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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66136 FOR REVIEWERS: this is ready to review, test failures comes from somewhere else in stack.. Takes in a TensorExprGraph of static shapes and generalizes the input shapes to symbolic dimensions. Dimensions of value 1 will be preserved, otherwise dimensions with the same value will be bucketed to the same symbolic shape. E.g. `Tensor(5, 3), Tensor(3, 1) -> Tensor(SS(-1), SS(-2)), Tensor(SS(-2), 1)` From there, runs symbolic shape inference on the graph, and creates a versioning if in the graph with prim::TensorExprDynamicGuard checking if the inputs at runtime match the Generalized Symbolic Shapes that are inputs to the TE Kernel. The computate to calculate all symbolic dimensions is inlined in to the if block with the TE Kernel. All Sym Dim Value* are appended to the end of the TE Kernel Graph/Node inputs, and the Node is augmented with a integer list attr `symbolic_shape_inputs` that gives the mapping from Value * -> Symbolic Shape int64_t value. For more lengthy IR examples and walkthrough look at ShapeAnalysisTest.DynamicShapesFusion in `test_shape_analysis` Returns True on Success, False on Failure, can fail if shape propagation fails to propagate # of dims or if complete shapes on inputs not set. Example transformation ``` graph(%x_inp : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y_inp : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z_inp : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %3 : Tensor = prim::TensorExprGroup_0(%x_inp, %y_inp, %z_inp) return () with prim::TensorExprGroup_0 = graph(%x.1 : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y.1 : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %3 : int = prim::Constant[value=0]() %4 : Tensor = aten::tanh(%x.1) %5 : Tensor = aten::erf(%4) %6 : Tensor = aten::relu(%y.1) %7 : Tensor[] = prim::ListConstruct(%5, %6) %8 : Tensor = aten::cat(%7, %3) %9 : Tensor = aten::hardswish(%8) %10 : Tensor = aten::mul(%9, %z) return (%9) ``` -> ``` graph(%x_inp : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y_inp : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z_inp : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %4 : bool = prim::TensorExprDynamicGuard[types=[Float(SS(-2), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), Float(SS(-4), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)]](%x_inp, %y_inp, %z_inp) %5 : Tensor = prim::If(%4) block0(): %15 : int[] = aten::size(%x_inp) %16 : int[] = aten::size(%y_inp) %17 : int = prim::Constant[value=1]() %18 : int = prim::Constant[value=0]() %elem.3 : int = aten::__getitem__(%15, %18) # <string>:40:10 %elem.5 : int = aten::__getitem__(%15, %17) # <string>:40:10 %elem.11 : int = aten::__getitem__(%16, %18) # <string>:40:10 %cat_dim_size.48 : int = aten::add(%elem.3, %elem.11) # <string>:321:29 %3 : Tensor = prim::TensorExprGroup_0[symbolic_shape_inputs=[-5, -4, -3, -2]](%x_inp, %y_inp, %z_inp, %cat_dim_size.48, %elem.11, %elem.5, %elem.3) -> (%3) block1(): %14 : Tensor = prim::FallbackGraph_1(%x_inp, %y_inp, %z_inp) -> (%14) return () with prim::TensorExprGroup_0 = graph(%x.1 : Float(SS(-2), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), %y.1 : Float(SS(-4), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), %z : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu), %SS_5 : int, %SS_4 : int, %SS_3 : int, %SS_2 : int): %3 : int = prim::Constant[value=0]() %4 : Tensor(SS(-2), SS(-3)) = aten::tanh(%x.1) %5 : Tensor(SS(-2), SS(-3)) = aten::erf(%4) %6 : Tensor(SS(-4), SS(-3)) = aten::relu(%y.1) %7 : Tensor[] = prim::ListConstruct(%5, %6) %8 : Tensor(SS(-5), SS(-3)) = aten::cat(%7, %3) %9 : Tensor(SS(-5), SS(-3)) = aten::hardswish(%8) %10 : Tensor(SS(-5), SS(-3)) = aten::mul(%9, %z) return (%9) ``` Test Plan: Imported from OSS Reviewed By: navahgar Differential Revision: D31732414 Pulled By: eellison fbshipit-source-id: 290a94a667c20467717202a43c60e4f9ca4c00e2 |
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|---|---|---|
| .. | ||
| __init__.py | ||
| CMakeLists.txt | ||
| README.md | ||
| script_module_v4.ptl | ||
| script_module_v5.ptl | ||
| script_module_v6.ptl | ||
| test_alias_analysis.cpp | ||
| test_argument_spec.cpp | ||
| test_autodiff.cpp | ||
| test_backend.cpp | ||
| test_backend_compiler_lib.cpp | ||
| test_backend_compiler_preprocess.cpp | ||
| test_backend_lib.cpp | ||
| test_class_import.cpp | ||
| test_class_parser.cpp | ||
| test_class_type.cpp | ||
| test_cleanup_passes.cpp | ||
| test_code_template.cpp | ||
| test_concat_opt.cpp | ||
| test_constant_pooling.cpp | ||
| test_create_autodiff_subgraphs.cpp | ||
| test_cs_debug_info_serialization.cpp | ||
| test_custom_class.cpp | ||
| test_custom_class_registrations.cpp | ||
| test_custom_class_registrations.h | ||
| test_custom_operators.cpp | ||
| test_dce.cpp | ||
| test_fuser.cpp | ||
| test_gpu.cpp | ||
| test_gpu_shift.cpp | ||
| test_gpu_validator.h | ||
| test_graph_executor.cpp | ||
| test_graph_iterator.cpp | ||
| test_inliner.cpp | ||
| test_interface.cpp | ||
| test_interpreter.cpp | ||
| test_interpreter_async.pt | ||
| test_ir.cpp | ||
| test_irparser.cpp | ||
| test_jit_logging_levels.cpp | ||
| test_jit_type.cpp | ||
| test_lite_interpreter.cpp | ||
| test_lite_trainer.cpp | ||
| test_memory_dag.cpp | ||
| test_misc.cpp | ||
| test_mobile_type_parser.cpp | ||
| test_module_api.cpp | ||
| test_peephole_optimize.cpp | ||
| test_qualified_name.cpp | ||
| test_save_load.cpp | ||
| test_schema_matching.cpp | ||
| test_script_profile.cpp | ||
| test_shape_analysis.cpp | ||
| test_stack_opt.cpp | ||
| test_subgraph_matcher.cpp | ||
| test_subgraph_rewriter.cpp | ||
| test_subgraph_utils.cpp | ||
| test_union.cpp | ||
| test_utils.cpp | ||
| test_utils.h | ||
| tests_setup.py | ||
| torch_python_test.cpp | ||
JIT C++ Tests
Adding a new test
First, create a new test file. Test files should have be placed in this
directory, with a name that starts with test_, like test_foo.cpp.
In general a single test suite
Add your test file to the JIT_TEST_SRCS list in test/cpp/jit/CMakeLists.txt.
A test file may look like:
#include <gtest/gtest.h>
using namespace ::torch::jit
TEST(FooTest, BarBaz) {
// ...
}
// Append '_CUDA' to the test case name will automatically filter it out if CUDA
// is not compiled.
TEST(FooTest, NeedsAGpu_CUDA) {
// ...
}
// Similarly, if only one GPU is detected, tests with `_MultiCUDA` at the end
// will not be run.
TEST(FooTest, NeedsMultipleGpus_MultiCUDA) {
// ...
}
Building and running the tests
The following commands assume you are in PyTorch root.
# ... Build PyTorch from source, e.g.
python setup.py develop
# (re)build just the binary
ninja -C build bin/test_jit
# run tests
build/bin/test_jit --gtest_filter='glob_style_filter*'