[Build/CI] Fix CUDA 11.8 build (#17679)

Signed-off-by: Tyler Michael Smith <[email protected]>
Signed-off-by: Lucas Wilkinson <[email protected]>
Signed-off-by: Tyler Michael Smith <[email protected]>
Co-authored-by: Lucas Wilkinson <[email protected]>

Author: tlrmchlsmth

CMakeLists.txt

@@ -30,7 +30,11 @@ set(ignoreMe "${VLLM_PYTHON_PATH}")
 set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 
 # Supported NVIDIA architectures.
-set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
+if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL)
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
+else()
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
+endif()
 
 # Supported AMD GPU architectures.
 set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")

csrc/moe/moe_ops.h

@@ -28,4 +28,6 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor num_tokens_post_pad, int64_t top_k,
                              int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
                              int64_t BLOCK_SIZE_K, int64_t bit);
-#endif
+#endif
+
+bool moe_permute_unpermute_supported();

csrc/moe/moe_permute_unpermute_op.cu

@@ -5,6 +5,9 @@
 #include "permute_unpermute_kernels/dispatch.h"
 #include "core/registration.h"
 
+// moe_permute kernels require at least CUDA 12.0
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+
 void moe_permute(
     const torch::Tensor& input,                      // [n_token, hidden]
     const torch::Tensor& topk_weights,               //[n_token, topk]
@@ -127,7 +130,45 @@ void moe_unpermute(
   });
 }
 
+#else
+
+void moe_permute(const torch::Tensor& input, const torch::Tensor& topk_weights,
+                 torch::Tensor& topk_ids,
+                 const torch::Tensor& token_expert_indicies,
+                 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) {
+  TORCH_CHECK(false, "moe_unpermute 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_indicies,
+                   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) {
+  TORCH_CHECK(false, "moe_unpermute is not supported on CUDA < 12.0");
+}
+
+#endif
+
+bool moe_permute_unpermute_supported() {
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+  return true;
+#else
+  return false;
+#endif
+}
+
 TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
   m.impl("moe_permute", &moe_permute);
   m.impl("moe_unpermute", &moe_unpermute);
-}
+}

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -1,6 +1,9 @@
 
 #include "moe_permute_unpermute_kernel.h"
 
+// moe_permute kernels require at least CUDA 12.0
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+
 // CubKeyValueSorter definition begin
 CubKeyValueSorter::CubKeyValueSorter()
     : num_experts_(0), num_bits_(sizeof(int) * 8) {}
@@ -131,9 +134,6 @@ __global__ void preprocessTopkIdKernel(int* topk_id_ptr, int size,
                                        int num_experts) {
   auto tidx = threadIdx.x;
   auto bidx = blockIdx.x;
-  auto lidx = tidx & 31;
-  auto widx = tidx >> 5;
-  auto warp_count = (blockDim.x + 31) >> 5;
   auto offset = bidx * blockDim.x;
   auto bound = min(offset + blockDim.x, size);
   extern __shared__ int smem_expert_map[];
@@ -226,4 +226,6 @@ void getMIndices(int64_t* expert_first_token_offset,
         expert_first_token_offset, align_expert_first_token_offset, m_indices,
         num_local_expert, align_block_size);
   }
-}
+}
+
+#endif

csrc/moe/torch_bindings.cpp

@@ -77,7 +77,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "Tensor topk_ids,Tensor src_row_id2dst_row_id_map, Tensor "
       "expert_first_token_offset, int n_expert, int n_local_expert,int "
       "topk, Tensor! hidden_states)->()");
-  // conditionally compiled so impl registration is in source file
+
+  m.def("moe_permute_unpermute_supported() -> bool");
+  m.impl("moe_permute_unpermute_supported", &moe_permute_unpermute_supported);
 
 #endif
 }

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -123,7 +123,7 @@ bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability) {
 }
 
 bool cutlass_group_gemm_supported(int64_t cuda_device_capability) {
-  // CUTLASS groped FP8 kernels need at least CUDA 12.3
+  // CUTLASS grouped FP8 kernels need at least CUDA 12.3
   // and SM90 (Hopper)
 
 #if defined CUDA_VERSION

docker/Dockerfile

@@ -263,8 +263,11 @@ if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
         export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'; \
     else \
         export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0+PTX'; \
-    fi && \
-    export FLASHINFER_ENABLE_AOT=1; \
+    fi; \
+    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
+    if [ "$CUDA_MAJOR" -lt 12 ]; then \
+        export FLASHINFER_ENABLE_SM90=0; \
+    fi; \
     uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@21ea1d2545f74782b91eb8c08fd503ac4c0743fc" ; \
 fi
 COPY examples examples
@@ -275,7 +278,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 . /etc/environment && \
 uv pip list
 
-# Although we build Flashinfer with AOT mode, there's still
+# Even when we build Flashinfer with AOT mode, there's still
 # some issues w.r.t. JIT compilation. Therefore we need to
 # install build dependencies for JIT compilation.
 # TODO: Remove this once FlashInfer AOT wheel is fixed
@@ -303,8 +306,11 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
 
 # install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements/dev.txt
+RUN --mount=type=cache,target=/root/.cache/uv \   
+    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
+    if [ "$CUDA_MAJOR" -ge 12 ]; then \
+        uv pip install --system -r requirements/dev.txt; \
+    fi
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \

tests/kernels/moe/test_moe_permute_unpermute.py

@@ -13,7 +13,7 @@
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
 from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
-    moe_permute, moe_unpermute)
+    moe_permute, moe_permute_unpermute_supported, moe_unpermute)
 from vllm.platforms import current_platform
 
 NUM_EXPERTS = [16, 64]
@@ -167,6 +167,8 @@ def torch_unpermute(permuted_hidden_states: torch.Tensor,
 def test_moe_permute_unpermute(n_token: int, n_hidden: int, topk: int,
                                n_expert: int, ep_size: int, dtype: torch.dtype,
                                align_block_size: Optional[int]):
+    if not moe_permute_unpermute_supported():
+        pytest.skip("moe_permute_unpermute is not supported on this platform.")
     fill_invalid_expert = 0
     ep_rank = np.random.randint(0, ep_size)
     expert_map = None

vllm/model_executor/layers/fused_moe/moe_permute_unpermute.py

@@ -182,3 +182,7 @@ def moe_unpermute(
                                    expert_first_token_offset, n_expert,
                                    n_local_expert, topk, hidden_states)
     return hidden_states
+
+
+def moe_permute_unpermute_supported():
+    return torch.ops._moe_C.moe_permute_unpermute_supported()