Support nvfp4 low latency mode dispatch

csrc/deep_ep.cpp

@@ -1094,8 +1094,10 @@ std::tuple<torch::Tensor, std::optional<torch::Tensor>, torch::Tensor, torch::Te
 Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_idx,
                              const std::optional<torch::Tensor>& cumulative_local_expert_recv_stats,
                              const std::optional<torch::Tensor>& dispatch_wait_recv_cost_stats,
+                             const std::optional<torch::Tensor>& x_global_scale,
                              int num_max_dispatch_tokens_per_rank, int num_experts,
                              bool use_fp8, bool round_scale, bool use_ue8m0,
+                             bool use_nvfp4, bool use_ue8m0_for_sf,
                              bool async, bool return_recv_hook) {
 #ifndef DISABLE_NVSHMEM
     EP_HOST_ASSERT(low_latency_mode);
@@ -1140,8 +1142,8 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
         stream_wait(launch_stream, compute_stream);
 
     // Allocate packed tensors
-    auto packed_recv_x = torch::empty({num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, hidden},
-                                      x.options().dtype(use_fp8 ? torch::kFloat8_e4m3fn: torch::kBFloat16));
+    auto packed_recv_x = torch::empty({num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, use_nvfp4 ? hidden / 2 : hidden},
+                                      x.options().dtype(use_nvfp4 ? torch::kUInt8 : (use_fp8 ? torch::kFloat8_e4m3fn: torch::kBFloat16)));
     auto packed_recv_src_info = torch::empty({num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank}, torch::dtype(torch::kInt32).device(torch::kCUDA));
     auto packed_recv_layout_range = torch::empty({num_local_experts, num_ranks}, torch::dtype(torch::kInt64).device(torch::kCUDA));
     auto packed_recv_count = torch::empty({num_local_experts}, torch::dtype(torch::kInt32).device(torch::kCUDA));
@@ -1151,6 +1153,7 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
     void* packed_recv_x_scales_ptr = nullptr;
     EP_HOST_ASSERT((num_ranks * num_max_dispatch_tokens_per_rank) % 4 == 0 and "TMA requires the number of tokens to be multiple of 4");
 
+    EP_HOST_ASSERT(not (use_fp8 and use_nvfp4));
     if (use_fp8) {
         // TODO: support unaligned cases
         EP_HOST_ASSERT(hidden % 512 == 0);
@@ -1164,6 +1167,35 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
         }
         packed_recv_x_scales = torch::transpose(packed_recv_x_scales.value(), 1, 2);
         packed_recv_x_scales_ptr = packed_recv_x_scales->data_ptr();
+    }else if (use_nvfp4) {
+        constexpr int kNumPerChannels = 16;
+        constexpr int NUM_SF_ELEMS_PER_PACK = 4;
+        constexpr int mTileSize_dim_0 = 32;
+        constexpr int mTileSize_dim_1 = 4;
+        constexpr int mTileSize = mTileSize_dim_0 * mTileSize_dim_1;
+
+        assert(hidden % kNumPerChannels == 0);
+        auto l = num_local_experts;
+        auto m = num_ranks * num_max_dispatch_tokens_per_rank;
+        auto rm = (m + 127) / 128;
+        auto rk = (hidden + (kNumPerChannels * NUM_SF_ELEMS_PER_PACK) -1 ) / (kNumPerChannels * NUM_SF_ELEMS_PER_PACK);
+        // The physical layout is (l, rm, rk, 32, 4, 4).
+        if (use_ue8m0_for_sf) {
+            packed_recv_x_scales = torch::empty({l, rm, rk, 32, 4, 4},
+                                                torch::dtype(torch::kInt).device(torch::kCUDA));
+        } else {
+            packed_recv_x_scales = torch::empty({l, rm, rk, 32, 4, 4},
+                                                torch::dtype(torch::kFloat8_e4m3fn).device(torch::kCUDA));
+        }
+        // After permute, the logical shape is (32, 4, rm, 4, rk, l)
+        packed_recv_x_scales = packed_recv_x_scales.value().permute({3, 4, 1, 5, 2, 0});
+
+        // The physical layout is (l, m, k // 2). 
+        // After permute, the logical shape is (m, k // 2, l).
+        packed_recv_x = packed_recv_x.permute({1, 2, 0});
+
+        packed_recv_x_scales_ptr = packed_recv_x_scales->data_ptr();
+        EP_HOST_ASSERT(packed_recv_x_scales_ptr != nullptr);
     }
 
     // Kernel launch
@@ -1174,13 +1206,15 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
                                packed_recv_count.data_ptr<int>(),
                                cumulative_local_expert_recv_stats.has_value() ? cumulative_local_expert_recv_stats->data_ptr<int>() : nullptr,
                                dispatch_wait_recv_cost_stats.has_value() ? dispatch_wait_recv_cost_stats->data_ptr<int64_t>() : nullptr,
+                               x_global_scale.has_value() ? x_global_scale->data_ptr<float>() : nullptr,
                                buffer.dispatch_rdma_recv_data_buffer, buffer.dispatch_rdma_recv_count_buffer,
                                buffer.dispatch_rdma_send_buffer,
                                x.data_ptr(), topk_idx.data_ptr<int64_t>(),
                                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, round_scale, use_ue8m0,
+                               use_nvfp4, use_ue8m0_for_sf,
                                workspace, num_device_sms,
                                launch_stream, phases);
     };

csrc/deep_ep.hpp

@@ -167,8 +167,10 @@ struct Buffer {
     low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_idx,
                          const std::optional<torch::Tensor>& cumulative_local_expert_recv_stats,
                          const std::optional<torch::Tensor>& dispatch_wait_recv_cost_stats,
+                         const std::optional<torch::Tensor>& x_global_scale,
                          int num_max_dispatch_tokens_per_rank, int num_experts,
                          bool use_fp8, bool round_scale, bool use_ue8m0,
+                         bool use_nvfp4, bool use_ue8m0_for_sf,
                          bool async, bool return_recv_hook);
 
     std::tuple<torch::Tensor, std::optional<EventHandle>, std::optional<std::function<void()>>>

csrc/kernels/api.cuh

@@ -145,12 +145,14 @@ void dispatch(void* packed_recv_x, void* packed_recv_x_scales,
               int* packed_recv_count,
               int* cumulative_local_expert_recv_stats,
               int64_t* dispatch_wait_recv_cost_stats,
+              const float* x_global_scale,
               void* rdma_recv_x, int* rdma_recv_count, void* rdma_x,
               const void* x, const int64_t* topk_idx,
               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, bool round_scale, bool use_ue8m0,
+              bool use_nvfp4, bool use_ue8m0_for_sf,
               void* workspace, int num_device_sms,
               cudaStream_t stream, int phases);