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[Hardware][Intel] Optimize CPU backend and add more performance tips (v…
…llm-project#4971) Co-authored-by: Jianan Gu <jianan.gu@intel.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| from typing import Dict, List, Optional, Tuple | ||
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| import intel_extension_for_pytorch.llm.modules as ipex_modules | ||
| import torch | ||
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| from vllm import _custom_ops as ops | ||
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| class PagedAttention: | ||
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| @staticmethod | ||
| def get_supported_head_sizes() -> List[int]: | ||
| return [64, 80, 96, 112, 128, 256] | ||
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| @staticmethod | ||
| def get_kv_cache_shape( | ||
| num_blocks: int, | ||
| block_size: int, | ||
| num_kv_heads: int, | ||
| head_size: int, | ||
| *args, | ||
| ) -> Tuple[int, ...]: | ||
| return (2, num_blocks, block_size * num_kv_heads * head_size) | ||
|
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| @staticmethod | ||
| def split_kv_cache( | ||
| kv_cache: torch.Tensor, | ||
| num_kv_heads: int, | ||
| head_size: int, | ||
| *args, | ||
| ) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| num_blocks = kv_cache.shape[1] | ||
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| key_cache = kv_cache[0] | ||
| key_cache = key_cache.view(num_blocks, num_kv_heads, -1, head_size) | ||
| value_cache = kv_cache[1] | ||
| value_cache = value_cache.view(num_blocks, num_kv_heads, -1, head_size) | ||
| return key_cache, value_cache | ||
|
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| @staticmethod | ||
| def write_to_paged_cache( | ||
| key: torch.Tensor, | ||
| value: torch.Tensor, | ||
| key_cache: torch.Tensor, | ||
| value_cache: torch.Tensor, | ||
| slot_mapping: torch.Tensor, | ||
| kv_cache_dtype: str, | ||
| kv_scale: float, | ||
| *args, | ||
| ) -> None: | ||
| ipex_modules.PagedAttention.reshape_and_cache( | ||
| key, value, key_cache, value_cache, | ||
| slot_mapping.flatten().int()) | ||
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| @staticmethod | ||
| def forward_decode( | ||
| query: torch.Tensor, | ||
| key_cache: torch.Tensor, | ||
| value_cache: torch.Tensor, | ||
| block_tables: torch.Tensor, | ||
| context_lens: torch.Tensor, | ||
| max_context_len: int, | ||
| kv_cache_dtype: str, | ||
| num_kv_heads: int, | ||
| scale: float, | ||
| alibi_slopes: Optional[torch.Tensor], | ||
| kv_scale: float, | ||
| *args, | ||
| ) -> torch.Tensor: | ||
| output = torch.empty_like(query) | ||
| block_size = value_cache.shape[2] | ||
| head_mapping = torch.arange( | ||
| 0, | ||
| num_kv_heads, | ||
| device="cpu", | ||
| dtype=torch.int32, | ||
| ).view(num_kv_heads, | ||
| 1).repeat_interleave(query.size(1) // num_kv_heads).flatten() | ||
| ipex_modules.PagedAttention.single_query_cached_kv_attention( | ||
| output, query.contiguous(), key_cache, value_cache, head_mapping, | ||
| scale, block_tables, context_lens, block_size, max_context_len, | ||
| alibi_slopes) | ||
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| return output | ||
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| @staticmethod | ||
| def forward_prefix( | ||
| query: torch.Tensor, | ||
| key: torch.Tensor, | ||
| value: torch.Tensor, | ||
| key_cache: torch.Tensor, | ||
| value_cache: torch.Tensor, | ||
| block_tables: torch.Tensor, | ||
| subquery_start_loc: torch.Tensor, | ||
| prompt_lens_tensor: torch.Tensor, | ||
| context_lens: torch.Tensor, | ||
| max_subquery_len: int, | ||
| alibi_slopes: Optional[torch.Tensor], | ||
| *args, | ||
| ) -> torch.Tensor: | ||
| raise NotImplementedError | ||
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| @staticmethod | ||
| def swap_blocks( | ||
| src_kv_cache: torch.Tensor, | ||
| dst_kv_cache: torch.Tensor, | ||
| src_to_dst: Dict[int, int], | ||
| *args, | ||
| ) -> None: | ||
| raise NotImplementedError | ||
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| @staticmethod | ||
| def copy_blocks( | ||
| kv_caches: List[torch.Tensor], | ||
| src_to_dists: Dict[int, List[int]], | ||
| *args, | ||
| ) -> None: | ||
| key_caches = [kv_cache[0] for kv_cache in kv_caches] | ||
| value_caches = [kv_cache[1] for kv_cache in kv_caches] | ||
| ops.copy_blocks(key_caches, value_caches, src_to_dists) |