| /* |
| * 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::ArrayRef; |
| using exec_aten::optional; |
| using exec_aten::ScalarType; |
| using exec_aten::Tensor; |
| using torch::executor::testing::TensorFactory; |
| |
| class OpConvOutTest : public OperatorTest { |
| protected: |
| Tensor& op_convolution_out( |
| const Tensor& input, |
| const Tensor& weight, |
| const optional<Tensor>& bias, |
| ArrayRef<int64_t> stride, |
| ArrayRef<int64_t> padding, |
| ArrayRef<int64_t> dilation, |
| bool transposed, |
| ArrayRef<int64_t> output_padding, |
| int64_t groups, |
| Tensor& out) { |
| return torch::executor::aten::convolution_outf( |
| context_, |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| transposed, |
| output_padding, |
| groups, |
| out); |
| } |
| |
| /* Correctness Test Template for test code generation via Python */ |
| /* %python |
| correctness_test_template = f""" |
| {declare_tensor_factory("ScalarType::$DTYPE$", "tf")} |
| |
| {declare_tensor_make_t("input", "tf")} |
| {declare_tensor_make_t("weight", "tf")} |
| {declare_optional_tensor_make_t("bias", "tf")} |
| {declare_tensor_make_t("expected", "tf")} |
| Tensor out = tf.zeros($out_size$, $dynamism$); |
| |
| {declare_array_ref_t("stride", "int64_t")} |
| {declare_array_ref_t("padding", "int64_t")} |
| {declare_array_ref_t("dilation", "int64_t")} |
| {declare_array_ref_t("output_padding", "int64_t")} |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| $transposed$, |
| output_padding, |
| $groups$, |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected);""" |
| */ |
| |
| /* %python |
| import torch |
| torch.manual_seed(0) |
| input = (torch.randint(10, 100, (1, 2, 5)).to(torch.double) / 10.0); |
| weight = (torch.randint(10, 100, (4, 2, 3)).to(torch.double) / 10.0); |
| bias = torch.ones(4).to(torch.double) |
| stride = [2] |
| padding = [0] |
| dilation = [1] |
| transposed = False |
| output_padding = [0] |
| groups = 1 |
| expected = torch.nn.functional.conv1d( |
| input, weight, bias, stride, padding, dilation, groups) |
| |
| DTYPE = "Float" |
| out_size = "out_shape" |
| dynamism = "dynamism" |
| */ |
| |
| void test_dynamic_shape( |
| const std::vector<int32_t>& out_shape, |
| enum torch::executor::TensorShapeDynamism dynamism) { |
| /* %python |
| %past-rewrite(correctness_test_template) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = |
| tf.make({1, 2, 5}, {5.4, 1.9, 9.3, 7.0, 5.3, 7.9, 1.7, 8.3, 4.7, 7.3}); |
| Tensor weight = |
| tf.make({4, 2, 3}, {8.1, 6.6, 1.6, 4.9, 3.8, 6.6, 4.6, 2.8, |
| 2.4, 1.3, 3.6, 3.9, 8.1, 8.4, 5.4, 5.1, |
| 8.9, 9.9, 7.9, 1.0, 1.1, 8.2, 6.3, 7.0}); |
| optional<Tensor> bias(tf.make({4}, {1.0, 1.0, 1.0, 1.0})); |
| Tensor expected = tf.make( |
| {1, 4, 2}, |
| {172.11, 237.72, 102.24, 132.28, 248.51, 320.18, 189.38, 236.07}); |
| Tensor out = tf.zeros(out_shape, dynamism); |
| |
| int64_t stride[] = {2}; |
| int64_t padding[] = {0}; |
| int64_t dilation[] = {1}; |
| int64_t output_padding[] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| false, |
| output_padding, |
| 1, |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| }; |
| |
| class OpConvCorrectnessTest : public OpConvOutTest {}; |
| |
| // |
| // Correctness Tests |
| // |
| |
| TEST_F(OpConvCorrectnessTest, GenericSmokeTest) { |
| TensorFactory<ScalarType::Int> tf; |
| |
| auto input = tf.make({1, 2, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); |
| auto weight = |
| tf.make({4, 2, 3}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, |
| 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); |
| auto bias = tf.ones({4}); |
| auto expected = tf.make({1, 4, 2}, {80, 110, 206, 308, 332, 506, 458, 704}); |
| auto out = tf.zeros({1, 4, 2}); |
| |
| int64_t stride[1] = {2}; |
| int64_t padding[1] = {0}; |
| int64_t dilation[1] = {1}; |
| int64_t output_padding[1] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| false, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| int64_t(1), |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| /* %python |
| import torch |
| torch.manual_seed(0) |
| input = (torch.randint(10, 100, (1, 2, 5)).to(torch.double) / 10.0); |
| weight = (torch.randint(10, 100, (4, 2, 3)).to(torch.double) / 10.0); |
| bias = torch.ones(4).to(torch.double) |
| stride = [2] |
| padding = [2] |
| dilation = [1] |
| transposed = False |
| output_padding = [0] |
| groups = 1 |
| expected = torch.nn.functional.conv1d( |
| input, weight, bias, stride, padding, dilation, groups) |
| |
| DTYPE = "Float" |
| out_size = expected.size() |
| dynamism = "torch::executor::TensorShapeDynamism::STATIC" |
| */ |
| TEST_F(OpConvCorrectnessTest, NonZeroPadding) { |
| /* %python |
| %past-rewrite(correctness_test_template) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = |
| tf.make({1, 2, 5}, {5.4, 1.9, 9.3, 7.0, 5.3, 7.9, 1.7, 8.3, 4.7, 7.3}); |
| Tensor weight = tf.make( |
| {4, 2, 3}, {8.1, 6.6, 1.6, 4.9, 3.8, 6.6, 4.6, 2.8, 2.4, 1.3, 3.6, 3.9, |
| 8.1, 8.4, 5.4, 5.1, 8.9, 9.9, 7.9, 1.0, 1.1, 8.2, 6.3, 7.0}); |
| optional<Tensor> bias(tf.make({4}, {1.0, 1.0, 1.0, 1.0})); |
| Tensor expected = tf.make( |
| {1, 4, 4}, |
| {61.78, |
| 172.11, |
| 237.72, |
| 79.7, |
| 44.77, |
| 102.24, |
| 132.28, |
| 34.87, |
| 108.37, |
| 248.51, |
| 320.18, |
| 81.16, |
| 62.24, |
| 189.38, |
| 236.07, |
| 102.73}); |
| Tensor out = |
| tf.zeros({1, 4, 4}, torch::executor::TensorShapeDynamism::STATIC); |
| |
| int64_t stride[] = {2}; |
| int64_t padding[] = {2}; |
| int64_t dilation[] = {1}; |
| int64_t output_padding[] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| false, |
| output_padding, |
| 1, |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| /* %python |
| import torch |
| torch.manual_seed(0) |
| input = (torch.randint(10, 100, (3, 2, 5)).to(torch.double) / 10.0); |
| weight = (torch.randint(10, 100, (4, 2, 3)).to(torch.double) / 10.0); |
| bias = torch.ones(4).to(torch.double) |
| stride = [2] |
| padding = [2] |
| dilation = [1] |
| transposed = False |
| output_padding = [0] |
| groups = 1 |
| expected = torch.nn.functional.conv1d( |
| input, weight, bias, stride, padding, dilation, groups) |
| |
| DTYPE = "Float" |
| out_size = expected.size() |
| dynamism = "torch::executor::TensorShapeDynamism::STATIC" |
| */ |
| TEST_F(OpConvCorrectnessTest, MultipleInputBatches) { |
| /* %python |
| %past-rewrite(correctness_test_template) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = |
| tf.make({3, 2, 5}, {5.4, 1.9, 9.3, 7.0, 5.3, 7.9, 1.7, 8.3, 4.7, 7.3, |
| 8.1, 6.6, 1.6, 4.9, 3.8, 6.6, 4.6, 2.8, 2.4, 1.3, |
| 3.6, 3.9, 8.1, 8.4, 5.4, 5.1, 8.9, 9.9, 7.9, 1.0}); |
| Tensor weight = tf.make( |
| {4, 2, 3}, {1.1, 8.2, 6.3, 7.0, 6.5, 2.5, 9.2, 9.9, 8.1, 9.8, 4.8, 1.3, |
| 2.6, 8.9, 1.1, 8.7, 2.3, 3.5, 4.2, 7.1, 5.0, 3.9, 3.3, 4.1}); |
| optional<Tensor> bias(tf.make({4}, {1.0, 1.0, 1.0, 1.0})); |
| Tensor expected = tf.make( |
| {3, 4, 4}, {54.77, 168.21, 208.92, 57.93, 55.01, 241.19, 312.18, 121.3, |
| 34.59, 143.87, 201.88, 78.29, 60.39, 154.12, 194.07, 51.73, |
| 68.53, 157.21, 105.33, 14.28, 75.19, 244.22, 135.66, 48.70, |
| 33.01, 160.36, 87.38, 22.19, 68.56, 142.28, 85.68, 22.03, |
| 36.43, 206.27, 235.96, 13.94, 36.79, 243.91, 338.66, 60.48, |
| 22.81, 153.47, 210.56, 23.74, 39.91, 174.16, 190.44, 27.58}); |
| Tensor out = |
| tf.zeros({3, 4, 4}, torch::executor::TensorShapeDynamism::STATIC); |
| |
| int64_t stride[] = {2}; |
| int64_t padding[] = {2}; |
| int64_t dilation[] = {1}; |
| int64_t output_padding[] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| false, |
| output_padding, |
| 1, |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| /* %python |
| import torch |
| torch.manual_seed(0) |
| input = (torch.randint(10, 100, (1, 4, 8, 8)).to(torch.double) / 10.0); |
| weight = (torch.randint(10, 100, (2, 4, 3, 3)).to(torch.double) / 10.0); |
| bias = torch.ones(2).to(torch.double) |
| stride = [2, 2] |
| padding = [1, 1] |
| dilation = [1, 1] |
| transposed = False |
| output_padding = [0] |
| groups = 1 |
| expected = torch.nn.functional.conv2d( |
| input, weight, bias, stride, padding, dilation, groups) |
| |
| DTYPE = "Float" |
| out_size = expected.size() |
| dynamism = "torch::executor::TensorShapeDynamism::STATIC" |
| */ |
| TEST_F(OpConvCorrectnessTest, 2DSanityCheck) { |
| /* %python |
| %past-rewrite(correctness_test_template) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.make( |
| {1, 4, 8, 8}, |
| {5.4, 1.9, 9.3, 7.0, 5.3, 7.9, 1.7, 8.3, 4.7, 7.3, 8.1, 6.6, 1.6, 4.9, |
| 3.8, 6.6, 4.6, 2.8, 2.4, 1.3, 3.6, 3.9, 8.1, 8.4, 5.4, 5.1, 8.9, 9.9, |
| 7.9, 1.0, 1.1, 8.2, 6.3, 7.0, 6.5, 2.5, 9.2, 9.9, 8.1, 9.8, 4.8, 1.3, |
| 2.6, 8.9, 1.1, 8.7, 2.3, 3.5, 4.2, 7.1, 5.0, 3.9, 3.3, 4.1, 8.1, 6.0, |
| 3.3, 8.6, 6.6, 5.7, 5.9, 8.6, 7.3, 3.4, 9.5, 6.0, 6.8, 6.2, 1.8, 3.2, |
| 2.7, 7.5, 7.0, 8.0, 2.8, 5.1, 4.9, 8.6, 1.1, 9.0, 4.2, 9.9, 2.4, 5.3, |
| 4.9, 9.3, 2.9, 5.3, 8.9, 4.8, 9.5, 2.3, 9.2, 3.8, 6.5, 9.6, 2.6, 3.5, |
| 2.7, 9.2, 1.5, 7.6, 5.6, 8.5, 5.4, 7.0, 8.8, 5.1, 2.7, 1.8, 7.5, 4.4, |
| 2.4, 4.8, 1.4, 3.4, 8.9, 4.0, 4.7, 3.4, 2.5, 8.3, 8.3, 1.7, 2.3, 9.0, |
| 2.9, 2.9, 5.3, 7.1, 3.8, 7.1, 1.7, 9.8, 2.4, 4.1, 6.0, 8.4, 4.0, 1.4, |
| 7.9, 7.7, 4.0, 4.0, 9.1, 7.4, 4.9, 3.9, 3.5, 8.9, 2.2, 3.2, 8.2, 7.1, |
| 5.4, 2.9, 8.1, 5.1, 3.0, 9.3, 2.0, 3.6, 8.7, 6.6, 9.9, 3.1, 7.6, 3.4, |
| 4.1, 5.0, 8.5, 9.2, 7.5, 5.8, 6.1, 5.8, 4.1, 4.2, 9.8, 2.0, 7.3, 2.8, |
| 7.9, 8.2, 9.7, 9.0, 4.8, 7.8, 6.6, 5.8, 4.5, 7.8, 4.6, 8.5, 7.2, 4.4, |
| 1.2, 7.7, 2.2, 2.4, 2.9, 1.8, 2.5, 2.6, 3.4, 6.3, 9.3, 8.4, 3.0, 8.2, |
| 1.5, 2.1, 3.2, 5.8, 5.2, 6.4, 1.8, 7.3, 7.6, 1.5, 2.8, 7.8, 9.0, 5.5, |
| 4.1, 2.3, 3.0, 8.8, 7.1, 7.1, 9.1, 3.7, 6.2, 6.2, 2.2, 1.3, 4.3, 5.6, |
| 8.7, 6.8, 5.0, 9.5, 5.0, 5.3, 5.5, 4.5, 3.3, 6.6, 6.2, 8.2, 5.5, 8.5, |
| 2.9, 9.4, 8.3, 8.3}); |
| Tensor weight = tf.make( |
| {2, 4, 3, 3}, |
| {4.7, 1.3, 7.8, 3.0, 9.7, 2.5, 3.8, 5.2, 4.4, 7.7, 2.3, 6.2, |
| 1.5, 9.5, 6.3, 4.9, 8.1, 9.8, 2.0, 6.6, 4.7, 2.4, 6.7, 5.6, |
| 2.9, 1.3, 7.8, 5.4, 2.4, 6.9, 6.4, 1.4, 8.9, 7.9, 7.5, 6.7, |
| 4.0, 8.3, 5.2, 4.0, 4.8, 7.6, 7.1, 5.9, 9.1, 9.6, 3.9, 6.8, |
| 7.6, 2.5, 8.1, 7.3, 7.5, 7.5, 9.3, 5.6, 5.2, 4.7, 4.5, 8.7, |
| 8.7, 1.3, 4.1, 4.5, 4.9, 6.5, 7.9, 4.6, 7.0, 8.0, 1.6, 3.5}); |
| optional<Tensor> bias(tf.make({2}, {1.0, 1.0})); |
| Tensor expected = tf.make( |
| {1, 2, 4, 4}, |
| {642.33, 714.6, 687.96, 717.12, 859.79, 939.27, 996.79, 1189.59, |
| 700.73, 1083.28, 1010.33, 1167.78, 776.33, 1138.92, 1073.43, 1140.64, |
| 539.83, 851.42, 754.16, 815.01, 822.66, 1191.95, 1063.46, 1330.28, |
| 662.97, 1240.69, 1254.52, 1281.46, 766.25, 1273.41, 1148.57, 1217.47}); |
| Tensor out = |
| tf.zeros({1, 2, 4, 4}, torch::executor::TensorShapeDynamism::STATIC); |
| |
| int64_t stride[] = {2, 2}; |
| int64_t padding[] = {1, 1}; |
| int64_t dilation[] = {1, 1}; |
| int64_t output_padding[] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| false, |
| output_padding, |
| 1, |
| out); |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, 2DSanityCheckChannelsLast) { |
| /* %python |
| %past-rewrite(correctness_test_template) */ |
| |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.make_channels_last( |
| {1, 4, 8, 8}, |
| {5.4, 1.9, 9.3, 7.0, 5.3, 7.9, 1.7, 8.3, 4.7, 7.3, 8.1, 6.6, 1.6, 4.9, |
| 3.8, 6.6, 4.6, 2.8, 2.4, 1.3, 3.6, 3.9, 8.1, 8.4, 5.4, 5.1, 8.9, 9.9, |
| 7.9, 1.0, 1.1, 8.2, 6.3, 7.0, 6.5, 2.5, 9.2, 9.9, 8.1, 9.8, 4.8, 1.3, |
| 2.6, 8.9, 1.1, 8.7, 2.3, 3.5, 4.2, 7.1, 5.0, 3.9, 3.3, 4.1, 8.1, 6.0, |
| 3.3, 8.6, 6.6, 5.7, 5.9, 8.6, 7.3, 3.4, 9.5, 6.0, 6.8, 6.2, 1.8, 3.2, |
| 2.7, 7.5, 7.0, 8.0, 2.8, 5.1, 4.9, 8.6, 1.1, 9.0, 4.2, 9.9, 2.4, 5.3, |
| 4.9, 9.3, 2.9, 5.3, 8.9, 4.8, 9.5, 2.3, 9.2, 3.8, 6.5, 9.6, 2.6, 3.5, |
| 2.7, 9.2, 1.5, 7.6, 5.6, 8.5, 5.4, 7.0, 8.8, 5.1, 2.7, 1.8, 7.5, 4.4, |
| 2.4, 4.8, 1.4, 3.4, 8.9, 4.0, 4.7, 3.4, 2.5, 8.3, 8.3, 1.7, 2.3, 9.0, |
| 2.9, 2.9, 5.3, 7.1, 3.8, 7.1, 1.7, 9.8, 2.4, 4.1, 6.0, 8.4, 4.0, 1.4, |
| 7.9, 7.7, 4.0, 4.0, 9.1, 7.4, 4.9, 3.9, 3.5, 8.9, 2.2, 3.2, 8.2, 7.1, |
| 5.4, 2.9, 8.1, 5.1, 3.0, 9.3, 2.0, 3.6, 8.7, 6.6, 9.9, 3.1, 7.6, 3.4, |
| 4.1, 5.0, 8.5, 9.2, 7.5, 5.8, 6.1, 5.8, 4.1, 4.2, 9.8, 2.0, 7.3, 2.8, |
| 7.9, 8.2, 9.7, 9.0, 4.8, 7.8, 6.6, 5.8, 4.5, 7.8, 4.6, 8.5, 7.2, 4.4, |
| 1.2, 7.7, 2.2, 2.4, 2.9, 1.8, 2.5, 2.6, 3.4, 6.3, 9.3, 8.4, 3.0, 8.2, |
| 1.5, 2.1, 3.2, 5.8, 5.2, 6.4, 1.8, 7.3, 7.6, 1.5, 2.8, 7.8, 9.0, 5.5, |
| 4.1, 2.3, 3.0, 8.8, 7.1, 7.1, 9.1, 3.7, 6.2, 6.2, 2.2, 1.3, 4.3, 5.6, |
| 8.7, 6.8, 5.0, 9.5, 5.0, 5.3, 5.5, 4.5, 3.3, 6.6, 6.2, 8.2, 5.5, 8.5, |
| 2.9, 9.4, 8.3, 8.3}); |
| Tensor weight = tf.make_channels_last( |
| {2, 4, 3, 3}, |
| {4.7, 1.3, 7.8, 3.0, 9.7, 2.5, 3.8, 5.2, 4.4, 7.7, 2.3, 6.2, |
| 1.5, 9.5, 6.3, 4.9, 8.1, 9.8, 2.0, 6.6, 4.7, 2.4, 6.7, 5.6, |
| 2.9, 1.3, 7.8, 5.4, 2.4, 6.9, 6.4, 1.4, 8.9, 7.9, 7.5, 6.7, |
| 4.0, 8.3, 5.2, 4.0, 4.8, 7.6, 7.1, 5.9, 9.1, 9.6, 3.9, 6.8, |
| 7.6, 2.5, 8.1, 7.3, 7.5, 7.5, 9.3, 5.6, 5.2, 4.7, 4.5, 8.7, |
| 8.7, 1.3, 4.1, 4.5, 4.9, 6.5, 7.9, 4.6, 7.0, 8.0, 1.6, 3.5}); |
| optional<Tensor> bias(tf.make({2}, {1.0, 1.0})); |
| Tensor expected = tf.make_channels_last( |
| {1, 2, 4, 4}, |
| {624.92, 656.07, 710.91, 800.45, 622.48, 596.14, 831.26, 882.43, |
| 812.8, 947.49, 1069.65, 1155.81, 964.84, 1057.19, 1121.77, 1328.68, |
| 748.23, 799.7, 1090.23, 1203.45, 1043.71, 1124.75, 1140.41, 1265.35, |
| 688.62, 807.57, 1073.07, 1109.53, 1110, 1221.82, 1210.86, 1324.26}); |
| Tensor out = tf.full_channels_last({1, 2, 4, 4}, 0); |
| |
| int64_t stride[] = {2, 2}; |
| int64_t padding[] = {1, 1}; |
| int64_t dilation[] = {1, 1}; |
| int64_t output_padding[] = {0}; |
| |
| op_convolution_out( |
| input, |
| weight, |
| bias, |
| stride, |
| padding, |
| dilation, |
| false, |
| output_padding, |
| 1, |
| out); |
| |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| TEST_F(OpConvOutTest, DynamicShapeUpperBoundSameAsExpected) { |
| test_dynamic_shape( |
| {1, 4, 2}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND); |
| } |
| |
| TEST_F(OpConvOutTest, DynamicShapeUpperBoundLargerThanExpected) { |
| test_dynamic_shape( |
| {10, 10, 10}, torch::executor::TensorShapeDynamism::DYNAMIC_BOUND); |
| } |
| |
| TEST_F(OpConvOutTest, DynamicShapeUnbound) { |
| if (!torch::executor::testing::SupportedFeatures::get()->output_resize) { |
| GTEST_SKIP() << "Dynamic shape unbound not supported"; |
| } |
| test_dynamic_shape( |
| {1, 1, 1}, torch::executor::TensorShapeDynamism::DYNAMIC_UNBOUND); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, InvalidInputShape) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.ones({2, 4, 4, 5}); |
| Tensor weight = tf.ones({8, 3, 2, 2}); |
| optional<Tensor> bias; |
| Tensor out = tf.zeros({2, 8, 3, 4}); |
| |
| int64_t stride[1] = {1}; |
| int64_t padding[1] = {0}; |
| int64_t dilation[1] = {1}; |
| int64_t output_padding[1] = {0}; |
| int64_t groups = 2; |
| |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| false, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out)); |
| |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| true, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out)); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, TransposedDefaultParams) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.full({2, 4, 3, 2}, 2.0); |
| Tensor weight = tf.full({4, 1, 2, 2}, 0.5); |
| optional<Tensor> bias; |
| Tensor out = tf.full({2, 2, 4, 3}, 0.7); |
| Tensor expected = |
| tf.make({2, 2, 4, 3}, {2, 4, 2, 4, 8, 4, 4, 8, 4, 2, 4, 2, 2, 4, 2, 4, |
| 8, 4, 4, 8, 4, 2, 4, 2, 2, 4, 2, 4, 8, 4, 4, 8, |
| 4, 2, 4, 2, 2, 4, 2, 4, 8, 4, 4, 8, 4, 2, 4, 2}); |
| |
| int64_t stride[1] = {1}; |
| int64_t padding[1] = {0}; |
| int64_t dilation[1] = {1}; |
| bool transposed = true; |
| int64_t output_padding[1] = {0}; |
| int64_t groups = 2; |
| |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| transposed, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out); |
| |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, TransposedNonDefaultParams) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.full({2, 6, 4, 5}, 2.0); |
| Tensor weight = tf.full({6, 1, 2, 2}, 0.5); |
| Tensor bias = tf.make({3}, {1, 2, 3}); |
| Tensor out = tf.full({2, 3, 3, 6}, 0.7); |
| Tensor expected = tf.make( |
| {2, 3, 3, 6}, |
| {1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 2, 2, 2, 2, |
| 2, 2, 2, 4, 4, 2, 4, 4, 2, 4, 4, 2, 4, 4, 3, 3, 3, 3, 3, 3, 3, 5, |
| 5, 3, 5, 5, 3, 5, 5, 3, 5, 5, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, |
| 1, 3, 3, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 4, 4, 2, 4, 4, 2, |
| 4, 4, 3, 3, 3, 3, 3, 3, 3, 5, 5, 3, 5, 5, 3, 5, 5, 3, 5, 5}); |
| |
| int64_t stride[1] = {3}; |
| int64_t padding[1] = {7}; |
| int64_t dilation[1] = {5}; |
| bool transposed = true; |
| int64_t output_padding[1] = {2}; |
| int64_t groups = 3; |
| |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| transposed, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out); |
| |
| EXPECT_TENSOR_CLOSE(out, expected); |
| } |
| |
| template <typename T> |
| std::vector<T> get_channels_last_data(const Tensor& t) { |
| const std::vector<int32_t> sizes(t.sizes().begin(), t.sizes().end()); |
| std::vector<T> contiguous_data( |
| t.const_data_ptr<T>(), t.const_data_ptr<T>() + t.numel()); |
| std::vector<T> channels_last_data(t.numel()); |
| int32_t N = sizes[0]; |
| int32_t C = sizes[1]; |
| int32_t H = sizes[2]; |
| int32_t W = sizes[3]; |
| for (int32_t n = 0; n < N; ++n) { |
| for (int32_t c = 0; c < C; ++c) { |
| for (int32_t h = 0; h < H; ++h) { |
| for (int32_t w = 0; w < W; ++w) { |
| // Calculate the index in the original blob |
| int32_t old_index = ((n * C + c) * H + h) * W + w; |
| // Calculate the index in the new blob |
| int32_t new_index = ((n * H + h) * W + w) * C + c; |
| // Copy the data |
| channels_last_data[new_index] = contiguous_data[old_index]; |
| } |
| } |
| } |
| } |
| return channels_last_data; |
| } |
| |
| TEST_F(OpConvCorrectnessTest, TransposedDefaultParamsChannelsLast) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.full_channels_last({2, 4, 3, 2}, 2.0); |
| Tensor weight = tf.full_channels_last({4, 1, 2, 2}, 0.5); |
| optional<Tensor> bias; |
| Tensor out = tf.full_channels_last({2, 2, 4, 3}, 0.7); |
| Tensor expected = |
| tf.make({2, 2, 4, 3}, {2, 4, 2, 4, 8, 4, 4, 8, 4, 2, 4, 2, 2, 4, 2, 4, |
| 8, 4, 4, 8, 4, 2, 4, 2, 2, 4, 2, 4, 8, 4, 4, 8, |
| 4, 2, 4, 2, 2, 4, 2, 4, 8, 4, 4, 8, 4, 2, 4, 2}); |
| |
| const std::vector<int32_t> sizes( |
| expected.sizes().begin(), expected.sizes().end()); |
| std::vector<float> channels_last_data = |
| get_channels_last_data<float>(expected); |
| Tensor expected_channels_last = |
| tf.make_channels_last(sizes, channels_last_data); |
| |
| int64_t stride[1] = {1}; |
| int64_t padding[1] = {0}; |
| int64_t dilation[1] = {1}; |
| bool transposed = true; |
| int64_t output_padding[1] = {0}; |
| int64_t groups = 2; |
| |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| transposed, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out); |
| |
| EXPECT_TENSOR_CLOSE(out, expected_channels_last); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, TransposedNonDefaultParamsChannelsLast) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.full_channels_last({2, 6, 4, 5}, 2.0); |
| Tensor weight = tf.full_channels_last({6, 1, 2, 2}, 0.5); |
| Tensor bias = tf.make({3}, {1, 2, 3}); |
| Tensor out = tf.full_channels_last({2, 3, 3, 6}, 0.7); |
| Tensor expected = tf.make( |
| {2, 3, 3, 6}, |
| {1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 2, 2, 2, 2, |
| 2, 2, 2, 4, 4, 2, 4, 4, 2, 4, 4, 2, 4, 4, 3, 3, 3, 3, 3, 3, 3, 5, |
| 5, 3, 5, 5, 3, 5, 5, 3, 5, 5, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, |
| 1, 3, 3, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 4, 4, 2, 4, 4, 2, |
| 4, 4, 3, 3, 3, 3, 3, 3, 3, 5, 5, 3, 5, 5, 3, 5, 5, 3, 5, 5}); |
| |
| const std::vector<int32_t> sizes( |
| expected.sizes().begin(), expected.sizes().end()); |
| std::vector<float> channels_last_data = |
| get_channels_last_data<float>(expected); |
| Tensor expected_channels_last = |
| tf.make_channels_last(sizes, channels_last_data); |
| |
| int64_t stride[1] = {3}; |
| int64_t padding[1] = {7}; |
| int64_t dilation[1] = {5}; |
| bool transposed = true; |
| int64_t output_padding[1] = {2}; |
| int64_t groups = 3; |
| |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| transposed, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out); |
| |
| EXPECT_TENSOR_CLOSE(out, expected_channels_last); |
| } |
| |
| TEST_F(OpConvCorrectnessTest, InvalidOutputPadding) { |
| TensorFactory<ScalarType::Float> tf; |
| |
| Tensor input = tf.full({2, 6, 4, 5}, 2.0); |
| Tensor weight = tf.full({6, 1, 2, 2}, 0.5); |
| Tensor bias = tf.make({3}, {1, 2, 3}); |
| Tensor out = tf.zeros({2, 3, 6, 9}); |
| |
| int64_t stride[1] = {3}; |
| int64_t padding[1] = {7}; |
| int64_t dilation[1] = {5}; |
| bool transposed = true; |
| int64_t output_padding[1] = {5}; |
| int64_t groups = 3; |
| |
| ET_EXPECT_KERNEL_FAILURE( |
| context_, |
| op_convolution_out( |
| input, |
| weight, |
| exec_aten::optional<Tensor>(bias), |
| exec_aten::ArrayRef<int64_t>{stride, 1}, |
| exec_aten::ArrayRef<int64_t>{padding, 1}, |
| exec_aten::ArrayRef<int64_t>{dilation, 1}, |
| transposed, |
| exec_aten::ArrayRef<int64_t>{output_padding, 1}, |
| groups, |
| out)); |
| } |
