[Misc] Use deprecated for seed_everything

benchmarks/kernels/benchmark_activation.py

@@ -8,10 +8,9 @@
 
 import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
-from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 batch_size_range = [1, 16, 128]
 seq_len_range = [1, 16, 64, 1024, 4096]
@@ -30,7 +29,7 @@ def benchmark_activation(
     device = "cuda"
     num_tokens = batch_size * seq_len
     dim = intermediate_size
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     if func_name == "gelu_and_mul":

benchmarks/kernels/benchmark_layernorm.py

@@ -6,9 +6,8 @@
 import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
@@ -22,7 +21,7 @@ def main(
     num_warmup_iters: int = 5,
     num_iters: int = 100,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device("cuda")
 
     layer = RMSNorm(hidden_size).to(dtype=dtype)

benchmarks/kernels/benchmark_moe.py

@@ -24,6 +24,7 @@
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
@@ -431,7 +432,7 @@ def merge_unique_dicts(list1, list2):
 class BenchmarkWorker:
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
-        current_platform.seed_everything(seed)
+        set_random_seed(seed)
         self.seed = seed
         # Get the device ID to allocate tensors and kernels
         # on the respective GPU. This is required for Ray to work
@@ -451,7 +452,7 @@ def benchmark(
         block_quant_shape: list[int] = None,
         use_deep_gemm: bool = False,
     ) -> tuple[dict[str, int], float]:
-        current_platform.seed_everything(self.seed)
+        set_random_seed(self.seed)
         dtype_str = _get_config_dtype_str(
             dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
         )

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -18,6 +18,7 @@
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
@@ -261,7 +262,7 @@ def run(input: tuple):
 class BenchmarkWorker:
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
-        current_platform.seed_everything(seed)
+        set_random_seed(seed)
         self.seed = seed
         # Get the device ID to allocate tensors and kernels
         # on the respective GPU. This is required for Ray to work
@@ -279,7 +280,7 @@ def benchmark(
         use_int8_w8a16: bool,
         use_customized_permute: bool = False,
     ) -> tuple[dict[str, int], float]:
-        current_platform.seed_everything(self.seed)
+        set_random_seed(self.seed)
 
         permute_time = benchmark_permute(
             num_tokens,

benchmarks/kernels/benchmark_mrope.py

@@ -37,9 +37,9 @@
 import torch
 
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -94,7 +94,7 @@ def benchmark_mrope(
     benchmark_iter: int = 100,
     csv_writer=None,
 ):
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     # the parameters to compute the q k v size based on tp_size
     mrope_helper_class = get_rope(

benchmarks/kernels/benchmark_paged_attention.py

@@ -13,6 +13,7 @@
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random,
+    set_random_seed,
 )
 
 logger = init_logger(__name__)
@@ -38,7 +39,7 @@ def main(
     device: str = "cuda",
     kv_cache_dtype: str | None = None,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     scale = float(1.0 / (head_size**0.5))
     query = torch.empty(

benchmarks/kernels/benchmark_quant.py

@@ -6,9 +6,8 @@
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
@@ -23,7 +22,7 @@ def main(
     num_warmup_iters: int = 5,
     num_iters: int = 100,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device("cuda")
 
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -8,11 +8,11 @@
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random,
+    set_random_seed,
 )
 
 logger = init_logger(__name__)
@@ -36,7 +36,7 @@ def run_benchmark(
     if kv_cache_dtype == "fp8" and head_size % 16:
         raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
 
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     # create random key / value tensors [T, H, D].

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -11,11 +11,11 @@
     triton_reshape_and_cache_flash,
 )
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random_flash,
+    set_random_seed,
 )
 
 logger = init_logger(__name__)
@@ -49,7 +49,7 @@ def run_benchmark(
     if implementation == "triton" and kv_cache_layout == "HND":
         return float("nan")  # Triton does not support HND layout yet.
 
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     # create random key / value tensors [T, H, D].

benchmarks/kernels/benchmark_silu_mul_fp8_quant.py

@@ -23,9 +23,9 @@
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
     persistent_masked_m_silu_mul_quant,
 )
-from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
+from vllm.utils.torch_utils import set_random_seed
 
 
 @triton.jit
@@ -207,7 +207,7 @@ def benchmark(
 ):
     def generate_data(seed_offset=0):
         """Generate input data with given seed offset"""
-        current_platform.seed_everything(42 + seed_offset)
+        set_random_seed(42 + seed_offset)
         y = torch.rand((E, T, 2 * H), dtype=torch.bfloat16, device="cuda").contiguous()
 
         if gen_strategy == "random_imbalanced":

docs/design/plugin_system.md

@@ -154,4 +154,4 @@ The interface for the model/module may change during vLLM's development. If you
 !!! warning "Deprecations"
     - `use_v1` parameter in `Platform.get_attn_backend_cls` is deprecated. It has been removed in v0.13.0.
     - `_Backend` in `vllm.attention` is deprecated. It has been removed in v0.13.0. Please use `vllm.attention.backends.registry.register_backend` to add new attention backend to `AttentionBackendEnum` instead.
-    - `seed_everything` platform interface is deprecated. It will be removed in v0.14.0 or later. Please use `vllm.utils.torch_utils.set_random_seed` instead.
+    - `seed_everything` platform interface is deprecated. It will be removed in v0.15.0 or later. Please use `vllm.utils.torch_utils.set_random_seed` instead.

vllm/platforms/interface.py

@@ -11,6 +11,7 @@
 
 import numpy as np
 import torch
+from typing_extensions import deprecated
 
 from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.logger import init_logger
@@ -365,17 +366,17 @@ def inference_mode(cls):
         return torch.inference_mode(mode=True)
 
     @classmethod
+    @deprecated(
+        "`seed_everything` is deprecated. It will be removed in v0.15.0 or later. "
+        "Please use `vllm.utils.torch_utils.set_random_seed` instead."
+    )
     def seed_everything(cls, seed: int | None = None) -> 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
         """
-        logger.info_once(
-            "`seed_everything` is deprecated. It will be removed in v0.14.0 or later. "
-            "Please use `vllm.utils.torch_utils.set_random_seed` instead."
-        )
         if seed is not None:
             random.seed(seed)
             np.random.seed(seed)