Introduce CudaDeviceMixin and CudaSRTPlatform (#24096)

python/sglang/srt/managers/scheduler.py

@@ -203,6 +203,7 @@ from sglang.srt.observability.scheduler_metrics_mixin import (
 )
 from sglang.srt.observability.trace import process_tracing_init, trace_set_thread_info
 from sglang.srt.parser.reasoning_parser import ReasoningParser
+from sglang.srt.platforms import current_platform
 from sglang.srt.plugins import load_plugins
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.server_args import PortArgs, ServerArgs, get_global_server_args
@@ -214,7 +215,6 @@ from sglang.srt.utils import (
     broadcast_pyobj,
     configure_gc_logger,
     configure_logger,
-    empty_device_cache,
     freeze_gc,
     get_available_gpu_memory,
     get_bool_env_var,
@@ -3454,7 +3454,7 @@ class Scheduler(
                 self.draft_worker.clear_cache_pool()
 
             if empty_cache:
-                empty_device_cache(self.device_module)
+                current_platform.empty_cache()
             logger.info("Cache flushed successfully!")
             success = True
         else:
@@ -3852,7 +3852,7 @@ class IdleSleeper:
             and real_time() - self.last_empty_time > self.empty_cache_interval
         ):
             self.last_empty_time = real_time()
-            empty_device_cache()
+            current_platform.empty_cache()
 
 
 def is_health_check_generate_req(recv_req):

python/sglang/srt/mem_cache/memory_pool.py

@@ -406,7 +406,7 @@ class MambaPool:
         return dst_index
 
     def get_cpu_copy(self, indices):
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         conv_cpu = [
             conv[:, indices].to("cpu", non_blocking=True)
             for conv in self.mamba_cache.conv
@@ -414,18 +414,18 @@ class MambaPool:
         temporal_cpu = self.mamba_cache.temporal[:, indices].to(
             "cpu", non_blocking=True
         )
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         return conv_cpu, temporal_cpu
 
     def load_cpu_copy(self, mamba_cache_cpu, indices):
         conv_cpu, temporal_cpu = mamba_cache_cpu
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         for i, conv in enumerate(self.mamba_cache.conv):
             conv[:, indices] = conv_cpu[i].to(conv.device, non_blocking=True)
         self.mamba_cache.temporal[:, indices] = temporal_cpu.to(
             self.mamba_cache.temporal.device, non_blocking=True
         )
-        torch.cuda.synchronize()
+        current_platform.synchronize()
 
     def get_contiguous_buf_infos(self):
         """
@@ -982,7 +982,7 @@ class MHATokenToKVPool(KVCache):
         return kv_data_ptrs, kv_data_lens, kv_item_lens
 
     def get_cpu_copy(self, indices, mamba_indices=None):
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         kv_cache_cpu = []
         chunk_size = self.cpu_offloading_chunk_size
         for layer_id in range(self.layer_num):
@@ -996,11 +996,11 @@ class MHATokenToKVPool(KVCache):
                     "cpu", non_blocking=True
                 )
                 kv_cache_cpu[-1].append([k_cpu, v_cpu])
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         return kv_cache_cpu
 
     def load_cpu_copy(self, kv_cache_cpu, indices, mamba_indices=None):
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         chunk_size = self.cpu_offloading_chunk_size
         for layer_id in range(self.layer_num):
             for i in range(0, len(indices), chunk_size):
@@ -1014,7 +1014,7 @@ class MHATokenToKVPool(KVCache):
                 v_chunk = v_cpu.to(self.v_buffer[0].device, non_blocking=True)
                 self.k_buffer[layer_id][chunk_indices] = k_chunk
                 self.v_buffer[layer_id][chunk_indices] = v_chunk
-        torch.cuda.synchronize()
+        current_platform.synchronize()
 
     def _get_key_buffer(self, layer_id: int):
         # for internal use of referencing
@@ -1822,7 +1822,7 @@ class MLATokenToKVPool(KVCache):
         return cache_k_nope, cache_k_rope
 
     def get_cpu_copy(self, indices, mamba_indices=None):
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         kv_cache_cpu = []
         chunk_size = self.cpu_offloading_chunk_size
         for layer_id in range(self.layer_num):
@@ -1833,11 +1833,11 @@ class MLATokenToKVPool(KVCache):
                     "cpu", non_blocking=True
                 )
                 kv_cache_cpu[-1].append(kv_cpu)
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         return kv_cache_cpu
 
     def load_cpu_copy(self, kv_cache_cpu, indices, mamba_indices=None):
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         chunk_size = self.cpu_offloading_chunk_size
         for layer_id in range(self.layer_num):
             for i in range(0, len(indices), chunk_size):
@@ -1846,7 +1846,7 @@ class MLATokenToKVPool(KVCache):
                 assert kv_cpu.shape[0] == len(chunk_indices)
                 kv_chunk = kv_cpu.to(self.kv_buffer[0].device, non_blocking=True)
                 self.kv_buffer[layer_id][chunk_indices] = kv_chunk
-        torch.cuda.synchronize()
+        current_platform.synchronize()
 
 
 class MLATokenToKVPoolFP4(MLATokenToKVPool):

python/sglang/srt/model_executor/model_runner.py

@@ -1288,7 +1288,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
                 # Single warmup all_reduce to initialize NCCL/RCCL communicator
                 warmup_tensor = torch.zeros(1, device=torch.cuda.current_device())
                 dist.all_reduce(warmup_tensor, group=tp_group_handle)
-                torch.cuda.synchronize()
+                current_platform.synchronize()
 
                 warmup_elapsed = time.perf_counter() - warmup_start
                 logger.info(
@@ -1847,7 +1847,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             f"group_rank={group_rank}, world_size={world_size}, group_name={group_name}, backend={backend}"
         )
 
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
         success = False
         message = ""
         try:
@@ -1867,7 +1867,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             message = f"Failed to init group: {e}."
             logger.error(message)
 
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
         return success, message
 
     def send_weights_to_remote_instance(
@@ -1895,7 +1895,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             logger.error(message)
             return False, message
 
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
         success = False
         na = NetworkAddress(master_address, group_port)
         message = ""
@@ -1915,7 +1915,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         # destroy the process group after sending weights
         del self._weights_send_group[group_name]
         torch.distributed.distributed_c10d.destroy_process_group(send_group)
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
         return success, message
 
     def init_weights_update_group(

python/sglang/srt/model_loader/loader.py

@@ -106,6 +106,7 @@ from sglang.srt.model_loader.weight_utils import (
     safetensors_weights_iterator,
     set_runai_streamer_env,
 )
+from sglang.srt.platforms import current_platform
 from sglang.srt.utils import (
     get_bool_env_var,
     get_device_capability,
@@ -1221,7 +1222,7 @@ class QuantizedRLModelLoader(DefaultModelLoader):
         del current_param_data
         if is_last_update:
             gc.collect()
-            torch.cuda.empty_cache()
+            current_platform.empty_cache()
 
         logger.info("[QuantizedRL] Reload complete")
         return updated_param_names, is_last_update
@@ -1912,7 +1913,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
 
         model.load_weights(qweight_iterator)
 
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
 
         param_dict = dict(model.named_parameters())
         stacked_quant_state_dict: Dict[str, Dict[int, Any]] = {}
@@ -2220,7 +2221,7 @@ class RemoteInstanceModelLoader(BaseModelLoader):
             tp_rank=load_config.tp_rank,
             instance_ip=instance_ip,
         )
-        torch.cuda.synchronize()
+        current_platform.synchronize()
         end_build_group_tic = time.time()
         logger.debug(
             f"finish building group for remote instance, time used: {(end_build_group_tic - start_build_group_tic):.4f}s"
@@ -2246,7 +2247,7 @@ class RemoteInstanceModelLoader(BaseModelLoader):
                     src=0,
                     group=client._model_update_group,
                 )
-            torch.cuda.synchronize()
+            current_platform.synchronize()
 
             _post_load_weights(model)
         end_get_weights_tic = time.time()
@@ -2257,7 +2258,7 @@ class RemoteInstanceModelLoader(BaseModelLoader):
         torch.distributed.distributed_c10d.destroy_process_group(
             client._model_update_group
         )
-        torch.cuda.empty_cache()
+        current_platform.empty_cache()
 
     def load_model_from_remote_instance_by_transfer_engine(
         self, model, transfer_engine, seed_url, tp_rank

python/sglang/srt/platforms/__init__.py

@@ -14,8 +14,12 @@ import logging
 import pkgutil
 from importlib.metadata import entry_points
 
+import torch
+
 from sglang.srt.environ import envs
+from sglang.srt.platforms.cuda import CudaSRTPlatform
 from sglang.srt.platforms.interface import SRTPlatform
+from sglang.srt.platforms.rocm import RocmSRTPlatform
 from sglang.srt.plugins import PLATFORM_PLUGINS_GROUP, load_plugins_by_group
 
 logger = logging.getLogger(__name__)
@@ -23,6 +27,14 @@ logger = logging.getLogger(__name__)
 _current_platform: SRTPlatform | None = None
 
 
+def _is_cuda_available() -> bool:
+    return bool(torch.cuda.is_available() and torch.version.hip is None)
+
+
+def _is_rocm_available() -> bool:
+    return bool(torch.cuda.is_available() and torch.version.hip is not None)
+
+
 def _resolve_platform() -> SRTPlatform:
     """
     Discover and instantiate the active platform.
@@ -39,7 +51,9 @@ def _resolve_platform() -> SRTPlatform:
 
        SGLANG_PLATFORM unset (auto-discover):
          - Import and activate all discovered plugins
-         - 0 activated → fallback base SRTPlatform
+         - 0 activated + CUDA available → fallback CudaSRTPlatform
+         - 0 activated + ROCm available → fallback RocmSRTPlatform
+         - 0 activated + neither → fallback base SRTPlatform
          - 1 activated → use it
          - N activated → RuntimeError (must set SGLANG_PLATFORM)
 
@@ -90,7 +104,17 @@ def _resolve_platform() -> SRTPlatform:
             logger.exception("Failed to activate platform plugin: %s", name)
 
     if len(activated) == 0:
-        logger.debug("No platform detected. Using base SRTPlatform with defaults.")
+        if _is_cuda_available():
+            logger.debug(
+                "No platform plugin detected. Using CUDA SRTPlatform defaults."
+            )
+            return CudaSRTPlatform()
+        if _is_rocm_available():
+            logger.debug(
+                "No platform plugin detected. Using ROCm SRTPlatform defaults."
+            )
+            return RocmSRTPlatform()
+        logger.debug("No platform detected. Using base SRTPlatform.")
         return SRTPlatform()
 
     if len(activated) == 1:

python/sglang/srt/platforms/cuda.py

@@ -0,0 +1,75 @@
+"""CUDA device operations for the SRT platform layer."""
+
+from typing import Optional
+
+import torch
+
+from sglang.srt.platforms.device_mixin import (
+    DeviceCapability,
+    DeviceMixin,
+    PlatformEnum,
+)
+from sglang.srt.platforms.interface import SRTPlatform
+
+
+class CudaDeviceMixin(DeviceMixin):
+    """CUDA implementation of the shared device operations."""
+
+    _enum: PlatformEnum = PlatformEnum.CUDA
+    device_name: str = "cuda"
+    device_type: str = "cuda"
+
+    def get_device_total_memory(self, device_id: int = 0) -> int:
+        return int(torch.cuda.get_device_properties(device_id).total_memory)
+
+    def get_current_memory_usage(
+        self, device: Optional["torch.device"] = None
+    ) -> float:
+        return float(torch.cuda.max_memory_allocated(device))
+
+    def get_device(self, local_rank: int) -> "torch.device":
+        return torch.device("cuda", local_rank)
+
+    def set_device(self, device: "torch.device") -> None:
+        torch.cuda.set_device(device)
+
+    def get_device_name(self, device_id: int = 0) -> str:
+        return str(torch.cuda.get_device_name(device_id))
+
+    def get_device_uuid(self, device_id: int = 0) -> str:
+        return str(torch.cuda.get_device_properties(device_id).uuid)
+
+    def get_device_capability(self, device_id: int = 0) -> DeviceCapability:
+        major, minor = torch.cuda.get_device_capability(device_id)
+        return DeviceCapability(major, minor)
+
+    def empty_cache(self) -> None:
+        torch.cuda.empty_cache()
+
+    def synchronize(self) -> None:
+        torch.cuda.synchronize()
+
+    def get_available_memory(self, device_id: int = 0) -> tuple[int, int]:
+        return torch.cuda.mem_get_info(device_id)
+
+    def get_torch_distributed_backend_str(self) -> str:
+        return "nccl"
+
+    @classmethod
+    def seed_everything(cls, seed: int | None = None) -> None:
+        if seed is not None:
+            super().seed_everything(seed)
+            torch.cuda.manual_seed_all(seed)
+
+
+class CudaSRTPlatform(CudaDeviceMixin, SRTPlatform):
+    """Default in-tree CUDA SRT platform."""
+
+    def supports_fp8(self) -> bool:
+        return True
+
+    def support_cuda_graph(self) -> bool:
+        return True
+
+    def support_piecewise_cuda_graph(self) -> bool:
+        return True

python/sglang/srt/platforms/device_mixin.py

@@ -26,10 +26,11 @@ Method status annotations:
 """
 
 import enum
-from typing import TYPE_CHECKING, NamedTuple, Optional
+import random
+from typing import NamedTuple, Optional
 
-if TYPE_CHECKING:
+import numpy as np
-    import torch
+import torch
 
 
 class PlatformEnum(enum.Enum):
@@ -203,19 +204,12 @@ class DeviceMixin:
     @classmethod
     def inference_mode(cls):
         """[Planned] Return inference mode context manager."""
-        import torch
-
         return torch.inference_mode(mode=True)
 
     @classmethod
     def seed_everything(cls, seed: int | None = None) -> None:
         """[Planned] Set random seeds for reproducibility across all libraries."""
         if seed is not None:
-            import random
-
-            import numpy as np
-            import torch
-
             random.seed(seed)
             np.random.seed(seed)
             torch.manual_seed(seed)

python/sglang/srt/platforms/interface.py

@@ -7,16 +7,11 @@ and adds SRT-specific subsystem factory methods, capability flags, and
 configuration lifecycle hooks.
 
 Out-of-tree platforms register via setuptools entry_points under the
-"sglang.platform_plugins" group and should subclass SRTPlatform.
+"sglang.srt.platforms" group and should subclass SRTPlatform.
 """
 
-from typing import TYPE_CHECKING
-
 from sglang.srt.platforms.device_mixin import DeviceMixin, PlatformEnum
 
-if TYPE_CHECKING:
-    pass
-
 # Re-export for convenience
 __all__ = ["SRTPlatform", "PlatformEnum"]
 

python/sglang/srt/platforms/rocm.py

@@ -0,0 +1,31 @@
+"""ROCm device operations for the SRT platform layer.
+
+PyTorch exposes ROCm through the same ``torch.cuda.*`` API surface as CUDA
+(HIP is a binary shim, and ``torch.device("rocm")`` does not exist). So
+``RocmDeviceMixin`` inherits all device ops from ``CudaDeviceMixin`` and
+only overrides identity (``_enum``, ``device_name``).
+"""
+
+from sglang.srt.platforms.cuda import CudaDeviceMixin
+from sglang.srt.platforms.device_mixin import PlatformEnum
+from sglang.srt.platforms.interface import SRTPlatform
+
+
+class RocmDeviceMixin(CudaDeviceMixin):
+    """ROCm device ops — identical surface to CUDA via torch.cuda's HIP shim."""
+
+    _enum: PlatformEnum = PlatformEnum.ROCM
+    device_name: str = "rocm"
+    # device_type stays "cuda" — torch.device("cuda") is the only valid
+    # device-type string for HIP devices in PyTorch.
+
+
+class RocmSRTPlatform(RocmDeviceMixin, SRTPlatform):
+    """Default in-tree ROCm SRT platform.
+
+    Capability flags (supports_fp8, support_cuda_graph, support_piecewise_cuda_graph)
+    keep the conservative SRTPlatform defaults rather than mirroring CudaSRTPlatform.
+    They are currently only consulted in OOT branches gated on is_out_of_tree(),
+    so the defaults are behaviorally inert for the in-tree ROCm path. A follow-up
+    that migrates AMD-specific gating off legacy is_hip() should set these here.
+    """

test/registered/unit/platforms/test_platform_interface.py

@@ -7,7 +7,10 @@ and the platform discovery / lazy initialization mechanism.
 
 from unittest.mock import MagicMock, patch
 
+import torch
+
 from sglang.srt.platforms import _load_platform_class, _resolve_platform
+from sglang.srt.platforms.cuda import CudaDeviceMixin, CudaSRTPlatform
 from sglang.srt.platforms.device_mixin import (
     CpuArchEnum,
     DeviceCapability,
@@ -222,6 +225,111 @@ class TestSRTPlatform(CustomTestCase):
         base = SRTPlatform()
         self.assertEqual(base.get_compile_backend(mode="npugraph_ex"), "inductor")
 
+    def test_base_device_identity_stays_unspecified(self):
+        """The abstract SRT base should not claim any concrete in-tree device."""
+        base = SRTPlatform()
+        self.assertFalse(base.is_cuda())
+        self.assertFalse(base.is_cuda_alike())
+
+
+class TestCudaDeviceMixin(CustomTestCase):
+    """Tests for CUDA device operation defaults."""
+
+    def test_default_get_device_returns_cuda_device(self):
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_device(2), torch.device("cuda", 2))
+
+    def test_cuda_platform_identity(self):
+        base = CudaSRTPlatform()
+        self.assertTrue(base.is_cuda())
+        self.assertTrue(base.is_cuda_alike())
+        self.assertIsInstance(base, CudaDeviceMixin)
+
+    @patch("torch.cuda.get_device_properties")
+    def test_default_get_device_total_memory_uses_cuda(
+        self, mock_get_device_properties
+    ):
+        mock_get_device_properties.return_value.total_memory = 123
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_device_total_memory(1), 123)
+        mock_get_device_properties.assert_called_once_with(1)
+
+    @patch("torch.cuda.max_memory_allocated", return_value=456)
+    def test_default_get_current_memory_usage_uses_cuda(
+        self, mock_max_memory_allocated
+    ):
+        base = CudaSRTPlatform()
+        device = torch.device("cuda", 1)
+        self.assertEqual(base.get_current_memory_usage(device), 456.0)
+        mock_max_memory_allocated.assert_called_once_with(device)
+
+    @patch("torch.cuda.set_device")
+    def test_default_set_device_uses_cuda(self, mock_set_device):
+        base = CudaSRTPlatform()
+        device = torch.device("cuda", 1)
+        base.set_device(device)
+        mock_set_device.assert_called_once_with(device)
+
+    @patch("torch.cuda.get_device_name", return_value="NVIDIA H100")
+    def test_default_get_device_name_uses_cuda(self, mock_get_device_name):
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_device_name(1), "NVIDIA H100")
+        mock_get_device_name.assert_called_once_with(1)
+
+    @patch("torch.cuda.get_device_properties")
+    def test_default_get_device_uuid_uses_cuda(self, mock_get_device_properties):
+        mock_get_device_properties.return_value.uuid = "1234"
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_device_uuid(1), "1234")
+        mock_get_device_properties.assert_called_once_with(1)
+
+    @patch("torch.cuda.get_device_capability", return_value=(9, 0))
+    def test_default_get_device_capability_uses_cuda(self, mock_get_device_capability):
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_device_capability(1), DeviceCapability(9, 0))
+        mock_get_device_capability.assert_called_once_with(1)
+
+    @patch("torch.cuda.empty_cache")
+    def test_default_empty_cache_uses_cuda(self, mock_empty_cache):
+        base = CudaSRTPlatform()
+        base.empty_cache()
+        mock_empty_cache.assert_called_once_with()
+
+    @patch("torch.cuda.synchronize")
+    def test_default_synchronize_uses_cuda(self, mock_synchronize):
+        base = CudaSRTPlatform()
+        base.synchronize()
+        mock_synchronize.assert_called_once_with()
+
+    @patch("torch.cuda.mem_get_info", return_value=(123, 456), create=True)
+    def test_default_get_available_memory_uses_cuda(self, mock_mem_get_info):
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_available_memory(1), (123, 456))
+        mock_mem_get_info.assert_called_once_with(1)
+
+    def test_default_distributed_backend_is_nccl(self):
+        base = CudaSRTPlatform()
+        self.assertEqual(base.get_torch_distributed_backend_str(), "nccl")
+
+    @patch("torch.cuda.manual_seed_all")
+    @patch("torch.manual_seed")
+    @patch("sglang.srt.platforms.device_mixin.np.random.seed")
+    @patch("sglang.srt.platforms.device_mixin.random.seed")
+    def test_default_seed_everything_seeds_cuda(
+        self, mock_random_seed, mock_np_seed, mock_torch_seed, mock_cuda_seed
+    ):
+        CudaSRTPlatform.seed_everything(123)
+        mock_random_seed.assert_called_once_with(123)
+        mock_np_seed.assert_called_once_with(123)
+        mock_torch_seed.assert_called_once_with(123)
+        mock_cuda_seed.assert_called_once_with(123)
+
+    def test_cuda_srt_platform_capabilities(self):
+        base = CudaSRTPlatform()
+        self.assertTrue(base.supports_fp8())
+        self.assertTrue(base.support_cuda_graph())
+        self.assertTrue(base.support_piecewise_cuda_graph())
+
 
 class TestSRTPlatformOverrides(CustomTestCase):
     """Tests for SRTPlatform method overrides via plugins."""
@@ -320,6 +428,16 @@ class TestResolvePlatformWithEnv(CustomTestCase):
 class TestResolvePlatformAutoDiscover(CustomTestCase):
     """Tests for _resolve_platform auto-discovery when SGLANG_PLATFORM is not set."""
 
+    @patch("sglang.srt.platforms.torch")
+    def test_is_cuda_available_excludes_rocm(self, mock_torch):
+        """ROCm exposes torch.cuda, but should not use the CUDA platform identity."""
+        mock_torch.cuda.is_available.return_value = True
+        mock_torch.version.hip = "6.0"
+
+        import sglang.srt.platforms as plat_mod
+
+        self.assertFalse(plat_mod._is_cuda_available())
+
     @patch("sglang.srt.platforms.load_plugins_by_group")
     @patch("sglang.srt.platforms.envs")
     def test_single_plugin_activates(self, mock_envs, mock_load):
@@ -335,13 +453,48 @@ class TestResolvePlatformAutoDiscover(CustomTestCase):
             self.assertEqual(result, mock_instance)
 
     @patch("sglang.srt.platforms.load_plugins_by_group")
+    @patch("sglang.srt.platforms._is_cuda_available")
     @patch("sglang.srt.platforms.envs")
-    def test_no_plugin_activates_fallback(self, mock_envs, mock_load):
+    def test_no_plugin_activates_cuda_fallback(
-        """When no plugin activates, return base SRTPlatform with warning."""
+        self, mock_envs, mock_is_cuda_available, mock_load
+    ):
+        """When CUDA is available and no plugin activates, return CUDA defaults."""
         mock_envs.SGLANG_PLATFORM.get.return_value = ""
+        mock_is_cuda_available.return_value = True
+        mock_load.return_value = {}
+        result = _resolve_platform()
+        self.assertIsInstance(result, CudaSRTPlatform)
+
+    @patch("sglang.srt.platforms.load_plugins_by_group")
+    @patch("sglang.srt.platforms._is_cuda_available")
+    @patch("sglang.srt.platforms.envs")
+    def test_no_plugin_no_cuda_activates_base_fallback(
+        self, mock_envs, mock_is_cuda_available, mock_load
+    ):
+        """When no plugin or CUDA is available, return the abstract base platform."""
+        mock_envs.SGLANG_PLATFORM.get.return_value = ""
+        mock_is_cuda_available.return_value = False
         mock_load.return_value = {}
         result = _resolve_platform()
         self.assertIsInstance(result, SRTPlatform)
+        self.assertNotIsInstance(result, CudaSRTPlatform)
+
+    @patch("sglang.srt.platforms.load_plugins_by_group")
+    @patch("sglang.srt.platforms.torch")
+    @patch("sglang.srt.platforms.envs")
+    def test_no_plugin_rocm_does_not_activate_cuda_fallback(
+        self, mock_envs, mock_torch, mock_load
+    ):
+        """ROCm exposes torch.cuda but must not use the CUDA fallback platform."""
+        mock_envs.SGLANG_PLATFORM.get.return_value = ""
+        mock_torch.cuda.is_available.return_value = True
+        mock_torch.version.hip = "6.0"
+        mock_load.return_value = {}
+
+        result = _resolve_platform()
+
+        self.assertIsInstance(result, SRTPlatform)
+        self.assertNotIsInstance(result, CudaSRTPlatform)
 
     @patch("sglang.srt.platforms.load_plugins_by_group")
     @patch("sglang.srt.platforms.envs")