Deepseek V3 fix

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -33,26 +33,26 @@ def fused_moe_kernel(
         expert_ids_ptr,
         num_tokens_post_padded_ptr,
         # Matrix dimensions
-        N,
-        K,
-        EM,
-        num_valid_tokens,
+        N: tl.int64,
+        K: tl.int64,
+        EM: tl.int64,
+        num_valid_tokens: tl.int64,
         # The stride variables represent how much to increase the ptr by when
         # moving by 1 element in a particular dimension. E.g. `stride_am` is
         # how much to increase `a_ptr` by to get the element one row down
         # (A has M rows).
-        stride_am,
-        stride_ak,
-        stride_be,
-        stride_bk,
-        stride_bn,
-        stride_cm,
-        stride_cn,
-        stride_asm,
-        stride_ask,
-        stride_bse,
-        stride_bsk,
-        stride_bsn,
+        stride_am: tl.int64,
+        stride_ak: tl.int64,
+        stride_be: tl.int64,
+        stride_bk: tl.int64,
+        stride_bn: tl.int64,
+        stride_cm: tl.int64,
+        stride_cn: tl.int64,
+        stride_asm: tl.int64,
+        stride_ask: tl.int64,
+        stride_bse: tl.int64,
+        stride_bsk: tl.int64,
+        stride_bsn: tl.int64,
         # Block size for block-wise quantization
         group_n: tl.constexpr,
         group_k: tl.constexpr,
@@ -114,18 +114,17 @@ def fused_moe_kernel(
     num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr)
     if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
         return
-    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(
-        tl.int64)
+    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
     offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
     token_mask = offs_token < num_valid_tokens
 
     offs_bn = (pid_n * BLOCK_SIZE_N +
-               tl.arange(0, BLOCK_SIZE_N).to(tl.int64)) % N
+               tl.arange(0, BLOCK_SIZE_N)) % N
     offs_k = tl.arange(0, BLOCK_SIZE_K)
     a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am +
                       offs_k[None, :] * stride_ak)
 
-    off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
+    off_experts = tl.load(expert_ids_ptr + pid_m)
     b_ptrs = b_ptr + off_experts * stride_be + (offs_k[:, None] * stride_bk +
                                                 offs_bn[None, :] * stride_bn)
     if use_int8_w8a16: