[Feature] Collectors - Weight Sync Scheme Integration

docs/source/reference/llms.rst

@@ -633,7 +633,7 @@ Collectors
 .. _Collectors:
 
 TorchRL offers specialized collector classes (:class:`~torchrl.collectors.llm.LLMCollector` and :class:`~torchrl.collectors.llm.RayLLMCollector`) 
-that are tailored for LLM use cases. We also provide dedicated updaters for some inference engines.
+that are tailored for LLM use cases. We also provide weight synchronization schemes for vLLM inference engines.
 
 See :ref:`ref_collectors` for more details on the collector API. In brief, the idea of a collector is to isolate the inference part of the pipeline
 in a dedicated class. 
@@ -649,8 +649,126 @@ Collectors are defined by the following parameters and features:
   In other cases, the collector can be iterated over to collect data.
 - **Steps**: A collector is built with a certain number of steps budget, as well as a number of steps to be
   included in each batch yield during collection.
-- **Weight Updater**: Weight updaters are the classes that update the policy weights. Isolating the weight update
-  in a dedicated class allows to easily implement different weight update strategies depending on the policy specification.
+- **Weight Synchronization Schemes**: Weight sync schemes handle the synchronization of weights between the training model
+  and the inference engine. The new scheme-based approach provides flexible, high-performance weight updates for vLLM and
+  other inference backends.
+
+vLLM Weight Synchronization Schemes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TorchRL provides two weight synchronization schemes for vLLM engines, offering different trade-offs between
+performance and simplicity:
+
+**1. NCCL-Based Synchronization** (:class:`~torchrl.weight_update.llm.VLLMWeightSyncScheme`)
+
+Uses NCCL collectives for high-bandwidth GPU-to-GPU weight transfers. Best for:
+
+- High-frequency weight updates
+- Large models where transfer speed is critical
+- Setups with GPU interconnect (NVLink, InfiniBand)
+
+**2. Double-Buffer Synchronization** (:class:`~torchrl.weight_update.llm.VLLMDoubleBufferSyncScheme`)
+
+Uses memory-mapped file storage for asynchronous weight transfers. Best for:
+
+- Simpler setup without NCCL coordination
+- Distributed setups with shared filesystems (NFS)
+- Cases where update frequency is lower
+
+**Usage Example with NCCL:**
+
+.. code-block:: python
+
+    from torchrl.collectors.llm import RayLLMCollector
+    from torchrl.weight_update.llm import VLLMWeightSyncScheme
+    from torchrl.modules.llm import AsyncVLLM, vLLMWrapper
+
+    # Create vLLM engine
+    vllm_engine = AsyncVLLM.from_pretrained(
+        "Qwen/Qwen2.5-7B",
+        num_devices=2,
+        num_replicas=2,
+    )
+    policy = vLLMWrapper(vllm_engine, input_mode="history")
+
+    # Create NCCL weight sync scheme
+    weight_sync_scheme = VLLMWeightSyncScheme(
+        master_address="localhost",
+        master_port=29500,
+        gpus_per_replica=2,  # tp_size × dp_size × pp_size
+        num_replicas=2,
+        strategy="state_dict"
+    )
+
+    # Create collector with weight sync scheme
+    collector = RayLLMCollector(
+        env=make_env,
+        policy=policy,
+        dialog_turns_per_batch=256,
+        total_dialog_turns=10000,
+        weight_sync_schemes={"policy": weight_sync_scheme},
+        track_policy_version=True,
+    )
+
+    # During training, get the sender and update weights
+    sender = collector._weight_senders["policy"]
+    sender.register_model(training_model)
+
+    # Initialize collective group (must be called before first update)
+    metadata = get_model_metadata(training_model)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
+
+    # Update weights during training
+    for i, data in enumerate(collector):
+        # ... training step ...
+        if i % 10 == 0:
+            sender.update_weights()  # Broadcasts via NCCL
+
+**Usage Example with Double-Buffer:**
+
+.. code-block:: python
+
+    from torchrl.collectors.llm import RayLLMCollector
+    from torchrl.weight_update.llm import VLLMDoubleBufferSyncScheme
+    from torchrl.modules.llm import AsyncVLLM, vLLMWrapper
+
+    # Create vLLM engine
+    vllm_engine = AsyncVLLM.from_pretrained(
+        "Qwen/Qwen2.5-7B",
+        num_devices=2,
+        num_replicas=1,
+    )
+    policy = vLLMWrapper(vllm_engine, input_mode="history")
+
+    # Create double-buffer weight sync scheme
+    weight_sync_scheme = VLLMDoubleBufferSyncScheme(
+        remote_addr="/tmp/weights",  # Or "/mnt/shared/weights" for NFS
+        num_threads=128,
+        strategy="state_dict"
+    )
+
+    # Create collector with weight sync scheme
+    collector = RayLLMCollector(
+        env=make_env,
+        policy=policy,
+        dialog_turns_per_batch=256,
+        total_dialog_turns=10000,
+        weight_sync_schemes={"policy": weight_sync_scheme},
+        track_policy_version=True,
+    )
+
+    # During training, get the sender and receiver
+    sender = collector._weight_senders["policy"]
+    sender.register_model(training_model)
+
+    # No initialization needed for double-buffer scheme!
+
+    # Update weights during training
+    for i, data in enumerate(collector):
+        # ... training step ...
+        if i % 10 == 0:
+            sender.update_weights()  # Writes to shared storage
+            # vLLM workers can poll and apply: receiver.poll_and_apply()
 
 Policy Version Tracking
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -662,19 +780,52 @@ transform, or a boolean to the collector constructor.
 
     >>> from torchrl.envs.llm.transforms import PolicyVersion
     >>> from torchrl.collectors.llm import LLMCollector
-    >>> from torchrl.collectors.llm.weight_update import vLLMUpdater
+    >>> from torchrl.weight_update.llm import VLLMWeightSyncScheme, get_model_metadata
     >>> env = make_env() # place your code here
     >>> policy = make_policy() # place your code here
-    >>> collector = LLMCollector(env, policy=policy, weight_updater=vLLMUpdater(), track_policy_version=True)
-    >>> # init the updater
-    >>> collector.weight_updater.init(...)
-    >>> # the version is incremented after each weight update
-    >>> collector.update_policy_weights_(state_dict=...)
+    >>> scheme = VLLMWeightSyncScheme(master_port=29500, gpus_per_replica=1, num_replicas=1)
+    >>> collector = LLMCollector(env, policy=policy, weight_sync_schemes={"policy": scheme}, track_policy_version=True)
+    >>> # Get the sender and register model
+    >>> sender = collector._weight_senders["policy"]
+    >>> sender.register_model(training_model)
+    >>> # Initialize the collective group
+    >>> metadata = get_model_metadata(training_model)
+    >>> sender.init_all_workers_group(metadata, vllm_engine=policy.model)
+    >>> # Update weights
+    >>> sender.update_weights()
     >>> print(collector.policy_version_tracker.version)
     >>> # the policy version is written in the data
     >>> for data in collector:
     ...     print(data["policy_version"])
 
+.. currentmodule:: torchrl.weight_update.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    VLLMWeightSyncScheme
+    VLLMWeightSender
+    VLLMWeightReceiver
+    VLLMCollectiveTransport
+    VLLMDoubleBufferSyncScheme
+    VLLMDoubleBufferWeightSender
+    VLLMDoubleBufferWeightReceiver
+    VLLMDoubleBufferTransport
+    get_model_metadata
+
+Legacy Weight Updaters (Deprecated)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. deprecated:: 0.11
+    The `vLLMUpdater` and `vLLMUpdaterV2` classes are deprecated in favor of the new weight synchronization schemes
+    (:class:`~torchrl.weight_update.llm.VLLMWeightSyncScheme` and :class:`~torchrl.weight_update.llm.VLLMDoubleBufferSyncScheme`).
+    These schemes provide better performance, more flexibility, and cleaner integration with collectors.
+    The legacy updaters will be removed in a future release.
+
+    The legacy weight updaters (`vLLMUpdater` and `vLLMUpdaterV2`) are still available but are no longer recommended.
+    Please migrate to the new weight synchronization schemes shown above.
+
 .. currentmodule:: torchrl.collectors.llm
 
 .. autosummary::

examples/collectors/multi_weight_updates.py

@@ -0,0 +1,115 @@
+"""Example of updating weights of several models at once in a multiprocessed data collector.
+
+This example demonstrates:
+1. Using different weight sync schemes for different models
+2. Updating the policy (via pipes with MultiProcessWeightSyncScheme)
+3. Updating Ray-based transforms in env and replay buffer (via RayModuleTransformScheme)
+4. Atomic multi-model weight updates using weights_dict
+
+Note:
+- Ray actors are shared across all workers, so RayModuleTransformScheme uses a
+  single transport rather than per-worker pipes.
+- When using transform_factory with a replay buffer, delayed_init automatically defaults
+  to True for proper serialization in multiprocessing contexts.
+- extend_buffer defaults to True in all collectors, extending the buffer with entire
+  rollouts rather than individual frames for better compatibility with postprocessing.
+"""
+
+from functools import partial
+
+import torch.nn as nn
+from tensordict import TensorDict
+from tensordict.nn import TensorDictModule
+
+from torchrl.collectors import MultiSyncDataCollector
+from torchrl.data import LazyTensorStorage, ReplayBuffer
+from torchrl.envs.libs.gym import GymEnv
+from torchrl.envs.transforms.module import ModuleTransform
+from torchrl.weight_update.weight_sync_schemes import MultiProcessWeightSyncScheme
+
+
+def make_module():
+    # A module that transforms the observations
+    return TensorDictModule(
+        nn.Linear(3, 3), in_keys=["observation"], out_keys=["observation"]
+    )
+
+
+def policy_factory():
+    # A module that produces the actions
+    return TensorDictModule(
+        nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"]
+    )
+
+
+def make_env():
+    env_module = ModuleTransform(
+        module_factory=make_module, inverse=False, no_grad=True
+    )
+    return GymEnv("Pendulum-v1").append_transform(env_module)
+
+
+def main():
+    rb = ReplayBuffer(
+        storage=LazyTensorStorage(10000, shared_init=True),
+        transform_factory=partial(
+            ModuleTransform,
+            module_factory=make_module,
+            inverse=True,
+            no_grad=True,
+        ),
+        # delayed_init automatically defaults to True when transform_factory is provided
+    )
+
+    policy = policy_factory()
+
+    weight_sync_schemes = {
+        "policy": MultiProcessWeightSyncScheme(strategy="state_dict"),
+        "replay_buffer.transform[0].module": MultiProcessWeightSyncScheme(
+            strategy="tensordict"
+        ),
+        "env.transform[0].module": MultiProcessWeightSyncScheme(strategy="tensordict"),
+    }
+
+    collector = MultiSyncDataCollector(
+        create_env_fn=[make_env, make_env],
+        policy_factory=policy_factory,
+        total_frames=2000,
+        max_frames_per_traj=50,
+        frames_per_batch=200,
+        init_random_frames=-1,
+        device="cpu",
+        storing_device="cpu",
+        weight_sync_schemes=weight_sync_schemes,
+        replay_buffer=rb,
+        local_init_rb=True,
+        # extend_buffer=True is the default for MultiSyncDataCollector
+    )
+
+    policy_weights = TensorDict.from_module(policy).data
+    env_module_weights = TensorDict.from_module(make_module()).data
+    rb_module_weights = TensorDict.from_module(make_module()).data
+
+    for i, _data in enumerate(collector):
+        env_module_weights.zero_()
+        rb_module_weights.zero_()
+        policy_weights.zero_()
+
+        collector.update_policy_weights_(
+            weights_dict={
+                "policy": policy_weights,
+                "env.transform[0].module": env_module_weights,
+                "replay_buffer.transform[0].module": rb_module_weights,
+            }
+        )
+
+        assert len(rb) == i * 200 + 200
+
+        if i >= 10:
+            break
+
+    collector.shutdown()
+
+
+if __name__ == "__main__":
+    main()

sota-implementations/expert-iteration/ei_utils.py

@@ -15,10 +15,10 @@
 from torch import device as torch_device, dtype as torch_dtype
 
 from torchrl._utils import logger as torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm import vLLMUpdater
 from torchrl.envs.llm import RetrieveLogProb
 from torchrl.envs.llm.datasets.ifeval import IFEvalEnv
 from torchrl.modules.llm import TransformersWrapper, vLLMWrapper
+from torchrl.weight_update.llm import VLLMWeightSyncScheme
 from transformers.models.auto.modeling_auto import AutoModelForCausalLM
 from transformers.tokenization_utils import PreTrainedTokenizer
 
@@ -479,42 +479,40 @@ def get_hf_model(
         torch.set_default_dtype(original_dtype)
 
 
-def make_weight_updater(
-    policy_training=None,
+def make_weight_sync_scheme(
     master_address=None,
     master_port=None,
-    model_metadata=None,
-    vllm_tp_size=None,
-) -> vLLMUpdater:
-    """Creates a vLLM weight updater for the policy.
+    vllm_tp_size=1,
+) -> VLLMWeightSyncScheme:
+    """Creates a vLLM weight synchronization scheme using NCCL collectives.
 
-    This function can be used in two ways:
-    1. Synchronous mode (expert-iteration-sync.py): Pass policy_training to get an initialized updater with metadata
-    2. Async mode (expert-iteration-async.py): Pass master_address, master_port, model_metadata, and remote_actor
+    This function creates a weight sync scheme that uses NCCL for high-performance
+    GPU-to-GPU weight transfers from the training model to vLLM inference workers.
 
     Args:
-        policy_training (Optional[TransformersWrapper]): The training policy model. Required for sync mode.
-        master_address (Optional[str]): Ray master address for async mode.
-        master_port (Optional[int]): Ray master port for async mode.
-        model_metadata (Optional[dict]): Model metadata for async mode. If not provided but policy_training is,
-            it will be extracted from the policy.
-        vllm_tp_size (Optional[int]): vLLM tensor parallel size. If not provided, will be set to 1.
+        master_address (Optional[str]): Address of the master node for distributed init.
+            Defaults to "localhost".
+        master_port (Optional[int]): Port of the master node for distributed init.
+            If None, will auto-assign.
+        vllm_tp_size (int): vLLM tensor parallel size (gpus_per_replica). Defaults to 1.
 
     Returns:
-        vLLMUpdater: An instance of the weight updater configured to update
-            the vLLM worker's weights.
+        VLLMWeightSyncScheme: A weight sync scheme configured for the vLLM engine.
     """
-    if model_metadata is None and policy_training is not None:
-        # Extract metadata from training policy
-        model_metadata = {
-            k: (v.dtype, v.shape) for k, v in policy_training.model.state_dict().items()
-        }
+    if master_address is None:
+        master_address = "localhost"
+
+    torchrl_logger.info(
+        f"Creating VLLMWeightSyncScheme with tp_size={vllm_tp_size}, "
+        f"master_address={master_address}, master_port={master_port}"
+    )
 
-    return vLLMUpdater(
+    return VLLMWeightSyncScheme(
         master_address=master_address,
         master_port=master_port,
-        model_metadata=model_metadata,
-        vllm_tp_size=vllm_tp_size,
+        gpus_per_replica=vllm_tp_size,
+        num_replicas=1,  # For expert iteration, typically 1 replica
+        strategy="state_dict",
     )