Move tensor implicit conversions to test_builtins.py (#55532)

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/55532

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D27729682

Pulled By: nikithamalgifb

fbshipit-source-id: d2517ee68b83e59cde87b8fb7d5bf7203f02cbc6

test/jit/test_builtins.py

@@ -5,6 +5,7 @@ import unittest
 from typing import Dict, List
 
 import torch
+from torch.testing import FileCheck
 
 # Make the helper files in test/ importable
 pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
@@ -219,3 +220,73 @@ class TestTensorBuiltins(JitTestCase):
             return a
 
         self.checkScript(fn6, (torch.zeros(2, dtype=torch.float32, device="cuda"),))
+
+    def test_tensor_item(self):
+        def test_scalar_cast(x):
+            scalar = x.item()
+            return int(scalar), float(scalar)
+
+        graph = torch.jit.script(test_scalar_cast).graph
+        FileCheck().check("(int, float) = prim::TupleConstruct").run(graph)
+        self.checkScript(test_scalar_cast, (torch.tensor(1.0),))
+        self.checkScript(test_scalar_cast, (torch.tensor(1),))
+
+    def test_method_on_number(self):
+        def func():
+            c = 1
+            return c.add(1)
+        with self.assertRaisesRegex(RuntimeError, 'nonexistent attribute or method'):
+            torch.jit.script(func)
+
+    # testing implicit conversion of tensors to scalars to match function arguments
+    def test_scalar_to_num_conversions(self):
+        @torch.jit.script
+        def multiple_defs(x):
+            c = 1
+            x = x + c
+            return x
+
+        self.assertTrue("ImplicitTensorToNum" not in str(multiple_defs.graph))
+
+        @torch.jit.script
+        def tensor_to_int_script(x, tensor):
+            return x.unsqueeze(tensor)
+
+        # location present in error message
+        with self.assertRaisesRegex(RuntimeError, "x.unsqueeze"):
+            tensor_to_int_script(torch.tensor([2]), torch.tensor([2, 2]))
+
+        def tensor_to_int(x, tensor):
+            return x.unsqueeze(tensor)
+
+        @torch.jit.script
+        def tensor_to_float_script(x, tensor):
+            return x.addcmul(tensor, tensor, value=tensor)
+
+        def tensor_to_float(x, tensor):
+            return x.addcmul(tensor, tensor, value=tensor)
+
+        x = torch.zeros(10)
+        # float tensor, float tensor with grad, int tensor (can't set grad on int tensor)
+        tensors = [torch.tensor(1.1),
+                   torch.tensor(1.1, requires_grad=True),
+                   torch.tensor(0),
+                   torch.tensor([2])]
+
+        script_funs = [tensor_to_int_script, tensor_to_float_script]
+        funs = [tensor_to_int, tensor_to_float]
+
+        # return the result, or whether exception was thrown
+        def test_func(func, x, tensor):
+            try:
+                result = func(x, tensor)
+            except RuntimeError as e:
+                result = True
+            except TypeError as e:
+                result = True
+            return result
+
+        # assert result or exception equal for each (function, inputs)
+        for tensor in tensors:
+            for i in range(len(script_funs)):
+                self.assertEqual(test_func(script_funs[i], x, tensor), test_func(funs[i], x, tensor))

test/test_jit.py

@@ -4622,76 +4622,6 @@ a")
                     test(backward=True)
                     test(backward=True)
 
-    def test_tensor_item(self):
-        def test_scalar_cast(x):
-            scalar = x.item()
-            return int(scalar), float(scalar)
-
-        graph = torch.jit.script(test_scalar_cast).graph
-        FileCheck().check("(int, float) = prim::TupleConstruct").run(graph)
-        self.checkScript(test_scalar_cast, (torch.tensor(1.0),))
-        self.checkScript(test_scalar_cast, (torch.tensor(1),))
-
-    def test_method_on_number(self):
-        def func():
-            c = 1
-            return c.add(1)
-        with self.assertRaisesRegex(RuntimeError, 'nonexistent attribute or method'):
-            torch.jit.script(func)
-
-    # testing implicit conversion of tensors to scalars to match function arguments
-    def test_scalar_to_num_conversions(self):
-        @torch.jit.script
-        def multiple_defs(x):
-            c = 1
-            x = x + c
-            return x
-
-        self.assertTrue("ImplicitTensorToNum" not in str(multiple_defs.graph))
-
-        @torch.jit.script
-        def tensor_to_int_script(x, tensor):
-            return x.unsqueeze(tensor)
-
-        # location present in error message
-        with self.assertRaisesRegex(RuntimeError, "x.unsqueeze"):
-            tensor_to_int_script(torch.tensor([2]), torch.tensor([2, 2]))
-
-        def tensor_to_int(x, tensor):
-            return x.unsqueeze(tensor)
-
-        @torch.jit.script
-        def tensor_to_float_script(x, tensor):
-            return x.addcmul(tensor, tensor, value=tensor)
-
-        def tensor_to_float(x, tensor):
-            return x.addcmul(tensor, tensor, value=tensor)
-
-        x = torch.zeros(10)
-        # float tensor, float tensor with grad, int tensor (can't set grad on int tensor)
-        tensors = [torch.tensor(1.1),
-                   torch.tensor(1.1, requires_grad=True),
-                   torch.tensor(0),
-                   torch.tensor([2])]
-
-        script_funs = [tensor_to_int_script, tensor_to_float_script]
-        funs = [tensor_to_int, tensor_to_float]
-
-        # return the result, or whether exception was thrown
-        def test_func(func, x, tensor):
-            try:
-                result = func(x, tensor)
-            except RuntimeError as e:
-                result = True
-            except TypeError as e:
-                result = True
-            return result
-
-        # assert result or exception equal for each (function, inputs)
-        for tensor in tensors:
-            for i in range(len(script_funs)):
-                self.assertEqual(test_func(script_funs[i], x, tensor), test_func(funs[i], x, tensor))
-
     def test_module_copy_with_attributes(self):
         class Vocabulary(torch.jit.ScriptModule):
             def __init__(self, vocab_list):