vllm.v1.kv_offload.worker.cpu_gpu ¶

SingleDirectionOffloadingHandler ¶

Bases: 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.

Source code in vllm/v1/kv_offload/worker/cpu_gpu.py

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,
        gpu_tensors: list[torch.Tensor],
        cpu_tensors: list[torch.Tensor],
        block_size_factor: int,
        kv_cache_groups_data_refs: list[list[CanonicalKVCacheRef]],
        gpu_to_cpu: bool,
    ):
        """
        Initialize a SingleDirectionOffloadingHandler.

        Args:
            gpu_tensors: list of GPU KV cache tensors.
                Each of shape (num_gpu_blocks, gpu_page_size_bytes) with dtype int8.
            cpu_tensors: list of CPU KV cache tensors.
                Each of shape (num_cpu_blocks, cpu_page_size_bytes) with dtype int8.
                Order should match gpu_tensors.
            kv_cache_groups_data_refs: list of CanonicalKVCacheRef per group.
            gpu_to_cpu: if True, transfer from GPU to CPU; otherwise CPU to GPU.
        """
        assert len(gpu_tensors) == len(cpu_tensors)
        assert len(gpu_tensors) > 0

        # assert input tensors are as expected
        for gpu_tensor, cpu_tensor in zip(gpu_tensors, cpu_tensors):
            assert gpu_tensor.dtype == torch.int8
            assert gpu_tensor.ndim == 2
            assert gpu_tensor.is_cuda
            assert cpu_tensor.dtype == torch.int8
            assert cpu_tensor.ndim == 2
            assert cpu_tensor.device.type == "cpu"
            _, gpu_page_size = gpu_tensor.shape
            _, cpu_page_size = cpu_tensor.shape
            assert cpu_page_size == gpu_page_size * block_size_factor

        self.src_tensors: list[torch.Tensor] = (
            gpu_tensors if gpu_to_cpu else cpu_tensors
        )
        self.dst_tensors: list[torch.Tensor] = (
            cpu_tensors if gpu_to_cpu else gpu_tensors
        )
        self.gpu_to_cpu: bool = gpu_to_cpu
        self.kv_cache_groups_data_refs = kv_cache_groups_data_refs

        # GPU blocks may be smaller
        # cpu_page_size = gpu_page_size * block_size_factor.
        self.src_block_size_factor = 1 if self.gpu_to_cpu else block_size_factor
        self.dst_block_size_factor = block_size_factor if self.gpu_to_cpu else 1

        self.transfer_type = ("GPU", "CPU") if self.gpu_to_cpu else ("CPU", "GPU")
        # job_id -> event
        self._transfer_events: dict[int, torch.Event] = {}
        # queue of transfers (job_id, stream, event)
        self._transfers: deque[Transfer] = 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

        num_src_blocks = len(src_blocks)
        num_dst_blocks = len(dst_blocks)

        # There are 2 types of transfers:
        # 1. GPU -> CPU
        # 2. CPU -> GPU
        #
        # transfers are also to CPU blocks, EXCEPT MAYBE for the first and last block.
        # i.e. the first and last CPU blocks in src_blocks can match against
        # a smaller (byte-wise) set of GPU blocks in dst_blocks.
        # In such cases, we may need to skip some gpu-sized sub-blocks,
        # and start reading/writing from the middle of the first CPU block.
        # If we have multiple KV cache groups (when using HMA with hybrid models),
        # we may have a partial first/last CPU block per each group.
        # The group_sizes parameter encodes the size of each group of blocks
        # in the GPU dst_blocks.
        # If group_sizes is None, we assume all blocks belong to a single group.
        # The logical_offset parameter maps each group of blocks to its logical
        # offset inside the request, counting in GPU blocks.
        # This allows us to find the correct starting position
        # in the matching first CPU block.

        # extract group_sizes from the GPU spec
        gpu_spec = src_spec if self.gpu_to_cpu else dst_spec
        assert isinstance(gpu_spec, GPULoadStoreSpec)
        group_sizes = gpu_spec.group_sizes
        assert len(group_sizes) == len(self.kv_cache_groups_data_refs)

        # extract block indices from the GPU spec
        block_indices = gpu_spec.block_indices
        assert len(block_indices) == len(self.kv_cache_groups_data_refs)

        num_copy_ops = 0
        for group_size, group_data_refs in zip(
            group_sizes, self.kv_cache_groups_data_refs
        ):
            num_copy_ops += group_size * len(group_data_refs)

        all_src = np.empty(num_copy_ops, dtype=np.int64)
        all_dst = np.empty(num_copy_ops, dtype=np.int64)
        all_sizes = np.empty(num_copy_ops, dtype=np.int64)

        src_offset = 0
        dst_offset = 0
        op_idx = 0
        # count total number of bytes copied
        num_transfer_bytes = 0
        for group_size, block_idx, group_data_refs in zip(
            group_sizes, block_indices, self.kv_cache_groups_data_refs
        ):
            if group_size == 0:
                continue

            src_logical_blocks_to_skip = block_idx % self.src_block_size_factor
            dst_logical_blocks_to_skip = block_idx % self.dst_block_size_factor
            src_logical_blocks_count = group_size + src_logical_blocks_to_skip
            dst_logical_blocks_count = group_size + dst_logical_blocks_to_skip

            dst_blocks_count = cdiv(
                dst_logical_blocks_count, self.dst_block_size_factor
            )
            dst_end_offset = dst_offset + dst_blocks_count
            assert dst_end_offset <= num_dst_blocks

            src_blocks_count = cdiv(
                src_logical_blocks_count, self.src_block_size_factor
            )
            src_end_offset = src_offset + src_blocks_count
            assert src_end_offset <= num_src_blocks

            group_src = src_blocks[src_offset:src_end_offset]
            group_dst = dst_blocks[dst_offset:dst_end_offset]

            for data_ref in group_data_refs:
                t_idx = data_ref.tensor_idx
                end_idx = op_idx + group_size

                compute_sub_block_ptrs(
                    group_src,
                    self.src_block_size_factor,
                    all_src[op_idx:end_idx],
                    self.src_tensors[t_idx],
                    skip_count=src_logical_blocks_to_skip,
                )
                compute_sub_block_ptrs(
                    group_dst,
                    self.dst_block_size_factor,
                    all_dst[op_idx:end_idx],
                    self.dst_tensors[t_idx],
                    skip_count=dst_logical_blocks_to_skip,
                )

                all_sizes[op_idx:end_idx] = data_ref.page_size_bytes
                num_transfer_bytes += group_size * data_ref.page_size_bytes
                op_idx = end_idx

            src_offset = src_end_offset
            dst_offset = dst_end_offset

        assert src_offset == num_src_blocks
        assert dst_offset == num_dst_blocks
        assert op_idx == num_copy_ops

        batch_src = torch.from_numpy(all_src)
        batch_dst = torch.from_numpy(all_dst)
        batch_sizes = torch.from_numpy(all_sizes)

        stream = self._stream_pool.pop() if self._stream_pool else torch.cuda.Stream()
        start_event = (
            self._event_pool.pop()
            if self._event_pool
            else torch.Event(enable_timing=True)
        )
        end_event = (
            self._event_pool.pop()
            if self._event_pool
            else torch.Event(enable_timing=True)
        )

        if self.gpu_to_cpu:
            # wait for model computation to finish before offloading
            stream.wait_stream(torch.cuda.current_stream())
        if self._transfers:
            last_transfer: Transfer = self._transfers[-1]
            last_event = last_transfer.end_event
            # assure job will start only after the previous one completes
            stream.wait_event(last_event)
        with torch.cuda.stream(stream):
            start_event.record(stream)
            if num_copy_ops > 0:
                ops.swap_blocks_batch(batch_src, batch_dst, batch_sizes)
            end_event.record(stream)

        self._transfer_events[job_id] = end_event
        self._transfers.append(
            Transfer(
                job_id=job_id,
                stream=stream,
                start_event=start_event,
                end_event=end_event,
                num_bytes=num_transfer_bytes,
            )
        )

        # success
        return True

    def get_finished(self) -> list[TransferResult]:
        results: list[TransferResult] = []
        while self._transfers and self._transfers[0].end_event.query():
            transfer = self._transfers.popleft()
            transfer_time = (
                transfer.start_event.elapsed_time(transfer.end_event) * 1e-3
            )  # elapsed_time is in milliseconds
            result = TransferResult(
                job_id=transfer.job_id,
                success=True,
                transfer_size=transfer.num_bytes,
                transfer_time=transfer_time,
                transfer_type=self.transfer_type,
            )

            results.append(result)
            self._stream_pool.append(transfer.stream)
            self._event_pool.append(transfer.end_event)
            self._event_pool.append(transfer.start_event)
            del self._transfer_events[transfer.job_id]
        return results

    def wait(self, job_ids: set[int]):
        for job_id in job_ids:
            event = self._transfer_events.get(job_id)
            if event is not None:
                event.synchronize()

    def shutdown(self) -> None:
        while self._transfers:
            transfer = self._transfers.popleft()
            transfer.end_event.synchronize()
        self._transfer_events.clear()
        self._stream_pool.clear()
        self._event_pool.clear()
        self.src_tensors.clear()
        self.dst_tensors.clear()

init ¶

__init__(
    gpu_tensors: list[Tensor],
    cpu_tensors: list[Tensor],
    block_size_factor: int,
    kv_cache_groups_data_refs: list[
        list[CanonicalKVCacheRef]
    ],
    gpu_to_cpu: bool,
)

Initialize a SingleDirectionOffloadingHandler.

Parameters:

Name	Type	Description	Default
`gpu_tensors`	`list[Tensor]`	list of GPU KV cache tensors. Each of shape (num_gpu_blocks, gpu_page_size_bytes) with dtype int8.	required
`cpu_tensors`	`list[Tensor]`	list of CPU KV cache tensors. Each of shape (num_cpu_blocks, cpu_page_size_bytes) with dtype int8. Order should match gpu_tensors.	required
`kv_cache_groups_data_refs`	`list[list[CanonicalKVCacheRef]]`	list of CanonicalKVCacheRef per group.	required
`gpu_to_cpu`	`bool`	if True, transfer from GPU to CPU; otherwise CPU to GPU.	required

Source code in vllm/v1/kv_offload/worker/cpu_gpu.py

def __init__(
    self,
    gpu_tensors: list[torch.Tensor],
    cpu_tensors: list[torch.Tensor],
    block_size_factor: int,
    kv_cache_groups_data_refs: list[list[CanonicalKVCacheRef]],
    gpu_to_cpu: bool,
):
    """
    Initialize a SingleDirectionOffloadingHandler.

    Args:
        gpu_tensors: list of GPU KV cache tensors.
            Each of shape (num_gpu_blocks, gpu_page_size_bytes) with dtype int8.
        cpu_tensors: list of CPU KV cache tensors.
            Each of shape (num_cpu_blocks, cpu_page_size_bytes) with dtype int8.
            Order should match gpu_tensors.
        kv_cache_groups_data_refs: list of CanonicalKVCacheRef per group.
        gpu_to_cpu: if True, transfer from GPU to CPU; otherwise CPU to GPU.
    """
    assert len(gpu_tensors) == len(cpu_tensors)
    assert len(gpu_tensors) > 0

    # assert input tensors are as expected
    for gpu_tensor, cpu_tensor in zip(gpu_tensors, cpu_tensors):
        assert gpu_tensor.dtype == torch.int8
        assert gpu_tensor.ndim == 2
        assert gpu_tensor.is_cuda
        assert cpu_tensor.dtype == torch.int8
        assert cpu_tensor.ndim == 2
        assert cpu_tensor.device.type == "cpu"
        _, gpu_page_size = gpu_tensor.shape
        _, cpu_page_size = cpu_tensor.shape
        assert cpu_page_size == gpu_page_size * block_size_factor

    self.src_tensors: list[torch.Tensor] = (
        gpu_tensors if gpu_to_cpu else cpu_tensors
    )
    self.dst_tensors: list[torch.Tensor] = (
        cpu_tensors if gpu_to_cpu else gpu_tensors
    )
    self.gpu_to_cpu: bool = gpu_to_cpu
    self.kv_cache_groups_data_refs = kv_cache_groups_data_refs

    # GPU blocks may be smaller
    # cpu_page_size = gpu_page_size * block_size_factor.
    self.src_block_size_factor = 1 if self.gpu_to_cpu else block_size_factor
    self.dst_block_size_factor = block_size_factor if self.gpu_to_cpu else 1

    self.transfer_type = ("GPU", "CPU") if self.gpu_to_cpu else ("CPU", "GPU")
    # job_id -> event
    self._transfer_events: dict[int, torch.Event] = {}
    # queue of transfers (job_id, stream, event)
    self._transfers: deque[Transfer] = 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] = []

compute_sub_block_ptrs ¶

compute_sub_block_ptrs(
    block_ids: ndarray,
    block_size_factor: int,
    output: ndarray,
    tensor: Tensor,
    skip_count: int = 0,
)

Compute byte pointers for sub-blocks of the given block IDs.

Each block in block_ids contains block_size_factor sub-blocks. The pointer for sub-block j of block b is: base_ptr + b * row_stride + j * sub_block_size

where sub_block_size = tensor.shape[1] // block_size_factor (gpu page size).

This handles tensors where row_stride != block_size_factor * sub_block_size (e.g. non-contiguous CPU tensors).

Parameters:

Name	Type	Description	Default
`block_ids`	`ndarray`	array of block IDs at the tensor's native granularity.	required
`block_size_factor`	`int`	number of sub-blocks per block.	required
`output`	`ndarray`	pre-allocated int64 array to write pointers into.	required
`tensor`	`Tensor`	the source or destination tensor.	required
`skip_count`	`int`	sub-blocks to skip in the first block.	`0`

Source code in vllm/v1/kv_offload/worker/cpu_gpu.py

def compute_sub_block_ptrs(
    block_ids: np.ndarray,
    block_size_factor: int,
    output: np.ndarray,
    tensor: torch.Tensor,
    skip_count: int = 0,
):
    """
    Compute byte pointers for sub-blocks of the given block IDs.

    Each block in block_ids contains block_size_factor sub-blocks.
    The pointer for sub-block j of block b is:
        base_ptr + b * row_stride + j * sub_block_size

    where sub_block_size = tensor.shape[1] // block_size_factor (gpu page size).

    This handles tensors where row_stride != block_size_factor * sub_block_size
    (e.g. non-contiguous CPU tensors).

    Args:
        block_ids: array of block IDs at the tensor's native granularity.
        block_size_factor: number of sub-blocks per block.
        output: pre-allocated int64 array to write pointers into.
        tensor: the source or destination tensor.
        skip_count: sub-blocks to skip in the first block.
    """
    assert skip_count < block_size_factor

    num_sub_blocks = len(output)
    base_ptr = tensor.data_ptr()
    row_stride = tensor.stride(0)

    if block_size_factor == 1:
        # Fast path: 1:1 mapping, no sub-block expansion needed.
        output[:] = base_ptr + block_ids[:num_sub_blocks] * row_stride
        return

    # Vectorized expansion for block_size_factor > 1.
    assert tensor.shape[1] % block_size_factor == 0
    sub_block_size = tensor.shape[1] // block_size_factor
    sub_offsets = np.arange(block_size_factor, dtype=np.int64) * sub_block_size
    # (num_blocks, 1) + (1, block_size_factor) -> (num_blocks, block_size_factor)
    all_ptrs = (
        base_ptr + block_ids.astype(np.int64)[:, np.newaxis] * row_stride
    ) + sub_offsets[np.newaxis, :]
    # Flatten and apply skip_count / truncation
    flat = all_ptrs.ravel()
    output[:] = flat[skip_count : skip_count + num_sub_blocks]

pin_mmap_region ¶

pin_mmap_region(region: SharedOffloadRegion) -> None

Register the entire mmap as CUDA pinned memory via cudaHostRegister.

Source code in vllm/v1/kv_offload/worker/cpu_gpu.py

def pin_mmap_region(region: SharedOffloadRegion) -> None:
    """Register the entire mmap as CUDA pinned memory via cudaHostRegister."""
    rank = region.rank

    base_ptr = region._base.data_ptr()
    result = torch.cuda.cudart().cudaHostRegister(base_ptr, region.total_size_bytes, 0)
    if result.value != 0:
        logger.warning(
            "cudaHostRegister failed for rank=%d (code=%d) — "
            "transfers will still work but may be slower (unpinned DMA)",
            rank,
            result,
        )
    else:
        logger.debug(
            "cudaHostRegister rank=%d %.2f GB",
            rank,
            region.total_size_bytes / 1e9,
        )
        region.is_pinned = True

vllm.v1.kv_offload.worker.cpu_gpu ¶

SingleDirectionOffloadingHandler ¶

__init__ ¶

compute_sub_block_ptrs ¶

pin_mmap_region ¶

init ¶