[Kernel] CUTLASS MoE FP8: Integrate cuda moe permute/unpermute (#23045)

Signed-off-by: Shixian Cui <shixian@amazon.com>

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -80,6 +80,11 @@ def bench_run(
         a, score, topk, renormalize=False
     )
 
+    ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
+    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
+    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+
     def run_triton_moe(
         a: torch.Tensor,
         w1: torch.Tensor,
@@ -111,6 +116,10 @@ def bench_run(
         w2: torch.Tensor,
         w1_scale: torch.Tensor,
         w2_scale: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         per_act_token: bool,
@@ -125,6 +134,10 @@ def bench_run(
                 topk_ids,
                 w1_scale,
                 w2_scale,
+                ab_strides1,
+                ab_strides2,
+                c_strides1,
+                c_strides2,
                 per_act_token,
                 a1_scale=None,
             )
@@ -136,6 +149,10 @@ def bench_run(
         w2_q: torch.Tensor,
         w1_scale: torch.Tensor,
         w2_scale: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
     ):
@@ -150,6 +167,10 @@ def bench_run(
                 topk_ids,
                 w1_scale,
                 w2_scale,
+                ab_strides1,
+                ab_strides2,
+                c_strides1,
+                c_strides2,
                 per_act_token,
                 a1_scale=None,
             )
@@ -194,6 +215,10 @@ def bench_run(
             w2_q,
             w1_scale,
             w2_scale,
+            ab_strides1,
+            ab_strides2,
+            c_strides1,
+            c_strides2,
             topk_weights,
             topk_ids,
         )
@@ -231,6 +256,10 @@ def bench_run(
         "w1_scale": w1_scale,
         "w2_scale": w2_scale,
         "per_act_token": per_act_token,
+        "ab_strides1": ab_strides1,
+        "ab_strides2": ab_strides2,
+        "c_strides1": c_strides1,
+        "c_strides2": c_strides2,
         # cuda graph params
         "cutlass_graph": cutlass_graph,
         "triton_graph": triton_graph,
@@ -289,6 +318,10 @@ def bench_run(
         w2_q,
         w1_scale,
         w2_scale,
+        ab_strides1,
+        ab_strides2,
+        c_strides1,
+        c_strides2,
         topk_weights,
         topk_ids,
         per_act_token,
@@ -297,7 +330,7 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, ab_strides1, ab_strides2, c_strides1, c_strides2, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,

csrc/moe/moe_permute_unpermute_op.cu

@@ -45,8 +45,6 @@ void moe_permute(
   auto copy_topk_ids = topk_ids.clone();  // copy topk_ids for preprocess
   auto permuted_experts_id = torch::empty_like(topk_ids);
   auto sorted_row_idx = torch::empty_like(inv_permuted_idx);
-  auto align_expert_first_token_offset =
-      torch::zeros_like(expert_first_token_offset);
 
   CubKeyValueSorter sorter{};
   int64_t* valid_num_ptr = nullptr;
@@ -85,12 +83,14 @@ void moe_permute(
   });
 
   // get m_indices and update expert_first_token_offset with align block
+  // this is only required for DeepGemm and not required for CUTLASS group gemm
+  if (align_block_size.has_value()) {
+    auto align_expert_first_token_offset =
+        torch::zeros_like(expert_first_token_offset);
     getMIndices(get_ptr<int64_t>(expert_first_token_offset),
                 get_ptr<int64_t>(align_expert_first_token_offset),
                 get_ptr<int>(m_indices), n_local_expert, align_block_size_value,
                 stream);
-  if (align_block_size.has_value()) {
-    // update align_expert_first_token_offset
     expert_first_token_offset.copy_(align_expert_first_token_offset);
   }
 }
@@ -195,19 +195,14 @@ void moe_permute(const torch::Tensor& input, const torch::Tensor& topk_weights,
                  torch::Tensor& expert_first_token_offset,
                  torch::Tensor& src_row_id2dst_row_id_map,
                  torch::Tensor& m_indices) {
-  TORCH_CHECK(false, "moe_unpermute is not supported on CUDA < 12.0");
+  TORCH_CHECK(false, "moe_permute is not supported on CUDA < 12.0");
 }
 
-void moe_unpermute(const torch::Tensor& input,
-                   const torch::Tensor& topk_weights, torch::Tensor& topk_ids,
-                   const torch::Tensor& token_expert_indices,
-                   const std::optional<torch::Tensor>& expert_map,
-                   int64_t n_expert, int64_t n_local_expert, int64_t topk,
-                   const std::optional<int64_t>& align_block_size,
-                   torch::Tensor& permuted_input,
-                   torch::Tensor& expert_first_token_offset,
-                   torch::Tensor& src_row_id2dst_row_id_map,
-                   torch::Tensor& m_indices) {
+void moe_unpermute(
+    const torch::Tensor& permuted_hidden_states,
+    const torch::Tensor& topk_weights, const torch::Tensor& inv_permuted_idx,
+    const std::optional<torch::Tensor>& expert_first_token_offset, int64_t topk,
+    torch::Tensor& hidden_states) {
   TORCH_CHECK(false, "moe_unpermute is not supported on CUDA < 12.0");
 }
 

csrc/ops.h

@@ -229,6 +229,11 @@ void get_cutlass_moe_mm_data(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
+void get_cutlass_moe_mm_problem_sizes(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets);
+
 void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
                                   torch::Tensor& problem_sizes1,
                                   torch::Tensor& problem_sizes2,

csrc/quantization/cutlass_w8a8/moe/get_group_starts.cuh

@@ -10,7 +10,7 @@
 
 template <typename ElementAB, typename ElementC, typename ElementAccumulator>
 __global__ void get_group_gemm_starts(
-    int32_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
+    int64_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
     ElementC** out_offsets, ElementAccumulator** a_scales_offsets,
     ElementAccumulator** b_scales_offsets, ElementAB* a_base_as_int,
     ElementAB* b_base_as_int, ElementC* out_base_as_int,
@@ -34,7 +34,7 @@ __global__ void get_group_gemm_starts(
   else if (out_tensors.dtype() == TENSOR_C_TYPE) {                         \
     get_group_gemm_starts<cutlass::float_e4m3_t, C_TYPE, float>            \
         <<<1, num_experts, 0, stream>>>(                                   \
-            static_cast<int32_t*>(expert_offsets.data_ptr()),              \
+            static_cast<int64_t*>(expert_offsets.data_ptr()),              \
             static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),       \
             static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()),       \
             static_cast<C_TYPE**>(out_ptrs.data_ptr()),                    \
@@ -61,6 +61,8 @@ void run_get_group_gemm_starts(
   TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
   TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
   TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
+  // expect int64_t to avoid overflow during offset calculations
+  TORCH_CHECK(expert_offsets.dtype() == torch::kInt64);
 
   int num_experts = static_cast<int>(expert_offsets.size(0));
   bool per_act_token = a_scales.numel() != 1;

csrc/quantization/cutlass_w8a8/moe/moe_data.cu

@@ -104,6 +104,53 @@ __global__ void compute_arg_sorts(const int32_t* __restrict__ topk_ids,
   }
 }
 
+namespace {
+inline void launch_compute_problem_sizes(const torch::Tensor& topk_ids,
+                                         torch::Tensor& problem_sizes1,
+                                         torch::Tensor& problem_sizes2,
+                                         torch::Tensor& atomic_buffer,
+                                         int64_t num_experts, int64_t n,
+                                         int64_t k, cudaStream_t stream,
+                                         const bool swap_ab) {
+  int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
+
+  const int32_t* topk_ptr = static_cast<const int32_t*>(topk_ids.data_ptr());
+  int32_t* ps1_ptr = static_cast<int32_t*>(problem_sizes1.data_ptr());
+  int32_t* ps2_ptr = static_cast<int32_t*>(problem_sizes2.data_ptr());
+  int32_t* atomic_ptr = static_cast<int32_t*>(atomic_buffer.data_ptr());
+
+  if (swap_ab) {
+    compute_problem_sizes<true><<<num_experts, num_threads, 0, stream>>>(
+        topk_ptr, ps1_ptr, ps2_ptr, atomic_ptr,
+        static_cast<int>(topk_ids.numel()), static_cast<int>(n),
+        static_cast<int>(k));
+  } else {
+    compute_problem_sizes<false><<<num_experts, num_threads, 0, stream>>>(
+        topk_ptr, ps1_ptr, ps2_ptr, atomic_ptr,
+        static_cast<int>(topk_ids.numel()), static_cast<int>(n),
+        static_cast<int>(k));
+  }
+}
+}  // namespace
+
+void get_cutlass_moe_mm_problem_sizes_caller(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets) {
+  auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
+  auto options_int32 =
+      torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
+  torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
+
+  // Swap-AB should be disabled for FP4 path
+  bool may_swap_ab = (!blockscale_offsets.has_value()) &&
+                     (topk_ids.numel() <= SWAP_AB_THRESHOLD);
+
+  launch_compute_problem_sizes(topk_ids, problem_sizes1, problem_sizes2,
+                               atomic_buffer, num_experts, n, k, stream,
+                               may_swap_ab);
+}
+
 void get_cutlass_moe_mm_data_caller(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
@@ -121,21 +168,9 @@ void get_cutlass_moe_mm_data_caller(
   bool may_swap_ab = (!blockscale_offsets.has_value()) &&
                      (topk_ids.numel() <= SWAP_AB_THRESHOLD);
 
-  if (may_swap_ab) {
-    compute_problem_sizes<true><<<num_experts, num_threads, 0, stream>>>(
-        static_cast<const int32_t*>(topk_ids.data_ptr()),
-        static_cast<int32_t*>(problem_sizes1.data_ptr()),
-        static_cast<int32_t*>(problem_sizes2.data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
-        k);
-  } else {
-    compute_problem_sizes<false><<<num_experts, num_threads, 0, stream>>>(
-        static_cast<const int32_t*>(topk_ids.data_ptr()),
-        static_cast<int32_t*>(problem_sizes1.data_ptr()),
-        static_cast<int32_t*>(problem_sizes2.data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
-        k);
-  }
+  launch_compute_problem_sizes(topk_ids, problem_sizes1, problem_sizes2,
+                               atomic_buffer, num_experts, n, k, stream,
+                               may_swap_ab);
 
   if (blockscale_offsets.has_value()) {
     // fp4 path

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -76,6 +76,11 @@ void get_cutlass_moe_mm_data_caller(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
+void get_cutlass_moe_mm_problem_sizes_caller(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets);
+
 void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
                                          torch::Tensor& problem_sizes1,
                                          torch::Tensor& problem_sizes2,
@@ -293,6 +298,25 @@ void get_cutlass_moe_mm_data(
       version_num, ". Required capability: 90 or 100");
 }
 
+void get_cutlass_moe_mm_problem_sizes(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets) {
+  int32_t version_num = get_sm_version_num();
+#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) || \
+    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100)
+  get_cutlass_moe_mm_problem_sizes_caller(topk_ids, problem_sizes1,
+                                          problem_sizes2, num_experts, n, k,
+                                          blockscale_offsets);
+  return;
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled get_cutlass_moe_mm_problem_sizes: no cutlass_scaled_mm "
+      "kernel for CUDA device capability: ",
+      version_num, ". Required capability: 90 or 100");
+}
+
 void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
                                   torch::Tensor& problem_sizes1,
                                   torch::Tensor& problem_sizes2,

csrc/torch_bindings.cpp

@@ -440,6 +440,19 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       {stride_tag});
   ops.impl("get_cutlass_moe_mm_data", torch::kCUDA, &get_cutlass_moe_mm_data);
 
+  // A function that computes problem sizes for each expert's multiplication
+  // used by the two mms called from fused MoE operation. It takes topk_ids as
+  // an input, and computes problem_sizes1 and problem_sizes2 only.
+  ops.def(
+      "get_cutlass_moe_mm_problem_sizes(Tensor topk_ids, "
+      "                                 Tensor! problem_sizes1, "
+      "                                 Tensor! problem_sizes2, "
+      "                                 int num_experts, int n, int k, "
+      "                                 Tensor? blockscale_offsets) -> ()",
+      {stride_tag});
+  ops.impl("get_cutlass_moe_mm_problem_sizes", torch::kCUDA,
+           &get_cutlass_moe_mm_problem_sizes);
+
   // A function that computes data required to run fused MoE with w8a8 grouped
   // GEMM and PPLX. It takes expert_num_tokens and non_zero_expert_idxs
   // as an input, and computes expert_offsets (token start indices of each

tests/kernels/moe/test_cutlass_moe.py

@@ -207,6 +207,10 @@ def run_8_bit(moe_tensors: MOETensors8Bit,
         'topk_ids': topk_ids,
         'w1_scale': moe_tensors.w1_scale,
         'w2_scale': moe_tensors.w2_scale,
+        'ab_strides1': moe_tensors.ab_strides1,
+        'ab_strides2': moe_tensors.ab_strides2,
+        'c_strides1': moe_tensors.c_strides1,
+        'c_strides2': moe_tensors.c_strides2,
         'per_act_token': per_act_token,
         'a1_scale': None  #moe_tensors.a_scale
     }
@@ -424,8 +428,8 @@ def test_run_cutlass_moe_fp8(
         topk_ids[0][1] = 1
 
         workspace13_shape = (m * topk, max(2 * n, k))
-        workspace2_shape = (m * topk, n)
-        output_shape = (m * topk, k)
+        workspace2_shape = (m * topk, max(n, k))
+        output_shape = (m, k)
 
         workspace13 = torch.empty(prod(workspace13_shape),
                                   device="cuda",
@@ -440,6 +444,11 @@ def test_run_cutlass_moe_fp8(
         expert_map[start:end] = list(range(num_local_experts))
         expert_map = torch.tensor(expert_map, dtype=torch.int32, device="cuda")
 
+        ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
+        ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
+        c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
+        c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
+
         activation = lambda o, i: torch.ops._C.silu_and_mul(o, i)
         a1q, a1q_scale = moe_kernel_quantize_input(mt.a, mt.a_scale,
                                                    torch.float8_e4m3fn,
@@ -448,8 +457,9 @@ def test_run_cutlass_moe_fp8(
         func = lambda output: run_cutlass_moe_fp8(
             output, a1q, mt.w1_q, mt.w2_q, topk_ids, activation,
             global_num_experts, expert_map, mt.w1_scale, mt.w2_scale,
-            a1q_scale, None, workspace13, workspace2, None, mt.a.dtype,
-            per_act_token, per_out_channel, False)
+            a1q_scale, None, ab_strides1, ab_strides2, c_strides1, c_strides2,
+            workspace13, workspace2, None, mt.a.dtype, per_act_token,
+            per_out_channel, False, topk_weights)
 
         workspace13.random_()
         output_random_workspace = torch.empty(output_shape,

tests/kernels/moe/test_moe_permute_unpermute.py

@@ -238,7 +238,11 @@ def test_moe_permute_unpermute(n_token: int, n_hidden: int, topk: int,
                                atol=0,
                                rtol=0)
     # check mindice
+    # current kernel usage assumes deepgemm requires align_block_size
+    # when it's not provided then we don't compute m_indices (for cutlass)
+    if align_block_size is not None:
         torch.testing.assert_close(gold_m_indices, m_indices, atol=0, rtol=0)
+
     # check permuted_hidden_states, only valid token
     torch.testing.assert_close(gold_permuted_hidden_states[valid_row_idx],
                                permuted_hidden_states[valid_row_idx],

tests/kernels/moe/test_pplx_cutlass_moe.py

@@ -76,6 +76,7 @@ def pplx_cutlass_moe(
     assert torch.cuda.current_device() == pgi.local_rank
 
     num_tokens, hidden_dim = a.shape
+    intermediate_dim = w2.shape[2]
     num_experts = w1.shape[0]
     block_size = hidden_dim  # TODO support more cases
     device = pgi.device
@@ -124,8 +125,27 @@ def pplx_cutlass_moe(
         num_local_experts=num_local_experts,
         num_dispatchers=num_dispatchers)
 
+    ab_strides1 = torch.full((num_local_experts, ),
+                             hidden_dim,
+                             device="cuda",
+                             dtype=torch.int64)
+    ab_strides2 = torch.full((num_local_experts, ),
+                             intermediate_dim,
+                             device="cuda",
+                             dtype=torch.int64)
+    c_strides1 = torch.full((num_local_experts, ),
+                            2 * intermediate_dim,
+                            device="cuda",
+                            dtype=torch.int64)
+    c_strides2 = torch.full((num_local_experts, ),
+                            hidden_dim,
+                            device="cuda",
+                            dtype=torch.int64)
+
     experts = CutlassBatchedExpertsFp8(num_local_experts, num_dispatchers,
-                                       out_dtype, per_act_token, per_out_ch)
+                                       out_dtype, per_act_token, per_out_ch,
+                                       ab_strides1, ab_strides2, c_strides1,
+                                       c_strides2)
 
     fused_cutlass_experts = FusedMoEModularKernel(
         prepare_finalize,

tests/kernels/quantization/test_cutlass_scaled_mm.py

@@ -535,7 +535,7 @@ def test_cutlass_fp8_group_gemm(num_experts: int, per_act_token: bool,
 
     expert_offsets = torch.zeros((num_experts + 1),
                                  device=device,
-                                 dtype=torch.int32)
+                                 dtype=torch.int64)
 
     problem_sizes = torch.zeros((num_experts, 3),
                                 device=device,

vllm/_custom_ops.py

@@ -844,6 +844,28 @@ def get_cutlass_moe_mm_data(topk_ids: torch.Tensor,
                                                 blockscale_offsets)
 
 
+def get_cutlass_moe_mm_problem_sizes(
+        topk_ids: torch.Tensor,
+        problem_sizes1: torch.Tensor,
+        problem_sizes2: torch.Tensor,
+        num_experts: int,
+        n: int,
+        k: int,
+        blockscale_offsets: Optional[torch.Tensor] = None):
+    """
+    Compute only the per-expert problem sizes needed by the two grouped matrix
+    multiplications used in CUTLASS-based fused MoE.
+
+    The function takes in topk_ids (token→expert mapping) and computes:
+    - problem_sizes1, problem_sizes2: M×N×K sizes of each expert's
+                                    multiplication for the two grouped MMs
+                                    used in the fused MoE operation.
+    """
+    return torch.ops._C.get_cutlass_moe_mm_problem_sizes(
+        topk_ids, problem_sizes1, problem_sizes2, num_experts, n, k,
+        blockscale_offsets)
+
+
 def shuffle_rows(input_tensor: torch.Tensor, dst2src_map: torch.Tensor):
     """
     Shuffle and expand the input tensor according to the dst2src_map and store the result in output_tensor.

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -9,12 +9,13 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
+    moe_permute, moe_unpermute)
 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)
-from vllm.model_executor.layers.fused_moe.utils import (_fp8_perm,
-                                                        _fp8_quantize,
+from vllm.model_executor.layers.fused_moe.utils import (_fp8_quantize,
                                                         _resize_cache)
 from vllm.scalar_type import scalar_types
 
@@ -34,6 +35,10 @@ def run_cutlass_moe_fp8(
     w2_scale: Optional[torch.Tensor],
     a1q_scale: Optional[torch.Tensor],
     a2_scale: Optional[torch.Tensor],
+    ab_strides1: torch.Tensor,
+    ab_strides2: torch.Tensor,
+    c_strides1: torch.Tensor,
+    c_strides2: torch.Tensor,
     workspace13: torch.Tensor,
     workspace2: torch.Tensor,
     expert_num_tokens: Optional[torch.Tensor],
@@ -41,6 +46,7 @@ def run_cutlass_moe_fp8(
     per_act_token: bool,
     per_out_ch: bool,
     use_batched_format: bool,
+    topk_weights: Optional[torch.Tensor],
 ):
     a1q = hidden_states
 
@@ -99,6 +105,22 @@ def run_cutlass_moe_fp8(
     topk = local_topk_ids.size(1)
     local_E = w1.size(0)
 
+    if use_batched_format:
+        mm1_out = _resize_cache(workspace13, (local_E * padded_M, N * 2))
+        act_out = _resize_cache(workspace2, (local_E * padded_M, N))
+        quant_out = _resize_cache(workspace13.view(dtype=torch.float8_e4m3fn),
+                                  (local_E * padded_M, N))
+        mm2_out = _resize_cache(workspace2, (local_E * padded_M, K))
+    else:
+        a1q_perm = _resize_cache(workspace2.view(dtype=torch.float8_e4m3fn),
+                                 (M * topk, K))
+        mm1_out = _resize_cache(workspace13, (M * topk, N * 2))
+        act_out = _resize_cache(workspace2, (M * topk, N))
+        # original workspace are based on input hidden_states dtype (bf16)
+        quant_out = _resize_cache(workspace13.view(dtype=torch.float8_e4m3fn),
+                                  (M * topk, N))
+        mm2_out = _resize_cache(workspace2, (M * topk, K))
+
     if use_batched_format:
         assert expert_num_tokens is not None
 
@@ -120,11 +142,10 @@ def run_cutlass_moe_fp8(
         w2_scale = w2_scale.reshape(w2_scale.size(0), -1)
         a1q = a1q.reshape(-1, a1q.size(2))
         a1q_scale = a1q_scale.reshape(-1, a1q_scale.size(2)).contiguous()
-
+        # c3x get_group_gemm_starts expects int64 to avoid overflow
+        # during offset calculations
+        expert_offsets = expert_offsets.to(torch.int64)
     else:
-        expert_offsets = torch.empty((global_num_experts + 1),
-                                     dtype=torch.int32,
-                                     device=device)
         problem_sizes1 = torch.empty((global_num_experts, 3),
                                      dtype=torch.int32,
                                      device=device)
@@ -132,84 +153,57 @@ def run_cutlass_moe_fp8(
                                      dtype=torch.int32,
                                      device=device)
 
-        # With expert_map each Rank processes only a subset of experts. As
-        # a result not all of a_map and c2 tensors are filled. We fill it
-        # zeros for correctness.
-        if expert_map is not None:
-            a_map = torch.zeros((local_topk_ids.numel()),
-                                dtype=torch.int32,
-                                device=device)
-        else:
-            a_map = torch.empty((local_topk_ids.numel()),
-                                dtype=torch.int32,
-                                device=device)
-
-        c_map = torch.empty((local_topk_ids.numel()),
-                            dtype=torch.int32,
-                            device=device)
-
-        ops.get_cutlass_moe_mm_data(local_topk_ids, expert_offsets,
-                                    problem_sizes1, problem_sizes2, a_map,
-                                    c_map, global_num_experts, N, K)
-
-        a1q = _fp8_perm(a1q, a_map)
-        a1q_scale = a1q_scale[a_map] if per_act_token else a1q_scale
+        num_expert = global_num_experts if expert_map is None \
+                     else expert_map.size(0)
+        # permuted a1q reuses workspace2
+        a1q, a1q_scale, expert_offsets, inv_perm, _ = moe_permute(
+            a1q,
+            a1q_scale,
+            topk_ids,
+            num_expert,
+            local_E,
+            expert_map,
+            permuted_hidden_states=a1q_perm)
         expert_offsets = expert_offsets[:-1]
 
-    ab_strides1 = torch.full((w1.size(0), ),
-                             K,
-                             device=device,
-                             dtype=torch.int64)
-    c_strides1 = torch.full((w1.size(0), ),
-                            2 * N,
-                            device=device,
-                            dtype=torch.int64)
-    ab_strides2 = torch.full((w1.size(0), ),
-                             N,
-                             device=device,
-                             dtype=torch.int64)
-    c_strides2 = torch.full((w1.size(0), ),
-                            K,
-                            device=device,
-                            dtype=torch.int64)
-
-    if use_batched_format:
-        c1 = _resize_cache(workspace13, (local_E * padded_M, N * 2))
-        c2 = _resize_cache(workspace2, (local_E * padded_M, N))
-        c3 = _resize_cache(workspace13, (local_E * padded_M, K))
-    else:
-        c1 = _resize_cache(workspace13, (M * topk, N * 2))
-        c2 = _resize_cache(workspace2, (M * topk, N))
-        c3 = _resize_cache(workspace13, (M * topk, K))
+        ops.get_cutlass_moe_mm_problem_sizes(local_topk_ids, problem_sizes1,
+                                             problem_sizes2,
+                                             global_num_experts, N, K)
 
     if not per_act_token and (expert_map is not None or use_batched_format):
         # this is necessary to avoid imprecise scale calculation caused by
         # random data in the unused workspace. The workspace is unused when
         # this rank handles only partial tokens, or when it is batched .
-        c1.fill_(0)
+        mm1_out.fill_(0)
 
-    ops.cutlass_moe_mm(c1, a1q, w1, a1q_scale, w1_scale, expert_offsets,
+    ops.cutlass_moe_mm(mm1_out, a1q, w1, a1q_scale, w1_scale, expert_offsets,
                        problem_sizes1, ab_strides1, ab_strides1, c_strides1,
                        per_act_token, per_out_ch)
 
-    activation_callable(c2, c1)
+    activation_callable(act_out, mm1_out)
 
     a2q, a2q_scale = ops.scaled_fp8_quant(
-        c2, a2_scale, use_per_token_if_dynamic=per_act_token)
+        act_out,
+        a2_scale,
+        use_per_token_if_dynamic=per_act_token,
+        output=quant_out)
 
     if expert_map is not None:
-        c3.fill_(0)
+        mm2_out.fill_(0)
 
-    ops.cutlass_moe_mm(c3, a2q, w2, a2q_scale, w2_scale, expert_offsets,
+    ops.cutlass_moe_mm(mm2_out, a2q, w2, a2q_scale, w2_scale, expert_offsets,
                        problem_sizes2, ab_strides2, ab_strides2, c_strides2,
                        per_act_token, per_out_ch)
 
     if use_batched_format:
-        output.copy_(c3.reshape(local_E, padded_M, K), non_blocking=True)
+        output.copy_(mm2_out.reshape(local_E, padded_M, K), non_blocking=True)
     else:
-        # We can't do this inplace because output may point to the same tensor
-        # as c3.
-        output.copy_(c3[c_map].view(M * topk, K), non_blocking=True)
+        # for non-chunking mode the output is resized from workspace13
+        # so we need to make sure mm2_out uses workspace2.
+        moe_unpermute(out=output,
+                      permuted_hidden_states=mm2_out,
+                      topk_weights=topk_weights,
+                      inv_permuted_idx=inv_perm)
 
 
 class CutlassExpertsFp8Base(mk.FusedMoEPermuteExpertsUnpermute):
@@ -219,6 +213,10 @@ class CutlassExpertsFp8Base(mk.FusedMoEPermuteExpertsUnpermute):
         out_dtype: Optional[torch.dtype],
         per_act_token_quant: bool,
         per_out_ch_quant: bool,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
         block_shape: Optional[list[int]] = None,
     ):
         super().__init__(
@@ -229,6 +227,10 @@ class CutlassExpertsFp8Base(mk.FusedMoEPermuteExpertsUnpermute):
                 block_shape=block_shape,
             ))
         self.out_dtype = out_dtype
+        self.ab_strides1 = ab_strides1
+        self.ab_strides2 = ab_strides2
+        self.c_strides1 = c_strides1
+        self.c_strides2 = c_strides2
 
     def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
         # Let PrepareAndFinalize::finalize() decide the impl.
@@ -272,10 +274,11 @@ class CutlassExpertsFp8Base(mk.FusedMoEPermuteExpertsUnpermute):
         run_cutlass_moe_fp8(
             output, hidden_states, w1, w2, topk_ids, activation_callable,
             global_num_experts, expert_map, w1_scale, w2_scale, a1q_scale,
-            a2_scale, workspace13, workspace2, expert_num_tokens,
+            a2_scale, self.ab_strides1, self.ab_strides2, self.c_strides1,
+            self.c_strides2, workspace13, workspace2, expert_num_tokens,
             self.out_dtype if self.out_dtype is not None else in_dtype,
             self.per_act_token_quant, self.per_out_ch_quant,
-            use_batched_format)
+            use_batched_format, topk_weights)
 
 
 class CutlassExpertsFp8(CutlassExpertsFp8Base):
@@ -285,12 +288,20 @@ class CutlassExpertsFp8(CutlassExpertsFp8Base):
         out_dtype: Optional[torch.dtype],
         per_act_token_quant: bool,
         per_out_ch_quant: bool,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
         block_shape: Optional[list[int]] = None,
     ):
         super().__init__(
             out_dtype,
             per_act_token_quant,
             per_out_ch_quant,
+            ab_strides1,
+            ab_strides2,
+            c_strides1,
+            c_strides2,
             block_shape,
         )
 
@@ -307,6 +318,10 @@ class CutlassExpertsFp8(CutlassExpertsFp8Base):
     def supports_expert_map(self) -> bool:
         return True
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # topk weights and reduction are fused in moe_unpermute cuda kernel
+        return TopKWeightAndReduceNoOP()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,
@@ -320,8 +335,8 @@ class CutlassExpertsFp8(CutlassExpertsFp8Base):
         expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
     ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
         workspace1 = (M * topk, max(N, K))
-        workspace2 = (M * topk, N // 2)
-        output = (M * topk, K)
+        workspace2 = (M * topk, max(N // 2, K))
+        output = (M, K)
         return (workspace1, workspace2, output,
                 self.out_dtype if self.out_dtype is not None else a.dtype)
 
@@ -335,12 +350,20 @@ class CutlassBatchedExpertsFp8(CutlassExpertsFp8Base):
         out_dtype: Optional[torch.dtype],
         per_act_token_quant: bool,
         per_out_ch_quant: bool,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
         block_shape: Optional[list[int]] = None,
     ):
         super().__init__(
             out_dtype,
             per_act_token_quant,
             per_out_ch_quant,
+            ab_strides1,
+            ab_strides2,
+            c_strides1,
+            c_strides2,
             block_shape,
         )
         assert max_experts_per_worker > 0
@@ -378,7 +401,8 @@ class CutlassBatchedExpertsFp8(CutlassExpertsFp8Base):
         assert num_dp is not None
         workspace1 = (self.max_experts_per_worker, padded_M * num_dp,
                       max(N, K))
-        workspace2 = (self.max_experts_per_worker, padded_M * num_dp, (N // 2))
+        workspace2 = (self.max_experts_per_worker, padded_M * num_dp,
+                      max(N // 2, K))
         output = (self.max_experts_per_worker, padded_M, K)
         return (workspace1, workspace2, output,
                 self.out_dtype if self.out_dtype is not None else a.dtype)
@@ -392,6 +416,10 @@ def cutlass_moe_fp8(
     topk_ids: torch.Tensor,
     w1_scale: torch.Tensor,
     w2_scale: torch.Tensor,
+    ab_strides1: torch.Tensor,
+    ab_strides2: torch.Tensor,
+    c_strides1: torch.Tensor,
+    c_strides2: torch.Tensor,
     per_act_token: Optional[bool] = None,
     activation: str = "silu",
     a1_scale: Optional[torch.Tensor] = None,
@@ -419,6 +447,17 @@ def cutlass_moe_fp8(
         Shape: [num_experts] or [num_experts, 2N]
     - w2_scale (torch.Tensor): The fp32 scale to dequantize w2_q.
         Shape: [num_experts] or [num_experts, K]
+    - ab_strides1 (torch.Tensor): The input/weight strides for the first gemm.
+        Shape: [num_experts]
+    - ab_strides2 (torch.Tensor): The input/weight strides for the second gemm.
+        Shape: [num_experts]
+    - c_strides1 (torch.Tensor): The output strides for the first gemm.
+        Shape: [num_experts]
+    - c_strides2 (torch.Tensor): The output strides for the second gemm.
+        Shape: [num_experts]
+    - per_act_token (Optional[bool]): Whether the scale is per-token or
+                                      per-tensor.
+    - activation (str): The activation function to use.
     - a1_scale (Optional[torch.Tensor]): The optional fp32 scale to quantize a.
         Shape: scalar or [M]
     - a2_scale (Optional[torch.Tensor]): The optional fp32 scale to
@@ -450,6 +489,10 @@ def cutlass_moe_fp8(
             out_dtype=a.dtype,
             per_act_token_quant=per_act_token,
             per_out_ch_quant=per_out_ch,
+            ab_strides1=ab_strides1,
+            ab_strides2=ab_strides2,
+            c_strides1=c_strides1,
+            c_strides2=c_strides2,
         ),
     )
 

vllm/model_executor/layers/fused_moe/moe_permute_unpermute.py

@@ -82,7 +82,8 @@ def moe_permute(
     n_local_expert: int = -1,
     expert_map: Optional[torch.Tensor] = None,
     align_block_size: Optional[int] = None,
-    fill_invalid_expert: int = -1
+    fill_invalid_expert: int = -1,
+    permuted_hidden_states: Optional[torch.Tensor] = None,
 ) -> tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor, torch.Tensor,
            torch.Tensor]:
     """
@@ -100,9 +101,12 @@ def moe_permute(
     - align_block_size (Optional[int]): align group gemm block size for deepgemm
     - fill_invalid_expert(int): fill expert id in m_indices for invalid expert
       to workaround DeepGemm unsupported -1 in m_indices
+    - permuted_hidden_states (Optional[torch.Tensor]): Optional output tensor.
+        If None, the output tensor will be created in this function.
     Returns:
     - permuted_hidden_states (torch.Tensor): permuted activation.
-    - a1q_scale (Optional[torch.Tensor]): quant scale for hidden_states
+    - a1q_scale (Optional[torch.Tensor]): permuted quant scale for hidden_states
+        if original scale not per-tensor scaling
     - expert_first_token_offset (torch.Tensor): offset of the first token
        of each expert for standard grouped gemm. if enable 'align_block_size'
        expert_first_token_offset will align up to 'align_block_size'.
@@ -122,11 +126,16 @@ def moe_permute(
                              1) // align_block_size * align_block_size
     if n_local_expert == -1:
         n_local_expert = n_expert
+    if permuted_hidden_states is None:
         permuted_hidden_states = torch.empty(
             (permuted_row_size, n_hidden),
             dtype=hidden_states.dtype,
             device=hidden_states.device,
         )
+    assert permuted_hidden_states.size() == (permuted_row_size, n_hidden), (
+        f"Expected permuted hidden states to be {(permuted_row_size, n_hidden)}"
+        f" but got {permuted_hidden_states.size()}")
+
     token_expert_indices = torch.arange(0,
                                         n_token * topk,
                                         dtype=torch.int32,
@@ -153,7 +162,8 @@ def moe_permute(
                                  align_block_size, permuted_hidden_states,
                                  expert_first_token_offset, inv_permuted_idx,
                                  permuted_idx, m_indices)
-    if a1q_scale is not None:
+
+    if a1q_scale is not None and a1q_scale.dim() > 1:
         a1q_scale = a1q_scale[permuted_idx.clamp(max=n_token * topk - 1) //
                               topk]
     return (permuted_hidden_states, a1q_scale, expert_first_token_offset,
@@ -185,6 +195,7 @@ def moe_unpermute(
     n_hidden = permuted_hidden_states.size(-1)
     assert (n_hidden * permuted_hidden_states.element_size()
             ) % 16 == 0, "unpermue kernel need hidden dim align to 16B"
+
     torch.ops._moe_C.moe_unpermute(permuted_hidden_states, topk_weights,
                                    inv_permuted_idx, expert_first_token_offset,
                                    topk, out)

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -669,6 +669,25 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
             from vllm.model_executor.layers.fused_moe import fused_experts
             self.fused_experts_func = fused_experts
 
+        if self.use_cutlass:
+            device = layer.w13_weight.device
+            # ab_strides1 and c_strides2 are the same
+            self.ab_strides1_c_strides2 = torch.full(
+                (layer.local_num_experts, ),
+                layer.hidden_size,
+                device=device,
+                dtype=torch.int64)
+            self.ab_strides2 = torch.full(
+                (layer.local_num_experts, ),
+                layer.intermediate_size_per_partition,
+                device=device,
+                dtype=torch.int64)
+            self.c_strides1 = torch.full(
+                (layer.local_num_experts, ),
+                2 * layer.intermediate_size_per_partition,
+                device=device,
+                dtype=torch.int64)
+
     def select_gemm_impl(
         self,
         prepare_finalize: FusedMoEPrepareAndFinalize,
@@ -693,6 +712,10 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                     moe.in_dtype,
                     self.input_quant.strategy == QuantizationStrategy.TOKEN,
                     self.weight_quant.strategy == QuantizationStrategy.CHANNEL,
+                    ab_strides1=self.ab_strides1_c_strides2,
+                    ab_strides2=self.ab_strides2,
+                    c_strides1=self.c_strides1,
+                    c_strides2=self.ab_strides1_c_strides2,
                 )
             else:
                 logger.debug("CutlassExpertsFp8(%s)", self.__class__.__name__)
@@ -700,6 +723,10 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                     moe.in_dtype,
                     self.input_quant.strategy == QuantizationStrategy.TOKEN,
                     self.weight_quant.strategy == QuantizationStrategy.CHANNEL,
+                    ab_strides1=self.ab_strides1_c_strides2,
+                    ab_strides2=self.ab_strides2,
+                    c_strides1=self.c_strides1,
+                    c_strides2=self.ab_strides1_c_strides2,
                 )
 
             self.disable_expert_map = (num_dispatchers > 1
@@ -822,6 +849,10 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                     expert_map=None if self.disable_expert_map else expert_map,
                     w1_scale=layer.w13_weight_scale,
                     w2_scale=layer.w2_weight_scale,
+                    ab_strides1=self.ab_strides1_c_strides2,
+                    ab_strides2=self.ab_strides2,
+                    c_strides1=self.c_strides1,
+                    c_strides2=self.ab_strides1_c_strides2,
                     a1_scale=layer.w13_input_scale,
                     a2_scale=layer.w2_input_scale,
                 )