feat: add trtllm-gen mha from direct call (#8782)

Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>

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

+from __future__ import annotations
+
+from python.sglang.srt.layers.radix_attention import RadixAttention
+
+"""
+Support attention backend for TRTLLM MLA kernels from flashinfer.
+"""
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from sglang.srt.layers.attention.flashinfer_backend import FlashInferAttnBackend
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
+from sglang.srt.utils import is_flashinfer_available
+
+if is_flashinfer_available():
+    import flashinfer
+
+if TYPE_CHECKING:
+    from sglang.srt.layers.radix_attention import RadixAttention
+    from sglang.srt.model_executor.model_runner import ModelRunner
+    from sglang.srt.speculative.spec_info import SpecInfo
+
+# Constants
+DEFAULT_WORKSPACE_SIZE_MB = 128  # Memory workspace size in MB
+
+
+@dataclass
+class TRTLLMMHAMetadata:
+    # Sequence lengths for the forward batch
+    cache_seqlens_int32: torch.Tensor = None
+    # Maximum sequence length for query
+    max_seq_len_q: int = 1
+    # Maximum sequence length for key
+    max_seq_len_k: int = 0
+    # Cumulative sequence lengths for `query
+    cu_seqlens_q: torch.Tensor = None
+    # Cumulative sequence lengths for key
+    cu_seqlens_k: torch.Tensor = None
+    # Page table, the index of KV Cache Tables/Blocks
+    page_table: torch.Tensor = None
+
+
+class TRTLLMHAAttnBackend(FlashInferAttnBackend):
+    """TRTLLM MHA attention kernel from flashinfer."""
+
+    def __init__(
+        self,
+        model_runner: ModelRunner,
+        skip_prefill: bool = False,
+        kv_indptr_buf: Optional[torch.Tensor] = None,
+        q_indptr_decode_buf: Optional[torch.Tensor] = None,
+    ):
+        super().__init__(model_runner, skip_prefill, kv_indptr_buf, q_indptr_decode_buf)
+
+        config = model_runner.model_config
+
+        # MHA-specific dimensions
+        self.max_context_len = model_runner.model_config.context_len
+        self.sliding_window_size = (
+            model_runner.sliding_window_size
+            if model_runner.sliding_window_size is not None
+            else -1  # -1 indicates full attention
+        )
+        self.hidden_size = config.hidden_size
+
+        # Runtime parameters
+        self.data_type = model_runner.kv_cache_dtype
+        self.q_data_type = model_runner.dtype
+        self.page_size = model_runner.page_size
+        self.req_to_token = model_runner.req_to_token_pool.req_to_token
+        self.device = model_runner.device
+
+        # Workspace allocation
+        self.workspace_size = DEFAULT_WORKSPACE_SIZE_MB * 1024 * 1024
+        self.workspace_buffer = torch.empty(
+            self.workspace_size, dtype=torch.int8, device=self.device
+        )
+
+        # CUDA graph state
+        self.decode_cuda_graph_metadata = {}
+
+        # Forward metadata
+        self.forward_metadata: Optional[TRTLLMMHAMetadata] = None
+
+    def init_cuda_graph_state(
+        self,
+        max_bs: int,
+        max_num_tokens: int,
+        kv_indices_buf: Optional[torch.Tensor] = None,
+    ):
+        """Initialize CUDA graph state for TRTLLM MHA."""
+        self.decode_cuda_graph_metadata = {
+            "cache_seqlens": torch.zeros(max_bs, dtype=torch.int32, device=self.device),
+            "page_table": torch.zeros(
+                max_bs,
+                (self.max_context_len + self.page_size - 1) // self.page_size,
+                dtype=torch.int32,
+                device=self.device,
+            ),
+            "strided_indices": torch.arange(
+                0, self.max_context_len, self.page_size, device=self.device
+            ),
+        }
+
+    def init_forward_metadata_capture_cuda_graph(
+        self,
+        bs: int,
+        num_tokens: int,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        encoder_lens: Optional[torch.Tensor],
+        forward_mode: ForwardMode,
+        spec_info: Optional[SpecInfo],
+    ):
+        """Initialize metadata for CUDA graph capture."""
+        metadata = TRTLLMMHAMetadata()
+
+        # Get sequence information
+        metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
+
+        # Precompute maximum sequence length
+        metadata.max_seq_len_k = seq_lens.max().item()
+
+        # Precompute page table
+        metadata.page_table = self.decode_cuda_graph_metadata["page_table"][:bs, :]
+        self.decode_cuda_graph_metadata[bs] = metadata
+        self.forward_metadata = metadata
+
+    def init_forward_metadata_replay_cuda_graph(
+        self,
+        bs: int,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        seq_lens_sum: int,
+        encoder_lens: Optional[torch.Tensor],
+        forward_mode: ForwardMode,
+        spec_info: Optional[SpecInfo],
+        seq_lens_cpu: Optional[torch.Tensor],
+    ):
+        """Replay CUDA graph with new inputs."""
+        seq_lens = seq_lens[:bs]
+        seq_lens_cpu = seq_lens_cpu[:bs]
+        req_pool_indices = req_pool_indices[:bs]
+        device = seq_lens.device
+        metadata = None
+
+        # Normal Decode
+        metadata = self.decode_cuda_graph_metadata[bs]
+        max_len = seq_lens_cpu.max().item()
+        max_seq_pages = (max_len + self.page_size - 1) // self.page_size
+        metadata.max_seq_len_k = max_len
+
+        metadata.cache_seqlens_int32.copy_(seq_lens)
+        page_indices = self.req_to_token[
+            req_pool_indices[:, None],
+            self.decode_cuda_graph_metadata["strided_indices"][:max_seq_pages][None, :],
+        ]
+        metadata.page_table[:, :max_seq_pages].copy_(page_indices // self.page_size)
+        self.forward_metadata = metadata
+
+    def get_cuda_graph_seq_len_fill_value(self) -> int:
+        """Get the fill value for sequence lengths in CUDA graph."""
+        return 1
+
+    def init_forward_metadata(self, forward_batch: ForwardBatch):
+        """Initialize the metadata for a forward pass."""
+
+        metadata = TRTLLMMHAMetadata()
+        seqlens_in_batch = forward_batch.seq_lens
+        batch_size = forward_batch.batch_size
+        device = seqlens_in_batch.device
+
+        if forward_batch.forward_mode.is_decode_or_idle():
+            # Normal Decode
+            metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
+            metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
+            metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
+                forward_batch.req_pool_indices, : metadata.max_seq_len_k
+            ]
+        else:
+            metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
+            metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
+            metadata.cu_seqlens_k = torch.nn.functional.pad(
+                torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
+            )
+            metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
+                forward_batch.req_pool_indices, : metadata.max_seq_len_k
+            ]
+
+            if any(forward_batch.extend_prefix_lens_cpu):
+                extend_seq_lens = forward_batch.extend_seq_lens
+                metadata.max_seq_len_q = max(forward_batch.extend_seq_lens_cpu)
+                metadata.cu_seqlens_q = torch.nn.functional.pad(
+                    torch.cumsum(extend_seq_lens, dim=0, dtype=torch.int32), (1, 0)
+                )
+            else:
+                metadata.max_seq_len_q = metadata.max_seq_len_k
+                metadata.cu_seqlens_q = metadata.cu_seqlens_k
+
+        # Convert the page table to a strided format
+        if self.page_size > 1:
+            self.strided_indices = torch.arange(
+                0, metadata.page_table.shape[1], self.page_size, device=self.device
+            )
+            metadata.page_table = (
+                metadata.page_table[:, self.strided_indices] // self.page_size
+            )
+
+        self.forward_metadata = metadata
+
+    def forward_decode(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer: RadixAttention,
+        forward_batch: ForwardBatch,
+        save_kv_cache: bool = True,
+    ) -> torch.Tensor:
+        """Run forward for decode using TRTLLM MHA kernel."""
+        cache_loc = forward_batch.out_cache_loc
+        if save_kv_cache and k is not None:
+            forward_batch.token_to_kv_pool.set_kv_buffer(
+                layer, cache_loc, k, v, layer.k_scale, layer.v_scale
+            )
+
+        q = q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim)
+        k_cache, v_cache = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)
+        # shape conversion:
+        # [bs, page_size, num_kv_heads, head_dim] -> [bs, num_kv_heads, page_size, head_dim]
+        k_cache = k_cache.view(
+            -1, self.page_size, layer.tp_k_head_num, layer.head_dim
+        ).permute(0, 2, 1, 3)
+        v_cache = v_cache.view(
+            -1, self.page_size, layer.tp_v_head_num, layer.head_dim
+        ).permute(0, 2, 1, 3)
+        kv_cache = (k_cache, v_cache)
+
+        # TODO: bmm1_scale and bmm2_scale might require modification
+        q_scale = 1.0
+        k_scale = (
+            layer.k_scale_float
+            if getattr(layer, "k_scale_float", None) is not None
+            else 1.0
+        )
+        bmm1_scale = q_scale * k_scale * layer.scaling
+        bmm2_scale = 1.0
+
+        # Call TRT-LLM kernel
+        # raw_out: like q, [bs, acc_q_len, num_q_heads, head_dim] but with output dtype
+        o = flashinfer.decode.trtllm_batch_decode_with_kv_cache(
+            query=q,
+            kv_cache=kv_cache,
+            workspace_buffer=self.workspace_buffer,
+            block_tables=self.forward_metadata.page_table,
+            seq_lens=self.forward_metadata.cache_seqlens_int32,
+            max_seq_len=self.forward_metadata.max_seq_len_k,
+            bmm1_scale=bmm1_scale,
+            bmm2_scale=bmm2_scale,
+            window_left=self.sliding_window_size,
+            # TODO: add attention_sink operation or nvfp4 scale factor if needed
+        )
+
+        return o.view(-1, layer.tp_q_head_num * layer.head_dim)
+
+    def forward_extend(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer: RadixAttention,
+        forward_batch: ForwardBatch,
+        save_kv_cache=True,
+    ):
+        cache_loc = forward_batch.out_cache_loc
+        if save_kv_cache and k is not None:
+            forward_batch.token_to_kv_pool.set_kv_buffer(
+                layer, cache_loc, k, v, layer.k_scale, layer.v_scale
+            )
+        q = q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim)
+        k_cache, v_cache = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)
+        k_cache = k_cache.view(
+            -1, self.page_size, layer.tp_k_head_num, layer.head_dim
+        ).permute(0, 2, 1, 3)
+        v_cache = v_cache.view(
+            -1, self.page_size, layer.tp_v_head_num, layer.head_dim
+        ).permute(0, 2, 1, 3)
+        kv_cache = (k_cache, v_cache)
+
+        # TODO: bmm1_scale and bmm2_scale might require modification
+        # TODO: Change once quantization is supported
+        q_scale = 1.0
+        k_scale = (
+            layer.k_scale_float
+            if getattr(layer, "k_scale_float", None) is not None
+            else 1.0
+        )
+        bmm1_scale = q_scale * k_scale * layer.scaling
+        bmm2_scale = 1.0
+
+        o = flashinfer.prefill.trtllm_batch_context_with_kv_cache(
+            query=q,
+            kv_cache=kv_cache,
+            workspace_buffer=self.workspace_buffer,
+            block_tables=self.forward_metadata.page_table,
+            seq_lens=self.forward_metadata.cache_seqlens_int32,
+            max_q_len=self.forward_metadata.max_seq_len_q,
+            max_kv_len=self.forward_metadata.max_seq_len_k,
+            bmm1_scale=bmm1_scale,
+            bmm2_scale=bmm2_scale,
+            batch_size=forward_batch.batch_size,
+            cum_seq_lens_q=self.forward_metadata.cu_seqlens_q,
+            cum_seq_lens_kv=self.forward_metadata.cu_seqlens_k,
+            window_left=self.sliding_window_size,
+            # TODO: add attention_sink operation or nvfp4 scale factor if needed
+        )
+
+        return o.view(-1, layer.tp_q_head_num * layer.head_dim)

python/sglang/srt/managers/schedule_batch.py

@@ -1705,6 +1705,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             or attention_backend_str == "flashmla"
             or attention_backend_str == "cutlass_mla"
             or attention_backend_str == "ascend"
+            or attention_backend_str == "trtllm_mha"
             or global_server_args_dict["enable_two_batch_overlap"]
         ):
             seq_lens_cpu = (

python/sglang/srt/model_executor/model_runner.py

@@ -1449,6 +1449,17 @@ class ModelRunner:
             from sglang.srt.layers.attention.trtllm_mla_backend import TRTLLMMLABackend
 
             return TRTLLMMLABackend(self)
+        elif self.server_args.attention_backend == "trtllm_mha":
+            if self.use_mla_backend:
+                raise ValueError(
+                    "trtllm_mha backend can only be used with non-MLA models."
+                )
+            from sglang.srt.layers.attention.trtllm_mha_backend import (
+                TRTLLMHAAttnBackend,
+            )
+
+            return TRTLLMHAAttnBackend(self)
+
         elif self.server_args.attention_backend == "intel_amx":
             from sglang.srt.layers.attention.intel_amx_backend import (
                 IntelAMXAttnBackend,

python/sglang/srt/server_args.py

@@ -441,6 +441,23 @@ class ServerArgs:
                     "trtllm_mla backend does not support speculative decoding yet."
                 )
 
+        if self.attention_backend == "trtllm_mha":
+            if not is_sm100_supported():
+                raise ValueError(
+                    "TRTLLM MHA backend is only supported on Blackwell GPUs (SM100). Please use a different backend."
+                )
+
+            if self.page_size not in [16, 32, 64]:
+                logger.warning(
+                    f"TensorRT-LLM MHA only supports page_size of 16, 32 or 64, changing page_size from {self.page_size} to 64."
+                )
+                self.page_size = 64
+
+            if self.speculative_algorithm is not None:
+                raise ValueError(
+                    "trtllm_mla backend does not support speculative decoding yet."
+                )
+
         # Set page size
         if self.page_size is None:
             self.page_size = 1
@@ -1275,6 +1292,7 @@ class ServerArgs:
                 "ascend",
                 "triton",
                 "trtllm_mla",
+                "trtllm_mha",
             ],
             default=ServerArgs.attention_backend,
             help="Choose the kernels for attention layers.",