[data.llm] Return a batch of rows in the udf instead of row by row

python/ray/llm/_internal/batch/stages/base.py

@@ -160,14 +160,8 @@ async def __call__(self, batch: Dict[str, Any]) -> AsyncIterator[Dict[str, Any]]
         for idx, row in enumerate(inputs):
             row[self.IDX_IN_BATCH_COLUMN] = idx
 
-        # Always stream the outputs one by one to better overlapping
-        # batches. For example, when the output batch size is 64, Ray Data
-        # will collect 64 outputs, and 1) send the batch of 64 to the next stage,
-        # 2) get the next batch of this stage. Assuming the input batch size
-        # is 63 and we yield all 63 results at once, then Ray Data will wait
-        # for 2 batches (63 + 63 > 64) to continue proceeding. On the other hand,
-        # if we stream outputs one-by-one, Ray Data can form a batch of 64 before
-        # the second batch is done.
+        # Collect all outputs first, then return them in the original order
+        # This is a requirement set by https://github.com/ray-project/ray/pull/54190/
         not_outputed_rows = set(range(len(inputs)))
         async for output in self.udf(inputs):
             if self.IDX_IN_BATCH_COLUMN not in output:
@@ -186,11 +180,13 @@ async def __call__(self, batch: Dict[str, Any]) -> AsyncIterator[Dict[str, Any]]
             # Add stage outputs to the data column of the row.
             inputs[idx_in_batch].pop(self.IDX_IN_BATCH_COLUMN)
             inputs[idx_in_batch].update(output)
-            yield {self.data_column: [inputs[idx_in_batch]]}
 
         if not_outputed_rows:
             raise ValueError(f"The rows {not_outputed_rows} are not outputed.")
 
+        # Return all updated inputs in the original order
+        yield {self.data_column: inputs}
+
     def validate_inputs(self, inputs: List[Dict[str, Any]]):
         """Validate the inputs to make sure the required keys are present.
 

python/ray/llm/tests/batch/cpu/stages/test_chat_template_stage.py

@@ -43,10 +43,10 @@ async def test_chat_template_udf_basic(mock_tokenizer_setup):
 
     results = []
     async for result in udf(batch):
-        results.append(result)
+        results.extend(result["__data"])
 
     assert len(results) == 1
-    assert results[0]["__data"][0]["prompt"] == "<chat>Hello AI</chat>"
+    assert results[0]["prompt"] == "<chat>Hello AI</chat>"
     mock_tokenizer.apply_chat_template.assert_called_once()
 
 
@@ -83,9 +83,9 @@ async def test_chat_template_udf_multiple_messages(mock_tokenizer_setup):
     async for result in udf(batch):
         results.append(result)
 
-    assert len(results) == 2
+    assert len(results) == 1
     assert results[0]["__data"][0]["prompt"] == "<chat>Hello AI</chat>"
-    assert results[1]["__data"][0]["prompt"] == "<chat>How are you?</chat>"
+    assert results[0]["__data"][1]["prompt"] == "<chat>How are you?</chat>"
     assert mock_tokenizer.apply_chat_template.call_count == 2
 
 
@@ -123,14 +123,12 @@ async def test_chat_template_udf_assistant_prefill(mock_tokenizer_setup):
 
     results = []
     async for result in udf(batch):
-        results.append(result)
+        results.extend(result["__data"])
 
     assert len(results) == 2
     assert mock_tokenizer.apply_chat_template.call_count == 2
-    assert (
-        results[0]["__data"][0]["prompt"] == "<chat>Hello AI<assistant><think>\n</chat>"
-    )
-    assert results[1]["__data"][0]["prompt"] == "<chat>Hello AI</chat>"
+    assert results[0]["prompt"] == "<chat>Hello AI<assistant><think>\n</chat>"
+    assert results[1]["prompt"] == "<chat>Hello AI</chat>"
     # check if kwargs were set properly
     call_args_list = mock_tokenizer.apply_chat_template.call_args_list
     args1, kwargs1 = call_args_list[0]

python/ray/llm/tests/batch/cpu/stages/test_http_request_stage.py

@@ -73,7 +73,7 @@ async def test_http_request_udf_with_qps(mock_session):
 
         results = []
         async for result in udf(batch):
-            results.append(result)
+            results.extend(result["__data"])
 
         assert len(results) == 2
         assert mock_sleep.called  # Should have called sleep for QPS limiting
@@ -113,7 +113,7 @@ async def test_http_request_udf_with_retry(mock_response):
     with patch("asyncio.sleep") as mock_sleep:
         results = []
         async for result in udf(batch):
-            results.append(result)
+            results.extend(result["__data"])
 
         assert len(results) == 2
         mock_sleep.assert_called()

python/ray/llm/tests/batch/cpu/stages/test_stage_base.py

@@ -73,11 +73,10 @@ async def test_basic_processing(self):
 
         results = []
         async for result in udf(batch):
-            results.append(result)
+            results.extend(result["__data"])
 
         assert len(results) == 2
-        for result in results:
-            data = result["__data"][0]
+        for data in results:
             val = data["value"]
             assert data["processed"] == val * 2
             assert data["extra"] == 10 * val

python/ray/llm/tests/batch/cpu/stages/test_tokenize_stage.py

@@ -35,7 +35,7 @@ async def test_tokenize_udf_basic(mock_tokenizer_setup):
 
     results = []
     async for result in udf(batch):
-        results.append(result["__data"][0])
+        results.extend(result["__data"])
 
     assert len(results) == 2
     assert all(result["tokenized_prompt"] == [1, 2, 3] for result in results)
@@ -64,7 +64,7 @@ async def test_detokenize_udf_basic(mock_tokenizer_setup):
 
     results = []
     async for result in udf(batch):
-        results.append(result["__data"][0])
+        results.extend(result["__data"])
 
     assert len(results) == 2
     assert results[0]["generated_text"] == "Hello"

python/ray/llm/tests/batch/gpu/stages/test_sglang_engine_stage.py

@@ -168,7 +168,7 @@ async def test_sglang_engine_udf_basic(mock_sglang_wrapper, model_llama_3_2_216M
 
     responses = []
     async for response in udf(batch):
-        responses.append(response["__data"][0])
+        responses.extend(response["__data"])
 
     assert len(responses) == 2
     assert all("batch_uuid" in r for r in responses)