From 0d1a855d8177604a85170384477fa6043947c0a1 Mon Sep 17 00:00:00 2001 From: Chenggang Zhao Date: Mon, 9 Jun 2025 14:37:13 +0800 Subject: [PATCH] Add low-latency kernel PCIe usage flag (#195) * Add low-latency kernel usage flag * Update comments --- csrc/deep_ep.cpp | 17 +++++++++++++++-- csrc/deep_ep.hpp | 6 ++++++ csrc/kernels/api.cuh | 7 ++++--- csrc/kernels/internode_ll.cu | 26 ++++++++++++++++++-------- deep_ep/buffer.py | 13 +++++++++++++ tests/test_low_latency.py | 1 + 6 files changed, 57 insertions(+), 13 deletions(-) diff --git a/csrc/deep_ep.cpp b/csrc/deep_ep.cpp index b46a3a0..a51eba8 100644 --- a/csrc/deep_ep.cpp +++ b/csrc/deep_ep.cpp @@ -76,6 +76,13 @@ Buffer::Buffer(int rank, int num_ranks, int64_t num_nvl_bytes, int64_t num_rdma_ CUDA_CHECK(cudaHostGetDevicePointer(&moe_recv_rdma_counter_mapped, const_cast(moe_recv_rdma_counter), 0)); *moe_recv_rdma_counter = -1; } + + // Low-latency kernels' usage flag + if (low_latency_mode) { + CUDA_CHECK(cudaMallocHost(&low_latency_usage_flag, sizeof(int), cudaHostAllocMapped)); + CUDA_CHECK(cudaHostGetDevicePointer(&low_latency_usage_flag_mapped, const_cast(low_latency_usage_flag), 0)); + *low_latency_usage_flag = 0; + } } Buffer::~Buffer() noexcept(false) { @@ -997,6 +1004,11 @@ Buffer::internode_combine(const torch::Tensor& x, const std::optional(low_latency_usage_flag); +} + void Buffer::clean_low_latency_buffer(int num_max_dispatch_tokens_per_rank, int hidden, int num_experts) { EP_HOST_ASSERT(low_latency_mode); @@ -1078,7 +1090,7 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i next_clean_meta.first, next_clean_meta.second, num_tokens, hidden, num_max_dispatch_tokens_per_rank, num_topk, num_experts, rank, num_ranks, use_fp8, - workspace, launch_stream, phases); + workspace, low_latency_usage_flag_mapped, launch_stream, phases); }; launcher(return_recv_hook ? LOW_LATENCY_SEND_PHASE : (LOW_LATENCY_SEND_PHASE | LOW_LATENCY_RECV_PHASE)); @@ -1165,7 +1177,7 @@ Buffer::low_latency_combine(const torch::Tensor& x, const torch::Tensor& topk_id next_clean_meta.first, next_clean_meta.second, num_combined_tokens, hidden, num_max_dispatch_tokens_per_rank, num_topk, num_experts, rank, num_ranks, - workspace, launch_stream, + workspace, low_latency_usage_flag_mapped, launch_stream, phases, zero_copy); }; launcher(return_recv_hook ? LOW_LATENCY_SEND_PHASE : (LOW_LATENCY_SEND_PHASE | LOW_LATENCY_RECV_PHASE)); @@ -1238,6 +1250,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { .def("intranode_combine", &deep_ep::Buffer::intranode_combine) .def("internode_dispatch", &deep_ep::Buffer::internode_dispatch) .def("internode_combine", &deep_ep::Buffer::internode_combine) + .def("get_low_latency_usage_flag", &deep_ep::Buffer::get_low_latency_usage_flag) .def("clean_low_latency_buffer", &deep_ep::Buffer::clean_low_latency_buffer) .def("low_latency_dispatch", &deep_ep::Buffer::low_latency_dispatch) .def("low_latency_combine", &deep_ep::Buffer::low_latency_combine) diff --git a/csrc/deep_ep.hpp b/csrc/deep_ep.hpp index a12a8a0..3d6aab4 100644 --- a/csrc/deep_ep.hpp +++ b/csrc/deep_ep.hpp @@ -71,6 +71,10 @@ private: volatile int* moe_recv_rdma_counter = nullptr; int* moe_recv_rdma_counter_mapped = nullptr; + // Host-side low-latency kernels' usages + volatile int* low_latency_usage_flag = nullptr; + int* low_latency_usage_flag_mapped = nullptr; + private: void move_fifo_slots(int num_slots = 1); @@ -132,6 +136,8 @@ public: const torch::Tensor& combined_rdma_head, const torch::Tensor& combined_nvl_head, const Config& config, std::optional& previous_event, bool async, bool allocate_on_comm_stream); + uint64_t get_low_latency_usage_flag() const; + void clean_low_latency_buffer(int num_max_dispatch_tokens_per_rank, int hidden, int num_experts); std::tuple, torch::Tensor, torch::Tensor, torch::Tensor, std::optional, std::optional>> diff --git a/csrc/kernels/api.cuh b/csrc/kernels/api.cuh index 89937a8..b08cbab 100644 --- a/csrc/kernels/api.cuh +++ b/csrc/kernels/api.cuh @@ -138,7 +138,8 @@ void dispatch(void* packed_recv_x, float* packed_recv_x_scales, int* next_clean, int num_next_clean_int, int num_tokens, int hidden, int num_max_dispatch_tokens_per_rank, int num_topk, int num_experts, int rank, int num_ranks, bool use_fp8, - void* workspace, cudaStream_t stream, int phases); + void* workspace, int* usage_flag, + cudaStream_t stream, int phases); void combine(void* combined_x, void* rdma_recv_x, int* rdma_recv_flag, void* rdma_send_x, @@ -147,8 +148,8 @@ void combine(void* combined_x, int* next_clean, int num_next_clean_int, int num_combined_tokens, int hidden, int num_max_dispatch_tokens_per_rank, int num_topk, int num_experts, int rank, int num_ranks, - void* workspace, cudaStream_t stream, - int phases, bool zero_copy); + void* workspace, int* usage_flag, + cudaStream_t stream, int phases, bool zero_copy); } // namespace internode_ll diff --git a/csrc/kernels/internode_ll.cu b/csrc/kernels/internode_ll.cu index 899cc11..fd80ad2 100644 --- a/csrc/kernels/internode_ll.cu +++ b/csrc/kernels/internode_ll.cu @@ -47,7 +47,7 @@ dispatch(void* packed_recv_x, float* packed_recv_x_scales, int* next_clean, int num_next_clean_int, int num_tokens, int num_max_dispatch_tokens_per_rank, int num_topk, int num_experts, int rank, int num_ranks, - int phases) { + int* usage_flag, int phases) { const auto sm_id = static_cast(blockIdx.x); const auto thread_id = static_cast(threadIdx.x); const auto warp_id = thread_id / 32, lane_id = get_lane_id(); @@ -180,6 +180,10 @@ dispatch(void* packed_recv_x, float* packed_recv_x_scales, #pragma unroll for (int i = lane_id; i < num_experts; i += 32) atomic_add_release_global(atomic_finish_counter_per_expert + i, FINISHED_SUM_TAG); + } else if (sm_id == 1) { + // The second SM is also responsible for notifying PCIe usage + if (lane_id == 0) + atomicAdd_system(usage_flag, 1); } // This SM should be responsible for some destination experts, read `topk_idx` for them @@ -311,7 +315,8 @@ void dispatch(void* packed_recv_x, float* packed_recv_x_scales, int* next_clean, int num_next_clean_int, int num_tokens, int hidden, int num_max_dispatch_tokens_per_rank, int num_topk, int num_experts, int rank, int num_ranks, bool use_fp8, - void* workspace, cudaStream_t stream, int phases) { + void* workspace, int* usage_flag, + cudaStream_t stream, int phases) { constexpr int kNumMaxTopK = 9; constexpr int kNumWarpsPerGroup = 10; constexpr int kNumWarpGroups = 3; @@ -338,7 +343,8 @@ LAUNCH_KERNEL(&cfg, dispatch_func, \ atomic_counter_per_expert, atomic_finish_counter_per_expert, \ next_clean, num_next_clean_int, \ num_tokens, num_max_dispatch_tokens_per_rank, \ - num_topk, num_experts, rank, num_ranks, phases); } break + num_topk, num_experts, rank, num_ranks, \ + usage_flag, phases); } break SETUP_LAUNCH_CONFIG(num_sms, num_warps * 32, stream); SWITCH_HIDDEN(DISPATCH_LAUNCH_CASE); @@ -356,7 +362,7 @@ combine(void* combined_x, int num_combined_tokens, int hidden, int num_topk, int num_max_dispatch_tokens_per_rank, int num_experts, int rank, int num_ranks, - int phases, bool zero_copy) { + int* usage_flag, int phases, bool zero_copy) { const auto sm_id = static_cast(blockIdx.x); const auto num_sms = static_cast(gridDim.x); const auto thread_id = static_cast(threadIdx.x); @@ -451,11 +457,14 @@ combine(void* combined_x, if ((phases & LOW_LATENCY_RECV_PHASE) == 0) return; - // Wait all ranks to arrive + // Wait all ranks to arrive and notify usages if (responsible_expert_idx < num_experts) { EP_STATIC_ASSERT(kNumWarpsPerGroup > 1, "Invalid number of warps per group"); - if (sub_warp_id == 0 and lane_id == 0) + if (sub_warp_id == 0 and lane_id == 0) { while (ld_acquire_sys_global(rdma_recv_flag + responsible_expert_idx) == 0); + } else if (sm_id == 0 and sub_warp_id == 1 and lane_id == 0) { + atomicAdd_system(usage_flag, 1); + } } cg::this_grid().sync(); @@ -506,8 +515,8 @@ void combine(void* combined_x, int* next_clean, int num_next_clean_int, int num_combined_tokens, int hidden, int num_max_dispatch_tokens_per_rank, int num_topk, int num_experts, int rank, int num_ranks, - void* workspace, cudaStream_t stream, - int phases, bool zero_copy) { + void* workspace, int* usage_flag, + cudaStream_t stream, int phases, bool zero_copy) { constexpr int kNumWarpsPerGroup = 10; constexpr int kNumWarpGroups = 3; constexpr int kNumMaxTopk = 9; @@ -531,6 +540,7 @@ LAUNCH_KERNEL(&cfg, combine_func, \ num_combined_tokens, hidden, num_topk, \ num_max_dispatch_tokens_per_rank, \ num_experts, rank, num_ranks, \ + usage_flag, \ phases, zero_copy); } break SETUP_LAUNCH_CONFIG(num_sms, num_warps * 32, stream); diff --git a/deep_ep/buffer.py b/deep_ep/buffer.py index 25fcff5..a403589 100644 --- a/deep_ep/buffer.py +++ b/deep_ep/buffer.py @@ -443,6 +443,19 @@ class Buffer: async_finish, allocate_on_comm_stream) return combined_x, combined_topk_weights, EventOverlap(event) + def get_low_latency_usage_flag(self): + """ + Return a host-side integer flag, which indicates the stages of low-latency kernels. + The initial value is 0, the low-latency dispatch will add 1 before communication, the low-latency combine + will add 1 after communication. + This is useful when there is no two-batch overlap, and you want to overlap H2D/D2H transfer with attention layers. + + Returns: + flag: the host-side integer flag pointer. The value is in `int`, but returns a `uint64_t` pointer. Please + `reinterpret_cast` the returned value into `int*`. + """ + return self.runtime.get_low_latency_usage_flag() + def clean_low_latency_buffer(self, num_max_dispatch_tokens_per_rank: int, hidden: int, num_experts: int) -> None: """ As low-latency kernels require part of the buffer to be zero-initialized, so it is vital to clean the buffer diff --git a/tests/test_low_latency.py b/tests/test_low_latency.py index 3dba6f1..9805263 100644 --- a/tests/test_low_latency.py +++ b/tests/test_low_latency.py @@ -155,6 +155,7 @@ def test_loop(local_rank: int, num_local_ranks: int): print(f'Allocating buffer size: {num_rdma_bytes / 1e6} MB ...', flush=True) buffer = deep_ep.Buffer(group, num_rdma_bytes=num_rdma_bytes, low_latency_mode=True, num_qps_per_rank=num_experts // num_ranks) + buffer.get_low_latency_usage_flag() test_main(num_tokens, hidden, num_experts, num_topk, rank, num_ranks, group, buffer, seed=1) do_pressure_test = False