def main():
device = torch_mlir.mlir_device()
model = Net()
tensor = torch.randn((64, 1, 28, 28), requires_grad=True)
# CHECK: PASS! fwd check
fwd_path = test.check_ref(model, tensor)
target = torch.ones((64), dtype=torch.long)
loss = F.nll_loss
# CHECK: PASS! back check
test.check_back(fwd_path, target, loss)
# CHECK: PASS! fc1_weight_grad check
test.compare(model.fc1.weight.grad, fwd_path[0].fc1.weight.grad,
"fc1_weight_grad")
def main():
model = Net()
tensor = torch.randn((64, 1, 28, 28), requires_grad=True)
# CHECK: PASS! fwd check
fwd_path = test.check_fwd(model, tensor)
target = torch.ones((64), dtype=torch.long)
loss = F.nll_loss
# CHECK: PASS! back check
test.check_back(fwd_path, target, loss)
# CHECK: PASS! weight_grad check
test.compare(model.conv2.weight.grad, fwd_path[0].conv2.weight.grad,
"weight_grad")
# CHECK: PASS! bias_grad check
test.compare(model.conv2.bias.grad, fwd_path[0].conv2.bias.grad,
"bias_grad")
# CHECK: PASS! fc1_weight_grad check
test.compare(model.fc1.weight.grad, fwd_path[0].fc1.weight.grad,
"fc1_weight_grad")
# -*- Python -*- # This file is licensed under a pytorch-style license # See frontends/pytorch/LICENSE for license information. import torch import npcomp.frontends.pytorch as torch_mlir import npcomp.frontends.pytorch.test as test # RUN: %PYTHON %s | FileCheck %s model = torch.nn.LogSoftmax(dim=1) tensor = torch.randn(3, 5, requires_grad=True) # CHECK: PASS! fwd check fwd_path = test.check_fwd(model, tensor) target = torch.tensor([1, 0, 4]) loss = torch.nn.NLLLoss() # CHECK: PASS! back check test.check_back(fwd_path, target, loss)