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a176c73ed5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/53063 The problem was that a derived class was marked with "py::nodelete", while the base class wasn't. Now they both are marked correctly. Test Plan: Imported from OSS Reviewed By: bertmaher Differential Revision: D26737877 Pulled By: ZolotukhinM fbshipit-source-id: 17d9d430651c8f695fc7b6bf6784e7719e20a4d2
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
import torch
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.jit_utils import JitTestCase
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class kernel_arena_scope(object):
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def __enter__(self):
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self.scope = torch._C._te.KernelScope()
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def __exit__(self, typ, val, traceback):
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self.scope = None
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class TestTensorExprPyBind(JitTestCase):
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def test_simple_sum(self):
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with kernel_arena_scope():
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dtype = torch._C._te.Dtype.Float
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N = 32
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dN = torch._C._te.ExprHandle.int(N)
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A = torch._C._te.Placeholder('A', dtype, [dN])
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B = torch._C._te.Placeholder('B', dtype, [dN])
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def compute(i):
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return A.load([i]) + B.load([i])
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C = torch._C._te.Compute('C', [torch._C._te.DimArg(dN, 'i')], compute)
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loopnest = torch._C._te.LoopNest([C])
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loopnest.prepare_for_codegen()
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stmt = torch._C._te.simplify(loopnest.root_stmt())
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cg = torch._C._te.construct_codegen('ir_eval', stmt, [torch._C._te.BufferArg(x) for x in [A, B, C]])
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tA = torch.rand(N) * 5
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tB = torch.rand(N) * 6
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tC = torch.empty(N)
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cg.call([tA, tB, tC])
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torch.testing.assert_allclose(tA + tB, tC)
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def test_external_calls(self):
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with kernel_arena_scope():
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dtype = torch._C._te.Dtype.Float
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ZERO = torch._C._te.ExprHandle.int(0)
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ONE = torch._C._te.ExprHandle.int(1)
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FOUR = torch._C._te.ExprHandle.int(4)
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A = torch._C._te.BufHandle('A', [ONE, FOUR], dtype)
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B = torch._C._te.BufHandle('B', [FOUR, ONE], dtype)
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C = torch._C._te.BufHandle('C', [ONE, ONE], dtype)
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s = torch._C._te.ExternalCall(C, "nnc_aten_matmul", [A, B], [])
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loopnest = torch._C._te.LoopNest(s, [C])
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loopnest.prepare_for_codegen()
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codegen = torch._C._te.construct_codegen('ir_eval', s, [torch._C._te.BufferArg(x) for x in [A, B, C]])
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tA = torch.ones(1, 4)
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tB = torch.ones(4, 1)
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tC = torch.empty(1, 1)
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codegen.call([tA, tB, tC])
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torch.testing.assert_allclose(torch.matmul(tA, tB), tC)
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if __name__ == '__main__':
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run_tests()
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