mirror of https://github.com/llvm/torch-mlir
47 lines
1.1 KiB
Python
47 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 torch.nn as nn
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import torch.nn.functional as F
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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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N = 3
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Cin = 16
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Cout = 4
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w = 10
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h = 10
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(Cin, Cout, (3,3))
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def forward(self, x):
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x = self.conv1(x)
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output = F.log_softmax(x, dim=1)
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return output
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model = Net()
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tensor = torch.randn(N, Cin, h, w)
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# CHECK: PASS! fwd check
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fwd_path = test.check_ref(model, tensor)
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loss = torch.nn.NLLLoss()
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target = torch.empty(N, 8, 8, dtype=torch.long).random_(0, Cout)
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# CHECK: PASS! back check
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test.check_back(fwd_path, target, loss)
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# CHECK: PASS! weight_grad check
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test.compare(model.conv1.weight.grad, fwd_path[0].conv1.weight.grad, "weight_grad")
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# CHECK: PASS! bias_grad check
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test.compare(model.conv1.bias.grad, fwd_path[0].conv1.bias.grad, "bias_grad")
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