[Hardware][TPU] Optimize KV cache swapping (vllm-project#5878)

Signed-off-by: Alvant <alvasian@yandex.ru>

Author: WoosukKwon

vllm/attention/backends/pallas.py

@@ -28,21 +28,13 @@ def get_kv_cache_shape(
     ) -> Tuple[int, ...]:
         return (num_kv_heads, num_blocks, block_size, head_size)
 
-    @torch.compile(backend="openxla")
     @staticmethod
     def swap_blocks(
-        src_kv_cache: Tuple[torch.Tensor, torch.Tensor],
-        dst_kv_cache: Tuple[torch.Tensor, torch.Tensor],
-        src_to_dst: Tuple[torch.Tensor, torch.Tensor],
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
     ) -> None:
-        src_k_cache, src_v_cache = src_kv_cache
-        dst_k_cache, dst_v_cache = dst_kv_cache
-        src_indices, dst_indices = src_to_dst
-        device = dst_k_cache.device
-        torch.ops.xla.dynamo_set_buffer_donor_(dst_k_cache, True)
-        torch.ops.xla.dynamo_set_buffer_donor_(dst_v_cache, True)
-        dst_k_cache[:, dst_indices] = src_k_cache[:, src_indices].to(device)
-        dst_v_cache[:, dst_indices] = src_v_cache[:, src_indices].to(device)
+        raise RuntimeError("swap_blocks is not used for the TPU backend.")
 
     @torch.compile(backend="openxla")
     @staticmethod

vllm/worker/tpu_worker.py

@@ -3,6 +3,7 @@
 
 import torch
 import torch_xla.core.xla_model as xm
+import torch_xla.experimental.dynamo_set_buffer_donor  # noqa: F401
 import torch_xla.runtime as xr
 
 import vllm.envs as envs
@@ -152,8 +153,8 @@ def initialize_cache(
         num_kv_heads = self.model_config.get_num_kv_heads(self.parallel_config)
         head_size = self.model_config.get_head_size()
 
-        self.cpu_cache = []
-        self.tpu_cache = []
+        self.cpu_cache: List[Tuple[torch.Tensor, torch.Tensor]] = []
+        self.tpu_cache: List[Tuple[torch.Tensor, torch.Tensor]] = []
         tpu_cache_shape = self.model_runner.attn_backend.get_kv_cache_shape(
             num_gpu_blocks, self.block_size, num_kv_heads, head_size)
         cpu_cache_shape = self.model_runner.attn_backend.get_kv_cache_shape(
@@ -227,18 +228,25 @@ def cache_swap(
 
         if blocks_to_swap_in:
             # Swap from CPU to TPU.
-            src_to_dst = _make_src_to_dst(blocks_to_swap_in, "cpu",
-                                          self.device)
+            src_indices, dst_indices = _make_src_to_dst(
+                blocks_to_swap_in, "cpu", self.device)
             for i in range(num_layers):
-                attn_backend.swap_blocks(self.cpu_cache[i], self.tpu_cache[i],
-                                         src_to_dst)
+                tpu_k_cache, tpu_v_cache = self.tpu_cache[i]
+                cpu_k_cache, cpu_v_cache = self.cpu_cache[i]
+                k = cpu_k_cache[:, src_indices].to(self.device)
+                v = cpu_v_cache[:, src_indices].to(self.device)
+                _insert_kv(k, v, dst_indices, tpu_k_cache, tpu_v_cache)
+
         if blocks_to_swap_out:
             # Swap from TPU to CPU.
-            src_to_dst = _make_src_to_dst(blocks_to_swap_out, self.device,
-                                          "cpu")
+            src_indices, dst_indices = _make_src_to_dst(
+                blocks_to_swap_out, self.device, "cpu")
             for i in range(num_layers):
-                attn_backend.swap_blocks(self.tpu_cache[i], self.cpu_cache[i],
-                                         src_to_dst)
+                tpu_k_cache, tpu_v_cache = self.tpu_cache[i]
+                cpu_k_cache, cpu_v_cache = self.cpu_cache[i]
+                cpu_k_cache[:, dst_indices] = tpu_k_cache[:, src_indices].cpu()
+                cpu_v_cache[:, dst_indices] = tpu_v_cache[:, src_indices].cpu()
+
         if blocks_to_copy:
             src_to_dst = _make_src_to_dst(blocks_to_copy, self.device,
                                           self.device)
@@ -267,3 +275,17 @@ def _make_src_to_dst(
                                device=dst_device,
                                dtype=torch.int64)
     return src_indices, dst_indices
+
+
+@torch.compile(backend="openxla")
+def _insert_kv(
+    k: torch.Tensor,
+    v: torch.Tensor,
+    indices: torch.Tensor,
+    tpu_k_cache: torch.Tensor,
+    tpu_v_cache: torch.Tensor,
+) -> None:
+    torch.ops.xla.dynamo_set_buffer_donor_(tpu_k_cache, True)
+    torch.ops.xla.dynamo_set_buffer_donor_(tpu_v_cache, True)
+    tpu_k_cache[:, indices] = k
+    tpu_v_cache[:, indices] = v