init

vllm/v1/pool/metadata.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+from vllm.pooling_params import PoolingParams
+from vllm.utils import is_pin_memory_available
+
+pin_memory = is_pin_memory_available()
+
+
+@dataclass
+class PoolingCursor:
+    index: list[int]
+    first_token_indices_gpu: torch.Tensor
+    last_token_indices_gpu: torch.Tensor
+    prompt_lens_cpu: torch.Tensor
+    num_scheduled_tokens_cpu: torch.Tensor
+
+    def __getitem__(self, indices: slice):
+        return PoolingCursor(
+            index=self.index[indices],
+            first_token_indices_gpu=self.first_token_indices_gpu[indices],
+            last_token_indices_gpu=self.last_token_indices_gpu[indices],
+            prompt_lens_cpu=self.prompt_lens_cpu[indices],
+            num_scheduled_tokens_cpu=self.num_scheduled_tokens_cpu[indices],
+        )
+
+    def is_partial_prefill(self):
+        return not torch.all(
+            self.prompt_lens_cpu == self.num_scheduled_tokens_cpu)
+
+
+@dataclass
+class PoolingMetadata:
+    """Tensors for pooling."""
+    prompt_lens: torch.Tensor  # CPU Tensor
+    prompt_token_ids: Optional[torch.Tensor]
+    pooling_params: list[PoolingParams]
+    pooling_cursor: Optional[PoolingCursor] = None
+
+    def __getitem__(self, indices: slice):
+        return PoolingMetadata(
+            prompt_lens=self.prompt_lens[indices],
+            prompt_token_ids=None if self.prompt_token_ids is None else
+            self.prompt_token_ids[indices],
+            pooling_params=self.pooling_params[indices],
+            pooling_cursor=None
+            if self.pooling_cursor is None else self.pooling_cursor[indices],
+        )
+
+    def build_pooling_cursor(self, num_scheduled_tokens: list[int],
+                             device: torch.device):
+        self.pooling_cursor = build_pooling_cursor(num_scheduled_tokens,
+                                                   self.prompt_lens, device)
+
+
+def build_pooling_cursor(num_scheduled_tokens: list[int],
+                         prompt_lens: torch.Tensor, device: torch.device):
+    assert len(prompt_lens) == len(num_scheduled_tokens)
+
+    n_seq = len(num_scheduled_tokens)
+    index = list(range(n_seq))
+    num_scheduled_tokens = torch.tensor(num_scheduled_tokens, device="cpu")
+    cumsum = torch.zeros(n_seq + 1,
+                         dtype=torch.int64,
+                         pin_memory=pin_memory,
+                         device="cpu")
+    torch.cumsum(num_scheduled_tokens, dim=0, out=cumsum[1:])
+    cumsum = cumsum.to(device, non_blocking=True)
+    return PoolingCursor(index=index,
+                         first_token_indices_gpu=cumsum[:n_seq],
+                         last_token_indices_gpu=cumsum[1:] - 1,
+                         prompt_lens_cpu=prompt_lens,
+                         num_scheduled_tokens_cpu=num_scheduled_tokens)