AMD CI Test - unskip moe_sum test and moe_align_block_size tests (#32039)

Signed-off-by: Hongxia Yang <hongxia.yang@amd.com>

tests/kernels/moe/test_moe.py

@@ -1161,7 +1161,6 @@ def test_batched_moe_align_block_size_opcheck():
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("k", [128, 511, 1024])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
-@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
 def test_moe_sum(m: int, topk: int, k: int, dtype: torch.dtype):
     input = torch.randn((m, topk, k), device="cuda", dtype=dtype)
     actual = torch.empty((m, k), device="cuda", dtype=dtype)

tests/kernels/moe/test_moe_align_block_size.py

@@ -12,7 +12,6 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
     batched_moe_align_block_size,
     moe_align_block_size,
 )
-from vllm.platforms import current_platform
 from vllm.utils.math_utils import round_up
 from vllm.utils.torch_utils import set_random_seed
 
@@ -185,7 +184,6 @@ def torch_moe_align_block_size(
 @pytest.mark.parametrize("num_experts", NUM_EXPERTS)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("pad_sorted_ids", [False, True])
-@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
 def test_moe_align_block_size(
     m: int, topk: int, num_experts: int, block_size: int, pad_sorted_ids: bool
 ):
@@ -245,7 +243,6 @@ def test_moe_align_block_size(
 @pytest.mark.parametrize("topk", [2, 4])
 @pytest.mark.parametrize("num_experts", [8, 64])
 @pytest.mark.parametrize("block_size", [64])
-@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
 def test_moe_align_block_size_with_expert_map(
     m: int, topk: int, num_experts: int, block_size: int
 ):