[Hardware] using current_platform.seed_everything (#9785)

Signed-off-by: wangshuai09 <391746016@qq.com>

benchmarks/kernels/benchmark_layernorm.py

@@ -3,8 +3,8 @@ import time
 import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
-                        seed_everything)
+from vllm.platforms import current_platform
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()
@@ -16,7 +16,7 @@ def main(num_tokens: int,
          do_profile: bool = False,
          num_warmup_iters: int = 5,
          num_iters: int = 100) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device("cuda")
 
     layer = RMSNorm(hidden_size).to(dtype=dtype)

benchmarks/kernels/benchmark_moe.py

@@ -10,7 +10,8 @@ from ray.experimental.tqdm_ray import tqdm
 from transformers import AutoConfig
 
 from vllm.model_executor.layers.fused_moe.fused_moe import *
-from vllm.utils import FlexibleArgumentParser, seed_everything
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
 
 
 class BenchmarkConfig(TypedDict):
@@ -167,7 +168,7 @@ class BenchmarkWorker:
 
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
-        seed_everything(seed)
+        current_platform.seed_everything(seed)
         self.seed = seed
 
     def benchmark(
@@ -181,7 +182,7 @@ class BenchmarkWorker:
         use_fp8_w8a8: bool,
         use_int8_w8a16: bool,
     ) -> Tuple[Dict[str, int], float]:
-        seed_everything(self.seed)
+        current_platform.seed_everything(self.seed)
         dtype_str = get_config_dtype_str(dtype,
                                          use_int8_w8a16=use_int8_w8a16,
                                          use_fp8_w8a8=use_fp8_w8a8)

benchmarks/kernels/benchmark_paged_attention.py

@@ -5,8 +5,9 @@ from typing import List, Optional
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
-                        create_kv_caches_with_random, seed_everything)
+                        create_kv_caches_with_random)
 
 NUM_BLOCKS = 1024
 PARTITION_SIZE = 512
@@ -28,7 +29,7 @@ def main(
     device: str = "cuda",
     kv_cache_dtype: Optional[str] = None,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     scale = float(1.0 / (head_size**0.5))
     query = torch.empty(num_seqs,

benchmarks/kernels/benchmark_quant.py

@@ -3,8 +3,8 @@ import time
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
-                        seed_everything)
+from vllm.platforms import current_platform
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()
@@ -17,7 +17,7 @@ def main(num_tokens: int,
          do_profile: bool = False,
          num_warmup_iters: int = 5,
          num_iters: int = 100) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device("cuda")
 
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)

benchmarks/kernels/benchmark_rope.py

@@ -6,7 +6,8 @@ import torch
 
 from vllm.model_executor.layers.rotary_embedding import (RotaryEmbedding,
                                                          get_rope)
-from vllm.utils import FlexibleArgumentParser, seed_everything
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
 
 
 def benchmark_rope_kernels_multi_lora(
@@ -22,7 +23,7 @@ def benchmark_rope_kernels_multi_lora(
     max_position: int = 8192,
     base: int = 10000,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size

tests/kernels/test_activation.py

@@ -8,7 +8,7 @@ from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul,
                                                    GeluAndMul, NewGELU,
                                                    QuickGELU, SiluAndMul)
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 from .allclose_default import get_default_atol, get_default_rtol
 
@@ -37,7 +37,7 @@ def test_act_and_mul(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, 2 * d, dtype=dtype)
     if activation == "silu":
@@ -85,7 +85,7 @@ def test_activation(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, d, dtype=dtype)
     layer = activation[0]()

tests/kernels/test_attention.py

@@ -7,7 +7,7 @@ import torch
 from tests.kernels.utils import opcheck
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils import get_max_shared_memory_bytes, seed_everything
+from vllm.utils import get_max_shared_memory_bytes
 
 from .allclose_default import get_default_atol, get_default_rtol
 
@@ -144,7 +144,7 @@ def test_paged_attention(
             or (version == "rocm" and head_size not in (64, 128))):
         pytest.skip()
 
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     scale = float(1.0 / (head_size**0.5))
     num_query_heads, num_kv_heads = num_heads
@@ -382,7 +382,7 @@ def test_multi_query_kv_attention(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     # MAX_SEQ_LEN sometimes causes OOM in the reference implementation.
     # As the xformers library is already tested with its own tests, we can use

tests/kernels/test_awq_triton.py

@@ -7,7 +7,7 @@ import torch
 
 from vllm.model_executor.layers.quantization.awq_triton import (
     AWQ_TRITON_SUPPORTED_GROUP_SIZES, awq_dequantize_triton, awq_gemm_triton)
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 device = "cuda"
 
@@ -80,7 +80,7 @@ def test_dequantize(qweight_rows, qweight_cols, group_size):
     zeros_cols = qweight_cols
     zeros_dtype = torch.int32
 
-    seed_everything(0)
+    current_platform.seed_everything(0)
 
     qweight = torch.randint(0,
                             torch.iinfo(torch.int32).max,
@@ -134,7 +134,7 @@ def test_gemm(N, K, M, splitK, group_size):
     qzeros_rows = scales_rows
     qzeros_cols = qweight_cols
 
-    seed_everything(0)
+    current_platform.seed_everything(0)
 
     input = torch.rand((input_rows, input_cols),
                        dtype=input_dtype,

tests/kernels/test_blocksparse_attention.py

@@ -8,7 +8,7 @@ from vllm import _custom_ops as ops
 from vllm.attention.ops.blocksparse_attention.interface import (
     LocalStridedBlockSparseAttn)
 from vllm.platforms import current_platform
-from vllm.utils import get_max_shared_memory_bytes, seed_everything
+from vllm.utils import get_max_shared_memory_bytes
 
 from .allclose_default import get_default_atol, get_default_rtol
 
@@ -173,7 +173,7 @@ def test_paged_attention(
     blocksparse_block_size: int,
     blocksparse_head_sliding_step: int,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     scale = float(1.0 / (head_size**0.5))
     num_query_heads, num_kv_heads = num_heads
@@ -384,7 +384,7 @@ def test_varlen_blocksparse_attention_prefill(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     # MAX_SEQ_LEN sometimes causes OOM in the reference implementation.
     # As the xformers library is already tested with its own tests, we can use

tests/kernels/test_cache.py

@@ -6,7 +6,7 @@ import torch
 
 from tests.kernels.utils import DEFAULT_OPCHECK_TEST_UTILS, opcheck
 from vllm import _custom_ops as ops
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 COPYING_DIRECTION = [('cuda', 'cpu'), ('cuda', 'cuda'), ('cpu', 'cuda')]
 DTYPES = [torch.half, torch.bfloat16, torch.float]
@@ -56,7 +56,7 @@ def test_copy_blocks(
 ) -> None:
     if kv_cache_dtype == "fp8" and head_size % 16:
         pytest.skip()
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     # Generate random block mappings where each source block is mapped to two
     # destination blocks.
@@ -132,7 +132,7 @@ def test_reshape_and_cache(
 ) -> None:
     if kv_cache_dtype == "fp8" and head_size % 16:
         pytest.skip()
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     # Create a random slot mapping.
     num_slots = block_size * num_blocks
@@ -224,7 +224,7 @@ def test_reshape_and_cache_flash(
     device: str,
     kv_cache_dtype: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
 
     # Create a random slot mapping.
@@ -339,7 +339,7 @@ def test_swap_blocks(
     if kv_cache_dtype == "fp8" and head_size % 16:
         pytest.skip()
 
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     src_device = device if direction[0] == "cuda" else 'cpu'
     dst_device = device if direction[1] == "cuda" else 'cpu'
@@ -408,7 +408,7 @@ def test_fp8_e4m3_conversion(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     low = -224.0
     high = 224.0

tests/kernels/test_causal_conv1d.py

@@ -9,7 +9,7 @@ from vllm import _custom_ops as ops  # noqa: F401
 from vllm.attention.backends.utils import PAD_SLOT_ID
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_fn, causal_conv1d_update)
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 
 def causal_conv1d_ref(
@@ -161,7 +161,7 @@ def test_causal_conv1d(batch, dim, seqlen, width, has_bias, silu_activation,
     if itype == torch.bfloat16:
         rtol, atol = 1e-2, 5e-2
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
     x = torch.randn(batch, dim, seqlen, device=device,
                     dtype=itype).contiguous()
 
@@ -223,7 +223,7 @@ def test_causal_conv1d_update(dim, width, seqlen, has_bias, silu_activation,
     if itype == torch.bfloat16:
         rtol, atol = 1e-2, 5e-2
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
     batch = 2
     x = torch.randn(batch, dim, seqlen, device=device, dtype=itype)
     x_ref = x.clone()
@@ -270,7 +270,7 @@ def test_causal_conv1d_update_with_batch_gather(with_padding, dim, width,
         rtol, atol = 1e-2, 5e-2
 
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
 
     batch_size = 3
     padding = 5 if with_padding else 0
@@ -343,7 +343,7 @@ def test_causal_conv1d_varlen(with_padding, dim, seqlen, width, has_bias,
     if itype == torch.bfloat16:
         rtol, atol = 1e-2, 5e-2
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
     seqlens = []
     batch_size = 4
     if seqlen < 10:

tests/kernels/test_flash_attn.py

@@ -3,7 +3,7 @@ from typing import List, Optional, Tuple
 import pytest
 import torch
 
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 from vllm.vllm_flash_attn import (flash_attn_varlen_func,
                                   flash_attn_with_kvcache)
 
@@ -91,7 +91,7 @@ def test_flash_attn_with_paged_kv(
     sliding_window: Optional[int],
 ) -> None:
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]
@@ -161,7 +161,7 @@ def test_varlen_with_paged_kv(
     num_blocks: int,
 ) -> None:
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
     kv_lens = [x[1] for x in seq_lens]

tests/kernels/test_flashinfer.py

@@ -4,7 +4,7 @@ import flashinfer
 import pytest
 import torch
 
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 NUM_HEADS = [(16, 16), (32, 8), (64, 8), (6, 1)]
 HEAD_SIZES = [128, 256]
@@ -84,7 +84,7 @@ def test_flashinfer_decode_with_paged_kv(
     soft_cap: Optional[float],
 ) -> None:
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]
@@ -170,7 +170,7 @@ def test_flashinfer_prefill_with_paged_kv(seq_lens: List[Tuple[int, int]],
                                           block_size: int,
                                           soft_cap: Optional[float]) -> None:
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
     kv_lens = [x[1] for x in seq_lens]
@@ -268,7 +268,7 @@ def test_flashinfer_prefill_with_paged_fp8_kv(
         head_size: int, dtype: torch.dtype, block_size: int,
         soft_cap: Optional[float]) -> None:
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
     kv_lens = [x[1] for x in seq_lens]
@@ -381,7 +381,7 @@ def test_flashinfer_decode_with_paged_fp8_kv(
 ) -> None:
     # test doesn't work for num_heads = (16,16)
     torch.set_default_device("cuda")
-    seed_everything(0)
+    current_platform.seed_everything(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]

tests/kernels/test_fp8_quant.py

@@ -6,7 +6,7 @@ from tests.kernels.quant_utils import (FP8_DTYPE,
                                        ref_dynamic_per_tensor_fp8_quant,
                                        ref_dynamic_per_token_quant)
 from tests.kernels.utils import opcheck
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 HIDDEN_SIZES = [1, 2, 3, 4, 16, 67, 768, 2048, 5120, 5137, 8192,
@@ -46,7 +46,7 @@ def opcheck_fp8_quant(output,
 def test_dynamic_per_token_fp8_quant(num_tokens: int, hidden_size: int,
                                      dtype: torch.dtype, scale_ub: bool,
                                      seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype,
                    device="cuda") + 1e-6  # avoid nans
@@ -76,7 +76,7 @@ def test_dynamic_per_token_fp8_quant(num_tokens: int, hidden_size: int,
 @torch.inference_mode()
 def test_dynamic_per_tensor_fp8_quant(num_tokens: int, hidden_size: int,
                                       dtype: torch.dtype, seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda")
 
@@ -95,7 +95,7 @@ def test_dynamic_per_tensor_fp8_quant(num_tokens: int, hidden_size: int,
 @torch.inference_mode()
 @pytest.mark.parametrize("seed", SEEDS)
 def test_fp8_quant_large(seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     num_tokens = 1024000  # Mistral-Nemo's max_position_embeddings
     hidden_size = 1152  # Smallest hidden_size to reproduce the error

tests/kernels/test_gguf.py

@@ -7,7 +7,7 @@ from gguf import GGMLQuantizationType, GGUFReader, ReaderTensor, dequantize
 from huggingface_hub import snapshot_download
 
 import vllm._custom_ops as ops
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 GGUF_SAMPLE = snapshot_download("Isotr0py/test-gguf-sample")
 
@@ -75,7 +75,7 @@ def test_dequantize(hidden_size: int, dtype: torch.dtype,
 @torch.inference_mode()
 def test_mmvq(hidden_size: int, dtype: torch.dtype,
               quant_type: GGMLQuantizationType):
-    seed_everything(0)
+    current_platform.seed_everything(0)
 
     tensors = get_gguf_sample_tensors(hidden_size, quant_type)
     x = torch.rand((1, hidden_size), dtype=dtype, device="cuda")
@@ -111,7 +111,7 @@ def test_mmvq(hidden_size: int, dtype: torch.dtype,
 @torch.inference_mode()
 def test_mmq(num_tokens: int, hidden_size: int, dtype: torch.dtype,
              quant_type: GGMLQuantizationType):
-    seed_everything(0)
+    current_platform.seed_everything(0)
 
     tensors = get_gguf_sample_tensors(hidden_size, quant_type)
     x = torch.rand((num_tokens, hidden_size), dtype=dtype, device="cuda")

tests/kernels/test_int8_quant.py

@@ -4,7 +4,7 @@ import torch
 from tests.kernels.quant_utils import ref_dynamic_per_token_quant
 from tests.kernels.utils import opcheck
 from vllm._custom_ops import scaled_int8_quant
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 HIDDEN_SIZES = [16, 67, 768, 2048, 5120, 5137, 8192,
@@ -45,7 +45,7 @@ def opcheck_int8_quant_dynamic(output, input, symmetric=True):
 @torch.inference_mode()
 def test_dynamic_scaled_int8_quant(num_tokens: int, hidden_size: int,
                                    dtype: torch.dtype, seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
 
@@ -68,7 +68,7 @@ def test_dynamic_scaled_int8_quant(num_tokens: int, hidden_size: int,
 @torch.inference_mode()
 def test_dynamic_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
                                        dtype: torch.dtype, seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     int8_traits = torch.iinfo(torch.int8)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype,
@@ -112,7 +112,7 @@ def test_dynamic_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
 def test_static_scaled_int8_quant(num_tokens: int, hidden_size: int,
                                   dtype: torch.dtype, seed: int,
                                   scale: float) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     int8_traits = torch.iinfo(torch.int8)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
@@ -138,7 +138,7 @@ def test_static_scaled_int8_quant(num_tokens: int, hidden_size: int,
 def test_static_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
                                       dtype: torch.dtype, seed: int,
                                       scale: float, azp: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     int8_traits = torch.iinfo(torch.int8)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype,

tests/kernels/test_layernorm.py

@@ -3,7 +3,7 @@ import torch
 
 from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 NUM_TOKENS = [7, 83, 4096]  # Arbitrary values for testing
@@ -31,7 +31,7 @@ def test_rms_norm(
     seed: int,
     device: str,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     layer = RMSNorm(hidden_size).to(dtype=dtype)
     layer.weight.data.normal_(mean=1.0, std=0.1)

tests/kernels/test_mamba_ssm.py

@@ -8,7 +8,7 @@ from vllm import _custom_ops as ops  # noqa: F401
 from vllm.attention.backends.utils import PAD_SLOT_ID
 from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
     selective_scan_fn, selective_state_update)
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 
 def selective_state_update_ref(state,
@@ -235,7 +235,7 @@ def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_D,
         rtolw = max(rtolw, rtol)
         atolw = max(atolw, atol)
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
     batch_size = 1
     dim = 4
     dstate = 8
@@ -358,7 +358,7 @@ def test_selective_state_update(dim, dstate, has_z, itype):
         if torch.version.hip:
             atol *= 2
     # set seed
-    seed_everything(0)
+    current_platform.seed_everything(0)
     batch_size = 1
     state = torch.randn(batch_size, dim, dstate, dtype=itype, device=device)
     x = torch.randn(batch_size, dim, device=device, dtype=itype)

tests/kernels/test_moe.py

@@ -19,7 +19,6 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
 from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
-from vllm.utils import seed_everything
 
 
 @pytest.mark.parametrize("m", [1024 * 128, 512, 222, 33, 1])
@@ -115,7 +114,7 @@ def test_fused_marlin_moe(
     num_bits: int,
     is_k_full: bool,
 ):
-    seed_everything(7)
+    current_platform.seed_everything(7)
 
     # Filter act_order
     if act_order:

tests/kernels/test_pos_encoding.py

@@ -5,7 +5,7 @@ import pytest
 import torch
 
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 from .allclose_default import get_default_atol, get_default_rtol
 
@@ -48,7 +48,7 @@ def test_rotary_embedding(
     if rotary_dim is None:
         rotary_dim = head_size
 
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size
@@ -100,7 +100,7 @@ def test_batched_rotary_embedding(
     max_position: int = 8192,
     base: int = 10000,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size
@@ -160,7 +160,7 @@ def test_batched_rotary_embedding_multi_lora(
     max_position: int = 8192,
     base: int = 10000,
 ) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size