mirror of https://github.com/llvm/torch-mlir
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
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# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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import torch
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import npcomp.frontends.pytorch as torch_mlir
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import npcomp.frontends.pytorch.test as test
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# RUN: python %s | FileCheck %s
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dev = torch_mlir.mlir_device()
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x = torch.rand((3,64,8,8), device=dev)
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y = x*x
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print (y.stride())
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dim = [64,24,24]
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dim = [4,4,4]
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N = 2;
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count = dim[0]*dim[1]*dim[2]
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sizes = (N,dim[0],dim[1],dim[2])
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strides = (1,dim[1]*dim[2],dim[2],1)
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print(count)
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t0 = torch.randn((N,count), device=dev)
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t0_like = torch.randn((N,count))
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t1 = t0.as_strided(sizes, strides)
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t1_ref = t0.to('cpu').as_strided(sizes, strides)
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t1_like = t0_like.as_strided(sizes, strides)
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t1_ref = t1_ref.clone()
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# check that the IR has recorded the
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# stride properly before invoking JIT
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# CHECK: PASS! stride check
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test.compare_eq(t1.stride(), t1_like.stride(), "stride")
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# CHECK: PASS! as_stride check
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test.compare(t1_ref, t1, "as_stride")
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# CHECK: PASS! as_stride stride check
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test.compare_eq(t1_ref.stride(), t1.to("cpu").stride(), "as_stride stride")
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