fix python lint

deeplink_ext/internlm_ops/rms_norm/deeplink.py

@@ -12,7 +12,9 @@ class _DeepLinkRMSNormFunction(torch.autograd.Function):
     def forward(ctx, hidden_states, weight, bias, eps):
         output = torch.empty_like(hidden_states)
         inv_rms_shape = list(hidden_states.shape[:-1], 1)
-        inv_rms = torch.empty(inv_rms_shape, dtype=hidden_states.dtype, device=hidden_states.device)
+        inv_rms = torch.empty(
+            inv_rms_shape, dtype=hidden_states.dtype, device=hidden_states.device
+        )
         ext.rms_norm(output, inv_rms, hidden_states, None, weight, bias, eps)
 
         ctx.save_for_backward(hidden_states, inv_rms, weight, bias, torch.tensor(eps))
@@ -22,12 +24,23 @@ def forward(ctx, hidden_states, weight, bias, eps):
     def backward(ctx, grad_output):
         hidden_states, inv_rms, weight, bias, eps_tensor = ctx.saved_tensors
         eps = eps_tensor.item()
-        
+
         grad_input = torch.empty_like(hidden_states)
         grad_weight = torch.empty_like(weight)
         grad_bias = torch.empty_like(bias)
 
-        ext.rms_norm_backward(grad_input, grad_weight, grad_bias, grad_output, hidden_states, weight, bias, inv_rms, None, eps)
+        ext.rms_norm_backward(
+            grad_input,
+            grad_weight,
+            grad_bias,
+            grad_output,
+            hidden_states,
+            weight,
+            bias,
+            inv_rms,
+            None,
+            eps
+        )
         return grad_input, grad_weight, grad_bias, None
 
 
@@ -36,8 +49,18 @@ class _DeepLinkRMSNormFunctionWithNormalizedShape(torch.autograd.Function):
     def forward(ctx, hidden_states, weight, bias, eps, normalized_shape):
         output = torch.empty_like(hidden_states, dtype=torch.float32)
         inv_rms_shape = list(hidden_states.shape[:-1], 1)
-        inv_rms = torch.empty(inv_rms_shape, dtype=torch.float32, device=hidden_states.device)
-        ext.rms_norm(output, inv_rms, hidden_states.float(), normalized_shape, weight.float(), bias.float(), eps)
+        inv_rms = torch.empty(
+            inv_rms_shape, dtype=torch.float32, device=hidden_states.device
+        )
+        ext.rms_norm(
+            output,
+            inv_rms,
+            hidden_states.float(),
+            normalized_shape,
+            weight.float(),
+            bias.float(),
+            eps
+        )
         output = output.half()
         inv_rms = inv_rms.half()
         ctx.save_for_backward(hidden_states, inv_rms, weight, bias, torch.tensor(eps))
@@ -49,20 +72,22 @@ def backward(ctx, grad_output):
         hidden_states, inv_rms, weight, bias, eps_tensor = ctx.saved_tensors
         eps = eps_tensor.item()
         normalized_shape = ctx.intermediate_results
-        
+
         grad_input = torch.empty_like(hidden_states, dtype=torch.float32)
         grad_weight = torch.empty_like(weight, dtype=torch.float32)
         grad_bias = torch.empty_like(bias, dtype=torch.float32)
-        ext.rms_norm_backward(grad_input,
-                              grad_weight,
-                              grad_bias,
-                              grad_output.float(),
-                              hidden_states.float(),
-                              weight.float(),
-                              bias.float(),
-                              inv_rms.float(),
-                              normalized_shape,
-                              eps)
+        ext.rms_norm_backward(
+            grad_input,
+            grad_weight,
+            grad_bias,
+            grad_output.float(),
+            hidden_states.float(),
+            weight.float(),
+            bias.float(),
+            inv_rms.float(),
+            normalized_shape,
+            eps
+        )
         grad_output = grad_output.half()
         hidden_states = hidden_states.half()
         inv_rms = inv_rms.half()

tests/test_rms_lightlm.py

@@ -38,7 +38,7 @@
     bias,
     inv_rms,
     weight.shape,
-    1e-6
+    1e-6,
 )
 
 print("Output:", output)