update

vllm_old/v1/pool/metadata.py

@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+
+import torch
+
+from vllm.pooling_params import PoolingParams
+from vllm.utils.platform_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: torch.Tensor | None
+    pooling_params: list[PoolingParams]
+    pooling_cursor: PoolingCursor | None = 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,
+    )