[tvm] branch main updated: [Torch] Added tests in test_forward_linear (#13937)

[ma...@apache.org]

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The following commit(s) were added to refs/heads/main by this push:
     new 6f0e2edd71 [Torch] Added tests in test_forward_linear (#13937)
6f0e2edd71 is described below

commit 6f0e2edd714ba1cc7800dc445fec45115d0e5c1c
Author: padreofthegame <97...@users.noreply.github.com>
AuthorDate: Fri Feb 10 09:27:24 2023 +0100

    [Torch] Added tests in test_forward_linear (#13937)
---
 tests/python/frontend/pytorch/test_forward.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index d5cf053090..033ce64b3a 100755
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1830,15 +1830,17 @@ def test_forward_linear():
         def forward(self, inputs, weight):
             return F.linear(inputs, weight)
 
-    class LinearNested(torch.nn.Module):
+    class LinearNested(Module):
         def forward(self, x, y, z):
             return F.linear(x, F.linear(y, z))
 
+    input1d = torch.rand([2]).float()
     input2d = torch.rand([2, 2]).float()
     input3d = torch.rand([4, 3, 2]).float()
     weight1d = torch.rand([2]).float()
     weight2d = torch.rand([2, 2]).float()
     weight3x2 = torch.rand([3, 2]).float()
+    bias0d = torch.rand([]).float()
     bias1d = torch.rand([2]).float()
     bias2d = torch.rand([2, 2]).float()
     # 2D input, 2D weight, 1D bias
@@ -1858,11 +1860,14 @@ def test_forward_linear():
 
     verify_model(LinearNested(), input_data=[torch.randn(10, 10) for _ in range(3)])
 
-    # TODO: Add the following cases when matmul(1D, _) is supported by TVM
     # 1D input, 2D weight, 1D bias
+    verify_model(Linear(), input_data=[input1d, weight2d, bias1d])
     # 1D input, 2D weight, no bias
+    verify_model(LinearNoBias(), input_data=[input1d, weight2d])
     # 1D input, 1D weight, scalar bias
+    verify_model(Linear(), input_data=[input1d, weight1d, bias0d])
     # 1D input, 1D weight, no bias
+    verify_model(LinearNoBias(), input_data=[input1d, weight1d])
 
 
 @tvm.testing.uses_gpu