Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/vllm/v1/kv_offload/worker/cpu_gpu.py
+++ b/vllm/v1/kv_offload/worker/cpu_gpu.py
@@ -0,0 +1,280 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections import deque
+
+import numpy as np
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.logger import init_logger
+from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.v1.kv_offload.mediums import BlockIDsLoadStoreSpec
+from vllm.v1.kv_offload.worker.worker import (
+    OffloadingHandler,
+    TransferResult,
+    TransferSpec,
+)
+
+logger = init_logger(__name__)
+
+
+def expand_block_ids(
+    block_ids: np.ndarray,
+    block_size_factor: int,
+    output: np.ndarray,
+    skip_count: int = 0,
+):
+    """
+    Convert a list of block IDs to a list of matching block ids,
+    assuming each block is composed of actual block_size_factor blocks.
+    Outputs to output tensor.
+    The first skip_count blocks will be skipped.
+    Note that skip_count must be less than block_size_factor.
+
+    For example, if block_ids = [0, 1, 3] and block_size_factor =  4,
+    then it yields [0, 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15]
+    since 0 maps to [0, 1, 2, 3]
+    1 maps to [4, 5, 6, 7]
+    and 3 maps to [12, 13, 14, 15]
+    """
+    assert skip_count < block_size_factor
+
+    first_range = np.arange(skip_count, block_size_factor)
+    full_range = np.arange(0, block_size_factor)
+
+    output_idx = 0
+    for i, block_id in enumerate(block_ids):
+        base_block_id = block_id * block_size_factor
+        indices = first_range if i == 0 else full_range
+        output_end_idx = output_idx + len(indices)
+        output[output_idx:output_end_idx] = base_block_id + indices
+        output_idx = output_end_idx
+
+
+class SingleDirectionOffloadingHandler(OffloadingHandler):
+    """
+    SingleDirectionOffloadingHandler handles transfers for a single direction,
+    either CPU->GPU or GPU->CPU.
+    Transfers are guaranteed to be executed in order of their submission.
+    Each transfer uses a unique CUDA stream, and its stream will start
+    executing only after the streams of previous transfers have finished.
+    """
+
+    def __init__(
+        self,
+        src_tensors: list[torch.Tensor],
+        dst_tensors: list[torch.Tensor],
+        kv_dim_before_num_blocks: list[bool],
+        src_block_size_factor: int,
+        dst_block_size_factor: int,
+        priority: int,
+    ):
+        """
+        Initialize a SingleDirectionOffloadingHandler.
+
+        Args:
+            src_tensors: list of KV cache tensors to copy from.
+            dst_tensors: list of KV cache tensors to copy to.
+                Order should match src_tensors.
+            kv_dim_before_num_blocks: list of bools, indicating
+                whether the respective KV cache tensor has a KV
+                dimension before its num_blocks dimension.
+                e.g. (2, num_blocks, ...)
+            src_block_size_factor: The number of kernel blocks
+                per KV block in a source tensor.
+            dst_block_size_factor: The number of kernel blocks
+                per KV block in a destination tensor.
+            priority: The priority of the backing CUDA streams.
+                Lower numbers indicate higher priority.
+        """
+        assert len(src_tensors) == len(dst_tensors) == len(kv_dim_before_num_blocks)
+
+        self.src_tensors: list[torch.Tensor] = src_tensors
+        self.dst_tensors: list[torch.Tensor] = dst_tensors
+        self.kv_dim_before_num_blocks: list[bool] = kv_dim_before_num_blocks
+        self.src_block_size_factor: int = src_block_size_factor
+        self.dst_block_size_factor: int = dst_block_size_factor
+        self.priority = priority
+
+        # queue of transfers (job_id, stream, event)
+        self._transfers: deque[tuple[int, torch.cuda.Stream, torch.Event]] = deque()
+        # list of CUDA streams available for re-use
+        self._stream_pool: list[torch.cuda.Stream] = []
+        # list of CUDA events available for re-use
+        self._event_pool: list[torch.Event] = []
+
+    def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
+        src_spec, dst_spec = transfer_spec
+        assert isinstance(src_spec, BlockIDsLoadStoreSpec)
+        assert isinstance(dst_spec, BlockIDsLoadStoreSpec)
+
+        src_blocks = src_spec.block_ids
+        dst_blocks = dst_spec.block_ids
+        assert src_blocks.ndim == 1
+        assert dst_blocks.ndim == 1
+
+        src_sub_block_count = src_blocks.size * self.src_block_size_factor
+        dst_sub_block_count = dst_blocks.size * self.dst_block_size_factor
+        src_sub_blocks_to_skip = -dst_blocks.size % self.src_block_size_factor
+
+        assert dst_sub_block_count == src_sub_block_count - src_sub_blocks_to_skip
+
+        src_to_dst = np.empty((dst_sub_block_count, 2), dtype=np.int64)
+        expand_block_ids(
+            src_blocks,
+            self.src_block_size_factor,
+            src_to_dst[:, 0],
+            skip_count=src_sub_blocks_to_skip,
+        )
+        expand_block_ids(dst_blocks, self.dst_block_size_factor, src_to_dst[:, 1])
+        src_to_dst_tensor = torch.from_numpy(src_to_dst)
+
+        stream = (
+            self._stream_pool.pop()
+            if self._stream_pool
+            else torch.cuda.Stream(priority=self.priority)
+        )
+        event = self._event_pool.pop() if self._event_pool else torch.Event()
+        if self._transfers:
+            _, _, last_event = self._transfers[-1]
+            # assure job will start only after the previous one completes
+            stream.wait_event(last_event)
+        with torch.cuda.stream(stream):
+            for src_tensor, dst_tensor, kv_dim in zip(
+                self.src_tensors, self.dst_tensors, self.kv_dim_before_num_blocks
+            ):
+                if kv_dim:
+                    src_key_cache, src_value_cache = src_tensor
+                    dst_key_cache, dst_value_cache = dst_tensor
+                    ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst_tensor)
+                    ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst_tensor)
+                else:
+                    ops.swap_blocks(src_tensor, dst_tensor, src_to_dst_tensor)
+            event.record(stream)
+
+        self._transfers.append((job_id, stream, event))
+
+        # success
+        return True
+
+    def get_finished(self) -> list[TransferResult]:
+        results: list[TransferResult] = []
+        while self._transfers and self._transfers[0][2].query():
+            job_id, stream, event = self._transfers.popleft()
+            results.append((job_id, True))
+            self._stream_pool.append(stream)
+            self._event_pool.append(event)
+        return results
+
+
+class CpuGpuOffloadingHandlers:
+    def __init__(
+        self,
+        gpu_block_size: int,
+        cpu_block_size: int,
+        num_cpu_blocks: int,
+        gpu_caches: dict[str, torch.Tensor],
+        attn_backends: dict[str, type[AttentionBackend]],
+    ):
+        assert gpu_caches
+        assert cpu_block_size % gpu_block_size == 0
+        block_size_factor = cpu_block_size // gpu_block_size
+
+        pin_memory = is_pin_memory_available()
+
+        # allocate cpu tensors
+        logger.info("Allocating %d CPU tensors...", len(gpu_caches))
+        gpu_tensors: list[torch.Tensor] = []
+        cpu_tensors: list[torch.Tensor] = []
+        kv_dim_before_num_blocks: list[bool] = []
+        kernel_block_size: int | None = None
+        for layer_name, gpu_tensor in gpu_caches.items():
+            gpu_tensors.append(gpu_tensor)
+
+            gpu_shape = gpu_tensor.shape
+            attn_backend = attn_backends[layer_name]
+            test_shape = attn_backend.get_kv_cache_shape(
+                num_blocks=1234, block_size=16, num_kv_heads=8, head_size=256
+            )
+
+            has_layers_dim = False
+            if len(gpu_shape) != len(test_shape):
+                # cross-layers tensor
+                # shape is (num_blocks, ...)
+                assert len(gpu_shape) == len(test_shape) + 1
+                num_blocks_idx = 0
+                has_layers_dim = True
+                kv_dim_before_num_blocks.append(False)
+
+                # prepend a dummy num_layers=80 to test_shape
+                test_shape = (80,) + test_shape
+            elif test_shape[0] == 1234:
+                # shape is (num_blocks, ...)
+                num_blocks_idx = 0
+                kv_dim_before_num_blocks.append(False)
+            else:
+                # shape should be (2, num_blocks, ...)
+                assert test_shape[0] == 2
+                assert test_shape[1] == 1234
+                assert gpu_shape[0] == 2
+
+                num_blocks_idx = 1
+                kv_dim_before_num_blocks.append(True)
+
+            try:
+                kv_cache_stride_order = attn_backend.get_kv_cache_stride_order(
+                    include_num_layers_dimension=has_layers_dim
+                )
+                assert len(kv_cache_stride_order) == len(gpu_shape)
+            except (AttributeError, NotImplementedError):
+                kv_cache_stride_order = tuple(range(len(gpu_shape)))
+
+            # permute test_shape according to stride_order
+            test_shape = tuple(test_shape[i] for i in kv_cache_stride_order)
+
+            # find block_size (16) dimension index
+            block_size_idx = test_shape.index(16)
+            if kernel_block_size is not None:
+                assert kernel_block_size == gpu_shape[block_size_idx]
+            else:
+                kernel_block_size = gpu_shape[block_size_idx]
+                assert gpu_block_size % kernel_block_size == 0
+
+            cpu_shape = list(gpu_shape)
+            cpu_shape[num_blocks_idx] = num_cpu_blocks * block_size_factor
+
+            logger.debug("Allocating CPU tensor of shape %r", cpu_shape)
+            cpu_tensors.append(
+                torch.zeros(
+                    cpu_shape,
+                    dtype=gpu_tensor.dtype,
+                    device="cpu",
+                    pin_memory=pin_memory,
+                )
+            )
+
+        assert kernel_block_size is not None
+        gpu_block_size_factor = gpu_block_size // kernel_block_size
+        cpu_block_size_factor = cpu_block_size // kernel_block_size
+
+        # TODO (orozery): adapt swap_blocks to support gpu_block_size_factor
+        assert gpu_block_size_factor == 1
+
+        self.gpu_to_cpu_handler = SingleDirectionOffloadingHandler(
+            src_tensors=gpu_tensors,
+            dst_tensors=cpu_tensors,
+            kv_dim_before_num_blocks=kv_dim_before_num_blocks,
+            src_block_size_factor=gpu_block_size_factor,
+            dst_block_size_factor=cpu_block_size_factor,
+            priority=1,
+        )
+
+        self.cpu_to_gpu_handler = SingleDirectionOffloadingHandler(
+            src_tensors=cpu_tensors,
+            dst_tensors=gpu_tensors,
+            kv_dim_before_num_blocks=kv_dim_before_num_blocks,
+            src_block_size_factor=cpu_block_size_factor,
+            dst_block_size_factor=gpu_block_size_factor,
+            priority=-1,
+        )