enabled test_conv_transpose; all tests passing

tripy/tests/integration/test_conv_transpose.py

@@ -81,7 +81,7 @@ class ConvTestCase:
 @pytest.mark.parametrize("torch_dtype,tp_dtype", DTYPES)
 class TestConvolution:
     @pytest.mark.parametrize("test_case", test_cases_transpose_1d)
-    def test_transposed_convolution_1d(self, torch_dtype, tp_dtype, test_case):
+    def test_transposed_convolution_1d(self, torch_dtype, tp_dtype, test_case, compile_fixture):
         if not test_case.torch_pad:
             test_case.torch_pad = 0
         if not test_case.stride:
@@ -129,14 +129,14 @@ def test_transposed_convolution_1d(self, torch_dtype, tp_dtype, test_case):
             conv_layer.bias = tp.cast(tp.Tensor(conv_layer_torch.bias.data), tp_dtype)
 
         expected = conv_layer_torch(input_torch).to(torch_dtype)
-        output = conv_layer(input)
+        output = compile_fixture(conv_layer, input)
 
         rtol_ = 1e-3
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)
         assert output.shape == expected.shape
 
     @pytest.mark.parametrize("test_case", test_cases_transpose_2d)
-    def test_transposed_convolution_2d(self, torch_dtype, tp_dtype, test_case):
+    def test_transposed_convolution_2d(self, torch_dtype, tp_dtype, test_case, compile_fixture):
         if not test_case.torch_pad:
             test_case.torch_pad = 0
         if not test_case.stride:
@@ -184,14 +184,14 @@ def test_transposed_convolution_2d(self, torch_dtype, tp_dtype, test_case):
             conv_layer.bias = tp.cast(tp.Tensor(conv_layer_torch.bias.data), tp_dtype)
 
         expected = conv_layer_torch(input_torch).to(torch_dtype)
-        output = conv_layer(input)
+        output = compile_fixture(conv_layer, input)
 
         rtol_ = 1e-3
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)
         assert output.shape == expected.shape
 
     @pytest.mark.parametrize("test_case", test_cases_transpose_3d)
-    def test_transposed_convolution_3d(self, torch_dtype, tp_dtype, test_case):
+    def test_transposed_convolution_3d(self, torch_dtype, tp_dtype, test_case, compile_fixture):
         if not test_case.torch_pad:
             test_case.torch_pad = 0
         if not test_case.stride:
@@ -239,12 +239,12 @@ def test_transposed_convolution_3d(self, torch_dtype, tp_dtype, test_case):
             conv_layer.bias = tp.cast(tp.Tensor(conv_layer_torch.bias.data), tp_dtype)
 
         expected = conv_layer_torch(input_torch).to(torch_dtype)
-        output = conv_layer(input)
+        output = compile_fixture(conv_layer, input)
         rtol_ = 1.3e-6 if tp_dtype == tp.float32 else 1.6e-3
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)
         assert output.shape == expected.shape
 
-    def test_transposed_equivalency(self, torch_dtype, tp_dtype):
+    def test_transposed_equivalency(self, torch_dtype, tp_dtype, compile_fixture):
         input_torch = torch.arange(9, dtype=torch.float32, device=torch.device("cuda")).reshape(*(1, 1, 3, 3))
         input = tp.cast(tp.Tensor(input_torch), tp_dtype)
 
@@ -277,8 +277,8 @@ def test_transposed_equivalency(self, torch_dtype, tp_dtype):
 
         expected = conv_layer_torch(input_torch).to(torch_dtype)
         expected_transpose = conv_transpose_layer_torch(input_torch).to(torch_dtype)
-        output = conv_layer(input)
-        output_transpose = conv_transpose_layer(input)
+        output = compile_fixture(conv_layer, input)
+        output_transpose = compile_fixture(conv_transpose_layer, input)
 
         rtol_ = 2e-7 if tp_dtype == tp.float32 else 9e-4
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)
@@ -291,7 +291,7 @@ def test_transposed_equivalency(self, torch_dtype, tp_dtype):
         assert expected.shape == expected_transpose.shape
 
     @pytest.mark.parametrize("test_case", test_cases_transpose_downscale)
-    def test_transposed_downscale(self, torch_dtype, tp_dtype, test_case):
+    def test_transposed_downscale(self, torch_dtype, tp_dtype, test_case, compile_fixture):
         input_torch = torch.arange(9, dtype=torch.float32, device=torch.device("cuda")).reshape(*(1, 1, 3, 3))
         input = tp.cast(tp.Tensor(input_torch), tp_dtype)
 
@@ -320,7 +320,7 @@ def test_transposed_downscale(self, torch_dtype, tp_dtype, test_case):
         conv_layer.weight = tp.cast(tp.Tensor(conv_layer_torch.weight.data), tp_dtype)
 
         expected = conv_layer_torch(input_torch).to(torch_dtype)
-        output = conv_layer(input)
+        output = compile_fixture(conv_layer, input)
 
         rtol_ = 1e-15 if tp_dtype == tp.float32 else 1e-10
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)