audio server improve.

lightllm/models/qwen3_omni_moe_thinker/qwen3_omni_audio.py

@@ -16,7 +16,6 @@
 
 from lightllm.server.multimodal_params import AudioItem
 from lightllm.server.embed_cache.utils import read_shm, get_shm_name_data
-from lightllm.server.embed_cache.embed_cache_client import CpuEmbedCacheClient
 from lightllm.common.basemodel.layer_infer.cache_tensor_manager import g_cache_manager
 from lightllm.models.vit.triton_kernel.flashattention_nopad import flash_attention_fwd
 from lightllm.models.qwen3_omni_moe_thinker.audio_process import WhisperFeatureExtractor
@@ -207,9 +206,6 @@ def __init__(
         self.proj2 = nn.Linear(d_model, output_dim)
         self.n_window_infer = n_window_infer
         self.conv_chunksize = conv_chunksize
-
-        self.cache_port = kvargs["cache_port"]
-        self.cache_client = rpyc.connect("localhost", self.cache_port, config={"allow_pickle": True})
         self._init_datatype()
 
     def _init_datatype(self):
@@ -337,7 +333,7 @@ def forward(
         hidden_states = self.proj2(hidden_states)
         return hidden_states
 
-    def encode(self, audio_items: List[AudioItem], cpu_embed_cache_client: CpuEmbedCacheClient):
+    def encode(self, audio_items: List[AudioItem]):
         uuids = []
         items: List[AudioItem] = []
         per_audio_features: List[torch.Tensor] = []
@@ -368,24 +364,9 @@ def encode(self, audio_items: List[AudioItem], cpu_embed_cache_client: CpuEmbedC
             )
             per_audio_features.append(audio_features)
 
-        ready_audio = obtain(self.cache_client.root.get_items_embed(uuids))
-        ids_to_set = []
-        for i, ready in enumerate(ready_audio):
-            if ready:
-                continue
-
-            uid = uuids[i]
-            item = items[i]
-
+        all_embeds = []
+        for i in range(len(audio_items)):
             cur_embed = per_audio_features[i]
-            cpu_embed_cache_client.copy_to_cache(
-                embed_tensor=cur_embed, start_index_in_cache=item.start_index_in_embed_cache
-            )
-            assert (
-                item.token_num == cur_embed.shape[0]
-            ), f"audio token num not match {item.token_num} vs {cur_embed.shape[0]} "
-            ids_to_set.append(uid)
-
-        if ids_to_set:
-            self.cache_client.root.set_items_embed(ids=ids_to_set)
-            torch.cuda.current_stream().synchronize()
+            all_embeds.append(cur_embed)
+
+        return all_embeds, audio_items

lightllm/models/whisper/whisper_audio.py

@@ -12,7 +12,6 @@
 from lightllm.server.embed_cache.utils import read_shm, get_shm_name_data
 from lightllm.server.multimodal_params import AudioItem
 from rpyc.utils.classic import obtain
-from lightllm.server.embed_cache.embed_cache_client import CpuEmbedCacheClient
 
 # tokenizer_class removed
 class WhisperProcessor(ProcessorMixin):
@@ -89,8 +88,6 @@ def __init__(self, kvargs):
         self.max_seconds = 30
         self.sampling_rate = 16000
         self.max_length = self.max_seconds * self.sampling_rate
-        self.cache_port = kvargs["cache_port"]
-        self.cache_client = rpyc.connect("localhost", self.cache_port, config={"allow_pickle": True})
         data_type = kvargs["data_type"]
         if data_type in ["bf16", "bfloat16"]:
             self.data_type = torch.bfloat16
@@ -162,7 +159,7 @@ def forward(self, audio_values, audio_lens_after_cnn):
         x = F.linear(x, weight=self.projector_weights["mlp2.3.weight"], bias=self.projector_weights["mlp2.3.bias"])
         return x
 
-    def encode(self, audio_items: List[AudioItem], cpu_embed_cache_client: CpuEmbedCacheClient):
+    def encode(self, audio_items: List[AudioItem]):
         # 每个元素是一个chunk
         batch_audios = []
         batch_audio_lens = []
@@ -222,22 +219,13 @@ def encode(self, audio_items: List[AudioItem], cpu_embed_cache_client: CpuEmbedC
                 continue
             per_audio_embeds[owner].append(audios[chunk_idx][:token_len])
 
-        ready_audio = obtain(self.cache_client.root.get_items_embed(uuids))
-        ids_to_set = []
-        for i, ready in enumerate(ready_audio):
-            if ready:
-                continue
+        ans_embeds = []
+        for i in range(len(uuids)):
 
-            uid = uuids[i]
             item = items[i]
 
             # 拼接该 audio 的所有 chunk embedding
             cur_embed = torch.cat(per_audio_embeds[i], dim=0)
-            cpu_embed_cache_client.copy_to_cache(
-                embed_tensor=cur_embed, start_index_in_cache=item.start_index_in_embed_cache
-            )
-            ids_to_set.append(uid)
-
-        if ids_to_set:
-            self.cache_client.root.set_items_embed(ids=ids_to_set)
-            torch.cuda.current_stream().synchronize()
+            ans_embeds.append(cur_embed)
+
+        return ans_embeds, audio_items

lightllm/server/api_cli.py

@@ -484,6 +484,32 @@ def make_argument_parser() -> argparse.ArgumentParser:
             transfer image to embed.
             """,
     )
+    parser.add_argument(
+        "--audio_gpu_ids", nargs="+", type=int, default=None, help="GPU IDs for audio encoder, e.g., 0 1 2"
+    )
+    parser.add_argument(
+        "--audio_tp",
+        type=int,
+        default=1,
+        help="Tensor parallel size for audio encoder (only 1 is supported; use audio_dp to scale)",
+    )
+    parser.add_argument("--audio_dp", type=int, default=1, help="Data parallel replicas for audio encoder")
+    parser.add_argument(
+        "--audio_nccl_ports",
+        nargs="+",
+        type=int,
+        default=None,
+        help="NCCL ports per audio DP group; if omitted, auto-allocated in api_start (reserved until audio_tp>1)",
+    )
+    parser.add_argument(
+        "--audio_infer_batch_size",
+        type=int,
+        default=None,
+        help="""
+        Max audio items per GPU infer batch in audio worker (default: max(4, audio_dp),
+        must be multiple of audio_dp)
+        """,
+    )
     parser.add_argument(
         "--visual_use_proxy_mode",
         action="store_true",

lightllm/server/api_start.py

@@ -204,6 +204,32 @@ def normal_or_p_d_start(args):
             f"a positive integer multiple of visual_dp ({args.visual_dp})"
         )
 
+    if not args.disable_audio:
+        if args.audio_tp != 1:
+            raise ValueError(
+                "audio_tp > 1 is not supported for the audio encoder yet; use --audio_dp for multi-GPU data parallel."
+            )
+        if args.audio_gpu_ids is None:
+            args.audio_gpu_ids = list(range(args.audio_dp * args.audio_tp))
+        total_audio_gpus = args.audio_dp * args.audio_tp
+        if len(args.audio_gpu_ids) < total_audio_gpus:
+            raise ValueError(
+                f"Not enough audio GPUs specified. Need at least {total_audio_gpus}, "
+                f"but got {len(args.audio_gpu_ids)}."
+            )
+        args.audio_gpu_ids = args.audio_gpu_ids[:total_audio_gpus]
+        if args.audio_dp <= 0:
+            raise ValueError("audio_dp must be a positive integer.")
+        if args.audio_infer_batch_size is None:
+            args.audio_infer_batch_size = args.audio_dp * 4
+        if args.audio_infer_batch_size < 1:
+            raise ValueError("audio_infer_batch_size must be >= 1.")
+        if args.audio_infer_batch_size // args.audio_dp < 1 or args.audio_infer_batch_size % args.audio_dp != 0:
+            raise ValueError(
+                f"audio_infer_batch_size ({args.audio_infer_batch_size}) must be "
+                f"a positive integer multiple of audio_dp ({args.audio_dp})."
+            )
+
     if args.disable_chunked_prefill:
         args.chunked_prefill_size = args.max_req_total_len
         # 普通模式下
@@ -248,6 +274,8 @@ def normal_or_p_d_start(args):
         already_uesd_ports.append(args.pd_decode_rpyc_port)
     if args.visual_nccl_ports is not None:
         already_uesd_ports.extend(args.visual_nccl_ports[: args.visual_dp])
+    if not args.disable_audio and args.audio_nccl_ports is not None:
+        already_uesd_ports.extend(args.audio_nccl_ports[: args.audio_dp])
 
     # 提前锁定端口，防止在单个机器上启动多个实列的时候，要到模型启动的时候才能
     # 捕获到端口设置冲突的问题
@@ -256,7 +284,7 @@ def normal_or_p_d_start(args):
 
     node_world_size = args.tp // args.nnodes
     can_use_ports = alloc_can_use_network_port(
-        num=10 + node_world_size + args.visual_dp * args.visual_tp + args.visual_dp,
+        num=10 + node_world_size + args.visual_dp * args.visual_tp + args.visual_dp + args.audio_dp,
         used_ports=already_uesd_ports,
     )
     logger.info(f"alloced ports: {can_use_ports}")
@@ -274,14 +302,17 @@ def normal_or_p_d_start(args):
     ) = can_use_ports[0:10]
     can_use_ports = can_use_ports[10:]
 
-    visual_nccl_ports = []
-    for _ in range(args.visual_dp):
-        if args.visual_nccl_ports is None:
-            visual_nccl_ports.append(can_use_ports[0])
-            can_use_ports = can_use_ports[1:]
+    if args.visual_nccl_ports is None:
+        args.visual_nccl_ports = can_use_ports[: args.visual_dp]
+        can_use_ports = can_use_ports[args.visual_dp :]
+    else:
+        args.visual_nccl_ports = args.visual_nccl_ports[: args.visual_dp]
 
-    if args.visual_nccl_ports is not None:
-        visual_nccl_ports = args.visual_nccl_ports[: args.visual_dp]
+    if args.audio_nccl_ports is None:
+        args.audio_nccl_ports = can_use_ports[: args.audio_dp]
+        can_use_ports = can_use_ports[args.audio_dp :]
+    else:
+        args.audio_nccl_ports = args.audio_nccl_ports[: args.audio_dp]
 
     # 将申请好的端口放入args参数中
     if args.nccl_port is None:
@@ -296,7 +327,6 @@ def normal_or_p_d_start(args):
     args.cache_port = cache_port
     args.metric_port = metric_port
     args.multi_level_kv_cache_port = multi_level_kv_cache_port
-    args.visual_nccl_ports = visual_nccl_ports
     # 申请在 p d 分离模式下，会用的端口
     args.pd_node_infer_rpyc_ports = can_use_ports[0:node_world_size]
     # p d 分离模式下用于标识节点的id