Merge branch 'v0.9.2-dev-ds' of...

Merge branch 'v0.9.2-dev-ds' of ssh://10.16.6.30:10022/dcutoolkit/deeplearing/vllm into v0.9.2-dev-ds

vllm/attention/layer.py

@@ -81,7 +81,7 @@ class Attention(nn.Module):
             calculate_kv_scales = cache_config.calculate_kv_scales
         else:
             kv_cache_dtype = "auto"
-            block_size = 64 if envs.VLLM_USE_FLASH_ATTN_PA or envs.VLLM_USE_FLASH_MLA else 16
+            block_size = 64 if envs.VLLM_USE_FLASH_ATTN_PA and envs.VLLM_USE_FLASH_MLA else 16
             is_attention_free = False
             calculate_kv_scales = False
         if num_kv_heads is None:
@@ -312,7 +312,7 @@ class MultiHeadAttention(nn.Module):
         attn_backend = get_attn_backend(head_size,
                                         dtype,
                                         kv_cache_dtype=None,
-                                        block_size=64 if envs.VLLM_USE_FLASH_ATTN_PA or envs.VLLM_USE_FLASH_MLA else 16,
+                                        block_size=64 if envs.VLLM_USE_FLASH_ATTN_PA and envs.VLLM_USE_FLASH_MLA else 16,
                                         is_attention_free=False)
         backend = backend_name_to_enum(attn_backend.get_name())
         if current_platform.is_rocm():

vllm/config.py

@@ -1499,7 +1499,7 @@ PrefixCachingHashAlgo = Literal["builtin", "sha256"]
 class CacheConfig:
     """Configuration for the KV cache."""
 
-    block_size: BlockSize = 64 if envs.VLLM_USE_FLASH_ATTN_PA or envs.VLLM_USE_FLASH_MLA else 16  # type: ignore
+    block_size: BlockSize = 64 if envs.VLLM_USE_FLASH_ATTN_PA and envs.VLLM_USE_FLASH_MLA else 16  # type: ignore
     """Size of a contiguous cache block in number of tokens. This is ignored on
     neuron devices and set to `--max-model-len`. On CUDA devices, only block
     sizes up to 32 are supported. On HPU devices, block size defaults to 128.

vllm/model_executor/models/deepseek_v2.py

@@ -242,11 +242,6 @@ class DeepseekV2MoE(nn.Module):
                     # See DeepseekV2DecoderLayer for more details.
                     final_hidden_states = self.experts(hidden_states=hidden_states,
                                                     router_logits=router_logits)
-        else:        
-            final_hidden_states = self.experts(hidden_states=hidden_states,
-                                                router_logits=router_logits)
-
-        if not self.use_mori_ep:
                 if shared_output is not None:
                     if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
                         final_hidden_states = final_hidden_states + shared_output
@@ -255,7 +250,11 @@ class DeepseekV2MoE(nn.Module):
                         # See DeepseekV2DecoderLayer for more details.
                         final_hidden_states = final_hidden_states + shared_output \
                             * (1. / self.routed_scaling_factor)
+        else:        
+            final_hidden_states = self.experts(hidden_states=hidden_states,
+                                                router_logits=router_logits)
 
+        if not self.use_mori_ep:
             if self.tp_size > 1:
                 if envs.VLLM_ENABLE_TBO:
                     final_hidden_states = self.tbo_all_reduce(final_hidden_states)