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9ce833879f
Summary: **Summary** There is often a need to create a Tensor when writing IR by hand for JIT optimisation pass unit tests. The only options for this today are real Tensor creation functions like `aten::ones`. Any test that uses these functions must also use the same default arguments as the Python/C++ API, which means that all of the tests have to be updated when the API is updated. This commit introduces a new primitive, `prim::MakeTestTensor` with schema `() -> Tensor` that should be used in unit tests instead of real Tensor creation functions. This new primitive has no public-facing API, so the maintenance burden is much lower. **Testing** This commit updates the alias analysis and DCE tests to use `prim::MakeTestTensor` instead of `aten::rand`, `aten::ones`, and `aten::zeros`. ``` $ ./bin/test_jit CUDA not available. Disabling CUDA and MultiCUDA tests Note: Google Test filter = *-*_CUDA:*_MultiCUDA [==========] Running 75 tests from 1 test case. [----------] Global test environment set-up. [----------] 75 tests from JitTest [ RUN ] JitTest.ADFormulas [ OK ] JitTest.ADFormulas (82 ms) [ RUN ] JitTest.Attributes [ OK ] JitTest.Attributes (0 ms) ... ... ... [ RUN ] JitTest.LiteInterpreterPrim [ OK ] JitTest.LiteInterpreterPrim (0 ms) [ RUN ] JitTest.LiteInterpreterLoadOrigJit [ OK ] JitTest.LiteInterpreterLoadOrigJit (2 ms) [----------] 75 tests from JitTest (150 ms total) [----------] Global test environment tear-down [==========] 75 tests from 1 test case ran. (150 ms total) [ PASSED ] 75 tests. ``` **Fixes** This pull request fixes https://github.com/pytorch/pytorch/issues/33500. Pull Request resolved: https://github.com/pytorch/pytorch/pull/33914 Differential Revision: D20150304 Pulled By: SplitInfinity fbshipit-source-id: c88f5289055a02dc20b7a5dcdf87469f9816d020
53 lines
1.6 KiB
C++
53 lines
1.6 KiB
C++
#include <test/cpp/jit/test_base.h>
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#include <test/cpp/jit/test_utils.h>
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#include <torch/csrc/jit/passes/dead_code_elimination.h>
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#include <torch/csrc/jit/testing/file_check.h>
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namespace torch {
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namespace jit {
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void testDCE() {
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auto graph = std::make_shared<Graph>();
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// Consider the following loop:
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// for i in range(3):
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// tot += a[0][0]
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// b = a[0]
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// b[0] += 1
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// print(tot)
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// We want to check that b[0] and b are properly marked as live and thus not
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// DCE'd.
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const std::string input =
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R"IR(
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graph():
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%48 : None = prim::Constant()
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%50 : bool = prim::Constant[value=1]()
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%0 : int = prim::Constant[value=2]()
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%12 : int = prim::Constant[value=1]()
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%24 : int = prim::Constant[value=3]()
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%31 : int = prim::Constant[value=0]()
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%2 : int[] = prim::ListConstruct(%0, %0)
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%a.1 : Tensor = prim::MakeTestTensor()
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%14 : int[] = prim::ListConstruct(%12)
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%tot.1 : Tensor = prim::MakeTestTensor()
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%tot : Tensor = prim::Loop(%24, %50, %tot.1)
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block0(%i : int, %tot.6 : Tensor):
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%33 : Tensor = aten::select(%a.1, %31, %31)
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%35 : Tensor = aten::select(%33, %31, %31)
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# CHECK: add_
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%tot.3 : Tensor = aten::add_(%tot.6, %35, %12)
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%b.1 : Tensor = aten::select(%a.1, %31, %31)
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%44 : Tensor = aten::select(%b.1, %31, %31)
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# CHECK: add_
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%46 : Tensor = aten::add_(%44, %12, %12)
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-> (%50, %tot.3)
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return (%tot)
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)IR";
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script::parseIR(input, graph.get());
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EliminateDeadCode(graph);
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// Check that dead code elimin
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testing::FileCheck().run(input, *graph);
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}
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} // namespace jit
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} // namespace torch
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