47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""Attention layer."""
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from typing import List, Optional
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import torch
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import torch.nn as nn
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.attention.selector import get_attn_backend
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class Attention(nn.Module):
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"""Attention layer.
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This class takes query, key, and value tensors as input. The input tensors
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can either contain prompt tokens or generation tokens.
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The class does the following:
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1. Store the input key and value tensors in the KV cache.
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2. Perform (multi-head/multi-query/grouped-query) attention.
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3. Return the output tensor.
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"""
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: Optional[int] = None,
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alibi_slopes: Optional[List[float]] = None,
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sliding_window: Optional[int] = None,
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) -> None:
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super().__init__()
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self.backend = get_attn_backend(torch.get_default_dtype())
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impl_cls = self.backend.get_impl_cls()
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self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window)
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: Optional[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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return self.impl.forward(query, key, value, kv_cache, attn_metadata)
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