First commit

2025-08-05 19:02:46 +08:00
parent 9efe891f99
commit 99fb9f5cb0
1412 changed files with 203615 additions and 0 deletions
--- a/vllm/attention/ops/ipex_attn.py
+++ b/vllm/attention/ops/ipex_attn.py
@@ -0,0 +1,123 @@
+from typing import Dict, List, Optional, Tuple
+
+import intel_extension_for_pytorch.llm.modules as ipex_modules
+import torch
+
+from vllm import _custom_ops as ops
+
+
+class PagedAttention:
+
+    @staticmethod
+    def get_supported_head_sizes() -> List[int]:
+        return [64, 80, 96, 112, 128, 256]
+
+    @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)
+
+    @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]
+
+        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
+
+    @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,
+        k_scale: float,
+        v_scale: float,
+        *args,
+    ) -> None:
+        ipex_modules.PagedAttention.reshape_and_cache(
+            key, value, key_cache, value_cache,
+            slot_mapping.flatten().int())
+
+    @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],
+        k_scale: float,
+        v_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)
+
+        return output
+
+    @staticmethod
+    def forward_prefix(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache_dtype: str,
+        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
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: Dict[int, int],
+        *args,
+    ) -> None:
+        raise NotImplementedError
+
+    @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)