[Performance] Improve performance of compiled ReplayBuffer (#2529)

(cherry picked from commit 2a07f4c)

Author: kurtamohler

benchmarks/test_replaybuffer_benchmark.py

@@ -172,6 +172,82 @@ def test_rb_populate(benchmark, rb, storage, sampler, size):
     )
 
 
+class create_compiled_tensor_rb:
+    def __init__(
+        self, rb, storage, sampler, storage_size, data_size, iters, compilable=False
+    ):
+        self.storage = storage
+        self.rb = rb
+        self.sampler = sampler
+        self.storage_size = storage_size
+        self.data_size = data_size
+        self.iters = iters
+        self.compilable = compilable
+
+    def __call__(self):
+        kwargs = {}
+        if self.sampler is not None:
+            kwargs["sampler"] = self.sampler()
+        if self.storage is not None:
+            kwargs["storage"] = self.storage(
+                self.storage_size, compilable=self.compilable
+            )
+
+        rb = self.rb(batch_size=3, compilable=self.compilable, **kwargs)
+        data = torch.randn(self.data_size, 1)
+        return ((rb, data, self.iters), {})
+
+
+def extend_and_sample(rb, td, iters):
+    for _ in range(iters):
+        rb.extend(td)
+        rb.sample()
+
+
+def extend_and_sample_compiled(rb, td, iters):
+    @torch.compile
+    def fn(td):
+        rb.extend(td)
+        rb.sample()
+
+    for _ in range(iters):
+        fn(td)
+
+
+@pytest.mark.parametrize(
+    "rb,storage,sampler,storage_size,data_size,iters,compiled",
+    [
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 10_000, 10_000, 100, True],
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 10_000, 10_000, 100, False],
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 100_000, 10_000, 100, True],
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 100_000, 10_000, 100, False],
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 1_000_000, 10_000, 100, True],
+        [ReplayBuffer, LazyTensorStorage, RandomSampler, 1_000_000, 10_000, 100, False],
+    ],
+)
+def test_rb_extend_sample(
+    benchmark, rb, storage, sampler, storage_size, data_size, iters, compiled
+):
+    if compiled:
+        torch._dynamo.reset_code_caches()
+
+    benchmark.pedantic(
+        extend_and_sample_compiled if compiled else extend_and_sample,
+        setup=create_compiled_tensor_rb(
+            rb=rb,
+            storage=storage,
+            sampler=sampler,
+            storage_size=storage_size,
+            data_size=data_size,
+            iters=iters,
+            compilable=compiled,
+        ),
+        iterations=1,
+        warmup_rounds=10,
+        rounds=50,
+    )
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)

test/test_rb.py

@@ -171,18 +171,24 @@
 )
 @pytest.mark.parametrize("size", [3, 5, 100])
 class TestComposableBuffers:
-    def _get_rb(self, rb_type, size, sampler, writer, storage):
+    def _get_rb(self, rb_type, size, sampler, writer, storage, compilable=False):
 
         if storage is not None:
-            storage = storage(size)
+            storage = storage(size, compilable=compilable)
 
         sampler_args = {}
         if sampler is samplers.PrioritizedSampler:
             sampler_args = {"max_capacity": size, "alpha": 0.8, "beta": 0.9}
 
         sampler = sampler(**sampler_args)
-        writer = writer()
-        rb = rb_type(storage=storage, sampler=sampler, writer=writer, batch_size=3)
+        writer = writer(compilable=compilable)
+        rb = rb_type(
+            storage=storage,
+            sampler=sampler,
+            writer=writer,
+            batch_size=3,
+            compilable=compilable,
+        )
         return rb
 
     def _get_datum(self, datatype):
@@ -407,6 +413,84 @@ def data_iter():
         ) if cond else contextlib.nullcontext():
             rb.extend(data2)
 
+    @pytest.mark.skipif(
+        TORCH_VERSION < version.parse("2.5.0"), reason="requires Torch >= 2.5.0"
+    )
+    # Compiling on Windows requires "cl" compiler to be installed.
+    # <https://github.com/pytorch/pytorch/blob/8231180147a096a703d8891756068c89365292e0/torch/_inductor/cpp_builder.py#L143>
+    # Our Windows CI jobs do not have "cl", so skip this test.
+    @pytest.mark.skipif(_os_is_windows, reason="windows tests do not support compile")
+    @pytest.mark.parametrize("avoid_max_size", [False, True])
+    def test_extend_sample_recompile(
+        self, rb_type, sampler, writer, storage, size, datatype, avoid_max_size
+    ):
+        if rb_type is not ReplayBuffer:
+            pytest.skip(
+                "Only replay buffer of type 'ReplayBuffer' is currently supported."
+            )
+        if sampler is not RandomSampler:
+            pytest.skip("Only sampler of type 'RandomSampler' is currently supported.")
+        if storage is not LazyTensorStorage:
+            pytest.skip(
+                "Only storage of type 'LazyTensorStorage' is currently supported."
+            )
+        if writer is not RoundRobinWriter:
+            pytest.skip(
+                "Only writer of type 'RoundRobinWriter' is currently supported."
+            )
+        if datatype == "tensordict":
+            pytest.skip("'tensordict' datatype is not currently supported.")
+
+        torch._dynamo.reset_code_caches()
+
+        # Number of times to extend the replay buffer
+        num_extend = 10
+        data_size = size
+
+        # These two cases are separated because when the max storage size is
+        # reached, the code execution path changes, causing necessary
+        # recompiles.
+        if avoid_max_size:
+            storage_size = (num_extend + 1) * data_size
+        else:
+            storage_size = 2 * data_size
+
+        rb = self._get_rb(
+            rb_type=rb_type,
+            sampler=sampler,
+            writer=writer,
+            storage=storage,
+            size=storage_size,
+            compilable=True,
+        )
+        data = self._get_data(datatype, size=data_size)
+
+        @torch.compile
+        def extend_and_sample(data):
+            rb.extend(data)
+            return rb.sample()
+
+        # NOTE: The first three calls to 'extend' and 'sample' can currently
+        # cause recompilations, so avoid capturing those.
+        num_extend_before_capture = 3
+
+        for _ in range(num_extend_before_capture):
+            extend_and_sample(data)
+
+        try:
+            torch._logging.set_logs(recompiles=True)
+            records = []
+            capture_log_records(records, "torch._dynamo", "recompiles")
+
+            for _ in range(num_extend - num_extend_before_capture):
+                extend_and_sample(data)
+
+        finally:
+            torch._logging.set_logs()
+
+        assert len(rb) == min((num_extend * data_size), storage_size)
+        assert len(records) == 0
+
     def test_sample(self, rb_type, sampler, writer, storage, size, datatype):
         if rb_type is RemoteTensorDictReplayBuffer and _os_is_windows:
             pytest.skip(
@@ -730,6 +814,52 @@ def test_storage_state_dict(self, storage_in, storage_out, init_out, backend):
         s = new_replay_buffer.sample()
         assert (s.exclude("index") == 1).all()
 
+    @pytest.mark.skipif(
+        TORCH_VERSION < version.parse("2.5.0"), reason="requires Torch >= 2.5.0"
+    )
+    @pytest.mark.skipif(_os_is_windows, reason="windows tests do not support compile")
+    # This test checks if the `torch._dynamo.disable` wrapper around
+    # `TensorStorage._rand_given_ndim` is still necessary.
+    def test__rand_given_ndim_recompile(self):
+        torch._dynamo.reset_code_caches()
+
+        # Number of times to extend the replay buffer
+        num_extend = 10
+        data_size = 100
+        storage_size = (num_extend + 1) * data_size
+        sample_size = 3
+
+        storage = LazyTensorStorage(storage_size, compilable=True)
+        sampler = RandomSampler()
+
+        # Override to avoid the `torch._dynamo.disable` wrapper
+        storage._rand_given_ndim = storage._rand_given_ndim_impl
+
+        @torch.compile
+        def extend_and_sample(data):
+            storage.set(torch.arange(data_size) + len(storage), data)
+            return sampler.sample(storage, sample_size)
+
+        data = torch.randint(100, (data_size, 1))
+
+        try:
+            torch._logging.set_logs(recompiles=True)
+            records = []
+            capture_log_records(records, "torch._dynamo", "recompiles")
+
+            for _ in range(num_extend):
+                extend_and_sample(data)
+
+        finally:
+            torch._logging.set_logs()
+
+        assert len(storage) == num_extend * data_size
+        assert len(records) == 8, (
+            "If this ever decreases, that's probably good news and the "
+            "`torch._dynamo.disable` wrapper around "
+            "`TensorStorage._rand_given_ndim` can be removed."
+        )
+
     @pytest.mark.parametrize("storage_type", [LazyMemmapStorage, LazyTensorStorage])
     def test_extend_lazystack(self, storage_type):
 

torchrl/_utils.py

@@ -252,6 +252,11 @@ class implement_for:
     Keyword Args:
         class_method (bool, optional): if ``True``, the function will be written as a class method.
             Defaults to ``False``.
+        compilable (bool, optional): If ``False``, the module import happens
+            only on the first call to the wrapped function. If ``True``, the
+            module import happens when the wrapped function is initialized. This
+            allows the wrapped function to work well with ``torch.compile``.
+            Defaults to ``False``.
 
     Examples:
         >>> @implement_for("gym", "0.13", "0.14")
@@ -290,11 +295,13 @@ def __init__(
         to_version: str = None,
         *,
         class_method: bool = False,
+        compilable: bool = False,
     ):
         self.module_name = module_name
         self.from_version = from_version
         self.to_version = to_version
         self.class_method = class_method
+        self._compilable = compilable
         implement_for._setters.append(self)
 
     @staticmethod
@@ -386,18 +393,27 @@ def __call__(self, fn):
         self.fn = fn
         implement_for._lazy_impl[self.func_name].append(self._call)
 
-        @wraps(fn)
-        def _lazy_call_fn(*args, **kwargs):
-            # first time we call the function, we also do the replacement.
-            # This will cause the imports to occur only during the first call to fn
+        if self._compilable:
+            _call_fn = self._delazify(self.func_name)
 
-            result = self._delazify(self.func_name)(*args, **kwargs)
-            return result
+            if self.class_method:
+                return classmethod(_call_fn)
 
-        if self.class_method:
-            return classmethod(_lazy_call_fn)
+            return _call_fn
+        else:
+
+            @wraps(fn)
+            def _lazy_call_fn(*args, **kwargs):
+                # first time we call the function, we also do the replacement.
+                # This will cause the imports to occur only during the first call to fn
+
+                result = self._delazify(self.func_name)(*args, **kwargs)
+                return result
+
+            if self.class_method:
+                return classmethod(_lazy_call_fn)
 
-        return _lazy_call_fn
+            return _lazy_call_fn
 
     def _call(self):
 

torchrl/data/replay_buffers/replay_buffers.py

@@ -19,6 +19,11 @@
 
 import torch
 
+try:
+    from torch.compiler import is_dynamo_compiling
+except ImportError:
+    from torch._dynamo import is_compiling as is_dynamo_compiling
+
 from tensordict import (
     is_tensor_collection,
     is_tensorclass,
@@ -132,6 +137,9 @@ class ReplayBuffer:
             .. warning:: As of now, the generator has no effect on the transforms.
         shared (bool, optional): whether the buffer will be shared using multiprocessing or not.
             Defaults to ``False``.
+        compilable (bool, optional): whether the writer is compilable.
+            If ``True``, the writer cannot be shared between multiple processes.
+            Defaults to ``False``.
 
     Examples:
         >>> import torch
@@ -217,11 +225,20 @@ def __init__(
         checkpointer: "StorageCheckpointerBase" | None = None,  # noqa: F821
         generator: torch.Generator | None = None,
         shared: bool = False,
+        compilable: bool = None,
     ) -> None:
-        self._storage = storage if storage is not None else ListStorage(max_size=1_000)
+        self._storage = (
+            storage
+            if storage is not None
+            else ListStorage(max_size=1_000, compilable=compilable)
+        )
         self._storage.attach(self)
         self._sampler = sampler if sampler is not None else RandomSampler()
-        self._writer = writer if writer is not None else RoundRobinWriter()
+        self._writer = (
+            writer
+            if writer is not None
+            else RoundRobinWriter(compilable=bool(compilable))
+        )
         self._writer.register_storage(self._storage)
 
         self._get_collate_fn(collate_fn)
@@ -600,7 +617,9 @@ def _add(self, data):
         return index
 
     def _extend(self, data: Sequence) -> torch.Tensor:
-        with self._replay_lock, self._write_lock:
+        is_compiling = is_dynamo_compiling()
+        nc = contextlib.nullcontext()
+        with self._replay_lock if not is_compiling else nc, self._write_lock if not is_compiling else nc:
             if self.dim_extend > 0:
                 data = self._transpose(data)
             index = self._writer.extend(data)
@@ -653,7 +672,7 @@ def update_priority(
 
     @pin_memory_output
     def _sample(self, batch_size: int) -> Tuple[Any, dict]:
-        with self._replay_lock:
+        with self._replay_lock if not is_dynamo_compiling() else contextlib.nullcontext():
             index, info = self._sampler.sample(self._storage, batch_size)
             info["index"] = index
             data = self._storage.get(index)