update

vllm/v1/worker/workspace.py

@@ -0,0 +1,252 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import inspect
+import os
+from itertools import accumulate
+from math import prod
+
+import torch
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.utils.math_utils import round_up
+from vllm.v1.worker.ubatching import dbo_current_ubatch_id
+
+logger = init_logger(__name__)
+
+
+def _compute_bytes(shape: tuple[int, ...], dtype: torch.dtype) -> int:
+    return prod(shape) * dtype.itemsize
+
+
+# Constants
+_MB = 1024**2
+_GiB = 1024**3
+
+# Global workspace manager instance
+_manager: "WorkspaceManager | None" = None
+
+
+class WorkspaceManager:
+    """Manager for workspace allocation.
+
+    Manages workspace buffers for DBO (Dual Batch Overlap) execution.
+    Can be locked to prevent further growth during execution.
+    """
+
+    def __init__(self, device: torch.device, num_ubatches: int | None = None):
+        self._device = device
+        # Cache num ubatches at init based on configuration (default to 1)
+        self._num_ubatches = num_ubatches if num_ubatches is not None else 1
+        self._current_workspaces: list[torch.Tensor | None] = [None, None]
+        self._locked: bool = False
+
+    @staticmethod
+    def _workspace_size_bytes(workspace: torch.Tensor | None) -> int:
+        """Get size of workspace in bytes."""
+        if workspace is None:
+            return 0
+        return workspace.numel() * workspace.element_size()
+
+    def lock(self) -> None:
+        """Lock the workspace to prevent further growth.
+
+        After locking, any attempt to allocate a larger workspace will raise
+        an assertion error. This ensures workspace size is fixed during execution.
+        """
+        self._locked = True
+        if envs.VLLM_DEBUG_WORKSPACE:
+            logger.info(
+                "[WORKSPACE DEBUG] Workspace locked. Current sizes: %s",
+                [
+                    self._workspace_size_bytes(ws) / _MB
+                    for ws in self._current_workspaces
+                    if ws is not None
+                ],
+            )
+
+    def is_locked(self) -> bool:
+        """Check if workspace is locked."""
+        return self._locked
+
+    def get_simultaneous(
+        self, *shapes_and_dtypes: tuple[tuple[int, ...], torch.dtype]
+    ) -> list[torch.Tensor]:
+        """Get multiple workspace tensors simultaneously from a single allocation.
+
+        Args:
+            *shapes_and_dtypes: One or more (shape, dtype) tuples.
+
+        Returns:
+            List of tensor views into the workspace buffer, one per shape/dtype pair.
+        """
+        actual_bytes = [_compute_bytes(s, d) for s, d in shapes_and_dtypes]
+        aligned_bytes = [round_up(actual, 256) for actual in actual_bytes]
+        total_bytes = sum(aligned_bytes)
+
+        # Calculate cumulative offsets using itertools.accumulate
+        offsets = list(accumulate([0] + aligned_bytes[:-1]))
+
+        current_workspace = self._ensure_workspace_size(total_bytes)
+
+        return [
+            current_workspace[offsets[i] : offsets[i] + actual_bytes[i]]
+            .view(shapes_and_dtypes[i][1])
+            .reshape(shapes_and_dtypes[i][0])
+            for i in range(len(shapes_and_dtypes))
+        ]
+
+    def _ensure_workspace_size(self, required_bytes: int) -> torch.Tensor:
+        """Ensure workspace is allocated and large enough, return current workspace.
+
+        Args:
+            required_bytes: The number of bytes required.
+
+        Returns:
+            The current workspace tensor.
+        """
+        ubatch_id = dbo_current_ubatch_id()
+        current_workspace = self._current_workspaces[ubatch_id]
+        current_size = self._workspace_size_bytes(current_workspace)
+
+        if current_size < required_bytes:
+
+            def get_caller_info() -> str:
+                """Find first frame outside WorkspaceManager."""
+                curr_frame = inspect.currentframe()
+                if curr_frame is None:
+                    return "unknown"
+                # Walk up the stack skipping WorkspaceManager frames
+                curr_frame = curr_frame.f_back
+                while curr_frame is not None:
+                    # TODO: This only catches instance methods (self), missing
+                    # classmethods and staticmethods. Once Python 3.11+ is the
+                    # minimum supported version, use co_qualname instead:
+                    #   qualname = curr_frame.f_code.co_qualname
+                    #   if qualname.startswith("WorkspaceManager."):
+                    if isinstance(curr_frame.f_locals.get("self"), WorkspaceManager):
+                        curr_frame = curr_frame.f_back
+                        continue
+                    filename = os.path.basename(curr_frame.f_code.co_filename)
+                    return (
+                        f"{filename}:{curr_frame.f_lineno}:{curr_frame.f_code.co_name}"
+                    )
+                return "unknown"
+
+            if self._locked:
+                raise AssertionError(
+                    f"Workspace is locked but allocation from '{get_caller_info()}' "
+                    f"requires {required_bytes / _MB:.2f} MB, current size is "
+                    f"{current_size / _MB:.2f} MB. "
+                    "Workspace growth is not allowed after locking."
+                )
+
+            for ubatch_id in range(self._num_ubatches):
+                current_workspace = self._current_workspaces[ubatch_id]
+                if (
+                    current_workspace is None
+                    or self._workspace_size_bytes(current_workspace) < required_bytes
+                ):
+                    # Delete old tensor before allocating new one to avoid
+                    # memory spike from resize_(). resize_() allocates new
+                    # memory before freeing old, which can cause OOM.
+                    # Must clear the list reference first since local var
+                    # is just a copy of the reference.
+                    self._current_workspaces[ubatch_id] = None
+                    del current_workspace
+                    self._current_workspaces[ubatch_id] = torch.empty(
+                        (required_bytes,), dtype=torch.uint8, device=self._device
+                    )
+
+            if envs.VLLM_DEBUG_WORKSPACE:
+                logger.info(
+                    "[WORKSPACE DEBUG] Resized workspace from '%s': %.2f MB -> "
+                    "%.2f MB (%d ubatches, total memory %.2f MB)",
+                    get_caller_info(),
+                    current_size / _MB,
+                    required_bytes / _MB,
+                    self._num_ubatches,
+                    required_bytes * self._num_ubatches / _MB,
+                )
+
+            current_workspace = self._current_workspaces[dbo_current_ubatch_id()]
+
+        return current_workspace
+
+
+def is_workspace_manager_initialized() -> bool:
+    """Check if workspace manager has been initialized.
+
+    Returns:
+        True if workspace manager is initialized, False otherwise.
+    """
+    return _manager is not None
+
+
+def current_workspace_manager() -> "WorkspaceManager":
+    """Get the current workspace manager instance.
+
+    Raises:
+        AssertionError: If workspace manager has not been initialized.
+    """
+    assert _manager is not None, (
+        "WorkspaceManager not initialized. Call init_workspace_manager() "
+        "with a device before using workspace functions."
+    )
+    return _manager
+
+
+def init_workspace_manager(
+    device: torch.device, num_ubatches: int | None = None
+) -> None:
+    """Initialize the workspace manager with a device.
+
+    Must be called before using any workspace functions. Typically called
+    from GPUModelRunner.__init__.
+
+    Args:
+        device: The device to allocate workspace on.
+        num_ubatches: Number of micro-batches. Defaults to 1.
+    """
+    global _manager
+    if _manager is not None:
+        logger.warning(
+            "WorkspaceManager already initialized on device %s, "
+            "reinitializing on device %s",
+            _manager._device,
+            device,
+        )
+    _manager = WorkspaceManager(device, num_ubatches)
+
+
+def lock_workspace() -> None:
+    """Lock the workspace to prevent further growth.
+
+    After calling this function, any attempt to allocate a workspace larger
+    than the current size will raise an AssertionError. This ensures that
+    workspace size is fixed during execution and prevents unexpected memory
+    allocations in the hot path.
+
+    Example:
+        # During initialization
+        init_workspace_manager(device)
+        reserve_workspace(shape1, dtype1)
+        reserve_workspace(shape2, dtype2)
+
+        # Lock after warmup/profiling
+        lock_workspace()
+
+        # Now all get_workspace calls must fit in pre-allocated size
+    """
+    current_workspace_manager().lock()
+
+
+def reset_workspace_manager() -> None:
+    """Reset the workspace manager to uninitialized state.
+
+    This is primarily intended for testing purposes to allow tests
+    to reinitialize the workspace manager cleanly.
+    """
+    global _manager
+    _manager = None