First commit

vllm/attention/backends/openvino.py

@@ -0,0 +1,130 @@
+from dataclasses import dataclass
+from typing import List, Tuple, Type
+
+import openvino as ov
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata)
+from vllm.attention.backends.utils import CommonAttentionState
+
+
+def copy_cache_block(src_tensor: ov.Tensor, dst_tensor: ov.Tensor,
+                     src_offset: int, dst_offset: int) -> None:
+
+    def create_roi_tensor(
+        tensor: ov.Tensor,
+        block_number: int,
+    ) -> ov.Tensor:
+        roi_begin = ov.runtime.Coordinate([0, 0, 0, 0])
+        roi_end = ov.runtime.Coordinate(tensor.get_shape())
+
+        roi_begin[0] = block_number
+        roi_end[0] = block_number + 1
+
+        if isinstance(tensor, ov.Tensor):
+            return ov.Tensor(tensor, roi_begin, roi_end)
+        else:
+            return ov.RemoteTensor(tensor, roi_begin, roi_end)
+
+    src_roi_tensor = \
+        create_roi_tensor(src_tensor, src_offset)
+    dst_roi_tensor = \
+        create_roi_tensor(dst_tensor, dst_offset)
+    src_roi_tensor.copy_to(dst_roi_tensor)
+
+
+class OpenVINOAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "openvino"
+
+    @staticmethod
+    def get_impl_cls():
+        # OpenVINO implements PagedAttention as part of the Optimum
+        # exported model
+        raise NotImplementedError
+
+    @staticmethod
+    def make_metadata(*args, **kwargs) -> "AttentionMetadata":
+        raise NotImplementedError
+
+    @staticmethod
+    def get_state_cls() -> Type["CommonAttentionState"]:
+        return CommonAttentionState
+
+    @staticmethod
+    def make_openvino_metadata(*args, **kwargs) -> "OpenVINOAttentionMetadata":
+        return OpenVINOAttentionMetadata(*args, **kwargs)
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (2, num_blocks, num_kv_heads, block_size, head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_tensor: ov.Tensor,
+        dst_tensor: ov.Tensor,
+        src_to_dists: List[Tuple[int, int]],
+    ) -> None:
+        for src, dst in src_to_dists:
+            copy_cache_block(src_tensor, dst_tensor, src, dst)
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[Tuple[ov.Tensor, ov.Tensor]],
+        src_to_dists: List[Tuple[int, int]],
+    ) -> None:
+        for src, dst in src_to_dists:
+            for key_cache, value_cache in kv_caches:
+                copy_cache_block(key_cache, key_cache, src, dst)
+                copy_cache_block(value_cache, value_cache, src, dst)
+
+
+@dataclass
+class OpenVINOAttentionMetadata:
+    """Metadata for OpenVINOAttentionBackend.
+
+    Basic terms used below:
+    - batch_size_in_sequences - total number of sequences to execute​
+    - prompt_lens – per sequence size number of scheduled tokens​
+    - batch_size_in_tokens = sum(prompt_lens)​
+    - max_context_len = max(context_lens)​
+    - max_num_blocks = div_up(max_context_len / BLOCK_SIZE)​
+    - num_blocks – total number of blocks in block_indices​
+    """
+
+    # Describes past KV cache size for each sequence within a batch
+    # Shape: [batch_size_in_sequences]
+    # Type: i32​
+    past_lens: torch.Tensor
+
+    # Describes start indices of input / speculative tokens from
+    # current sequences within a batch sequence​
+    # Shape: [batch_size_in_sequences + 1]​
+    # Type: i32
+    subsequence_begins: torch.Tensor
+
+    # Describes block tables for each sequence within a batch​ -
+    # indices along 0th dimension in key_cache and value_cache inputs​
+    # Shape: [num_blocks]
+    # Type: i32​
+    block_indices: torch.Tensor
+
+    # Describes block tables for each sequence within a batch​ -
+    # for i-th element, it is an index in block_indices with the
+    # first block belonging to i-th sequence​
+    # Shape: [batch_size_in_sequences + 1]
+    # Type: i32​
+    block_indices_begins: torch.Tensor
+
+    # Describes max context length
+    # Shape: scalar
+    # Type: i32
+    max_context_len: torch.Tensor