Update vllm to 0.6.3 (#1711) (#1720)

Co-authored-by: Ke Bao <ISPObaoke@163.com>

examples/frontend_language/usage/llava_video/srt_example_llava_v.py

@@ -208,7 +208,7 @@ if __name__ == "__main__":
         model_override_args["image_token_index"] = 64002
 
     if args.num_frames == 32:
-        model_override_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_override_args["rope_scaling"] = {"factor": 2.0, "rope_type": "linear"}
         model_override_args["max_sequence_length"] = 4096 * 2
         model_override_args["tokenizer_model_max_length"] = 4096 * 2
     elif args.num_frames < 32:

python/pyproject.toml

@@ -26,7 +26,7 @@ runtime_common = ["aiohttp", "decord", "fastapi", "hf_transfer", "huggingface_hu
     "outlines>=0.0.44", "modelscope"]
 # xpu is not enabled in public vllm and torch whl,
 # need to follow https://docs.vllm.ai/en/latest/getting_started/xpu-installation.htmlinstall vllm
-srt = ["sglang[runtime_common]", "torch", "vllm==0.5.5"]
+srt = ["sglang[runtime_common]", "torch", "vllm==0.6.3.post1"]
 srt_xpu = ["sglang[runtime_common]"]
 
 openai = ["openai>=1.0", "tiktoken"]

python/sglang/launch_server_llavavid.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
     model_override_args["num_frames"] = 16
     model_override_args["model_type"] = "llavavid"
     if model_override_args["num_frames"] == 32:
-        model_override_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_override_args["rope_scaling"] = {"factor": 2.0, "rope_type": "linear"}
         model_override_args["max_sequence_length"] = 4096 * 2
         model_override_args["tokenizer_model_max_length"] = 4096 * 2
         model_override_args["model_max_length"] = 4096 * 2

python/sglang/srt/layers/linear.py

@@ -20,8 +20,10 @@ from vllm.distributed import (
 from vllm.model_executor.layers.linear import LinearBase
 from vllm.model_executor.parameter import (
     BasevLLMParameter,
+    PackedColumnParameter,
     PackedvLLMParameter,
     PerTensorScaleParameter,
+    RowvLLMParameter,
 )
 
 from sglang.srt.layers.quantization.base_config import (
@@ -39,6 +41,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
     "GPTQMarlinLinearMethod",
     "Fp8LinearMethod",
     "MarlinLinearMethod",
+    "GPTQLinearMethod",
 ]
 
 
@@ -50,7 +53,7 @@ def adjust_marlin_shard(param, shard_size, shard_offset):
     return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
 
 
-def adjust_bitsandbytes_shard(
+def adjust_bitsandbytes_4bit_shard(
     param: Parameter, qkv_offsets: Dict[str, Tuple[int, int]], loaded_shard_id: str
 ) -> Tuple[int, int]:
     """Adjust the quantization offsets and sizes for BitsAndBytes sharding."""
@@ -207,7 +210,6 @@ class ReplicatedLinear(LinearBase):
             self.output_size,
             self.params_dtype,
             weight_loader=self.weight_loader,
-            prefix=prefix,
         )
 
         if bias:
@@ -315,7 +317,6 @@ class ColumnParallelLinear(LinearBase):
                 if self.quant_method.__class__.__name__ in WEIGHT_LOADER_V2_SUPPORTED
                 else self.weight_loader
             ),
-            prefix=prefix,
         )
         if bias:
             self.bias = Parameter(
@@ -345,8 +346,12 @@ class ColumnParallelLinear(LinearBase):
         if is_gguf_weight and isinstance(param, UninitializedParameter):
             param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)
 
+        use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+
         param_data = param.data
-        if output_dim is not None:
+        # bitsandbytes loads the weights of the specific portion
+        # no need to narrow here
+        if output_dim is not None and not use_bitsandbytes_4bit:
             shard_size = param_data.shape[output_dim]
             start_idx = tp_rank * shard_size
             loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
@@ -454,17 +459,22 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
             return
 
-        if is_gguf_weight and isinstance(param, UninitializedParameter):
-            from gguf.constants import GGML_QUANT_SIZES
+        if is_gguf_weight:
+            tp_size = get_tensor_model_parallel_world_size()
+            tp_rank = get_tensor_model_parallel_rank()
+
+            output_dim = getattr(param, "output_dim", None)
+            shard_size = loaded_weight.size(output_dim) // tp_size
+            start_idx = tp_rank * shard_size
 
-            ori_shape = param.tensor_shape
-            weight_types = self.qweight_type.shard_weight_type.values()
-            row_size = []
-            for weight_type in weight_types:
-                block_size, type_size = GGML_QUANT_SIZES[weight_type]
-                row_size.append(ori_shape[1] // block_size * type_size)
-            q_shape = (ori_shape[0], max(row_size))
-            param.materialize(q_shape, dtype=loaded_weight.dtype)
+            loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+
+            param.shard_id.append(loaded_shard_id)
+            param.shard_id_map[loaded_shard_id] = len(param.data_container)
+            param.data_container.append(loaded_weight)
+            if len(param.data_container) == 2:
+                self.qweight = param.materialize_nested()
+            return
 
         param_data = param.data
         output_dim = getattr(param, "output_dim", None)
@@ -526,26 +536,17 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                     param, shard_size, shard_offset
                 )
 
-            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
-            if use_bitsandbytes:
+            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+            if use_bitsandbytes_4bit:
                 shard_size = loaded_weight.shape[output_dim]
                 shard_offset = loaded_weight.shape[output_dim] * loaded_shard_id
 
-            if is_gguf_weight:
-                tp_size = get_tensor_model_parallel_world_size()
-                output_dim = getattr(param, "output_dim", None)
-                shard_shape = list(loaded_weight.shape)
-                shard_shape[output_dim] = shard_shape[output_dim] // tp_size
-                param.shard_id.append(loaded_shard_id)
-                param.shard_size[loaded_shard_id] = shard_shape
-
-                input_dim = getattr(param, "input_dim", None)
-                input_size = loaded_weight.shape[input_dim]
-                param_data = param_data.narrow(input_dim, 0, input_size)
-
             param_data = param_data.narrow(output_dim, shard_offset, shard_size)
             start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+            # bitsandbytes loads the weights of the specific portion
+            # no need to narrow here
+            if not use_bitsandbytes_4bit:
+                loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
         # Special case for AQLM codebooks.
         elif is_metadata:
             # metadata indicates fixed size concatenated along dim 0
@@ -595,7 +596,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
             if (
-                isinstance(param, PackedvLLMParameter)
+                isinstance(param, (PackedColumnParameter, PackedvLLMParameter))
                 and param.packed_dim == param.output_dim
             ):
                 shard_size, shard_offset = param.adjust_shard_indexes_for_packing(
@@ -617,7 +618,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             if isinstance(param, PerTensorScaleParameter):
                 param.load_merged_column_weight(loaded_weight=loaded_weight, shard_id=0)
                 return
-            elif type(param) is BasevLLMParameter:
+            elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                 param.load_merged_column_weight(loaded_weight=loaded_weight)
                 return
             self._load_fused_module_from_checkpoint(param, loaded_weight)
@@ -760,7 +761,7 @@ class QKVParallelLinear(ColumnParallelLinear):
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
             if (
-                isinstance(param, PackedvLLMParameter)
+                isinstance(param, (PackedColumnParameter, PackedvLLMParameter))
                 and param.packed_dim == param.output_dim
             ):
                 shard_size, shard_offset = param.adjust_shard_indexes_for_packing(
@@ -780,10 +781,10 @@ class QKVParallelLinear(ColumnParallelLinear):
     ):
         if loaded_shard_id is None:  # special case for certain models
             if isinstance(param, PerTensorScaleParameter):
-                param.load_merged_column_weight(loaded_weight=loaded_weight, shard_id=0)
+                param.load_qkv_weight(loaded_weight=loaded_weight, shard_id=0)
                 return
-            elif type(param) is BasevLLMParameter:
-                param.load_merged_column_weight(loaded_weight=loaded_weight)
+            elif type(param) in (RowvLLMParameter, BasevLLMParameter):
+                param.load_qkv_weight(loaded_weight=loaded_weight)
                 return
             self._load_fused_module_from_checkpoint(param, loaded_weight)
             return
@@ -818,17 +819,22 @@ class QKVParallelLinear(ColumnParallelLinear):
             param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
             return
 
-        if is_gguf_weight and isinstance(param, UninitializedParameter):
-            from gguf.constants import GGML_QUANT_SIZES
+        if is_gguf_weight:
+            tp_size = get_tensor_model_parallel_world_size()
+            tp_rank = get_tensor_model_parallel_rank()
+
+            output_dim = getattr(param, "output_dim", None)
+            shard_size = loaded_weight.size(output_dim) // tp_size
+            start_idx = tp_rank * shard_size
 
-            ori_shape = param.tensor_shape
-            weight_types = self.qweight_type.shard_weight_type.values()
-            row_size = []
-            for weight_type in weight_types:
-                block_size, type_size = GGML_QUANT_SIZES[weight_type]
-                row_size.append(ori_shape[1] // block_size * type_size)
-            q_shape = (ori_shape[0], max(row_size))
-            param.materialize(q_shape, dtype=loaded_weight.dtype)
+            loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+
+            param.shard_id.append(loaded_shard_id)
+            param.shard_id_map[loaded_shard_id] = len(param.data_container)
+            param.data_container.append(loaded_weight)
+            if len(param.data_container) == 3:
+                self.qweight = param.materialize_nested()
+            return
 
         param_data = param.data
         output_dim = getattr(param, "output_dim", None)
@@ -863,6 +869,8 @@ class QKVParallelLinear(ColumnParallelLinear):
                     self.total_num_kv_heads * self.head_size,
                 ),
             ]
+            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+
             packed_dim = getattr(param, "packed_dim", None)
             for shard_id, shard_offset, shard_size in shard_offsets:
                 # Special case for Quantized Weights.
@@ -877,6 +885,29 @@ class QKVParallelLinear(ColumnParallelLinear):
                         param, shard_size, shard_offset
                     )
 
+                if use_bitsandbytes_4bit:
+                    orig_qkv_offsets = {
+                        "q": (0, self.total_num_heads * self.head_size),
+                        "k": (
+                            self.total_num_heads * self.head_size,
+                            self.total_num_kv_heads * self.head_size,
+                        ),
+                        "v": (
+                            (self.total_num_heads + self.total_num_kv_heads)
+                            * self.head_size,
+                            self.total_num_kv_heads * self.head_size,
+                        ),
+                        "total": (
+                            (self.total_num_heads + 2 * self.total_num_kv_heads)
+                            * self.head_size,
+                            0,
+                        ),
+                    }
+
+                    shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
+                        param, orig_qkv_offsets, shard_id
+                    )
+
                 loaded_weight_shard = loaded_weight.narrow(
                     output_dim, shard_offset, shard_size
                 )
@@ -910,8 +941,8 @@ class QKVParallelLinear(ColumnParallelLinear):
                     param, shard_size, shard_offset
                 )
 
-            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
-            if use_bitsandbytes:
+            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+            if use_bitsandbytes_4bit:
                 orig_qkv_offsets = {
                     "q": (0, self.num_heads * self.head_size),
                     "k": (
@@ -927,29 +958,22 @@ class QKVParallelLinear(ColumnParallelLinear):
                         0,
                     ),
                 }
-                shard_size, shard_offset = adjust_bitsandbytes_shard(
+                shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
                     param, orig_qkv_offsets, loaded_shard_id
                 )
 
-            if is_gguf_weight:
-                tp_size = get_tensor_model_parallel_world_size()
-                output_dim = getattr(param, "output_dim", None)
-                shard_shape = list(loaded_weight.shape)
-                shard_shape[output_dim] = shard_shape[output_dim] // tp_size
-                param.shard_id.append(loaded_shard_id)
-                param.shard_size[loaded_shard_id] = shard_shape
-
-                input_dim = getattr(param, "input_dim", None)
-                input_size = loaded_weight.shape[input_dim]
-                param_data = param_data.narrow(input_dim, 0, input_size)
-
             param_data = param_data.narrow(output_dim, shard_offset, shard_size)
             if loaded_shard_id == "q":
                 shard_id = tp_rank
             else:
                 shard_id = tp_rank // self.num_kv_head_replicas
             start_idx = shard_id * shard_size
-            loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+
+            # bitsandbytes loads the weights of the specific portion
+            # no need to narrow here
+            if not use_bitsandbytes_4bit:
+                loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+
         # Special case for for AQLM codebooks.
         elif is_metadata:
             # metadata indicates fixed size concatenated along dim 0
@@ -1037,7 +1061,6 @@ class RowParallelLinear(LinearBase):
                 if self.quant_method.__class__.__name__ in WEIGHT_LOADER_V2_SUPPORTED
                 else self.weight_loader
             ),
-            prefix=prefix,
         )
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError(
@@ -1061,6 +1084,7 @@ class RowParallelLinear(LinearBase):
         tp_rank = get_tensor_model_parallel_rank()
         tp_size = get_tensor_model_parallel_world_size()
         input_dim = getattr(param, "input_dim", None)
+        use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
 
         # Special case for GGUF
         is_gguf_weight = getattr(param, "is_gguf_weight", False)
@@ -1076,7 +1100,9 @@ class RowParallelLinear(LinearBase):
             param.materialize(tuple(weight_shape), dtype=loaded_weight.dtype)
 
         param_data = param.data
-        if input_dim is not None:
+        # bitsandbytes loads the weights of the specific portion
+        # no need to narrow here
+        if input_dim is not None and not use_bitsandbytes_4bit:
             shard_size = param_data.shape[input_dim]
             start_idx = tp_rank * shard_size
             loaded_weight = loaded_weight.narrow(input_dim, start_idx, shard_size)

python/sglang/srt/lora/lora.py

@@ -351,7 +351,9 @@ class LoRAAdapter(nn.Module):
         loader = DefaultModelLoader(self.load_config)
         revision = getattr(self.config.hf_config, "revision", None)
         for name, loaded_weight in loader._get_weights_iterator(
-            model_path, revision=revision, fall_back_to_pt=True
+            DefaultModelLoader.Source(
+                model_path, revision=revision, fall_back_to_pt=True
+            )
         ):
             match = re.search(r"layers\.(\d+)\.", name)
             if match is not None:

python/sglang/srt/model_executor/model_runner.py

@@ -59,8 +59,11 @@ from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     enable_show_time_cost,
     get_available_gpu_memory,
+    is_attention_free_model,
+    is_embedding_model,
     is_generation_model,
     is_multimodal_model,
+    model_has_inner_state,
     monkey_patch_vllm_dummy_weight_loader,
     monkey_patch_vllm_p2p_access_check,
 )
@@ -316,11 +319,13 @@ class ModelRunner:
 
         def get_weight_iter(config):
             iter = loader._get_weights_iterator(
-                config.model,
-                config.revision,
-                fall_back_to_pt=getattr(
-                    self.model, "fall_back_to_pt_during_load", True
-                ),
+                DefaultModelLoader.Source(
+                    config.model,
+                    revision=config.revision,
+                    fall_back_to_pt=getattr(
+                        self.model, "fall_back_to_pt_during_load", True
+                    ),
+                )
             )
             return iter
 
@@ -662,3 +667,7 @@ def load_model_cls_srt(model_arch: str) -> Optional[Type[nn.Module]]:
 
 # Monkey patch model loader
 setattr(ModelRegistry, "_try_load_model_cls", load_model_cls_srt)
+setattr(ModelRegistry, "is_multimodal_model", is_multimodal_model)
+setattr(ModelRegistry, "is_attention_free_model", is_attention_free_model)
+setattr(ModelRegistry, "model_has_inner_state", model_has_inner_state)
+setattr(ModelRegistry, "is_embedding_model", is_embedding_model)

python/sglang/srt/models/deepseek_v2.py

@@ -250,7 +250,7 @@ class DeepseekV2Attention(nn.Module):
             bias=False,
             quant_config=quant_config,
         )
-        rope_scaling["type"] = "deepseek_yarn"
+        rope_scaling["rope_type"] = "deepseek_yarn"
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
             rotary_dim=qk_rope_head_dim,
@@ -398,7 +398,7 @@ class DeepseekV2AttentionMLA(nn.Module):
             bias=False,
             quant_config=quant_config,
         )
-        rope_scaling["type"] = "deepseek_yarn"
+        rope_scaling["rope_type"] = "deepseek_yarn"
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
             rotary_dim=qk_rope_head_dim,

python/sglang/srt/utils.py

@@ -215,6 +215,26 @@ def is_multimodal_model(model_architectures):
         return False
 
 
+def is_attention_free_model(model_architectures):
+    return False
+
+
+def model_has_inner_state(model_architectures):
+    return False
+
+
+def is_embedding_model(model_architectures):
+    if (
+        "LlamaEmbeddingModel" in model_architectures
+        or "MistralModel" in model_architectures
+        or "LlamaForSequenceClassification" in model_architectures
+        or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures
+    ):
+        return True
+    else:
+        return False
+
+
 def is_generation_model(model_architectures, is_embedding: bool = False):
     # We have two ways to determine whether a model is a generative model.
     # 1. Check the model architectue

test/srt/test_server_args.py

@@ -11,13 +11,13 @@ class TestPrepareServerArgs(unittest.TestCase):
                 "--model-path",
                 "model_path",
                 "--json-model-override-args",
-                '{"rope_scaling": {"factor": 2.0, "type": "linear"}}',
+                '{"rope_scaling": {"factor": 2.0, "rope_type": "linear"}}',
             ]
         )
         self.assertEqual(server_args.model_path, "model_path")
         self.assertEqual(
             json.loads(server_args.json_model_override_args),
-            {"rope_scaling": {"factor": 2.0, "type": "linear"}},
+            {"rope_scaling": {"factor": 2.0, "rope_type": "linear"}},
         )