formatting changes from black 22.3.0

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

torchmultimodal/models/flava.py

@@ -1326,7 +1326,7 @@ def __init__(self, n_in: int, n_out: int, kw: int):
         super().__init__()
 
         w = torch.empty((n_out, n_in, kw, kw), dtype=torch.float32)
-        w.normal_(std=1 / math.sqrt(n_in * kw ** 2))
+        w.normal_(std=1 / math.sqrt(n_in * kw**2))
 
         b = torch.zeros((n_out,), dtype=torch.float32)
         self.w, self.b = nn.Parameter(w), nn.Parameter(b)
@@ -1340,7 +1340,7 @@ class DalleEncoderBlock(nn.Module):
     def __init__(self, n_in: int, n_out: int, n_layers: int):
         super().__init__()
         n_hid = n_out // 4
-        self.post_gain = 1 / (n_layers ** 2)
+        self.post_gain = 1 / (n_layers**2)
 
         self.id_path = DalleConv2d(n_in, n_out, 1) if n_in != n_out else nn.Identity()
         self.res_path = nn.Sequential(

torchmultimodal/modules/encoders/clip_resnet_encoder.py

@@ -83,7 +83,7 @@ def __init__(
     ):
         super().__init__()
         self.positional_embedding = nn.Parameter(
-            torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5
+            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
         )
         self.k_proj = nn.Linear(embed_dim, embed_dim)
         self.q_proj = nn.Linear(embed_dim, embed_dim)
@@ -200,7 +200,7 @@ def _make_layer(self, planes: int, blocks: int, stride=1) -> nn.Module:
 
     def initialize_parameters(self):
         if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features ** -0.5
+            std = self.attnpool.c_proj.in_features**-0.5
             nn.init.normal_(self.attnpool.q_proj.weight, std=std)
             nn.init.normal_(self.attnpool.k_proj.weight, std=std)
             nn.init.normal_(self.attnpool.v_proj.weight, std=std)

torchmultimodal/modules/encoders/clip_text_encoder.py

@@ -71,8 +71,8 @@ def initialize_parameters(self) -> None:
             std=self.POS_EMBEDDING_INIT_STD,
         )
 
-        proj_std = (self.width ** -0.5) * ((2 * self.encoder.layers.num_layers) ** -0.5)
-        attn_std = self.width ** -0.5
+        proj_std = (self.width**-0.5) * ((2 * self.encoder.layers.num_layers) ** -0.5)
+        attn_std = self.width**-0.5
         fc_std = (2 * self.width) ** -0.5
         for layer in self.encoder.layers.layers:
             nn.init.normal_(layer.self_attn.in_proj_weight, std=attn_std)
@@ -83,7 +83,7 @@ def initialize_parameters(self) -> None:
             nn.init.normal_(layer.linear2.weight, std=proj_std)
 
         # Initialize projection
-        nn.init.normal_(self.projection.weight, std=self.width ** -0.5)
+        nn.init.normal_(self.projection.weight, std=self.width**-0.5)
 
     def build_attention_mask(self) -> torch.Tensor:
         mask = torch.full((self.context_length, self.context_length), True).triu(1)