Enable custom all-reduce for ROCm.

CMakeLists.txt

@@ -187,6 +187,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/custom_all_reduce.cu")
 endif()
 
+if(VLLM_GPU_LANG STREQUAL "HIP")
+  list(APPEND VLLM_EXT_SRC
+    "csrc/custom_all_reduce.cu")
+endif()
+
 define_gpu_extension_target(
   _C
   DESTINATION vllm

csrc/custom_all_reduce.cu

@@ -84,13 +84,20 @@ void _all_reduce(fptr_t _fa, torch::Tensor &inp, torch::Tensor &out,
                           out.numel());
       break;
     }
-#if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
+#if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__)) && !defined(USE_ROCM)
     case at::ScalarType::BFloat16: {
       fa->allreduce<nv_bfloat16>(
           stream, reinterpret_cast<nv_bfloat16 *>(inp.data_ptr()),
           reinterpret_cast<nv_bfloat16 *>(out.data_ptr()), out.numel());
       break;
     }
+#elif defined(USE_ROCM)
+    case at::ScalarType::BFloat16: {
+      fa->allreduce<__hip_bfloat16>(
+          stream, reinterpret_cast<__hip_bfloat16 *>(inp.data_ptr()),
+          reinterpret_cast<__hip_bfloat16 *>(out.data_ptr()), out.numel());
+      break;
+    }
 #endif
     default:
       throw std::runtime_error(

csrc/custom_all_reduce.cuh

@@ -1,7 +1,11 @@
 #pragma once
 
 #include <cuda.h>
+#ifndef USE_ROCM
 #include <cuda_bf16.h>
+#else
+#include <hip/amd_detail/amd_hip_bf16.h>
+#endif
 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
 
@@ -31,9 +35,17 @@ struct Signal {
   alignas(128) uint32_t end[kMaxBlocks][8];
 };
 
+#ifndef USE_ROCM
 struct __align__(16) RankData { const void *__restrict__ ptrs[8]; };
+#else
+struct __align__(16) RankData { const void * ptrs[8]; };
+#endif
 
+#ifndef USE_ROCM
 struct __align__(16) RankSignals { volatile Signal *signals[8]; };
+#else
+struct __align__(16) RankSignals { Signal *signals[8]; };
+#endif
 
 // like std::array, but aligned
 template <typename T, int sz>
@@ -74,6 +86,7 @@ DINLINE half &assign_add(half &a, half b) {
 }
 DINLINE float &assign_add(float &a, float b) { return a += b; }
 
+#ifndef USE_ROCM
 #if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
 DINLINE float upcast_s(nv_bfloat16 val) { return __bfloat162float(val); }
 template <>
@@ -85,6 +98,17 @@ DINLINE nv_bfloat16 &assign_add(nv_bfloat16 &a, nv_bfloat16 b) {
   return a;
 }
 #endif
+#else
+DINLINE float upcast_s(__hip_bfloat16 val) { return __bfloat162float(val); }
+template <>
+DINLINE __hip_bfloat16 downcast_s(float val) {
+  return __float2bfloat16(val);
+}
+DINLINE __hip_bfloat16 &assign_add(__hip_bfloat16 &a, __hip_bfloat16 b) {
+  a = __hadd(a, b);
+  return a;
+}
+#endif
 
 template <typename T, int N>
 DINLINE array_t<T, N> &packed_assign_add(array_t<T, N> &a, array_t<T, N> b) {
@@ -128,16 +152,30 @@ DINLINE O downcast(array_t<float, O::size> val) {
 // prior memory accesses. Note: volatile writes will not be reordered against
 // other volatile writes.
 template <int ngpus>
+#ifndef USE_ROCM
 DINLINE void start_sync(const RankSignals &sg, volatile Signal *self_sg,
-                        int rank) {
+                int rank) {
+#else
+DINLINE void start_sync(const RankSignals &sg, Signal *self_sg, int rank) {
+#endif
   if (threadIdx.x < ngpus) {
     // reset flag for next time
+#ifndef USE_ROCM
     self_sg->end[blockIdx.x][threadIdx.x] = 0;
     // simultaneously write to the corresponding flag of all ranks.
     // Latency = 1 p2p write
     sg.signals[threadIdx.x]->start[blockIdx.x][rank] = 1;
     // wait until we got true from all ranks
     while (!self_sg->start[blockIdx.x][threadIdx.x])
+#else
+    __atomic_store_n(&self_sg->end[blockIdx.x][threadIdx.x], 0, __ATOMIC_RELAXED);
+    // simultaneously write to the corresponding flag of all ranks.
+    // Latency = 1 p2p write
+    __atomic_store_n(&sg.signals[threadIdx.x]->start[blockIdx.x][rank], 1, __ATOMIC_RELAXED);
+    __atomic_thread_fence(__ATOMIC_ACQ_REL);
+    // wait until we got true from all ranks
+    while (!__atomic_load_n(&self_sg->start[blockIdx.x][threadIdx.x], __ATOMIC_RELAXED))
+#endif
       ;
   }
   __syncthreads();
@@ -147,13 +185,18 @@ DINLINE void start_sync(const RankSignals &sg, volatile Signal *self_sg,
 // barrier in the all reduce kernel. If it's the final synchronization barrier,
 // we don't need to make any visibility guarantees for prior memory accesses.
 template <int ngpus, bool final_sync = false>
+#ifndef USE_ROCM
 DINLINE void end_sync(const RankSignals &sg, volatile Signal *self_sg,
                       int rank) {
+#else
+DINLINE void end_sync(const RankSignals &sg, Signal *self_sg, int rank) {
+#endif
   __syncthreads();
   // eliminate the case that prior writes are not visible after signals become
   // visible. Note that I did not managed to make this happen through a lot of
   // testing. Might be the case that hardware provides stronger guarantee than
   // the memory model. 
+#ifndef USE_ROCM
   if constexpr (!final_sync) __threadfence_system();
   if (threadIdx.x < ngpus) {
     // reset flag for next time
@@ -164,6 +207,18 @@ DINLINE void end_sync(const RankSignals &sg, volatile Signal *self_sg,
     // wait until we got true from all ranks
     while (!self_sg->end[blockIdx.x][threadIdx.x])
       ;
+#else
+  if (threadIdx.x < ngpus) {
+    // reset flag for next time
+    __atomic_store_n(&self_sg->start[blockIdx.x][threadIdx.x], 0, __ATOMIC_RELAXED);
+    // simultaneously write to the corresponding flag of all ranks.
+    // Latency = 1 p2p write
+    __atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank], 1, __ATOMIC_RELAXED);
+    __atomic_thread_fence(__ATOMIC_ACQ_REL);
+    // wait until we got true from all ranks
+    while (!__atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x], __ATOMIC_RELAXED))
+      ;
+#endif
   }
   if constexpr (!final_sync) __syncthreads();
 }
@@ -179,10 +234,16 @@ DINLINE P packed_reduce(const P *ptrs[], int idx) {
 }
 
 template <typename T, int ngpus>
+#ifndef USE_ROCM
 __global__ void __launch_bounds__(512, 1)
     cross_device_reduce_1stage(RankData *_dp, RankSignals sg,
                                volatile Signal *self_sg, T *__restrict__ result,
                                int rank, int size) {
+#else
+__global__ void __launch_bounds__(1024, 1)
+    cross_device_reduce_1stage(RankData *_dp, RankSignals sg,
+                               Signal *self_sg, T *__restrict__ result, int rank, int size) {
+#endif
   using P = typename packed_t<T>::P;
   using A = typename packed_t<T>::A;
   // note: we don't reorder the address so the accumulation order is the same
@@ -199,15 +260,26 @@ __global__ void __launch_bounds__(512, 1)
 }
 
 template <typename P>
+#ifndef USE_ROCM
 DINLINE P *get_tmp_buf(volatile Signal *sg) {
+#else
+DINLINE P *get_tmp_buf(Signal *sg) {
+#endif
   return (P *)(((Signal *)sg) + 1);
 }
 
 template <typename T, int ngpus>
+#ifndef USE_ROCM
 __global__ void __launch_bounds__(512, 1)
     cross_device_reduce_2stage(RankData *_dp, RankSignals sg,
                                volatile Signal *self_sg, T *__restrict__ result,
                                int rank, int size) {
+#else
+__global__ void __launch_bounds__(1024, 1)
+    cross_device_reduce_2stage(RankData *_dp, RankSignals sg,
+                               Signal *self_sg, T *__restrict__ result,
+                               int rank, int size) {
+#endif
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = gridDim.x * blockDim.x;
   using P = typename packed_t<T>::P;
@@ -327,8 +399,12 @@ class CustomAllreduce {
       // note: must share the base address of each allocation, or we get wrong
       // address
       if (cuPointerGetAttribute(&base_ptr,
+#ifndef USE_ROCM
                                 CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,
-                                (CUdeviceptr)ptr) != CUDA_SUCCESS)
+#else
+                                HIP_POINTER_ATTRIBUTE_RANGE_START_ADDR,
+#endif
+				(CUdeviceptr)ptr) != CUDA_SUCCESS)
         throw std::runtime_error("failed to get pointer attr");
       CUDACHECK(cudaIpcGetMemHandle(
           (cudaIpcMemHandle_t *)&handles[i * handle_sz], base_ptr));
@@ -406,7 +482,11 @@ class CustomAllreduce {
    */
   template <typename T>
   void allreduce(cudaStream_t stream, T *input, T *output, int size,
+#ifndef USE_ROCM
                  int threads = 512, int block_limit = 36) {
+#else
+                 int threads = 1024, int block_limit = 36) {
+#endif
     auto d = packed_t<T>::P::size;
     if (size % d != 0)
       throw std::runtime_error(

csrc/custom_all_reduce_test.cu

@@ -4,6 +4,10 @@
  * export MPI_HOME=XXX
  * nvcc -O2 -arch=native -std=c++17 custom_all_reduce_test.cu -o
  * custom_all_reduce_test -lnccl -I${MPI_HOME}/include -lmpi
+ * to hipify and compile
+ * export MPI_HOME=XXX
+ * hipify-perl custom_all_reduce_test.cu > custom_all_reduce_test.hip
+ * hipcc -O2 -std=c++17 custom_all_reduce_test.hip -o custom_all_reduce_test -lrccl -I${MPI_HOME}/include -L${MPI_HOME}/lib -lmpi -DUSE_ROCM=1
  *
  * Warning: this C++ test is not designed to be very readable and was used
  * during the rapid prototyping process.
@@ -12,17 +16,29 @@
  * mpirun -np 8 ./custom_all_reduce_test
  */
 #include <cuda.h>
+#ifndef USE_ROCM
 #include <curand_kernel.h>
+#else
+#include <hiprand/hiprand_kernel.h>
+#endif
 #include <stdio.h>
 #include <stdlib.h>
 
 #include <limits>
 #include <vector>
 
 #include "cuda_profiler_api.h"
+#ifndef USE_ROCM
 #include "custom_all_reduce.cuh"
+#else
+#include "custom_all_reduce_hip.cuh"
+#endif
 #include "mpi.h"
+#ifndef USE_ROCM
 #include "nccl.h"
+#else
+#include <rccl/rccl.h>
+#endif
 
 #define MPICHECK(cmd)                                                  \
   do {                                                                 \
@@ -44,7 +60,12 @@
   } while (0)
 
 __global__ void dummy_kernel() {
+#ifndef USE_ROCM
   for (int i = 0; i < 100; i++) __nanosleep(1000000);  // 100ms
+#else
+  #pragma unroll
+  for (int i = 0; i < 100; i++) __builtin_amdgcn_s_sleep(127);
+#endif
 }
 
 template <typename T>
@@ -164,7 +185,11 @@ void run(int myRank, int nRanks, ncclComm_t &comm, int threads, int block_limit,
   ncclDataType_t ncclDtype;
   if (std::is_same<T, half>::value) {
     ncclDtype = ncclFloat16;
+#ifndef USE_ROCM
   } else if (std::is_same<T, nv_bfloat16>::value) {
+#else
+  } else if (std::is_same<T, __hip_bfloat16>::value) {
+#endif
     ncclDtype = ncclBfloat16;
   } else {
     ncclDtype = ncclFloat;
@@ -308,9 +333,14 @@ int main(int argc, char **argv) {
   //   }
   // }
   for (int sz = 512; sz <= (8 << 20); sz *= 2) {
+#ifndef USE_ROCM
     run<half>(myRank, nRanks, comm, 512, 36, sz + 8 * 47, performance_test);
+#else
+    run<half>(myRank, nRanks, comm, 1024, 16, sz + 8 * 47, performance_test);
+#endif
   }
 
   cudaProfilerStop();
+  MPICHECK(MPI_Finalize());
   return EXIT_SUCCESS;
 }

csrc/ops.h

@@ -139,7 +139,6 @@ void moe_align_block_size(
   torch::Tensor experts_ids,
   torch::Tensor num_tokens_post_pad);
 
-#ifndef USE_ROCM
 using fptr_t = uint64_t;
 fptr_t init_custom_ar(torch::Tensor &meta, torch::Tensor &rank_data,
                     const std::vector<std::string> &handles,
@@ -158,7 +157,6 @@ void register_buffer(fptr_t _fa, torch::Tensor &t,
 std::pair<std::vector<uint8_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
 void register_graph_buffers(fptr_t _fa, const std::vector<std::string> &handles,
                             const std::vector<std::vector<int64_t>> &offsets);
-#endif
 
 void convert_fp8(
   torch::Tensor& src_cache,
@@ -180,4 +178,4 @@ torch::Tensor fp8_gemm_16(
   torch::Tensor& scaleA,
   torch::Tensor& scaleB,
   int algo_idx
-);
+);

csrc/pybind.cpp

@@ -112,7 +112,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     &get_max_shared_memory_per_block_device_attribute,
     "Gets the maximum shared memory per block device attribute.");
 
-#ifndef USE_ROCM
   // Custom all-reduce kernels
   pybind11::module custom_ar = m.def_submodule("custom_ar", "custom allreduce");
   custom_ar.def("init_custom_ar", &init_custom_ar, "init_custom_ar");
@@ -126,6 +125,5 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
                 "get_graph_buffer_ipc_meta");
   custom_ar.def("register_graph_buffers", &register_graph_buffers,
                 "register_graph_buffers");
-#endif
 
 }

vllm/config.py

@@ -526,10 +526,9 @@ def _verify_args(self) -> None:
                 "Pipeline parallelism is not supported yet.")
         if not self.disable_custom_all_reduce and self.world_size > 1:
             if is_hip():
-                self.disable_custom_all_reduce = True
+                self.disable_custom_all_reduce = False
                 logger.info(
-                    "Disabled the custom all-reduce kernel because it is not "
-                    "supported on AMD GPUs.")
+                    "Enable the custom all-reduce kernel on AMD GPUs.")
             elif self.pipeline_parallel_size > 1:
                 self.disable_custom_all_reduce = True
                 logger.info(

vllm/model_executor/parallel_utils/custom_all_reduce.py

@@ -9,12 +9,14 @@
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 
 try:
-    import pynvml
-
     from vllm._C import custom_ar
 except ImportError:
-    # For AMD GPUs
     custom_ar = None
+
+try:
+    import pynvml
+except ImportError:
+    # For AMD GPUs
     pynvml = None
 
 logger = init_logger(__name__)