Support Qwen models' dp>1 in PD

docs/CN/source/tutorial/api_server_args_zh.rst

@@ -160,9 +160,6 @@ PD 分离模式参数
 .. option:: --dp
 
     数据并行大小，默认为 ``1``
-
-    这是 deepseekv2 的有用参数。使用 deepseekv2 模型时，将 dp 设置为等于 tp 参数。
-    其他情况下请不要设置，保持默认值 1。
 
 .. option:: --nccl_host
 

docs/EN/source/tutorial/api_server_args_zh.rst

@@ -160,9 +160,6 @@ Different Parallel Mode Setting Parameters
 .. option:: --dp
 
     Data parallelism size, default is ``1``
-
-    This is a useful parameter for deepseekv2. When using deepseekv2 model, set dp equal to the tp parameter.
-    In other cases, please do not set it, keep the default value of 1.
 
 .. option:: --nccl_host
 

lightllm/common/mem_manager.py

@@ -2,12 +2,13 @@
 import os
 import torch
 import torch.distributed as dist
-from typing import List, Union
+from typing import List, Union, Optional
 from lightllm.server.pd_io_struct import KVMoveTask
 from lightllm.utils.log_utils import init_logger
 from lightllm.server.router.dynamic_prompt.shared_arr import SharedInt
 from lightllm.utils.profile_max_tokens import get_available_gpu_memory, get_total_gpu_memory
 from lightllm.common.kv_trans_kernel.kv_trans import kv_trans
+from lightllm.common.kv_trans_kernel.kv_trans_v2 import kv_trans_v2_for_d_node, kv_trans_v2_for_p_node
 from lightllm.utils.dist_utils import get_current_rank_in_node
 from lightllm.utils.envs_utils import get_unique_server_name, get_env_start_args
 from lightllm.distributed.pynccl import PyNcclCommunicator
@@ -103,7 +104,10 @@ def send_to_decode_node(
         dp_size_in_node: int,
         nccl_comm: PyNcclCommunicator,
     ):
-        assert dp_size_in_node == 1
+        if dp_size_in_node > 1:
+            return self.send_to_decode_node_p2p(
+                move_tasks, mem_managers, dp_size_in_node, nccl_comm
+            )
 
         # 先将数据发送到指定的一张卡上的buffer，再发送。
 
@@ -143,8 +147,10 @@ def receive_from_prefill_node(
         dp_size_in_node: int,
         nccl_comm: PyNcclCommunicator,
     ):
-        assert dp_size_in_node == 1
-
+        if dp_size_in_node > 1:
+            return self.receive_from_prefill_node_p2p(
+                move_tasks, mem_managers, dp_size_in_node, nccl_comm
+            )
         # 先将数据接受到指定的一张卡上的buffer，再复制到其他的卡上。
 
         move_token_indexes = []
@@ -183,29 +189,73 @@ def send_to_decode_node_p2p(
         """
         使用 p2p triton kernel 进行数据复制和传输的实现方式。
         """
-        assert dp_size_in_node == 1
-
-        # 先将数据发送到指定的一张卡上的buffer，再发送。
+        if dp_size_in_node > 1:
+            mem_ptrs_dict = {}
+            # 一个 dp 副本占用的 device 数量
+            group_stride = max(1, len(mem_managers) // dp_size_in_node)
+            for layer_index in range(self.layer_num):
+                mems_ptr = []
+                for i in range(0, len(mem_managers), group_stride):
+                    mems_ptr.append(mem_managers[i].kv_buffer[layer_index, :, :, :].data_ptr())
+                mem_ptrs_dict[layer_index] = torch.tensor(mems_ptr, dtype=torch.uint64, device="cuda")
 
         move_token_indexes = []
+        token_dp_indexes = []
         for task in move_tasks:
             if task.move_kv_len != 0:
                 move_token_indexes.extend(task.prefill_token_indexes[-task.move_kv_len :])
+                if dp_size_in_node > 1:
+                    token_dp_indexes.extend([task.prefill_dp_index for _ in range(task.move_kv_len)])
+
+        if len(move_token_indexes) == 0:
+            return
 
         move_token_indexes = torch.tensor(move_token_indexes, dtype=torch.int64, device="cuda")
-        for i, mem in enumerate(mem_managers):
-            for layer_index in range(mem.layer_num):
-                move_buffer = mem._get_kv_move_data_p2p(move_token_indexes, layer_index, self.kv_move_buffer)
-                nccl_comm.send(move_buffer, dst=1)
+        token_dp_tensor = (
+            torch.tensor(token_dp_indexes, dtype=torch.int32, device="cuda") if dp_size_in_node > 1 else None
+        )
+
+        for layer_index in range(self.layer_num):
+            move_buffer = self._get_kv_move_data_p2p(
+                move_token_indexes,
+                layer_index,
+                self.kv_move_buffer,
+                token_dp_indexes=token_dp_tensor,
+                dp_size_in_node=dp_size_in_node,
+                mem_ptrs_dict=mem_ptrs_dict
+            )
+            nccl_comm.send(move_buffer, dst=1)
         return
 
-    def _get_kv_move_data_p2p(self, token_indexes: torch.Tensor, layer_index: int, kv_move_buffer: torch.Tensor):
+    def _get_kv_move_data_p2p(
+        self,
+        token_indexes: torch.Tensor,
+        layer_index: int,
+        kv_move_buffer: torch.Tensor,
+        token_dp_indexes: Optional[torch.Tensor] = None,
+        dp_size_in_node: int = 1,
+        mem_ptrs_dict: Optional[dict] = None
+    ):
         move_token_num = len(token_indexes)
         move_size = self.token_dim_size * move_token_num
         move_buffer = kv_move_buffer.view(-1)[0:move_size].view(move_token_num, 2 * self.head_num, self.head_dim)
-        kv_trans(
-            self.kv_buffer[layer_index, :, :, :], token_indexes, move_buffer, self.kv_move_buf_indexes[0:move_token_num]
-        )
+
+        if dp_size_in_node == 1 or token_dp_indexes is None:
+            kv_trans(
+                self.kv_buffer[layer_index, :, :, :],
+                token_indexes,
+                move_buffer,
+                self.kv_move_buf_indexes[0:move_token_num],
+            )
+        else:
+            kv_trans_v2_for_p_node(
+                input_mems=mem_ptrs_dict[layer_index],
+                input_idx=token_indexes,
+                input_dp_idx=token_dp_indexes,
+                output=move_buffer,
+                output_idx=self.kv_move_buf_indexes[0:move_token_num],
+                dp_size_in_node=dp_size_in_node,
+            )
         return move_buffer
 
     def receive_from_prefill_node_p2p(
@@ -215,29 +265,72 @@ def receive_from_prefill_node_p2p(
         dp_size_in_node: int,
         nccl_comm: PyNcclCommunicator,
     ):
-        assert dp_size_in_node == 1
-
-        # 先将数据接受到指定的一张卡上的buffer，再复制到其他的卡上。
+        if dp_size_in_node > 1:
+            mem_ptrs_dict = {}
+            for layer_index in range(self.layer_num):
+                mems_ptr = []
+                for mem in mem_managers:
+                    mems_ptr.append(mem.kv_buffer[layer_index, :, :, :].data_ptr())
+                mem_ptrs_dict[layer_index] = torch.tensor(mems_ptr, dtype=torch.uint64, device="cuda")
 
         move_token_indexes = []
+        token_dp_indexes = []
         for task in move_tasks:
             if task.move_kv_len != 0:
                 move_token_indexes.extend(task.decode_token_indexes[-task.move_kv_len :])
+                if dp_size_in_node > 1:
+                    token_dp_indexes.extend([task.decode_dp_index for _ in range(task.move_kv_len)])
+
+        if len(move_token_indexes) == 0:
+            return
 
         move_token_indexes = torch.tensor(move_token_indexes, dtype=torch.int64, device="cuda")
+        token_dp_tensor = (
+            torch.tensor(token_dp_indexes, dtype=torch.int32, device="cuda") if dp_size_in_node > 1 else None
+        )
 
-        token_num = len(move_token_indexes)
-        move_size = self.token_dim_size * token_num
-        recive_buffer = self.kv_move_buffer.view(-1)[0:move_size].view(token_num, 2 * self.head_num, self.head_dim)
-        for i, mem in enumerate(mem_managers):
-            for layer_index in range(mem.layer_num):
-                nccl_comm.recv(recive_buffer, src=0)
-                mem._write_kv_move_data_p2p(move_token_indexes, recive_buffer, layer_index)
+        move_token_num = len(move_token_indexes)
+        move_size = self.token_dim_size * move_token_num
+        recive_buffer = self.kv_move_buffer.view(-1)[0:move_size].view(move_token_num, 2 * self.head_num, self.head_dim)
+
+        for layer_index in range(self.layer_num):
+            nccl_comm.recv(recive_buffer, src=0)
+            self._write_kv_move_data_p2p(
+                move_token_indexes,
+                recive_buffer,
+                layer_index,
+                token_dp_indexes=token_dp_tensor,
+                dp_size_in_node=dp_size_in_node,
+                mem_ptrs_dict=mem_ptrs_dict
+            )
         return
 
-    def _write_kv_move_data_p2p(self, token_indexes: torch.Tensor, buffer_tensor: torch.Tensor, layer_index):
+    def _write_kv_move_data_p2p(
+        self,
+        token_indexes: torch.Tensor,
+        buffer_tensor: torch.Tensor,
+        layer_index: int,
+        token_dp_indexes: Optional[torch.Tensor] = None,
+        dp_size_in_node: int = 1,
+        mem_ptrs_dict: Optional[dict] = None
+    ):
         move_token_num = len(token_indexes)
-        kv_trans(buffer_tensor, self.kv_move_buf_indexes[0:move_token_num], self.kv_buffer[layer_index], token_indexes)
+        if dp_size_in_node == 1 or token_dp_indexes is None:
+            kv_trans(
+                buffer_tensor,
+                self.kv_move_buf_indexes[0:move_token_num],
+                self.kv_buffer[layer_index],
+                token_indexes,
+            )
+        else:
+            kv_trans_v2_for_d_node(
+                output_mems=mem_ptrs_dict[layer_index],
+                output_idx=token_indexes,
+                output_dp_idx=token_dp_indexes,
+                input=buffer_tensor,
+                input_idx=self.kv_move_buf_indexes[0:move_token_num],
+                dp_size_in_node=dp_size_in_node,
+            )
         return
 
     def _free_buffers(self):