Enable RDMA by default

src/forge/actors/_torchstore_utils.py

@@ -10,6 +10,7 @@
 import torch
 import torch.distributed.checkpoint as dcp
 from torch.distributed.checkpoint.metadata import Metadata as DcpMeta
+from torchstore.transport.buffers import rdma_available
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -69,3 +70,8 @@ def extract_param_name(key: str) -> str:
 
 def get_dcp_whole_state_dict_key(policy_version: int) -> str:
     return f"{get_param_prefix(policy_version)}{KEY_DELIM}{DCP_WHOLE_STATE_TAG}"
+
+
+def rdma_enabled() -> bool:
+    """Return if TorchStore thinks we're using RDMA"""
+    return rdma_available()

src/forge/actors/generator.py

@@ -46,6 +46,7 @@
     get_param_key,
     get_param_prefix,
     load_tensor_from_dcp,
+    rdma_available,
 )
 
 from forge.controller import (
@@ -56,7 +57,6 @@
 )
 from forge.data_models.completion import Completion
 from forge.data_models.prompt import to_prompt
-from forge.env import TORCHSTORE_USE_RDMA
 from forge.observability.metrics import record_metric, Reduce
 from forge.observability.perf_tracker import Tracer
 from forge.types import ProcessConfig
@@ -112,7 +112,7 @@ def __post_init__(self):
             self.sampling_params.output_kind = RequestOutputKind.FINAL_ONLY
 
         if self.use_dcp_for_weight_sync is None:
-            self.use_dcp_for_weight_sync = not TORCHSTORE_USE_RDMA.get_value()
+            self.use_dcp_for_weight_sync = not rdma_available()
         logger.debug(f"{self.use_dcp_for_weight_sync=}")
 
     @endpoint
@@ -492,14 +492,16 @@ async def shutdown(  # pyright: ignore[reportIncompatibleMethodOverride]
         await stop_proc_mesh(actor._generator_proc)
 
     @endpoint
-    async def save_model_params(self):
-        """Used for debugging purpose. Save model parameters before weight update."""
-        await self.worker.save_model_params.call()
+    async def _test_save_model_params(self):
+        """Save model parameters before weight update, used for tesing purposes only."""
+        logger.info("[Generator] save model parameters for testing.")
+        await self.worker._test_save_model_params.call()
 
     @endpoint
-    async def validate_model_params(self, validate_fn):
-        """Used for debugging purpose. Validate saved params using validate_fn."""
-        return await self.worker.validate_model_params.call(validate_fn)
+    async def _test_validate_model_params(self, validate_fn):
+        """Validate updated model params using validate_fn."""
+        logger.info("[Generator] start validating model parameters.")
+        return await self.worker._test_validate_model_params.call(validate_fn)
 
 
 @dataclass
@@ -512,6 +514,8 @@ class GeneratorWorker(ForgeActor):
     """
 
     vllm_config: VllmConfig
+    # TODO: Remove below param
+    _test_prev_params = {}
 
     @endpoint
     async def setup(self):
@@ -601,19 +605,20 @@ async def update_weights(self, version: int) -> None:
         t.stop()
 
     @endpoint
-    async def save_model_params(self):
-        """Used for debugging purposes. Save model parameters before weight update."""
-        self._debug_saved_params = {}
+    async def _test_save_model_params(self):
+        """Save model parameters before weight update, used for tesing purposes only."""
+        logger.info("[GeneratorWorker] save model parameters for testing.")
         for name, param in self.worker.model_runner.model.named_parameters():
-            self._debug_saved_params[name] = param.detach().cpu()
+            self._test_prev_params[name] = param.detach().cpu()
         logger.info(
             "[GeneratorWorker] finished saving model parameters, len = %d",
-            len(self._debug_saved_params),
+            len(self._test_prev_params),
         )
 
     @endpoint
-    async def validate_model_params(self, validate_fn):
-        """Used for debugging purposes. Validate saved params using validate_fn."""
+    async def _test_validate_model_params(self, validate_fn):
+        """Validate updated model params using validate_fn."""
+        logger.info("[GeneratorWorker] start validating model parameters.")
         return validate_fn(
-            self._debug_saved_params, self.worker.model_runner.model, logger
+            self._test_prev_params, self.worker.model_runner.model, logger
         )

src/forge/actors/trainer.py

@@ -41,11 +41,11 @@
     DcpHandle,
     get_dcp_whole_state_dict_key,
     get_param_key,
+    rdma_available,
 )
 
 from forge.controller import ForgeActor
 from forge.data.utils import batch_to_device
-from forge.env import TORCHSTORE_USE_RDMA
 from forge.observability.metrics import record_metric, Reduce
 from forge.observability.perf_tracker import Tracer
 
@@ -131,9 +131,7 @@ class RLTrainer(ForgeActor):
     # Non JobConfig-related fields
     loss: Callable = lambda logits, **targets: logits
     state_dict_key: str = "model_state_dict"
-    use_dcp: bool = (
-        TORCHSTORE_USE_RDMA.get_value() == 0
-    )  # torchstore currently only accepts 0 or 1
+    use_dcp: bool = not rdma_available()
     dcp_path: str = "forge_dcp_tmp"
 
     def __post_init__(self):

src/forge/env.py

@@ -101,7 +101,7 @@ def get_value(self) -> Any:
 
 TORCHSTORE_USE_RDMA = EnvVar(
     name="TORCHSTORE_RDMA_ENABLED",
-    default=0,
+    default=1,
     description="Whether or not to use RDMA in TorchStore.",
 )