Fix

tests/test_Tensor_index_fill.py

@@ -51,7 +51,7 @@ def test_case_3():
         indices = torch.tensor([0, 1])
         dim = 0
         value = -1
-        result = torch.eye(3, 4).index_fill(dim, indices, value)
+        result = torch.eye(3, 4).index_fill(index=indices, dim=dim, value=value)
         """
     )
     obj.run(pytorch_code, ["result"])

tests/test_Tensor_index_fill_.py

@@ -53,7 +53,7 @@ def test_case_3():
         indices = torch.tensor([0, 1])
         dim = 0
         value = -1
-        result = x.index_fill_(dim, indices, value)
+        result = x.index_fill_(index=indices, dim=dim, value=value)
         """
     )
     obj.run(pytorch_code, ["result", "x"])

tests/test_Tensor_masked_fill.py

@@ -73,7 +73,7 @@ def test_case_5():
         import torch
         a = torch.Tensor([[1.0,0.2], [0.3,0.4]])
         b = torch.Tensor([[1,0], [1,1]])
-        result = a.masked_fill(value=0.1, mask=b==1)
+        result = a.masked_fill(b==1, 0.1)
         """
     )
     obj.run(pytorch_code, ["result"])

tests/test_Tensor_masked_fill_.py

@@ -73,7 +73,7 @@ def test_case_5():
         import torch
         a = torch.Tensor([[1.0,0.2], [0.3,0.4]])
         b = torch.Tensor([[1,0], [1,1]])
-        result = a.masked_fill_(value=0.1, mask=b==1)
+        result = a.masked_fill_(b==1, 0.1)
         """
     )
     obj.run(pytorch_code, ["result", "a"])

tests/test_Tensor_signbit.py

@@ -23,7 +23,7 @@ def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = x.signbit()
         """
     )
@@ -34,7 +34,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float64')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float64)
         result = x.signbit()
         """
     )

tests/test_combinations.py

@@ -25,7 +25,7 @@ def test_case_1():
         """
         import torch
         x = torch.tensor([1, 2, 3], dtype=torch.int32)
-        result = torch.combinations(input=x, r=2)
+        result = torch.combinations(input=x)
         """
     )
     obj.run(pytorch_code, ["result"])
@@ -36,7 +36,7 @@ def test_case_2():
         """
         import torch
         x = torch.tensor([1, 2, 3], dtype=torch.int32)
-        result = torch.combinations(x, r=2)
+        result = torch.combinations(x)
         """
     )
     obj.run(pytorch_code, ["result"])

tests/test_histogramdd.py

@@ -26,7 +26,7 @@ def test_case_1():
         x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
         bins = [3,3]
         weights = torch.tensor([1., 2., 4., 8.])
-        result = torch.histogramdd(x, bins=bins, weight=weights)
+        result = torch.histogramdd(x, bins=bins)
         """
     )
     obj.run(pytorch_code, ["result"])
@@ -39,7 +39,7 @@ def test_case_2():
         x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
         bins = [3,3]
         weights = torch.tensor([1., 2., 4., 8.])
-        result = torch.histogramdd(input=x, bins=bins, weight=weights)
+        result = torch.histogramdd(input=x, bins=bins)
         """
     )
     obj.run(pytorch_code, ["result"])

tests/test_hypot.py

@@ -49,7 +49,7 @@ def test_case_3():
         a = torch.tensor([1., 2, 3])
         b = torch.tensor([4., 5, 6])
         out = torch.tensor([4., 5, 6])
-        result = torch.hypot(input=a, other=b, out=out)
+        result = torch.hypot(other=b, input=a, out=out)
         """
     )
     obj.run(pytorch_code, ["out"])

tests/test_nn_Module_type.py

@@ -33,8 +33,8 @@ def test_case_1():
     obj.run(pytorch_code, ["result"])
 
 
-# Will match torch.nn.Module, the named parameter "dst_type" cannot be resolved.
-def _test_case_2():
+# Will match torch.Tensor.type to resolve "dst_type" parameter.
+def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch

tests/test_optim_lr_scheduler_CosineAnnealingWarmRestarts.py

@@ -15,14 +15,14 @@
 import textwrap
 
 from apibase import APIBase
-from lr_scheduler_helper import generate_torch_code
+from lr_scheduler_helper import generate_lr_scheduler_test_code
 
 obj = APIBase("torch.optim.lr_scheduler.CosineAnnealingWarmRestarts")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, 10)"
         )
     )
@@ -31,58 +31,55 @@ def test_case_1():
 
 def test_case_2():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
-            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, T_0=10)"
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, T_0=10, T_mult=1)"
         )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_3():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
-            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10)"
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(T_mult=1, optimizer=sgd, T_0=10)"
         )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_4():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
-            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=-1, verbose=True)"
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, T_mult=1, eta_min=0.0, last_epoch=-1, verbose=True)"
         )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_5():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
-            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.05, verbose=True)"
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, T_mult=1, eta_min=0.05, verbose=True)"
         )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_6():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, 10, 1, 1.0, -1, False)"
         )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
-# reference: https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/optimizer/lr/CosineAnnealingDecay_en.html
-# note: paddle not support restart
-# paddle result has diff with pytorch result
 def test_case_7():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             [
-                "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=-1, verbose=False)",
-                "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=scheduler_1.last_epoch, verbose=False)",
+                "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, T_mult=1, eta_min=0.0, last_epoch=-1, verbose=False)",
+                "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, T_mult=2, eta_min=0.0, last_epoch=scheduler_1.last_epoch, verbose=False)",
             ]
         )
     )

tests/test_optim_lr_scheduler_LinearLR.py

@@ -15,21 +15,23 @@
 import textwrap
 
 from apibase import APIBase
-from lr_scheduler_helper import generate_torch_code
+from lr_scheduler_helper import generate_lr_scheduler_test_code
 
 obj = APIBase("torch.optim.lr_scheduler.LinearLR")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, verbose=True)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, verbose=True)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_2():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(sgd, start_factor=0.05, end_factor=1.0)"
         )
     )
@@ -38,21 +40,25 @@ def test_case_2():
 
 def test_case_3():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, total_iters=3)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, total_iters=3)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_4():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_5():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3)"
         )
     )
@@ -61,7 +67,7 @@ def test_case_5():
 
 def test_case_6():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(start_factor=0.05, end_factor=1.0, total_iters=3, optimizer=sgd)"
         )
     )
@@ -70,7 +76,7 @@ def test_case_6():
 
 def test_case_7():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1.0, 3, -1, False)"
         )
     )
@@ -79,7 +85,7 @@ def test_case_7():
 
 def test_case_8():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=-1, verbose=False)"
         )
     )
@@ -88,7 +94,7 @@ def test_case_8():
 
 def test_case_9():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             [
                 "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=-1, verbose=False)",
                 "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=scheduler_1.last_epoch, verbose=False)",
@@ -100,6 +106,6 @@ def test_case_9():
 
 def test_case_10():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd)")
+        generate_lr_scheduler_test_code("torch.optim.lr_scheduler.LinearLR(sgd)")
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)

tests/test_signbit.py

@@ -23,7 +23,7 @@ def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = torch.signbit(x)
         """
     )
@@ -34,7 +34,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = torch.signbit(input=x)
         """
     )
@@ -45,8 +45,8 @@ def test_case_3():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(out=out, input=x)
         """
     )
@@ -57,8 +57,8 @@ def test_case_4():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(input=x, out=out)
         """
     )
@@ -69,8 +69,8 @@ def test_case_5():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(x, out=out)
         """
     )