[fix] fix illegal mem access and clean up triton attention backend (#4571)

python/sglang/srt/layers/attention/base_attn_backend.py

@@ -39,7 +39,6 @@ class AttentionBackend(ABC):
     def init_forward_metadata_replay_cuda_graph(
         self,
         bs: int,
-        num_kv_heads: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         seq_lens_sum: int,

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -349,7 +349,6 @@ class FlashInferAttnBackend(AttentionBackend):
     def init_forward_metadata_replay_cuda_graph(
         self,
         bs: int,
-        num_kv_heads: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         seq_lens_sum: int,
@@ -1063,7 +1062,6 @@ class FlashInferMultiStepDraftBackend:
         def call_fn(i, forward_batch):
             self.attn_backends[i].init_forward_metadata_replay_cuda_graph(
                 bs,
-                -1,
                 forward_batch.req_pool_indices,
                 forward_batch.seq_lens,
                 seq_lens_sum=-1,

python/sglang/srt/layers/attention/flashinfer_mla_backend.py

@@ -279,7 +279,6 @@ class FlashInferMLAAttnBackend(AttentionBackend):
     def init_forward_metadata_replay_cuda_graph(
         self,
         bs: int,
-        num_kv_heads: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         seq_lens_sum: int,
@@ -792,7 +791,6 @@ class FlashInferMLAMultiStepDraftBackend:
         def call_fn(i, forward_batch):
             self.attn_backends[i].init_forward_metadata_replay_cuda_graph(
                 bs,
-                -1,
                 forward_batch.req_pool_indices,
                 forward_batch.seq_lens,
                 seq_lens_sum=-1,

python/sglang/srt/layers/attention/triton_backend.py

 from __future__ import annotations
 
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Optional, Union
 
 import torch
@@ -22,20 +23,21 @@ if TYPE_CHECKING:
 def get_num_kv_splits_triton(
     num_kv_splits_ptr,
     seq_lens_ptr,
-    bs,
+    num_seq,
+    num_group,
     num_head,
     num_kv_head,
     max_kv_splits,
     device_core_count,
-    MAX_BS: tl.constexpr,
+    MAX_NUM_SEQ: tl.constexpr,
 ):
-    # TODO: this method is tunable
-    offs_b = tl.arange(0, MAX_BS)
-    mask_b = offs_b < bs
+    # TODO: this method is tunable, we need more online serving data to tune it
+    offs_seq = tl.arange(0, MAX_NUM_SEQ)
+    mask_seq = offs_seq < num_seq
 
-    seq_lens = tl.load(seq_lens_ptr + offs_b, mask=mask_b, other=0)
+    seq_lens = tl.load(seq_lens_ptr + offs_seq, mask=mask_seq, other=0)
     max_seq_len = tl.max(seq_lens)
-    seq_lens = tl.load(seq_lens_ptr + offs_b, mask=mask_b, other=max_seq_len)
+    seq_lens = tl.load(seq_lens_ptr + offs_seq, mask=mask_seq, other=max_seq_len)
     min_seq_len = tl.min(seq_lens)
     if max_seq_len * 8 < min_seq_len * 10:
         min_seq_len = max_seq_len
@@ -43,24 +45,43 @@ def get_num_kv_splits_triton(
     kv_chunk_size_1 = tl.cdiv(max_seq_len, max_kv_splits_1)
 
     # NOTE: this is a hack to let num_kv_split grows up with seqlen gradually
-    ext_seq_len = tl.cast(tl.cdiv(max_seq_len, 256), tl.float32)
-    ext_device_core_count = device_core_count * tl.maximum(
-        tl.cast(tl.ceil(tl.log2(ext_seq_len)), tl.int32), 1
+    ext_seq_len = tl.cast(max_seq_len, tl.float32) / 64.0
+    ext_device_core_count = tl.cast(
+        device_core_count * tl.maximum(tl.log2(ext_seq_len), 1.0), tl.int32
     )
     block_h, num_kv_group = 16, num_head // num_kv_head
     if num_kv_group == 1:
-        bh_grid = bs * num_head
+        token_grid = num_seq * num_group * num_head
     else:
         # from triton_ops/decode_attention.py:_decode_grouped_att_m_fwd
         block_h = tl.minimum(block_h, num_kv_group)
-        bh_grid = bs * tl.cdiv(num_head, block_h)
-    max_kv_splits_2 = tl.minimum(tl.cdiv(ext_device_core_count, bh_grid), max_kv_splits)
+        token_grid = num_seq * num_group * tl.cdiv(num_head, block_h)
+    max_kv_splits_2 = tl.minimum(
+        tl.cdiv(ext_device_core_count, token_grid), max_kv_splits
+    )
     kv_chunk_size_2 = tl.cdiv(max_seq_len, max_kv_splits_2)
 
     num_kv_splits = tl.maximum(
         tl.cdiv(seq_lens, kv_chunk_size_1), tl.cdiv(seq_lens, kv_chunk_size_2)
     )
-    tl.store(num_kv_splits_ptr + offs_b, num_kv_splits, mask=mask_b)
+
+    offs_token = offs_seq * num_group
+    mask_token = offs_token < num_seq * num_group
+    for i in range(0, num_group):
+        tl.store(num_kv_splits_ptr + i + offs_token, num_kv_splits, mask=mask_token)
+
+
+@dataclass
+class ForwardMetadata:
+    attn_logits: torch.Tensor
+    attn_lse: torch.Tensor
+    max_extend_len: int
+    num_kv_splits: torch.Tensor
+    kv_indptr: torch.Tensor
+    kv_indices: torch.Tensor
+    qo_indptr: torch.Tensor
+    custom_mask: torch.Tensor
+    mask_indptr: torch.Tensor
 
 
 class TritonAttnBackend(AttentionBackend):
@@ -110,6 +131,9 @@ class TritonAttnBackend(AttentionBackend):
         self.num_head = (
             model_runner.model_config.num_attention_heads // get_attention_tp_size()
         )
+        self.num_kv_head = model_runner.model_config.get_num_kv_heads(
+            get_attention_tp_size()
+        )
 
         self.static_kv_splits = get_bool_env_var(
             "SGLANG_TRITON_DECODE_ATTN_STATIC_KV_SPLITS", "false"
@@ -117,7 +141,7 @@ class TritonAttnBackend(AttentionBackend):
         self.max_kv_splits = model_runner.server_args.triton_attention_num_kv_splits
         self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]
 
-        self.forward_metadata = None
+        self.forward_metadata: ForwardMetadata = None
 
         self.max_context_len = model_runner.model_config.context_len
 
@@ -128,23 +152,33 @@ class TritonAttnBackend(AttentionBackend):
         self,
         num_kv_splits: torch.Tensor,
         seq_lens: torch.Tensor,
-        bs: int,
-        num_kv_head: int,
     ):
-        MAX_SCHEDULE_BS = 4096
-        if self.static_kv_splits or self.device_core_count <= 0 or bs > MAX_SCHEDULE_BS:
+        num_token, num_seq = num_kv_splits.shape[0], seq_lens.shape[0]
+        num_group = num_token // num_seq
+
+        assert (
+            num_group * num_seq == num_token
+        ), f"num_seq({num_seq}), num_token({num_token}), something goes wrong!"
+
+        if self.static_kv_splits or self.device_core_count <= 0:
             num_kv_splits.fill_(self.max_kv_splits)
             return
 
+        if num_seq < 256:
+            SCHEDULE_SEQ = 256
+        else:
+            SCHEDULE_SEQ = triton.next_power_of_2(num_seq)
+
         get_num_kv_splits_triton[(1,)](
             num_kv_splits,
             seq_lens,
-            bs,
+            num_seq,
+            num_group,
             self.num_head,
-            num_kv_head,
+            self.num_kv_head,
             self.max_kv_splits,
             self.device_core_count,
-            MAX_BS=MAX_SCHEDULE_BS,
+            MAX_NUM_SEQ=SCHEDULE_SEQ,
         )
 
     def init_forward_metadata(self, forward_batch: ForwardBatch):
@@ -174,36 +208,19 @@ class TritonAttnBackend(AttentionBackend):
                 kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
                 bs = kv_indptr.shape[0] - 1
 
-            attn_logits = [
-                torch.empty(
-                    (
-                        bs,
-                        self.num_head,
-                        self.max_kv_splits,
-                        self.v_head_dim,
-                    ),
-                    dtype=torch.float32,
-                    device=self.device,
-                ),
-                torch.empty(
-                    (
-                        bs,
-                        self.num_head,
-                        self.max_kv_splits,
-                    ),
-                    dtype=torch.float32,
-                    device=self.device,
-                ),
-            ]
+            attn_logits = torch.empty(
+                (bs, self.num_head, self.max_kv_splits, self.v_head_dim),
+                dtype=torch.float32,
+                device=self.device,
+            )
+            attn_lse = torch.empty(
+                (bs, self.num_head, self.max_kv_splits),
+                dtype=torch.float32,
+                device=self.device,
+            )
             num_kv_splits = torch.empty((bs,), dtype=torch.int32, device=self.device)
 
-            num_kv_heads = self.num_head
-            if hasattr(forward_batch.token_to_kv_pool, "k_buffer"):
-                if isinstance(forward_batch.token_to_kv_pool.k_buffer, list):
-                    num_kv_heads = forward_batch.token_to_kv_pool.k_buffer[0].shape[1]
-            self.get_num_kv_splits(
-                num_kv_splits, forward_batch.seq_lens, bs, num_kv_heads
-            )
+            self.get_num_kv_splits(num_kv_splits, forward_batch.seq_lens)
 
             qo_indptr = None
             custom_mask = None
@@ -244,6 +261,7 @@ class TritonAttnBackend(AttentionBackend):
             max_extend_len = self.num_draft_tokens
             num_kv_splits = None
             attn_logits = None
+            attn_lse = None
         elif forward_batch.forward_mode.is_draft_extend():
             kv_indices, kv_indptr, qo_indptr, custom_mask = (
                 spec_info.generate_attn_arg_prefill(
@@ -254,9 +272,13 @@ class TritonAttnBackend(AttentionBackend):
                 )
             )
             mask_indptr = None
+            # TODO(FIXME): This will trigger an invalid Eagle tree when using
+            # `max(spec_info.accept_length_cpu)`.
+            # It might have been forgotten to update somewhere.
             max_extend_len = torch.max(spec_info.accept_length).item()
             num_kv_splits = None
             attn_logits = None
+            attn_lse = None
         else:
             kv_indptr[1 : bs + 1] = torch.cumsum(
                 forward_batch.extend_prefix_lens, dim=0
@@ -283,11 +305,13 @@ class TritonAttnBackend(AttentionBackend):
             custom_mask = None
             mask_indptr = None
             attn_logits = None
+            attn_lse = None
             max_extend_len = torch.max(forward_batch.extend_seq_lens).item()
             num_kv_splits = None
 
-        self.forward_metadata = (
+        self.forward_metadata = ForwardMetadata(
             attn_logits,
+            attn_lse,
             max_extend_len,
             num_kv_splits,
             kv_indptr,
@@ -300,18 +324,16 @@ class TritonAttnBackend(AttentionBackend):
     def init_cuda_graph_state(
         self, max_bs: int, kv_indices_buf: Optional[torch.Tensor] = None
     ):
-        self.cuda_graph_attn_logits = [
-            torch.zeros(
-                (max_bs, self.num_head, self.max_kv_splits, self.v_head_dim),
-                dtype=torch.float32,
-                device=self.device,
-            ),
-            torch.zeros(
-                (max_bs, self.num_head, self.max_kv_splits),
-                dtype=torch.float32,
-                device=self.device,
-            ),
-        ]
+        self.cuda_graph_attn_logits = torch.zeros(
+            (max_bs, self.num_head, self.max_kv_splits, self.v_head_dim),
+            dtype=torch.float32,
+            device=self.device,
+        )
+        self.cuda_graph_attn_lse = torch.zeros(
+            (max_bs, self.num_head, self.max_kv_splits),
+            dtype=torch.float32,
+            device=self.device,
+        )
         self.cuda_graph_num_kv_splits = torch.full(
             (max_bs,), self.max_kv_splits, dtype=torch.int32, device=self.device
         )
@@ -362,6 +384,7 @@ class TritonAttnBackend(AttentionBackend):
                 kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
 
             attn_logits = self.cuda_graph_attn_logits
+            attn_lse = self.cuda_graph_attn_lse
             max_extend_len = None
             num_kv_splits = self.cuda_graph_num_kv_splits
             qo_indptr = None
@@ -396,13 +419,15 @@ class TritonAttnBackend(AttentionBackend):
             max_extend_len = self.num_draft_tokens
             num_kv_splits = None
             attn_logits = None
+            attn_lse = None
         else:
             raise ValueError(
                 f"Invalid forward mode: {forward_mode=} for CUDA Graph capture."
             )
 
-        self.forward_metadata = (
+        self.forward_metadata = ForwardMetadata(
             attn_logits,
+            attn_lse,
             max_extend_len,
             num_kv_splits,
             kv_indptr,
@@ -415,7 +440,6 @@ class TritonAttnBackend(AttentionBackend):
     def init_forward_metadata_replay_cuda_graph(
         self,
         bs: int,
-        num_kv_head: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         seq_lens_sum: int,
@@ -442,10 +466,12 @@ class TritonAttnBackend(AttentionBackend):
                     kv_indices,
                     self.req_to_token.stride(0),
                 )
+                num_token = bs
             else:
                 kv_indptr[: spec_info.kv_indptr.shape[0]] = spec_info.kv_indptr
                 kv_indices[: spec_info.kv_indices.shape[0]] = spec_info.kv_indices
-            self.get_num_kv_splits(num_kv_splits, seq_lens, bs, num_kv_head)
+                num_token = spec_info.kv_indptr.shape[0] - 1
+            self.get_num_kv_splits(num_kv_splits[:num_token], seq_lens[:bs])
         elif forward_mode.is_target_verify():
             # Update qo_indptr, kv_indptr, kv_indices, custom_mask, mask_indptr
             bs = len(req_pool_indices)
@@ -502,17 +528,6 @@ class TritonAttnBackend(AttentionBackend):
                 layer, forward_batch.out_cache_loc, k, v
             )
 
-        (
-            _,
-            max_extend_len,
-            _,
-            kv_indptr,
-            kv_indices,
-            qo_indptr,
-            custom_mask,
-            mask_indptr,
-        ) = self.forward_metadata
-
         self.extend_attention_fwd(
             q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
             k.contiguous(),
@@ -520,12 +535,12 @@ class TritonAttnBackend(AttentionBackend):
             o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
             forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
             forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
-            qo_indptr,
-            kv_indptr,
-            kv_indices,
-            custom_mask,
-            mask_indptr,
-            max_extend_len,
+            self.forward_metadata.qo_indptr,
+            self.forward_metadata.kv_indptr,
+            self.forward_metadata.kv_indices,
+            self.forward_metadata.custom_mask,
+            self.forward_metadata.mask_indptr,
+            self.forward_metadata.max_extend_len,
             layer.scaling,
             layer.logit_cap,
         )
@@ -550,10 +565,6 @@ class TritonAttnBackend(AttentionBackend):
         else:
             o = torch.empty_like(q)
 
-        attn_logits, _, num_kv_splits, kv_indptr, kv_indices, _, _, _ = (
-            self.forward_metadata
-        )
-
         if save_kv_cache:
             forward_batch.token_to_kv_pool.set_kv_buffer(
                 layer, forward_batch.out_cache_loc, k, v
@@ -564,10 +575,11 @@ class TritonAttnBackend(AttentionBackend):
             forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
             forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
             o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
-            kv_indptr,
-            kv_indices,
-            attn_logits,
-            num_kv_splits,
+            self.forward_metadata.kv_indptr,
+            self.forward_metadata.kv_indices,
+            self.forward_metadata.attn_logits,
+            self.forward_metadata.attn_lse,
+            self.forward_metadata.num_kv_splits,
             self.max_kv_splits,
             layer.scaling,
             layer.logit_cap,
@@ -700,15 +712,9 @@ class TritonMultiStepDraftBackend:
     def init_forward_metadata_replay_cuda_graph(
         self, forward_batch: ForwardBatch, bs: int
     ):
-        num_kv_heads = self.num_head
-        if hasattr(forward_batch.token_to_kv_pool, "k_buffer"):
-            if isinstance(forward_batch.token_to_kv_pool.k_buffer, list):
-                num_kv_heads = forward_batch.token_to_kv_pool.k_buffer[0].shape[1]
-
         def call_fn(i, forward_batch):
             self.attn_backends[i].init_forward_metadata_replay_cuda_graph(
                 bs,
-                num_kv_heads,
                 forward_batch.req_pool_indices,
                 forward_batch.seq_lens,
                 seq_lens_sum=-1,

python/sglang/srt/layers/attention/triton_ops/decode_attention.py

@@ -609,6 +609,7 @@ def decode_attention_fwd_normal(
     kv_indptr,
     kv_indices,
     attn_logits,
+    attn_lse,
     num_kv_splits,
     max_kv_splits,
     sm_scale,
@@ -618,8 +619,8 @@ def decode_attention_fwd_normal(
         q,
         k_buffer,
         v_buffer,
-        attn_logits[0],
-        attn_logits[1],
+        attn_logits,
+        attn_lse,
         kv_indptr,
         kv_indices,
         num_kv_splits,
@@ -628,8 +629,8 @@ def decode_attention_fwd_normal(
         logit_cap,
     )
     _decode_softmax_reducev_fwd(
-        attn_logits[0],
-        attn_logits[1],
+        attn_logits,
+        attn_lse,
         q,
         o,
         v_buffer,
@@ -647,6 +648,7 @@ def decode_attention_fwd_grouped(
     kv_indptr,
     kv_indices,
     attn_logits,
+    attn_lse,
     num_kv_splits,
     max_kv_splits,
     sm_scale,
@@ -656,8 +658,8 @@ def decode_attention_fwd_grouped(
         q,
         k_buffer,
         v_buffer,
-        attn_logits[0],
-        attn_logits[1],
+        attn_logits,
+        attn_lse,
         kv_indptr,
         kv_indices,
         num_kv_splits,
@@ -666,8 +668,8 @@ def decode_attention_fwd_grouped(
         logit_cap,
     )
     _decode_softmax_reducev_fwd(
-        attn_logits[0],
-        attn_logits[1],
+        attn_logits,
+        attn_lse,
         q,
         o,
         v_buffer,
@@ -685,14 +687,15 @@ def decode_attention_fwd(
     kv_indptr,
     kv_indices,
     attn_logits,
+    attn_lse,
     num_kv_splits,
     max_kv_splits,
     sm_scale,
     logit_cap=0.0,
 ):
-    assert max_kv_splits == attn_logits[0].shape[2]
+    assert max_kv_splits == attn_logits.shape[2]
     assert q.shape[0] <= kv_indptr.shape[0] - 1
-    assert q.shape[0] <= attn_logits[0].shape[0]
+    assert q.shape[0] <= attn_logits.shape[0]
 
     kv_group_num = q.shape[1] // v_buffer.shape[1]
 
@@ -706,6 +709,7 @@ def decode_attention_fwd(
             kv_indptr,
             kv_indices,
             attn_logits,
+            attn_lse,
             num_kv_splits,
             max_kv_splits,
             sm_scale,
@@ -721,6 +725,7 @@ def decode_attention_fwd(
             kv_indptr,
             kv_indices,
             attn_logits,
+            attn_lse,
             num_kv_splits,
             max_kv_splits,
             sm_scale,

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -26,7 +26,6 @@ import tqdm
 from sglang.srt.custom_op import CustomOp
 from sglang.srt.distributed import get_tensor_model_parallel_rank
 from sglang.srt.distributed.parallel_state import GroupCoordinator, graph_capture
-from sglang.srt.layers.dp_attention import get_attention_tp_size
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.moe.fused_moe_native import fused_moe_forward_native
 from sglang.srt.layers.torchao_utils import save_gemlite_cache
@@ -196,9 +195,6 @@ class CudaGraphRunner:
         # Attention backend
         self.max_bs = max(self.capture_bs)
         self.max_num_token = self.max_bs * self.num_tokens_per_bs
-        self.num_head = (
-            model_runner.model_config.num_attention_heads // get_attention_tp_size()
-        )
         self.model_runner.attn_backend.init_cuda_graph_state(self.max_num_token)
         self.seq_len_fill_value = (
             self.model_runner.attn_backend.get_cuda_graph_seq_len_fill_value()
@@ -507,15 +503,9 @@ class CudaGraphRunner:
         if hasattr(forward_batch.spec_info, "hidden_states"):
             self.hidden_states[:raw_num_token] = forward_batch.spec_info.hidden_states
 
-        num_kv_heads = self.num_head
-        if hasattr(forward_batch.token_to_kv_pool, "k_buffer"):
-            if isinstance(forward_batch.token_to_kv_pool.k_buffer, list):
-                num_kv_heads = forward_batch.token_to_kv_pool.k_buffer[0].shape[1]
-
         # Attention backend
         self.model_runner.attn_backend.init_forward_metadata_replay_cuda_graph(
             bs,
-            num_kv_heads,
             self.req_pool_indices,
             self.seq_lens,
             forward_batch.seq_lens_sum + (bs - raw_bs),

test/srt/test_triton_attention_kernels.py

@@ -265,7 +265,8 @@ class TestTritonAttention(unittest.TestCase):
             o,
             kv_indptr,
             kv_indices,
-            (attn_logits, attn_lse),
+            attn_logits,
+            attn_lse,
             num_kv_splits,
             max_kv_splits,
             sm_scale,
@@ -329,7 +330,8 @@ class TestTritonAttention(unittest.TestCase):
             o,
             kv_indptr,
             kv_indices,
-            (attn_logits, attn_lse),
+            attn_logits,
+            attn_lse,
             num_kv_splits,
             max_kv_splits,
             sm_scale,
@@ -353,7 +355,8 @@ class TestTritonAttention(unittest.TestCase):
             o_grouped,
             kv_indptr,
             kv_indices,
-            (attn_logits1, attn_lse1),
+            attn_logits1,
+            attn_lse1,
             num_kv_splits,
             max_kv_splits,
             sm_scale,