Apply isort and black reformatting

Signed-off-by: WoodieDudy <WoodieDudy@users.noreply.github.com>

nemo/collections/asr/parts/submodules/adapters/multi_head_attention_adapter_module.py

@@ -136,7 +136,9 @@ def __init__(
         adapter_strategy: MHAResidualAddAdapterStrategy = None,
         use_pytorch_sdpa: bool = True,
     ):
-        super().__init__(n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=0, use_pytorch_sdpa=use_pytorch_sdpa)
+        super().__init__(
+            n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=0, use_pytorch_sdpa=use_pytorch_sdpa
+        )
 
         self.pre_norm = nn.LayerNorm(n_feat)
 
@@ -230,7 +232,13 @@ def __init__(
         use_pytorch_sdpa: bool = False,
     ):
         super().__init__(
-            n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, pos_bias_u=None, pos_bias_v=None, max_cache_len=0, use_pytorch_sdpa=use_pytorch_sdpa
+            n_head=n_head,
+            n_feat=n_feat,
+            dropout_rate=dropout_rate,
+            pos_bias_u=None,
+            pos_bias_v=None,
+            max_cache_len=0,
+            use_pytorch_sdpa=use_pytorch_sdpa,
         )
 
         self.pre_norm = nn.LayerNorm(n_feat)

nemo/collections/asr/parts/submodules/multi_head_attention.py

@@ -159,13 +159,11 @@ def forward(self, query, key, value, mask, pos_emb=None, cache=None):
 
                 # add extra col for key and value to handle problem with nan after solfmax
                 extra_column = torch.zeros(k.shape[:-2] + (1, k.shape[-1])).to(k.device)
-                k = torch.cat([k, extra_column], dim=-2) 
+                k = torch.cat([k, extra_column], dim=-2)
 
                 extra_column = torch.zeros(v.shape[:-2] + (1, v.shape[-1])).to(v.device)
-                v = torch.cat([v, extra_column], dim=-2) 
-                out = torch.nn.functional.scaled_dot_product_attention(
-                    q, k, v, attn_mask=mask, dropout_p=dropout_rate
-                )
+                v = torch.cat([v, extra_column], dim=-2)
+                out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=dropout_rate)
                 out = out.transpose(1, 2).reshape(n_batch, -1, self.h * self.d_k)  # (batch, time1, d_model)
                 out = self.linear_out(out)  # (batch, time1, d_model)
             else:
@@ -303,7 +301,7 @@ def forward(self, query, key, value, mask, pos_emb, cache=None):
                 dropout_rate = self.dropout_rate if self.training else 0
                 # add extra col for key and value to handle problem with nan after solfmax
                 extra_column = torch.zeros(k.shape[:-2] + (1, k.shape[-1])).to(k.device)
-                k = torch.cat([k, extra_column], dim=-2) 
+                k = torch.cat([k, extra_column], dim=-2)
 
                 extra_column = torch.zeros(v.shape[:-2] + (1, v.shape[-1])).to(v.device)
                 v = torch.cat([v, extra_column], dim=-2)

tests/collections/asr/mixins/adapters/test_asr_adapter_modules.py

@@ -204,7 +204,7 @@ def test_relmha_adapter_with_torch_sdpa(self):
             _, pos_emb = relpos_enc(x)
             out = adapter(x, x, x, att_mask, pos_emb)
             out_sdpa = adapter_torch_sdpa(x, x, x, att_mask, pos_emb)
-            assert torch.allclose(out_sdpa, out, atol=1e-5) 
+            assert torch.allclose(out_sdpa, out, atol=1e-5)
 
     @pytest.mark.unit
     def test_mha_adapter_with_torch_sdpa(self):
@@ -229,7 +229,7 @@ def test_mha_adapter_with_torch_sdpa(self):
         with torch.no_grad():
             out = adapter(x, x, x, att_mask)
             out_sdpa = adapter_torch_sdpa(x, x, x, att_mask)
-            assert torch.allclose(out_sdpa, out, atol=1e-5) 
+            assert torch.allclose(out_sdpa, out, atol=1e-5)
 
     @pytest.mark.unit
     def test_abspos_encoding_init(self):

tests/collections/asr/test_conformer_encoder.py

@@ -74,7 +74,11 @@ def test_stochastic_depth_model_creation(self):
         for start_layer in [-1, 0, 5]:
             with pytest.raises(ValueError, match="stochastic_depth_start_layer has to be in"):
                 ConformerEncoder(
-                    feat_in=10, n_layers=n_layers, d_model=4, feat_out=8, stochastic_depth_start_layer=start_layer,
+                    feat_in=10,
+                    n_layers=n_layers,
+                    d_model=4,
+                    feat_out=8,
+                    stochastic_depth_start_layer=start_layer,
                 )
 
     @pytest.mark.pleasefixme