[Misc] LoRA + Chunked Prefill (#9057)

tests/lora/test_chatglm3_tp.py

@@ -53,7 +53,8 @@ def test_chatglm3_lora(chatglm3_lora_files):
                    max_loras=4,
                    max_lora_rank=64,
                    tensor_parallel_size=1,
-                   trust_remote_code=True)
+                   trust_remote_code=True,
+                   enable_chunked_prefill=True)
 
     output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
     for i in range(len(EXPECTED_LORA_OUTPUT)):
@@ -73,7 +74,8 @@ def test_chatglm3_lora_tp4(chatglm3_lora_files):
                    max_lora_rank=64,
                    tensor_parallel_size=4,
                    trust_remote_code=True,
-                   fully_sharded_loras=False)
+                   fully_sharded_loras=False,
+                   enable_chunked_prefill=True)
 
     output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
     for i in range(len(EXPECTED_LORA_OUTPUT)):
@@ -93,7 +95,8 @@ def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files):
                    max_lora_rank=64,
                    tensor_parallel_size=4,
                    trust_remote_code=True,
-                   fully_sharded_loras=True)
+                   fully_sharded_loras=True,
+                   enable_chunked_prefill=True)
     output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
     for i in range(len(EXPECTED_LORA_OUTPUT)):
         assert output1[i] == EXPECTED_LORA_OUTPUT[i]

tests/lora/test_gemma.py

@@ -37,7 +37,8 @@ def test_gemma_lora(gemma_lora_files):
     llm = vllm.LLM(MODEL_PATH,
                    max_model_len=1024,
                    enable_lora=True,
-                   max_loras=4)
+                   max_loras=4,
+                   enable_chunked_prefill=True)
 
     expected_lora_output = [
         "more important than knowledge.\nAuthor: Albert Einstein\n",

tests/lora/test_llama_tp.py

@@ -78,7 +78,8 @@ def test_llama_lora(sql_lora_files):
                    enable_lora=True,
                    max_num_seqs=16,
                    max_loras=4,
-                   tensor_parallel_size=1)
+                   tensor_parallel_size=1,
+                   enable_chunked_prefill=True)
     generate_and_test(llm, sql_lora_files)
 
 
@@ -120,6 +121,7 @@ def test_llama_lora_tp4(sql_lora_files):
         max_num_seqs=16,
         max_loras=4,
         tensor_parallel_size=4,
+        enable_chunked_prefill=True,
     )
     generate_and_test(llm, sql_lora_files)
 
@@ -135,6 +137,7 @@ def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files):
         max_loras=4,
         tensor_parallel_size=4,
         fully_sharded_loras=True,
+        enable_chunked_prefill=True,
     )
     generate_and_test(llm, sql_lora_files)
 
@@ -151,5 +154,6 @@ def test_llama_lora_tp4_fully_sharded_enable_bias(sql_lora_files):
         tensor_parallel_size=4,
         fully_sharded_loras=True,
         enable_lora_bias=True,
+        enable_chunked_prefill=True,
     )
     generate_and_test(llm, sql_lora_files)

tests/lora/test_long_context.py

@@ -124,7 +124,8 @@ def lora_llm(long_context_infos):
         tensor_parallel_size=4,
         # FIXME enable async output processor
         disable_async_output_proc=True,
-        distributed_executor_backend="mp")
+        distributed_executor_backend="mp",
+        enable_chunked_prefill=True)
     yield llm
     del llm
 

tests/lora/test_minicpmv.py

@@ -67,7 +67,8 @@ def test_minicpmv_lora(minicpmv_lora_files):
         max_loras=4,
         max_lora_rank=64,
         trust_remote_code=True,
-        gpu_memory_utilization=0.97  # This model is pretty big for CI gpus
+        gpu_memory_utilization=0.97,  # This model is pretty big for CI gpus
+        enable_chunked_prefill=True,
     )
     output1 = do_sample(llm, minicpmv_lora_files, lora_id=1)
     for i in range(len(EXPECTED_OUTPUT)):

tests/lora/test_minicpmv_tp.py

@@ -69,6 +69,7 @@ def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded):
         tensor_parallel_size=2,
         trust_remote_code=True,
         fully_sharded_loras=fully_sharded,
+        enable_chunked_prefill=True,
     )
 
     output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
@@ -89,6 +90,7 @@ def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded):
         tensor_parallel_size=4,
         trust_remote_code=True,
         fully_sharded_loras=fully_sharded,
+        enable_chunked_prefill=True,
     )
     output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
     for i in range(len(EXPECTED_OUTPUT)):

tests/lora/test_mixtral.py

@@ -47,6 +47,7 @@ def test_mixtral_lora(mixtral_lora_files, tp_size):
         max_loras=4,
         distributed_executor_backend="ray",
         tensor_parallel_size=tp_size,
+        enable_chunked_prefill=True,
     )
 
     expected_lora_output = [

tests/lora/test_phi.py

@@ -53,7 +53,8 @@ def test_phi2_lora(phi2_lora_files):
                    max_model_len=1024,
                    enable_lora=True,
                    max_loras=2,
-                   enforce_eager=True)
+                   enforce_eager=True,
+                   enable_chunked_prefill=True)
 
     expected_lora_output = [
         "SELECT catalog_publisher, COUNT(*) as num_catalogs FROM catalogs GROUP BY catalog_publisher ORDER BY num_catalogs DESC LIMIT 1;",  # noqa: E501

tests/lora/test_quant_model.py

@@ -84,7 +84,8 @@ def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model,
         tensor_parallel_size=tp_size,
         gpu_memory_utilization=0.2,  #avoid OOM
         quantization=model.quantization,
-        trust_remote_code=True)
+        trust_remote_code=True,
+        enable_chunked_prefill=True)
 
     if model.quantization is None:
         expected_no_lora_output = [
@@ -176,7 +177,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available,
         tensor_parallel_size=1,
         gpu_memory_utilization=0.2,  #avoid OOM
         quantization=model.quantization,
-        trust_remote_code=True)
+        trust_remote_code=True,
+        enable_chunked_prefill=True)
     output_tp1 = do_sample(llm_tp1, tinyllama_lora_files, lora_id=1)
 
     del llm_tp1
@@ -189,7 +191,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available,
         max_loras=4,
         tensor_parallel_size=2,
         gpu_memory_utilization=0.2,  #avoid OOM
-        quantization=model.quantization)
+        quantization=model.quantization,
+        enable_chunked_prefill=True)
     output_tp2 = do_sample(llm_tp2, tinyllama_lora_files, lora_id=1)
 
     del llm_tp2

vllm/config.py

@@ -1698,7 +1698,8 @@ class LoRAConfig:
         # Reminder: Please update docs/source/usage/compatibility_matrix.rst
         # If the feature combo become valid
         if scheduler_config.chunked_prefill_enabled:
-            raise ValueError("LoRA is not supported with chunked prefill yet.")
+            logger.warning("LoRA with chunked prefill is still experimental "
+                           "and may be unstable.")
 
 
 @dataclass

vllm/core/scheduler.py

@@ -166,9 +166,18 @@ class SchedulerOutputs:
                 and not self.blocks_to_swap_out and not self.blocks_to_copy)
 
     def _sort_by_lora_ids(self):
-        self.scheduled_seq_groups = sorted(
-            self.scheduled_seq_groups,
-            key=lambda g: (g.seq_group.lora_int_id, g.seq_group.request_id))
+        assert 0 <= self.num_prefill_groups <= len(self.scheduled_seq_groups)
+
+        def key_fn(group: ScheduledSequenceGroup):
+            key = (group.seq_group.lora_int_id, group.seq_group.request_id)
+            if 0 < self.num_prefill_groups < len(self.scheduled_seq_groups):
+                # Sort sequence groups so that all prefills come before all
+                # decodes as required by chunked prefill.
+                return (not group.seq_group.is_prefill(), *key)
+            return key
+
+        self.scheduled_seq_groups = sorted(self.scheduled_seq_groups,
+                                           key=key_fn)
 
     @property
     def lora_requests(self) -> Set[LoRARequest]:

vllm/worker/model_runner.py

@@ -622,11 +622,13 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             inter_data.lora_requests.add(seq_group_metadata.lora_request)
         query_len = inter_data.query_lens[seq_idx]
         inter_data.lora_index_mapping.append([lora_id] * query_len)
-        inter_data.lora_prompt_mapping.append(
-            [lora_id] *
-            (query_len if seq_group_metadata.sampling_params
-             and seq_group_metadata.sampling_params.prompt_logprobs is not None
-             else 1))
+        sampling_params = seq_group_metadata.sampling_params
+        if sampling_params and sampling_params.prompt_logprobs is not None:
+            inter_data.lora_prompt_mapping.append([lora_id] * query_len)
+        elif not self.chunked_prefill_enabled or seq_group_metadata.do_sample:
+            inter_data.lora_prompt_mapping.append([lora_id])
+        else:
+            inter_data.lora_prompt_mapping.append([])
 
     def _compute_prompt_adapter_input(
             self, inter_data: InterDataForSeqGroup,