, /*start=*/0, /*end=*/1, /*step=*/1, out); |
| EXPECT_TENSOR_EQ(ret, out); |
| |
| // All operations in this test share same ground truth |
| EXPECT_TENSOR_EQ(ret, expect); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, EmptySizeInputDies) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.ones({}); |
| Tensor out = tf.ones({}); |
| |
| // The operation shall die whatever the end is. |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/0, /*step=*/1, out)); |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/1, /*step=*/1, out)); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, ZeroLengthSupported) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.ones({2, 3}); |
| Tensor out = tf.ones({2, 0}); |
| |
| Tensor expect = tf.ones({2, 0}); |
| |
| Tensor ret = op_slice_copy_tensor_out( |
| input, /*dim=*/1, /*start=*/1, /*end=*/1, /*step=*/1, out); |
| EXPECT_TENSOR_EQ(ret, out); |
| EXPECT_TENSOR_EQ(ret, expect); |
| |
| ret = op_slice_copy_tensor_out( |
| input, /*dim=*/1, /*start=*/-1, /*end=*/-1, /*step=*/1, out); |
| EXPECT_TENSOR_EQ(ret, out); |
| EXPECT_TENSOR_EQ(ret, expect); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, NonPostiveStepsDies) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.ones({1, 1, 1}); |
| Tensor out = tf.zeros({1, 1, 1}); |
| |
| // Some invalid step values. |
| const std::vector<int64_t> invalid_steps = {-2, -1, 0}; |
| for (int64_t step : invalid_steps) { |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/1, /*step=*/step, out)); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, DimOutOfBoundDies) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.ones({1, 1, 1}); |
| Tensor out = tf.zeros({1, 1, 1}); |
| |
| // Some invalid dim values. |
| const std::vector<int64_t> invalid_dims = {3, 4, 5, -4, -5, -6}; |
| for (int64_t dim : invalid_dims) { |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, dim, /*start=*/0, /*end=*/1, /*step=*/1, out)); |
| } |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, MismatchedDtypesDies) { |
| TensorFactory<ScalarType::Int> tf_int; |
| TensorFactory<ScalarType::Float> tf_float; |
| Tensor input = tf_int.zeros({1, 2, 2}); |
| |
| // Size is compatible to the output, but a mismatched dtype. |
| Tensor out = tf_float.ones({1, 2, 2}); |
| |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/1, /*step=*/1, out)); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, OutSizeMismatchDimDies) { |
| if (torch::executor::testing::SupportedFeatures::get()->is_aten) { |
| GTEST_SKIP() << "ATen kernel can handle out with mismatched dimensions"; |
| } |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.zeros({2, 4, 7, 5}); |
| |
| // Should be {2, 4, 7, 5} |
| Tensor out = tf.zeros({2, 4, 7}); |
| |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_slice_copy_tensor_out( |
| input, /*dim=*/0, /*start=*/0, /*end=*/2, /*step=*/1, out)); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, DefaultStartValSupported) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.zeros({2, 4, 7, 5}); |
| |
| Tensor out = tf.ones({2, 4, 7, 5}); |
| Tensor expected = tf.zeros({2, 4, 7, 5}); |
| |
| Tensor ret_default_start = op_slice_copy_tensor_out( |
| input, |
| /*dim=*/0, |
| /*start=*/exec_aten::nullopt, |
| /*end=*/2, |
| /*step=*/1, |
| out); |
| EXPECT_TENSOR_EQ(ret_default_start, out); |
| EXPECT_TENSOR_EQ(ret_default_start, expected); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, DefaultEndValSupported) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| Tensor input = tf.zeros({2, 4, 7, 5}); |
| |
| Tensor out = tf.ones({2, 4, 7, 5}); |
| Tensor expected = tf.zeros({2, 4, 7, 5}); |
| |
| Tensor ret_default_end = op_slice_copy_tensor_out( |
| input, |
| /*dim=*/0, |
| /*start=*/0, |
| /*end=*/exec_aten::nullopt, |
| /*step=*/1, |
| out); |
| EXPECT_TENSOR_EQ(ret_default_end, out); |
| EXPECT_TENSOR_EQ(ret_default_end, expected); |
| } |
| |
| /* %python |
| import torch |
| torch.manual_seed(0) |
| x = torch.rand(2, 6, 3) |
| res = x[:, 1:5:2, :] |
| print(res.size()) |
| op = "op_slice_copy_tensor_out" |
| opt_extra_params = "1, 1, 5, 2," |
| dtype = "ScalarType::Float" |
| check = "EXPECT_TENSOR_EQ" */ |
| |
| TEST_F(OpSliceCopyTensorOutTest, DynamicShapeUpperBoundSameAsExpected) { |
| /* %python |
| out_args = "{2, 2, 3}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND" |
| %rewrite(unary_op) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor x = tf.make( |
| {2, 6, 3}, |
| {0.49625658988952637, 0.7682217955589294, 0.08847743272781372, |
| 0.13203048706054688, 0.30742281675338745, 0.6340786814689636, |
| 0.4900934100151062, 0.8964447379112244, 0.455627977848053, |
| 0.6323062777519226, 0.3488934636116028, 0.40171730518341064, |
| 0.022325754165649414, 0.16885894536972046, 0.2938884496688843, |
| 0.518521785736084, 0.6976675987243652, 0.800011396408081, |
| 0.16102945804595947, 0.28226858377456665, 0.6816085577011108, |
| 0.9151939749717712, 0.39709991216659546, 0.8741558790206909, |
| 0.41940832138061523, 0.5529070496559143, 0.9527381062507629, |
| 0.036164820194244385, 0.1852310299873352, 0.37341737747192383, |
| 0.3051000237464905, 0.9320003986358643, 0.17591017484664917, |
| 0.2698335647583008, 0.15067976713180542, 0.03171950578689575}); |
| Tensor expected = tf.make( |
| {2, 2, 3}, |
| {0.13203048706054688, |
| 0.30742281675338745, |
| 0.6340786814689636, |
| 0.6323062777519226, |
| 0.3488934636116028, |
| 0.40171730518341064, |
| 0.9151939749717712, |
| 0.39709991216659546, |
| 0.8741558790206909, |
| 0.036164820194244385, |
| 0.1852310299873352, |
| 0.37341737747192383}); |
| |
| Tensor out = |
| tf.zeros({2, 2, 3}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND); |
| op_slice_copy_tensor_out(x, 1, 1, 5, 2, out); |
| EXPECT_TENSOR_EQ(out, expected); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, DynamicShapeUpperBoundLargerThanExpected) { |
| if (!torch::executor::testing::SupportedFeatures::get()->output_resize) { |
| GTEST_SKIP() << "Dynamic shape not supported"; |
| } |
| /* %python |
| out_args = "{10, 10, 10}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND" |
| %rewrite(unary_op) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor x = tf.make( |
| {2, 6, 3}, |
| {0.49625658988952637, 0.7682217955589294, 0.08847743272781372, |
| 0.13203048706054688, 0.30742281675338745, 0.6340786814689636, |
| 0.4900934100151062, 0.8964447379112244, 0.455627977848053, |
| 0.6323062777519226, 0.3488934636116028, 0.40171730518341064, |
| 0.022325754165649414, 0.16885894536972046, 0.2938884496688843, |
| 0.518521785736084, 0.6976675987243652, 0.800011396408081, |
| 0.16102945804595947, 0.28226858377456665, 0.6816085577011108, |
| 0.9151939749717712, 0.39709991216659546, 0.8741558790206909, |
| 0.41940832138061523, 0.5529070496559143, 0.9527381062507629, |
| 0.036164820194244385, 0.1852310299873352, 0.37341737747192383, |
| 0.3051000237464905, 0.9320003986358643, 0.17591017484664917, |
| 0.2698335647583008, 0.15067976713180542, 0.03171950578689575}); |
| Tensor expected = tf.make( |
| {2, 2, 3}, |
| {0.13203048706054688, |
| 0.30742281675338745, |
| 0.6340786814689636, |
| 0.6323062777519226, |
| 0.3488934636116028, |
| 0.40171730518341064, |
| 0.9151939749717712, |
| 0.39709991216659546, |
| 0.8741558790206909, |
| 0.036164820194244385, |
| 0.1852310299873352, |
| 0.37341737747192383}); |
| |
| Tensor out = tf.zeros( |
| {10, 10, 10}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND); |
| op_slice_copy_tensor_out(x, 1, 1, 5, 2, out); |
| EXPECT_TENSOR_EQ(out, expected); |
| } |
| |
| TEST_F(OpSliceCopyTensorOutTest, DynamicShapeUnbound) { |
| if (!torch::executor::testing::SupportedFeatures::get()->output_resize) { |
| GTEST_SKIP() << "Dynamic shape not supported"; |
| } |
| /* %python |
| out_args = "{1, 1, 1}, torch::executor::TensorShapeDynamism::DYNAMIC_UNBOUND" |
| %rewrite(unary_op) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor x = tf.make( |
| {2, 6, 3}, |
| {0.49625658988952637, 0.7682217955589294, 0.08847743272781372, |
| 0.13203048706054688, 0.30742281675338745, 0.6340786814689636, |
| 0.4900934100151062, 0.8964447379112244, 0.455627977848053, |
| 0.6323062777519226, 0.3488934636116028, 0.40171730518341064, |
| 0.022325754165649414, 0.16885894536972046, 0.2938884496688843, |
| 0.518521785736084, 0.6976675987243652, 0.800011396408081, |
| 0.16102945804595947, 0.28226858377456665, 0.6816085577011108, |
| 0.9151939749717712, 0.39709991216659546, 0.8741558790206909, |
| 0.41940832138061523, 0.5529070496559143, 0.9527381062507629, |
| 0.036164820194244385, 0.1852310299873352, 0.37341737747192383, |
| 0.3051000237464905, 0.9320003986358643, 0.17591017484664917, |
| 0.2698335647583008, 0.15067976713180542, 0.03171950578689575}); |
| Tensor expected = tf.make( |
| {2, 2, 3}, |
| {0.13203048706054688, |
| 0.30742281675338745, |
| 0.6340786814689636, |
| 0.6323062777519226, |
| 0.3488934636116028, |
| 0.40171730518341064, |
| 0.9151939749717712, |
| 0.39709991216659546, |
| 0.8741558790206909, |
| 0.036164820194244385, |
| 0.1852310299873352, |
| 0.37341737747192383}); |
| |
| Tensor out = tf.zeros( |
| {1, 1, 1}, torch::executor::TensorShapeDynamism::DYNAMIC_UNBOUND); |
| op_slice_copy_tensor_out(x, 1, 1, 5, 2, out); |
| EXPECT_TENSOR_EQ(out, expected); |
| } |
