Fix (tests): skip some MHA tests with torch 2.0.1

tests/brevitas/graph/equalization_fixtures.py

@@ -109,6 +109,10 @@ def linearmha_model(bias, add_bias_kv, batch_first):
     if torch_version < version.parse('1.9.1'):
         pytest.skip(f"batch_first not supported in MHA with torch version {torch_version}")
 
+    # Skip due to following issue https://github.com/pytorch/pytorch/issues/97128
+    if torch_version == version.parse('2.0.1') and not bias and batch_first and not add_bias_kv:
+        pytest.skip(f"Skip due to a pytorch regression in torch 2.0.1")
+
     class LinearMhaModel(nn.Module):
 
         def __init__(self) -> None:
@@ -135,6 +139,10 @@ def layernormmha_model(bias, add_bias_kv, batch_first):
     if torch_version < version.parse('1.9.1'):
         pytest.skip(f"batch_first not supported in MHA with torch version {torch_version}")
 
+    # Skip due to following issue https://github.com/pytorch/pytorch/issues/97128
+    if torch_version == version.parse('2.0.1') and not bias and batch_first and not add_bias_kv:
+        pytest.skip(f"Skip due to a pytorch regression in torch 2.0.1")
+
     class LayerNormMhaModel(nn.Module):
 
         def __init__(self) -> None:
@@ -164,6 +172,10 @@ def mhalinear_model(bias, add_bias_kv, batch_first):
     if torch_version < version.parse('1.9.1'):
         pytest.skip(f"batch_first not supported in MHA with torch version {torch_version}")
 
+    # Skip due to following issue https://github.com/pytorch/pytorch/issues/97128
+    if torch_version == version.parse('2.0.1') and not bias and batch_first and not add_bias_kv:
+        pytest.skip(f"Skip due to a pytorch regression in torch 2.0.1")
+
     class MhaLinearModel(nn.Module):
 
         def __init__(self) -> None:

tests/brevitas/graph/test_equalization.py

@@ -124,7 +124,9 @@ def test_models(toy_model, merge_bias, request):
     inp = torch.randn(in_shape)
 
     model.eval()
-    expected_out = model(inp)
+    with torch.no_grad():
+        expected_out = model(inp)
+
     model = symbolic_trace(model)
     regions = _extract_regions(model)
     scale_factor_regions = equalize_test(
@@ -135,7 +137,8 @@ def test_models(toy_model, merge_bias, request):
         scale_computation_type='maxabs')
     shape_scale_regions = [scale.shape for scale in scale_factor_regions]
 
-    out = model(inp)
+    with torch.no_grad():
+        out = model(inp)
     assert len(regions) > 0
     assert torch.allclose(expected_out, out, atol=ATOL)
     # Check that at least one region performs "true" equalization