[Feature][Kernel]FusedMoE LoRA (#21229)

Signed-off-by: wuchen <cntryroa@gmail.com>
Signed-off-by: banjuede <lmklhc@163.com>
Signed-off-by: Chen Wu <cntryroa@gmail.com>
Signed-off-by: Danielle Robinson <dmmaddix@amazon.com>
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Signed-off-by: bk-201 <joy25810@foxmail.com>
Co-authored-by: wuchen <wuchen@zetyun.com>
Co-authored-by: Nathan Van Gheem <vangheem@gmail.com>
Co-authored-by: banjuede <lmklhc@163.com>
Co-authored-by: Danielle Robinson <dmmaddix@amazon.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: bk-201 <joy25810@foxmail.com>

.buildkite/test-pipeline.yaml

@@ -384,7 +384,12 @@ steps:
       --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
       --ignore=lora/test_chatglm3_tp.py \
       --ignore=lora/test_llama_tp.py \
-      --ignore=lora/test_llm_with_multi_loras.py
+      --ignore=lora/test_llm_with_multi_loras.py \
+      --ignore=lora/test_olmoe_tp.py \
+      --ignore=lora/test_deepseekv2_tp.py \
+      --ignore=lora/test_gptoss.py \
+      --ignore=lora/test_qwen3moe_tp.py
+      
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests # 15min
@@ -1065,6 +1070,7 @@ steps:
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
+    - pytest -v -s -x lora/test_olmoe_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min

CMakeLists.txt

@@ -883,6 +883,7 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
+  "csrc/moe/moe_lora_align_sum_kernels.cu"
   "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")

csrc/moe/moe_lora_align_sum_kernels.cu

@@ -0,0 +1,173 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <time.h>
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/Atomic.cuh>
+
+#include "../cuda_compat.h"
+#include "../dispatch_utils.h"
+#include "core/math.hpp"
+
+namespace {
+
+__device__ __forceinline__ int32_t index(int32_t total_col, int32_t row,
+                                         int32_t col) {
+  return row * total_col + col;
+}
+
+}  // namespace
+
+// TODO: Refactor common parts with moe_align_sum_kernels
+template <typename scalar_t, typename token_cnts_t>
+__global__ void moe_lora_align_sum_kernel(
+    scalar_t* __restrict__ topk_ids, int32_t* token_lora_mapping,
+    int64_t block_size, int num_experts, int max_loras, size_t numel,
+    int max_num_tokens_padded, int max_num_m_blocks,
+    int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
+    int topk_num, int32_t* total_tokens_post_pad) {
+  const size_t tokens_per_thread = div_ceil(numel, blockDim.x);
+  const size_t start_idx = threadIdx.x * tokens_per_thread;
+
+  int lora_id = blockIdx.x;
+  extern __shared__ int32_t shared_mem[];
+  int32_t* cumsum = shared_mem;
+  token_cnts_t* tokens_cnts = (token_cnts_t*)(shared_mem + num_experts + 1);
+
+  // Initialize sorted_token_ids with numel
+  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
+    sorted_token_ids[lora_id * max_num_tokens_padded + it] = numel;
+  }
+
+  // Initialize expert_ids with -1
+  for (size_t it = threadIdx.x; it < max_num_m_blocks; it += blockDim.x) {
+    expert_ids[lora_id * max_num_m_blocks + it] = -1;
+  }
+
+  // Initialize total_tokens_post_pad with 0
+  if (threadIdx.x == 0) {
+    total_tokens_post_pad[lora_id] = 0;
+  }
+
+  for (int i = 0; i < num_experts; ++i) {
+    tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0;
+  }
+
+  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
+    int mask = token_lora_mapping[i / topk_num] == lora_id;
+    int idx = index(num_experts, threadIdx.x + 1, topk_ids[i]);
+    tokens_cnts[idx] += mask;
+  }
+
+  __syncthreads();
+
+  // For each expert we accumulate the token counts from the different threads.
+  if (threadIdx.x < num_experts) {
+    tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0;
+    for (int i = 1; i <= blockDim.x; ++i) {
+      tokens_cnts[index(num_experts, i, threadIdx.x)] +=
+          tokens_cnts[index(num_experts, i - 1, threadIdx.x)];
+    }
+  }
+
+  __syncthreads();
+
+  // We accumulate the token counts of all experts in thread 0.
+  if (threadIdx.x == 0) {
+    cumsum[0] = 0;
+    for (int i = 1; i <= num_experts; ++i) {
+      cumsum[i] = cumsum[i - 1] +
+                  div_ceil(tokens_cnts[index(num_experts, blockDim.x, i - 1)],
+                           block_size) *
+                      block_size;
+    }
+    total_tokens_post_pad[lora_id] = static_cast<int32_t>(cumsum[num_experts]);
+  }
+
+  __syncthreads();
+
+  /**
+   * For each expert, each thread processes the tokens of the corresponding
+   * blocks and stores the corresponding expert_id for each block.
+   */
+  if (threadIdx.x < num_experts) {
+    for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1];
+         i += block_size) {
+      expert_ids[index(max_num_m_blocks, lora_id, i / block_size)] =
+          threadIdx.x;
+    }
+  }
+
+  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
+    int32_t expert_id = topk_ids[i];
+    /** The cumsum[expert_id] stores the starting index of the tokens that the
+     * expert with expert_id needs to process, and
+     * tokens_cnts[threadIdx.x][expert_id] stores the indices of the tokens
+     * processed by the expert with expert_id within the current thread's token
+     * shard.
+     */
+    int32_t rank_post_pad =
+        tokens_cnts[index(num_experts, threadIdx.x, expert_id)] +
+        cumsum[expert_id];
+
+    int mask = (int)token_lora_mapping[i / topk_num] == lora_id;
+    atomicAdd(
+        &sorted_token_ids[index(max_num_tokens_padded, lora_id, rank_post_pad)],
+        (i - numel) * mask);
+    tokens_cnts[index(num_experts, threadIdx.x, expert_id)] += mask;
+  }
+}
+
+void moe_lora_align_block_size(torch::Tensor topk_ids,
+                               torch::Tensor token_lora_mapping,
+                               int64_t num_experts, int64_t block_size,
+                               int64_t max_loras,
+                               torch::Tensor sorted_token_ids,
+                               torch::Tensor expert_ids,
+                               torch::Tensor num_tokens_post_pad) {
+  const int topk_num = topk_ids.size(1);
+
+  int max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1);
+
+  TORCH_CHECK(block_size > 0, "block_size should be greater than 0. ");
+  max_num_tokens_padded = round_to_next_multiple_of(
+      max_num_tokens_padded, static_cast<int>(block_size));
+  int max_num_m_blocks = div_ceil(max_num_tokens_padded, block_size);
+
+  int device_max_shared_mem;
+  auto dev = topk_ids.get_device();
+  cudaDeviceGetAttribute(&device_max_shared_mem,
+                         cudaDevAttrMaxSharedMemoryPerBlockOptin, dev);
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  const int32_t num_thread = max((int32_t)num_experts, 128);  // WARP_SIZE,
+  TORCH_CHECK(num_thread <= 1024,
+              "num_thread must be less than 1024, "
+              "and fallback is not implemented yet.");
+  const int32_t shared_mem = (num_thread + 1) * num_experts * sizeof(int32_t) +
+                             (num_experts + 1) * sizeof(int32_t);
+
+  if (shared_mem > device_max_shared_mem) {
+    TORCH_CHECK(false,
+                "Shared memory usage exceeds device limit, and global memory "
+                "fallback is not implemented yet.");
+  }
+
+  VLLM_DISPATCH_INTEGRAL_TYPES(
+      topk_ids.scalar_type(), "moe_lora_align_sum_kernel", [&] {
+        dim3 blockDim(num_thread);
+        auto kernel = moe_lora_align_sum_kernel<scalar_t, int32_t>;
+        AT_CUDA_CHECK(VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(
+            (void*)kernel, shared_mem));
+        kernel<<<max_loras, blockDim, shared_mem, stream>>>(
+            topk_ids.data_ptr<scalar_t>(),
+            token_lora_mapping.data_ptr<int32_t>(), block_size, num_experts,
+            max_loras, topk_ids.numel(), max_num_tokens_padded,
+            max_num_m_blocks, sorted_token_ids.data_ptr<int32_t>(),
+            expert_ids.data_ptr<int32_t>(), topk_num,
+            num_tokens_post_pad.data_ptr<int32_t>());
+      });
+}

csrc/moe/moe_ops.h

@@ -20,6 +20,13 @@ void batched_moe_align_block_size(int64_t max_tokens_per_batch,
                                   torch::Tensor expert_ids,
                                   torch::Tensor num_tokens_post_pad);
 
+void moe_lora_align_block_size(torch::Tensor topk_ids,
+                               torch::Tensor token_lora_mapping,
+                               int64_t num_experts, int64_t block_size,
+                               int64_t max_loras,
+                               torch::Tensor sorted_token_ids,
+                               torch::Tensor expert_ids,
+                               torch::Tensor num_tokens_post_pad);
 #ifndef USE_ROCM
 torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor b_qweight, torch::Tensor b_scales,

csrc/moe/torch_bindings.cpp

@@ -33,6 +33,18 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
   m.impl("batched_moe_align_block_size", torch::kCUDA,
          &batched_moe_align_block_size);
 
+  // Aligning the number of tokens to be processed by each expert such
+  // that it is divisible by the block size.
+  m.def(
+      "moe_lora_align_block_size(Tensor topk_ids,"
+      "                     Tensor token_lora_mapping,"
+      "                     int num_experts,"
+      "                     int block_size, int max_loras, "
+      "                     Tensor !sorted_token_ids,"
+      "                     Tensor !experts_ids,"
+      "                     Tensor !num_tokens_post_pad) -> () ");
+  m.impl("moe_lora_align_block_size", torch::kCUDA, &moe_lora_align_block_size);
+
 #ifndef USE_ROCM
   m.def(
       "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "

tests/lora/conftest.py

@@ -230,6 +230,26 @@ def tinyllama_lora_files():
     return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")
 
 
+@pytest.fixture(scope="session")
+def deepseekv2_lora_files():
+    return snapshot_download(repo_id="wuchen01/DeepSeek-V2-Lite-Chat-All-LoRA")
+
+
+@pytest.fixture(scope="session")
+def gptoss20b_lora_files():
+    return snapshot_download(repo_id="LevinZheng/gpt-oss-20b-lora-adapter")
+
+
+@pytest.fixture(scope="session")
+def qwen3moe_lora_files():
+    return snapshot_download(repo_id="jeeejeee/qwen3-moe-text2sql-spider")
+
+
+@pytest.fixture(scope="session")
+def olmoe_lora_files():
+    return snapshot_download(repo_id="jeeejeee/olmoe-instruct-text2sql-spider")
+
+
 @pytest.fixture
 def reset_default_device():
     """

tests/lora/test_deepseekv2_tp.py

@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+
+PROMPT_TEMPLATE = "<｜begin▁of▁sentence｜>You are a helpful assistant.\n\nUser: {context}\n\nAssistant:"  # noqa: E501
+
+
+def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int):
+    prompts = [
+        PROMPT_TEMPLATE.format(context="Who are you?"),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    # return generated_texts
+    expected_lora_output = [
+        "I am \u5f20\u5b50\u8c6a, an AI assistant developed by \u9648\u58eb\u680b.",  # noqa: E501
+    ]
+    for i in range(len(expected_lora_output)):
+        assert generated_texts[i].startswith(expected_lora_output[i])
+
+
+def test_deepseekv2_lora(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 1)
+
+
+def test_deepseekv2(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 1)
+
+
+@multi_gpu_test(num_gpus=2)
+def test_deepseekv2_tp2(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        tensor_parallel_size=2,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 2)
+
+
+@multi_gpu_test(num_gpus=4)
+def test_deepseekv2_tp4(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        tensor_parallel_size=4,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 2)

tests/lora/test_fused_moe_lora_kernel.py

@@ -0,0 +1,287 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import random
+
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.lora.ops.triton_ops import fused_moe_lora
+from vllm.platforms import current_platform
+
+
+@pytest.fixture(autouse=True)
+def reset_device(reset_default_device):
+    pass
+
+
+def round_up(x, base):
+    return ((x + base - 1) // base) * base
+
+
+def CEILDIV(x, y):
+    return (x + y - 1) // y
+
+
+def assign_loras_to_tokens(num_tokens: int, num_sequences: int, max_loras: int):
+    """
+    Split `num_tokens` into `num_sequences` sequences.
+    Each sequence randomly selects 1 LoRA index from [0, max_loras),
+    and all tokens in that sequence are assigned this LoRA index.
+
+    Args:
+        num_tokens (int): Total number of tokens.
+        num_sequences (int): Number of sequences to split the tokens into.
+        max_loras (int): Total number of available LoRA modules.
+
+    Returns:
+        torch.Tensor: 1D tensor of shape [num_tokens], where each value
+                      is the LoRA index assigned to that token.
+    """
+    assert num_sequences > 0 and max_loras > 0
+    assert num_tokens >= num_sequences, "num_tokens must be >= num_sequences"
+
+    # Compute token distribution per sequence (distribute remainder evenly)
+    tokens_per_seq = num_tokens // num_sequences
+    remainder = num_tokens % num_sequences
+
+    token_lora_mapping = torch.empty(num_tokens, dtype=torch.int32)
+
+    start = 0
+    for seq_idx in range(num_sequences):
+        # Determine the token range for this sequence
+        end = start + tokens_per_seq + (1 if seq_idx < remainder else 0)
+
+        # Randomly select one LoRA ID for this sequence
+        lora_id = random.randint(0, max_loras - 1)
+
+        # Assign the same LoRA ID to all tokens in this sequence
+        token_lora_mapping[start:end] = lora_id
+
+        start = end
+
+    return token_lora_mapping
+
+
+def assign_experts_to_tokens(num_tokens: int, num_experts: int, top_k_num: int):
+    """
+    For each token, randomly select `top_k_num` distinct experts out of `num_experts`,
+    and assign normalized random weights that sum to 1.
+
+    Args:
+        num_tokens (int): Total number of tokens.
+        num_experts (int): Total number of available experts.
+        top_k_num (int): Number of experts to select per token.
+
+    Returns:
+        expert_indices (torch.Tensor): shape [num_tokens, top_k_num],
+                                       expert index for each token.
+        expert_weights (torch.Tensor): shape [num_tokens, top_k_num],
+                                       normalized weights (sum = 1 per row).
+    """
+    assert top_k_num <= num_experts, "top_k_num must be <= num_experts"
+
+    # Randomly select top_k_num distinct experts for each token
+    expert_indices = torch.empty((num_tokens, top_k_num), dtype=torch.int32)
+    for i in range(num_tokens):
+        # Randomly choose unique expert indices
+        selected = torch.randperm(num_experts)[:top_k_num]
+        expert_indices[i] = selected
+
+    # Generate random weights and normalize along dim=1
+    expert_weights = torch.rand((num_tokens, top_k_num), dtype=torch.float32)
+    expert_weights = expert_weights / expert_weights.sum(dim=1, keepdim=True)
+
+    return expert_indices, expert_weights
+
+
+def sample_data(
+    num_tokens: int,
+    num_sequences: int,
+    max_loras: int,
+    num_experts: int,
+    top_k_num: int,
+):
+    topk_ids, topk_weights = assign_experts_to_tokens(
+        num_tokens, num_experts, top_k_num
+    )
+    token_lora_mapping = assign_loras_to_tokens(num_tokens, num_sequences, max_loras)
+    return topk_ids, topk_weights, token_lora_mapping
+
+
+def use_fused_moe_lora_kernel(
+    topk_ids,
+    topk_weights,
+    token_lora_mapping,
+    max_lora_rank,
+    top_k_num,
+    lora_a_stacked,
+    lora_b_stacked,
+    hidden_states,
+    output,
+    max_loras,
+    num_experts,
+    block_size,
+):
+    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+    max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
+    max_num_m_blocks = CEILDIV(max_num_tokens_padded, block_size)
+
+    # init output tensors
+    sorted_token_ids = torch.empty(
+        (max_loras * max_num_tokens_padded,),
+        dtype=torch.int32,
+    )
+    expert_ids = torch.empty((max_loras * max_num_m_blocks,), dtype=torch.int32)
+    num_tokens_post_padded = torch.empty((max_loras,), dtype=torch.int32)
+
+    # call kernel
+    ops.moe_lora_align_block_size(
+        topk_ids,
+        token_lora_mapping,
+        num_experts,
+        block_size,
+        max_loras,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_padded,
+    )
+
+    config = {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+    }
+
+    mul_routed_weight = False
+    expert_ids = expert_ids.view(max_loras, -1)
+    sorted_token_ids = sorted_token_ids.view(max_loras, -1)
+
+    fused_moe_lora(
+        output,
+        hidden_states,
+        lora_a_stacked,
+        lora_b_stacked,
+        topk_weights,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_padded,
+        max_lora_rank,
+        top_k_num,
+        config["BLOCK_SIZE_M"],
+        config["BLOCK_SIZE_N"],
+        config["BLOCK_SIZE_K"],
+        config["GROUP_SIZE_M"],
+        mul_routed_weight,
+    )
+
+    return output
+
+
+def use_torch(
+    hidden_states,
+    token_lora_mapping,
+    topk_ids,
+    lora_a_stacked,
+    lora_b_stacked,
+    top_k_num,
+):
+    outputs = []
+    for i in range(hidden_states.shape[0]):
+        lora_idx = token_lora_mapping[i]
+        expert_ids = topk_ids[i]
+        lora_a = lora_a_stacked[0][lora_idx][expert_ids]
+        lora_b = lora_b_stacked[0][lora_idx][expert_ids]
+        tensors = [
+            hidden_states[i] @ lora_a[x].T @ lora_b[x].T for x in range(top_k_num)
+        ]
+        outputs.append(torch.stack(tensors, dim=0))
+    return torch.stack(outputs, dim=0)
+
+
+@pytest.mark.parametrize("num_tokens", [100])
+@pytest.mark.parametrize("top_k_num", [6, 12])
+@pytest.mark.parametrize("num_experts", [64])
+@pytest.mark.parametrize("max_loras", [4, 6, 16])
+@pytest.mark.parametrize("N", [1408])
+@pytest.mark.parametrize("K", [2048])
+@pytest.mark.parametrize("max_lora_rank", [16, 32, 64])
+@pytest.mark.parametrize("block_size", [16])
+def test_fused_moe_lora_kernel(
+    num_tokens,
+    top_k_num,
+    num_experts,
+    max_loras,
+    N,
+    K,
+    max_lora_rank,
+    block_size,
+):
+    torch.set_default_device("cuda:0")
+    current_platform.seed_everything(42)
+    # the number of randomly generated sentences.
+    num_sequences = 10
+    # generate data
+    topk_ids, topk_weights, token_lora_mapping = sample_data(
+        num_tokens, num_sequences, max_loras, num_experts, top_k_num
+    )
+
+    # init lora weights
+    lora_a_stacked = [
+        torch.rand(
+            (
+                max_loras,
+                num_experts,
+                max_lora_rank,
+                K,
+            ),
+            dtype=torch.bfloat16,
+        )
+    ]
+    lora_b_stacked = [
+        torch.rand(
+            (
+                max_loras,
+                num_experts,
+                N,
+                max_lora_rank,
+            ),
+            dtype=torch.bfloat16,
+        )
+    ]
+    hidden_states = torch.rand(
+        (
+            num_tokens,
+            K,
+        ),
+        dtype=torch.bfloat16,
+    )
+
+    # fused_moe_lora_kernel output
+    output = torch.zeros((num_tokens, top_k_num, N), dtype=torch.bfloat16)
+    use_fused_moe_lora_kernel(
+        topk_ids,
+        topk_weights,
+        token_lora_mapping,
+        max_lora_rank,
+        top_k_num,
+        lora_a_stacked,
+        lora_b_stacked,
+        hidden_states,
+        output,
+        max_loras,
+        num_experts,
+        block_size,
+    )
+    # pytorch output
+    output2 = use_torch(
+        hidden_states,
+        token_lora_mapping,
+        topk_ids,
+        lora_a_stacked,
+        lora_b_stacked,
+        top_k_num,
+    )
+
+    torch.testing.assert_close(output, output2, atol=1e-1, rtol=1e-1)

tests/lora/test_gptoss.py

@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "openai/gpt-oss-20b"
+
+PROMPT_TEMPLATE = "<｜begin▁of▁sentence｜>You are a helpful assistant.\n\nUser: {context}\n\nAssistant:"  # noqa: E501
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
+    prompts = [
+        PROMPT_TEMPLATE.format(context="Who are you?"),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+# FIXME: Load gpt-oss adapter
+def test_gptoss20b_lora(gptoss20b_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_loras=1,
+        trust_remote_code=True,
+    )
+
+    expected_lora_output = [
+        "I am an AI language model developed by OpenAI. "
+        "I am here to help you with any questions or "
+        "tasks you may have."
+    ]
+
+    output1 = do_sample(llm, gptoss20b_lora_files, lora_id=1)
+    print(output1)
+    for i in range(len(expected_lora_output)):
+        assert output1[i].startswith(expected_lora_output[i])

tests/lora/test_moe_lora_align_sum.py

@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import random
+
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+
+
+def round_up(x, base):
+    return ((x + base - 1) // base) * base
+
+
+def CEILDIV(x, y):
+    return (x + y - 1) // y
+
+
+def sample_data(num_experts, max_loras, num_tokens, topk_num):
+    topk_ids = torch.zeros((num_tokens, topk_num), dtype=torch.int32)
+    token_lora_mapping = torch.zeros((num_tokens,), dtype=torch.int32)
+
+    for i in range(num_tokens):
+        pool = list(range(num_experts))
+        random.shuffle(pool)
+        for j in range(topk_num):
+            topk_ids[i, j] = pool[j]
+        token_lora_mapping[i] = random.randint(0, max_loras - 1)
+
+    return topk_ids.to("cuda"), token_lora_mapping.to("cuda")
+
+
+@pytest.mark.parametrize("num_tokens", [100, 200, 1024, 4096])  # 81920
+@pytest.mark.parametrize("topk_num", [6])
+@pytest.mark.parametrize("num_experts", [64, 128])
+@pytest.mark.parametrize("max_loras", [2, 32])
+@pytest.mark.parametrize("block_size", [16])
+def test_moe_lora_align_block_size(
+    num_tokens, topk_num, num_experts, max_loras, block_size
+):
+    # sample data
+    random.seed(1)
+    topk_ids, token_lora_mapping = sample_data(
+        num_experts, max_loras, num_tokens, topk_num
+    )
+
+    # compute paddings
+    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+    max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
+    max_num_m_blocks = CEILDIV(max_num_tokens_padded, block_size)
+
+    # init output tensors
+    sorted_token_ids = torch.full(
+        (max_loras * max_num_tokens_padded,),
+        topk_ids.numel(),
+        dtype=torch.int32,
+        device="cuda",
+    )
+    expert_ids = torch.full(
+        (max_loras * max_num_m_blocks,), num_experts, dtype=torch.int32, device="cuda"
+    )
+    num_tokens_post_pad = torch.zeros((max_loras,), dtype=torch.int32, device="cuda")
+
+    # call kernel
+    ops.moe_lora_align_block_size(
+        topk_ids,
+        token_lora_mapping,
+        num_experts,
+        block_size,
+        max_loras,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_pad,
+    )
+
+    # verify values
+    expert_ids = expert_ids.view(max_loras, -1)
+    sorted_token_ids = sorted_token_ids.view(max_loras, -1, block_size)
+
+    for lora_idx in range(max_loras):
+        for token_idx in range(sorted_token_ids.size(1)):
+            block = sorted_token_ids[lora_idx][token_idx]
+            indices = block[block != topk_ids.numel()]
+            if indices.numel() > 0:
+                expert_id = expert_ids[lora_idx][token_idx]
+                assert torch.all(topk_ids.view(-1)[indices] == expert_id)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

tests/lora/test_olmoe_tp.py

@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "allenai/OLMoE-1B-7B-0125-Instruct"
+
+PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.
+"
+##Instruction:
+candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
+Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
+The People_ID of candidate is the foreign key of People_ID of people.
+
+
+###Input:
+{context}
+
+###Response:"""  # noqa: E501
+
+EXPECTED_LORA_OUTPUT = [
+    "SELECT count(*) FROM candidate",
+    "SELECT count(*) FROM candidate",
+    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+]
+
+
+def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
+    prompts = [
+        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
+        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
+        PROMPT_TEMPLATE.format(
+            context="Which poll resource provided the most number of candidate information?"  # noqa: E501
+        ),
+        PROMPT_TEMPLATE.format(
+            context="Return the poll resource associated with the most candidates."
+        ),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])
+
+
+def test_olmoe_lora(olmoe_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=1)
+    generate_and_test(llm, olmoe_lora_files, lora_id=2)
+
+
+@multi_gpu_test(num_gpus=2)
+def test_olmoe_lora_tp2(olmoe_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=2,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=1)
+    generate_and_test(llm, olmoe_lora_files, lora_id=2)
+
+
+@multi_gpu_test(num_gpus=4)
+def test_olmoe_lora_tp4(olmoe_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=4,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=1)
+    generate_and_test(llm, olmoe_lora_files, lora_id=2)

tests/lora/test_qwen3moe_tp.py

@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "Qwen/Qwen3-30B-A3B"
+
+PROMPT_TEMPLATE = """<|im_start|>user
+I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.
+"
+##Instruction:
+candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
+Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
+The People_ID of candidate is the foreign key of People_ID of people.
+
+
+###Input:
+{context}
+
+###Response:<|im_end|>
+<|im_start|>assistant"""  # noqa: E501
+
+EXPECTED_LORA_OUTPUT = [
+    "<think>\n\n</think>\n\nSELECT count(*) FROM candidate",
+    "<think>\n\n</think>\n\nSELECT count(*) FROM candidate",
+    "<think>\n\n</think>\n\nSELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+    "<think>\n\n</think>\n\nSELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+]
+
+
+def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
+    prompts = [
+        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
+        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
+        PROMPT_TEMPLATE.format(
+            context="Which poll resource provided the most number of candidate information?"  # noqa: E501
+        ),
+        PROMPT_TEMPLATE.format(
+            context="Return the poll resource associated with the most candidates."
+        ),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])
+
+
+def test_qwen3moe_lora(qwen3moe_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=2)
+
+
+@multi_gpu_test(num_gpus=2)
+def test_qwen3moe_lora_tp2(qwen3moe_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=2,
+    )
+
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=2)
+
+
+@multi_gpu_test(num_gpus=4)
+def test_qwen3moe_lora_tp4(qwen3moe_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=4,
+    )
+
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
+    generate_and_test(llm, qwen3moe_lora_files, lora_id=2)

vllm/_custom_ops.py

@@ -1795,6 +1795,28 @@ def batched_moe_align_block_size(
     )
 
 
+def moe_lora_align_block_size(
+    topk_ids: torch.Tensor,
+    token_lora_mapping: torch.Tensor,
+    num_experts: int,
+    block_size: int,
+    max_loras: int,
+    sorted_token_ids: torch.Tensor,
+    experts_ids: torch.Tensor,
+    num_tokens_post_pad: torch.Tensor,
+) -> None:
+    torch.ops._moe_C.moe_lora_align_block_size(
+        topk_ids,
+        token_lora_mapping,
+        num_experts,
+        block_size,
+        max_loras,
+        sorted_token_ids,
+        experts_ids,
+        num_tokens_post_pad,
+    )
+
+
 def moe_wna16_gemm(
     input: torch.Tensor,
     output: torch.Tensor,

vllm/lora/layers/__init__.py

@@ -11,6 +11,7 @@ from vllm.lora.layers.column_parallel_linear import (
     QKVParallelLinearWithLoRA,
     QKVParallelLinearWithShardedLoRA,
 )
+from vllm.lora.layers.fused_moe import FusedMoEWithLoRA
 from vllm.lora.layers.logits_processor import LogitsProcessorWithLoRA
 from vllm.lora.layers.replicated_linear import ReplicatedLinearWithLoRA
 from vllm.lora.layers.row_parallel_linear import (
@@ -36,4 +37,5 @@ __all__ = [
     "RowParallelLinearWithShardedLoRA",
     "ReplicatedLinearWithLoRA",
     "LoRAMapping",
+    "FusedMoEWithLoRA",
 ]

vllm/lora/layers/fused_moe.py

@@ -0,0 +1,410 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import functools
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm import envs
+from vllm.config.lora import LoRAConfig
+from vllm.distributed.parallel_state import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.lora.layers.base import BaseLayerWithLoRA
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.config import (
+    FUSED_MOE_UNQUANTIZED_CONFIG,
+    _get_config_dtype_str,
+    mxfp4_w4a16_moe_quant_config,
+)
+from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+    modular_marlin_fused_moe,
+)
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    modular_triton_fused_moe,
+    try_get_optimal_moe_config,
+)
+from vllm.model_executor.layers.quantization.mxfp4 import Mxfp4Config
+
+
+class FusedMoEWithLoRA(BaseLayerWithLoRA):
+    def __init__(self, base_layer: FusedMoE) -> None:
+        super().__init__()
+        self.base_layer = base_layer
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.device = base_layer.w2_weight.device
+        self._inject_lora_into_fused_moe()
+
+    def _inject_lora_into_fused_moe(self):
+        moe_state_dict = {}
+        top_k = self.base_layer.top_k
+
+        if self.base_layer.quant_config is None:
+            quant_config = FUSED_MOE_UNQUANTIZED_CONFIG
+        elif not isinstance(self.base_layer.quant_config, Mxfp4Config):
+            quant_config = self.base_layer.quant_config
+        else:
+            quant_config = mxfp4_w4a16_moe_quant_config(
+                w1_bias=self.base_layer.w13_bias,
+                w2_bias=self.base_layer.w2_bias,
+                w1_scale=self.base_layer.w13_weight_scale,
+                w2_scale=self.base_layer.w2_weight_scale,
+            )
+
+        m_fused_moe_fn = (
+            modular_triton_fused_moe(
+                quant_config, shared_experts=self.base_layer.shared_experts
+            )
+            if not quant_config.use_mxfp4_w4a16
+            else modular_marlin_fused_moe(
+                quant_config, shared_experts=self.base_layer.shared_experts
+            )
+        )
+
+        def fwd_decorator(layer, func):
+            def wrapper(*args, **kwargs):
+                moe_state_dict["hidden_states"] = kwargs["hidden_states"]
+                moe_state_dict["topk_ids"] = kwargs["topk_ids"]
+                moe_state_dict["topk_weights"] = kwargs["topk_weights"]
+                moe_state_dict["global_num_experts"] = kwargs["global_num_experts"]
+                moe_state_dict["expert_map"] = kwargs["expert_map"]
+                moe_state_dict["apply_router_weight_on_input"] = kwargs[
+                    "apply_router_weight_on_input"
+                ]
+                moe_state_dict["max_loras"] = layer.w1_lora_a_stacked.shape[0]
+                result = func(*args, **kwargs)
+                return result
+
+            return wrapper
+
+        def act_decorator(layer, func):
+            def wrapper(*args, **kwargs):
+                _, output, input = args
+
+                hidden_states = moe_state_dict["hidden_states"]
+                topk_weights = moe_state_dict["topk_weights"]
+                curr_topk_ids = moe_state_dict["topk_ids"]
+                global_num_experts = moe_state_dict["global_num_experts"]
+                expert_map = moe_state_dict["expert_map"]
+                max_loras = moe_state_dict["max_loras"]
+
+                config_dtype = _get_config_dtype_str(
+                    dtype=hidden_states.dtype,
+                    use_fp8_w8a8=False,
+                    use_int8_w8a16=False,
+                    use_int4_w4a16=False,
+                )
+                CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
+                num_tokens = hidden_states.size(0)
+                M = min(num_tokens, CHUNK_SIZE)
+
+                get_config_func = functools.partial(
+                    try_get_optimal_moe_config,
+                    layer.w13_weight.size(),
+                    layer.w2_weight.size(),
+                    top_k,
+                    config_dtype,
+                    block_shape=layer.quant_method.moe_quant_config.block_shape,
+                )
+
+                config = get_config_func(M)
+                (
+                    sorted_token_ids_lora,
+                    expert_ids_lora,
+                    num_tokens_post_padded_lora,
+                ) = self.punica_wrapper.moe_lora_align_block_size(
+                    curr_topk_ids,
+                    num_tokens,
+                    config["BLOCK_SIZE_M"],
+                    global_num_experts,
+                    max_loras,
+                    expert_map,
+                )
+
+                moe_state_dict["sorted_token_ids_lora"] = sorted_token_ids_lora
+                moe_state_dict["expert_ids_lora"] = expert_ids_lora
+                moe_state_dict["num_tokens_post_padded_lora"] = (
+                    num_tokens_post_padded_lora
+                )
+
+                w13_lora_a_stacked = [self.w1_lora_a_stacked, self.w3_lora_a_stacked]
+                w13_lora_b_stacked = [self.w1_lora_b_stacked, self.w3_lora_b_stacked]
+                max_lora_rank = self.w1_lora_a_stacked.shape[-2]
+                expert_ids_lora = expert_ids_lora.view(max_loras, -1)
+                sorted_token_ids_lora = sorted_token_ids_lora.view(max_loras, -1)
+
+                self.punica_wrapper.add_lora_fused_moe(
+                    input.view(-1, top_k, input.shape[-1]),
+                    hidden_states,
+                    w13_lora_a_stacked,
+                    w13_lora_b_stacked,
+                    topk_weights,
+                    sorted_token_ids_lora,
+                    expert_ids_lora,
+                    num_tokens_post_padded_lora,
+                    max_lora_rank,
+                    top_k,
+                    config,
+                )
+
+                result = func(*args, **kwargs)
+
+                moe_state_dict["intermediate_cache2"] = output
+                return result
+
+            return wrapper
+
+        def moe_sum_decorator(layer, func):
+            def wrapper(*args, **kwargs):
+                hidden_states = moe_state_dict["hidden_states"]
+                topk_weights = moe_state_dict["topk_weights"]
+                max_loras = moe_state_dict["max_loras"]
+
+                config_dtype = _get_config_dtype_str(
+                    dtype=hidden_states.dtype,
+                    use_fp8_w8a8=False,
+                    use_int8_w8a16=False,
+                    use_int4_w4a16=False,
+                )
+                CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
+                num_tokens = hidden_states.size(0)
+                M = min(num_tokens, CHUNK_SIZE)
+
+                get_config_func = functools.partial(
+                    try_get_optimal_moe_config,
+                    layer.w13_weight.size(),
+                    layer.w2_weight.size(),
+                    top_k,
+                    config_dtype,
+                    block_shape=layer.quant_method.moe_quant_config.block_shape,
+                )
+
+                config = get_config_func(M)
+
+                sorted_token_ids_lora = moe_state_dict["sorted_token_ids_lora"]
+                expert_ids_lora = moe_state_dict["expert_ids_lora"]
+                num_tokens_post_padded_lora = moe_state_dict[
+                    "num_tokens_post_padded_lora"
+                ]
+
+                expert_ids_lora = expert_ids_lora.view(max_loras, -1)
+                sorted_token_ids_lora = sorted_token_ids_lora.view(max_loras, -1)
+                intermediate_cache2 = moe_state_dict["intermediate_cache2"]
+                intermediate_cache3 = args[0]
+                max_lora_rank = self.w1_lora_a_stacked.shape[-2]
+                self.punica_wrapper.add_lora_fused_moe(
+                    intermediate_cache3,
+                    intermediate_cache2,
+                    [self.w2_lora_a_stacked],
+                    [self.w2_lora_b_stacked],
+                    topk_weights,
+                    sorted_token_ids_lora,
+                    expert_ids_lora,
+                    num_tokens_post_padded_lora,
+                    max_lora_rank,
+                    top_k,
+                    config,
+                    True,
+                )
+
+                result = func(*args, **kwargs)
+                return result
+
+            return wrapper
+
+        fused_experts = m_fused_moe_fn.fused_experts
+
+        m_fused_moe_fn.forward = fwd_decorator(self.base_layer, m_fused_moe_fn.forward)
+        fused_experts.activation = act_decorator(
+            self.base_layer, fused_experts.activation
+        )
+        fused_experts.moe_sum = moe_sum_decorator(
+            self.base_layer, fused_experts.moe_sum
+        )
+
+        self.base_layer.quant_method.old_fused_experts = (
+            self.base_layer.quant_method.fused_experts
+        )
+        self.base_layer.quant_method.fused_experts = m_fused_moe_fn
+
+    def create_lora_weights(
+        self,
+        max_loras: int,
+        lora_config: LoRAConfig,
+        model_config: PretrainedConfig | None = None,
+    ) -> None:
+        """Initializes lora matrices."""
+
+        assert not self.base_layer.use_ep, (
+            "EP support for Fused MoE LoRA is not implemented yet."
+        )
+
+        self.w1_lora_a_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                lora_config.max_lora_rank,
+                self.base_layer.hidden_size,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+        self.w1_lora_b_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                self.base_layer.intermediate_size_per_partition,
+                lora_config.max_lora_rank,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+
+        self.w2_lora_a_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                lora_config.max_lora_rank,
+                self.base_layer.intermediate_size_per_partition,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+        self.w2_lora_b_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                self.base_layer.hidden_size,
+                lora_config.max_lora_rank,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+
+        self.w3_lora_a_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                lora_config.max_lora_rank,
+                self.base_layer.hidden_size,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+        self.w3_lora_b_stacked = torch.zeros(
+            (
+                max_loras,
+                self.base_layer.global_num_experts,
+                self.base_layer.intermediate_size_per_partition,
+                lora_config.max_lora_rank,
+            ),
+            dtype=lora_config.lora_dtype,
+            device=self.device,
+        )
+
+        self.base_layer.w1_lora_a_stacked = self.w1_lora_a_stacked
+        self.base_layer.w1_lora_b_stacked = self.w1_lora_b_stacked
+        self.base_layer.w2_lora_a_stacked = self.w2_lora_a_stacked
+        self.base_layer.w2_lora_b_stacked = self.w2_lora_b_stacked
+        self.base_layer.w3_lora_a_stacked = self.w3_lora_a_stacked
+        self.base_layer.w3_lora_b_stacked = self.w3_lora_b_stacked
+        # They will be used by 'LoRALayerWeights.create_dummy_lora_weights'
+        # to create a dummy LoRA weights.
+        self.lora_a_stacked = []
+        self.lora_b_stacked = []
+        for lora_id in range(max_loras):
+            for experts_id in range(self.base_layer.global_num_experts):
+                # gate_proj,down_proj,up_proj
+                self.lora_a_stacked.append(self.w1_lora_a_stacked[lora_id][experts_id])
+                self.lora_a_stacked.append(self.w2_lora_a_stacked[lora_id][experts_id])
+                self.lora_a_stacked.append(self.w3_lora_a_stacked[lora_id][experts_id])
+
+                self.lora_b_stacked.append(self.w1_lora_b_stacked[lora_id][experts_id])
+                self.lora_b_stacked.append(self.w2_lora_b_stacked[lora_id][experts_id])
+                self.lora_b_stacked.append(self.w3_lora_b_stacked[lora_id][experts_id])
+
+    def reset_lora(self, index: int):
+        """Resets the lora weights at index back to 0."""
+        self.w1_lora_a_stacked[index] = 0
+        self.w1_lora_b_stacked[index] = 0
+        self.w3_lora_a_stacked[index] = 0
+        self.w3_lora_b_stacked[index] = 0
+        self.w2_lora_a_stacked[index] = 0
+        self.w2_lora_b_stacked[index] = 0
+
+    def set_lora(
+        self,
+        index: int,
+        lora_a: torch.Tensor,
+        lora_b: torch.Tensor,
+        embeddings_tensor: torch.Tensor | None,
+        bias: torch.Tensor | None = None,
+    ):
+        """Overwrites lora tensors at index."""
+        for eid in range(len(lora_a) // 3):
+            w1_lora_a = lora_a[eid * 3]
+            w2_lora_a = lora_a[eid * 3 + 1]
+            w3_lora_a = lora_a[eid * 3 + 2]
+            w1_lora_b = lora_b[eid * 3]
+            w2_lora_b = lora_b[eid * 3 + 1]
+            w3_lora_b = lora_b[eid * 3 + 2]
+
+            if self.tp_size > 1:
+                shard_size = self.base_layer.intermediate_size_per_partition
+                start_idx = self.tp_rank * shard_size
+                end_idx = (self.tp_rank + 1) * shard_size
+
+                w1_lora_b = w1_lora_b[start_idx:end_idx, :]
+                w3_lora_b = w3_lora_b[start_idx:end_idx, :]
+                w2_lora_a = w2_lora_a[:, start_idx:end_idx]
+
+            self.w1_lora_a_stacked[
+                index, eid, : w1_lora_a.shape[0], : w1_lora_a.shape[1]
+            ].copy_(w1_lora_a, non_blocking=True)
+
+            self.w3_lora_a_stacked[
+                index, eid, : w3_lora_a.shape[0], : w3_lora_a.shape[1]
+            ].copy_(w3_lora_a, non_blocking=True)
+
+            self.w2_lora_b_stacked[
+                index, eid, : w2_lora_b.shape[0], : w2_lora_b.shape[1]
+            ].copy_(w2_lora_b, non_blocking=True)
+
+            self.w1_lora_b_stacked[
+                index, eid, : w1_lora_b.shape[0], : w1_lora_b.shape[1]
+            ].copy_(w1_lora_b, non_blocking=True)
+            self.w3_lora_b_stacked[
+                index, eid, : w3_lora_b.shape[0], : w3_lora_b.shape[1]
+            ].copy_(w3_lora_b, non_blocking=True)
+            self.w2_lora_a_stacked[
+                index, eid, : w2_lora_a.shape[0], : w2_lora_a.shape[1]
+            ].copy_(w2_lora_a, non_blocking=True)
+
+    @classmethod
+    def can_replace_layer(
+        cls,
+        source_layer: nn.Module,
+        lora_config: LoRAConfig,
+        packed_modules_list: list,
+        model_config: PretrainedConfig | None,
+    ) -> bool:
+        """Returns True if the layer can be replaced by this LoRA layer."""
+        # return type(source_layer) is FusedMoE
+        return isinstance(source_layer, FusedMoE)
+
+    def forward(self, *args, **kwargs):
+        return self.base_layer.forward(*args, **kwargs)
+
+    def maybe_all_reduce_tensor_model_parallel(self, *args, **kwargs):
+        return self.base_layer.maybe_all_reduce_tensor_model_parallel(*args, **kwargs)
+
+    @property
+    def _shared_experts(self):
+        return self.base_layer._shared_experts
+
+    @property
+    def quant_method(self):
+        return self.base_layer.quant_method

vllm/lora/models.py

@@ -13,7 +13,7 @@ from torch import nn
 
 from vllm.config.lora import LoRAConfig
 from vllm.logger import init_logger
-from vllm.lora.layers import BaseLayerWithLoRA, LoRAMapping
+from vllm.lora.layers import BaseLayerWithLoRA, FusedMoEWithLoRA, LoRAMapping
 from vllm.lora.lora_weights import LoRALayerWeights, PackedLoRALayerWeights
 from vllm.lora.peft_helper import PEFTHelper
 from vllm.lora.punica_wrapper import get_punica_wrapper
@@ -23,15 +23,14 @@ from vllm.lora.utils import (
     get_supported_lora_modules,
     is_regex_target_modules,
     parse_fine_tuned_lora_name,
+    process_packed_modules_mapping,
     replace_submodule,
 )
-from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 from vllm.model_executor.models import SupportsLoRA, supports_multimodal
 from vllm.model_executor.models.interfaces import is_pooling_model
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.utils import PPMissingLayer, WeightsMapper
-from vllm.model_executor.utils import get_packed_modules_mapping
 from vllm.utils import is_pin_memory_available
 from vllm.utils.cache import LRUCache
 
@@ -60,18 +59,6 @@ def get_lora_id():
     return _GLOBAL_LORA_ID
 
 
-def is_moe_model(model: nn.Module) -> bool:
-    """Checks if the model contains FusedMoE layers and warns the user."""
-    if any(isinstance(module, FusedMoE) for module in model.modules()):
-        logger.warning_once(
-            "For MoE models, vLLM currently does not support fused MoE LoRA "
-            "inference. Please ensure that the loaded LoRA model does not "
-            "contain expert weights."
-        )
-        return True
-    return False
-
-
 class LoRAModel:
     """A LoRA fine-tuned model."""
 
@@ -229,9 +216,19 @@ class LoRAModel:
         def check_unexpected_modules(modules: dict):
             for lora_module in modules.keys():  # noqa
                 module_name, _ = parse_fine_tuned_lora_name(lora_module, weights_mapper)
-                part_name = module_name.split(".")[-1]
-                if part_name not in expected_lora_modules:
+                # Handle FSDP file format where experts.base_layer is the
+                # gate_up_proj and experts is the down_proj
+                if "base_layer" in lora_module:
+                    continue
+                # Case for expert lora weights
+                if ".experts" in module_name:
+                    if not any(
+                        module_name.endswith(ele) for ele in expected_lora_modules
+                    ):
+                        unexpected_modules.append(module_name)
+                elif module_name.split(".")[-1] not in expected_lora_modules:
                     unexpected_modules.append(module_name)
+
             if unexpected_modules:
                 raise ValueError(
                     f"While loading {lora_dir}, expected"
@@ -371,7 +368,7 @@ class LoRAModelManager:
         assert self.supported_lora_modules, "No supported LoRA modules found in"
         f" {self.model.__class__.__name__}."
 
-        self.packed_modules_mapping = get_packed_modules_mapping(self.model)
+        self.packed_modules_mapping = process_packed_modules_mapping(self.model)
         # Used to indicate whether the model is a multimodal model
         self.supports_mm: bool = (
             supports_multimodal(self.model)
@@ -380,7 +377,6 @@ class LoRAModelManager:
             and hasattr(self.model, "get_mm_mapping")
         )
         self.is_pooling_model = is_pooling_model(self.model)
-        self.is_moe_model = is_moe_model(self.model)
         self.packed_modules: dict[str, list[str]] = {}
         self.modules: dict[str, BaseLayerWithLoRA] = {}
         # Dict instead of a set for compatibility with LRUCache.
@@ -431,6 +427,50 @@ class LoRAModelManager:
             module_lora = self._get_lora_layer_weights(lora_model, module_name)
             if module_lora:
                 module_lora.optimize()
+                # Note (gnovack) - If MOE lora weights are not split into
+                # num_experts chunks, we split them here
+                if isinstance(module, FusedMoEWithLoRA) and torch.is_tensor(
+                    module_lora.lora_a
+                ):
+                    # Handle FSDP file format where experts.base_layer is the
+                    # gate_up_proj and experts is the down_proj
+                    gate_up_proj_lora = self._get_lora_layer_weights(
+                        lora_model, module_name + ".base_layer"
+                    )
+
+                    assert gate_up_proj_lora is not None
+                    assert module_lora is not None
+
+                    down_proj_lora = module_lora
+                    num_experts = module_lora.lora_a.shape[0] // module_lora.rank
+
+                    gate_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
+                    up_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
+
+                    gate_proj_b = gate_up_proj_lora.lora_b[::2, ...].chunk(
+                        num_experts, dim=-1
+                    )
+                    up_proj_b = gate_up_proj_lora.lora_b[1::2, ...].chunk(
+                        num_experts, dim=-1
+                    )
+
+                    down_proj_a = down_proj_lora.lora_a.chunk(num_experts, dim=0)
+                    down_proj_b = down_proj_lora.lora_b.chunk(num_experts, dim=-1)
+
+                    lora_a = []
+                    lora_b = []
+                    for i in range(num_experts):
+                        lora_a.append(gate_proj_a[i])
+                        lora_a.append(down_proj_a[i])
+                        lora_a.append(up_proj_a[i])
+
+                        lora_b.append(gate_proj_b[i])
+                        lora_b.append(down_proj_b[i])
+                        lora_b.append(up_proj_b[i])
+
+                    module_lora.lora_a = lora_a
+                    module_lora.lora_b = lora_b
+
                 module.set_lora(
                     index,
                     module_lora.lora_a,
@@ -486,6 +526,7 @@ class LoRAModelManager:
         for module_name, module in self.model.named_modules(remove_duplicate=False):
             if isinstance(module, PPMissingLayer):
                 continue
+
             if not self._match_target_modules(module_name):
                 continue
             # A temporary approach for multimodal models to support LoRA
@@ -549,7 +590,10 @@ class LoRAModelManager:
             new_module.set_mapping(self.punica_wrapper)
 
     def register_module(self, module_name: str, module: "BaseLayerWithLoRA"):
-        assert isinstance(module, BaseLayerWithLoRA)
+        assert isinstance(module, BaseLayerWithLoRA), (
+            f"Module {module_name} must be a BaseLayerWithLoRA instance,"
+        )
+        f" got {type(module)}"
         self.modules[module_name] = module
 
     def create_dummy_lora(

vllm/lora/ops/triton_ops/__init__.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from vllm.lora.ops.triton_ops.fused_moe_lora_op import fused_moe_lora
 from vllm.lora.ops.triton_ops.lora_expand_op import lora_expand
 from vllm.lora.ops.triton_ops.lora_kernel_metadata import LoRAKernelMeta
 from vllm.lora.ops.triton_ops.lora_shrink_op import lora_shrink
@@ -9,4 +10,5 @@ __all__ = [
     "lora_expand",
     "lora_shrink",
     "LoRAKernelMeta",
+    "fused_moe_lora",
 ]

vllm/lora/ops/triton_ops/fused_moe_lora_op.py

@@ -0,0 +1,350 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+import triton
+import triton.language as tl
+
+from vllm.utils.torch_utils import direct_register_custom_op
+
+_LORA_PTR_DICT: dict[tuple[int, ...], torch.tensor] = {}
+
+
+def _get_ptr(lora_weights: list[torch.Tensor], device: torch.device):
+    """
+    `_LORA_PTR_DICT` collects the required information during `profile_run`,
+    After this, it remains constant and subsequent usage is through LUT.
+    Refer to:
+    https://github.com/triton-lang/triton/blob/release/3.1.x/python/tutorials/08-grouped-gemm.py
+    """
+    key = tuple(lora_weight.data_ptr() for lora_weight in lora_weights)
+
+    if (ptr_tensor := _LORA_PTR_DICT.get(key)) is not None:
+        return ptr_tensor
+
+    tensor_ptrs = []
+    for lora_weight in lora_weights:
+        tensor_ptrs.append(lora_weight.data_ptr())
+    ptr_tensor = torch.tensor(tensor_ptrs, device=device)
+
+    _LORA_PTR_DICT[key] = ptr_tensor
+    return _LORA_PTR_DICT.get(key)
+
+
+@triton.jit
+def _fused_moe_lora_kernel(
+    a_ptr,
+    b_ptr,
+    c_ptr,
+    topk_weights_ptr,
+    sorted_token_ids_ptr,
+    expert_ids_ptr,
+    num_tokens_post_padded_ptr,
+    # Matrix dimensions
+    N,
+    K,
+    EM,
+    num_valid_tokens,
+    num_experts,
+    # The stride variables represent how much to increase the ptr by when
+    # moving by 1 element in a particular dimension. E.g. `stride_am` is
+    # how much to increase `a_ptr` by to get the element one row down
+    # (A has M rows).
+    stride_am,
+    stride_ak,
+    stride_bl,
+    stride_be,
+    stride_bk,
+    stride_bn,
+    stride_cm,
+    stride_cn,
+    stride_tl,
+    stride_el,
+    # Meta-parameters
+    num_slice_a: tl.constexpr,
+    num_slice_c: tl.constexpr,
+    slice_a_size: tl.constexpr,
+    slice_c_size: tl.constexpr,
+    top_k: tl.constexpr,
+    MUL_ROUTED_WEIGHT: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    GROUP_SIZE_M: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    slice_id = tl.program_id(axis=1)
+    lora_idx = tl.program_id(axis=2)
+    max_loras = tl.num_programs(axis=2)
+
+    # calculate pid_m,pid_n
+    num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr + lora_idx)
+    if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
+        return
+
+    # get the expert_id to process curr shard
+    ind = lora_idx * stride_el + pid_m
+    expert_id = tl.load(expert_ids_ptr + ind)
+    if expert_id == -1:
+        return
+
+    # get a_ptr,b_ptr,c_ptr
+    cur_a_ptr = a_ptr + (slice_id % num_slice_a) * slice_a_size
+    cur_b_ptr = tl.load(b_ptr + slice_id).to(tl.pointer_type(tl.bfloat16))
+    cur_c_ptr = c_ptr + (slice_id % num_slice_c) * slice_c_size
+
+    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N).to(tl.int64)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+
+    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(tl.int64)
+    token_ind = stride_tl * lora_idx + offs_token_id
+    offs_token = tl.load(
+        sorted_token_ids_ptr + token_ind, token_ind < max_loras * stride_tl, 0.0
+    )
+    token_mask = offs_token < num_valid_tokens
+
+    # get a_ptrs,b_ptrs
+    a_ptrs = cur_a_ptr + (
+        offs_token[:, None] // top_k * stride_am + offs_k[None, :] * stride_ak
+    )
+
+    b_ptrs = (
+        cur_b_ptr
+        + lora_idx * stride_bl
+        + expert_id * stride_be
+        + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
+    )
+
+    # accumulator
+    accumulator = 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=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
+            other=0.0,
+        )
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
+        accumulator += tl.dot(a, b)
+        # Advance the ptrs to the next K block.
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    if MUL_ROUTED_WEIGHT:
+        moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0)
+        accumulator = accumulator * moe_weight[:, None]
+
+    accumulator = accumulator.to(tl.bfloat16)
+    # Write back the block of the output
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = cur_c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[None, :]
+    c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+
+
+@torch.inference_mode()
+def _fused_moe_lora(
+    output: torch.Tensor,  # (num_tokens, top_k_num, N*len(lora_a_stacked),)
+    qcurr_hidden_states: torch.Tensor,  # (num_tokens, K,)
+    lora_a_stacked: list[
+        torch.Tensor
+    ],  # [(max_loras, num_experts, max_lora_rank, K,),...]
+    lora_b_stacked: list[
+        torch.Tensor
+    ],  # [(max_loras, num_experts, N, max_lora_rank,),...]
+    topk_weights: torch.Tensor,  # (num_tokens, top_k_num)
+    sorted_token_ids: torch.Tensor,  # (max_loras, _)
+    expert_ids: torch.Tensor,  # (max_loras, _ ,)
+    num_tokens_post_padded: torch.Tensor,  # (max_loras, )
+    max_lora_rank: int,
+    top_k_num: int,
+    block_size_m: int,
+    block_size_n: int,
+    block_size_k: int,
+    group_size_m: int,
+    mul_routed_weight: bool = False,
+) -> None:
+    assert len(lora_a_stacked) == len(lora_b_stacked) > 0
+    assert (
+        sorted_token_ids.dim()
+        == expert_ids.dim()
+        == topk_weights.dim()
+        == qcurr_hidden_states.dim()
+        == 2
+    )
+    assert (
+        sorted_token_ids.shape[0]
+        == expert_ids.shape[0]
+        == num_tokens_post_padded.shape[0]
+    )
+    assert len(lora_b_stacked) * lora_b_stacked[0].shape[-2] == output.shape[-1]
+    assert output.shape[0] == topk_weights.shape[0]
+    assert top_k_num == topk_weights.shape[1]
+
+    device = qcurr_hidden_states.device
+    num_slices = len(lora_a_stacked)
+
+    config = {
+        "BLOCK_SIZE_M": block_size_m,
+        "BLOCK_SIZE_N": block_size_n,
+        "BLOCK_SIZE_K": block_size_k,
+        "GROUP_SIZE_M": group_size_m,
+    }
+
+    w1_lora_a_stacked = lora_a_stacked[0]
+    w1_lora_b_stacked = lora_b_stacked[0]
+    num_experts = lora_a_stacked[0].shape[1]
+
+    N = max_lora_rank
+    M = topk_weights.shape[0]
+    EM = sorted_token_ids.shape[1]
+    K = qcurr_hidden_states.shape[1]
+    num_tokens = M * top_k_num
+    w1_output_dim_size = w1_lora_b_stacked.shape[2]
+
+    lora_intermediate_cache1 = torch.zeros(
+        (num_slices * M * top_k_num * (max_lora_rank + w1_output_dim_size)),
+        dtype=torch.bfloat16,
+        device=device,
+    )
+
+    # slices
+    a_intermediate_size = num_slices * M * top_k_num * max_lora_rank
+    a_intermediate_cache1 = lora_intermediate_cache1[:a_intermediate_size].view(
+        num_slices, M, top_k_num, max_lora_rank
+    )
+    b_intermediate_cache1 = lora_intermediate_cache1[a_intermediate_size:].view(
+        num_slices, M, top_k_num, w1_output_dim_size
+    )
+
+    b_ptr = _get_ptr(lora_a_stacked, device)
+
+    grid = lambda META: (
+        triton.cdiv(EM, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        len(lora_a_stacked),
+        lora_a_stacked[0].shape[0],
+    )
+
+    _fused_moe_lora_kernel[grid](
+        qcurr_hidden_states,
+        b_ptr,
+        a_intermediate_cache1,
+        topk_weights,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_padded,
+        N,
+        K,
+        EM,
+        num_tokens,
+        num_experts,
+        qcurr_hidden_states.stride(0),
+        qcurr_hidden_states.stride(1),
+        w1_lora_a_stacked.stride(0),
+        w1_lora_a_stacked.stride(1),
+        w1_lora_a_stacked.stride(3),
+        w1_lora_a_stacked.stride(2),
+        a_intermediate_cache1.stride(2),
+        a_intermediate_cache1.stride(3),
+        sorted_token_ids.stride(0),
+        expert_ids.stride(0),
+        num_slice_a=1,
+        num_slice_c=num_slices,
+        slice_a_size=qcurr_hidden_states.numel(),
+        slice_c_size=a_intermediate_cache1.numel() // num_slices,
+        top_k=1 if mul_routed_weight else top_k_num,
+        MUL_ROUTED_WEIGHT=False,
+        **config,
+    )
+
+    b_ptr = _get_ptr(lora_b_stacked, device)
+    K = max_lora_rank
+    N = w1_output_dim_size
+
+    # a_intermediate_cache1 = a_intermediate_cache1.view(
+    #     M, -1, a_intermediate_cache1.shape[3]
+    # )
+
+    a_intermediate_cache1 = a_intermediate_cache1.view(
+        -1, a_intermediate_cache1.shape[3]
+    )
+
+    grid = lambda META: (
+        triton.cdiv(EM, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        len(lora_b_stacked),
+        lora_b_stacked[0].shape[0],
+    )
+    _fused_moe_lora_kernel[grid](
+        a_intermediate_cache1,
+        b_ptr,
+        b_intermediate_cache1,
+        topk_weights,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_padded,
+        N,
+        K,
+        EM,
+        num_tokens,
+        num_experts,
+        a_intermediate_cache1.stride(0),
+        a_intermediate_cache1.stride(1),
+        w1_lora_b_stacked.stride(0),
+        w1_lora_b_stacked.stride(1),
+        w1_lora_b_stacked.stride(3),
+        w1_lora_b_stacked.stride(2),
+        b_intermediate_cache1.stride(2),
+        b_intermediate_cache1.stride(3),
+        sorted_token_ids.stride(0),
+        expert_ids.stride(0),
+        num_slice_a=num_slices,
+        num_slice_c=num_slices,
+        slice_a_size=a_intermediate_cache1.numel() // num_slices,
+        slice_c_size=b_intermediate_cache1.numel() // num_slices,
+        top_k=1,
+        MUL_ROUTED_WEIGHT=mul_routed_weight,
+        **config,
+    )
+    for i in range(num_slices):
+        output[:, :, i * N : (i + 1) * N] += b_intermediate_cache1[i]
+
+
+def _fused_moe_lora_fake(
+    output: torch.Tensor,
+    qcurr_hidden_states: torch.Tensor,
+    lora_a_stacked: list[torch.Tensor],
+    lora_b_stacked: list[torch.Tensor],
+    topk_weights: torch.Tensor,
+    sorted_token_ids: torch.Tensor,
+    expert_ids: torch.Tensor,
+    num_tokens_post_padded: torch.Tensor,
+    max_lora_rank: int,
+    top_k_num: int,
+    block_size_m: int,
+    block_size_n: int,
+    block_size_k: int,
+    group_size_m: int,
+    mul_routed_weight: bool = False,
+) -> None:
+    return
+
+
+try:
+    direct_register_custom_op(
+        op_name="fused_moe_lora",
+        op_func=_fused_moe_lora,
+        mutates_args=["output"],
+        fake_impl=_fused_moe_lora_fake,
+    )
+    fused_moe_lora = torch.ops.vllm.fused_moe_lora
+
+except AttributeError:
+    fused_moe_lora = _fused_moe_lora

vllm/lora/punica_wrapper/punica_base.py

@@ -448,3 +448,42 @@ class PunicaWrapperBase(PunicaWrapperABC):
         """
         # TODO: implement it based on torch ops
         raise NotImplementedError
+
+    def moe_lora_align_block_size(
+        self,
+        topk_ids: torch.Tensor,
+        num_tokens: int,
+        block_size: int,
+        num_experts: int,
+        max_loras: int,
+        expert_map: torch.Tensor | None = None,
+        pad_sorted_ids: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Aligns tokens and experts into block-sized chunks for LoRA-based
+        mixture-of-experts (MoE) execution.
+        """
+        # TODO: implement it based on torch ops
+        raise NotImplementedError
+
+    def add_lora_fused_moe(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: list[torch.Tensor],
+        lora_b_stacked: list[torch.Tensor],
+        topk_weights: torch.Tensor,
+        sorted_token_ids: torch.Tensor,
+        expert_ids: torch.Tensor,
+        num_tokens_post_padded: torch.Tensor,
+        max_lora_rank: int,
+        top_k_num: int,
+        config,
+        mul_routed_weight=False,
+    ):
+        """
+        Performs a fused forward computation for LoRA of
+        Mixture-of-Experts (MoE) layer.
+        """
+        # TODO: implement it based on torch ops
+        raise NotImplementedError

vllm/lora/punica_wrapper/punica_gpu.py

@@ -12,10 +12,18 @@ from typing import final
 import torch
 
 from vllm.lora.layers import LoRAMapping
-from vllm.triton_utils import HAS_TRITON
+from vllm.triton_utils import HAS_TRITON, triton
+from vllm.utils import round_up
 
 if HAS_TRITON:
-    from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
+    from vllm.lora.ops.triton_ops import (
+        LoRAKernelMeta,
+        fused_moe_lora,
+        lora_expand,
+        lora_shrink,
+    )
+
+from vllm import _custom_ops as ops
 
 from .punica_base import PunicaWrapperBase
 
@@ -289,3 +297,91 @@ class PunicaWrapperGPU(PunicaWrapperBase):
             add_inputs=True,
         )
         y = y.view_as(y_org)
+
+    def moe_lora_align_block_size(
+        self,
+        topk_ids: torch.Tensor,
+        num_tokens: int,
+        block_size: int,
+        num_experts: int,
+        max_loras: int,
+        expert_map: torch.Tensor | None = None,
+        pad_sorted_ids: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Aligns tokens and experts into block-sized chunks for LoRA-based
+        mixture-of-experts (MoE) execution.
+        """
+        max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+        if pad_sorted_ids:
+            max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
+        sorted_ids = torch.empty(
+            (max_loras * max_num_tokens_padded,),
+            dtype=torch.int32,
+            device=topk_ids.device,
+        )
+        max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
+        # Expert ids must be set default to -1 to prevent a blank block
+        expert_ids = torch.empty(
+            (max_loras * max_num_m_blocks,),
+            dtype=torch.int32,
+            device=topk_ids.device,
+        )
+        num_tokens_post_pad = torch.empty(
+            (max_loras), dtype=torch.int32, device=topk_ids.device
+        )
+
+        (token_lora_mapping, _, _, _, _, _) = self.token_mapping_meta.meta_args(
+            num_tokens
+        )
+
+        ops.moe_lora_align_block_size(
+            topk_ids,
+            token_lora_mapping,
+            num_experts,
+            block_size,
+            max_loras,
+            sorted_ids,
+            expert_ids,
+            num_tokens_post_pad,
+        )
+        if expert_map is not None:
+            expert_ids = expert_map[expert_ids]
+
+        return sorted_ids, expert_ids, num_tokens_post_pad
+
+    def add_lora_fused_moe(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        lora_a_stacked: list[torch.Tensor],
+        lora_b_stacked: list[torch.Tensor],
+        topk_weights: torch.Tensor,
+        sorted_token_ids: torch.Tensor,
+        expert_ids: torch.Tensor,
+        num_tokens_post_padded: torch.Tensor,
+        max_lora_rank: int,
+        top_k_num: int,
+        config,
+        mul_routed_weight=False,
+    ):
+        """
+        Performs a fused forward computation for LoRA of Mixture-of-Experts (MoE) layer.
+        """
+        fused_moe_lora(
+            y,
+            x,
+            lora_a_stacked,
+            lora_b_stacked,
+            topk_weights,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            max_lora_rank,
+            top_k_num,
+            config["BLOCK_SIZE_M"],
+            config["BLOCK_SIZE_N"],
+            config["BLOCK_SIZE_K"],
+            config["GROUP_SIZE_M"],
+            mul_routed_weight,
+        )

vllm/lora/utils.py

@@ -23,6 +23,7 @@ from vllm.lora.layers import (
     BaseLayerWithLoRA,
     ColumnParallelLinearWithLoRA,
     ColumnParallelLinearWithShardedLoRA,
+    FusedMoEWithLoRA,
     LogitsProcessorWithLoRA,
     MergedColumnParallelLinearWithLoRA,
     MergedColumnParallelLinearWithShardedLoRA,
@@ -35,7 +36,9 @@ from vllm.lora.layers import (
     RowParallelLinearWithShardedLoRA,
     VocabParallelEmbeddingWithLoRA,
 )
+from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.utils import get_moe_expert_mapping, get_packed_modules_mapping
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -58,9 +61,18 @@ _all_lora_classes: set[type[BaseLayerWithLoRA]] = {
     MergedColumnParallelLinearWithShardedLoRA,
     MergedQKVParallelLinearWithShardedLoRA,
     RowParallelLinearWithShardedLoRA,
+    FusedMoEWithLoRA,
 }
 
 
+def is_moe_model(model: nn.Module) -> bool:
+    """Checks if the model contains FusedMoE layers and warns the user."""
+    if any(isinstance(module, FusedMoE) for module in model.modules()):
+        logger.info_once("MoE model detected. Using fused MoE LoRA implementation.")
+        return True
+    return False
+
+
 def from_layer(
     layer: nn.Module,
     max_loras: int,
@@ -205,6 +217,9 @@ def get_supported_lora_modules(model: nn.Module) -> list[str]:
         if isinstance(module, (LinearBase,)):
             supported_lora_modules.add(name.split(".")[-1])
 
+        if isinstance(module, (FusedMoE,)):
+            supported_lora_modules.add(name.split(".")[-1])
+
     return list(supported_lora_modules)
 
 
@@ -252,3 +267,27 @@ def get_adapter_absolute_path(lora_path: str) -> str:
         return lora_path
 
     return local_snapshot_path
+
+
+def process_packed_modules_mapping(model: nn.Module) -> dict[str, list[str]]:
+    if is_moe_model(model):
+        if moe_packed_mapping := get_moe_expert_mapping(model):
+            # This method generates and returns a dictionary mapping packed module
+            # names to lists of their corresponding submodule names. It includes
+            # both static mappings and dynamic mappings for expert layers, where
+            # the expert indices are expanded based on the configured number
+            # of routed experts.
+            packed_modules_mapping = get_packed_modules_mapping(model)
+
+            packed_modules_mapping["experts"] = [
+                weight_name.rstrip(".") for _, weight_name, _, _ in moe_packed_mapping
+            ]
+
+            return packed_modules_mapping
+        else:
+            raise AttributeError(
+                "To support LoRA for MoE model, "
+                "'get_expert_mapping' must be implemented"
+            )
+    else:
+        return get_packed_modules_mapping(model)

vllm/lora/worker_manager.py

@@ -94,7 +94,8 @@ class WorkerLoRAManager:
                     expected_lora_modules.extend(packed_modules_mapping[module])
                 else:
                     expected_lora_modules.append(module)
-
+                if module == "experts":
+                    expected_lora_modules.append(module)
             expected_lora_modules = list(set(expected_lora_modules))
             lora_path = get_adapter_absolute_path(lora_request.lora_path)
 

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Fused MoE utilities for GPTQ."""
 
+from collections.abc import Callable
+
 import torch
 
 import vllm._custom_ops as ops
@@ -11,6 +13,9 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
     batched_moe_align_block_size,
     moe_align_block_size,
 )
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate,
     TopKWeightAndReduceNoOP,
@@ -24,6 +29,21 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils import (
 from vllm.scalar_type import ScalarType, scalar_types
 
 
+def default_activation_func(
+    activation: str, output: torch.Tensor, input: torch.Tensor
+) -> None:
+    if activation == "silu":
+        torch.ops._C.silu_and_mul(output, input)
+    elif activation == "swigluoai":
+        # alpha = 1.702, limit = 7.0
+        torch.ops._C.swigluoai_and_mul(output, input)
+    else:
+        raise ValueError(
+            f"Unsupported activation: {activation}. "
+            "Only silu and swigluoai activations are supported."
+        )
+
+
 def _fused_marlin_moe(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
@@ -36,12 +56,15 @@ def _fused_marlin_moe(
     num_topk: int,
     quant_type: ScalarType,
     apply_router_weight_on_input: bool,
-    activation: str,
     expert_map: torch.Tensor | None,
     block_size_m: int,
     sorted_token_ids: torch.Tensor,
     expert_ids: torch.Tensor,
     num_tokens_post_padded: torch.Tensor,
+    activation: str = "silu",
+    activation_func: Callable[
+        [str, torch.Tensor, torch.Tensor], None
+    ] = default_activation_func,
     global_scale1: torch.Tensor | None = None,
     global_scale2: torch.Tensor | None = None,
     g_idx1: torch.Tensor | None = None,
@@ -118,20 +141,9 @@ def _fused_marlin_moe(
         is_zp_float=False,
     )
 
-    if activation == "silu":
-        torch.ops._C.silu_and_mul(
-            intermediate_cache2, intermediate_cache1.view(-1, 2 * N)
-        )
-    elif activation == "swigluoai":
-        # alpha = 1.702, limit = 7.0
-        torch.ops._C.swigluoai_and_mul(
-            intermediate_cache2, intermediate_cache1.view(-1, 2 * N)
-        )
-    else:
-        raise ValueError(
-            f"Unsupported activation: {activation}. "
-            "Only silu and swigluoai activations are supported."
-        )
+    activation_func(
+        activation, intermediate_cache2, intermediate_cache1.view(-1, 2 * N)
+    )
 
     if output is None:
         output = intermediate_cache3
@@ -185,7 +197,11 @@ def fused_marlin_moe(
     quant_type_id: int,
     apply_router_weight_on_input: bool = False,
     global_num_experts: int = -1,
-    activation: str | None = "silu",
+    activation: str = "silu",
+    activation_func: Callable[
+        [str, torch.Tensor, torch.Tensor], None
+    ] = default_activation_func,
+    moe_sum: Callable[[torch.Tensor, torch.Tensor], None] | None = None,
     expert_map: torch.Tensor | None = None,
     global_scale1: torch.Tensor | None = None,
     global_scale2: torch.Tensor | None = None,
@@ -290,12 +306,13 @@ def fused_marlin_moe(
         num_topk=topk,
         quant_type=quant_type,
         apply_router_weight_on_input=apply_router_weight_on_input,
-        activation=activation,
         expert_map=expert_map,
         block_size_m=block_size_m,
         sorted_token_ids=sorted_token_ids,
         expert_ids=expert_ids,
         num_tokens_post_padded=num_tokens_post_padded,
+        activation=activation,
+        activation_func=activation_func,
         global_scale1=global_scale1,
         global_scale2=global_scale2,
         g_idx1=g_idx1,
@@ -317,7 +334,10 @@ def fused_marlin_moe(
         else:
             output = torch.empty_like(hidden_states)
 
-    return torch.sum(moe_output.view(-1, topk, K), dim=1, out=output)
+    if moe_sum is None:
+        return torch.sum(moe_output.view(-1, topk, K), dim=1, out=output)
+    else:
+        return moe_sum(moe_output, output)
 
 
 def batched_fused_marlin_moe(
@@ -600,6 +620,8 @@ class MarlinExperts(MarlinExpertsBase):
             apply_router_weight_on_input=apply_router_weight_on_input,
             global_num_experts=global_num_experts,
             activation=activation,
+            activation_func=self.activation,
+            moe_sum=self.moe_sum,
             expert_map=expert_map,
             output=output,
             # Workspaces are swapped in workspace_shapes() to account for proper
@@ -608,6 +630,19 @@ class MarlinExperts(MarlinExpertsBase):
             intermediate_cache2=workspace13,
         )
 
+    def moe_sum(self, input: torch.Tensor, output: torch.Tensor) -> None:
+        ops.moe_sum(input, output)
+
+
+def modular_marlin_fused_moe(
+    quant_config: FusedMoEQuantConfig, shared_experts: torch.nn.Module | None = None
+) -> mk.FusedMoEModularKernel:
+    return mk.FusedMoEModularKernel(
+        MoEPrepareAndFinalizeNoEP(),
+        MarlinExperts(quant_config),
+        shared_experts,
+    )
+
 
 class BatchedMarlinExperts(MarlinExpertsBase):
     def __init__(

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -2135,13 +2135,18 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
             B_bias=self.w2_bias,
         )
 
-        ops.moe_sum(intermediate_cache3, output)
+        # separate function is required for MoE + LoRA
+        self.moe_sum(intermediate_cache3, output)
+
+    def moe_sum(self, input: torch.Tensor, output: torch.Tensor) -> None:
+        ops.moe_sum(input, output)
 
 
 def modular_triton_fused_moe(
-    quant_config: FusedMoEQuantConfig,
+    quant_config: FusedMoEQuantConfig, shared_experts: torch.nn.Module | None = None
 ) -> mk.FusedMoEModularKernel:
     return mk.FusedMoEModularKernel(
         MoEPrepareAndFinalizeNoEP(),
         TritonExperts(quant_config),
+        shared_experts,
     )

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -557,6 +557,9 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
             torch.ops._C.silu_and_mul(output, input)
         elif activation == "gelu":
             torch.ops._C.gelu_and_mul(output, input)
+        elif activation == "swigluoai":
+            # alpha = 1.702, limit = 7.0
+            torch.ops._C.swigluoai_and_mul(output, input)
         else:
             raise ValueError(f"Unsupported FusedMoe activation: {activation}")
 

vllm/model_executor/models/deepseek_v2.py

@@ -1313,6 +1313,17 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts, SupportsLoR
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return SharedFusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts,
+            num_redundant_experts=0,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)

vllm/model_executor/models/gpt_oss.py

@@ -32,7 +32,7 @@ from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
 from vllm.utils import cdiv
 
-from .interfaces import SupportsEagle3, SupportsPP
+from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     WeightsMapper,
@@ -627,7 +627,7 @@ class GptOssModel(nn.Module):
             )
 
 
-class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3):
+class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
     packed_modules_mapping = {"qkv": ["q_proj", "k_proj", "v_proj"]}
 
     hf_to_vllm_mapper = WeightsMapper(
@@ -696,6 +696,17 @@ class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3):
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, weight scales, activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_local_experts,
+            num_redundant_experts=0,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(
             self,

vllm/model_executor/models/olmoe.py

@@ -49,7 +49,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     is_pp_missing_parameter,
@@ -349,8 +349,6 @@ class OlmoeModel(nn.Module):
             ("qkv_proj", "q_proj", "q"),
             ("qkv_proj", "k_proj", "k"),
             ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
         ]
 
         params_dict = dict(self.named_parameters())
@@ -433,17 +431,13 @@ class OlmoeModel(nn.Module):
         return loaded_params
 
 
-class OlmoeForCausalLM(nn.Module, SupportsPP):
+class OlmoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
             "k_proj",
             "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
+        ]
     }
 
     def __init__(