fp8量化细节调整

csrc/deep_ep.cu

@@ -1330,9 +1330,9 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
     auto packed_recv_x_dtype = torch::kBFloat16;
     switch (quant_type) {
         case 1: packed_recv_x_dtype = torch::kInt8;             break;
-        case 2: packed_recv_x_dtype = torch::kFloat8_e4m3fnuz;  break;
-        case 3: packed_recv_x_dtype = torch::kFloat8_e4m3fnuz;  break;
-        case 4: packed_recv_x_dtype = torch::kFloat8_e5m2fnuz;  break;
+        case 2: packed_recv_x_dtype = torch::kFloat8_e4m3fn;    break;
+        case 3: packed_recv_x_dtype = torch::kFloat8_e4m3fn;    break;
+        case 4: packed_recv_x_dtype = torch::kFloat8_e5m2;      break;
     }
     
     auto packed_recv_x = torch::empty({num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, hidden}, x.options().dtype(packed_recv_x_dtype));

csrc/kernels/internode_ll.cu

@@ -152,10 +152,10 @@ __forceinline__ __device__ void pack_quantized_values(
 
             if constexpr (kQuantType == 4) {
                 // FP8 E5M2
-                fp8x2_ptr[j / 2] = __hip_cvt_float2_to_fp8x2(fp32x2, __HIP_SATFINITE, __HIP_E5M2_FNUZ);
+                fp8x2_ptr[j / 2] = __hip_cvt_float2_to_fp8x2(fp32x2, __HIP_SATFINITE, __HIP_E5M2);
             } else {
                 // FP8 E4M3 或 UE8M0
-                fp8x2_ptr[j / 2] = __hip_cvt_float2_to_fp8x2(fp32x2, __HIP_SATFINITE, __HIP_E4M3_FNUZ);
+                fp8x2_ptr[j / 2] = __hip_cvt_float2_to_fp8x2(fp32x2, __HIP_SATFINITE, __HIP_E4M3);
             }
         }
     }
@@ -179,9 +179,9 @@ __global__ __launch_bounds__(16 * kWarpSize, 1) void
     enum class QuantType {
         None        = 0,        // 不进行量化
         Int8        = 1,        // 采用 Int8 量化
-        FP8_E4M3    = 2,        // 采用 FP8 量化 __HIP_E4M3_FNUZ
+        FP8_E4M3    = 2,        // 采用 FP8 量化 __HIP_E4M3
         FP8_UE8M0   = 3,        // 采用 FP8 量化 DeepseekV3.1的 UE8M0
-        FP8_E5M2    = 4         // 采用 FP8 量化 __HIP_E5M2_FNUZ
+        FP8_E5M2    = 4         // 采用 FP8 量化 __HIP_E5M2
     };
 
     const auto sm_id = static_cast<int>(blockIdx.x);
@@ -247,7 +247,7 @@ __global__ __launch_bounds__(16 * kWarpSize, 1) void
             __shared__ float channel_amaxf[kNumScales];
             if constexpr(kUseQuant8Bit && kQuantGroupSize == 0) {
                 if (thread_id < kNumScales) {
-                    channel_amaxf[thread_id] = kFP8Margin;
+                    channel_amaxf[thread_id] = 0.0;
                 }
                 __syncthreads();
             }
@@ -262,7 +262,7 @@ __global__ __launch_bounds__(16 * kWarpSize, 1) void
                     // Calculate local amax
                     auto bf16_values = reinterpret_cast<hip_bfloat16*>(&int4_value);
                     float fp32_values[kNumElemsPerRead];
-                    float amax = kFP8Margin, scale, scale_inv;
+                    float amax = 0.0, scale, scale_inv;
                     #pragma unroll
                     for (int j = 0; j < kNumElemsPerRead; ++ j) {
                         fp32_values[j] = static_cast<float>(bf16_values[j]);
@@ -294,7 +294,7 @@ __global__ __launch_bounds__(16 * kWarpSize, 1) void
             __syncthreads();
 
             if constexpr(kUseQuant8Bit && kQuantGroupSize == 0) {
-                float amax_per_token = kFP8Margin;
+                float amax_per_token = 0.0;
                 // 并行规约，计算每个token的amax
                 for (int s = 0; s < kNumScales; s+=kWarpSize) {
                     int src_idx = s + lane_id;
@@ -310,7 +310,7 @@ __global__ __launch_bounds__(16 * kWarpSize, 1) void
 
                 // 根据最大值计算scale
                 float scale, scale_inv;
-                calculate_quant8bit_scales<kQuantType>(amax_per_token, scale, scale_inv);
+                calculate_quant8bit_scales<kQuantType>(amax_per_token, scale, scale_inv, fp8_round_scale);
                 if (thread_id == 0) {
                     rdma_x_scales[0] = scale_inv;
                 }
@@ -571,9 +571,9 @@ void dispatch(void* packed_recv_x, void* packed_recv_x_scales,
     /*量化类型枚举
     0 -> None          不量化，保持原始精度
     1 -> Int8          使用 INT8 对称量化
-    2 -> FP8_E4M3      使用 FP8 E4M3 格式 (__HIP_E4M3_FNUZ)
+    2 -> FP8_E4M3      使用 FP8 E4M3 格式 (__HIP_E4M3)
     3 -> FP8_UE8M0     使用 DeepSeekV3.1 提出的 UE8M0 格式 (仅支持round_scale=True)
-    4 -> FP8_E5M2      使用 FP8 E5M2 格式 (__HIP_E5M2_FNUZ)
+    4 -> FP8_E5M2      使用 FP8 E5M2 格式 (__HIP_E5M2)
     */
 
 #define DISPATCH_LAUNCH_CASE(hidden)                                                \

csrc/kernels/utils.cuh

@@ -342,8 +342,7 @@ __device__ __forceinline__ dtype_t broadcast(dtype_t &ptr, int src_lane_idx) {
 }
 
 // 设置不同的量化方式的最大值与相反数
-constexpr float kFP8Margin = 0.0;
-constexpr float kFinfoAmaxE4M3 = 240.0f;
+constexpr float kFinfoAmaxE4M3 = 448.0f;
 constexpr float kFinfoAmaxInvE4M3 = 1.0f / kFinfoAmaxE4M3;
 constexpr float kFinfoAmaxE5M2 = 57344.0f; 
 constexpr float kFinfoAmaxInvE5M2 = 1.0f / kFinfoAmaxE5M2;