[Misc] fix olmoe model layer can't laod in tp gt 1 (vllm-project#18828)

Signed-off-by: rongfu.leng <[email protected]>

Author: lengrongfu

vllm/model_executor/models/olmoe.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Inference-only OLMoE model compatible with HuggingFace weights."""
 from collections.abc import Iterable
+from functools import partial
 from typing import Any, Optional, Union
 
 import torch
@@ -22,7 +23,10 @@
 from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
-from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_gather)
+from vllm.distributed.utils import split_tensor_along_last_dim
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -140,8 +144,11 @@ def __init__(
             bias=False,
             quant_config=quant_config,
         )
-        self.q_norm = RMSNorm(hidden_size, eps=1e-5)
-        self.k_norm = RMSNorm(hidden_size, eps=1e-5)
+        self.tp_size = tp_size
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.q_norm = RMSNorm(self.total_num_heads * self.head_dim, eps=1e-5)
+        self.k_norm = RMSNorm(self.total_num_kv_heads * self.head_dim,
+                              eps=1e-5)
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
@@ -165,14 +172,28 @@ def __init__(
                               quant_config=quant_config,
                               prefix=f"{prefix}.attn")
 
+    def _apply_qk_norm(self, q: torch.Tensor,
+                       k: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.tp_size > 1:
+            q = tensor_model_parallel_all_gather(q.contiguous())
+            k = tensor_model_parallel_all_gather(k.contiguous())
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        if self.tp_size > 1:
+            splitter = partial(split_tensor_along_last_dim,
+                               num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+        return q, k
+
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        q, k = self.q_norm(q.contiguous()), self.k_norm(k.contiguous())
+        q, k = self._apply_qk_norm(q, k)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v)
         output, _ = self.o_proj(attn_output)