[`RWKV`] Add Gradient Checkpointing support for RWKV (#24955)

add GC support for RWKV

src/transformers/models/rwkv/modeling_rwkv.py

@@ -406,6 +406,7 @@ class RwkvPreTrainedModel(PreTrainedModel):
     base_model_prefix = "rwkv"
     _no_split_modules = ["RwkvBlock"]
     _keep_in_fp32_modules = ["time_decay", "time_first"]
+    supports_gradient_checkpointing = True
 
     def _init_weights(self, module):
         """Initialize the weights."""
@@ -605,6 +606,8 @@ class RwkvModel(RwkvPreTrainedModel):
 
         self.layers_are_rescaled = False
 
+        self.gradient_checkpointing = False
+
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -659,14 +662,35 @@ class RwkvModel(RwkvPreTrainedModel):
             ]
             state[4] -= 1e30
 
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
         hidden_states = inputs_embeds
 
         all_self_attentions = () if output_attentions else None
         all_hidden_states = () if output_hidden_states else None
         for idx, block in enumerate(self.blocks):
-            hidden_states, state, attentions = block(
-                hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions
-            )
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
+
+                    return custom_forward
+
+                hidden_states, state, attentions = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block), hidden_states, state
+                )
+            else:
+                hidden_states, state, attentions = block(
+                    hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions
+                )
+
             if (
                 self.layers_are_rescaled
                 and self.config.rescale_every > 0