fix typehints conv1d (ivy-llc#9880)

Co-authored-by: WilliamHirst [email protected]
vedpatwardhan · Jan 21, 2023 · 38fd084 · 38fd084
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commit 38fd084
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Showing 7 changed files with 57 additions and 33 deletions.
diff --git a/ivy/array/layers.py b/ivy/array/layers.py
@@ -303,12 +303,12 @@ def multi_head_attention(
     def conv1d(
         self: ivy.Array,
         filters: Union[ivy.Array, ivy.NativeArray],
-        strides: int,
+        strides: Union[int, Tuple[int]],
         padding: str,
         /,
         *,
         data_format: str = "NWC",
-        dilations: int = 1,
+        dilations: Union[int, Tuple[int]] = 1,
         out: Optional[ivy.Array] = None,
     ) -> ivy.Array:
         """
@@ -318,17 +318,17 @@ def conv1d(
 
         Parameters
         ----------
-        x
-            Input image *[batch_size,w,d_in]*.
+        self
+            Input image *[batch_size,w,d_in]* or *[batch_size,d_in,w]*.
         filters
             Convolution filters *[fw,d_in,d_out]*.
         strides
             The stride of the sliding window for each dimension of input.
         padding
-            SAME" or "VALID" indicating the algorithm, or list indicating the
+            "SAME" or "VALID" indicating the algorithm, or list indicating the
             per-dimension paddings.
         data_format
-            NWC" or "NCW". Defaults to "NWC".
+            "NWC" or "NCW". Defaults to "NWC".
         dilations
             The dilation factor for each dimension of input. (Default value = 1)
         out

diff --git a/ivy/container/layers.py b/ivy/container/layers.py
@@ -510,7 +510,7 @@ def static_scaled_dot_product_attention(
         prune_unapplied: bool = False,
         map_sequences: bool = False,
         out: Optional[ivy.Container] = None,
-    ) -> Union[ivy.Array, ivy.NativeArray, ivy.Container]:
+    ) -> ivy.Container:
         """
         ivy.Container static method variant of ivy.scaled_dot_product_attention.
         This method simply wraps the function, and so the docstring for
@@ -519,7 +519,7 @@ def static_scaled_dot_product_attention(
 
         Parameters
         ----------
-        self
+        q
             The queries input container. The shape of queries input array leaves should
             be in *[batch_shape,num_queries,feat_dim]*. The queries input array leaves
             should have the same size as keys and values.
@@ -592,7 +592,6 @@ def static_scaled_dot_product_attention(
                         [4.4, 5.6],
                         [4.4, 5.6]]])
         }
-
         """
         return ContainerBase.cont_multi_map_in_function(
             "scaled_dot_product_attention",
@@ -621,9 +620,9 @@ def scaled_dot_product_attention(
         prune_unapplied: bool = False,
         map_sequences: bool = False,
         out: Optional[ivy.Container] = None,
-    ) -> Union[ivy.Array, ivy.NativeArray, ivy.Container]:
+    ) -> ivy.Container:
         """
-        ivy.Container method variant of ivy.scaled_dot_product_attention.
+        ivy.Container instance method variant of ivy.scaled_dot_product_attention.
         This method simply wraps the function, and so the docstring for
         ivy.scaled_dot_product_attention also applies to this method with minimal
         changes.
@@ -702,7 +701,6 @@ def scaled_dot_product_attention(
                         [4.4, 5.6],
                         [4.4, 5.6]]])
         }
-
         """
         return self.static_scaled_dot_product_attention(
             self,
@@ -801,12 +799,12 @@ def multi_head_attention(
     def static_conv1d(
         x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
         filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
-        strides: int,
+        strides: Union[int, Tuple[int]],
         padding: str,
         /,
         *,
         data_format: str = "NWC",
-        dilations: int = 1,
+        dilations: Union[int, Tuple[int]] = 1,
         key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
         to_apply: bool = True,
         prune_unapplied: bool = False,
@@ -833,6 +831,17 @@ def static_conv1d(
             "NWC" or "NCW". Defaults to "NWC".
         dilations
             The dilation factor for each dimension of input. (Default value = 1)
+        key_chains
+            The key-chains to apply or not apply the method to. Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains, otherwise key_chains
+            will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was not applied.
+            Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
         out
             optional output container, for writing the result to. It must have a shape
             that the inputs broadcast to.
@@ -873,13 +882,13 @@ def static_conv1d(
 
     def conv1d(
         self: ivy.Container,
-        filters: Union[ivy.Array, ivy.NativeArray],
-        strides: int,
+        filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
+        strides: Union[int, Tuple[int]],
         padding: str,
         /,
         *,
         data_format: str = "NWC",
-        dilations: int = 1,
+        dilations: Union[int, Tuple[int]] = 1,
         key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
         to_apply: bool = True,
         prune_unapplied: bool = False,
@@ -893,7 +902,7 @@ def conv1d(
 
         Parameters
         ----------
-        x
+        self
             Input image *[batch_size,w, d_in]*.
         filters
             Convolution filters *[fw,d_in, d_out]*. (d_in must be the same as d from x)
@@ -906,6 +915,17 @@ def conv1d(
             "NWC" or "NCW". Defaults to "NWC".
         dilations
             The dilation factor for each dimension of input. (Default value = 1)
+        key_chains
+            The key-chains to apply or not apply the method to. Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains, otherwise key_chains
+            will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was not applied.
+            Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
         out
             optional output container, for writing the result to. It must have a shape
             that the inputs broadcast to.

diff --git a/ivy/functional/backends/jax/layers.py b/ivy/functional/backends/jax/layers.py
@@ -37,12 +37,12 @@ def _conv_transpose_padding(k, s, padding, dilation, diff=0):
 def conv1d(
     x: JaxArray,
     filters: JaxArray,
-    strides: int,
+    strides: Union[int, Tuple[int]],
     padding: str,
     /,
     *,
-    data_format: Optional[str] = "NWC",
-    dilations: Optional[int] = 1,
+    data_format: str = "NWC",
+    dilations: Union[int, Tuple[int]] = 1,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     strides = (strides,) if isinstance(strides, int) else strides

diff --git a/ivy/functional/backends/numpy/layers.py b/ivy/functional/backends/numpy/layers.py
@@ -26,12 +26,12 @@ def _add_dilations(x, dilations, axis):
 def conv1d(
     x: np.ndarray,
     filters: np.ndarray,
-    strides: int,
+    strides: Union[int, Tuple[int]],
     padding: str,
     /,
     *,
-    data_format: Optional[str] = "NWC",
-    dilations: Optional[int] = 1,
+    data_format: str = "NWC",
+    dilations: Union[int, Tuple[int]] = 1,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if isinstance(strides, (tuple, list)):

diff --git a/ivy/functional/backends/tensorflow/layers.py b/ivy/functional/backends/tensorflow/layers.py
@@ -17,12 +17,12 @@
 def conv1d(
     x: Union[tf.Tensor, tf.Variable],
     filters: Union[tf.Tensor, tf.Variable],
-    strides: int,
+    strides: Union[int, Tuple[int]],
     padding: str,
     /,
     *,
-    data_format: Optional[str] = "NWC",
-    dilations: Optional[int] = 1,
+    data_format: str = "NWC",
+    dilations: Union[int, Tuple[int]] = 1,
     out: Optional[Union[tf.Tensor, tf.Variable]] = None,
 ) -> Union[tf.Tensor, tf.Variable]:
     if data_format == "NCW":

diff --git a/ivy/functional/backends/torch/layers.py b/ivy/functional/backends/torch/layers.py
@@ -24,12 +24,12 @@ def _out_shape(x, strides, pad, dilations, filters):
 def conv1d(
     x: torch.Tensor,
     filters: torch.Tensor,
-    strides: int,
+    strides: Union[int, Tuple[int]],
     padding: str,
     /,
     *,
-    data_format: Optional[str] = "NWC",
-    dilations: Optional[int] = 1,
+    data_format: str = "NWC",
+    dilations: Union[int, Tuple[int]] = 1,
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     if isinstance(strides, (tuple, list)):

diff --git a/ivy/functional/ivy/layers.py b/ivy/functional/ivy/layers.py
@@ -771,12 +771,12 @@ def call_einops(t):
 def conv1d(
     x: Union[ivy.Array, ivy.NativeArray],
     filters: Union[ivy.Array, ivy.NativeArray],
-    strides: int,
+    strides: Union[int, Tuple[int]],
     padding: str,
     /,
     *,
-    data_format: Optional[str] = "NWC",
-    dilations: Optional[int] = 1,
+    data_format: str = "NWC",
+    dilations: Union[int, Tuple[int]] = 1,
     out: Optional[ivy.Array] = None,
 ) -> ivy.Array:
     """Computes a 1-D convolution given 3-D input x and filters arrays.
@@ -807,6 +807,10 @@ def conv1d(
     ret
         The result of the convolution operation.
 
+    Both the description and the type hints above assumes an array input for simplicity,
+    but this function is *nestable*, and therefore also accepts :class:`ivy.Container`
+    instances in place of any of the arguments.
+
     Examples
     --------
     With :class:`ivy.Array` input: