init

vllm/model_executor/input_metadata.py

@@ -0,0 +1,54 @@
+from typing import Optional
+
+import torch
+
+
+class InputMetadata:
+    """Metadata for input sequences. Used in PagedAttention.
+
+    Args:
+        prompt_lens: Lengths of prompts.
+        slot_mapping: The address to write the new KV to of each token.
+        max_context_len: The maximum context length.
+        context_lens: the length of attention context for each sequence.
+        block_tables: The block tables. (Seq id -> list of physical block)
+        kv_cache_dtype: Data type to store kv cache.
+    """
+
+    def __init__(
+        self,
+        is_prompt: bool,
+        slot_mapping: torch.Tensor,
+        prompt_lens: Optional[torch.Tensor],
+        max_seq_len: Optional[int],
+        start_loc: Optional[torch.Tensor],
+        max_context_len: Optional[int],
+        context_lens: Optional[torch.Tensor],
+        block_tables: Optional[torch.Tensor],
+        use_cuda_graph: bool,
+        kv_cache_dtype: str,
+    ) -> None:
+        self.is_prompt = is_prompt
+        self.prompt_lens = prompt_lens
+        self.max_seq_len = max_seq_len
+        self.start_loc = start_loc
+        self.max_context_len = max_context_len
+        self.slot_mapping = slot_mapping
+        self.context_lens = context_lens
+        self.block_tables = block_tables
+        self.use_cuda_graph = use_cuda_graph
+        self.kv_cache_dtype = kv_cache_dtype
+
+        # Set during the execution of the first attention op.
+        # FIXME(woosuk): This is a hack.
+        self.attn_bias = None
+
+    def __repr__(self) -> str:
+        return ("InputMetadata("
+                f"is_prompt={self.is_prompt}, "
+                f"max_context_len={self.max_context_len}, "
+                f"slot_mapping={self.slot_mapping}, "
+                f"context_lens={self.context_lens}, "
+                f"block_tables={self.block_tables}, "
+                f"use_cuda_graph={self.use_cuda_graph}, "
+                f"kv_cache_dtype={self.kv_cache_dtype})")