feat: page size > 1 support

lightllm/common/basemodel/attention/create_utils.py

@@ -2,7 +2,7 @@
 
 import os
 import torch
-from lightllm.utils.envs_utils import get_env_start_args
+from lightllm.utils.envs_utils import get_env_start_args, get_page_size
 from lightllm.utils.log_utils import init_logger
 from lightllm.utils.backend_validator import validate
 from .base_att import BaseAttBackend
@@ -13,18 +13,24 @@
 from .fa3.fp import Fa3AttBackend
 from .fa3.fp8 import Fp8Fa3AttBackend
 from .fa3.mla import MlaFa3AttBackend
+from .paged_fa3.fp import PagedFa3AttBackend
+from .paged_fa3.mla import PagedMlaFa3AttBackend
 from .flashinfer.fp8 import Fp8FlashInferAttBackend
 from .flashinfer.fp import FlashInferAttBackend
 from .flashinfer.mla import MlaFlashInferAttBackend
+from .paged_flashinfer.fp import PagedFlashInferAttBackend
+from .paged_flashinfer.mla import PagedMlaFlashInferAttBackend
 
 logger = init_logger(__name__)
 
+_PAGE_ENABLED = get_page_size() > 1
+
 # Backend class mappings by data type
 data_type_to_backend = {
     "None": {
-        "triton": TritonAttBackend,
-        "fa3": Fa3AttBackend,
-        "flashinfer": FlashInferAttBackend,
+        "triton": TritonAttBackend,  # triton后端已支持任意page size
+        "fa3": PagedFa3AttBackend if _PAGE_ENABLED else Fa3AttBackend,
+        "flashinfer": PagedFlashInferAttBackend if _PAGE_ENABLED else FlashInferAttBackend,
     },
     "int4kv": {
         "triton": Int4kvTritonAttBackend,
@@ -41,8 +47,8 @@
 mla_data_type_to_backend = {
     "None": {
         "triton": MlaTritonAttBackend,
-        "fa3": MlaFa3AttBackend,
-        "flashinfer": MlaFlashInferAttBackend,
+        "fa3": PagedMlaFa3AttBackend if _PAGE_ENABLED else MlaFa3AttBackend,
+        "flashinfer": PagedMlaFlashInferAttBackend if _PAGE_ENABLED else MlaFlashInferAttBackend,
     },
 }
 

lightllm/common/basemodel/attention/fa3/fp.py

@@ -66,15 +66,15 @@ def prefill_att(
         alloc_func=torch.empty,
     ) -> torch.Tensor:
         assert att_control.use_alibi is False
-        return self._nomarl_prefill_att(
+        return self._normal_prefill_att(
             q=q,
             k=k,
             v=v,
             att_control=att_control,
             alloc_func=alloc_func,
         )
 
-    def _nomarl_prefill_att(
+    def _normal_prefill_att(
         self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, att_control: AttControl, alloc_func=torch.empty
     ) -> torch.Tensor:
         self.backend: Fa3AttBackend = self.backend  # for typing

lightllm/common/basemodel/attention/flashinfer/fp.py

@@ -99,14 +99,14 @@ def prefill_att(
             and att_control.use_sliding_window is False
             and att_control.use_att_sink is False
         )
-        return self._nomarl_prefill_att(
+        return self._normal_prefill_att(
             q=q,
             k=k,
             v=v,
             alloc_func=alloc_func,
         )
 
-    def _nomarl_prefill_att(
+    def _normal_prefill_att(
         self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, alloc_func=torch.empty
     ) -> torch.Tensor:
         self.backend: FlashInferAttBackend = self.backend  # for typing

lightllm/common/basemodel/attention/paged_fa3/fp.py

@@ -0,0 +1,188 @@
+import dataclasses
+import torch
+import triton
+from ..base_att import BaseAttBackend, BasePrefillAttState, BaseDecodeAttState, AttControl
+from lightllm.utils.dist_utils import get_current_device_id
+from lightllm.utils.sgl_utils import flash_attn_with_kvcache
+from lightllm.utils.envs_utils import get_env_start_args, get_page_size
+from lightllm.common.basemodel.triton_kernel.fa3_utils import page_table_copy
+from lightllm.common.basemodel.triton_kernel.gen_prefill_params import gen_cumsum_pad0_tensor
+
+
+class PagedFa3AttBackend(BaseAttBackend):
+    def __init__(self, model, page_size=None):
+        super().__init__(model=model)
+        self.page_size = page_size or get_page_size()
+        self.get_page_table_buffer()
+
+    def get_page_table_buffer(self):
+        model = self.model
+        if not hasattr(self, "_shared_page_table_buffer"):
+            shared_len = model.graph_max_batch_size * triton.cdiv(model.graph_max_len_in_batch, self.page_size)
+            self._shared_page_table_buffer = [
+                torch.empty(shared_len, dtype=torch.int32).to(get_current_device_id()),
+                torch.empty(shared_len, dtype=torch.int32).to(get_current_device_id()),
+            ]
+        return self._shared_page_table_buffer
+
+    def create_att_prefill_state(self, infer_state):
+        return PagedFa3PrefillAttState(backend=self, infer_state=infer_state)
+
+    def create_att_decode_state(self, infer_state):
+        return PagedFa3DecodeAttState(backend=self, infer_state=infer_state)
+
+
+@dataclasses.dataclass
+class PagedFa3PrefillAttState(BasePrefillAttState):
+    cu_seqlens_q: torch.Tensor = None
+    cu_seqlens_k: torch.Tensor = None
+    page_table: torch.Tensor = None
+
+    def init_state(self):
+        self.cu_seqlens_q = self.infer_state.b1_cu_q_seq_len.int()
+        self.cu_seqlens_k = self.infer_state.b1_cu_kv_seq_len.int()
+        table_len = triton.cdiv(self.infer_state.max_kv_seq_len, self.backend.page_size)
+        self.page_table = torch.empty(
+            (self.infer_state.batch_size, table_len),
+            dtype=torch.int32,
+            device=self.infer_state.input_ids.device,
+        )
+        page_table_copy(
+            page_table=self.page_table,
+            req_to_token_indexs=self.infer_state.req_manager.req_to_token_indexs,
+            b_req_idx=self.infer_state.b_req_idx,
+        )
+
+    def prefill_att(self, q, k, v, att_control: AttControl = AttControl(), alloc_func=torch.empty):
+        assert att_control.use_alibi is False
+        return self._normal_prefill_att(q=q, k=k, v=v, att_control=att_control, alloc_func=alloc_func)
+
+    def _normal_prefill_att(self, q, k, v, att_control: AttControl, alloc_func=torch.empty):
+        if att_control.use_sliding_window:
+            window_size = att_control.sliding_window
+        else:
+            window_size = (-1, -1)
+
+        if att_control.use_att_sink:
+            sink_weight = att_control.sink_weight
+        else:
+            sink_weight = None
+
+        sm_scale = 1.0 / (q.shape[-1] ** 0.5)
+        return flash_attn_with_kvcache(
+            q=q,
+            k_cache=k.view(-1, self.backend.page_size, k.shape[1], k.shape[2]),
+            v_cache=v.view(-1, self.backend.page_size, v.shape[1], v.shape[2]),
+            page_table=self.page_table,
+            cache_seqlens=self.infer_state.b_seq_len,
+            cu_seqlens_q=self.cu_seqlens_q,
+            cu_seqlens_k_new=self.cu_seqlens_k,
+            max_seqlen_q=self.infer_state.max_q_seq_len,
+            softmax_scale=sm_scale,
+            causal=True,
+            window_size=window_size,
+            softcap=0.0,
+            k_descale=None,
+            v_descale=None,
+            return_softmax_lse=False,
+            sinks=sink_weight,
+        )
+
+
+@dataclasses.dataclass
+class PagedFa3DecodeAttState(BaseDecodeAttState):
+    cu_seqlens_q: torch.Tensor = None
+    cu_seqlens_k: torch.Tensor = None
+    page_table: torch.Tensor = None
+    b_att_seq_len: torch.Tensor = None
+    decode_max_q_seq_len: int = None
+
+    def init_state(self):
+        args_mtp_step = get_env_start_args().mtp_step
+        if args_mtp_step > 0:
+            mtp_size = args_mtp_step + 1
+            b_q_seq_len = torch.full(
+                (self.infer_state.b_seq_len.shape[0] // mtp_size,),
+                fill_value=mtp_size,
+                dtype=torch.int32,
+                device=self.infer_state.b_seq_len.device,
+            )
+            b_kv_seq_len = self.infer_state.b_seq_len[mtp_size - 1 :: mtp_size]
+            b1_cu_q_seq_len, b1_cu_kv_seq_len = gen_cumsum_pad0_tensor(b_q_seq_len, b_kv_seq_len)
+            self.cu_seqlens_q = b1_cu_q_seq_len.int()
+            self.cu_seqlens_k = b1_cu_kv_seq_len.int()
+        else:
+            self.cu_seqlens_q = self.infer_state.b1_cu_q_seq_len.int()
+            self.cu_seqlens_k = self.infer_state.b1_cu_kv_seq_len.int()
+
+        att_batch_size = self.infer_state.batch_size // (args_mtp_step + 1)
+        assert self.infer_state.batch_size % (args_mtp_step + 1) == 0
+        model = self.backend.model
+        table_len = triton.cdiv(self.infer_state.max_kv_seq_len, self.backend.page_size)
+        if (
+            self.infer_state.batch_size <= model.graph_max_batch_size
+            and self.infer_state.max_kv_seq_len <= model.graph_max_len_in_batch
+        ):
+            page_buffer = self.backend.get_page_table_buffer()
+            shared_table_len = triton.cdiv(model.graph_max_len_in_batch, self.backend.page_size)
+            self.page_table = page_buffer[self.infer_state.microbatch_index][
+                : att_batch_size * shared_table_len
+            ].reshape(att_batch_size, shared_table_len)
+        else:
+            self.page_table = torch.empty(
+                (att_batch_size, table_len),
+                dtype=torch.int32,
+                device=self.infer_state.input_ids.device,
+            )
+
+        if args_mtp_step > 0:
+            page_table_copy(
+                page_table=self.page_table[:, :table_len],
+                req_to_token_indexs=model.req_manager.req_to_token_indexs,
+                b_req_idx=self.infer_state.b_req_idx[args_mtp_step :: (args_mtp_step + 1)],
+            )
+            self.b_att_seq_len = self.infer_state.b_seq_len[args_mtp_step :: (args_mtp_step + 1)].contiguous()
+            self.decode_max_q_seq_len = args_mtp_step + 1
+        else:
+            page_table_copy(
+                page_table=self.page_table[:, :table_len],
+                req_to_token_indexs=model.req_manager.req_to_token_indexs,
+                b_req_idx=self.infer_state.b_req_idx,
+            )
+            self.b_att_seq_len = self.infer_state.b_seq_len
+            self.decode_max_q_seq_len = 1
+
+    def decode_att(self, q, k, v, att_control: AttControl = AttControl(), alloc_func=torch.empty):
+        assert att_control.use_alibi is False
+        return self._normal_decode_att(q=q, k=k, v=v, att_control=att_control, alloc_func=alloc_func)
+
+    def _normal_decode_att(self, q, k, v, att_control: AttControl, alloc_func=torch.empty):
+        if att_control.use_sliding_window:
+            window_size = att_control.sliding_window
+        else:
+            window_size = (-1, -1)
+
+        if att_control.use_att_sink:
+            sink_weight = att_control.sink_weight
+        else:
+            sink_weight = None
+
+        sm_scale = 1.0 / (q.shape[-1] ** 0.5)
+        return flash_attn_with_kvcache(
+            q=q,
+            k_cache=k.view(-1, self.backend.page_size, k.shape[1], k.shape[2]),
+            v_cache=v.view(-1, self.backend.page_size, v.shape[1], v.shape[2]),
+            page_table=self.page_table,
+            cache_seqlens=self.b_att_seq_len,
+            cu_seqlens_q=self.cu_seqlens_q,
+            cu_seqlens_k_new=self.cu_seqlens_k,
+            max_seqlen_q=self.decode_max_q_seq_len,
+            softmax_scale=sm_scale,
+            causal=True,
+            window_size=window_size,
+            softcap=0.0,
+            k_descale=None,
+            v_descale=None,
+            return_softmax_lse=False,
+            sinks=sink_weight,
+        )