ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
124 lines
3.5 KiB
Python
124 lines
3.5 KiB
Python
from typing import Dict, List, Optional, Tuple
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import intel_extension_for_pytorch.llm.modules as ipex_modules
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import torch
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from vllm import _custom_ops as ops
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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 [64, 80, 96, 112, 128, 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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*args,
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) -> Tuple[int, ...]:
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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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*args,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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num_blocks = kv_cache.shape[1]
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key_cache = kv_cache[0]
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key_cache = key_cache.view(num_blocks, num_kv_heads, -1, head_size)
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value_cache = kv_cache[1]
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value_cache = value_cache.view(num_blocks, num_kv_heads, -1, head_size)
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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: float,
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v_scale: float,
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*args,
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) -> None:
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ipex_modules.PagedAttention.reshape_and_cache(
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key, value, key_cache, value_cache,
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slot_mapping.flatten().int())
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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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context_lens: torch.Tensor,
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max_context_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: float,
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v_scale: float,
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*args,
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) -> torch.Tensor:
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output = torch.empty_like(query)
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block_size = value_cache.shape[2]
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head_mapping = torch.arange(
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0,
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num_kv_heads,
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device="cpu",
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dtype=torch.int32,
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).view(num_kv_heads,
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1).repeat_interleave(query.size(1) // num_kv_heads).flatten()
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ipex_modules.PagedAttention.single_query_cached_kv_attention(
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output, query.contiguous(), key_cache, value_cache, head_mapping,
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scale, block_tables, context_lens, block_size, max_context_len,
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alibi_slopes)
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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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subquery_start_loc: torch.Tensor,
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prompt_lens_tensor: torch.Tensor,
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context_lens: torch.Tensor,
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max_subquery_len: int,
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alibi_slopes: Optional[torch.Tensor],
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*args,
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) -> torch.Tensor:
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raise NotImplementedError
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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: Dict[int, int],
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*args,
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) -> None:
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raise NotImplementedError
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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: Dict[int, List[int]],
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*args,
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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)
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