287 lines
11 KiB
Python
287 lines
11 KiB
Python
"""Multi-head attention."""
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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 xformers import ops as xops
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from xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
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LowerTriangularMaskWithTensorBias)
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from vllm._C import ops
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from vllm._C import cache_ops
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.utils import is_hip
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_SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128, 256]
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# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
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_PARTITION_SIZE = 512
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class PagedAttention(nn.Module):
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"""MHA/MQA/GQA layer with PagedAttention.
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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. Wait for the cache operations (e.g., swap, copy) to finish. The cache
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operations are issued by the cache engine before executing the forward
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pass of the model, and they are executed asynchronously.
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2. Reshape and store the input key and value tensors in the KV cache.
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3. Perform (multi-head/multi-query/grouped-query) attention using either
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xformers or the PagedAttention custom op.
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4. 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.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
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self.sliding_window = sliding_window
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if alibi_slopes is not None:
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alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
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self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
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assert self.num_heads % self.num_kv_heads == 0
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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if self.head_size not in _SUPPORTED_HEAD_SIZES:
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raise ValueError(f"head_size ({self.head_size}) is not supported. "
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f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
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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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key_cache: Optional[torch.Tensor],
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value_cache: Optional[torch.Tensor],
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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"""PagedAttention forward pass.
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Args:
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query: shape = [batch_size, seq_len, num_heads * head_size]
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key: shape = [batch_size, seq_len, num_kv_heads * head_size]
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value: shape = [batch_size, seq_len, num_kv_heads * head_size]
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key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
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block_size, x]
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value_cache: shape = [num_blocks, num_kv_heads, head_size,
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block_size]
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input_metadata: metadata for the inputs.
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cache_event: event to wait for the cache operations to finish.
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Returns:
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shape = [batch_size, seq_len, num_heads * head_size]
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"""
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batch_size, seq_len, hidden_size = query.shape
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# Reshape the query, key, and value tensors.
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query = query.view(-1, self.num_heads, self.head_size)
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key = key.view(-1, self.num_kv_heads, self.head_size)
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value = value.view(-1, self.num_kv_heads, self.head_size)
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slot_mapping = input_metadata.slot_mapping.flatten()
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if cache_event is not None:
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cache_event.wait()
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# Reshape the keys and values and store them in the cache.
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# If key_cache and value_cache are not provided, the new key and value
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# vectors will not be cached. This happens during the initial memory
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# profiling run.
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if key_cache is not None and value_cache is not None:
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cache_ops.reshape_and_cache(
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key,
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value,
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key_cache,
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value_cache,
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slot_mapping,
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)
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if input_metadata.is_prompt:
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# Prompt run.
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if self.num_kv_heads != self.num_heads:
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# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
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# project the key and value tensors to the desired number of
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# heads.
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# TODO(woosuk): Use MQA/GQA kernels for higher performance.
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query = query.view(query.shape[0], self.num_kv_heads,
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self.num_queries_per_kv, query.shape[-1])
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key = key[:, :,
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None, :].expand(key.shape[0], self.num_kv_heads,
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self.num_queries_per_kv,
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key.shape[-1])
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value = value[:, :, None, :].expand(value.shape[0],
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self.num_kv_heads,
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self.num_queries_per_kv,
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value.shape[-1])
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# Set attention bias if not provided. This typically happens at the
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# very attention layer of every iteration.
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# FIXME(woosuk): This is a hack.
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if input_metadata.attn_bias is None:
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if self.alibi_slopes is None:
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attn_bias = BlockDiagonalCausalMask.from_seqlens(
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[seq_len] * batch_size)
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if self.sliding_window is not None:
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attn_bias = attn_bias.make_local_attention(
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self.sliding_window)
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input_metadata.attn_bias = attn_bias
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else:
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input_metadata.attn_bias = _make_alibi_bias(
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self.alibi_slopes, self.num_kv_heads, batch_size,
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seq_len, query.dtype)
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# TODO(woosuk): Too many view operations. Let's try to reduce them
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# in the future for code readability.
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if self.alibi_slopes is None:
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query = query.unsqueeze(0)
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key = key.unsqueeze(0)
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value = value.unsqueeze(0)
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else:
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query = query.unflatten(0, (batch_size, seq_len))
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key = key.unflatten(0, (batch_size, seq_len))
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value = value.unflatten(0, (batch_size, seq_len))
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out = xops.memory_efficient_attention_forward(
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query,
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key,
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value,
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attn_bias=input_metadata.attn_bias,
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p=0.0,
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scale=self.scale,
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op=xops.fmha.MemoryEfficientAttentionFlashAttentionOp[0] if
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(is_hip()) else None,
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)
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output = out.view_as(query)
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else:
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# Decoding run.
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output = _paged_attention(
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query,
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key_cache,
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value_cache,
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input_metadata,
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self.num_kv_heads,
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self.scale,
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self.alibi_slopes,
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)
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# Reshape the output tensor.
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return output.view(batch_size, seq_len, hidden_size)
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def _make_alibi_bias(
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alibi_slopes: torch.Tensor,
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num_kv_heads: int,
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batch_size: int,
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seq_len: int,
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dtype: torch.dtype,
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) -> LowerTriangularMaskWithTensorBias:
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bias = torch.arange(seq_len, dtype=dtype, device="cuda")
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# NOTE(zhuohan): HF uses
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# `bias = bias[None, :].repeat(prompt_len, 1)`
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# here. We find that both biases give the same results, but
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# the bias below more accurately follows the original ALiBi
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# paper.
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bias = bias[None, :] - bias[:, None]
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# When using custom attention bias, xformers requires the bias to
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# be sliced from a tensor whose length is a multiple of 8.
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padded_len = (seq_len + 7) // 8 * 8
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num_heads = alibi_slopes.shape[0]
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bias = torch.empty(
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batch_size,
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num_heads,
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seq_len,
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padded_len,
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device=alibi_slopes.device,
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dtype=dtype,
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)[:, :, :, :seq_len].copy_(bias)
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bias.mul_(alibi_slopes[:, None, None])
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if num_heads != num_kv_heads:
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bias = bias.unflatten(1, (num_kv_heads, num_heads // num_kv_heads))
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attn_bias = LowerTriangularMaskWithTensorBias(bias)
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return attn_bias
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def _paged_attention(
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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input_metadata: InputMetadata,
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num_kv_heads: int,
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scale: float,
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alibi_slopes: Optional[torch.Tensor],
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) -> torch.Tensor:
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output = torch.empty_like(query)
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block_size = value_cache.shape[3]
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num_seqs, num_heads, head_size = query.shape
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max_num_partitions = (
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(input_metadata.max_context_len + _PARTITION_SIZE - 1) //
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_PARTITION_SIZE)
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# NOTE(woosuk): We use a simple heuristic to decide whether to use
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# PagedAttention V1 or V2. If the number of partitions is 1, we use
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# V1 to avoid the overhead of reduction. Also, if the number of
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# sequences or heads is large, we use V1 since there is enough work
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# to parallelize.
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# TODO(woosuk): Tune this heuristic.
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# For context len > 8192, use V2 kernel to avoid shared memory shortage.
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use_v1 = input_metadata.max_context_len <= 8192 and (
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max_num_partitions == 1 or num_seqs * num_heads > 512)
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if use_v1:
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# Run PagedAttention V1.
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ops.paged_attention_v1(
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output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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input_metadata.block_tables,
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input_metadata.context_lens,
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block_size,
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input_metadata.max_context_len,
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alibi_slopes,
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)
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else:
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# Run PagedAttention V2.
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assert _PARTITION_SIZE % block_size == 0
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tmp_output = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions, head_size),
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dtype=output.dtype,
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device=output.device,
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)
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exp_sums = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions),
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dtype=torch.float32,
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device=output.device,
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)
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max_logits = torch.empty_like(exp_sums)
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ops.paged_attention_v2(
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output,
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exp_sums,
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max_logits,
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tmp_output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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input_metadata.block_tables,
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input_metadata.context_lens,
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block_size,
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input_metadata.max_context_len,
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alibi_slopes,
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)
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return output
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