[Pytorch] Add get_backward_dw_params api for TE module

transformer_engine/pytorch/graph.py

@@ -853,12 +853,22 @@ def functionalized(*user_args, **user_kwargs):
         return functionalized
 
     def make_graphed_attribute_functions(graph_idx):
+        # Get te modules for current graph
+        te_modules = visited_te_modules.get(graph_idx, set())
 
         # Attach backward_dw as an attribute to the graphed callable.
         def backward_dw():
             if need_bwd_dw_graph.get(graph_idx, False):
                 bwd_dw_graphs[graph_idx].replay()
 
+                # Trigger the grad accumulation hook for wgrad graphs.
+                for module in te_modules:
+                    if (
+                        isinstance(module, TransformerEngineBaseModule)
+                        and module.need_backward_dw()
+                    ):
+                        module._trigger_wgrad_accumulation_and_reduce_hooks()
+
         # Attach reset as an attribute to the graphed callable.
         def reset():
             fwd_graphs[graph_idx].reset()

transformer_engine/pytorch/module/base.py

@@ -1526,8 +1526,14 @@ def backward_dw(self):
                     bias_tensor.grad = bgrad.to(bias_tensor.dtype)
             del wgrad
             del bgrad
-            for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
-                wgrad_accumulation_and_reduce_hook()
+            self._trigger_wgrad_accumulation_and_reduce_hooks()
+
+    def _trigger_wgrad_accumulation_and_reduce_hooks(self):
+        """
+        Trigger the wgrad accumulation and reduce hooks.
+        """
+        for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
+            wgrad_accumulation_and_reduce_hook()
 
     def is_debug_iter(self) -> bool:
         """

transformer_engine/pytorch/module/grouped_linear.py

@@ -873,8 +873,7 @@ def backward_dw(self):
             del grad_biases_
             del wgrad_list
             del tensor_list
-            for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
-                wgrad_accumulation_and_reduce_hook()
+            self._trigger_wgrad_accumulation_and_reduce_hooks()
 
     def _customize_quantizers_float8_current_scaling(self, fwd: bool, recipe: Recipe) -> None:
         """Customize quantizers based on current scaling recipe + linear."""

transformer_engine/pytorch/module/layernorm_mlp.py

@@ -2506,5 +2506,4 @@ def backward_dw(self):
             del fc2_wgrad
             del fc1_wgrad
             del fc1_bias_grad
-            for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
-                wgrad_accumulation_and_reduce_hook()
+            self._trigger_wgrad_accumulation_and_reduce_hooks()