Add cache engine

cacheflow/worker/cache_engine.py

+from typing import List, Tuple
+
+import torch
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class CacheEngine:
+
+    def __init__(
+        self,
+        worker_id: int,
+        gpu_id: int,
+        num_layers: int,
+        num_heads: int,
+        head_size: int,
+        num_gpu_blocks: int,
+        num_cpu_blocks: int,
+        block_size: int,
+        dtype: torch.dtype = torch.float16,
+    ) -> None:
+        self.worker_id = worker_id
+        self.gpu_id = gpu_id
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.num_gpu_blocks = num_gpu_blocks
+        self.num_cpu_blocks = num_cpu_blocks
+        self.block_size = block_size
+        self.dtype = dtype
+
+        # Initialize the cache.
+        self.gpu_cache = self.allocate_gpu_cache()
+        self.cpu_cache = self.allocate_cpu_cache()
+
+        # Initialize the streams.
+        self.copy_stream = torch.cuda.Stream(device=gpu_id)
+        self.swap_stream = torch.cuda.Stream(device=gpu_id)
+        assert self.copy_stream != self.swap_stream
+        current_stream = torch.cuda.current_stream(device=gpu_id)
+        assert self.copy_stream != current_stream
+        assert self.swap_stream != current_stream
+
+        # Initialize the events for synchronization.
+
+    def allocate_gpu_cache(self) -> List[List[KVCache]]:
+        gpu_cache: List[List[KVCache]] = []
+        for _ in range(self.num_layers):
+            layer_cache: List[KVCache] = []
+            for _ in range(self.num_heads):
+                key_blocks = torch.empty(
+                    (self.num_gpu_blocks, self.block_size * self.head_size),
+                    dtype=self.dtype,
+                    device=self.gpu_id,
+                )
+                value_blocks = torch.empty(
+                    (self.num_gpu_blocks, self.block_size * self.head_size),
+                    dtype=self.dtype,
+                    device=self.gpu_id,
+                )
+                layer_cache.append((key_blocks, value_blocks))
+            gpu_cache.append(layer_cache)
+        return gpu_cache
+
+    def allocate_cpu_cache(self) -> List[List[KVCache]]:
+        cpu_cache: List[List[KVCache]] = []
+        for _ in range(self.num_layers):
+            layer_cache: List[KVCache] = []
+            for _ in range(self.num_heads):
+                key_blocks = torch.empty(
+                    (self.num_cpu_blocks, self.block_size * self.head_size),
+                    dtype=self.dtype,
+                    pin_memory=True,
+                )
+                value_blocks = torch.empty(
+                    (self.num_cpu_blocks, self.block_size * self.head_size),
+                    dtype=self.dtype,
+                    pin_memory=True,
+                )
+                layer_cache.append((key_blocks, value_blocks))
+            cpu_cache.append(layer_cache)
+        return cpu_cache
+
+    def copy(
+        self,
+        src_block_numbers: List[int],
+        dst_block_numbers: List[int],
+    ) -> None:
+        for layer in range(self.num_layers):
+            # TODO: Call the COPY op.
+            pass
+
+    def swap_out(
+        self,
+        gpu_block_numbers: List[int],
+        cpu_block_numbers: List[int],
+    ) -> None:
+        for layer in range(self.num_layers):
+            # TODO: Call the SWAP_OUT op on the swap stream.
+            pass
+
+    def swap_in(
+        self,
+        gpu_block_numbers: List[int],
+        cpu_block_numbers: List[int],
+    ) -> None:
+        for layer in range(self.num_layers):
+            # TODO: Call the SWAP_IN op on the swap stream.
+            pass