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[feat] Add support for argmax and argmin (#1312)
* [feat] Add support for argmax and argmin Adds support for aten::argmax and aten::argmin. Fixes # (issue) Please delete options that are not relevant and/or add your own. - Bug fix (non-breaking change which fixes an issue) - New feature (non-breaking change which adds functionality) - Breaking change (fix or feature that would cause existing functionality to not work as expected) - This change requires a documentation update - [ ] My code follows the style guidelines of this project (You can use the linters) - [ ] I have performed a self-review of my own code - [ ] I have commented my code, particularly in hard-to-understand areas and hacks - [ ] I have made corresponding changes to the documentation - [ ] I have added tests to verify my fix or my feature - [ ] New and existing unit tests pass locally with my changes - [ ] I have added the relevant labels to my PR in so that relevant reviewers are notified * move max.cpp tests to test_max.cpp no functional change * fix permissions on max.cpp
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| #include <string> | ||
| #include "core/compiler.h" | ||
| #include "gtest/gtest.h" | ||
| #include "tests/util/util.h" | ||
| #include "torch/csrc/jit/ir/irparser.h" | ||
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||
| TEST(Converters, ATenMaxDimConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=0]() | ||
| %3 : bool = prim::Constant[value=0]() | ||
| %4 : Tensor, %5 : Tensor = aten::max(%x.1, %2, %3) | ||
| return (%4, %5))IR"; | ||
|
|
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| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
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| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
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| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
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|
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| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6)); | ||
| } | ||
|
|
||
| TEST(Converters, ATenMinDimConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=0]() | ||
| %3 : bool = prim::Constant[value=0]() | ||
| %4 : Tensor, %5 : Tensor = aten::min(%x.1, %2, %3) | ||
| return (%4, %5))IR"; | ||
|
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| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
|
||
| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
|
|
||
| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
|
|
||
| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6)); | ||
| } | ||
|
|
||
| TEST(Converters, ATenArgMaxConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=0]() | ||
| %3 : bool = prim::Constant[value=0]() | ||
| %4 : Tensor = aten::argmax(%x.1, %2, %3) | ||
| return (%4))IR"; | ||
|
|
||
| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
|
||
| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
|
|
||
| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
|
|
||
| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| } | ||
|
|
||
| TEST(Converters, ATenArgMaxKeepdimConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=1]() | ||
| %3 : bool = prim::Constant[value=1]() | ||
| %4 : Tensor = aten::argmax(%x.1, %2, %3) | ||
| return (%4))IR"; | ||
|
|
||
| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
|
||
| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
|
|
||
| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
|
|
||
| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| } | ||
|
|
||
| TEST(Converters, ATenArgMinConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=0]() | ||
| %3 : bool = prim::Constant[value=0]() | ||
| %4 : Tensor = aten::argmin(%x.1, %2, %3) | ||
| return (%4))IR"; | ||
|
|
||
| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
|
||
| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
|
|
||
| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
|
|
||
| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| } | ||
|
|
||
| TEST(Converters, ATenArgMinKeepdimConvertsCorrectly) { | ||
| const auto graph = R"IR( | ||
| graph(%x.1 : Tensor): | ||
| %2 : int = prim::Constant[value=1]() | ||
| %3 : bool = prim::Constant[value=1]() | ||
| %4 : Tensor = aten::argmin(%x.1, %2, %3) | ||
| return (%4))IR"; | ||
|
|
||
| auto g = std::make_shared<torch::jit::Graph>(); | ||
| torch::jit::parseIR(graph, g.get()); | ||
|
|
||
| auto in = at::rand({2, 3, 5, 5}, {at::kCUDA}); | ||
|
|
||
| auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in}); | ||
|
|
||
| params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {}); | ||
| auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}); | ||
|
|
||
| ASSERT_TRUE( | ||
| torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6)); | ||
| } |
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