fix(ci): unexpected keyword argument streaming

.github/workflows/benchmark.yml

@@ -40,7 +40,6 @@ jobs:
         run: python3 -m pip install -r benchmark_v2/requirements.txt kernels
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
         run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]"
 
       - name: Run benchmark

src/transformers/generation/continuous_batching/cache.py

@@ -335,6 +335,14 @@ def update(
         # Return the new KV values
         return key_states_with_cache, value_states_with_cache
 
+    @traced
+    def close(self):
+        self.key_cache.clear()
+        self.value_cache.clear()
+
+        torch._dynamo.reset()
+        torch._dynamo.reset_code_caches()
+
 
 # TODO: rework computation with the groups and their sizes
 class PagedAttentionMemoryHandler:

src/transformers/generation/continuous_batching/continuous_api.py

@@ -708,6 +708,35 @@ def _sample(self, probs: torch.Tensor, do_sample: bool) -> None:
         tokens = next_tokens.size(1)  # Get seq_len dimension
         self.output_ids[:, :tokens].copy_(next_tokens)
 
+    def close(self):
+        self.cache.close()
+        self.requests_in_batch.clear()
+
+        if self._graphs is not None:
+            self._graphs.clear()
+
+        del self.input_ids
+        del self.position_ids
+        del self.cumulative_seqlens_q
+        del self.logits_indices
+        del self.output_ids
+
+        self.cumulative_seqlens_k.clear()
+
+        if self.attention_mask is not None:
+            self.attention_mask.clear()
+            self.attention_mask = None
+
+        self.write_index_storage.clear()
+        self.read_index_storage.clear()
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+            import gc
+
+            gc.collect()
+            torch.cuda.empty_cache()
+
 
 # Manager Class (User Interface)
 @attach_tracer()
@@ -826,6 +855,10 @@ def stop(self, block: bool = True, timeout: Optional[float] = None) -> None:
         if block:
             self.join(stop_trigger_time, timeout)
 
+        if self.batch_processor is not None:
+            self.batch_processor.close()
+            self.batch_processor = None  # NOTE: this is enough to clear memory after stop, still calling `close()` because it calls torch cache intrinsics
+
     def join(self, stop_trigger_time: float, timeout: Optional[float] = None) -> None:
         """Wait for the background thread to finish.