Feature/support longcat flash lite (#17838)

Co-authored-by: sunjiaqi11 <sunjiaqi11@meituan.com>
Co-authored-by: ispobock <ispobaoke@gmail.com>

python/sglang/jit_kernel/csrc/ngram_embedding.cuh

@@ -0,0 +1,295 @@
+#include <sgl_kernel/tensor.h>
+#include <sgl_kernel/utils.h>
+
+#include <sgl_kernel/utils.cuh>
+
+#include <dlpack/dlpack.h>
+
+#include <algorithm>
+#include <concepts>
+#include <cstddef>
+#include <cstdint>
+#include <type_traits>
+
+namespace device::ngram_embedding {
+
+__global__ void ComputeNGramIdsKernel(
+    int batch_size,
+    int ne_n,
+    int ne_k,
+    int* ne_weights,                      // [ne_n-1,ne_k,ne_n]
+    int* ne_mods,                         // [ne_n-1,ne_k]
+    int* exclusive_ne_embeder_size_sums,  // [(ne_n-1)*ne_k]
+    int* tokens,                          // [token_num]
+    int* exclusive_req_len_sums,          // [batch_size+1]
+    int* ne_token_table,                  // [max_running_reqs, max_context_len]
+    int max_context_len,                  // max_context_len
+    long* row_indices,                    // [batch_size]
+    int* column_starts,                   // [batch_size]
+    int* n_gram_ids                       // [ne_n-1,ne_k,token_num]
+) {
+  // Determine which n, k, and request this block handles.
+  /**
+  Example: [req0, req1, req2] with n=3, k=2
+  n       k       req_id      blockIdx.x  config_id (combination of n and k)
+  2       1       0           0           0
+  2       1       1           1           0
+  2       1       2           2           0
+  2       2       0           3           1
+  2       2       1           4           1
+  2       2       2           5           1
+  3       1       0           0           2
+  3       1       1           1           2
+  3       1       2           2           2
+  3       2       0           3           3
+  3       2       1           4           3
+  3       2       2           5           3
+  */
+  const int req_id = blockIdx.x % batch_size;
+  const int config_id = (blockIdx.x - req_id) / batch_size;
+  // n and k here are offset from their physical meanings: n = real_n - 2, k = real_k - 1.
+  // This offset exists because n and k are used as indices into ne_weights and ne_mods.
+  const int k = config_id % ne_k;
+  const int n = (config_id - config_id % ne_k) / ne_k;
+  // ne_weights has shape [ne_n-1, ne_k, ne_n]; last dim is token distance, so compute base index first
+  const int ne_weight_base_idx = n * ne_k * ne_n + k * ne_n;
+  // ne_mods has shape [ne_n-1, ne_k]
+  const int ne_mod = ne_mods[n * ne_k + k];
+  // stride loop
+  for (int i = exclusive_req_len_sums[req_id] + threadIdx.x; i < exclusive_req_len_sums[req_id + 1]; i += blockDim.x) {
+    uint64_t n_gram_id = 0;
+    // Token offset within the current request
+    int current_token_offset = i - exclusive_req_len_sums[req_id];
+    // Start index of this request in the token table; tokens before this belong to other requests
+    int req_token_table_index = row_indices[req_id] * max_context_len;
+    // Position of the current token in the token table
+    int current_token_table_index = req_token_table_index + column_starts[req_id] + current_token_offset;
+    for (int j = 0; j < n + 2; j++) {
+      if (current_token_table_index - j < req_token_table_index) {
+        // Out of this request's range, stop computing n_gram_id
+        break;
+      }
+      if (ne_token_table[current_token_table_index - j] < 0) {
+        // Token was marked as ignored during write
+        break;
+      }
+      const uint64_t term =
+          (uint64_t)ne_token_table[current_token_table_index - j] * (uint64_t)ne_weights[ne_weight_base_idx + j];
+      n_gram_id += term % ne_mod;
+    }
+    n_gram_id %= ne_mod;
+    n_gram_id += exclusive_ne_embeder_size_sums[n * ne_k + k];
+    // [token_num, ne_n-1, ne_k]
+    n_gram_ids[i * (ne_n - 1) * ne_k + n * ne_k + k] = (int)(n_gram_id);
+  }
+}
+
+__global__ void UpdateTokenTableKernel(
+    int batch_size,
+    int* tokens,           // [token_num]
+    int* ne_token_table,   // [max_running_reqs, max_context_len]
+    int max_context_len,   // max_context_len
+    long* row_indices,     // [batch_size]
+    int* column_starts,    // [batch_size]
+    int* req_lens,         // [batch_size]
+    int ignore_token_num,  // number of tokens to ignore
+    int* ignore_tokens     // [ignore_token_num]
+) {
+  // Each block processes one request.
+  const int req_id = blockIdx.x % batch_size;
+  int start = 0;
+  int end = 0;
+  for (int i = 0; i < req_id; i++) {
+    start += req_lens[i];
+  }
+  end = start + req_lens[req_id];
+  // stride loop
+  for (int i = start + threadIdx.x; i < end; i += blockDim.x) {
+    // Token offset within the current request
+    int current_token_offset = i - start;
+    // Start index of this request in the token table
+    int req_token_table_index = row_indices[req_id] * max_context_len;
+    // Position of the current token in the token table
+    int current_token_table_index = req_token_table_index + column_starts[req_id] + current_token_offset;
+    ne_token_table[current_token_table_index] = tokens[i];
+    for (int j = 0; j < ignore_token_num; j++) {
+      if (ignore_tokens[j] == tokens[i]) {
+        ne_token_table[current_token_table_index] = -tokens[i];
+        break;
+      }
+    }
+  }
+}
+
+}  // namespace device::ngram_embedding
+
+namespace {
+
+struct NgramEmbeddingKernel {
+  static void compute_n_gram_ids(
+      const int64_t ne_n,
+      const int64_t ne_k,
+      const tvm::ffi::TensorView ne_weights,
+      const tvm::ffi::TensorView ne_mods,
+      const tvm::ffi::TensorView exclusive_ne_embeder_size_sums,
+      const tvm::ffi::TensorView tokens,
+      const tvm::ffi::TensorView exclusive_req_len_sums,
+      const tvm::ffi::TensorView ne_token_table,
+      const tvm::ffi::TensorView row_indices,
+      const tvm::ffi::TensorView column_starts,
+      const tvm::ffi::TensorView n_gram_ids) {
+    using namespace host;
+
+    auto device_ = SymbolicDevice{};
+
+    // Verify tensor shapes and types using -1 (kAnySize) for dynamic dimensions
+    TensorMatcher({-1, -1, -1})  // [ne_n-1, ne_k, ne_n]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>(device_)
+        .verify(ne_weights);
+
+    TensorMatcher({-1, -1})  // [ne_n-1, ne_k]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(ne_mods);
+
+    TensorMatcher({-1})  // [(ne_n-1)*ne_k + 1]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(exclusive_ne_embeder_size_sums);
+
+    TensorMatcher({-1})  // [token_num]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(tokens);
+
+    TensorMatcher({-1})  // [batch_size+1]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(exclusive_req_len_sums);
+
+    TensorMatcher({-1, -1})  // [max_running_reqs, max_context_len]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(ne_token_table);
+
+    TensorMatcher({-1})  // [batch_size]
+        .with_dtype<int64_t>()
+        .with_device<kDLCUDA>()
+        .verify(row_indices);
+
+    TensorMatcher({-1})  // [batch_size]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(column_starts);
+
+    TensorMatcher({-1, -1})  // [token_num, (ne_n-1)*ne_k]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(n_gram_ids);
+
+    const int batch_size = static_cast<int>(exclusive_req_len_sums.size(0) - 1);
+    const int max_context_len = static_cast<int>(ne_token_table.size(1));
+    const auto stream = LaunchKernel::resolve_device(device_.unwrap());
+
+    constexpr int BLOCK_THREADS = 256;
+    const int num_configs = (static_cast<int>(ne_n) - 1) * static_cast<int>(ne_k);
+    const int grid_size = num_configs * batch_size;
+
+    LaunchKernel(grid_size, BLOCK_THREADS, stream)(
+        device::ngram_embedding::ComputeNGramIdsKernel,
+        batch_size,
+        static_cast<int>(ne_n),
+        static_cast<int>(ne_k),
+        static_cast<int*>(ne_weights.data_ptr()),
+        static_cast<int*>(ne_mods.data_ptr()),
+        static_cast<int*>(exclusive_ne_embeder_size_sums.data_ptr()),
+        static_cast<int*>(tokens.data_ptr()),
+        static_cast<int*>(exclusive_req_len_sums.data_ptr()),
+        static_cast<int*>(ne_token_table.data_ptr()),
+        max_context_len,
+        static_cast<long*>(row_indices.data_ptr()),
+        static_cast<int*>(column_starts.data_ptr()),
+        static_cast<int*>(n_gram_ids.data_ptr()));
+  }
+
+  static void update_token_table(
+      const tvm::ffi::TensorView tokens,
+      const tvm::ffi::TensorView ne_token_table,
+      const tvm::ffi::TensorView row_indices,
+      const tvm::ffi::TensorView column_starts,
+      const tvm::ffi::TensorView req_lens,
+      const tvm::ffi::TensorView ignore_tokens) {
+    using namespace host;
+
+    auto device_ = SymbolicDevice{};
+
+    // Verify tensor shapes and types using -1 (kAnySize) for dynamic dimensions
+    TensorMatcher({-1})  // [token_num]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>(device_)
+        .verify(tokens);
+
+    TensorMatcher({-1, -1})  // [max_running_reqs, max_context_len]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(ne_token_table);
+
+    TensorMatcher({-1})  // [batch_size]
+        .with_dtype<int64_t>()
+        .with_device<kDLCUDA>()
+        .verify(row_indices);
+
+    TensorMatcher({-1})  // [batch_size]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(column_starts);
+
+    TensorMatcher({-1})  // [batch_size]
+        .with_dtype<int32_t>()
+        .with_device<kDLCUDA>()
+        .verify(req_lens);
+
+    // ignore_tokens can be empty or have values
+    void* ignore_tokens_ptr = ignore_tokens.data_ptr();
+    const bool has_ignore_tokens = ignore_tokens_ptr != nullptr && ignore_tokens.numel() > 0;
+    if (has_ignore_tokens) {
+      TensorMatcher({-1})  // [ignore_token_num]
+          .with_dtype<int32_t>()
+          .with_device<kDLCUDA>()
+          .verify(ignore_tokens);
+    }
+
+    const int batch_size = static_cast<int>(req_lens.size(0));
+    if (batch_size <= 0) {
+      return;
+    }
+
+    const int max_context_len = static_cast<int>(ne_token_table.size(1));
+    const auto stream = LaunchKernel::resolve_device(device_.unwrap());
+
+    constexpr int BLOCK_THREADS = 256;
+    const int grid_size = batch_size;
+
+    int ignore_token_num = 0;
+    int* ignore_tokens_typed_ptr = nullptr;
+    if (has_ignore_tokens) {
+      ignore_token_num = static_cast<int>(ignore_tokens.numel());
+      ignore_tokens_typed_ptr = static_cast<int*>(ignore_tokens_ptr);
+    }
+
+    LaunchKernel(grid_size, BLOCK_THREADS, stream)(
+        device::ngram_embedding::UpdateTokenTableKernel,
+        batch_size,
+        static_cast<int*>(tokens.data_ptr()),
+        static_cast<int*>(ne_token_table.data_ptr()),
+        max_context_len,
+        static_cast<long*>(row_indices.data_ptr()),
+        static_cast<int*>(column_starts.data_ptr()),
+        static_cast<int*>(req_lens.data_ptr()),
+        ignore_token_num,
+        ignore_tokens_typed_ptr);
+  }
+};
+
+}  // namespace

python/sglang/jit_kernel/ngram_embedding.py

@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import TYPE_CHECKING
+
+from sglang.jit_kernel.utils import load_jit
+
+if TYPE_CHECKING:
+    import torch
+    from tvm_ffi.module import Module
+
+
+@lru_cache(maxsize=None)
+def _jit_ngram_embedding_module() -> Module:
+    return load_jit(
+        "ngram_embedding",
+        cuda_files=["ngram_embedding.cuh"],
+        cuda_wrappers=[
+            ("compute_n_gram_ids", "&NgramEmbeddingKernel::compute_n_gram_ids"),
+            ("update_token_table", "&NgramEmbeddingKernel::update_token_table"),
+        ],
+    )
+
+
+def compute_n_gram_ids(
+    ne_n: int,
+    ne_k: int,
+    ne_weights: torch.Tensor,
+    ne_mods: torch.Tensor,
+    exclusive_ne_embeder_size_sums: torch.Tensor,
+    tokens: torch.Tensor,
+    exclusive_req_len_sums: torch.Tensor,
+    ne_token_table: torch.Tensor,
+    row_indices: torch.Tensor,
+    column_starts: torch.Tensor,
+    n_gram_ids: torch.Tensor,
+) -> None:
+    """
+    Compute n-gram IDs for embedding.
+
+    Args:
+        ne_n: n value for n-gram
+        ne_k: k value for n-gram configurations
+        ne_weights: weights tensor with shape [ne_n-1, ne_k, ne_n]
+        ne_mods: mods tensor with shape [ne_n-1, ne_k]
+        exclusive_ne_embeder_size_sums: exclusive sum of embedder sizes
+        tokens: input token ids
+        exclusive_req_len_sums: exclusive sum of request lengths
+        ne_token_table: token table for all requests
+        row_indices: row indices for each request
+        column_starts: column start positions for each request
+        n_gram_ids: output tensor for n-gram ids
+    """
+    module = _jit_ngram_embedding_module()
+    module.compute_n_gram_ids(
+        ne_n,
+        ne_k,
+        ne_weights,
+        ne_mods,
+        exclusive_ne_embeder_size_sums,
+        tokens,
+        exclusive_req_len_sums,
+        ne_token_table,
+        row_indices,
+        column_starts,
+        n_gram_ids,
+    )
+
+
+def update_token_table(
+    tokens: torch.Tensor,
+    ne_token_table: torch.Tensor,
+    row_indices: torch.Tensor,
+    column_starts: torch.Tensor,
+    req_lens: torch.Tensor,
+    ignore_tokens: torch.Tensor | None = None,
+) -> None:
+    """
+    Update the token table with new tokens.
+
+    Args:
+        tokens: input token ids
+        ne_token_table: token table for all requests
+        row_indices: row indices for each request
+        column_starts: column start positions for each request
+        req_lens: request lengths
+        ignore_tokens: tokens to be ignored (marked as negative in table)
+    """
+    module = _jit_ngram_embedding_module()
+    if ignore_tokens is None:
+        # Create an empty tensor for ignore_tokens
+        ignore_tokens = tokens.new_empty(0, dtype=tokens.dtype)
+    module.update_token_table(
+        tokens,
+        ne_token_table,
+        row_indices,
+        column_starts,
+        req_lens,
+        ignore_tokens,
+    )

python/sglang/srt/configs/longcat_flash.py

@@ -53,6 +53,9 @@ class LongcatFlashConfig(PretrainedConfig):
         zero_expert_type="identity",
         nextn_use_scmoe=False,
         num_nextn_predict_layers=1,
+        ngram_vocab_size_ratio=None,
+        emb_neighbor_num=None,
+        emb_split_num=None,
         **kwargs,
     ):
         super().__init__(
@@ -102,3 +105,8 @@ class LongcatFlashConfig(PretrainedConfig):
         self.zero_expert_type = zero_expert_type
         self.routed_scaling_factor = routed_scaling_factor
         self.hidden_act = "silu"
+        self.use_ngram_embedding = ngram_vocab_size_ratio is not None
+        if self.use_ngram_embedding:
+            self.ngram_embedding_m = int(ngram_vocab_size_ratio * vocab_size)
+            self.ngram_embedding_n = emb_neighbor_num
+            self.ngram_embedding_k = emb_split_num

python/sglang/srt/configs/model_config.py

@@ -193,6 +193,7 @@ class ModelConfig:
         self.is_local_attention_model = is_local_attention_model(
             self.hf_config.architectures
         )
+        self.use_ngram_embedding = getattr(self.hf_config, "use_ngram_embedding", False)
         self.is_piecewise_cuda_graph_disabled_model = (
             is_piecewise_cuda_graph_disabled_model(self.hf_config.architectures)
             or is_deepseek_nsa(self.hf_text_config)

python/sglang/srt/layers/n_gram_embedding.py

@@ -0,0 +1,172 @@
+import torch
+from torch import nn
+from torch.nn import Parameter
+
+from sglang.jit_kernel.ngram_embedding import compute_n_gram_ids
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+
+class NgramEmbedding(torch.nn.Module):
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        over_embedding_m: int,
+        over_embedding_k: int,
+        over_embedding_n: int,
+    ):
+        super().__init__()
+        assert (
+            over_embedding_n > 1
+        ), f"over_embedding_n must be > 1, got {over_embedding_n}"
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.over_embedding_m = over_embedding_m
+        self.over_embedding_k = over_embedding_k
+        self.over_embedding_n = over_embedding_n
+
+        self.word_embeder = VocabParallelEmbedding(
+            num_embeddings,
+            embedding_dim,
+            enable_tp=is_dp_attention_enabled(),
+        )
+        self.n_grams = (over_embedding_n - 1) * over_embedding_k
+        oe_hidden_dim = embedding_dim // (over_embedding_k * (over_embedding_n - 1))
+        self.exclusive_oe_embeder_size_sums = torch.zeros(
+            [over_embedding_k * (over_embedding_n - 1) + 1],
+            dtype=torch.int32,
+            device="cuda",
+        )
+        for i in range(over_embedding_k * (over_embedding_n - 1)):
+            self.exclusive_oe_embeder_size_sums[i + 1] = (
+                self.exclusive_oe_embeder_size_sums[i]
+                + int(over_embedding_m + i * 2 + 1)
+            )
+        self.oe_embeder = VocabParallelEmbedding(
+            num_embeddings=self.exclusive_oe_embeder_size_sums[-1],
+            embedding_dim=oe_hidden_dim,
+            enable_tp=is_dp_attention_enabled(),
+        )
+
+        self.oe_projection = nn.Parameter(
+            torch.empty(
+                (over_embedding_n - 1) * over_embedding_k, oe_hidden_dim, embedding_dim
+            ),
+            requires_grad=False,
+        )
+
+        self.oe_mods = torch.zeros(
+            [self.over_embedding_n - 1, self.over_embedding_k], dtype=torch.int32
+        )
+        self.oe_weights = torch.zeros(
+            [self.over_embedding_n - 1, self.over_embedding_k, self.over_embedding_n],
+            dtype=torch.int32,
+        )
+        for n in range(2, self.over_embedding_n + 1):
+            for k in range(self.over_embedding_k):
+                mod = (
+                    self.over_embedding_m
+                    + 2 * ((n - 2) * self.over_embedding_k + k)
+                    + 1
+                )
+                self.oe_mods[n - 2][k] = mod
+                for delta in range(self.over_embedding_n):
+                    self.oe_weights[n - 2][k][delta] = pow(num_embeddings, delta, mod)
+
+    def init_buffers(
+        self, max_running_requests: int, chunked_prefill_size: int, device: str
+    ):
+        max_tokens = max(chunked_prefill_size, max_running_requests)
+        self.oe_n_gram_ids = torch.zeros(
+            [max_tokens, self.n_grams],
+            dtype=torch.int32,
+            device=device,
+        )
+        self.exclusive_req_len_sums = torch.zeros(
+            max_running_requests + 1, dtype=torch.int32, device=device
+        )
+
+    def load_weight(
+        self, param: Parameter, weight_name: str, loaded_weight: torch.Tensor
+    ):
+        if ".embed_tokens." in weight_name:
+            param.weight_loader(param, loaded_weight)
+        elif "model.ngram_embeddings.embedders." in weight_name:
+            index = int(
+                weight_name.replace("model.ngram_embeddings.embedders.", "").replace(
+                    ".weight", ""
+                )
+            )
+            oe_weight_start = self.exclusive_oe_embeder_size_sums[index]
+            oe_weight_end = self.exclusive_oe_embeder_size_sums[index + 1]
+            assert (
+                oe_weight_end - oe_weight_start == loaded_weight.shape[0]
+            ), f"{oe_weight_end - oe_weight_start=} {loaded_weight.shape[0]=}"
+            tp_start = self.oe_embeder.shard_indices.org_vocab_start_index
+            tp_end = self.oe_embeder.shard_indices.org_vocab_end_index
+            to_load_start = max(oe_weight_start, tp_start)
+            to_load_end = min(oe_weight_end, tp_end)
+            if to_load_start < to_load_end:
+                src_start = to_load_start - oe_weight_start
+                src_end = to_load_end - oe_weight_start
+                dest_start = to_load_start - tp_start
+                dest_end = to_load_end - tp_start
+                self.oe_embeder.weight.data[dest_start:dest_end] = loaded_weight[
+                    src_start:src_end
+                ]
+            else:
+                return
+        elif "model.ngram_embeddings.post_projs." in weight_name:
+            index = int(
+                weight_name.replace("model.ngram_embeddings.post_projs.", "").replace(
+                    ".weight", ""
+                )
+            )
+            self.oe_projection[index].copy_(loaded_weight.data.t())
+        else:
+            assert False, f"Unknown ngram embedding weight name: {weight_name}"
+
+    def forward(self, input_ids: torch.Tensor, forward_batch: ForwardBatch):
+        if (
+            forward_batch.forward_mode.is_extend()
+            or forward_batch.forward_mode.is_decode()
+        ):
+            ngram_embedding_info = forward_batch.ngram_embedding_info
+            torch.cumsum(
+                ngram_embedding_info.req_lens,
+                dim=0,
+                dtype=torch.int32,
+                out=self.exclusive_req_len_sums[1 : 1 + forward_batch.batch_size],
+            )
+            compute_n_gram_ids(
+                ne_n=self.over_embedding_n,
+                ne_k=self.over_embedding_k,
+                ne_weights=self.oe_weights,
+                ne_mods=self.oe_mods,
+                tokens=input_ids.to(torch.int32),
+                exclusive_ne_embeder_size_sums=self.exclusive_oe_embeder_size_sums,
+                exclusive_req_len_sums=self.exclusive_req_len_sums[
+                    : forward_batch.batch_size + 1
+                ],
+                ne_token_table=ngram_embedding_info.token_table,
+                row_indices=forward_batch.req_pool_indices,
+                column_starts=ngram_embedding_info.column_starts,
+                n_gram_ids=self.oe_n_gram_ids[: len(input_ids)],
+            )
+
+        # [13, seq_len, hidden_dim]
+        all_hidden_states = torch.empty(
+            [self.n_grams + 1, len(input_ids), self.embedding_dim],
+            dtype=self.oe_projection.dtype,
+            device=input_ids.device,
+        )
+        all_hidden_states[0] = self.word_embeder(input_ids)
+        # oe_hidden_states: [12, seq_len, hidden_dim / 12]
+        oe_hidden_states = self.oe_embeder(
+            self.oe_n_gram_ids[: len(input_ids)].permute(1, 0).contiguous()
+        )
+        torch.bmm(oe_hidden_states, self.oe_projection, out=all_hidden_states[1:])
+        return all_hidden_states.mean(dim=0)

python/sglang/srt/managers/schedule_batch.py

@@ -1216,6 +1216,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     # Batched arguments to model runner
     input_ids: torch.Tensor = None  # shape: [b], int64
     input_embeds: torch.Tensor = None  # shape: [b, hidden_size], float32
+    ne_token_table: torch.Tensor = None
     token_type_ids: torch.Tensor = None  # shape: [b], int64
     req_pool_indices: torch.Tensor = None  # shape: [b], int64
     seq_lens: torch.Tensor = None  # shape: [b], int64
@@ -1932,7 +1933,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         self.seq_lens_cpu = torch.empty(0, dtype=torch.int64)
         self.orig_seq_lens = torch.empty(0, dtype=torch.int32, device=self.device)
         self.out_cache_loc = torch.empty(0, dtype=torch.int64, device=self.device)
-        self.req_pool_indices = torch.empty(0, dtype=torch.int32, device=self.device)
+        self.req_pool_indices = torch.empty(0, dtype=torch.int64, device=self.device)
         self.seq_lens_sum = 0
         self.extend_num_tokens = 0
         self.sampling_info = SamplingBatchInfo.from_schedule_batch(
@@ -2234,6 +2235,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             lora_ids=[req.lora_id for req in self.reqs],
             sampling_info=self.sampling_info,
             input_embeds=self.input_embeds,
+            ne_token_table=self.ne_token_table,
             token_type_ids=self.token_type_ids,
             spec_algorithm=self.spec_algorithm,
             spec_info=self.spec_info,
@@ -2416,6 +2418,9 @@ class ModelWorkerBatch:
     # The input Embeds
     input_embeds: Optional[torch.Tensor] = None
 
+    # token table for ngram embedding
+    ne_token_table: Optional[torch.Tensor] = None
+
     # For corss-encoder model
     token_type_ids: Optional[torch.Tensor] = None
 

python/sglang/srt/managers/scheduler.py

@@ -33,6 +33,7 @@ from torch.cuda import Stream as CudaStream
 from torch.cuda import StreamContext as CudaStreamContext
 from torch.distributed import barrier
 
+from sglang.jit_kernel.ngram_embedding import update_token_table
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.constrained.grammar_manager import GrammarManager
 from sglang.srt.disaggregation.decode import (
@@ -394,6 +395,9 @@ class Scheduler(
         # Init overlap schedule
         self.init_overlap()
 
+        # Init Ngram Embedding
+        self.maybe_init_ngram_embedding()
+
         # Init prefill kv split size when deterministic inference is enabled with various attention backends
         self.init_deterministic_inference_config()
 
@@ -1050,6 +1054,57 @@ class Scheduler(
         self.batch_record_buf = [None] * 2
         self.batch_record_ct = 0
 
+    def maybe_init_ngram_embedding(self):
+        self.use_ngram_embedding = self.tp_worker.model_config.use_ngram_embedding
+        if self.use_ngram_embedding:
+            self.token_table = self.tp_worker.model_runner.token_table
+            hf_config = self.tp_worker.model_config.hf_config
+            self.ngram_embedding_n = hf_config.ngram_embedding_n
+            self.ngram_embedding_k = hf_config.ngram_embedding_k
+
+    def _maybe_prepare_ngram_embedding(
+        self, batch: Optional[ScheduleBatch]
+    ) -> Optional[ScheduleBatch]:
+        """Fill the token table for ngram embedding before a forward pass."""
+        if batch is None or not self.use_ngram_embedding:
+            return batch
+        batch.ne_token_table = self.token_table
+        if batch.forward_mode == ForwardMode.EXTEND:
+            all_tokens = []
+            column_starts = []
+            request_lengths = []
+            for req in batch.reqs:
+                start = len(req.prefix_indices)
+                end = start + req.extend_input_len
+                fill_ids = req.origin_input_ids + req.output_ids
+                if start == 0:
+                    tokens = fill_ids[start:end]
+                    column_starts.append(0)
+                elif start < self.ngram_embedding_n:
+                    tokens = fill_ids[0:end]
+                    column_starts.append(0)
+                else:
+                    # Prepend n-1 tokens before prefix_len for n-gram context
+                    tokens = fill_ids[start - self.ngram_embedding_n + 1 : end]
+                    column_starts.append(start - self.ngram_embedding_n + 1)
+                all_tokens.extend(tokens)
+                request_lengths.append(len(tokens))
+            dtype = self.token_table.dtype
+            device = self.token_table.device
+            update_token_table(
+                ne_token_table=self.token_table,
+                tokens=torch.tensor(all_tokens, dtype=dtype, device=device),
+                row_indices=batch.req_pool_indices,
+                column_starts=torch.tensor(
+                    column_starts, dtype=torch.int32, device=device
+                ),
+                req_lens=torch.tensor(
+                    request_lengths, dtype=torch.int32, device=device
+                ),
+                ignore_tokens=None,
+            )
+        return batch
+
     def init_deterministic_inference_config(self):
         """Initialize deterministic inference configuration for different attention backends."""
         if not self.server_args.enable_deterministic_inference:
@@ -2035,6 +2090,9 @@ class Scheduler(
         # Handle DP attention and log stats
         ret = self.maybe_prepare_mlp_sync_batch(ret, need_sync=need_mlp_sync)
 
+        # Handle ngram embedding
+        ret = self._maybe_prepare_ngram_embedding(ret)
+
         if ret:
             set_schedule_time_batch(ret)
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -58,6 +58,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
     ForwardBatch,
     ForwardMode,
+    NgramEmbeddingInfo,
     PPProxyTensors,
     compute_local_num_token_non_padded,
     enable_num_token_non_padded,
@@ -126,6 +127,7 @@ class DecodeInputBuffers(ForwardInputBuffers):
     global_num_tokens_for_logprob_gpu: torch.Tensor
     encoder_lens: Optional[torch.Tensor]
     pp_proxy_tensors: Optional[Dict[str, torch.Tensor]]
+    ngram_embedding_info: Optional["NgramEmbeddingInfo"]
 
     @classmethod
     def create(
@@ -146,11 +148,12 @@ class DecodeInputBuffers(ForwardInputBuffers):
         num_tokens_per_bs: int,
         cache_loc_dtype: torch.dtype,
         enable_mamba_track: bool,
+        ne_token_table: Optional[torch.Tensor] = None,
     ) -> "DecodeInputBuffers":
         with torch.device(device):
             input_ids = torch.zeros((max_num_token,), dtype=torch.int64)
             input_embeds = torch.zeros((max_num_token, hidden_size), dtype=dtype)
-            req_pool_indices = torch.zeros((max_bs,), dtype=torch.int32)
+            req_pool_indices = torch.zeros((max_bs,), dtype=torch.int64)
             seq_lens = torch.full((max_bs,), seq_len_fill_value, dtype=torch.int32)
             out_cache_loc = torch.zeros((max_num_token,), dtype=cache_loc_dtype)
             positions = torch.zeros((max_num_token,), dtype=torch.int64)
@@ -197,6 +200,18 @@ class DecodeInputBuffers(ForwardInputBuffers):
                 global_num_tokens_gpu = torch.zeros((1,), dtype=torch.int32)
                 global_num_tokens_for_logprob_gpu = torch.zeros((1,), dtype=torch.int32)
 
+            ngram_embedding_info = (
+                NgramEmbeddingInfo(
+                    token_table=ne_token_table,
+                    column_starts=torch.zeros([max_bs], dtype=torch.int32),
+                    req_lens=torch.ones([max_bs], dtype=torch.int32),
+                    out_column_starts=torch.zeros([max_bs], dtype=torch.int32),
+                    out_req_lens=torch.ones([max_bs], dtype=torch.int32),
+                )
+                if ne_token_table is not None
+                else None
+            )
+
         # Keep seq_lens_cpu as a true CPU tensor, like the old implementation.
         seq_lens_cpu = torch.full(
             (max_bs,),
@@ -223,6 +238,7 @@ class DecodeInputBuffers(ForwardInputBuffers):
             global_num_tokens_gpu=global_num_tokens_gpu,
             global_num_tokens_for_logprob_gpu=global_num_tokens_for_logprob_gpu,
             pp_proxy_tensors=pp_proxy_tensors,
+            ngram_embedding_info=ngram_embedding_info,
         )
 
     def populate_from_forward_batch(
@@ -263,6 +279,15 @@ class DecodeInputBuffers(ForwardInputBuffers):
             forward_batch.positions,
         ]
 
+        if self.ngram_embedding_info is not None:
+            ngram_embedding_info = forward_batch.ngram_embedding_info
+            self.ngram_embedding_info.column_starts[:raw_bs].copy_(
+                ngram_embedding_info.column_starts
+            )
+            self.ngram_embedding_info.req_lens[:raw_bs].copy_(
+                ngram_embedding_info.req_lens
+            )
+
         if (
             self.mamba_track_indices is not None
             and forward_batch.mamba_track_indices is not None
@@ -484,6 +509,11 @@ class CudaGraphRunner:
         self.enable_two_batch_overlap = (
             model_runner.server_args.enable_two_batch_overlap
         )
+        self.use_ngram_embedding = model_runner.use_ngram_embedding
+        if self.use_ngram_embedding:
+            hf_config = model_runner.model_config.hf_config
+            self.ngram_embedding_n = hf_config.ngram_embedding_n
+            self.ngram_embedding_k = hf_config.ngram_embedding_k
         self.speculative_algorithm = model_runner.server_args.speculative_algorithm
         self.enable_profile_cuda_graph = (
             model_runner.server_args.enable_profile_cuda_graph
@@ -582,6 +612,9 @@ class CudaGraphRunner:
             num_tokens_per_bs=self.num_tokens_per_bs,
             cache_loc_dtype=self._cache_loc_dtype(),
             enable_mamba_track=enable_mamba_track,
+            ne_token_table=(
+                model_runner.token_table if self.use_ngram_embedding else None
+            ),
         )
         self.buffers.share_buffers()
 
@@ -910,6 +943,9 @@ class CudaGraphRunner:
             global_forward_mode=self.capture_forward_mode,
             lora_ids=lora_ids,
         )
+        if buffers.ngram_embedding_info is not None:
+            forward_batch.ngram_embedding_info = buffers.ngram_embedding_info.slice(bs)
+
         self.tbo_plugin.capture_one_batch_size(forward_batch, num_tokens=num_tokens)
 
         if lora_ids is not None:

python/sglang/srt/model_executor/forward_batch_info.py

@@ -227,6 +227,48 @@ def compute_local_num_token_non_padded(
     )
 
 
+@dataclass
+class NgramEmbeddingInfo:
+    """Ngram embedding state for LongCat models."""
+
+    token_table: torch.Tensor
+    column_starts: torch.Tensor
+    req_lens: torch.Tensor
+    out_column_starts: torch.Tensor
+    out_req_lens: torch.Tensor
+
+    @classmethod
+    def create(
+        cls,
+        token_table: torch.Tensor,
+        batch_size: int,
+        device: torch.device,
+        column_starts=None,
+        req_lens=None,
+    ) -> NgramEmbeddingInfo:
+        info = cls(
+            token_table=token_table,
+            column_starts=torch.empty(batch_size, dtype=torch.int32, device=device),
+            req_lens=torch.empty(batch_size, dtype=torch.int32, device=device),
+            out_column_starts=torch.empty(batch_size, dtype=torch.int32, device=device),
+            out_req_lens=torch.empty(batch_size, dtype=torch.int32, device=device),
+        )
+        if column_starts is not None:
+            info.column_starts[:] = column_starts
+        if req_lens is not None:
+            info.req_lens[:] = req_lens
+        return info
+
+    def slice(self, bs: int) -> NgramEmbeddingInfo:
+        return NgramEmbeddingInfo(
+            token_table=self.token_table,
+            column_starts=self.column_starts[:bs],
+            req_lens=self.req_lens[:bs],
+            out_column_starts=self.out_column_starts[:bs],
+            out_req_lens=self.out_req_lens[:bs],
+        )
+
+
 @dataclass
 class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
     """Store all inputs of a forward pass."""
@@ -374,6 +416,9 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
     # For hidden states before normal
     return_hidden_states_before_norm: bool = False
 
+    # For ngram embedding
+    ngram_embedding_info: Optional[NgramEmbeddingInfo] = None
+
     # For dumper: request IDs for cross-step sequence tracking
     rids: Optional[List[str]] = None
 
@@ -511,6 +556,9 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
             ret.extend_seq_lens_cpu = batch.extend_seq_lens
             ret.extend_logprob_start_lens_cpu = batch.extend_logprob_start_lens
 
+        if model_runner.use_ngram_embedding:
+            ret._init_ngram_embedding_info(batch, model_runner, device)
+
         if model_runner.model_is_mrope:
             if (
                 ret.spec_info is not None
@@ -602,6 +650,21 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
             or self.contains_image_inputs()
         )
 
+    def _init_ngram_embedding_info(
+        self, batch: ModelWorkerBatch, model_runner: ModelRunner, device: torch.device
+    ):
+        if self.forward_mode.is_decode():
+            column_starts, req_lens = self.seq_lens - 1, 1
+        else:
+            column_starts, req_lens = self.extend_prefix_lens, self.extend_seq_lens
+        self.ngram_embedding_info = NgramEmbeddingInfo.create(
+            batch.ne_token_table,
+            self.batch_size,
+            device,
+            column_starts=column_starts,
+            req_lens=req_lens,
+        )
+
     def _compute_spec_mrope_positions(
         self, model_runner: ModelRunner, batch: ModelWorkerBatch
     ):

python/sglang/srt/model_executor/input_buffers.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import dataclasses
 from dataclasses import dataclass, fields
 from typing import Dict
 
@@ -38,7 +39,11 @@ class ForwardInputBuffers:
 
             if buffer is None:
                 continue
-            elif isinstance(buffer, dict):
+
+            if dataclasses.is_dataclass(buffer):
+                buffer = vars(buffer)
+
+            if isinstance(buffer, dict):
                 for sub_name, sub_buffer in buffer.items():
                     assert isinstance(
                         sub_buffer, torch.Tensor
@@ -50,6 +55,6 @@ class ForwardInputBuffers:
             else:
                 assert isinstance(
                     buffer, torch.Tensor
-                ), f"Field {name} is expected to be a torch.Tensor or a dict of torch.Tensor, but got {type(buffer)}."
+                ), f"Field {name} is expected to be a torch.Tensor, a dict of torch.Tensor, or a dataclass of torch.Tensor, but got {type(buffer)}."
                 new_buffer = self._share_one_buffer(name, buffer)
                 setattr(self, name, new_buffer)

python/sglang/srt/model_executor/model_runner.py

@@ -30,6 +30,7 @@ import torch
 import torch.distributed as dist
 from torch import nn
 
+from sglang.jit_kernel.ngram_embedding import update_token_table
 from sglang.srt.configs import (
     BailingHybridConfig,
     FalconH1Config,
@@ -602,6 +603,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         # Init memory pool and attention backends
         self.init_memory_pool(pre_model_load_memory)
 
+        # Init ngram embedding token table
+        self.maybe_init_ngram_embedding()
+
         # Init routed experts capturer
         self.init_routed_experts_capturer()
 
@@ -2108,6 +2112,49 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         with torch.inference_mode(), run_ctx or empty_context():
             run_once()
 
+    def maybe_init_ngram_embedding(self):
+        self.use_ngram_embedding = self.model_config.use_ngram_embedding
+        if self.use_ngram_embedding:
+            from sglang.srt.layers.n_gram_embedding import NgramEmbedding
+
+            self.token_table = torch.empty(
+                self.req_to_token_pool.size,
+                self.model_config.context_len,
+                dtype=torch.int32,
+                device=self.device,
+            )
+            chunked_prefill_size = self.server_args.chunked_prefill_size
+            assert (
+                chunked_prefill_size is not None and chunked_prefill_size > 0
+            ), "Ngram embedding requires chunked prefill to be enabled (chunked_prefill_size > 0)"
+            for module in self.model.modules():
+                if isinstance(module, NgramEmbedding):
+                    module.init_buffers(
+                        self.max_running_requests, chunked_prefill_size, self.device
+                    )
+
+    def maybe_update_ngram_token_table(
+        self,
+        next_token_ids: torch.Tensor,
+        forward_batch: "ForwardBatch",
+    ):
+        """Update the ngram embedding token table after sampling."""
+        ngram_embedding_info = forward_batch.ngram_embedding_info
+        if ngram_embedding_info is None:
+            return
+        ngram_embedding_info.out_column_starts[: forward_batch.batch_size] = (
+            forward_batch.seq_lens
+        )
+        ngram_embedding_info.out_req_lens[: forward_batch.batch_size] = 1
+        update_token_table(
+            ne_token_table=ngram_embedding_info.token_table,
+            tokens=next_token_ids.to(torch.int32),
+            row_indices=forward_batch.req_pool_indices,
+            column_starts=ngram_embedding_info.out_column_starts,
+            req_lens=torch.ones_like(ngram_embedding_info.out_column_starts),
+            ignore_tokens=None,
+        )
+
     def init_device_graphs(self):
         """Capture device graphs."""
         self.graph_runner = None
@@ -2570,6 +2617,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
                 else forward_batch.seq_lens - 1
             ),
         )
+        self.maybe_update_ngram_token_table(next_token_ids, forward_batch)
         return next_token_ids
 
     def compute_logprobs_only(

python/sglang/srt/models/longcat_flash.py

@@ -64,6 +64,7 @@ from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class
 from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
 from sglang.srt.layers.moe.topk import StandardTopKOutput, TopK
 from sglang.srt.layers.moe.utils import filter_moe_weight_param_global_expert
+from sglang.srt.layers.n_gram_embedding import NgramEmbedding
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
 from sglang.srt.layers.quantization.fp8_utils import (
@@ -329,7 +330,7 @@ class LongcatFlashDecoderLayer(nn.Module):
                     q_lora_rank=config.q_lora_rank,
                     kv_lora_rank=config.kv_lora_rank,
                     rope_theta=config.rope_theta,
-                    rope_scaling=None,
+                    rope_scaling=getattr(config, "rope_scaling", None),
                     max_position_embeddings=config.max_position_embeddings,
                     quant_config=(
                         None
@@ -500,6 +501,17 @@ class LongcatFlashModel(nn.Module):
         super().__init__()
         self.vocab_size = config.vocab_size
 
+        if config.use_ngram_embedding:
+            self.use_ngram_embedding = True
+            self.embed_tokens = NgramEmbedding(
+                num_embeddings=config.vocab_size,
+                embedding_dim=config.hidden_size,
+                over_embedding_m=config.ngram_embedding_m,
+                over_embedding_k=config.ngram_embedding_k,
+                over_embedding_n=config.ngram_embedding_n,
+            )
+        else:
+            self.use_ngram_embedding = False
             self.embed_tokens = VocabParallelEmbedding(
                 config.vocab_size,
                 config.hidden_size,
@@ -540,6 +552,9 @@ class LongcatFlashModel(nn.Module):
             device=device,
         )
         if input_embeds is None:
+            if self.use_ngram_embedding:
+                hidden_states = self.embed_tokens(input_ids, forward_batch)
+            else:
                 hidden_states = self.embed_tokens(input_ids)
         else:
             hidden_states = input_embeds
@@ -597,6 +612,7 @@ class LongcatFlashForCausalLM(nn.Module):
         self.model = LongcatFlashModel(
             config, quant_config, prefix=add_prefix("model", prefix)
         )
+        self.use_ngram_embedding = config.use_ngram_embedding
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.hidden_size,
@@ -874,7 +890,6 @@ class LongcatFlashForCausalLM(nn.Module):
             self.config.q_lora_rank is not None
         )
         cached_a_proj = {} if fuse_qkv_a_proj else None
-
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = []
             params_dict = dict(self.named_parameters())
@@ -883,6 +898,12 @@ class LongcatFlashForCausalLM(nn.Module):
                 use_async_loading = should_async_load(loaded_weight)
                 if "mtp" in name:
                     continue
+                if self.use_ngram_embedding:
+                    if ".embed_tokens." in name:
+                        name = "model.embed_tokens.word_embeder.weight"
+                    if ".ngram_embeddings" in name:
+                        self.model.embed_tokens.load_weight(None, name, loaded_weight)
+                        continue
                 weight_names.append(name)
                 if "rotary_emb.inv_freq" in name:
                     continue

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -105,7 +105,7 @@ class EAGLEDraftCudaGraphRunner:
         # Graph inputs
         with torch.device(model_runner.device):
             input_ids = torch.zeros((self.max_num_token,), dtype=torch.int64)
-            req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int32)
+            req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int64)
             out_cache_loc = torch.zeros(
                 (self.max_num_token * self.speculative_num_steps,),
                 dtype=self._cache_loc_dtype(),

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -110,7 +110,7 @@ class EAGLEDraftExtendCudaGraphRunner:
         # Graph inputs
         with torch.device(model_runner.device):
             input_ids = torch.zeros((self.max_num_token,), dtype=torch.int64)
-            req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int32)
+            req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int64)
             out_cache_loc = torch.ones(
                 (self.max_num_token,), dtype=self._cache_loc_dtype()
             )

python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py

@@ -669,7 +669,7 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner:
                 dtype=torch.int32,
             )
             self.cuda_graph_buffers["req_pool_indices"] = torch.zeros(
-                (self.max_bs,), dtype=torch.int32
+                (self.max_bs,), dtype=torch.int64
             )
             self.cuda_graph_buffers["accept_length"] = torch.full(
                 (self.max_bs,), 1, dtype=torch.int32

python/sglang/srt/utils/hf_transformers_utils.py

@@ -342,6 +342,14 @@ def get_config(
             "patch_size": 14,
         }
         config.vision_config = SiglipVisionConfig(**vision_config)
+
+    if config.architectures in [
+        ["LongcatCausalLM"],
+        ["LongcatFlashForCausalLM"],
+        ["LongcatFlashNgramForCausalLM"],
+    ]:
+        config.model_type = "longcat_flash"
+
     text_config = get_hf_text_config(config=config)
 
     if isinstance(model, str) and text_config is not None:
@@ -389,6 +397,9 @@ def get_config(
     if config.model_type == "multi_modality":
         config.update({"architectures": ["MultiModalityCausalLM"]})
 
+    if config.model_type == "longcat_flash":
+        config.update({"architectures": ["LongcatFlashForCausalLM"]})
+
     if model_override_args:
         config.update(model_override_args)