Recompute: fix bug with transformer attention mask

python/paddle/distributed/fleet/utils/recompute.py

@@ -145,23 +145,25 @@ def backward(ctx, *args):
 
             # run backward() with only tensor that requires grad
             forward_outputs_with_grad = []
-            backward_inputs = list(args)
+            # NOTE In Transformer-like network, if user put the attention mask into the recompute segment output,
+            # pylayer will force the stop_gradient of attention mask to be False, which will make the number of 
+            # tensor that need grad does not match.
+            # the following backward_inputs_with_grad is used to avoid this case.
+            backward_inputs_with_grad = []
             for i in range(len(outputs)):
                 if isinstance(outputs[i],
                               core.VarBase) and not outputs[i].stop_gradient:
                     forward_outputs_with_grad.append(outputs[i])
+                    backward_inputs_with_grad.append(args[i])
+
             if len(forward_outputs_with_grad) == 0:
                 raise RuntimeError(
                     "none of output has requires_grad=True, this recompute() is not necessary"
                 )
 
-            assert len(backward_inputs) == len(
-                forward_outputs_with_grad
-            ), "number of forward outputs is [{}], but the backward got [{}] inputs".format(
-                len(forward_outputs_with_grad), len(backward_inputs))
-
             # actually backward            
-            paddle.autograd.backward(forward_outputs_with_grad, backward_inputs)
+            paddle.autograd.backward(forward_outputs_with_grad,
+                                     backward_inputs_with_grad)
 
             grads = list(inp._grad_ivar() for inp in detached_inputs
                          if isinstance(inp, core.VarBase))