[model] Add mamba2 and Falcon-H1 support. (#10988)

Co-authored-by: Younes Belkada <younes.belkada@tii.ae>
Co-authored-by: Younes B <49240599+younesbelkada@users.noreply.github.com>

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

@@ -69,6 +69,7 @@ class MambaAttnBackend(AttentionBackend):
 
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         bs = forward_batch.batch_size
+
         if forward_batch.forward_mode.is_decode_or_idle():
             query_start_loc = torch.arange(
                 0, bs + 1, dtype=torch.int32, device=self.device

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

@@ -1,6 +1,39 @@
-from typing import Callable, List, Tuple
+from typing import Callable, List, Optional, Tuple, Union
 
 import torch
+import torch.nn as nn
+
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.custom_op import CustomOp
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.distributed.utils import divide
+from sglang.srt.layers.attention.fla.layernorm_gated import layernorm_fn
+from sglang.srt.layers.attention.mamba.causal_conv1d import (
+    causal_conv1d_fn,
+    causal_conv1d_update,
+)
+from sglang.srt.layers.attention.mamba.mamba_utils import MambaStateShapeCalculator
+from sglang.srt.layers.attention.mamba.ops import (
+    mamba_chunk_scan_combined,
+    selective_state_update,
+)
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import (
+    composed_weight_loader,
+    sharded_weight_loader,
+)
+from sglang.srt.utils import set_weight_attrs
 
 LoaderFunction = Callable[[torch.Tensor, torch.Tensor], None]
 
@@ -62,3 +95,535 @@ def mamba_v2_sharded_weight_loader(
             loaded_boundary += full_dim - extra
 
     return loader
+
+
+class Mixer2RMSNormGated(CustomOp):
+
+    def __init__(
+        self,
+        full_hidden_size: int,
+        full_n_groups: int,
+        use_rms_norm: bool = True,
+        eps: float = 1e-6,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.full_hidden_size = full_hidden_size
+        self.group_size = full_hidden_size // full_n_groups
+        self.per_rank_hidden_size = full_hidden_size // self.tp_size
+        self.n_groups = full_hidden_size // self.group_size
+
+        self.variance_epsilon = eps
+        self.use_rms_norm = use_rms_norm
+        if self.use_rms_norm:
+            # Register norm weight only if we're actually applying RMSNorm
+            self.weight = nn.Parameter(torch.ones(self.per_rank_hidden_size))
+            set_weight_attrs(self.weight, {"weight_loader": sharded_weight_loader(0)})
+        else:
+            # Avoid checkpoint mismatch by skipping unused parameter
+            self.register_parameter("weight", None)
+        assert (
+            self.full_hidden_size % self.tp_size == 0
+        ), "Tensor parallel world size must divide hidden size."
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        gate: torch.Tensor,
+    ):
+        # Three tensor-parallel cases:
+        #   1. n_groups is 1
+        #      In this case we parallelize along the reduction dim.
+        #      Each rank computes a local sum of squares followed by AllReduce
+        #   2. tp_size divides n_groups
+        #      Each rank only reduces within its local group(s).
+        #      No collective ops necessary.
+        #   3. The general case can be pretty complicated so we AllGather
+        #      the input and then redundantly compute the RMSNorm.
+        input_dtype = x.dtype
+        x = x * nn.functional.silu(gate.to(torch.float32))
+        if not self.use_rms_norm:
+            return x.to(input_dtype)
+
+        if self.n_groups == 1:
+            if self.tp_size > 1:
+                # Compute local sum and then reduce to obtain global sum
+                local_sums = x.pow(2).sum(dim=-1, keepdim=True)
+                global_sums = tensor_model_parallel_all_reduce(local_sums)
+                # Calculate the variance
+                count = self.tp_size * x.shape[-1]
+                variance = global_sums / count
+
+            else:
+                variance = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.variance_epsilon)
+        else:
+            redundant_tp: bool = self.n_groups % self.tp_size != 0
+            if redundant_tp:
+                # To handle the general case, redundantly apply the variance
+                x = tensor_model_parallel_all_gather(x, -1)
+
+            *prefix_dims, hidden_dim = x.shape
+            group_count = hidden_dim // self.group_size
+            x_grouped = x.view(*prefix_dims, group_count, self.group_size)
+            variance = x_grouped.pow(2).mean(-1, keepdim=True)
+            x_grouped = x_grouped * torch.rsqrt(variance + self.variance_epsilon)
+            x = x_grouped.view(*prefix_dims, hidden_dim)
+
+            if redundant_tp:
+                start = self.per_rank_hidden_size * self.tp_rank
+                end = start + self.per_rank_hidden_size
+                x = x[..., start:end]
+
+        return self.weight * x.to(input_dtype)
+
+    def forward_cuda(
+        self,
+        x: torch.Tensor,
+        gate: torch.Tensor,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        input_dtype = x.dtype
+        if not self.use_rms_norm:
+            # Keep gate in float32 for numerical stability during silu
+            return x * nn.functional.silu(gate.to(torch.float32)).to(input_dtype)
+
+        if ((self.n_groups % self.tp_size) != 0) or self.n_groups != 1:
+            return self.forward_native(x, gate)
+
+        return layernorm_fn(
+            x,
+            self.weight.data,
+            bias=None,
+            z=gate,
+            eps=self.variance_epsilon,
+            norm_before_gate=False,
+        )
+
+
+class MambaMixer2(torch.nn.Module):
+    """
+    Compute ∆, A, B, C, and D the state space parameters and compute
+    the `contextualized_states`. A, D are input independent
+    (see Mamba paper [1] Section 3.5.2 "Interpretation of A"
+    for why A isn't selective) ∆, B, C are input-dependent
+    (this is a key difference between Mamba and the linear time
+    invariant S4, and is why Mamba is called
+    **selective** state spaces)
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        ssm_state_size: int,
+        conv_kernel_size: int,
+        intermediate_size: int,
+        use_conv_bias: bool,
+        use_bias: bool,
+        chunk_size: int,
+        layer_id: int,
+        n_groups: int = 1,
+        num_heads: int = 128,
+        head_dim: int = 64,
+        rms_norm_eps: float = 1e-5,
+        activation: str = "silu",
+        use_rms_norm: bool = True,
+        model_config: Optional[ModelConfig] = None,
+        #  cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        # For TP, the sharding plan is as follows:
+        # - for the conv modules, since
+        #   conv_dim = intermediate_size * 2 * n_groups * ssm_state_size,
+        #   we shard intermediate_size and n_groups
+        # - since intermediate_size = n_heads * head_dim, sharding on
+        #   intermediate_size is achieved by sharding on n_heads.
+        # - IF, world_size divides groups, then sharding
+        #   (n_groups / world_size, n_heads / world_size)
+        #   also maintains the invariant n_heads % n_groups == 0
+        # - HOWEVER IF, world_size DOES NOT divide groups, then we need
+        #   to allocate extra space in the shard, such that groups
+        #   may be replicated to follow the head shard.
+        # - NOTE: currently for the world size DOES NOT divide groups
+        #   case, we only support the case when n_groups == 1
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        assert (
+            num_heads % self.tp_size == 0
+        ), "Tensor parallel world size must divide num heads."
+
+        assert (n_groups % self.tp_size) == 0 or n_groups == 1, (
+            "If tensor parallel world size does not divide num_groups, "
+            "then num_groups must equal 1."
+        )
+
+        self.ssm_state_size = ssm_state_size
+        self.conv_kernel_size = conv_kernel_size
+        self.activation = activation
+        self.layer_id = layer_id
+
+        self.intermediate_size = intermediate_size
+        self.head_dim = head_dim
+        self.num_heads = num_heads
+        self.chunk_size = chunk_size
+
+        self.n_groups = n_groups
+        if n_groups % self.tp_size != 0:
+            # - for TP we shard conv_dim by sharding on n_groups,
+            # - but if n_groups cannot divide tp_size, we need to
+            #   extend some extra groups
+            groups = MambaStateShapeCalculator.extra_groups_for_head_shards(
+                n_groups, self.tp_size
+            )
+            self.n_groups = n_groups + groups
+
+        self.groups_ssm_state_size = self.n_groups * self.ssm_state_size
+        self.conv_dim = intermediate_size + 2 * self.groups_ssm_state_size
+
+        self.conv1d = MergedColumnParallelLinear(
+            input_size=conv_kernel_size,
+            output_sizes=[
+                intermediate_size,
+                self.groups_ssm_state_size,
+                self.groups_ssm_state_size,
+            ],
+            bias=use_conv_bias,
+            quant_config=None,
+            prefix=f"{prefix}.conv1d",
+        )
+
+        self.in_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[
+                intermediate_size,
+                intermediate_size,
+                self.groups_ssm_state_size,
+                self.groups_ssm_state_size,
+                self.num_heads,
+            ],
+            bias=use_bias,
+            prefix=f"{prefix}.in_proj",
+        )
+        if n_groups % self.tp_size != 0:
+            # This is the n_groups == 1 case,
+            # where we need to duplicate groups if TP>1.
+
+            # - because in_proj is a concatenation of 3 weights, we
+            #   need to interleave them before sharding
+            # - use the custom weight loader mamba_v2_sharded_weight_loader
+            #   for conv1d.bias, covn1d.weight and in_proj.weight
+            # - need to set these settings, to assign the groups
+            #   to the head shards
+            group_shard_settings = (
+                self.groups_ssm_state_size,  # expected model size
+                (self.n_groups - n_groups) * self.ssm_state_size,  # extra dims assigned
+                n_groups == 1,  # if there was only one group
+            )
+            intermediate_settings = (intermediate_size, 0, False)
+            head_settings = (self.num_heads, 0, False)
+
+            # - the weight already has a "weight_loader" attribute
+            #   which set_weight_attrs will raise if we do not
+            #   delete before trying to override it
+            # - ditto for the other two weights below
+            delattr(self.conv1d.bias, "weight_loader")
+            set_weight_attrs(
+                self.conv1d.bias,
+                {
+                    "weight_loader": mamba_v2_sharded_weight_loader(
+                        [
+                            intermediate_settings,
+                            group_shard_settings,
+                            group_shard_settings,
+                        ],
+                        self.tp_size,
+                        self.tp_rank,
+                    )
+                },
+            )
+
+            delattr(self.conv1d.weight, "weight_loader")
+            set_weight_attrs(
+                self.conv1d.weight,
+                {
+                    "weight_loader": mamba_v2_sharded_weight_loader(
+                        [
+                            intermediate_settings,
+                            group_shard_settings,
+                            group_shard_settings,
+                        ],
+                        self.tp_size,
+                        self.tp_rank,
+                    )
+                },
+            )
+
+            if quant_config is None:
+                # - quant layers do not have a weight loader
+                delattr(self.in_proj.weight, "weight_loader")
+                set_weight_attrs(
+                    self.in_proj.weight,
+                    {
+                        "weight_loader": mamba_v2_sharded_weight_loader(
+                            [
+                                intermediate_settings,  # for gate
+                                intermediate_settings,
+                                group_shard_settings,
+                                group_shard_settings,
+                                head_settings,  # for dt
+                            ],
+                            self.tp_size,
+                            self.tp_rank,
+                        )
+                    },
+                )
+
+        # unsqueeze to fit conv1d weights shape into the linear weights shape.
+        # Can't do this in `weight_loader` since it already exists in
+        # `ColumnParallelLinear` and `MergedColumnParallelLinear`,
+        # and `set_weight_attrs` doesn't allow to override it
+        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
+
+        # - these are TPed by heads to reduce the size of the
+        #   temporal shape
+        self.A = nn.Parameter(
+            torch.empty(
+                divide(num_heads, self.tp_size),
+                dtype=torch.float32,
+            )
+        )
+        self.D = nn.Parameter(torch.ones(num_heads // self.tp_size))
+        self.dt_bias = nn.Parameter(torch.ones(num_heads // self.tp_size))
+        self.use_rms_norm = use_rms_norm
+
+        set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
+        a_weight_loader = composed_weight_loader(
+            sharded_weight_loader(0), lambda x: -torch.exp(x.float())
+        )
+        set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
+        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
+
+        self.out_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=use_bias,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+            reduce_results=False,
+        )
+
+        self.norm = Mixer2RMSNormGated(
+            intermediate_size, n_groups, self.use_rms_norm, eps=rms_norm_eps
+        )
+
+        # The tuple is (conv_state, ssm_state)
+        self.kv_cache = (torch.tensor([]), torch.tensor([]))
+
+        self.model_config = model_config
+        self.prefix = prefix
+
+    def forward_native(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+        mup_vector: Optional[torch.Tensor] = None,
+    ):
+        pass
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+        forward_batch: ForwardBatch,
+        mup_vector: Optional[torch.Tensor] = None,
+    ):
+        # attn_backend_list[-1] gives access to MambaAttnBackend
+        mamba_backend = forward_batch.attn_backend.attn_backend_list[-1]
+        attn_metadata = mamba_backend.forward_metadata
+        state_indices_tensor = attn_metadata.mamba_cache_indices
+        chunk_size = self.chunk_size
+
+        conv_state, ssm_state, *rest = mamba_backend.req_to_token_pool.get_mamba_params(
+            self.layer_id
+        )
+
+        assert (
+            ssm_state.size(1) == self.ssm_state_size
+        ), f"dstate must be {self.ssm_state_size}, got {ssm_state.size(1)}"
+
+        query_start_loc = attn_metadata.query_start_loc
+
+        chunk_size = self.chunk_size
+
+        # TODO: properly support this
+        prep_initial_states = False
+
+        # 1. Gated MLP's linear projection
+        projected_states, _ = self.in_proj(hidden_states)
+
+        if mup_vector is not None:
+            projected_states = projected_states * mup_vector
+
+        gate, hidden_states_B_C, dt = torch.split(
+            projected_states,
+            [
+                self.intermediate_size // self.tp_size,
+                self.conv_dim // self.tp_size,
+                self.num_heads // self.tp_size,
+            ],
+            dim=-1,
+        )
+        conv_weights = self.conv1d.weight.view(
+            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
+        )
+
+        # - get hidden_states, B and C after depthwise convolution.
+        split_hidden_states_B_C_fn = lambda hidden_states_B_C: torch.split(
+            hidden_states_B_C,
+            [
+                self.intermediate_size // self.tp_size,
+                self.groups_ssm_state_size // self.tp_size,
+                self.groups_ssm_state_size // self.tp_size,
+            ],
+            dim=-1,
+        )
+
+        preallocated_ssm_out = torch.empty(
+            [
+                projected_states.shape[0],
+                (self.num_heads * self.head_dim) // self.tp_size,
+            ],
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        # Process prefill requests
+        if forward_batch.forward_mode.is_extend():
+            # 2. Convolution sequence transformation
+            # - "cache_indices" updates the conv_state cache in positions
+            #   pointed to by "state_indices_tensor"
+            num_prefill_tokens = forward_batch.extend_num_tokens or 0
+            has_initial_states = forward_batch.extend_prefix_lens > 0
+            cache_indices = attn_metadata.mamba_cache_indices
+
+            x = hidden_states_B_C.transpose(
+                0, 1
+            )  # this is the form that causal-conv see
+            hidden_states_B_C = causal_conv1d_fn(
+                x,
+                conv_weights,
+                self.conv1d.bias,
+                activation=self.activation,
+                conv_states=conv_state,
+                has_initial_state=has_initial_states,
+                cache_indices=cache_indices,
+                query_start_loc=query_start_loc,
+                seq_lens_cpu=forward_batch.extend_seq_lens_cpu,
+            ).transpose(0, 1)
+
+            hidden_states, B, C = split_hidden_states_B_C_fn(hidden_states_B_C)
+
+            # 3. State Space Model sequence transformation
+            initial_states = None
+
+            if has_initial_states is not None and prep_initial_states:
+                initial_states = torch.where(
+                    has_initial_states[:, None, None, None],
+                    ssm_state[state_indices_tensor],
+                    0,
+                )
+
+            # NOTE: final output is an in-place update of out tensor
+            varlen_state = mamba_chunk_scan_combined(
+                hidden_states.view(
+                    1, num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim
+                ),
+                dt.unsqueeze(0),
+                self.A,
+                B.view(1, num_prefill_tokens, self.n_groups // self.tp_size, -1),
+                C.view(1, num_prefill_tokens, self.n_groups // self.tp_size, -1),
+                chunk_size=chunk_size,
+                D=self.D,
+                z=None,
+                dt_bias=self.dt_bias,
+                cu_seqlens=query_start_loc,
+                initial_states=initial_states,
+                return_varlen_states=True,
+                return_final_states=False,
+                dt_softplus=True,
+                dt_limit=(0.0, float("inf")),
+                out=preallocated_ssm_out.view(1, num_prefill_tokens, -1, self.head_dim),
+                state_dtype=ssm_state.dtype,
+            )
+
+            # update ssm states
+            # - varlen state is a (num_prefills, nheads, headdim, dstate) tensor
+            ssm_state[state_indices_tensor] = varlen_state.permute(0, 3, 2, 1)
+        elif forward_batch.forward_mode.is_decode():
+            num_decodes = len(query_start_loc) - 1
+            # 2. Convolution sequence transformation
+            hidden_states_B_C = causal_conv1d_update(
+                hidden_states_B_C,
+                conv_state,
+                conv_weights,
+                self.conv1d.bias,
+                self.activation,
+                conv_state_indices=state_indices_tensor,
+            )
+
+            hidden_states, B, C = split_hidden_states_B_C_fn(hidden_states_B_C)
+
+            # 3. State Space Model sequence transformation
+            n_groups = self.n_groups // self.tp_size
+            A = (
+                self.A[:, None, ...][:, :, None]
+                .expand(-1, self.head_dim, self.ssm_state_size)
+                .to(dtype=torch.float32)
+            )
+            dt = dt[:, :, None].expand(-1, -1, self.head_dim)
+            dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
+            D = self.D[:, None, ...].expand(-1, self.head_dim)
+            B = B.view(-1, n_groups, B.shape[1] // n_groups)
+            C = C.view(-1, n_groups, C.shape[1] // n_groups)
+            hidden_states = hidden_states.view(
+                -1, self.num_heads // self.tp_size, self.head_dim
+            )
+
+            # - the hidden is reshaped into (bs, num_heads, head_dim)
+            # - mamba_cache_params.ssm_state's slots will be selected
+            #   using state_indices_tensor_d
+            # NOTE: final output is an in-place update of out tensor
+            selective_state_update(
+                ssm_state.permute(0, 3, 2, 1),
+                hidden_states,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+                state_batch_indices=state_indices_tensor,
+                out=preallocated_ssm_out.view(num_decodes, -1, self.head_dim),
+            )
+        elif forward_batch.forward_mode.is_idle():
+            preallocated_ssm_out = preallocated_ssm_out
+
+        # 4. gated MLP
+        # GatedRMSNorm internally applying SiLU to the gate
+        # SiLU is applied internally before normalization, unlike standard
+        # norm usage
+        hidden_states = self.norm(preallocated_ssm_out, gate)
+
+        # 5. Final linear projection
+        output[:], _ = self.out_proj(hidden_states)
+
+    @property
+    def mamba_type(self) -> str:
+        return "mamba2"

python/sglang/srt/layers/attention/mamba/mamba_utils.py

@@ -0,0 +1,81 @@
+# Adapted from: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/mamba/mamba_utils.py
+from sglang.srt.distributed.utils import divide
+
+
+class MambaStateShapeCalculator:
+
+    @classmethod
+    def linear_attention_state_shape(
+        cls,
+        num_heads: int,
+        tp_size: int,
+        head_dim: int,
+    ) -> tuple[tuple[int, int, int], ...]:
+
+        state_shape = (num_heads // tp_size, head_dim, head_dim)
+        return (state_shape,)
+
+    @classmethod
+    def mamba1_state_shape(
+        cls,
+        tp_world_size: int,
+        intermediate_size: int,
+        state_size: int,
+        conv_kernel: int,
+    ) -> tuple[tuple[int, int], tuple[int, int]]:
+        conv_state_shape = (divide(intermediate_size, tp_world_size), conv_kernel - 1)
+
+        temporal_state_shape = (divide(intermediate_size, tp_world_size), state_size)
+
+        conv_state_shape = conv_state_shape[1], conv_state_shape[0]
+
+        return conv_state_shape, temporal_state_shape
+
+    @classmethod
+    def mamba2_state_shape(
+        cls,
+        tp_world_size: int,
+        intermediate_size: int,
+        n_groups: int,
+        num_heads: int,
+        head_dim: int,
+        state_size: int,
+        conv_kernel: int,
+    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
+        # if n_groups is not divisible by world_size, need to extend the shards
+        # to ensure all groups needed by a head is sharded along with it
+        n_groups = n_groups + cls.extra_groups_for_head_shards(n_groups, tp_world_size)
+        # heads and n_groups are TP-ed
+        conv_dim = intermediate_size + 2 * n_groups * state_size
+
+        # contiguous along 'dim' axis
+        conv_state_shape = (conv_kernel - 1, divide(conv_dim, tp_world_size))
+
+        # These are not TP-ed as they depend on A, dt_bias, D
+        # - they are typically small
+        #   e.g., (h_heads, head_dim, state_size) = (128, 64, 128)
+        temporal_state_shape = (divide(num_heads, tp_world_size), head_dim, state_size)
+        return conv_state_shape, temporal_state_shape
+
+    @classmethod
+    def short_conv_state_shape(
+        cls,
+        tp_world_size: int,
+        intermediate_size: int,
+        conv_kernel: int,
+    ) -> tuple[tuple[int, int]]:
+        conv_dim = divide(intermediate_size, tp_world_size)
+        conv_state_shape = (conv_kernel - 1, conv_dim)
+        return (conv_state_shape,)
+
+    @classmethod
+    def extra_groups_for_head_shards(cls, ngroups: int, tp_size: int):
+        """Compute the increase in group numbers to account for
+        replication in order to accompany the head shards."""
+
+        # in the case ngoups % tp_size == 0, this will be zero
+        if ngroups % tp_size == 0:
+            return 0
+
+        # for n_groups == 1, this is exactly tp_size - n_groups
+        return tp_size - ngroups

python/sglang/srt/layers/attention/mamba/ops/__init__.py

@@ -0,0 +1,2 @@
+from .mamba_ssm import selective_state_update
+from .ssd_combined import mamba_chunk_scan_combined

python/sglang/srt/layers/attention/mamba/ops/layernorm_gated.py

@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2024, Tri Dao.
+# Adapted from https://github.com/state-spaces/mamba/blob/60dadf2e0ee730ac337035d5533de10bc26e4847/mamba_ssm/ops/triton/layernorm_gated.py
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
+@triton.heuristics({"HAS_Z": lambda args: args["Z"] is not None})
+@triton.jit
+def _layer_norm_fwd_1pass_kernel(
+    X,  # pointer to the input
+    Y,  # pointer to the output
+    W,  # pointer to the weights
+    B,  # pointer to the biases
+    Z,  # pointer to the other branch
+    Mean,  # pointer to the mean
+    Rstd,  # pointer to the 1/std
+    stride_x_row: tl.int64,
+    stride_y_row: tl.int64,
+    stride_z_row: tl.int64,
+    M: tl.int64,  # number of rows in X
+    N: tl.int64,  # number of columns in X
+    eps,  # epsilon to avoid division by zero
+    BLOCK_N: tl.constexpr,
+    HAS_BIAS: tl.constexpr,
+    HAS_Z: tl.constexpr,
+    NORM_BEFORE_GATE: tl.constexpr,
+    IS_RMS_NORM: tl.constexpr,
+):
+    # Map the program id to the row of X and Y it should compute.
+    row = tl.program_id(0)
+    group = tl.program_id(1)
+    X += row * stride_x_row + group * N
+    Y += row * stride_y_row + group * N
+    if HAS_Z:
+        Z += row * stride_z_row + group * N
+    if not IS_RMS_NORM:
+        Mean += group * M
+    Rstd += group * M
+    W += group * N
+    if HAS_BIAS:
+        B += group * N
+    # Compute mean and variance
+    cols = tl.arange(0, BLOCK_N)
+    x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
+    if HAS_Z and not NORM_BEFORE_GATE:
+        z = tl.load(Z + cols, mask=cols < N).to(tl.float32)
+        x *= z * tl.sigmoid(z)
+    if not IS_RMS_NORM:
+        mean = tl.sum(x, axis=0) / N
+        tl.store(Mean + row, mean)
+        xbar = tl.where(cols < N, x - mean, 0.0)
+        var = tl.sum(xbar * xbar, axis=0) / N
+    else:
+        xbar = tl.where(cols < N, x, 0.0)
+        var = tl.sum(xbar * xbar, axis=0) / N
+    rstd = 1 / tl.sqrt(var + eps)
+    tl.store(Rstd + row, rstd)
+    # Normalize and apply linear transformation
+    mask = cols < N
+    w = tl.load(W + cols, mask=mask).to(tl.float32)
+    if HAS_BIAS:
+        b = tl.load(B + cols, mask=mask).to(tl.float32)
+    x_hat = (x - mean) * rstd if not IS_RMS_NORM else x * rstd
+    y = x_hat * w + b if HAS_BIAS else x_hat * w
+    if HAS_Z and NORM_BEFORE_GATE:
+        z = tl.load(Z + cols, mask=mask).to(tl.float32)
+        y *= z * tl.sigmoid(z)
+    # Write output
+    tl.store(Y + cols, y, mask=mask)
+
+
+def _layer_norm_fwd(
+    x,
+    weight,
+    bias,
+    eps,
+    z=None,
+    out=None,
+    group_size=None,
+    norm_before_gate=True,
+    is_rms_norm=False,
+):
+    M, N = x.shape
+    if group_size is None:
+        group_size = N
+    assert N % group_size == 0
+    ngroups = N // group_size
+    assert x.stride(-1) == 1
+    if z is not None:
+        assert z.stride(-1) == 1
+        assert z.shape == (M, N)
+    assert weight.shape == (N,)
+    assert weight.stride(-1) == 1
+    if bias is not None:
+        assert bias.stride(-1) == 1
+        assert bias.shape == (N,)
+    # allocate output
+    if out is not None:
+        assert out.shape == x.shape
+    else:
+        out = torch.empty_like(x)
+    assert out.stride(-1) == 1
+    mean = (
+        torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
+        if not is_rms_norm
+        else None
+    )
+    rstd = torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
+    # Less than 64KB per feature: enqueue fused kernel
+    MAX_FUSED_SIZE = 65536 // x.element_size()
+    BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(group_size))
+    if group_size > BLOCK_N:
+        raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
+    # heuristics for number of warps
+    num_warps = min(max(BLOCK_N // 256, 1), 8)
+    grid = (M, ngroups)
+    with torch.cuda.device(x.device.index):
+        _layer_norm_fwd_1pass_kernel[grid](
+            x,
+            out,
+            weight,
+            bias,
+            z,
+            mean,
+            rstd,
+            x.stride(0),
+            out.stride(0),
+            z.stride(0) if z is not None else 0,
+            M,
+            group_size,
+            eps,
+            BLOCK_N=BLOCK_N,
+            NORM_BEFORE_GATE=norm_before_gate,
+            IS_RMS_NORM=is_rms_norm,
+            num_warps=num_warps,
+        )
+    return out, mean, rstd
+
+
+def rms_norm_gated(
+    x, weight, bias, z=None, eps=1e-6, group_size=None, norm_before_gate=True
+):
+    x_shape_og = x.shape
+    # reshape input data into 2D tensor
+    x = x.reshape(-1, x.shape[-1])
+    if x.stride(-1) != 1:
+        x = x.contiguous()
+    if z is not None:
+        assert z.shape == x_shape_og
+        z = z.reshape(-1, z.shape[-1])
+        if z.stride(-1) != 1:
+            z = z.contiguous()
+    weight = weight.contiguous()
+    if bias is not None:
+        bias = bias.contiguous()
+    y, _, _ = _layer_norm_fwd(
+        x,
+        weight,
+        bias,
+        eps,
+        z=z,
+        group_size=group_size,
+        norm_before_gate=norm_before_gate,
+        is_rms_norm=True,
+    )
+
+    return y.reshape(x_shape_og)

python/sglang/srt/layers/attention/mamba/ops/mamba_ssm.py

@@ -0,0 +1,442 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/mamba_ssm.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/selective_state_update.py
+
+import torch
+import triton
+import triton.language as tl
+from packaging import version
+
+from sglang.srt import _custom_ops as ops
+
+PAD_SLOT_ID = -1
+
+TRITON3 = version.parse(triton.__version__) >= version.parse("3.0.0")
+
+if TRITON3:
+
+    @triton.jit
+    def softplus(dt):
+        dt = tl.where(dt <= 20.0, tl.math.log(tl.math.exp(dt) + 1), dt)
+        return dt
+
+else:
+
+    @triton.jit
+    def softplus(dt):
+        dt = tl.where(dt <= 20.0, tl.math.log1p(tl.exp(dt)), dt)
+        return dt
+
+
+@triton.heuristics({"HAS_DT_BIAS": lambda args: args["dt_bias_ptr"] is not None})
+@triton.heuristics({"HAS_D": lambda args: args["D_ptr"] is not None})
+@triton.heuristics({"HAS_Z": lambda args: args["z_ptr"] is not None})
+@triton.heuristics(
+    {
+        "HAS_STATE_BATCH_INDICES": lambda args: args["state_batch_indices_ptr"]
+        is not None
+    }
+)
+@triton.heuristics(
+    {"BLOCK_SIZE_DSTATE": lambda args: triton.next_power_of_2(args["dstate"])}
+)
+@triton.jit
+def _selective_scan_update_kernel(
+    # Pointers to matrices
+    state_ptr,
+    x_ptr,
+    dt_ptr,
+    dt_bias_ptr,
+    A_ptr,
+    B_ptr,
+    C_ptr,
+    D_ptr,
+    z_ptr,
+    out_ptr,
+    state_batch_indices_ptr,
+    pad_slot_id,
+    # Matrix dimensions
+    batch,
+    nheads,
+    dim,
+    dstate,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_state_batch,
+    stride_state_head,
+    stride_state_dim,
+    stride_state_dstate,
+    stride_x_batch,
+    stride_x_head,
+    stride_x_dim,
+    stride_dt_batch,
+    stride_dt_head,
+    stride_dt_dim,
+    stride_dt_bias_head,
+    stride_dt_bias_dim,
+    stride_A_head,
+    stride_A_dim,
+    stride_A_dstate,
+    stride_B_batch,
+    stride_B_group,
+    stride_B_dstate,
+    stride_C_batch,
+    stride_C_group,
+    stride_C_dstate,
+    stride_D_head,
+    stride_D_dim,
+    stride_z_batch,
+    stride_z_head,
+    stride_z_dim,
+    stride_out_batch,
+    stride_out_head,
+    stride_out_dim,
+    # Meta-parameters
+    DT_SOFTPLUS: tl.constexpr,
+    TIE_HDIM: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    HAS_DT_BIAS: tl.constexpr,
+    HAS_D: tl.constexpr,
+    HAS_Z: tl.constexpr,
+    HAS_STATE_BATCH_INDICES: tl.constexpr,
+    BLOCK_SIZE_DSTATE: tl.constexpr,
+):
+    pid_m = tl.program_id(axis=0)
+    pid_b = tl.program_id(axis=1)
+    pid_h = tl.program_id(axis=2)
+
+    # If HAS_STATE_BATCH_INDICES is true, then the ssm state's batch coordinate
+    # is taken from the state_batch_indices_ptr Otherwise, the state coordinate
+    # is the same as the batch id.
+    if HAS_STATE_BATCH_INDICES:
+        state_batch_indices_ptr += pid_b
+        state_batch_idx = tl.load(state_batch_indices_ptr).to(tl.int64)
+        state_ptr += state_batch_idx * stride_state_batch + pid_h * stride_state_head
+    else:
+        state_ptr += pid_b * stride_state_batch + pid_h * stride_state_head
+
+    x_ptr += pid_b * stride_x_batch + pid_h * stride_x_head
+    dt_ptr += pid_b * stride_dt_batch + pid_h * stride_dt_head
+    if HAS_DT_BIAS:
+        dt_bias_ptr += pid_h * stride_dt_bias_head
+    A_ptr += pid_h * stride_A_head
+    B_ptr += pid_b * stride_B_batch + (pid_h // nheads_ngroups_ratio) * stride_B_group
+    C_ptr += pid_b * stride_C_batch + (pid_h // nheads_ngroups_ratio) * stride_C_group
+    if HAS_Z:
+        z_ptr += pid_b * stride_z_batch + pid_h * stride_z_head
+    out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = tl.arange(0, BLOCK_SIZE_DSTATE)
+    state_ptrs = state_ptr + (
+        offs_m[:, None] * stride_state_dim + offs_n[None, :] * stride_state_dstate
+    )
+    x_ptrs = x_ptr + offs_m * stride_x_dim
+    dt_ptrs = dt_ptr + offs_m * stride_dt_dim
+    if HAS_DT_BIAS:
+        dt_bias_ptrs = dt_bias_ptr + offs_m * stride_dt_bias_dim
+    if HAS_D:
+        D_ptr += pid_h * stride_D_head
+    A_ptrs = A_ptr + (
+        offs_m[:, None] * stride_A_dim + offs_n[None, :] * stride_A_dstate
+    )
+    B_ptrs = B_ptr + offs_n * stride_B_dstate
+    C_ptrs = C_ptr + offs_n * stride_C_dstate
+    if HAS_D:
+        D_ptrs = D_ptr + offs_m * stride_D_dim
+    if HAS_Z:
+        z_ptrs = z_ptr + offs_m * stride_z_dim
+    out_ptrs = out_ptr + offs_m * stride_out_dim
+    mask = (offs_m[:, None] < dim) & (offs_n[None, :] < dstate)
+    if HAS_STATE_BATCH_INDICES:
+        mask &= state_batch_idx != pad_slot_id
+    state = tl.load(state_ptrs, mask=mask, other=0.0)
+
+    x = tl.load(x_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+    if not TIE_HDIM:
+        dt = tl.load(dt_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+        if HAS_DT_BIAS:
+            dt += tl.load(dt_bias_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+        if DT_SOFTPLUS:
+            dt = softplus(dt)
+        A = tl.load(
+            A_ptrs, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate), other=0.0
+        ).to(tl.float32)
+        dA = tl.exp(A * dt[:, None])
+    else:
+        dt = tl.load(dt_ptr).to(tl.float32)
+        if HAS_DT_BIAS:
+            dt += tl.load(dt_bias_ptr).to(tl.float32)
+        if DT_SOFTPLUS:
+            dt = softplus(dt)
+        A = tl.load(A_ptr).to(tl.float32)
+        dA = tl.exp(A * dt)  # scalar, not a matrix
+
+    B = tl.load(B_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+    C = tl.load(C_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+    if HAS_D:
+        D = tl.load(D_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+    if HAS_Z:
+        z = tl.load(z_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+    dB = B[None, :] * dt[:, None] if not TIE_HDIM else B * dt
+    state = state * dA + dB * x[:, None]
+
+    mask = (offs_m[:, None] < dim) & (offs_n[None, :] < dstate)
+    if HAS_STATE_BATCH_INDICES:
+        mask &= state_batch_idx != pad_slot_id
+    tl.store(state_ptrs, state, mask=mask)
+    out = tl.sum(state * C[None, :], axis=1)
+    if HAS_D:
+        out += x * D
+    if HAS_Z:
+        out *= z * tl.sigmoid(z)
+    tl.store(out_ptrs, out, mask=offs_m < dim)
+
+
+def selective_state_update(
+    state,
+    x,
+    dt,
+    A,
+    B,
+    C,
+    D=None,
+    z=None,
+    dt_bias=None,
+    dt_softplus=False,
+    state_batch_indices=None,
+    pad_slot_id=PAD_SLOT_ID,
+    out=None,
+):
+    """
+    Argument:
+        state: (batch, dim, dstate) or (batch, nheads, dim, dstate)
+        x: (batch, dim) or (batch, nheads, dim)
+        dt: (batch, dim) or (batch, nheads, dim)
+        A: (dim, dstate) or (nheads, dim, dstate)
+        B: (batch, dstate) or (batch, ngroups, dstate)
+        C: (batch, dstate) or (batch, ngroups, dstate)
+        D: (dim,) or (nheads, dim)
+        z: (batch, dim) or (batch, nheads, dim)
+        dt_bias: (dim,) or (nheads, dim)
+        pad_slot_id: int
+            if cache_indices is passed, lets the kernel identify padded
+            entries that will not be processed,
+            for example: cache_indices = [pad_slot_id, 1, 20, pad_slot_id]
+            in this case, the kernel will not process entries at
+            indices 0 and 3
+        out: Preallocated ssm output tensor. Assume same shape as x.
+             In-place updated.
+    """
+    if state.dim() == 3:
+        state = state.unsqueeze(1)
+    if x.dim() == 2:
+        x = x.unsqueeze(1)
+    if dt.dim() == 2:
+        dt = dt.unsqueeze(1)
+    if A.dim() == 2:
+        A = A.unsqueeze(0)
+    if B.dim() == 2:
+        B = B.unsqueeze(1)
+    if C.dim() == 2:
+        C = C.unsqueeze(1)
+    if D is not None and D.dim() == 1:
+        D = D.unsqueeze(0)
+    if z is not None and z.dim() == 2:
+        z = z.unsqueeze(1)
+    if dt_bias is not None and dt_bias.dim() == 1:
+        dt_bias = dt_bias.unsqueeze(0)
+    if out.dim() == 2:
+        out = out.unsqueeze(1)
+
+    _, nheads, dim, dstate = state.shape
+    batch = x.shape[0]
+
+    assert x.shape == (batch, nheads, dim)
+    assert dt.shape == x.shape
+    assert A.shape == (nheads, dim, dstate)
+    ngroups = B.shape[1]
+    assert nheads % ngroups == 0, "nheads must be divisible by ngroups"
+    assert B.shape == (batch, ngroups, dstate)
+    assert C.shape == B.shape
+    if D is not None:
+        assert D.shape == (nheads, dim)
+    if z is not None:
+        assert z.shape == x.shape
+    if dt_bias is not None:
+        assert dt_bias.shape == (nheads, dim)
+    if state_batch_indices is not None:
+        assert state_batch_indices.shape == (batch,)
+    assert out.shape == x.shape
+
+    grid = lambda META: (triton.cdiv(dim, META["BLOCK_SIZE_M"]), batch, nheads)
+    z_strides = (z.stride(0), z.stride(1), z.stride(2)) if z is not None else (0, 0, 0)
+    # We don't want autotune since it will overwrite the state
+    # We instead tune by hand.
+    BLOCK_SIZE_M, num_warps = (
+        (32, 4)
+        if dstate <= 16
+        else (
+            (16, 4)
+            if dstate <= 32
+            else ((8, 4) if dstate <= 64 else ((4, 4) if dstate <= 128 else ((4, 8))))
+        )
+    )
+    tie_hdim = (
+        A.stride(-1) == 0
+        and A.stride(-2) == 0
+        and dt.stride(-1) == 0
+        and dt_bias.stride(-1) == 0
+    )
+    with torch.cuda.device(x.device.index):
+        _selective_scan_update_kernel[grid](
+            state,
+            x,
+            dt,
+            dt_bias,
+            A,
+            B,
+            C,
+            D,
+            z,
+            out,
+            state_batch_indices,
+            pad_slot_id,
+            batch,
+            nheads,
+            dim,
+            dstate,
+            nheads // ngroups,
+            state.stride(0),
+            state.stride(1),
+            state.stride(2),
+            state.stride(3),
+            x.stride(0),
+            x.stride(1),
+            x.stride(2),
+            dt.stride(0),
+            dt.stride(1),
+            dt.stride(2),
+            *(dt_bias.stride(0), dt_bias.stride(1)) if dt_bias is not None else 0,
+            A.stride(0),
+            A.stride(1),
+            A.stride(2),
+            B.stride(0),
+            B.stride(1),
+            B.stride(2),
+            C.stride(0),
+            C.stride(1),
+            C.stride(2),
+            *(D.stride(0), D.stride(1)) if D is not None else 0,
+            z_strides[0],
+            z_strides[1],
+            z_strides[2],
+            out.stride(0),
+            out.stride(1),
+            out.stride(2),
+            dt_softplus,
+            tie_hdim,
+            BLOCK_SIZE_M,
+            num_warps=num_warps,
+        )
+
+
+def selective_scan_fn(
+    u,
+    ssm_states,
+    delta,
+    A,
+    B,
+    C,
+    D=None,
+    z=None,
+    delta_bias=None,
+    delta_softplus=False,
+    query_start_loc=None,
+    cache_indices=None,
+    has_initial_state=None,
+    pad_slot_id=PAD_SLOT_ID,
+) -> torch.Tensor:
+    """
+    u: (dim, total_length) for varlen or (batch, dim, seqlen)
+        applies changes in place.
+    ssm_states: (batch, dim, dstate) or (batch, nheads, dim, dstate)
+        applies changes in place.
+    delta: (dim, total_length) for varlen or (batch, dim, seqlen)
+    A: (dim, dstate)
+    B: (ngroups, dstate, total_length) for varlen or
+                                        (batch,ngroups,dstate,seqlen)
+    C: (ngroups, dstate, total_length) for varlen or
+                                        (batch,ngroups,dstate,seqlen)
+    D: (dim,)
+    z: (dim, total_length) for varlen or (batch, dim, seqlen)
+    dt_bias: (dim,) or (dim)
+    query_start_loc: (batch + 1) int32
+        The cumulative sequence lengths of the sequences in
+        the batch, used to index into sequence. prepended with 0.
+        for example: query_start_loc = torch.Tensor([0,10,16,17]),
+        x.shape=(dim,17)
+    cache_indices: (batch) int32
+        A tensor with each cell is a correspondent
+        input and output ssm_state index
+    has_initial_state: (batch) bool
+        A tensor populated with ones and zeros,
+        indicate if the ssm_state at the corresponding index should be
+        used as initial state. Not providing argument assumes
+        there's no initial state
+    pad_slot_id: int
+        if cache_indices is passed, lets the kernel identify padding entries
+        that will not be processed,
+        for example: cache_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
+        in this case, the kernel will not process entries at indices 0 and 3
+    returns
+        output: (dim, total_length) for varlen or (batch, dim, seqlen)
+                supports inplace replacement
+    """
+    if u.stride(-1) != 1:
+        u = u.contiguous()
+    if delta.stride(-1) != 1:
+        delta = delta.contiguous()
+    if D is not None:
+        D = D.contiguous()
+    if B.stride(-1) != 1:
+        B = B.contiguous()
+    if C.stride(-1) != 1:
+        C = C.contiguous()
+    if z is not None and z.stride(-1) != 1:
+        z = z.contiguous()
+    if B.dim() == 3 and query_start_loc is None:
+        B = B.unsqueeze(1)
+    if B.dim() == 2 and query_start_loc is not None:
+        B = B.unsqueeze(0)
+    if C.dim() == 3 and query_start_loc is None:
+        C = C.unsqueeze(1)
+    if C.dim() == 2 and query_start_loc is not None:
+        C = C.unsqueeze(0)
+
+    ops.selective_scan_fwd(
+        u,
+        delta,
+        A,
+        B,
+        C,
+        D,
+        z,
+        delta_bias,
+        delta_softplus,
+        query_start_loc,
+        cache_indices,
+        has_initial_state,
+        ssm_states,
+        pad_slot_id,
+    )
+
+    if z is None:
+        return delta  # output written inplace to delta
+    else:
+        return z  # output written inplace to z

python/sglang/srt/layers/attention/mamba/ops/ssd_bmm.py

@@ -0,0 +1,264 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_bmm.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_bmm.py
+
+# ruff: noqa: E501,SIM102
+
+import math
+
+import torch
+import triton
+import triton.language as tl
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 64},
+#             num_stages=3,
+#             num_warps=8,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=2,
+#         ),
+#     ],
+#     key=["chunk_size", "K", "IS_CAUSAL"],
+# )
+@triton.jit
+def _bmm_chunk_fwd_kernel(
+    # Pointers to matrices
+    a_ptr,
+    b_ptr,
+    out_ptr,
+    seq_idx_ptr,
+    # Matrix dimensions
+    seqlen,
+    chunk_size,
+    K,
+    ngroups,
+    stride_a_batch,
+    stride_a_seqlen,
+    stride_a_head,
+    stride_ak,
+    stride_b_batch,
+    stride_b_seqlen,
+    stride_b_head,
+    stride_bk,
+    stride_out_batch,
+    stride_out_chunk,
+    stride_out_head,
+    stride_outm,
+    stride_outn,
+    stride_seq_idx_batch,
+    stride_seq_idx_seqlen,
+    # Meta-parameters
+    IS_CAUSAL: tl.constexpr,
+    dot_dtype: tl.constexpr,
+    HAS_SEQ_IDX: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr = 16,
+    BLOCK_SIZE_N: tl.constexpr = 16,
+    BLOCK_SIZE_K: tl.constexpr = 16,
+):
+    pid_b = tl.program_id(axis=1)
+    pid_ch = tl.program_id(axis=2).to(tl.int64)
+    pid_c = pid_ch // ngroups
+    pid_h = pid_ch - pid_c * ngroups
+    num_pid_n = tl.cdiv(chunk_size, BLOCK_SIZE_N)
+    pid_m = tl.program_id(axis=0) // num_pid_n
+    pid_n = tl.program_id(axis=0) % num_pid_n
+    if IS_CAUSAL:
+        if pid_n * BLOCK_SIZE_N >= (pid_m + 1) * BLOCK_SIZE_M:
+            return
+    a_ptr += (
+        pid_b * stride_a_batch
+        + pid_c * chunk_size * stride_a_seqlen
+        + pid_h * stride_a_head
+    )
+    b_ptr += (
+        pid_b * stride_b_batch
+        + pid_c * chunk_size * stride_b_seqlen
+        + pid_h * stride_b_head
+    )
+    if HAS_SEQ_IDX:
+        seq_idx_ptr += (
+            pid_b * stride_seq_idx_batch + pid_c * chunk_size * stride_seq_idx_seqlen
+        )
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_m[:, None] * stride_a_seqlen + offs_k[None, :] * stride_ak)
+    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_n[None, :] * stride_b_seqlen)
+    chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size)
+
+    acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        a = tl.load(
+            a_ptrs,
+            mask=(offs_m[:, None] < chunk_size_limit)
+            & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
+            other=0.0,
+        ).to(dot_dtype)
+        b = tl.load(
+            b_ptrs,
+            mask=(offs_k[:, None] < K - k * BLOCK_SIZE_K)
+            & (offs_n[None, :] < chunk_size_limit),
+            other=0.0,
+        ).to(dot_dtype)
+        acc += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    if HAS_SEQ_IDX:
+        chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size)
+        seq_idx_m = tl.load(
+            seq_idx_ptr + offs_m * stride_seq_idx_seqlen,
+            mask=offs_m < chunk_size_limit,
+            other=-1,
+        )
+        seq_idx_n = tl.load(
+            seq_idx_ptr + offs_n * stride_seq_idx_seqlen,
+            mask=offs_n < chunk_size_limit,
+            other=-2,
+        )
+        acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0)
+    out = acc.to(out_ptr.dtype.element_ty)
+
+    out_ptr += (
+        pid_b * stride_out_batch + pid_c * stride_out_chunk + pid_h * stride_out_head
+    )
+    out_ptrs = out_ptr + (stride_outm * offs_m[:, None] + offs_n[None, :] * stride_outn)
+    tl.store(
+        out_ptrs,
+        out,
+        mask=(offs_m[:, None] < chunk_size) & (offs_n[None, :] < chunk_size),
+    )
+
+
+def _bmm_chunk_fwd(a, b, chunk_size, seq_idx=None, causal=False, output_dtype=None):
+    """
+    Argument:
+        a: (batch, seqlen, k) or (batch, seqlen, ngroups, k)
+        b: (batch, seqlen, k) or (batch, seqlen, ngroups, k)
+        seq_idx: (batch, seqlen) or None. out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out.
+        causal: if True, then out[i, j] for i > j will be arbitrary, only out[i, j] for i <= j are
+            guaranteed to be correct.
+    Return:
+        out: (batch, nchunks, chunk_size, chunk_size) or (batch, nchunks, ngroups, chunk_size, chunk_size)
+    """
+    # Check constraints.
+    has_groups = a.dim() == 4
+    if not has_groups:
+        batch, seqlen, k = a.shape
+    else:
+        batch, seqlen, ngroups, k = a.shape
+    assert b.shape == a.shape
+    if seq_idx is not None:
+        assert seq_idx.shape == (batch, seqlen)
+    if a.stride(-1) != 1 and a.stride(1) != 1:
+        a = a.contiguous()
+    if b.stride(-1) != 1 and b.stride(1) != 1:
+        b = b.contiguous()
+    nchunks = math.ceil(seqlen / chunk_size)
+    # Allocates output.
+    out_dtype = a.dtype if output_dtype is None else output_dtype
+    out = torch.empty(
+        (
+            (batch, nchunks, chunk_size, chunk_size)
+            if not has_groups
+            else (batch, nchunks, ngroups, chunk_size, chunk_size)
+        ),
+        device=a.device,
+        dtype=out_dtype,
+    )
+    dot_dtype = (
+        tl.bfloat16
+        if a.dtype == torch.bfloat16 or b.dtype == torch.bfloat16
+        else (
+            tl.float16
+            if a.dtype == torch.float16 or b.dtype == torch.float16
+            else tl.float32
+        )
+    )
+    grid = lambda META: (
+        triton.cdiv(chunk_size, META["BLOCK_SIZE_M"])
+        * triton.cdiv(chunk_size, META["BLOCK_SIZE_N"]),
+        batch,
+        nchunks if not has_groups else nchunks * ngroups,
+    )
+    with torch.cuda.device(a.device.index):
+        _bmm_chunk_fwd_kernel[grid](
+            a,
+            b,
+            out,
+            seq_idx,
+            seqlen,
+            chunk_size,
+            k,
+            ngroups if has_groups else 1,
+            a.stride(0),
+            a.stride(1),
+            0 if not has_groups else a.stride(2),
+            a.stride(-1),
+            b.stride(0),
+            b.stride(1),
+            0 if not has_groups else b.stride(2),
+            b.stride(-1),
+            out.stride(0),
+            out.stride(1),
+            0 if not has_groups else out.stride(2),
+            out.stride(-2),
+            out.stride(-1),
+            *(
+                (seq_idx.stride(0), seq_idx.stride(1))
+                if seq_idx is not None
+                else (0, 0)
+            ),
+            causal,
+            dot_dtype,
+            HAS_SEQ_IDX=seq_idx is not None,
+        )
+    return out

python/sglang/srt/layers/attention/mamba/ops/ssd_chunk_scan.py

@@ -0,0 +1,622 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_chunk_scan.py
+
+# ruff: noqa: E501,SIM102
+
+import torch
+import triton
+import triton.language as tl
+from packaging import version
+
+TRITON_22 = version.parse(triton.__version__) >= version.parse("2.2.0")
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 64},
+#             num_stages=3,
+#             num_warps=8,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 64},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=2,
+#         ),
+#     ],
+#     key=["chunk_size", "hdim", "dstate", "IS_CAUSAL"],
+# )
+@triton.jit
+def _chunk_scan_fwd_kernel(
+    # Pointers to matrices
+    cb_ptr,
+    x_ptr,
+    z_ptr,
+    out_ptr,
+    out_x_ptr,
+    dt_ptr,
+    dA_cumsum_ptr,
+    seq_idx_ptr,
+    C_ptr,
+    states_ptr,
+    D_ptr,
+    initstates_ptr,
+    chunk_indices_ptr,
+    chunk_offsets_ptr,
+    chunk_meta_num,
+    # Matrix dimensions
+    chunk_size,
+    hdim,
+    dstate,
+    batch,
+    seqlen,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_cb_batch,
+    stride_cb_chunk,
+    stride_cb_head,
+    stride_cb_csize_m,
+    stride_cb_csize_k,
+    stride_x_batch,
+    stride_x_seqlen,
+    stride_x_head,
+    stride_x_hdim,
+    stride_z_batch,
+    stride_z_seqlen,
+    stride_z_head,
+    stride_z_hdim,
+    stride_out_batch,
+    stride_out_seqlen,
+    stride_out_head,
+    stride_out_hdim,
+    stride_dt_batch,
+    stride_dt_chunk,
+    stride_dt_head,
+    stride_dt_csize,
+    stride_dA_cs_batch,
+    stride_dA_cs_chunk,
+    stride_dA_cs_head,
+    stride_dA_cs_csize,
+    stride_seq_idx_batch,
+    stride_seq_idx_seqlen,
+    stride_C_batch,
+    stride_C_seqlen,
+    stride_C_head,
+    stride_C_dstate,
+    stride_states_batch,
+    stride_states_chunk,
+    stride_states_head,
+    stride_states_hdim,
+    stride_states_dstate,
+    stride_init_states_batch,
+    stride_init_states_head,
+    stride_init_states_hdim,
+    stride_init_states_dstate,
+    stride_D_head,
+    # Meta-parameters
+    IS_CAUSAL: tl.constexpr,
+    HAS_D: tl.constexpr,
+    D_HAS_HDIM: tl.constexpr,
+    HAS_Z: tl.constexpr,
+    HAS_SEQ_IDX: tl.constexpr,
+    BLOCK_SIZE_DSTATE: tl.constexpr,
+    IS_TRITON_22: tl.constexpr,
+    HAS_INITSTATES: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr = 16,
+    BLOCK_SIZE_N: tl.constexpr = 16,
+    BLOCK_SIZE_K: tl.constexpr = 16,
+):
+    pid_bc = tl.program_id(axis=1).to(tl.int64)
+    pid_c = pid_bc // batch
+    pid_b = pid_bc - pid_c * batch
+    if not HAS_INITSTATES:
+        c_idx = pid_c
+        c_off = 0
+    else:
+        c_idx = tl.load(chunk_indices_ptr + pid_c, mask=pid_c > -1, other=0)
+        c_off = tl.load(chunk_offsets_ptr + pid_c, mask=pid_c > -1, other=0)
+
+    pid_h = tl.program_id(axis=2)
+    num_pid_n = tl.cdiv(hdim, BLOCK_SIZE_N)
+    pid_m = tl.program_id(axis=0) // num_pid_n
+    pid_n = tl.program_id(axis=0) % num_pid_n
+    cb_ptr += (
+        pid_b * stride_cb_batch
+        + c_idx * stride_cb_chunk
+        + (pid_h // nheads_ngroups_ratio) * stride_cb_head
+    )
+    x_ptr += (
+        pid_b * stride_x_batch
+        + c_idx * chunk_size * stride_x_seqlen
+        + pid_h * stride_x_head
+    )
+    dt_ptr += pid_b * stride_dt_batch + c_idx * stride_dt_chunk + pid_h * stride_dt_head
+    dA_cumsum_ptr += (
+        pid_b * stride_dA_cs_batch
+        + c_idx * stride_dA_cs_chunk
+        + pid_h * stride_dA_cs_head
+    )
+    C_ptr += (
+        pid_b * stride_C_batch
+        + c_idx * chunk_size * stride_C_seqlen
+        + (pid_h // nheads_ngroups_ratio) * stride_C_head
+    )
+
+    # M-block offsets and prev states
+    #  - logic in next block may override these if there is an active offset
+    offs_m = pid_m * BLOCK_SIZE_M + c_off + tl.arange(0, BLOCK_SIZE_M)
+    prev_states_ptr = (
+        states_ptr
+        + pid_b * stride_states_batch
+        + c_idx * stride_states_chunk
+        + pid_h * stride_states_head
+    )
+    prev_states_hdim = stride_states_hdim
+    prev_states_dstate = stride_states_dstate
+
+    chunk_size_limit = min(chunk_size, seqlen - c_idx * chunk_size)
+    if HAS_SEQ_IDX:
+        seq_idx_ptr += (
+            pid_b * stride_seq_idx_batch + c_idx * chunk_size * stride_seq_idx_seqlen
+        )
+
+        # - we only need seq_idx_prev to be aligned to chunk boundary
+        seq_idx_prev = tl.load(
+            seq_idx_ptr - stride_seq_idx_seqlen, mask=c_idx >= 1, other=0
+        )
+
+        if HAS_INITSTATES:
+            # if there are init states, we only need seq_idx_m to point
+            # what is the current seq_idx
+
+            # get current seq idx
+            if (pid_m * BLOCK_SIZE_M + c_off) < chunk_size_limit:
+                seq_idx_m = tl.load(
+                    seq_idx_ptr
+                    + (pid_m * BLOCK_SIZE_M + c_off) * stride_seq_idx_seqlen,
+                )
+
+                # - recall that in ssd_state_passing, for the case c_off == 0
+                # i.e., the very first sequence, we made states_ptr hold its initial state
+                # so this edge case is taken care of
+                if (
+                    (c_off == 0)
+                    and (
+                        seq_idx_prev != seq_idx_m
+                    )  # if a seq is changed exactly on boundary
+                    or (c_off > 0)  # implies a new example (pseudo chunk)
+                ):
+
+                    # - replace prev_states_ptr with init_states
+                    prev_states_ptr = (
+                        initstates_ptr
+                        + seq_idx_m * stride_init_states_batch
+                        + pid_h * stride_init_states_head
+                    )
+                    prev_states_hdim = stride_init_states_hdim  # override strides
+                    prev_states_dstate = stride_init_states_dstate
+
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    dA_cs_m = tl.load(
+        dA_cumsum_ptr + offs_m * stride_dA_cs_csize, mask=offs_m < chunk_size, other=0.0
+    ).to(tl.float32)
+
+    # - handle chunk state limit
+    if HAS_INITSTATES:
+
+        # have to split this if otherwise compilation will have problems
+        dA_cs_m_boundary = 0.0
+
+        # get the c_idx for the next (logica) chunk
+        c_idx_n = tl.load(
+            chunk_indices_ptr + (pid_c + 1),
+            mask=pid_c > -1 and (pid_c + 1) < chunk_meta_num,
+            other=-1,  # to trigger different chunk
+        )
+
+        # - there are things to consider
+        # A. if c_off > 0 then we need to move the dA_cs boundary to ensure correct
+        #    contribution of past states
+        # B. if c_off_n < chunk_size_limit, then we need to adjust this so as not to
+        #    encroach into the next sequence, where c_off_n is the offset of the next
+        #    (logical) chunk.
+        # An equivalent check for B is c_idx == c_idx_n, where there is repetition in
+        # (logical) chunk indices.
+
+        if (c_idx == c_idx_n) or c_off > 0:
+
+            # get the next offset
+            c_off_n = tl.load(
+                chunk_offsets_ptr + (pid_c + 1),
+                mask=pid_c > -1 and (pid_c + 1) < chunk_meta_num,
+                other=chunk_size,
+            )
+
+            # in this case, adjust down the chunk_size_limit
+            if c_idx == c_idx_n:
+                chunk_size_limit = min(c_off_n, chunk_size_limit)
+
+            # get the cs at the offset boundary
+            # - c_off == 0 is a passthrough
+            # - We need dA_cs at the boundary, defined by c_off - no need
+            #   to increase pointer by pid_m (it is a constant offset,
+            #   i.e. the same for all blocks)
+            dA_cs_m_boundary = tl.load(
+                dA_cumsum_ptr + (c_off - 1) * stride_dA_cs_csize,
+                mask=(((c_off - 1) > -1) and ((c_off) < chunk_size)),
+                other=0.0,
+            ).to(tl.float32)
+
+    if HAS_SEQ_IDX:
+        # - handle seq idx when HAS_INITSTATES==False
+        if not HAS_INITSTATES:
+            seq_idx_m = tl.load(
+                seq_idx_ptr + offs_m * stride_seq_idx_seqlen,
+                mask=offs_m < chunk_size_limit,
+                other=-1,
+            )
+
+    acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+    # Without the if (pid_c > -1), with Triton 2.1.0, I get
+    # Assertion `!(srcMmaLayout && dstMmaLayout) && "Unexpected mma -> mm a layout conversion"' failed.
+    # With Triton 2.2.0, this works
+    if IS_TRITON_22 or c_idx > -1:
+        # Faster to just do 1 iteration with larger BLOCK_SIZE_K, up to block size 128
+        offs_k_dstate = tl.arange(
+            0, BLOCK_SIZE_DSTATE if BLOCK_SIZE_DSTATE <= 128 else BLOCK_SIZE_K
+        )
+        C_ptrs = C_ptr + (
+            offs_m[:, None] * stride_C_seqlen + offs_k_dstate[None, :] * stride_C_dstate
+        )
+
+        prev_states_ptrs = prev_states_ptr + (
+            offs_n[None, :] * prev_states_hdim
+            + offs_k_dstate[:, None] * prev_states_dstate
+        )
+        if HAS_SEQ_IDX:
+
+            if not HAS_INITSTATES:
+                # - this is for continuous batching where there is no init states
+                scale_m = tl.where(seq_idx_m == seq_idx_prev, tl.exp(dA_cs_m), 0.0)
+            else:
+                # - if there is initstates, we will rely on prev_states, no zeroing
+                #   required.
+                scale_m = tl.exp(dA_cs_m - dA_cs_m_boundary)
+        else:
+            scale_m = tl.exp(dA_cs_m)
+        if BLOCK_SIZE_DSTATE <= 128:
+            C = tl.load(
+                C_ptrs,
+                mask=(offs_m[:, None] < chunk_size_limit)
+                & (offs_k_dstate[None, :] < dstate),
+                other=0.0,
+            )
+
+            prev_states = tl.load(
+                prev_states_ptrs,
+                mask=(offs_k_dstate[:, None] < dstate) & (offs_n[None, :] < hdim),
+                other=0.0,
+            )
+            prev_states = prev_states.to(C_ptr.dtype.element_ty)
+            acc = tl.dot(C, prev_states) * scale_m[:, None]
+        else:
+            for k in range(0, dstate, BLOCK_SIZE_K):
+                C = tl.load(
+                    C_ptrs,
+                    mask=(offs_m[:, None] < chunk_size_limit)
+                    & (offs_k_dstate[None, :] < dstate - k),
+                    other=0.0,
+                )
+                # C = (C * scale_m[:, None]).to(C_ptr.dtype.element_ty)
+                prev_states = tl.load(
+                    prev_states_ptrs,
+                    mask=(offs_k_dstate[:, None] < dstate - k)
+                    & (offs_n[None, :] < hdim),
+                    other=0.0,
+                )
+                prev_states = prev_states.to(C_ptr.dtype.element_ty)
+                acc += tl.dot(C, prev_states)
+                C_ptrs += BLOCK_SIZE_K
+                prev_states_ptrs += BLOCK_SIZE_K
+            acc *= scale_m[:, None]
+
+    offs_k = tl.arange(0, BLOCK_SIZE_K) + c_off
+    cb_ptrs = cb_ptr + (
+        offs_m[:, None] * stride_cb_csize_m + offs_k[None, :] * stride_cb_csize_k
+    )
+    x_ptrs = x_ptr + (
+        offs_k[:, None] * stride_x_seqlen + offs_n[None, :] * stride_x_hdim
+    )
+    dt_ptrs = dt_ptr + offs_k * stride_dt_csize
+    dA_cumsum_ptrs = dA_cumsum_ptr + offs_k * stride_dA_cs_csize
+    K_MAX = (
+        chunk_size_limit
+        if not IS_CAUSAL
+        else min((pid_m + 1) * BLOCK_SIZE_M, chunk_size_limit)
+    )
+    for k in range(0, K_MAX, BLOCK_SIZE_K):
+        cb = tl.load(
+            cb_ptrs,
+            mask=(offs_m[:, None] < chunk_size) & (offs_k[None, :] < chunk_size - k),
+            other=0.0,
+        ).to(tl.float32)
+        dA_cs_k = tl.load(dA_cumsum_ptrs, mask=offs_k < chunk_size - k, other=0.0).to(
+            tl.float32
+        )
+        # If there's seq_idx, we already set cb[i, j] = 0 for seq_idx[i] != seq_idx[j].
+        # So we don't need masking wrt seq_idx here.
+        cb *= tl.exp(dA_cs_m[:, None] - dA_cs_k[None, :])
+        dt_k = tl.load(dt_ptrs, mask=offs_k < chunk_size - k, other=0.0).to(tl.float32)
+        cb *= dt_k
+        if IS_CAUSAL:
+            mask = offs_m[:, None] >= k + offs_k[None, :]
+            cb = tl.where(mask, cb, 0.0)
+        cb = cb.to(x_ptr.dtype.element_ty)
+        x = tl.load(
+            x_ptrs,
+            mask=(offs_k[:, None] < chunk_size_limit - k) & (offs_n[None, :] < hdim),
+            other=0.0,
+        )
+        acc += tl.dot(cb, x)
+        cb_ptrs += BLOCK_SIZE_K * stride_cb_csize_k
+        x_ptrs += BLOCK_SIZE_K * stride_x_seqlen
+        dt_ptrs += BLOCK_SIZE_K * stride_dt_csize
+        dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize
+
+    offs_out_m = pid_m * BLOCK_SIZE_M + c_off + tl.arange(0, BLOCK_SIZE_M)
+    offs_out_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+
+    if HAS_D:
+        if D_HAS_HDIM:
+            D = tl.load(
+                D_ptr + pid_h * stride_D_head + offs_n, mask=offs_n < hdim, other=0.0
+            ).to(tl.float32)
+        else:
+            D = tl.load(D_ptr + pid_h * stride_D_head).to(tl.float32)
+        x_residual = tl.load(
+            x_ptr
+            + (offs_m[:, None] * stride_x_seqlen + offs_n[None, :] * stride_x_hdim),
+            mask=(offs_m[:, None] < chunk_size_limit) & (offs_n[None, :] < hdim),
+            other=0.0,
+        ).to(tl.float32)
+        acc += x_residual * D
+
+    if HAS_Z:
+        out_x_ptr += (
+            pid_b * stride_out_batch
+            + c_idx * chunk_size * stride_out_seqlen
+            + pid_h * stride_out_head
+        )
+        out_x_ptrs = out_x_ptr + (
+            stride_out_seqlen * offs_out_m[:, None] + offs_out_n[None, :]
+        )
+        tl.store(
+            out_x_ptrs,
+            acc,
+            mask=(offs_out_m[:, None] < chunk_size_limit)
+            & (offs_out_n[None, :] < hdim),
+        )
+
+        z_ptr += (
+            pid_b * stride_z_batch
+            + c_idx * chunk_size * stride_z_seqlen
+            + pid_h * stride_z_head
+        )
+        z_ptrs = z_ptr + (
+            stride_z_seqlen * offs_out_m[:, None] + stride_z_hdim * offs_out_n[None, :]
+        )
+        z = tl.load(
+            z_ptrs,
+            mask=(offs_out_m[:, None] < chunk_size_limit)
+            & (offs_out_n[None, :] < hdim),
+            other=0.0,
+        ).to(tl.float32)
+        acc *= z * tl.sigmoid(z)
+
+    out_ptr += (
+        pid_b * stride_out_batch
+        + c_idx * chunk_size * stride_out_seqlen
+        + pid_h * stride_out_head
+    )
+    out_ptrs = out_ptr + (
+        stride_out_seqlen * offs_out_m[:, None] + offs_out_n[None, :] * stride_out_hdim
+    )
+    tl.store(
+        out_ptrs,
+        acc,
+        mask=(offs_out_m[:, None] < chunk_size_limit) & (offs_out_n[None, :] < hdim),
+    )
+
+
+def _chunk_scan_fwd(
+    cb,
+    x,
+    dt,
+    dA_cumsum,
+    C,
+    states,
+    D=None,
+    z=None,
+    seq_idx=None,
+    chunk_indices=None,
+    chunk_offsets=None,
+    initial_states=None,
+    out=None,
+):
+    batch, seqlen, nheads, headdim = x.shape
+    _, _, nchunks, chunk_size = dt.shape
+    _, _, ngroups, dstate = C.shape
+    assert nheads % ngroups == 0
+    assert C.shape == (batch, seqlen, ngroups, dstate)
+    assert cb.shape == (batch, nchunks, ngroups, chunk_size, chunk_size)
+    if z is not None:
+        assert z.shape == x.shape
+    if D is not None:
+        assert D.shape == (nheads, headdim) or D.shape == (nheads,)
+    assert dt.shape == (batch, nheads, nchunks, chunk_size)
+    assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size)
+    assert states.shape == (batch, nchunks, nheads, headdim, dstate)
+
+    if seq_idx is not None:
+        assert seq_idx.shape == (batch, seqlen)
+
+        if initial_states is not None:
+            # with initial states, we need to take care of how
+            # seq_idx crosses the boundaries
+            assert batch == 1, "chunk scan only supports initial states with batch 1"
+            assert (
+                chunk_indices is not None and chunk_offsets is not None
+            ), "chunk_indices and chunk_offsets should have been set"
+        else:
+            chunk_indices, chunk_offsets = None, None
+    else:
+        chunk_indices, chunk_offsets = None, None
+
+    assert out.shape == x.shape
+
+    if z is not None:
+        out_x = torch.empty_like(x)
+        assert out_x.stride() == out.stride()
+    else:
+        out_x = None
+
+    grid = lambda META: (
+        triton.cdiv(chunk_size, META["BLOCK_SIZE_M"])
+        * triton.cdiv(headdim, META["BLOCK_SIZE_N"]),
+        batch * nchunks if chunk_offsets is None else len(chunk_offsets),
+        nheads,
+    )
+    z_strides = (
+        (z.stride(0), z.stride(1), z.stride(2), z.stride(3))
+        if z is not None
+        else (0, 0, 0, 0)
+    )
+    _chunk_scan_fwd_kernel[grid](
+        cb,
+        x,
+        z,
+        out,
+        out_x,
+        dt,
+        dA_cumsum,
+        seq_idx,
+        C,
+        states,
+        D,
+        initial_states,
+        chunk_indices,
+        chunk_offsets,
+        len(chunk_indices) if chunk_indices is not None else 0,
+        chunk_size,
+        headdim,
+        dstate,
+        batch,
+        seqlen,
+        nheads // ngroups,
+        cb.stride(0),
+        cb.stride(1),
+        cb.stride(2),
+        cb.stride(3),
+        cb.stride(4),
+        x.stride(0),
+        x.stride(1),
+        x.stride(2),
+        x.stride(3),
+        z_strides[0],
+        z_strides[1],
+        z_strides[2],
+        z_strides[3],
+        out.stride(0),
+        out.stride(1),
+        out.stride(2),
+        out.stride(3),
+        dt.stride(0),
+        dt.stride(2),
+        dt.stride(1),
+        dt.stride(3),
+        dA_cumsum.stride(0),
+        dA_cumsum.stride(2),
+        dA_cumsum.stride(1),
+        dA_cumsum.stride(3),
+        *((seq_idx.stride(0), seq_idx.stride(1)) if seq_idx is not None else (0, 0)),
+        C.stride(0),
+        C.stride(1),
+        C.stride(2),
+        C.stride(3),
+        states.stride(0),
+        states.stride(1),
+        states.stride(2),
+        states.stride(3),
+        states.stride(4),
+        *(
+            (
+                initial_states.stride(0),
+                initial_states.stride(1),
+                initial_states.stride(2),
+                initial_states.stride(3),
+            )
+            if initial_states is not None
+            else (0, 0, 0, 0)
+        ),
+        D.stride(0) if D is not None else 0,
+        True,
+        D is not None,
+        D.dim() == 2 if D is not None else True,
+        BLOCK_SIZE_DSTATE=max(triton.next_power_of_2(dstate), 16),
+        HAS_Z=z is not None,
+        HAS_SEQ_IDX=seq_idx is not None,
+        IS_TRITON_22=TRITON_22,
+        HAS_INITSTATES=initial_states is not None,
+    )
+    return out_x

python/sglang/srt/layers/attention/mamba/ops/ssd_chunk_state.py

@@ -0,0 +1,757 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_chunk_state.py
+
+# ruff: noqa: E501
+
+import math
+
+import torch
+import triton
+import triton.language as tl
+
+from .mamba_ssm import softplus
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config({"BLOCK_SIZE_H": 2}),
+#         triton.Config({"BLOCK_SIZE_H": 4}),
+#         triton.Config({"BLOCK_SIZE_H": 8}),
+#         triton.Config({"BLOCK_SIZE_H": 16}),
+#         triton.Config({"BLOCK_SIZE_H": 32}),
+#         triton.Config({"BLOCK_SIZE_H": 64}),
+#     ],
+#     key=["chunk_size", "nheads"],
+# )
+@triton.jit
+def _chunk_cumsum_fwd_kernel(
+    # Pointers to matrices
+    dt_ptr,
+    A_ptr,
+    dt_bias_ptr,
+    dt_out_ptr,
+    dA_cumsum_ptr,
+    # Matrix dimension
+    batch,
+    seqlen,
+    nheads,
+    chunk_size,
+    dt_min,
+    dt_max,
+    # Strides
+    stride_dt_batch,
+    stride_dt_seqlen,
+    stride_dt_head,
+    stride_A_head,
+    stride_dt_bias_head,
+    stride_dt_out_batch,
+    stride_dt_out_chunk,
+    stride_dt_out_head,
+    stride_dt_out_csize,
+    stride_dA_cs_batch,
+    stride_dA_cs_chunk,
+    stride_dA_cs_head,
+    stride_dA_cs_csize,
+    # Meta-parameters
+    DT_SOFTPLUS: tl.constexpr,
+    HAS_DT_BIAS: tl.constexpr,
+    BLOCK_SIZE_CHUNK: tl.constexpr,
+    BLOCK_SIZE_H: tl.constexpr = 16,
+):
+    pid_b = tl.program_id(axis=0)
+
+    # if dt is long, may cause problems, so use 64 bit
+    # https://github.com/triton-lang/triton/issues/1058
+    pid_c = tl.program_id(axis=1).to(tl.int64)
+    pid_h = tl.program_id(axis=2)
+    dt_ptr += pid_b * stride_dt_batch + pid_c * chunk_size * stride_dt_seqlen
+    dt_out_ptr += pid_b * stride_dt_out_batch + pid_c * stride_dt_out_chunk
+    dA_cumsum_ptr += pid_b * stride_dA_cs_batch + pid_c * stride_dA_cs_chunk
+
+    offs_h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H)
+    offs_c = tl.arange(0, BLOCK_SIZE_CHUNK)
+    dt_ptrs = dt_ptr + (
+        offs_h[:, None] * stride_dt_head + offs_c[None, :] * stride_dt_seqlen
+    )
+    A_ptrs = A_ptr + offs_h * stride_A_head
+    dt_out_ptrs = dt_out_ptr + (
+        offs_h[:, None] * stride_dt_out_head + offs_c[None, :] * stride_dt_out_csize
+    )
+    dA_cs_ptrs = dA_cumsum_ptr + (
+        offs_h[:, None] * stride_dA_cs_head + offs_c[None, :] * stride_dA_cs_csize
+    )
+    chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size)
+
+    dt = tl.load(
+        dt_ptrs,
+        mask=(offs_h[:, None] < nheads) & (offs_c[None, :] < chunk_size_limit),
+        other=0.0,
+    ).to(tl.float32)
+    if HAS_DT_BIAS:
+        dt_bias = tl.load(
+            dt_bias_ptr + offs_h * stride_dt_bias_head, mask=offs_h < nheads, other=0.0
+        ).to(tl.float32)
+        dt += dt_bias[:, None]
+    if DT_SOFTPLUS:
+        dt = tl.where(dt <= 20.0, softplus(dt), dt)
+    # As of Triton 2.2.0, tl.clamp is not available yet
+    # dt = tl.clamp(dt, dt_min, dt_max)
+    dt = tl.minimum(tl.maximum(dt, dt_min), dt_max)
+    dt = tl.where(
+        (offs_h[:, None] < nheads) & (offs_c[None, :] < chunk_size_limit), dt, 0.0
+    )
+    tl.store(
+        dt_out_ptrs,
+        dt,
+        mask=(offs_h[:, None] < nheads) & (offs_c[None, :] < chunk_size),
+    )
+    A = tl.load(A_ptrs, mask=offs_h < nheads, other=0.0).to(tl.float32)
+    dA = dt * A[:, None]
+    dA_cs = tl.cumsum(dA, axis=1)
+    tl.store(
+        dA_cs_ptrs,
+        dA_cs,
+        mask=(offs_h[:, None] < nheads) & (offs_c[None, :] < chunk_size),
+    )
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 64},
+#             num_stages=3,
+#             num_warps=8,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=2,
+#         ),
+#     ],
+#     key=["hdim", "dstate", "chunk_size"],
+# )
+@triton.jit
+def _chunk_state_fwd_kernel(
+    # Pointers to matrices
+    x_ptr,
+    b_ptr,
+    states_ptr,
+    dt_ptr,
+    dA_cumsum_ptr,
+    seq_idx_ptr,
+    # Matrix dimensions
+    hdim,
+    dstate,
+    chunk_size,
+    batch,
+    seqlen,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_x_batch,
+    stride_x_seqlen,
+    stride_x_head,
+    stride_x_hdim,
+    stride_b_batch,
+    stride_b_seqlen,
+    stride_b_head,
+    stride_b_dstate,
+    stride_states_batch,
+    stride_states_chunk,
+    stride_states_head,
+    stride_states_hdim,
+    stride_states_dstate,
+    stride_dt_batch,
+    stride_dt_chunk,
+    stride_dt_head,
+    stride_dt_csize,
+    stride_dA_cs_batch,
+    stride_dA_cs_chunk,
+    stride_dA_cs_head,
+    stride_dA_cs_csize,
+    stride_seq_idx_batch,
+    stride_seq_idx_seqlen,
+    # Meta-parameters
+    HAS_SEQ_IDX: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr = 16,
+    BLOCK_SIZE_N: tl.constexpr = 16,
+    BLOCK_SIZE_K: tl.constexpr = 16,
+):
+    pid_bc = tl.program_id(axis=1).to(tl.int64)
+    pid_c = pid_bc // batch
+    pid_b = pid_bc - pid_c * batch
+    pid_h = tl.program_id(axis=2)
+    num_pid_n = tl.cdiv(dstate, BLOCK_SIZE_N)
+    pid_m = tl.program_id(axis=0) // num_pid_n
+    pid_n = tl.program_id(axis=0) % num_pid_n
+    b_ptr += (
+        pid_b * stride_b_batch
+        + pid_c * chunk_size * stride_b_seqlen
+        + (pid_h // nheads_ngroups_ratio) * stride_b_head
+    )
+    x_ptr += (
+        pid_b * stride_x_batch
+        + pid_c * chunk_size * stride_x_seqlen
+        + pid_h * stride_x_head
+    )
+    dt_ptr += pid_b * stride_dt_batch + pid_c * stride_dt_chunk + pid_h * stride_dt_head
+    dA_cumsum_ptr += (
+        pid_b * stride_dA_cs_batch
+        + pid_c * stride_dA_cs_chunk
+        + pid_h * stride_dA_cs_head
+    )
+    if HAS_SEQ_IDX:
+        seq_idx_ptr += (
+            pid_b * stride_seq_idx_batch + pid_c * chunk_size * stride_seq_idx_seqlen
+        )
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    x_ptrs = x_ptr + (
+        offs_m[:, None] * stride_x_hdim + offs_k[None, :] * stride_x_seqlen
+    )
+    b_ptrs = b_ptr + (
+        offs_n[None, :] * stride_b_dstate + offs_k[:, None] * stride_b_seqlen
+    )
+    dt_ptrs = dt_ptr + offs_k * stride_dt_csize
+    dA_cs_last = tl.load(dA_cumsum_ptr + (chunk_size - 1) * stride_dA_cs_csize).to(
+        tl.float32
+    )
+    dA_cumsum_ptrs = dA_cumsum_ptr + offs_k * stride_dA_cs_csize
+    if HAS_SEQ_IDX:
+        seq_idx_ptrs = seq_idx_ptr + offs_k * stride_seq_idx_seqlen
+
+    chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size)
+    if HAS_SEQ_IDX:
+        seq_idx_last = tl.load(
+            seq_idx_ptr + (chunk_size_limit - 1) * stride_seq_idx_seqlen
+        )
+
+    acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, chunk_size_limit, BLOCK_SIZE_K):
+        x = tl.load(
+            x_ptrs,
+            mask=(offs_m[:, None] < hdim) & (offs_k[None, :] < chunk_size_limit - k),
+            other=0.0,
+        )
+        b = tl.load(
+            b_ptrs,
+            mask=(offs_k[:, None] < chunk_size_limit - k) & (offs_n[None, :] < dstate),
+            other=0.0,
+        ).to(tl.float32)
+        dA_cs_k = tl.load(
+            dA_cumsum_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0
+        ).to(tl.float32)
+        if HAS_SEQ_IDX:
+            seq_idx_k = tl.load(
+                seq_idx_ptrs, mask=offs_k < chunk_size_limit - k, other=-1
+            )
+        dt_k = tl.load(dt_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(
+            tl.float32
+        )
+        if not HAS_SEQ_IDX:
+            scale = tl.exp(dA_cs_last - dA_cs_k) * dt_k
+        else:
+            scale = tl.where(
+                seq_idx_k == seq_idx_last, tl.exp(dA_cs_last - dA_cs_k) * dt_k, 0.0
+            )
+        b *= scale[:, None]
+        b = b.to(x_ptr.dtype.element_ty)
+        acc += tl.dot(x, b)
+        x_ptrs += BLOCK_SIZE_K * stride_x_seqlen
+        b_ptrs += BLOCK_SIZE_K * stride_b_seqlen
+        dt_ptrs += BLOCK_SIZE_K * stride_dt_csize
+        dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize
+        if HAS_SEQ_IDX:
+            seq_idx_ptrs += BLOCK_SIZE_K * stride_seq_idx_seqlen
+    states = acc.to(states_ptr.dtype.element_ty)
+
+    states_ptr += (
+        pid_b * stride_states_batch
+        + pid_c * stride_states_chunk
+        + pid_h * stride_states_head
+    )
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    states_ptrs = states_ptr + (
+        offs_m[:, None] * stride_states_hdim + offs_n[None, :] * stride_states_dstate
+    )
+    c_mask = (offs_m[:, None] < hdim) & (offs_n[None, :] < dstate)
+    tl.store(states_ptrs, states, mask=c_mask)
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 64},
+#             num_stages=3,
+#             num_warps=8,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=4,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=5,
+#             num_warps=2,
+#         ),
+#         triton.Config(
+#             {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32},
+#             num_stages=4,
+#             num_warps=2,
+#         ),
+#     ],
+#     key=["hdim", "dstate", "chunk_size"],
+# )
+@triton.jit
+def _chunk_state_varlen_kernel(
+    # Pointers to matrices
+    x_ptr,
+    b_ptr,
+    dt_ptr,
+    dA_cumsum_ptr,
+    chunk_states_ptr,
+    cu_seqlens_ptr,
+    states_ptr,
+    initstates_ptr,
+    # Matrix dimensions
+    hdim,
+    dstate,
+    chunk_size,
+    seqlen,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_x_seqlen,
+    stride_x_head,
+    stride_x_hdim,
+    stride_b_seqlen,
+    stride_b_head,
+    stride_b_dstate,
+    stride_dt_chunk,
+    stride_dt_head,
+    stride_dt_csize,
+    stride_dA_cs_chunk,
+    stride_dA_cs_head,
+    stride_dA_cs_csize,
+    stride_chunk_states_chunk,
+    stride_chunk_states_head,
+    stride_chunk_states_hdim,
+    stride_chunk_states_dstate,
+    stride_states_batch,
+    stride_states_head,
+    stride_states_hdim,
+    stride_states_dstate,
+    stride_init_states_batch,
+    stride_init_states_head,
+    stride_init_states_hdim,
+    stride_init_states_dstate,
+    # Meta-parameters
+    HAS_INITSTATES: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr = 16,
+    BLOCK_SIZE_N: tl.constexpr = 16,
+    BLOCK_SIZE_K: tl.constexpr = 16,
+):
+    pid_b = tl.program_id(axis=1)
+    pid_h = tl.program_id(axis=2)
+    num_pid_n = tl.cdiv(dstate, BLOCK_SIZE_N)
+    pid_m = tl.program_id(axis=0) // num_pid_n
+    pid_n = tl.program_id(axis=0) % num_pid_n
+    end_idx = tl.load(cu_seqlens_ptr + pid_b + 1)
+    pid_c = (end_idx - 1) // chunk_size
+    b_ptr += (
+        pid_c * chunk_size * stride_b_seqlen
+        + (pid_h // nheads_ngroups_ratio) * stride_b_head
+    )
+    x_ptr += pid_c * chunk_size * stride_x_seqlen + pid_h * stride_x_head
+    dt_ptr += pid_c * stride_dt_chunk + pid_h * stride_dt_head
+    dA_cumsum_ptr += pid_c * stride_dA_cs_chunk + pid_h * stride_dA_cs_head
+    chunk_states_ptr += (
+        pid_c * stride_chunk_states_chunk + pid_h * stride_chunk_states_head
+    )
+
+    if HAS_INITSTATES:
+        # if there are init states provided, we differentiate between states (which
+        # are boundary conditions at a chunk boundary) and initstates (which are boundary
+        # conditions when a new example in a cont batch starts)
+        initstates_ptr += pid_h * stride_init_states_head
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    x_ptrs = x_ptr + (
+        offs_m[:, None] * stride_x_hdim + offs_k[None, :] * stride_x_seqlen
+    )
+    b_ptrs = b_ptr + (
+        offs_n[None, :] * stride_b_dstate + offs_k[:, None] * stride_b_seqlen
+    )
+    dt_ptrs = dt_ptr + offs_k * stride_dt_csize
+    dA_cs_last = tl.load(
+        dA_cumsum_ptr + (end_idx - pid_c * chunk_size - 1) * stride_dA_cs_csize
+    ).to(tl.float32)
+    dA_cumsum_ptrs = dA_cumsum_ptr + offs_k * stride_dA_cs_csize
+
+    chunk_size_limit = end_idx - pid_c * chunk_size
+    start_idx = tl.load(cu_seqlens_ptr + pid_b)
+    start_idx_cur = tl.maximum(start_idx - pid_c * chunk_size, 0)
+
+    acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, chunk_size_limit, BLOCK_SIZE_K):
+        x = tl.load(
+            x_ptrs,
+            mask=(offs_m[:, None] < hdim)
+            & (offs_k[None, :] < chunk_size_limit - k)
+            & (offs_k[None, :] >= start_idx_cur - k),
+            other=0.0,
+        )
+        b = tl.load(
+            b_ptrs,
+            mask=(offs_k[:, None] < chunk_size_limit - k)
+            & (offs_n[None, :] < dstate)
+            & (offs_k[:, None] >= start_idx_cur - k),
+            other=0.0,
+        ).to(tl.float32)
+        dA_cs_k = tl.load(
+            dA_cumsum_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0
+        ).to(tl.float32)
+        dt_k = tl.load(dt_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(
+            tl.float32
+        )
+        scale = tl.where(
+            (offs_k >= start_idx_cur - k) & (offs_k < chunk_size_limit - k),
+            tl.exp(dA_cs_last - dA_cs_k) * dt_k,
+            0.0,
+        )
+        b *= scale[:, None]
+        b = b.to(x_ptr.dtype.element_ty)
+        acc += tl.dot(x, b)
+        x_ptrs += BLOCK_SIZE_K * stride_x_seqlen
+        b_ptrs += BLOCK_SIZE_K * stride_b_seqlen
+        dt_ptrs += BLOCK_SIZE_K * stride_dt_csize
+        dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize
+
+    # If the sequence starts after the last chunk idx, we don't need to add the contribution from the last chunk
+    # If HAS_INITSTATES==True need to consider two possibilities
+    # - if start_idx < pid_c * chunk_size, then we need to take the past_states_ptrs
+    # - if state_idx >= pid * chunk_size, then we need to insert initstates
+    if (start_idx < pid_c * chunk_size) or (HAS_INITSTATES):  # first chunk
+
+        dA_cs_boundary = 0.0  # default
+
+        if not HAS_INITSTATES:
+            past_states_ptrs = chunk_states_ptr + (
+                offs_m[:, None] * stride_chunk_states_hdim
+                + offs_n[None, :] * stride_chunk_states_dstate
+            )
+        else:
+
+            # - this seems repetitive, buts its to help the compiler
+            if start_idx < pid_c * chunk_size:
+                past_states_ptrs = chunk_states_ptr + (
+                    offs_m[:, None] * stride_chunk_states_hdim
+                    + offs_n[None, :] * stride_chunk_states_dstate
+                )
+            else:
+                past_states_ptrs = initstates_ptr + (
+                    pid_b * stride_init_states_batch
+                    + offs_m[:, None] * stride_init_states_hdim
+                    + offs_n[None, :] * stride_init_states_dstate
+                )
+
+                # need to adjust the boundary
+                if start_idx > pid_c * chunk_size:
+                    dA_cs_boundary = tl.load(
+                        dA_cumsum_ptr
+                        + (start_idx - pid_c * chunk_size - 1) * stride_dA_cs_csize
+                    ).to(tl.float32)
+
+        past_states = tl.load(
+            past_states_ptrs,
+            mask=(offs_m[:, None] < hdim) & (offs_n[None, :] < dstate),
+            other=0.0,
+        ).to(tl.float32)
+
+        scale = tl.exp(dA_cs_last - dA_cs_boundary)
+        acc += past_states * scale
+
+    states = acc.to(states_ptr.dtype.element_ty)
+
+    states_ptr += pid_b * stride_states_batch + pid_h * stride_states_head
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    states_ptrs = states_ptr + (
+        offs_m[:, None] * stride_states_hdim + offs_n[None, :] * stride_states_dstate
+    )
+    c_mask = (offs_m[:, None] < hdim) & (offs_n[None, :] < dstate)
+    tl.store(states_ptrs, states, mask=c_mask)
+
+
+def _chunk_cumsum_fwd(
+    dt, A, chunk_size, dt_bias=None, dt_softplus=False, dt_limit=(0.0, float("inf"))
+):
+    batch, seqlen, nheads = dt.shape
+    assert A.shape == (nheads,)
+    if dt_bias is not None:
+        assert dt_bias.shape == (nheads,)
+    nchunks = math.ceil(seqlen / chunk_size)
+    dt_out = torch.empty(
+        batch, nheads, nchunks, chunk_size, device=dt.device, dtype=torch.float32
+    )
+    dA_cumsum = torch.empty(
+        batch, nheads, nchunks, chunk_size, device=dt.device, dtype=torch.float32
+    )
+    grid_chunk_cs = lambda META: (
+        batch,
+        nchunks,
+        triton.cdiv(nheads, META["BLOCK_SIZE_H"]),
+    )
+    with torch.cuda.device(dt.device.index):
+        _chunk_cumsum_fwd_kernel[grid_chunk_cs](
+            dt,
+            A,
+            dt_bias,
+            dt_out,
+            dA_cumsum,
+            batch,
+            seqlen,
+            nheads,
+            chunk_size,
+            dt_limit[0],
+            dt_limit[1],
+            dt.stride(0),
+            dt.stride(1),
+            dt.stride(2),
+            A.stride(0),
+            dt_bias.stride(0) if dt_bias is not None else 0,
+            dt_out.stride(0),
+            dt_out.stride(2),
+            dt_out.stride(1),
+            dt_out.stride(3),
+            dA_cumsum.stride(0),
+            dA_cumsum.stride(2),
+            dA_cumsum.stride(1),
+            dA_cumsum.stride(3),
+            dt_softplus,
+            HAS_DT_BIAS=dt_bias is not None,
+            BLOCK_SIZE_CHUNK=triton.next_power_of_2(chunk_size),
+        )
+    return dA_cumsum, dt_out
+
+
+def _chunk_state_fwd(
+    B, x, dt, dA_cumsum, seq_idx=None, states=None, states_in_fp32=True
+):
+    batch, seqlen, nheads, headdim = x.shape
+    _, _, nchunks, chunk_size = dt.shape
+    _, _, ngroups, dstate = B.shape
+    assert nheads % ngroups == 0
+    assert B.shape == (batch, seqlen, ngroups, dstate)
+    assert dt.shape == (batch, nheads, nchunks, chunk_size)
+    assert dA_cumsum.shape == dt.shape
+    if seq_idx is not None:
+        assert seq_idx.shape == (batch, seqlen)
+    if states is not None:
+        assert states.shape == (batch, nchunks, nheads, headdim, dstate)
+    else:
+        states_dtype = torch.float32 if states_in_fp32 else B.dtype
+        states = torch.empty(
+            (batch, nchunks, nheads, headdim, dstate),
+            device=x.device,
+            dtype=states_dtype,
+        )
+    grid = lambda META: (
+        triton.cdiv(headdim, META["BLOCK_SIZE_M"])
+        * triton.cdiv(dstate, META["BLOCK_SIZE_N"]),
+        batch * nchunks,
+        nheads,
+    )
+    with torch.cuda.device(x.device.index):
+        _chunk_state_fwd_kernel[grid](
+            x,
+            B,
+            states,
+            dt,
+            dA_cumsum,
+            seq_idx,
+            headdim,
+            dstate,
+            chunk_size,
+            batch,
+            seqlen,
+            nheads // ngroups,
+            x.stride(0),
+            x.stride(1),
+            x.stride(2),
+            x.stride(3),
+            B.stride(0),
+            B.stride(1),
+            B.stride(2),
+            B.stride(-1),
+            states.stride(0),
+            states.stride(1),
+            states.stride(2),
+            states.stride(3),
+            states.stride(4),
+            dt.stride(0),
+            dt.stride(2),
+            dt.stride(1),
+            dt.stride(3),
+            dA_cumsum.stride(0),
+            dA_cumsum.stride(2),
+            dA_cumsum.stride(1),
+            dA_cumsum.stride(3),
+            *(
+                (seq_idx.stride(0), seq_idx.stride(1))
+                if seq_idx is not None
+                else (0, 0)
+            ),
+            HAS_SEQ_IDX=seq_idx is not None,
+        )
+    return states
+
+
+def chunk_state_varlen(
+    B, x, dt, dA_cumsum, cu_seqlens, chunk_states, initial_states=None
+):
+    total_seqlen, nheads, headdim = x.shape
+    _, nchunks, chunk_size = dt.shape
+    _, ngroups, dstate = B.shape
+    batch = cu_seqlens.shape[0] - 1
+    cu_seqlens = cu_seqlens.contiguous()
+    assert nheads % ngroups == 0
+    assert B.shape == (total_seqlen, ngroups, dstate)
+    assert dt.shape == (nheads, nchunks, chunk_size)
+    assert dA_cumsum.shape == dt.shape
+    assert chunk_states.shape == (nchunks, nheads, headdim, dstate)
+
+    if initial_states is not None:
+        assert initial_states.shape == (batch, nheads, headdim, dstate)
+
+    states = torch.empty(
+        batch,
+        nheads,
+        headdim,
+        dstate,
+        dtype=chunk_states.dtype,
+        device=chunk_states.device,
+    )
+    grid = lambda META: (
+        triton.cdiv(headdim, META["BLOCK_SIZE_M"])
+        * triton.cdiv(dstate, META["BLOCK_SIZE_N"]),
+        batch,
+        nheads,
+    )
+    with torch.cuda.device(x.device.index):
+        _chunk_state_varlen_kernel[grid](
+            x,
+            B,
+            dt,
+            dA_cumsum,
+            chunk_states,
+            cu_seqlens,
+            states,
+            initial_states,
+            headdim,
+            dstate,
+            chunk_size,
+            total_seqlen,
+            nheads // ngroups,
+            x.stride(0),
+            x.stride(1),
+            x.stride(2),
+            B.stride(0),
+            B.stride(1),
+            B.stride(2),
+            dt.stride(1),
+            dt.stride(0),
+            dt.stride(2),
+            dA_cumsum.stride(1),
+            dA_cumsum.stride(0),
+            dA_cumsum.stride(2),
+            chunk_states.stride(0),
+            chunk_states.stride(1),
+            chunk_states.stride(2),
+            chunk_states.stride(3),
+            states.stride(0),
+            states.stride(1),
+            states.stride(2),
+            states.stride(3),
+            *(
+                (
+                    initial_states.stride(0),
+                    initial_states.stride(1),
+                    initial_states.stride(2),
+                    initial_states.stride(3),
+                )
+                if initial_states is not None
+                else (0, 0, 0, 0)
+            ),
+            HAS_INITSTATES=initial_states is not None,
+        )
+    return states

python/sglang/srt/layers/attention/mamba/ops/ssd_combined.py

@@ -0,0 +1,262 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_combined.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_combined.py
+
+# ruff: noqa: E501
+
+import torch
+import triton
+import triton.language as tl
+from einops import rearrange
+from packaging import version
+
+from .ssd_bmm import _bmm_chunk_fwd
+from .ssd_chunk_scan import _chunk_scan_fwd
+from .ssd_chunk_state import _chunk_cumsum_fwd, _chunk_state_fwd, chunk_state_varlen
+from .ssd_state_passing import _state_passing_fwd
+
+TRITON_22 = version.parse(triton.__version__) >= version.parse("2.2.0")
+
+
+def is_int_pow_2(n):
+    return isinstance(n, int) and n > 0 and (n & (n - 1)) == 0
+
+
+def _mamba_chunk_scan_combined_fwd(
+    x,
+    dt,
+    A,
+    B,
+    C,
+    chunk_size,
+    D=None,
+    z=None,
+    dt_bias=None,
+    initial_states=None,
+    seq_idx=None,
+    chunk_indices=None,
+    chunk_offsets=None,
+    cu_seqlens=None,
+    dt_softplus=False,
+    dt_limit=(0.0, float("inf")),
+    state_dtype=None,
+    out=None,
+):
+    assert is_int_pow_2(chunk_size), "chunk_size must be integer power of 2"
+    batch, seqlen, nheads, headdim = x.shape
+    _, _, ngroups, dstate = B.shape
+    assert nheads % ngroups == 0
+    assert B.shape == (batch, seqlen, ngroups, dstate)
+    assert dt.shape == (batch, seqlen, nheads)
+    assert A.shape == (nheads,)
+    assert C.shape == B.shape
+    if z is not None:
+        assert z.shape == x.shape
+    if D is not None:
+        assert D.shape == (nheads, headdim) or D.shape == (nheads,)
+    if seq_idx is not None:
+        assert seq_idx.shape == (batch, seqlen)
+    if B.stride(-1) != 1:
+        B = B.contiguous()
+    if C.stride(-1) != 1:
+        C = C.contiguous()
+    if (
+        x.stride(-1) != 1 and x.stride(1) != 1
+    ):  # Either M or K dimension should be contiguous
+        x = x.contiguous()
+    if (
+        z is not None and z.stride(-1) != 1 and z.stride(1) != 1
+    ):  # Either M or K dimension should be contiguous
+        z = z.contiguous()
+    if D is not None and D.stride(-1) != 1:
+        D = D.contiguous()
+    if initial_states is not None:
+        if cu_seqlens is None:
+            assert initial_states.shape == (batch, nheads, headdim, dstate)
+        else:
+            assert initial_states.shape == (
+                len(cu_seqlens) - 1,
+                nheads,
+                headdim,
+                dstate,
+            )
+
+    # This function executes 5 sub-functions for computing mamba
+    # - a good resource is the blog https://goombalab.github.io/blog/2024/mamba2-part3-algorithm/
+    #   which has a minimal implementation to understand the below operations
+    # - as explained by the blog, mamba is a special case of causal attention
+    # - the idea is to chunk the attention matrix and compute each
+    #   submatrix separately using different optimizations.
+    # - see the blog and paper for a visualization of the submatrices
+    #   which we refer to in the comments below
+
+    # 1. Compute chunked cumsum of A * dt
+    # - here dt may go through a softplus activation
+    dA_cumsum, dt = _chunk_cumsum_fwd(
+        dt, A, chunk_size, dt_bias=dt_bias, dt_softplus=dt_softplus, dt_limit=dt_limit
+    )
+
+    # 2. Compute the state for each intra-chunk
+    # (right term of low-rank factorization of off-diagonal blocks; B terms)
+    states = _chunk_state_fwd(B, x, dt, dA_cumsum, seq_idx=seq_idx, states_in_fp32=True)
+
+    # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
+    # (middle term of factorization of off-diag blocks; A terms)
+    # - for handling chunked prefill, this requires i) initial_states
+    #   ii) seq_idx iii) is_cont_batched and (iv) chunk_offsets to be all specified.
+    # - When a new seq_idx is detected, we will stop passing the prev_state
+    #   and switch accordingly to the init_state corresponding to the new seq_idx.
+    # - We will also make sure that the dA_cumsum is taken only from the start of the
+    #   sequence (hence we need the full dA_cumsum tensor and not just the values at chunk boundaries)
+    # - this will ensure that states will be updated with the rightmost flushed seq_idx
+    #   of the previous chunk. This implies that the first chunk of states is either 0
+    #   or equal to init_states of the first example.
+    states, final_states = _state_passing_fwd(
+        rearrange(states, "... p n -> ... (p n)"),
+        dA_cumsum,
+        initial_states=(
+            rearrange(initial_states, "... p n -> ... (p n)")
+            if initial_states is not None
+            else None
+        ),
+        seq_idx=seq_idx,
+        chunk_size=chunk_size,
+        out_dtype=state_dtype if state_dtype is not None else C.dtype,
+        is_cont_batched=cu_seqlens is not None,
+        chunk_offsets=chunk_offsets,
+    )
+    states, final_states = (
+        rearrange(t, "... (p n) -> ... p n", n=dstate) for t in [states, final_states]
+    )
+
+    # 4. Compute batched matrix multiply for C_j^T B_i terms
+    CB = _bmm_chunk_fwd(C, B, chunk_size, seq_idx=seq_idx, output_dtype=torch.float32)
+
+    # 5. Scan and compute the diagonal blocks, taking into
+    #    account past causal states.
+    # - if initial states are provided, then states information will be
+    #   augmented with initial_states.
+    # - to do this properly, we need to account for example changes in
+    #   the continuous batch, therefore we introduce pseudo chunks, which is
+    #   a chunk that is split up each time an example changes.
+    # - in each (pseudo) chunk, we detect if the previous (pseudo) chunk had
+    #   a seq_idx change, in which case we take states information from
+    #   init_states.
+    out_x = _chunk_scan_fwd(
+        CB,
+        x,
+        dt,
+        dA_cumsum,
+        C,
+        states,
+        D=D,
+        z=z,
+        seq_idx=seq_idx,
+        chunk_indices=chunk_indices,
+        chunk_offsets=chunk_offsets,
+        initial_states=initial_states,
+        out=out,
+    )
+    if cu_seqlens is None:
+        return out_x, dt, dA_cumsum, states, final_states
+    else:
+        assert (
+            batch == 1
+        ), "passing cu_seqlens to get the varlen states is only supported if batch dimension is 1"
+        varlen_states = chunk_state_varlen(
+            B.squeeze(0),
+            x.squeeze(0),
+            dt.squeeze(0),
+            dA_cumsum.squeeze(0),
+            cu_seqlens,
+            states.squeeze(0),
+            initial_states=initial_states,
+        )
+        return out_x, dt, dA_cumsum, states, final_states, varlen_states
+
+
+def mamba_chunk_scan_combined(
+    x,
+    dt,
+    A,
+    B,
+    C,
+    chunk_size,
+    D=None,
+    z=None,
+    dt_bias=None,
+    initial_states=None,
+    seq_idx=None,
+    chunk_indices=None,
+    chunk_offsets=None,
+    cu_seqlens=None,
+    dt_softplus=False,
+    dt_limit=(0.0, float("inf")),
+    out=None,
+    return_final_states=False,
+    return_varlen_states=False,
+    state_dtype=None,
+):
+    """
+    Argument:
+        x: (batch, seqlen, nheads, headdim)
+        dt: (batch, seqlen, nheads)
+        A: (nheads)
+        B: (batch, seqlen, ngroups, dstate)
+        C: (batch, seqlen, ngroups, dstate)
+        chunk_size: int
+        D: (nheads, headdim) or (nheads,)
+        z: (batch, seqlen, nheads, headdim)
+        dt_bias: (nheads,)
+        initial_states: (batch, nheads, headdim, dstate)
+        seq_idx: (batch, seqlen)
+        cu_seqlens: (num_sequences + 1) or None, only used if return_varlen_states is True
+        dt_softplus: Whether to apply softplus to dt
+        out: Preallocated output tensor
+        state_dtype: The data type of the ssm state
+    """
+
+    if not return_varlen_states:
+        cu_seqlens = None
+    else:
+        assert (
+            cu_seqlens is not None
+        ), "cu_seqlens must be provided if return_varlen_states is True"
+    out_x, dt_out, dA_cumsum, states, final_states, *rest = (
+        _mamba_chunk_scan_combined_fwd(
+            x,
+            dt,
+            A,
+            B,
+            C,
+            chunk_size,
+            D=D,
+            z=z,
+            dt_bias=dt_bias,
+            initial_states=initial_states,
+            seq_idx=seq_idx,
+            chunk_indices=chunk_indices,
+            chunk_offsets=chunk_offsets,
+            cu_seqlens=cu_seqlens,
+            dt_softplus=dt_softplus,
+            dt_limit=dt_limit,
+            out=out,
+            state_dtype=state_dtype,
+        )
+    )
+    if not return_varlen_states:
+        if not return_final_states:
+            return
+        else:
+            return final_states
+    else:
+        varlen_states = rest[0]
+        return (
+            (varlen_states)
+            if not return_final_states
+            else (final_states, varlen_states)
+        )

python/sglang/srt/layers/attention/mamba/ops/ssd_state_passing.py

@@ -0,0 +1,275 @@
+# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_state_passing.py
+
+# ruff: noqa: E501
+
+import torch
+import triton
+import triton.language as tl
+
+
+# @triton.autotune(
+#     configs=[
+#         triton.Config({"BLOCK_SIZE": 64}),
+#         triton.Config({"BLOCK_SIZE": 128}),
+#         triton.Config({"BLOCK_SIZE": 256}),
+#         triton.Config({"BLOCK_SIZE": 512}),
+#         triton.Config({"BLOCK_SIZE": 1024}),
+#         triton.Config({"BLOCK_SIZE": 2048}),
+#     ],
+#     key=["dim"],
+# )
+@triton.jit
+def _state_passing_fwd_kernel(
+    # Pointers to matrices
+    states_ptr,
+    out_ptr,
+    final_states_ptr,
+    dA_cs_ptr,
+    initstates_ptr,
+    seq_idx_ptr,
+    chunk_offsets_ptr,
+    chunk_meta_num,
+    # Matrix dimensions
+    dim,
+    nchunks,
+    seqlen,
+    chunk_size,
+    # Strides
+    stride_states_batch,
+    stride_states_chunk,
+    stride_states_head,
+    stride_states_dim,
+    stride_out_batch,
+    stride_out_chunk,
+    stride_out_head,
+    stride_out_dim,
+    stride_final_states_batch,
+    stride_final_states_head,
+    stride_final_states_dim,
+    stride_dA_cs_batch,
+    stride_dA_cs_chunk,
+    stride_dA_cs_head,
+    stride_dA_cs_csize,
+    stride_initstates_batch,
+    stride_initstates_head,
+    stride_initstates_dim,
+    stride_seq_idx_batch,
+    stride_seq_idx_seqlen,
+    # Meta-parameters
+    HAS_INITSTATES: tl.constexpr,
+    HAS_SEQ_IDX: tl.constexpr,
+    IS_CONT_BATCHED: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr = 16,
+):
+    pid_b = tl.program_id(axis=1)
+    pid_h = tl.program_id(axis=2)
+    pid_m = tl.program_id(axis=0)
+    states_ptr += pid_b * stride_states_batch + pid_h * stride_states_head
+    dA_cs_ptr += (
+        pid_b * stride_dA_cs_batch
+        + pid_h * stride_dA_cs_head
+        + (chunk_size - 1) * stride_dA_cs_csize
+    )
+    out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head
+    final_states_ptr += (
+        pid_b * stride_final_states_batch + pid_h * stride_final_states_head
+    )
+    if HAS_INITSTATES:
+        initstates_ptr += pid_h * stride_initstates_head
+        if not IS_CONT_BATCHED:
+            initstates_ptr += pid_b * stride_initstates_batch
+
+    if HAS_SEQ_IDX:
+        seq_idx_ptr += pid_b * stride_seq_idx_batch
+
+    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    states_ptrs = states_ptr + offs_m * stride_states_dim
+    out_ptrs = out_ptr + offs_m * stride_out_dim
+    final_states_ptrs = final_states_ptr + offs_m * stride_final_states_dim
+
+    # - states will be the past state of the sequence that continues on the current check
+    if not HAS_INITSTATES:
+        states = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)
+    else:
+        initstates_ptr += offs_m * stride_initstates_dim
+        initstates_ptrs = initstates_ptr
+        # - for cont batches, for the first chunk mean it will be the first batch's
+        #   init state
+        states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+    tl.store(out_ptrs, states, mask=offs_m < dim)
+    out_ptrs += stride_out_chunk
+    prev_seq_idx_chunk_end = 0
+    logical_chunk_idx = 0
+    for c in range(nchunks):
+        new_states = tl.load(states_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+        dA_cs = tl.load(dA_cs_ptr).to(tl.float32)
+        scale_mask = True
+        if HAS_SEQ_IDX:
+            # - the seq to pass forward is the one that is flushed to the right
+            #   boundary.
+            # - that is given by seq_idx_chunk_end below: the sequence index at the end of the chunk.
+            seq_idx_chunk_end = tl.load(
+                seq_idx_ptr
+                + (min((c + 1) * chunk_size, seqlen) - 1) * stride_seq_idx_seqlen
+            )
+            if HAS_INITSTATES:
+                if IS_CONT_BATCHED and prev_seq_idx_chunk_end != seq_idx_chunk_end:
+                    # this means in the current chunk the rightmost flushed seq
+                    # has changed.
+                    # - so we do not propagate the state from previous chunk
+                    # - but rather we load that sequence's init state
+                    initstates_ptrs = (
+                        initstates_ptr + seq_idx_chunk_end * stride_initstates_batch
+                    )
+
+                    # - update state with seq_idx_new's init state
+                    states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(
+                        tl.float32
+                    )
+
+                    # - we need to consider the cumsum only of the last sequence in the chunk
+                    # - find its starting position (given by c_off of the logical chunk index)
+                    # - and subtract the cumsum just before that position from the total cumsum
+                    # - first, update the logical chunk index (add the number of sequences in the current physical chunk):
+                    # sequence index at the start of the current chunk
+                    seq_idx_chunk_start = tl.load(
+                        seq_idx_ptr
+                        + min(c * chunk_size, seqlen) * stride_seq_idx_seqlen
+                    )
+                    logical_chunk_idx += seq_idx_chunk_end - seq_idx_chunk_start
+                    # - load the chunk offset:
+                    c_off = tl.load(
+                        chunk_offsets_ptr + logical_chunk_idx,
+                        mask=logical_chunk_idx < chunk_meta_num,
+                        other=0,
+                    )
+                    # - if offset is 0, then the sequence starts at the beginning of the chunk, and we don't need to subtract anything
+                    if c_off > 0:
+                        # - dA_cs_ptr currently points to the cumsum at the end of the chunk - subtract the chunk size and add the offset
+                        dA_cs_boundary = tl.load(
+                            dA_cs_ptr
+                            - (chunk_size - 1) * stride_dA_cs_csize
+                            + (c_off - 1) * stride_dA_cs_csize,
+                            mask=(c_off - 1) > -1 and c_off < chunk_size,
+                            other=0.0,
+                        )
+                        dA_cs -= dA_cs_boundary
+
+                # - increment logical chunk index for every physical chunk
+                logical_chunk_idx += 1
+            else:
+                scale_mask = seq_idx_chunk_end == prev_seq_idx_chunk_end
+            prev_seq_idx_chunk_end = seq_idx_chunk_end
+
+        scale = tl.where(scale_mask, tl.exp(dA_cs), 0.0)
+        states = scale * states + new_states
+        if c < nchunks - 1:
+            tl.store(out_ptrs, states, mask=offs_m < dim)
+        else:
+            tl.store(final_states_ptrs, states, mask=offs_m < dim)
+        states_ptrs += stride_states_chunk
+        dA_cs_ptr += stride_dA_cs_chunk
+        out_ptrs += stride_out_chunk
+
+
+def _state_passing_fwd(
+    states,
+    dA_cumsum,
+    initial_states=None,
+    seq_idx=None,
+    chunk_size=None,
+    out_dtype=None,
+    is_cont_batched=False,
+    chunk_offsets=None,
+):
+    batch, nchunks, nheads, dim = states.shape
+    if chunk_size is None:
+        chunk_size = dA_cumsum.shape[-1]
+    else:
+        assert chunk_size == dA_cumsum.shape[-1]
+    assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size)
+    if initial_states is not None:
+        if is_cont_batched:
+            # - if cu_seqlens is provided, then the initial states
+            #   are used for continuous batching. In which case we
+            #   require seq_idx to be provided
+            assert (
+                seq_idx is not None
+            ), "seq_idx must be provided for continuous batching"
+            # - we also need chunk_offsets to be provided, to account
+            #   for computation of dA_cumsum from the start of the
+            #   sequence
+            assert (
+                chunk_offsets is not None
+            ), "chunk_offsets must be provided for continuous batching"
+        else:
+            # - this is the regular batching case, where initial
+            #   states are used are for each example of the batch.
+            assert initial_states.shape == (batch, nheads, dim)
+
+    if seq_idx is not None:
+        seqlen = seq_idx.shape[-1]
+        assert seq_idx.shape == (batch, seqlen)
+    out_dtype = states.dtype if out_dtype is None else out_dtype
+    out = torch.empty(
+        (batch, nchunks, nheads, dim), device=states.device, dtype=out_dtype
+    )
+    final_states = torch.empty(
+        (batch, nheads, dim), device=states.device, dtype=torch.float32
+    )
+    grid = lambda META: (triton.cdiv(dim, META["BLOCK_SIZE"]), batch, nheads)
+    with torch.cuda.device(states.device.index):
+        _state_passing_fwd_kernel[grid](
+            states,
+            out,
+            final_states,
+            dA_cumsum,
+            initial_states,
+            seq_idx,
+            chunk_offsets,
+            len(chunk_offsets) if chunk_offsets is not None else 0,
+            dim,
+            nchunks,
+            seqlen if seq_idx is not None else 0,
+            chunk_size,
+            states.stride(0),
+            states.stride(1),
+            states.stride(2),
+            states.stride(3),
+            out.stride(0),
+            out.stride(1),
+            out.stride(2),
+            out.stride(3),
+            final_states.stride(0),
+            final_states.stride(1),
+            final_states.stride(2),
+            dA_cumsum.stride(0),
+            dA_cumsum.stride(2),
+            dA_cumsum.stride(1),
+            dA_cumsum.stride(3),
+            *(
+                (
+                    initial_states.stride(0),
+                    initial_states.stride(1),
+                    initial_states.stride(2),
+                )
+                if initial_states is not None
+                else (0, 0, 0)
+            ),
+            *(
+                (seq_idx.stride(0), seq_idx.stride(1))
+                if seq_idx is not None
+                else (0, 0)
+            ),
+            HAS_INITSTATES=initial_states is not None,
+            HAS_SEQ_IDX=seq_idx is not None,
+            IS_CONT_BATCHED=is_cont_batched,
+        )
+    return out, final_states