[Platform] Deprecate seed_everything (#31659)

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

docs/design/plugin_system.md

@@ -154,3 +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.

tests/compile/distributed/test_async_tp.py

@@ -26,6 +26,7 @@ from vllm.distributed.parallel_state import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.system_utils import update_environment_variables
+from vllm.utils.torch_utils import set_random_seed
 
 from ...models.registry import HF_EXAMPLE_MODELS
 from ...utils import (
@@ -301,7 +302,7 @@ def async_tp_pass_on_test_model(
     dtype: torch.dtype,
     dynamic: bool,
 ):
-    current_platform.seed_everything(0)
+    set_random_seed(0)
 
     device = torch.device(f"cuda:{local_rank}")
     torch.cuda.set_device(device)

tests/compile/distributed/test_fusion_all_reduce.py

@@ -32,6 +32,7 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.system_utils import update_environment_variables
+from vllm.utils.torch_utils import set_random_seed
 
 from ...utils import has_module_attribute, multi_gpu_test
 from ..backend import TestBackend
@@ -263,7 +264,7 @@ def all_reduce_fusion_pass_on_test_model(
     enable_rms_norm_custom_op,
     enable_quant_fp8_custom_op,
 ):
-    current_platform.seed_everything(0)
+    set_random_seed(0)
 
     device = torch.device(f"cuda:{local_rank}")
     torch.cuda.set_device(device)

tests/compile/distributed/test_sequence_parallelism.py

@@ -31,6 +31,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import Fp8LinearOp
 from vllm.platforms import current_platform
 from vllm.utils.system_utils import update_environment_variables
+from vllm.utils.torch_utils import set_random_seed
 
 from ...utils import multi_gpu_test
 from ..backend import TestBackend
@@ -232,7 +233,7 @@ def sequence_parallelism_pass_on_test_model(
     fuse_norm_quant: bool,
     dynamic: bool,
 ):
-    current_platform.seed_everything(0)
+    set_random_seed(0)
 
     device = torch.device(f"cuda:{local_rank}")
     torch.cuda.set_device(device)

tests/kernels/attention/test_aiter_flash_attn.py

@@ -8,6 +8,7 @@ import torch
 import vllm.v1.attention.backends.rocm_aiter_fa  # noqa: F401
 from vllm.attention.utils.fa_utils import is_flash_attn_varlen_func_available
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]
@@ -104,7 +105,7 @@ def test_varlen_with_paged_kv(
     if not is_flash_attn_varlen_func_available():
         pytest.skip("flash_attn_varlen_func required to run this test.")
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(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/attention/test_attention.py

@@ -13,6 +13,7 @@ from vllm.attention.layer import Attention
 from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.platforms import current_platform
 from vllm.utils.mem_utils import get_max_shared_memory_bytes
+from vllm.utils.torch_utils import set_random_seed
 
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 # This will change depending on the compute capability.
@@ -150,7 +151,7 @@ def test_paged_attention(
 
     global PARTITION_SIZE
 
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     scale = float(1.0 / (head_size**0.5))
     num_query_heads, num_kv_heads = num_heads

tests/kernels/attention/test_cache.py

@@ -9,6 +9,7 @@ import torch
 from tests.kernels.utils import DEFAULT_OPCHECK_TEST_UTILS, opcheck
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 COPYING_DIRECTION = [("cuda", "cpu"), ("cuda", "cuda"), ("cpu", "cuda")]
 DTYPES = [torch.bfloat16, torch.float]
@@ -64,7 +65,7 @@ def test_reshape_and_cache(
 ) -> None:
     if kv_cache_dtype == "fp8" and head_size % 16:
         pytest.skip()
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
     # Create a random slot mapping.
@@ -185,7 +186,7 @@ def test_reshape_and_cache_flash(
     kv_cache_layout: str,
     implementation: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
     assert implementation in ["cuda", "triton"]
@@ -355,7 +356,7 @@ def test_swap_blocks(
     if kv_cache_dtype == "fp8" and head_size % 16:
         pytest.skip()
 
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     src_device = device if direction[0] == "cuda" else "cpu"
     dst_device = device if direction[1] == "cuda" else "cpu"
@@ -444,7 +445,7 @@ def test_fp8_e4m3_conversion(
     seed: int,
     device: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     low = -224.0
     high = 224.0
@@ -507,7 +508,7 @@ def test_concat_and_cache_mla(
     device: str,
     kv_cache_dtype: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
 
@@ -584,7 +585,7 @@ def test_concat_and_cache_ds_mla(
     if dtype.itemsize != 2:
         pytest.skip("ds_mla only supports 16-bit input")
     kv_cache_dtype = "fp8_ds_mla"
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
 
@@ -695,7 +696,7 @@ def test_swap_blocks_mla(
     device: str,
     kv_cache_dtype: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
 
@@ -947,7 +948,7 @@ def test_concat_and_cache_mla_cpu(
 ) -> None:
     device = "cpu"
     kv_cache_dtype = "auto"
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
 
     total_slots = num_blocks * block_size

tests/kernels/attention/test_cascade_flash_attn.py

@@ -6,6 +6,7 @@ import pytest
 import torch
 
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.attention.backends.flash_attn import cascade_attention, merge_attn_states
 
 try:
@@ -39,7 +40,7 @@ def test_merge_kernel(
     dtype: torch.dtype,
 ):
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]
     assert num_query_heads % num_kv_heads == 0
@@ -103,7 +104,7 @@ def test_cascade(
             f'to: "{fa_version_unsupported_reason(fa_version)}"'
         )
 
-    current_platform.seed_everything(0)
+    set_random_seed(0)
 
     window_size = (-1, -1)
     scale = head_size**-0.5

tests/kernels/attention/test_cpu_attn.py

@@ -8,6 +8,7 @@ import pytest
 import torch
 
 from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.attention.backends.cpu_attn import _get_attn_isa
 
 if not current_platform.is_cpu():
@@ -190,7 +191,7 @@ def varlen_with_paged_kv(
     use_sink: bool,
     isa: str,
 ) -> None:
-    current_platform.seed_everything(0)
+    set_random_seed(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/attention/test_flash_attn.py

@@ -6,6 +6,7 @@ import pytest
 import torch
 
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 try:
     from vllm.vllm_flash_attn import (
@@ -129,7 +130,7 @@ def test_varlen_with_paged_kv(
             "Flash attention with quantized inputs is only "
             "supported on version 3 with bfloat16 base type"
         )
-    current_platform.seed_everything(0)
+    set_random_seed(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/attention/test_flashinfer.py

@@ -5,6 +5,7 @@
 import pytest
 
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 try:
     import flashinfer
@@ -101,7 +102,7 @@ def test_flashinfer_decode_with_paged_kv(
     sliding_window: int | None,
 ) -> None:
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]
@@ -196,7 +197,7 @@ def test_flashinfer_prefill_with_paged_kv(
     sliding_window: int | None,
 ) -> None:
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
     kv_lens = [x[1] for x in seq_lens]
@@ -299,7 +300,7 @@ def test_flashinfer_prefill_with_paged_fp8_kv(
 ) -> None:
     pytest.skip("TODO: fix the accuracy issue")
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
     kv_lens = [x[1] for x in seq_lens]
@@ -409,7 +410,7 @@ def test_flashinfer_decode_with_paged_fp8_kv(
 ) -> None:
     # test doesn't work for num_heads = (16,16)
     torch.set_default_device("cuda")
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
     num_kv_heads = num_heads[1]

tests/kernels/attention/test_flashinfer_trtllm_attention.py

@@ -10,6 +10,7 @@ from tests.kernels.quantization.nvfp4_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import round_up
+from vllm.utils.torch_utils import set_random_seed
 
 if not current_platform.is_device_capability_family(100):
     pytest.skip(
@@ -80,7 +81,7 @@ def test_flashinfer_trtllm_decode_with_baseline(
     has_sinks: bool,
 ) -> None:
     torch.set_default_device("cuda")
-    current_platform.seed_everything(42)
+    set_random_seed(42)
 
     q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
     q_quant_dtype = q_quant_dtype or dtype
@@ -279,7 +280,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     has_sinks: bool,
 ) -> None:
     torch.set_default_device("cuda")
-    current_platform.seed_everything(42)
+    set_random_seed(42)
 
     q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
     q_quant_dtype = q_quant_dtype or dtype

tests/kernels/attention/test_lightning_attn.py

@@ -5,7 +5,7 @@ import pytest
 import torch
 
 from vllm.model_executor.layers.lightning_attn import linear_decode_forward_triton
-from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 NUM_HEADS = [4, 8]
 HEAD_SIZES = [64]
@@ -124,7 +124,7 @@ def test_linear_decode_forward_triton(
     torch.set_default_device("cuda")
     torch.manual_seed(42)
     torch.cuda.manual_seed_all(42)
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     base = 0.01
     q = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
     k = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
@@ -167,7 +167,7 @@ def test_linear_decode_forward_triton_with_padding(
     torch.set_default_device("cuda")
     torch.manual_seed(42)
     torch.cuda.manual_seed_all(42)
-    current_platform.seed_everything(42)
+    set_random_seed(42)
 
     batch_size = 4
     base = 0.01
@@ -231,7 +231,7 @@ def test_lightning_attention_reference(
     torch.set_default_device("cuda")
     torch.manual_seed(42)
     torch.cuda.manual_seed_all(42)
-    current_platform.seed_everything(42)
+    set_random_seed(42)
 
     base = 0.01
     q = base * torch.randn(batch_size, num_heads, seq_len, head_size, dtype=dtype)

tests/kernels/attention/test_mha_attn.py

@@ -19,6 +19,7 @@ from vllm.platforms import current_platform
 from vllm.platforms.cpu import CpuPlatform
 from vllm.platforms.cuda import CudaPlatform
 from vllm.platforms.rocm import RocmPlatform
+from vllm.utils.torch_utils import set_random_seed
 
 
 @pytest.fixture(autouse=True)
@@ -123,7 +124,7 @@ def test_mha_attn_forward(
     dtype: torch.dtype,
     device: str,
 ):
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     torch.set_default_device(device)
     torch.set_default_dtype(dtype)
 
@@ -168,7 +169,7 @@ def test_mha_attn_varlen_forward(
     dtype: torch.dtype,
     device: str,
 ):
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     torch.set_default_device(device)
     torch.set_default_dtype(dtype)
 

tests/kernels/attention/test_prefix_prefill.py

@@ -13,7 +13,7 @@ import torch.nn.functional as F
 from vllm.attention.ops.chunked_prefill_paged_decode import chunked_prefill_paged_decode
 from vllm.attention.ops.prefix_prefill import context_attention_fwd
 from vllm.platforms import current_platform
-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
 
 NUM_HEADS = [64]
 NUM_QUERIES_PER_KV = [1, 64]
@@ -125,7 +125,7 @@ def test_contexted_kv_attention(
     ):
         pytest.skip("ROCm custom paged attention does not support fp8_e5m2 KV cache")
 
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     torch.set_default_device(device)
 
     # Need this, otherwise when we capture the graph the process
@@ -346,7 +346,7 @@ def test_contexted_kv_attention_alibi(
     ):
         pytest.skip("ROCm custom paged attention does not support fp8_e5m2 KV cache")
 
-    current_platform.seed_everything(0)
+    set_random_seed(0)
     torch.set_default_device(device)
 
     # Need this, otherwise when we capture the graph the process

tests/kernels/attention/test_triton_unified_attention.py

@@ -8,6 +8,7 @@ import torch
 from vllm.attention.ops.triton_unified_attention import unified_attention
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import next_power_of_2
+from vllm.utils.torch_utils import set_random_seed
 
 NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]
@@ -113,7 +114,7 @@ def test_triton_unified_attn(
 ) -> None:
     torch.set_default_device("cuda")
 
-    current_platform.seed_everything(0)
+    set_random_seed(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/core/test_activation.py

@@ -18,7 +18,7 @@ from vllm.model_executor.layers.activation import (
     SiluAndMul,
     SwigluOAIAndMul,
 )
-from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 NUM_TOKENS = [7, 83, 2048]  # Arbitrary values for testing
@@ -52,7 +52,7 @@ def test_act_and_mul(
     seed: int,
     device: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, 2 * d, dtype=dtype)
     if activation == "silu_and_mul":
@@ -129,7 +129,7 @@ def test_activation(
     seed: int,
     device: str,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, d, dtype=dtype)
     layer = activation[0]()

tests/kernels/core/test_fused_qk_norm_rope.py

@@ -8,6 +8,7 @@ from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 DTYPES = [torch.bfloat16, torch.float16]
 IS_NEOX = [True, False]
@@ -64,7 +65,7 @@ def test_fused_qk_norm_rope_matches_reference(
     rotary_ratio: float,
 ):
     torch.set_default_device(device)
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     num_heads, num_kv_heads, head_dim = 16, 4, 128
     num_tokens = 4
 

tests/kernels/core/test_layernorm.py

@@ -7,7 +7,7 @@ import torch
 from tests.kernels.quant_utils import FP8_DTYPE
 from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 NUM_TOKENS = [7, 83, 4096]  # Arbitrary values for testing
@@ -34,7 +34,7 @@ def test_rms_norm(
     device: str,
     strided_input: bool,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     layer = RMSNorm(hidden_size).to(dtype=dtype)
     layer.weight.data.normal_(mean=1.0, std=0.1)
@@ -88,7 +88,7 @@ def test_fused_rms_norm_quant(
     device: str,
     strided_input: bool,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
 
     weight = torch.empty(hidden_size, dtype=dtype).normal_(mean=1.0, std=0.1)

tests/kernels/core/test_mrope.py

@@ -10,6 +10,7 @@ from transformers import __version__ as TRANSFORMERS_VERSION
 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.torch_utils import set_random_seed
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -24,7 +25,7 @@ def generate_test_data(
     device: torch.device,
 ):
     """Generate test data for given configuration."""
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     # Create 2D positions (3, num_tokens) for multimodal case
     positions = torch.randint(
         0, max_position_embeddings // 4, (3, num_tokens), device=device