refactor: Extract repeated member variables in KVCache subclasses to base class. (#6323)

python/sglang/srt/mem_cache/memory_pool.py

@@ -94,6 +94,33 @@ class ReqToTokenPool:
 
 
 class KVCache(abc.ABC):
+    @abc.abstractmethod
+    def __init__(
+        self,
+        size: int,
+        page_size: int,
+        dtype: torch.dtype,
+        layer_num: int,
+        device: str,
+        enable_memory_saver: bool,
+        start_layer: Optional[int] = None,
+        end_layer: Optional[int] = None,
+    ):
+        self.size = size
+        self.page_size = page_size
+        self.dtype = dtype
+        self.device = device
+        if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
+            # NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
+            self.store_dtype = torch.uint8
+        else:
+            self.store_dtype = dtype
+        self.layer_num = layer_num
+        self.start_layer = start_layer or 0
+        self.end_layer = end_layer or layer_num - 1
+        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
+            enable=enable_memory_saver
+        )
 
     @abc.abstractmethod
     def get_key_buffer(self, layer_id: int) -> torch.Tensor:
@@ -217,25 +244,20 @@ class MHATokenToKVPool(KVCache):
         start_layer: Optional[int] = None,
         end_layer: Optional[int] = None,
     ):
-        self.size = size
-        self.page_size = page_size
-        self.dtype = dtype
-        self.device = device
-        if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
-            # NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
-            self.store_dtype = torch.uint8
-        else:
-            self.store_dtype = dtype
-        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=enable_memory_saver
+        super().__init__(
+            size,
+            page_size,
+            dtype,
+            layer_num,
+            device,
+            enable_memory_saver,
+            start_layer,
+            end_layer,
         )
 
         self.head_num = head_num
         self.head_dim = head_dim
-        self.layer_num = layer_num
         self._create_buffers()
-        self.start_layer = start_layer or 0
-        self.end_layer = end_layer or layer_num - 1
 
         self.layer_transfer_counter = None
         self.capture_mode = False
@@ -493,26 +515,21 @@ class MLATokenToKVPool(KVCache):
         start_layer: Optional[int] = None,
         end_layer: Optional[int] = None,
     ):
-        self.size = size
-        self.page_size = page_size
-        self.dtype = dtype
-        self.device = device
-        if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
-            # NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
-            self.store_dtype = torch.uint8
-        else:
-            self.store_dtype = dtype
-        self.kv_lora_rank = kv_lora_rank
-        self.qk_rope_head_dim = qk_rope_head_dim
-        self.layer_num = layer_num
-        self.start_layer = start_layer or 0
-        self.end_layer = end_layer or layer_num - 1
-
-        memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=enable_memory_saver
+        super().__init__(
+            size,
+            page_size,
+            dtype,
+            layer_num,
+            device,
+            enable_memory_saver,
+            start_layer,
+            end_layer,
         )
 
-        with memory_saver_adapter.region():
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+
+        with self.memory_saver_adapter.region():
             # The padded slot 0 is used for writing dummy outputs from padded tokens.
             self.kv_buffer = [
                 torch.zeros(
@@ -636,20 +653,18 @@ class DoubleSparseTokenToKVPool(KVCache):
         start_layer: Optional[int] = None,
         end_layer: Optional[int] = None,
     ):
-        self.size = size
-        self.page_size = page_size
-        self.dtype = dtype
-        self.device = device
-        if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
-            # NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
-            self.store_dtype = torch.uint8
-        else:
-            self.store_dtype = dtype
-        memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=enable_memory_saver
+        super().__init__(
+            size,
+            page_size,
+            dtype,
+            layer_num,
+            device,
+            enable_memory_saver,
+            start_layer,
+            end_layer,
         )
 
-        with memory_saver_adapter.region():
+        with self.memory_saver_adapter.region():
             # [size, head_num, head_dim] for each layer
             self.k_buffer = [
                 torch.zeros(
@@ -672,9 +687,6 @@ class DoubleSparseTokenToKVPool(KVCache):
                 for _ in range(layer_num)
             ]
 
-        self.start_layer = start_layer or 0
-        self.end_layer = end_layer or layer_num - 1
-
     def get_key_buffer(self, layer_id: int):
         return self.k_buffer[layer_id - self.start_layer]
 
@@ -742,7 +754,7 @@ class HostKVCache(abc.ABC):
 
     def __init__(
         self,
-        device_pool: MHATokenToKVPool,
+        device_pool: KVCache,
         host_to_device_ratio: float,
         host_size: int,
         pin_memory: bool,
@@ -914,6 +926,8 @@ class HostKVCache(abc.ABC):
 
 
 class MHATokenToKVPoolHost(HostKVCache):
+    device_pool: MHATokenToKVPool
+
     def __init__(
         self,
         device_pool: MHATokenToKVPool,
@@ -997,6 +1011,8 @@ class MHATokenToKVPoolHost(HostKVCache):
 
 
 class MLATokenToKVPoolHost(HostKVCache):
+    device_pool: MLATokenToKVPool
+
     def __init__(
         self,
         device_pool: MLATokenToKVPool,