[Hardware] using current_platform.seed_everything (#9785)

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

tests/kernels/test_prefix_prefill.py

@@ -9,7 +9,8 @@ from xformers.ops.fmha.attn_bias import BlockDiagonalCausalFromBottomRightMask
 
 from vllm.attention.backends.xformers import _make_alibi_bias
 from vllm.attention.ops.prefix_prefill import context_attention_fwd
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, seed_everything
+from vllm.platforms import current_platform
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 
 NUM_HEADS = [64]
 NUM_QUERIES_PER_KV = [1, 8, 64]
@@ -39,7 +40,7 @@ def test_contexted_kv_attention(
     kv_cache_dtype: str,
     device: str,
 ) -> None:
-    seed_everything(0)
+    current_platform.seed_everything(0)
     torch.set_default_device(device)
 
     # Need this, otherwise when we capture the graph the process
@@ -234,7 +235,7 @@ def test_contexted_kv_attention_alibi(
     kv_cache_dtype: str,
     device: str,
 ) -> None:
-    seed_everything(0)
+    current_platform.seed_everything(0)
     torch.set_default_device(device)
 
     # Need this, otherwise when we capture the graph the process

tests/lora/test_layers.py

@@ -39,7 +39,7 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding, get_masked_input_and_mask)
 from vllm.model_executor.utils import set_random_seed
-from vllm.utils import seed_everything
+from vllm.platforms import current_platform
 
 from .utils import DummyLoRAManager
 
@@ -923,7 +923,7 @@ def test_rotary_embedding_long_context(dist_init, num_loras, device,
                                        seq_len) -> None:
     dtype = torch.float16
     seed = 0
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
     torch.set_default_device(device)
     punica_wrapper = PunicaWrapper(8192, 256, device)
     max_loras = 8

tests/lora/test_punica_sizes.py

 """
-This script is mainly used to tests various hidden_sizes. We have collected the 
+This script is mainly used to tests various hidden_sizes. We have collected the
 hidden_sizes included in the LoRA models currently supported by vLLM. It tests
 whether the corresponding Triton kernel can run normally when tensor parallelism
 is set to [1, 2, 4, 8, 16, 32, 64].
@@ -15,8 +15,8 @@ from vllm.lora.ops.bgmv_shrink import bgmv_shrink
 from vllm.lora.ops.sgmv_expand import sgmv_expand
 from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice
 from vllm.lora.ops.sgmv_shrink import sgmv_shrink
+from vllm.platforms import current_platform
 from vllm.triton_utils.libentry import LibEntry
-from vllm.utils import seed_everything
 
 from .utils import (generate_data, generate_data_for_expand_nslices,
                     ref_torch_groupgemm)
@@ -146,7 +146,7 @@ def test_punica_sgmv(
     device: str,
 ):
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 128
     (
@@ -239,7 +239,7 @@ def test_punica_bgmv(
     from vllm.lora.ops.bgmv_shrink import _bgmv_shrink_kernel
 
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 1
     (
@@ -327,7 +327,7 @@ def test_punica_expand_nslices(
     from vllm.lora.ops.bgmv_expand_slice import _bgmv_expand_slice_kernel
 
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 128 if op_type == "sgmv" else 1
     (

tests/lora/test_punica_variation.py

 """
-This script is mainly used to test whether trtion kernels can run normally 
-under different conditions, including various batches, numbers of LoRA , and 
+This script is mainly used to test whether trtion kernels can run normally
+under different conditions, including various batches, numbers of LoRA , and
 maximum ranks.
 """
 from unittest.mock import patch
@@ -14,8 +14,8 @@ from vllm.lora.ops.bgmv_shrink import bgmv_shrink
 from vllm.lora.ops.sgmv_expand import sgmv_expand
 from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice
 from vllm.lora.ops.sgmv_shrink import sgmv_shrink
+from vllm.platforms import current_platform
 from vllm.triton_utils.libentry import LibEntry
-from vllm.utils import seed_everything
 
 from .utils import (generate_data, generate_data_for_expand_nslices,
                     ref_torch_groupgemm)
@@ -61,7 +61,7 @@ def test_punica_sgmv(
     device: str,
 ):
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 128
     (
@@ -154,7 +154,7 @@ def test_punica_bgmv(
     from vllm.lora.ops.bgmv_shrink import _bgmv_shrink_kernel
 
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 1
     (
@@ -242,7 +242,7 @@ def test_punica_expand_nslices(
     from vllm.lora.ops.bgmv_expand_slice import _bgmv_expand_slice_kernel
 
     torch.set_default_device(device)
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     seq_length = 128 if op_type == "sgmv" else 1
     (

vllm/model_executor/utils.py

@@ -4,11 +4,10 @@ from typing import Any, Dict, Optional
 import torch
 
 from vllm.platforms import current_platform
-from vllm.utils import seed_everything
 
 
 def set_random_seed(seed: int) -> None:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
 
 def set_weight_attrs(

vllm/platforms/interface.py

 import enum
+import random
 from typing import NamedTuple, Optional, Tuple, Union
 
+import numpy as np
 import torch
 
 
@@ -111,6 +113,18 @@ class Platform:
         """
         return torch.inference_mode(mode=True)
 
+    @classmethod
+    def seed_everything(cls, seed: int) -> None:
+        """
+        Set the seed of each random module.
+        `torch.manual_seed` will set seed on all devices.
+
+        Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
+        """
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+
 
 class UnspecifiedPlatform(Platform):
     _enum = PlatformEnum.UNSPECIFIED

vllm/utils.py

@@ -7,7 +7,6 @@ import gc
 import inspect
 import ipaddress
 import os
-import random
 import socket
 import subprocess
 import sys
@@ -331,22 +330,6 @@ def get_cpu_memory() -> int:
     return psutil.virtual_memory().total
 
 
-def seed_everything(seed: int) -> None:
-    """
-    Set the seed of each random module.
-
-    Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-
-    if current_platform.is_cuda_alike():
-        torch.cuda.manual_seed_all(seed)
-
-    if current_platform.is_xpu():
-        torch.xpu.manual_seed_all(seed)
-
-
 def random_uuid() -> str:
     return str(uuid.uuid4().hex)
 
@@ -643,7 +626,7 @@ def create_kv_caches_with_random_flash(
     seed: int = 0,
     device: Optional[str] = "cuda",
 ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
     key_value_cache_shape = (num_blocks, 2, block_size, num_heads, head_size)
@@ -685,7 +668,7 @@ def create_kv_caches_with_random(
             f"Does not support key cache of type fp8 with head_size {head_size}"
         )
 
-    seed_everything(seed)
+    current_platform.seed_everything(seed)
 
     torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)