支持更复杂的量化，包括fp8/int8/ue8m0，且支持per-group/per-channel

csrc/config.hpp

@@ -136,7 +136,7 @@ struct LowLatencyLayout {
 
     LowLatencyLayout(void *rdma_buffer, int num_max_dispatch_tokens_per_rank, int hidden,
                      int num_ranks, int num_experts) {
-        const int num_scales = hidden / FP8_QUANTIZATION_NUM_PER_CHANNEL;
+        const int num_scales = hidden / QUANTIZATION_GROUPSIZE;
 
         // Dispatch and combine layout:
         //  - 2 symmetric odd/even send buffer

csrc/deep_ep.cu

@@ -1293,7 +1293,7 @@ void Buffer::clean_low_latency_buffer(int num_max_dispatch_tokens_per_rank, int
 std::tuple<torch::Tensor, std::optional<torch::Tensor>, torch::Tensor, torch::Tensor, torch::Tensor, std::optional<EventHandle>, std::optional<std::function<void()>>>
 Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_idx,
                              int num_max_dispatch_tokens_per_rank, int num_experts,
-                             bool use_fp8, bool round_scale, bool use_ue8m0, bool use_int8,
+                             int quant_type, int quant_group_size, bool fp8_round_scale,
                              bool async, bool return_recv_hook) {
     EP_HOST_ASSERT(low_latency_mode);
 
@@ -1327,8 +1327,15 @@ 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_int8 ? torch::kInt8 : use_fp8 ? torch::kFloat8_e4m3fnuz: torch::kBFloat16));
+    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;
+    }
+    
+    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));
     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));
@@ -1336,21 +1343,28 @@ Buffer::low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_i
     // Allocate column-majored scales
     auto packed_recv_x_scales = std::optional<torch::Tensor>();
     void* packed_recv_x_scales_ptr = nullptr;
-    if (use_fp8) {
+    if (quant_type > 0) {
         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");
         // TODO: support unaligned cases
-        EP_HOST_ASSERT(hidden % (FP8_QUANTIZATION_NUM_PER_CHANNEL * 4) == 0);
-        EP_HOST_ASSERT(!(use_ue8m0 && use_int8));
+        EP_HOST_ASSERT(hidden % (QUANTIZATION_GROUPSIZE * 4) == 0);
+        
+        // 计算scale_col的大小
+        int scales_col_size = 1;    // 默认为per-channel
+        if (quant_group_size > 0) {
+            if (quant_type == 3) {  // FP8_UE8M0比较特殊
+                scales_col_size = hidden / (QUANTIZATION_GROUPSIZE * 4);
+            } else {
+                scales_col_size = hidden / QUANTIZATION_GROUPSIZE;
+            }
+        }
 
-        if (use_ue8m0) {
-            EP_HOST_ASSERT(round_scale);
-            packed_recv_x_scales = torch::empty({num_local_experts, hidden / (FP8_QUANTIZATION_NUM_PER_CHANNEL * 4), num_ranks * num_max_dispatch_tokens_per_rank},
+        // 设置packed_recv_x_scales
+        if (quant_type == 3) {  // FP8_UE8M0比较特殊，需要单独处理
+            EP_HOST_ASSERT(fp8_round_scale && quant_group_size == 128);
+            packed_recv_x_scales = torch::empty({num_local_experts, scales_col_size, num_ranks * num_max_dispatch_tokens_per_rank},
                                                 torch::dtype(torch::kInt).device(torch::kCUDA));
-        } else if (use_int8) {
-            packed_recv_x_scales = torch::empty({num_local_experts, 1, num_ranks * num_max_dispatch_tokens_per_rank},
-                                                torch::dtype(torch::kFloat32).device(torch::kCUDA));
         } else {
-            packed_recv_x_scales = torch::empty({num_local_experts, hidden / FP8_QUANTIZATION_NUM_PER_CHANNEL, num_ranks * num_max_dispatch_tokens_per_rank},
+            packed_recv_x_scales = torch::empty({num_local_experts, scales_col_size, num_ranks * num_max_dispatch_tokens_per_rank},
                                                 torch::dtype(torch::kFloat32).device(torch::kCUDA));
         }
         packed_recv_x_scales = torch::transpose(packed_recv_x_scales.value(), 1, 2);
@@ -1370,7 +1384,7 @@ 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, round_scale, use_ue8m0, use_int8,
+                            quant_type, quant_group_size, fp8_round_scale,
                             workspace, num_device_sms, launch_stream, phases);
     };
     launcher(return_recv_hook ? LOW_LATENCY_SEND_PHASE : (LOW_LATENCY_SEND_PHASE | LOW_LATENCY_RECV_PHASE));

csrc/deep_ep.hpp

@@ -177,7 +177,7 @@ public:
     std::tuple<torch::Tensor, std::optional<torch::Tensor>, torch::Tensor, torch::Tensor, torch::Tensor, std::optional<EventHandle>, std::optional<std::function<void()>>>
     low_latency_dispatch(const torch::Tensor& x, const torch::Tensor& topk_idx,
                          int num_max_dispatch_tokens_per_rank, int num_experts,
-                         bool use_fp8, bool round_scale, bool use_ue8m0, bool use_int8,
+                         int quant_type, int quant_group_size, bool fp8_round_scale,
                          bool async, bool return_recv_hook);
 
     std::tuple<torch::Tensor, std::optional<EventHandle>, std::optional<std::function<void()>>>

csrc/kernels/api.cuh

@@ -147,7 +147,7 @@ void dispatch(void* packed_recv_x, void* packed_recv_x_scales,
                 int64_t* 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_int8,
+                int quant_type, int group_size, bool fp8_round_scale,
                 void* workspace, int num_device_sms, hipStream_t stream, int phases);
 
 void combine(void* combined_x,

csrc/kernels/configs.cuh

@@ -23,7 +23,7 @@
 #define LOW_LATENCY_SEND_PHASE 1
 #define LOW_LATENCY_RECV_PHASE 2
 #define NUM_INTERNODE_DISPATCH_BLOCKS_PER_CHANNEL 3
-#define FP8_QUANTIZATION_NUM_PER_CHANNEL 128
+#define QUANTIZATION_GROUPSIZE 128
 
 #define DEFAULT_NUM_CU 20
 #define DEFAULT_NUM_MAX_XGMI_CHUNKED_SEND_TOKENS 6

csrc/kernels/utils.cuh

@@ -341,10 +341,14 @@ __device__ __forceinline__ dtype_t broadcast(dtype_t &ptr, int src_lane_idx) {
     return *reinterpret_cast<dtype_t *>(recv_int_values);
 }
 
-constexpr float kFP8Margin = 1e-4;
+// 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 kFinfoAmaxInvE4M3 = 1.0f / kFinfoAmaxE4M3;
-constexpr float kInt8Amax = 127.0f;
+constexpr float kFinfoAmaxE5M2 = 57344.0f; 
+constexpr float kFinfoAmaxInvE5M2 = 1.0f / kFinfoAmaxE5M2;
+constexpr float kFinfoAmaxInt8 = 127.0f;
+constexpr float kFinfoAmaxInvInt8 = 1.0f / 127.0f;
 
 __forceinline__ __device__ float fast_pow2(int x) {
     // We can ensure `-126 <= x and x <= 127`
@@ -359,22 +363,33 @@ __forceinline__ __device__ int fast_log2_ceil(float x) {
     return exp_x - 127 + (man_bits != 0);
 }
 
-__forceinline__ __device__ void calculate_fp8_scales(float amax, float& scale, float& scale_inv, bool round_scale) {
-    if (round_scale) {
-        auto exp_scale_inv = fast_log2_ceil(amax * kFinfoAmaxInvE4M3);
-        scale = fast_pow2(-exp_scale_inv);
-        scale_inv = fast_pow2(exp_scale_inv);
-    } else {
-        scale_inv = amax * kFinfoAmaxInvE4M3;
-        scale = kFinfoAmaxE4M3 / amax;
+template <int kQuantType>
+__forceinline__ __device__ void calculate_quant8bit_scales(float amax, float& scale, float& scale_inv, bool round_scale=0) {
+    amax = fmaxf(amax, 1e-6f);
+    if constexpr(kQuantType == 1) { // 使用 INT8 对称量化
+        scale_inv = kFinfoAmaxInvInt8 * amax;
+        scale = kFinfoAmaxInt8 / amax;
+    } else if constexpr(kQuantType == 2 || kQuantType == 3) {   // 使用 FP8_E4M3 或 FP8_UE8M0 非对称量化
+        if (round_scale) {
+            auto exp_scale_inv = fast_log2_ceil(amax * kFinfoAmaxInvE4M3);
+            scale = fast_pow2(-exp_scale_inv);
+            scale_inv = fast_pow2(exp_scale_inv);
+        } else {
+            scale_inv = amax * kFinfoAmaxInvE4M3;
+            scale = kFinfoAmaxE4M3 / amax;
+        }
+    } else if constexpr(kQuantType == 4) { // 使用 FP8_E5M2 对称量化
+        if (round_scale) {
+            auto exp_scale_inv = fast_log2_ceil(amax * kFinfoAmaxInvE5M2);
+            scale = fast_pow2(-exp_scale_inv);
+            scale_inv = fast_pow2(exp_scale_inv);
+        } else {
+            scale_inv = amax * kFinfoAmaxInvE5M2;
+            scale = kFinfoAmaxE5M2 / amax;
+        }
     }
 }
 
-__forceinline__ __device__ void calculate_int8_scales(float amax, float& scale, float& scale_inv) {
-    scale = kInt8Amax / amax;
-    scale_inv = amax / kInt8Amax;
-}
-
 template <bool kIsUE8M0, typename out_dtype_t = std::conditional_t<kIsUE8M0, uint8_t, float>>
 __forceinline__ __device__ out_dtype_t extract_required_scale_format(float value) {
     if constexpr (kIsUE8M0) {

deep_ep/buffer.py

@@ -841,7 +841,7 @@ class Buffer:
     # noinspection PyTypeChecker
     def low_latency_dispatch(self, x: torch.Tensor, topk_idx: torch.Tensor,
                              num_max_dispatch_tokens_per_rank: int, num_experts: int,
-                             use_fp8: bool = True, round_scale: bool = False, use_ue8m0: bool = False, use_int8: bool = False,
+                             quant_type: int = 1, quant_group_size: int = 0, fp8_round_scale: bool = False,
                              async_finish: bool = False, return_recv_hook: bool = False) -> \
             Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, Tuple, EventOverlap, Callable]:
         """
@@ -858,10 +858,20 @@ class Buffer:
                 only several top-k shapes are supported. `-1` indices (not selecting any expert) are supported.
             num_max_dispatch_tokens_per_rank: the maximum number of tokens to dispatch, all the ranks must hold the same value.
             num_experts: the number of all experts.
-            use_fp8: whether to enable FP8 casting, with this, the received data will be a tuple of FP8 tensor and scaling factors.
-            round_scale: whether round the scaling factors into power of 2.
-            use_ue8m0: whether use UE8M0 as scaling factor format (available only with `round_scale=True`).
-            use_int8: whether to enable INT8 casting.
+            量化配置
+            quant_type:          int 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)
+                                 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)
+            quant_group_size:    int 量化分组大小
+                                 0  -> 逐token量化 (per-channel) 
+                                 128-> 每 128 元素一组 (per-group) 量化
+            fp8_round_scale:     bool 是否将 FP8 缩放因子取整为 2 的幂
+                                 true  -> 缩放因子 = 2^k，硬件零开销
+                                 false -> 缩放因子 = 任意浮点，精度更高
+            异步配置
             async_finish: the current stream will not wait for the communication kernels to be finished if set.
             return_recv_hook: return a receiving hook if set. If set, the kernel will just do the RDMA request issues,
                 but **without actually receiving the data**. You must call the received hook to make sure the data's arrival.
@@ -869,15 +879,25 @@ class Buffer:
 
         Returns:
             recv_x: a tensor or tuple with received tokens for each expert.
-                With `use_fp8=True`: the first element is a `torch.Tensor` shaped as
-                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden]` with `torch.float8_e4m3fn`.
-                The second tensor is the corresponding scales for the first element with shape
-                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden // 128]` with `torch.float`,
-                if `use_ue8m0=False`. With `use_ue8m0=True`, the second one is packed and shaped as
-                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden // 512]` with type `torch.int`.
-                Notice that, the last-two-dimension of the scaling tensors are in column-major for TMA compatibility.
-                With `use_fp8=False`, the result would be a tensor shaped as
-                `[num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, hidden]` with `torch.bfloat16`.
+                - packed_recv_x:
+                     存储接收到的 Token 数据，形状为
+                     `[num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, hidden]`。
+                     数据类型取决于 quant_type：
+                      quant_type == 1 -> torch.int8
+                         quant_type == 2 -> torch.float8_e4m3fnuz
+                         quant_type == 3 -> torch.float8_e4m3fnuz (UE8M0 使用 E4M3 格式存储)
+                         quant_type == 4 -> torch.float8_e5m2fnuz
+                         其他 (非量化)   -> torch.bfloat16
+                - packed_recv_x_scales (可选):
+                     仅在 quant_type > 0 时存在，存储量化的 Scale 值。
+                     形状为 `[num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, scales_col_size]`。
+                     - 当 quant_type == 3 (UE8M0) 时:
+                         scales_col_size = hidden // 512
+                         数据类型为 torch.int (内部打包存储 4-bit scale)。
+                         *注意：此模式强制要求 fp8_round_scale=True 且 group_size=128。
+                     - 当 quant_type == 1, 2, 4 时:
+                         scales_col_size = hidden // 128 (若使用 group_size) 或 1 (per-channel)。
+                         数据类型为 torch.float32。
                 Moreover, not all tokens are valid, only some of the `num_max_dispatch_tokens_per_rank * num_ranks` are,
                 as we do not synchronize CPU received count with GPU (also not incompatible with CUDA graph if synced).
             recv_count: a tensor shaped `[num_local_experts]` with type `torch.int`, indicating how many tokens each
@@ -889,14 +909,15 @@ class Buffer:
         packed_recv_x, packed_recv_x_scales, packed_recv_count, packed_recv_src_info, packed_recv_layout_range, event, hook = \
             self.runtime.low_latency_dispatch(x, topk_idx,
                                               num_max_dispatch_tokens_per_rank, num_experts,
-                                              use_fp8, round_scale, use_ue8m0, use_int8,
+                                              quant_type, quant_group_size, fp8_round_scale,
                                               async_finish, return_recv_hook)
         handle = (packed_recv_src_info, packed_recv_layout_range, num_max_dispatch_tokens_per_rank, x.size(1), num_experts)
         tensors_to_record = (x, topk_idx,
                              packed_recv_x, packed_recv_x_scales, packed_recv_count,
                              packed_recv_src_info, packed_recv_layout_range)
-        return (packed_recv_x, packed_recv_x_scales) if use_fp8 else packed_recv_x, packed_recv_count, handle, \
-            EventOverlap(event, tensors_to_record if async_finish else None), hook
+
+        recv_x = (packed_recv_x, packed_recv_x_scales) if (quant_type > 0) else packed_recv_x
+        return recv_x, packed_recv_count, handle, EventOverlap(event, tensors_to_record if async_finish else None), hook
 
     # noinspection PyTypeChecker
     def low_latency_combine(self, x: torch.Tensor, topk_idx: torch.Tensor, topk_weights: torch.Tensor,

tests/test_low_latency_new.py

@@ -58,7 +58,8 @@ def test_main(num_tokens: int,
     x_list = [x]
     # # NOTES: the last one is for performance testing
     # # Most of the values in the perf case is lower than the threshold, casting most channels
-    # x_list.append(torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') * 0.1)
+    # x_rand = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') * 0.1
+    # x_list = [x_rand]
 
     scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1
     topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=True)[1]
@@ -80,14 +81,19 @@ def test_main(num_tokens: int,
     hash_value, num_times = 0, 0
     for current_x in x_list:
         for return_recv_hook in (False, True):
-            for dispatch_use_fp8 in (False, True):
-                for round_scale in (False, True) if dispatch_use_fp8 else (False,):
-                    for use_ue8m0 in (False, True) if round_scale else (False,):
+            for quant_type in (0, 2, 3, ): # 0: 不量化, 2: FP8_E4M3, 3: FP8_UE8M0 (仅支持round_scale=True)
+                dispatch_use_fp8 = quant_type > 0
+                for fp8_round_scale in (False, True) if dispatch_use_fp8 else (False, ):
+                    for quant_group_size in (128, ):
+                        # 跳过不支持的情况
+                        if quant_type == 3 and fp8_round_scale == False:
+                            continue
+
                         num_times += 1
                         for _ in range((num_times % 2) + 1):
                             packed_recv_x, packed_recv_count, handle, event, hook = \
                                 buffer.low_latency_dispatch(current_x, topk_idx, num_tokens, num_experts,
-                                                            use_fp8=dispatch_use_fp8, round_scale=round_scale, use_ue8m0=use_ue8m0,
+                                                            quant_type=quant_type, fp8_round_scale=fp8_round_scale, quant_group_size=quant_group_size,
                                                             async_finish=not return_recv_hook, return_recv_hook=return_recv_hook)
                             hook() if return_recv_hook else event.current_stream_wait()
                         packed_recv_x = (packed_recv_x[0], packed_recv_x[1].contiguous()) if dispatch_use_fp8 else packed_recv_x
@@ -115,13 +121,13 @@ def test_main(num_tokens: int,
                                 recv_x_amin = recv_x[:, :-128].amin(dim=-1)
                                 recv_src_info = recv_src_info[:num_valid_tokens]
                                 assert torch.equal(recv_x_amin, recv_x[:, :-128].amax(dim=-1))
-                                if round_scale:
+                                if fp8_round_scale:
                                     assert calc_diff(recv_x[:, -1], recv_src_info.view(-1)) < 0.007
                                 else:
                                     assert (recv_x[:, -128:] - recv_src_info.view(-1, 1) % num_tokens).sum().item() == 0
                                 for j in range(num_ranks):
                                     begin_idx, count = (recv_layout_range[j] >> 32).item(), (recv_layout_range[j] & int_mask).item()
-                                    if not round_scale:
+                                    if not fp8_round_scale:
                                         assert (recv_x_amin == j - rank_offset).sum().item() == (all_topk_idx[j] == expert_id).sum().item()
                                         assert (recv_x[begin_idx:begin_idx + count, :-128] - j + rank_offset).sum().item() == 0
                             if dispatch_use_fp8:
@@ -147,7 +153,7 @@ def test_main(num_tokens: int,
                             if do_check:
                                 diff = calc_diff(current_x * topk_weights.masked_fill(topk_idx == -1, 0).sum(dim=1).view(-1, 1), combined_x)
                                 assert torch.isnan(combined_x).sum().item() == 0
-                                # if not round_scale:
+                                # if not fp8_round_scale:
                                 assert diff < (9e-4 if dispatch_use_fp8 else 1e-5), f'Error: diff={diff}, dispatch_use_fp8={dispatch_use_fp8}, zero_copy={zero_copy}'
                                 hash_value ^= hash_tensor(combined_x)
 
@@ -162,7 +168,8 @@ def test_main(num_tokens: int,
     def test_func(return_recv_hook: bool):
         recv_x, recv_count, handle, event, hook = \
             buffer.low_latency_dispatch(current_x, topk_idx, num_tokens, num_experts,
-                                        use_fp8=True, async_finish=False, return_recv_hook=return_recv_hook)
+                                        quant_type=2, quant_group_size=128,
+                                        async_finish=False, return_recv_hook=return_recv_hook)
         large_gemm_with_hook(hook) if return_recv_hook else None
         combined_x, event, hook = buffer.low_latency_combine(simulated_gemm_x,
                                                              topk_idx,

tests/test_low_latency_new_int8.py

@@ -54,16 +54,16 @@ def test_main(num_tokens: int,
     do_check = True
     hash_value, num_times = 0, 0
     for current_x in x_list:
-        for return_recv_hook in (False, ):
-            for dispatch_use_fp8 in (True, ):
-                for round_scale in (False, ):
-                    for use_ue8m0 in (False, ):
+        for return_recv_hook in (False, True):
+            for quant_type in (1, ):
+                for fp8_round_scale in (False, ):
+                    for quant_group_size in (0, ):
+                        dispatch_use_fp8 = quant_type > 0
                         num_times += 1
-                        use_int8 = True
                         for _ in range(1):
                             packed_recv_x, packed_recv_count, handle, event, hook = \
                                 buffer.low_latency_dispatch(current_x, topk_idx, num_tokens, num_experts,
-                                                            use_fp8=dispatch_use_fp8, round_scale=round_scale, use_ue8m0=use_ue8m0, use_int8=use_int8,
+                                                            quant_type=quant_type, quant_group_size=quant_group_size,
                                                             async_finish=not return_recv_hook, return_recv_hook=return_recv_hook)
                             hook() if return_recv_hook else event.current_stream_wait()
 
@@ -97,9 +97,7 @@ def test_main(num_tokens: int,
 
                                 assert torch.equal(recv_x_amin, recv_x_amax)
 
-                                if round_scale:
-                                    assert calc_diff(recv_x[:, -1], recv_src_info.view(-1)) < 0.007
-                                elif use_int8:
+                                if quant_type == 1:
                                     assert calc_diff(recv_x[:, -1], recv_src_info.view(-1)) < 0.01
                                 else:
                                     assert (recv_x[:, -128:] - recv_src_info.view(-1, 1) % num_tokens).sum().item() == 0
@@ -131,7 +129,7 @@ def test_main(num_tokens: int,
     def test_func(return_recv_hook: bool):
         recv_x, recv_count, handle, event, hook = \
             buffer.low_latency_dispatch(current_x, topk_idx, num_tokens, num_experts,
-                                        use_fp8=True, round_scale=False, use_ue8m0=False, use_int8=True,
+                                        quant_type=1, quant_group_size=0,
                                         async_finish=False, return_recv_hook=return_recv_hook)
         large_gemm_with_hook(hook) if return_recv_hook else None
         combined_x, event, hook = buffer.low_latency_combine(simulated_gemm_x,