chore: Add test for torch.Tensor.uniform_ and remove unnecessary comm…

…ents
aibenStunner · Nov 17, 2023 · 79d36a1 · 79d36a1
1 parent e73a282
commit 79d36a1
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Showing 3 changed files with 61 additions and 3 deletions.
diff --git a/ivy/functional/frontends/torch/nn/parameter.py b/ivy/functional/frontends/torch/nn/parameter.py
@@ -20,7 +20,6 @@ def __init__(self, data=None, device=None, requires_grad=True):
         self.grad_fn = None
 
     def __deepcopy__(self, memo):
-        # TODO: Need to add test. Adding for KLA demo (priority)
         if id(self) in memo:
             return memo[id(self)]
         else:

diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
@@ -45,7 +45,6 @@ def __repr__(self):
         )
 
     def __hash__(self):
-        # TODO: Need to add test. Adding for KLA demo (priority)
         return id(self)
 
     # Properties #
@@ -1995,8 +1994,19 @@ def random_(
         )
         return self.ivy_array
 
+    @with_unsupported_dtypes(
+        {
+            "2.1.1 and below": (
+                "integer",
+                "unsigned",
+                "bfloat16",
+                "bool",
+                "complex",
+            )
+        },
+        "torch",
+    )
     def uniform_(self, from_=0, to=1, *, generator=None):
-        # TODO: Need to add test. Adding for KLA demo (priority)
         ret = ivy.random_uniform(
             low=from_, high=to, shape=self.shape, dtype=self.dtype, seed=generator
         )

diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -13500,6 +13500,55 @@ def test_torch_unfold(
     )
 
 
+# uniform_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="uniform_",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_value=1,
+        max_value=5,
+        min_num_dims=1,
+        max_num_dims=5,
+    ),
+    from_=helpers.floats(min_value=-1000, max_value=0),
+    to=helpers.floats(min_value=1, max_value=1000),
+    test_inplace=st.just(True),
+)
+def test_torch_uniform_(
+    dtype_and_x,
+    from_,
+    to,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    method_flags.num_positional_args = 3
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "from_": from_,
+            "to": to,
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+        test_values=False,
+    )
+
+
 # unique
 @handle_frontend_method(
     class_tree=CLASS_TREE,