[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/configs/__init__.py

@@ -4,6 +4,7 @@ from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
 from sglang.srt.configs.dots_ocr import DotsOCRConfig
 from sglang.srt.configs.dots_vlm import DotsVLMConfig
 from sglang.srt.configs.exaone import ExaoneConfig
+from sglang.srt.configs.falcon_h1 import FalconH1Config
 from sglang.srt.configs.janus_pro import MultiModalityConfig
 from sglang.srt.configs.kimi_vl import KimiVLConfig
 from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
@@ -30,4 +31,5 @@ __all__ = [
     "Qwen3NextConfig",
     "DotsVLMConfig",
     "DotsOCRConfig",
+    "FalconH1Config",
 ]

python/sglang/srt/configs/falcon_h1.py

+# coding=utf-8
+# Copyright 2024 TII and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Falcon-H1 model configuration"""
+
+import enum
+import os
+
+import numpy as np
+import torch
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+from sglang.srt.distributed.utils import divide
+from sglang.srt.layers.attention.mamba.mamba_utils import MambaStateShapeCalculator
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_size,
+    get_tensor_model_parallel_world_size,
+)
+
+logger = logging.get_logger(__name__)
+
+
+class FalconH1Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`FalconH1Model`]. It is used to instantiate a
+    FalconH1Model model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with defaults taken from [ibm-fms/FalconH1-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/FalconH1-9.8b-2.2T-hf).
+    The FalconH1Model is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU.
+    The checkpoints are  jointly trained by IBM, Princeton, and UIUC.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 128000):
+            Vocabulary size of the FalconH1 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`FalconH1Model`]
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
+            model has a output word embedding layer.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
+            Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
+            integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
+            logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
+            sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
+            significantly.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            Max cached sequence length for the model
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mamba_d_ssm (`int`, *optional*, defaults to 1024):
+            The dimension of the SSM state space latents.
+        mamba_n_heads (`int`, *optional*, defaults to 128):
+            The number of mamba heads used in the v2 implementation.
+        mamba_d_head (`int`, *optional*, defaults to `"auto"`):
+            Head embedding dimension size
+        mamba_n_groups (`int`, *optional*, defaults to 1):
+            The number of the mamba groups used in the v2 implementation.
+        mamba_d_state (`int`, *optional*, defaults to 256):
+            The dimension the mamba state space latents
+        mamba_d_conv (`int`, *optional*, defaults to 4):
+            The size of the mamba convolution kernel
+        mamba_expand (`int`, *optional*, defaults to 2):
+            Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
+        mamba_chunk_size (`int`, *optional*, defaults to 256):
+            The chunks in which to break the sequence when doing prefill/training
+        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
+        mamba_norm_before_gate (`bool`, *optional*, defaults to `True`):
+            Whether to use RMSNorm before the gate in the Mamba block
+        mamba_rms_norm (`bool`, *optional*, defaults to `False`):
+            Whether to use RMSNorm instead of LayerNorm in the Mamba block
+        projectors_bias (`bool`, *optional*, defaults to `False`):
+            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the attention block
+        rope_theta (`float`, *optional*, defaults to 100000.0):
+            The theta value used for the RoPE embeddings.
+        rope_scaling (`float`, *optional*):
+            The scaling value used for the RoPE embeddings. If `None`, no scaling is applied.
+        lm_head_multiplier (`float`, *optional*, defaults to 1.0):
+            The multiplier for the LM head. This is used to scale the output of the LM head.
+        embedding_multiplier (`float`, *optional*, defaults to 1.0):
+            The multiplier for the embedding layer. This is used to scale the output of the embedding layer.
+        mlp_multipliers (`list[float]`, *optional*):
+            The multipliers for the MLP layers. This is used to scale the output of the MLP layers. The first value is
+            the multiplier of gate layer, the second value is the multiplier of the down_proj layer.
+        key_multiplier (`float`, *optional*):
+            The multiplier for the key layer. This is used to scale the output of the key layer.
+        attention_out_multiplier (`float`, *optional*):
+            The multiplier for the attention output layer. This is used to scale the output of the attention output
+        attention_in_multiplier (`float`, *optional*):
+            The multiplier for the attention input layer. This is used to scale the output of the attention input layer.
+        ssm_multipliers (`list[float]`, *optional*):
+            The multipliers for the SSM layers. This is used to scale the output of the SSM layers.
+        ssm_in_multiplier (`float`, *optional*):
+            The multiplier for the SSM input layer. This is used to scale the output of the SSM input layer.
+        ssm_out_multiplier (`float`, *optional*):
+            The multiplier for the SSM output layer. This is used to scale the output of the SSM output layer.
+    """
+
+    model_type = "falcon_h1"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=128000,
+        tie_word_embeddings=False,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        num_logits_to_keep=1,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        max_position_embeddings=8192,
+        attention_dropout=0.0,
+        mamba_d_ssm=1024,
+        mamba_n_heads=128,
+        mamba_d_head="auto",
+        mamba_n_groups=1,
+        mamba_d_state=256,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_chunk_size=256,
+        mamba_conv_bias=True,
+        mamba_proj_bias=False,
+        mamba_norm_before_gate=True,
+        mamba_rms_norm=False,
+        projectors_bias=False,
+        rope_theta=100000.0,
+        rope_scaling=None,
+        lm_head_multiplier=1.0,
+        embedding_multiplier=1.0,
+        mlp_multipliers=None,
+        key_multiplier=None,
+        attention_out_multiplier=None,
+        attention_in_multiplier=None,
+        ssm_multipliers=None,
+        ssm_in_multiplier=None,
+        ssm_out_multiplier=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.attention_dropout = attention_dropout
+        self.attention_bias = False
+        self.mlp_bias = False
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+
+        self.use_cache = use_cache
+        self.num_logits_to_keep = num_logits_to_keep
+
+        self.rope_theta = rope_theta
+        self.rope_scaling = None
+        self.rope_scaling = rope_scaling
+        self.projectors_bias = projectors_bias
+        mamba_intermediate = (
+            mamba_expand * hidden_size if mamba_d_ssm is None else mamba_d_ssm
+        )
+
+        if mamba_intermediate % mamba_n_heads != 0:
+            raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size")
+
+        # for the mamba_v2, must satisfy the following
+        if mamba_d_head == "auto":
+            mamba_d_head = mamba_intermediate // mamba_n_heads
+
+        if mamba_d_head * mamba_n_heads != mamba_intermediate:
+            raise ValueError(
+                "The dimensions for the Mamba head state do not match the model intermediate_size"
+            )
+
+        self.mamba_d_ssm = mamba_d_ssm
+        self.mamba_n_heads = mamba_n_heads
+        self.mamba_d_head = mamba_d_head
+        self.mamba_n_groups = mamba_n_groups
+        self.mamba_d_state = mamba_d_state
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_expand = mamba_expand
+        self.mamba_chunk_size = mamba_chunk_size
+        self.mamba_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+
+        self.mamba_norm_before_gate = mamba_norm_before_gate
+        self.mamba_rms_norm = mamba_rms_norm
+
+        self.lm_head_multiplier = lm_head_multiplier
+        self.embedding_multiplier = embedding_multiplier
+
+        if mlp_multipliers is not None:
+            self.mlp_multipliers = mlp_multipliers
+        else:
+            self.mlp_multipliers = [1.0, 1.0]
+
+        if attention_out_multiplier is not None:
+            self.attention_out_multiplier = attention_out_multiplier
+        else:
+            self.attention_out_multiplier = 1.0
+
+        if attention_in_multiplier is not None:
+            self.attention_in_multiplier = attention_in_multiplier
+        else:
+            self.attention_in_multiplier = 1.0
+
+        if key_multiplier is not None:
+            self.key_multiplier = key_multiplier
+        else:
+            self.key_multiplier = 1.0
+
+        if ssm_multipliers is not None:
+            self.ssm_multipliers = ssm_multipliers
+        else:
+            self.ssm_multipliers = [1.0, 1.0, 1.0, 1.0, 1.0]
+
+        if ssm_in_multiplier is not None:
+            self.ssm_in_multiplier = ssm_in_multiplier
+        else:
+            self.ssm_in_multiplier = 1.0
+
+        if ssm_out_multiplier is not None:
+            self.ssm_out_multiplier = ssm_out_multiplier
+        else:
+            self.ssm_out_multiplier = 1.0
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @property
+    def layers_block_type(self):
+        return ["falcon_h1" for i in range(self.num_hidden_layers)]
+
+    @property
+    def mamba_cache_per_req(self):
+        conv_state_shape, temporal_state_shape, conv_dtype, ssm_dtype, mamba_layers = (
+            self.hybrid_gdn_params
+        )
+        mamba_layers_len = len(mamba_layers)
+
+        return (
+            int(np.prod(conv_state_shape)) * conv_dtype.itemsize
+            + int(np.prod(temporal_state_shape)) * ssm_dtype.itemsize
+        ) * mamba_layers_len
+
+    @property
+    def full_attention_layer_ids(self):
+        # For Falcon-H1, we do have attention on all layers
+        return range(self.num_hidden_layers)
+
+    @property
+    def linear_layer_ids(self):
+        # For Falcon-H1, we do have mamba on all layers
+        return range(self.num_hidden_layers)
+
+    @property
+    def hybrid_gdn_params(self):
+        world_size = get_tensor_model_parallel_world_size()
+
+        n_groups = self.mamba_n_groups
+        if self.mamba_n_groups % world_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
+            extra_groups = MambaStateShapeCalculator.extra_groups_for_head_shards(
+                self.mamba_n_groups, world_size
+            )
+            n_groups += extra_groups
+
+        conv_dim = self.mamba_d_ssm + 2 * n_groups * self.mamba_d_state
+
+        conv_state_shape = (
+            divide(conv_dim, world_size),
+            self.mamba_d_conv - 1,
+        )
+
+        # we TP-ize on the heads dimension
+        temporal_state_shape = (
+            self.mamba_d_state,
+            self.mamba_d_head,
+            divide(self.mamba_n_heads, world_size),
+        )
+        conv_dtype = torch.bfloat16
+        dtype_map = {
+            "float32": torch.float32,
+            "bfloat16": torch.bfloat16,
+        }
+        ssm_dtype = dtype_map[os.environ["SGLANG_MAMBA_SSM_DTYPE"]]
+        mamba_layers = self.linear_layer_ids
+
+        return (
+            conv_state_shape,
+            temporal_state_shape,
+            conv_dtype,
+            ssm_dtype,
+            mamba_layers,
+        )

python/sglang/srt/hf_transformers_utils.py

@@ -41,6 +41,7 @@ from sglang.srt.configs import (
     DotsOCRConfig,
     DotsVLMConfig,
     ExaoneConfig,
+    FalconH1Config,
     KimiVLConfig,
     LongcatFlashConfig,
     MultiModalityConfig,
@@ -62,6 +63,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     Step3VLConfig.model_type: Step3VLConfig,
     LongcatFlashConfig.model_type: LongcatFlashConfig,
     Qwen3NextConfig.model_type: Qwen3NextConfig,
+    FalconH1Config.model_type: FalconH1Config,
     DotsVLMConfig.model_type: DotsVLMConfig,
     DotsOCRConfig.model_type: DotsOCRConfig,
 }

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_utils.py

+# 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

+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

+# 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

+# 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

+# 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_combined.py

+# 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

+# 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

python/sglang/srt/model_executor/model_runner.py

@@ -1297,6 +1297,7 @@ class ModelRunner:
         return self.model_config.hf_config.architectures[0] in [
             "Qwen3NextForCausalLM",
             "Qwen3NextForCausalLMMTP",
+            "FalconH1ForCausalLM",
         ]
 
     def set_num_token_hybrid(self):

test/srt/models/test_falcon_h1_models.py

+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+
+class TestFalconH1(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "tiiuae/Falcon-H1-0.5B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tensor-parallel-size",
+                "1",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval(args)
+        print(f"{metrics=}")
+        self.assertGreater(metrics["accuracy"], 0.74)
+
+
+class TestFalconH1TP4(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "tiiuae/Falcon-H1-0.5B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tensor-parallel-size",
+                "4",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval(args)
+        print(f"{metrics=}")
+        self.assertGreater(metrics["accuracy"], 0.74)
+
+
+class TestFalconH1NoGatedRMS(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "tiiuae/Falcon-H1-1.5B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tensor-parallel-size",
+                "1",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval(args)
+        print(f"{metrics=}")
+        self.assertGreater(metrics["accuracy"], 0.74)
+
+
+class TestFalconH1NoGatedTP4(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "tiiuae/Falcon-H1-1.5B-Instruct"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tensor-parallel-size",
+                "4",
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval(args)
+        print(f"{metrics=}")
+        self.assertGreater(metrics["accuracy"], 0.74)

test/srt/run_suite.py

@@ -140,6 +140,7 @@ suites = {
         TestFile("test_local_attn.py", 250),
         TestFile("test_pp_single_node.py", 372),
         TestFile("models/test_qwen3_next_models.py", 200),
+        TestFile("models/test_falcon_h1_models.py", 200),
         TestFile("test_multi_instance_release_memory_occupation.py", 64),
     ],
     "per-commit-8-gpu": [