[Kernel][MoE] optimize `moe_align_block_size` (#29642)

Signed-off-by: Jinzhen Lin <jinzhen.ljz@antgroup.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

benchmarks/kernels/benchmark_moe_align_block_size.py

@@ -24,12 +24,15 @@ def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
 num_tokens_range = [1, 16, 256, 4096]
 num_experts_range = [16, 64, 224, 256, 280, 512]
 topk_range = [1, 2, 8]
-configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range))
+ep_size_range = [1, 8]
+configs = list(
+    itertools.product(num_tokens_range, num_experts_range, topk_range, ep_size_range)
+)
 
 
 @triton.testing.perf_report(
     triton.testing.Benchmark(
-        x_names=["num_tokens", "num_experts", "topk"],
+        x_names=["num_tokens", "num_experts", "topk", "ep_size"],
         x_vals=configs,
         line_arg="provider",
         line_vals=["vllm"],
@@ -38,16 +41,26 @@ configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range
         args={},
     )
 )
-def benchmark(num_tokens, num_experts, topk, provider):
+def benchmark(num_tokens, num_experts, topk, ep_size, provider):
     """Benchmark function for Triton."""
     block_size = 256
+    torch.cuda.manual_seed_all(0)
     topk_ids = get_topk_ids(num_tokens, num_experts, topk)
 
+    e_map = None
+    if ep_size != 1:
+        local_e = num_experts // ep_size
+        e_ids = torch.randperm(num_experts, device="cuda", dtype=torch.int32)[:local_e]
+        e_map = torch.full((num_experts,), -1, device="cuda", dtype=torch.int32)
+        e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
+
     quantiles = [0.5, 0.2, 0.8]
 
     if provider == "vllm":
         ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: moe_align_block_size(topk_ids, block_size, num_experts),
+            lambda: moe_align_block_size(
+                topk_ids, block_size, num_experts, e_map, ignore_invalid_experts=True
+            ),
             quantiles=quantiles,
         )
 

csrc/moe/moe_align_sum_kernels.cu

@@ -83,14 +83,22 @@ template <typename scalar_t>
 __global__ void moe_align_block_size_kernel(
     const scalar_t* __restrict__ topk_ids,
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
-    int32_t* __restrict__ total_tokens_post_pad, int32_t num_experts,
+    int32_t* __restrict__ total_tokens_post_pad,
+    int32_t* __restrict__ expert_map, int32_t num_experts,
     int32_t padded_num_experts, int32_t experts_per_warp, int32_t block_size,
-    size_t numel, int32_t* __restrict__ cumsum, int32_t max_num_tokens_padded) {
+    size_t numel, int32_t* __restrict__ cumsum, int32_t max_num_tokens_padded,
+    bool has_expert_map) {
   extern __shared__ int32_t shared_counts[];
 
-  // Initialize sorted_token_ids with numel
-  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
-    sorted_token_ids[it] = numel;
+  // Use a separate threadblock to fill sorted_token_ids.
+  // This is safe since the current kernel does not use sorted_token_ids.
+  if (blockIdx.x == 1) {
+    // Initialize sorted_token_ids with numel
+    for (size_t it = threadIdx.x; it < max_num_tokens_padded;
+         it += blockDim.x) {
+      sorted_token_ids[it] = numel;
+    }
+    return;
   }
 
   const int warp_id = threadIdx.x / WARP_SIZE;
@@ -112,6 +120,11 @@ __global__ void moe_align_block_size_kernel(
     if (expert_id >= num_experts) {
       continue;
     }
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid experts
+      if (expert_id == -1) continue;
+    }
     int warp_idx = expert_id / experts_per_warp;
     int expert_offset = expert_id % experts_per_warp;
     atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset], 1);
@@ -163,7 +176,8 @@ template <typename scalar_t>
 __global__ void count_and_sort_expert_tokens_kernel(
     const scalar_t* __restrict__ topk_ids,
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ cumsum_buffer,
-    size_t numel, int32_t num_experts) {
+    int32_t* __restrict__ expert_map, size_t numel, int32_t num_experts,
+    bool has_expert_map) {
   const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
   const size_t stride = blockDim.x * gridDim.x;
 
@@ -172,6 +186,11 @@ __global__ void count_and_sort_expert_tokens_kernel(
     if (expert_id >= num_experts) {
       continue;
     }
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid experts
+      if (expert_id == -1) continue;
+    }
     int32_t rank_post_pad = atomicAdd(&cumsum_buffer[expert_id], 1);
     sorted_token_ids[rank_post_pad] = i;
   }
@@ -193,50 +212,69 @@ __global__ void moe_sum_kernel(
   }
 }
 
-template <typename scalar_t>
+template <typename scalar_t, int32_t fill_threads>
 __global__ void moe_align_block_size_small_batch_expert_kernel(
     const scalar_t* __restrict__ topk_ids,
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
-    int32_t* __restrict__ total_tokens_post_pad, int32_t num_experts,
-    int32_t block_size, size_t numel, int32_t max_num_tokens_padded) {
-  // Initialize sorted_token_ids with numel
-  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
-    sorted_token_ids[it] = numel;
+    int32_t* __restrict__ total_tokens_post_pad,
+    int32_t* __restrict__ expert_map, int32_t num_experts, int32_t block_size,
+    size_t numel, int32_t max_num_tokens_padded, bool has_expert_map) {
+  // Use an additional group of threads to fill sorted_token_ids.
+  // Since the current kernel will use sorted_token_ids afterward,
+  // we fill sorted_token_ids within the same threadblock to make
+  // synchronization easier.
+  if (threadIdx.x < fill_threads) {
+    // Initialize sorted_token_ids with numel
+    for (size_t it = threadIdx.x; it < max_num_tokens_padded;
+         it += fill_threads) {
+      sorted_token_ids[it] = numel;
+    }
+    // Three __syncthreads() corresponding to the other threads
+    __syncthreads();
+    __syncthreads();
+    __syncthreads();
+    return;
   }
 
-  const size_t tid = threadIdx.x;
-  const size_t stride = blockDim.x;
+  const size_t tid = threadIdx.x - fill_threads;
+  const size_t stride = blockDim.x - fill_threads;
 
   extern __shared__ int32_t shared_mem[];
   int32_t* cumsum = shared_mem;
   int32_t* tokens_cnts = (int32_t*)(shared_mem + num_experts + 1);
 
   for (int i = 0; i < num_experts; ++i) {
-    tokens_cnts[(threadIdx.x + 1) * num_experts + i] = 0;
+    tokens_cnts[(tid + 1) * num_experts + i] = 0;
   }
 
   for (size_t i = tid; i < numel; i += stride) {
-    ++tokens_cnts[(threadIdx.x + 1) * num_experts + topk_ids[i]];
+    int32_t expert_id = topk_ids[i];
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid expert
+      if (expert_id == -1) continue;
+    }
+    ++tokens_cnts[(tid + 1) * num_experts + expert_id];
   }
 
   __syncthreads();
 
-  if (threadIdx.x < num_experts) {
-    tokens_cnts[threadIdx.x] = 0;
-    for (int i = 1; i <= blockDim.x; ++i) {
-      tokens_cnts[i * num_experts + threadIdx.x] +=
-          tokens_cnts[(i - 1) * num_experts + threadIdx.x];
+  if (tid < num_experts) {
+    tokens_cnts[tid] = 0;
+    for (int i = 1; i <= stride; ++i) {
+      tokens_cnts[i * num_experts + tid] +=
+          tokens_cnts[(i - 1) * num_experts + tid];
     }
   }
 
   __syncthreads();
 
-  if (threadIdx.x == 0) {
+  if (tid == 0) {
     cumsum[0] = 0;
     for (int i = 1; i <= num_experts; ++i) {
       cumsum[i] =
           cumsum[i - 1] +
-          CEILDIV(tokens_cnts[blockDim.x * num_experts + i - 1], block_size) *
+          CEILDIV(tokens_cnts[stride * num_experts + i - 1], block_size) *
               block_size;
     }
     *total_tokens_post_pad = static_cast<int32_t>(cumsum[num_experts]);
@@ -244,26 +282,30 @@ __global__ void moe_align_block_size_small_batch_expert_kernel(
 
   __syncthreads();
 
-  if (threadIdx.x < num_experts) {
-    for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1];
-         i += block_size) {
-      expert_ids[i / block_size] = threadIdx.x;
+  if (tid < num_experts) {
+    for (int i = cumsum[tid]; i < cumsum[tid + 1]; i += block_size) {
+      expert_ids[i / block_size] = tid;
     }
   }
 
   // Fill remaining expert_ids with 0
-  const size_t fill_start_idx = cumsum[num_experts] / block_size + threadIdx.x;
+  const size_t fill_start_idx = cumsum[num_experts] / block_size + tid;
   const size_t expert_ids_size = CEILDIV(max_num_tokens_padded, block_size);
-  for (size_t i = fill_start_idx; i < expert_ids_size; i += blockDim.x) {
+  for (size_t i = fill_start_idx; i < expert_ids_size; i += stride) {
     expert_ids[i] = 0;
   }
 
   for (size_t i = tid; i < numel; i += stride) {
     int32_t expert_id = topk_ids[i];
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid expert
+      if (expert_id == -1) continue;
+    }
     int32_t rank_post_pad =
-        tokens_cnts[threadIdx.x * num_experts + expert_id] + cumsum[expert_id];
+        tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];
     sorted_token_ids[rank_post_pad] = i;
-    ++tokens_cnts[threadIdx.x * num_experts + expert_id];
+    ++tokens_cnts[tid * num_experts + expert_id];
   }
 }
 
@@ -275,7 +317,8 @@ __global__ void moe_align_block_size_small_batch_expert_kernel(
 void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                           int64_t block_size, torch::Tensor sorted_token_ids,
                           torch::Tensor experts_ids,
-                          torch::Tensor num_tokens_post_pad) {
+                          torch::Tensor num_tokens_post_pad,
+                          std::optional<torch::Tensor> maybe_expert_map) {
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   int64_t padded_num_experts =
@@ -287,14 +330,19 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
   // BlockScan uses 1024 threads and assigns one thread per expert.
   TORCH_CHECK(padded_num_experts < 1024,
               "padded_num_experts must be less than 1024");
+  auto options_int =
+      torch::TensorOptions().dtype(torch::kInt).device(topk_ids.device());
+  bool has_expert_map = maybe_expert_map.has_value();
+  torch::Tensor expert_map;
+  if (has_expert_map) {
+    expert_map = maybe_expert_map.value();
+  } else {
+    expert_map = torch::empty({0}, options_int);
+  }
 
   VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
       topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
         // calc needed amount of shared mem for `cumsum` tensors
-        auto options_int =
-            torch::TensorOptions().dtype(torch::kInt).device(topk_ids.device());
-        torch::Tensor cumsum_buffer =
-            torch::empty({num_experts + 1}, options_int);
         bool small_batch_expert_mode =
             (topk_ids.numel() < 1024) && (num_experts <= 64);
 
@@ -304,30 +352,41 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
               ((threads + 1) * num_experts + (num_experts + 1)) *
               sizeof(int32_t);
 
+          // threadIdx.x >= fill_threads: counting experts and aligning
+          // threadIdx.x < fill_threads: filling sorted_token_ids
+          constexpr int32_t fill_threads = 256;
           auto small_batch_expert_kernel =
               vllm::moe::moe_align_block_size_small_batch_expert_kernel<
-                  scalar_t>;
-          small_batch_expert_kernel<<<1, threads, shared_mem_size, stream>>>(
+                  scalar_t, fill_threads>;
+          small_batch_expert_kernel<<<1, fill_threads + threads,
+                                      shared_mem_size, stream>>>(
               topk_ids.data_ptr<scalar_t>(),
               sorted_token_ids.data_ptr<int32_t>(),
               experts_ids.data_ptr<int32_t>(),
-              num_tokens_post_pad.data_ptr<int32_t>(), num_experts, block_size,
-              topk_ids.numel(), sorted_token_ids.size(0));
+              num_tokens_post_pad.data_ptr<int32_t>(),
+              expert_map.data_ptr<int32_t>(), num_experts, block_size,
+              topk_ids.numel(), sorted_token_ids.size(0), has_expert_map);
         } else {
+          torch::Tensor cumsum_buffer =
+              torch::empty({num_experts + 1}, options_int);
           auto align_kernel = vllm::moe::moe_align_block_size_kernel<scalar_t>;
 
           size_t num_warps = CEILDIV(padded_num_experts, experts_per_warp);
           size_t shared_mem_size =
               num_warps * experts_per_warp * sizeof(int32_t);
 
-          align_kernel<<<1, threads, shared_mem_size, stream>>>(
+          // launch two threadblocks
+          // blockIdx.x == 0: counting experts and aligning
+          // blockIdx.x == 1: filling sorted_token_ids
+          align_kernel<<<2, threads, shared_mem_size, stream>>>(
               topk_ids.data_ptr<scalar_t>(),
               sorted_token_ids.data_ptr<int32_t>(),
               experts_ids.data_ptr<int32_t>(),
-              num_tokens_post_pad.data_ptr<int32_t>(), num_experts,
-              padded_num_experts, experts_per_warp, block_size,
-              topk_ids.numel(), cumsum_buffer.data_ptr<int32_t>(),
-              sorted_token_ids.size(0));
+              num_tokens_post_pad.data_ptr<int32_t>(),
+              expert_map.data_ptr<int32_t>(), num_experts, padded_num_experts,
+              experts_per_warp, block_size, topk_ids.numel(),
+              cumsum_buffer.data_ptr<int32_t>(), sorted_token_ids.size(0),
+              has_expert_map);
 
           const int block_threads = std::min(256, (int)threads);
           const int num_blocks =
@@ -340,7 +399,8 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
           sort_kernel<<<actual_blocks, block_threads, 0, stream>>>(
               topk_ids.data_ptr<scalar_t>(),
               sorted_token_ids.data_ptr<int32_t>(),
-              cumsum_buffer.data_ptr<int32_t>(), topk_ids.numel(), num_experts);
+              cumsum_buffer.data_ptr<int32_t>(), expert_map.data_ptr<int32_t>(),
+              topk_ids.numel(), num_experts, has_expert_map);
         }
       });
 }

csrc/moe/moe_ops.h

@@ -11,7 +11,8 @@ void moe_sum(torch::Tensor& input, torch::Tensor& output);
 void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                           int64_t block_size, torch::Tensor sorted_token_ids,
                           torch::Tensor experts_ids,
-                          torch::Tensor num_tokens_post_pad);
+                          torch::Tensor num_tokens_post_pad,
+                          std::optional<torch::Tensor> maybe_expert_map);
 
 void batched_moe_align_block_size(int64_t max_tokens_per_batch,
                                   int64_t block_size,

csrc/moe/torch_bindings.cpp

@@ -19,7 +19,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "moe_align_block_size(Tensor topk_ids, int num_experts,"
       "                     int block_size, Tensor! sorted_token_ids,"
       "                     Tensor! experts_ids,"
-      "                     Tensor! num_tokens_post_pad) -> ()");
+      "                     Tensor! num_tokens_post_pad,"
+      "                     Tensor? maybe_expert_map) -> ()");
   m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
 
   // Aligning the number of tokens to be processed by each expert such

tests/kernels/moe/test_moe.py

@@ -955,9 +955,22 @@ def test_fused_marlin_moe_with_bias(m):
     torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)
 
 
-def test_moe_align_block_size_opcheck():
+@pytest.mark.parametrize("ep_size", [1, 2])
+def test_moe_align_block_size_opcheck(ep_size):
     num_experts = 4
     block_size = 4
+
+    expert_map = None
+    if ep_size != 1:
+        local_num_experts = num_experts // ep_size
+        expert_ids = torch.randint(
+            0, num_experts, (local_num_experts,), device="cuda", dtype=torch.int32
+        )
+        expert_map = torch.full((num_experts,), -1, device="cuda", dtype=torch.int32)
+        expert_map[expert_ids] = torch.arange(
+            local_num_experts, device="cuda", dtype=torch.int32
+        )
+
     topk_ids = torch.randint(0, num_experts, (3, 4), dtype=torch.int32, device="cuda")
 
     max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
@@ -980,6 +993,7 @@ def test_moe_align_block_size_opcheck():
             sorted_ids,
             expert_ids,
             num_tokens_post_pad,
+            expert_map,
         ),
     )
 

tests/kernels/moe/test_moe_align_block_size.py

@@ -106,6 +106,8 @@ def torch_moe_align_block_size(
     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)
+    if topk_ids.numel() < num_experts:
+        max_num_tokens_padded = topk_ids.numel() * block_size
 
     flattened_token_indices = torch.arange(
         topk_ids.numel(), device=topk_ids.device, dtype=torch.int32
@@ -126,6 +128,8 @@ def torch_moe_align_block_size(
     )
     for expert_id in range(num_experts):
         original_count = expert_token_counts[expert_id]
+        if expert_map is not None and expert_map[expert_id] == -1:
+            continue
         if original_count > 0:
             expert_padded_counts[expert_id] = (
                 (original_count + block_size - 1) // block_size
@@ -143,6 +147,9 @@ def torch_moe_align_block_size(
     current_pos = 0
     current_block = 0
     for expert_id in range(num_experts):
+        if expert_map is not None and expert_map[expert_id] == -1:
+            continue
+
         expert_mask = sorted_expert_ids == expert_id
         expert_tokens = sorted_token_indices[expert_mask]
         num_expert_tokens = expert_tokens.shape[0]
@@ -153,7 +160,13 @@ def torch_moe_align_block_size(
             )
 
             expert_blocks_needed = expert_padded_counts[expert_id] // block_size
-            expert_ids[current_block : current_block + expert_blocks_needed] = expert_id
+
+            expert_id_new = expert_id
+            if expert_map is not None:
+                expert_id_new = expert_map[expert_id]
+            expert_ids[current_block : current_block + expert_blocks_needed] = (
+                expert_id_new
+            )
 
             current_pos += expert_padded_counts[expert_id]
             current_block += expert_blocks_needed
@@ -163,8 +176,6 @@ def torch_moe_align_block_size(
         [total_padded_tokens], dtype=torch.int32, device=topk_ids.device
     )
 
-    if expert_map is not None:
-        expert_ids = expert_map[expert_ids]
     return sorted_token_ids, expert_ids, num_tokens_post_pad
 
 
@@ -229,9 +240,9 @@ def test_moe_align_block_size(
     )
 
 
-@pytest.mark.parametrize("m", [16, 32])
+@pytest.mark.parametrize("m", [16, 32, 2048])
 @pytest.mark.parametrize("topk", [2, 4])
-@pytest.mark.parametrize("num_experts", [8])
+@pytest.mark.parametrize("num_experts", [8, 64])
 @pytest.mark.parametrize("block_size", [64])
 @pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
 def test_moe_align_block_size_with_expert_map(
@@ -253,6 +264,7 @@ def test_moe_align_block_size_with_expert_map(
         block_size=block_size,
         num_experts=num_experts,
         expert_map=expert_map,
+        ignore_invalid_experts=True,
     )
     golden_sorted_ids, golden_expert_ids, golden_num_tokens = (
         torch_moe_align_block_size(

vllm/_custom_ops.py

@@ -1877,6 +1877,7 @@ def moe_align_block_size(
     sorted_token_ids: torch.Tensor,
     experts_ids: torch.Tensor,
     num_tokens_post_pad: torch.Tensor,
+    expert_map: torch.Tensor | None = None,
 ) -> None:
     torch.ops._moe_C.moe_align_block_size(
         topk_ids,
@@ -1885,6 +1886,7 @@ def moe_align_block_size(
         sorted_token_ids,
         experts_ids,
         num_tokens_post_pad,
+        expert_map,
     )
 
 

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -316,7 +316,11 @@ def fused_marlin_moe(
     if global_num_experts == -1:
         global_num_experts = E
     sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-        topk_ids, block_size_m, global_num_experts, expert_map
+        topk_ids,
+        block_size_m,
+        global_num_experts,
+        expert_map,
+        ignore_invalid_experts=True,
     )
 
     assert activation is not None

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1887,7 +1887,11 @@ def fused_experts_impl(
         )
 
         sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-            curr_topk_ids, config["BLOCK_SIZE_M"], global_num_experts, expert_map
+            curr_topk_ids,
+            config["BLOCK_SIZE_M"],
+            global_num_experts,
+            expert_map,
+            ignore_invalid_experts=True,
         )
 
         invoke_fused_moe_kernel(
@@ -1946,6 +1950,9 @@ def fused_experts_impl(
             block_shape=block_shape,
         )
 
+        if expert_map is not None:
+            intermediate_cache3.zero_()
+
         invoke_fused_moe_kernel(
             qintermediate_cache2,
             w2,

vllm/model_executor/layers/fused_moe/moe_align_block_size.py

@@ -14,6 +14,7 @@ def moe_align_block_size(
     num_experts: int,
     expert_map: torch.Tensor | None = None,
     pad_sorted_ids: bool = False,
+    ignore_invalid_experts: bool = False,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Aligns the token distribution across experts to be compatible with block
@@ -35,7 +36,13 @@ def moe_align_block_size(
         expert parallel shard. If the expert is not in the current expert
         parallel shard, the mapping is set to -1.
     - pad_sorted_ids: A flag indicating whether the sorted_token_ids length
-      should be padded to a multiple of block_size,
+        should be padded to a multiple of block_size,
+    - ignore_invalid_experts: A flag indicating whether to ignore invalid
+        experts. When False, all expert_ids in topk_ids will participate in
+        counting and ranking, but invalid experts in expert_ids will be marked
+        as -1. When True, all invalid expert_ids in topk_ids will be ignored
+        and will not participate in counting or ranking, and there will be no
+        -1 in expert_ids.
 
     Returns:
     - sorted_token_ids: A tensor containing the sorted token indices according
@@ -67,6 +74,10 @@ def moe_align_block_size(
     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)
+    if topk_ids.numel() < num_experts:
+        max_num_tokens_padded = min(
+            topk_ids.numel() * block_size, max_num_tokens_padded
+        )
     sorted_ids = torch.empty(
         (max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
     )
@@ -77,9 +88,16 @@ def moe_align_block_size(
     num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)
 
     ops.moe_align_block_size(
-        topk_ids, num_experts, block_size, sorted_ids, expert_ids, num_tokens_post_pad
+        topk_ids,
+        num_experts,
+        block_size,
+        sorted_ids,
+        expert_ids,
+        num_tokens_post_pad,
+        expert_map if ignore_invalid_experts else None,
     )
-    if expert_map is not None:
+
+    if expert_map is not None and not ignore_invalid_experts:
         expert_ids = expert_map[expert_ids]
 
     return sorted_ids, expert_ids, num_tokens_post_pad