fix run error

vllm/attention/ops/flashmla.py

@@ -226,14 +226,16 @@ def flash_mla_with_kvcache(
             out, softmax_lse = flash_mla_cuda.fwd_kvcache_mla(
                 q, 
                 k_cache, 
-                None, 
                 head_dim_v, 
                 cache_seqlens, 
                 block_table, 
                 softmax_scale,
                 causal, 
                 tile_scheduler_metadata, 
-                num_splits)
+                num_splits,
+                is_fp8_kvcache,
+                indices,
+            )
         else:
             out, softmax_lse = torch.ops._flashmla_C.fwd_kvcache_mla(
                 q,

vllm/model_executor/layers/fused_moe/layer.py

@@ -2062,8 +2062,6 @@ class FusedMoE(CustomOp):
                     router_logits=router_logits,
                     use_nn_moe=self.use_nn_moe,
                     use_fused_gate=self.use_fused_gate,
-                    use_nn_moe=self.use_nn_moe,
-                    use_fused_gate=self.use_fused_gate,
                     i_q=i_q,
                     i_s=i_s,
                 )

vllm/platforms/rocm.py

@@ -228,11 +228,10 @@ class RocmPlatform(Platform):
             logger.info_once("Using Sparse MLA backend on V1 engine.")
             return AttentionBackendEnum.ROCM_AITER_MLA_SPARSE.get_path()
                 
-        if use_mla:
-            # if use_sparse:
+        if attn_selector_config.use_mla:
+            # if attn_selector_config.use_sparse:
             #     logger.info_once("Using Sparse MLA backend on V1 engine.")
-            #     return ("vllm.v1.attention.backends.mla.flashmla_sparse."
-            #             "FlashMLASparseBackend")
+            #     return AttentionBackendEnum.FLASHMLA_SPARSE.get_path()
                 
             use_flashmla = selected_backend == AttentionBackendEnum.FLASHMLA or envs.VLLM_USE_FLASH_MLA 
             use_triton = selected_backend == AttentionBackendEnum.TRITON_MLA or (

vllm/v1/attention/backends/flash_attn.py

@@ -56,6 +56,7 @@ from vllm.v1.attention.backends.utils import (
     get_kv_cache_layout,
 )
 from vllm.v1.kv_cache_interface import AttentionSpec
+import vllm.envs as envs
 
 logger = init_logger(__name__)
 

vllm/v1/worker/gpu_worker.py

@@ -81,8 +81,8 @@ class Worker(WorkerBase):
         )
 
         # configure float32 matmul precision according to vLLM env.
-        precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
-        torch.backends.cuda.matmul.fp32_precision = precision
+        # precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
+        # torch.backends.cuda.matmul.fp32_precision = precision
 
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing