[V1] Add BlockTable class

vllm/v1/worker/gpu_block_table.py

@@ -0,0 +1,75 @@
+from typing import List
+
+import numpy as np
+import torch
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class BlockTable:
+
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_model_len: int,
+        max_num_blocks_per_req: int,
+        pin_memory: bool,
+        device: torch.device,
+    ):
+        self.max_num_reqs = max_num_reqs
+        self.max_model_len = max_model_len
+        self.max_num_blocks_per_req = max_num_blocks_per_req
+        self.pin_memory = pin_memory
+        self.device = device
+
+        self.block_table = torch.zeros(
+            (max_num_reqs, max_num_blocks_per_req),
+            device=self.device,
+            dtype=torch.int32,
+        )
+        self.block_table_cpu = torch.zeros(
+            (max_num_reqs, max_num_blocks_per_req),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.block_table_np = self.block_table_cpu.numpy()
+        self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
+
+    def append_row(
+        self,
+        row_idx: int,
+        start: int,
+        block_ids: List[int],
+    ) -> None:
+        num_blocks = len(block_ids)
+        self.block_table_np[row_idx, start:start + num_blocks] = block_ids
+        self.num_blocks_per_row[row_idx] = start + num_blocks
+
+    def add_row(self, row_idx: int, block_ids: List[int]) -> None:
+        self.append_row(row_idx, 0, block_ids)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        num_blocks = self.num_blocks_per_row[src]
+        self.block_table_np[tgt, :num_blocks] = self.block_table_np[
+            src, :num_blocks]
+        self.num_blocks_per_row[tgt] = num_blocks
+
+    def commit(self, num_reqs: int) -> None:
+        self.block_table[:num_reqs].copy_(self.block_table_cpu[:num_reqs],
+                                          non_blocking=True)
+
+    def clear(self) -> None:
+        self.block_table.fill_(0)
+        self.block_table_cpu.fill_(0)
+
+    def cuda(self) -> torch.Tensor:
+        return self.block_table
+
+    def cpu(self) -> torch.Tensor:
+        return self.block_table_cpu
+
+    def numpy(self) -> np.ndarray:
+        return self.block_table_np

vllm/v1/worker/gpu_input_batch.py

@@ -9,6 +9,7 @@
 from vllm.multimodal import MultiModalKwargs
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.worker.gpu_block_table import BlockTable
 
 if TYPE_CHECKING:
     from vllm.multimodal.inputs import PlaceholderRange
@@ -69,19 +70,14 @@ def __init__(
         self.num_computed_tokens_cpu = np.empty(max_num_reqs, dtype=np.int32)
         self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
 
-        # Attention-related.
-        self.block_table = torch.zeros(
-            (max_num_reqs, max_num_blocks_per_req),
-            device=self.device,
-            dtype=torch.int32,
-        )
-        self.block_table_cpu_tensor = torch.zeros(
-            (max_num_reqs, max_num_blocks_per_req),
-            device="cpu",
-            dtype=torch.int32,
+        # Block table.
+        self.block_table = BlockTable(
+            max_num_reqs=max_num_reqs,
+            max_model_len=max_model_len,
+            max_num_blocks_per_req=max_num_blocks_per_req,
             pin_memory=pin_memory,
+            device=device,
         )
-        self.block_table_cpu = self.block_table_cpu_tensor.numpy()
 
         # Sampling-related.
         self.temperature = torch.empty((max_num_reqs, ),
@@ -191,8 +187,7 @@ def add_request(
                            start_idx:end_idx] = request.output_token_ids
 
         self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
-        num_blocks = len(request.block_ids)
-        self.block_table_cpu[req_index, :num_blocks] = request.block_ids
+        self.block_table.add_row(req_index, request.block_ids)
 
         sampling_params = request.sampling_params
         self.temperature_cpu[req_index] = sampling_params.temperature
@@ -298,8 +293,7 @@ def condense(self, empty_req_indices: List[int]) -> None:
                 self.num_prompt_tokens[last_req_index]
             self.num_computed_tokens_cpu[
                 empty_index] = self.num_computed_tokens_cpu[last_req_index]
-            self.block_table_cpu[empty_index] = self.block_table_cpu[
-                last_req_index]
+            self.block_table.move_row(last_req_index, empty_index)
             self.temperature_cpu[empty_index] = self.temperature_cpu[
                 last_req_index]
             self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]

vllm/v1/worker/gpu_model_runner.py

@@ -211,10 +211,9 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
             if num_new_blocks == 0:
                 continue
             start_index = len(req_state.block_ids)
-            end_index = start_index + num_new_blocks
             req_state.block_ids.extend(req_data.new_block_ids)
-            self.input_batch.block_table_cpu[
-                req_index, start_index:end_index] = req_data.new_block_ids
+            self.input_batch.block_table.append_row(req_index, start_index,
+                                                    req_data.new_block_ids)
 
         req_ids_to_add: List[str] = []
         # Add new requests to the cached states.
@@ -275,9 +274,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
 
         # OPTIMIZATION: Start copying the block table first.
         # This way, we can overlap the copy with the following CPU operations.
-        self.input_batch.block_table[:num_reqs].copy_(
-            self.input_batch.block_table_cpu_tensor[:num_reqs],
-            non_blocking=True)
+        self.input_batch.block_table.commit(num_reqs)
 
         # Get the number of scheduled tokens for each request.
         # TODO: The Python loop can be slow. Optimize.
@@ -333,7 +330,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
         # NOTE(woosuk): We use torch.index_select instead of np.take here
         # because torch.index_select is much faster than np.take for large
         # tensors.
-        block_numbers = (self.input_batch.block_table_cpu_tensor.flatten()
+        block_numbers = (self.input_batch.block_table.cpu().flatten()
                          [block_table_indices].numpy())
         block_offsets = positions_np % self.block_size
         np.add(block_numbers * self.block_size,
@@ -450,7 +447,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
             query_start_loc=query_start_loc,
             max_seq_len=max_seq_len,
             seq_start_loc=seq_start_loc,
-            block_table=self.input_batch.block_table[:num_reqs],
+            block_table=self.input_batch.block_table.cuda()[:num_reqs],
             slot_mapping=slot_mapping,
             use_cascade=use_cascade,
             common_prefix_len=common_prefix_len,