297 lines
10 KiB
Python
297 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from dataclasses import dataclass
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from typing import List, Optional, Tuple
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from vllm.platforms import current_platform
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from vllm_kunlun.ops._kunlun_ops import KunlunOps as ops
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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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@dataclass
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class PagedAttentionMetadata:
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"""Metadata for PagedAttention."""
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# (batch_size,). The length of sequences (entire tokens seen so far) per
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# sequence.
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seq_lens_tensor: Optional[torch.Tensor]
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# Maximum sequence length in the batch. 0 if it is prefill-only batch.
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max_decode_seq_len: int
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# (batch_size, max_blocks_per_seq).
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# Block addresses per sequence. (Seq id -> list of physical block)
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# E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
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# in the kv cache. Each block can contain up to block_size tokens.
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# 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
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# captured.
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block_tables: Optional[torch.Tensor]
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class PagedAttention:
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@staticmethod
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def get_supported_head_sizes() -> List[int]:
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return [32, 64, 80, 96, 112, 120, 128, 192, 256]
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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"""
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获取KV缓存的形状，根据是否在芯片上进行计算返回不同的形状。
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如果在芯片上（is_kunlun()为True），则返回形状(2, num_blocks, num_kv_heads, block_size, head_size)；
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否则，返回形状(2, num_blocks, block_size * num_kv_heads * head_size)。
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Args:
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num_blocks (int): 块数量。
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block_size (int): 每个块大小。
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num_kv_heads (int): KV头数量。
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head_size (int): 每个头大小。
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Returns:
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Tuple[int, ...]: KV缓存的形状，包括两个元素：第一个元素为2，表示维度数量为2；第二个元素为num_blocks、num_kv_heads、block_size和head_size中的任意一个。
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"""
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if current_platform.is_kunlun():
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return (2, num_blocks, num_kv_heads, block_size, head_size)
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return (2, num_blocks, block_size * num_kv_heads * head_size)
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@staticmethod
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def split_kv_cache(
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kv_cache: torch.Tensor,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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将一个缓存张量（包含key和value）分成两部分，每个部分是一个张量。
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如果在KUNLUN上运行，则返回的第一个张量是key缓存，第二个张量是value缓存。
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否则，第一个张量是key缓存，第二个张量是key缓存的view，其形状为(num_blocks, num_kv_heads, head_size//x, -1, x)，
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第三个张量是value缓存，其形状为(num_blocks, num_kv_heads, head_size, -1)。
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Args:
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kv_cache (torch.Tensor): 包含key和value的张量，形状为(2, num_blocks, kv_cache_size)。
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num_kv_heads (int): 多头注意力中的头数。
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head_size (int): 每个头的大小。
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Returns:
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Tuple[torch.Tensor, torch.Tensor]:
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- key_cache (torch.Tensor): 形状为(num_blocks, num_kv_heads, head_size//x, -1, x)，包含key缓存。
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- value_cache (torch.Tensor): 形状为(num_blocks, num_kv_heads, head_size, -1)，包含value缓存。
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"""
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x = 16 // kv_cache.element_size()
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num_blocks = kv_cache.shape[1]
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if current_platform.is_kunlun():
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key_cache = kv_cache[0]
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value_cache = kv_cache[1]
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else:
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key_cache = kv_cache[0]
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key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
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-1, x)
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value_cache = kv_cache[1]
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value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
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return key_cache, value_cache
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@staticmethod
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def write_to_paged_cache(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slot_mapping: torch.Tensor,
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kv_cache_dtype: str,
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k_scale: torch.Tensor,
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v_scale: torch.Tensor,
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) -> None:
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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.flatten(),
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kv_cache_dtype,
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k_scale,
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v_scale,
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)
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@staticmethod
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def forward_decode(
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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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block_tables: torch.Tensor,
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seq_lens: torch.Tensor,
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context_lens_cpu: torch.Tensor,
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is_context,
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max_seq_len: int,
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kv_cache_dtype: str,
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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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k_scale: torch.Tensor,
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v_scale: torch.Tensor,
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tp_rank: int = 0,
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blocksparse_local_blocks: int = 0,
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blocksparse_vert_stride: int = 0,
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blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> torch.Tensor:
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if blocksparse_vert_stride is not None and blocksparse_vert_stride > 1:
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# use blocksparse paged attention
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block_size = value_cache.size(-1)
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assert (blocksparse_block_size > 0 and
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blocksparse_block_size % block_size == 0), \
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(f"{blocksparse_block_size=} needs to be a multiple of"
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f"{block_size=} used in block_tables.")
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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 = ((max_seq_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 = (max_seq_len <= 8192
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and (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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block_tables,
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seq_lens,
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context_lens_cpu,
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is_context,
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block_size,
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max_seq_len,
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alibi_slopes,
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kv_cache_dtype,
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k_scale,
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v_scale,
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tp_rank,
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blocksparse_local_blocks,
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blocksparse_vert_stride,
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blocksparse_block_size,
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blocksparse_head_sliding_step,
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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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block_tables,
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seq_lens,
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context_lens_cpu,
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is_context,
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block_size,
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max_seq_len,
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alibi_slopes,
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kv_cache_dtype,
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k_scale,
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v_scale,
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tp_rank,
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blocksparse_local_blocks,
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blocksparse_vert_stride,
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blocksparse_block_size,
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blocksparse_head_sliding_step,
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)
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return output
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@staticmethod
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def forward_prefix(
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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_dtype: str,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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block_tables: torch.Tensor,
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query_start_loc: torch.Tensor,
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seq_lens_tensor: torch.Tensor,
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max_query_len: int,
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alibi_slopes: Optional[torch.Tensor],
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sliding_window: Optional[int],
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k_scale: torch.Tensor,
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v_scale: torch.Tensor,
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) -> torch.Tensor:
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output = torch.empty_like(query)
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max_seq_len = None
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context_attention_fwd(
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query,
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key,
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value,
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output,
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kv_cache_dtype,
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key_cache,
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value_cache,
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block_tables,
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# query_start_loc is (batch_size + 1,)
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query_start_loc,
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seq_lens_tensor,
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max_seq_len,
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max_query_len,
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k_scale,
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v_scale,
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alibi_slopes,
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sliding_window,
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)
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return output
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@staticmethod
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def swap_blocks(
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src_kv_cache: torch.Tensor,
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dst_kv_cache: torch.Tensor,
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src_to_dst: torch.Tensor,
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) -> None:
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src_key_cache = src_kv_cache[0]
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dst_key_cache = dst_kv_cache[0]
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ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
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src_value_cache = src_kv_cache[1]
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dst_value_cache = dst_kv_cache[1]
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ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst)
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: torch.Tensor,
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) -> None:
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key_caches = [kv_cache[0] for kv_cache in kv_caches]
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value_caches = [kv_cache[1] for kv_cache in kv_caches]
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ops.copy_blocks(key_caches, value_caches, src_to_dists) |