Sync from v0.13

vllm/v1/pool/metadata.py

@@ -0,0 +1,126 @@
+# 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.tasks import PoolingTask
+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
+    seq_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],
+            seq_lens_cpu=self.seq_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)
+
+    def is_finished(self):
+        return self.prompt_lens_cpu == self.seq_lens_cpu
+
+
+class PoolingStates:
+    def __init__(self):
+        # for chunked prefill with ALL pooling
+        self.hidden_states_cache: list[torch.Tensor] = []
+
+    def clean(self):
+        self.hidden_states_cache.clear()
+
+
+@dataclass
+class PoolingMetadata:
+    """Tensors for pooling."""
+
+    prompt_lens: torch.Tensor  # CPU Tensor
+    prompt_token_ids: torch.Tensor | None
+    pooling_params: list[PoolingParams]
+    pooling_states: list[PoolingStates]
+    pooling_cursor: PoolingCursor | None = None
+
+    def __post_init__(self) -> None:
+        pooling_params = self.pooling_params
+
+        tasks: list[PoolingTask] = [
+            task
+            for pooling_param in pooling_params
+            if (task := pooling_param.task) is not None
+        ]
+        assert len(pooling_params) == len(tasks)
+
+        self.tasks = tasks
+
+    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_states=self.pooling_states[indices],
+            pooling_cursor=None
+            if self.pooling_cursor is None
+            else self.pooling_cursor[indices],
+        )
+
+    def get_prompt_token_ids(self) -> list[torch.Tensor]:
+        prompt_token_ids = self.prompt_token_ids
+        assert prompt_token_ids is not None, (
+            "Please set `requires_token_ids=True` in `get_pooling_updates`"
+        )
+
+        return [prompt_token_ids[i, :num] for i, num in enumerate(self.prompt_lens)]
+
+    def build_pooling_cursor(
+        self,
+        num_scheduled_tokens: list[int],
+        seq_lens_cpu: torch.Tensor,
+        device: torch.device,
+    ):
+        self.pooling_cursor = build_pooling_cursor(
+            num_scheduled_tokens, seq_lens_cpu, self.prompt_lens, device
+        )
+
+
+def build_pooling_cursor(
+    num_scheduled_tokens: list[int],
+    seq_lens_cpu: torch.Tensor,
+    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_cpu = 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_cpu, 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,
+        seq_lens_cpu=seq_lens_cpu,
+        num_scheduled_tokens_cpu=num_scheduled_tokens_cpu,
+    )